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Topical application of CYP11B1 inhibitorsto reduce cutaneous cortisol is a novel strategy to promote healingof chronic wounds. Pyridyl substituted arylsulfonyltetrahydroquinolineswere designed and synthesized resulting in a strong inhibitor 34(IC50= 5 nM). It showed no inhibition of CYP17and CYP19 and no mutagenic effects. It exhibited inverse metabolicstability in plasma (t1/2≫ 150min), which is similar to wound fluid in composition, and in liverS9 fractions (t1/2= 16 min). [ABSTRACT FROM AUTHOR]

To further research the extracting method of the pig gait features, the paper firstly focus on the extraction of the pig gait frequency. A gait frequency extraction method was proposed based on analyzing the skeleton endpoints of depth image. Firstly, a series of processes, including skeleton extracting and pruning, were taken to the frames of depth image sequences. Secondly, a path similarity skeleton graph matching method was introduced to distinguish the fore-leg endpoints and the hind-leg endpoints from the skeleton graph. Then considering the characteristics of the depth image, a rule to distinguish the far-side endpoint and the near-side endpoint was constructed by calculating the average value of neighbor skeleton points of the endpoint. After ascertaining the skeleton endpoints of four legs, a variable was defined to represent the relative position of the far-side endpoint and the near-side endpoint, along the horizontal direction between frames in a sequence. Furthermore, the fitting sine curves were used to represent the variations of the fore-leg endpoints and the hind-leg endpoints separately. At last, the reciprocal of the fitting sine curve frequency can be calculated and the INTPART of double reciprocal was regarded as the fore-leg steps (FS) or the hind-leg steps (HS). The complete step (CS) was defined as the minimum of FS and HS. The finally gait frequency can be calculated by using the CS value to divide the duration of the sequence. To verify the proposal method, 28 depth image sequences of pig moving were acquired by using the KINECT depth camera, at the Rongxin pig farming of Danyang city in Jiangsu province, China. Another 28 sequences were achieved by mirror transforming along the horizontal direction to the native sequences. Experiments were taken for all the 56 sequences by using the proposal method. Experimental results show that the success rate of the method proposed in this paper is 82.1%, up to 92% for the situation when the pig moves continuously and the moving directions is perpendicular or nearly perpendicular to the axis of the depth camera only. Incorrect results often appear when the pig stays for a long time between steps or by non-cross steps, it needs to further adapt the proposal method. For the situation of rough variation of the pig body occurring in the sequence, the proposal method is not suited because the matching of skeleton points can not be achieved. That is the insufficient point of the proposal method. The proposal method would help to carry the further research of the abnormal gait of pig and construct the abnormal monitoring system by fusion of multi-source vision features. [ABSTRACT FROM AUTHOR]

Thousands of kilometers of shelterbelt plantations of Casuarina equisetifolia have been planted to protect the southeast coastline of China. These plantations also play an important role in the regional carbon (C) cycling. In this study, we examined plant biomass increment and C accumulation in four different aged C. equisetifolia plantations in sandy beaches in South China. The C accumulated in the C. equisetifolia plant biomass increased markedly with stand age. The annual rate of C accumulation in the C. equisetifolia plant biomass during 0–3, 3–6, 6–13 and 13–18 years stage was 2.9, 8.2, 4.2 and 1.0 Mg C ha−1 yr−1, respectively. Soil organic C (SOC) at the top 1 m soil layer in these plantations was 17.74, 5.14, 6.93, and 11.87 Mg C ha−1, respectively, with SOC density decreasing with increasing soil depth. Total C storage in the plantation ecosystem averaged 26.57, 38.50, 69.78, and 79.79 Mg C ha−1 in the 3, 6, 13 and 18- yrs plantation, with most of the C accumulated in the aboveground biomass rather than in the belowground root biomass and soil organic C. Though our results suggest that C. equisetifolia plantations have the characteristics of fast growth, high biomass accumulation, and the potential of high C sequestration despite planting in poor soil conditions, the interactive effects of soil condition, natural disturbance, and human policies on the ecosystem health of the plantation need to be further studied to fully realize the ecological and social benefits of the C equisetifolia shelterbelt forests in South China. [ABSTRACT FROM AUTHOR]

