A social-ecological approach for identifying and mapping ecosystem service trade-offs and conservation priorities in peri-urban areas.

Considering both ecological and social dimensions in the assessment of ecosystem services (ESs) can facilitate acceptable and inclusive management strategies, especially in peri-urban areas characterized by intricate human-ecosystem interactions. A limited body of research, however, has mapped the plural values of ESs and their different types of trade-offs in such areas. This research aimed to execute an interdisciplinary analysis of the biophysical and social values of ESs in peri-urban Shanghai, China, through a social-ecological approach that integrates spatial biophysical assessment with participatory mapping. Trade-off analysis in both ES types and ES valuations were then conducted, and multicriteria decision-making was applied for conservation. Our results reveal that trade-off intensities were lower within the social values compared to the biophysical values. Within both value dimensions, relatively stronger trade-offs were found between food production and other ESs. Areas with both high biophysical and social values were infrequently observed across ESs. Based on the characteristics of diverse values, our study identified priority conservation areas and provided management implications. We argue that adopting the integrated social-ecological perspective in sustainable environmental management contributes to the realization of harmonious coexistence between people and nature in peri-urban areas.

Electro-oxidative quinylation of sulfides to sulfur ylides in batch and continuous flow.

An unprecedented strategy for preparing a series of sulfur ylides through electro-oxidative quinylation of sulfides in batch and continuous flow has been developed. Good to excellent yields were obtained with excellent functional group compatibility and good concentration tolerance under exogenous oxidant- and transition metal-free conditions. Advantageously, this electrosynthesis methodology was scalable with higher daily production and steady production was achieved attributing to the use of micro-flow cells.

Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings.

Linear B-cell epitopes (BCEs) play a key role in the development of peptide vaccines and immunodiagnostic reagents. Therefore, the accurate identification of linear BCEs is of great importance in the prevention of infectious diseases and the diagnosis of related diseases. The experimental methods used to identify BCEs are both expensive and time-consuming and they do not meet the demand for identification of large-scale protein sequence data. As a result, there is a need to develop an efficient and accurate computational method to rapidly identify linear BCE sequences. In this work, we developed the new linear BCE prediction method LBCE-BERT. This method is based on peptide chain sequence information and natural language model BERT embedding information, using an XGBoost classifier. The models were trained on three benchmark datasets. The model was training on three benchmark datasets for hyperparameter selection and was subsequently evaluated on several test datasets. The result indicate that our proposed method outperforms others in terms of AUROC and accuracy. The LBCE-BERT model is publicly available at: https://github.com/Lfang111/LBCE-BERT .

RNA N6-methyladenosine demethylase FTO inhibits glucocorticoid-induced osteoblast differentiation and function in bone marrow mesenchymal stem cells.

Excess glucocorticoids (GCs) have been reported as key factors that impair osteoblast (OB) differentiation and function. However, the role of RNA N6-methyladenosine (m6 A) in this process has not yet been elucidated. In this study, we report that both the mRNA and protein expression of fat mass and obesity-associated gene (FTO), a key m6 A demethylase, were dose-dependently downregulated during OB differentiation by dexamethasone (DEX) in bone marrow mesenchymal stem cells (BMSCs), and FTO was gradually increased during OB differentiation. Meanwhile, FTO knockdown suppressed OB differentiation and mineralization, whereas overexpression of wide-type FTO, but not mutant FTO (mutated m6 A demethylase active site), reversed DEX-induced osteogenesis impairment. Interfering with FTO inhibited proliferation and the expression of Ki67 and Pcna in BMSCs during OB differentiation, whereas forced expression of wide-type FTO improved DEX-induced inhibition of BMSCs proliferation. Moreover, FTO knockdown reduced the mRNA stability of the OB marker genes Alpl and Col1a1, and FTO-modulated OB differentiation via YTHDF1 and YTHDF2. In conclusion, our results suggest that FTO inhibits the GCs-induced OB differentiation and function of BMSCs.

Assessing the ecological risk of croplands in loess drylands by combining environmental disturbance with ecosystem vulnerability.

