Cross-Species Comparative DNA Methylation Reveals Novel Insights into Complex Trait Genetics among Cattle, Sheep, and Goats.

The cross-species characterization of evolutionary changes in the functional genome can facilitate the translation of genetic findings across species and the interpretation of the evolutionary basis underlying complex phenotypes. Yet, this has not been fully explored between cattle, sheep, goats, and other mammals. Here, we systematically characterized the evolutionary dynamics of DNA methylation and gene expression in 3 somatic tissues (i.e. brain, liver, and skeletal muscle) and sperm across 7 mammalian species, including 3 ruminant livestock species (cattle, sheep, and goats), humans, pigs, mice, and dogs, by generating and integrating 160 DNA methylation and transcriptomic data sets. We demonstrate dynamic changes of DNA hypomethylated regions and hypermethylated regions in tissue-type manner across cattle, sheep, and goats. Specifically, based on the phylo-epigenetic model of DNA methylome, we identified a total of 25,074 hypomethylated region extension events specific to cattle, which participated in rewiring tissue-specific regulatory network. Furthermore, by integrating genome-wide association studies of 50 cattle traits, we provided novel insights into the genetic and evolutionary basis of complex phenotypes in cattle. Overall, our study provides a valuable resource for exploring the evolutionary dynamics of the functional genome and highlights the importance of cross-species characterization of multiomics data sets for the evolutionary interpretation of complex phenotypes in cattle livestock.

AACFlow: an end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides.

MOTIVATION: Anticancer peptides (ACPs) have natural cationic properties and can act on the anionic cell membrane of cancer cells to kill cancer cells. Therefore, ACPs have become a potential anticancer drug with good research value and prospect. RESULTS: In this article, we propose AACFlow, an end-to-end model for identification of ACPs based on deep learning. End-to-end models have more room to automatically adjust according to the data, making the overall fit better and reducing error propagation. The combination of attention augmented convolutional neural network (AAConv) and multi-layer convolutional neural network (CNN) forms a deep representation learning module, which is used to obtain global and local information on the sequence. Based on the concept of flow network, multi-head flow-attention mechanism is introduced to mine the deep features of the sequence to improve the efficiency of the model. On the independent test dataset, the ACC, Sn, Sp, and AUC values of AACFlow are 83.9%, 83.0%, 84.8%, and 0.892, respectively, which are 4.9%, 1.5%, 8.0%, and 0.016 higher than those of the baseline model. The MCC value is 67.85%. In addition, we visualize the features extracted by each module to enhance the interpretability of the model. Various experiments show that our model is more competitive in predicting ACPs.

Targeted PLK1 suppression through RNA interference mediated by high-fidelity Cas13d mitigates osteosarcoma progression via TGF-ß/Smad3 signalling.

Osteosarcoma is the most common primary bone malignancy in children and adolescents. Overexpression of polo-like kinase 1 (PLK1) is frequent in osteosarcoma and drives disease progression and metastasis, making it a promising therapeutic target. In this study, we explored PLK1 knockdown in osteosarcoma cells using RNA interference mediated by high-fidelity Cas13d (hfCas13d). PLK1 was found to be significantly upregulated in osteosarcoma tumour tissues compared to normal bone. sgRNA-mediated PLK1 suppression via hfCas13d transfection inhibited osteosarcoma cell proliferation, induced G2/M cell cycle arrest, promoted apoptosis, reduced cell invasion and increased expression of the epithelial marker E-cadherin. Proximity labelling by TurboID coupled with co-immunoprecipitation identified novel PLK1 interactions with Smad3, a key intracellular transducer of TGF-ß signalling. PLK1 knockdown impaired Smad2/3 phosphorylation and modulated TGF-ß/Smad3 pathway inactivation. Finally, in vivo delivery of hfCas13d vectors targeting PLK1 substantially attenuated osteosarcoma xenograft growth in nude mice. Taken together, this study highlights PLK1 as a potential therapeutic target and driver of disease progression in osteosarcoma. It also demonstrates the utility of hfCas13d-mediated gene knockdown as a strategy for targeted therapy. Further optimization of PLK1 suppression approaches may ultimately improve clinical outcomes for osteosarcoma patients.

Design of Multifunctional Electrocatalysts for ORR/OER/HER/HOR: Janus Makes Difference.

