Explore the vast range of titles available.

Recent advances in single-cell biotechnologies have resulted in high-dimensional datasets with increased complexity, making feature selection an essential technique for single-cell data analysis. Here, we revisit feature selection techniques and summarise recent developments. We review their application to a range of single-cell data types generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions and finally consider their scalability and make general recommendations on each type of feature selection method. We hope this review stimulates future research and application of feature selection in the single-cell era.

Image phase from gradient echo MRI provides a unique contrast that reflects brain tissue composition variations, such as iron and myelin distribution. Phase imaging is emerging as a powerful tool for the investigation of functional brain anatomy and disease diagnosis. However, the quantitative value of phase is compromised by its nonlocal and orientation dependent properties. There is an increasing need for reliable quantification of magnetic susceptibility, the intrinsic property of tissue. In this study, we developed a novel and accurate susceptibility mapping method that is also phase-wrap insensitive. The proposed susceptibility mapping method utilized two complementary equations: (1) the Fourier relationship of phase and magnetic susceptibility; and (2) the first-order partial derivative of the first equation in the spatial frequency domain. In numerical simulation, this method reconstructed the susceptibility map almost free of streaking artifact. Further, the iterative implementation of this method allowed for high quality reconstruction of susceptibility maps of human brain in vivo. The reconstructed susceptibility map provided excellent contrast of iron-rich deep nuclei and white matter bundles from surrounding tissues. Further, it also revealed anisotropic magnetic susceptibility in brain white matter. Hence, the proposed susceptibility mapping method may provide a powerful tool for the study of brain physiology and pathophysiology. Further elucidation of anisotropic magnetic susceptibility in vivo may allow us to gain more insight into the white matter micro-architectures. ► Phase wrap insensitive removal of background phase ► High quality reconstruction of magnetic susceptibility map of human brain ► Susceptibility map shows excellent delineation of iron-rich deep nuclei ► Susceptibility map exhibits good contrast between gray and white matter ► Susceptibility of white matter is dependent on its underlying microstructure

As a vital mode in which teachers can participate in university management, voice behavior is an important way of enhancing the efficiency of organizational decision-making, promoting democratic management, and facilitating sustainable development in universities. Although previous studies have confirmed the positive impact of inclusive leadership on employees' voice behavior, the mechanism underlying this effect remains unclear. Therefore, based on the cognitive-affective system theory of personality, this study aims to examine the mediating effects of psychological empowerment and organizational identification on the relationship between inclusive leadership and voice behavior among university teachers. A total of 517 valid questionnaires were administered to university teachers in mainland China using a convenience sampling approach. Structural equation modeling and bootstrap testing were used to analyze the data, and the results reveal that inclusive leadership is positively related to teachers’ promotive and prohibitive voice behavior. This relationship is mediated by psychological empowerment and organizational identification, in which context a partial mediating effect is observed in the relationship between inclusive leadership and promotive voice and a full mediating effect is observed in the relationship between inclusive leadership and prohibitive voice. These findings can enrich the extant research on the impact of inclusive leadership in the field of higher education to a certain extent. Moreover, they provide a new perspective that can support an in-depth analysis of the mechanism underlying the effect of inclusive leadership and generate valuable practical insights into ways of stimulating voice behavior among university teachers.

•A method named DECOMPOSE-QSM is developed to decompose sub-voxel paramagnetic and diamagnetic susceptibilities only use multi-echo GRE data.•The method is validated with numerical simulations, phantom, ex vivo and in vivo experiments.•The resulting susceptibility composition maps reveal more detailed subregion structures than conventional QSM.•The proposed method may be applied to various susceptibility-based studies. A method named DECOMPOSE-QSM is developed to decompose bulk susceptibility measured with QSM into sub-voxel paramagnetic and diamagnetic components based on a three-pool complex signal model. Multi-echo gradient echo signal is modeled as a summation of three weighted exponentials corresponding to three types of susceptibility sources: reference susceptibility, diamagnetic and paramagnetic susceptibility relative to the reference. Paramagnetic component susceptibility (PCS) and diamagnetic component susceptibility (DCS) maps are constructed to represent the sub-voxel compartments by solving for linear and nonlinear parameters in the model. Numerical forward simulation and phantom validation confirmed the ability of DECOMPOSE-QSM to separate the mixture of paramagnetic and diamagnetic components. The PCS obtained from temperature-variant brainstem imaging follows the Curie's Law, which further validated the model and the solver. Initial in vivo investigation of human brain images showed the ability to extract sub-voxel PCS and DCS sources that produce visually enhanced contrast between brain structures comparing to threshold QSM.

