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The nitrogen application rate (NAR) has a significant effect on the contents of wheat grain protein and its composition. There is still no consensus regarding the appropriate NAR, given the differences in studied conditions and influence of factors such as geographical location, climate, and soil nutrient contents. In this study, 66 papers related to wheat grain protein and its composition published from 1984 to 2021 were selected for meta-analysis in comprehensively evaluating the response of wheat grain protein content and composition to NAR in China. The results reveal that NAR significantly increased total protein content by 9.49–28.6%, gliadin by 9.13–30.5%, glutenin by 12.9–45.4%, albumin by 5.06–15.8%, and globulin by 8.52–24.0% of wheat grain in China, respectively, compared to no nitrogen application. The optimal NAR is 240–300 kg ha−1 when specific planting conditions are not being considered. Under different growing conditions, the NAR that provided the greatest increase in wheat grain protein and its composition varied as follows: 180–240 kg ha−1 in Northwest China and at >100 m altitudes; >300 kg ha−1 in North China and at <100 m altitudes and lower soil base nutrient levels; 240–300 kg ha−1 in Southeast China, with higher soil nutrients levels and for all average annual temperatures and precipitation ranges. In conclusion, the results of the present study reveal that it is feasible to systematically enhance the contents of wheat grain protein and its related fractions by appropriate NAR under different cropping conditions. [ABSTRACT FROM AUTHOR]

Floating wheat is a classical herbal with potential efficacy in the treatment of hyperhidrosis. Aiming at revealing the main components and potential mechanisms of floating wheat, a comprehensive and unique phytopharmacology profile study was carried out. First, common wheat was used as a control to look for chemical markers of floating wheat. In the screening analysis, a total of 180 shared compounds were characterized in common wheat and floating wheat, respectively. The results showed that floating wheat and common wheat contain similar types of compounds. In addition, in non-targeted metabolomic analysis, when taking the contents of the constituents into account, it was found that there indeed existed quite a difference between floating wheat and common wheat and 17 potential biomarkers for floating wheat. Meanwhile, a total of seven components targeted for hyperhidrosis were screened out based on network pharmacology. Seven key differential components were screened, among which kaempferol, asiatic acid, sclareol, enoxolone, and secoisolariciresinol had higher degree values than the others. The analysis of interacting genes revealed three key genes, namely, MAP2K1, ESR1, and ESR2. The Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses showed that various signaling pathways were involved. Prolactin signaling, thyroid cancer, endocrine resistance, gonadotropin secretion, and estrogen signaling pathways were the main pathways of the intervention of floating wheat in excessive sweating, which was associated with the estrogenic response, hormone receptor binding, androgen metabolism, apoptosis, cancer, and many other biological processes. Molecular docking showed that the screened key components could form good bindings with the target proteins through intermolecular forces. This study reveals the active ingredients and potential molecular mechanism of floating wheat in the treatment of hyperhidrosis and provides a reference for subsequent basic research. [ABSTRACT FROM AUTHOR]

In driverless systems (scenarios such as subways, buses, trucks, etc.), multi-modal data fusion, such as light detection and ranging (LiDAR) points and camera images, is essential for accurate 3D object detection. In the fusion process, the information interaction between the modes is challenging due to the different coordinate systems of various sensors and the significant difference in the density of the collected data. It is necessary to fully consider the consistency and complementarity of multi-modal information, make up for the gap between multi-source data density, and achieve the joint interactive processing of multi-source information. Therefore, this paper is based on Transformer to improve a new multi-modal fusion model called PIDFusion for 3D object detection. Firstly, the method uses the results of 2D instance segmentation to generate dense 3D virtual points to enhance the original sparse 3D point clouds. This optimizes the issue that the nearest Euclidean distance in the 2D image space cannot ensure the nearest in the 3D space. Secondly, a new cross-modal fusion architecture is designed to maintain individual per-modality features to take advantage of their unique characteristics during 3D object detection. Finally, an instance-level fusion module is proposed to enhance semantic consistency through cross-modal feature interaction. Experiments show that PIDFusion is far ahead of existing 3D object detection methods, especially for small and long-range objects, with 70.8 mAP and 73.5 NDS on the nuScenes test set. [ABSTRACT FROM AUTHOR]

