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. 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.

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. By aligning high-quality TCM and modern medicine data, TCMM boasts the most extensive TCM modernization knowledge, including 20 types of modernized TCM concepts such as prescription, ingredient, target and 46 biological relations among them, totaling 3,447,023 records. We demonstrate the efficacy and reliability of TCMM with two features, prescription generation and knowledge discovery, the outcomes show consistency with biological experimental results. A publicly available web interface is at https://www.tcmm.net.cn/.

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. The dataset, including 39 cancers, is constructed by integrating 684 tumor scRNA-seq samples from multiple public repositories. After screening and processing, the dataset retains only the expression data of immune cells. With a machine learning classification model, unique cytokine expression patterns are identified for various cancer categories and pioneering applied to cancer classification with an accuracy rate of 78.01%. Our method will not only boost the understanding of cancer-type-specific immune modulations in TIME but also serve as a crucial reference for future diagnostic and therapeutic research in cancer immunity.

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. To facilitate the integration of multi-modal molecular data, we propose GIT-Former, a novel architecture that is capable of aligning all modalities into a unified latent space. We achieve a 5%-10% accuracy increase in properties prediction and a 20.2% boost in molecule generation validity compared to the baselines. With the any-to-language molecular translation strategy, our model has the potential to perform more downstream tasks, such as compound name recognition and chemical reaction prediction.

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 (T50) of visible PeLEDs under high current densities (>10 mA cm-2) remain unsatisfactory (normally <100 h), limiting the possibilities in solid-state lighting and AR/VR applications. This problem becomes more pronounced for mixed-halide (e.g., red and blue) perovskite emitters in which critical challenges such as halide segregation and spectral instability are present. Here, we demonstrate bright and stable red PeLEDs based on mixed-halide perovskites, showing measured T50 lifetimes of up to ∼357 h at currents of ≥25 mA cm-2, a record for the operational stability of visible PeLEDs under high current densities. The devices produce intense and stable emission with a maximum luminance of 28,870 cd m-2 (radiance: 1584 W sr-1 m-2), which is record-high for red PeLEDs. Key to this demonstration is the introduction of sulfonamide, a dipolar molecular stabilizer that effectively interacts with the ionic species in the perovskite emitters. It suppresses halide segregation and migration into the charge-transport layers, resulting in enhanced stability and brightness of the mixed-halide PeLEDs. These results represent a substantial step toward bright and stable PeLEDs for emerging applications.

The novel HLA-A*11:01:124 allele was identified in a Chinese individual.

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.

Low-temperature CeCoMnOx spinel-type catalysts prepared by oxalate co-precipitation for selective catalytic reduction of NO using NH3: A structure-activity relationship study.

CeCoMnOx spinel-type catalysts for the selective catalytic reduction of NO using NH3 (NH3-SCR) are usually prepared by alkaline co-precipitation. In this paper, a series of CeCoMnOx spinel-type catalysts with different calcination temperatures were prepared by acidic oxalate co-precipitation. The physicochemical structures and NH3-SCR activities of the CeCoMnOx spinel-type catalysts prepared by oxalate co-precipitation and conventional ammonia co-precipitation were systematically compared. The results show that the CeCoMnOx spinel-type catalysts prepared by the oxalate precipitation method (CeCoMnOx-C) have larger specific surface area, more mesopores and surface active sites, stronger redox properties and adsorption activation properties than those prepared by the traditional ammonia co-precipitation method at 400 °C (CeCoMnOx-N-400), and thus CeCoMnOx-C have better low-temperature NH3-SCR performance. At the same calcination temperature of 400 °C, the NO conversion of CeCoMnOx-C-400 exceeds 89 % and approaches 100 % within the reaction temperature of 100-125 °C, which is 14.8 %-2.5 % higher than that of CeCoMnOx-N-400 at 100-125 °C. In addition, the enhanced redox and acid cycle matching mechanisms on the CeCoMnOx-C surface, as well as the enhanced monoadsorption Eley-Rideal (E-R) and double adsorption Langmuir-Hinshelwood (L-H) reaction mechanisms, are also derived from XPS and in situ DRIFTS characterization.

