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Premature petal senescence dramatically reduces flower quality and value. Ethylene and reactive oxygen species (ROS) are key players in accelerating rose petal senescence, but the molecular mechanism by which ethylene antagonizes ROS scavenging is not well understood. Here, we show that ethylene reduces ascorbic acid (AsA) production, leading to the accumulation of ROS and hastening petal senescence. Ethylene treatment suppresses the expression of GDP-L-galactose phosphorylase 1 (RhGGP1), encoding the rate-controlling enzyme in AsA biosynthesis. A HOMEODOMAIN-LEUCINE ZIPPER (HD-ZIP) II transcriptional factor, RhHB22, directly binds to the promoter of RhGGP1 and inhibits its transcription. RhHB22 is induced by ethylene, and silencing of RhHB22 increases RhGGP1 expression and AsA production, resulting in reduced H2O2 accumulation and delayed petal senescence. Additionally, the delayed petal senescence symptoms of RhHB22-silenced plants were suppressed by silencing RhGGP1. Moreover, the expression of RhGGP1, which is suppressed by ethylene in wild-type petals, is significantly compromised in RhHB22-silenced petals. These findings uncover the transcriptional regulatory mechanism by which ethylene promotes ROS accumulation and petal senescence by inhibiting AsA biosynthesis, enhance our understanding of ethylene-induced petal senescence, and provide novel insights for improving the longevity of cut flowers., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Corynebacterium glutamicum is a key industrial workhorse for producing amino acids and high-value chemicals. Balancing metabolic flow between cell growth and product synthesis is crucial for enhancing production efficiency. Developing dynamic, broadly applicable, and minimally toxic gene regulation tools for C. glutamicum remains challenging, as optogenetic tools ideal for dynamic regulatory strategies have not yet been developed. This study introduces an advanced light-controlled gene expression system using light-controlled RNA-binding proteins (RBP), a first for Corynebacterium glutamicum. We established a gene expression regulation system, 'LightOnC.glu', utilizing the light-controlled RBP to construct light-controlled transcription factors in C. glutamicum. Simultaneously, we developed a high-performance light-controlled gene interference system using CRISPR/Cpf1 tools. The metabolic flow in the synthesis network was designed to enable the production of chitin oligosaccharides (CHOSs) and chondroitin sulphate oligosaccharides A (CSA) for the first time in C. glutamicum. Additionally, a light-controlled bioreactor was constructed, achieving a CHOSs production concentration of 6.2 g/L, the highest titer recorded for CHOSs biosynthesis to date. Herein, we have established a programmable light-responsive genetic circuit in C. glutamicum, advancing the theory of dynamic regulation based on light signaling. This breakthrough has potential applications in optimizing metabolic modules in other chassis cells and synthesizing other compounds., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: Vitiligo is a clinically prevalent acquired skin disorder characterized by depigmentation. Currently, the therapeutic options for vitiligo are restricted, and numerous issues exist, such as a prolonged treatment course, unsatisfactory therapeutic efficacy, adverse reactions, and a high propensity for recurrence after treatment cessation., Aim: To assess the safety and efficacy of combined treatment involving oral tofacitinib citrate (TC) and a 308-nm excimer laser (EL), with the aim of discovering a rapid and effective treatment approach to minimize the side effects of various drugs., Methods: A total of 63 patients with progressive vitiligo and a Vitiligo Disease Activity Score (VIDA) score of four from January 2022 to January 2024 were enrolled and divided into three groups. The three groups were the TC combined with 308-nm EL and methylprednisolone treatment group, the TC and 308-nm EL treatment group, and the methylprednisolone treatment alone group. The treatment was continued for 24 weeks. Finally, assessment of therapy outcomes was performed., Results: When TC was combined with 308-nm EL and methylprednisolone treatment, the effect was more rapid. When TC was combined with 308-nm EL, it had a more favorable treatment effect than that of methylprednisolone alone, despite having a slower effect onset. After 24 weeks of treatment, there was no significant difference between the two groups in terms of the total response rate or significant efficiency. However, when only methylprednisolone treatment was applied, the effect is relatively slow and inefficient., Conclusion: This study confirmed the rapid and efficient treatment of vitiligo through the combination of TC and 308-nm EL, and no serious adverse reactions were reported, which could offer an optimal treatment plan for patients with vitiligo., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Association of Dermatologists.)

Agitated depression (A-MDD) is a severe subtype of major depressive disorder, with an increased risk of suicidality and the potential to evolve into bipolar disorder. Despite its clinical significance, the neural basis remains unclear. We hypothesize that psychomotor agitation, marked by pressured speech and racing thoughts, is linked to disruptions in brain dynamics. To test this hypothesis, we examined brain dynamics using time delay estimation and edge-centre time series, as well as dynamic connections between the somatomotor network (SMN) and the default mode network in 44 patients with A-MDD, 75 with non-agitated MDD (NA-MDD), and 94 healthy controls. Our results revealed that the neural co-activity duration was shorter in the A-MDD group compared with both the NA-MDD and controls (A-MDD versus NA-MDD: t = 2.295; A-MDD versus controls: t = 2.192, all P < 0.05). In addition, the dynamic of neural fluctuation in SMN altered in the A-MDD group than in the NA-MDD group ( t = -2.616, P = 0.011) and was correlated with agitation severity ( β = -0.228, P = 0.011). The inter-network connection was reduced in the A-MDD group compared with the control group ( t = 2.102, P = 0.037), especially at low-amplitude time points ( t = 2.139, P = 0.034). These findings indicate rapid neural fluctuations and disrupted dynamic coupling between the SMN and default mode network in A-MDD, potentially underlying the psychomotor agitation characteristic of this subtype. These insights contribute to a more nuanced understanding of the heterogeneity of depression and have implications for differential diagnosis and treatment strategies., Competing Interests: The authors report no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Single-cell multi-omics refers to the various types of biological data at the single-cell level. These data have enabled insight and resolution to cellular phenotypes, biological processes, and developmental stages. Current advances hold high potential for breakthroughs by integrating multiple different omics layers. However, singlecell multi-omics data usually have different feature dimensions and direct or indirect relationships. How to keep the data structure of these different data and extract hidden relationships is a major challenge for omics data integration, and effective integration models are urgently needed. In this paper, we propose an irregular tensor decomposition model (GSTRPCA) based on tensor robust principal component analysis (TRPCA). We developed a weighted threshold model for the decomposition of irregular tensor data by combining low-rank and sparsity constraints, which requires that the low-dimensional embeddings of the data remain lowrank and sparse. The major advantage of the GSTRPCA algorithm is its ability to keep the original data structure and explore hidden related features among omics data. For GSTRPCA, we also designed an effective algorithm that theoretically guarantees global convergence for the tensor decomposition. The computational experiments on irregular tensor datasets demonstrate that GSTRPCA significantly outperformed the state-of-the-art methods and hence confirm the superiority of GSTRPCA in clustering single-cell multiomics data. To our knowledge, this is the first tensor decomposition method for irregular tensor data to keep the data structure and hence improve the clustering performance for single-cell multi-omics data. GSTRPCA is a Matlabbased algorithm, and the code is available from https://github.com/GGL-B/GSTRPCA., (© The Author(s) 2024. Published by Oxford University Press.)