Chitin is an aminopolysaccharide present in insects as a major structural component of the cuticle. However, current knowledge on the chitin biosynthetic machinery, especially its constituents and mechanism, is limited. Using three independent binding assays, including co-immunoprecipitation, split-ubiquitin membrane yeast two-hybrid assay, and pull-down assay, we demonstrate that choline transporter-like protein 2 (Ctl2) interacts with krotzkopf verkehrt (kkv) in Drosophila melanogaster. The global knockdown of Ctl2 by RNA interference (RNAi) induced lethality at the larval stage. Tissue-specific RNAi to silence Ctl2 in the tracheal system and in the epidermis of the flies resulted in lethality at the first larval instar. The knockdown of Ctl2 in wings led to shrunken wings containing accumulated fluid. Calcofluor White staining demonstrated reduced chitin content in the first longitudinal vein of Ctl2 knockdown wings. The pro-cuticle, which was thinner compared to wildtype, exhibited a reduced number of chitin laminar layers. Phylogenetic analyses revealed orthologues of Ctl2 in different insect orders with highly conserved domains. Our findings provide new insights into cuticle formation, wherein Ctl2 plays an important role as a chitin-synthase interacting protein. [Display omitted] • Choline transporter-like protein 2 interacts with chitin synthase 1. • Ctl2 is necessary for chitin synthesis in chitin-containing organs. • Multiple motifs in Ctl2 are involved in the interaction to N-terminal region of kkv. • Putative orthologues of Ctl2 are found in different insect orders. • The gene expression pattern of Ctl2 during development is similar to that of kkv. [ABSTRACT FROM AUTHOR]

Background and aims: The relationship between tree species and soil nutrient availability is critical for evaluating plantation succession and promoting forest restoration. This study was conducted to evaluate the impact of exotic and native tress species on soil nutrient availability. Methods: Four exotic species ( Eucalyptus urophylla, E. tereticornis, Acaia auriculaeformis, A. mangium) and four native species ( Castanopsis fissa, Schima superba, C. hystrix, Michelia macclurei) were planted and grown for one-year. Soil solution (DOC, DON, NH−N, NO−N) was sampled and analyzed during the study. After the experiment, soil properties were determined, and plant tissues were analyzed. Results: DOC levels were greater in soils with trees planted than controls without trees. Compared to native species, exotic species had much faster growth rates and greatly reduced DON and NO−N concentrations. Exotic species always had less P concentrations in leaves and stems than native species. Furthermore, N-fixing A. auriculaeformis led to greater soil available P compared to other species. Conclusions: Based on these findings, we provide some recommendations for afforestation practice. This study highlights that a better understanding of the pros and cons of exotic species would be beneficial to advance afforestation in China and the world. [ABSTRACT FROM AUTHOR]

Atmospheric nitrogen (N) deposition in subtropical metropolitan regions has increased greatly because of rapid urbanization, and such increase could lead to N-related changes in soil properties and plant diversity in remnant forests of urban ecosystems. To investigate the pattern of atmospheric N deposition along an urban-rural gradient in metropolitan Guangzhou, southern China, and to assess the potential influence of N deposition on soil properties and understory plant diversity in remnant forests, precipitation, and soil samples were collected and vegetation was surveyed from four forest sites between March 2010 and March 2011. The atmospheric inorganic nitrogen deposition (DIN) decreased with increasing distance from the urban center: DIN inputs were 43.3, 41.2, 35.2, and 30.1 kg N ha year in two urban sites, a suburban site and a rural site, respectively. However, forest soil N status (NH-N, NO-N, and total nitrogen) showed the opposite pattern. Understory herb-layer diversity was negatively correlated to DIN input and positively correlated to soil calcium (Ca) and potassium (K) concentrations and pH; with highest herb-layer diversity found in the rural site receiving the lowest amount of DIN input. These results indicated that higher DIN along with soil acidification and leaching of base cations (Ca and K) might change the current N status and increase nutrients leaching and thereby cause reductions in understory plant diversity. A regional policy linking atmospheric pollution and land protection is needed to protect the most N-sensitive herb species (e.g., forbs and ferns) in these remnant forests. [ABSTRACT FROM AUTHOR]

