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Mediator of IRF3 activation (MITA, also named as STING/ERIS/MPYS/TMEM173), is essential to DNA virus- or cytosolic DNA-triggered innate immune responses. In this study, we demonstrated the negative regulatory role of RING-finger protein (RNF) 90 in innate immune responses targeting MITA. RNF90 promoted K48-linked ubiquitination of MITA and its proteasome-dependent degradation. Overexpression of RNF90 inhibited HSV-1- or cytosolic DNA-induced immune responses whereas RNF90 knockdown had the opposite effects. Moreover, RNF90-deficient bone marrow-derived dendritic cells (BMDCs), bone marrow-derived macrophages (BMMs) and mouse embryonic fibroblasts (MEFs) exhibited increased DNA virus- or cytosolic DNA-triggered signaling and RNF90 deficiency protected mice from DNA virus infection. Taken together, our findings suggested a novel function of RNF90 in innate immunity.

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10 −8 ). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine . The biological basis of brain aging is not well understood, but it has implications for human health. Here, the authors explore the genetic basis of human brain aging, finding genetic variants, genes and potential causal relationships with disease.

To address the severe surface imperfections induced during ultrafast pulsed laser fabrication of fused silica microfluidic chips, a high-precision CO2 laser polishing strategy based on shallow-layer melting and reflow was employed. This method enables localized melting within an extremely thin surface layer, effectively smoothing the topography without altering the original microstructure geometry. An L9(33) orthogonal experimental design was conducted to systematically investigate the influence of key parameters on polishing quality, identifying defocus distance as the dominant factor affecting surface roughness, followed by scanning speed and laser power. The optimal parameter combination was determined to be a laser power of 8 W, a defocus distance of 6 mm, and a scanning speed of 5 mm/s. Furthermore, an overlap rate between 38% and 63% was found to ensure sufficient fusion without excessive remelting, with the minimum surface roughness of 0.157 µm achieved at a 50% overlap rate. Based on the optimized parameters, adaptive scanning paths were designed for different functional units of a fused silica microfluidic chip. Surface characterization demonstrated that the surface roughness was remarkably reduced from 303 nm to 0.33 nm, meeting optical-grade surface quality requirements.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.

Hydrophilic colloids are ideal materials for preparing edible films; however, their intrinsic hydrophilicity leads to poor hydrophobicity in the resulting films. Emulsion-based films can significantly improve the hydrophobicity of films made from hydrophilic colloids, but this approach tends to disrupt intermolecular interactions within the film matrix. Phenolic compounds can compensate for this drawback by promoting crosslinking among film-forming polymers. In this study, hemp seed protein was used as the film-forming matrix, and rose essential oil was incorporated to prepare emulsion-based films. Different amounts of propolis flavonoids were added to investigate their effects on the physicochemical properties of the films. The results show that the addition of propolis flavonoids significantly reduced film whiteness (9%–45%), thickness (6%–37%), light transmittance (9%–60%), water vapor transmission rate (34%–65%), and peroxide value (25%–76%) of oil, while increasing tensile strength (15%–149%), elongation at break (24%–95%), Young’s modulus (26%–140%), surface hydrophobicity, thermal stability, and antioxidant and antimicrobial activities. Furthermore, pork wrapped with flavonoid-containing films exhibited inhibition of microbial growth, lipid oxidation, protein degradation, and maintained firmness. Therefore, propolis flavonoids represent a potential active ingredient for improving the physicochemical properties and preservative performance of emulsion-based films.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN – a multi-view, weakly-supervised deep clustering method – which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer’s disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes. Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.