Identifying the essential factors and underlying mechanisms regulating plant heat stress (HS) responses is crucial for mitigating the threat posed by HS on plant growth, development, distribution, and productivity. In this study, we found that the Arabidopsis (Arabidopsis thaliana) super-killer2 (ski2) dicer-like4 (dcl4) mutant, characterized by RNA processing defects and the accumulation of abundant 22-nt small interfering RNAs derived from protein-coding transcripts, displayed significantly increased expression levels of HS-responsive genes and enhanced thermotolerance. These traits primarily resulted from the suppression of SMAX1-LIKE4 (SMXL4) and SMXL5, which encode 2 putative transcriptional regulators that belong to the SMXL protein family. While smxl4 and smxl5 single mutants were similar to wild type, the smxl4 smxl5 double mutant displayed substantially heightened seedling thermotolerance. Further investigation demonstrated that SMXL4 and SMXL5 repressed the transcription of HEAT-SHOCK TRANSCRIPTION FACTOR A2 (HSFA2), encoding a master regulator of thermotolerance, independently of ethylene-response factor-associated amphiphilic repression motifs. Moreover, SMXL4 and SMXL5 interacted with HSFA1d and HSFA1e, central regulators sensing and transducing HS stimuli, and antagonistically affected their transactivation activity. In addition, HSFA2 directly bound to the SMXL4 and SMXL5 promoters, inducing their expression during recovery from HS. Collectively, our findings elucidate the role of the SMXL4/SMXL5-HSFA2 regulatory module in orchestrating plant thermotolerance under HS., Competing Interests: Conflict of interest statement. The authors declare no conflict of interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society of Plant Biologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Chilling stress threatens plant growth and development, particularly affecting membrane fluidity and cellular integrity. Understanding plant membrane responses to chilling stress is important for unraveling the molecular mechanisms of stress tolerance. Whereas core transcriptional responses to chilling stress and stress tolerance are conserved across species, the associated changes in membrane lipids appear to be less conserved, as which lipids are affected by chilling stress varies by species. Here, we investigated changes in gene expression and membrane lipids in response to chilling stress during one 24 h cycle in chilling-tolerant foxtail millet (Setaria italica), and chilling-sensitive sorghum (Sorghum bicolor) and Urochloa (browntop signal grass, Urochloa fusca, lipids only), leveraging their evolutionary relatedness and differing levels of chilling stress tolerance. We show that most chilling-induced lipid changes are conserved across the three species, while we observed distinct, time-specific responses in chilling-tolerant foxtail millet, indicating the presence of a finely orchestrated adaptive mechanism. We detected rhythmicity in lipid responses to chilling stress in the three grasses, which were also present in Arabidopsis thaliana, suggesting the conservation of rhythmic patterns across species and highlighting the importance of accounting for time of day. When integrating lipid datasets with gene expression profiles, we identified potential candidate genes that showed corresponding transcriptional changes in response to chilling stress, providing insights into the differences in regulatory mechanisms between chilling-sensitive sorghum and chilling-tolerant foxtail millet., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Predicting associations between microbes and diseases opens up new avenues for developing diagnostic, preventive, and therapeutic strategies. Given that laboratory-based biological tests to verify these associations are often time-consuming and expensive, there is a critical need for innovative computational frameworks to predict new microbe-disease associations. In this work, we introduce a novel prediction algorithm called Predicting Human Disease-Microbe Associations using Cross-Domain Matrix Factorization (CMFHMDA). Initially, we calculate the composite similarity of diseases and the Gaussian interaction profile similarity of microbes. We then apply the Weighted K Nearest Known Neighbors (WKNKN) algorithm to refine the microbe-disease association matrix. Our CMFHMDA model is subsequently developed by integrating the network data of both microbes and diseases to predict potential associations. The key innovations of this method include using the WKNKN algorithm to preprocess missing values in the association matrix and incorporating cross-domain information from microbes and diseases into the CMFHMDA model. To validate CMFHMDA, we employed three different cross-validation techniques to evaluate the model's accuracy. The results indicate that the CMFHMDA model achieved Area Under the Receiver Operating Characteristic Curve scores of 0.9172, 0.8551, and 0.9351$\pm $0.0052 in global Leave-One-Out Cross-Validation (LOOCV), local LOOCV, and five-fold CV, respectively. Furthermore, many predicted associations have been confirmed by published experimental studies, establishing CMFHMDA as an effective tool for predicting potential disease-associated microbes., (© The Author(s) 2024. Published by Oxford University Press.)

Acephalic spermatozoa syndrome (ASS) represents a rare genetic and reproductive disease, which is defined as semen composed of mostly headless spermatozoa. The connecting piece in the neck region, also known as the head-to-tail coupling apparatus (HTCA), plays a crucial role in the tight linkage between the sperm head and tail. Dysfunction of this structure can lead to separation of sperm heads and tails, and male infertility. Using the mouse as an experimental model, several proteins have been identified as associated with the HTCA and disruption of these proteins causes acephalic spermatozoa. However, the molecular mechanism underlying this morphologic anomaly and HTCA remains elusive. In this study, we focused on coiled-coil domain containing 188 (Ccdc188), which shows testis-enriched expression. To elucidate the physiological role of CCDC188, we generated a knockout (KO) mouse line using the CRISPR/Cas9 system. Ccdc188 KO male mice were sterile, indicating that CCDC188 is indispensable for male fertility. Most Ccdc188-null spermatozoa were acephalic. Transmission electron microscopy revealed that while the sperm HTCA could assemble properly without CCDC188, the HTCA failed to attach to the nucleus during spermiogenesis, leading to sperm head and neck separation. In addition, we found almost all of the spermatozoa in the cauda epididymis lacked a mitochondrial sheath. Taken together, we demonstrated that CCDC188 plays a crucial role in forming a tight sperm head-neck junction., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for the Study of Reproduction. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Hemoglobin (Hb) Hekinan II (A27; Glu-Asp) is an α-chain variant, and its interaction with the common Southeast Asian (--SEA/) α-thalassemia (α-thal) deletion is rarely reported. This study provides a clinical update of Hb Hekinan II associated with (--SEA/) α-thal., Methods: A total of 11 simple heterozygotes and 20 composite heterozygotes for Hb Hekinan II and (--SEA/) α-thal were included based on molecular diagnosis., Results: Hb Hekinan II exhibited a significant increase in hemoglobin, mean corpuscular volume, and mean corpuscular hemoglobin content, but a decrease in red blood cell level compared with α+ thalassemia deletion. Compared with (--SEA/) α-thal, composite heterozygotes for Hb Hekinan II and (--SEA/) α-thal showed similar erythrocyte parameters. Both heterozygotes with and without (--SEA/) α-thal showed low Hb A2 level. Hb Hekinan II showed abnormal performance in high-performance liquid chromatography but not in capillary electrophoresis., Conclusion: Hb Hekinan II is a benign Hb variant. The heterozygotes exhibit clinically asymptomatic coinheritance with (--SEA/) α-thal having comparable hematological phenotype to simple (--SEA/) α-thal. The combination of hematological and molecular analysis helped to improve the detection rate of this rare variant., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Objective: Most existing fine-tuned biomedical large language models (LLMs) focus on enhancing performance in monolingual biomedical question answering and conversation tasks. To investigate the effectiveness of the fine-tuned LLMs on diverse biomedical natural language processing (NLP) tasks in different languages, we present Taiyi, a bilingual fine-tuned LLM for diverse biomedical NLP tasks., Materials and Methods: We first curated a comprehensive collection of 140 existing biomedical text mining datasets (102 English and 38 Chinese datasets) across over 10 task types. Subsequently, these corpora were converted to the instruction data used to fine-tune the general LLM. During the supervised fine-tuning phase, a 2-stage strategy is proposed to optimize the model performance across various tasks., Results: Experimental results on 13 test sets, which include named entity recognition, relation extraction, text classification, and question answering tasks, demonstrate that Taiyi achieves superior performance compared to general LLMs. The case study involving additional biomedical NLP tasks further shows Taiyi's considerable potential for bilingual biomedical multitasking., Conclusion: Leveraging rich high-quality biomedical corpora and developing effective fine-tuning strategies can significantly improve the performance of LLMs within the biomedical domain. Taiyi shows the bilingual multitasking capability through supervised fine-tuning. However, those tasks such as information extraction that are not generation tasks in nature remain challenging for LLM-based generative approaches, and they still underperform the conventional discriminative approaches using smaller language models., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

