Input: 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.

Output: contrast


Input: 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. . .

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: pass


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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].

Output: corroborate


Input: 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.

Output: contrast


Input: 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?

Output: pass


Input: 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.

Output: pass


Input: 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.

Output: contrast


Input: 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.

Output: pass


Input: 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.

Output: corroborate


Input: 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 \(Recall\).

Output: contrast


Input: 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?

Output: pass


Input: 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.

Output: corroborate


Input: 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.

Output: contrast


Input: 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?

Output: pass


Input: 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.

Output: corroborate


Input: 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].

Output: contrast


Input: Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: Explain the total solids content of the gel material used in the study. In the results section, a description of the histopathological picture is explained in relation to the analysis results from figures 1,2 and 3. It would be easier to explain in the discussion section. In the discussion section, please explain clearly the bioactive compounds contained in Aloe vera other than glucomannan and acemannan. Are there other active ingredients? What do the authors mean treatment days 14 and 21? Describe the histopathological picture with figures 1,2 and 3. In the introduction and abstract, Aloe vera intervention changes macrophages, fibroblasts and increases bioactive compounds and their potential for healing functional burns. The conclusion states that it is a potential pharmaceutical product. Please be consistent. 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: Physiology human, exercise, and much research-related use plants as herbal medicine I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as 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: We used the ANOVA test because in this study we used more than two groups with parametric data types We have presented and correlated the changes that occur on days 3,14,21 in macrophages and fibroblasts that could be seen in Figures 1 and 2 and had been discussed in the discussion section. Minor: Aloe vera contained 10% of the total weight of the gel (see gel preparation) In journal writing, repetition of words should be avoided, therefore we chose to explain in detail the number and comparison between groups in the results section.

Output: pass


Input: Claim: Stem cells are often used in bioartificial organ development because they can differentiate into different types of cells. [SEP] Text: Thus, we believe that hAECs function primarily by reactivating host gem line stem cells. Additional experiments are necessary to elucidate the exact mechanisms by which stem cell transplantation restores ovarian function. Previous studies indicate that amniotic epithelial cells had no karyotypic abnormalities or transformation potential in vitro and no tumorigenic effects in vivo[14]. Evidence also suggested that the amniotic epithelial cells had immunosuppressive properties[38, 39]. Here C57BL/6 mice (not SCID mice) were used to model ovarian failure. Mice receive no immunosuppressive agents before hAEC transplantation, and no transplant-related deaths were observed after hAEC transplantation. However, the immunological rejection of hAECs needs to be further elucidated before clinical application. ConclusionIn summary, hAECs derived from term placentas after live birth may have the intrinsic ability to restore folliculogenesis, and hAEC transplantation may be a testable clinical strategy for preserving or resurrecting ovarian function in female cancer patients after chemotherapy. Our findings have implications for the future use of stem cells for the improvement of the quality of life in cancer survivors. AbbreviationsAMH: Anti-Müllerian hormone BLIMP1: B-lymphocyte-induced maturation protein BM: Bone marrow c-MOS: Oocyte maturation factor mos CTX: Cyclophosphamide DAZL: Deleted in azoospermia-like DMSO: Dimethyl sulfoxide EGFP: Green fluorescent protein FSHR: Follicular stimulating hormone receptor GFP: Green fluorescent protein GnRH: Gonadotropin-releasing hormone H&E: Hemotoxylin and eosin hAECs: Human amniotic epithelial cells MSCs: Mesenchymal stem cells PBS: Phosphate-buffered saline PGCs: Primordial germ cells SCID: Severe combined immunodeficiency SCP1 and SCP3: Syntaptonemal complex protein 1 and 3 : Developmental pluripotency-associated protein 3, stella-related protein STRA8: Stimulated by retinoic acid gene 8 VASA: Probable ATP-dependent RNA helicase DDX4, vasa homolog. 543. 10. 1093/humupd/7. 6. 535. 405.

