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Background As RNA-seq becomes the assay of choice for measuring gene expression levels, differential expression analysis has received extensive attentions of researchers. To date, for the evaluation of DE methods, most attention has been paid on validity. Yet another important aspect of DE methods, stability, is overlooked and has not been studied to the best of our knowledge. Results In this study, we empirically show the need of assessing stability of DE methods and propose a stability metric, called Area Under the Correlation curve (AUCOR), that generates the perturbed datasets by a mixture distribution and combines the information of similarities between sets of selected features from these perturbed datasets and the original dataset. Conclusion Empirical results support that AUCOR can effectively rank the DE methods in terms of stability for given RNA-seq datasets. In addition, we explore how biological or technical factors from experiments and data analysis affect the stability of DE methods. AUCOR is implemented in the open-source R package AUCOR, with source code freely available at https://github.com/linbingqing/stableDE .

While single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression in individual cells, its unique characteristics like over-dispersion, zero-inflation, high gene-gene correlation, and large data volume with many features pose challenges for most existing feature selection methods. In this paper, we present a feature selection method based on neural network (scFSNN) to solve classification problem for the scRNA-seq data. scFSNN is an embedded method that can automatically select features (genes) during model training, control the false discovery rate of selected features and adaptively determine the number of features to be eliminated. Extensive simulation and real data studies demonstrate its excellent feature selection ability and predictive performance.

Background High-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, normalization serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. Results In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and by using the hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. Conclusions Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named “SCBN”, which is freely available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html .

We propose a kernel density based estimation for multiplicative linear regression models. The method proposed in this article makes use of kernel smoothing nonparametric techniques to estimate the unknown density function of model error. For the hypothesis testing of parametric components, restricted estimators under the null hypothesis and test statistics are proposed. The asymptotic properties for the estimators and test statistics are established. We illustrate our proposals through simulations and an analysis of the QSAR fish bioconcentration factor data set. Our analysis provides strong evidence that the proposed kernel density based estimator is superior than the least squares estimator and least product relative error estimator in the literature, particularly for multimodal or asymmetric or heavy-tailed distributions of the model error.

This study aims to enhance the aquatic eco-functional zoning by incorporating the spatial variability of hydrological processes during the zoning process. We propose a method for watershed eco-functional zoning based on distributed hydrological modeling. Using the Jinjiang Basin in Southeast China as a case study, we applied the Soil Water and Assessment Tool (SWAT) model to delineate the basic zoning units and simulate their hydrological processes. We integrated natural environmental indicators—specifically topography, vegetation, meteorology, and hydrology—with land use as a measure of human activity, while accounting for their spatial variability. This approach enabled us to conduct both first-level and second-level eco-functional zoning of the watershed. The results indicated that (1) the Jinjiang Basin can be categorized into three main groups consisting of six first-level aquatic ecological zones, which reflect the spatial variability of terrestrial natural environmental factors and their influence on aquatic ecosystems; (2) building on this categorization, the first-level aquatic ecological regions were further divided into five categories comprising 18 second-level aquatic ecological functional zones, emphasizing the impact of human activities on aquatic ecosystems and their associated first-level ecological service functions; and (3) the application of hydrological simulation techniques allows for a comprehensive assessment of the spatial variability of hydrological processes, thereby enhancing the validity of the ecological function zoning results and providing robust technical support for watershed ecological function zoning.

Accelerated repair of Achilles tendon rupture and prevention of re-rupture continue to pose significant technical challenges in orthopedic surgery and rehabilitation. Extracellular vesicles (EVs) derived from bone marrow mesenchymal stem cells exhibit substantial therapeutic potential for various degenerative diseases and tissue regeneration. However, the use of EVs alone for repairing ruptured Achilles tendons requires multiple invasive administrations, such as repeated injections, to maintain a therapeutic effect, which increases patient discomfort and the risk of infection. In this study, we innovatively combined EVs with sodium alginate-based piezoelectric hydrogel (SPH) to develop SPH-EVs. By leveraging the slow degradation of SPH in vivo, SPH-EVs enable sustained-release of EVs while generating electrical stimulation, ensuring that an effective therapeutic concentration is maintained at the Achilles tendon fracture site. Additionally, the integrated near-field communication (NFC) module within SPH-EVs allows for real-time monitoring of rehabilitation exercise intensity in the affected area, guiding patients to conduct rehabilitation training within a safe range and minimizing the risk of re-rupture. Graphical Abstract

