Lead-Free Perovskite Light-Emitting Diodes.

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.

CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction.

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.

Improved Molecular Packing of Self-Assembled Monolayer Charge Injectors for Perovskite Light-Emitting Diodes.

Self-assembled monolayers (SAMs) have shown great potential as hole injection materials for perovskite light-emitting diodes due to their low parasitic absorption and ability to adjust energy level alignment. However, the head and anchoring groups on SAM molecules with significant differences in polarity can lead to the formation of micelles in the commonly used alcoholic processing solvent, inhibiting the formation of an intact SAM. In this work, the introduction of methyl groups on carbazole in the phosphonic-acid-based SAM materials is found to facilitate energy level alignment and promote the formation of compact SAMs.

TCMM: A unified database for traditional Chinese medicine modernization and therapeutic innovations.

Mining the potential of traditional Chinese medicine (TCM) in treating modern diseases requires a profound understanding of its action mechanism and a comprehensive knowledge system that seamlessly bridges modern medical insights with traditional theories. However, existing databases for modernizing TCM are plagued by varying degrees of information loss, which impede the multidimensional dissection of pharmacological effects. To address this challenge, we introduce traditional Chinese medicine modernization (TCMM), the currently largest modernized TCM database that integrates pioneering intelligent pipelines.

Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction.

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.

Characterization of the novel HLA-DRB1*16:76 allele in a Chinese individual.

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.

GIT-Mol: A multi-modal large language model for molecular science with graph, image, and text.

Large language models have made significant strides in natural language processing, enabling innovative applications in molecular science by processing textual representations of molecules. However, most existing language models cannot capture the rich information with complex molecular structures or images. In this paper, we introduce GIT-Mol, a multi-modal large language model that integrates the Graph, Image, and Text information.

Cytokine expression patterns: A single-cell RNA sequencing and machine learning based roadmap for cancer classification.

Cytokines are small protein molecules that exhibit potent immunoregulatory properties, which are known as the essential components of the tumor immune microenvironment (TIME). While some cytokines are known to be universally upregulated in TIME, the unique cytokine expression patterns have not been fully resolved in specific types of cancers. To address this challenge, we develop a TIME single-cell RNA sequencing (scRNA-seq) dataset, which is designed to study cytokine expression patterns for precise cancer classification.

Bright and Stable Red Perovskite LEDs under High Current Densities.

Perovskite LEDs (PeLEDs) have emerged as a next-generation light-emitting technology. Recent breakthroughs were made in achieving highly stable near-infrared and green PeLEDs. However, the operational lifetimes () of visible PeLEDs under high current densities (>10 mA cm) remain unsatisfactory (normally <100 h), limiting the possibilities in solid-state lighting and AR/VR applications.

Low-temperature CeCoMnO spinel-type catalysts prepared by oxalate co-precipitation for selective catalytic reduction of NO using NH: A structure-activity relationship study.

CeCoMnO spinel-type catalysts for the selective catalytic reduction of NO using NH (NH-SCR) are usually prepared by alkaline co-precipitation. In this paper, a series of CeCoMnO spinel-type catalysts with different calcination temperatures were prepared by acidic oxalate co-precipitation. The physicochemical structures and NH-SCR activities of the CeCoMnO spinel-type catalysts prepared by oxalate co-precipitation and conventional ammonia co-precipitation were systematically compared.

Oncolytic adenoviruses expressing checkpoint inhibitors for cancer therapy.

Despite the remarkable success of immune checkpoint inhibitors (ICIs), primary resistance to ICIs causes only subsets of patients to achieve durable responses due to the complex tumor microenvironment (TME). Oncolytic viruses (OVs) can overcome the immunosuppressive TME and promote systemic antitumor immunity in hosts. Engineered OVs armed with ICIs would likely have improved effectiveness as a cancer therapy.

DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model.

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.

A novel high activity MnFeO spinel catalyst for selective catalytic reduction of NO using NH prepared by a short process from natural minerals for low-temperature sintering flue gas: Effect of X value on catalytic mechanism.

The process of smelting and purifying the catalyst precursor salt from minerals is extremely complex, which directly leads to high catalyst costs and serious secondary pollution. In order to achieve energy saving and emission reduction in the catalyst preparation process, in-situ synthesis of catalyst materials from natural minerals is a new research direction. In this study, we firstly explored the optimal X value of MnFeO for the NH selective catalytic reduction of NO (NH-SCR) reaction, i.

A Large-Scale Dataset of Three-Dimensional Solar Magnetic Fields Extrapolated by Nonlinear Force-Free Method.