Floristic regions, conventionally established using species distribution patterns, have often overlooked the phylogenetic relationships among taxa. However, how phylogenetic relationships influence the historical interconnections within and among biogeographic regions remains inadequately understood. In this research, we compiled distribution data for seed plants in Gansu, a region of significant biogeographic diversity located in northwestern China.We proposed a novel framework for floristic regions within Gansu, integrating distribution data and phylogenetic relationships of genera-level native seed plants, aiming to explore the relationship between phylogenetic relatedness, taxonomic composition, and regional phylogenetic delineation. We found that (1) phylogenetic relatedness was strongly correlated with the taxonomic composition among floras in Gansu. (2) The southeastern Gansu region showed the lowest level of spatial turnover in both phylogenetic relationships and the taxonomic composition of floristic assemblages across the Gansu region. (3) Null model analyses indicated nonrandom phylogenetic structure across the region, where most areas showed higher phylogenetic turnover than expected given the underlying taxonomic composition between sites. (4) Our results demonstrated a consistent pattern across various regionalization schemes and highlighted the preference for employing the phylogenetic dissimilarity approach in biogeographical regionalization investigations. (5) Employing the phylogenetic dissimilarity approach, we identified nine distinct floristic regions in Gansu that are categorized into two broader geographical units, namely the northwest and southeast. (6) Based on the phylogenetic graphic regions of China across this area. [ABSTRACT FROM AUTHOR]

In recent years, semantic segmentation of high-resolution remote sensing images has been gradually applied to many important scenes. However, with the rapid development of remote sensing data acquisition technology, the existing image data processing methods are facing major challenges. Especially in the accuracy of extraction and the integrity of the edges of objects, there are often problems such as small objects being assimilated by large objects. In order to solve the above problems, based on the excellent performance of Transformer, convolution and its variants, and feature pyramids in the field of deep learning image segmentation, we designed two encoders with excellent performance to extract global high-order interactive features and low-order local feature information. These encoders are then used as the backbone to construct a global and local feature fusion network with a dual encoder (GLFFNet) to effectively complete the segmentation of remote sensing images. Furthermore, a new auxiliary training module is proposed that uses the semantic attention layer to process the extracted feature maps separately, adjust the losses, and more specifically optimize each encoder of the backbone, thus optimizing the training process of the entire network. A large number of experiments show that our model achieves 87.96% mIoU on the Potsdam dataset and 80.42% mIoU on the GID dataset, and it has superior performance compared with some state-of-the-art methods on semantic segmentation tasks in the field of remote sensing. [ABSTRACT FROM AUTHOR]

Automotive radar pursues low cost and high performance, and especially hopes to improve the angular resolution under the condition of a limited number of multiple-input–multiple-output (MIMO) radar channels. Conventional time division multiplexing (TDM) MIMO technology has a limited ability to improve the angular resolution without increasing the number of channels. In this paper, a random time division multiplexing MIMO radar is proposed. First, the non-uniform linear array (NULA) and random time division transmission mechanism are combined in the MIMO system, and then a three-order sparse receiving tensor of a range-virtual aperture-pulse sequence is obtained during echo receiving. Next, this sparse three-order receiving tensor is recovered by using tensor completion technology. Finally, the range, velocity and angle measurements are completed for the recovered three-order receiving tensor signals. The effectiveness of this method is verified via simulations. [ABSTRACT FROM AUTHOR]

Congenital cataracts account for approximately 5–20% of childhood blindness worldwide and 22–30% of childhood blindness in developing countries. Genetic disorders are the primary cause of congenital cataracts. In this work, we investigated the underlying molecular mechanism of G149V point missense mutation in βB2-crystallin, which was first identified in a three-generation Chinese family with two affected members diagnosed with congenital cataracts. Spectroscopic experiments were performed to determine the structural differences between the wild type (WT) and the G149V mutant of βB2-crystallin. The results showed that the G149V mutation significantly changed the secondary and tertiary structure of βB2-crystallin. The polarity of the tryptophan microenvironment and the hydrophobicity of the mutant protein increased. The G149V mutation made the protein structure loose and the interaction between oligomers was reduced, which decreased the stability of the protein. Furthermore, we compared βB2-crystallin WT and the G149V mutant with their biophysical properties under environmental stress. We found that the G149V mutation makes βB2-crystallin more sensitive to environmental stresses (oxidative stress, UV irradiation, and heat shock) and more likely to aggregate and form precipitation. These features might be important to the pathogenesis of βB2-crystallin G149V mutant related to congenital cataracts. [ABSTRACT FROM AUTHOR]

In recent years, the application of semantic segmentation methods based on the remote sensing of images has become increasingly prevalent across a diverse range of domains, including but not limited to forest detection, water body detection, urban rail transportation planning, and building extraction. With the incorporation of the Transformer model into computer vision, the efficacy and accuracy of these algorithms have been significantly enhanced. Nevertheless, the Transformer model's high computational complexity and dependence on a pre-training weight of large datasets leads to a slow convergence during the training for remote sensing segmentation tasks. Motivated by the success of the adapter module in the field of natural language processing, this paper presents a novel adapter module (ResAttn) for improving the model training speed for remote sensing segmentation. The ResAttn adopts a dual-attention structure in order to capture the interdependencies between sets of features, thereby improving its global modeling capabilities, and introduces a Swin Transformer-like down-sampling method to reduce information loss and retain the original architecture while reducing the resolution. In addition, the existing Transformer model is limited in its ability to capture local high-frequency information, which can lead to an inadequate extraction of edge and texture features. To address these issues, this paper proposes a Local Feature Extractor (LFE) module, which is based on a convolutional neural network (CNN), and incorporates multi-scale feature extraction and residual structure to effectively overcome this limitation. Further, a mask-based segmentation method is employed and a residual-enhanced deformable attention block (Deformer Block) is incorporated to improve the small target segmentation accuracy. Finally, a sufficient number of experiments were performed on the ISPRS Potsdam datasets. The experimental results demonstrate the superior performance of the model described in this paper. [ABSTRACT FROM AUTHOR]

