Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: Existing plant diseases and pests classification network mostly use the muture network structures in computer vision, including AlexNet [26], GoogleLeNet [27], VGGNet [28], ResNet [29], Inception V4 [30], DenseNets [31], MobileNet [32] and SqueezeNet [33]. There are also some studies which have designed network structures based on practical problems [34,35,36,37]. By inputting a test image into the classification network, the network analyses the input image and returns a label that classifies the image. According to the difference of tasks achieved by the classification network method, it can be subdivided into three subcategories: using the network as a feature extractor, using the network for classification directly and using the network for lesions location. Using network as feature extractorIn the early studies on plant diseases and pests classification methods based on deep learning, many researchers took advantage of the powerful feature extraction capability of CNN, and the methods were combined with traditional classifiers [38]. First, the images are input into a pretrained CNN network to obtain image characterization features, and the acquired features are then input into a conventional machine learning classifier (e. g. , SVM) for classification. Yalcin et al. [39] proposed a convolutional neural network architecture to extract the features of images while performing experiments using SVM classifiers with different kernels and feature descriptors such as LBP and GIST, the experimental results confirmed the effectiveness of the approach. Fuentes et al. [40] put forward the idea of CNN based meta architecture with different feature extractors, and the input images included healthy and infected plants, which were identified as their respective classes after going through the meta architecture. Hasan et al. [41] identified and classified nine different types of rice diseases by using the features extracted from DCNN model and input into SVM, and the accuracy achieved 97. 5%. Using network for classification directlyDirectly using classification network to classify lesions is the earliest common means of CNN applied in plant diseases and pests detection. According to the characteristics of existing research work, it can be further subdivided into original image classification, classification after locating Region of Interest (ROI) and multi-category classification. 1. Original image classification. That is, directly put the collected complete plant diseases and pests image into the network for learning and training. Thenmozhi et al. [SEP] Justify:

Output: The text contrasts the claim by discussing various plant diseases and pests classification methods and the use of convolutional neural networks (CNN) to identify and classify plant diseases and pests. This implies that despite controlled environments, diseases and pests still exist and require management, indicating that vertical farms are not completely immune.


Input: Task: pass the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: 43 ± 0. 45 vs 5. 15 ± 0. 36; p=0. 0001), while group I-3 vs. III-3 (5. 88 ± 0. 76 vs 5. 15 ± 0. 36; p=0. 182) did not show a significant result (see Figure 3). On the 14th day after injury, the mean epidermal thickness was highest in the treatment group (III), followed by the negative control group (II), and followed by the standard group (I). From the post hoc test results, it appeared that there were significant differences in the mean epidermal thickness (p<0. 05) in groups I-14 vs II-14 (6. 01 ± 0. 54 vs 11. 46 ± 0. 92; p=0. 0001), group I-14 vs III-14 (6. 01 ± 0. 54 vs 16. 75 ± 1. 31; p=0. 0001), and group II-14 vs III-14 (11. 46 ± 0. 92 vs 16. 75 ± 1. 31; p=0. 0001) (see Figure 3). On the 21st day after injury, the mean epidermal thickness was highest in the treatment group (III), followed by the negative control group (II), and followed by the standard group (I). From the post hoc test results, it appeared that there were significant differences in the mean epidermal thickness (p<0. 05) in groups I-21 vs II-21 (5. 92 ± 0. 68 vs 23. 10 ± 1. 37; p=0. 0001), group I-21 vs III-21 (5. 92 ± 0. 68 vs 30. 47 ± 1. 25; p=0. 0001), and group II-21 vs III-21 (23. 10 ± 1. 37 vs 30. 47 ± 1. 25; p=0. 0001) (see Figure 3). DiscussionMacrophages are cells that multiply when there is a wound to phagocytose debris and bacteria and they are the predominant cells on the 3rd day post wound infliction (Gurtner & Wong, 2013). This study showed that both negative control and treatment groups had more number of macrophages than standard group on the 3rd day after injury. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses statistical results related to epidermal thickness and macrophage activity in response to treatment without specifying that Aloe vera is being used for treating skin ailments. There are no references or indications of Aloe vera or its historical use in the text.


Input: Task: corroborate the claim using the given text. Claim: The prefrontal cortex is heavily involved in decision-making processes, particularly when assessing long-term risks and rewards. [SEP] Text: To expand on the above: as far as I can tell, the main novel claims seem to be: 1) the prefrontal cortex (PFC) expresses theta sequences 2) theta sequences in both the hippocampus (HC) and PFC are trajectory-specific ("splitter sequences") 3) PFC, but not HC, theta sequence content predicts future choice 4) joint HC-PFC activity during replay predicts error vs. correct trials Based on these findings, the authors conclude that interaction at multiple timescales between HC and PFC supports memory-guided decision-making. If adequately supported, each of these claims would be a useful addition to the field, with the dissociation between HC and PFC theta sequences of particular interest. However, as currently presented, the claims seem out of step with the underlying evidence; in each case, this overall issue could be addressed by bolstering the analyses and/or by making the claims more precise or toned down. We thank the reviewer for noting the importance of our work and providing many constructive suggestions to improve our manuscript. Regarding primary new claims, the findings of prefrontal theta sequences, theta cycle skipping and theta sequence prediction are all novel, to the best of our knowledge. Further, we would like to argue that it is a significant strength of the manuscript that by presenting these findings in context of hippocampal phenomena, we are able to integrate both hippocampal and prefrontal results for behavioral sequences, theta sequences and replay sequences in a unified framework for memory-guided decision making. While we are aware of the extensive work on hippocampal sequences which is cited in the manuscript, there have been only very few studies investigating the relationship between these different sequences, even just in the hippocampus (e. g. , Papale et al. , 2016; Drieu et al. , 2018), which is a major goal in the field (Pezzulo et al. , Ann. N. Y. Acad. Sci. , 2017). In the current study, we therefore present the new prefrontal results in the context of hippocampal sequences, and also build upon our previous replay results (Shin et al. , 2019) to contrast the roles of the three types of sequences in memory-guided decision making. We hope the reviewer will share our vision and appreciate the strength of these findings. [SEP] Justify:

Output: The text corroborates the claim by discussing how the prefrontal cortex (PFC) is involved in memory-guided decision-making, highlighting that PFC theta sequence content can predict future choices. This supports the assertion of the PFC's significant role in decision-making processes, particularly in the context of assessing long-term outcomes.


Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: [42] proposed an effective deep CNN model, and transfer learning is used to fine-tune the pre-training model. Insect species were classified on three public insect datasets with accuracy of 96. 75%, 97. 47% and 95. 97%, respectively. Fang et al. [43] used ResNet50 in plant diseases and pests detection. The focus loss function was used instead of the standard cross-entropy loss function, and the Adam optimization method was used to identify the leaf disease grade, and the accuracy achieved 95. 61%. 2. Classification after locating ROI. For the whole image acquired, we should focus on whether there is a lesion in a fixed area, so we often obtain the region of interest (ROI) in advance, and then input the ROI into the network to judge the category of diseases and pests. Nagasubramanian et al. [44] used a new three-dimensional deep convolution neural network (DCNN) and salience map visualization method to identify healthy and infected samples of soybean stem rot, and the classification accuracy achieved 95. 73%. 3. Multi-category classification. When the number of plant diseases and pests class to be classified exceed 2 class, the conventional plant diseases and pests classification network is the same as the original image classification method, that is, the output nodes of the network are the number of plant diseases and pests class + 1 (including normal class). However, multi-category classification methods often use a basic network to classify lesions and normal samples, and then share feature extraction parts on the same network to modify or increase the classification branches of lesion categories. This approach is equivalent to preparing a pre-training weight parameter for subsequent multi-objective plant diseases and pests classification network, which is obtained by binary training between normal samples and plant diseases and pests samples. Picon et al. [45] proposed a CNN architecture to identify 17 diseases in 5 crops, which seamlessly integrates context metadata, allowing training of a single multi-crop model. The model can achieve the following goals: (a) obtains richer and more robust shared visual features than the corresponding single crop; (b) is not affected by different diseases in which different crops have similar symptoms; (c) seamlessly integrates context to perform crop conditional disease classification. Experiments show that the proposed model alleviates the problem of data imbalance, and the average balanced accuracy is 0. [SEP] Justify:

Output: The text contrasts with the claim by discussing various models for identifying and classifying plant diseases and pests, indicating that pests and diseases are indeed present and can be detected even in controlled environments like vertical farms. This suggests that vertical farms are not completely immune, as effective detection models are needed to manage these issues.