Nitric oxide (NO) plays a critical role in atmospheric chemistry and also is a precursor of nitrate, which affects particle matter formation and nitrogen deposition. Agricultural soil has been recognized as a main source of atmospheric NO. However, quantifying the NO fluxes emitted from croplands remains a challenge and in situ long-term measurements of NO are still limited. In this study, we used an automated sampling system to measure NO fluxes with a high temporal resolution over two years (April 2017 to March 2019) from a rainfed maize field in the Northeast China. The cumulative annual NO emissions were 8.9 and 2.3 kg N ha−1 in year 1 (April 2017 to March 2018) and year 2 (April 2018 to March 2019), respectively. These interannual differences were largely related to different weather conditions encountered. In year 1, a month-long drought before and after the seeding and fertilizing reduced plant N uptake and dramatically increased soil N concentration. The following moderate rainfalls promoted large amount of NO emissions, which remained high until late September. The NO fluxes in both years showed clearer seasonal patterns, being highest after fertilizer application in summer, and lowest in winter. The seasonal patterns of NO fluxes were mainly controlled by soil available N concentrations and soil temperatures. The contribution of NO fluxes during the spring freeze-thaw in both years was no more than 0.2% of the annual NO budget, indicating that the freeze-thaw effect on agricultural NO emissions was minimal. In addition, with high-resolution monitoring, we found that soil not only act as a NO source but also a sink. Long-term and high-resolution measurements help us better understand the diurnal, seasonal, and annual dynamics of NO emissions, build more accurate models and better estimate global NO budget and develop more effective policy responses to global climate change. [Display omitted] • Soil NO flux was measured in a maize field with an automatic method over 2 years. • Annual NO emissions were 8.9 and 2.3 kg N ha−1 in year 1 and 2, respectively. • Intensified drought caused NO as an important pathway in rainfed agriculture N loss. • The NO emissions during freeze-thaw period contributed <0.2% to annual NO loss. [ABSTRACT FROM AUTHOR]

• Computer vision methods for livestock behaviour recognition were evaluated. • Journey from computer vision to deep learning was elaborated. • This paper involves with segmentation, identification and behaviour recognition. • Research trend of livestock behaviour recognition was illustrated from four aspects. • This review could provide researchers and producers with technical references. The increasing demand for sustainable livestock products also demands new considerations in animal breeding. Breeding programs are now seeking to integrate animal behavioural phenotypes, as these relate to the productivity, health and welfare of the animals and thereby can influence yield and economic benefits in the industry. Traditional manual observation of pig behaviour is time-consuming, laborious, subjective, and difficult to achieve in continuous and large-scale operations. It is not surprising that computer vision technology with the advantages of being objective, non-invasive and continuous has been widely researched for its use in the recognition of livestock behaviours over recent years. Nevertheless, in studies of livestock behaviour recognition, computer vision technology faces some challenges, e.g., complex scenes, variable illumination, occlusion, touching and overlapping between livestock, which has limited the fast translation of technology to industry. On the other hand, deep learning technology has proven to solve these difficulties to a certain extent and is being adopted to recognise livestock behaviours. This paper mainly evaluates the recent developments in computer vision methods for recognition of these behaviours in pigs and cattle. The focus on these species is made possible by the number of studies exist quantifying behaviours that are of importance for their health, welfare and productivity such as aggression, drinking, feeding, lameness, mounting, posture, tail-biting and nursing. This review paper especially analyses the development of image segmentation, identification and behaviour recognition using tradition computer vision and more recent deep learning methods, and evaluates the evolution of key research in the field. We elaborate the research trend of livestock behaviour recognition from four aspects, i.e., development of robust livestock identification algorithms, recognition of livestock behaviours for different growth stages, further quantification of the results of behaviour recognition, and building evaluation system of growth status, health and welfare. [ABSTRACT FROM AUTHOR]

