RPI-GGCN: Prediction of RNA-Protein Interaction Based on Interpretability Gated Graph Convolution Neural Network and Co-Regularized Variational Autoencoders.

RNA-protein interactions (RPIs) play an important role in several fundamental cellular physiological processes, including cell motility, chromosome replication, transcription and translation, and signaling. Predicting RPI can guide the exploration of cellular biological functions, intervening in diseases, and designing drugs. Given this, this study proposes the RPI-gated graph convolutional network (RPI-GGCN) method for predicting RPI based on the gated graph convolutional neural network (GGCN) and co-regularized variational autoencoder (Co-VAE). First, different types of feature information were extracted from RNA and protein sequences by nine feature extraction methods. Second, Co-VAEs are used to eliminate the redundancy of fused features and generate optimal features. Finally, this study introduces gated cyclic units into graph convolutional networks (GCNs) to construct a model for RPI prediction, which efficiently extracts topological information and improves the model's interpretable feature learning and expression capabilities. In the fivefold cross-validation test, the RPI-GGCN method achieved prediction accuracies of 97.27%, 97.32%, 96.54%, 95.76%, and 94.98% on the RPI369, RPI488, RPI1446, RPI1807, and RPI2241 datasets. To test the generalization performance of the model, we used the model trained on RPI369 to predict the independent NPInter v3.0 dataset and achieved excellent performance in all six independent validation sets. By visualizing the RPI network graph based on the prediction results, we aim to provide a new perspective and reference for studying RPI mechanisms and exploring new RPIs. Extensive experimental results demonstrate that RPI-GGCN can provide an efficient, accurate, and stable RPI prediction method.

Longitudinal analysis of epigenome-wide DNA methylation reveals novel loci associated with BMI change in East Asians.

BACKGROUND: Obesity is a global public health concern linked to chronic diseases such as cardiovascular disease and type 2 diabetes (T2D). Emerging evidence suggests that epigenetic modifications, particularly DNA methylation, may contribute to obesity. However, the molecular mechanism underlying the longitudinal change of BMI has not been well-explored, especially in East Asian populations. METHODS: This study performed a longitudinal epigenome-wide association analysis of DNA methylation to uncover novel loci associated with BMI change in 533 individuals across two Chinese cohorts with repeated DNA methylation and BMI measurements over four years. RESULTS: We identified three novel CpG sites (cg14671384, cg25540824, and cg10848724) significantly associated with BMI change. Two of the identified CpG sites were located in regions previously associated with body shape and basal metabolic rate. Annotation of the top 20 BMI change-associated CpGs revealed strong connections to obesity and T2D. Notably, these CpGs exhibited active regulatory roles and located in genes with high expression in the liver and digestive tract, suggesting a potential regulatory pathway from genome to phenotypes of energy metabolism and absorption via DNA methylation. Cross-sectional and longitudinal EWAS comparisons indicated different mechanisms between CpGs related to BMI and BMI change. CONCLUSION: This study enhances our understanding of the epigenetic dynamics underlying BMI change and emphasizes the value of longitudinal analyses in deciphering the complex interplay between epigenetics and obesity.

A novel feature extraction method PSS-CSP for binary motor imagery - based brain-computer interfaces.

In order to improve the performance of binary motor imagery (MI) - based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel method (PSS-CSP) is proposed, which combines spectral subtraction with common spatial pattern. Spectral subtraction is an effective denoising method which is initially adopted to process MI-based EEG signals for binary BCIs in this work. On this basis, we proposed a novel feature extraction method called power spectral subtraction-based common spatial pattern (PSS-CSP) , which calculates the differences in power spectrum between binary classes of EEG signals and uses the differences in the feature extraction process. Additionally, support vector machine (SVM) algorithm is used for signal classification. Results show the proposed method (PSS-CSP) outperforms certain existing methods, achieving a classification accuracy of 76.8% on the BCIIV dataset 2b, and 76.25% and 77.38% on the OpenBMI dataset session 1 and session 2, respectively.

A robust multi-scale feature extraction framework with dual memory module for multivariate time series anomaly detection.

