Search

Abstract Background Abdominal obesity, a significant risk factor for the progression of diabetic retinopathy (DR), may lead to improved visual outcomes through early assessment. This study aims to evaluate any potential associations between DR and novel lipid metabolism markers, including the Atherogenic Index of Plasma (AIP), Visceral Adiposity Index (VAI), and Lipid Accumulation Product (LAP). Methods This study aimed to elucidate the association between various lipid markers and DR by screening the National Health and Nutrition Examination Survey (NHANES) database in the United States from 2005 to 2008. To examine the correlation, multifactor logistic regression analysis, subgroup analysis, threshold effect analysis, interaction test, and smooth curve fitting were used. Results Among the 2591 participants included, the incidence of DR was 13.6% and the mean age was 59.55 ± 12.26 years. After adjusting for important confounding covariates, logistic regression studies suggested a possible positive association between LAP, VAI, AIP, and DR occurrence (odds ratio [OR] = 1.004; 95% confidence interval [CI]: 1.002, 1.006; P 0.05 for all interactions). Conclusion Studies have shown that elevated levels of LAP, VAI, and AIP increase the likelihood of DR, suggesting that they have the potential to be predictive markers of DR, emphasizing their potential utility in risk assessment and prevention strategies, and advocating for early intervention to mitigate the likelihood of DR.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) and is characterized by high heterogeneity. Assessment of its prognosis and genetic subtyping hold significant clinical implications. However, existing DLBCL prognostic models are mainly based on transcriptomic profiles, while genetic variation detection is more commonly used in clinical practice. In addition, current clustering-based subtyping methods mostly focus on genes with high mutation frequencies, providing insufficient explanations for the heterogeneity of DLBCL. Here, we proposed VNNSurv (https://bio-web1.nscc-gz.cn/app/VNNSurv), a survival model for DLBCL patients based on a biologically informed visible neural network (VNN). VNNSurv achieved an average C-index of 0.72 on the cross-validation set (HMRN cohort, n = 928), outperforming the baseline methods. The remarkable interpretability of VNNSurv facilitated the identification of the most impactful genes and the underlying pathways through which they act on patient outcomes. When only the 30 highest-impact genes were used as genetic input, the overall performance of VNNSurv improved, and a C-index of 0.70 was achieved on the external TCGA cohort (n = 48). Leveraging these high-impact genes, including 16 genes with low (

Abstract Aim Recent imaging studies have found significant abnormalities in the brain’s functional or structural connectivity among patients with high myopia (HM), indicating a heightened risk of cognitive impairment and other behavioral changes. However, there is a lack of research on the topological characteristics and connectivity changes of the functional networks in HM patients. In this study, we employed graph theoretical analysis to investigate the topological structure and regional connectivity of the brain function network in HM patients. Methods We conducted rs-fMRI scans on 82 individuals with HM and 59 healthy controls (HC), ensuring that the two groups were matched for age and education level. Through graph theoretical analysis, we studied the topological structure of whole-brain functional networks among participants, exploring the topological properties and differences between the two groups. Results In the range of 0.05 to 0.50 of sparsity, both groups demonstrated a small-world architecture of the brain network. Compared to the control group, HM patients showed significantly lower values of normalized clustering coefficient (γ) (P = 0.0101) and small-worldness (σ) (P = 0.0168). Additionally, the HM group showed lower nodal centrality in the right Amygdala (P

The highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) poses a significant threat to the global swine industry. Vaccination is a preventive measure against viral infections. However, the use of vaccines in livestock healthcare programs faces the challenge of safety and delayed immune responses. Earlier studies have shown the potential of modified Bazhen powder as an immunomodulator with significant biological properties, but its effect on vaccines against HP-PRRSV is yet to be studied. This study elucidated how modified Bazhen powder could improve the safety and efficacy of the conventional PRRSV vaccine by evaluating T-cell responses, antibody levels, clinical symptoms, levels of viremia, organ health, and cytokine production. The results revealed that the oral application of modified Bazhen powder in combination with PRRS vaccination improved both cellular and humoral immunity, accelerated viremia clearance, improved lung injury scores, and reduced viral load in the tonsils. The modified Bazhen powder also effectively reduced inflammatory responses following a PRRSV challenge. These findings further highlight the pharmacological properties of modified Bazhen powder as a potential oral immunomodulatory agent that could enhance vaccine efficacy and ensure broad-spectrum protection against HP-PRRSV in pigs.

Calcium ion is a type of indispensable metal elements in biology, which participates in processes like maintaining the excitability of neuromuscular muscles. However, calcium content should be monitored in a safety range. Herein, a novel electrochemical method is developed for Ca2+ assay by monitoring electrochemical response after DNAzyme catalyzed DNA hydrogel degradation. Pure DNA hydrogel is first built with three-way junction scaffolds and linkers containing Ca2+-dependent DNAzyme sequence. In the presence of target Ca2+, the substrates in linkers are cleaved and DNA hydrogel can be degraded gradually. The encapsulated electrochemical species thus facilely interact with the electrode, leading to the increase of electrochemical responses. This electrochemical method for Ca2+ quantification is selective and sensitive, which also performs satisfactorily challenging biological samples like sweat and urine.

Background: Survival prediction is one of the crucial goals in precision medicine, as accurate survival assessment can aid physicians in selecting appropriate treatment for individual patients. To achieve this aim, extensive data must be utilized to train the prediction model and prevent overfitting. However, the collection of patient data for disease prediction is challenging due to potential variations in data sources across institutions and concerns regarding privacy and ownership issues in data sharing. To facilitate the integration of cancer data from different institutions without violating privacy laws, we developed a federated learning-based data integration framework called AdFed, which can be used to evaluate patients’ survival while considering the privacy protection problem by utilizing the decentralized federated learning technology and regularization method. Results: AdFed was tested on different cancer datasets that contain the patients’ information from different institutions. The experimental results show that AdFed using distributed data can achieve better performance in cancer survival prediction (AUC = 0.605) than the compared federated-learning-based methods (average AUC = 0.554). Additionally, to assess the biological interpretability of our method, in the case study we list 10 identified genes related to liver cancer selected by AdFed, among which 5 genes have been proved by literature review. Conclusions: The results indicate that AdFed outperforms better than other federated-learning-based methods, and the interpretable algorithm can select biologically significant genes and pathways while ensuring the confidentiality and integrity of data.

Abstract Background Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information. Results The model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study. Conclusions Our proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet .

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory loss, cognitive disorder, language dysfunction, and mental disability. The main neuropathological changes in AD mainly include amyloid plaque deposition, neurofibrillary tangles, synapse loss, and neuron reduction. However, the current anti-AD drugs do not demonstrate a favorable effect in altering the pathological course of AD. Moreover, long-term use of these drugs is usually accompanied with various side effects. Ginsenosides are the major active constituents of ginseng and have protective effects on AD through various mechanisms in both in vivo and in vitro studies. In this review, we focused on discussing the therapeutic potential effects and the mechanisms of pharmacological activities of ginsenosides in AD, to provide new insight for further research and clinical application of ginsenosides in the future. Recent studies on the pharmacological effects and mechanisms of ginsenosides were retrieved from Chinese National Knowledge Infrastructure, National Science and Technology Library, Wanfang Data, Elsevier, ScienceDirect, PubMed, SpringerLink, and the Web of Science database up to April 2023 using relevant keywords. Network pharmacology and bioinformatics analysis were used to predict the therapeutic effects and mechanisms of ginsenosides against AD. Ginsenosides presented a wide range of therapeutic and biological activities, including alleviating Aβ deposition, decreasing tau hyperphosphorylation, regulating the cholinergic system, resisting oxidative stress, modulating Ca2+ homeostasis, as well as anti-inflammation and anti-apoptosis in neurons, respectively. For further developing the therapeutic potential as well as clinical applications, the network pharmacology approach was combined with a summary of published studies.

Alfalfa is a perennial herbaceous forage legume that is significantly and adversely affected by monocropping. Crop rotation is the most effective measure to overcome continuous cropping obstacles. However, the mechanisms of how bacterial communities are affected and the potential links between these effects and cropping systems remain poorly understood. Based on a long-term field experiments with continuous alfalfa and forage crops with alfalfa rotation in the black soil region of the western Songnen Plain in Northeast China, the alterations in soil bacterial community structure using high-throughput sequencing of the 16S rRNA gene and soil chemical properties and enzyme activities were analyzed. The alfalfa–forage oats–silage maize–alfalfa and alfalfa–silage maize–forage oats–alfalfa system significantly increase the levels of total phosphorus and available phosphorus, and promote the activities of acid phosphatase, β-glucosidase, leucine aminopeptidase, and N-acetyl-β-glucosaminidase in comparison to continuous alfalfa. While alfalfa crop rotation did not affect the α-diversity of soil bacteria, it significantly altered the bacterial community composition and structure. Some key taxa are significantly enriched in the crop rotation system soils, including Bacillus, Sphingobium, Paenibacillus, Hydrogenispora, Rubrobacter, Haliangium, and Rubellimicrobium. Additionally, crop rotation with alfalfa increased the stability and complexity of the soil bacterial co-occurrence network. Based on our findings, we recommend promoting the alfalfa–forage oats–silage maize–alfalfa and alfalfa–silage maize–forage oats–alfalfa rotation systems as ideal practices for overcoming the challenges associated with continuous cropping of alfalfa. These systems not only enhance soil nutrient content and enzyme activities but also foster a beneficial microbial community, ultimately improving soil functionality and crop performance.

Complex diseases do not always follow gradual progressions. Instead, they may experience sudden shifts known as critical states or tipping points, where a marked qualitative change occurs. Detecting such a pivotal transition or pre-deterioration state holds paramount importance due to its association with severe disease deterioration. Nevertheless, the task of pinpointing the pre-deterioration state for complex diseases remains an obstacle, especially in scenarios involving high-dimensional data with limited samples, where conventional statistical methods frequently prove inadequate. In this study, we introduce an innovative quantitative approach termed sample-specific causality network entropy (SCNE), which infers a sample-specific causality network for each individual and effectively quantifies the dynamic alterations in causal relations among molecules, thereby capturing critical points or pre-deterioration states of complex diseases. We substantiated the accuracy and efficacy of our approach via numerical simulations and by examining various real-world datasets, including single-cell data of epithelial cell deterioration (EPCD) in colorectal cancer, influenza infection data, and three different tumor cases from The Cancer Genome Atlas (TCGA) repositories. Compared to other existing six single-sample methods, our proposed approach exhibits superior performance in identifying critical signals or pre-deterioration states. Additionally, the efficacy of computational findings is underscored by analyzing the functionality of signaling biomarkers.

In this work, a novel strand displacement polymerization strategy is designed with a single hairpin-structured DNA probe acting as both of primer and template. After target miRNA-induced structural transitions, extension and displacement reactions occur at the dimer with recycled target miRNA. The generated double-stranded products further activate CRISPR/Cas12a collateral cleavage of substrate DNA at the electrode surface, which can be reflected by the silver stripping peak variations. By analyzing the reduced peak intensity, the concentration of miRNA can be determined. This method combines the strand displacement amplification and CRISPR/Cas12a’s shearing capability. Thus a highly sensitive electrochemical biosensor is established with the limit of detection as low as 31 aM. It may also inspire new ideas of electrochemical platforms integrating CRISPR/Cas system for biological studies and clinical applications.

Analytical coarse alignment and Kalman filter fine alignment based on zero-velocity are typically used to obtain initial attitude for inertial navigation systems (SINS) on a static base. However, in the shipboard mooring state, the static observation condition is corrupted. This paper presents a rapid alignment method for SINS on swaying bases. The proposed method begins with a coarse alignment technique in the inertial frame to obtain an initial rough attitude. Subsequently, a Kalman filter with position updates is employed to estimate the remaining misalignment error. To enhance the filter estimation performance, an appropriate lower boundary is set to the target states’ variances according to a carefully designed relative convergence index. The variance-constraint Kalman filter (VCKF) approach is proposed in this paper, and the shipborne experiments validate its effectiveness. The results demonstrate that the VCKF approach significantly reduces the time requirement for fine alignment to achieve the same accuracy on a swaying base, from 90 min in the classic Kalman filter to 30 min. Additionally, the parameter estimation performance in the Kalman filter is also improved, particularly in situations where unpredicted external interference is involved during fine alignment.

AIM: To explore Bland-Altman analysis of corneal curvature and astigmatism measured by new swept-source optical coherence tomography(SS-OCT)and Scheimpflug anterior segment analyzer in patients with age-related cataract.METHODS: A total of 177 patients(282 eyes)with age-related cataract admitted to the hospital between January and December 2021 were enrolled. The steep-axis curvature(Ks), flat-axis curvature(Kf), mean corneal curvature(Km), corneal astigmatism and astigmatism axis of anterior corneal surface, posterior surface and the whole cornea were measured by SS-OCT and Scheimpflug anterior segment analyzer respectively. All parameters were detected by paired sample t-test, intra-group repeatability test, Pearson correlation and Bland-Altman consistency analysis.RESULTS: There was no significant difference in Ks, Kf and Km of anterior corneal surface measured by SS-OCT and Scheimpflug anterior segment analyzer(P>0.05). Ks, Kf and Km of posterior corneal surface and whole cornea measured by Scheimpflug anterior segment analyzer were all greater than those measured by SS-OCT(P0.05). The intraclass correlation coefficient(ICC)of all parameters was greater than 0.88, indicating a good intra-group repeatability. Ks, Kf, Km, astigmatism and axis of anterior corneal surface, posterior surface and whole cornea measured by SS-OCT were positively correlated with those measured by Scheimpflug anterior segment analyzer(P

Abstract Solar irradiance is the main factor affecting the output of a photovoltaic (PV) power station, which is chiefly determined by the cloud distribution over the power station. For ultra‐short‐term, especially the intro‐hour time scale irradiance forecasting, ground‐based cloud image is considered as a very necessary data as Global Horizontal Irradiance (GHI). However, the information content in the image is much higher than that of GHI record, and there is even a difference in magnitude between them. Therefore, how to effectively extract the key features in the cloud images and fuse them with GHI record data is the decisive factor affecting the performance of the forecasting model. Here, a novel convolutional auto‐encoder based cloud distribution feature (CDF) extraction method is first proposed. Then for feature fusion part, an LSTM‐FUSION irradiance forecasting model is established based on long short‐term memory (LSTM) neural network and feature fusion by time steps considering the one‐to‐one correlation between CDFs and GHI. Finally, a novel determination method of input time step length based on attention distribution analysis is also proposed. Simulation results show that the proposed LSTM‐FUSION model is overall superior to the benchmark models.

