Genome-wide identified VvOFP genes family and VvOFP4 functional characterization provide insight into fruit shape in grape.

Ovate Family Proteins (OFPs) are emerging as novel transcriptional regulators of fruit shape. Despite their established role in various species, their involvement in regulating grape fruit shape remains understudied. This study encompassed a comprehensive evaluation of 16 grape OFP genes in total at the whole genome level. Phylogenetic and synteny analyses established a close relationship between grape VvOFP genes and their tomato counterparts. Expression profiling post-treatment with gibberellic acid (GA3) and thidiazuron (TDZ) revealed that certain OFP genes responded to these regulators, with VvOFP4 showing peak expression three days post-anthesis. Functional assays via overexpression of VvOFP4 in tobacco and tomato altered the morphology of both vegetative and reproductive organs, including leaves, stamens, and fruits/pods. Paraffin sections of transgenic tobacco stems and tomato fruits demonstrated that VvOFP4 overexpression modifies cell dimensions, leading to changes in organ morphology. Additionally, treatments with GA3 and TDZ similarly influenced the shape of grape pulp cells and thereby the overall fruit morphology. These findings suggest that the VvOFP4 gene plays a crucial role in fruit shape determination by modulating cell shape and presents a potential target for future grape breeding programs aimed at diversifying fruit shapes.

GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction.

Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein-ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein-ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods.

Model selection of GLMMs in the analysis of count data in single-case studies: A Monte Carlo simulation.

Generalized linear mixed models (GLMMs) have great potential to deal with count data in single-case experimental designs (SCEDs). However, applied researchers have faced challenges in making various statistical decisions when using such advanced statistical techniques in their own research. This study focused on a critical issue by investigating the selection of an appropriate distribution to handle different types of count data in SCEDs due to overdispersion and/or zero-inflation. To achieve this, I proposed two model selection frameworks, one based on calculating information criteria (AIC and BIC) and another based on utilizing a multistage-model selection procedure. Four data scenarios were simulated including Poisson, negative binominal (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB). The same set of models (i.e., Poisson, NB, ZIP, and ZINB) were fitted for each scenario. In the simulation, I evaluated 10 model selection strategies within the two frameworks by assessing the model selection bias and its consequences on the accuracy of the treatment effect estimates and inferential statistics. Based on the simulation results and previous work, I provide recommendations regarding which model selection methods should be adopted in different scenarios. The implications, limitations, and future research directions are also discussed.

Immobile polyanionic backbone enables a 900-µm-thick electrode for compact energy storage with unprecedented areal capacitance.

Thickening of electrodes is crucial for maximizing the proportion of active components and thus improving the energy density of practical energy storage cells. Nevertheless, trade-offs between electrode thickness and electrochemical performance persist because of the considerably increased ion transport resistance of thick electrodes. Herein, we propose accelerating ion transport through thick and dense electrodes by establishing an immobile polyanionic backbone within the electrode pores; and as a proof of concept, gel polyacrylic electrolytes as such a backbone are in situ synthesized for supercapacitors. During charge and discharge, protons rapidly hop among RCOO- sites for oriented transport, fundamentally reducing the effects of electrode tortuosity and polarization resulting from concentration gradients. Consequently, nearly constant ion transport resistance per unit thickness is achieved, even in the case of a 900-µm-thick dense electrode, leading to unprecedented areal capacitances of 14.85 F cm-2 at 1 mA cm-2 and 4.26 F cm-2 at 100 mA cm-2. This study provides an efficient method for accelerating ion transport through thick and dense electrodes, indicating a significant solution for achieving high energy density in energy storage devices, including but not limited to supercapacitors.

Calcium(II)-Mediated Three-Component Selenylation of gem-Difluoroalkenes: Access to α,α-Difluoroalkyl-ß-selenides.

A calcium-mediated three-component selenylation of gem-difluoroalkenes using alcohols as nucleophiles and N-(phenylseleno)phthalimide as the selenylation agent has been developed for the efficient synthesis of various α,α-difluoroalkyl-ß-selenides. This selenylation reaction exhibits broad substrate and functional group tolerance, along with high levels of chemo- and regioselectivity. Additionally, the synthetic utility of the developed transformation in the late-stage functionalization of drug molecules was demonstrated.

Unveiling the detrimental vicious cycle linking skeletal muscle and COVID-19: A systematic review and meta-analysis.