Output: corroborate


Input: Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: In the actual early identification of plant diseases and pests, due to the small size of the lesion object itself, multiple down sampling processes in the deep feature extraction network tend to cause small-scale objects to be ignored. Moreover, due to the background noise problem on the collected images, large-scale complex background may lead to more false detection, especially on low-resolution images. In view of the shortage of existing algorithms, the improvement direction of small object detection algorithm is analyzed, and several strategies such as attention mechanism are proposed to improve the performance of small target detection. The use of attention mechanism makes resources allocated more rationally. The essence of attention mechanism is to quickly find region of interest and ignore unimportant information. By learning the characteristics of plant diseases and pests images, features can be separated using weighted sum method with weighted coefficient, and the background noise in the image can be suppressed. Specifically, the attention mechanism module can get a salient image, and seclude the object from the background, and the Softmax function can be used to manipulate the feature image, and combine it with the original feature image to obtain new fusion features for noise reduction purposes. In future studies on early recognition of plant diseases and pests, attention mechanisms can be used to effectively select information and allocate more resources to region of interest to achieve more accurate detection. Karthik et al. [100] applied attention mechanism on the residual network and experiments were carried out using the plantVillage dataset, which achieved 98% overall accuracy. Fine-grained identificationFirst, there is a large difference within the class, that is, the visual characteristics of plant diseases and pests belonging to the same class are quite different. The reason is that the aforementioned external factors such as uneven illumination, dense occlusion, blurred equipment dithering and other interferences, resulting in different image samples belonging to the same kind of diseases and pests differ greatly. Plant diseases and pests detection in complex scenarios is a very challenging task of fine-grained recognition [101]. The existence of growth variations of diseases and pests results in distinct differences in the characterization of the same diseases and pests at different stages, forming the “intra-class difference” fine-grained characteristics. Secondly, there is fuzziness between classes, that is, objects of different classes have some similarity.

Output: contrast


Input: Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: While in the discussion section we explained the comparison of our findings with other studies Aloe vera has active ingredients other than acemannan and glucomannan, but based on the literature that we read, it proved that they could play a role in burn repair. We did not state that aloe vera can be used as a pharmaceutical product in the conclusion section (see part conclusion). We only stated that aloe vera could accelerate the healing process as proven by this study. Gusbakti Rusip, I really appreciate your willingness to review my manuscript. explain more information to improve understanding in this 10. 6084/m9. figshare. 17194973. v1) We used the ANOVA test because in this study we used more than two groups with parametric data types We have presented and correlated the changes that occur on days 3,14,21 in macrophages and fibroblasts that could be seen in Figures 1 and 2 and had been discussed in the discussion section. Minor: Aloe vera contained 10% of the total weight of the gel (see gel preparation) In journal writing, repetition of words should be avoided, therefore we chose to explain in detail the number and comparison between groups in the results section. While in the discussion section we explained the comparison of our findings with other studies Aloe vera has active ingredients other than acemannan and glucomannan, but based on the literature that we read, it proved that they could play a role in burn repair. We did not state that aloe vera can be used as a pharmaceutical product in the conclusion section (see part conclusion). We only stated that aloe vera could accelerate the healing process as proven by this study. Competing Interests: No competing interests were disclosed. a concern Respond or 10 Mar 2022 Pane, Pharmacology and Therapeutics, Utara, Medan, 20155, Indonesia 10 Mar 2022 really appreciate your willingness to review my manuscript. explain more information to improve understanding in this 10. 6084/m9. figshare. 17194973. v1) We used the ANOVA test because in this study we used more than two groups with parametric data types We have presented and correlated the changes that occur on days 3,14,21 in macrophages and fibroblasts that could be seen in Figures 1 and 2 and had been discussed in the discussion section.

Output: pass


Input: Claim: Stem cells are often used in bioartificial organ development because they can differentiate into different types of cells. [SEP] Text: AbstractAutologous skin grafts are used to treat severe burn wounds, however, the availability of adequate donor sites makes this option less practical. Recently, stem cells have been used successfully in tissue engineering and in regenerative medicine. The current study aims to differentiate umbilical cord tissue derived mesenchymal stem cells (CT-MSCs) into skin cells (fibroblasts and keratinocytes) for use to treat severe burn wounds. After isolation, MSCs were characterized and their growth characteristics were determined. The cells were induced to differentiate into fibroblasts and keratinocytes using respective induction medium. Results indicated that CT-MSCs were spindle shaped, plastic adherent and positive for CD29, CD44, CD73, CD90 markers. CT-MSCs also showed high proliferative potential as indicated by cumulative population doubling, doubling time and plating efficiency. The MSCs were successfully differentiated into fibroblast and keratinocytes as indicated by morphological changes and expression of lineage specific genes. We propose that these differentiated skin cells which are derived from CT-MSCs can thus be used for the development of bioengineered skin; however, further studies are required to evaluate the utility of these substitutes. Keywords: mesenchymal stem cells; cord tissue; bioengineered skin; skin cells; keratinocytes; fibroblasts1 IntroductionSkin wound healing requires restoration of both the dermal and epidermal layers of skin. Normally re-epithelization occurs by proliferation of keratinocytes that migrate from the wound edges. Similarly, fibroblast proliferation and release of growth factors restore dermis. In severe burn injuries, the normal repair process becomes defective. Autologous skin grafts is a treatment for severe burn wounds. However, problems such as the availability of adequate donor sites (for use as a graft) and chances of infection (due to additional injury) make this option less practical [1]. Recently, the use of adult stem cells has become a promising approach for the treatment of several diseases and disorders [2]. These cells are found in different adult and neonatal tissues. Stem cells possess two important characteristics, this is the ability of self-renewal and the ability to differentiate into specific cell types under appropriate culture conditions.