The classification of wetland plants using unmanned aerial vehicle (UAV) and satellite synergies has received increasing attention in recent years. In this study, UAV-derived training and validation data and WorldView-3 satellite imagery are integrated in the classification of five dominant wetland plants in the Old Woman Creek (OWC) estuary, USA. Several classifiers are explored: (1) pixel-based methods: maximum likelihood (ML), support vector machine (SVM), and neural network (NN), and (2) object-based methods: Naïve Bayes (NB), support vector machine (SVM), and k-nearest neighbors (k-NN). The study evaluates the performance of the classifiers for different image feature combinations such as single bands, vegetation indices, principal components (PCs), and texture information. The results showed that all classifiers reached high overall accuracy (>85%). Pixel-based SVM and object-based NB exhibited the best performance with overall accuracies of 93.76% and 93.30%, respectively. Insignificantly lower overall accuracy was achieved with ML (92.29), followed by NN (90.95) and object-oriented SVM (90.61). The k-NN method showed the lowest (but still high) accuracy of 86.74%. All classifiers except for the pixel-based SVM required additional input features. The pixel-based SVM achieved low errors of commission and omission, and unlike the other classifiers, exhibited low variability and low sensitivity to additional image features. Our study shows the efficacy of combining very high spatial resolution UAV-derived information and the super spectral observation capabilities of WorldView-3 in machine learning for mapping wetland vegetation. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) is being widely used to compress and reconstruct data for processing electrocardiogram (ECG) signals obtained through Wireless Body Area Networks. However, the conventional measurement matrix generator and compression computations for CS are in parallel, resulting in significant power consumption and a large area. This paper proposes a serial measurement matrix generator, which reduces the clock frequencies by using linear feedback shift registers and latches. A CS circuit for ECG signals processing based on the proposed measurement matrix generator is proposed and implemented in a SMIC 55 nm CMOS process. The experimental results show that the power consumption is only 1.690 μW at 1.2 V, and the chip area is 0.608 mm2, which has obvious advantages over the traditional parallel architecture. The reconstruction results show that the Percentage Root-mean-square Difference is 1.32%, which means that the design meets the basic clinical requirements. [ABSTRACT FROM AUTHOR]

The metabolism disorders are a common convergence of Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM). The characteristics of AD are senile plaques and neurofibrillary tangles (NFTs) composed by deposits of amyloid−β (Aβ) and phosphorylated tau, respectively. Advanced glycation end−products (AGEs) are a stable modification of proteins by non−enzymatic reactions, which could result in the protein dysfunction. AGEs are associated with some disease developments, such as diabetes mellitus and AD, but the effects of the glycated γ2 subunit of AMPK on its activity and the roles in AD onset are unknown. Methods: We studied the effect of glycated γ2 subunit of AMPK on its activity in N2a cells. In 3 × Tg mice, we administrated L−arginine once every two days for 45 days and evaluated the glycation level of γ2 subunit and function of AMPK and alternation of pathologies. Results: The glycation level of γ2 subunit was significantly elevated in 3 × Tg mice as compared with control mice, meanwhile, the level of pT172−AMPK was obviously lower in 3 × Tg mice than that in control mice. Moreover, we found that arginine protects the γ2 subunit of AMPK from glycation, preserves AMPK function, and improves pathologies and cognitive deficits in 3 × Tg mice. Conclusions: Arginine treatment decreases glycated γ2 subunit of AMPK and increases p−AMPK levels in 3 × Tg mice, suggesting that reduced glycation of the γ2 subunit could ameliorate AMPK function and become a new target for AD therapy in the future. [ABSTRACT FROM AUTHOR]