OBJECTIVE: Skeletal muscle catabolism supports multiple organs and systems during severe trauma and infection, but its role in COVID-19 remains unclear. This study investigates the interactions between skeletal muscle and COVID-19. METHODS: The PubMed, EMbase, and The Cochrane Library databases were systematically searched from January 2020 to August 2023 for cohort studies focusing on the impact of skeletal muscle on COVID-19 prevalence and outcomes, and longitudinal studies examining skeletal muscle changes caused by COVID-19. Skeletal muscle quantity (SMQN) and quality (SMQL) were assessed separately. The random-effect model was predominantly utilized for statistical analysis. RESULTS: Seventy studies with moderate to high quality were included. Low SMQN/SMQL was associated with an increased risk of COVID-19 infection (OR = 1.62, p < 0.001). Both the low SMQN and SMQL predicted COVID-19-related mortality (OR = 1.53, p = 0.016; OR = 2.18, p = 0.001, respectively). Mortality risk decreased with increasing SMQN (OR = 0.979, p = 0.009) and SMQL (OR = 0.972, p = 0.034). Low SMQN and SMQL were also linked to the need for intensive care unit/mechanical ventilation, increased COVID-19 severity, and longer hospital stays. Significant skeletal muscle wasting, characterized by reduced volume and strength, was observed during COVID-19 infection and the pandemic. CONCLUSIONS: This study reveals a detrimental vicious circle between skeletal muscle and COVID-19. Effective management of skeletal muscle could be beneficial for treating COVID-19 infections and addressing the broader pandemic. These findings have important implications for the management of future virus pandemics. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023395476.

Highly Efficient and Reversible Carbon Dioxide Capture by Carbanion-Functionalized Ionic Liquids.

Due to the active unstable nature of carbon anions, it is challenging to develop carbanion-functionalized ionic liquids (ILs) for efficient and reversible carbon dioxide (CO2) capture. Here, a series of carbanion-based ILs with large conjugated structures were designed and a promising system was achieved through tuning the nucleophilicity of carbanions and screening the cation. The ideal carbanion-functionalized IL trihexyl(tetradecyl)phosphonium N,N-diethycyanoacetoamide ([P66614][DECA]) showed equimolar chemisorption of CO2 (up to 0.98 mol CO2 /mol IL) under ambient pressure and excellent absorption rate. What's more, the combined CO2 can be released easily, leading to excellent reversibility due to high stability of anion conjugated structures. More importantly, the presence of water had negligible effect on the absorption capacity, which makes it potential to be applied to the CO2 capture in industrial flue gas. The chemisorption mechanism of the carbanion and CO2 was confirmed by spectroscopic investigations and DFT calculations, where carboxylic acid product was formed through proton transfer after the carbanions reacted with CO2. Considering that high capacity, quick rate as well as excellent reversibility, these carbanion-functionalized IL should certainly represent competitive candidates for further scale up and practical application in CO2 capture.

Construct Validity, Longitudinal Measurement Invariance, Incremental Validity, and Predictive Validity of the Original Grit Scale in Chinese Young Adults.

Although many studies have attempted to validate grit scales because of the construct's popularity, most have considered the shorter rather than the longer Original Grit Scale (Grit-O). We examined the Grit-O's construct validity, longitudinal measurement invariance, incremental validity for academic performance, and longitudinal predictive validity for subjective well-being among young Chinese. We used a cross-sectional sample of 3,322 college students and a longitudinal sample of 1,884 college students, tested twice over 10 months. The first-order factor model fit the data better than other models and showed partial configural and metric measurement invariance over time. Grit and its two facets longitudinally predicted subjective well-being (i.e., life satisfaction, happiness, positive affect, negative affect, and depression) but had negligible incremental validity for two semesters' grades after controlling for conscientiousness. So, while the Grit-O could be a useful construct for young adults, its predictive value overlaps with a better-established construct, conscientiousness.

Optical in situ deciphering of the surface reconstruction-assistant multielectron transfer event of single Co3O4 nanoparticles.

Surface reconstruction determines the fate of catalytic sites on the near-surface during the oxygen evolution reaction. However, deciphering the conversion mechanism of various intermediate-states during surface reconstruction remains a challenge. Herein, we employed an optical imaging technique to draw the landscape of dynamic surface reconstruction on individual Co3O4 nanoparticles. By regulating the surface states of Co3O4 nanoparticles, we explored dynamic growth of the CoOx(OH)y sublayer on single Co3O4 nanoparticles and directly identified the conversion between two dynamics. Rich oxygen vacancies induced more active sites on the surface and prolonged surface reconstruction, which enhanced electrochemical redox and oxygen evolution. These results were further verified by in situ electrochemical extinction spectroscopy of single Co3O4 nanoparticles. We elucidate the heterogeneous evolution of surface reconstruction on individual Co3O4 nanoparticles and present a unique perspective to understand the fate of catalytic species on the nanosurface, which is of enduring significance for investigating the heterogeneity of multielectron-transfer events.