Output: corroborate


Input: Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: There are many detailed classifications of biological subspecies and subclasses of different kinds of diseases and pests, and there are some similarities of biological morphology and life habits among the subclasses, which lead to the problem of fine-grained identification of “inter-class similarity”. Barbedo believed that similar symptoms could be produced, which even phytopathologists could not correctly distinguish [102]. Thirdly, background disturbance makes it impossible for plant diseases and pests to appear in a very clean background in the real world. Background can be very complex and interfere with objects of interest, which makes plant diseases and pests detection more difficult. Some literature often ignores this issue because images are captured under controlled conditions [103]. Relying on the existing deep learning methods can not effectively identify the fine-grained characteristics of diseases and pests that exist naturally in the application of the above actual agricultural scenarios, resulting in technical difficulties such as low identification accuracy and generalization robustness, which has long restricted the performance improvement of decision-making management of diseases and pests by the Internet of Things [104]. The existing research is only suitable for fine-grained identification of fewer class of diseases and pests, can not solve the problem of large-scale, large-category, accurate and efficient identification of diseases and pests, and is difficult to deploy directly to the mobile terminals of smart agriculture. Detection performance under the influence of illumination and occlusionLighting problemsPrevious studies have collected images of plant diseases and pests mostly in indoor light boxes [105]. Although this method can effectively eliminate the influence of external light to simplify image processing, it is quite different from the images collected under real natural light. Because natural light changes very dynamically, and the range in which the camera can accept dynamic light sources is limited, it is easy to cause image color distortion when above or below this limit. In addition, due to the difference of view angle and distance during image collection, the apparent characteristics of plant diseases and pests change greatly, which brings great difficulties to the visual recognition algorithm. Occlusion problemAt present, most researchers intentionally avoid the recognition of plant diseases and pests in complex environments. They only focus on a single background. They use the method of directly intercepting the area of interest to the collected images, but seldom consider the occlusion problem. As a result, the recognition accuracy under occlusion is low and the practicability is greatly reduced.

Output: contrast


Input: Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: Minor: Aloe vera contained 10% of the total weight of the gel (see gel preparation) In journal writing, repetition of words should be avoided, therefore we chose to explain in detail the number and comparison between groups in the results section. While in the discussion section we explained the comparison of our findings with other studies Aloe vera has active ingredients other than acemannan and glucomannan, but based on the literature that we read, it proved that they could play a role in burn repair. We did not state that aloe vera can be used as a pharmaceutical product in the conclusion section (see part conclusion). We only stated that aloe vera could accelerate the healing process as proven by this study. Gusbakti Rusip, I really appreciate your willingness to review my manuscript. explain more information to improve understanding in this 10. 6084/m9. figshare. 17194973. v1) We used the ANOVA test because in this study we used more than two groups with parametric data types We have presented and correlated the changes that occur on days 3,14,21 in macrophages and fibroblasts that could be seen in Figures 1 and 2 and had been discussed in the discussion section. Minor: Aloe vera contained 10% of the total weight of the gel (see gel preparation) In journal writing, repetition of words should be avoided, therefore we chose to explain in detail the number and comparison between groups in the results section. While in the discussion section we explained the comparison of our findings with other studies Aloe vera has active ingredients other than acemannan and glucomannan, but based on the literature that we read, it proved that they could play a role in burn repair. We did not state that aloe vera can be used as a pharmaceutical product in the conclusion section (see part conclusion). We only stated that aloe vera could accelerate the healing process as proven by this study. Competing Interests: No competing interests were disclosed. a concern on this 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"?