As an element with metalloid properties, arsenic is pervasively present in the environment and is recognized as a potent carcinogen. Consequently, the issue of human arsenic exposure has become a significant concern within the global public health sector. Numerous studies have indicated that arsenic induces cellular senescence through various mechanisms, including triggering epigenetic alterations, inducing the senescence-associated secretory phenotype (SASP), promoting telomere shortening, and causing mitochondrial dysfunction. This article collates and summarizes the latest research advancements on the involvement of cellular senescence in arsenic toxicity and explores the mechanisms of arsenic-induced toxicity. This study aims to provide new perspectives and directions for future research on arsenic toxicity and the development of prevention and treatment strategies., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Achieving long-term clinical remission in Crohn's disease (CD) with antitumor necrosis factor α (anti-TNF-α) agents remains challenging., Aims: This study aims to establish a prediction model based on patients' clinical characteristics using a machine-learning approach to predict the long-term efficacy of infliximab (IFX)., Methods: Three cohorts comprising 746 patients with CD were included from 3 inflammatory bowel disease (IBD) centers between June 2013 and January 2022. Clinical records were collected from baseline, 14-, 30-, and 52-week post-IFX treatment. Three machine-learning approaches were employed to develop predictive models based on 23 baseline predictors. The SHapley Additive exPlanations (SHAP) algorithm was used to dissect underlying predictors, and latent class mixed model (LCMM) was applied for trajectory analysis of the longitudinal change of blood routine tests along with long-term IFX therapy., Results: The XGBoost model exhibited the best discrimination between long-term responders and nonresponders. In the internal training and testing set, the model achieved an AUC of 0.91 (95% CI, 0.86-0.95) and 0.71 (95% CI, 0.66-0.87), respectively. Moreover, it achieved a moderate predictive performance in the independent external cohort, with an AUC of 0.68 (95% CI, 0.59-0.77). The SHAP algorithm revealed disease-relevant laboratory measurements, notably hemoglobin (HB), white blood cells (WBC), erythrocyte sedimentation rate (ESR), albumin (ALB), and platelets (PLT), alongside age at diagnosis and the Montreal classification, as the most influential predictors. Furthermore, 2 distinct patient clusters based on dynamic laboratory tests were identified for monitoring the long-term remission., Conclusions: The established prediction model demonstrated remarkable discriminatory power in distinguishing long-term responders from nonresponders to IFX therapy. The identification of distinct patient clusters further emphasizes the need for tailored therapeutic approaches in CD management., (© The Author(s) 2024. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Context: Resting heart rate (HR) is increasingly recognized as an indicator of disease and overall morbidity and mortality. Whether chronic coffee consumption affects resting HR is an important consideration for individual consumers as well as from a public health perspective., Objective: A meta-analysis of randomized controlled trials (RCTs) was conducted to examine the effectiveness of coffee consumption on resting HR., Data Sources: Original RCTs assessing the effect of coffee consumption on resting HR and published prior to March 2023 were identified by searching online databases, including PubMed, Web of Science, and Cochrane Library databases., Data Extraction and Analysis: Data searches and extraction and risk-of-bias assessments were performed according to the Cochrane guidelines, and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for systematic reviews were followed. Data on study characteristics, type, and amount of coffee and net change and measurement resting HR were extracted. A random-effects or a fixed-effects model was used to estimate the pooled effect sizes. Homogeneity was determined with the Cochran Q test, and publication bias was assessed through Begg's test, Egger's test, and funnel plots., Results: A total of 6 RCTs with 11 intervention trials or arms involving 485 individuals were included. The participants were generally healthy, although some had hypertension, hypercholesterolemia, or were overweight. The trial duration ranged from 2 weeks to 24 weeks. The overall pooled analysis showed that coffee consumption resulted in a negligible increase in resting HR of 0.40 beats per minute (95% CI: -0.78 to 1.57; P = 0.506), which was statistically insignificant. Subgroup analysis of all specified categories was consistent with the overall analysis. No heterogeneity was observed among included trials (I2 = 0.0%, P = 0.756)., Conclusion: The results of the present meta-analysis study demonstrate that daily coffee consumption of 3 to 6 cups for a period of 2 to 24 weeks has no statistically significant effect on resting HR., (© The Author(s) 2023. Published by Oxford University Press on behalf of the International Life Sciences Institute. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background and Hypothesis: This study investigated the role of the medial prefrontal cortex (mPFC)-basolateral amygdala (BLA) pathway in schizophrenia (SCZ)-related cognitive impairments using various techniques., Study Design: This study utilized clinical scales, magnetic resonance imaging, single-cell RNA sequencing, and optogenetics to investigate the mPFC-BLA pathway in SCZ patients. In the mouse model, 6-week-old methylazoxymethanol acetate-induced mice demonstrated significant cognitive deficits, which were addressed through stereotaxic injections of an adeno-associated viral vector to unveil the neural connection between the mPFC and BLA., Study Results: Significant disparities in brain volume and neural activity, particularly in the dorsolateral prefrontal cortex (DLPFC) and BLA regions, were found between SCZ patients and healthy controls. Additionally, we observed correlations indicating that reduced volumes of the DLPFC and BLA were associated with lower cognitive function scores. Activation of the mPFC-BLA pathway notably improved cognitive performance in the SCZ model mice, with the targeting of excitatory or inhibitory neurons alone failing to replicate this effect. Single-cell transcriptomic profiling revealed gene expression differences in excitatory and inhibitory neurons in the BLA of SCZ model mice. Notably, genes differentially expressed in the BLA of these model mice were also found in the blood exosomes of SCZ patients., Conclusions: Our research provides a comprehensive understanding of the role of the PFC-BLA pathway in SCZ, underscoring its significance in cognitive impairment and offering novel diagnostic and therapeutic avenues. Additionally, our research highlights the potential of blood exosomal mRNAs as noninvasive biomarkers for SCZ diagnosis, underscoring the clinical feasibility and utility of this method., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Context: Large-for-gestational-age (LGA), one of the most common complications of gestational diabetes mellitus (GDM), has become a global concern. The predictive performance of common continuous glucose monitoring (CGM) metrics for LGA is limited., Objective: We aimed to develop and validate an artificial intelligence (AI) based model to determine the probability of women with GDM giving birth to LGA infants during pregnancy using CGM measurements together with demographic data and metabolic indicators., Methods: A total of 371 women with GDM from a prospective cohort at a university hospital were included. CGM was performed during 20-34 gestational weeks, and glycemic fluctuations were evaluated and visualized in women with GDM who gave birth to LGA and non-LGA infants. A convolutional neural network (CNN)-based fusion model was developed to predict LGA. Comparisons among the novel fusion model and three conventional models were made using the area under the receiver-operating characteristic curve (AUCROC) and accuracy., Results: Overall, 76 (20.5%) out of 371 GDM women developed LGA neonates. The visualized 24-h glucose profiles differed at midmorning. This difference was consistent among subgroups categorized by pregestational BMI, therapeutic protocol and CGM administration period. The AI based fusion prediction model using 24-h CGM data and 15 clinical variables for LGA prediction (AUCROC 0.852, 95% CI 0.680-0.966, accuracy 84.4%) showed superior discriminative power compared with the three classic models., Conclusions: We demonstrated better performance in predicting LGA infants among women with GDM using the AI based fusion model. The characteristics of the CGM profiles allowed us to determine the appropriate window for intervention., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. See the journal About page for additional terms.)