Multifunctional electrocatalysts for hydrogen evolution reaction (HER), hydrogen oxidation reaction (HOR), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR) have broad application prospects; However, realization of such kinds of materials remain difficulties because it requires the materials to have not only unique electronic properties, but multiple active centers to deal with different reactions. Here, employing density functional theory (DFT) computations, it is demonstrated that by decorating the Janus-type 2D transition metal dichalcogenide (TMD) of TaSSe with the single atoms, the materials can achieve multifunctionality to catalyze the ORR/OER/HER/HOR. Out of sixteen catalytic systems, Pt-VS (i.e., Pt atom embedded in the sulfur vacancy), Pd-VSe, and Pt-VSe@TaSSe are promising multifunctional catalysts with superior stability. Among them, the Pt-VS@TaSSe catalyst exhibits the highest activity with theoretical overpotentials ηORR = 0.40 V, ηOER = 0.39 V, and ηHER/HOR = 0.07 V, respectively, better than the traditional Pt (111), IrO2 (110). The interplays between the catalyst and the reaction intermediate over the course of the reaction are then systematically investigated. Generally, this study presents a viable approach for the design and development of advanced multifunctional electrocatalysts. It enriches the application of Janus, a new 2D material, in electrochemical energy storage and conversion technology.

Revealing the Thermodynamic Mechanism of Phase Transition Induced by Activation Polarization in Lithium-Ion Batteries.

Enhancing the phase transition reversibility of electrode materials is an effective strategy to alleviate capacity degradation in the cycling of lithium-ion batteries (LIBs). However, a comprehensive understanding of phase transitions under microscopic electrode dynamics is still lacking. In this paper, the activation polarization is quantified as the potential difference between the applied potential (Uabs) and the zero-charge potential (ZCP) of electrode materials. The polarization potential difference facilitates the phase transition by driving Li-ion adsorption and supplying an electron-rich environment. A novel thermodynamic phase diagram is constructed to characterize the phase transition of the example MoS2 under various Li-ion concentrations and operating voltages using the grand canonic fixed-potential method (FPM). At thermodynamic quasi-equilibrium, the ZCP is close to the Uabs, and thus is used to form the discharge curve in the phase diagram. The voltage plateau is observed within the phase transition region in the simulation, which will disappear as the phase transition reversibility is impaired. The obtained discharge curve and phase transition concentration both closely match the experimental results. Overall, the study provides a theoretical understanding of how polarization affects phase evolution in electrode dynamics, which may provide a guideline to improve battery safety and cycle life.

Targeted Endoperoxides Delivering Singlet Oxygen to Cancer Cell Mitochondria: Exploration of the Therapeutic Potential.

The clinical practice of photodynamic therapy of cancer (PDT) is mostly limited to superficial types of cancer. The major reason behind this limited applicability is the need for light in the photogeneration of ROS, and in particular singlet oxygen. In order to circumvent this major roadblock, we designed and synthesized naphthalene-derived endoperoxides with mitochondria targeting triphenylphosphonium moieties. Here, we show that these compounds release singlet oxygen by thermal cycloreversion, and initiate cell death with IC50<10 µM in cancer cell cultures. The mouse 4T1 breast tumor model study, where the endoperoxide compound was introduced intraperitoneally, also showed highly promising results, with negligible systemic toxicity. Targeted delivery of singlet oxygen to cancer cell mitochondria could be the breakthrough needed to transform Photodynamic Therapy into a broadly applicable methodology for cancer treatment by keeping the central tenet and discarding problematic dependencies on oxygen or external light.

NeuroPred-ResSE: Predicting neuropeptides by integrating residual block and squeeze-excitation attention mechanism.

Neuropeptides play crucial roles in regulating neurological function acting as signaling molecules, which provide new opportunity for developing drugs for the treatment of neurological diseases. Therefore, it is very necessary to develop a rapid and accurate prediction model for neuropeptides. Although a few prediction tools have been developed, there is room for improvement in prediction accuracy by using deep learning approach. In this paper, we establish the NeuroPred-ResSE model based on residual block and squeeze-excitation attention mechanism. Firstly, we extract multi-features by using one-hot coding based on the NT5CT5 sequence, dipeptide deviation from expected mean and natural vector. Then, we integrate residual block and squeeze-excitation attention mechanism, which can capture and identify the most relevant attribute features. Finally, the accuracies of the training set and test set are 97.16 % and 96.60 % based on the 5-fold cross-validation and independent test, respectively, and other evaluation metrics have also obtained satisfactory results. The experimental results show that the performance of the NeuroPred-ResSE model outperforms those of existing state-of-the-art models, and our model is an effective, intelligent and robust prediction tool. The datasets and source codes are available at https://github.com/yunyunliang88/NeuroPred-ResSE.