Output: pass


Input: Claim: Stem cells are often used in bioartificial organ development because they can differentiate into different types of cells. [SEP] Text: Mesenchymal stem cells (MSCs) are a type of adult stem cells that have remarkable differentiation potential and could be utilized for repair and regeneration of lost tissues [3]. The high proliferation, multi-lineage differentiation capacity, immunomodulatory and immunosuppressive properties have made MSCs promising therapeutic candidates [4, 5, 6]. The MSCs can be isolated from adult (such as bone marrow, adipose tissue, synovial membrane, teeth etc. ) and neonatal sources (such as cord blood, cord tissue) [7, 8, 9, 10]. The current study has focused on umbilical cord tissue derived MSCs (CT-MSCs) as human cord tissue is readily available. There are certain advantages in using cord tissue, firstly, a large number of MSCs can be harvested and secondly, its isolation poses no risk to donors. Finally, CT-MSCs are less immunogenic and can be expanded extensively in vitro [11, 12]. In the current study, MSCs were isolated from human umbilical cord tissue by an explant culture technique. These cells were characterized and their growth characteristics were determined. CT-MSCs were differentiated into fibroblasts and keratinocytes by culturing in the respective induction medium. Results show that CT-MSCs exhibited spindle shape morphology, plastic adherent growth and expression of CD29, CD44, CD73, CD90 markers. The CT-MSCs exhibited high proliferative potential as indicated by cumulative population doubling and plating efficiency. On the other hand, the differentiated fibroblast and keratinocyte exhibited a changed morphology and had expression of lineage specific genes. We show that CT-MSCs can be differentiated into both types of skin cells (i. e. keratinocytes and fibroblasts) that could be used in the future development of bioengineered skin. However, further studies will be required to evaluate the utility of such substitutes. 2 Methods2. 1 Collection and isolation of cord tissuesHuman umbilical cord tissues were collected following full-term cesarean births. Around 3-5 inches of cord tissue was transferred from the hospital to the cell processing facility under sterilized conditions. All samples were obtained after written consent from the donors. All procedures were performed according to the protocol approved by local Board at the Medical University.

Output: corroborate


Input: Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: Occlusion problems are common in real natural environments, including blade occlusion caused by changes in blade posture, branch occlusion, light occlusion caused by external lighting, and mixed occlusion caused by different types of occlusion. The difficulties of plant diseases and pests identification under occlusion are the lack of features and noise overlap caused by occlusion. Different occlusion conditions have different degrees of impact on the recognition algorithm, resulting in false detection or even missed detection. In recent years, with the maturity of deep learning algorithms under restricted conditions, some researchers have gradually challenged the identification of plant diseases and pests under occluded conditions [106, 107], and significant progress has been made, which lays a good foundation for the application of plant diseases and pests identification in real-world scenarios. However, occlusion is random and complex. The training of the basic framework is difficult and the dependence on the performance of hardware devices still exists, we should strengthen the innovation and optimization of the basic framework, including the design of lightweight network architecture. The exploration of GAN and other aspects should be enhanced, while ensuring the accuracy of detection, the difficulty of model training should be reduced. GAN has prominent advantages in dealing with posture changes and chaotic background, but its design is not yet mature, and it is easy to crash in learning and cause model uncontrollable problems during training. We should strengthen the exploration of network performance to make it easier to quantify the quality of the model. Detection speed problemCompared with traditional methods, deep learning algorithms have better results, but their computational complexity is also higher. If the detection accuracy is guaranteed, the model needs to fully learn the characteristics of the image and increase the computational load, which will inevitably lead to slow detection speed and can not meet the needs of real-time. In order to ensure the detection speed, it is usually necessary to reduce the amount of calculation. However, this will cause insufficient training and result in false or missed detection. Therefore, it is important to design an efficient algorithm with both detection accuracy and detection speed. Plant diseases and pests detection methods based on deep learning include three main links in agricultural applications: data labeling, model training and model inference. In real-time agricultural applications, more attention is paid to model inference. Currently, most plant diseases and pests detection methods focus on the accuracy of recognition. Little attention is paid to the efficiency of model inference.

Output: contrast


Input: Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: 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? 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.