Cloud detection is an essential step in optical remote sensing data processing. With the development of deep learning technology, cloud detection methods have made remarkable progress. Among them, researchers have started to try to introduce Transformer into cloud detection tasks due to its excellent performance in image semantic segmentation tasks. However, the current Transformer-based methods suffer from training difficulty and low detection accuracy of small clouds. To solve these problems, this paper proposes Cloudformer V2 based on the previously proposed Cloudformer. For the training difficulty, Cloudformer V2 uses Set Attention Block to extract intermediate features as Set Prior Prediction to participate in supervision, which enables the model to converge faster. For the detection of small clouds, Cloudformer V2 decodes the features by a multi-scale Transformer decoder, which uses multi-resolution features to improve the modeling accuracy. In addition, a binary mask weighted loss function (BW Loss) is designed to construct weights by counting pixels classified as clouds; thus, guiding the network to focus on features of small clouds and improving the overall detection accuracy. Cloudformer V2 is experimented on the dataset from GF-1 satellite and has excellent performance. [ABSTRACT FROM AUTHOR]

Forest ecosystem services are intrinsically linked. We design a spatially explicit approach to quantify and analyze the co-benefits and trade-offs between the main forest ecosystem services. Our goal is to develop criteria for forest management that include ecosystem service interactions. Chinese fir and pine plantations provide the largest portion of the overall ecosystem services currently provided. They are volume stock and water yield service hotspots, but these have negative effects on soil retention and carbon storage, causing environmental problems. The natural forests (broad-leaf and bamboo forests) are carbon storage and volume stock hotspots and show the lowest erosion modulus. Thus, their protection, combined with expanding the plantation area under forest management should be considered in order to increase ecosystem service synergies. In contrast, an increased area of broad-leaf plantations reduces water yield service due to their lower water production capacity, in comparison with plantations of fast-growing species. Our study shows that the inclusion of ecosystem services as part of forest management could provide opportunities for optimal allocation of forest resources and sustainable utilization. Management based only on economically beneficial ecosystem services can be detrimental to the forest ecosystem and can cause environmental problems. [ABSTRACT FROM AUTHOR]

To reduce the negative impacts of stall and surge on compressor performance, a novel combined casing treatment (CCT) structure with axial skewed slots and injection groove is proposed in this paper. The aerodynamic performance, as well as the mechanisms of loss generation, of a transonic axial compressor with NASA Rotor 67 are investigated numerically. The simulation results indicate that, compared with individual casing treatment method, the CCT works effectively with regard to operation performance. The stall margin (SM) is increased by 14.7% with 1.12% decrease in the peak efficiency. The interaction of axial skewed slots and injection groove can be explained by the enhancement of exchange flow in slots and axial motion of fluid. As a result, the leakage flow near the blade tip is eliminated and the flow separation is further suppressed. What is more, an analysis of entropy generation is also conducted. The results reveal that the effect of CCT on loss reduction mainly concentrates in the tip part of the blade, with the loss decrease about 14.46% compared with the original rotor. The best control effect can be expected by appropriate match between geometrical parameters of axial skewed slots and mass flow rate of injection from the parameter analysis. [ABSTRACT FROM AUTHOR]

Road segmentation is one of the essential tasks in remote sensing. Large-scale high-resolution remote sensing images originally have larger pixel sizes than natural images, while the existing models based on Transformer have the high computational cost of square complexity, leading to more extended model training and inference time. Inspired by the long text Transformer model, this paper proposes a novel hybrid attention mechanism to improve the inference speed of the model. By calculating several diagonals and random blocks of the attention matrix, hybrid attention achieves linear time complexity in the token sequence. Using the superposition of adjacent and random attention, hybrid attention introduces the inductive bias similar to convolutional neural networks (CNNs) and retains the ability to acquire long-distance dependence. In addition, the dense road segmentation result of remote sensing image still has the problem of insufficient continuity. However, multiscale feature representation is an effective means in the network based on CNNs. Inspired by this, we propose a multi-scale patch embedding module, which divides images by patches with different scales to obtain coarse-to-fine feature representations. Experiments on the Massachusetts dataset show that the proposed HA-RoadFormer could effectively preserve the integrity of the road segmentation results, achieving a higher Intersection over Union (IoU) 67.36% of road segmentation compared to other state-of-the-art (SOTA) methods. At the same time, the inference speed has also been greatly improved compared with other Transformer based models. [ABSTRACT FROM AUTHOR]

In the software development life cycle, the quality and reliability of software are critical to software developers. Poor quality and reliability not only cause the loss of customers and sales but also increase the operational risk due to unreliable codes. Therefore, software developers should try their best to reduce such potential software defects by undertaking a software testing project. However, to pursue perfect and faultless software is unrealistic since the budget, time, and testing resources are limited, and the software developers need to reach a compromise that balances software reliability and the testing cost. Using the model presented in this study, software developers can devise multiple alternatives for a software testing project, and each alternative has its distinct allocation of human resources. The best alternative can therefore be selected. Furthermore, the allocation incorporates debuggers' learning and negligent factors, both of which influence the efficiency of software testing in practice. Accordingly, the study considers both human factors and the nature of errors during the debugging process to develop a software reliability growth model to estimate the related costs and the reliability indicator. Additionally, the issue of error classification is also extended by considering the impacts of errors on the system, and the expected time required to remove simple or complex errors can be estimated based on different truncated exponential distributions. Finally, numerical examples are presented and sensitivity analyses are performed to provide managerial insights and useful directions to inform software release strategies. [ABSTRACT FROM AUTHOR]