Agroecosystems suffer various ecological risks due to the intensive production of crops. However, comprehensive assessments of cropland ecological risks remain limited. This study developed an assessment method for cropland ecological risks by combining environmental disturbance with ecosystem vulnerability. Environmental disturbance reflects stresses caused by risk sources in an environment, while ecosystem vulnerability is the susceptibility of an ecosystem to adverse disturbances and its capacity to cope and adapt. The proposed method is conducive to understanding the complex exposure-response relationship between croplands and environmental stresses. Cropland ecological risk was evaluated by conducting a case study on a loess dryland region in Shaanxi. The hot spots and driving factors of risk were explored using spatial autocorrelation and quantile regression methods, respectively. Results show that overall cropland ecological risk is at medium low level. Risk hot spots are concentrated in the north of the loess dryland. Ecosystem vulnerability exerts greater effect on the distribution of hot spots than environmental disturbance in the study area. Road density (RDD), river density, and soil organic matter exert the most important effects on cropland ecological risk. Moreover, the same driving factor exhibits various effects on cropland ecological risk in different risk level areas. RDD, slope, precipitation, elevation, fertilizer application rate, gross domestic product, and distance to town center have greater effects on risk in regions with high cropland ecological risk than in regions with low cropland ecological risk. The findings of this study must be considered in formulating targeted policies for controlling cropland ecological risk in loess drylands to realize sustainable crop production.

[Electrocardiogram signal classification based on fusion method of residual network and self-attention mechanism].

In the diagnosis of cardiovascular diseases, the analysis of electrocardiogram (ECG) signals has always played a crucial role. At present, how to effectively identify abnormal heart beats by algorithms is still a difficult task in the field of ECG signal analysis. Based on this, a classification model that automatically identifies abnormal heartbeats based on deep residual network (ResNet) and self-attention mechanism was proposed. Firstly, this paper designed an 18-layer convolutional neural network (CNN) based on the residual structure, which helped model fully extract the local features. Then, the bi-directional gated recurrent unit (BiGRU) was used to explore the temporal correlation for further obtaining the temporal features. Finally, the self-attention mechanism was built to weight important information and enhance model's ability to extract important features, which helped model achieve higher classification accuracy. In addition, in order to mitigate the interference on classification performance due to data imbalance, the study utilized multiple approaches for data augmentation. The experimental data in this study came from the arrhythmia database constructed by MIT and Beth Israel Hospital (MIT-BIH), and the final results showed that the proposed model achieved an overall accuracy of 98.33% on the original dataset and 99.12% on the optimized dataset, which demonstrated that the proposed model can achieve good performance in ECG signal classification, and possessed potential value for application to portable ECG detection devices.

Copper(II) complex enhanced chemodynamic therapy through GSH depletion and autophagy flow blockade.

Three copper(II) complexes C1-C3 were synthesized and fully characterized as chemodynamic therapy (CDT) anticancer agents. C1-C3 showed greater cytotoxicity than their ligands toward SK-OV-3 and T24 cells. Particularly, C2 showed high cytotoxicity toward T24 cells and low cytotoxicity toward normal human HL-7702 and WI-38 cells. Mechanistic studies demonstrated that C2 oxidized GSH to GSSG and produced ˙OH, which induced mitochondrial dysfunction and ER stress, finally leading to apoptosis of T24 cells. In addition, C2 inhibited autophagy by blocking autophagy flow, thereby closing the self-protection pathway of oxidative stress to enhance CDT. Importantly, C2 significantly inhibited T24 tumor growth with 57.1% inhibition in a mouse xenograft model. C2 is a promising lead as a potential CDT anticancer agent.

A Framework for Deep Multitask Learning With Multiparametric Magnetic Resonance Imaging for the Joint Prediction of Histological Characteristics in Breast Cancer.

The clinical management and decision-making process related to breast cancer are based on multiple histological indicators. This study aims to jointly predict the Ki-67 expression level, luminal A subtype and histological grade molecular biomarkers using a new deep multitask learning method with multiparametric magnetic resonance imaging. A multitask learning network structure was proposed by introducing a common-task layer and task-specific layers to learn the high-level features that are common to all tasks and related to a specific task, respectively. A network pretrained with knowledge from the ImageNet dataset was used and fine-tuned with MRI data. Information from multiparametric MR images was fused using the strategy at the feature and decision levels. The area under the receiver operating characteristic curve (AUC) was used to measure model performance. For single-task learning using a single image series, the deep learning model generated AUCs of 0.752, 0.722, and 0.596 for the Ki-67, luminal A and histological grade prediction tasks, respectively. The performance was improved by freezing the first 5 convolutional layers, using 20% shared layers and fusing multiparametric series at the feature level, which achieved AUCs of 0.819, 0.799 and 0.747 for Ki-67, luminal A and histological grade prediction tasks, respectively. Our study showed advantages in jointly predicting correlated clinical biomarkers using a deep multitask learning framework with an appropriate number of fine-tuned convolutional layers by taking full advantage of common and complementary imaging features. Multiparametric image series-based multitask learning could be a promising approach for the multiple clinical indicator-based management of breast cancer.

Position-Aware Participation-Contributed Temporal Dynamic Model for Group Activity Recognition.