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. 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/.

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. According to the diverse immune cell landscapes among different types of tumors, we rationally and precisely generated three recombinant oncolytic adenoviruses (OAds): OAd-SIRPα-Fc, OAd-Siglec10-Fc and OAd-TIGIT-Fc. These viruses were designed to locally deliver SIRPα-Fc, Siglec10-Fc or TIGIT-Fc fusion proteins recognizing CD47, CD24 or CD155, respectively, in the TME to achieve enhanced antitumor effects. Our results suggested that OAd-SIRPα-Fc and OAd-Siglec10-Fc both showed outstanding efficacy in tumor suppression of macrophage-dominated tumors, while OAd-TIGIT-Fc showed the best antitumor immunity in CD8+ T-cell-dominated tumors. Importantly, the recombinant OAds activated an inflammatory immune response and generated long-term antitumor memory. In addition, the combination of OAd-Siglec10-Fc with anti-PD-1 significantly enhanced the antitumor effect in a 4T1 tumor model by remodeling the TME. In summary, rationally designed OAds expressing ICIs tailored to the immune cell landscape in the TME can precisely achieve tumor-specific immunotherapy of cancer.

A novel high activity MnXFe3-XO4 spinel catalyst for selective catalytic reduction of NO using NH3 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 MnXFe3-XO4 for the NH3 selective catalytic reduction of NO (NH3-SCR) reaction, i.e., the Mn, Fe ratio, and then prepared a novel highly active mineral-based pure phase MnXFe3-XO4 spinel NH3-SCR catalyst by natural ferromanganese ore fines with iron-red fines (Fe2O3) allotment through in situ solid-phase synthesis and magnetic separation methods according to this ratio. The results show that the X value of 1.5 (Mn1.5Fe1.5O4) is the best for NH3-SCR reaction. Mn1.5Fe1.5O4 nano-particles (201 nm) has nearly 100 % NO conversion (with 5 % H2O(g)) at 125-300 °C. The combination of characterizations and density functional theory (DFT) calculation shows that the catalytic process of Eley-Rideal (E-R) dehydrogenation is enhanced at both the active site Mn site and Fe site, which is a key factor in the acceleration of the NH3-SCR reaction with increasing X value at the MnXFe3-XO4 surface.

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. Methods: Single-cell RNA sequencing (scRNA-seq) and subsequent bioinformatic analysis was performed on three patient-derived immature ovarian teratomas (PDT) samples to reveal the heterogeneity, evolution trajectory, and cell communication within the tumor microenvironment of PDT. Validations were conducted in additional seven samples through multiplex immunofluorescence. Result: A total of qualified 22,153 cells were obtained and divided into 28 clusters, which can match to the scRNA-seq annotation of CDT as well as human fetal Cell Atlas, but with higher heterogeneity and more prolific cell-cell crosstalk. Radial glia cells (tagged by SOX2) and immature neuron (tagged by DCX) exhibited mutually exclusive expression and differentiated along distinct evolutionary trajectory from cycling neural progenitors. Proportions of these neuroectodermal cell subtypes may play important roles in PDT through contributing to the internal heterogeneity of PDTs. Moreover, the immune cells in PDTs were infiltrated rather than teratoma-derived, with more abundant macrophage in immature neuron than those in radial glia cells, and the infiltrated macrophage subtypes (i.e., M1 and M2) were significantly correlated to clinical grade. Overall, suppressed evolution process and transcriptome regulation in neuroectodermal cells, reduced cell-cell crosstalk, higher M1/M2 proportion ratio, and enhanced T cell effects in tumor microenvironment are enriched in patients with favorable prognosis. Discussion: This study provides a comprehensive profile of PDT at the single cell level, shedding light on the heterogeneity and evolution of neuroectodermal cells within PDTs and the role of immune cells within the tumor microenvironment. Also, our findings highlight the potential usage of CDTs as a model for research on PDT.