In this study, an imperfect debugging software reliability growth model (SRGM) with Bayesian analysis was proposed to determine an optimal software release in order to minimize software testing costs and also enhance the practicability. Generally, it is not easy to estimate the model parameters by applying MLE (maximum likelihood estimation) or LSE (least squares estimation) with insufficient historical data. Therefore, in the situation of insufficient data, the proposed Bayesian method can adopt domain experts' prior judgments and utilize few software testing data to forecast the reliability and the cost to proceed with the prior analysis and the posterior analysis. Moreover, the debugging efficiency involves testing staff's learning and negligent factors, and therefore, the human factors and the nature of debugging process are taken into consideration in developing the fundamental model. Based on this, the estimation of the model's parameters would be more intuitive and can be easily evaluated by domain experts, which is the major advantage for extending the related applications in practice. Finally, numerical examples and sensitivity analyses are performed to provide managerial insights and useful directions for software release strategies. [ABSTRACT FROM AUTHOR]

Leaf functional traits are the essential components of adaption plant strategies and have different responses to various environments, but our knowledge of how plants adapt to highly complex urban environments through coordinated changes in leaf functional traits is limited. In this study, we studied the response of landscape plants to the environments of sports field (SF), park (PAR), residential green space (RES), and greenway (GW), and analyzed the effects of the different green space environments on trade-off strategies of plants based on leaf functional traits. The results showed that leaf functional traits of plants and adaptation strategies varied among different urban environments in Lanzhou, China. Leaf length (LL), width (LW), area (LA), and special leaf area (SLA) were PAR > SF > RES > GW. Leaf nitrogen (LNC) and phosphorus content (LPC) were SF > PAR > RES > GW. Leaf carbon content (LCC), leaf dry matter content (LDMC), the ratio of leaf carbon and nitrogen (C/N), and the ratio of leaf carbon and phosphorus (C/P) was GW > RES > PAR > SF. The landscape plants in SF and PAR were more adaptive to the urban environment than those in RES and GW. Among different green space environments, landscape plants in SF and PAR tended to have an acquisitive strategy with high LL, LW, LA, SLA, LNC, and LPC. In contrast, plants in RES and GW tended to have a conservative strategy with a high level of concentration of LCC, LDMC, C/N, and C/P. [ABSTRACT FROM AUTHOR]

Brassica napus L. provides high-quality edible oil and clean energy for humans. For a long time, rapeseed breeders have tried to breed improved varieties through traditional breeding strategies. However, B. napus is an allotetraploid species containing many repetitive sequences. It is very inefficient to change traits through traditional genetic methods such as hybridization and random mutagenesis due to gene redundancy. Today, the burgeoning CRISPR/Cas9 technology has been applied in polyploid rapeseed for gene function research and targeted genetic improvement because of its unique advantages of high efficiency and simplicity. This review summarizes current reports about the application of CRISPR/Cas9 system for gene function research and genetic improvement in rapeseed, involving important agronomic traits such as yield, oil content, and fatty acid composition. The application status of emerging precise genome editing technology in plants and several potential limitations and technical bottlenecks in rapeseed gene editing is discussed, which will provide confidence for researchers in rapeseed gene function research and genetic improvement through genome editing technology. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning have shown remarkable performance in road segmentation from remotely sensed images. However, these methods based on convolutional neural networks (CNNs) cannot obtain long-range dependency and global contextual information because of the intrinsic inductive biases. Motivated by the success of Transformer in computer vision (CV), excellent models based on Transformer are emerging endlessly. However, patches with a fixed scale limit the further improvement of the model performance. To address this problem, a dual-resolution road segmentation network (DCS-TransUperNet) with a features fusion module (FFM) was proposed for road segmentation. Firstly, the encoder of DCS-TransUperNet was designed based on CSwin Transformer, which uses dual subnetwork encoders of different scales to obtain the coarse and fine-grained feature representations. Secondly, a new FFM was constructed to build enhanced feature representation with global dependencies, using different scale features from the subnetwork encoders. Thirdly, a mixed loss function was designed to avoid the local optimum caused by the imbalance between road and background pixels. Experiments using the Massachusetts dataset and DeepGlobe dataset showed that the proposed DCS-TransUperNet could effectively solve the discontinuity problem and preserve the integrity of the road segmentation results, achieving a higher IoU (65.36% on Massachusetts dataset and 56.74% on DeepGlobe) of road segmentation compared to other state-of-the-art methods. The considerable performance also proves the powerful generation ability of our method. [ABSTRACT FROM AUTHOR]