Output: pass


Input: Claim: Stem cells are often used in bioartificial organ development because they can differentiate into different types of cells. [SEP] Text: The fibroblast differentiation medium contained DMEM (Gibco, Cat# 11995-065), 10% FBS (Merck, Cat# 2020-07-31), 1% penicillin/streptomycin solution (Capricon, Cat# CP13-1019), 5 ug/ml insulin (Sigma, Cat# 19278-5ml) and 1 ng/ml basic fibroblast growth factor (Sigma, Cat# F02901) while keratinocyte differentiation medium consisted of DMEM, 10% FBS, 1% penicillin/streptomycin solution, 0. 5 mg/ml hydrocortisone (Sigma, Cat# H-4001), 1% insulin transferrin (Roche, Cat# 13532600) and 15 ng/ml keratinocytes growth factor (Sigma, Cat# H6666). Differentiation of CT-MSCs into fibroblasts and keratinocytes was confirmed by morphological changes and through polymerase chain reaction (PCR) using marker genes for fibroblast (desmin, collagen 3, vimentin, FGF7) and keratinocytes (CK1, CK10, CK14). 2. 3 Reverse transcription polymerase chain reaction (RT-PCR)Expression of lineage specific genes was carried out by RT-PCR. Briefly, cells were cultured for 15 days in the respective differentiation medium and total RNA was extracted using Trizol. Following extraction, the RNA was quantified using NanoDrop. For cDNA synthesis, 1. 5ug of RNA sample was used using Wizscript cDNA synthesis kit. The WizPure™ PCR master mix was used for quantitating the expression level of genes. Sequences for primer pairs and their product lengths (bp) are shown in Table 1. Gel bands were measured with image J software. Table 1List of used primers and their sequences.

Output: corroborate


Input: Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: In reference [108], to improve the efficiency of the model calculation process to meet the actual agricultural needs, a deep separable convolution structure model for plant leaf disease detection was introduced. Several models were trained and tested. The classification accuracy of Reduced MobileNet was 98. 34%, the parameters were 29 times less than VGG, and 6 times less than MobileNet. This shows an effective compromise between delay and accuracy, which is suitable for real-time crop diseases diagnosis on resource-constrained mobile devices. Conclusions and future directionsCompared with traditional image processing methods, which deal with plant diseases and pests detection tasks in several steps and links, plant diseases and pests detection methods based on deep learning unify them into end-to-end feature extraction, which has a broad development prospects and great potential. Although plant diseases and pests detection technology is developing rapidly, it has been moving from academic research to agricultural application, there is still a certain distance from the mature application in the real natural environment, and there are still some problems to be solved. Plant diseases and pests detection datasetDeep learning technology has made some achievements in the identification of plant diseases and pests. Various image recognition algorithms have also been further developed and extended, which provides a theoretical basis for the identification of specific diseases and pests. However, the collection of image samples in previous studies mostly come from the characterization of disease spots, insect appearance characteristics or the characterization of insect pests and leaves. Most of the research results are limited to the laboratory environment and are applicable only to the plant diseases and pests images obtained at the time. The main reason for this is that the growth of plants is cyclical, continuous, seasonal and regional. Similarly, the characteristics of the same disease or pest at different growing stages of crops are different. Images of different plant species vary from region to region. As a result, most of the existing research results are not universal. Even with a high recognition rate in a single trial, the validity of the data obtained at other times cannot be guaranteed. Most of the existing studies are based on the images generated in the visible range, but the electromagnetic wave outside the visible range also contains a lot of information, so the comprehensive information such as visible light, near infrared, multi-spectral should be fused to achieve the acquisition of plant diseases and pests dataset. Future research should focus on multi-information fusion method to obtain and identify plant diseases and pests information.

Output: contrast


Input: Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: The limited time of this research makes this research must be completed within the specified time. View more View less No competing interests were disclosed. reply a concern keyboard_arrow_left Back to all reports 0 Views copyright © 2022 Rusip G. This is an open access peer review report distributed under the terms of the Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 01 Aug 2022 | for Version 2 Gusbakti Rusip, Department of Physiology, Faculty of 0 Views copyright © 2022 Rusip G. This is an open access peer review report distributed under the terms of the Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses(0) Approved info_outline Alongside their report, reviewers assign a status to the 3. It would be easier to explain in the discussion section. In the discussion section, please explain clearly the bioactive compounds contained in Aloe vera other than glucomannan and acemannan. Are there other active ingredients? What do the authors mean treatment days 14 and 21? Describe the histopathological picture with figures 1,2 and 3. In the introduction and abstract, Aloe vera intervention changes macrophages, fibroblasts and increases bioactive compounds and their potential for healing functional burns. The conclusion states that it is a potential pharmaceutical product. Please be consistent. 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? competing interests were disclosed. Physiology human, exercise, and much research-related use plants as herbal medicine I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. replyRespond to this report Responses (1) 10 Mar 2022 Pane, Pharmacology and Therapeutics, Utara, Medan, 20155, really appreciate your willingness to review my manuscript. explain more information to improve understanding in this 10.