Although existing reconstruction-based multivariate time series anomaly detection (MTSAD) methods have shown advanced performance, most assume the training data is clean. When faced with noise or contamination in training data, they can also reconstruct the anomaly well, weakening the distinction between normal and anomaly. Some probabilistic generation-based methods have been used to address this issue because of their implicit robust structure to noise, but the training process and suppression of anomalous generalization are not stable. The recently proposed explicit method based on the memory module would also sacrifice the reconstruction effect of normal patterns, resulting in limited performance improvement. Moreover, most existing MTSAD methods use a single fixed-length window for input, which weakens their ability to extract long-term dependency. This paper proposes a robust multi-scale feature extraction framework with the dual memory module to comprehensively extract features fusing different levels of semantic information and lengths of temporal dependency. First, this paper designs consecutive neighboring windows as inputs to allow the model to extract local and long-term dependency information. Secondly, a dual memory-augmented encoder is proposed to extract global typical patterns and local common features. It ensures the reconstruction ability of normal data while suppressing the generalization of the anomaly. Finally, this paper proposes a multi-scale fusion module to fuse latent variables representing different levels of semantic information and uses the reconstructed latent variables to reconstruct samples for anomaly detection. Experimental results on five datasets from diverse domains show that the proposed method outperforms 16 typical baseline methods.

Twin prime editing mediated exon skipping/reinsertion for restored collagen VII expression in recessive dystrophic epidermolysis bullosa.

Gene editing nucleases, base editors, and prime editors are potential locus specific genetic treatment strategies for recessive dystrophic epidermolysis bullosa (RDEB); however, many RDEB COL7A1 mutations are unique, making the development of personalized editing reagents challenging. 270 of the ∼320 COL7A1 EB mutations reside in exons that can be skipped, and antisense oligonucleotides (ASO) and gene editing nucleases have been used to create in-frame deletions. ASOs are transient and nucleases generate deleterious double stranded DNA breaks (DSB) and uncontrolled mixtures of allele products. We developed a twin prime editing (twinPE) strategy using the PEmax and recently evolved PE6 prime editors and dual prime editing guide RNAs flanking COL7A1 exon five. Prime editing-mediated deletion of exon 5 with a homozygous premature stop codon was achieved in RDEB fibroblasts, keratinocytes, and iPSC with minimal DSBs, and collagen type VII (C7) protein was restored. TwinPE can replace the target exon with recombinase attachment sequences, and we exploited this to re-insert a normal copy of exon 5 using the Bxb1 recombinase. These findings demonstrate that twinPE can facilitate locus-specific, predictable, in-frame deletions and sequence replacement with few DSBs as a strategy that may enable a single therapeutic agent to treat multiple RDEB patient cohorts.

KaryoXpert: An accurate chromosome segmentation and classification framework for karyotyping analysis without training with manually labeled metaphase-image mask annotations.

Automated karyotyping is of great importance for cytogenetic research, as it speeds up the process for cytogeneticists through incorporating AI-driven automated segmentation and classification techniques. Existing frameworks confront two primary issues: Firstly the necessity for instance-level data annotation with either detection bounding boxes or semantic masks for training, and secondly, its poor robustness particularly when confronted with domain shifts. In this work, we first propose an accurate segmentation framework, namely KaryoXpert. This framework leverages the strengths of both morphology algorithms and deep learning models, allowing for efficient training that breaks the limit for the acquirement of manually labeled ground-truth mask annotations. Additionally, we present an accurate classification model based on metric learning, designed to overcome the challenges posed by inter-class similarity and batch effects. Our framework exhibits state-of-the-art performance with exceptional robustness in both chromosome segmentation and classification. The proposed KaryoXpert framework showcases its capacity for instance-level chromosome segmentation even in the absence of annotated data, offering novel insights into the research for automated chromosome segmentation. The proposed method has been successfully deployed to support clinical karyotype diagnosis.

Drymariamides A-J, Antiadipogenic Cyclopeptides Incorporating Noncanonical Amino Acids from Drymaria cordata.