Cloud detection is an important step in the processing of optical satellite remote-sensing data. In recent years, deep learning methods have achieved excellent results in cloud detection tasks. However, most of the current models have difficulties to accurately classify similar objects (e.g., clouds and snow) and to accurately detect clouds that occupy a few pixels in an image. To solve these problems, a cloud-detection framework (Cloudformer) combining CNN and Transformer is being proposed to achieve high-precision cloud detection in optical remote-sensing images. The framework achieves accurate detection of thin and small clouds using a pyramidal structure encoder. It also achieves accurate classification of similar objects using a dual-path decoder structure of CNN and Transformer, reducing the rate of missed detections and false alarms. In addition, since the Transformer model lacks the perception of location information, an asynchronous position-encoding method is being proposed to enhance the position information of the data entering the Transformer module and to optimize the detection results. Cloudformer is experimented on two datasets, AIR-CD and 38-Cloud, and the results show that it has state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Hydroxyflutamide (HF), an active metabolite of the first generation antiandrogen flutamide, was used in clinic to treat prostate cancer targeting androgen receptor (AR). However, a drug resistance problem appears after about one year's treatment. AR T877A is the first mutation that was found to cause a resistance problem. Then W741C-T877A and F876L-T877A mutations were also reported to cause resistance to HF, while W741C and F876L single mutations cannot. In this study, molecular dynamics (MD) simulations combined with the molecular mechanics generalized Born surface area (MM-GBSA) method have been carried out to analyze the interaction mechanism between HF and wild-type (WT)/mutant ARs. The obtained results indicate that AR helix 12 (H12) plays a pivotal role in the resistance of HF. It can affect the coactivator binding site at the activation function 2 domain (AF2, surrounded by H3, H4, and H12). When H12 closes to the AR ligand-binding domain (LBD) like a lid, the coactivator binding site can be formed to promote transcription. However, once H12 is opened to expose LBD, the coactivator binding site will be distorted, leading to invalid transcription. Moreover, per-residue free energy decomposition analyses indicate that N705, T877, and M895 are vital residues in the agonist/antagonist mechanism of HF. [ABSTRACT FROM AUTHOR]

Lactate was recently found to mediate histone lysine lactylation and facilitate polarization of M1 macrophages, indicating its role in metabolic regulation of gene expression. During somatic cell reprogramming, lactate promotes histone lactylation of pluripotency genes and improves reprogramming efficiency. However, the function of lactate in cell fate control in embryonic stem cells (ESCs) remains elusive. In this study, we revealed that lactate supplementation activated germline genes in mouse ESCs. Lactate also induced global upregulation of cleavage embryo genes, such as members of the Zscan4 gene family. Further exploration demonstrated that lactate stimulated H3K18 lactylation accumulation on germline and cleavage embryo genes, which in turn promoted transcriptional elongation. Our findings indicated that lactate supplementation expanded the transcriptional network in mouse ESCs. [ABSTRACT FROM AUTHOR]

Abstract: The microtubule-associated protein tau plays a major role in maintaining the normal morphology of the neurons. The major biological activity of tau is to promote microtubule assembly and stabilize the microtubules. In the brain of Alzheimer''s disease (AD) patients, tau protein is abnormally hyperphosphorylated and thus become incompetent in promoting microtubule assemble and maintaining the stability of the microtubules. These detrimental effects of tau may lead the neurons to degeneration. Recent studies show that tau hyperphorylation may be neuroprotective in the early stages of the disease process. The primary aim of this review is to summarize the latest developments and perspectives in our understanding about the roles of tau hyperphosphorylation in neurodegeneration and neuroprotection. [Copyright &y& Elsevier]

In this paper, the effects of an aging treatment on the corrosion resistance/mechanism of a tensile deformed Al-Cu-Mn-Fe-Zr alloy are investigated. The impedance magnitude and polarization resistance increase, while the corrosion current decreases with the increased aging time and temperature. The discontinuously-distributed precipitates and precipitation-free zone, which can cut the corrosion channels, appear at grain boundaries when the temperature is relatively high and the aging time is relatively long. They can improve the corrosion resistance. Additionally, the intergranular and pitting corrosion are the main mechanisms. The intergranular corrosion is likely to occur in an under-aged alloy. This is because the potential difference between the grain boundaries and grains is high, due to the segregation of Cu atoms. When the aging degree is increased, the grain boundary precipitates reduce the potential difference, and the intragranular precipitates make the surrounding matrix prone to dissolution. As such, the pitting corrosion is likely to occur in the over-aged alloys. [ABSTRACT FROM AUTHOR]