Output: pass


Input: Claim: Stem cells are often used in bioartificial organ development because they can differentiate into different types of cells. [SEP] Text: 3. 2 Growth CharacteristicsThe growth characteristics of CT-MSCs were assessed by measuring plating efficiency, cumulative population doublings, and doubling time. Plating efficiency ofCT-MSCs was evaluated by culturing the cells at low numbers for the eventual growth into colonies. The number of colonies were counted after 2 weeks and this number was used to determine plating efficiency which was 4. 225±1. 7 (Figure 2). Figure 2Plating efficiency of CT-MSCs. Inset shows a single colony of MSCs at high power (10X). To determine the number and time of population doublings at each passage, cells were counted with a hemocytometer and 10% cells were seeded into new culture flasks. The number of cumulative population doublings was 24. 67±0. 445 for CT-MSCs. Similarly, the average population doubling time for CT-MSCs was 51±3 hours. 3. 3 CT-MSCs can differentiate into keratinocytes and fibroblastsCT-MSCs at passage 2 (n=5) were differentiated into skin cells, either fibroblasts or keratinocytes. For differentiation into fibroblasts and keratinocytes, respective differentiation medium was used for 2 weeks. To serve as a control, cells were cultured in parallel in regular expansion medium. Compared to the spindle shaped morphology of control group (Figure 3A), MSCs in the keratinocyte differentiated medium showed polygonal shape (Figure 3B). Similarly, RT-PCR results indicated that differentiated MSCs were positive for keratinocyte lineage markers (CK1, CK10, CK14) at day 7 and day 14 (Figure 3C, D). In contrast, the expression of OCT4 and SSEA4 stem cell markers was downregulated (Figure 3E, F). Figure 3In vitro differentiation of CT-MSCs into keratinocytes: Untreated CT-MSCs exhibit spindle shape morphology (A). MSCs induced into keratinocytes exhibits a polygonal shape (B). Results of RT-PCR show that differentiated cells were positive for keratinocyte specific genes CK1, CK10 and CK14 (C). Quantification of gel bands using ImageJ software (D). Expression of SSEA4 and OCT4 decreased during differentiation into keratinocytes (E, F).

Output: corroborate


Input: Claim: Vertical farms are completely immune to pests and plant diseases due to their controlled environments. [SEP] Text: In addition, image databases of different kinds of plant diseases and pests in real natural environments are still in the blank stage. Future research should make full use of the data information acquisition platform such as portable field spore auto-capture instrument, unmanned aerial vehicle aerial photography system, agricultural internet of things monitoring equipment, which performs large-area and coverage identification of farmland and makes up for the lack of randomness of image samples in previous studies. Also, it can ensures the comprehensiveness and accuracy of dataset, and improves the generality of the algorithm. Early recognition of plant diseases and pestsIn the application of plant diseases and pests identification, the manifestation symptoms are not obvious, so early diagnosis is very difficult whether it is by visual observation or computer interpretation. However, the research significance and demand of early diagnosis are greater, which is more conducive to the prevention and control of plant diseases and pests and prevent their spread and development. The best image quality can be obtained when the sunlight is sufficient, and taking pictures in cloudy weather will increase the complexity of image preprocessing and reduce the recognition effect. In addition, in the early stage of plant diseases and pests occurrence, even high-resolution images are difficult to analyze. It is necessary to combine meteorological and plant protection data such as temperature and humidity to realize the recognition and prediction of diseases and pests. By consulting the existing research literatures, there are few reports on the early diagnosis of plant diseases and pests. Network training and learningWhen plant diseases and pests are visually identified manually, it is difficult to collect samples of all plant diseases and pests types, and many times only healthy data (positive samples) are available. However, most of the current plant diseases and pests detection methods based on deep learning are supervised learning based on a large number of diseases and pests samples, so manual collection of labelled datasets requires a lot of manpower, so unsupervised learning needs to be explored. Deep learning is a black box, which requires a large number of labelled training samples for end-to-end learning and has poor interpretability. Therefore, how to use the prior knowledge of brain-inspired computing and human-like visual cognitive model to guide the training and learning of the network is also a direction worthy of studying. At the same time, deep models need a large amount of memory and are extremely time-consuming during testing, which makes them unsuitable for deployment on mobile platforms with limited resources.

Output: contrast