Herein, we report an extensive phytochemical study on the whole plant of Drymaria cordata, which led to the isolation of ten new orbitides, named drymariamides A-J (1-10). Compounds 2, 3, and 5 incorporate rare residues of noncanonical amino acids of kynurenine (Kyn) or 3a-hydroxypyrroloindoline (HPI). Their structures with absolute configurations were elucidated by a combination of spectroscopic analysis, advanced Marfey's method, X-ray diffraction, and electronic circular dichroism analysis. Compounds 1-10 exhibited antiadipogenic effects in 3T3-L1 adipocytes, and the most potent compound 7 showed an EC50 value of 1.17 ± 0.19 µM.

Surface mobility gradient and emergent facilitation in glassy films.

Confining glassy polymers into films can substantially modify their local and film-averaged properties. We present a lattice model of film geometry with void-mediated facilitation behaviors but free from any elasticity effect. We analyze the spatially varying viscosity to delineate the transport properties of glassy films. The film mobility measurements reported by Yang et al., Science, 2010, 328, 1676 are successfully reproduced. The flow exhibits a crossover from a simple viscous flow to a surface-dominated regime as the temperature decreases. The propagation of a highly mobile front induced by the free surface is visualized in real space. Our approach provides a microscopic treatment of the observed glassy phenomena.

Stochastic process is main factor to affect plateau river fish community assembly.

Plateau river ecosystems are often highly vulnerable and responsive to environmental change. The driving mechanism of fish diversity and community assembly in plateau rivers under changing environments presents a significant complexity to the interdisciplinary study of ecology and environment. This study integrated molecular biological techniques and mathematical models to identify the mechanisms influencing spatial heterogeneity of freshwater fish diversity and driving fish community assembly in plateau rivers. By utilizing environmental-DNA metabarcoding and the null model, this study revealed the impact of the stochastic process on fish diversity variations and community assembly in the Huangshui Plateau River of the Yellow River Basin (YRB) in China. This research identified 30 operational taxonomic units (OTUs), which correspond to 20 different fish species. The findings of this study revealed that the fish α-diversity in the upstream region of Xining is significantly higher than in the middle-lower reach (Shannon index: P = 0.017 and Simpson: P = 0.035). This pattern was not found to be related to any other environmental factors besides altitude (P = 0.023) that we measured. Further, the study indicated that the assembly of fish communities in the Huangshui River primarily depends on stochastic ecological processes. These findings suggested that elevation was not the primary factor impacting the biodiversity patterns of fish in plateau rivers. In plateau rivers, spatial heterogeneity of fish community on elevation is mainly determined by stochastic processes under habitat fragmentation, rather than any other physicochemical environmental factors. The limitations of connectivity in the downstream channel of the river could be taken the mainly responsibility for stochastic processes of fish community in Huangshui River. Incorporating ecological processes in the eDNA approach holds great potential for future monitoring and evaluation of fish biodiversity and community assembly in plateau rivers.

Palladium-Catalyzed Ligand-Controlled Switchable Hetero-(5 + 3)/Enantioselective [2σ+2σ] Cycloadditions of Bicyclobutanes with Vinyl Oxiranes.

For nearly 60 years, significant research efforts have been focused on developing strategies for the cycloaddition of bicyclobutanes (BCBs). However, higher-order cycloaddition and catalytic asymmetric cycloaddition of BCBs have been long-standing formidable challenges. Here, we report Pd-catalyzed ligand-controlled, tunable cycloadditions for the divergent synthesis of bridged bicyclic frameworks. The dppb ligand facilitates the formal (5+3) cycloaddition of BCBs and vinyl oxiranes, yielding valuable eight-membered ethers with bridged bicyclic scaffolds in 100% regioselectivity. The Cy-DPEphos ligand promotes selective hetero-[2σ+2σ] cycloadditions to access pharmacologically important 2-oxabicyclo[3.1.1]heptane (O-BCHeps). Furthermore, the corresponding catalytic asymmetric synthesis of O-BCHeps with 94-99% ee has been achieved using chiral (S)-DTBM-Segphos, representing the first catalytic asymmetric cross-dimerization of two strained rings. The obtained O-BCHeps are promising bioisosteres for ortho-substituted benzenes.