The unit of visual working memory is a fundamental issue under debate in the fields of cognitive psychology and neuroscience, with some traditional research suggesting that it is an object, while other recent studies demonstrating that a Boolean map offers a better account. The controversy surrounding the unit of visual working memory often centers on the representation of objects consist of same dimensional features (e.g. bicolor objects). For 2 colors in a bicolor object, some behavioral studies have suggested that they need to be represented by separate units, while some other studies using electrophysiological measures have found that they can be represented within a single unit. This apparent conflict hints that Boolean map and object may reconcile as the unit of visual working memory. Adopting the contralateral delay activity as an electrophysiological marker of visual working memory, experiments 1 and 2 consistently found that the contralateral delay activity amplitude for memorizing bicolor circles at P7/P8 conformed the Boolean map-based storage throughout the whole maintenance, while the contralateral delay activity amplitude at P3/P4 just conformed the object-based storage during the early period. It suggests though Boolean map got stronger supporting evidence than object, they 2 may coexist as the unit of visual working memory., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Motivation: Biomedical relation extraction at the document level (Bio-DocRE) involves extracting relation instances from biomedical texts that span multiple sentences, often containing various entity concepts such as genes, diseases, chemicals, variants, etc. Currently, this task is usually implemented based on graphs or transformers. However, most work directly models entity features to relation prediction, ignoring the effectiveness of entity pair information as an intermediate state for relation prediction. In this article, we decouple this task into a three-stage process to capture sufficient information for improving relation prediction., Results: We propose an innovative framework HTGRS for Bio-DocRE, which constructs a hierarchical tree graph (HTG) to integrate key information sources in the document, achieving relation reasoning based on entity. In addition, inspired by the idea of semantic segmentation, we conceptualize the task as a table-filling problem and develop a relation segmentation (RS) module to enhance relation reasoning based on the entity pair. Extensive experiments on three datasets show that the proposed framework outperforms the state-of-the-art methods and achieves superior performance., Availability and Implementation: Our source code is available at https://github.com/passengeryjy/HTGRS., (© The Author(s) 2024. Published by Oxford University Press.)

Background and Aims: Emerging evidence has raised an obesity paradox in observational studies of body mass index (BMI) and health among the oldest-old (aged ≥80 years), as an inverse relationship of BMI with mortality was reported. This study was to investigate the causal associations of BMI, waist circumference (WC), or both with mortality in the oldest-old people in China., Methods: A total of 5306 community-based oldest-old (mean age 90.6 years) were enrolled in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) between 1998 and 2018. Genetic risk scores were constructed from 58 single-nucleotide polymorphisms (SNPs) associated with BMI and 49 SNPs associated with WC to subsequently derive causal estimates for Mendelian randomization (MR) models. One-sample linear MR along with non-linear MR analyses were performed to explore the associations of genetically predicted BMI, WC, and their joint effect with all-cause mortality, cardiovascular disease (CVD) mortality, and non-CVD mortality., Results: During 24 337 person-years of follow-up, 3766 deaths were documented. In observational analyses, higher BMI and WC were both associated with decreased mortality risk [hazard ratio (HR) 0.963, 95% confidence interval (CI) 0.955-0.971 for a 1-kg/m2 increment of BMI and HR 0.971 (95% CI 0.950-0.993) for each 5 cm increase of WC]. Linear MR models indicated that each 1 kg/m2 increase in genetically predicted BMI was monotonically associated with a 4.5% decrease in all-cause mortality risk [HR 0.955 (95% CI 0.928-0.983)]. Non-linear curves showed the lowest mortality risk at the BMI of around 28.0 kg/m2, suggesting that optimal BMI for the oldest-old may be around overweight or mild obesity. Positive monotonic causal associations were observed between WC and all-cause mortality [HR 1.108 (95% CI 1.036-1.185) per 5 cm increase], CVD mortality [HR 1.193 (95% CI 1.064-1.337)], and non-CVD mortality [HR 1.110 (95% CI 1.016-1.212)]. The joint effect analyses indicated that the lowest risk was observed among those with higher BMI and lower WC., Conclusions: Among the oldest-old, opposite causal associations of BMI and WC with mortality were observed, and a body figure with higher BMI and lower WC could substantially decrease the mortality risk. Guidelines for the weight management should be cautiously designed and implemented among the oldest-old people, considering distinct roles of BMI and WC., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Brassinosteroids (BRs) are widely used as plant growth regulators in modern agriculture. Understanding how BRs regulate nutrient signaling is crucial for reducing fertilizer usage. Here we elucidate that the central BR signaling inhibitor GSK3/SHAGGY-LIKE KINASE2 (GSK2) interacts directly with and phosphorylates PHOSPHATE STARVATION RESPONSE2 (OsPHR2), the key regulator of phosphate (Pi) signaling, to suppress its transcription factor activity in rice (Oryza sativa). We identify a critical phosphorylation site at serine residue S269 of OsPHR2 and demonstrate that phosphorylation by GSK2 or phosphor-mimic mutation of S269 substantially impairs the DNA-binding activity of OsPHR2, and thus diminishes expression of OsPHR2-induced genes and reduces Pi levels. Like BRs, Pi starvation noticeably induces GSK2 instability. We further show that this site-specific phosphorylation event is conserved in Arabidopsis (Arabidopsis thaliana), but varies among the PHR-family members, being present only in most land plants. These results unveil a distinctive post-transcriptional regulatory mechanism in Pi signaling by which BRs promote Pi acquisition, with a potential contribution to the environmental adaptability of plants during their evolution., Competing Interests: Conflict of interest statement. We have no conflicts of interest to disclose., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society of Plant Biologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Use of local anesthesia and conscious sedation with a combination of a sedative and anesthetic drug during a surgical procedure is an approach designed to avoid intubation, which produces fewer adverse events compared to general anesthesia. In the present study, a comparison was made between the efficacy and safety of remimazolam besylate and propofol for facial plastic surgery., Objectives: The objective was to evaluate the clinical efficacy, comfort, and incidence of adverse events of remimazolam compared with propofol combined with alfentanil in outpatient facial plastic surgery., Methods: In this randomized, single-blind, single-center, comparative study, facial plastic surgery patients were randomly divided into remimazolam-alfentanil (n = 50) and propofol-alfentanil (n = 50) groups for sedation and analgesia. The primary endpoint was the incidence of hypoxemia, while secondary endpoints included efficacy and safety evaluations., Results: There were no significant differences regarding the surgical procedure, sedation and induction times, pain and comfort scores, muscle strength recovery, heart rate, respiratory rate, and blood pressure, but the dosage of alfentanil administered to the remimazolam group (387.5 μg) was lower than that for the propofol group (600 μg). The incidence of hypoxemia (P = .046) and towing of the mandibular (P = .028), as well as wake-up (P = .027) and injection pain (P = .008), were significantly higher in the propofol group than the remimazolam group., Conclusions: Remimazolam and propofol had similar efficacies for sedation and analgesia during facial plastic surgery, but especially the incidence of respiratory depression was significantly lower in patients given remimazolam., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Aesthetic Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Motivation: Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging., Results: This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework., Availability and Implementation: scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780., (© The Author(s) 2024. Published by Oxford University Press.)

