Browse Articles

Transcriptomic signatures in brain and blood related to cognitive and psychiatric phenotypes of Prader–Willi syndrome

Abstract This study defined gene expression changes across different cell types in prefrontal cortex (PFC) of donors with Prader-Willi syndrome (PWS) compared to controls and examined relationships between these changes in blood and PWS symptoms. 8,338 long non-coding RNAs and 17,079 protein-coding genes were examined using single-nucleus RNA-sequencing (snRNA-seq) in the PFC of 8 donors with PWS (4 deletion, 4 non-deletion), and 4 age- and sex-matched neurotypical controls. snRNA-seq analyses showed an increased proportion of interneurons in both PWS groups compared to controls. Fifty-four genes and related pathways were consistently dysregulated across all cell types in the PFC of the PWS group compared to controls, with RPS18 being the only protein-coding gene upregulated in PWS PFC across all comparisons. Increase in RPS18 mRNA levels in peripheral blood mononuclear cells (PBMCs) of another cohort (16 deletion, 20 non-deletion, ages 1–45 years) assessed using droplet digital PCR was found to be associated with intellectual functioning and challenging behaviors, but not autistic traits in children with PWS due to non-deletion (< 13 years old; N  = 19). If confirmed in future studies, these findings may lead to development of prognostic biomarkers and therapeutics targeting consistently dysregulated genes and related pathways between brain and periphery.

Structural Insights into Recognition and Translocation of Oxidized Phospholipid by CD36 Using Mass Spectrometry, Molecular Docking, Dynamics, and Metadynamics Simulations

Dynamic adaptive synergistic attention network for visible-infrared person re-identification

Electrochemical CO ₂ Capture by a Quinone-Based Covalent Organic Framework

Enhancing groundwater level prediction with a hybrid deep learning model in Jinan City, China

Abstract Accurate prediction of groundwater levels (GWL) is critical for sustainable utilization and scientific management of groundwater resources. However, precise forecasting of GWL fluctuations faces significant challenges due to the complex nonlinear coupling effects of hydrogeological conditions and hydro-meteorological factors. In recent years, research on GWL prediction based on deep learning models has become a cutting-edge topic in the field of hydrogeology. This study focused on Jinan City, China, and constructed a novel hybrid deep learning model that integrates graph neural networks to capture spatial relationships and recurrent neural networks to model temporal dynamics, effectively learning the complex spatio-temporal patterns in the data, namely the Spatio-Temporal Graph Prediction Model (STGPM). Our approach uniquely captures both hydrological connectivity between monitoring wells and multi-scale temporal dependencies, overcoming key limitations of conventional time-series models. Comparative experiments demonstrate that STGPM outperforms the benchmark models on the test set, achieving the lowest prediction errors (MAE = 0.039, RMSE = 0.052) and the highest coefficient of determination (R 2 =0.988). Notably, for the monitoring well data not involved in model training, the STGPM still maintains excellent predictive accuracy (MAE = 0.062, RMSE = 0.087, R 2 =0.980), demonstrating the model’s strong generalization ability to unmonitored locations. This study provides water resource managers with a reliable decision-support tool for sustainable groundwater management and spring conservation strategies. The proposed methodological framework also offers a transferable solution for addressing various environmental forecasting challenges characterized by spatial heterogeneity.

Extraordinary Tolerance of the Cogwheel Mechanism of Helical Self-Organization to Structural Defects

Theoretical, biological, and electrochemical explanation of the complexity of Peramivir’s drug–DNA interaction

Control of Antibody Orientation on Graphene Using Porphyrin Linker Molecules for High-Performance Graphene-Based Immuno-Biosensors

Equity of total health expenditure in China, 2015–2021

Effect of Fluoride Anions on Nd(III) Electrode Processes and Nd Metal Recovery in LiCl–KCl–NdCl ₃

Visualizing the dynamic polymerization of the bacterial actin-like cytoskeleton for magnetic organelle positioning

Contact with Electrically Conductive Inert Solids Alters Intrinsic Heterogeneous Brønsted Acid Catalysis

Investigating the potential selective effects of a Kiperin multi-component magnesium supplement on colon cancer and normal cells