High performance few-mode fiber-based light field direction sensing system using deep convolutional neural network: fiber speckle demodulation network (FSDNET).

Precisely sensing the light field direction information plays the essential role in the fields of three-dimensional (3D) imaging, light field sensing, target positioning and tracking, remote sensing, etc. It is thrilling to find that the optical fiber can be used as a sensing component due to its high sensitivity, compact size, and strong resistance to electromagnetic interference. According to the core principle that the few-mode fiber output speckle pattern is sensitive to the change of incident light field direction, the variation characteristics is further investigated in this research study. Based on the simulation and analysis of the fiber transmission characteristics, the output speckle corresponding to the incident light field with the direction in the range of ±6° horizontally and vertically are calculated. Furthermore, a deep convolutional neural network (CNN): fiber speckle demodulation network (FSDNET) is proposed and constructed to establish what we believe to be a novel way to reveal and identify the mapping relationship between the light field direction and the output speckle. The theoretical simulation shows that the mean absolute error (MAE) between the perceived light field directions and the true directions is 0.01°. Then, a light field direction sensing system based on the few-mode fiber is developed. Regarding to the performance of the sensing system, the MAE of the FSDNET for the light field directions that have appeared in the training set is 0.0389°, and for testing set of the unknown directions that have not appeared in the training set, the MAE is 0.0570°. Therefore, the simulation and experimental results prove that high performance sensing of light field direction can be achieved by the proposed few-mode fiber sensing system and the FSDNET.

Multi-mechanism antitumor/antibacterial effects of Cu-EGCG self-assembling nanocomposite in tumor nanotherapy and drug-resistant bacterial wound infections.

Chemotherapy and surgery stand as primary cancer treatments, yet the unique traits of the tumor microenvironment hinder their effectiveness. The natural compound epigallocatechin gallate (EGCG) possesses potent anti-tumor and antibacterial traits. However, the tumor's adaptability to chemotherapy due to its acidic pH and elevated glutathione (GSH) levels, coupled with the challenges posed by drug-resistant bacterial infections post-surgery, impede treatment outcomes. To address these challenges, researchers strive to explore innovative treatment strategies, such as multimodal combination therapy. This study successfully synthesized Cu-EGCG, a metal-polyphenol network, and detailly characterized it by using synchrotron radiation and high-resolution mass spectrometry (HRMS). Through chemodynamic therapy (CDT), photothermal therapy (PTT), and photodynamic therapy (PDT), Cu-EGCG showed robust antitumor and antibacterial effects. Cu+ in Cu-EGCG actively participates in a Fenton-like reaction, generating hydroxyl radicals (·OH) upon exposure to hydrogen peroxide (H2O2) and converting to Cu2+. This Cu2+ interacts with GSH, weakening the oxidative stress response of bacteria and tumor cells. Density functional theory (DFT) calculations verified Cu-EGCG's efficient GSH consumption during its reaction with GSH. Additionally, Cu-EGCG exhibited outstanding photothermal conversion when exposed to 808 nm near-infrared (NIR) radiation and produced singlet oxygen (1O2) upon laser irradiation. In both mouse tumor and wound models, Cu-EGCG showcased remarkable antitumor and antibacterial properties.

PROX1 drives neuroendocrine plasticity and liver metastases in prostate cancer.

With the widespread use of anti-androgen therapy, such as abiraterone and enzalutamide, the incidence of neuroendocrine prostate cancer (NEPC) is increasing. NEPC is a lethal form of prostate cancer (PCa), with a median overall survival of less than one year after diagnosis. In addition to the common bone metastases seen in PCa, NEPC exhibits characteristics of visceral metastases, notably liver metastasis, which serves as an indicator of a poor prognosis clinically. Key factors driving the neuroendocrine plasticity of PCa have been identified, yet the underlying mechanism behind liver metastasis remains unclear. In this study, we identified PROX1 as a driver of neuroendocrine plasticity in PCa, responsible for promoting liver metastases. Mechanistically, anti-androgen therapy alleviates transcriptional inhibition of PROX1. Subsequently, elevated PROX1 levels drive both neuroendocrine plasticity and liver-specific transcriptional reprogramming, promoting liver metastases. Moreover, liver metastases in PCa induced by PROX1 depend on reprogrammed lipid metabolism, a disruption that effectively reduces the formation of liver metastases.

