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Functional peptides play crucial roles in various biological processes and hold significant potential in many fields such as drug discovery and biotechnology. Accurately predicting the functions of peptides is essential for understanding their diverse effects and designing peptide-based therapeutics. Here, we propose CELA-MFP, a deep learning framework that incorporates feature Contrastive Enhancement and Label Adaptation for predicting Multi-Functional therapeutic Peptides. CELA-MFP utilizes a protein language model (pLM) to extract features from peptide sequences, which are then fed into a Transformer decoder for function prediction, effectively modeling correlations between different functions. To enhance the representation of each peptide sequence, contrastive learning is employed during training. Experimental results demonstrate that CELA-MFP outperforms state-of-the-art methods on most evaluation metrics for two widely used datasets, MFBP and MFTP. The interpretability of CELA-MFP is demonstrated by visualizing attention patterns in pLM and Transformer decoder. Finally, a user-friendly online server for predicting multi-functional peptides is established as the implementation of the proposed CELA-MFP and can be freely accessed at http://dreamai.cmii.online/CELA-MFP. [ABSTRACT FROM AUTHOR]

With the increasing awareness of architectural heritage conservation and the development of digital technology, there is an urgent need in the field of architectural heritage restoration for a novel solution that can enhance data security, collaboration efficiency, and file management capabilities. This study proposes an Architectural Heritage Restoration Distributed Common Data Environment (AHR-DCDE) framework based on blockchain and IPFS technologies to address the above challenges. The AHR-DCDE framework significantly improves data security and collaborative efficiency in architectural heritage restoration projects by creating a decentralized collaborative design process that achieves data immutability, traceability, and efficient large-scale file processing capabilities. The AHR-DCDE framework significantly improves data security and collaborative efficiency in architectural heritage restoration projects by creating a decentralized collaborative design process that achieves data immutability, traceability, and efficient large-scale file processing capabilities. In this study, the practicality and effectiveness of the AHR-DCDE framework is verified by taking the heritage restoration design project of Pinghe Packing Factory in Wuhan, Hubei Province, as an example. Evaluation of the framework's network latency, throughput, and storage costs indicates that AHR-DCDE can meet the requirements of architectural heritage restoration projects, possessing efficient capabilities for handling and sharing project data. Furthermore, the implementation of the AHR-DCDE framework also facilitates efficient collaboration among interdisciplinary teams, providing robust technical support for the protection and restoration of architectural heritage. [ABSTRACT FROM AUTHOR]

Language models pretrained by self-supervised learning (SSL) have been widely utilized to study protein sequences, while few models were developed for genomic sequences and were limited to single species. Due to the lack of genomes from different species, these models cannot effectively leverage evolutionary information. In this study, we have developed SpliceBERT, a language model pretrained on primary ribonucleic acids (RNA) sequences from 72 vertebrates by masked language modeling, and applied it to sequence-based modeling of RNA splicing. Pretraining SpliceBERT on diverse species enables effective identification of evolutionarily conserved elements. Meanwhile, the learned hidden states and attention weights can characterize the biological properties of splice sites. As a result, SpliceBERT was shown effective on several downstream tasks: zero-shot prediction of variant effects on splicing, prediction of branchpoints in humans, and cross-species prediction of splice sites. Our study highlighted the importance of pretraining genomic language models on a diverse range of species and suggested that SSL is a promising approach to enhance our understanding of the regulatory logic underlying genomic sequences. [ABSTRACT FROM AUTHOR]

Motivation Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information. Results In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein–protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite. Availability and implementation The datasets and source codes can be accessed at https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/. [ABSTRACT FROM AUTHOR]

The never-ending emergence of SARS-CoV-2 variations of concern (VOCs) has challenged the whole world for pandemic control. In order to develop effective drugs and vaccines, one needs to efficiently simulate SARS-CoV-2 spike receptor-binding domain (RBD) mutations and identify high-risk variants. We pretrain a large protein language model with approximately 408 million protein sequences and construct a high-throughput screening for the prediction of binding affinity and antibody escape. As the first work on SARS-CoV-2 RBD mutation simulation, we successfully identify mutations in the RBD regions of 5 VOCs and can screen millions of potential variants in seconds. Our workflow scales to 4096 NPUs with 96.5% scalability and 493.9× speedup in mixed-precision computing, while achieving a peak performance of 366.8 PFLOPS (reaching 34.9% theoretical peak) on Pengcheng Cloudbrain-II. Our method paves the way for simulating coronavirus evolution in order to prepare for a future pandemic that will inevitably take place. Our models are released at https://github.com/ZhiweiNiepku/SARS-CoV-2%5fmutation%5fsimulation to facilitate future related work. [ABSTRACT FROM AUTHOR]