Roses are among the most popular ornamental plants cultivated worldwide for their great economic, symbolic, and cultural importance. Nevertheless, rapid petal senescence markedly reduces rose (Rosa hybrida) flower quality and value. Petal senescence is a developmental process tightly regulated by various phytohormones. Ethylene accelerates petal senescence, while gibberellic acid (GA) delays this process. However, the molecular mechanisms underlying the crosstalk between these phytohormones in the regulation of petal senescence remain largely unclear. Here, we identified SENESCENCE-ASSOCIATED F-BOX (RhSAF), an ethylene-induced F-box protein gene encoding a recognition subunit of the SCF-type E3 ligase. We demonstrated that RhSAF promotes degradation of the GA receptor GIBBERELLIN INSENSITIVE DWARF1 (RhGID1) to accelerate petal senescence. Silencing RhSAF expression delays petal senescence, while suppressing RhGID1 expression accelerates petal senescence. RhSAF physically interacts with RhGID1s and targets them for ubiquitin/26S proteasome-mediated degradation. Accordingly, ethylene-induced RhGID1C degradation and RhDELLA3 accumulation are compromised in RhSAF-RNAi lines. Our results demonstrate that ethylene antagonizes GA activity through RhGID1 degradation mediated by the E3 ligase RhSAF. These findings enhance our understanding of the phytohormone crosstalk regulating petal senescence and provide insights for improving flower longevity., Competing Interests: Conflict of interest statement. The authors declare no competing interests., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society of Plant Biologists. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Motivation: The technology for analyzing single-cell multi-omics data has advanced rapidly and has provided comprehensive and accurate cellular information by exploring cell heterogeneity in genomics, transcriptomics, epigenomics, metabolomics and proteomics data. However, because of the high-dimensional and sparse characteristics of single-cell multi-omics data, as well as the limitations of various analysis algorithms, the clustering performance is generally poor. Matrix factorization is an unsupervised, dimensionality reduction-based method that can cluster individuals and discover related omics variables from different blocks. Here, we present a novel algorithm that performs joint dimensionality reduction learning and cell clustering analysis on single-cell multi-omics data using non-negative matrix factorization that we named scMNMF. We formulate the objective function of joint learning as a constrained optimization problem and derive the corresponding iterative formulas through alternating iterative algorithms. The major advantage of the scMNMF algorithm remains its capability to explore hidden related features among omics data. Additionally, the feature selection for dimensionality reduction and cell clustering mutually influence each other iteratively, leading to a more effective discovery of cell types. We validated the performance of the scMNMF algorithm using two simulated and five real datasets. The results show that scMNMF outperformed seven other state-of-the-art algorithms in various measurements., Availability and Implementation: scMNMF code can be found at https://github.com/yushanqiu/scMNMF., (© The Author(s) 2024. Published by Oxford University Press.)

Hi-C data are commonly normalized using single sample processing methods, with focus on comparisons between regions within a given contact map. Here, we aim to compare contact maps across different samples. We demonstrate that unwanted variation, of likely technical origin, is present in Hi-C data with replicates from different individuals, and that properties of this unwanted variation change across the contact map. We present band-wise normalization and batch correction, a method for normalization and batch correction of Hi-C data and show that it substantially improves comparisons across samples, including in a quantitative trait loci analysis as well as differential enrichment across cell types., (© The Author(s) 2024. Published by Oxford University Press.)

Motivation: Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR., Results: In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis., Availability and Implementation: R package is freely available for download at https://cran.r-project.org/package=PCGII., (© The Author(s) 2024. Published by Oxford University Press.)

Indocyanine green angiography (ICGA) has been widely employed for quantitative evaluation of the rat comb burn model, but the imaging equipment, imaging protocol, and fluorescence data interpretation of ICGA remain unsatisfactory. This study aims to provide better solutions for the application of ICGA in perfusion analysis. The rat comb burn model was established under a series of different comb contact durations, including 10, 20, 25, 30, 35, and 40 s. Indocyanine green angiography was used to analyze wound perfusion. In total, 16 rats were divided into ibuprofen and control groups for the burn model, and their perfusion was compared. A total of 16 identical models were divided into standard- and high-dose indocyanine green (ICG) groups, and ICGA was conducted to investigate the dynamic change in wound fluorescence. Escharectomy was performed under real-time fluorescence mapping and navigation. The results showed that a comb contact duration of 30 s was optimum for the burn model. Indocyanine green angiography could accurately evaluate the histologically determined depth of thermal injury and wound perfusion in the rat comb model. Digital subtraction of residual fluorescence was necessary for multiple comparisons of perfusion. Dynamic changes in fluorescence and necrotic tissues were observed more clearly by high-dose (0.5 mg/kg) ICG in angiography. In conclusion, perfusion analysis by ICGA can be used to assess the histologically determined depth of thermal injury and the impact of a specific treatment on wound perfusion. Indocyanine green angiography can help to identify necrotic tissue. The above findings and related imaging protocols lay the foundation for future research., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Burn Association. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Foliar diseases of maize are among the most important diseases of maize worldwide. This study focused on 4 major foliar diseases of maize: Goss's wilt, gray leaf spot, northern corn leaf blight, and southern corn leaf blight. QTL mapping for resistance to Goss's wilt was conducted in 4 disease resistance introgression line populations with Oh7B as the common recurrent parent and Ki3, NC262, NC304, and NC344 as recurrent donor parents. Mapping results for Goss's wilt resistance were combined with previous studies for gray leaf spot, northern corn leaf blight, and southern corn leaf blight resistance in the same 4 populations. We conducted (1) individual linkage mapping analysis to identify QTL specific to each disease and population; (2) Mahalanobis distance analysis to identify putative multiple disease resistance regions for each population; and 3) joint linkage mapping to identify QTL across the 4 populations for each disease. We identified 3 lines that were resistant to all 4 diseases. We mapped 13 Goss's wilt QTLs in the individual populations and an additional 6 using joint linkage mapping. All Goss's wilt QTL had small effects, confirming that resistance to Goss's wilt is highly quantitative. We report several potentially important chromosomal bins associated with multiple disease resistance including 1.02, 1.03, 3.04, 4.06, 4.08, and 9.03. Together, these findings indicate that disease QTL distribution is not random and that there are locations in the genome that confer resistance to multiple diseases. Furthermore, resistance to bacterial and fungal diseases is not entirely distinct, and we identified lines resistant to both fungi and bacteria, as well as loci that confer resistance to both bacterial and fungal diseases., Competing Interests: Conflicts of interest The author(s) declare no conflicts of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