Background: The prognosis of patients with acute coronary syndrome (ACS) varies greatly, and risk assessment models can help clinicians to identify and manage high-risk patients. While the Global Registry of Acute Coronary Events (GRACE) model is widely used, the clinical pathways for acute coronary syndromes (CPACS), which was constructed based on the Chinese population, and acute coronary treatment and intervention outcomes network (ACTION) have not yet been validated in the Chinese population. Methods: Patients with ACS who underwent coronary angiography or percutaneous coronary intervention from 2011 to 2020, were retrospectively recruited and the appropriate corresponding clinical indicators was obtained. The primary endpoint was in-hospital mortality. The performance of the GRACE, GRACE 2.0, ACTION, thrombolysis in myocardial infarction (TIMI) and CPACS risk models was evaluated and compared. Results: A total of 19,237 patients with ACS were included. Overall, in-hospital mortality was 2.2%. ACTION showed the highest accuracy in predicting discriminated risk (c-index 0.945, 95% confidence interval [CI] 0.922–0.955), but the calibration was not satisfactory. GRACE and GRACE 2.0 did not significantly differ in discrimination (p = 0.1480). GRACE showed the most accurate calibration in all patients and in the subgroup analysis of all models. CPACS (c-index 0.841, 95% CI 0.821–0.861) and TIMI (c-index 0.811, 95% CI 0.787–0.835) did not outperform (c-index 0.926, 95% CI 0.911–0.940). Conclusions: In contemporary Chinese ACS patients, the ACTION risk model’s calibration is not satisfactory, although outperformed the gold standard GRACE model in predicting hospital mortality. The CPACS model developed for Chinese patients did not show better predictive performance than the GRACE model.

With the frequent occurrence of various disasters, serious damage has been caused to social and economic development. Therefore, disaster management plays an increasingly significant role in controlling disasters and reducing losses. This study aims to provide a dynamic interaction assessment method for the emergency management department to manage disasters. For this purpose, the classical Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is first extended with bipolar 2-tuple linguistic information to model both the negative and positive influences among factors involved in coping with disaster. Then, the weights of influential factors are determined according to their total interaction relationships derived by extended DEMATEL. After that, the performances or states of factors are suggested to be appraised under a bipolar 2-tuple linguistic environment. Further, the performance or state simulation rule of factors is proposed based on their initial states and the interactions among them during disaster management. According to the simulation results, a weighted average operator is employed to obtain the overall performance values of emergency scenarios. Finally, an illustrative example and comparative analysis are presented for elucidating the feasibility and usefulness of the suggested method. Results of a case study show that the proposed method has the abilities to capture the interactions among influential factors and explore how the factors and their interactions affect disaster management. The proposed method could provide valuable information to emergency management departments for managing disasters more effectively.

The ongoing development of renewable energy and microgrid technologies has gradually transformed the conventional energy infrastructure and upgraded it into a modernized system with more distributed generation and localized energy storage options. Compared with power grids utilizing synchronous generation, inverter-based networks cannot physically provide large amounts of inertia, which means that more advanced and extensive studies regarding stability considerations are required for such systems. Therefore, appropriate analytical methods are needed for the voltage stability analysis of renewable-dominated power systems, which incorporate a large number of inverters and distributed energy sources. This paper provides a comprehensive literature review of voltage stability analyses of power systems with high levels of renewable energy penetration. A series of generalized evaluation schemes and improvement methods relating to the voltage stability of power systems integrated with various distributed energy resources are discussed. The existing voltage stability analysis methods and corresponding simulation verification models for microgrids are also reviewed in a systematic manner. The traditional and improved voltage stability analysis methods are reviewed according to the microgrid operation mode, the types of distributed generators, and the microgrid configurations. Moreover, the voltage stability indices, which play a crucial role in voltage stability assessments, are critically evaluated in terms of the applicable conditions. The associated modeling and simulation techniques are also presented and discussed. This contribution presents guidelines for voltage stability analysis and instability mitigation methods for modern renewable-rich power systems.

BackgroundThe soil fungal community is one of the most important drivers of the soil nutrient cycling that sustains plant growth. However, little research has been done on the effects of different land uses on soil fungal communities in northeast China.MethodsIn this study, we conducted a field experiment to investigate the effects of continuous cropping of grass, maize, and alfalfa on their respective fungal communities and co-occurrence networks.ResultsWe showed that the physicochemical properties of the soil, such as nitrate (NO3-N), available phosphorus, and soil pH, were the most important driving factors affecting the structure of the soil fungal community in different cropping systems. In addition, compared to the cultivation of grass and maize, the continuous cropping of alfalfa increased the abundance of several beneficial as well as pathogenic species, such as Mortierella and Gaiellales. In addition, the networks differed among plant species and according to the number of years of continuous cultivation.ConclusionThis suggests that the continuous cropping of alfalfa results in greater cooperation among fungi, which may be beneficial to the soil as well as to the development of the alfalfa.

Nitrous oxide (N2O) is one of the most important greenhouse gases contributing to global climate warming. Recently, studies have shown that arbuscular mycorrhizal fungi (AMF) could reduce N2O emissions in terrestrial ecosystems; however, the microbial mechanisms of how AMF reduces N2O emissions under climate change are still not well understood. We tested the influence of AMF on N2O emissions by setting up a gradient of precipitation intensity (+50%, +30%, ambient (0%), −30%, −50%, and −70%) and manipulating the presence or exclusion of AMF hyphae in a semiarid grassland located in northeast China. Our results showed that N2O fluxes dramatically declined with the decrease in precipitation gradient during the peak growing season (June–August) in both 2019 and 2020. There was a significantly positive correlation between soil water content and N2O fluxes. Interestingly, N2O fluxes significantly decreased when AMF were present compared to when they were absent under all precipitation conditions. The contribution of AMF to mitigate N2O emission increased gradually with decreasing precipitation magnitudes, but no contribution in the severe drought (−70%). AMF significantly reduced the soil’s available nitrogen concentration and altered the composition of the soil bacteria community including those associated with N2O production. Hyphal length density was negatively correlated with the copy numbers of key genes for N2O production (nirK and nirS) and positively correlated with the copy numbers of key genes for N2O consumption (nosZ). Our results highlight that AMF would reduce the soil N2O emission under precipitation variability in a temperate grassland except for extreme drought.