Quantitative susceptibility mapping (QSM) is a novel MRI method for quantifying tissue magnetic property. In the brain, it reflects the molecular composition and microstructure of the local tissue. However, susceptibility maps reconstructed from single-orientation data still suffer from streaking artifacts which obscure structural details and small lesions. We propose and have developed a general method for estimating streaking artifacts and subtracting them from susceptibility maps. Specifically, this method uses a sparse linear equation and least-squares (LSQR)-algorithm-based method to derive an initial estimation of magnetic susceptibility, a fast quantitative susceptibility mapping method to estimate the susceptibility boundaries, and an iterative approach to estimate the susceptibility artifact from ill-conditioned k-space regions only. With a fixed set of parameters for the initial susceptibility estimation and subsequent streaking artifact estimation and removal, the method provides an unbiased estimate of tissue susceptibility with negligible streaking artifacts, as compared to multi-orientation QSM reconstruction. This method allows for improved delineation of white matter lesions in patients with multiple sclerosis and small structures of the human brain with excellent anatomical details. The proposed methodology can be extended to other existing QSM algorithms. •This method provides an unbiased quantification of magnetic susceptibility.•This method improves assessment of white matter lesions in multiple sclerosis.•This method allows for delineation of small gray matter structures in excellent detail.

Beta amyloid is a protein fragment snipped from the amyloid precursor protein (APP). Aggregation of these peptides into amyloid plaques is one of the hallmarks of Alzheimer's disease. MR imaging of beta amyloid plaques has been attempted using various techniques, notably with T2* contrast. The non-invasive detectability of beta amyloid plaques in MR images has so far been largely attributed to focal iron deposition accompanying the plaques. It is believed that the T2* shortening effects of paramagnetic iron are the primary source of contrast between plaques and surrounding tissue. Amyloid plaque itself has been reported to induce no magnetic susceptibility effect. We hypothesized that aggregations of beta amyloid would increase electron density and induce notable changes in local susceptibility value, large enough to generate contrast relative to surrounding normal tissues that can be visualized by quantitative susceptibility mapping (QSM) MR imaging. To test this hypothesis, we first demonstrated in a phantom that beta amyloid is diamagnetic and can generate strong contrast on susceptibility maps. We then conducted experiments on a transgenic mouse model of Alzheimer's disease that is known to mimic the formation of human beta amyloid but without neurofibrillary tangles or neuronal death. Over a period of 18 months, we showed that QSM can be used to longitudinally monitor beta amyloid accumulation and accompanied iron deposition in vivo. Individual beta amyloid plaque can also be visualized ex vivo in high resolution susceptibility maps. Moreover, the measured negative susceptibility map and positive susceptibility map could provide histology-like image contrast for identifying deposition of beta amyloid plaques and iron. Finally, we demonstrated that the diamagnetic susceptibility of beta amyloid can also be observed in brain specimens of AD patients. The ability to assess beta amyloid aggregation non-invasively with QSM MR imaging may aid the diagnosis of Alzheimer's disease. •We demonstrated in a phantom experiment that beta amyloid has diamagnetic susceptibility contrary to previous hypothesis.•This diamagnetic susceptibility can be measured and used to monitor longitudinal accumulation of beta amyloid in a mouse model and even visualize individual plaques.•The diamagnetic susceptibility map provided image contrast for identifying dominating magnetic sources of beta amyloid plaques, which were validated by histology.•The ability to image and quantify beta amyloid aggregation non-invasively with MRI may aid the diagnosis of Alzheimer’s disease.