Group activity recognition (GAR) aiming at understanding the behavior of a group of people in a video clip has received increasing attention recently. Nevertheless, most of the existing solutions ignore that not all the persons contribute to the group activity of the scene equally. That is to say, the contribution from different individual behaviors to group activity is different; meanwhile, the contribution from people with different spatial positions is also different. To this end, we propose a novel Position-aware Participation-Contributed Temporal Dynamic Model (P2CTDM), in which two types of the key actor are constructed and learned. Specifically, we focus on the behaviors of key actors, who maintain steady motions (long moving time, called long motions) or display remarkable motions (but closely related to other people and the group activity, called flash motions) at a certain moment. For capturing long motions, we rank individual motions according to their intensity measured by stacking optical flows. For capturing flash motions that are closely related to other people, we design a position-aware interaction module (PIM) that simultaneously considers the feature similarity and position information. Beyond that, for capturing flash motions that are highly related to the group activity, we also present an aggregation long short-term memory (Agg-LSTM) to fuse the outputs from PIM by time-varying trainable attention factors. Four widely used benchmarks are adopted to evaluate the performance of the proposed P2CTDM compared to the state of the art.

Vitamin D supplementation for improving children with bone mineral density: A protocol for systematic review and meta-analysis.

BACKGROUND: Osteoporosis is usually one of the less perceived complications of chronic illness among children. Previous studies have shown that vitamin D supplementation may be valuable to bone density, especially among children with a deficiency of vitamin D. Yet, the results often remain inconsistent. Therefore, the present study investigates the clinical therapeutic effects of vitamin D supplementation to enhance children with bone mineral density. METHODS: We will search the randomised controlled experiment literature of vitamin D supplementation for bone mineral density, focusing on children, in 3 distinct English databases (EMBASE, MEDLINE via PubMed, and Cochrane Library) and 2 specific Chinese databases (China National Knowledge Infrastructure (CNKI) and WanFang databases). Additionally, we intend to explore the Clinical Trials.gov, reference lists of identified publication and the grey literature. Accordingly, we will use 2 independent authors to screen the literature, extract data, and research quality assessment. We will carry out all statistical analyses using RevMan 5.3 software. RESULTS: We will systematically evaluate the clinical therapeutic effects of vitamin D supplementation to enhance children with bone mineral density. CONCLUSION: The present study will summarise the currently published pieces of evidence of vitamin D supplementation for bone mineral density in children to further comprehend its promotion and application. ETHICS AND DISSEMINATION: The present study is a systematic review and meta-analysis founded upon existing or published studies; therefore, ethical approval is not applicable. OSF REGISTRATION NUMBER: October 24, 2020. osf.io/7vtey. (https://osf.io/7vtey/).

Visible-light-promoted N-centered radical generation for remote heteroaryl migration.

Herein, an efficient visible-light-mediated N-H heteroarylation via remote heteroaryl ipso-migration has been accomplished. Moderate to good yields were obtained with good functional group tolerance. Moreover, a simple and readily available organic photoredox catalyst was employed, avoiding the use of complex and costly noble metal compounds.

A national assessment of the effect of intensive agro-land use practices on nonpoint source pollution using emission scenarios and geo-spatial data.

China's intensive agriculture has led to a broad range of adverse impacts upon ecosystems and thereby caused environmental quality degradation. One of the fundamental problems that face land managers when dealing with agricultural nonpoint source (NPS) pollution is to quantitatively assess the NPS pollution loads from different sources at a national scale. In this study, export scenarios and geo-spatial data were used to calculate the agricultural NPS pollution loads of nutrient, pesticide, plastic film residue, and crop straw burning in China. The results provided the comprehensive and baseline knowledge of agricultural NPS pollution from China's arable farming system in 2014. First, the nitrogen (N) and phosphorus (P) emission loads to water environment were estimated to be 1.44 Tg N and 0.06 Tg P, respectively. East and south China showed the highest load intensities of nutrient release to aquatic system. Second, the amount of pesticide loss to water of seven pesticides that are widely used in China was estimated to be 30.04 tons (active ingredient (ai)). Acetochlor was the major source of pesticide loss to water, contributing 77.65% to the total loss. The environmental impacts of pesticide usage in east and south China were higher than other parts. Third, 19.75% of the plastic film application resided in arable soils. It contributed a lot to soil phthalate ester (PAE) contamination. Fourth, 14.11% of straw produce were burnt in situ, most occurring in May to July (post-winter wheat harvest) in North China Plain and October to November (post-rice harvest days) in southeast China. All the above agricultural NPS pollution loadings were unevenly distributed across China. The spatial correlations between pollution loads at land unit scale were also estimated. Rising labor cost in rural China might be a possible explanation for the general positive correlations of the NPS pollution loads. It also indicated a co-occurred higher NPS pollution loads and a higher human exposure risk in eastern regions. Results from this research might provide full-scale information on the status and spatial variation of various agricultural NPS pollution loads for policy makers to control the NPS pollution in China.