The hippocampus (HC) and the orbitofrontal cortex (OFC) jointly encode a map-like representation of a task space to guide behavior. It remains unclear how the OFC and HC interact in encoding this map-like representation, though previous studies indicated that both regions have different functions. We acquired the functional magnetic resonance imaging data under a social navigation task in which participants interacted with characters in a two-dimensional "social space." We calculate the social relationships between the participants and characters and used a drift-diffusion model to capture the inner process of social interaction. Then we used multivoxel pattern analysis to explore the brain-behavior relationship. We found that (i) both the HC and the OFC showed higher activations during the selective trial than the narrative trial; (ii) the neural pattern of the right HC was associated with evidence accumulation during social interaction, and the pattern of the right lateral OFC was associated with the social relationship; (iii) the neural pattern of the HC can decode the participants choices, while the neural pattern of the OFC can decode the task information about trials. The study provided evidence for distinct roles of the HC and the OFC in encoding different information when representing social space., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Viruses with split genomes are classified as being either segmented or multipartite based on whether their genomic segments occur within a single virion or across different virions. Despite variations in number and sequence during evolution, the genomic segments of many viruses are conserved within the untranslated regions (UTRs). In this study, we present a methodology that combines RNA sequencing with iterative BLASTn of UTRs (https://github.com/qq371260/Iterative-blast-v.1.0) to identify new viral genomic segments. Some novel multipartite-like viruses related to the phylum Kitrinoviricota were annotated using sequencing data from field plant samples and public databases. We identified potentially plant-infecting jingmen-related viruses (order Amarillovirales ) and jivi-related viruses (order Martellivirales ) with at least six genomic components. The number of RNA molecules associated with a genome of the novel viruses in the families Closteroviridae, Kitaviridae , and Virgaviridae within the order Martellivirales reached five. Several of these viruses seem to represent new taxa at the subgenus, genus, and family levels. The diversity of novel genomic components and the multiple duplication of proteins or protein domains within single or multiple genomic components emphasize the evolutionary roles of genetic recombination (horizontal gene transfer), reassortment, and deletion. The relatively conserved UTRs at the genome level might explain the relationships between monopartite and multipartite viruses, as well as how subviral agents such as defective RNAs and satellite viruses can coexist with their helper viruses., Competing Interests: The authors declare no competing interests., (© The Author(s) 2024. Published by Oxford University Press.)

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Atopic dermatitis (AD) is a chronic inflammatory skin condition characterized by dysregulated immune responses. The key mediators of AD pathogenesis are T helper 2 (TH2) cells and TH2 cytokines. Targeting interleukin 4 (IL4), IL13 or IL31 has become a pivotal focus in both research and clinical treatments for AD. However, the need remains pressing for the development of a more effective and safer therapy, as the current approaches often yield low response rates and adverse effects. In response to this challenge, we have engineered a immunoglobulin G-single-chain fragment variable (scFv) format bispecific antibody (Ab) designed to concurrently target IL4R and IL31R. Our innovative design involved sequence optimization of VL-VH and the introduction of disulfide bond (VH44-VL100) within the IL31Rα Ab scFv region to stabilize the scFv structure. Our bispecific Ab efficiently inhibited the IL4/IL13/IL31 signaling pathways in vitro and reduced serum immunoglobulin E and IL31 levels in vivo . Consequently, this intervention led to improved inflammation profiles and notable amelioration of AD symptoms. This research highlighted a novel approach to AD therapy by employing bispecific Ab targeting IL4Rα and IL31Rα with potent efficacy., Competing Interests: Feiyan Deng, Yuxin Qiu, Xiangling Zhang, Nining Guo, Junhong Hu, Wenjie Yang, Wei Shang and Suofu Qin are employees in Shenzhen Kexing BioPharma Co., Ltd. Bicheng Liu is the professor of Southeast University School of Medicine., (© The Author(s) 2024. Published by Oxford University Press on behalf of Antibody Therapeutics. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Objectives: Blood dendritic cell antigen 2 (BDCA2) is exclusively expressed on plasmacytoid dendritic cells (pDCs) whose uncontrolled production of type I IFN (IFN-I) is crucial in pathogenesis of SLE and other autoimmune diseases. Although anti-BDCA2 antibody therapy reduced disease activity in SLE patients, its clinical efficacy needs further improvement. We developed a novel glucocorticoid receptor agonist and used it as a payload to conjugate with an anti-BDCA2 antibody to form an BDCA2 antibody-drug conjugate (BDCA2-ADC). The activation of BDCA2-ADC was evaluated in vitro., Methods: Inhibitory activity of BDCA2-ADC was evaluated in peripheral blood mononuclear cells or in purified pDCs under ex vivo toll-like receptor agonistic stimulation. The global gene regulation in purified pDCs was analysed by RNA-seq. The antigen-dependent payload delivery was measured by reporter assay., Results: The BDCA2-ADC molecule causes total suppression of IFNα production and broader inhibition of inflammatory cytokine production compared with the parental antibody in human pDCs. Global gene expression analysis confirmed that the payload and antibody acted synergistically to regulate both type I IFN signature genes and glucocorticoid responsive genes in pDCs., Conclusion: Taken together, these data suggest dual mechanisms of BDCA2-ADC on pDCs and the potential for BDCA2-ADC to be the first ADC treatment for SLE in the world and a better treatment option than anti-BDCA2 antibody for SLE patients., (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The US standard for maize commercially grown for grain specifies that yellow corn can contain at maximum 5% corn of other colors. Inbred parents of commercial hybrids typically have clear pericarp, but transgressive segregants in breeding populations can display variation in pericarp pigmentation. We identified 10 doubled haploid biparental populations segregating for pigmented pericarp and evaluated qualitative genetic models using chi-square tests of observed and expected frequencies. Pigmentation ranged from light to dark brown color, and pigmentation intensity was quantitatively measured across 1,327 inbred lines using hue calculated from RGB pixel values. Genetic mapping was used to identify loci associated with pigmentation intensity. For 9 populations, pigmentation inheritance best fit a hypothesis of a 2- or 3-gene epistatic model. Significant differences in pigment intensity were observed across populations. W606S-derived inbred lines with the darkest pericarp often had clear glumes, suggesting the presence of a novel P1-rw allele, a hypothesis supported by a significant quantitative trait locus peak at P1. A separate quantitative trait locus region on chromosome 2 between 221.64 and 226.66 Mbp was identified in LH82-derived populations, and the peak near p1 was absent. A genome-wide association study using 416 inbred lines from the Wisconsin Diversity panel with full genome resequencing revealed 4 significant associations including the region near P1. This study supports that pericarp pigmentation among dent maize inbreds can arise by transgressive segregation when pigmentation in the parental generation is absent and is partially explained by functional allelic variation at the P1 locus., Competing Interests: Conflicts of interest The authors declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