Background Feature selection is an essential task in single-cell RNA-seq (scRNA-seq) data analysis and can be critical for gene dimension reduction and downstream analyses, such as gene marker identification and cell type classification. Most popular methods for feature selection from scRNA-seq data are based on the concept of differential distribution wherein a statistical model is used to detect changes in gene expression among cell types. Recent development of deep learning-based feature selection methods provides an alternative approach compared to traditional differential distribution-based methods in that the importance of a gene is determined by neural networks. Results In this work, we explore the utility of various deep learning-based feature selection methods for scRNA-seq data analysis. We sample from Tabula Muris and Tabula Sapiens atlases to create scRNA-seq datasets with a range of data properties and evaluate the performance of traditional and deep learning-based feature selection methods for cell type classification, feature selection reproducibility and diversity, and computational time. Conclusions Our study provides a reference for future development and application of deep learning-based feature selection methods for single-cell omics data analyses.

Global warming is expected to enhance fluxes of fresh water between the surface and atmosphere, causing wet regions to become wetter and dry regions drier, with serious implications for water resource management. Defining the wet and dry regions as the upper 30% and lower 70% of the precipitation totals across the tropics (30° S-30° N) each month we combine observations and climate model simulations to understand changes in the wet and dry regions over the period 1850-2100. Observed decreases in precipitation over dry tropical land (1950-2010) are also simulated by coupled atmosphere-ocean climate models (−0.3% decade) with trends projected to continue into the 21st century. Discrepancies between observations and simulations over wet land regions since 1950 exist, relating to decadal fluctuations in El Niño southern oscillation, the timing of which is not represented by the coupled simulations. When atmosphere-only simulations are instead driven by observed sea surface temperature they are able to adequately represent this variability over land. Global distributions of precipitation trends are dominated by spatial changes in atmospheric circulation. However, the tendency for already wet regions to become wetter (precipitation increases with warming by 3% K−1 over wet tropical oceans) and the driest regions drier (precipitation decreases of −2% K−1 over dry tropical land regions) emerges over the 21st century in response to the substantial surface warming.

Interferon-Induced Protein with Tetratricopeptide Repeats 3 (IFIT3) plays a dual role in innate immunity and tumor immunity, functioning as both a viral defense molecule and a regulator of tumor progression. This review explores the mechanisms through which IFIT3 modulates immune responses, including interferon signaling, RIG-I-like receptors, and the NF-κB pathway. IFIT3 facilitates immune evasion and promotes inflammation-mediated tumor growth by regulating immune checkpoints and the tumor microenvironment, its emerging role as a target for cancer immunotherapy opens new avenues for therapeutic strategies. Finally, this paper underscores IFIT3’s potential clinical applications in the modulation of tumor immunity, highlighting the need for further research on IFIT3-targeted therapies.

With the increasingly intense academic competition, the stressors faced by doctoral students are gradually escalating. Based on the Job Demands-Resources Model, this study proposed a moderated moderation to explore how hindrance research stressors affect doctoral students’ research creativity. Explicitly, the present study investigates whether the relationship between hindrance research stressors and research creativity is contingent on inclusive mentoring style and academic resilience. By analyzing the survey data from 538 valid questionnaire responses of doctoral students in China, this study has revealed that hindrance research stressors negatively relate to doctoral students’ research creativity, and inclusive mentoring style from academic supervisors can mitigate the negative impact of hindrance research stressors on the research creativity of doctoral students. Furthermore, academic resilience strengthens the moderating effect of inclusive mentoring style. Specifically, it buffers the negative impact of hindrance research stressors on research creativity among doctoral students who receive high inclusive mentoring, but not among those with low levels of inclusive mentoring. These findings emphasize that effective strategies to enhance the research creativity of doctoral students who encounter hindrance stressors may require the joint consideration of contextual and personal resources.