Effects of NEAT1 levels on cardiovascular events and prognosis in diabetic nephropathy patients undergoing peritoneal dialysis.

The purpose of this study was to provide observational indicators for clinically predicting cardiovascular events in patients with diabetic nephropathy (DN) undergoing peritoneal dialysis by determining the effects of nuclear enriched abundant transcript 1 (NEAT1) levels on the cardiovascular events and prognosis in DN patients receiving continuous ambulatory peritoneal dialysis (CAPD). A retrospective analysis was conducted on the data of 80 DN patients undergoing CAPD. Patients were assigned to NEAT1 high expression group and NEAT1 low expression group. NEAT1 had a substantially increased expression in the serum of DN patients, and it could serve as a potential biomarker for predicting the development of DN. Patients with highly expressed NEAT1 had an higher level of high-sensitivity C-reactive protein (hs-CRP), larger cardiac structural parameters left ventricular end-diastolic diameter (LVED), left ventricular end-systolic diameter (LVESD), interventricular septal diameter (IVSD) and left ventricular posterior wall diameter (LVPWD), but a notably lower cardiac function evaluation indicator left ventricular ejection fraction (LVEF) than those with lowly expressed NEAT1. The coefficient (r) of correlation between NEAT1 and hs-CRP level was 0.3585 (P=0.0011). The incidence rates of acute myocardial infarction, congestive heart failure and angina in NEAT1 high expression group were higher than those in NEAT1 low expression group. Patients with NEAT1 high expression exhibited a higher mortality rate than NEAT1 low expression group. With the increase in NEAT1 levels, the level of hs-CRP rose in DN patients undergoing CAPD. A higher expression level of NEAT1 indicates poorer cardiac function, higher incidence rates of cardiovascular adverse events and a poorer prognosis in diabetics undergoing CAPD.

First Report of Fruit Rot Disease on melon (Cucumis melo L.) Caused by Fusarium ipomoeae in China.