Background: Insulin feedback is a critical mechanism responsible for the poor clinical efficacy of phosphatidylinositol 3-kinase (PI3K) inhibition in cancer, and hyperglycemia is an independent factor associated with poor prognosis in glioblastoma (GBM). We investigated combination anti-hyperglycemic therapy in a mouse model of GBM and evaluated the association of glycemic control in clinical trial data from patients with GBM., Methods: The effect of the anti-hyperglycemic regimens, metformin and the ketogenic diet, was evaluated in combination with PI3K inhibition in patient-derived GBM cells and in an orthotopic GBM mouse model. Insulin feedback and the immune microenvironment were retrospectively evaluated in blood and tumor tissue from a Phase 2 clinical trial of buparlisib in patients with recurrent GBM., Results: We found that PI3K inhibition induces hyperglycemia and hyperinsulinemia in mice and that combining metformin with PI3K inhibition improves the treatment efficacy in an orthotopic GBM xenograft model. Through examination of clinical trial data, we found that hyperglycemia was an independent factor associated with poor progression-free survival in patients with GBM. We also found that PI3K inhibition increased insulin receptor activation and T-cell and microglia abundance in tumor tissue from these patients., Conclusion: Reducing insulin feedback improves the efficacy of PI3K inhibition in GBM in mice, and hyperglycemia worsens progression-free survival in patients with GBM treated with PI3K inhibition. These findings indicate that hyperglycemia is a critical resistance mechanism associated with PI3K inhibition in GBM and that anti-hyperglycemic therapy may enhance PI3K inhibitor efficacy in GBM patients., (Published by Oxford University Press on behalf of the Society for Neuro-Oncology 2023.)

Identification of viruses and further assembly of viral genomes from the next-generation-sequencing data are essential steps in virome studies. This study presented a one-stop tool named VIGA (available at https://github.com/viralInformatics/VIGA) for eukaryotic virus identification and genome assembly from NGS data. It was composed of four modules, namely, identification, taxonomic annotation, assembly and novel virus discovery, which integrated several third-party tools such as BLAST, Trinity, MetaCompass and RagTag. Evaluation on multiple simulated and real virome datasets showed that VIGA assembled more complete virus genomes than its competitors on both the metatranscriptomic and metagenomic data and performed well in assembling virus genomes at the strain level. Finally, VIGA was used to investigate the virome in metatranscriptomic data from the Human Microbiome Project and revealed different composition and positive rate of viromes in diseases of prediabetes, Crohn's disease and ulcerative colitis. Overall, VIGA would help much in identification and characterization of viromes, especially the known viruses, in future studies., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Over the past few years, therapeutic drug monitoring (TDM) has gained practical significance in antimicrobial precision therapy. Yet two categories of mainstream TDM techniques (chromatographic analysis and immunoassays) that are widely adopted nowadays retain certain inherent limitations. The use of biosensors, an innovative strategy for rapid evaluation of antimicrobial concentrations in biological samples, enables the implementation of point-of-care testing (POCT) and continuous monitoring, which may circumvent the constraints of conventional TDM and provide strong technological support for individualized antimicrobial treatment. This comprehensive review summarizes the investigations that have harnessed biosensors to detect antimicrobial drugs in biological matrices, provides insights into the performance and characteristics of each sensing form, and explores the feasibility of translating them into clinical practice. Furthermore, the future trends and obstacles to achieving POCT and continuous monitoring are discussed. More efforts are necessary to address the four key 'appropriateness' challenges to deploy biosensors in clinical practice, paving the way for personalized antimicrobial stewardship., (© The Author(s) 2023. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Ribonucleotide reductase subunit M2 (RRM2) plays a key role in cell and hepatitis B virus (HBV) replication. Nevertheless, its clinical implications for managing liver diseases have been inadequately studied., Methods: A total of 412 participants were enrolled, including 60 healthy control individuals, 55 patients with chronic hepatitis B (CHB), 173 patients with cirrhosis, and 124 patients with hepatocellular carcinoma (HCC). Serum RRM2 was measured via ELISA., Results: The level of serum RRM2 in patients with CHB, cirrhosis, and HCC was higher than that in healthy controls (P < .05). A large difference in serum RRM2 was found between HBV-related and non-HBV-related patients in the cirrhosis group (P < .001), compared with the difference between HBV-related HCC and non-HBV-related HCC (P = .86). In the HBV-related cirrhosis group, the serum RRM2 level showed significant positive correlations with HBV DNA, hepatitis B surface antigen, hepatitis B e antigen, Child-Pugh scores, and MELD scores and played a strong role in diagnosing HBV-related cirrhosis in CHB, compared with fibrosis-4 score and aspartate aminotransferase-to-platelet ratio index., Conclusions: Serum RRM2 is a reliable biomarker for accurate HBV-related cirrhosis diagnosis and evaluation. Also, serum RRM2 could reflect the expression state of HBV replication in patients with HBV-related cirrhosis., (© The Author(s) 2023. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Water caltrop ( Trapa spp., Lythraceae) is a traditional but currently underutilized non-cereal crop. Here, we generated chromosome-level genome assemblies for the two diploid progenitors of allotetraploid Trapa. natans (4x, AABB), i.e., diploid T. natans (2x, AA) and Trapa incisa (2x, BB). In conjunction with four published (sub)genomes of Trapa , we used gene-based and graph-based pangenomic approaches and a pangenomic transposable element (TE) library to develop Trapa genomic resources. The pangenome displayed substantial gene-content variation with dispensable and private gene clusters occupying a large proportion (51.95%) of the total cluster sets in the six (sub)genomes. Genotyping of presence-absence variation (PAVs) identified 40 453 PAVs associated with 2570 genes specific to A- or B-lineages, of which 1428 were differentially expressed, and were enriched in organ development process, organic substance metabolic process and response to stimulus. Comparative genome analyses showed that the allotetraploid T. natans underwent asymmetric subgenome divergence, with the B-subgenome being more dominant than the A-subgenome. Multiple factors, including PAVs, asymmetrical amplification of TEs, homeologous exchanges (HEs), and homeolog expression divergence, together affected genome evolution after polyploidization. Overall, this study sheds lights on the genome architecture and evolution of Trapa, and facilitates its functional genomic studies and breeding program., Competing Interests: All authors confirm that they have no conflict of interest., (© The Author(s) 2023. Published by OxfordUniversity Press on behalf of Nanjing Agricultural University.)

