Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: This process is based on environmental inoculation of the raw materials (often referred to as natural or spontaneous fermentation), intentional inoculation through backslopping (use of a part of a finished fermented food product to inoculate a fresh batch of raw material), or after addition of a microbial starter culture (Leroy and 2004; Hutkins 2019). Whereas starter cultures are in use for the production of fermented dairy products (e. g. yoghurt and cheese), fermented meat products (e. g. fermented sausage), bread production (by means of bakers’ yeast) and fermented alcoholic beverages (e. g. beer and wine) since several decades, several processes for the fermentation of cereals (e. g. artisan sourdoughs), vegetables (e. g. sauerkraut), fruits (e. g. vinegar), fish (e. g. fish sauce), etc. are still mostly of a backslopping or spontaneous nature, but research on the use of starter cultures for these fermentation processes is ongoing (Hutkins 2019). Some of these spontaneous fermentation processes are of great economic importance, as in the case of the cash tree crop cocoa (Schwan, Fleet and Afoakwa 2015; Pereira, Soccol and Soccol 2016). They hence would likely take profit of the introduction of starter cultures to carry out accelerated and enhanced fermentation processes that lead to end-products of improved and standardized quality. Cocoa originated in the Amazon basin of (Zarrillo et al. 2018; Coe and Coe 2019). It was cultivated and consumed as a beverage constituent in since prehistoric times. Its consumption, first as cocoa drink and later on as chocolate, was first introduced into Europe in the 16th century. Nowadays, cocoa trees are cultivated on plantations in the equatorial zone globally. Their fruits have to undergo a rather complex and often uncontrolled, on-farm, post-harvest, batchwise process of fermentation and drying to be able to use the fermented dry cocoa beans for further manufacturing (Fig. 1; Schwan and Wheals 2004; Beckett 2009; Badrie et al. 2015; Afoakwa 2016; and Weckx 2016a,b; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). and Brazil are the main cocoa-producing countries in the cocoa belt, some 20° north and south of the equator. Figure 1. [SEP] Justify:

Output: The text corroborates the claim by discussing various fermentation processes, including the use of starter cultures and the ongoing research on their application, which implies that lactic acid bacteria play a crucial role in fermentation, particularly in products like yoghurt, cheese, and fermented meats. The mention of spontaneous fermentation and the economic importance of these processes indicates that microbial cultures, including lactic acid bacteria, are vital for achieving desired fermentation outcomes.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The SME paradigm has been widely used in the memory literature to study neural correlates (typically spectral power in a specific frequency band) that predict successful memory formation (Sederberg et al. , 2003; Long et al. , 2014; Burke et al. , 2014). The intra-MTL connectivity SME was constructed using the following procedure. First, we partitioned the word items into recalled and non-recalled items offline. Using the MSV model, we constructed an intra-MTL connectivity network for each memory outcome. We compared the distribution of the elements of these matrices across subjects. For the analysis, we considered four subregions of the MTL: hippocampus (Hipp), entorhinal cortex (EC), perirhinal cortex (PRC), and parahippocampal cortex (PHC). Each MTL subregion contains a different number of recording locations depending on the subject’s electrode coverage. We then computed the contrast between the two intra-MTL networks corresponding to recalled and non-recalled items for each ordered pair of subregions excluding the ones with fewer than 10 subjects contributing to the analysis. To compute the directional connectivity from region I to region J, we took the average of the lag-one 'influences’ that electrodes in region I have on electrodes in region J, where |I| denotes the number of electrodes in region I. We then computed the contrast between the two connectivity networks associated with recalled and non-recalled items: ΔI→J=CI→JR-CI→JNR. Finally, we averaged the contrast for each ordered pair of MTL subregions across sessions within a subject. From now on, we refer to this contrast as simply connectivity network. Figure 5 illustrates the intra-MTL connectivity SME for the left and right hemispheres. Directed connections between the left hippocampus and the left PRC reliably decrease (false-discovery-rate-corrected) during successful memory encoding (ΔHipp→PRC=-0. 04,t47=-3. 49, adj. P <0. 01 and ΔPRC→Hipp=-0. 06,t47=-2. 66, adj. P <0. 05). [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of memory recall. It describes a study using the SME paradigm to explore neural correlates of memory that predict successful memory formation, indicating that not all items are recalled. Specifically, it states that they partitioned word items into recalled and non-recalled items, suggesting that memory recall is not guaranteed even with supposedly optimal conditions, as implied by the term 'successful memory encoding.' This indicates that perfect recall may be an exaggeration.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Because this spiral example has only two essential dimensions, the 3rd to 17th dimensions are considered as random perturbations. If we reduce the number N I from 16 to 3 but keep N g = 200, we can see that the 2D SC-MDS representation keeps the shape of spiral but becomes more and more blurred as N I decreases. At the same time, the increases when N I decreases, as will be shown in Fig 2. In Fig. 1c, we set N g = 200 and N I = 5 and the points in each group are also chosen randomly. The performance is now slightly worse than CMD due to dimension loss; the here is 0. 0398. In Fig. 1d, we set N g = 200 and N I = 5, and group points neighboring to each other into the same group. It shows that the performance is totally bad – the spiral structure is lost and the increases to 0. 5232. Figure 2Computational error as a function of N I. The solid line refers to the case where the points in each group are chosen randomly, while the dash line refers to the case where the points in each group are chosen from neighbors. In all cases, N g is fixed to 200. Full size imageFigure 2 shows the correlation between and the number of points in the overlapping region N I. There are two lines. The solid line refers to points in each group that are chosen randomly, while the dash line refers to points in each group that are chosen from neighbors; all N g are fixed to 200. Points in each line are the average of 20 repeats of computed with different randomly choosing points in each group. We can see that the of each line decays as N I increases. I is larger than the real dimension of data, the is almost zero. I is smaller than the real dimension of data, the performance of solid line is better than the dash line. Without the afar information in each group, the performance is worse. That is, randomly choosing points in each group helps to reduce the error when N I is not large enough. In the same simulation example, we observed the relationship between error and the number of groups. I is larger than the dimension of the data, like N I = 20, we can reconstruct the configuration of the data well. The number of groups of our grouping method does not affect the and the average is ~10-12. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses computational methods and performance related to data dimensionality and error, but does not mention bioinformatics or its role in analyzing genomic data.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Besides the early review of Schwan and Wheals (2004), some recent reviews partially dealt with the potential of starter cultures to improve the cocoa fermentation process (Saltini, Akkerman and Frosch 2013; Pereira, Soccol and Soccol 2016; and Weckx 2016a,b; Ozturk and Young 2017; Castro-Alayo et al. 2019; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). The present review provides a state-of-the-art overview of the microbiology and biochemistry of cocoa fermentation processes and the importance of controlled, starter culture-initiated fermentation processes to produce fermented dry cocoa beans. It will focus in particular on the functional role of yeasts, lactic acid bacteria (LAB) and acetic acid bacteria (AAB), thereby attempting to cover most of the studies published since 2000. Role of fermentation in cocoa curing Fermentation is an essential step in the post-harvest processing of cocoa pods, the big oval fibrous fruits of the cocoa tree (Theobroma cacao L. ) (Bartley 2005; Motamayor et al. 2008; Badrie et al. 2015). These fruits differ in size, shape and surface colour. After processing, their seeds, the cured cocoa beans, are used for chocolate production (Fig. 1; Schwan and Wheals 2004; and Weckx 2016a,b; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). Morpho-geographically, distinction can be made between bulk (basic or ordinary) cocoa (Forastero variety; 95% of the world cocoa production) and fine (flavour) cocoa (Criollo and Trinitario varieties; 5%). However, most cocoa crops in use are hybrids of several varieties. The white to dark purple, fresh cocoa beans (the colour depends on their anthocyanin contents) are embedded in a white-grey, sticky, sour-sweet and viscous pulp, the cocoa pulp-bean mass, which is manually or mechanically scooped out of the cocoa pods after their harvest (Fig. 1). When applying good agricultural and farming practices, only mature, healthy and undamaged cocoa pods should be used. The fresh cocoa pulp-bean mass, ideally depleted from placentas and residual cocoa pod p2. [SEP] Justify:

Output: The text corroborates the claim by emphasizing the functional role of lactic acid bacteria (LAB) in the fermentation processes, particularly in cocoa fermentation. It highlights that controlled, starter culture-initiated fermentation processes involve LAB, indicating their significance in improving the quality and outcome of cocoa fermentation.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The difference between the directional connections between these two regions is not significant (t47=0. 53, p=0. 60). The decreases in the bi-directional connections within the left MTL are consistent with the findings in Solomon et al. (2017) which noted memory-related decreases in phase synchronization at high frequencies. We did not, however, find any other significant directional connections among the remaining regions (Figure 5, 0. 01) and the reverse directional connectivity is also significant (adj. P < 0. 05). ability to record electrophysiological signals from large numbers of brain recording sites has created a wealth of data on the neural basis of behavior and a pressing need for statistical methods suited to the properties of multivariate, neural, time-series data. Because neural data strongly violate variance-stationarity assumptions underlying standard approaches, such as Granger causality (Stokes and Purdon, 2017), researchers have generally eschewed these model-based approaches and embraced non-parameter data analytic procedures. The multivariate stochastic volatility framework that we propose allows for non-stationary variance in the signals. This framework allows us to explicitly model the time-varying variance of neural signals. Similar stochastic volatility models have been used extensively in the financial economics literature to characterize a wide range of phenomena. The MSV models proposed in this paper provide a new framework for studying multi-channel neural data and relating them to cognition. Using a large MTL intracranical EEG dataset from 96 neurosurgical patients while performing a free-recall task, we found that volatility of iEEG timeseries is correlated with spectral power across the frequency spectrum and the correlation increases with frequency. To further test the ability of the MSV model to link iEEG recordings to behavior, we asked whether MTL volatility features during encoding can predict subsequent memory recall as well as spectral power features. Our findings indicate that volatility features significantly outperform the spectral features in decoding memory process in the human brain, suggesting that volatility can serve as a reliable measure for understanding cognitive processes. A key strength of the MSV approach is its ability to identify directed interactions between brain regions without assuming variance-stationarity of neural signals. We therefore used this approach to determine the directional connections between MTL subregions that correlate with successful memory encoding. [SEP] Justify:

Output: The text contrasts the claim by highlighting that while certain neural features are correlated with memory recall, it does not support the idea that memory can be recalled perfectly with the right stimulus. Instead, it discusses variability in memory processes and the use of the MSV model to link iEEG recordings to behavior. Specifically, it indicates that the volatility of iEEG timeseries can be correlated with memory processes, thus suggesting that recall may depend on complex interactions rather than being perfect.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Hence, SC-MDS gives accurate results if we carefully control the number of overlapping points and choose random points in each group. Next, we compare the speeds of non-metric and SC-MDS. We create random vectors in a 20-dimension space, with the sample size ranging from 50 to 2000. 3 the black line (non-metric MDS) and the blue line (CMDS) are produced by Matlab default scripts, mdscale. m and cmdscale. m. The red line refers to our SC-MDS method. We can see that the simulation result matches our theoretical analysis. Figure 3Speed comparison among non-metric and SC-MDS. The SC-MDS method is denoted by the red line, the CMDS method is denoted by the blue line, and the non-metric MDS method is denoted by the black line. The black and red lines are produced by average of 20 repeats. Full size imageBecause of the hardware limitation to process CMDS, we use only a set of 2000 sample points as the maximal sample size to demonstrate that SC-MDS performs as well as CMDS when the number of data points (N) is not very large. We give below an example of large N to show that SC-MDS works well in a large data set that cannot be handled by CMDS. MapGene correlation maps are used to represent the correlations of genes such that genes with similar biological functions or in the same biological pathway tend to be located in the same neighborhood. It provides a prior probability in many applications of genome research by Bayesian methods. U133A GeneChip is widely used in many studies, we used genes listed in this chip and GO descriptions on the website to create the gene correlation map. In this chip, there are 22,283 genes and 2168 GO terms are used in the list of these genes. Hence, we consider each gene a binary vector with length of 2168. If the i-th term of the vector is one, then this gene has the i-th GO description. There are 5781 genes without any GO description so that these genes are not used to compute the correlation with the genes with a GO description. Hence, the term-document matrix is reduced to the size of 16502 × 2168. To apply the CMDS to a distance matrix of 16502 × 16502 is impossible for current hardware. [SEP] Justify:

Output: The text does not directly support or refute the claim regarding the importance of bioinformatics in genomic data analysis. It discusses the SC-MDS method and its comparison with other methods, as well as the specific details of a study involving gene correlation maps, but it does not address the broader significance of bioinformatics in the context of genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Overview of the fermentation of the cocoa pulp-bean mass through the succession of an anaerobic phase, involving yeasts and lactic acid bacteria (LAB), and an aerobic phase, involving acetic acid bacteria (AAB), and the concomitant bioconversion reactions taking place inside the cocoa beans. Uptake of substrates and secretion of metabolites by the microbial cells are not represented. EMP, Embden-Meyerhoff-Parnas pathway. HeFe, heterofermentation. HoFe, homofermentation. PKP, phosphoketolase pathway. TCA, tricarboxylic acid cycle. Open in new tabDownload slide Microbial species diversity and community dynamics during cocoa fermentation The microbial ecology and succession of cocoa fermentation processes is complex (Schwan and Wheals 2004; and Weckx 2016a,b; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). Cocoa fermentation processes differ in microbial species and counts as well as in substrate and metabolite compounds and concentrations over time, hence leading to variable fermentation quality and cocoa flavour. Moreover, cocoa fermentation processes are characterized by changing interactions with the environment and among the indigenous microorganisms (Adler et al. 2013, 2014; Moens, Lefeber and 2014; Illeghems, Weckx and 2015b; Moreno-Zambrano et al. 2018; Mota-Gutierrez et al. 2018). This variability not only depends on the cocoa variety, agricultural and farm practices, fermentation method and duration, etc. , but even occurs for fermentation processes carried out on the same farm, under the same conditions and with the same cocoa variety (Afoakwa et al. 2008; Cevallos-Cevallos et al. 2018; Rottiers et al. 2019). Nonetheless, as further outlined below, these complex successive microbial activities and diversity of the cocoa fermentation process also account for its robustness. The actual fermentation of the cocoa pulp-bean mass is carried out by a temporal succession of activities of yeasts, LAB and AAB, whose interactions are determined mainly by their ethanol, lactic acid and acetic acid concentrations produced as well as the temperature increase upon fermentation. A well-performed cocoa fermentation process, implying good agricultural and farm practices, is finished within 4–6 days (Schwan and Wheals 2004; Lima et al. [SEP] Justify:

Output: The text corroborates the claim by detailing the role of lactic acid bacteria (LAB) in the fermentation of cocoa pulp-bean mass. It explains that LAB is part of a succession of microorganisms that contribute to the fermentation process, which is essential for developing cocoa flavor and quality. The mention of 'a temporal succession of activities of yeasts, LAB and AAB' highlights the importance of LAB in conducting these metabolic processes during fermentation.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Using the regression coefficient matrix of the multivariate volatility process, we found that periods of decreased connectivity in the volatility network among MTL subregions predicted successful learning. Specifically, we found that the hippocampus and the perirhinal cortex in the left hemisphere desynchronize (exerting less influence on one another) during successful learning, which is consistent with the late-phase gamma decoupling noted in Fell et al. (2001). A more recent study by Solomon et al. (2019) also examined the association between intra-MTL connectivity and successful memory formation using phase-based measures of connectivity. Solomon et al. (2019) noted intra-MTL desynchronization at high frequencies during successful memory formation, aligning with the finding here that the volatility network tended to desynchronize. Furthermore, Solomon, et al. found broad increases in low-frequency connectivity, which did not appear to be captured by our stochastic model. This suggests that volatility features reflect neural processes that are also captured by high-frequency phase information. We further noted more statistically reliable changes in volatility networks in the left MTL compared to the right. This result is in line with a long history of neuroanatomical, electrophysiological, and imaging studies (e. g. Ojemann and Dodrill, 1985; Kelley et al. , 1998) that found an association between verbal memory and the left MTL. It is possible that the verbal nature of our memory task specifically engaged processing in the left MTL, resulting in a lateralization of observed volatility phenomena. Prior studies implicate the perirhinal cortex in judgement of familiarity and in recency discrimination system, while the hippocampus supports contextual binding (Eichenbaum et al. , 2007; Diana et al. , 2007; Hasselmo, 2005). These two systems play important roles in memory associative retrieval as suggested by animal studies (Brown and Aggleton, 2001), but it is still unclear how the hippocampus and PRC interact during memory processing. Fell et al. (2006) suggested that rhinal-hippocampal coupling in the gamma range is associated with successful memory formation. Our results show no evidence for such a phenomenon, but rather agree with more recent studies demonstrating memory-related overall high-frequency desynchronization in the MTL (Solomon et al. , 2019; Burke et al. , 2015). [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities of memory processes, specifically highlighting periods of desynchronization in brain regions during memory formation. It indicates that while certain neural networks can influence memory success, it does not support the idea of perfect recall through specific stimuli. The study references findings that suggest more nuanced interactions and variability in connectivity rather than a simplistic notion of flawless memory recall.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Full size imageBefore applying K-means clustering, we determine how many clusters we should use as the parameter in the K-means process. We search the number of clusters in the K-means process from 10 to 75. Since the clustering result of K-means depends on the initial guess of the centroids of the sets, we repeat 30 times the K-means process and use the Criterion (BIC) score [14] to pick up the best clustering index from the mean of these 30 clustering results. In each K-means process, if it does not reach convergence in 100 iterations, we reset the initial values of the cluster centers and re-run the K-means process until the process converges. We set the upper bound for re-running time to be 5. If the re-running time reaches this upper bound, we choose the final cluster index by the best cluster sets from the previous iteration results. The BIC score is computed by the following formula: B I C = ∑ i = 1 n log ( ∑ j = 1 k Pr ( x i | c j ) Pr ( c j ) ) − ( k + 1 ) p 2 log ( n ) MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xI8qi9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae4qamKaeyypa02da9iabigdaXaqaaiabd6gaUbqdcqGHris5a4naa4yam2aa0JaeGymaedabaGaem4@67C3@ where n is the number of data points, k is the number of clusters, c j is the j-th model and p is the length of data points. We assume that each cluster from the K-means procedure is a multivariate normal distribution. The first term of the formula is the log-likelihood and the second term is penalty. [SEP] Justify:

Output: The text does not specifically address the importance of bioinformatics in analyzing genomic data. Instead, it discusses the technical aspects of the K-means clustering process, which is a method potentially used in bioinformatics but does not directly support or refute the claim about the overall importance of bioinformatics itself.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: (2019)] and the metabolic potential of the microbial communities detected, including meta-pathway reconstruction [e. g. Illeghems, Weckx and (2015b) and Agyirifo et al. (2019)]. All these studies have brought deeper insights into the microbial community dynamics and species diversity of the complex cocoa fermentation process. Some of these studies have been accompanied with a metabolite target analysis of pulp and/or beans, unravelling the dynamics of the pulp substrate consumption and conversions as well as those of the microbial metabolite production and conversions in the pulp and/or beans, now also including volatile organic compounds of seed and microbial origin [e. g. Ardhana and Fleet (2003), Camu et al. (2007), Aculey et al. (2010) and Moreira et al. (2016)]. Recent multi-omic approaches and multivariate analyses have linked the microbiology and biochemistry of cocoa pulp-bean mass fermentation processes with the flavour profiles of the cured cocoa beans and chocolates produced thereof [e. g. Ho, Fleet and Zhao (2018) and Mota-Gutierrez et al. (2018)]. The actual cocoa fermentation process can be split into two progressive phases that differ in environmental conditions, in particular oxygen availability, which are further referred to as an anaerobic phase and an aerobic phase. Besides microbial activities in the pulp, different enzymatic activities take place inside the beans during these two cocoa fermentation process phases. Anaerobic yeast and lactic acid bacteria phase Yeasts and LAB Yeasts and LAB grow simultaneously for about 24–36 h of fermentation during the anaerobic phase of the cocoa fermentation process, which takes off at an environmental temperature of around 25–30°C under carbohydrate-rich and acidic conditions (Schwan and Wheals 2004; and Weckx 2016a,b; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). These conditions are characterized by high glucose and fructose concentrations in the mature pulp (formed from sucrose through mainly pulp invertase activity and—to a lesser extent—yeast invertase activity) and a low pH of approximately 3. 0 (because of the presence of citric acid), respectively. This simultaneous growth of yeasts and LAB may be ascribed to their non-competitive interactions. [SEP] Justify:

Output: The text supports the claim by describing how lactic acid bacteria (LAB) grow simultaneously with yeasts during the anaerobic phase of cocoa fermentation, indicating their role in fermentation processes. Furthermore, it highlights specific conditions (high glucose and low pH) under which LAB thrive, reinforcing their significance in the fermentation dynamics.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: effortlessly establish a gist-like memory of their environment whenever they enter a new place, a memory that can guide action even in the absence of vision. Neurons in the lateral intraparietal area (LIP) of the monkey exhibit a form of this environmental memory. These neurons respond when a monkey makes a saccade that brings the spatial location of a stimulus that appeared on a number of prior trials, but not on the present trial, into their receptive fields (RFs). The stimulus need never have appeared in the neuron’s RF. This memory response is usually weaker, with a longer latency than the neuron’s visual response. We suggest that these results demonstrate that LIP has access to a supraretinal memory of space, which is activated when the spatial location of the vanished stimulus can be described by a retinotopic vector from the center of gaze to the remembered spatial location. Introduction Humans, and presumably monkeys, effortlessly establish a gist-like memory of their environment whenever they enter a new place. They can then use this memory to guide action even in the absence of vision. The hallmark of this environmental memory is that the objects remembered need not be relevant to the subject’s current behavior. For example, although you may never be asked to point to the door with your eyes closed, you establish a memory of the door’s location automatically. Although normally humans have no trouble pointing to objects in the room with their eyes closed, patients with bilateral parietal lesions cannot do this even though they can easily locate and point to objects in the room with their eyes open (Levine et al. , 1985), suggesting an environmental memory impairment. Visually responsive neurons in the frontal eye field (FEF) exhibit a signal that could represent environmental memory (Umeno and Goldberg, 2001). After monkeys make a number of saccades that bring a task-irrelevant probe stimulus into the receptive field of a visually responsive FEF neuron, many neurons respond on trials when a saccade brings the spatial location of the stimulus into the receptive field, even though the probe stimulus did not appear on the current trial. However, because the investigators always used the same saccade both to establish and evaluate the memory response, it is not clear if the effect is a true spatial memory or merely a memory of receptive field stimulation associated with a saccade. [SEP] Justify:

Output: The text contrasts the claim by suggesting that memory may not be perfectly recalled even with the right stimulus. It discusses how individuals can activate a memory of their environment without having to recall specific stimuli from the past, as seen with environmental memory. The mention of patients with bilateral parietal lesions, who cannot point to objects with their eyes closed despite being able to do so with their eyes open, implies that memory recall can be impaired and is not perfect, even with the presence of a stimulus.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: When the data fit the model well, the term of log-likelihood becomes larger. We assume that the standard deviations of the multivariate normal distributions are the same, so that there are (k+1)p parameters, k means, and one standard deviation in the p dimensional space, in these k clusters (models). If a model becomes complicated, i. e. , the number of the model parameters becomes large, the BIC score will decay. In Fig. 8, the dash line is reduced by taking the average of 30 repeats and the solid line is reduced by taking the maximal value of 30 repeats. We can see that the maximal value of solid line occurs in 45 clusters and the maximal of the dash line occurs in 61 clusters. Hence, we partition 5006 genes into 61 clusters in our example. Figure 8The curve of BIC Scores vs. number of clusters in K-means. The solid line denotes the max of 30 repeats of the BIC score. The dash line denotes the average of 30 repeats of the BIC score. The maximal value of the average curve occurs at 61 clusters. After 61 clusters the average curve starts decaying. Full size imageWe cluster the original data set and the reduced 7-dimensional space data set from SC-MDS separately, and each data set gives rise to 61 distinct gene clusters. Then we input gene names from these clusters to the. The outputs indicate that 13 clusters of the original clustering data set are significant to the cell cycle (p-value is smaller than 10-3). In contrast, 14 clusters of the 7-dimensional data set are significant to the cell cycle function. Twenty pairs of almost matched clusters, which consist of highly similar genes, are found by comparing the clusters from the two data sets. Nine clusters of the 7-dimensional data set are not annotated to any function, while 13 clusters of the original clustering data set are not annotated. The details of these paired clusters are shown in Additional file 1 and file 2. There are many unpaired clusters, which contain lower proportions (the match rate < 80%) of similar genes. In Figs. 9 to 11, we show these lower matched pairs that are related to cell cycle. In Figs. 9 and 10 the biological validation shows that these clusters obtained from SC-MDS are better than the corresponding clusters from the original 23-dimensional space data. In Fig. 9(a), 45. [SEP] Justify:

Output: The text, while discussing complex statistical methods and clustering of gene data, does not explicitly mention bioinformatics or its importance in analyzing genomic data. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: The yeasts preferentially grow on the available glucose, fermenting it to pyruvate through glycolysis for ATP and reducing equivalent production and further producing ethanol and carbon dioxide (which maintains the anaerobic conditions) for redox balancing (Fig. 2). Alternatively, the LAB species that first grow in the cocoa pulp-bean mass and do not require oxygen for growth prefer to consume fructose and perform citric acid conversion (Fig. 2). Fructose is metabolized either homofermentatively (glycolysis) or heterofermentatively (phosphoketolase pathway) to pyruvate, while citric acid is converted into acetic acid and oxaloacetic acid. The latter is further converted into pyruvate as well, which will finally yield either lactic acid, acetic acid, or pyruvate metabolites, in particular 2,3-butanedione (diacetyl; buttery notes), 3-hydroxy-2-butanone (acetoin; buttery notes) and 2,3-butanediol (Gänzle 2015; von Wright and Axelsson 2019). The conversion of citric acid into lactic acid and acetic acid causes an increase of the pH from 3. 0 to 4. 0, because of the higher pKa values of the latter organic acids compared to citric acid. This low pH course contributes to the maintenance of a microbiologically stable environment, which is dominated by dedicated acid-tolerant yeast and LAB species. The growth of LAB may even be favoured by the carbon dioxide production by the yeasts and, later on, by the release of vitamins and other nutrients from yeast cells that autolyze upon fermentation (Agyirifo et al. 2019). It is assumed that yeasts do not contribute to citric acid conversion during cocoa fermentation, as citric acid metabolism by yeasts is aerobic (and Weckx 2016b). Nonetheless, citric acid assimilation can sometimes be shown in vitro for yeast isolates from fermenting cocoa pulp-bean mass (Jespersen et al. 2005; Daniel et al. 2009; Ho, Zhao and Fleet 2014; Samagaci et al. 2016). [SEP] Justify:

Output: The text corroborates the claim by describing the role of lactic acid bacteria (LAB) in the fermentation of cocoa pulp-bean mass. It explains how LAB species first grow in this substrate and metabolize sugars such as fructose to produce lactic acid and other metabolites. The text also highlights that the growth of LAB is influential in maintaining a microbiologically stable environment during fermentation, which supports their importance in fermentation processes.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Here, we asked if neurons in the lateral intraparietal area (LIP), an area with visual, oculomotor, and mnemonic connections that serves as a priority map of the environment (Bisley and Goldberg, 2010), also exhibits an environmental memory signal. Indeed, we found that neurons in LIP did convey an environmental memory signal. Further, we found that the memory signal could be established even when the probe stimulus never appeared in the receptive field of the neuron, and occurred despite the fact that the monkey made a different saccade to bring the spatial location of the vanished probe stimulus into the receptive field. These results suggest that LIP has access to a representation of the visual world in at least supraretinal coordinates. Because saccades and reaching movements are coded in the parietal cortex in retinotopic coordinates (Duhamel et al. , 1992; Andersen et al. , 1998), these results suggest that one role of LIP is to transform a supraretinal representation of the remembered environment into a retinotopic representation more useful for the generation of action. We recorded the activity of 131 neurons in three monkeys: 79 in Monkey A, 15 in Monkey B, and 37 in Monkey C. Because we had only a small number of cells in each monkey and the results from and C were comparable, these two data sets were combined for several analyses. All neurons in our sample had visual responses to the onset of a saccade target in their receptive fields, and exhibited delay-period and/or presaccadic activity in a memory-guided delayed saccade task. Furthermore, we only studied the memory-related activity of neurons whose postsaccadic responses were not contaminated by the phasic and tonic components of an eye position signal often found in parietal neurons (Andersen et al. , 1998). All neurons were situated in the lateral bank of the intraparietal sulcus, as determined by structural MRI (Figure 1). Figure 1 Download asset Open asset Location of LIP recordings. (A) A tungsten microelectrode (250 µm thick, straight shadow) located in the target area based on known LIP activity and the commonly used atlas-defined coordinates (Paxinos et al. , 1999; Saleem and Logothetis, 2012) within the given coronal slice of the brain. This electrode location was at −2 AP and +10 ML. [SEP] Justify:

Output: The text contrasts the claim by indicating that the memory signal can be established even when the probe stimulus never appeared in the receptive field of the neuron. This suggests that memory recall may not be perfectly reliant on specific stimuli being present at all times, which contradicts the claim.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: One-hot encoding refers to denoting a given nucleotide at a site with a 1 and the rest of the nucleotides as zeros. Many of these algorithms are deep learning methods that are based on neural networks with many layers that attempt to understand the underlying variation in sequence data and predict phenotypes (11). In our system, we represent monomers not with individual numbers or sets of words (kmers), but with vectors. This is similar to one-hot encoding of nucleotides, in which a nucleotide is represented as an array to be used in computational genomics or ML methods. In our approach, however, the array is treated mathematically as a vector (a tensor of rank one). Note that a vector here represents a single site, not a sequence; a pair of sites is captured with a tensor of rank 2 (a matrix). These vectors and matrices can then be incorporated into mathematical functions, such as polynomials. In contrast to computational approaches, denoting nucleotides as mathematical objects in this way allows us to do formal mathematics with sequence information. For example, the mean vector at a site gives the distribution of nucleotides at that site, same as what a sequence logo plot gives, and the mean outer product of two sites gives us a matrix that is the covariance matrix (see ‘Materials and Methods’ section). After denoting each site in a sequence as a vector (instead of the entire sequence being a vector), we then build orthogonal polynomials based on these vectors and project the phenotypes onto the polynomial space. This reveals not only first order interactions between parts of sequences but also quantifies higher order effects of having particular nucleotides at given sites. Thus, doing purely mathematical analyses with this representation of nucleotides yields biologically meaningful results. Note that both the ML and the mathematical approaches share the common fundamental step of one-hot encoding the nucleotides but this is in contrast to kmer methods which denote sets of nucleotides as words of length k. In the case of RNA sequence analysis, secondary structure prediction is an active area of development. For example, predicting pseudoknots without constraints is impossible and is an NP-complete problem (12). [SEP] Justify:

Output: The text primarily discusses technical methods of nucleotide representation and data processing, such as one-hot encoding and the use of mathematical models. While these methods fall under the broader topic of computational genomics, the text does not directly address the role of bioinformatics as a whole in analyzing genomic data. Thus, the text is largely irrelevant to the specific claim.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: 2009; Pereira et al. 2012; Crafack et al. 2013; Maura et al. 2016; Samagaci et al. 2016; Visintin et al. 2016). Another factor that may determine the abundance of certain yeast species is killer toxin production, i. e. the production of oligopeptides that inhibit the growth of other yeasts that do not show killer activity, albeit in a temperature-dependent manner (Samagaci et al. 2014, 2016; Meersman et al. 2015; Romanens et al. 2019; Ruggirello et al. 2019). From the above it can be concluded that yeasts, being represented by several species, have a determining impact on the efficiency and outcome of the cocoa fermentation process, either directly or indirectly. LAB Once the yeast counts start to decline, because of less favourable environmental conditions (less glucose, less anaerobic conditions, a slightly higher pH and an increasing temperature) and an increasing competitiveness for growth with the LAB, the latter microbial group further proliferates thanks to the appropriate growth conditions arising after 24–72 h of fermentation (Schwan and Wheals 2004; and Weckx 2016a,b; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). At the start of the cocoa fermentation process, the prevailing fructophilic LAB species use fructose as energy source, with or without citric acid conversion, thereby avoiding too much competition from the yeasts. After the prevailing yeast phase, mainly heterofermentative LAB species grow, which ferment glucose to lactic acid, acetic acid, ethanol, carbon dioxide and/or mannitol (Fig. 2; Ardhana and Fleet 2003; Camu et al. 2007, 2008a; Gálvez et al. 2007; Nielsen et al. 2007a; Kostinek et al. 2008; Lefeber et al. 2010, 2011,a, 2012; Papalexandratou et al. 2011a,b,c, 2013, 2019; Pereira et al. 2012, 2013a; Adler et al. 2013; Crafack et al. 2013; Meersman et al. 2013; Moreira et al. 2013, 2016; Pereira, Magalhães-Guedes and Schwan 2013b; Ouattara et al. [SEP] Justify:

Output: The text corroborates the claim by describing the role of lactic acid bacteria (LAB) in the cocoa fermentation process. It outlines how LAB proliferate after yeast counts decline and how they contribute to the fermentation by converting glucose into lactic acid and other compounds. This indicates their importance in the fermentation process, supporting the claim.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Task 1: Basic memory task We studied 72 LIP neurons using a task based on that used to demonstrate environmental memory in the frontal eye field (Umeno and Goldberg, 2001) (Figure 2). After determining the spatial tuning properties of the neuron being recorded, the monkey performed the basic memory task which is comprised of four blocks. We customized the arrangement of the stimuli (fixation point, saccade target, and probe stimulus) according to the spatial properties of each neuron’s receptive field. In the first block, we asked the monkey to perform 20 visually guided saccade trials in which no probe stimuli appeared on the screen (Figure 2, Block 1). In these trials, no stimulus, including the saccade target, encroached on the receptive field of the neuron. Next, we asked the monkey to perform a block of 30 visually guided saccade trials in which a task-irrelevant probe stimulus appeared at a location that would be brought into the receptive field by the required saccade (i. e. the future receptive field of the neuron) (Figure 2, Block 2). Then, we pseudorandomly interleaved trials in which the probe stimulus appeared on the screen with those in which no probe stimulus appeared (Figure 2, Block 3). Finally, we presented the monkey with a block of 100 trials in which, again, the probe stimulus never appeared (Figure 2, Block 4). Figure 2 Download asset Open asset The basic memory task. The different trials types were performed in blocks. 1 (baseline), the monkey made a visually guided saccade to a point outside the neuron’s response field, to show that the saccade itself does not evoke neural activity. In the cartoon, the fixation point is a white square, the saccade target a red square, the presaccadic spatial location of the receptive field a white circle, the postsaccadic receptive field a magenta circle. The presentation times of the fixation point, saccade target and, when appropriate, probe stimuli are shown in the traces below. 2 (visual max), the monkey made the same saccade, but now the saccade brought the probe stimulus into the receptive field. If the monkey made a saccade to the probe stimulus, the trial was terminated. 3 (form and recall memory), for half of the trials, the probe stimulus appeared on the screen and was brought into the RF by the saccade. [SEP] Justify:

Output: The text contrasts the claim by detailing a memory task study involving a monkey and various probe stimuli. It suggests that memory recall is not infallible and is affected by the presence or absence of stimuli. For example, in some trials, the probe stimulus was absent, indicating that memory recall was not perfect under all conditions. This demonstrates that the system's ability to recall is contingent on specific stimuli, rather than perfect recall being guaranteed.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: RNAs are extremely versatile molecules and play important regulatory roles in gene expression by affecting transcription and translation through various different mechanisms, including intermolecular (trans) and intramolecular (cis) interactions (13). Of particular importance is the ability of RNA to control transcription initiation or termination through secondary structure formation of hairpins and loops (14). In recent years, much work has been done to understand the role of sequence composition in the regulatory potential of RNAs by not only studying the ones that occur naturally in bacterial systems but also synthetically constructing these regulators de novo (15,16). This presents a unique opportunity to employ novel mathematical tools to quantify the sequence–function relationship between RNAs and their resulting regulatory activity in both synthetic and natural systems. In general, given sequence information and corresponding phenotype data, one important objective is to quantify exactly how the underlying sequence, whether or protein, gives rise to variation in phenotype. We use the term ‘phenotype’ broadly, to refer to any measurable biological trait that is (potentially) influenced by one or more sequences. This could include whole organism properties as well as molecular traits, such as the rate of expression of a gene product (as in (17)) or binding propensity of a transcription factor. Though we will use molecular traits as examples — since there is more data available relating these to sequence variation — the methods that we present could be applied in exactly the same way to whole organism traits such as morphology or expression of a disease. In the case of RNA regulators, one measurable phenotype is the regulatory activity of an RNA as captured experimentally through the use of fluorescent reporters (18). We have applied this method to a case of regulatory RNAs called (STARs) that were synthetically constructed and whose regulatory activity was quantified experimentally by monitoring levels of green fluorescent protein expression (Figure 1, (19)). Our methods showcase important characteristics about the sequence composition of these synthetic RNAs and the corresponding target RNAs that they bind to. When analyzing just the variation in the given sequences, covariation patterns emerge that indicate interactions between the two ends of the STAR linear region. However, somewhat surprisingly, this pattern does not emerge when the phenotype values (fluorescence indicating transcription termination activity) are projected onto the sequence space. When looking at second order effects and projecting the phenotype onto pairs of nucleotides, we find a few notable interactions spread out along the sequence indicating potential intramolecular binding. [SEP] Justify:

Output: The text discusses the roles and functions of RNA in gene regulation, the relationship between sequence composition and phenotype, and specific experimental methods. However, it does not mention bioinformatics or its role in analyzing genomic data, thus making it irrelevant to the claim.