Fusarium rot on melon fruit has become an important postharvest disease for producers worldwide, typically involving multiple Fusarium pathogens (Khuna et al. 2022; Medeiros Araújo et al. 2021). In 2022, Fusarium fruit rot of muskmelon (Cucumis melo var. conomon) occurred sporadically in a field at Huainan Academy of Agricultural Sciences (32.658193º N, 117.064922º E) with an incidence of about 10%. Among these diseased muskmelons, a fruit exhibiting a white to yellowish colony athe intersection of the diseased and healthy tissues was collected and labeled TGGF22-17. The streak plate method was employed to isolate fungal spores on Bengal Red PDA (potato dextrose agar), which were then incubated at 25℃ in darkness. Following isolation and purification, a single-spore strain, TGGF22-17, was obtained and analyzed using morphological characters on PDA, synthetic nutrient agar (SNA) and carnation leaf agar (CLA) (Leslie and Summerell 2006), along with molecular identification. Colours were rated according to the color charts of Kornerup and Wanscher (1978). Based on the colony morphology on PDA, the isolate displayed a rosy buff or buff color with a white to buff margin. The colony margin was undulate, with the reverse transitioning from amber-yellow to honey-yellow. Aerial macroconidia on SNA were thin-walled, hyaline, mostly 3-5 septate, falcate, and measured 18.5-46.4 (x̄=34.2) × 2.9-4.8 (x̄ =3.9) µm in size (n =50). Sporodochial macroconidia on CLA were mostly five-septate with long apical and basal cells, exhibiting dorsiventral curvature. They were hyaline, with the apical cell hooked to tapering and the basal cell foot-shaped, measuring 46.5-89.6 (x̄ =72.3) × 3.5-5.0 (x̄ =4.3) µm in size (n = 100). Portions of three loci (TEF-1α, RPB1 and RPB2) were amplified and sequenced as described by Wang et al. (2019). Sequences were deposited in GenBank with accession number PP196583 to PP196585. The three gene sequences (TEF-1α, RPB1 and RPB2) of strain TGGF2022-17 shared 99.5% (629/632bp), 97.9% (1508/1540 bp) and 99.9% (1608/1609 bp) identity to the ex-type strain F. ipomoeae LC12165 respectively by pairwise DNA alignments on the FUSARIOID-ID database (https://www.fusarium.org). Phylogenetic analysis of the partial TEF-1α and RPB2 sequences with PhyloSuite (Zhang et al. 2020) showed the isolated fungus clustered with F. ipomoeae. Based on the morphological and phylogenetic analyses, TGGF22-17 was identified as F. ipomoeae. Pathogenicity tests were performed on healthy melons, which were surface-sterilized with 75% alcohol and wounded using a sterilized inoculation needle. A 4-mm diameter plug from a 7-day-old SNA culture of TGGF22-17 was aseptically inserted in the middle of the wound, sealed with plastic bag after absorbent cotton was included to maintain moisture. Five melons were each inoculated at three points. Noncolonized PDA agar plugs served as the negative control. The inoculated and uninoculated plugs were removed approximately 48 hours after inoculation. The melon inoculated with TGGF22-17 exhibited water-soaked black lesions 48h post-inoculation, resulting in a 100% infection rate (15/15). After 7 days, mycelium was obseved on the inoculated melons. No disease symptoms were observed on the uninoculated melons. To fulfill Koch's postulates, fungi were isolated from the inoculated fruit and confirmed as F. ipomoeae by morphological observation. Fusarium ipomoeae has been reported to cause fruit rot on winter squash (Cucurbita maxima) in Japan (Kitabayashi et al. 2023). To our knowledge, this is the first report of fruit rot on muskmelon caused by F. ipomoeae in China and this report will be valuable for monitoring and management of fruit rot disease on muskmelons.

The Inhibition Effect of Epigallocatechin-3-Gallate on the Co-Aggregation of Amyloid-ß and Human Islet Amyloid Polypeptide Revealed by Replica Exchange Molecular Dynamics Simulations.

Alzheimer's disease and Type 2 diabetes are two epidemiologically linked diseases which are closely associated with the misfolding and aggregation of amyloid proteins amyloid-ß (Aß) and human islet amyloid polypeptide (hIAPP), respectively. The co-aggregation of the two amyloid proteins is regarded as the fundamental molecular mechanism underlying their pathological association. The green tea extract epigallocatechin-3-gallate (EGCG) has been extensively demonstrated to inhibit the amyloid aggregation of Aß and hIAPP proteins. However, its potential role in amyloid co-aggregation has not been thoroughly investigated. In this study, we employed the enhanced-sampling replica exchange molecular dynamics simulation (REMD) method to investigate the effect of EGCG on the co-aggregation of Aß and hIAPP. We found that EGCG molecules substantially diminish the ß-sheet structures within the amyloid core regions of Aß and hIAPP in their co-aggregates. Through hydrogen-bond, π-π and cation-π interactions targeting polar and aromatic residues of Aß and hIAPP, EGCG effectively attenuates both inter-chain and intra-chain interactions within the co-aggregates. All these findings indicated that EGCG can effectively inhibit the co-aggregation of Aß and hIAPP. Our study expands the potential applications of EGCG as an anti-amyloidosis agent and provides therapeutic options for the pathological association of amyloid misfolding disorders.

Co-Mutations and Possible Variation Tendency of the Spike RBD and Membrane Protein in SARS-CoV-2 by Machine Learning.

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, SARS-CoV-2 variants capable of breakthrough infections have attracted global attention. These variants have significant mutations in the receptor-binding domain (RBD) of the spike protein and the membrane (M) protein, which may imply an enhanced ability to evade immune responses. In this study, an examination of co-mutations within the spike RBD and their potential correlation with mutations in the M protein was conducted. The EVmutation method was utilized to analyze the distribution of the mutations to elucidate the relationship between the mutations in the spike RBD and the alterations in the M protein. Additionally, the Sequence-to-Sequence Transformer Model (S2STM) was employed to establish mapping between the amino acid sequences of the spike RBD and M proteins, offering a novel and efficient approach for streamlined sequence analysis and the exploration of their interrelationship. Certain mutations in the spike RBD, G339D-S373P-S375F and Q493R-Q498R-Y505, are associated with a heightened propensity for inducing mutations at specific sites within the M protein, especially sites 3 and 19/63. These results shed light on the concept of mutational synergy between the spike RBD and M proteins, illuminating a potential mechanism that could be driving the evolution of SARS-CoV-2.

