A metagene based similarity network fusion approach for multi-omics data integration identified novel subtypes in renal cell carcinoma.

Renal cell carcinoma (RCC) ranks among the most prevalent cancers worldwide, with both incidence and mortality rates increasing annually. The heterogeneity among RCC patients presents considerable challenges for developing universally effective treatment strategies, emphasizing the necessity of in-depth research into RCC's molecular mechanisms, understanding the variations among RCC patients and further identifying distinct molecular subtypes for precise treatment. We proposed a metagene-based similarity network fusion (Meta-SNF) method for RCC subtype identification with multi-omics data, using a non-negative matrix factorization technique to capture alternative structures inherent in the dataset as metagenes.

A Novel One-Sample Mendelian Randomization Approach for Count-Type Outcomes That Is Robust to Correlated and Uncorrelated Pleiotropic Effects.

We propose two novel one-sample Mendelian randomization (MR) approaches to causal inference from count-type health outcomes, tailored to both equidispersion and overdispersion conditions. Selecting valid single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) poses a key challenge for MR approaches, as it requires meeting the necessary IV assumptions. To bolster the proposed approaches by addressing violations of IV assumptions, we incorporate a process for removing invalid SNPs that violate the assumptions.

Reverse Solute Diffusion Enhances Sludge Dewatering in Dead-End Forward Osmosis.

Wastewater treatment plants produce high quantities of excess sludge. However, traditional sludge dewatering technology has high energy consumption and occupies a large area. Dead-end forward osmosis (DEFO) is an efficient and energy-saving deep dewatering technology for sludge.

Uncertainty quantification in high-dimensional linear models incorporating graphical structures with applications to gene set analysis.

Motivation: The functions of genes in networks are typically correlated due to their functional connectivity. Variable selection methods have been developed to select important genes associated with a trait while incorporating network graphical information. However, no method has been proposed to quantify the uncertainty of individual genes under such settings.

MR-SPLIT: A novel method to address selection and weak instrument bias in one-sample Mendelian randomization studies.

Mendelian Randomization (MR) is a widely embraced approach to assess causality in epidemiological studies. Two-stage least squares (2SLS) method is a predominant technique in MR analysis. However, it can lead to biased estimates when instrumental variables (IVs) are weak.

SPACE: Spatially variable gene clustering adjusting for cell type effect for improved spatial domain detection.

Recent advances in spatial transcriptomics have significantly deepened our understanding of biology. A primary focus has been identifying spatially variable genes (SVGs) which are crucial for downstream tasks like spatial domain detection. Traditional methods often use all or a set number of top SVGs for this purpose.

MET receptor tyrosine kinase promotes the generation of functional synapses in adult cortical circuits.

JOURNAL/nrgr/04.03/01300535-202505000-00026/figure1/v/2024-07-28T173839Z/r/image-tiff Loss of synapse and functional connectivity in brain circuits is associated with aging and neurodegeneration, however, few molecular mechanisms are known to intrinsically promote synaptogenesis or enhance synapse function. We have previously shown that MET receptor tyrosine kinase in the developing cortical circuits promotes dendritic growth and dendritic spine morphogenesis.

Novel binary regulated silicon-carbon materials as high-performance anodes for lithium-ion batteries.

The massive volume dilation, unsteady solid electrolyte interphase, and weak conductivity about Si have failed to bring it to practical applications, although its potential capacity is up to 4200 mAh g. For solving these problems, novel binary regulated silicon-carbon materials (Si/BPC) were done by a sol-gel procedure combined with single carbonization. Analytical techniques were systematically utilized to examine the effects of element doping at several gradients on morphology, structure and electrochemical properties of composites, thus the optimal content was identified.

BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases.

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis.

SIRT1 Coordinates Transcriptional Regulation of Neural Activity and Modulates Depression-Like Behaviors in the Nucleus Accumbens.

Background: Major depression and anxiety disorders are significant causes of disability and socioeconomic burden. Despite the prevalence and considerable impact of these affective disorders, their pathophysiology remains elusive. Thus, there is an urgent need to develop novel therapeutics for these conditions.

Loss-of-function mutation in PRMT9 causes abnormal synapse development by dysregulation of RNA alternative splicing.

Protein arginine methyltransferase 9 (PRMT9) is a recently identified member of the PRMT family, yet its biological function remains largely unknown. Here, by characterizing an intellectual disability associated PRMT9 mutation (G189R) and establishing a Prmt9 conditional knockout (cKO) mouse model, we uncover an important function of PRMT9 in neuronal development. The G189R mutation abolishes PRMT9 methyltransferase activity and reduces its protein stability.

Recent advances in spatially variable gene detection in spatial transcriptomics.

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs.

wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning.

Background: Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping.

Placebo and Nocebo Responses in Pharmacological Trials of Tic Disorders: A Meta-Analysis.

Background: Clinical trials of new drugs for tic disorders (TD) often fail to yield positive results. Placebo and nocebo responses play a vital role in interpreting the outcomes of randomized controlled trials (RCTs), yet these responses in RCTs of TD remain unexplored.

Objective: The aim was to assess the magnitude of placebo and nocebo responses in RCTs of pharmacological interventions for TD and identify influencing factors.

A SELECTIVE REVIEW OF RECENT DEVELOPMENTS IN SPATIALLY VARIABLE GENE DETECTION FOR SPATIAL TRANSCRIPTOMICS.

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs.

BayesKAT: Bayesian Optimal Kernel-based Test for genetic association studies reveals joint genetic effects in complex diseases.

GWAS methods have identified individual SNPs significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis.

Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning.

Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer diagnosis and treatment. Taking the heterogeneity of different omics data types into account, we propose a hierarchical multi-kernel learning (hMKL) approach, a novel cancer molecular subtyping method to identify cancer subtypes by adopting a two-stage kernel learning strategy.

Disrupted Maturation of Prefrontal Layer 5 Neuronal Circuits in an Alzheimer's Mouse Model of Amyloid Deposition.

Mutations in genes encoding amyloid precursor protein (APP) and presenilins (PSs) cause familial forms of Alzheimer's disease (AD), a neurodegenerative disorder strongly associated with aging. It is currently unknown whether and how AD risks affect early brain development, and to what extent subtle synaptic pathology may occur prior to overt hallmark AD pathology. Transgenic mutant APP/PS1 over-expression mouse lines are key tools for studying the molecular mechanisms of AD pathogenesis.

Multi-omics data integration for hepatocellular carcinoma subtyping with multi-kernel learning.

Hepatocellular carcinoma (HCC) is a leading malignant liver tumor with high mortality and morbidity. Patients at the same stage can be defined as different molecular subtypes associated with specific genomic disorders and clinical features. Thus, identifying subtypes is essential to realize efficient treatment and improve survival outcomes of HCC patients.

Early impairment of cortical circuit plasticity and connectivity in the 5XFAD Alzheimer's disease mouse model.

Genetic risk factors for neurodegenerative disorders, such as Alzheimer's disease (AD), are expressed throughout the life span. How these risk factors affect early brain development and function remain largely unclear. Analysis of animal models with high constructive validity for AD, such as the 5xFAD mouse model, may provide insights on potential early neurodevelopmental effects that impinge on adult brain function and age-dependent degeneration.