OBOX regulates mouse zygotic genome activation and early development.

Zygotic genome activation (ZGA) activates the quiescent genome to enable the maternal-to-zygotic transition1,2. However, the identity of transcription factors that underlie mammalian ZGA in vivo remains elusive. Here we show that OBOX, a PRD-like homeobox domain transcription factor family (OBOX1-OBOX8)3-5, are key regulators of mouse ZGA. Mice deficient for maternally transcribed Obox1/2/5/7 and zygotically expressed Obox3/4 had a two-cell to four-cell arrest, accompanied by impaired ZGA. The Obox knockout defects could be rescued by restoring either maternal and zygotic OBOX, which suggests that maternal and zygotic OBOX redundantly support embryonic development. Chromatin-binding analysis showed that Obox knockout preferentially affected OBOX-binding targets. Mechanistically, OBOX facilitated the 'preconfiguration' of RNA polymerase II, as the polymerase relocated from the initial one-cell binding targets to ZGA gene promoters and distal enhancers. Impaired polymerase II preconfiguration in Obox mutants was accompanied by defective ZGA and chromatin accessibility transition, as well as aberrant activation of one-cell polymerase II targets. Finally, ectopic expression of OBOX activated ZGA genes and MERVL repeats in mouse embryonic stem cells. These data thus demonstrate that OBOX regulates mouse ZGA and early embryogenesis.

Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model.

The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition.

AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding.

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool.

DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction.

BACKGROUND: Drug-drug interactions (DDIs) can result in unexpected pharmacological outcomes, including adverse drug events, which are crucial for drug discovery. Graph neural networks have substantially advanced our ability to model molecular representations; however, the precise identification of key local structures and the capture of long-distance structural correlations for better DDI prediction and interpretation remain significant challenges. RESULTS: Here, we present DrugDAGT, a dual-attention graph transformer framework with contrastive learning for predicting multiple DDI types. The dual-attention graph transformer incorporates attention mechanisms at both the bond and atomic levels, thereby enabling the integration of short and long-range dependencies within drug molecules to pinpoint key local structures essential for DDI discovery. Moreover, DrugDAGT further implements graph contrastive learning to maximize the similarity of representations across different views for better discrimination of molecular structures. Experiments in both warm-start and cold-start scenarios demonstrate that DrugDAGT outperforms state-of-the-art baseline models, achieving superior overall performance. Furthermore, visualization of the learned representations of drug pairs and the attention map provides interpretable insights instead of black-box results. CONCLUSIONS: DrugDAGT provides an effective tool for accurately predicting multiple DDI types by identifying key local chemical structures, offering valuable insights for prescribing medications, and guiding drug development. All data and code of our DrugDAGT can be found at https://github.com/codejiajia/DrugDAGT .

Unveiling the Intrinsic Photophysics in Quasi-Two-Dimensional Perovskites.

The two-dimensional (2D) perovskites have drawn intensive attention due to their unique stability and outstanding optoelectronic properties. However, the debate surrounding the spatial phase distribution and band alignment among different 2D phases in the quasi-2D perovskite has created complexities in understanding the carrier dynamics, hindering material and device development. In this study, we employed highly sensitive transient absorption spectroscopy to investigate the carrier dynamics of (BA)2(MA)n-1PbnI3n+1 quasi-2D Ruddlesden-Popper perovskite thin films, nominally prepared as n = 4. We observed the carrier-density-dependent electron and hole transfer dynamics between the 2D and three-dimensional (3D) phases. Under a low carrier density within the linear response range, we successfully resolved three ultrafast processes of both electron and hole transfers, spanning from hundreds of femtoseconds to several picoseconds, tens to hundreds of picoseconds, and hundreds of picoseconds to several nanoseconds, which can be attributed to lateral-epitaxial, partial-epitaxial, and disordered-interface heterostructures between 2D and 3D phases. By considering the interplay among the phase structure, band alignment, and carrier dynamics, we have proposed material synthesis strategies aimed at enhancing the carrier transport. Our results not only provide deep insights into an accurate intrinsic photophysics of quasi-2D perovskites but also inspire advancements in the practical application of these materials.

Pd Nanoparticle Size-Dependent H* Coverage for Cu-Catalyzed Nitrate Electro-Reduction to Ammonia in Neutral Electrolyte.