Exploring Antiviral Drugs on Monolayer Black Phosphorene: Atomistic Theory and Explainable Machine Learning-Assisted Platform.

Favipiravir (FP) and ebselen (EB) belong to a diverse class of antiviral drugs known for their significant efficacy in treating various viral infections. Utilizing molecular dynamics (MD) simulations, machine learning, and van der Waals density functional theory, we accurately elucidate the binding properties of these antiviral drugs on a phosphorene single-layer. To further investigate these characteristics, this study employs four distinct machine learning models-Random Forest, Gradient Boosting, XGBoost, and CatBoost. The Hamiltonian of antiviral molecules within a monolayer of phosphorene is appropriately trained. The key aspect of utilizing machine learning (ML) in drug design revolves around training models that are efficient and precise in approximating density functional theory (DFT). Furthermore, the study employs SHAP (SHapley Additive exPlanations) to elucidate model predictions, providing insights into the contribution of each feature. To explore the interaction characteristics and thermodynamic properties of the hybrid drug, we employ molecular dynamics and DFT calculations in a vacuum interface. Our findings suggest that this functionalized 2D complex exhibits robust thermostability, indicating its potential as an effective and enabled entity. The observed variations in free energy at different surface charges and temperatures suggest the adsorption potential of FP and EB molecules from the surrounding environment.

SIRT1 maintains bone homeostasis by regulating osteoblast glycolysis through GOT1.

The silent information regulator T1 (SIRT1) is linked to longevity and is a crucial mediator of osteoblast function. We investigated the direct role of Sirt1 during bone modeling and remodeling stages in vivo using Tamoxifen-inducible osteoblast-specific Sirt1 conditional knockout (cKO) mice. cKO mice exhibited lower trabecular and cortical bone mass in the distal femur. These phenotypes were coupled with lower bone formation and bone resorption. Metabolomics analysis revealed that the metabolites involved in glycolysis were significantly decreased in cKO mice. Further analysis of the quantitative acetylome revealed 11 proteins with upregulated acetylation levels in both the femur and calvaria of cKO mice. Cross-analysis identified four proteins with the same upregulated lysine acetylation site in both the femur and calvaria of cKO mice. A combined analysis of the metabolome and acetylome, as well as immunoprecipitation, gene knockout, and site-mutation experiments, revealed that Sirt1 deletion inhibited glycolysis by directly binding to and increasing the acetylation level of Glutamine oxaloacetic transaminase 1 (GOT1). In conclusion, our study suggested that Sirt1 played a crucial role in regulating osteoblast metabolism to maintain bone homeostasis through its deacetylase activity on GOT1. These findings provided a novel insight into the potential targeting of osteoblast metabolism for the treatment of bone-related diseases.

Theoretical Perspective on the Design of Surface Frustrated Lewis Pairs for Small-Molecule Activation.

The excellent reactivity of frustrated Lewis pairs (FLP) to activate small molecules has gained increasing attention in recent decades. Though the development of surface FLP (SFLP) is prompting the application of FLP in the chemical industry, the design of SFLP with superior activity, high density, and excellent stability for small-molecule activation is still challenging. Herein, we review the progress of designing SFLP by surface engineering, screening natural SFLP, and the dynamic formation of SFLP from theoretical perspectives. We highlight the breakthrough in fine-tuning the activity, density, and stability of the designed SFLP studied by using computational methods. We also discuss future challenges and directions in designing SFLP with outstanding capabilities for small-molecule activation.

AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data.

Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.

Waveguide-integrated twisted bilayer graphene photodetectors.

