Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: After a time interval of 6 h, the reducing sugars liberated in the reaction mixtures were determined by dinitrosalicyclic acid method [58]. Light microscopy was used for the examination of the effect of enzyme on raw starch granules using Olympus microscope BH-2. Advertisement 4. Results and discussion4. 1. Biochemical properties of amylolytic LAB isolatedFrom the 28 samples of soil collected from different localities of Cameroon, 90 amylolytic isolates were screened but only 9 isolates (04BBA15, 04BBA19, 05BBA22, 05BBA23, 14BYA42, 20BBA60, 17BNG51, 23BYA21, 26BMB81) presented very high amylolytic power (≥15mm) and were qualified as amylase overproducing isolates. The amylolytic power was defined as the average diameter (mm) of starch hydrolysis halo (Fig. 1. ) provoked by a strain after its inoculation in micro-well on MRS-starch agar plate for 48 h incubation at optimum temperature of growth for three assays. The amylolytic power is an expression of the capacity of an isolate to degrade starch during the culture. Among the amylase overproducing isolates, two (04BBA19, 26BMB81) were aero-anaerobic non spore forming, gram positive and catalase negative bacteria; this characteristic is proper to lactic acid bacteria. Microscopic observation showed rod cells. Biochemical characteristics of these isolates were carried out using API 50 CH kit bioMerieux system, the results are summarized in Table 1, the isolates were tested for their possibility to ferment 50 carbohydrates, and this fermentation profile was use for their numerical identification. According to their biochemical profile, 04BBA19 and 26BMB81 were respectively identified as Lactobacillus fermentum and Lactobacillus plantarum. The strain 04BBA19 (Lactobacillus fermentum) presented a very high amylolytic power, as it was able to cause a starch hydrolysis halo of 45 ±1. 5mm on MRS-starch agar plate after 48 h of incubation at 40 °C; consequently it was selected for further studies. [SEP] Justify:

Output: The text corroborates the claim by highlighting the biochemical properties of lactic acid bacteria (LAB) isolated from soil samples. Specifically, it mentions that two isolates, classified as LAB, were tested for their ability to ferment carbohydrates, which underscores their role in fermentation processes. The identification of these isolates as Lactobacillus fermentum and Lactobacillus plantarum further supports the claim by providing examples of lactic acid bacteria that are integral to fermentation.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In the recall phase, ‘Spotlight Mode’ is turned off and the subject performs four recall trials. For each recall trial, the subject presses the Clicker, then a voice instruction specifies which object to turn to, the subject faces in the recalled direction, and confirms with a voice command (‘Target confirmed’). The entire task was repeated in two blocks that differed in the arrangement of the objects. The object sequence from left to right was ‘piano’, ‘table’, ‘chair’, ‘lamp’, ‘trash bin’ (block 1), and ‘trash bin’, ‘piano’, ‘table’, ‘chair’,‘ lamp’ (block 2). The center object is never selected as a recall target because 0° is marked by sonar beeps and thus can be aimed at trivially. Task 3, direct navigation (Figure 3): In each trial, a single chair is placed at 2 m from the center of the arena at an azimuth angle randomly drawn from four possible choices: 0°, 90°, 180°, 270°. To start a trial, the subject must be in a starting zone of 1 m diameter in the center. During navigation, the subject can repeatedly press the Clicker to receive a spatialized call from the target. The trial completes when the subject arrives within 0. 5 m of the center of the target. Then the system guides the subject back to the starting zone using spatialized calls emanating from the center of the arena, and the next trial begins. Subjects performed 19–21 trials. All blind subjects moved freely without cane or guide dog during this task. To measure performance on a comparable search without CARA, each subject performed a single trial with audio feedback turned off. A real chair is placed at one of the locations previously used for virtual chairs. The subject wears the HoloLens for tracking and uses a cane or other walking aid as desired. The trial completes when the subject touches the target chair with a hand. All blind subjects used a cane during this silent trial. Task 4, long range guided navigation (Figure 4): The experimenter defined a guide path of ~36 m length from the first-floor lobby to the second-floor office by placing nine waypoints in the pre-scanned environment. [SEP] Justify:

Output: The text contrasts the claim by describing a series of trials where subjects, including blind individuals, are guided through navigation tasks to locate objects using stimuli such as spatialized calls and audio feedback. This suggests that recall may be dependent on conditions, tasks, and the absence or presence of specific guidance, implying that memory recall is not perfect and can be influenced by external factors.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Note that the same PDZ domain 3D structure (α 1-syntrophin accession number 2PDZ) has been used in all three pictures since the structures of Lap4(2/4) and Lin7(1/1) from C. elegans and D. melanogaster have not been solved. The residue labels refer to the amino acid at the given position in Lap4(2/4) and Lin7(1/1) and do thus not correspond to the structure of the depicted amino acids. Residue numbering is according to 2PDZ. pdb. Bayesian P-valuesTo explain the Bayesian P-values used in the 6026. 1259. 10. 1038/nbt1017216. 127. 10. 1186/1471-2105-7-1271965. 10. 1016/j. patrec. 2008. 06. 018539. 10. 1038/nbt1397140. 10. 1038/msb4100180125. 10. 1186/1471-2105-9-1252000. Book 1178. 10. 1038/nbt0904-11772007. 1985. 651. 10. 1038/451648a679. 3555. 10. 1073/pnas. 0506230102272. 10. 1093/jnci/djj05264. 10. 1186/bcr1325178. 10. 1093/bioinformatics/bth469492. 10. 1016/S0140-6736(05)17866-0210. 10. 1093/nar/30. 1. 2073214. 10. 1158/1078-0432. CCR-06-27652268. 10. 1093/bioinformatics/btl362369. 10. 1126/science. 11445921045. 10. 1038/nbt. 1489859. 10. 1093/protein/11. 10. 855242. 10. 1093/nar/28. 1. 2351194. 10. 1093/bioinformatics/btp116203. 10. 1126/science. 1169377299. 10. 1126/science. 286. 5438. 2951809. 10. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses a specific 3D structure and Bayesian P-values without any mention of 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: The preliminary test of thermostability carried out on its crude extract amylase showed that it produce a very high thermostable enzyme and it was selected for an application on simultaneous production of thermostable amylase and lactic acid from starchy material. Figure 1. Plate assays for detection of amylase activity of lactic acid bacteria (04BBA19, 26BMB81) on MRS-starch agar plate medium. The diameter of hydrolysis halo was revealed by flooding the plates with Iodine solution (0. 1% I2+1% KI) after 48 h of culture at 40°CTest number1234567891011121314151617181920212223242526272829Strains codHeterofermentativeGaz productionOptimum temperature of growthGrowth at 10°CDextranAmmonia from ArginineNitrate reductionGlycerolErythritolD-arabinoseL-arabinoseRiboseD-xyloseL-xyloseAdonitolß methyl-D-α-Methyl-D-mannosideα-Methyl-D-glucosideN-Acetyl-Glucosamine04BBA19++45°C--+-----+----++++---nc-----26BMB81-+40°C-------++----++++----++-++Test number303132333435363738394041424344454647484950515253545556Strains codß GentiobioseD-turanoseD-lyxoseD-tagatoseD-fucoseL-fucoseD-arabitolL-arabitolGluconate2-gh-Gluconate5-Keto-GluconateIdentification species (API 50CHL) 04BBA19--nc--++++---++----------+--Lactobacillus fermentum 26BMB81++++++++++nc++++-++------+--Lactobacillus plantarumTable 1. Biochemical characteristics of amylases overproducing Lactobacillus isolated from soils4. 2. Amylase and lactic acid productionIn the presence of starch as carbon source at 40°C, L. [SEP] Justify:

Output: The text discusses the role of lactic acid bacteria in producing lactic acid during the fermentation process. It specifically mentions the isolation of Lactobacillus species that overproduce amylases and lactic acid from starchy materials, indicating 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: This approach, which the authors call 'targeted dimensionality reduction' (TDR), also yields one component per task parameter: in our example, we obtain one component for the stimulus and one for the decision (Figure 1h; see Materials and methods for details). Both of these approaches are supervised, meaning that they are informed by the task parameters. At the same time, they do not seek to faithfully represent the whole dataset and are prone to losing some information about the neural activities. Indeed, the two components from Figure 1e explain only 23% of the total variance of the population firing rates and the two components from Figure 1h explain only 22% (see Materials and methods). Consequently, a naive observer would not be able to infer from the components what the original neural activities looked like. While such supervised approaches can be extended in various ways to produce more components and capture more variance, a more direct way to avoid this loss of information is to resort to unsupervised methods such as principal component analysis (PCA). This method extracts a set of principal components (PCs) that are linear combinations of the original PSTHs, just as the population averages above. However, the weights to form these linear combinations are chosen so as to maximize the amount of explained variance (first six components explain 69% of variance, see Figure 1i–k). The principal components can be thought of as 'building blocks' of neural activity: PSTHs of actual neurons are given by linear combinations of PCs, with the first PCs being more informative than the later ones. However, since PCA is an unsupervised method, information about stimuli and decisions is not taken into account, and the resulting components can retain mixed selectivity and therefore fail to highlight neural tuning to the task parameters. The most striking observation when comparing supervised and unsupervised approaches is how different the results look. Indeed, PCA paints a much more complex picture of the population activity, dominated by strong temporal dynamics, with several stimulus- and decision-related components. At the same time, none of the methods can fully demix the stimulus and decision information: even the supervised methods show decision-related activity in the stimulus components and stimulus-related activity in the decision components (Figure 1e,h). [SEP] Justify:

Output: The text contrasts the claim by explaining that neither supervised nor unsupervised methods can fully demix stimulus and decision information. It states that 'none of the methods can fully demix the stimulus and decision information,' implying that simply having the right stimulus does not guarantee perfect recall of memory, as there is always residual mixing of information.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 69. 92 the features "snoRNA," "scRNA," "snRNA," and "misc_RNA" were replaced by the new feature "ncRNA" and its mandatory qualifier "ncRNA_class. " The qualifier defines the kind of "ncRNA," and its value set is defined in an extensible form [11]. This suggests that further development in the area of non-coding RNA may be expected in the future. As new insights into MB are gained, the domain and its concepts quite often have to be viewed from a new perspective; sometimes new experimental or theoretical results shift even fundamental concepts. The term "concepts" means the notable entities in the domain, such as different kinds of molecules (and proteins) or special regions of molecules (genes or promoter). Software systems for MB must be sufficiently flexible to accommodate these changes in a reasonable amount of time; otherwise, the software soon becomes obsolete or its value diminishes [12]. Previous studies investigated extensibility as an important requirement to achieve flexibility for MB software; e. g. Jones and Paton [13]. Besides extensibility, changeability is equally required to achieve flexibility [14]. An approach often used to deal with the problem of flexibility is to consider only a small, well-defined area of the domain. By this means, knowledge changes are scarcer and, due to the lower program complexity, easier to implement. The drawback is an isolated domain understanding of these programs, which is very much dependent on the particular program's domain view. Furthermore, these programs very often use proprietary data formats. The explorative manner of biologists work is not well supported, and interoperability and data exchange are difficult to achieve. Performing large-scale "in-silico" analyses using several programs often requires an enormous amount of time and resources for interoperability. The challenge in developing software for MB is to satisfy both requirements appropriately: interoperability to support explorative work in MB, and flexibility to accommodate new knowledge in the domain. To meet this challenge, we approach the problem from the fundamental level of computer program development; namely, the domain model, also known as "conceptual model. " In object-oriented (OO) software development [15, 16], the domain model is a systematic description of entities in the subject domain and their relationships [14, 17]. [SEP] Justify:

Output: The text is largely focused on the development and flexibility of software systems for molecular biology (MB), discussing aspects such as extensibility, changeability, and interoperability in the context of software evolution. While bioinformatics generally relates to the analysis of genomic data, the specific content of the text does not directly address its importance or relevance in that context, thus rendering it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: fermentum 04BBA19 strain grew, exhibited amylolytic activity and produced lactic acid in the culture medium. The amylase production pattern in L. fermentum 04BBA19 (Fig. 2) indicates that the induction of amylase took place during the lag phase (after 10 h of incubation) in the presence of starch. The level of amylase production increased significantly during the exponential phase of growth. Lactic acid production became visible around 15 h after incubation and also increased considerably during the exponential phase of growth. Cell growth, amylase and lactic acid production reached maxima values at the same time (40 h of fermentation). The values of those maxima were 1. 1x109 cfu/ml, 107. 3±0. 5 U/ml, 8. 7±0. 5g/l for cell growth, amylase activity, and lactic acid production respectively. Such coincidence shows that amylase production by L. fermentum 04BBA19 was tightly linked to cell growth. These results are in agreement with the report of Goyal et al. [53], Liu and Xu [54] on the relationship between pattern of cell growth and amylase production. The decline of cell growth and amylase production after the peak occurred around 50 h of incubation and could be attributed to the rise of lactic acid concentration in fermented broth [6] or to the rise of protease levels [55]. The acidification was also expressed by the decrease of initial pH of culture broth (Fig. 2). The initial pH of culture broth declined significantly and reached a value of 3. 0 around 50 h of incubation and then remained constant. Figure 2. Time course of growth (○), pH (▽), α-amylase (▲) and lactic acid (□) production by L. fermentum 04BBA19 in 1% (w/v) soluble starch medium at 40°C, pH 6. 0. The data shown are averages of triplicates assays within 10% of the mean value. The study of cell growth and amylase production as a function of temperature (Fig. 3a) showed that L. fermentum 04BBA19 exhibited maximal growth and amylase activity at 45°C, confirming thus the strong relationships between cell growth and amylase production. [SEP] Justify:

Output: The text corroborates the claim by highlighting the role of L. fermentum 04BBA19 in fermentation processes, specifically noting the production of lactic acid during the fermentation. It details that lactic acid production became visible after 15 hours of incubation and increased significantly, demonstrating the connection between the growth of the bacteria and the fermentation process.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Demixed principal component analysis (dPCA) To address these problems, we developed a modified version of PCA that not only compresses the data, but also demixes the dependencies of the population activity on the task parameters. We will first explain that these two goals generally constitute a trade-off, then suggest a solution to this trade-off for a single task parameter, and then generalize to multiple task parameters. The trade-off between demixing and compression is illustrated in Figure 2, where we compare linear discriminant analysis (LDA, Figure 2a,b), PCA (Figure 2c,d), and dPCA (Figure 2e–h). We will first focus on a single task parameter and seek to reduce the activity of N=2 neurons responding to three different stimuli. For each stimulus, the joint activity of the two neurons traces out a trajectory in the space of firing rates as time progresses (Figure 2b). The aim of 'demixing' in this simplified case is to find a linear mapping (decoder) of the neural activity that separates the different stimuli (Figure 2a) and ignores the time-dependency. We can use LDA in order to determine a projection of the data that optimally separates the three stimuli. However, LDA will generally not preserve the 'geometry' of the original neural activity: firing patterns for stimuli 1 and 2 are close to each other and far away from stimulus 3, whereas in the LDA projection all three stimuli are equally spaced (Figure 2b). More generally, decoding is always prone to distorting the data and therefore tends to impede a proper reconstruction of the original data from the reduced description. Figure 2 Download asset Open asset Linear dimensionality reduction. (a) Linear discriminant analysis maps the firing rates of individual neurons onto a latent component that allows us to decode a task parameter of interest. Shades of grey inside each neuron show the proportion of variance due to the various task parameters (e. g. stimulus, decision, and time), illustrating mixed selectivity. In contrast, the LDA component is maximally demixed. (b) At any moment in time, the population firing rate of N neurons is represented by a point in the N-dimensional space; here N=2. Each trial is represented by a trajectory in this space. Colors indicate different stimuli and dot sizes represent time. [SEP] Justify:

Output: The text contrasts the claim by indicating that decoding neural activity, which is suggested as a method for memory recall, is inherently prone to distorting the data, thus impeding a proper reconstruction of the original data from the reduced description. It mentions that while techniques like linear discriminant analysis (LDA) aim to separate different stimuli, they may not preserve the 'geometry' of the original neural activity, which implies that perfect recall may not be achievable.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: It reduces the gap between mental and software models, and it supports communication between domain experts and software developers [18]. The domain model is created in an early development phase, the analysis phase. It is the basis for data models created in later phases, such as data formats for data exchange and file storage, or the database model, or the class model for the domain layer. Changes of the domain model affect many parts of a software system; they are therefore costly and complicated and should be avoided. So, besides flexibility, stability is also very important for domain models in MB. A comprehensive domain model for MB could provide a shared domain understanding for a wide range of programs and therefore support interoperability. It is difficult to fulfill both requirements using commonly applied OO modeling approaches. In these approaches, domain concepts are modeled statically in class hierarchies with associations between classes. New or changed knowledge leads to changes in the domain model, which should be avoided, as mentioned above. Therefore, we present a different modeling approach, which has adapted the "Model" pattern [19]. This approach promises to fulfill both requirements of a comprehensive domain model for MB – flexibility and stability – in a balanced manner and therefore supports interoperability. ResultsApproachOverviewThe essential idea behind our approach is the separation of fundamental domain concepts from domain knowledge, as proposed by Beale [12]. Fundamental concepts define the scope of the subject domain, and hence the scope of the software. If fundamental concepts were to change, the subject of the domain and the software would have to change, too. Therefore, fundamental concepts constitute a stable core; changes would only affect domain knowledge. Following this idea, fundamental concepts were modeled in an abstract class model and domain knowledge was reflected in an object model. This approach utilizes the "Model" pattern [19], also known as "Model" [20]. This is a composed pattern and its core is the "Type Object" pattern [21]. We named abstract classes corresponding to fundamental concepts typed classes. Instances of typed classes get a more concrete meaning from a special attribute, called type attribute, which describes a concrete concept. In other words, instances of typed classes are parameterized by their type attributes. These type attributes constitute the model for the domain knowledge. They are instances of special classes, called type definition classes. The following example explains this approach: In classical OO models, different molecules such as and proteins are represented as classes in a class hierarchy (Figure 1a). [SEP] Justify:

Output: The text discusses a modeling approach for software development and domain modeling, but it does not address bioinformatics or its role in analyzing genomic data. Therefore, it is irrelevant to the specific claim regarding bioinformatics.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: [63], Saxena et al. [4] who reported maximum amylase production when starch was used as carbohydrate source. In the presence of glucose and fructose, amylase production was almost nil; and that was a proof that glucose and fructose repressed amylase synthesis by L. fermentum 04BBA19. This observation is in agreement with the reports of Theodoro and Martin [64] showing that synthesis of carbohydrate degrading enzymes in some microbial species leads to catabolic repression by substrate such as glucose and fructose. Similar results were observed by Halsetine et al. [65] for the production of amylase by the hyperthemophilic archeon Sulfolobus solfataricus. According to them, glucose prevented α-amylase gene expression and not only secretion of performed enzyme. Since amylase yield is higher with amylose (92. 3 U/ml) as carbohydrate source than with amylopectin (50. 1 U/ml), the L. fermentum 04BBA19 amylase is more efficient for hydrolysis of alpha-1,4 linkages than those of alpha-1,6. The amylase production increased with the soluble starch concentration (Fig. 4), reaching a maximum (180. 5 ± 0. 3 U/ml) at the concentration range of 8-16 % (w/v). These optimum starch concentrations for amylase production by L. fermentum 04BBA19 are higher than that observed for amylase production in Bacillus sp. PN5 reported by Saxena et al. [4]. This microorganism presented an optimum soluble starch concentration of 0. 6% (w/v) for amylase production. The lactic acid production also increased with the soluble starch concentration, the optimum starch concentration for lactic acid production was achieved at the same range of concentration for amylase production. Figure 4. Effect of starch concentration on α-amylase (●) and lactic acid production () by L. fermentum 04BBA19. The data shown are averages of triplicate assays with SD within 10% of mean valueAmong the various gelatinized starchy sources tested, corn and sorghum flour were found to be the most suitable for α-amylase and lactic acid production by L. [SEP] Justify:

Output: The text corroborates the claim by demonstrating that lactic acid production increased with the concentration of soluble starch, indicating an active fermentation process facilitated by lactic acid bacteria, specifically L. fermentum 04BBA19. Reference to amylase production alongside lactic acid production further supports the importance of lactic acid bacteria in fermentation.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The LDA component for stimulus is given by the projection onto the LDA axis (black line); projections of all points are shown along this line. All three stimuli are clearly separated, but their geometrical relation to each other is lost. (c) Principal component analysis linearly maps the firing rates into a few principal components such that a second linear transformation can reconstruct the original firing rates. (d) The same set of points as in (b) is projected onto the first PCA axis. However, the stimuli are no longer separated. Rather, the points along the PCA axis have complex dependencies on stimulus and time (mixed selectivity). The PCA axis minimizes the distances between the original points and their projections. (e) Demixed principal component analysis also compresses and decompresses the firing rates through two linear transformations. However, here the transformations are found by both minimizing the reconstruction error and enforcing a demixing constraint on the latent variables. (f) The same set of points as in (b) projected onto the first dPCA decoder axis. The three stimuli are clearly separated (as in LDA), but some information about the relative distances between classes is preserved as well (as in PCA). (g) The same data as in (b) linearly decomposed into the time effect, the stimulus effect, and the noise. (h) The dPCA projection from (f) has to be mapped onto a different axis, given by the dPCA encoder, in order to reconstruct the stimulus class means (large colored circles). The decoder and encoder axes together minimize the reconstruction error between the original data and the stimulus class means. The aim of compression is to find a linear mapping (decoder) that reduces the dimensionality and preserves the original data as much as possible (Figure 2c,d). Using PCA, we determine a projection of the data that minimizes the reconstruction error between the projections and the original points. In contrast to seeks to preserve the geometry of the neural activity, and thereby yields the most faithful reduction of the data (Figure 2d). However, the PCA projection does not properly separate the stimuli and mixes the time-dependency with the stimulus-dependency. The wildly different projection axes for LDA (Figure 2b) and PCA (Figure 2d) seem to suggest that the goals of demixing and compression are essentially incompatible in this example. [SEP] Justify:

Output: The text contrasts the claim by discussing various data projection techniques (LDA and PCA) that demonstrate the complexities of mapping stimuli to memory recall. It explains that even though stimuli can be separated through various analyses, preserving the geometrical relationships and ensuring perfect recall is challenging due to the complexities of neural activity, such as mixed selectivity and the interference of time dependencies with stimulus dependencies. This suggests that perfect recall is not achievable solely through the right stimulus, as indicated by the loss of geometric information during certain projections.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: In contrast, the presented modeling approach uses classes as well as instances (Figure 1b): Molecule is a typed class and MoleculeType is a type definition class. Different molecule types are represented by instances of MoleculeType, not by subclasses of Molecule (see the object diagram in Figure 1b). These instances are type attributes of Molecule instances. By using this approach, new concepts can be introduced into the model without changing the class model. Figure 1Comparison of the classical and the proposed modeling approach. A) Classical OO-class model of different kinds of molecules. B) Model of different kinds of molecules following the proposed approach. On top, the class model with typed class Molecule and its type definition class MoleculeType. At the bottom, the object model that defines concrete kinds of molecules, instances of MoleculeType. They are used by instances of the typed class Molecule. Full size imageFollowing this approach, we realized that four models can be distinguished on different abstraction levels (Figure 2):Figure 2Four different models. The presented approach leads to four different models: operational model, meta model of the knowledge model (meta model), knowledge model, and information model. The operational model and meta model are OO-class models. The knowledge model consists of instantiated classes of the meta model. The information model consists of instantiated classes of the operational model that are parameterized by elements of the knowledge model. Elements of the operational and information model are shown in red. Elements of the meta model and knowledge model are depicted in blue. Instances are given in a lighter color. Full size image The operational model defines the scope of the domain model. It contains the fundamental abstract domain concepts. It is a classical OO-class model that consists of the typed classes. The knowledge model defines concrete concepts in the area of interest; these concepts and their relations are subject to changing knowledge. The elements of the knowledge model are instances of type definition classes that can be created and changed dynamically. They are used as type attributes in the operational model. The meta model of the knowledge model defines the structure of the knowledge model. It joins concrete concepts defined in the knowledge model with abstract concepts of the operational model. Classes in the meta model of the knowledge model are type definition classes. This model is therefore an OO-class model. The information model contains application data from the developed system. [SEP] Justify:

Output: The text is irrelevant to the claim about bioinformatics and genomic data analysis. It discusses a modeling approach involving classes and instances related to molecules and does not mention bioinformatics or genomic data.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: fermentum 04BBA19 while for the raw starchy sources tested, potato starch was most suitable (Table 2). On the other hand the level of lactic acid was more important when corn and sorghum flours were used. The good production of α-amylase and lactic acid when these starchy flours are used is based on their composition; they also contain proteins and vitamins which are required by lactic acid bacteria for their growth, enzymes and acids production [66]. Among nitrogen sources used in the present study, soya bean meal and yeast extract showed significant effect on α-amylase and lactic acid production. Soya bean meal, rich in protein is a potential nutrient for lactic acid fermentation. Similar results were obtained by several authors. Goyal et al. [53] reported that soybean meal presented a positive effect and was the best nitrogen source for raw starch digesting thermostable α-amylase production by the Bacillus sp I-3 strain. The yeast extract was also reported to be a potential nutrient for lactic acid fermentation, since it contains vitamins, amino acids [66]. Though all nitrogen sources are positively influencing enzyme production by L. fermentum 04BBA19, an inverse behaviour has been observed with other bacterial strains, for instance, Tanyildizi et al. [67] reported zero effect of yeast extract on amylase production by Bacillus sp. All metal salts tested in this study increased amylase and lactic acid production by L. fermentum 04BBA19, except CuSO4. 5H2O that acted as inhibitor. The inhibition of amylase production by CuSO4. 5H2O was also reported by Wu et al. [68] for the Bacillus sp CRP strain. Copper ion acted as poisonous compound for this strain and consequently inhibited amylase synthesis. The effect of CaCl2. 2H2O was the most important, and was in agreement with the observation of Gangadharan et al. [61] who described the rise of amylase production by B. amyloliquefaciens when CaCl2. 2H2O was supplemented to the culture medium. The supplementation of metal ions has been reported to provide good growth and also influence higher enzyme production. [SEP] Justify:

Output: The text corroborates the claim by describing the essential roles that lactic acid bacteria, specifically L. fermentum 04BBA19, play in the fermentation processes. It discusses how the production of lactic acid and α-amylase is influenced by various starchy substrates and the necessary nutrients that lactic acid bacteria require for growth and enzyme production. The mention of soya bean meal and yeast extract as potential nutrients for lactic acid fermentation supports the significance of lactic acid bacteria in fermentation.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: However, we can achieve both goals by assuming that the reconstruction of the original data works along a separate encoder axis (Figure 2f,h). Given this additional flexibility, we first choose a decoder axis that reconciles the decoding and compression objectives. Once projected onto this axis, all three stimuli are separated from each other, as in LDA, yet their geometrical arrangement is approximately preserved, as in PCA (Figure 2f). In turn, when reconstructed along the encoder axis, the projected data still approximates the original data (Figure 2h). To define these ideas more formally, we assume that we simultaneously recorded the spike trains of N neurons. Let 𝐗 be our data matrix with N rows, in which the i-th row contains the instantaneous firing rate (i. e. binned or smoothed spike train) of the i-th neuron for all task conditions and all trials (assumed to be centered, i. e. , with row means subtracted). Classical PCA compresses the data with a decoder matrix 𝐃. The resulting principal components can then be linearly de-compressed through an encoder matrix 𝐃⊤, approximately reconstructing the original data (Hastie et al. , 2009). The optimal decoder matrix is found by minimizing the squared error between the original data, 𝐗, and the reconstructed data, 𝐃⊤𝐃𝐗, given by LPCA=∥X−D⊤DX∥2. In the toy example of Figure 2, the data matrix 𝐗 is of size 2×15, and the decoder matrix 𝐃 is of size 1×2. Crucially, the information about task parameters does not enter the loss function and hence PCA neither decodes nor demixes these parameters. In our method, which we call demixed PCA (dPCA), we make two changes to this classical formulation. First, we require that the compression and decompression steps reconstruct not the neural activity directly, but the neural activity averaged over trials and over some of the task parameters. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities of reconstructing data and how certain methods, such as PCA and dPCA, approximate reconstruction rather than achieving perfect recall. The mention of the reconstruction being 'approximately' accurate suggests limitations to memory recall rather than the perfection implied by the claim.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: It is not developed in any phase of the software development process, but rather it originates during the execution of an implemented system. It consists of instances of classes from the operational model, which use type attributes. The names "operational model" and "knowledge model" are inspired by the terms "operational level" and "knowledge level," used by Fowler in [22]. Beale used the terms "knowledge level" and "information level" in [12]; the latter term was the inspiration behind the naming of the "information model. "Properties of knowledge model conceptsConcrete concepts usually define properties. For example, double-stranded DNA has a melting temperature and a GC ratio, and proteins can have annotations describing their function. In our approach, properties cannot be modeled as instance attributes of the typed class. This is because properties are not shared by all kinds of concepts; every concept has its own set of properties. To meet this challenge, we integrated properties using a generic property model (Figure 3). The basis of this model is the "Property" pattern [23]. Figure 3Modeling properties. Properties of concrete concepts are held by the typed class Property. Instances of its type definition class PropertyType (gcType, mtType, and funcType) specify the meaning of properties with their attribute name. The attributes valueType and cardinality restrict the data type and number of hold values. PropertyHolder and PropertyRegistry support the handling and definition of property sets. The PropertyHolder aDnaMol holds the Property gcProp of type gcType; the optional Property of type mtType is not set. Full size imageThe property model defines kinds of properties by instances of the type definition class PropertyType. These instances are elements of the knowledge model (the object diagram at the bottom right of Figure 3) and represent concrete kinds of properties (mtType, gcType, and funcType). Instances of the typed class Property (mtProp and funcProp) represent property values according to its PropertyType. Domain concepts usually have several properties; they have a property set. These sets are defined by instances of the type definition class PropertyRegistry (dnaPropReg and proteinPropReg in Figure 3). These instances define property sets for concrete concepts, they are also elements of the knowledge model. [SEP] Justify:

Output: The text does not address the claim directly or provide any information that supports or contrasts it. Instead, it discusses the modeling of properties in a knowledge model and the operational aspects of a property model without mentioning bioinformatics or its role in genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Most α-amylases are metalloenzymes and in most cases, Ca2+ ions are required for maintaining the spatial conformation of the enzyme, thus play an important role in enzyme stability [61]. From the surfactants tested in this study, Tween-80 appeared to be the best surfactant sources for amylase production by L. fermentum 04BBA19. Similar results were obtained by Reddy et al. [69]. These authors reported that the supplementation of culture medium with Tween-80 resulted in a marked increase in the yields of thermostable β-amylase and pullullanase by C. thermosulfurogenes SV2, and that the stimulation of enzyme production was greater when the surfactants were added after 18 h of incubation of culture. Beside stimulation, the surfactants caused and increased secretion of the enzymes into extracellular fluid [59]. From various environmental factors tested for α-amylase and lactic acid production by L. fermentum 04BBA19, it has been observed that all factors that increase amylase synthesis also positively affect lactic acid production. The optimization of the basal medium by supplementation of all carbohydrate, nitrogen, mineral and surfactant sources (excepted CuSO4. 5H2O4) in culture medium resulted to a significant improvement of enzyme and lactic acid yield. In the optimized medium, amylase activity and lactic acid content reached 732. 4±0. 4 U/ml and 53. 2±0. 4 g/l respectively. ParametersEnzyme yield (U/ml)Lactic acid(g/l)Carbohydrate sources (1% w/v)Glucose0. 1±0. 0d*14. 3±0. 5aFructose0. 2±0. 0d12. 1±0. 5bMaltose0. 1±0. 0d12. 8±0. 4bAmylose92. 3±0. 1b12. 2±0. 1bAmylopectin50. 1±0. 5c10. 3±0. 5cSoluble starch107. 3±0. 5a8. 7±0. 5dNitrogen sources (1. 5% w/v)Yeast Extract107. 3±0. 5b8. [SEP] Justify:

Output: The text corroborates the claim by discussing how various factors that enhance α-amylase synthesis also positively impact lactic acid production by L. fermentum 04BBA19. The optimization of the culture medium with different sources led to significant improvements in both enzyme and lactic acid yields, indicating the role of lactic acid bacteria in fermentation processes.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: For each neuron i, regress its firing rate at each time point t on stimulus, decision, and interaction between them: xi(t)=βio(t)+βis(t)s+βid(t)d+βisd(t)sd+ϵ, where d is any suitable parametrization of decision, e. g. d= ± 1, and s is stimulus value (e. g. actual stimulation frequency in the somatosensory working memory task in monkeys). values of βis(t) as defining an N-dimensional vector 𝜷s(t), and analogously define 𝜷d(t), 𝜷sd(t), and 𝜷o(t). Mante et al. (2013) did not use the condition-independent term 𝜷o(t) in their original study, but we treat it here on equal footing. Project each 𝜷ϕ(t) onto the space spanned by the leading PCA axes: 𝐊𝜷ϕ(t). For each of the three parameters select one vector 𝜷*ϕ=𝐊𝜷ϕ(t*ϕ), where t*ϕ is the time point at which the norm of 𝐊𝜷ϕ(t) is maximal. Finally, stack the obtained vectors to form a matrix 𝐁=[𝜷*s𝜷*d𝜷*sd𝜷*o] and perform QR decomposition 𝐁=𝐐𝐑 with 𝐐 orthonormal and 𝐑 upper triangular to find a set of three orthogonal demixing axes (as columns of 𝐐). If the individual vectors in 𝐁 are far from orthogonal, then the resulting axes will strongly depend on the order of stacking them into the matrix 𝐁. [SEP] Justify:

Output: The text contrasts the claim by discussing the complex modeling of neural responses and decision-making processes without asserting that memory can be perfectly recalled. It implies that memory recall might not be as straightforward as the claim suggests, particularly since it describes a method of examining neural activity that suggests variability and dependence on several factors rather than perfect recall.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The corresponding typed class for PropertyRegistry is PropertyHolder, which is a container class to manage instances of Property according to the PropertyRegistry. The property model is integrated into our domain model using inheritance (see "Modeling properties of concrete concepts"). If inheritance might cause problems, delegation can be used instead: For example, when the class that should have properties is the subclass of another class, delegation would avoid problems that multiple inheritance could imply [24]. Associations between concrete conceptsDomain objects are often related to other domain objects. For example, a promoter can be involved in gene regulation. To support relationships, Riehle suggests the "Objects" [19] pattern. It is a combination of "Association Classes" [22, 25] (classes that describe associations) and the "Type Object" pattern. "Objects" are "Association Classes" that have a type attribute; in our terms, they are typed association classes. On the basis of "Objects," we have developed a model for associations between typed classes (Figure 4). Figure 4Modeling relations. A relation model realizes relations between concrete concepts defined in the knowledge model. Relation is a typed association class that holds 8. Figure 8Example of the usage of properties. The property framework is integrated into the operational and meta model by inheritance. This is exemplified here by the Molecule class. Full size imageOverview of the operational modelThe preceding sections explained parts of the operational and the meta model in combination. The next two sections will explain both models separately to clarify their content and responsibility. This section describes the operational model (Figure 9). Figure 9Operational model. Full size imageMolecules and special regions within these molecules are the relevant concepts in the domain. They define the scope and have to be contained in the operational model. Both are represented by typed classes, Molecule and MoleculeSegment. Relations between molecules and special regions are also in the focus. They are realized by the relation model described in "Associations between concrete concepts. " The operational model contains the typed class Relation for this reason. Instances of these typed classes have to hold type specific properties. Therefore, Molecule, MoleculeSegment, and Relation are subclasses of PropertyHolder. The linear molecule structure is represented by the class Sequence, while the class MoleculeElement represents the monomers. The class Location specifies the position of a special region within a molecule. [SEP] Justify:

Output: The text does not provide any information that directly supports or contradicts the claim regarding the importance of bioinformatics in analyzing genomic data. Instead, it discusses a property model and class structures related to domain models without mentioning bioinformatics or genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: 5bOptimized medium732. 3±0. 453. 2±0. 4aTable 2. Effect of different parameters on α-amylase and lactic acid production by L. fermentum 04BBA19 in submerged state fermentation at 45 °C and initial pH 6. 5. The basal medium contained soluble starch, 1% (w/v); yeast extract, 0. 5 % (w/v); while the optimized medium contained all parameters without CuSO4. 5H2O. The data shown are averages of triplicate assays with SD within 10% of mean value. For each group of parameters (Carbohydrate, Nitrogen, Mineral, Starchy sources, media), means with different superscripts within columns are significantly different (p<0. 05). 4. 4. Enzyme propertiesThe amylase produced by L. fermentum 04BBA19 showed high affinity toward cassava raw starch granules with 80% adsorption and brought about 79% hydrolysis of 1% (w/v) suspension of raw cassava starch. On the other hand, the enzyme was able to hydrolyze blocked p-nitro phenyl methyl heptaoside, releasing a yellow compound (p-nitro phenol) with maximum absorption at 530 nm. This result was a proof that amylase from L. fermentum 04BBA19 is an endo acting amylase (α-amylase), since the blocked p-nitro phenyl methyl heptaoside is known to be hydrolysed only by endo-acting amylases [38]. The enzyme exhibited maximum activity at 60-70°C and maintained 100% of its initial activity at 80°C for 30 min of heat treatment (Fig 5-a). When the enzyme was treated for the same time (30 min. ) at 90°C and 100°C, the remaining activities were 90 and 87% respectively. These results showed the thermophilic character and very high thermostability of α-amylase from L. fermentum 04BBA19. In general, most of lactic acid bacteria do not produce amylases. However, this property have been observed in some genera of lactic acid bacteria, especially in L. plantarum and L. amylovorus [37], L. [SEP] Justify:

Output: The text corroborates the claim by discussing the enzyme properties of L. fermentum, a species of lactic acid bacteria, which highlights their role in hydrolyzing starch during fermentation processes. It specifies that certain genera of lactic acid bacteria, such as L. fermentum, L. plantarum, and L. amylovorus, exhibit enzyme activity relevant to fermentation, thus reinforcing the importance of lactic acid bacteria in these processes.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: We found that we obtain best results if we select this order ad hoc for each dataset; the orders we used were stimulus → decision → interaction for the somatosensory working memory task, stimulus → interaction → decision for the visuospatial working memory task, and interaction → decision → stimulus for both olfactory tasks. The vector 𝜷o(t) was always used last. We applied TDR to all our datasets and observed that dPCA consistently outperforms it in terms of capturing variance and demixing task parameters. First, unlike dyields only one component per task parameter. Second, even this component tends to retain more mixed selectivity than the corresponding dPCA component. Some representative components are shown in Figure 14. Figure 14 Download asset Open asset Some demixed components as given by three different demixing methods (rows) in various datasets and marginalizations (columns). Empty subplots mean that the corresponding method did not find any components. All projections were z-scored to make them of the same scale. Barplots on the right show fractions of variance in each marginalization for each component (stimulus in blue, decision in red, interaction in purple, condition-independent in gray): ∥𝐝𝐗~ϕ∥2/∥𝐝𝐗~∥2. Barplots consisting of a single colour correspond to perfect demixing. Comparison of dPCA with LDA Request a detailed protocol Analysis (LDA) is usually understood as a one-way technique: there is only one parameter (class id) associated with each data point, whereas in this manuscript we dealt with three parameters simultaneously. Therefore, LDA in its standard form cannot directly be applied to the demixing problem. We can, however, use the same data and covariance decomposition X=∑ϕXϕ+XnoiseC=∑ϕCϕ+Cnoise that dPCA is using and construct a separate LDA for each marginalization ϕ. To the best of our knowledge, this framework does not have an established name, so we call it factorial LDA. Let us first consider the case of finding demixed components for marginalization 𝐗ϕ. [SEP] Justify:

Output: The text does not directly address the claim regarding the perfection of memory recall with the right stimulus. While it discusses various methodologies and components related to tasks and memory, it does not provide evidence or insights that support or contrast the claim about memory recall.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Member holds 2. 3 [34, 38] and applies the notation "term-ID = term name" to reference ontology terms: The term "SO:0000110 = sequence_feature" is identified as a key concept in the ontology and is mapped to the class SegmentType. This term serves as an entry point, and according to this term, an instance of SegmentType is created and inserted into the knowledge model. The concept id "SO:0000110" is used as the name attribute for the new instance. All ontology terms with a direct or indirect "is-a" relation to the term "SO:0000110 = sequence_feature" are searched by reasoning. Every term contained in the result forces to insert a corresponding instance of SegmentType into the knowledge model. For example, the term "SO:0000001 = region" has a direct "is-a" relation to "SO:0000110 = sequence_feature" and is inserted into the knowledge model. The term "SO:0000833 = transcript_region" has an indirect "is-a" relation to "SO:0000110 = sequence_feature". This means, there is a path from "SO:0000110 = sequence_feature" over "SO:0000001 = region" to "SO:0000833 = transcript_region". Therefore, "SO:0000833 = transcript_region" is inserted as a SegmentType into the knowledge model. This strategy permits the derivation of the knowledge model from ontologies, and "is-a" relations (but also other relations) can now be expressed in the knowledge model by using reasoning. Current usage of ontologies and their relevance for deriving the knowledge modelOntologies are used extensively in current MB software and our approach can benefit from experiences made there. This section provides a short overview of current ontology usage, relevant to our approach. project [39] is an example for ontology usage to mediate between different domain models. A mediator component and several database wrappers use the ontology to provide transparent access to heterogeneous databases. strategy is to perform a mapping between the terms of the heterogeneous databases and the ontology terms. usage may be convenient for the user. However, due to continuous database development, the wrapper maintenance will be very labor intensive. On the other hand, the ontology proves the possibility of a comprehensive domain ontology. [SEP] Justify:

Output: The text does not address the claim regarding the importance of bioinformatics in analyzing genomic data. Instead, it discusses the use of ontology terms and mapping them to create a knowledge model, without specifically linking this process to bioinformatics or genomic data analysis.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: manihotivorans [70, 13], L. fermentum OGI E1 [38]. But amylases produced by these strains are not thermostable. Traditionally high thermostable and thermophiles amylases are found in Bacillus and Thermococcus genera as: B. amyloliquefaciens [71]; B. licheniformis [72]; B. stearothermophilus [73]; B. subtilis and T. aggreganes [74], T. profundus [75], Bacillus sp PN5 [4], B. cohnii US147 [35], Chromohalobacter sp. TVSP 101 [76]. Fig. 6 shows the thermostability pattern of α-amylase from L. fermentum 04BBA19 at 80°C, 90°C and 100°C when the time of heat treatment is beyond 30 min. Table 3 presents the thermal inactivation rate constant (ki) and half-life (T) at these temperatures. The half-life of this enzyme is higher than that of α-amylase from B. licheniformis: 120 min at 70°C [76]. The thermal stability was considerably improved by addition of 0. 1% (w/v) CaCl2. 2H2O. Goyal et al. [53] obtained a half-life value of 3. 5 h at 80°C with α-amylase from Bacillus sp. I-3 in the presence of 0. 1 % (w/v) calcium chloride, while under the same conditions; α-amylase from L. fermentum 04BBA19 displayed a half-life of 6. 1 h. Due to its high thermostability, α-amylase from L. fermentum 04BBA19 could be highly competitive in industrial bioconversion reactions, as compared to α-amylase from Bacillus. In addition, this competitiveness is enhanced by the fact that lactobacilli, due to their non-pathogen character, are easily used in food industry [6]. The L. fermentum 04BBA19 α-amylase is active and stable in pH range of 4. 0 – 7. 0 (Fig. 5-b), which is the pH range of many foods. [SEP] Justify:

Output: The text corroborates the claim by highlighting the role of lactobacilli, specifically 'L. fermentum 04BBA19,' in industrial bioconversion reactions. It notes that these bacteria possess high thermostability, making their enzymes competitive in various applications, which is indicative of their importance in fermentation processes. Additionally, the text points out that lactobacilli are 'easily used in the food industry' due to their non-pathogen character, further emphasizing their significance in fermentation.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In this case, the population response lies in the one-dimensional subspace spanned by 𝐚 and the covariance matrix has rank one: 𝐂=⟨𝐲(t)𝐲⊤(t)⟩t=𝐚𝐚⊤⟨z(t)2⟩t. Now consider the case in which the neurons are not recorded simultaneously but are pooled from different sessions. In behavioural experiments it is unavoidable that the onset of (self-timed) neural responses will vary by tenths or hundreds of milliseconds. Hence, the individual response yi(t) of neuron i will experience a small time-shift τi so that yi(t)=aiz(t+τi), see Figure 15 for an example with Gaussian tuning curves. If τi is small we can do a Taylor expansion around t, yi(t)=aiz(t)+aiτiz′(t)+𝒪(τi2). Figure 15 Download asset Open asset Fourier-like artifacts in PCA. (Left) In this toy example, single neuron responses are generated from the same underlying Gaussian but are randomly shifted in time. (Right) First three PCA components of the population data. While the leading component resembles the true signal, higher order components look like higher Fourier harmonics. They are artifacts of the jitter in time. where we neglect higher-order corrections for simplicity, but the extension is straight-forward. Let 𝝉 be the vector of time-shifts of all neurons and let 𝐛=𝐚∘𝝉 be the element-wise vector product of 𝐚 and 𝝉, i. e. [𝐚∘𝝉]i=aiτi. Then the population response can be written as y(t)≈az(t)+bz′(t). Hence, the covariance matrix becomes approximately C≈aa⊤⟨z2(t)⟩t+bb⊤⟨z′2(t)⟩t, where we assumed for simplicity that 𝐚⟂𝐛. [SEP] Justify:

Output: The text contrasts the claim by discussing how neural responses can vary in timing and how these time shifts can introduce artifacts in the recorded population response. It explains that when neurons are not recorded simultaneously and are subject to slight delays, the resultant data may not perfectly reflect the original signal due to these discrepancies. This indicates that memory recall might not be perfect due to variability in neural responses.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Another approach is to integrate ontologies into data models, e. g. , FuGE [40] and MAGE-OM [41]. In both models, classes, representing domain objects, use associations to the class OntologyTerm, that represents ontology terms. For example, to specify the experimental equipment (e. g. , a sequencer) more precisely, the FuGE model uses the association "equipmentModel" between the Equipment and OntologyTerm classes to specify the equipment product name. The approach allows the flexible integration of ontological terms into the models. Both models use this mechanism to specify domain entity types or generic properties. At first glance, this is similar to the type attributes and generic properties presented here. However, the models do not constrain the set of allowed properties. Our approach for deriving the knowledge model from ontologies is a further development of the FuGE and MAGE-OM approach. Another approach to utilize ontologies in domain modeling is to derive a class model from an ontology [42]. The approach defines transformation rules, which, for example, map concepts to classes and relations to associations. These rules allow the automatic conversion of ontologies into object-oriented models. However, a static class model is generated. Such a model is too inflexible to serve as a MB domain model. Changes in the ontology would require new transformations and result in new domain models. In contrast, our approach creates a dynamic representation of ontology concepts; it is a similar approach but enhances the flexibility significantly. Outlook and current workIntegration of data processing componentsAnother aspect of our current investigation are extension points that allow the integration of extended data processing components. These components should be seen as algorithms, formal described by type attributes. The description includes the I/O ports and configuration parameters of the algorithms, see section "Consequences for the structure of software components. " This description provides enhanced capabilities in using data processing components. For example, the description of I/O ports allows an easy and semantically correct combination of processing components. Together with the formal description of exchanged data, one can ensure semantic compatibility. In this way, users will be able to join data processing components safely and create their own computing pipelines. ConclusionOur aim was the development of a domain model for linear biological macromolecules that can easily integrate new or modified domain concepts and nevertheless provide a stable basis for implementation models. [SEP] Justify:

Output: The text does not address the claim regarding the importance of bioinformatics in analyzing genomic data. Instead, it focuses on the integration of ontologies into data models and the development of domain models without providing any context or evidence related to the role of bioinformatics in genomic analysis.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: 2H2O. The main ALAB that have been isolated for the past decade are summarized in Table 4. No study has dealt with the thermostability of their amylases, except the case reported by Aguilar et al. (2000) concerning the properties of the extracellular amylase produced by L. manihotivorans LMG 18010T. This strain produced an amylase with a moderate themostability exhibiting maximum activity at 55°C. The strain L. fermentum 04BBA19 appears as the first ALAB producing highly thermostable amylase. The potential industrial application of this strain could be the bioconversion of inexpensive raw material as starch into lactic acid in single step process. On the other hand this strain and its amylase are potential candidates for food industries (making of high density gruels, baking, brewing) and for the production of biodegradable plastic from starchy raw material. Figure 7. Effect of metal salts and EDTA on the activity of α-amylase from L. fermentum 04BBA19. The data shown are averages of triplicate assays with SD within 10% of mean value. BacteriaStrains4. The main amylolytic lactic bacteria strains isolated during the past two decade Advertisement 5. ConclusionL. fermentum 04BBA19 which is a soil isolate produced very high thermostable α-amylase. This is the first study dealing with high thermostable amylase from a lactic acid bacterium. According to its properties, this enzyme is a good candidate for starch hydrolysis at high temperature. An economical process could be attained through the use of this enzyme at the liquefaction stage at high temperatures. On the other hand the fact that thermostable amylase and lactic acid production can be combined in single fermentation step would not only provide a way to make gruels with high energy density, but also improve its safety, since lactic acid bacteria fermentation is an efficient way to inhibit food-borne pathogens. Owing to the importance of this finding, further studies will focus on the development of an accurate method for preparing high energy density complementary food using local starchy sources and the L. fermentum 04BBA19 strain. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of lactic acid bacteria, specifically the strain L. fermentum 04BBA19, in producing lactic acid through fermentation processes. It highlights that the fermentation by lactic acid bacteria not only facilitates high energy density food production but also ensures safety by inhibiting food-borne pathogens. This indicates the significance of lactic acid bacteria in fermentation applications.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The cross correlation functions are very similar between high (red) and low (blue) variance epochs. (f) Firing rate lag with respect to the stimulus, estimated from the location of the peak cross-correlation, vs. standard deviation of the stimulus. No significant change in lag was observed (p=0. 133, t-test). Figure 8 with 2 supplements Download asset Open asset A modified two state receptor model reproduces Weber’s Law and adaptive slowdown in LFP responses. (a). Or-Orco complexes (C) can be bound or unbound and active or inactive. (b) We assume (un)binding rates are much faster than (in)activation rates. Activity of the complex feeds back onto the free energy difference between active and inactive conformations, which also decreases the activation and inactivation rates of the complex (Equations 1–4). A mono-lobed filter converts receptor activity into LFP signals (Equation 5). We fit the model to Gaussian (Figures 3 and 5) and naturalistic data (Figures 1–2). In these fits, α=12. 5 s−1, β=1. 26 s−1, εL=0. 86, Kon=0. 1 V and Koff=400 V. (c) Model gain vs. mean stimulus. Red line is the Weber-Fechner prediction (ΔR/ΔS∼1/S). (c) LFP gain vs. model gain. (e) Model response lag with respect to stimulus vs. mean stimulus. (f) LFP and model responses to naturalistic stimulus. (g) The model reproduces LFP responses to similar-sized whiffs that vary inversely with the size of preceding whiffs. (cf. Figure 2). (h) LFP responses vs. model responses for every whiff in the naturalistic stimulus. Figure 8—figure supplement 1 Download asset Open asset Steady state activity as a function of the stimulus background. At high stimulus background, the steady state activity of the receptor complex is a0 (here, 1/2). The model is unable to adapt perfectly to lower stimulus backgrounds, since ε is bounded by εL. This causes the steady state activity to decrease. [SEP] Justify:

Output: The text does not address the claim directly or provide information relevant to the idea of memory recall with stimulus. It discusses various aspects of receptor activity, stimulation, and modeling but does not support or contrast the specific claim about memory. Therefore, it is classified as irrelevant (pass).


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: We achieved this through the strict separation of two model parts, the operational model and the knowledge model. These parts are connected by the meta model of the knowledge model to the whole system model. We validated our model against the EMBL standard and by implementing an MB framework (MCK) whose data structures are based on the model. We demonstrated that our model is able to integrate new domain concepts without changes to the operational model and therefore provides a stable interface for software programs. General use of the design pattern "Model" and its related patterns is the key to creating changeable, high-quality software solutions for MB. In the future, an integration of domain-specific ontologies will enrich the model and the modeling approach presented here even further; for example, in the form of enhanced expressiveness and automatic generation of knowledge models. The modeling approach allows the development of information systems for MB that can accommodate changes in the domain in a fast and flexible manner. This approach also provides the expressiveness and flexibility that is needed to develop a widely used domain model of MB, one which allows a shift to a global view of the domain and supports interoperability on a new level. Availability and requirementsOur models and the MCK are available at MCK requires Development Kit, version 1. 6 or higher, and Eclipse 3. 4 128. 714. 1306. 68. 120. 5. 4. 4. 12. 7. htm]Beale T: Archetypes: Constraint-based domain models for future-proof information systems. ; 4–8 November 2002; by: Baclawski K, Kilov H. 2002, 16–32. 235. 2005. 1999. 1992. 2006. 2004. 5. Edited by: Manolescu D, Völter M. Noble J: Addison-Wesley; 2005:3–24. 60. 3. Addison-Publishing Co. , Inc; 1997:47–65. 1997. 1997. 2. 0[ B: Components. Computer 1999, 32: 139–140. 2002. 2003. 384. 216. 290. 29. 274. 2005. , 6: 658. 90. 520. 2. 3[ : to Information Sources. Bioinformatics 2000, 16: 184–186. 1133. 0046. 0041-research0046. 0049. 2002. 2. [SEP] Justify:

Output: The text does not address the claim about the importance of bioinformatics in analyzing genomic data. It focuses on a specific modeling approach and how it integrates with a software framework, without mentioning genomic data or the field of bioinformatics.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: 2008Enzyme activities of lactic acid bacteria from a pearl millet fermented gruel (ben-saalga) of functional interest in nutrition. Int. J. of Food Microbiol. 1283954009. GiraudE. LelongB. RaimbaultM. 1991Influence of pH and initial lactate concentration on the growth of Lactobacillus plantarum. Biotechnol 3696910. Morlon-GuyotJ. GuyotJ. P. PotB. Jacobe deHaut. I. RaimbaultM. 1998Lactobacillus manihotivorans sp. Nov. , a new starch-hydrolysing lactic acid bacterium isolated during cassava sour starch fermentation. Int. J. Syst. Bacteriol. 481101110911. AgatiV. J. P. GuyotJ. Morlon-GuyotP. TalamondHounhouigan. D. J. 1998Isolation and characterization of new amylolytic strains of Lactobacillus fermentum from fermented maize doughs (mawe and ogi) from Benin. J 855122012. SanniA. Morlon-GuyotJ. GuyotJ. P. New efficient amylase-producing strains of Lactobacillus plantarum and L. fermentum isolated from different Nigerian traditional fermented foods. 200272536213. GuyotJ. P. CalderonM. Morlon-GuyotJ. P. 2000Effect of pH control on lactic acid fermentation of starch by Lactobacillus manihotivorans LMG 180010T. J. Appl. Microbiol. 8817618214. Ji, GE. , 1992Isolation of amylolytic Bifidobacterium sp. Int57. J. Microbiol. Biotechnol. 2859115. 1997Purification of amylase secreted from Bifidobacterium adolescentis. J. Appl. Microbiol. 8326727216. . Parka KH, In K,. Jib GE1999Fermentation of rice using amylolytic Bifidobacterium. 5015516117. [SEP] Justify:

Output: The text corroborates the claim by discussing various research studies and findings related to lactic acid bacteria, including their enzyme activities and roles in fermentation processes, such as in pearl millet fermented gruel and the fermentation of starch. It highlights the significance of lactic acid bacteria in different fermentation contexts, supporting the idea that they are important for fermentation.


Input: Task: contrast the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Because of the correspondence established in D, these virtual labels are tied to the physical objects in real space. Figure 2 with 1 supplement Download asset Open asset Spatial memory task. (A) Five objects are arranged on a half-circle; the subject explores the scene, then reports the recalled object identities and locations. (B) Recall performance during blocks 1 (left) and 2 (right). Recalled target angle potted against true angle. Shaded bar along the diagonal shows the 30 deg width of each object; data points within the bar indicate perfect recall. Dotted lines are linear regressions. (C) Slope and (D) correlation coefficient for the regressions in panel (B). Figure 2—figure supplement 1 Download asset Open asset Mental imagery task supplementary data. Spatial memory data (Figure 2) from blocks 1 (left) and 2 (right) by subject. Shaded areas indicate the true azimuthal extent of each object. Markers indicate recalled location. Most recalled locations overlap with the true extent of the object. Subjects 8–10 were normally sighted and performed the exploration phase using vision. Figure 3 with 2 supplements Download asset Open asset Direct navigation task. (A) For each trial, a target chair is randomly placed at one of four locations. The subject begins in the starting zone (red shaded circle), follows the voice of the chair, and navigates to the target zone (green shaded circle). (B) All raw trajectories from one subject (#6) including 1 s time markers. Oscillations from head movement are filtered out in subsequent analysis. (C) Filtered and aligned trajectories from all trials of 3 subjects (#3, 4, 7). Arrow highlights a trial where the subject started in the wrong direction. (D) Trajectories of subjects performing the task with only a cane and no HoloLens. (E) Deviation index, namely the excess length of the walking trajectory relative to the shortest distance between start and target. Note logarithmic axis and dramatic difference between HoloLens and Cane conditions. (F) Speed of each subject normalized to the free-walking speed. Figure 3—figure supplement 1 Download asset Open asset Direct navigation task extended data. Trial distance (A) and trial duration (B) for the first 20 trials of all subjects. A modest effect of practice on task duration can be observed across all subjects (B). [SEP] Justify:

Output: The text contrasts the claim by indicating that recall performance often shows deviation from perfect recall, as noted by the '30 deg width of each object' mentioned. The discussion of the 'shaded bar along the diagonal shows the 30 deg width of each object; data points within the bar indicate perfect recall' suggests that while there are instances of perfect recall, these are not the norm. Additionally, the ‘most recalled locations overlap with the true extent of the object’ implies that memory is not perfectly recalled but rather has variations that result in inaccuracies.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Results and discussionStart version of ArkMAP is now the default tool used to display maps in the ArkDB web application, replacing applet drawing code. This not only allows more flexible configuration options for the user, but also allows connection to third party data resources which was not possible from within a sandboxed applet. When accessed from is automatically preloaded with the user’s selected maps, otherwise maps are downloaded via the drop down ‘File’ menu: local XML files may be opened, new maps created with a text editor, or the user may download maps from one of the configured data sources (and Ensembl Bacteria). By selecting a data source the user can browse and select maps for available species, analyses or Ensembl releases. The same File menu allows screenshots of the canvas to be saved at any time as or PNG graphics files, or printed locally. The ArkMAP user interface is dominated by the drawing canvas upon which maps can be freely positioned and resized. The canvas is scrollable and automatically expands as new maps are added. Maps are sensitive to mouse actions, and on mouse entry the rectangular bounds of a map are drawn in grey; selecting a map by clicking within this area changes the border to green; grey stretch bars at the bottom allow the map to be resized by mouse dragging (see Figures 1 and 2). Maps may be dragged around the canvas and because maps are transparent they may overlap each other, allowing the user fine control of image composition. Maps may be stacked or even overlapped in ‘columns’, and because inter-marker relationships are only drawn between maps in adjacent ‘columns’ the user can sensitively control what information is visible (in Figure 3 maps 3 & 4, and maps 5 & 6 are in adjacent columns). Figure 1Screen shot of ArkMAP showing three gene maps uploaded from Ensembl. Map 1 (highlighted) of Cow chromosome 20 was retrieved from Ensembl release 63. Map 2 was retrieved by searching for human regions of conserved synteny to map 1. Map 3 was retrieved by fetching the more recent release version 70 for the region displayed in map 1. The upper configuration panel allows the selected map to be hidden or deleted, and the tabbed panels below allow rescaling, resizing, reorienting of the axis; changing the map title; choosing font types and sizes and selecting which markers to display. [SEP] Justify:

Output: The text describes the functionalities of the ArkMAP web application and its features for handling maps and data visualizations. It does not address bioinformatics or its role in analyzing genomic data, thus rendering it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Nakamura LK1981Lactobacillus amylovorus, a new starch-hydrolyzing species from cattle waste-corn fermentations. Syst Bacteriol: 31566318. ChatterjeeM. . R. K. 1997Production of lactic acid by direct fermentation of starchy wastes by an amylase-producing Lactobacillus. Biotechnol Lett: 19873419. GiraudE. ChampaillerA. RaimbaultM. 1994Degradation of raw starch by a wild amylolytic strain of Lactobacillus plantarum. Microbiol 6043192320. VishnuC. SeenayyaG. ReddyG. Direct fermentation of various pure and crude starchy substrates to L(+) lactic acid using Lactobacillus amylophilus GV6. 2002184293321. NaveenaB. J. MdAltafBhadrayya. K. MadhavendraS. S. ReddyG. 2005Direct fermentation of starch to L(+) lactic acid in SSF by Lactobacillus amylophilus GV6 using wheat bran as support and substrate-medium optimization using RSM. 406819022. AnuradhaR. SureshA. K. VenkateshK. V. 1999Simultaneous saccharification and fermentation of starch to lactic acid. 353677523. ZhangD. X. CheryanM. 1994Starch to lactic acid in a continuous membrane reactor. 291455024. NaveenaB. J. VishnuC. MdAltafReddy. G. 2003Wheat bran an inexpensive substrate for production of lactic acid in solid state fermentation by L actobacillus amylophilus GV6-optimization of fermentation conditions. J Res62453625. Goel MK. Biotechnology: an overview;1994 AltafM. VenkateshwarM. SrijanaM. ReddyG. 2007An economic approach for L-(+) lactic acid fermentation by Lactobacillus amylophilus GV6 using inexpensive carbon and nitrogen sources. J Appl Microbiol;1033728027. PandeyA. SoccolC. R. [SEP] Justify:

Output: The text provides multiple references related to lactic acid bacteria, specifically Lactobacillus species, and their roles in the fermentation of starchy substrates to produce lactic acid. This aligns with the claim that lactic acid bacteria are important for fermentation processes, as the text discusses various studies that utilize these bacteria for the production of lactic acid through fermentation.


Input: Task: contrast the claim using the given text. Claim: Neuroscience can cure all mental health disorders. [SEP] Text: “Essential reading for anyone who wants to understand why systems meant to save our lives end up killing us instead. ” — author of Ambulance: Choreographing the End of Life in Thailand “A richly detailed and theoretically innovative ethnography that illuminates how neoliberal health care systems can undermine a legally guaranteed right to health care. ” — author of State of Health: Pleasure and Politics in Care under Chávez “This book has much to offer to anthropologists, clinicians, bioethicists, philosophers, and policymakers who want to understand how inequities are remade and reframed by market-based health care reforms. ” — CÉSAR E. ABADÍA-author of Health in Ruins: Destruction of at a Sanz is of Anthropology at the University of de& reboots, franchises, and songs that remake old songs—does it feel like everything new in popular culture is just derivative of something old? Contrary to popular belief, the reason is not audiences or marketing, but Wall Street. In this book, Andrew deWaard shows how the financial sector is dismantling the creative capacity of cultural industries by upwardly redistributing wealth, consolidating corporate media, harming creative labor, and restricting our collective media culture. Moreover, financialization is transforming the very character of our mediascapes for branded transactions. Our media are increasingly shaped by the profit-extraction techniques of hedge funds, asset managers, venture capitalists, private equity firms, and derivatives traders. Illustrated with examples drawn from popular culture, offers readers the critical financial literacy necessary to understand the destructive financialization of film, television, and popular music—and provides a plan to reverse this dire threat to culture. “The thoughtful and thought-provoking 1524. The current movements in Cotzal, rooted in a long history of resistance, counter dominant narratives of that often portray them as “conquered. ” Egyptian Things: to the deaths of Antony and Cleopatra, Rome finally took control of Egypt. This occupation simultaneously facilitated and circumscribed the exchange of goods, people, and ideas along the paths carved by Rome’s burgeoning empire. In this book, sets out to recapture one of these systems of exchange: the vibrant literary tradition known as Aegyptiaca—or “Egyptian Things”—in which culturally mixed authors wrote about Egypt for a Greek and Roman audience. [SEP] Justify:

Output: The text contrasts the claim by discussing how various systems, including healthcare systems, can undermine health rather than cure it. It emphasizes that neoliberal healthcare systems can challenge the right to health, suggesting limitations in how such systems can address health issues, including mental health disorders. This implies that neuroscience alone may not be sufficient to 'cure' all mental health disorders, as it doesn't account for systemic factors impacting health outcomes.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Colour key: purple: identity, sky-blue: synonymy, black: one-to-one orthology, vermilion: apparent one-to-one orthology, orange: one-to-many orthology, blue-green: many-to-many orthology, blue: possible orthology, yellow: paralogy. ▲ forward transcribed gene; ▽reverse transcribed gene. Full size imageFigure 2Composite screen shot of ArkMAP illustrating various interactive options. Map 1 has been zoomed to show bases 1-40M of a chicken ePCR map from ArkDB, the selected (green highlighted) region of this map has been used to retrieve the cognate gene-annotated assembly from (map 2). As the mouse cursor is moved over the canvas it changes to reflect available actions for the underlying map object. Three versions of the cursor are shown here: cross hairs used to select regions on the axes, a double arrow used to stretch the selected map (2) and the open hand indicating that the map can be moved by mouse dragging. In addition the (hand) cursor has triggered display of a pop-up box displaying the coordinates of the blue-highlighted marker. Mouse clicking this selected marker would display a more detailed pop-up with further information and interaction options. The gene mappings from Ensembl include orientation information: ▲ forward or ▽ reverse transcribed gene. Alternate ‘Context Menus’ are shown for selected regions of each map. In both cases the user may zoom or project (duplicate) the selected map region. In the case of the ArkDB ePCR map the user may choose to import the cognate Ensembl chromosome (performed here), whilst for the Ensembl assembly map the user may import alternate release versions of the annotated assembly, import dbSNP variation maps (where available), search for regions of conserved synteny in a specified target species or choose to perform threshold filtering on any homology relationships for genes on this map. Full size imageFigure 3Composite screen shot of ArkMAP detailing a mapping analysis of low quality Salmon data. Map 1 is a private (and obfuscated) salmon QTL linkage map (Houston RD, Gonen S: per. comm. ) which uses SNP markers shared on published linkage map of salmon chromosome 26 (map 2 [25]). [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily provides a technical description of the ArkMAP tool and its functionalities related to gene mapping and visualization. It does not address the role of bioinformatics in analyzing genomic data or provide evidence that supports or refutes the claim.