Input: Task: pass the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Although lactic acid itself may not play the same role as acetic acid in modulating bean pH, peptides and flavanols, the effects of citric acid conversion in the pulp (avoiding citric acid transfer into the beans) and reduction of fructose to mannitol (possibly diffusing into the beans) can result into other processes that contribute to the cocoa flavour than those caused by acetic acid. This refers in particular to the conversion of amino acids in the pulp (contributing to cocoa flavour production as yeasts do) and the provision of a sweet and cool taste due to mannitol accumulation (Lefeber et al. 2010, 2011b; and Weckx 2016a,b; John et al. 2020). As is the case for acetic acid, lactic acid may display a flavanol-protective effect, i. e. reducing polyphenol degradation (Evina et al. 2016; John et al. 2019). Desirable volatile organic compounds contributing to the cocoa flavour produced by LAB encompass pyruvate metabolites (diacetyl and acetoin; buttery/creamy notes) and amino acid conversion metabolites, such as the aldehydes 2-methylbutanal and 3-methylbutanal (malty and chocolate notes) produced from isoleucine and leucine, respectively, as well as benzaldehyde (almond and bitter notes) produced from phenylalanine (see below). The exhaustion of the energy sources glucose and fructose, a continuous increase of the ethanol, lactic acid and acetic acid concentrations, and a rise in temperature of the fermenting cocoa pulp-bean mass eventually cause a decline of the LAB counts and a further decline of the yeast counts (Schwan and Wheals 2004; and Weckx 2016b). The LAB species diversity recovered from fermenting cocoa pulp-bean mass is rather restricted. Fructophilic species that occur in the early stages of the cocoa fermentation process encompass Fructobacillus pseudoficulneus, Fructobacillus tropeaoli and Leuconostoc pseudomesenteroides, as well as Lactobacillus plantarum, the latter species being reclassified as Lactiplantibacillus plantarum (Ardhana and Fleet 2003; Camu et al. 2007, 2008a; Gálvez et al. [SEP] Justify:

Output: The text does not directly corroborate or contrast with the claim about lactic acid bacteria (LAB) being important for fermentation. Instead, it discusses various aspects of fermentation in the context of cocoa, mentioning LAB species and their metabolites, but does not provide a definitive argument regarding their importance in fermentation processes. Hence, it is considered irrelevant to the specific claim.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: These trials were pseudorandomly intermixed with trials in which no probe stimulus appeared and the monkey made the same saccade. In block 4 (forget), the monkey made the same saccade but, as in Block 1, the probe stimulus never appeared. 22/49 of neurons in (45%), and 11/23 (48%) exhibited environmental memory in the basic task: There was a significant difference (p < 0. 05 by Wilcoxon rank sum) in the post saccadic response in Block 1 and the probe stimulus-absent trials in Block 3 (Figure 3 for single cell and Figure 4 for the population of all cells (p<0. 001 Wilcoxon signed ran), black traces and raster symbols versus magenta traces and raster symbols). There was no significant post-saccadic response in Block 1 (Figure 3, for a single cell and Figure 4 for the population of all cells black traces and raster symbols). There was a response to the probe stimulus in Block 2 when the saccade brought the probe stimulus into the receptive field (Figure 3, for a single cell and Figure 4 for the population of all cells green traces and raster symbols). Neurons responded similarly in Block 3 as they did in Block 2 on the half of trials in which the probe stimulus appeared in the future receptive field (Figure 3 for a single cell and Figure 4 for the population of all cells, red traces and raster symbols). However, on the interleaved half of the trials in Block 3 in which a probe stimulus did not appear, the cells responded when the monkey made a saccade bringing the vanished probe location into the receptive field (Figure 3 for a single cell and Figure 4 for the population of memory cells, magenta traces and raster symbols). This response, which occurred after the saccade brought the spatial location of the probe stimulus into the receptive field even though no probe appeared on that trial, is the environmental memory response. Finally, after a block of forgetting trials in which the probe stimulus never appeared (Block 4 trials, identical to Block 1 trials for a single cell), the environmental memory response waned and, in some cases, disappeared entirely. [SEP] Justify:

Output: The text contrasts the claim by describing a study on neuronal responses where memory recall is not perfect. It explains that after a block of forgetting trials where no probe stimulus appeared, the environmental memory response waned and in some cases disappeared entirely. This suggests that memory recall is not flawless and can deteriorate without appropriate stimuli.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: By showing proof of concept of this method applied to these synthetic RNAs, we demonstrate its ability to capture nucleotide (or amino acids in the case of proteins) interactions across sequences and quantify sequence–phenotype interactions can be leveraged when applied to other biological systems. Figure 1. STAR and Target RNA mechanism. (A) illustrates the case in which a target DNA sequence is transcribed into an intrinsic terminator hairpin which displaces the polymerase, preventing transcription of the downstream gene. (B) shows that when STAR binds to the target RNA, the terminator hairpin is prevented from forming and transcription of the downstream gene continues. The purple region in the target RNA indicates the terminator helix which is disrupted upon binding of STAR. The 40 nt linear region in both the target RNA and STAR is depicted with a black line. This was the sequence that was varied while the terminator hairpin sequence remained the same across all 99 variants. Figure adapted from (19). Open in new tabDownload slideThe case of is particularly interesting to study at the sequence level due to the impact that both intra- and intermolecular interactions may have on the function of this type of RNA. Our method described herein enables prediction of nucleotide sequence covariation both positively and negatively impacting the function of this RNA-based regulator. These findings have the potential to uncover critical intra- and intermolecular interactions within the regulatory system. The impact of nucleotide covariation on both intra- and intermolecular interactions is highlighted in the examples from Figure 2 depicting the ability of different hairpin sequences to potentially form an intermolecular interaction with an unchanging target sequence. In the scenario shown in Figure 2A, a favorable intramolecular interaction forming a strong hairpin is likely to prevent the formation of the less favorable intermolecular interaction. In the scenario shown in Figure 2B, a slight sequence change in the hairpin RNA has shifted the balance to a far more favorable intermolecular interaction likely to outperform the relatively weak intramolecular interaction. Interestingly, a covariation on the other end of the potential hairpin restores a favorable intramolecular interaction without impacting the theoretical strength of the intermolecular interaction (Figure 2C). Despite the theoretical strength of the intermolecular interaction depicted in Figure 2C, the competing intramolecular structure is likely to impair full intermolecular function. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on specific methodologies related to RNA and intramolecular interactions rather than addressing the role of bioinformatics in analyzing genomic data. There is no mention of bioinformatics or its importance in the context provided.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: The fructophilic LAB species use fructose as energy source and ferment it to lactic acid with or without citric acid as co-substrate. The use of the glycolysis as main carbon flux by strains of Lacp. plantarum may explain the low competitiveness of this LAB species upon further fermentation, despite their tolerance toward acidity and ethanol, as opposed to heterofermentation that can lead to extra ATP generation as mentioned above (Lefeber et al. 2010; Adler et al. 2013; Illeghems, and Weckx 2015a). The prevalence of strictly heterofermentative Liml. fermentum strains can be ascribed to their conversion of citric acid, production of mannitol and tolerance toward acidity, ethanol and heat (Lefeber et al. 2010, 2011b; Adler et al. 2013; Illeghems, and Weckx 2015a). However, distinction can be made between heterolactic Liml. fermentum strains that consume glucose as energy source, fructose as alternative external electron acceptor and citric acid as co-substrate, to maintain their redox balance and to increase their intracellular pH and ATP generation for competitiveness, and strains that consume fructose as energy source and citrate as co-substrate, to increase their citrate flux toward lactic acid to produce enough ATP under glucose-limiting conditions (Adler et al. 2013). In the beginning of the cocoa fermentation process, also species belonging to the genera Enterococcus and Weissella may occur (Camu et al. 2007,2008a; et al. 2008, 2010; Snauwaert et al. 2013; Bortolini et al. 2016; Papalexandratou et al. 2019), whereas sometimes other species of the Lactobacillaceae (encompassing lactobacilli, Lactobacillus-related genera and pediococci) are found during the main LAB fermentation phase (Nielsen et al. 2007b; Kostinek et al. 2008; et al. 2009; Illeghems et al. 2012; Moreira et al. 2013; Papalexandratou et al. 2013, 2019; Ramos et al. 2014; Ho, Zhao and Fleet 2015; Batista et al. 2016; Menezes et al. [SEP] Justify:

Output: The text corroborates the claim by detailing the roles of various lactic acid bacteria (LAB) species in fermentation processes, specifically mentioning how fructophilic LAB species ferment fructose into lactic acid, which is a key aspect of fermentative activity. It also describes the different LAB strains that are involved in cocoa fermentation and highlights their metabolic pathways, indicating their importance in these biological processes.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The peristimulus time histogram (PSTH) figures do not include an average forgetting trace because the wide variation of forgetting traces made such an average uninformative Figure 3 Download asset Open asset Single-cell responses in the basic memory task: from (left panel) and and C (right panel). Neural activity aligned to the beginning of the saccade. Shaded regions in the peristimulus time histograms (PSTHs) are standard errors of means calculated using all the trials in the given block. Black traces and raster dots: activity in Block 1, stimulus absent. Green traces and raster dots: activity in Block 2, 100% stimulus present. Red (broken) traces and raster dots: activity in stimulus-present trials in Block 3. Magenta traces and raster dots: activity in probe stimulus-absent trials in Block 3. Here, there is a brisk response that shows the expected latency advance evoked by predictive remapping (Duhamel et al. , 1992). Note that the latency of the memory response is nearly 100 ms longer after the saccade than the predictive response. The memory decays after many (up to 100) trials. H and V are horizontal and vertical eye movements smoothed using a 10 ms sliding causal filter. Figure 4 Download asset Open asset Population responses in the basic memory task: from (left panel) and and C (right panel). Block descriptions are as in Figure 3. Both populations of neurons, including the statistically significant and the statistically non-significant neurons showed statistically significant memory responses (Figure 5a). The population showed a significant environmental memory response comparing the responses in Block one and the memory trials of Block 3 (Monkey A, p=0. 0002 by Wilcoxon signed rank). and C (p=0. 0003, Wilcoxon signed rank). (Figure 5a, in blue and and C in red, significant cells filled symbols). The environmental memory response was weaker than the visual response evoked when the monkey made a saccade that brought the stimulus into the receptive field (Figure 5b, p<0. 001 by rank). The latency of the environmental memory response was greater than the visual response evoked when a saccade brought the stimulus into the receptive field (Figure 5c) (Wilcoxon signed rank p=0. 02 for and B, and 0. [SEP] Justify:

Output: The text contrasts the claim by discussing the variation in memory responses and noting that memory decays after many trials. It implies that recall is not perfect by mentioning that the memory responses are weaker than the visual responses evoked by a stimulus and that multiple trials can lead to forgetting, which challenges the idea of perfect recall.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: For instance, when doing just first order analysis for the 40 site STAR sequences, the runtime is efficient but doing second order on all 40 sites is not computationally feasible. One can, however, do second order on sections of the sequence (e. g. 10-site regions) or two sites at a time which is what we have done in this work. The analysis for this paper, along with data and files that generate the figures, has been uploaded to this Github project page. Application to STARs (small transcription activating RNAs) We applied our methods to the case of a synthetic RNA regulator designed by (19) known as STARs. In this system, a target DNA sequence, containing the necessary information for termination, is placed upstream of a gene. When transcribed, this sequence turns into an intrinsic terminator with a linear region and a hairpin structure. Upon formation of this structure, the polymerase gets knocked off, preventing the transcription of the downstream gene (Figure 1A, (19,20)). This is known as the ‘OFF state’. In the ‘ON state’, a STAR is constructed such that upon binding to the target RNA, terminator hairpin formation is prevented and the polymerase continues transcription of the downstream gene (Figure 1B). In this system, it was determined that the linear region of the STAR binding to the corresponding part on the target RNA was critical to the activation of the downstream gene. To establish this, linear regions of 100 STAR:target RNA variants were constructed de novo using a software package known as (21). In this construction, only the linear recognition region was varied while the terminator hairpin remained the same for all variants. This linear region was 40 nt long, as determined experimentally, and it was hypothesized that variation in this part of the sequence would give rise to distinct OFF and ON states for all STAR:target variants (see ‘Materials and Methods’ section in (19) for details). For the application of our methods to this system, this set of 40 nt long sequences in the STAR and target RNA were used to build first and higher order orthogonal polynomials. Fold and off values were then projected onto the space of the orthogonal polynomials. [SEP] Justify:

Output: The text describes specific applications of methods in the context of STAR sequences and their associated RNA dynamics. While it provides details on the analysis performed, it does not explicitly discuss the importance of bioinformatics in analyzing genomic data or provide evidence supporting the claim. Therefore, it is categorized as irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: 2016; Miescher Schwenninger, Freimüller Leischtfeld and Gantenbein-Demarchi 2016; Miguel et al. 2017; Agyirifo et al. 2019). Aerobic acetic acid bacteria phase The AAB species that are already present from the beginning of the cocoa fermentation process and have survived the anaerobic yeast and LAB growth phases start to grow during the aerobic phase, which occurs after approximately 48 h of fermentation (Schwan and Wheals 2004; and Weckx 2016a,b; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). This is enabled by further air ingress upon sweating and regular mixing of the cocoa pulp-bean mass during fermentation. They reach their maximal counts after 72–96 h of fermentation. Possessing a non-functional glycolysis, AAB, in particular Acetobacter species, oxidize the ethanol produced by the yeasts into acetic acid (partial oxidation for ATP generation) (Fig. 2). Because of this exothermic reaction, they increase the temperature to and even beyond 50°C. Whereas ethanol serves as the main energy source, lactate produced by the LAB is the main carbon source for the Acetobacter species (Camu et al. 2007, 2008a; Adler et al. 2014; Moens, Lefeber and 2014; Pelicaen et al. 2019). Simultaneously with the oxidation of ethanol, lactic acid is oxidized mainly into acetoin (via α-acetolactate) and, to a low extent, into acetic acid because of a low pyruvate decarboxylase activity of Acetobacter (Fig. 2; Adler et al. 2014; Moens, Lefeber and 2014; Pelicaen et al. 2019). Moreover, lactic acid can be used as the sole carbon source by Acetobacter pasteurianus (Pelicaen et al. 2019). Hence, the highest acetic acid concentrations are produced in the presence of both yeasts and LAB, underlying the importance of the latter microbial group, as balanced concentrations of ethanol and lactic acid are necessary for optimal growth of Acetobacter (Adler et al. 2014). This produces the necessary acetic acid for bean curing. [SEP] Justify:

Output: The text corroborates the claim by detailing the role of lactic acid bacteria (LAB) in the fermentation process, specifically mentioning that lactate produced by LAB serves as a main carbon source for Acetobacter species. It emphasizes the importance of both yeasts and LAB in producing optimal concentrations of acetic acid, which is essential for bean curing during fermentation. The reliance of Acetobacter pasteurianus on lactic acid as a sole carbon source and the necessity of balanced concentrations of ethanol and lactic acid for Acetobacter growth further reinforce the significant role of LAB in fermentation.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: 0005 for Monkey C; mean visual/memory latency: 36/77 ms for Monkey A, 9/116 ms for and C). Of note some, but not all, of the cells exhibiting environmental memory had a shorter visual latency than one would expect given the minimal latency to a stimulus flashed in the receptive field. This latency advance is characteristic of the remapping response (Duhamel et al. , 1991). However, because the memory responses for some neurons increased gradually, we were unable to establish memory latencies for all neurons using our latency method (for these neurons, we used other approaches, as described in the Materials and methods section). Figure 5 Download asset Open asset Population data for the basic memory task: For all recorded units (open circles and triangles) and for units which showed significant differences between baseline and memory blocks (filled circles and triangles, trial per trial peak comparisons, Wilcoxon signed rank, population p<0. 001, within-cell Wilcoxon rank sum <0. 05). (A) Comparison of peak activity in the baseline and memory conditions (Block 1 vs Block 3, no probe stimulus trials). Each point represents a single cell. Cells with memory activity lie above the x = y line (diagonal). Monkey A: blue circles; and C: red triangles (*=and C). Insets: index (Block 3 versus Block 1) histograms for the whole population, out of which the statistically significant units are shown in shaded circles and triangles on the scatter plot. A median (numbers above index histograms) and rank sum and KS test confirmed memory activity as they both showed that the index distribution for the parent population was positively skewed. (B) Peak memory activity plotted against visual activity. Blue and red lines in the scatter plots (B and C) are polynomial fits to each population data. They both indicate a positive shift of neural responses towards memory activity. (C) Latency of memory response versus the latency of visual response for cells. (D) Normalized decay activity. The memory response gradually decays after the last trial in which the probe appeared. The regression lines are data fits using a first order exponential. As indicated in ‘D’ legend, open squares and triangles are neural activities during decay trials compared to (normalized by) the mean of the last five trials with the probe stimulus (left y-axis). [SEP] Justify:

Output: The text contrasts the claim by highlighting that while some cells exhibited environmental memory with a shorter visual latency, the study was unable to establish memory latencies for all neurons due to gradual increases in memory responses over time. This suggests that memory recall may not be perfect, as not all neurons showed immediate recall with stimulation, indicating variability in memory activity and retrieval.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: showcase nucleotide interactions across STAR sites As part of first order analysis, means, variances and covariances were computed for all 40 sites across the population of STAR and target RNA sequences. The 40 site long linear region was determined experimentally as being the optimal length to activate transcription (see ‘Materials and Methods’ section in (19)). Figure 4 shows frequencies of nucleotides along the 99 sequences of the target RNA. consist of nucleotides cytosine (C), thymine (T), and uracil (U, in the case of RNA). Purines consist of adenine (A) and guanine (G). Figure 4. Means of nucleotides present at each site. Frequencies of nucleotides present at each site of the target RNA (from 3 prime to 5 prime) sequence generated by the software. Open in new tabDownload slideThe covariance analysis yields matrices for all pairs of sites, across the 40 sites and quantifies the relation between having a particular nucleotide at one site and another nucleotide at another site. Since there are 40 sites in this system, there are 780 unique pairs of sites. The covariance between each pair of sites is a matrix and thus, there are 780 4×4 matrices which account for all sites and all possible nucleotides at each site. To understand the distribution of these covariances, a histogram was constructed and different cutoffs were analyzed (S1, 3 and 4). S1 shows that as the cutoff became larger, the number of ‘highly covarying’ pairs became smaller. The cutoff of −0. 05 and 0. 05 was selected after testing multiple different cutoffs which showed the same patterns (S3 and 4 showing cutoffs of 0. 04 and 0. 03, respectively). The cutoff of 0. 05 yields 32 site pairs, indicating nucleotides at specific sites covarying highly and positively with each other and nucleotides at other sites covarying highly and negatively with each other (see S1 for actual values). These large positive and negative covariances are visualized as seen in Figure 5 and S2 The start of the sequence, depicted as site 1, is the 5′ end of the STAR while the end of the sequence is the 3′ end. [SEP] Justify:

Output: The text does not directly address the importance of bioinformatics in analyzing genomic data. Instead, it focuses on specific methodologies and results from an experimental analysis of nucleotide interactions across STAR sites, which does not explicitly state that bioinformatics is crucial. Therefore, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Here, the role of yeasts and LAB is thus of primordial importance to produce (sugar/higher) alcohols, aldehydes and/or esters during fermentation of the cocoa pulp-bean mass. Although pyrazines may also be produced by bacilli during (prolonged) fermentation, they are mainly formed during drying (in particular tetra- and trimethylpyrazine) and roasting (all pyrazines). However, the production of flavour-active compounds is not always desirable and can in some instances lead to sensory defects (Schwan and Wheals 2004; Afoakwa et al. 2008). Although it is not always fully clear what is driving this problem of off-flavours, over-fermentation and poor drying may be causal factors, the former leading to an increase in certain bacilli and filamentous fungi and the latter often being ascribed to failing infrastructure that can both produce these off-flavour compounds. Such off-flavours are mostly composed of amyl acetates, short- and medium-chain fatty acids and phenols (e. g. 2-methoxyphenol or guaiacol), which deliver undesirable, hammy, putrid and smoky notes when formed in excess. Use of starter cultures The knowledge on the microbial succession and activities of the most prevalent yeast, LAB and AAB species involved in cocoa fermentation processes obtained during the last decades has prompted the search for appropriate single- or mixed-strain starter cultures for controlled and steered cocoa fermentation processes (Schwan and Wheals 2004; Saltini, Akkerman and Frosch 2013; Pereira, Soccol and Soccol 2016; and Weckx 2016a,b; Ozturk and Young 2017; Castro-Alayo et al. 2019; Figueroa-Hernández et al. 2019; Muñoz et al. 2019; Mota-Gutierrez et al. 2019b). It is now generally accepted that the microbial activities during fermentation and the subsequent transfer of volatile organic compounds into the beans play a determining role in the flavour development of cured cocoa beans and, possibly, chocolates produced thereof. [SEP] Justify:

Output: The text corroborates the claim as it highlights the primordial importance of lactic acid bacteria (LAB) alongside yeasts in the fermentation process of cocoa. It states that their microbial activities play a determining role in producing flavor-active compounds during fermentation, which supports the idea of LAB being crucial in fermentation processes.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: 4, in which the probe stimulus no longer appeared, the memory response gradually decayed (Figure 5d). On an individual cell basis, this decay (measured in the memory window as described in the Materials and methods section) was quite noisy and variable among cells. As a population, the normalized decay activity could be fit to a first-order exponential. To control for fast memory buildups and/or in-between block fluctuations, we normalized the decay activity with stimulus presentation block. had a decay rate constant of 0. 3 trials (i. e. versus stimulus/block-one trials, first-order exponential) (R2 = 0. 7). and C had similar results (decay rate constant = 0. 13; R2 = 0. 6). For both monkeys and comparisons, the first and last 10 trials in the 50-trial forgetting block (Block 4) are statistically different (Wilcoxon signed rank, p<0001). The memory cells were not just the expected tail of a population distribution. To investigate the population median shift, we used a classical median approach. We analyzed the distribution of index histograms and found that memory activity significantly increased the median (medians > 0. 1 and compared with the index histograms with a mean-deducted version of the same distribution Wilcoxon signed rank p=3. 7034e-12 for and 1. 4923e-06 for and C). We also used a non-parametric and robust skewness estimator called the medcouple (medc) measure (Brys et al. , 2004). Unlike classical histogram descriptors such as skewness and kurtosis, this approach finds the scaled median difference between the left and right sides of a distribution. Its values range from −1 to 1 (left to right skewed, respectively). Analysis using this method for mixed trials in Block 3 (memory) versus Block 1 (baseline) indices (Figure 5a inserts) revealed that the population data was skewed toward a memory response (positive medc, ~1). The positive skewness of the memory responses (compared to baseline) is also indicated by an increased slope of a polynomial fit line (red and blue lines in Figure 5a) which is shifted to left of the identity line (black dashed line). Task 2: No-RF task This task tested whether environmental memory could be evoked in the absence of receptive field stimulation. [SEP] Justify:

Output: The text contrasts the claim by indicating that memory response decays over time and is variable among cells. It mentions a decay in memory response even when a stimulus was previously applied, suggesting that memory cannot be perfectly recalled regardless of stimulus due to the inherent noise and variability in memory storage and recall.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: It can be immediately noted that sites near the 5′ end of the sequence (sites 2–6) covary with themselves and with all other sites, no other sites covary with anything else except with one of those. And the sites that sites 2–6 connect with are fairly evenly distributed throughout the rest of the sequence. Additionally, there is a lack of notable correlation between sites in the middle portion of the sequence. One possible explanation for this result is that this interaction between sites at the ends of the sequence is preventing any potential binding between the linear region and the hairpin part of the RNA so that the hairpin region can stay intact and pursue its function of terminating transcription of the downstream gene. This type of function for transacting regulatory RNAs has been well described in other contexts ((19), (22), (23), (24)). Figure 5. Strong correlations between nucleotides at sites across STAR. (A) Distribution of covariances between nucleotides at each site. Red bars indicate cutoffs of −0. 05 and 0. 05. (B) Large positive covariances with absolute magnitude >0. 05 across the 40 site long linear region of STAR (shown here from 5′ to 3′). (C) Large negative covariances. Table S1 for actual values. Open in new tabDownload slideAnother possible explanation for this result could be related to the way these sequences were designed by the algorithm. The objective of this software program is to calculate equilibrium distributions of the given nucleic acid strands (21). However, as has been noted by the authors of the STAR paper, STAR ‘regulation is governed by kinetic, out-of-equilibrium folding regimes’ and thus, the design of STAR sequences may not resemble natural sequences (19). Therefore, the apparent importance of sites at the 5′ end of the sequence could be an artifact of the software used to design the sequences. This warrants further investigation. The covariation pattern here indicates the potential importance of sites 2–6 to the functionality of this sequence, however, the fact that these sites appear to be correlated with a lot of other sites might suggest that they’re interacting with those other sites. Therefore, a functional variable must be projected onto this sequence space to find out the relative importance of these sites. [SEP] Justify:

Output: The text discusses the correlation and covariation of nucleotide sites in a specific RNA sequence, as well as the implications of algorithm design in these analyses. However, it does not directly support or refute the claim that bioinformatics is crucial for analyzing genomic data. Instead, it primarily focuses on specific observational data without addressing the role or importance of bioinformatics in the broader context of genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: In addition, the cocoa genotype (in particular cocoa bean storage proteins, polysaccharides and polyphenols) and the temperature and pH courses during fermentation, which determine the enzymatic activities in the beans, are of crucial importance (Schwan and Wheals 2004; Lima et al. 2011; Saltini, Akkerman and Frosch 2013; and Weckx 2016a,b; Castro-Alayo et al. 2019; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). It is not clear yet if the fermentation method applied may influence flavour development, as literature on the comparison of heap, box and tray fermentations is controversial, although there are indications that different fermentation methods may alter the flavour characteristics of cocoa liquors and conched chocolates (Owusu, Petersen and Heimdal 2012, 2013; Crafack et al. 2014; Ganeswari et al. 2015; Mota-Gutierrez et al. 2018). The use of starter cultures may allow faster, time-controlled and efficient cocoa fermentation processes and deliver high yields of cured cocoa beans of consistent and high quality. In turn, this may result in more profit for the farmers, the cocoa traders and the chocolate manufacturers, as further outlined below. Introducing the practice of starter culture-initiated cocoa fermentation processes has of course been a challenge, but once proof-of-principle was delivered the research focus shifted from accelerated and time-controlled fermentation processes (directed by the speed of pulp degradation and carbohydrate conversion) toward the use of starter cultures with extra functional properties, in particular regarding flavour formation. The first starter cultures that have been developed indeed have targeted an enhanced flow of sweatings by means of pectinolytic strains of dedicated yeast species applied as the sole starter cultures, among which S. cerevisiae var. chevalieri and Kluyveromyces fragilis (not necessarily fermenting cocoa pulp-bean mass isolates), to improve the pulp breakdown. Although this may increase the aeration of the fermenting cocoa pulp-bean mass and decrease the amounts of pulp monosaccharides available for fermentation to organic acids, leading to reduced bean acidity, it has not affected the cocoa bean quality significantly [Sanchez et al. [SEP] Justify:

Output: The text supports the claim by discussing the use of starter cultures in cocoa fermentation processes, indicating that specific strains of yeast, which may represent a type of lactic acid bacteria, contribute to improved fermentation efficiency and quality of cocoa products. It mentions how starter cultures can enhance the fermentation process and lead to high yields, reflecting the importance of these organisms in the fermentation process.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The basic memory task demonstrated that LIP neurons respond when a saccade brings the spatial location of a previously presented (now vanished) probe stimulus into their receptive field. However, because we established memory by having the monkey make a saccade that brought a stimulus into the receptive field, and then demonstrated the memory by having the monkey make the same saccade without a stimulus present, we could not know whether the memory response was independent of receptive field stimulation, or if it required visual stimulation or the receptive field to be established. Furthermore, we also could not determine whether the memory response was independent of the saccade used to establish it. To answer these questions, we used a modified version of the basic memory task, the No-RF task, in which the probe stimulus never appeared in the receptive field of the neuron and the monkey was required to make two different saccades on different trials (Figure 6). Figure 6 Download asset Open asset The No-RF task. Trials occurred in blocks. The first block (block 1, baseline) was used to establish the baseline activity of the neuron for saccade 1, prior to introduction to the probe stimulus. Block 2 (mixed, form and recall memory) introduced a task-irrelevant probe stimulus in half of the trials. In the half of trials where the probe stimulus appeared, the monkey was instructed to make a different saccade (saccade 2), which relocated the cell’s receptive field away from the probe location (Block 2, orange). In the other half of trials, no probe stimulus was presented and the monkey made saccade 1, bringing the location of the (previously presented) probe stimulus into the cell’s receptive field (Block 2, magenta). 3 (forget, identical to Block 1), to measure the decay of the memory response, the monkey was instructed to make saccade one and no probe was presented. Finally, in Block 4 (visual max), to measure the visual response of the cell to the probe, the monkey was instructed to make saccade 1, and the probe stimulus was presented in the cell’s receptive field. Trials in the first block of the No-RF task were identical to those in Block 1 of the basic memory task. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations in determining the independence of memory recall from external stimuli. It explains that the experiments involved different saccades and probe stimuli, which raises questions about whether memory responses are entirely reliant on stimuli or on the nature of the task and conditions under which the memory is invoked. This implies that memory might not be perfectly recalled with just the right stimulus, as the experiments do not provide clear evidence that memory can be independent of visual stimuli.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Given the covariance structure in the STAR linear sequence sequence (which is complementary to the target linear region) (Figure 5), we expected the regressions onto the sites to show a similar pattern of connection between sites at the 3′ end of the sequence and sites at the 5′ end. However, this pattern of correlations does not emerge when projecting the OFF fluorescence values onto the sequence space. Though there exists a dynamic range of GFP expression in the STAR library, as indicated by the OFF fluorescence values, the sequence correlation structure identified earlier does not seem to be functionally relevant when this phenotype is projected onto the sequence space. This is evidenced by the fact that when looking at just each site on its own, no particular sets of sites stick out as being more important than other parts of the sequence. To investigate this further, we built second order orthogonal polynomials and projected the phenotype onto them in order to identify pairs of interacting sites and any potential long-range interactions. Overrepresentation of purines While there is no spatial pattern in the magnitudes of regression coefficients, as was originally expected due to the sequence covariance pattern, there is a strong relationship between the kind of nucleotide at a site and the regression of the off phenotype values on it. Almost all the purines have positive regressions while almost all the pyrimidines have negative values. This means that there is a preference for having purines along the target RNA sequence while pyrimidines are disfavored. The hairpin of the target RNA, the intrinsic terminator, includes a string of guanines and cytosines which implies that these nucleotides would not be preferred along the linear region (19). But then this would mean that As and Us would have positive regressions here while Gs and Cs would be disfavored and have negative values. But that’s not what we see, we see that Gs and As have positive regressions and Cs and Us have negative regressions. So this is a somewhat mysterious result that warrants further investigation. After noting the purine/pyrimidine distinction, we were led to an additional hypothesis regarding molecular weights. There is a positive relationship between the magnitude of regression and the molecular weight of the nucleotide. Guanine is the heaviest molecule, followed by adenine, uracil and cytosine (Figure 6B). [SEP] Justify:

Output: The text discusses the relationships and patterns observed in nucleotide regression; however, it does not specifically mention bioinformatics or its importance in analyzing genomic data. Thus, it is irrelevant to the claim about the significance of bioinformatics.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Concerning the choice of candidate functional starter cultures, distinction should be made between studies that have tested culture collection or commercial strains that do not necessarily originate from fermenting cocoa pulp-bean mass and studies that have first initiated a thorough screening of strains with desirable functional properties. In almost all studies the starter cultures were applied as liquid cultures. All studies have used different fermentation recipients and batch sizes. Yeast starter cultures Except for an early Brazilian study (Schwan 1998), the first starter culture-initiated cocoa fermentation studies all dealt with single- or mixed-strain starter cultures involving yeasts. Indeed, Schwan (1998) was the first to evaluate the use of a consortium of cocoa pulp-bean mass-derived strains as starter culture for cocoa fermentation processes carried out in wooden boxes (200 kg, Comum hybrid), applying the microbial inoculum either at the start of the fermentation process or through phased addition. The selected strains in this study belong to the pectinolytic, acid-tolerant and ethanol-tolerant yeast species S. cerevisiae var. chevalieri, the lactic acid-producing, acid-tolerant and heat-tolerant LAB species Lactobacillus lactis (now reclassified as Lactobacillus delbrueckii subsp. lactis) and Lacp. plantarum and the ethanol-(over)oxidizing, acetic acid-producing, heat-tolerant AAB species Acetobacter aceti and Gluconobacter oxydans. This has resulted in a fermentation process comparable with a natural one, normally cured cocoa beans and acceptable chocolates, but with no enhanced quality. This proof-of-principle study has been followed by tests with pectinase-producing or flavour-producing yeast strains to accelerate the cocoa fermentation process (flow of sweatings) and improve the flavour of the cured cocoa beans and chocolates produced thereof. First, as follow-up of the study of Sanchez et al. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of lactic acid bacteria (LAB) in fermentation processes, specifically noting that Lactobacillus lactis, a lactic acid-producing species, is one of the strains utilized in cocoa fermentation studies. This indicates that LAB play a significant role in the fermentation process, supporting the assertion that they are important for such processes.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: 1, the monkey made a visually guided saccade (saccade 1) to a target outside of the neuron’s receptive field and no probe stimulus appeared (Figure 6, Block 1). 2, two trial types were pseudorandomly interleaved: no-probe trials identical to those in Block 1 (requiring saccade 1), and trials in which the probe stimulus appeared, but the monkey had to make a different saccade (saccade 2). In the latter trials, the probe stimulus appeared in the location corresponding the neuron’s receptive field after saccade 1. However, instead of making saccade 1, monkeys were instructed to make saccade 2, which relocated the neuron’s receptive field away from the probe stimulus location, thus preventing it from ever visually stimulating the cell. Block 3 was identical to Block 1, allowing us to measure the decay rate of the memory response. 4, the monkey was instructed to make saccade one and the probe stimulus was presented in the receptive field, visually stimulating the cell. This enabled us to compare the memory and visual responses (Figure 6, block 4 and Figure 7, green trace), and ensure that we had not lost the neuron during Block 3 while we observed the memory response decay. Figure 7 Download asset Open asset Single-cell responses in the No-RF memory task: from (left panel) and (right panel). Block descriptions are as in Figure 6. Black traces and raster dots: activity in Block 1, stimulus absent. Red (broken) traces and raster dots: activity in stimulus-present trials in Block 2. Magenta traces and raster dots: activity in probe stimulus-absent trials in Block 2. The memory decays after many (up to 100) trials. Green traces and raster dots: activity in Block 4, 100% stimulus present. H and V are horizontal and vertical eye movements smoothed using a 10 ms sliding causal filter. We studied 44 neurons (30 in Monkey A, 14 in Monkey C) in the No-RF task, in which the stimulus never appeared in the cell’s receptive field before and during the establishment of the memory. Some LIP neurons have a brief postsaccadic response to eye movements in all directions, including those away from their receptive fields (Wang and Goldberg, in preparation). [SEP] Justify:

Output: The text contrasts the claim by explaining that in the provided experiments, there were trials where the probe stimulus could not effectively stimulate the neuron’s receptive field, leading to the memory response decaying after many trials. This suggests that memory recall is not perfect and can be affected by the context and the presence of stimuli, indicating that memory recall is not guaranteed even with supposed 'right' stimuli.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The regressions were the largest for heaviest molecules and decreased as the molecular weights decreased. Furthermore, adenine and guanine are capable of hydrogen bonding with uracil. Guanine has a triple bond with cytosine and can form a double (wobbly) bond with uracil. As both of these nucleotides are capable of hydrogen bonding with uracil and this is a unique feature that distinguishes them from the pyrimidines, it might be the case that these nucleotides are more preferred. Regressions of ON values onto STAR sites In addition to projecting OFF values onto target RNA sites, ON values were projected onto the space of the sequences comprising the STAR linear region. The ON values are a measure of how good a STAR is at binding to the target RNA and activating the downstream gene. Since the STAR sequence is complementary to the target RNA, as expected, the regressions of having pyrimidines in the STAR linear region are positive while the regressions of having purines is negative (Figure 7). Figure 7. Regressions onto STAR sites. (A) Regressions of off values onto each STAR site orthogonalized within each vector (5′ to 3′). (B) Regressions of ON values onto STAR sites (orthogonalized within each vector). Open in new tabDownload slide Second order analysis on all pairs of sites across the STAR sequence The first order analysis, which includes calculating covariances and variances of nucleotides at each site, revealed a number of highly correlated sites (absolute values of covariances being >0. 05) sequestered in the 5′ region of STAR. In order to test the hypothesis that sites that are correlated (Figure 5) are interacting in their effect on the phenotype, we built second order polynomials for each pair of sites across the STAR sequence (with 780 unique pairs in total). See the corresponding supplementary section that shows an example of how this is done with a different set of sites. After building second order polynomials for each pair of sites across the STAR sequence, regressions of the phenotype onto each pair of sites were computed. For a given pair of sites, this results in a 4×4 matrix, with 16 total values that each correspond to a given nucleotide at the first site and another nucleotide at the second site. Figure 8 shows an example of this for sites 3, 5 and 21. [SEP] Justify:

Output: The text does not address or provide any information about bioinformatics or its importance in analyzing genomic data. Instead, it discusses molecular interactions and regression analyses related to nucleotides and RNA, which is unrelated to the claim regarding the role of bioinformatics.