Input: Task: pass the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: On the same day, the treatment group had more macrophages than negative control group because there is acemannan in aloe gel that could increase the number of macrophages (Sierra-García et al. , 2014). The results of this study showed that there was reduction in number of macrophages in the treatment group than the negative control group on the 14th and 21st days after injury. Souza et al. (2017) reported a decrease in the number of macrophages in burn rats treated with 1% silver sulfadiazine. In this study, both the 3rd and 14th days after injury demonstrated a higher number of fibroblasts in the treatment group than the negative control group. This might have happened because of interaction between glucomannan in aloe gel and gibberellin, a growth hormone, with growth factor receptors on fibroblast. This interaction stimulated fibroblast activity and proliferation that could increase collagen synthesis (Chithra et al. , 1998; Surjushe et al. , 2008). Besides that, there was also acemannan in aloe gel which could increase the proliferation of fibroblasts (Xing et al. , 2014). Fibroblasts are cells that will increase their migration during the proliferation phase of wound healing. Therefore, this study showed highest number of fibroblasts on the 14th day after injury in the treatment group which was earlier than the negative control group that had the highest number on the 21st day after injury. At some point during the proliferation phase, there may be apoptosis of fibroblast when collagen matrix has filled the wound cavity (Gurtner & Wong, 2013). Therefore, there was a reduction in the number of fibroblasts on the 21st day after injury in the treatment group. Re-epithelialization occurs right after injury that consists of migration, proliferation, and differentiation of keratinocytes (Gurtner & Wong, 2013; Isrofah and Afandi, 2015). Acemannan could stimulate fibroblast to release keratinocyte growth factor-1 (KGF-1) that could hasten the re-epithelialization process by keratinocytes which could increase the epidermal thickness (Jettanacheawchankit et al. , 2009). [SEP] Justify:

Output: The text does not directly address the claim regarding the historical use of aloe vera for treating skin ailments. Instead, it discusses the effects of acemannan in aloe gel on macrophages and fibroblasts in wound healing, which does not provide historical context or confirm its long-standing use for skin conditions.


Input: Task: corroborate the claim using the given text. Claim: The prefrontal cortex is heavily involved in decision-making processes, particularly when assessing long-term risks and rewards. [SEP] Text: We have now provided explicit statements and an updated focus on these primary advances of our current study in the Discussion section: “In this work, we discovered theta sequences, theta cycle skipping and theta-sequence prediction of behavioral choices in PFC. These prefrontal phenomena follow a succession of results on hippocampal sequences, but our study points to different yet complementary roles of prefrontal and hippocampal sequences at multiple timescales. By dissecting fast cognitive-timescale sequences from slow behavioral-timescale sequences in a spatial working-memory and navigation task, these findings thus provide a unified framework that integrates hippocampal and prefrontal mechanisms of multi-timescale cell-assembly dynamics for memory-guided decision making. ” We have also followed the reviewer’s suggestions and provided additional evidence with new analyses and explanations to support our main novel conclusions. In overview, (1) we have now provided evidence that theta sequences in CA1 and PFC observed here, as ensemble-level phenomena, cannot be fully accounted for by single-neuronal phase precession, and thus the observation of the PFC theta sequences is a genuine and novel finding of the present work; (2) we have further clarified that our finding of “trajectory-specific” CA1 and PFC theta sequences is an important extension of the previously observed choice coding of theta sequences in the hippocampus; (3) we have demonstrated that the difference between behavioral- and theta-sequence coding of choices in CA1 and PFC is not a result of the methodological concerns suggested; (4) we have made changes in the presentation of the replay results, and added more explicit interpretation of the theta-sequence coordination result to enhance clarity. We believe that these changes, prompted by the reviewer’s suggestions, make the Results clearer and more accessible. Specifically: Claim 1: What counts as a "theta sequence" requires clarification. Do the authors consider the ensemble pattern generated from independently phase precessing cells a theta sequence? If yes, then the claim that mPFC has theta sequences follows directly from previous single cell results (Jones and Wilson, 2005 PLoS Biology; Zielinski et al. , 2019 J Neurosci) and they should acknowledge this. If no, they need provide a clear definition and associated analysis criterion of what constitutes a theta sequence, perhaps using a peer prediction analysis along the lines of Harris et al. [SEP] Justify:

Output: The text corroborates the claim by discussing the roles of theta sequences in the prefrontal cortex (PFC) and their significance in memory-guided decision making. It highlights how the findings provide a unified framework integrating PFC mechanisms with hippocampal functions in decision-making tasks, reinforcing the idea that the PFC is crucial in the decision-making process.


Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: 98, which is superior to other methods and eliminates 71% of classifier errors. Using network for lesions locationGenerally, the classification network can only complete the classification of image label level. In fact, it can also achieve the location of lesions and the pixel-by-pixel classification by combining different techniques and methods. According to the different means used, it can be further divided into three forms: sliding window, heatmap and multi-task learning network. 1. Sliding window. This is the simplest and intuitive method to achieve the location of lesion coarsely. The image in the sliding window is input into the classification network for plant diseases and pests detection by redundant sliding on the original image through a smaller size window. Finally, all sliding windows are connected to obtain the results of the location of lesion. Chen et al. [46] used CNN classification network based on sliding window to build a framework for characteristics automatic learning, feature fusion, recognition and location regression calculation of plant diseases and pests species, and the recognition rate of 38 common symptoms in the field was 50–90%. 2. Heatmap. This is an image that reflects the importance of each region in the image, the darker the color represents the more important. In the field of plant diseases and pests detection, the darker the color in the heatmap represents the greater the probability that it is the lesion. In 2017, Dechant et al. [47] trained CNN to make heatmap to show the probability of infection in each region in maize disease images, and these heatmaps were used to classify the complete images, dividing each image into containing or not containing infected leaves. At runtime, it takes about 2 min to generate a heatmap for an image (1. 6 GB of memory) and less than one second to classify a set of three heatmaps (800 MB of memory). Experiments show that the accuracy is 96. 7% on the test dataset. In 2019, Wiesner-Hanks et al. [48] used heatmap method to obtain accurate contour areas of maize diseases, the model can accurately depict lesions as low as millimeter scale from the images collected by UAVs, with an accuracy rate of 99. 79%, which is the best scale of aerial plant disease detection achieved so far. 3. Multi-task learning network. If the pure classified network does not add any other skills, it could only realize the image level classification. [SEP] Justify:

Output: The text contrasts the claim by discussing various techniques for detecting plant diseases and pests, suggesting that even in controlled environments, plants are still susceptible to diseases and pests. The details about classification networks, sliding windows, heatmaps, and multi-task learning networks for detecting lesions indicate that controlling the environment does not guarantee immunity from these issues. This contradicts the assertion of complete immunity in vertical farms.


Input: Task: pass the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: Author contributionsAulia L: Conceptualization, Formal Analysis, Investigation, Writing – Preparation; Pane YS: Conceptualization, Formal Analysis, Writing – Preparation, Writing – Review & EditingAcknowledgementThe authors give their appreciation and thanks to the of the Faculty of Medicine, Utara for allowing them to use the facility for collecting data for this research. 2018. ABA: 2019 - Association. 2019. accessed 9 April 2021. : Aloe vera gel facilitates re-epithelialization of corneal alkali burn in normal and diabetic rats. Clin. Ophthalmol. 2015. : Effect of Aloe vera extract in post-burn skin repair in rats. Figshare Dataset. 2022. : Topical silver sulfadiazine-induced acute renal failure. Annales de Dermatologie et de Venereologie. 2005; 132: 891–893. : Influence of Aloe vera on collagen characteristics in healing dermal wounds in rats. Mol. Cell. Biochem. 1998; 181: 71–76. : Experimental Design, Theory and Application. New York:Mac. Millan;1963; 544. : Leukopenia in non-septic burn patients receiving topical 1% silver sulfadiazine cream therapy: A survey. J. Rehabil. 1988; 9: 606–609. Pub| :. Hasanudin. 2013; 1–13. :Wound Healing: Normal and Abnormal. Grabb and Smith’ s Plastic Surgery. 7th edn. & Wilkins;2013. Isrofah S, Afandi M: Binahong ((Ten) Steenis) 2 Termal pada Tikus Putih. of Nursing. 2015. : Acemannan stimulates gingival fibroblast proliferation; expressions of keratinocyte growth factor-1, vascular endothelial growth factor, and type I collagen; and wound healing. J. Pharmacol. Sci. 2009; 109: 525–531. Pub| P: Buaya (Aloe Vera) (Linn) (Strain Wistar) Kediri. Kesehatan. 2016; 5: 114–121. Maulana RA: yang Berperan pada di dari Tahun 2011-2014. Utara;2014. Menkes. : - Luka Bakar. [SEP] Justify:

Output: The text discusses various research papers related to the use of Aloe vera in different contexts, such as wound healing and skin repair in rats, but does not specifically address its historical use for skin ailments. Thus, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: The prefrontal cortex is heavily involved in decision-making processes, particularly when assessing long-term risks and rewards. [SEP] Text: Given that p(SeqPFC = actual) and p(SeqPFC = alternative) are significantly different from the chance level (50%; p’s < 1e-4, signed-rank tests compared to 50%), stochastic independence would predict that the distributions of sequences coherently representing actual and alternative (i. e. , p(SeqPFC = actual| SeqCA1 = actual) and p(SeqPFC = alternative | SeqCA1 = alternative)) both significantly differ from the chance level. However, if they are dependent, different distributions for actual and alternative should be observed, which is indeed the case as shown in Figure 6L, i. e. , p(SeqPFC = actual| SeqCA1 = actual) significantly differs from the chance level (p’s = 0. 0312), but p(SeqPFC = alternative | SeqCA1= alternative) does not (p = 0. 94 and 0. 48 for outbound and inbound, respectively; signed-rank tests). In addition, we would like to note that the idea of CA1-PFC interactions at the theta timescale is also supported by previous evidence showing that PFC cells phase-lock and phase-precess to hippocampal theta oscillations (Jadhav et al. , 2016; Jones and Wilson, 2005a; Siapas et al. , 2005; Sigurdsson et al. , 2010), as well as theta-frequency synchrony and coherent spatial coding between the hippocampus and PFC (Benchenane et al. , 2010; Gordon, 2011; Hasz and Redish, 2020; Jones and Wilson, 2005b; Sigurdsson et al. , 2010; Zielinski et al. , 2019). Furthermore, disruption of prefrontal activity results in impaired theta sequences in the hippocampus (Schmidt et al. , 2019). We have discussed these studies in our manuscript. We have now explicitly mentioned and discussed this issue in the Results section: “Note that this result cannot be simply accounted for by the fact that the majority of PFC theta sequences depict the actual choice, assuming independence of CA1 and PFC theta sequences (see Materials and methods). [SEP] Justify:

Output: The text corroborates the claim by highlighting the interactions between the prefrontal cortex (PFC) and the hippocampus (CA1), which are relevant to decision-making processes. It discusses how the PFC's activity affects theta sequences in the hippocampus, a neural mechanism that may underlie the assessment of risks and rewards. Additionally, the mention of PFC cells phase-locking to hippocampal theta oscillations suggests a strong functional link between the PFC and decision-making mechanisms guided by time-sensitive rewards.


Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: Therefore, to accurately locate the location of plant diseases and pests, the designed network should often add an extra branch, and the two branches would share the results of the feature extracting. In this way, the network generally had the classification and segmentation output of the plant diseases and pests, forming a multi-task learning network. It takes into account the characteristics of both network. For segmentation network branches, each pixel in the image can be used as a training sample to train the network. Therefore, the multi-task learning network not only uses the segmentation branches to output the specific segmentation results of the lesions, but also greatly reduces the requirements of the classification network for samples. Ren et al. [49] constructed a Deconvolution-Guided VGNet (DGVGNet) model to identify plant leaf diseases which were easily disturbed by shadows, occlusions and light intensity. The deconvolution was used to guide the CNN classifier to focus on the real lesion sites. The test results show that the accuracy of disease class identification is 99. 19%, the pixel accuracy of lesion segmentation is 94. 66%, and the model has good robustness in occlusion, low light and other environments. To sum up, the method based on classification network is widely used in practice, and many scholars have carried out application research on the classification of plant diseases and pests [50,51,52,53]. At the same time, different sub-methods have their own advantages and disadvantages, as shown in Table 3. Table 3 Comparison of advantages and disadvantages of each sub-method of classification networkFull size tableDetection networkObject positioning is one of the most basic tasks in the field of computer vision. It is also the closest task to plant diseases and pests detections in the traditional sense. Its purpose is to obtain accurate location and category information of the object. At present, object detection methods based on deep learning emerge endlessly. Generally speaking, plant diseases and pests detection network based on deep learning can be divided into: two stage network represented by Faster R-CNN [54]; one stage network represented by SSD [55] and YOLO [56,57,58]. The main difference between the two networks is that the two-stage network needs to first generate a candidate box (proposal) that may contain the lesions, and then further execute the object detection process. [SEP] Justify:

Output: The text contrasts the claim by discussing methods for detecting plant diseases and pests, indicating that while advanced networks and technologies exist for detection, it suggests that pests and diseases can still be a concern. The mention of multi-task learning networks for classifying and segmenting diseases implies that vertical farms are not completely immune, as there is still a need for detection and classification of these issues.


Input: Task: pass the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: Indonesia. 2019. Reinke JM, Sorg H: Wound repair and regeneration. Eur. Surg. Res. 2012; 49: 35–43. : of Technique. London:Methuen& Co. Ltd;1992. (Reissued: for Animal Welfare, Herts, England. ) 1959. Sierra-García GD, Castro-Ríos R, González-Horta A, et al. : Acemannan, an extracted polysaccharide from Aloe vera: A literature review. Nat. Prod. Commun. 2014; 9: 1934578X1400900. -Daniel J, Forbes AA, et al. : Recent trends in burn epidemiology worldwide: A systematic review. Burns. 2017; 43: 249–257. Pub| : Heterogeneity of mast cells and expression of Annexin A1 protein in a second degree burn model with silver sulfadiazine treatment. PLoS One. 2017; 12: e0173417. Pub| : Aloe vera: A short review. Indian J. Dermatol. 2008; 53: 163–166. Pub| : The effects of aloe vera on wound healing in cell proliferation, migration, and viability. Wounds. 2018; 30: 263–268. : WHO|Burns. 2018. accessed 29 May 2021. : Acemannan accelerates cell proliferation and skin wound healing through AKT/mTOR signaling pathway. J. Dermatol. Sci. 2014; 79: 101–109. on this 10. 12688/f1000research. 79538. 3 version 2 Revised Published: 29 Jul 2022, 11:168 version 1 Published: 11 Feb 2022, 11:168 Copyright © 2022 S. This is an open access 10. 12688/f1000research. 79538. 2) NOTE: it is important to ensure the information in square brackets after the title is included in all 10. 5256/f1000research. 135733. r157960) The direct URL for this report is: Major concerns: In the method section, the authors mentioned that they divided the rats into 3 groups, group 1 no treatment, group 2 with gel base and group 3 with gel. Why did they. . . [SEP] Justify:

Output: The text does not directly address the claim regarding the historical use of Aloe vera for skin ailments. Instead, it presents a series of citations and studies related to wound healing and various scientific aspects of Aloe vera but lacks concrete information about its historical use in treating skin conditions.


Input: Task: corroborate the claim using the given text. Claim: The majority of people can train their brains to make perfect investment decisions by using neuroeconomic techniques. [SEP] Text: Download the 10. 33424/338 Link to the activity sheet 0 +1 Pinterest 0 LinkedIn 0 of the amygdala in the brain © Kateryna_Kon stock. adobe. com Young in her lab at the University of patient in an MRI scanner © stock. adobe. com The thermometer patients see that represents their amygdala activity which they are trying to increase with training. fMRI works by measuring tiny changes in oxygen levels in the blood (blood-oxygen) in an active part of the brain. “The more active a certain brain area is, the more blood-oxygen it uses. This is an indirect measure of neural activity,” says Kymberly. Before her patients enter the fMRI scanner, Kymberly works with them to come up with a few positive memories they will need to think about while they are in the machine. Positive memories are often quite difficult for patients with MDD to come up with, especially as they might struggle with low self-esteem. For example, a depressed patient who has what should be a positive memory of their graduation day might still have a negative association with this memory if they felt they had not made the most of their education since. Once her patients have decided upon a few positive memories, they enter the fMRI machine, and Kymberly asks them to recall these memories to themselves (they cannot speak while in the scanner as the images will be blurry). The fMRI machine measures the activity in the amygdala, which allows Kymberly and the patient to see what is happening in their brain when they are thinking about a positive emotion. “By focusing on the important and particularly positive aspects of their memory, their amygdala response will increase,” explains Kymberly. This procedure lets patients know what it feels like when their amygdala is active and engaging with positive emotions, so that they can work on making this happen more often. “Neurofeedback allows people to see how active certain parts of their brain are in real-time with the goal of teaching them to control it,” explains Kymberly. By presenting her patients with immediate feedback on their brain activity, they can be trained, over time, to learn to control that activity and increase their positive emotions. Why is the fMRI machine necessary? Using the fMRI machine to engage the amygdala of a patient is essential to Kymberly’s treatment. [SEP] Justify:

Output: The text supports the claim by explaining how neurofeedback and fMRI techniques can train individuals to control their brain activity, particularly in the amygdala, to engage with positive emotions. This implies a foundational mechanism through which neuroeconomic techniques could be applied to improve decision-making, including investment decisions. The statement from Kymberly about allowing patients to gain control over their emotional responses through feedback suggests that similar methods could be beneficial for training decision-making skills in a broader context.


Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: In contrast, the one-stage network directly uses the features extracted in the network to predict the location and class of the lesions. Plant diseases and pests detection based on two stages networkThe basic process of two-stage detection network (Faster R-CNN) is to obtain the feature map of the input image through the backbone network first, then calculate the anchor box confidence using RPN and get the proposal. Then, input the feature map of the proposal area after ROIpooling to the network, fine-tune the initial detection results, and finally get the location and classification results of the lesions. Therefore, according to the characteristics of plant diseases and pests detection, common methods often improve on the backbone structure or its feature map, anchor ratio, ROIpooling and loss function. In 2017, Fuentes et al. [59] first used Faster R-CNN to locate tomato diseases and pests directly, combined with deep feature extractors such as VGG-Net and ResNet, the mAP value reached 85. 98% in a dataset containing 5000 tomato diseases and pests of 9 categories. In 2019, Ozguven et al. [60] proposed a Faster R-CNN structure for automatic detection of beet leaf spot disease by changing the parameters of CNN model. 155 images were trained and tested. The results show that the overall correct classification rate of this method is 95. 48%. Zhou et al. [61] presented a fast rice disease detection method based on the fusion of FCM-KM and Faster R-CNN. The application results of 3010 images showed that: the detection accuracy and time of rice blast, bacterial blight, and sheath blight are 96. 71%/0. 65 s, 97. 53%/0. 82 s and 98. 26%/0. 53 s respectively. Xie et al. [62] proposed a Faster DR-IACNN model based on the self-built grape leaf disease dataset (GLDD) and Faster R-CNN detection algorithm, the Inception-v1 module, Inception-ResNet-v2 module and SE are introduced. The proposed model achieved higher feature extraction ability, the mAP accuracy was 81. 1% and the detection speed was 15. 01FPS. [SEP] Justify:

Output: The text contrasts the claim by discussing various methods for detecting plant diseases and pests, indicating that even with advanced detection methods like Faster R-CNN, there are still challenges related to pests and diseases, contradicting the idea that vertical farms are completely immune.