N-Phenyl-2-Pyridone-Derived Endoperoxide Suppressing both Lung Cancer and Idiopathic Pulmonary Fibrosis Progression by Three-Pronged Action.

We report an endoperoxide compound (E5) which can deliver three therapeutic components by a thermal cycloreversion, namely, singlet oxygen, triplet oxygen and 3-methyl-N-phenyl-2-pyridone (P5), thus targeting multiple mechanisms for treating non-small cell lung cancer and idiopathic pulmonary fibrosis. In aqueous environment, E5 undergoes clean reaction to afford three therapeutic components with a half-life of 8.3 hours without the generation of other by-products, which not only achieves good cytotoxicity toward lung cancer cells and decreases the levels of hypoxia-inducible factor 1α (HIF-1α) protein, but also inhibits the transforming growth factor ß1 (TGF-ß1) induced fibrosis in vitro. In vivo experiments also demonstrated the efficacy of E5 in inhibiting tumor growth and relieving idiopathic pulmonary fibrosis, while exhibiting good biocompatibility. Many lines of evidence reveal the therapeutic efficacy of singlet oxygen and 3-methyl-N-phenyl-2-pyridone for these two lung diseases, and triplet oxygen could downregulate HIF-1α and relieve tumor hypoxia which is a critical issue in photodynamic therapy (PDT). Unlike other combination therapies, in which multiple therapeutic agents are given in independent formulations, our work demonstrates single molecule endoperoxide prodrugs could be developed as new platforms for treatment of cancers and related diseases.

Exploration of the Character Representation of DNA Chiral Conformations and Deformations via a Curved Surface Discrete Frenet Frame.

While undergoing structural deformation, DNA experiences changes in the interactions between its internal base pairs, presenting challenges to conventional elastic methods. To address this, we propose the Discrete Critical State (DCS) model in this paper. This model combines surface discrete frame theory with gauge theory and Landau phase transition theory to investigate DNA's structural deformation, phase transitions, and chirality. Notably, the DCS model considers both the internal interactions within DNA and formulates an overall equation using unified physical and geometric parameters. By employing the discrete frame, we derive the evolution of physical quantities along the helical axis of DNA, including geodesic curvature, geodesic torsion, and others. Our findings indicate that B-DNA has a significantly lower free energy density compared to Z-DNA, which is in agreement with experimental observations. This research reveals that the direction of base pairs is primarily governed by the geodesic curve within the helical plane, aligning closely with the orientation of the base pairs. Moreover, the geodesic curve has a profound influence on the arrangement of base pairs at the microscopic level and effectively regulates the configuration and geometry of DNA through macroscopic-level free energy considerations.

EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.

BACKGROUND: The novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwide. Coronaviruses cause diseases such as severe acute respiratory syndrome (SARS-Cov) and SARS-COV-2. Many peptides in the host defense system have antiviral activity. How to establish a set of efficient models to identify anti-coronavirus peptides is a meaningful study. METHODS: Given this, a new prediction model EACVP is proposed. This model uses the evolutionary scale language model (ESM-2 LM) to characterize peptide sequence information. The ESM model is a natural language processing model trained by machine learning technology. It is trained on a highly diverse and dense dataset (UR50/D 2021_04) and uses the pre-trained language model to obtain peptide sequence features with 320 dimensions. Compared with traditional feature extraction methods, the information represented by ESM-2 LM is more comprehensive and stable. Then, the features are input into the convolutional neural network (CNN), and the convolutional block attention module (CBAM) lightweight attention module is used to perform attention operations on CNN in space dimension and channel dimension. To verify the rationality of the model structure, we performed ablation experiments on the benchmark and independent test datasets. We compared the EACVP with existing methods on the independent test dataset. RESULTS: Experimental results show that ACC, F1-score, and MCC are 3.95%, 35.65% and 0.0725 higher than the most advanced methods, respectively. At the same time, we tested EACVP on ENNAVIA-C and ENNAVIA-D data sets, and the results showed that EACVP has good migration and is a powerful tool for predicting anti-coronavirus peptides. CONCLUSION: The results prove that this model EACVP could fully characterize the peptide information and achieve high prediction accuracy. It can be generalized to different data sets. The data and code of the article have been uploaded to https://github.- com/JYY625/EACVP.git.