Highly Responsive and Self-Powered Photodetector Based on PtSe2/MoS2 Heterostructure.

In recent years, 2D materials and their heterostructures have started to offer an ideal platform for high-performance photodetection devices. In this work, a highly responsive, self-powered photodetector based on PtSe2/MoS2 van der Waals heterostructure is demonstrated. The device achieves a noteworthy wide band spectral response from visible (405 nm) range to the near infrared region (980 nm). The remarkable photoresponsivity and external quantum efficiency up to 4.52 A/W, and 1880% are achieved, respectively, at 405 nm illumination with fast response time of 20 ms. In addition, the photodetector exhibits a decent photoresponsivity of 33.4 mA/W at zero bias, revealing the photodetector works well in the self-driven mode. Our work suggests that a PtSe2/MoS2 heterostructure could be a potential candidate for the high-performance photodetection applications.

AutoESDCas: A Web-Based Tool for the Whole-Workflow Editing Sequence Design for Microbial Genome Editing Based on the CRISPR/Cas System.

Genome editing is the basis for the modification of engineered microbes. In the process of genome editing, the design of editing sequences, such as primers and sgRNA, is very important for the accurate positioning of editing sites and efficient sequence editing. The whole process of genome editing involves multiple rounds and types of editing sequence design, while the development of related whole-workflow design tools for high-throughput experimental requirements lags. Here, we propose AutoESDCas, an online tool for the end-to-end editing sequence design for microbial genome editing based on the CRISPR/Cas system. This tool facilitates all types of genetic manipulation covering diverse experimental requirements and design scenarios, enables biologists to quickly and efficiently obtain all editing sequences needed for the entire genome editing process, and empowers high-throughput strain modification. Notably, with its off-target risk assessment function for editing sequences, the usability of the design results is significantly improved. AutoESDCas is freely available at https://autoesdcas.biodesign.ac.cn/with the source code at https://github.com/tibbdc/AutoESDCas/.

GSEA analysis identifies potential drug targets and their interaction networks in coronary microcirculation disorders.

Coronary microcirculation dysfunction (CMD) is one of the main causes of cardiovascular disease. Traditional treatment methods lack specificity, making it difficult to fully consider the differences in patient conditions and achieve effective treatment and intervention. The complexity and diversity of CMD require more standardized diagnosis and treatment plans to clarify the best treatment strategy and long-term outcomes. The existing treatment measures mainly focus on symptom management, including medication treatment, lifestyle intervention, and psychological therapy. However, the efficacy of these methods is not consistent for all patients, and the long-term efficacy is not yet clear. GSEA is a bioinformatics method used to interpret gene expression data, particularly for identifying the enrichment of predefined gene sets in gene expression data. In order to achieve personalized treatment and improve the quality and effectiveness of interventions, this article combined GSEA (Gene Set Enrichment Analysis) technology to conduct in-depth research on potential drug targets and their interaction networks in coronary microcirculation dysfunctions. This article first utilized the Coremine medical database, GeneCards, and DrugBank public databases to collect gene data. Then, filtering methods were used to preprocess the data, and GSEA was used to analyze the preprocessed gene expression data to identify and calculate pathways and enrichment scores related to CMD. Finally, protein sequence features were extracted through the calculation of autocorrelation features. To verify the effectiveness of GSEA, this article conducted experimental analysis from four aspects: precision, receiver operating characteristic (ROC) curve, correlation, and potential drug targets, and compared them with Gene Regulatory Networks (GRN) and Random Forest (RF) methods. The results showed that compared to the GRN and RF methods, the average precision of GSEA improved by 0.11. The conclusion indicated that GSEA helped identify and explore potential drug targets and their interaction networks, providing new ideas for personalized quality of CMD.

Discovery of KT-413, a Targeted Protein Degrader of IRAK4 and IMiD Substrates Targeting MYD88 Mutant Diffuse Large B-Cell Lymphoma.

Developing therapies for the activated B-cell like (ABC) subtype of diffuse large B-cell lymphomas (DLBCL) remains an area of unmet medical need. A subset of ABC DLBCL tumors is driven by activating mutations in myeloid differentiation primary response protein 88 (MYD88), which lead to constitutive activation of interleukin-1 receptor associated kinase 4 (IRAK4) and cellular proliferation. IRAK4 signaling is driven by its catalytic and scaffolding functions, necessitating complete removal of this protein and its escape mechanisms for complete therapeutic suppression. Herein, we describe the identification and characterization of a dual-functioning molecule, KT-413 and show it efficiently degrades IRAK4 and the transcription factors Ikaros and Aiolos. KT-413 achieves concurrent degradation of these proteins by functioning as both a heterobifunctional degrader and a molecular glue. Based on the demonstrated activity and safety of KT-413 in preclinical studies, a phase 1 clinical trial in B-cell lymphomas, including MYD88 mutant ABC DLBCL, is currently underway.