Rapid urbanization has led to drastic land-use/cover changes (LUCCs) and urban heat islands (UHIs), negatively altering the urban climate and air quality. LUCC’s significant impacts on human health and energy consumption have inspired researchers to develop nature-based solutions to mitigate UHIs and improve air quality. However, integrating GIS-CFD modeling for urban heat mitigation towards climate change adaptation was largely neglected for eco-sustainable urban design in rapidly urbanizing areas. In this study, (1) long-term LUCC and meteorological analysis were conducted in the Wuhan metropolitan area from 1980 to 2016; (2) to mitigate the adverse effects of LUCC under a speedy development process, the role and relevance of optimizing building morphology and urban block configuration were discussed; (3) and particular design attention in strategy towards climate change adaptation for environmental performance improvement was paid in Wuhan’s fast-growing zones. The results show that UHII in 1980 was less severe than in 2016. Air temperature (Ta) increased by 0.4 °C on average per decade in developing areas. This increases the severity of UHII in urban fringes. It is found obligatory for a nature-based design to adopt urban morphology indicators (UMIs) such as average building height (μBH), sky view factors (ψSVF), and building density (BD/λp = % of built area) towards these changes. Further, on-site measurement revealed that λp is the most effective indicator for increasing urban heat around the buildings and boosting UHII. Using UMIs and a combined three-in-one regulation strategy based on μBH of common building types of high-rise (BHA), mid-rise (BHB), and low-rise (BHC) buildings can effectively contribute to regulating Ta and air movement within block configuration. As a result of this study’s strategy, urban heat is mitigated via reinforcing wind in order to adapt to climate change, which impacts the quality of life directly in developing areas.

Paphiopedilum (Orchidaceae) is one of the world’s most popular orchids that is found in tropical and subtropical forests and has an enormous ornamental value. SEPALLATA-like (SEP-like) MADS-box genes are responsible for floral organ specification. In this study, three SEP-like MADS-box genes, PhSEP1, PhSEP2, and PhSEP3, were identified in Paphiopedilum henryanum. These genes were 732–916 bp, with conserved SEPI and SEPII motifs. Phylogenetic analysis revealed that PhSEP genes were evolutionarily closer to the core eudicot SEP3 lineage, whereas none of them belonged to core eudicot SEP1/2/4 clades. PhSEP genes displayed non-ubiquitous expression, which was detectable across all floral organs at all developmental stages of the flower buds. Furthermore, subcellular localization experiments revealed the localization of PhSEP proteins in the nucleus. Yeast two-hybrid assays revealed no self-activation of PhSEPs. The protein–protein interactions revealed that PhSEPs possibly interact with B-class DEFICIENS-like and E-class MADS-box proteins. Our study suggests that the three SEP-like genes may play key roles in flower development in P. henryanum, which will improve our understanding of the roles of the SEP-like MADS-box gene family and provide crucial insights into the mechanisms underlying floral development in orchids.

Maize and alfalfa (Medicago sativa L.) have been used extensively in the animal husbandry to compensate for the lack of livestock and fodder yields in the chilly northeast of China. Little is known, however, about the impact on soil characteristics of consecutive plantings in various crops and alfalfa. In this research, the soil characteristics, bacterial community diversity, and structure of the meadow, maize, and alfalfa continuous cropping fields (i.e., 6, 10, 14, 20, and 30 years) were measured. The results showed that maize cropping and continuous cropping of alfalfa increased the soil bacterial alpha diversity compared with meadow cropping, and alpha diversity of alfalfa increased with the continuous planting years. Soil pH, total phosphorus (TP), available P, total potassium (TK), and nitrate nitrogen (NO3–) content were soil variables significantly impacting the structure of soil bacterial communities in different plant types and different alfalfa continuous cropping systems. In addition, the relative abundance of some beneficial microbial species, such as Arthrobacter and Gaiellales, in the cropping maize and continuous cropping of alfalfa was much higher than that in the meadow field. Moreover, the networks differ among different plant types, and also differ among different continuous cropping years of alfalfa, and topologies of the networks suggested that continuous planting of alfalfa promotes cooperation between bacteria, which facilitates the long growth of alfalfa and is beneficial to the soil.

Abstract Background Identifying molecular subtypes of ovarian cancer is important. Compared to identify subtypes using single omics data, the multi-omics data analysis can utilize more information. Autoencoder has been widely used to construct lower dimensional representation for multi-omics feature integration. However, learning in the deep architectures in Autoencoder is difficult for achieving satisfied generalization performance. To solve this problem, we proposed a novel deep learning-based framework to robustly identify ovarian cancer subtypes by using denoising Autoencoder. Results In proposed method, the composite features of multi-omics data in the Cancer Genome Atlas were produced by denoising Autoencoder, and then the generated low-dimensional features were input into k-means for clustering. At last based on the clustering results, we built the light-weighted classification model with L1-penalized logistic regression method. Furthermore, we applied the differential expression analysis and WGCNA analysis to select target genes related to molecular subtypes. We identified 34 biomarkers and 19 KEGG pathways associated with ovarian cancer. Conclusions The independent test results in three GEO datasets proved the robustness of our model. The literature reviewing show 19 (56%) biomarkers and 8(42.1%) KEGG pathways identified based on the classification subtypes have been proved to be associated with ovarian cancer. The outcomes indicate that our proposed method is feasible and can provide reliable results.

For physically understanding the dynamic response of the photovoltaic (PV) integrated power system for electromechanical oscillations damping, firstly, this paper develops the linear mathematical model of a single machine infinite bus system integrated by the PV grid-tied inverter under the electromechanical time scale. Then, based on the electric torque analysis method, and the functional route of the PV grid-tied inverter variable active power control (P-control) and reactive power control (Q-control) with the power angle or grid frequency feedback as the input control signal, the key factors and influence laws of the integrated system inertia, damping level and synchronization capability are investigated. The developed model shows that enlarging the proportional-integral-derivative (PID) controller parameters of P and/or Q-control loop can effectively change electromagnetic power distribution to suppress the imbalance power, and equivalently improve the system damping level, synchronization capability and inertial effect, respectively, contributing to actively adjust the grid frequency oscillation amplitude, period and recovery speed to maintain the system control stability according to the grid code demand. Besides, under the same inverter capacity constraint, P-control mode is proved more advantageous than Q-control mode to damp electromechanical oscillations. Finally, the correctness of the physical mechanism analysis is verified by MATLAB simulations and RT-LAB experiments.

The heterogeneity of cancer reflects the complexity of genetic mutations. Dissecting the heterogeneity plays an important role in the field of biomarker discovery, targeted therapy and drug designing. As it is time-consuming to identify new biomarkers in biological experiments, various machine learning methods have been developed. However, the current methods are limited because they ignore that patients may correspond to different disease-causing genotypes. In this article, a genotype-based ensemble classifier system (GECS) is proposed which aims to explain pathologies of NSCLC from the view of genotypes and identify the genetic subtypes of tumors. The core strategy of GECS is to construct multiple independent classifiers following the principle that one classifier is constructed based on one genotype of NSCLC. The analysis of synthetic data and three microarray datasets indicated that the proposed method outperforms existing approaches in the identification of genetic subtypes of tumors. The GECS method provides a useful tool for molecular pathology researches for dissecting the heterogeneity of cancer.