Electrochemical conversion of nitrate (NO3 -) to ammonia (NH3) is an effective approach to reduce nitrate pollutants in the environment and also a promising low-temperature, low-pressure method for ammonia synthesis. However, adequate H* intermediates are highly expected for NO3 - hydrogenation, while suppressing competitive hydrogen evolution. Herein, the effect of H* coverage on the NO3RR for ammonia synthesis by Cu electrocatalysts is investigated. The H* coverage can be adjusted by changing Pd nanoparticle sizes. The optimized Pd@Cu with an average Pd size of 2.88 nm shows the best activity for NO3RR, achieving a maximum Faradaic efficiency of 97% (at -0.8 V vs RHE) and an NH3 yield of 21 mg h-1 cm- 2, from an industrial wastewater level of 500 ppm NO3 -. In situ electrochemical experiments indicate that Pd particles with 2.88 nm can promote NO3 - hydrogenation to NH3 via well-modulated coverage of adsorbed H* species. Coupling the anodic glycerol oxidation reaction, ammonium and formate are successfully obtained as value-added products in a membrane electrode assembly electrolyzer. This work provides a feasible strategy for obtaining size-dependent H* intermediates for hydrogenation.

A Durable and High-Voltage Mn-Graphite Dual-Ion Battery Using Mn-Based Hybrid Electrolytes.

Rechargeable Mn-metal batteries (MMBs) can attract considerable attention because Mn has the intrinsic merits including high energy density (976 mAh g-1), high air stability, and low toxicity. However, the application of Mn in rechargeable batteries is limited by the lack of proper cathodes for reversible Mn2+ intercalation/de-intercalation, thus leading to low working voltage (<1.8 V) and poor cycling stability (≤200 cycles). Herein, a high-voltage and durable MMB with graphite as the cathode is successfully constructed using a LiPF6-Mn(TFSI)2 hybrid electrolyte, which shows a high discharge voltage of 2.34 V and long-term stability of up to 1000 cycles. Mn(TFSI)2 can reduce the plating/stripping overpotential of Mn ions, while LiPF6 can efficiently improve the conductivity of the electrolyte. Electrochemical in-situ characterization implies the dual-anions intercalation/de-intercalation at the cathode and Mn2+ plating/stripping reaction at the anode. Theoretical calculations unveil the top site of graphite is the energetically favorable for anions intercalation and TFSI- shows the low migration barrier. This work paves an avenue for designing high-performance rechargeable MMBs towards electricity storage.

Magnetic Activation: A Novel Approach to Enhance Hydrogen Evolution Activity of Co0.85Se@CNTs Heterostructured Catalyst.

The heterostructure strategy is currently an effective method for enhancing the catalytic activity of materials. However, the challenge that is how to further improve their catalytic performance, based on the principles of material modification is must addressed. Herein, a strategy is introduced for magnetically regulating the catalytic activity to further enhance the hydrogen evolution reaction (HER) activity for Co0.85Se@CNTs heterostructured catalyst. Building on heterostructure modulation, an external alternating magnetic field (AMF) is introduced to enhance the electronic localization at the active sites, which significantly boosts catalytic performance (71 to 43 mV at 10 mA cm-2). To elucidate the catalytic mechanism, especially under the influence of the AMF, in situ Raman spectroscopy is innovatively applied to monitor the HER process of Co0.85Se@CNTs, comparing conditions with and without the AMF. This study demonstrates that introducing the AMF does not induce a change in the true active site. Importantly, it shows that the Lorentz force generated by the AMF enhances HER activity by promoting water molecule adsorption and O─H bond cleavage, with the Stark tuning rate indicating increased water interaction and bond cleavage efficiency. Theoretical calculations further support that the AMF optimizes energy barriers for key reaction intermediates (steps of *H2O-TS and *H+*1/2H2).

Vertical Cross-Alignments of 2D Semiconductors with Steered Internal Electric Field for Urea Electrooxidation via Balancing Intermediates Adsorption.