• Feeding behaviour of multiple pigs at the nursery stage was recognised in this study. • Xception and LSTM were combined to extract discriminative spatial-temporal features. • These features can used to recognise feeding behaviour with an accuracy of 98.4%. • The identity and feeding time of each pig were determined in 4 feeding sub-regions. • The proposed algorithm could recognise 98.5% of feeding time of individual pigs. Monitoring the feeding behaviour of pigs and measuring their feeding time can help farmers to evaluate the pig health and welfare. The aim of this study was to develop a video-based deep learning algorithm to recognise feeding behaviour of nursery pigs and determine the feeding time of pigs on an individual level. In the experiment, two pens of pigs were video recorded for 3 days. In the video of feeding region of pen 1, 96,000 1 s feeding episodes and 96,000 1 s non-feeding episodes were generated. 70% of these data was randomly selected as training set and the remaining 30% as validation set. In the video of feeding region of pen 2, all the labelled 24,656 1 s feeding episodes and 61,744 1 s non-feeding episodes were used as test set. Firstly, the Convolutional Neural Network (CNN) architecture Xception was used to extract spatial features. These features were input into Long Short-term Memory (LSTM) framework to extract spatial-temporal features. Through the fully connected layer, the prediction function Softmax was finally used to classify these 1 s episodes as feeding or non-feeding. An image processing algorithm based on maximum entropy segmentation, HSV (Hue, Saturation and Value) colour space transformation and template matching was proposed to calculate the circularity of the head, the ratio of the head to the feeding sub-region, the accumulated pixels of the head motion, and the distance from the head to the number on pig back in order to determine the identity and feeding time of each pig. In the test set, the proposed algorithm could recognise feeding behaviour with an accuracy of 98.4%, a sensitivity of 98.8%, specificity of 98.3% and precision of 95.9%, and could correctly recognise 98.5% of feeding time of individual pigs from the total 45944 s feeding time of 8 pigs. The results indicate that the proposed method can be used to recognise feeding behavior of pigs and determine feeding time of each pig. [ABSTRACT FROM AUTHOR]

• It is the first time to use computer vision to recognise pig enrichment engagement. • A HSV colour space-based method was proposed to track the object region of interest. • InceptionV3 and LSTM were used to extract discriminative spatial-temporal features. • This algorithm has reference value for classifying similar behaviours of pigs. • Pigs preference to 3 enrichment objects was also determined in this study. As providing objects that pigs prefer can reduce the occurrence of tail-biting and aggression and consequently improve animal welfare, automatic recognition of pigs' engagement with different objects can have practical value. Therefore, aim of this study was to develop a computer vision based approach that utilised a recurrent neural network-based deep learning algorithm to recognise pig enrichment engagement (EE) behaviours and preliminarily determine the preference to objects. Two pig pens were studied. 1 day of video was recorded in pen 1, which generated 2400 1 s EE and 2400 1 s non-EE episodes. 80% of these data was randomly selected as training set and the remaining 20% as validation set. Moreover, 4 days of video were recorded and used as the test set in pen 2. Firstly, the HSV (Hue, Saturation, Value) colour space-based tracking algorithm was developed to locate object region of interest. Secondly, the convolutional neural network (CNN) architecture InceptionV3 was used to extract spatial features from each frame. These features were input into the long short-term memory (LSTM) framework to extract spatial-temporal features from each episode. Through the fully connected layer, the prediction function Softmax was finally used to classify these episodes as EE or non-EE behaviour. In the validation set, the proposed algorithm could recognise EE with blue ball, golden ball and wooden beam with an accuracy of 95.2%, 95.4% and 97.3%, respectively. By shortening the radius of the region of interest into a half of the average length of pig body, the corresponding accuracy could be improved into 96.9%, 97.1% and 97.9%, respectively. In the test set, the proposed algorithm could recognise EE with each of these 3 objects with an accuracy of 96.5%, 96.8% and 97.6%, respectively. The proportion of EE with each of these 3 objects was 75.8%, 6.0% and 18.2%, respectively. These results indicate that the proposed method can be used to recognise EE behaviours of pigs, and halving the radius of the region of interest can improve the recognition accuracy of EE behaviours. Moreover, the preference of pigs to objects based on EE duration were preliminarily determined as blue ball > wooden beam > golden ball. The obtained duration of EE behaviours can help farmers to evaluate the enrichment used and thereby to increase the health and welfare of the pigs in their care. Furthermore, the proposed algorithm has reference value for the classification of the behaviours with similar motion patterns. [ABSTRACT FROM AUTHOR]