Anomalous trajectory detection plays a significant role in fraud detection and adverse events monitoring for ride-hailing services. The spatial and temporal dynamics of road networks and the sparsity of trajectories make anomalous trajectory detection a challenging task. Most existing methods are based on density and isolation approaches, which ignore geographical information. Motivated by these challenges and shortcomings, we propose a novel method, which considers geospatial constraints of the trajectories and avoids sparsity issues. In our method, the geographical information and topological constraints of trajectories are embedded into structured vector space. Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are used to model common trajectory features. Our method could identify anomalous trajectories and determine which parts are responsible for anomalies by using these features. Experiments on two real-world datasets have been conducted, and results demonstrate the effectiveness and feasibility of the proposed method.

Acetaminophen (APAP) overdose is one of the leading causes of acute liver damage. Given N-acetylcysteine (NAC) and melatonin (MLT) both have an attenuated value for APAP-induced liver toxification, where an optimized integrated treatment has not been well deciphered. Here, by giving a single dose of APAP (500 mg/kg) to wild-type male mice, combined with a single dose of 500 mg/kg NAC or 100 mg/kg MLT separately as the therapeutic method, this study aimed to investigate the effects of NAC and melatonin (MLT) alone or combined on acetaminophen (APAP)-induced liver injury. In this study, NAC and MLT both partially have an alleviated function in APAP-challenged liver injury. However, MLT’s add-on role strengthens the hepatoprotective effect of NAC on APAP-induced liver damage and resolute the inflammatory infiltration. Meanwhile, the combination of two reagents attenuates the decreased glutathione (GSH) and activation of the p38/JNK pathway. The combination of MLT and NAC can further ameliorate APAP-induced liver injury, which provides a novel strategy for drug-induced liver injury (DILI).

Abstract Background Low temperature is one of the main environmental factors that limits crop growth, development, and production. Medicago falcata is an important leguminous herb that is widely distributed worldwide. M. falcata is related to alfalfa but is more tolerant to low temperature than alfalfa. Understanding the low temperature tolerance mechanism of M. falcata is important for the genetic improvement of alfalfa. Results In this study, we explored the transcriptomic changes in the roots of low-temperature-treated M. falcata plants by combining SMRT sequencing and NGS technologies. A total of 115,153 nonredundant sequences were obtained, and 8849 AS events, 73,149 SSRs, and 4189 lncRNAs were predicted. A total of 111,587 genes from SMRT sequencing were annotated, and 11,369 DEGs involved in plant hormone signal transduction, protein processing in endoplasmic reticulum, carbon metabolism, glycolysis/gluconeogenesis, starch and sucrose metabolism, and endocytosis pathways were identified. We characterized 1538 TF genes into 45 TF gene families, and the most abundant TF family was the WRKY family, followed by the ERF, MYB, bHLH and NAC families. A total of 134 genes, including 101 whose expression was upregulated and 33 whose expression was downregulated, were differentially coexpressed at all five temperature points. PB40804, PB75011, PB110405 and PB108808 were found to play crucial roles in the tolerance of M. falcata to low temperature. WGCNA revealed that the MEbrown module was significantly correlated with low-temperature stress in M. falcata. Electrolyte leakage was correlated with most genetic modules and verified that electrolyte leakage can be used as a direct stress marker in physiological assays to indicate cell membrane damage from low-temperature stress. The consistency between the qRT-PCR results and RNA-seq analyses confirmed the validity of the RNA-seq data and the analysis of the regulatory mechanism of low-temperature stress on the basis of the transcriptome. Conclusions The full-length transcripts generated in this study provide a full characterization of the transcriptome of M. falcata and may be useful for mining new low-temperature stress-related genes specific to M. falcata. These new findings could facilitate the understanding of the low-temperature-tolerance mechanism of M. falcata.

BackgroundPredicting hepatocellular carcinoma (HCC) prognosis is important for treatment selection, and it is increasingly interesting to predict prognosis through gene expression data. Currently, the prognosis remains of low accuracy due to the high dimension but small sample size of liver cancer omics data. In previous studies, a transfer learning strategy has been developed by pre-training models on similar cancer types and then fine-tuning the pre-trained models on the target dataset. However, transfer learning has limited performance since other cancer types are similar at different levels, and it is not trivial to balance the relations with different cancer types.MethodsHere, we propose an adaptive transfer-learning-based deep Cox neural network (ATRCN), where cancers are represented by 12 phenotype and 10 genotype features, and suitable cancers were adaptively selected for model pre-training. In this way, the pre-trained model can learn valuable prior knowledge from other cancer types while reducing the biases.ResultsATRCN chose pancreatic and stomach adenocarcinomas as the pre-training cancers, and the experiments indicated that our method improved the C-index of 3.8% by comparing with traditional transfer learning methods. The independent tests on three additional HCC datasets proved the robustness of our model. Based on the divided risk subgroups, we identified 10 HCC prognostic markers, including one new prognostic marker, TTC36. Further wet experiments indicated that TTC36 is associated with the progression of liver cancer cells.ConclusionThese results proved that our proposed deep-learning-based method for HCC prognosis prediction is robust, accurate, and biologically meaningful.

Motivation: Long non-coding RNAs (lncRNAs) play important roles in cancer development. Prediction of lncRNA–cancer association is necessary for efficiently discovering biomarkers and designing treatment for cancers. Currently, several methods have been developed to predict lncRNA–cancer associations. However, most of them do not consider the relationships between lncRNA with other molecules and with cancer prognosis, which has limited the accuracy of the prediction.Method: Here, we constructed relationship matrices between 1,679 lncRNAs, 2,759 miRNAs, and 16,410 genes and cancer prognosis on three types of cancers (breast, lung, and colorectal cancers) to predict lncRNA–cancer associations. The matrices were iteratively reconstructed by matrix factorization to optimize low-rank size. This method is called detecting lncRNA cancer association (DRACA).Results: Application of this method in the prediction of lncRNAs–breast cancer, lncRNA–lung cancer, and lncRNA–colorectal cancer associations achieved an area under curve (AUC) of 0.810, 0.796, and 0.795, respectively, by 10-fold cross-validations. The performances of DRACA in predicting associations between lncRNAs with three kinds of cancers were at least 6.6, 7.2, and 6.9% better than other methods, respectively. To our knowledge, this is the first method employing cancer prognosis in the prediction of lncRNA–cancer associations. When removing the relationships between cancer prognosis and genes, the AUCs were decreased 7.2, 0.6, and 5% for breast, lung, and colorectal cancers, respectively. Moreover, the predicted lncRNAs were found with greater numbers of somatic mutations than the lncRNAs not predicted as cancer-associated for three types of cancers. DRACA predicted many novel lncRNAs, whose expressions were found to be related to survival rates of patients. The method is available at https://github.com/Yanh35/DRACA.