Abstract Magnesium is vital for numerous cellular processes and has demonstrated therapeutic potential in various physiological and pathological contexts. Colorectal cancer remains a major cause of cancer-related mortality worldwide. This study evaluates the effects of a multi-component magnesium supplement on human colon fibroblasts (CCD-18Co) and colon cancer cells (HCT-116). CCD-18Co and HCT-116 cells were treated with 2.5 μL dose of the Kiperin multi-component magnesium supplement. Cell viability was assessed via cell counting, and migration was analyzed using wound healing assays. Oxidative stress was evaluated by measuring total oxidant status (TOS) and total antioxidant status (TAS). Gene expression levels of VDR and TNFα genes were measured by Real-Time PCR. Magnesium complex supplementation significantly reduced HCT-116 viability ( p  < 0.001) while enhancing CCD-18Co proliferation ( p  < 0.01). Cell migration increased in CCD-18Co cells at 18 h ( p  < 0.05), while HCT-116 migration decreased at 6 h ( p  < 0.05) and increased at 18 h. TOS levels were significantly decreased in both cell lines, indicating reduced oxidative stress. Additionally, magnesium supplementation upregulated VDR expression and downregulated TNFα in CCD-18Co cells. Similarly, in HCT-116 colon cancer cells, VDR expression was significantly increased, whereas TNFα expression showed no statistically significant change. Magnesium supplement may exert selective effects by inhibiting colon cancer cell survival while supporting proliferation and anti-inflammatory signaling in normal colon cells. These findings highlight its potential utility in colon cancer prevention and gut health.

Hydrogen Spillover-Mediated Spatial Decoupling Process Boosts Syngas Conversion to Higher Oxygenates

Structural, optical and dielectric properties of Cobalt incorporated Zinc oxide nanostructures: doping effect of Cobalt

Interfacial Water Frustration for Nitrate Semireduction to Hydroxylamine at Industrial-Relevant Currents

Targeted exercise improves functional movement performance and alters movement screening relationships in females with flexible flatfeet

Aryne Generation from Aryl Halides Via Photothermal Red-Light Activation

Synergetic effect of a DSP4-induced locus coeruleus lesion and systemic LPS exacerbates substantia nigra dopaminergic neuron loss

Abstract Parkinson’s disease (PD) involves progressive degeneration of dopaminergic neurons in the substantia nigra (SN) and noradrenergic neurons in the locus coeruleus (LC). Noradrenaline (NA), produced by LC neurons, has anti-inflammatory properties and seems to support SN dopaminergic neuron survival. This study used a dual-hit approach to examine how noradrenergic loss affects SN vulnerability to induced inflammation in Sprague-Dawley rats. LC denervation was produced with [N-(2-chloroethyl)-N-ethyl-2-bromobenzylamine] (DSP4), a selective LC noradrenergic neurotoxin, and a systemic inflammatory challenge was induced with a subthreshold dose of lipopolysaccharide (LPS). Behavioral assessments were performed, and neuronal populations were quantified by unbiased stereology. DSP4 or LPS alone reduced tyrosine hydroxylase-positive neurons in both LC and SN, with the DSP4-induced SN loss linked to NA depletion. DSP4 and LPS co-treatment produced greater dopaminergic degeneration in the substantia nigra than either treatment alone, suggesting an additive effect. Furthermore, immunoanalytical analysis using ELISA demonstrated altered cytokine levels in both SN and LC. Taken together, the results support a dual-hit theory, in which additive insults act to push the system toward a tipping point, beyond which SN dopaminergic cell loss becomes extensive.

A multistream attention based neural network for visual speech recognition and sign language understanding

Abstract This paper introduces SignKeyNet, a novel multi-stream keypoint-based neural network designed to enhance lip-reading recognition and support foundational sign language understanding. The architecture decouples signer movements into three primary streams hands, face, and body using 133 pose keypoints extracted via pose estimation techniques. Each stream is processed independently using specialized attention modules, followed by an attention-based fusion mechanism that models cross-modal spatiotemporal dependencies. SignKeyNet is evaluated on the MIRACL-VC1 lip-reading dataset, achieving superior performance over baseline models such as HMMs, DTW, CNNs, LSTMs, and Two-stream ConvNets, with results including an accuracy of 0.85, a Word Error Rate (WER) of 0.12, and a Character Error Rate (CER) of 0.06. These results highlight the effectiveness of attention-driven, multi-modal architectures for visual speech recognition tasks. While the current evaluation focuses on lip-reading due to dataset constraints, the proposed architecture is extendable to full Sign Language Translation (SLT) systems. SignKeyNet demonstrates strong potential for real-time deployment in accessibility technologies, particularly for the deaf and hard-of-hearing communities.