Diagnostic value of carbohydrate antigen 50 in biliary tract cancer: A large-scale multicenter study.

BACKGROUND: To date, carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) have been widely used for the screening, diagnosis and prediction of biliary tract cancer (BTC) patients. However, few studies with large sample sizes of carbohydrate antigen 50 (CA50) were reported in BTC patients. METHODS: A total of 1121 patients from the Liver Cancer Clin-Bio Databank of Anhui Hepatobiliary Surgery Union between January 2017 and December 2022 were included in this study (673 in the training cohort and 448 in the validation cohort): among them, 458 with BTC, 178 with hepatocellular carcinoma (HCC), 23 with combined hepatocellular-cholangiocarcinoma, and 462 with nontumor patients. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the diagnostic efficacy and clinical usefulness. RESULTS: ROC curves obtained by combining CA50, CA19-9, and AFP showed that the AUC value of the diagnostic MODEL 1 was 0.885 (95% CI 0.856-0.885, specificity 70.3%, and sensitivity 84.0%) in the training cohort and 0.879 (0.841-0.917, 76.7%, and 84.3%) in the validation cohort. In addition, comparing iCCA and HCC (235 in the training cohort, 157 in the validation cohort), the AUC values of the diagnostic MODEL 2 were 0.893 (95% CI 0.853-0.933, specificity 96%, and sensitivity 68.6%) in the training cohort and 0.872 (95% CI 0.818-0.927, 94.2%, and 64.6%) in the validation cohort. CONCLUSION: The model combining CA50, CA19-9, and AFP not only has good diagnostic value for BTC but also has good diagnostic value for distinguishing iCCA and HCC.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification.

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Urban biodiversity conservation: A framework for ecological network construction and priority areas identification considering habit differences within species.

The construction of ecological networks within the context of urbanization is an effective approach to cope with the challenges of urban biodiversity decline, representing a crucial goal in urban planning and development. However, existing studies often overlook the richness and uniqueness within species communities by homogenizing traits of species in the same class. This study proposes a framework for constructing and optimizing ecological networks focused on differential conservation within the same class. By classifying birds into three groups (specialists of water, forest or urban areas) based on their ecological requirements and urbanization tolerance, we constructed an ecological network tailored to their distinct migratory dispersal patterns. We then identified strategic areas including pinch points, barriers, and breakpoints specific to each bird group. Our findings reveal notable variations in suitable habitat distribution among different bird groups in urban environments. Corridor layouts varied according to habitat preferences and migratory dispersal patterns. Despite these differences, urban built-up areas persist as central hubs for the distribution of suitable habitats for 75% of bird species, with peripheral mountain-plain transition areas constituting 63% of crucial dispersal corridors. This emphasizes the critical role of urban built-up areas in maintaining biodiversity and ecological connectivity. Prioritizing connectivity between central urban areas and distant natural spaces is imperative. Our approach innovatively classifies and constructs networks to identify strategic areas with diverse species-specific attributes, providing valuable spatial information for land planning and guiding solutions to enhance target species. While the primary focus is on bird conservation in Beijing, our framework is broadly applicable to global biodiversity management and green planning under urbanization challenges. Overall, this study offers innovative insights for urban planning development and serves as decision support for prioritizing urban actions.

Chitosan-based packaging films with antibacterial-sterilization integrated continuous activity for extending the shelf life of perishable foods.

The current food packaging films can be preservative but lack the function of combining antibacterial and sterilization which lead to films can not maximize prolong shelf life of perishable foods. This study provided a new strategy to realize prolonging shelf life of perishable foods by integrating antibacterial and sterilization which focused on applying photodynamic inactivation to films with continuous activity, where curcumin (CUR) and sodium copper chlorophyll (SCC) were loaded into chitosan (CS) films. Compared to pure CS films, the barrier capacity (oxygen permeability and water vapor permeability) and mechanical properties of composite films were improved by introducing CUR and SCC. In addition, the composite film can effectively against food-borne pathogenic bacteria and significantly prolong the shelf life of cherries and pork. The provided strategy has potential application prospects in food preservation packaging.