The degradation of fluoroquinolones (FQs) via advanced oxidation processes (AOPs) is a promising avenue, yet the complete mineralization of certain FQ molecules remains elusive, raising concerns about the formation of toxic by-products. This study delineates five primary AOP degradation pathways for 16 commercially available FQ molecules, inferred from existing literature. Density functional theory (DFT) was employed to calculate the bond dissociation energies within these pathways to elucidate the correlation between bond strength and molecular architecture. Subsequently, Comparative Molecular Similarity Index Analysis (CoMSIA) models were constructed for various degradation reactions, including piperazine ring cleavage, defluorination, hydroxylation, and piperazine ring hydroxylation. Three-dimensional contour maps generated from these models provide a deeper understanding of the interplay between FQ molecular structure and bond dissociation energy. Furthermore, toxicity predictions for 16 FQ molecules and their advanced oxidation intermediates, conducted using VEGA 1.2.3 software, indicate that degradation products from pathways P2 and P5 pose a heightened health risk relative to their parent compounds. Furthermore, the application of the Multwfn program to compute the Fukui function for FQ molecules discerns the disparity in degradation propensities, highlighting that N atoms with higher f0 values can augment the likelihood of piperazine ring cleavage. HOMO-LUMO distribution diagrams further confirm that methoxy substitution at the 1-position leads to a dilution of HOMOs on the piperazine ring and an increased energy gap for free radical reactions, diminishing the reactivity with hydroxyl radicals. This study elucidates the pivotal role of structural characteristics in FQ antibiotics for their degradation efficiency within AOPs and unveils the underlying mechanisms of bond dissociation energy disparities. The toxicity parameter predictions for FQ molecules and their intermediates offer unique perspectives and theoretical underpinnings for mitigating the use of high-risk FQs and for devising targeted degradation strategies to circumvent the generation of toxic intermediates in AOPs through molecular structure optimization.

Nickel molybdate, which has a relatively high theoretical capacity, demonstrates potential for use in supercapacitors. However, its inferior electrical conductivity and cycling stability have led to poor electrochemical performance. Nanostructure engineering of NiMoO4 is an efficient strategy to overcome its performance limitations as an electrode. Here, a facile approach is reported for the precise phase regulation and nanostructure of NiMoO4 by manipulating the synthesis parameters, including duration, precursor selection, and urea concentration. The electrochemical properties of the electrode materials are also investigated. It is interesting to note that the β-NiMoO4 nanosheets show a decent specific capacity of 332.8 C/g at 1 A/g, surpassing the 252.6 C/g of the α-NiMoO4 nanorods. Furthermore, the supercapacitor device constructed with β-NiMoO4 and reduced graphene oxide hydrogel (rGH) electrodes achieves an acceptable energy density of 36.1 Wh kg−1, while retaining 70.2% after 5000 cycles.

ObjectiveTo investigate the quality standards and anti-proliferative effects of Gardenia jasminoides J.Ellis (GJE) at different harvesting periods, and to determine the optimal harvesting time for anti-cancer applications.MethodsA fast, simple, and accurate analytical method was established using UFLC/Q-TOF-MS technology in positive and negative ion modes to identify differential components in GJE samples collected at seven harvesting periods. The anti-proliferative effects of GJE extracts were evaluated against MCF7 breast cancer and HepG2 hepatocellular carcinoma cell lines using MTT assay.ResultsA total of 52 components were successfully identified from GJE samples, including iridoid glycosides, flavonoids, and phenolic acids. GJE demonstrated stronger anti-proliferative effects against HepG2 cells compared to MCF7 cells. The anti-proliferative activity increased progressively with fruit maturation, reaching peak efficacy at stages 5–6 (orange-red to red fruits) and declining at stage 7 (overripe dark red fruits). Most iridoid glycosides, flavonoids and phenolic acids showed increasing trends during the growth process, correlating with enhanced anti-proliferative effects.ConclusionGJE may serve as a potential source for developing anti-cancer therapeutic agents due to its demonstrated anti-proliferative activity. The mature fruits at stages 5–6 are most suitable for anti-cancer applications, providing scientific guidance for optimal harvesting time and quality standardization of GJE as a medicinal material.

Saw wood cracks are defects that affect the appearance and mechanical strength of sawn wood. Crack defects in the surface of sawn wood can be readily detected. Decisions regarding the presence and severity of such defects can affect the utilization rate of sawn timber. Due to the heavy workload, low efficiency, and low accuracy of manual inspection, traditional machine learning methods have strong specialization, complex methods, and high costs. By studying the semantic segmentation model of surface crack defects in sawn timber based on deep learning, the optimal model for segmentation and detection of surface cracks in sawn timber was established. The improved Attention U-Net model encoding stage was introduced into CBAM, and AdamW optimization was used instead of SGD and Adam to achieve better crack semantic segmentation results. The ECA module was introduced in the skip connection part, and the weighted fusion multi loss function was used instead of the original cross entropy loss function. The positions of the two modules were replaced to improve the accuracy of semantic segmentation of surface cracks in sawn timber. Through comparative experiments, the improved model also achieved higher scores in semantic segmentation indicators for surface cracks in sawn timber compared to other models.