Graphene photodetectors have exhibited high bandwidth and capability of being integrated with silicon photonics (SiPh), holding promise for future optical communication devices. However, they usually suffer from a low photoresponsivity due to weak optical absorption. In this work, we have implemented SiPh-integrated twisted bilayer graphene (tBLG) detectors and reported a responsivity of 0.65 A W-1 for telecom wavelength 1,550 nm. The high responsivity enables a 3-dB bandwidth of >65 GHz and a high data stream rate of 50 Gbit s-1. Such high responsivity is attributed to the enhanced optical absorption, which is facilitated by van Hove singularities in the band structure of high-mobility tBLG with 4.1o twist angle. The uniform performance of the fabricated photodetector arrays demonstrates a fascinating prospect of large-area tBLG as a material candidate for heterogeneous integration with SiPh.

Optimization of hydrolysis conditions of alginate based on high performance liquid chromatography.

Alginate is the most abundant polysaccharide compound in brown algae, which is widely used in various fields. At present, the determination of the content of alginate is mostly carried out using sulfuric acid and trifluoroacetic acid hydrolysis followed by the determination of the content, but the results are not satisfactory, and there are problems such as low hydrolysis degree and low recovery rate. Therefore, in this study, based on the optimization of high performance liquid chromatographic conditions for pre-column derivatization of 1-phenyl-3-methyl-5-pyrazolone (PMP), the hydrolysis effects of sulfuric acid, trifluoroacetic acid (TFA), oxalic acid, and formic acid were compared and the hydrolysis conditions were optimized. The results showed that formic acid was the best hydrolyzing acid. The optimal hydrolysis conditions were 95 % formic acid at 110 °C for 10 h. The hydrolysis effect was stable, with high recovery and low destruction of monosaccharides, which made it possible to introduce formic acid into the subsequent polysaccharide hydrolysis. The pre-column derivatization high performance liquid chromatography method established in this study was accurate and reliable, and the hydrolysis acid with better effect was screened, which provided a theoretical basis for the subsequent determination of alginate content.

Effect of Chinese quince proanthocyanidins on the inhibition of heterocyclic amines and quality of fried chicken meatballs and tofu.

Heterocyclic amines (HCAs) have potential carcinogenic and mutagenic activity and are generated in cooked protein-rich foods. Adding proanthocyanidins (PAs) to these foods before frying is an effective way to reduce HCAs. In this study, polymeric PAs (PPA) and ultrasound-assisted acid-catalyzed/catechin nucleophilic depolymerized PAs (UAPA, a type of oligomeric PA) were prepared from Chinese quince fruits (CQF). Different levels of PPA and UAPA (0.05%, 0.1%, and 0.15%) were added to chicken meatballs and tofu; then these foods were fried, and the content of HCAs in them after frying was investigated. The results showed that PPA and, particularly, UAPA significantly inhibited the formation of HCAs in fried meatballs and tofu, and this inhibition was dose-dependent. The inhibition of HCAs by both PPA and UAPA was stronger in the chicken meatballs than in fried tofu. The level of total HCAs was significantly reduced by 57.84% (from 11.93 to 5.03 ng/g) after treatment of meatballs with 0.15% UAPA, with inhibition rates of 78.94%, 50.37%, and 17.81% for norharman, harman, and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), respectively. Of note, there was a negative correlation between water, lipid, protein, creatine, and glucose content and HCA content in the crust, interior, and whole (crust-plus-interior) measurements of all fried samples. Interestingly, PPA and UAPA were found more effective in inhibiting HCAs in the exterior crust than in the interior of the fried chicken meatballs. These results provide evidence that further studies on the reduction of the formation of harmful HCAs in fried foods by adding CQF PAs could be valuable to the fried food industry. PRACTICAL APPLICATION: Chinese quince proanthocyanidins treatments significantly inhibited the generation of heterocyclic amines (HCAs) in chicken meatballs and tofu when deep-fried. These results suggest that Chinese quince proanthocyanidins can be used as natural food additive for reducing HCAs in fried foods, laying the foundation for using Chinese quince fruit proanthocyanidins for HCA inhibition in the food industry.

Genome-wide identification and analysis of the cotton ALDH gene family.