• A deep learning method based on CNN and LSTM was used to recognise pig aggression. • This study directly process video episodes rather than individual frames. • CNN features were input into LSTM framework to extract spatial-temporal features. • The proposed method could recognise aggressive episodes with an accuracy of 97.2%. • A frame skipping approach can improve the accuracy to 98.4% and halve running time. Aggression is considered as a major animal welfare problem in commercial pig farming. The aim of this study is to develop a deep learning method based on convolutional neural network (CNN) and long short-term memory (LSTM) to recognise aggressive episodes of pigs. Compared to previous studies of pig behaviours based on deep learning, this study directly process video episodes rather than individual frames. In the experiment, nursery pigs (8/pen) were mixed for 3 days and then 8 h of video was recorded in each day. From these videos, 600 aggressive 2 s-episodes were manually selected and then augmented into 2400 episodes by using horizontal, vertical and diagonal mirroring. From the videos, 2400 non-aggressive 2 s-episodes were also manually selected. 80% of the data were randomly allocated as training set and the remaining 20% as validation set. Firstly, the CNN architecture VGG-16 was used to extract spatial features. These features were then input into LSTM framework to further extract temporal features. Through fully connected layer, the prediction function Softmax was finally used to determine if the current episode is aggression or non-aggression. Using the proposed method, aggressive episodes could be recognised with an accuracy of 97.2%. This result indicates that this method can be used to recognise aggressive episodes of pigs. Additionally, this paper further investigates the validity of this method under the conditions of skipping frames and reducing the episode length. The results show that a frame skipping approach whereby 30 fps is reduced into 15 fps within each 2 s-episode can improve the accuracy into 98.4% and halve the total running time. [ABSTRACT FROM AUTHOR]

• A new depth sensor of RealSense D435 was used for pig aggression detection. • Advantages of group analysis and 3D cameras were combined for the first time. • Connected area threshold was set to remove moving pixels without aggression. • SVM was used to classify the 4 features of motion shape index in each 3 s-unit. • The proposed algorithm could detect aggression with higher evaluation parameters. The aim of this study is to develop a depth image analysis method to automatically detect aggressive behaviours of group-housed pigs. In this work 2 experiments were performed. In each experiment, 8 pigs from 3 pens were mixed for 3 days and then 8 h of video was recorded in each day. In the 24 h data of the first experiment, all the 883 aggressive 3 s-units and manually selected 883 non-aggressive 3 s-units were used as training set. In the 24 h data of the second experiment, all the 856 aggressive 3 s-units and manually selected 856 non-aggressive 3 s-units were used as test set. Firstly, frame-to-frame distance was set and then frame difference method was used to obtain moving pixels. Secondly, moving pixels caused by non-aggressive behaviours were removed by setting threshold of connected area. Number of filtered moving pixels were summed and defined as motion shape index (MSI) in each frame. After dividing all videos into 3 s-units, the maximum, mean, variance and standard deviation of MSI in each unit were extracted as features. Finally, support vector machine (SVM) was used to classify these features in order to detect aggression. Using the proposed algorithm, aggressive behaviours could be detected with an accuracy of 97.5%, a sensitivity of 98.2%, specificity of 96.7% and precision of 96.8%. The results indicate that this algorithm can be used to detect aggressive behaviours of pigs. [ABSTRACT FROM AUTHOR]