TMSC-m7G: A transformer architecture based on multi-sense-scaled embedding features and convolutional neural network to identify RNA N7-methylguanosine sites.

RNA N7-methylguanosine (m7G) is a crucial chemical modification of RNA molecules, whose principal duty is to maintain RNA function and protein translation. Studying and predicting RNA N7-methylguanosine sites aid in comprehending the biological function of RNA and the development of new drug therapy regimens. In the present scenario, the efficacy of techniques, specifically deep learning and machine learning, stands out in the prediction of RNA N7-methylguanosine sites, leading to improved accuracy and identification efficiency. In this study, we propose a model leveraging the transformer framework that integrates natural language processing and deep learning to predict m7G sites, called TMSC-m7G. In TMSC-m7G, a combination of multi-sense-scaled token embedding and fixed-position embedding is used to replace traditional word embedding for the extraction of contextual information from sequences. Moreover, a convolutional layer is added in the encoder to make up for the shortage of local information acquisition in transformer. The model's robustness and generalization are validated through 10-fold cross-validation and an independent dataset test. Results demonstrate outstanding performance in comparison to the most advanced models available. Among them, the Accuracy of TMSC-m7G reaches 98.70% and 92.92% on the benchmark dataset and independent dataset, respectively. To facilitate the popularization and use of the model, we have developed an intuitive online prediction tool, which is easily accessible for free at http://39.105.212.81/.

iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.

BACKGROUND: Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infection drugs with clear function, low cost, and high safety. Anti-coronavirus peptide (ACVP) is a key therapeutic agent against coronavirus. Traditional methods for finding ACVP need a great deal of money and man power. Hence, it is a significant task to establish intelligent computational tools to able rapid, efficient and accurate identification of ACVP. METHODS: In this paper, we construct an excellent model named iACVP-MR to identify ACVP based on multiple features and recurrent neural networks. Multiple features are extracted by using reduced amino acid component and dipeptide component, compositions of k-spaced amino acid pairs, BLOSUM62 encoder according to the N5C5 sequence, as well as second-order moving average approach based on 16 physicochemical properties. Then, two recurrent neural networks named long-short term memory (LSTM) and bidirectional gated recurrent unit (BiGRU) combined attention mechanism are used for feature fusion and classification, respectively. RESULTS: The accuracies of ENNAVIA-C and ENNAVIA-D datasets under the 10-fold cross-validation are 99.15% and 98.92%, respectively, and other evaluation indexes have also obtained satisfactory results. The experimental results show that our model is superior to other existing models. CONCLUSION: The iACVP-MR model can be viewed as a powerful and intelligent tool for the accurate identification of ACVP. The datasets and source codes for iACVP-MR are freely downloaded at https://github.com/yunyunliang88/iACVP-MR.

A deep learning model for anti-inflammatory peptides identification based on deep variational autoencoder and contrastive learning.

As a class of biologically active molecules with significant immunomodulatory and anti-inflammatory effects, anti-inflammatory peptides have important application value in the medical and biotechnology fields due to their unique biological functions. Research on the identification of anti-inflammatory peptides provides important theoretical foundations and practical value for a deeper understanding of the biological mechanisms of inflammation and immune regulation, as well as for the development of new drugs and biotechnological applications. Therefore, it is necessary to develop more advanced computational models for identifying anti-inflammatory peptides. In this study, we propose a deep learning model named DAC-AIPs based on variational autoencoder and contrastive learning for accurate identification of anti-inflammatory peptides. In the sequence encoding part, the incorporation of multi-hot encoding helps capture richer sequence information. The autoencoder, composed of convolutional layers and linear layers, can learn latent features and reconstruct features, with variational inference enhancing the representation capability of latent features. Additionally, the introduction of contrastive learning aims to improve the model's classification ability. Through cross-validation and independent dataset testing experiments, DAC-AIPs achieves superior performance compared to existing state-of-the-art models. In cross-validation, the classification accuracy of DAC-AIPs reached around 88%, which is 7% higher than previous models. Furthermore, various ablation experiments and interpretability experiments validate the effectiveness of DAC-AIPs. Finally, a user-friendly online predictor is designed to enhance the practicality of the model, and the server is freely accessible at http://dac-aips.online .