Abstract Background Ageing is a risk factor for both coronary artery disease (CAD) and reduced renal function (RRF), and it is also associated with poor prognosis in patients with CAD or RRF. However, little is known about whether the impact of RRF on clinical outcomes are different in CAD patients at different age groups. This study aimed to investigate whether ageing influences the effect of RRF on long-term risk of death in patients with CAD. Methods A retrospective analysis was conducted using data from a single-center cohort study. Three thousand and two consecutive patients with CAD confirmed by coronary angiography were enrolled. RRF was defined as an estimated glomerular filtration rate (eGFR) of less than 60 ml/min. The primary endpoint in this study was all-cause mortality. Results The mean follow-up time was 29.1 ± 12.5 months and death events occurred in 275 cases (all-cause mortality: 9.2%). The correlation analysis revealed a negative correlation between eGFR and age (r = − 0.386, P

Synthetic artificial DNA probes are excellent architectural scaffolds which can be assembled into various nanostructures for further applications. A DNA walker is usually designed in response to certain external stimuli, and can significantly enhance the obtained signals. Herein, we have developed a novel DNA walker biosensor for the detection of target DNA with enzymatic recycling cleavage. By using the target DNA as a driving force, a DNA star trigon nanostructure is formed on the electrode surface, which can be recognized by a nicking endonuclease. The DNA walker is then activated to move around for the cleavage reactions on the electrode. The resulting DNA biosensor shows excellent analytical performance with a low limit of detection of 1 fM. Good selectivity and practical utility are also demonstrated. This approach has many other advantages such as ease of use, low cost, mild conditions, and may hold great potential for a range of applications in the future. Keywords: DNA walker, DNA star trigon, Nicking endonuclease, Square wave voltammetry, Signal amplification

Abstract Background The role of triglyceride (TG) in secondary prevention of patients with coronary artery disease (CAD) was debated. In the present study, we assessed the association between admission TG levels and long-term mortality risk in CAD patients. Methods A retrospective analysis was conducted from a single registered database. 3061 consecutive patients with CAD confirmed by coronary angiography were enrolled and were grouped into 3 categories by the tertiles of admission serum TG levels. The primary end point in this study was all-cause mortality and the secondary end point was cardiovascular mortality. Results The mean follow-up time was 26.9 ± 13.6 months and death events occurred in 258 cases and cardiovascular death events occurred in 146 cases. Cumulative survival curves indicated that the risk of all-cause death decreased with increasing TG level (Tertile 1 vs. Tertile 2 vs. Tertile 3 = 10.3% vs. 8.6% vs. 6.3%, log rank test for overall p = 0.001). Cox regression analysis showed an independent correlation between TG level and risk of all-cause mortality [hazard ratio (HR) 0.71, 95% confidence interval (CI) 0.58–0.86] and cardiovascular mortality (HR 0.67, 95% CI 0.51–0.89) in total patients with CAD. Subgroup analysis found the similar results in patients with acute coronary syndrome and acute myocardial infarction. Conclusions This study found an inverse association between TG levels and mortality risk in CAD patients, which suggests that the “TG paradox” may exist in CAD patients. Trial registration ChiCTR, ChiCTR-OOC-17010433. Registered 17 February 2017 - Retrospectively registered.

Thioredoxin (TRX) is a small molecule protein that participates in the redox process and plays a decisive role in various functions of plants. However, the role of TRX in Medicago sativa (alfalfa), a widely cultivated perennial herb of legume, is still poorly understood. Here, we isolated MsTRX from alfalfa and determined the characteristics in improving salt tolerance by assaying the phenotype and physiological changes and the expression of stress-response genes in transgenic tobacco. The expression of MsTRX was similar in alfalfa roots, leaves, and inflorescences, and was downregulated in response to cold, drought, and salt treatment. The overexpression of MsTRX in tobacco promoted the accumulation of soluble sugar (SS) and proline; enhanced the activity of peroxidase (POD); and induced the upregulation of beta-amylase 1 (BAM1), lipid-transfer protein 1 (LTP1), candidate signal molecules/sensor relay proteins (CBSX3), superoxide dismutase [Cu-Zn] (Cu/Zn-SOD), superoxide dismutase [Mn] (Mn-SOD), protein gamma response 1 (GR1), dehydrin DHN1-like (ERD10B), and serine/threonine-protein kinase (SnRK2), as well as the downregulation of phyB activation-tagged suppressor1 (BAS1) and serine/threonine-protein kinase that phosphorylates LHCII protein 7 (STN7) under salt stress. These results indicated that MsTRX improves salt tolerance via maintaining osmotic homeostasis, scavenging reactive oxygen species (ROS), and regulating the transcription of stress-response genes in plants. In our study, we provided a new understanding of how MsTRX improves salt stress in plants and how MsTRX can be included in future breeding programs to improve salt tolerance in alfalfa.

Aim: This study aimed to evaluate the effects of centromere protein W (CENPW, also known as CUG2) in hepatocellular carcinoma (HCC). Methods: CENPW expression in HCC tissues and cells was detected by RT-qPCR assay. CCK-8 and colony formation assay were used to assess cell proliferation. Wound healing and Transwell assay was used to detect cell migration and invasion, respectively. The flow cytometry was used to analyze the cell cycle distribution and apoptosis. Results: CENPW expression was upregulated in HCC tissues and cells. Knockdown of CENPW inhibited cell proliferation, migration, and invasion and induced the G0/G1 phase arrest and cell apoptosis in HCC cells, which might involve the E2F signaling regulation. Conclusion: CENPW acted as an oncogenic role in HCC progression via activation E2F signaling. Our findings may provide new insights into the studying mechanisms of HCC.

Abstract Background The effect of statin treatment on circulating coenzyme Q10 (CoQ10) has been studied in numerous randomized controlled trails (RCTs). However, whether statin treatment decreases circulating CoQ10 is still controversial. Methods PubMed, EMBASE, and the Cochrane Library were searched to identify RCTs to investigate the effect of statin treatment on circulating CoQ10. We calculated the pooled standard mean difference (SMD) using a fixed effect model or random effect model to assess the effect of statin treatment on circulating CoQ10. The methodological quality of the studies was determined according to the Cochrane Handbook. Publication bias was evaluated by a funnel plot, the Egger regression test, and the Begg–Mazumdar correlation test. Results Twelve RCTs with a total of 1776 participants were evaluated. Compared with placebo, statin treatment resulted in a reduction of circulating CoQ10 (SMD, − 2.12; 95% CI, − 3.40 to − 0.84; p = 0.001), which was not associated with the duration of statin treatment (Exp, 1.00; 95% CI, 0.97 to 1.03; p = 0.994). Subgroup analysis demonstrated that both lipophilic statins (SMD, − 1.91; 95% CI, − 3.62 to 0.2; p = 0.017) and hydrophilic statins (SMD, − 2.36; 95% CI, − 4.30 to − 0.42; p = 0.028) decreased circulating CoQ10, and no obvious difference was observed between the two groups (SMD, − 0.20; 95% CI, − 0.208 to 0.618; p = 0.320). In addition, both low-middle intensity statins (SMD, − 2.403; 95% CI, − 3.992 to − 0.813; p

Microarray technology is a popular technique that has been extensively applied in cancer diagnosis. Many studies have used high-dimensional microarray data to identify informative features to classify the types of cancer, yet numerous irrelevant features that exist in microarray data may introduce the noise and decrease classification accuracy. Regularization techniques are common methods for feature selection, which can be used to reduce irrelevant features and avoid overfitting. In recent years, different regularization methods have been proposed. Theoretically, the Lq (0 q (0 q (0