Input: Task: corroborate the claim using the given text. Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Rodriguez-LeonJ. A. NigamP. 2001Solid state fermentation in biotechnology: fundamentals and applications. New Delhi: Asiatech Publishers28. MercierP. YerushalamiL. RouleauD. DochaniaD. 1992Kinetics of lactic acid fermentations on glucose and corn by Lactobacillus amylophilus. J Biotechnol 551112129. ChengP. MullerR. E. JaegerS. BajpaiR. JannottiE. L. 1991Lactic acid production from enzyme thinned cornstarch using Lactobacillus amylovorus. J 72734 30. KurusavaH. IshikawaH. TanakaH. L-lactic acid production from starch by co-immobilized mixed culture system of Aspergilus awamori and Streptococcus lactis. 198831183731. KristoficovaL. RosenbergM. VlnovaA. SajbidorJ. CetrikM. Selection of Rhizopus strains for L (+) lactic acid and gammalinolenic acid production. 199136451532. 1979Lactobacillus amylophilus, a new starch hydrolyzing species from swine waste-corn fermentation. Microbiol 205314033. Prasana VA2005Amylases and their applications. Afr. J Biotechnol 4131525152934. SivaramakrishnanS. GangadharanD. MadhavanK. 2006Amylase from microbial sources-An overview on recent developments. Food technol. Biotechnol. 44217318435. GhorbelR. E. MaktoufS. MassoudE. B. BejarS. ChaabouniS. E. 2009Amylase from Bacillus cohnii US147 with a Broad pH Applicability. Appl. Biochem. Biotechnol. 157506036. 1998Production and properties of a raw starch degrading amylase from thermophilic and alkaliphilic Bacillus sp TS-23. Biochem, 281616837. GiraudE. CunnyG. [SEP] Justify:

Output: The text discusses various studies and applications involving lactic acid bacteria, particularly focusing on their role in fermentations, such as 'Lactic acid production from enzyme thinned cornstarch using Lactobacillus amylovorus' and 'Kinetics of lactic acid fermentations on glucose and corn by Lactobacillus amylophilus.' These examples illustrate the importance of lactic acid bacteria in fermentation processes, thereby corroborating the claim.


Input: Task: contrast the claim using the given text. Claim: Neuroscience can cure all mental health disorders. [SEP] Text: Close navigation menu in Comorbidity of Mental and Edited by N. Sartorius; N. for other works by this author on: PubMed 10. 1159/isbn. 978-3-318-02604-7 Status: Available ISBN electronic: 978-3-318-02604-7 ISBN print: 978-3-318-02603-0 Subject Area: and Gerontology Oncology Psychiatry and Psychology Book Series: in Publication date: 2014 This publication presents evidence about the magnitude and severe consequences of comorbidity of mental and physical illnesses from a personal and societal perspective. Leading experts address the huge burden of co-morbidity to the affected individual as well as the public health aspects, the costs to society and interaction with factors stemming from the context of socioeconomic developments. The authors discuss the clinical challenge of managing cardiovascular illnesses, cancer, infectious diseases and other physical illness when they occur with a range of mental and behavioral disorders, including substance abuse, eating disorders and anxiety. Also covered are the organization of health services, the training of different categories of health personnel and the multidisciplinary engagement necessary to prevent and manage comorbidity effectively. The book is essential reading for general practitioners, internists, public health specialists, psychiatrists, cardiologists, oncologists, medical educationalists and other health care professionals. ‹ › 10. 1159/000365941 Published: 2014 Subject Area: and Gerontology Oncology Psychiatry and Psychology Book Series: in Split-2014. "the Costs: Impact of Comorbidity", Comorbidity of Mental and Physical Disorders, N. Sartorius, R. I. G. Holt, M. citation file: Ris (Zotero) toolbar search search input Search input auto suggest filter your search © 2015 S. Karger AG, Basel2014License DisclaimerThis 4. 0 (CC BY-NC-ND). Usage and distribution for commercial purposes as well as any distribution of modified material requires written permission. Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexity of managing mental health disorders in conjunction with physical illnesses, indicating that there are significant challenges involved in treating them. It highlights the burden of comorbidity and emphasizes the need for a multidisciplinary approach, suggesting that the assertion that neuroscience alone can cure all mental health disorders is overly simplistic and fails to accommodate the broader context of health care management.


Input: Task: pass the claim using the given text. Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The best BlastX hits for sequences flanking the salmon SNPS were mapped as ‘orthologous sequences’ on the stickleback (map 3) and medaka (map 4) genome assemblies. These maps were then projected onto the gene annotated assemblies downloaded from Ensembl (maps 5 and 6). The figure is a composite screenshot from ArkMAP showing ‘Identity’ relationships in purple and ‘Synonymy’ relationships in blue (key as in Figure 1). Full size imageWhen a map is selected various (collapsible) configuration panels are displayed (see Figure 1). In the upper panel maps may be hidden (using the ‘eye’ icon) or permanently removed. Separate tabbed control panes allow rescaling, resizing, reorienting of the axis; changing the map title; choosing font types and sizes and selecting which markers to display. Maps are drawn vertically as a scaled red axis, with labelled markers positioned as points or ranges; where available marker orientation is shown (see Figures 1 and 2). The user can select a sub-region of a map by mouse dragging along the labelled side of the axis, causing a context-dependent menu to be displayed beside the highlighted selection (see Figure 2). As described above, in addition to facilitating zooming or duplication (projection) of the region, various further options are presented according to type of map. Figure 1 illustrates an ArkMAP workflow that compared bovine genome builds between Ensembl releases, which was informed by querying a map for regions of conserved synteny in a better characterized model species (human). The gene-annotated map of bovine chromosome 20 (map 1) was loaded from Ensembl release 63 which used assembly version Btau_4. 1. This map has been zoomed to show the first 4. 5 MB, and the majority of marker labels have been hidden using the configuration panel. This region was selected, and searched via the ‘Context Menu’ for regions of conserved synteny with the annotated human genome. Conservation queries are performed by the JEnsembl API, which successively searches the data source for human homologues to each bovine gene located on the selected region. It should be noted that as the search time is proportional to the number of genes selected, searching large chromosomal regions may cause ArkMAP to become non-responsive. [SEP] Justify:

Output: The text does not specifically address the role of bioinformatics in analyzing genomic data. Instead, it describes technical aspects of mapping sequences and compares genomic assemblies without discussing the broader context of bioinformatics as a field or its importance for data analysis. Thus, 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: 1997Molecular characterization of the α-amylase genes of Lactobacillus plantarum A6 and Lactobacillus amylovorus reveals an unusual 3’ end structure with direct tandem repeats and suggest a common evolutionnary origin. Gene 198149157 38. TalamondP. DesseauxV. MoreauY. SantimoneM. Marchis-mourenG. 2002Isolation, characterization and inhibition by acarbose of the α-amylase from Lactobacillus fermentum : comparison with Lb. manihotivorans and Lb. plantarum. Comparative Biochem. and Physiol. 13335136039. Qui and Yao2007Lactic acid production, rice straw. Bioresources 2341942940. Sawadogo-LinganiH. DiawaraB. ASTraoréJakobsen. M. 2008Technological properties of Lactobacillus fermentum involved in the processing of dolo and pito, sorghum beers, for the selection of starter cultures. J. Appl Microbiol, 104387388241. ChampagneC. 1998La production des ferments dans l’industrie laitière Ed. Agriculture et Agroalimentaire, Canada,32145 42. EdelmanS. Westerlund-WikströmB. LeskeläS. KettunenH. RautonenN. ApajalahtiJ. KorhonenT. K. 2002In vitro adhesion specificity of indigenous lactobacilli within the avian intestinal tract. Microbiol 685155515943. HofvendahlK. Hahn-HagerdalB. 1997L-lactic acid production from whole wheat flour hydrolysate using strains of Lactobacilli and Lactococci. Technol 20301307 44. . MoonS. H. FrankJ. R. 1993Technological and economic potential of polylactic acid and lactic acid derivatives. International congress on chemicals from biotechnology, Hannover, Germany 1845. 2006Biotechnological production of lactic acid and its recent applications. Biotechnol 441637246. SenthuranA. SenthuranV. MattiassonB. KaulR. [SEP] Justify:

Output: The text corroborates the claim by mentioning various studies and works that involve lactic acid bacteria, specifically Lactobacillus species, in fermentation processes such as lactic acid production and in the processing of traditional beverages like dolo and pito. This indicates that lactic acid bacteria play a significant role in fermentation.


Input: Task: contrast the claim using the given text. Claim: Neuroscience can cure all mental health disorders. [SEP] Text: However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug. Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product 4. 0. 2008. Mean healthcare costs were significantly higher in the schizophrenia group; a key driver of greater healthcare costs was the significantly higher rate of physical health problems, including congestive heart failure (45. 1 vs. 38. 8%), chronic obstructive pulmonary disease (52. 7 vs. 41. 4%), hypothyroidism (36. 7 vs. 26. 7%) and dementia (64. 5 vs. 32. 1%) [7]. Turning to bipolar disorder, in the USA the medical records (spanning 1 year) of more than 28,000 people with bipolar disorder were compared with matched controls without any mental health problems [8]. Those with bipolar disorder had a significantly higher prevalence of metabolic comorbidities than the general population (37 vs. 30%). Annual healthcare costs for metabolic conditions were twice those of controls (USD 531 vs. 233). The bipolar cohort also had significantly higher overall medical service and prescription drug costs than those of the control cohort (USD 12,764 vs. 3,140). Prescription medication costs for metabolic conditions were also higher, with bipolar cohort per-patient costs of USD 571 versus 301 for the control cohort. Analysis of data on 67,000 members of a health insurance fund in seven US states also suggests that 67% of total healthcare costs of bipolar disorder were related to the treatment of comorbid physical health conditions [9]. Comorbid depression or anxiety disorders and physical health problems have also been associated with higher levels of cost to healthcare systems. One US study reported that the costs of 11 chronic health problems are significantly greater when an individual has comorbid depression. Costs related to diabetes, coronary artery disease and congestive heart failure were approximately twice the costs of individuals without depression [10]. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities and challenges associated with the treatment of mental health disorders. It highlights the higher healthcare costs and prevalence of comorbid physical health problems in individuals with bipolar disorder, as well as the significant costs associated with comorbidities like depression and diabetes. This suggests that while neuroscience may contribute to understanding and treating mental health issues, it is not a guaranteed cure for all disorders, indicating a limitation in the claim that neuroscience alone can cure all mental health disorders.