Assessing the impacts of the changes in farming systems on food security and environmental sustainability of a Chinese rural region under different policy scenarios: an agent-based model.

Since China has undergone a series of economic reforms and implemented opening up policies, its farming systems have significantly changed and have dramatically influenced the society, economy, and environment of China. To assess the comprehensive impacts of these changes on food security and environmental sustainability, and establish effective and environment-friendly subsidy policies, this research constructed an agent-based model (ABM). Daligang Town, which is located in the two-season rice region of Southern China, was selected as the case study site. Four different policy scenarios, i.e., "sharply increasing" (SI), "no-increase" (NI), "adjusted-method" (AM), and "trend" (TD) scenarios were investigated from 2015 to 2029. The validation result shows that the relative prediction errors between the simulated and actual values annually ranged from -20 to 20%, indicating the reliability of the proposed model. The scenario analysis revealed that the four scenarios generated different variations in cropping systems, rice yield, and fertilizer and pesticide inputs when the purchase price of rice and the non-agricultural income were assumed to increase annually by 0.1 RMB per kg and 10% per person, respectively. Among the four different policy scenarios in Daligang, the TD scenario was considered the best, because it had a relatively high rice yield, fairly minimal use of fertilizers and pesticides, and a lower level of subsidy. Despite its limitations, ABM could be considered a useful tool in analyzing, exploring, and discussing the comprehensive effects of the changes in farming system on food security and environmental sustainability.

Assessing the effects of rural livelihood transition on non-point source pollution: a coupled ABM-IECM model.

Water pollution caused by anthropogenic activities and driven by changes in rural livelihood strategies in an agricultural system has received increasing attention in recent decades. To simulate the effects of rural household livelihood transition on non-point source (NPS) pollution, a model combining an agent-based model (ABM) and an improved export coefficient model (IECM) was developed. The ABM was adopted to simulate the dynamic process of household livelihood transition, and the IECM was employed to estimate the effects of household livelihood transition on NPS pollution. The coupled model was tested in a small catchment in the Dongting Lake region, China. The simulated results reveal that the transition of household livelihood strategies occurred with the changes in the prices of rice, pig, and labor. Thus, the cropping system, land-use intensity, resident population, and number of pigs changed in the small catchment from 2000 to 2014. As a result of these changes, the total nitrogen load discharged into the river initially increased from 6841.0 kg in 2000 to 8446.3 kg in 2004 and then decreased to 6063.9 kg in 2014. Results also suggest that rural living, livestock, paddy field, and precipitation alternately became the main causes of NPS pollution in the small catchment, and the midstream region of the small catchment was the primary area for NPS pollution from 2000 to 2014. Despite some limitations, the coupled model provides an innovative way to simulate the effects of rural household livelihood transition on NPS pollution with the change of socioeconomic factors, and thereby identify the key factors influencing water pollution to provide valuable suggestions on how agricultural environmental risks can be reduced through the regulation of the behaviors of farming households in the future.

Assessing environmental risks for high intensity agriculture using the material flow analysis method--a case study of the Dongting Lake basin in South Central China.

This study primarily examined the assessment of environmental risk in high intensity agricultural areas. Dongting Lake basin was taken as a case study, which is one of the major grain producing areas in China. Using data obtained from 1989 to 2012, we applied Material Flow Analysis (MFA) to show the material consumption, pollutant output and production storage in the agricultural-environmental system and assessed the environmental risk index on the basis of the MFA results. The results predicted that the status of the environmental quality of the Dongting Lake area is unsatisfactory for the foreseeable future. The direct material input (DMI) declined by 13.9%, the domestic processed output (DPO) increased by 28.21%, the intensity of material consumption (IMC) decreased by 36.7%, the intensity of material discharge (IMD) increased by 10%, the material productivity (MP) increased by 27 times, the environmental efficiency (EE) increased by 15.31 times, and the material storage (PAS) increased by 0.23%. The DMI and DPO was higher at rural places on the edge of cities, whereas the risk of urban agriculture has arisen due to the higher increasing rate of DMI and DPO in cities compared with the counties. The composite environmental risk index increased from 0.33 to 0.96, indicating that the total environmental risk changed gradually but seriously during the 24 years assessed. The driving factors that affect environmental risk in high intensity agriculture can be divided into five classes: social, economic, human, natural and disruptive incidents. This study discussed a number of effective measures for protecting the environment while ensuring food production yields. Additional research in other areas and certain improvements of this method in future studies may be necessary to develop a more effective method of managing and controlling agricultural-environmental interactions.