Abstract Background Elderly patients with coronary artery disease (CAD) frequently complicated with more cardiovascular risk factors, but received fewer evidence-based medications (EBMs). This study explored the association of EBMs compliance in different age groups and the risk of long-term death. Methods A retrospective analysis was conducted from a single registered database. 2830 consecutive patients with CAD were enrolled and grouped into 3 categories by age. The primary end point was all-cause mortality and secondary endpoint is cardiovascular mortality. Results The mean follow-up time was 30.25 ± 11.89 months and death occurred in 270 cases,including 150 cases of cardiac death. Cumulative survival curves indicated that the incidence rates of all-cause death and cardiovascular death increased with age (older than 75 years old vs. 60 to 75 years old vs. younger than 60 years old, mortality: 18.7% vs. 9.6% vs. 4.1%, p

As one of the most important limiting factors of grassland productivity, drought is predicted to increase in intensity and frequency. Greenhouse studies suggest that arbuscular mycorrhizal fungi (AMF) can improve plant drought resistance. However, whether AMF can improve plant drought resistance in field conditions and whether the effects of AMF on drought resistance differ among plants with different photosynthetic pathways remain unclear. To evaluate the effect of indigenous AMF on plant drought resistance, an in situ rainfall exclusion experiment was conducted in a temperate meadow in northeast China. The results showed that AMF significantly reduced the negative effects of drought on plant growth. On average, AMF enhanced plant biomass, photosynthetic rate (A), stomatal conductance (gs), intrinsic water use efficiency (iWUE), and superoxide dismutase (SOD) activity of the C3 species Leymus chinensis by 58, 63, 38, 15, and 45%, respectively, and reduced levels of malondialdehyde (MDA) by 32% under light and moderate drought (rainfall exclusion of 30 and 50%, respectively). However, under extreme drought (rainfall exclusion of 70%), AMF elevated only aboveground biomass and catalase (CAT) activities. Averagely, AMF increased the aboveground biomass, A, and CAT activity of Hemarthria altissima (C4) by 37, 28, and 30%, respectively, under light and moderate droughts. The contribution of AMF to plant drought resistance was higher for the C3 species than that for the C4 species under both light and moderate drought conditions. The results highlight potential photosynthetic type differences in the magnitude of AMF-associated enhancement in plant drought resistance. Therefore, AMF may determine plant community structure under future climate change scenarios by affecting the drought resistance of different plant functional groups.

For studying cancer and genetic diseases, the issue of identifying high correlation genes from high-dimensional data is an important problem. It is a great challenge to select relevant biomarkers from gene expression data that contains some important correlation structures, and some of the genes can be divided into different groups with a common biological function, chromosomal location or regulation. In this paper, we propose a penalized accelerated failure time model CHR-DE using a non-convex regularization (local search) with differential evolution (global search) in a wrapper-embedded memetic framework. The complex harmonic regularization (CHR) can approximate to the combination [Formula: see text] and ℓq (1 ≤ q < 2) for selecting biomarkers in group. And differential evolution (DE) is utilized to globally optimize the CHR's hyperparameters, which make CHR-DE achieve strong capability of selecting groups of genes in high-dimensional biological data. We also developed an efficient path seeking algorithm to optimize this penalized model. The proposed method is evaluated on synthetic and three gene expression datasets: breast cancer, hepatocellular carcinoma and colorectal cancer. The experimental results demonstrate that CHR-DE is a more effective tool for feature selection and learning prediction.

Current evidence suggests that age-associated inflammation, a strong risk factor for the health status of elderly individuals, is closely associated with gut microbiota. Previous animal studies have demonstrated a benefit of microbiota-driven therapy in decreasing low-grade chronic inflammation in elderly individuals; however, it remains controversial in clinical studies. Therefore, the present systematic review and meta-analysis were designed to assess the effects of microbiota-driven therapy on inflammatory markers in elderly individuals. PubMed, EMBASE, and the Cochrane Library were searched with no language restrictions from the inception of the database to November 11th, 2018 to identify all existing literature. We calculated pooled standard mean difference (SMD) using fixed effect model or random effect model to assess the effects of microbiota-driven therapy on elderly individuals. The methodological quality of the studies was determined according to the Cochrane Handbook. The publication bias was evaluated by funnel plot and Egger regression test. Ten randomized controlled studies, with 689 elderly individuals (347 individuals in the microbiota-driven therapy group and 342 individuals in the placebo group), were included in the analysis. Compared with placebo, microbiota-driven therapy did not decrease the levels of tumor necrosis factor-α (SMD, -0.24; 95% CI, -0.69 to 0.21; p = 0.30; I2 = 82.7%), interleukin-6 (SMD, -0.13; 95% CI, -0.74 to 0.49; p = 0.69; I2 = 90.7%) and interleukin-10 (SMD, 1.00; 95% CI, -0.15 to 2.15; p = 0.09; I2 = 96.3%). In addition, the microbiota-driven therapy also did not decrease the levels of C reactive protein (SMD, -1.28; 95% CI, -2.62 to 0.06; p = 0.06; I2 = 96.2%), interleukin-1β (SMD, -0.22; 95% CI, -0.81 to 0.37; p = 0.46; I2 = 73.8%), interleukin-8 (SMD, -0.03; 95% CI, -0.67 to 0.61; p = 0.93; I2 = 88.0%) and monocyte chemoattractant protein-1 (SMD, -0.11; 95% CI, -0.41 to 0.20; p = 0.49; I2 = 0%) when compared with placebo. No obvious publication bias was observed (p>0.05). In conclusion, the present meta-analysis of available randomized controlled studies did not suggest any significant benefit of microbiota-driven therapy in decreasing the inflammatory responses of elderly individuals.

Grassland soils represent a significant potential pool for the sequestering of atmospheric CO _2 ; however, the magnitude of this pool depends to a certain extent on the grazing management of the grassland. Whether different herbivore species and combinations (herbivore assemblage) influence the soil organic carbon (SOC) in various grasslands remains unclear. Here we have investigated the impacts of herbivore assemblages (NG, no grazing; SG, sheep grazing; CG, cattle grazing; and MG, mixed grazing of sheep and cattle) under moderate grazing intensity on a shallow layer of SOC storage across the three different grassland types (meadow steppe, typical steppe, and desert steppe) in the temperate steppes of Eastern Eurasia. We found that herbivore-species-specific effects on SOC storage depended on the grassland type. In mesic grasslands (i.e. meadow steppe and typical steppe), CG enhanced SOC storage, but there were no effects from SG. In contrast, SG in the xeric grassland (i.e. desert steppe) dramatically increased SOC storage while no effects were observed for CG. Importantly, MG of sheep and cattle consistently increased SOC storage across all grassland types. We suggest that adopting herbivore-type-specific stewardship in different grasslands could assist with the enhancement of ecosystem C functionality and services; large herbivores and small herbivores are suitable for grazing in mesic grasslands and xeric grasslands, respectively. Moreover, MG with diverse herbivores may be the optimal moderate grazing mode for soil C sequestration in most grasslands of northern China.