BACKGROUND: Aldehyde dehydrogenases (ALDHs) are a family of enzymes that catalyze the oxidation of aldehyde molecules into the corresponding carboxylic acid, regulate the balance of aldehydes and protect plants from the poisoning caused by excessive accumulation of aldehydes; however, this gene family has rarely been studied in cotton. RESULTS: In the present study, genome-wide identification was performed, and a total of 114 ALDH family members were found in three cotton species, Gossypium hirsutum, Gossypium arboreum and Gossypium raimondii. The ALDH genes were divided into six subgroups by evolutionary analysis. ALDH genes in the same subgroup showed similar gene structures and conserved motifs, but some genes showed significant differences, which may result in functional differences. Chromosomal location analysis and selective pressure analysis revealed that the ALDH gene family had experienced many fragment duplication events. Cis-acting element analysis revealed that this gene family may be involved in the response to various biotic and abiotic stresses. The RT‒qPCR results showed that the expression levels of some members of this gene family were significantly increased under salt stress conditions. Gohir.A11G040800 and Gohir.D06G046200 were subjected to virus-induced gene silencing (VIGS) experiments, and the sensitivity of the silenced plants to salt stress was significantly greater than that of the negative control plants, suggesting that Gohir.A11G040800 and Gohir.D06G046200 may be involved in the response of cotton to salt stress. CONCLUSIONS: In total, 114 ALDH genes were identified in three Gossypium species by a series of bioinformatics analysis. Gene silencing of the ALDH genes of G. hirsutum revealed that ALDH plays an important role in the response of cotton to salt stress.

Soluble Dietary Fiber from Citrus unshiu Peel Promotes Antioxidant Activity in Oxidative Stress Mice and Regulates Intestinal Microecology.

Aging is characterized by the progressive degeneration of bodily tissues and decline in physiological functions, a process that may be exacerbated by imbalances in intestinal flora. Soluble dietary fiber (PSDF) from Citrus unshiu peel has demonstrated strong free radical scavenging ability to regulate intestinal flora in vitro. However, further evidence is required to ascertain the effectiveness of PSDF in vivo. In our study, 8-week-old mice were artificially aged through subcutaneous injections of a 200 mg/kg/d D-galactose solution for 42 days, followed by a 28-day dietary intervention with varying doses of PSDF, insoluble dietary fiber (PIDF), and vitamin C. After the intervention, we observed a significant mitigation of D-galactose-induced oxidative stress, as evident by weight normalization and reduced oxidative damage. 16S rRNA gene sequencing revealed that PSDF significantly altered the composition of intestinal flora, increasing Firmicutes and reducing Bacteroidota percentages, while also enriching colonic short-chain fatty acids (SCFAs). Spearman correlation analysis further identified a positive correlation between Firmicutes and isovaleric acid, and negative correlations between Muribaculaceae and acetic acid, and between Lachnospiraceae_NK4A136_group and caproic acid. These findings support the potential of Citrus PSDF to alleviate oxidative stress.

Species' geographical range, environmental range and traits lead to specimen collection preference of dominant plant species of grasslands in Northern China.

Many different factors, such as species traits, socio-economic factors, geographical and environmental factors, can lead to specimen collection preference. This study aims to determine whether grassland specimen collection in China is preferred by species traits (i.e., plant height, flowering and fruiting period), environmental range (i.e., the temperature and precipitation range) and geographical range (i.e., distribution range and altitudinal range). Ordinary least squares models and phylogenetic generalized linear mixed models were used to analyze the relationships between specimen number and the explanatory variables. Random Forest models were then used to find the most parsimonious multivariate model. The results showed that interannual variation in specimen number between 1900 and 2020 was considerable. Specimen number of these species in southeast China was notably lower than that in northwest China. Environmental range and geographical range of species had significant positive correlations with specimen number. In addition, there were relatively weak but significant associations between specimen number and species trait (i.e., plant height and flowering and fruiting period). Random Forest models indicated that distribution range was the most important variable, followed by flowering and fruiting period, and altitudinal range. These findings suggest that future floristic surveys should pay more attention to species with small geographical range, narrow environmental range, short plant height, and short flowering and fruiting period. The correction of specimen collection preference will also make the results of species distribution model, species evolution and other works based on specimen data more accurate.