The impacts of anthropogenic nitrogen (N) deposition on forest ecosystems depend in large part on its fate. However, our understanding of the fates of different forms of deposited N as well as the redistribution over time within different ecosystems is limited. In this study, we used the 15N‐tracer method to investigate both the short‐term (1 week to 3 months) and long‐term (1–3 yr) fates of deposited NH4+ or NO3− by following the recovery of the 15N in different ecosystem compartments in a larch plantation forest and a mixed forest located in northeastern China. The results showed similar total ecosystem retention for deposited NH4+ and NO3−, but their distribution within the ecosystems (plants vs. soil) differed distinctly particularly in the short‐term, with higher 15NO3− recoveries in plants (while lower recoveries in organic layer) than found for 15NH4+. The different short‐term fate was likely related to the higher mobility of 15NO3− than 15NH4+ in soils instead of plant uptake preferences for NO3− over NH4+. In the long‐term, differences between N forms became less prevalent but higher recoveries in trees (particularly in the larch forest) of 15NO3− than 15NH4+ tracer persisted, suggesting that incoming NO3− may contribute more to plant biomass increment and forest carbon sequestration than incoming NH4+. Differences between the two forests in recoveries were largely driven by a higher 15N recovery in the organic layer (both N forms) and in trees (for 15NO3−) in the larch forest compared to the mixed forest. This was due to a more abundant organic layer and possibly higher tree N demand in the larch forest than in the mixed forest. Leachate 15N loss was minor (<1% of the added 15N) for both N forms and in both forests. Total 15N recovery averaged 78% in the short‐term and decreased to 55% in the long‐term but with increasing amount of 15N label (re)‐redistributed into slow turn‐over pools (e.g., trees and mineral soil). The different retention dynamics of deposited NH4+ and NO3− may have implications in environmental policy related to the anthropogenic emissions of the two N forms. [ABSTRACT FROM AUTHOR]

Roads and highways play an important function in the human-dominated Earth landscape and strongly affect the environment. Roadside ecosystems receive a number of pollutants, including deicing salt and inorganic nitrogen (N) from automobiles. We investigated how soil carbon (C) and N cycling were impacted by the application of salt (NaCl) and nitrate (NO3−) to experimental plots in a field that is adjacent to Interstate 81, in Binghamton, NY. Experimental plots were constructed on two parallel transects; one was adjacent to the highway (0-m) and the other 50 m away from the highway (50-m). We hypothesized that the 0-m transect was exposed to roadway-derived pollutants over a long-term period of time, while the 50-m transect was exposed to fewer pollutants due to its distance from the road. Soils were collected in July and November 2011 and June and October 2012. Salt significantly decreased the rates of soil C mineralization and in situ soil respiration in both 0- and 50-m transects (p < 0.001), though it did not discernibly affect the rates of N mineralization or nitrification. Applications of NO3− had no significant impact on soil C or N mineralization. The effects of roadway pollutants were reflected in higher soil conductivities and pH at the 0-m transect. Under experimental salt treatment, C mineralization was reduced by 75% in the 50-m transect, compared to 20% reduction in the 0-m transect. We conclude that microbial communities near roads might have evolved to better withstand the impacts of roadway pollutants. Nevertheless, roads and vehicle traffic have strong impacts on the environment, and the application of road salt has important environmental consequences. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Increasing atmospheric nitrogen (N) deposition represents a major global change factor, but its long‐term effect on tree growth and carbon (C) sequestration remains uncertain. Our manipulation experiment and meta‐analysis reveal that N deposition in temperate and boreal forests promoted tree growth and the allocation of more C into wood than into leaves in China and worldwide. Thus, N deposition may increase forest C sequestration through enhanced wood production and distribution of C into stable sinks. In the context of achieving "Carbon Neutrality," understanding how N deposition affect long‐term forest C sinks will help us with mitigation strategies under climate change. Summary: Increased nitrogen (N) deposition is driving many temperate and boreal forests in the Northern Hemisphere towards N saturation. However, it is uncertain how long‐term N deposition affects tree growth and carbon (C) allocation in these forests.To investigate this, we treated temperate larch and mixed forests in northeastern China with N additions for 8 years. In addition, we collected data from 25 N‐addition experiments in temperate and boreal forests worldwide to reveal the overall effects of N on tree growth and C allocation.Nitrogen additions significantly promoted total biomass increment by 24% in both study forests, with on average additional 8 kg C per kg N gain into woody biomass over the study period. Nitrogen additions increased the ratio of woody biomass increment to foliage litterfall production in the larch forest (by 34%). Literature data analysis also revealed greater N promotion on wood (24%) over foliage (9%) production. However, the positive effect on foliage diminished over time.These results combined imply that N deposition may promote tree growth in temperate and boreal regions and drive proportionally more photosynthate allocation into wood than leaves, thus may enhance forest C sequestration in the long run. [ABSTRACT FROM AUTHOR]