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). In this dataset, all space-weather HMI active region patches (SHARPs) with the corresponding serial numbers of national oceanic and atmospheric administration (NOAA) are included. They are downloaded from the SHARP 720 s series of JSOC every 96 minutes. In addition, each sample is labelled with a finer grained label for solar flare forecast. This paper is with the purpose of open availability of data resource and source code to the peers for refraining from repeated labor of data preparation. Meanwhile, with such a large-scale, high spatio-temporal resolution and high quality scientific data, we anticipate a wide attention and interest from artificial intelligence (AI) and computer vision communities, for exploring AI for astronomy over such a large-scale dataset.

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. Moreover, minor CAF components represent the alternative origin from other TME components (e.g., endothelia and macrophages). Particularly, the ubiquitous presentation of endothelial-to-mesenchymal transition CAF, which may interact with proximal SPP1+ tumor-associated macrophages, is implicated in endothelial-to-mesenchymal transition and survival stratifications. Our study comprehensively profiles the shared characteristics and dynamics of CAFs, and highlight their heterogeneity and plasticity across different cancer types. Browser of integrated pan-cancer single-cell information is available at https://gist-fgl.github.io/sc-caf-atlas/ .

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 0.6-1.4 eV per photon. For 17 types of LEDs with different modes of charge injection and recombination (dark saturation currents of ~10-39-10-15 mA cm-2), their emission intensity-voltage curves under low voltages show similar behaviours. These observations and their consistency with the diode simulations suggest the ultralow-voltage electroluminescence arises from a universal origin-the radiative recombination of non-thermal-equilibrium band-edge carriers whose populations are determined by the Fermi-Dirac function perturbed by a small external bias. These results indicate the potential of low-voltage LEDs for communications, computational and energy applications.

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. The prognostic value of the genomic alterations was evaluated in a total of 58 GCA patients. RESULTS: Mutations were commonly observed in reported GCA-related driver genes, including TP53 (32%), CDKN2A (20%), SKT11 (20%), BRCA2 (12%), SMAD4 (12%), and ERBB2 (12%). Recurrent novel trunk mutations were also observed in PBRM1 (12%), FRMPD4 (12%), and NOP2 (8%) with high variant allele frequency. Moreover, enrichment of the APOBEC signature was attributed to frequent gain of somatic copy number alteration (SCNA) of APOBEC3B (20%), which perfectly matched the nuclear-positive staining of APOBEC3B through immunohistochemistry. In contrast, APOBEC3B alteration was absent in patients with conventional type of ECA (N = 52). Notably, positive APOBEC3B was consistently enriched in patients with favorable prognosis in both the discovery cohort and an additional 33 GCA patients, thus indicating a significant association with lower relapse risk of GCA independent of cancer stage (P = 0.02). CONCLUSION: Our results can aid understanding of the molecular basis of GCA in the Chinese population by providing genomic profiles and highlighting the potential prognostic value of APOBEC3B for GCA through routine clinical IHC.

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. The underlying mechanisms of pleckstrin homology-like domain, family B, member 2 (PHLDB2) in CRC were investigated by RNA immunoprecipitation assay, immunohistochemistry, mass spectrometry analysis, and Duolink in situ proximity ligation assay (Sigma-Aldrich, Shanghai, China). The efficacy of targeting PHLDB2 in cetuximab treatment was elucidated in CRC cell lines and mouse models. RESULTS: Based on the transcriptional profile of paired primary and metastatic tumor samples, we identified PHLDB2 as a potential regulator in latent liver metastasis. A detailed mechanistic study showed that chemotherapeutic agent-induced oxidative stress promotes methyltransferase-like 14 (METTL14)-mediated N6-methyladenosine modification of PHLDB2 messenger RNA, facilitating its protein expression. Up-regulated PHLDB2 stabilizes epidermal growth factor receptor (EGFR) and promotes its nuclear translocation, which in turn results in EGFR signaling activation and consequent cetuximab resistance. Moreover, Arg1163 (R1163) of PHLDB2 is crucial for interaction with EGFR, and the R1163A mutation abrogates its regulatory function in EGFR signaling. CONCLUSIONS: PHLDB2 plays a crucial role in cetuximab resistance and is proposed to be a potential target for the treatment of CRC.