Progress of charge carrier dynamics and regulation strategies in 2D CxNy-based heterojunctions.

Two-dimensional carbon nitrides (CxNy) have gained significant attention in various fields including hydrogen energy development, environmental remediation, optoelectronic devices, and energy storage owing to their extensive surface area, abundant raw materials, high chemical stability, and distinctive physical and chemical characteristics. One effective approach to address the challenges of limited visible light utilization and elevated carrier recombination rates is to establish heterojunctions for CxNy-based single materials (e.g. C2N3, g-C3N4, C3N4, C4N3, C2N, and C3N). The carrier generation, migration, and recombination of heterojunctions with different band alignments have been analyzed starting from the application of CxNy with metal oxides, transition metal sulfides (selenides), conductive carbon, and Cx'Ny' heterojunctions. Additionally, we have explored diverse strategies to enhance heterojunction performance from the perspective of carrier dynamics. In conclusion, we present some overarching observations and insights into the challenges and opportunities associated with the development of advanced CxNy-based heterojunctions.

Molecular Insights into the Inhibition and Disaggregation Effects of EGCG on Aß40 and Aß42 Cofibrillation.

The misfolding and aggregation of amyloid-ß (Aß) peptides play a pivotal role in the pathogenesis of Alzheimer's disease (AD). Aß40 and Aß42, the two primary isoforms of Aß, can not only self-aggregate into homogeneous aggregates but also coaggregate to form mixed fibrils. Epigallocatechin-3-gallate (EGCG), a prominent tea polyphenol, has shown the capability to prevent the self-aggregation of Aß40 and Aß42 peptides and disaggregate their homogeneous fibrils. However, its effects on the cofibrillation of Aß40 and Aß42 have not yet been explored. Here, we employed molecular dynamic simulations to investigate the effects of EGCG on the coaggregation of Aß40 and Aß42, as well as on their mixed fibril. Our findings indicated that EGCG effectively inhibits the codimerization of Aß40 and Aß42 primarily by impeding the interchain interaction between the two isoforms. The key binding sites for EGCG on Aß40 and Aß42 are the polar residues and aromatic residues, engaging in hydrogen-bond , π-π, and cation-π interactions with EGCG. Additionally, EGCG disaggregates the Aß40-Aß42 mixed fibril by reducing its long-range interaction through similar binding sites and interactions as those between EGCG and Aß40-Aß42 heterodimers. Our research reveals the comprehensive inhibition and disaggregation effects of EGCG on the cofibrillation of Aß isoforms, which provides further support for the development of EGCG as an effective antiaggregation agent for AD.

A next-generation transistor with low supply voltage operation constructed based on 2D materials' metal-semiconductor phase transition.

Power dissipation, a fundamental limitation for realizing high-performance electronic devices, may be effectively reduced by an external supply voltage. However, a small supply voltage simultaneously brings another serious challenge, that is, a remarkable device inability in transistors. To deal with this issue, we propose a new transistor design based on the metal-semiconductor phase transition in a AsGeC3 monolayer, which provides a switching mechanism of band-to-band tunneling at on- and off-states by gate-voltage modulation. Our first-principles calculations uncover that the monolayer AsGeC3 field-effect transistors (FETs) with gate lengths of 5, 4, and 3 nm may meet well the requirements for on-state current (Ion), power dissipation (PDP), and delay period (τ) as outlined by the International Technology Roadmap for Semiconductors (ITRS) in 2013 to achieve higher performance by the year 2028. Importantly, high performances are achieved only under a very low supply voltage (VDD = 0.05/0.10 V). Significantly, the AsGeC3 FETs exhibit remarkably lower values of both PDP and τ than those of nearly all the transistors reported up to date. These novel 2D metal-semiconductor phase transition-based FETs open up a new door for designing next-generation low-power electronic devices.