Background: Glioma stem cells (GSCs) are a subpopulation of tumor cells with self-renewal and tumorigenic capabilities in glioblastomas (GBMs). Diffuse infiltration of GSCs facilitates tumor progression and frustrates efforts at effective treatment. Further compounding this situation is the currently limited understanding of what drives GSC invasion. Here we comprehensively evaluated the significance of a novel invasion-related protein, Family with Sequence Similarity 129 Member A (FAM129A), in infiltrative GSCs., Methods: Western blotting, immunohistochemistry, and gene expression analysis were used to quantify FAM129A in glioma specimens and cancer datasets. Overexpression and knockdown of FAM129A in GSCs were used to investigate its effects on tumor growth and invasion. RNA-seq, qRT-PCR, western blotting, and co-precipitation assays were used to investigate FAM129A signaling mechanisms., Results: FAM129A is preferentially expressed in invasive frontiers. Targeting FAM129A impairs GSC invasion and self-renewal. Mechanistically, FAM129A acted as a positive regulator of Notch signaling by binding with the Notch1 intracellular domain (NICD1) and preventing its degradation., Conclusions: FAM129A and NICD1 provide a precise indicator for identifying tumor margins and aiding prognosis. Targeting them may provide a significantly therapeutic strategy for GSCs., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Spatial transcriptomics is a rapidly growing field that aims to comprehensively characterize tissue organization and architecture at single-cell or sub-cellular resolution using spatial information. Such techniques provide a solid foundation for the mechanistic understanding of many biological processes in both health and disease that cannot be obtained using traditional technologies. Several methods have been proposed to decipher the spatial context of spots in tissue using spatial information. However, when spatial information and gene expression profiles are integrated, most methods only consider the local similarity of spatial information. As they do not consider the global semantic structure, spatial domain identification methods encounter poor or over-smoothed clusters. We developed ConSpaS, a novel node representation learning framework that precisely deciphers spatial domains by integrating local and global similarities based on graph autoencoder (GAE) and contrastive learning (CL). The GAE effectively integrates spatial information using local similarity and gene expression profiles, thereby ensuring that cluster assignment is spatially continuous. To improve the characterization of the global similarity of gene expression data, we adopt CL to consider the global semantic information. We propose an augmentation-free mechanism to construct global positive samples and use a semi-easy sampling strategy to define negative samples. We validated ConSpaS on multiple tissue types and technology platforms by comparing it with existing typical methods. The experimental results confirmed that ConSpaS effectively improved the identification accuracy of spatial domains with biologically meaningful spatial patterns, and denoised gene expression data while maintaining the spatial expression pattern. Furthermore, our proposed method better depicted the spatial trajectory by integrating local and global similarities., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development., (© The Author(s) 2023. Published by Oxford University Press.)

Context: Imbalance of the skin microbial community could impair skin immune homeostasis and thus trigger skin lesions. Dysbiosis of skin microbiome may be involved in the early pathogenesis of diabetic foot (DF). However, the potential mechanism remains unclear., Objective: To investigate the dynamic composition and function of the foot skin microbiome with risk stratification for DF and assess whether dysbiosis of the skin microbiome induces diabetic skin lesions., Methods: We enrolled 90 consecutive subjects who were divided into 5 groups based on DF risk stratification: very low, low, moderate, and high risk for ulcers and a healthy control group. Integrated analysis of 16S ribosomal RNA and metagenomic sequencing of cotton swab samples was applied to identify the foot skin microbiome composition and functions in subjects. Then a mouse model of microbiota transplantation was used to evaluate the effects of the skin microbiome on diabetic skin lesions., Results: The results demonstrated that, with the progression of diabetic complications, the proportion of gram-negative bacteria in plantar skin increased. At the species level, metagenome sequencing analyses showed Moraxella osloensis to be a representative core strain in the high-risk group. The major microbial metabolites affecting diabetic skin lesions were increased amino acid metabolites, and antibiotic resistance genes in microorganisms were abundant. Skin microbiota from high-risk patients induced more inflammatory cell infiltration, similar to the lipopolysaccharide (LPS)-stimulated response, which was inhibited by Toll-like receptor 4 (TLR4) antagonists., Conclusions: The skin microbiome in patients with diabetes undergoes dynamic changes at taxonomic and functional levels with the progression of diabetic complications. The increase in gram-negative bacteria on the skin surface through LPS-TLR4 signal transduction could induce inflammatory response in early diabetic skin lesions., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society.)

MADS-box transcription factors (TFs), among the first TFs extensively studied, exhibit a wide distribution across eukaryotes and play diverse functional roles. Varying by domain architecture, MADS-box TFs in land plants are categorized into Type I (M-type) and Type II (MIKC-type). Type I and II genes have been considered orthologous to the SRF and MEF2 genes in animals, respectively, presumably originating from a duplication before the divergence of eukaryotes. Here, we exploited the increasing availability of eukaryotic MADS-box sequences and reassessed their evolution. While supporting the ancient duplication giving rise to SRF- and MEF2-types, we found that Type I and II genes originated from the MEF2-type genes through another duplication in the most recent common ancestor (MRCA) of land plants. Protein structures predicted by AlphaFold2 and OmegaFold support our phylogenetic analyses, with plant Type I and II TFs resembling the MEF2-type structure, rather than SRFs. We hypothesize that the ancestral SRF-type TFs were lost in the MRCA of Archaeplastida (the kingdom Plantae sensu lato). The retained MEF2-type TFs acquired a Keratin-like domain and became MIKC-type before the divergence of Streptophyta. Subsequently in the MRCA of land plants, M-type TFs evolved from a duplicated MIKC-type precursor through loss of the Keratin-like domain, leading to the Type I clade. Both Type I and II TFs expanded and functionally differentiated in concert with the increasing complexity of land plant body architecture. The recruitment of these originally stress-responsive TFs into developmental programs, including those underlying reproduction, may have facilitated the adaptation to the terrestrial environment., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Postoperative recovery, as a window to observe perioperative treatment effect and patient prognosis, is a common outcome indicator in clinical research and has attracted more and more attention of surgeons and anaesthesiologists. Postoperative recovery is a subjective, multidimensional, long-term, complex process, so it is unreasonable to only use objective indicators to explain it. Currently, with the widespread use of patient-reported outcomes, various scales become the primary tools for assessing postoperative recovery. Through systematic search, we found 14 universal recovery scales, which have different structures, contents and measurement properties, as well as their own strengths and weaknesses. We also found that it is urgently necessary to perform further researches and develop a scale that can serve as the gold universal standard to evaluate postoperative recovery. In addition, with the rapid development of intelligent equipment, the establishment and validation of electronic scales is also an interesting direction., (© The Author(s) 2022. Published by Oxford University Press on behalf of Postgraduate Medical Journal. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)