The HLA‐DRB1*16:76 allele differs from HLA‐DRB1*16:02:01 by one nucleotide substitution (A > G) at position 37 in exon 1. [ABSTRACT FROM AUTHOR]

The HLA‐A*11:01:124 allele differs from HLA‐A*11:01:01 by one nucleotide substitution, (C > T) position 459 in exon 3. [ABSTRACT FROM AUTHOR]

This paper reported synergistic bimetallic AuCu/TiO2 catalysts for selective oxidation of 1,2-propanediol to lactic acid under very mild conditions (T < 90 °C, 1 MPa O2). The addition of Cu to monometallic Au/TiO2 catalyst leads to a twofold activity enhancement for Au catalysts (TOF: 11894 h−1) with good selectivity towards lactic acid (S > 92%). Surface characterization reveals that while AuCu forms alloy structure with larger particle size based on TEM images, strong interaction between Au and Cu species is critical for performance enhancement. According to experimental studies on the influence of 1,2-propanediol and NaOH concentration on reaction rates, it is highly possible that 1,2-propanediol is reacted following single-site Langmuir-Hinshelwood mechanism while NaOH acts as a promoter and may block surface sites under relatively higher concentration.Graphical Abstract: [ABSTRACT FROM AUTHOR]

The objective of this paper is twofold. First, we introduce an effective region-based solution for saliency detection. Then, we apply the achieved saliency map to better encode the image features for solving object recognition task. To find the perceptually and semantically meaningful salient regions, we extract superpixels based on an adaptive mean shift algorithm as the basic elements for saliency detection. The saliency of each superpixel is measured by using its spatial compactness, which is calculated according to the results of Gaussian mixture model (GMM) clustering. To propagate saliency between similar clusters, we adopt a modified PageRank algorithm to refine the saliency map. Our method not only improves saliency detection through large salient region detection and noise tolerance in messy background, but also generates saliency maps with a well-defined object shape. Experimental results demonstrate the effectiveness of our method. Since the objects usually correspond to salient regions, and these regions usually play more important roles for object recognition than background, we apply our achieved saliency map for object recognition by incorporating a saliency map into sparse coding-based spatial pyramid matching (ScSPM) image representation. To learn a more discriminative codebook and better encode the features corresponding to the patches of the objects, we propose a weighted sparse coding for feature coding. Moreover, we also propose a saliency weighted max pooling to further emphasize the importance of those salient regions in feature pooling module. Experimental results on several datasets illustrate that our weighted ScSPM framework greatly outperforms ScSPM framework, and achieves excellent performance for object recognition. [ABSTRACT FROM PUBLISHER]

We present a multi-layer group sparse coding framework for concurrent single-label image classification and annotation. By leveraging the dependency between image class label and tags, we introduce a multi-layer group sparse structure of the reconstruction coefficients. Such structure fully encodes the mutual dependency between the class label, which describes image content as a whole, and tags, which describe the components of the image content. Therefore we propose a multi-layer group based tag propagation method, which combines the class label and subgroups of instances with similar tag distribution to annotate test images. To make our model more suitable for nonlinear separable features, we also extend our multi-layer group sparse coding in the Reproducing Kernel Hilbert Space (RKHS), which further improves performances of image classification and annotation. Moreover, we also integrate our multi-layer group sparse coding with kNN strategy, which greatly improves the computational efficiency. Experimental results on the LabelMe, UIUC-Sports and NUS-WIDE-Object databases show that our method outperforms the baseline methods, and achieves excellent performances in both image classification and annotation tasks. [ABSTRACT FROM PUBLISHER]

Metal halide perovskites have been identified as a promising class of materials for light-emitting applications. The development of lead-based perovskite light-emitting diodes (PeLEDs) has led to substantial improvements, with external quantum efficiencies (EQEs) now surpassing 30% and operational lifetimes comparable to those of organic LEDs (OLEDs). However, the concern over the potential toxicity of lead has motivated a search for alternative materials that are both eco-friendly and possess excellent optoelectronic properties, with lead-free perovskites emerging as a strong contender. In this review, the properties of various lead-free perovskite emitters are analyzed, with a particular emphasis on the more well-reported tin-based variants. Recent progress in enhancing device efficiencies through refined crystallization processes and the optimization of device configurations is also discussed. Additionally, the remaining challenges are examined, and propose strategies that may lead to stable device operation. Looking forward, the potential future developments for lead-free PeLEDs are considered, including the extension of spectral range, the adoption of more eco-friendly deposition techniques, and the exploration of alternative materials., (© 2024 Wiley‐VCH GmbH.)