Input: Task: pass the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: Continue reading concerns: In the method section, the authors mentioned that they divided the rats into 3 groups, group 1 no treatment, group 2 with gel base and group 3 with gel. Why did they name group 1 as standard group? What do they mean by "standard"? This study lacks the visual observation with camera and authors did not show any images of the appearance of the wounds and wound improvement. This is a major concern and needs to be addressed. The conclusion is a bold statement. What are the next steps? What are the limitations of the study? Is the work clearly and accurately presented and does it cite the current literature? the study design appropriate and is the work technically sound? sufficient details of methods and analysis provided to allow replication by others? applicable, is the statistical analysis and its interpretation appropriate? all the source data underlying the results available to ensure full reproducibility? the conclusions drawn adequately supported by the results? Interests: No competing interests were disclosed. Reviewer Expertise: Burn injuries and reconstructive surgery I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. . For: Effect of Aloe vera extract in post-burn skin repair in rats [version 2; peer review: 1 approved, 2 not approved]. F1000Research 2022, 11:168 ( The direct URL for this report is: In the method section, the authors mentioned that they divided the rats into 3 groups, group 1 without treatment, group 2 with gel base and group 3. . . Continue reading 1. In the method section, the authors mentioned that they divided the rats into 3 groups, group 1 without treatment, group 2 with gel base and group 3 with gel. Why did they name group 1 as standard group? What do they mean by "standard"? Answer: Naming group 1 as the standard group because group I is a group consisting of normal rat samples without being given burns or treatment only as a comparison to normal skin histology. This research is a true experimental study which has a control variable and the sample is randomly selected. At the beginning of the study we divided 54 rats into 3 large groups (group-I, II and III) with 3 small groups in them (day 3, 14 and 21) of each group @ 6 rats, namely: consisted of 18 rats divided into 3 small groups of 6 rats each. [SEP] Justify:

Output: The text is irrelevant to the claim about Aloe vera being used for centuries to treat skin ailments. Instead, it primarily discusses methodological concerns and details related to a specific study on the effects of Aloe vera extract in post-burn skin repair in rats, without addressing its historical use or effectiveness for skin ailments.


Input: Task: pass the claim using the given text. Claim: The majority of human decisions involving money are entirely based on unconscious processes that cannot be influenced. The majority of exoplanet atmosphere studies focus on detecting gases like methane, water vapor, and carbon dioxide, which could indicate the presence of life. [SEP] Text: Download figure:Standard image High-resolution image The K-band continuum shape can also be used to estimate the relative abundance of H2O and CO. Given the wider angular separation of b than c (1. 7 versus 1'') from the star, the continuum of b is less affected by residual speckles. The observed spectrum, with continuum intact, was compared to the same set of atmosphere models and C and O abundances. The distribution from this comparison is plotted in the top panel of Figure 11 and shows both planets having C/O between ∼0. 55 and 0. 7, for similar as in the continuum filtered comparison. The mole fractions of H2O, CO, and CH4 in the photosphere of the best-matching CA-specific atmosphere model can be compared to those found in Section 4. 4, where the molecular abundances were determined to be independent of any formation model. The mole fractions for the CA-specific atmosphere models are plotted in Figure 12 as functions of C/O, for the range of and explored here. Solar (non-equilibrium) mole fractions and solar C/O are indicated in this figure by the vertical dashed line. The CA-specific atmosphere model with C/O ∼ 0. 66 has H2O and CH4 mole fractions that closely match those found in Section 4. 4. These values are plotted as red symbols with error bars estimated from the formal uncertainties in Table 1 and the CO abundances corresponding to the 1 range in C/O. CO, however, is higher than found in Section 4. 4 for all C/O explored in the CA-specific atmospheres. Even if and are varied (see Table 1), the CO found by freely scaling the abundances remains below what is predicted in the CA-specific atmospheres. Lower would lower CO, but doing so would quickly increase CH4 to values well above what is observed. A lower CO could indicate additional chemical processes not accounted for in the atmosphere or formation models. The significance of this CO difference is hard to assess given the various source of uncertainty in the models not accounted for in the formal mole fraction error-bars. It is possible, for example, that the CO line broadening in the atmosphere models is under (or over) estimated, resulting in systematic errors in the inferred abundances (this will be investigated in a future paper). Reset image size Figure 12. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the K-band continuum shape and the estimation of gas abundances in exoplanet atmospheres, specifically addressing the comparisons of C and O abundances. It does not provide information on human decisions involving money or their unconscious processing. Therefore, it does not corroborate or contrast with the claim.


Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: The two-stage detection network has been devoted to improving the detection speed to improve the real-time and practicability of the detection system, but compared with the single-stage detection network, it is still not concise enough, and the inference speed is still not fast enough. Plant diseases and pests detection based on one stage networkThe one-stage object detection algorithm has eliminated the region proposal stage, but directly adds the detection head to the backbone network for classification and regression, thus greatly improving the inference speed of the detection network. The single-stage detection network is divided into two types, SSD and YOLO, both of which use the whole image as the input of the network, and directly return the position of the bounding box and the category to which it belongs at the output layer. Compared with the traditional convolutional neural network, the SSD selects VGG16 as the trunk of the network, and adds a feature pyramid network to obtain features from different layers and make predictions. Singh et al. [63] built the PlantDoc dataset for plant disease detection. Considering that the application should predict in mobile CPU in real time, an application based on MobileNets and SSD was established to simplify the detection of model parameters. Sun et al. [64] presented an instance detection method of multi-scale feature fusion based on convolutional neural network, which is improved on the basis of SSD to detect maize leaf blight under complex background. The proposed method combined data preprocessing, feature fusion, feature sharing, disease detection and other steps. The mAP of the new model is higher (from 71. 80 to 91. 83%) than that of the original SSD model. The FPS of the new model has also improved (from 24 to 28. 4), reaching the standard of real-time detection. YOLO considers the detection task as a regression problem, and uses global information to directly predict the bounding box and category of the object to achieve end-to-end detection of a single CNN network. YOLO can achieve global optimization and greatly improve the detection speed while satisfying higher accuracy. Prakruti et al. [65] presented a method to detect pests and diseases on images captured under uncontrolled conditions in tea gardens. YOLOv3 was used to detect pests and diseases. While ensuring real-time availability of the system, about 86% mAP was achieved with 50% IOU. Zhang et al. [SEP] Justify:

Output: The text contrasts the claim by discussing various methods and technologies developed for the detection of plant diseases and pests, indicating that despite the advancements, pests and diseases are still a concern. The mention of the proposed detection methods and their applications suggests that vertical farms are not completely immune to these issues, contrary to the claim.


Input: Task: pass the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: This group will be executed and their skin tissue will be taken on day-3, day-14 and day-21. The skin tissue taken was then examined histopathologically and used as a control skin of rats without being treated for BURN. Therefore we call as the "". Group II consisted of 18 rats divided into 3 small groups of 6 rats each which were treated with on the back of each rat, and every day they were smeared with gel base (gel without active substance) to see how the burn wound healed physiologically. We named this Group II the "" because it was not given any treatment. Whereas group III consisted of 18 rats divided into 3 small groups of 6 rats each which were treated with BURNS on the back of each rat, and smeared with Aloe vera gel (a gel with the active ingredient Aloe vera herb) every day to see how the process is progressed to healing burns with Aloe vera treatment. We call this Group III the "" because they were given the Aloe vera gel treatment. 2. This study lacks visual observation with a camera and the authors do not show a picture of the appearance of the wound and wound repair. This is a major concern and needs to be addressed. Answer: 6 in the new version. 3. The conclusion is a bold statement. What's the next step? Answer: See the conclusion in the new version. 4. What are the limitations of the study? Answer: Application of gel to burns in rats that cannot be controlled because rats can lick/clean it even though it has been prepared by making burns on the rat's back which is difficult for them to reach compared to the stomach, legs and other areas. The rat rubbed the wound so that the application of Aloe vera gel became less/ineffective. The limited time of this research makes this research must be completed within the specified time. 1. In the method section, the authors mentioned that they divided the rats into 3 groups, group 1 without treatment, group 2 with gel base and group 3 with gel. Why did they name group 1 as standard group? What do they mean by "standard"? Answer: Naming group 1 as the standard group because group I is a group consisting of normal rat samples without being given burns or treatment only as a comparison to normal skin histology. This research is a true experimental study which has a control variable and the sample is randomly selected. [SEP] Justify:

Output: The text does not address the historical use of Aloe vera for skin ailments but instead discusses an experimental study involving rats treated with Aloe vera gel to observe healing effects on burns. Since the text is focused on a specific study's methodology and limitations rather than its historical context or traditional uses of Aloe vera, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Many exoplanets have thick atmospheres of hydrogen and helium, similar to the gas giants in our solar system. [SEP] Text: In 1846, it was discovered near the predicted position. Neptune resides at about 30 AU from the Sun, and its orbital period is about 160 years [7, 15]. As noted in Table 3, Neptune has an atmosphere dominated by hydrogen and helium with a smaller amount of methane, and is similar to the atmosphere on Uranus. Voyager 2 revealed a blue color and the fastest winds in the with speeds reaching 2100 km/h [7]. The blue color is derived from small amounts of methane in its hydrogen and helium atmosphere [7]. As noted in Table 2, Neptune has a mass about 17 times the Earth’s, and it is about four times wider than Earth [15]. In a manner similar to Jupiter and Saturn, Neptune generates more energy than it receives from the Sun [15]. In a similar manner to the other giant planets, Neptune has a system of multiple rings as well as 14 moons [28]. However, Neptune’s rings are not as extensive as Saturn’s. Voyager 2 observed that Neptune’s rings appear to be composed of a high concentration of dust p2. 2 System BodiesIn addition to planets, there are additional bodies that reside in the and have the potential to affect planetary motion as well as their environment. These bodies include planetary moons, the Asteroid Belt, comets, Objects, and meteorites. Each of these bodies is addressed in subsequent discussion. 2. 2. 1 Planetary MoonsAs noted in Table 2, the number of moons varies with the planet and their characteristics [1, 3, 4, 10, 15]. The larger planets tend to have more moons than the smaller ones. However, dwarf planet Pluto has five moons, which is an exception to the aforementioned trend [28, 29]. Its moon Charon is larger than any other moon relative to its planet size. Table 4 summarizes the four largest Jupiter moons (Callisto, Io, Europa, and Ganymede), Saturn’s largest moon (Titan), Neptune’s largest moon (Triton), and Earth’s moon that are more massive and physically larger than Charon. Characteristics of these planetary satellites including their mass, radius, density, orbital period, and semimajor axis are summarized in Table 4. GasComposition (Fractional number density of p0. 840. 860. 960. 850. 85HeliumHe:0. 200. 160. [SEP] Justify:

Output: The text corroborates the claim by discussing Neptune, a gas giant in our solar system. It states that Neptune has an atmosphere dominated by hydrogen and helium, which aligns with the claim that many exoplanets have similar atmospheres to gas giants. Neptune's atmospheric composition is highlighted, supporting the notion that thick atmospheres of hydrogen and helium are prevalent among gas giants.


Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: [66] combined the pooling of spatial pyramids with the improved YOLOv3, deconvolution is implemented by using the combination of up-sampling and convolution operation, which enables the algorithm to effectively detect small size crop pest samples in the image and reduces the problem of relatively low recognition accuracy due to the diversity of crop pest attitudes and scales. The average recognition accuracy can reach 88. 07% by testing 20 class of pests collected in real scene. In addition, there are many studies on using detection network to identify diseases and pests [47, 67,68,69,70,71,72,73]. With the development of object detection network in computer vision, it is believed that more and more new detection models will be applied in plant diseases and pests detection in the future. In summary, in the field of plant diseases and pests detection which emphasizes detection accuracy at this stage, more models based on two-stage are used, and in the field of plant diseases and pests detection which pursue detection speed more models based on one-stage are used. Can detection network replace classification network? The task of detection network is to solve the location problem of plant diseases and pests. The task of classification network is to judge the class of plant diseases and pests. Visually, the hidden information of detection network includes the category information, that is, the category information of plant diseases and pests that need to be located needs to be known beforehand, and the corresponding annotation information should be given in advance to judge the location of plant diseases and pests. From this point of view, the detection network seems to include the steps of the classification network, that is, the detection network can answer “what kind of plant diseases and pests are in what place”. But there is a misconception, in which “what kind of plant diseases and pests” is given a priori, that is, what is labelled during training is not necessarily the real result. In the case of strong model differentiation, that is, when the detection network can give accurate results, the detection network can answer “what kind of plant diseases and pests are in what place” to a certain extent. However, in the real world, in many cases, it cannot uniquely reflect the uniqueness of plant diseases and pests categories, only can answer “what kind of plant diseases and pests may be in what place”, then the involvement of the classification network is necessary. [SEP] Justify:

Output: The text contrasts the claim by discussing the detection of pests and plant diseases in agricultural settings, indicating that even in controlled environments, such as vertical farms, there can still be issues with pests and diseases that require detection mechanisms. It mentions the use of object detection networks to identify pests and diseases, implying that these challenges still exist despite the controlled conditions.


Input: Task: pass the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: At the beginning of the study we divided 54 rats into 3 large groups (group-I, II and III) with 3 small groups in them (day 3, 14 and 21) of each group @ 6 rats, namely: consisted of 18 rats divided into 3 small groups of 6 rats each. This group will be executed and their skin tissue will be taken on day-3, day-14 and day-21. The skin tissue taken was then examined histopathologically and used as a control skin of rats without being treated for BURN. Therefore we call as the "". Group II consisted of 18 rats divided into 3 small groups of 6 rats each which were treated with on the back of each rat, and every day they were smeared with gel base (gel without active substance) to see how the burn wound healed physiologically. We named this Group II the "" because it was not given any treatment. Whereas group III consisted of 18 rats divided into 3 small groups of 6 rats each which were treated with BURNS on the back of each rat, and smeared with Aloe vera gel (a gel with the active ingredient Aloe vera herb) every day to see how the process is progressed to healing burns with Aloe vera treatment. We call this Group III the "" because they were given the Aloe vera gel treatment. 2. This study lacks visual observation with a camera and the authors do not show a picture of the appearance of the wound and wound repair. This is a major concern and needs to be addressed. Answer: 6 in the new version. 3. The conclusion is a bold statement. What's the next step? Answer: See the conclusion in the new version. 4. What are the limitations of the study? Answer: Application of gel to burns in rats that cannot be controlled because rats can lick/clean it even though it has been prepared by making burns on the rat's back which is difficult for them to reach compared to the stomach, legs and other areas. The rat rubbed the wound so that the application of Aloe vera gel became less/ineffective. The limited time of this research makes this research must be completed within the specified time. Competing Interests: No competing interests were disclosed. a concern Respond or 28 Feb 2023 Pane, Department of Pharmacology and Therapeutics, Utara, Medan, 20155, Indonesia 28 Feb 2023 1. [SEP] Justify:

Output: The text does not provide information supporting the claim that Aloe vera has been used for centuries to treat skin ailments. Instead, it focuses on a scientific study concerning the healing effects of Aloe vera on burn wounds in rats, without referencing historical use or traditional applications of Aloe vera for skin ailments.


Input: Task: corroborate the claim using the given text. Claim: Many exoplanets have thick atmospheres of hydrogen and helium, similar to the gas giants in our solar system. [SEP] Text: Meteorite material is also produced by the fracturing of comets that are exposed to the increased heat of the Sun particularly in the vicinity of the inner planets. These occurrences often lead to showers of micrometeorites that impact a planet’s atmosphere [15]. Meteorites play a key role in planetary development. By depositing chemical elements or prebiotic material, meteorites can influence the evolution of a plant during its formative years. The extent of this influence depends on their size, frequency of impact, and composition. Meteorites striking a mature planet can cause ecological harm including impacts on the climate. 2. 3 Exoplanetary SystemsExoplanets are planets that orbit stars outside the Solar System. Given the diversity of star types and sizes, exoplanets have a wider range of physical characteristics than the planets that inhabit the Solar System. The various types of exoplanets include the most massive or gas giant planets, intermediate mass or Neptune planets, and low mass planets that include terrestrial and ocean planets or water worlds [9, 26]. Although most exoplanets orbit their host stars, rogue exoplanets are also possible [8]. 2. 3. 1 Gas GiantsGas giant exoplanets are similar to Jupiter and Saturn. Their composition is dominated by hydrogen and helium with smaller contributions from heavier elements and complex molecules. Their structures may include cores of rock or ice [9, 15, 26]. However, a recent publication suggests that hot Jupiters could exhibit a more diverse chemical composition [34]. For example, an analysis of HD 209458b atmospheric data suggests the presence of water, carbon monoxide, hydrogen cyanide, methane, ammonia, and acetylene [34]. are a classification of gas giants that typically reside near their star usually within 0. 05 to 0. 5 AU [23]. They could have formed near their host star or migrated toward the star after forming at a more distant location. Given the proximity to their host star, temperatures can exceed 2,000 K [23]. Considering their size and proximity to their host star, they are readily detected and are one of the most common types of exoplanet detected to date. A commentary of detection methods is presented in subsequent discussion. 2. 3. 2 Neptune ClassExoplanets known as the Neptune class are also giant planets, but heavy elements comprise most of their mass [9, 26]. [SEP] Justify:

Output: The text corroborates the claim as it discusses gas giant exoplanets which are described as having a composition dominated by hydrogen and helium, akin to the gas giants Jupiter and Saturn in our solar system. It states, 'Gas giant exoplanets are similar to Jupiter and Saturn. Their composition is dominated by hydrogen and helium with smaller contributions from heavier elements and complex molecules.' This supports the claim that many exoplanets possess thick atmospheres composed of hydrogen and helium.


Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: Thus, the detection network cannot replace the classification network. Segmentation networkSegmentation network converts the plant diseases and pests detection task to semantic and even instance segmentation of lesions and normal areas. It not only finely divides the lesion area, but also obtains the location, category and corresponding geometric properties (including length, width, area, outline, center, etc. ). It can be roughly divided into: Networks (FCN) [74] and Mask R-CNN [75]. FCNFull convolution neural network (FCN) is the basis of image semantics segmentation. At present, almost all semantics segmentation models are based on FCN. FCN first extracts and codes the features of the input image using convolution, then gradually restores the feature image to the size of the input image by deconvolution or up sampling. Based on the differences in FCN network structure, the plant diseases and pests segmentation methods can be divided into conventional FCN, U-net [76] and SegNet [77]. 1. Conventional FCN. Wang et al. [78] presented a new method of maize leaf disease segmentation based on full convolution neural network to solve the problem that traditional computer vision is susceptible to different illumination and complex background, and the segmentation accuracy reached 96. 26. Wang et al. [79] proposed a plant diseases and pests segmentation method based on improved FCN. In this method, a convolution layer was used to extract multi-layer feature information from the input maize leaf lesion image, and the size and resolution of the input image were restored by deconvolution operation. Compared with the original FCN method, not only the integrity of the lesion was guaranteed, but also the segmentation of small lesion area was highlighted, and the accuracy rate reached 95. 87%. 2. U-net. U-net is not only a classical FCN structure, but also a typical encoder-decoder structure. It is characterized by introducing a layer-hopping connection, fusing the feature map in the coding stage with that in the decoding stage, which is beneficial to the recovery of segmentation details. Lin et al. [80] used U-net based convolutional neural network to segment 50 cucumber powdery mildew leaves collected in natural environment. [SEP] Justify:

Output: The text contrasts the claim by discussing various methods and networks for detecting and segmenting plant diseases and pests, implying that such issues still exist and can affect crops. The mention of segmentation networks and methods developed for identifying and analyzing pest and disease lesions suggests that vertical farms are not completely immune to these agricultural challenges.


Input: Task: pass the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: In the method section, the authors mentioned that they divided the rats into 3 groups, group 1 without treatment, group 2 with gel base and group 3. . . Continue reading 1. In the method section, the authors mentioned that they divided the rats into 3 groups, group 1 without treatment, group 2 with gel base and group 3 with gel. Why did they name group 1 as standard group? What do they mean by "standard"? Answer: Naming group 1 as the standard group because group I is a group consisting of normal rat samples without being given burns or treatment only as a comparison to normal skin histology. This research is a true experimental study which has a control variable and the sample is randomly selected. At the beginning of the study we divided 54 rats into 3 large groups (group-I, II and III) with 3 small groups in them (day 3, 14 and 21) of each group @ 6 rats, namely: consisted of 18 rats divided into 3 small groups of 6 rats each. This group will be executed and their skin tissue will be taken on day-3, day-14 and day-21. The skin tissue taken was then examined histopathologically and used as a control skin of rats without being treated for BURN. Therefore we call as the "". Group II consisted of 18 rats divided into 3 small groups of 6 rats each which were treated with on the back of each rat, and every day they were smeared with gel base (gel without active substance) to see how the burn wound healed physiologically. We named this Group II the "" because it was not given any treatment. Whereas group III consisted of 18 rats divided into 3 small groups of 6 rats each which were treated with BURNS on the back of each rat, and smeared with Aloe vera gel (a gel with the active ingredient Aloe vera herb) every day to see how the process is progressed to healing burns with Aloe vera treatment. We call this Group III the "" because they were given the Aloe vera gel treatment. 2. This study lacks visual observation with a camera and the authors do not show a picture of the appearance of the wound and wound repair. This is a major concern and needs to be addressed. Answer: 6 in the new version. 3. The conclusion is a bold statement. What's the next step? Answer: See the conclusion in the new version. 4. What are the limitations of the study? [SEP] Justify:

Output: The text does not address the claim regarding Aloe vera's historical use for treating skin ailments. Instead, it describes an experimental study involving rats treated with Aloe vera gel, providing details about the experimental groups and methodology without mentioning the traditional use or historical context of Aloe vera in treating skin conditions.


Input: Task: pass the claim using the given text. Claim: Stem cells are often used in bioartificial organ development because they can differentiate into different types of cells. [SEP] Text: It is suggestive of how conserved cell states may be generated in new body locations during evolution, and firmly debunks the view that typical spinal intermediate states are necessary to pass down a MN lineage. 7) Finally, to definitely proof that both the SD and DP protocol give rise to MN via different intermediate stages, it will be critical to isolate the presumed intermediate cells by FACS (either by using specific cell surface markers inferred from the single cell data or at least they should use their MNX1:GFP line) and show that these "presumed" intermediates are indeed the source of the mature MNs. That is, the authors should re-culture the isolated cells and demonstrate by IF characterisation that they give rise to the final end product. We thank the reviewer for this suggestion, which we have now carried out. In the revised manuscript submission we include exactly this experiment – see revised Figure 4C. By using the Mnx1:GFP reporter cell line, we isolated the DP transitional state cells on day 3 of differentiation, cultured them for an additional 10 days, and show by immunostaining that they indeed generate mature MNs (Vacht/Map2+). Reviewer #1: This manuscript uses single cell transcriptomics to compare the cell state transitions that occur when mouse embryonic stem cells are directly reprogrammed into motor neurons using defined transcription factors (direct protocol) to those that occur during a directed differentiation designed to simulate normal embryological induction processes (standard protocol). The main conclusion is that the two pathways converge on a similar endpoint via distinct transition states. The authors assess this endpoint through functional assays and through comparison with bona fide neurons isolated from animals. The manuscript addresses an important question in cellular differentiation and stem cell biology. It is fair to point out that the direct reprogramming of embryonic stem cells (which are pluripotent) is liable to be significantly different in important ways to direct reprogramming of differentiated adult somatic cells (the more likely starting point for many direct reprogramming protocols). The latter type of conversion most often requires crossing or upwards traverse of cell lineages whereas the former might be expected to be more closely related to normal developmental pathways. All the same this study is certainly a valid model for direct reprogramming, and having a common starting point for the standard and direct protocols simplifies the analysis. Though the overall concept behind this study is interesting, there are some significant issues with the experimental design. [SEP] Justify:

Output: The text does not directly reference the use of stem cells in bioartificial organ development or their ability to differentiate into various cell types. Instead, it discusses a study on differentiating mouse embryonic stem cells into motor neurons and the methodologies involved in that process. While it touches on aspects of stem cell differentiation, it does not corroborate, contrast, or directly relate to the claim about bioartificial organs.


Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: Compared with the original U-net, a batch normalization layer was added behind each convolution layer, making the neural network insensitive to weight initialization. The experiment shows that the convolutional neural network based on U-net can accurately segment powdery mildew on cucumber leaves at the pixel level with an average pixel accuracy of 96. 08%, which is superior to the existing K-means, Random-forest and GBDT methods. The U-net method can segment the lesion area in a complex background, and still has good segmentation accuracy and segmentation speed with fewer samples. 3. SegNet. It is also a classical encoder–decoder structure. Its feature is that the up-sampling operation in the decoder takes advantage of the index of the largest pooling operation in the encoder. Kerkech et al. [81] presented an image segmentation method for unmanned aerial vehicles. Visible and infrared images (480 samples from each range) were segmented using SegNet to identify four categories: shadows, ground, healthy and symptomatic grape vines. The detection rates of the proposed method on grape vines and leaves were 92% and 87%, respectively. Mask R-CNNMask R-CNN is one of the most commonly used image instance segmentation methods at present. It can be considered as a multitask learning method based on detection and segmentation network. When multiple lesions of the same type have adhesion or overlap, instance segmentation can separate individual lesions and further count the number of lesions. However, semantic segmentation often treats multiple lesions of the same type as a whole. Stewart et al. [82] trained a Mask R-CNN model to segment maize northern leaf blight (NLB) lesions in an unmanned aerial vehicle image. The trained model can accurately detect and segment a single lesion. At the IOU threshold of 0. 50, the IOU between the baseline true value and the predicted lesion was 0. 73, and the average accuracy was 0. 96. Also, some studies combine the Mask R-CNN framework with object detection networks for plant diseases and pests detection. Wang et al. [83] used two different models, Faster R-CNN and ask R-CNN, in which Faster R-CNN was used to identify the class of tomato diseases and Mask R-CNN was used to detect and segment the location and shape of the infected area. [SEP] Justify:

Output: The text contrasts the claim by discussing methodologies for detecting and segmenting plant diseases like powdery mildew and northern leaf blight using image segmentation techniques. This implies that despite the controlled environments of vertical farms, plant diseases can still occur and be identified, which supports the notion that they are not completely immune.