Decidualization is a crucial step for human reproduction, which is a prerequisite for embryo implantation, placentation and pregnancy maintenance. Despite rapid advances over recent years, the molecular mechanism underlying decidualization remains poorly understood. Here, we used the mouse as an animal model and generated a single-cell transcriptomic atlas of a mouse uterus during decidualization. By analyzing the undecidualized inter-implantation site of the uterus as a control, we were able to identify global gene expression changes associated with decidualization in each cell type. Additionally, we identified intercellular crosstalk between decidual cells and niche cells, including immune cells, endothelial cells and trophoblast cells. Our data provide a valuable resource for deciphering the molecular mechanism underlying decidualization. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a growing concern in modern society, and effective drugs for its treatment are lacking. Uncaria rhynchophylla (UR) and its main alkaloids have been studied to treat neurodegenerative diseases such as AD. This study aimed to uncover the key components and mechanism of the anti-AD effect of UR alkaloids through a network pharmacology approach. The analysis identified 10 alkaloids from UR based on HPLC that corresponded to 90 anti-AD targets. A potential alkaloid target-AD target network indicated that corynoxine, corynantheine, isorhynchophylline, dihydrocorynatheine, and isocorynoxeine are likely to become key components for AD treatment. KEGG pathway enrichment analysis revealed the Alzheimers disease (hsa05010) was the pathway most significantly enriched in alkaloids against AD. Further analysis revealed that 28 out of 90 targets were significantly correlated with Aβ and tau pathology. These targets were validated using a Gene Expression Omnibus (GEO) dataset. Molecular docking studies were carried out to verify the binding of corynoxine and corynantheine to core targets related to Aβ and tau pathology. In addition, the cholinergic synapse (hsa04725) and dopaminergic synapse (hsa04728) pathways were significantly enriched. Our findings indicate that UR alkaloids directly exert an AD treatment effect by acting on multiple pathological processes in AD. [ABSTRACT FROM AUTHOR]

Horizontal wells with multi-stage fractures have been widely used to improve coalbed methane (CBM) production from coalbed methane reservoirs. The main focus of this work is to establish a new semi-analytical method in the Laplace domain and investigate the transient pressure behavior in coalbed methane reservoirs. With the new semi-analytical method, flow regimes of a multi-fractured horizontal well in coalbed methane reservoirs were identified. In addition, the sensitivities of fracture conductivity, diffusion model, storability ratio, inter-porosity flow coefficient, adsorption index, fracture spacing, fracture asymmetry, non-planar angle, and wellbore storage were studied. Results indicate that six characteristic flow regimes can be identified for multi-fractured horizontal wells in coalbed methane reservoirs, which are bilinear flow, first linear flow, desorption-diffusion flow, first pseudo-radial flow, second linear flow, and second pseudo-radial flow. Furthermore, the sensitivity analysis shows that the early flow is mainly determined by the fracture conductivity, the asymmetry factor, the non-planar angle, and the wellbore storage; while the desorption-diffusion flow regime is mainly influenced by the diffusion model, the storability ratio, the inter-porosity flow coefficient, the adsorption index, and the fracture spacing. Our work can provide a deep insight into the fluid flow mechanism of multi-fractured horizontal wells in coalbed methane reservoirs. [ABSTRACT FROM AUTHOR]

Human society and ecosystems worldwide are increasinAagly threatened by water shortages. Despite numerous studies of climatic impacts on water availability, little is known about the influences of socioeconomic development on streamflow and water sustainability. Here, we show that the streamflow from the Yellow River to the sea has decreased by more than 80% in total over the last 60 years due to increased water consumption by agricultural, industrial and urban developments (76% of the streamflow decrease, similarly hereinafter), decreased precipitation (13%), reservoir construction (6%) and revegetation (5%). We predict that if the past trends in streamflow will continue, year-round dry-up in the lower Yellow River will commence in the late 2020s or early 2030s, unless effective countermeasures such as water diversion from the Yangtze River are taken. These results suggest that streamflow in semiarid basins is highly vulnerable to human impacts and that streamflow decline would in turn hinder further socioeconomic development and endanger river-sea ecosystems. [ABSTRACT FROM AUTHOR]

With the increasing demand from unmanned driving and robotics, more attention has been paid to point-cloud-based 3D object accurate detection technology. However, due to the sparseness and irregularity of the point cloud, the most critical problem is how to utilize the relevant features more efficiently. In this paper, we proposed a point-based object detection enhancement network to improve the detection accuracy in the 3D scenes understanding based on the distance features. Firstly, the distance features are extracted from the raw point sets and fused with the raw features regarding reflectivity of the point cloud to maximize the use of information in the point cloud. Secondly, we enhanced the distance features and raw features, which we collectively refer to as self-features of the key points, in set abstraction (SA) layers with the self-attention mechanism, so that the foreground points can be better distinguished from the background points. Finally, we revised the group aggregation module in SA layers to enhance the feature aggregation effect of key points. We conducted experiments on the KITTI dataset and nuScenes dataset and the results show that the enhancement method proposed in this paper has excellent performance.