To improve the noise immunity of a dq frame based open-loop phase detection (OPD) under high-frequency noise grid conditions, this paper develops a detailed model to quantitatively evaluate the phase detection error and noise immunity. It is found that the OPD behaves differently in terms of noise immunity when the dq frame is in different angle positions with the grid voltage. When the grid voltage coincides with the d axis, the high-frequency noise has the smallest impact on the phase detection accuracy, and the OPD thus has the strongest noise immunity. Inspired by this conclusion, an improved OPD algorithm is proposed in this paper, which can effectively reduce the phase detection error by fine-tuning the rotation angle of the dq frame to ensure that the angle between the voltage vector and d axis is always close to 0. The improved OPD algorithm has a fast and precise character to detect the phase information with less error and is flexible for application. Under heavy noise grid conditions, it can also effectively shorten the dynamic response time in the phase-detecting process using a low-pass filter (LPF) with a higher cut-off frequency. The correctness of the noise immunity analysis and the effectiveness of the improved OPD algorithm are verified by the simulations and experimental results in MATLAB and RT-LAB.

Background Previous studies have demonstrated a possible association between the induction of coenzyme Q10 (CoQ10) after statin treatment and statin‐induced myopathy. However, whether CoQ10 supplementation ameliorates statin‐induced myopathy remains unclear. Methods and Results PubMed, EMBASE, and Cochrane Library were searched to identify randomized controlled trials investigating the effect of CoQ10 on statin‐induced myopathy. We calculated the pooled weighted mean difference (WMD) using a fixed‐effect model and a random‐effect model to assess the effects of CoQ10 supplementation on statin‐associated muscle symptoms and plasma creatine kinase. The methodological quality of the studies was determined, according to the Cochrane Handbook. Publication bias was evaluated by a funnel plot, Egger regression test, and the Begg‐Mazumdar correlation test. Twelve randomized controlled trials with a total of 575 patients were enrolled; of them, 294 patients were in the CoQ10 supplementation group and 281 were in the placebo group. Compared with placebo, CoQ10 supplementation ameliorated statin‐associated muscle symptoms, such as muscle pain (WMD, −1.60; 95% confidence interval [CI], −1.75 to −1.44; P

Whether photosynthetic pathway differences exist in the amplitude of nighttime variations in the carbon isotope composition of leaf dark-respired CO2 (δ13Cl) and respiratory apparent isotope fractionation relative to biomass (ΔR,biomass) in response to drought stress is unclear. These differences, if present, would be important for the partitioning of C3-C4 mixed ecosystem C fluxes. We measured δ13Cl, the δ13C of biomass and of potential respiratory substrates and leaf gas exchange in one C3 (Leymus chinensis) and two C4 (Chloris virgata and Hemarthria altissima) grasses during a manipulated drought period. For all studied grasses, δ13Cl decreased from 21:00 to 03:00 h. The magnitude of the nighttime shift in δ13Cl decreased with increasing drought stress. The δ13Cl values were correlated with the δ13C of respiratory substrates, whereas the magnitude of the nighttime shift in δ13Cl strongly depended on the daytime carbon assimilation rate and the range of nighttime variations in the respiratory substrate content. The ΔR,biomass in the C3 and C4 grasses varied in opposite directions with the intensification of the drought stress. The contribution of C4 plant-associated carbon flux is likely to be overestimated if carbon isotope signatures are used for the partitioning of ecosystem carbon exchange and the δ13C of biomass is used as a substitute for leaf dark-respired CO2. The detected drought sensitivities in δ13Cl and differences in respiratory apparent isotope fractionation between C3 and C4 grasses have marked implications for isotope partitioning studies at the ecosystem level.

A highly stereoselective BF3∙OEt2-promoted tandem hydroamination/glycosylation on glycal scaffolds has been developed to form propargyl 3-tosylamino-2,3-dideoxysugars in a one-pot manner. Subsequent construction of multivalent 3-tosylamino-2,3-dideoxyneoglycoconjugates with potential biochemical applications was presented herein involving click conjugations as the key reaction step. The copper-catalyzed regioselective click reaction was tremendously accelerated with assistance of microwave irradiation.

Condition monitoring of an operating apparatus is essential for lifespan assessment and maintenance planning in a power system. Electrical insulation is a critical aspect to be monitored, since it is susceptible to failure under high electrical stress. To avoid unexpected breakdowns, the level of partial discharge (PD) activity should be continuously monitored because PD occurrence can accelerate the aging process of insulation in high voltage equipment and result in catastrophic failure if the associated defects are not treated at an early stage. For on-site PD detection, the ultra-high frequency (UHF) method was employed in the field and showed its effectiveness as a detection technique. The main advantage of the UHF method is its immunity to external electromagnetic interference with a high signal-to-noise ratio, which is necessary for on-site monitoring. Considering the detection process, sensors play a critical role in capturing signals from PD sources and transmitting them onto the measurement system. In this paper, UHF sensors applied in PD detection were comprehensively reviewed. In particular, for power transformers, the effects of the physical structure on UHF signals and practical applications of UHF sensors including PD localization techniques were discussed. The aim of this review was to present state-of-the-art UHF sensors in PD detection and facilitate future improvements in the UHF method.

The quantitative structure-activity relationship (QSAR) model searches for a reliable relationship between the chemical structure and biological activities in the field of drug design and discovery. (1) Background: In the study of QSAR, the chemical structures of compounds are encoded by a substantial number of descriptors. Some redundant, noisy and irrelevant descriptors result in a side-effect for the QSAR model. Meanwhile, too many descriptors can result in overfitting or low correlation between chemical structure and biological bioactivity. (2) Methods: We use novel log-sum regularization to select quite a few descriptors that are relevant to biological activities. In addition, a coordinate descent algorithm, which uses novel univariate log-sum thresholding for updating the estimated coefficients, has been developed for the QSAR model. (3) Results: Experimental results on artificial and four QSAR datasets demonstrate that our proposed log-sum method has good performance among state-of-the-art methods. (4) Conclusions: Our proposed multiple linear regression with log-sum penalty is an effective technique for both descriptor selection and prediction of biological activity.

An experiment comparing the location accuracy of gravity matching-aided navigation in the ocean and simulation is very important to evaluate the feasibility and the performance of an INS/gravity-integrated navigation system (IGNS) in underwater navigation. Based on a 1′ × 1′ marine gravity anomaly reference map and multi-model adaptive Kalman filtering algorithm, a matching location experiment of IGNS was conducted using data obtained using marine gravimeter. The location accuracy under actual ocean conditions was 2.83 nautical miles (n miles). Several groups of simulated data of marine gravity anomalies were obtained by establishing normally distributed random error N ( u , σ 2 ) with varying mean u and noise variance σ 2 . Thereafter, the matching location of IGNS was simulated. The results show that the changes in u had little effect on the location accuracy. However, an increase in σ 2 resulted in a significant decrease in the location accuracy. A comparison between the actual ocean experiment and the simulation along the same route demonstrated the effectiveness of the proposed simulation method and quantitative analysis results. In addition, given the gravimeter (1–2 mGal accuracy) and the reference map (resolution 1′ × 1′; accuracy 3–8 mGal), location accuracy of IGNS was up to reach ~1.0–3.0 n miles in the South China Sea.