The Soil and Water Assessment Tool (SWAT) has been widely applied to simulate the hydrological cycle, investigate cause-and-effect relationships, and aid decision-making for better watershed management. However, the software tools for model dataset analysis and visualization to support informed decision-making in a web environment are not considered fully fledged and are technically intensive to implement. This study focuses on addressing these issues by establishing a tool and library (named PAVLIB4SWAT) that can largely reduce technical expertise requirements for developers to adopt and customize this work to their own demands. Specifically, we created PAVLIB4SWAT based on a Kepler.gl widget to visualize SWAT model data, including shapefiles from the watershed delineation process, model inputs, and simulated results via dynamic and interactive maps. We evaluated PAVLIB4SWAT through a Jinjiang watershed SWAT model use case to demonstrate its utility and ease of adoption. The case study shows that PAVLIB4SWAT can provide various geospatial analysis and mapping functionalities for SWAT models and can flexibly distribute visualized results as standalone offline web pages and web servers. In addition, PAVLIB4SWAT was designed as an open-source project and implemented purely in the Python programming language; thus, developers can easily adapt and customize it to suit their demands.

Whitmania pigra Whitman (W. pigra) has been widely employed in decoction for the treatment of blood stasis syndrome for many years in China. The aim of the present study was to explore the anti-venous thrombosis (VT) mechanism of the aqueous extract of W. pigra (AEW) in rats. Rats were orally administered with AEW. A inferior vena cava (IVC) thrombosis model was established. Thrombosed IVC was weighed and histopathological analyses were performed. Blood coagulation, blood fibrinolysis, blood cell count, blood viscosity and platelet activity were evaluated. Reactive oxygen species (ROS) accumulation was analyzed. Malondialdehyde (MDA) content in thrombosed IVC and antioxidants in serum were detected. Protein expression of nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase 1 (HO-1) in thrombosed IVC was determined. AEW significantly reduced thrombus weight. It did not affect blood coagulation, blood fibrinolysis, blood cell count, platelet activity, or whole blood viscosity. However, AEW remarkably alleviated vascular injury, reduced ROS accumulation and MDA content, enhanced the total antioxidant capacity and the activities of superoxide dismutase, glutathione peroxidase and glutathione reductase. It increased the glutathione/oxidized glutathione ratio and the protein expression levels of Nrf2 and HO-1. In summary, W. pigra may prevent VT via Nrf2-mediated antioxidation.

We propose a two-stage model selection procedure for the linear mixed-effects models. The procedure consists of two steps: First, penalized restricted log-likelihood is used to select the random effects, and this is done by adopting a Newton-type algorithm. Next, the penalized log-likelihood is used to select the fixed effects via pathwise coordinate optimization to improve the computation efficiency. We prove that our procedure has the oracle properties. Both simulation studies and a real data example are carried out to examine finite sample performance of the proposed fixed and random effects selection procedure. Supplementary materials including R code used in this article and proofs for the theorems are available online.

Uncertainty analysis plays a pivotal role in identifying the important parameters affecting building energy consumption and estimate their effects at the early design stages. In this work, we consider the adaptive Lasso for uncertainty analysis in building performance simulation. This procedure has several appealing features: (1) We can introduce a large number of possible physical and environmental parameters at the initial stage to obtain a more complete picture of the building energy consumption. (2) The procedure could automatically select parameters and estimate influences simultaneously and no prior knowledge is required. (3) Due to computational efficiency of the procedure, non-linear relationship between the building performance and the input parameters could be accommodated. (4) The proposed adaptive Lasso can use a small number of samples to achieve high modeling accuracy and further reduce the huge computational cost of running building energy simulation programs. Furthermore, we propose a stable algorithm to rank input parameters to better identify important input parameters that affect energy consumption. A case study shows the superior performance of the procedure compared with LS and OMP in terms of modeling accuracy and computational cost.