Hydroxyflutamide (HF), an active metabolite of the first generation antiandrogen flutamide, was used in clinic to treat prostate cancer targeting androgen receptor (AR). However, a drug resistance problem appears after about one year's treatment. AR T877A is the first mutation that was found to cause a resistance problem. Then W741C&#95;T877A and F876L&#95;T877A mutations were also reported to cause resistance to HF, while W741C and F876L single mutations cannot. In this study, molecular dynamics (MD) simulations combined with the molecular mechanics generalized Born surface area (MM-GBSA) method have been carried out to analyze the interaction mechanism between HF and wild-type (WT)/mutant ARs. The obtained results indicate that AR helix 12 (H12) plays a pivotal role in the resistance of HF. It can affect the coactivator binding site at the activation function 2 domain (AF2, surrounded by H3, H4, and H12). When H12 closes to the AR ligand-binding domain (LBD) like a lid, the coactivator binding site can be formed to promote transcription. However, once H12 is opened to expose LBD, the coactivator binding site will be distorted, leading to invalid transcription. Moreover, per-residue free energy decomposition analyses indicate that N705, T877, and M895 are vital residues in the agonist/antagonist mechanism of HF., Competing Interests: The authors report no conflict of interest.

As current efforts have limited effects on the clinical outcome of intracerebral hemorrhage (ICH), the mechanisms including microglia/macrophages that involved inflammation need further investigation. Here, 0.4 units of collagenase VII were injected into the left caudate putamen (CPu) to duplicate ICH rat models. In the brains of ICH rats, microglia/macrophages, the nearest cells to the hemorrhagic center, were observed as ameboid and Prussian-blue positive. Furthermore, the ameboid microglia/macrophages were differentiation (CD) 68 and interleukin-1β (IL-1β) positive, and neither CD206 nor chitinase3-like 3 (Ym1) positive, suggesting their strong abilities of phagocytosis and secretion of IL-1β. According to the distance to the hemorrhagic center, we selected four areas-I, II, III, and IV-to analyze the morphology of microglia/macrophages. The processes decreased successively from region I to region IV. Microglia/macrophages in region IV had no processes. The processes in region I were radially distributed, however, they showed obvious directivity towards the hemorrhagic center in regions II and III. Region III had the largest density of compactly arrayed microglia/macrophages. All these in vivo results present the high morphologic plasticity of microglia/macrophages and their functions in the pathogenesis of ICHs.

To estimate the tritium doses of the residents living in the vicinity of a nuclear power plant, urine samples of 34 adults were collected from residents living near the Qinshan nuclear power plant. The tritium-in-urine (HTO plus OBT) was measured by liquid scintillation counting. The doses of tritium-in-urine from participants living at 2, 10 and 22 km were in a range of 1.26-6.73 Bq/L, 1.31-3.09 Bq/L and 2.21-3.81 Bq/L, respectively, while the average activity concentrations of participants from the three groups were 3.53 ± 1.62, 2.09 ± 0.62 and 2.97 ± 0.78 Bq/L, respectively. The personal committed effective doses for males were 2.5 ± 1.7 nSv and for females they were 2.9 ± 1.3 nSv. These results indicate that tritium concentrations in urine samples from residents living at 2 km from a nuclear power plant are significantly higher than those at 10 km. It may be the downwind direction that caused a higher dose in participants living at 22 km. All the measured doses of tritium-in-urine are in a background level range.

Alzheimer's disease (AD), an age-related neurodegenerative disorder with progressive cognition deficit, is characterized by extracellular senile plaques (SP) of aggregated β-amyloid (Aβ) and intracellular neurofibrillary tangles, mainly containing the hyperphosphorylated microtubule-associated protein tau. Multiple factors contribute to the etiology of AD in terms of initiation and progression. Melatonin is an endogenously produced hormone in the brain and decreases during aging and in patients with AD. Data from clinical trials indicate that melatonin supplementation improves sleep, ameliorates sundowning and slows down the progression of cognitive impairment in AD patients. Melatonin efficiently protects neuronal cells from Aβ-mediated toxicity via antioxidant and anti-amyloid properties. It not only inhibits Aβ generation, but also arrests the formation of amyloid fibrils by a structure-dependent interaction with Aβ. Our studies have demonstrated that melatonin efficiently attenuates Alzheimer-like tau hyperphosphorylation. Although the exact mechanism is still not fully understood, a direct regulatory influence of melatonin on the activities of protein kinases and protein phosphatases is proposed. Additionally, melatonin also plays a role in protecting the cholinergic system and in anti-inflammation. The aim of this review is to stimulate interest in melatonin as a potentially useful agent in the prevention and treatment of AD.