It has been widely accepted that solar magnetic field manipulates all solar activities, especially violent solar bursts in solar corona. Thus, it is extremely important to reconstruct three-dimentional (3D) magnetic field of solar corona from really observed photospheric magnetogram. In this paper, a large-scale dataset of 3D solar magnetic fields of active regions is built by using the nonlinear force-free magnetic field (NLFFF) extrapolation from vector magnetograms of Helioseismic and Magnetic Imager (HMI) on Solar Dynamics Observatory (SDO).

Characterization of immature ovarian teratomas through single-cell transcriptome.

Introduction: Immature ovarian teratomas are a type of malignant germ cell tumor composed of complicated cell types and are characterized by pathological features of immature neuroectodermal tubules/rosettes. However, there is a lack of understanding of patient-derived immature ovarian teratomas (PDT) at the single cell level. Moreover, whether stem cell lines derived from immature teratomas (CDT) can be used as models for research on PDT remains to be elucidated.

Pan-cancer single-cell analysis reveals the heterogeneity and plasticity of cancer-associated fibroblasts in the tumor microenvironment.

Cancer-associated fibroblasts (CAFs) are the predominant components of the tumor microenvironment (TME) and influence cancer hallmarks, but without systematic investigation on their ubiquitous characteristics across different cancer types. Here, we perform pan-cancer analysis on 226 samples across 10 solid cancer types to profile the TME at single-cell resolution, illustrating the commonalities/plasticity of heterogenous CAFs. Activation trajectory of the major CAF types is divided into three states, exhibiting distinct interactions with other cell components, and relating to prognosis of immunotherapy.

Ultralow-voltage operation of light-emitting diodes.

For a light-emitting diode (LED) to generate light, the minimum voltage required is widely considered to be the emitter's bandgap divided by the elementary charge. Here we show for many classes of LEDs, including those based on perovskite, organic, quantum-dot and III-V semiconductors, light emission can be observed at record-low voltages of 36-60% of their bandgaps, exhibiting a large apparent energy gain of 0.6-1.

Association of recurrent APOBEC3B alterations with the prognosis of gastric-type cervical adenocarcinoma.

Objective: Gastric-type cervical adenocarcinoma (GCA) is a rare and aggressive type of endocervical adenocarcinoma (ECA) with distinct histopathologic features and unfavorable treatment outcomes, but no genomic prognostic factor has been revealed. We aimed to systematically investigate the somatic alterations of GCA at genome-wide level and evaluate their prognostic value.

Methods: We performed whole-exome sequencing (WES) on 25 pairs of tumor and matched normal samples to characterize the genomic features of Chinese patients with GCA and investigated their relations to histopathological characterizations and prognosis.

PHLDB2 Mediates Cetuximab Resistance via Interacting With EGFR in Latent Metastasis of Colorectal Cancer.

Background & Aims: Latent metastasis of colorectal cancer (CRC) frequently develops months or years after primary surgery, followed by adjuvant therapies, and may progress rapidly even with targeted therapy administered, but the underlying mechanism remains unclear. Here, we aim to explore the molecular basis for the aggressive behavior of latent metastasis in CRC.

Methods: Transcriptional profiling and pathway enrichment analysis of paired primary and metastatic tumor samples were performed.

Novel Recurrent Altered Genes in Chinese Patients With Anaplastic Thyroid Cancer.

Background: Anaplastic thyroid cancer (ATC) is a rare but lethal malignancy, and few systematic investigations on genomic profiles of ATC have been performed in Chinese patients.

Methods: Fifty-four ATC patients in West China Hospital between 2010 to 2020 were retrospectively analyzed, while 29 patients with available samples were sequenced by whole-exome sequencing (WES). The associations between genomic alterations and clinical characteristics were statistically evaluated.

Effect of Calcination Temperature on the Activation Performance and Reaction Mechanism of Ce-Mn-Ru/TiO Catalysts for Selective Catalytic Reduction of NO with NH.

In this study, anatase TiO-supported cerium, manganese, and ruthenium mixed oxides (CeO -MnO -RuO /TiO; CMRT catalysts) were synthesized at different calcination temperatures via conventional impregnation methods and used for selective catalytic reduction (SCR) of NO with NH. The effect of calcination temperature on the structure, redox properties, activation performance, surface-acidity properties, and catalytic properties of the CMRT catalysts was investigated. The results show that the CMRT catalyst calcined at 350 °C exhibits the most efficient low-temperature (<120 °C) denitration activity.

Regularized feature reconstruction for spatio-temporal saliency detection.

Multimedia applications such as image or video retrieval, copy detection, and so forth can benefit from saliency detection, which is essentially a method to identify areas in images and videos that capture the attention of the human visual system. In this paper, we propose a new spatio-temporal saliency detection framework on the basis of regularized feature reconstruction. Specifically, for video saliency detection, both the temporal and spatial saliency detection are considered.