Single-component electrocatalysts generally lead to unbalanced adsorption of OH- and urea during urea oxidation reaction (UOR), thus obtaining low activity and selectivity especially when oxygen evolution reaction (OER) competes at high potentials (>1.5 V). Herein, a cross-alignment strategy of in situ vertically growing Ni(OH)2 nanosheets on 2D semiconductor g-C3N4 is reported to form a hetero-structured electrocatalyst. Various spectroscopy measurements including in situ experiments indicate the existence of enhanced internal electric field at the interfaces of vertical Ni(OH)2 and g-C3N4 nanosheets, favorable for balancing adsorption of reaction intermediates. This heterojunction electrocatalyst shows high-selectivity UOR compared to pure Ni(OH)2, even at high potentials (>1.5 V) and large current density. The computational results show the vertical heterojunction could steer the internal electric field to increase the adsorption of urea, thus efficiently avoiding poisoning of strongly adsorbed OH- on active sites. A membrane electrode assembly (MEA)-based electrolyzer with the heterojunction anode could operate at an industrial-level current density of 200 mA cm-2. This work paves an avenue for designing high-performance electrocatalysts by vertical cross-alignments of active components.

Electrochemical Activation Inducing Rocksalt-to-Spinel Transformation for Prolonged Service Life of LiMn2O4 Cathodes.

LiMn2O4 spinel is emerging as a promising cathode material for lithium-ion batteries, largely due to its open framework that facilitates Li+ diffusion and excellent rate performance. However, the charge-discharge cycling of the LiMn2O4 cathode leads to severe structural degradation and rapid capacity decay. Here, an electrochemical activation strategy is introduced, employing a facile galvano-potentiostatic charging operation, to restore the lost capacity of LiMn2O4 cathode without damaging the battery configuration. With an electrochemical activation strategy, the cycle life of the LiMn2O4 cathode is extended from an initial 1500 to an impressive 14 000 cycles at a 5C rate with Li metal as the anode, while increasing the total discharge energy by ten times. Remarkably, the electrochemical activation enhances the diffusion kinetics of Li+, with the diffusion coefficient experiencing a 37.2% increase. Further investigation reveals that this improvement in capacity and diffusion kinetics results from a transformation of the redox-inert LiMnO2 rocksalt layer on the surface of degraded cathodes back into active spinel. This transformation is confirmed through electron microscopy and corroborated by density functional theory simulations. Moreover, the viability of this electrochemical activation strategy has been demonstrated in pouch cell configurations with Li metal as the anode, underscoring its potential for broader application.

A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data.

The emergence of single cell RNA sequencing has facilitated the studied of genomes, transcriptomes and proteomes. As available single-cell RNA-seq datasets are released continuously, one of the major challenges facing traditional RNA analysis tools is the high-dimensional, high-sparsity, high-noise and large-scale characteristics of single-cell RNA-seq data. Deep learning technologies match the characteristics of single-cell RNA-seq data perfectly and offer unprecedented promise. Here, we give a systematic review for most popular single-cell RNA-seq analysis methods and tools based on deep learning models, involving the procedures of data preprocessing (quality control, normalization, data correction, dimensionality reduction and data visualization) and clustering task for downstream analysis. We further evaluate the deep model-based analysis methods of data correction and clustering quantitatively on 11 gold standard datasets. Moreover, we discuss the data preferences of these methods and their limitations, and give some suggestions and guidance for users to select appropriate methods and tools.

Deep learning models for disease-associated circRNA prediction: a review.

Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.

A co-adaptive duality-aware framework for biomedical relation extraction.

MOTIVATION: Biomedical relation extraction is a vital task for electronic health record mining and biomedical knowledge base construction. Previous work often adopts pipeline methods or joint methods to extract subject, relation, and object while ignoring the interaction of subject-object entity pair and relation within the triplet structure. However, we observe that entity pair and relation within a triplet are highly related, which motivates us to build a framework to extract triplets that can capture the rich interactions among the elements in a triplet. RESULTS: We propose a novel co-adaptive biomedical relation extraction framework based on a duality-aware mechanism. This framework is designed as a bidirectional extraction structure that fully takes interdependence into account in the duality-aware extraction process of subject-object entity pair and relation. Based on the framework, we design a co-adaptive training strategy and a co-adaptive tuning algorithm as collaborative optimization methods between modules to promote better mining framework performance gain. The experiments on two public datasets show that our method achieves the best F1 among all state-of-the-art baselines and provides strong performance gain on complex scenarios of various overlapping patterns, multiple triplets, and cross-sentence triplets. AVAILABILITY AND IMPLEMENTATION: Code is available at https://github.com/11101028/CADA-BioRE.

Risk Factors of Distant Metastatic Parathyroid Carcinoma and Insights into Therapeutic Perspectives.