Input: Task: pass the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: Answer: Application of gel to burns in rats that cannot be controlled because rats can lick/clean it even though it has been prepared by making burns on the rat's back which is difficult for them to reach compared to the stomach, legs and other areas. The rat rubbed the wound so that the application of Aloe vera gel became less/ineffective. The limited time of this research makes this research must be completed within the specified time. 1. In the method section, the authors mentioned that they divided the rats into 3 groups, group 1 without treatment, group 2 with gel base and group 3 with gel. Why did they name group 1 as standard group? What do they mean by "standard"? Answer: Naming group 1 as the standard group because group I is a group consisting of normal rat samples without being given burns or treatment only as a comparison to normal skin histology. This research is a true experimental study which has a control variable and the sample is randomly selected. At the beginning of the study we divided 54 rats into 3 large groups (group-I, II and III) with 3 small groups in them (day 3, 14 and 21) of each group @ 6 rats, namely: consisted of 18 rats divided into 3 small groups of 6 rats each. This group will be executed and their skin tissue will be taken on day-3, day-14 and day-21. The skin tissue taken was then examined histopathologically and used as a control skin of rats without being treated for BURN. Therefore we call as the "". Group II consisted of 18 rats divided into 3 small groups of 6 rats each which were treated with on the back of each rat, and every day they were smeared with gel base (gel without active substance) to see how the burn wound healed physiologically. We named this Group II the "" because it was not given any treatment. Whereas group III consisted of 18 rats divided into 3 small groups of 6 rats each which were treated with BURNS on the back of each rat, and smeared with Aloe vera gel (a gel with the active ingredient Aloe vera herb) every day to see how the process is progressed to healing burns with Aloe vera treatment. We call this Group III the "" because they were given the Aloe vera gel treatment. 2. This study lacks visual observation with a camera and the authors do not show a picture of the appearance of the wound and wound repair. [SEP] Justify:

Output: The text does not directly address the claim about Aloe vera's historical use in treating skin ailments. Instead, it focuses on a specific experimental study involving rats and the application of Aloe vera gel on burns. The discussion primarily revolves around the methodology and results of that study without mentioning the centuries-old practice or traditional uses of Aloe vera for skin ailments.


Input: Task: corroborate the claim using the given text. Claim: Stem cells are often used in bioartificial organ development because they can differentiate into different types of cells. [SEP] Text: The cellular system is appropriate too since three types of MNs can be compared: DP, SP, and pMN. The manuscript is clearly written following a logical argument. I believe it suffers from a very poor description of the experiment and methods that hurst the interpretation of the data. Also, it is superficial in important description as those single cell RNA-seq in Figure 5. We have increased the amount of detail provided in the manuscript regarding our definitions of cell states and our experimental methods, which should address all of the concerns here, including the data in Figure 5. Reviewer #3: The study, "Mouse embryonic stem cells can differentiate via multiple paths to the same state" by Briggs et al. uses single cell sequencing to demonstrate that mouse ESCs can give rise to motor neurons via alternative transcriptional trajectories. In detail the authors compare a traditional cytokine driven differentiation protocol (SD) with a transcription factor based direct "programming" approach. While the authors were able to detect discreet developmental intermediates in the SD protocol that correspond to known in vivo counter parts, the DP approach followed a mostly alternative path. While both DP and SD cultures appeared to transition into neural progenitors initially, beyond that they seemed to directly transition into an early motor neuron state bypassing a number of intermediate stages. The authors postulate that the same cell fate can be derived from mESCs via alternative routes that diverge after the neural commitment stage. This is an important and highly interesting study that tries to unveil how direct programming produces its target cell type and whether this is achieved by an accelerated form of differentiation (transcription factor guided) or whether this occurs via an alternative route/more direct cell state transition. While the authors use state of the art bioinformatical analyses to mine their data set is well done, and allowed them reach interesting results, I believe that a few key experiments are required for the authors to mechanistically support their key findings. – Single cell RNA seq data is biased towards detecting highly expressed genes. While the DP protocol appears to follow a very artificial trajectory it is possibly that key genes that are crucial for generation of the SD intermediates that the DP approach seems to skip are transiently expressed at very low levels. The authors could/should show that shRNA mediated knockdown of the key TF that are important for in vivo/SD generation of MNs but appear to get skipped in the DP protocol, reduce differentiation outcomes for the SD but not the DP protocol. [SEP] Justify:

Output: The text corroborates the claim by discussing the differentiation of mouse embryonic stem cells (mESCs) into motor neurons via alternative transcriptional trajectories. It highlights that mESCs can be differentiated using different protocols, such as the traditional cytokine-driven differentiation and the direct programming approach, thereby confirming the ability of stem cells to give rise to different cell types as needed in bioartificial organ development.


Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: The results showed that the proposed model can quickly and accurately identify 11 class of tomato diseases, and divide the location and shape of infected areas. Mask R-CNN reached a high detection rate of 99. 64% for all class of tomato diseases. Compared with the classification and detection network methods, the segmentation method has advantages in obtaining the lesion information. However, like the detection network, it requires a lot of annotation data, and its annotation information is pixel by pixel, which often takes a lot of effort and cost. Dataset and performance comparisonThis section first gives a brief introduction to the plant diseases and pests related datasets and the evaluation index of deep learning model, then compares and analyses the related models of plant diseases and pests detection based on deep learning in recent years. Datasets for plant diseases and pests detectionPlant diseases and pests detection datasets are the basis for research work. Compared with -VOC2007/2012 and COCO in computer vision tasks, there is not a large and unified dataset for plant diseases and pests detection. The plant diseases and pests dataset can be acquired by self-collection, network collection and use of public datasets. Among them, self-collection of image dataset is often obtained by unmanned aerial remote sensing, ground camera photography, Internet of Things monitoring video or video recording, aerial photography of unmanned aerial vehicle with camera, hyperspectral imager, near-infrared spectrometer, and so on. Public datasets typically come from PlantVillage, an existing well-known public standard library. Relatively, self-collected datasets of plant diseases and pests in real natural environment are more practical. Although more and more researchers have opened up the images collected in the field, it is difficult to compare them uniformly based on different class of diseases under different detection objects and scenarios. This section provides links to a variety of plant diseases and pests detection datasets in conjunction with existing studies. As shown in Table 4. Table 4 Common datasets for plant diseases and pests detectionFull size tableEvaluation indicesEvaluation indices can vary depending on the focus of the study. Common evaluation indices include \(Precision\), \(Recall\), mean (mAP) and the harmonic Mean F1 score based on \(Precision\) and \(Recall\). [SEP] Justify:

Output: The text contrasts the claim by discussing various aspects of plant disease and pest detection, highlighting that there are issues with datasets and methods to identify and analyze diseases in plants, including those in controlled environments. The mention of tomato diseases and the complexities of identifying and classifying these diseases suggests that while vertical farms may mitigate some issues, they are not completely immune to the challenges posed by pests and diseases.


Input: Task: pass the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: This is a major concern and needs to be addressed. Answer: 6 in the new version. 3. The conclusion is a bold statement. What's the next step? Answer: See the conclusion in the new version. 4. What are the limitations of the study? Answer: Application of gel to burns in rats that cannot be controlled because rats can lick/clean it even though it has been prepared by making burns on the rat's back which is difficult for them to reach compared to the stomach, legs and other areas. The rat rubbed the wound so that the application of Aloe vera gel became less/ineffective. The limited time of this research makes this research must be completed within the specified time. Competing Interests: No competing interests were disclosed. a concern 0 to cite this report: Rusip G. For: Effect of Aloe vera extract in post-burn skin repair in rats [version 2; peer review: 1 approved, 2 not approved]. F1000Research 2022, 11:168 ( The direct URL for this report is: The answer from the author is by the. . . Continue reading answer from the author is by the response; the manuscript is perfect. I approve the 10. 5256/f1000research. 135733. r145982) The direct URL for this report is: The direct URL for this report is: Major: The authors mainly focuses on wound repair after burn injury but fail to show the result. Only the data of fibroblast and macrophages did not justify the whole questions? To answer this question author must. . . Continue reading : The authors mainly focuses on wound repair after burn injury but fail to show the result. Only the data of fibroblast and macrophages did not justify the whole questions? To answer this question author must show picture of wound healing, histopathological picture, wound healing area, How authors say that the size of 2×2 cm2 is reduced? Author must show the data about the wound healing in term of wound healing picture and wound healing area? Images of a H&E staining should be provided and correlate it with number of fibroblast and other mentioned cells. Improve the scientific language. Is the work clearly and accurately presented and does it cite the current literature? the study design appropriate and is the work technically sound? sufficient details of methods and analysis provided to allow replication by others? [SEP] Justify:

Output: The text does not address the claim regarding the historical use of Aloe vera for skin ailments. Instead, it discusses a specific study focused on the application of Aloe vera extract in a controlled experiment involving burn injuries in rats, and critiques the study's methodology and results. Thus, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Stem cells are often used in bioartificial organ development because they can differentiate into different types of cells. [SEP] Text: Human amniotic epithelial cells can differentiate into granulosa cells and restore folliculogenesis in a mouse model of chemotherapy-induced premature ovarian failure access Published: 14 October 2013 Human amniotic epithelial cells can differentiate into granulosa cells and restore folliculogenesis in a mouse model of chemotherapy-induced premature ovarian failure Fangyuan Wang1, Li Wang2, Xiaofen Yao1, Dongmei Lai1 & …Lihe Guo1 Show authors Research & Therapy 0. 5 to 1. 0 cm2 segments. Placental segments were digested with 0. 25% trypsin/EDTA at 37°C for 45 minutes. The resulting cell suspensions were seeded in a six-well plate in RPMI 1640 medium supplemented with 10% FBS (ölbe, Germany), streptomycin (100 U/mL; ), penicillin (100 U/Ml; Gibco, Grand Island, NY), and glutamine (0. 3 mg/Ml; Gibco), and incubated at 37°C 5% CO2 in humidified air. Once hAECs reached 80 to 90% confluence, cells were ready for experiments. RNA extraction and real-time qPCR analysisTotal RNAs were isolated from samples using an RKit (). Five hundred nanograms of total RNA from each sample were used in reverse transcription (RT) using an iScript cDNA synthesis kit (Bio-). Real-time RT-qPCR was performed on cDNA using (Bio-Rad) on the Mastercycler® ep realplex (Hamburg, Germany). All reactions were performed in a 25-μl volume. Primer sequences are listed in Additional file1: Table S1. [SEP] Justify:

Output: The text corroborates the claim by providing an example of human amniotic epithelial cells (hAECs) that can differentiate into granulosa cells, demonstrating stem cell differentiation capability. This aligns with the assertion that stem cells are used in bioartificial organ development due to their ability to transform into varying cell types.


Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: Performance comparison of existing algorithmsAt present, the research on plant diseases and pests based on deep learning involves a wide range of crops, including all kinds of vegetables, fruits and food crops. The tasks completed include not only the basic tasks of classification, detection and segmentation, but also more complex tasks such as the judgment of infection degree. At present, most of the current deep learning-based methods for plant diseases and pests detection are applied on specific datasets, many datasets are not publicly available, there is still no single publicly available and comprehensive dataset that will allow all algorithms to be uniformly compared. With the continuous development of deep learning, the application performance of some typical algorithms on different datasets has been gradually improved, and the mAP, F1 score and FPS of the algorithms have all been increased. The breakthroughs achieved in the existing studies are amazing, but due to the fact that there is still a certain gap between the complexity of the infectious diseases and pests images in the existing studies and the real-time field diseases and pests detection based on mobile devices. Subsequent studies will need to find breakthroughs in larger, more complex, and more realistic datasets. ChallengesSmall dataset size problemAt present, deep learning methods are widely used in various computer vision tasks, plant diseases and pests detection is generally regarded as specific application in the field of agriculture. There are too few agricultural plant diseases and pests samples available. Compared with open standard libraries, self-collected data sets are small in size and laborious in labeling data. Compared with more than 14 million sample data in ImageNet datasets, the most critical problem facing plant diseases and pests detection is the problem of small samples. In practice, some plant diseases have low incidence and high cost of disease image acquisition, resulting in only a few or dozen training data collected, which limits the application of deep learning methods in the field of plant diseases and pests identification. In fact, for the problem of small samples, there are currently three different solutions. Data amplification, synthesis and generationData amplification is a key component of training deep learning models. An optimized data amplification strategy can effectively improve the plant diseases and pests detection effect. The most common method of plant diseases and pests image expansion is to acquire more samples using image processing operations such as mirroring, rotating, shifting, warping, filtering, contrast adjustment, and so on for the original plant diseases and pests samples. [SEP] Justify:

Output: The text contrasts the claim by discussing the challenge of detecting plant diseases and pests, which indicates that even with efforts in deep learning for detection, pests and diseases remain a concern in agriculture. It highlights that there are limited datasets available for study and that plant diseases can still occur, suggesting that vertical farms are not completely immune to these issues.


Input: Task: pass the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: applicable, is the statistical analysis and its interpretation appropriate? all the source data underlying the results available to ensure full reproducibility? the conclusions drawn adequately supported by the results? Interests: No competing interests were disclosed. Reviewer Expertise: Stem cell I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. . For: Effect of Aloe vera extract in post-burn skin repair in rats [version 2; peer review: 1 approved, 2 not approved]. F1000Research 2022, 11:168 ( The direct URL for this report is: The direct URL for this report is: The manuscript by Pane presents the intervening Effect of Aloe vera extract in post-burn skin repair in rats. In general, I find this manuscript to make an important contribution in developing palatable and superior bioactive compounds in Aloe. . . Continue reading manuscript by Pane presents the intervening Effect of Aloe vera extract in post-burn skin repair in rats. In general, I find this manuscript to make an important contribution in developing palatable and superior bioactive compounds in Aloe vera containing various active ingredients that help the wound healing process, such as glucomannan and acemannan. However, I have outlined below some of the issues that need to be addressed prior to indexing, and some minor comments and suggestions to further improve the quality of the manuscript. the abstract and introduction, the author states that one of the objectives of this study is to improve post-burn skin with the intervention of Aloe vera extract. However, histopathological description of the healing process of burns is not presented and discussed. The method part is not clear. It is not clear the analytical method for macrophage and fibroblast counts and the analytical formula is not specified, the authors need to explain it. Please clarify and make changes to the methods section as appropriate. In the discussion section, it is not explained how this study shows a synergistic interaction between changes that occur on days 3, 14 and 21 and between a number of macrophages and fibroblasts in figures 1, 2 and 3 with no visible histopathologic changes. It's better to call it the calculation score. the method section, how much Aloe vera contains? [SEP] Justify:

Output: The text is irrelevant to the claim because it discusses the effects of Aloe vera extract in a specific research study related to post-burn skin repair in rats, without providing information about the historical use of Aloe vera for skin ailments.


Input: Task: corroborate the claim using the given text. Claim: Stem cells are often used in bioartificial organ development because they can differentiate into different types of cells. [SEP] Text: Unfortunately, fertility preservation methods are used infrequently for people with cancer, and the quality of life of cancer survivors after chemotherapeutic or radiologic treatments is an increasing concern, specifically with respect to restoring gonadal function and reviving fertility[23]. Stem cells are unique cells with the potential for not only self-renewal but also the ability to differentiate into specialized progeny. Evidence suggests that transplantation of stem cells from bone marrow can restore ovarian function that has been destroyed by gonadotoxic insults[8, 10]. Recently, Ghadami and colleagues reported that bone marrow stem cell transplantation could restore follicular maturation and steroid hormone production in a follitropin receptor knockout (FORKO) mouse model used to study primary ovarian failure[24]. Irrespective of current stem cell controversies, general agreement exists regarding the utility of stem cell therapy, such as bone marrow stem cells, for improving ovarian function. Adult bone marrow (BM) is the most common source of clinically used mesenchymal stem cells (MSCs). However, adult BM has limitations. First, the frequency of MSCs in adult BM is low. Second, harvesting BM from a patient is invasive. Therefore, alternative sources of MSCs are needed for future clinical applications. Compared with BM or other adult stem cells, hAECs have enormous potential for serving as a stem cell source for basic science and regenerative medicine. hAECs can differentiate into cells from all three embryonic tissue types, such as ectoderm cells (neural cells), mesodermal cells (cardiomyocytes) and endodermal cells (hepatocytes and pancreatic cells)[11–13]. Moreover, hAECs can be isolated from term placentas without the sacrifice of human embryos and the cells can be readily cryopreserved for cell banking. Therefore, hAECs represent a powerful tool for cellular therapy for different human diseases, such as neurodegenerative diseases and nervous system injuries[25], hepatic fibrosis[26] and myocardial infarction (cardiac and smooth muscles)[27]. However, at this time whether hAECs can restore ovarian function is unclear. To address this, we analyzed hAECs for expression of germ cell markers. [SEP] Justify:

Output: The text corroborates the claim by explaining that stem cells possess the unique ability to differentiate into specialized progeny, specifically detailing how human amniotic epithelial cells (hAECs) can differentiate into various cell types including neural cells, cardiomyocytes, and hepatocytes. This capacity for differentiation supports their use in regenerative medicine and bioartificial organ development.


Input: Task: contrast the claim using the given text. Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: In addition, Networks (GANs) [93] and Variational automatic encoder (VAE) [94] can generate more diverse samples to enrich limited datasets. Transfer learning and fine-tuning classical network modelTransfer learning (TL) transfers knowledge learned from generic large datasets to specialized areas with relatively small amounts of data. When transfer learning develops a model for newly collected unlabeled samples, it can start with a training model by a similar known dataset. After fine-tuning parameters or modifying components, it can be applied to localized plant disease and pest detection, which can reduce the cost of model training and enable the convolution neural network to adapt to small sample data. Oppenheim et al. [95] collected infected potato images of different sizes, hues and shapes under natural light and classified by fine-tuning the VGG network. The results showed that, the transfer learning and training of new networks were effective. Too et al. [96] evaluated various classical networks by fine-tuning and contrast. The experimental results showed that the accuracy of Dense-Nets improved with the number of iterations. Chen et al. [97] used transfer learning and fine-tuning to identify rice disease images under complex background conditions and achieved an average accuracy of 92. 00%, which proves that the performance of transfer learning is better than training from scratch. Reasonable network structure designBy designing a reasonable network structure, the sample requirements can be greatly reduced. Zhang et al. [98] constructed a three-channel convolution neural network model for plant leaf disease recognition by combining three color components. Each channel TCCNN component is composed of three color RGB leaf disease images. Liu et al. [99] presented an improved CNN method for identifying grape leaf diseases. The model used a depth-separable convolution instead of a standard convolution to alleviate overfitting and reduce the number of parameters. For the different size of grape leaf lesions, the initial structure was applied to the model to improve the ability of multi-scale feature extraction. Compared with the standard ResNet and GoogLeNet structures, this model has faster convergence speed and higher accuracy during training. The recognition accuracy of this algorithm was 97. 22%. Fine-grained identification of small-size lesions in early identificationSmall-size lesions in early identificationAccurate early detection of plant diseases is essential to maximize the yield [36]. [SEP] Justify:

Output: The text contrasts the claim by discussing the importance of accurate early detection of plant diseases, which implies that pests and diseases can still affect plants. It highlights the use of advanced technologies like transfer learning for identifying and classifying plant diseases, suggesting that despite controlled environments, there can still be challenges related to pests and diseases.