Real-time semantic segmentation is a challenging task as both segmentation accuracy and inference speed need to be considered at the same time. In this paper, a Dual Context Network (DCNet) is presented to address this challenge. It contains two independent sub-networks: Region Context Network and Pixel Context Network. Region Context Network is main network with low-resolution input and features re-weighting module to achieve sufficient receptive field. Meanwhile, Pixel Context Network with location attention module is to capture the location dependencies of each pixel for assisting the main network to recover spatial detail. A contextual feature fusion is introduced to combine output features of these two sub-networks. The experiments show that DCNet can achieve high-quality segmentation while keeping a high speed. Specifically, for Cityscapes test dataset, it can achieve 76.1% Mean IOU with the speed of 82 FPS on a single GTX 2080Ti GPU when using ResNet50 as backbone and 71.2% Mean IOU with the speed of 142 FPS when using ResNet18 as backbone. [ABSTRACT FROM AUTHOR]

Chitin, the major structural polysaccharide in arthropods such as insects and mites, is a linear polymer of N -acetylglucosamine units. The growth and development of insects are intimately coupled with chitin biosynthesis. The membrane-bound β-glycosyltransferase chitin synthase is known to catalyze the key polymerization step of N -acetylglucosamine. However, the additional proteins that might assist chitin synthase during chitin biosynthesis are not well understood. Recently, fatty acid binding protein (Fabp) has been suggested as a candidate that interacts with the chitin synthase Krotzkopf verkehrt (Kkv) in Drosophila melanogaster. Here, using split-ubiquitin membrane yeast two-hybrid and pull-down assays, we have demonstrated that the Fabp-B splice variant physically interacts with Kkv in vitro. The global knockdown of Fabp in D. melanogaster using RNA interference (RNAi) induced lethality at the larval stage. Moreover, in tissue-specific RNAi experiments, silenced Fabp expression in the epidermis and tracheal system caused a lethal larval phenotype. Fabp knockdown in the wings resulted in an abnormal wing development and uneven cuticular surface. In addition to reducing the chitin content in the first longitudinal vein of wings, Fabp silencing also caused the loss of procuticle laminate structures. This study revealed that Fabp plays an important role in chitin synthesis and contributes to a comprehensive understanding of the complex insect chitin biosynthesis. [Display omitted] • Fatty acid binding protein associates with chitin biosynthesis in Drosophila melanogaster. • Whole-body knock down of fatty acid binding protein induced lethal larval phenotypes. • Wing-specific knockdown led to abnormal wing development with uneven surfaces. • Fatty acid binding protein suppression caused loss of pro-cuticle laminate structures. [ABSTRACT FROM AUTHOR]

Environmental change factors can significantly affect the composition and physiology of soil microbes. How the resulting changes in the community composition are related to microbial functions, however, remains poorly understood. We investigated the effects of climate warming (+ 1.4°C of air temperature and + 0.75°C of soil temperature at 10 cm depth) and reactive nitrogen (N) input (12 g N m–2 year–1) on the community composition and physiologies of soil bacteria in a semiarid Loess grassland. Soil bacterial communities were assessed by Miseq sequencing of 16S rRNA gene amplicons while their physiological properties were assessed by microbial metabolic quotients (qCO2, microbial respiration per unit of microbial biomass) and microbial community-level physiological profiles (CLPPs). Our results showed that N input, but not warming, altered bacterial community structure, although both warming and N input significantly affected the abundances of certain phyla. While phyla Verrucomicrobia and Chloroflexi were sensitive to warming, Saccharibacteria, Bacteroidetes and Actinobacteria were primarily responsive to N input. Both warming and N input increased microbial metabolic quotients, but only warming significantly impacted soil microbial CLPPs with L-cysteine, oxalic acid, oxoglutaric acid and aminobutyric acid being the sensitive C sources. Structural equation modeling showed that warming and N input influenced soil bacterial phyla through soil moisture, soil NO3––N and plant biomass. The sensitive bacterial phyla, not the whole community property, were significantly correlated with qCO2 and microbial C utilization. Our findings suggest that responses of bacterial groups sensitive to environmental change factors, rather than the whole community, may exert dominant effects on soil microbial functions under future climate change scenarios. [ABSTRACT FROM AUTHOR]