BACKGROUND: Distant metastatic parathyroid carcinoma (DM-PC) is a rare but often lethal entity with limited data about prognostic indicators. We sought to investigate the risk factors, patterns, and outcomes of DM-PC. METHODS: In this observational cohort study, 126 patients who underwent surgery for PC at a tertiary referral center from 2010 to 2023 were enrolled, among whom 38 had DMs. Univariate and multivariate Cox regression analyses were used to assess the effects of prognostic factors on DM. RESULTS: The cumulative incidence of DM was 14.1%, 33.8%, and 66.9% at 5, 10, and 20 years in the duration of disease course, respectively. DM-PC patients suffered a worse 5-year overall survival of 37.1% compared with 89.8% in the non-DM patients (p < 0.001). DM-PC patients also suffered more previous operations (p < 0.001), higher preoperative serum calcium (p<0.001) and parathyroid hormone (PTH) levels (p < 0.001), lower frequencies of R0 resection (p < 0.001), higher rates of pathological vascular invasion (p = 0.020), thyroid infiltration (p = 0.027), extraglandular extension (p = 0.001), upper aerodigestive tract (UAT) invasion (p < 0.001), and lymph node metastasis (p < 0.001). Multivariate Cox regression revealed that non-R0 resection (HR 6.144, 95% CI 2.881-13.106, p < 0.001), UAT invasion (HR 3.718, 95% CI 1.782-7.756, p < 0.001), and higher preoperative PTH levels (HR 1.001, 95% CI 1.000-1.001, p = 0.012) were independent risk factors of DM. CONCLUSIONS: Upper aerodigestive tract invasion and higher preoperative PTH levels might be risk factors for possible metastatic involvement of PC. R0 resection and closer surveillance should be considered in such cases to minimize the risk of DM and to optimize patient care.

Modulating the D-Band Center of Electrocatalysts for Enhanced Water Splitting.

To tackle the global energy scarcity and environmental degradation, developing efficient electrocatalysts is essential for achieving sustainable hydrogen production via water splitting. Modulating the d-band center of transition metal electrocatalysts is an effective approach to regulate the adsorption energy of intermediates, alter reaction pathways, lower the energy barrier of the rate-determining step, and ultimately improve electrocatalytic water splitting performance. In this review, a comprehensive overview of the recent advancements in modulating the d-band center for enhanced electrocatalytic water splitting is offered. Initially, the basics of the d-band theory are discussed. Subsequently, recent modulation strategies that aim to boost electrocatalytic activity, with particular emphasis on the d-band center as a key indicator in water splitting are summarized. Lastly, the importance of regulating electrocatalytic activity through d-band center, along with the challenges and prospects for improving electrocatalytic water splitting performance by fine-tuning the transition metal d-band center, are provided.

Double C-H Amination of Naphthylamine Derivatives by the Cross-Dehydrogenation Coupling Reaction.

A high-efficiency tandem process has been developed for the formation of two C-N bonds through a cross-dehydrogenative coupling (CDC) amination of spiro[acridine-9,9'-fluorene]s (SAFs) with amines. This method offers a strategically innovative and atom-economical approach to obtaining diamine-substituted SAFs. Notably, the approach eliminates the need for metal catalysts and other additives, relying solely on O2 as the oxidant. A self-activation mechanism has been proposed to elucidate the effective double amination in the CDC process.

Unraveling the role of BMI and blood markers in the relationship between plant-based diets and osteoporosis: A prospective cohort study.

BACKGROUND: The potential adverse effects of plant-based diets on bone health have raised significant concern, while the prospective evidence is limited. This study aimed to evaluate the association between plant-based diet indexes and incident osteoporosis while exploring the underlying mechanisms involved in this relationship. METHODS: The analysis included 202,063 UK Biobank participants conducted between 2006 and 2022. Plant-based diet indexes (hPDI and uPDI) were calculated using the 24-h dietary questionnaire. Cox proportional risk regression and mediation analysis were used to explore the associations of plant-based diet indexes with osteoporosis, estimating the contribution of BMI and blood markers. RESULTS: We found the highest quintile for hPDI (HR = 1.16; 95% CI: 1.05 to 1.28) and uPDI (HR = 1.15; 95% CI: 1.05 to 1.26) were associated with an increased risk of osteoporosis. BMI was identified as an important mediator in the association between hPDI and osteoporosis, with mediation proportions of 46.17%. For blood markers, the mediating (suppressing) effects of C-reactive protein, alkaline phosphatase, and insulin-like growth factor-1 on the association between uPDI (hPDI) and osteoporosis were significant, ranging from 5.63%-16.87% (4.57%-6.22%). CONCLUSION: Adherence to a plant-based diet is associated with a higher risk of osteoporosis, with BMI and blood markers potentially contributing to this relationship. Notably, even a healthy plant-based diet necessitates attention to weight management to mitigate its impact on bone loss. These findings emphasize the importance of personalized dietary recommendations and lifestyle interventions to decrease the risk of osteoporosis.