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. RESULTS: The median overall survival was 3.0 months in the entire cohort, which was impacted by multiple clinical features, including age, tumor size, and different treatment strategies. In the WES cohort, totally 797 nonsilent mutations were detected; the most frequently altered genes were TP53 (48%), BRAF (24%), PIK3CA (24%), and TERT promoter (21%). Although these mutations have been well-reported in previous studies, ethnic specificity was exhibited in terms of mutation frequency. Moreover, several novel significantly mutated genes were identified including RBM15 (17%), NOTCH2NL (14%), CTNNA3 (10%), and KATNAL2 (10%). WES-based copy number alteration analysis also revealed a high frequent gain of NOTCH2NL (41%), which induced its increased expression. Gene mutations and copy number alterations were enriched in phosphatidylinositol 3-kinase/AKT/mechanistic target of rapamycin (mTOR), NOTCH, and WNT pathways. CONCLUSIONS: This study reveals shared and ethnicity-specific genomic profiles of ATC in Chinese patients and suggests NOTCH2NL may act as a novel candidate driver gene for ATC tumorigenesis.

Effect of Calcination Temperature on the Activation Performance and Reaction Mechanism of Ce-Mn-Ru/TiO2 Catalysts for Selective Catalytic Reduction of NO with NH3.

In this study, anatase TiO2-supported cerium, manganese, and ruthenium mixed oxides (CeO x -MnO x -RuO x /TiO2; CMRT catalysts) were synthesized at different calcination temperatures via conventional impregnation methods and used for selective catalytic reduction (SCR) of NO x with NH3. 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. Moreover, the selective catalytic oxidation (SCO) reaction of ammonia is intensified when the reaction temperature is >200 °C, which leads to a decrease in the N2 selectivity of the CMRT catalysts and further results in an increase in the production of NO and N2O byproducts. X-ray photoelectron spectroscopy and in situ diffuse reflectance infrared Fourier transform spectroscopy show that the CMRT catalyst calcined at 350 °C contains more Ce4+, Mn4+, Ru4+, and lattice oxygen, which greatly improve the catalyst's ability to activate NO that promotes the NH3-SCR reaction. The Ru n+ sites of the CMRT catalyst calcined at 250 °C are the competitive adsorption sites of NO and NH3 molecules, while those of the CMRT catalyst calcined at 350 and 450 °C are active sites. Both the Langmuir-Hinshelwood (L-H) mechanism and the Eley-Rideal (E-R) mechanism occur on the surface of the CMRT catalyst at the low reaction temperature (100 °C).

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. For temporal saliency, we model the movement of the target patch as a reconstruction process using the patches in neighboring frames. A Laplacian smoothing term is introduced to model the coherent motion trajectories. With psychological findings that abrupt stimulus could cause a rapid and involuntary deployment of attention, our temporal model combines the reconstruction error, regularizer, and local trajectory contrast to measure the temporal saliency. For spatial saliency, a similar sparse reconstruction process is adopted to capture the regions with high center-surround contrast. Finally, the temporal saliency and spatial saliency are combined together to favor salient regions with high confidence for video saliency detection. We also apply the spatial saliency part of the spatio-temporal model to image saliency detection. Experimental results on a human fixation video dataset and an image saliency detection dataset show that our method achieves the best performance over several state-of-the-art approaches.