Lipid metabolism mediates the association between body mass index change and bone mineral density: The Taizhou imaging study.

BACKGROUND: Limited research explores the impact of body mass index (BMI) change on osteoporosis, regarding the role of lipid metabolism. We aimed to cross-sectionally investigate these relationships in 820 Chinese participants aged 55-65 from the Taizhou Imaging Study. METHODS: We used the baseline data collected between 2013 and 2018. T-score was calculated by standardizing bone mineral density and was used for osteoporosis and osteopenia diagnosis. Multinomial logistic regression was used to examine the effect of BMI change on bone health status. Multivariable linear regression was employed to identify the metabolites corrected with BMI change and T-score. Exploratory factor analysis (EFA) and mediation analysis were conducted to ascertain the involvement of the metabolites. RESULTS: BMI increase served as a protective factor against osteoporosis (OR = 0.79[0.71-0.88], P-value<0.001) and osteopenia (OR = 0.88[0.82-0.95], P-value<0.001). Eighteen serum metabolites were associated with both BMI change and T-score. Specifically, high-density lipoprotein (HDL) substructures demonstrated negative correlations (ß = -0.08 to -0.06 and - 0.12 to -0.08, respectively), while very low-density lipoprotein (VLDL) substructions showed positive correlations (ß = 0.09 to 0.10 and 0.10 to 0.11, respectively). The two lipid factors (HDL and VLDL) extracted by EFA acted as mediators between BMI change and T-score (Prop. Mediated = 8.16% and 10.51%, all P-value<0.01). CONCLUSION: BMI gain among Chinese aged 55-65 is beneficial for reducing the risk of osteoporosis. The metabolism of HDL and VLDL partially mediates the effect of BMI change on bone loss. Our research offers novel insights into the prevention of osteoporosis, approached from the perspective of weight management and lipid metabolomics.

Isolation and functional identification of immune cells in hemolymph of blood clams Tegillarca granosa.

Blood clam Tegillarca granosa is a type of economically cultivated bivalve mollusk with red blood, and it primarily relies on hemocytes in its hemolymph for immune defense. However, there are currently no reports on the isolation and identification of immune cells in T. granosa, which hinders our understanding of their immune defense. In this study, we employed single-cell transcriptome sequencing (scRNA-seq) to visualize the molecular profile of hemocytes in T. granosa. Based on differential expression of immune genes and hemoglobin genes, hemocytes can be molecularly classified into immune cells and erythrocytes. In addition, we separated immune cells using density gradient centrifugation and demonstrated their stronger phagocytic capacity compared to erythrocytes, as well as higher levels of ROS and NO. In summary, our experiments involved the isolation and functional identification of immune cells in hemolymph of T. granosa. This study will provide valuable insights into the innate immune system of red-blood mollusks and further deepen the immunological research of mollusks.

DNA-based nanosized functional ruthenium(II) complex as potential phosphorescent probe to track tumor cells.

Identifying tumor cells can be challenging due to cancer's complex and heterogeneous nature. Here, an efficacious phosphorescent probe that can precisely highlight tumor cells has been created. By combining the ruthenium(II) complex with oligonucleotides, we have developed a nanosized functional ruthenium(II) complex (Ru@DNA) with dimensions ranging from 300 to 500 nm. Our research demonstrates that Ru@DNA can readily traverse biomembranes via ATP-dependent endocytosis without carriers. Notably, the nanosized ruthenium(II) complex exhibits rapid and selective accumulation within tumor cells, possibly attributed to the nanoparticles' enhanced permeation and retention (EPR) effect. Ru@DNA can also effectively discern and label the transplanted cancer cells in the zebrafish model. Moreover, Ru@DNA is efficiently absorbed into the intestine and further distributed in the pancreas. Our findings underscore the potential of Ru@DNA as a DNA-based nanodevice derived from a functional ruthenium(II) complex. This innovative nanodevice holds promise as an efficient phosphorescent probe for both in vitro and in vivo imaging of living tumor cells.