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Abstract Nitrogen (N) fertilizer application ensures crop production and food security worldwide. N-controlled boosting of shoot branching that is also referred as tillering can improve planting density for increasing grain yield of cereals. Here, we report that Sugar Transporter Protein 28 (OsSTP28) as a key regulator of N-responsive tillering and yield formation in rice. N supply inhibits the expression of OsSTP28, resulting in glucose accumulation in the apoplast of tiller buds, which in turn suppresses the expression of a transcriptional inhibitor ORYZA SATIVA HOMEOBOX 15 (OSH15) via an epigenetic mechanism to activate gibberellin 2-oxidases (GA2oxs)-facilitated gibberellin catabolism in shoot base. Thereby, OsSTP28-OSH15-GA2oxs module reduces the level of bioactive gibberellin in shoot base upon increased N supply, and consequently promotes tillering and grain yield. Moreover, we identify an elite allele of OsSTP28 that can effectively promote N-responsive tillering and yield formation, thus representing a valuable breeding target of N use efficiency improvement for agricultural sustainability.

Abstract Background The pregnant women with intrahepatic cholestasis were at high risk of fetal distress, preterm birth and unexpected stillbirth. Intrahepatic cholestasis of pregnancy (ICP) was mainly caused by disorder of bile acid metabolism, whereas the specific mechanism was obscure. Methods We performed proteomics analysis of 10 ICP specimens and 10 placenta specimens from patients without ICP through data-independent acquisition (DIA) technique to disclose differentially expressed proteins. We executed metabolomic analysis of 30 ICP specimens and 30 placenta specimens from patients without ICP through UPLC-MS/MS to identify differentially expressed metabolites. Enrichment and correlation analysis was used to obtain the direct molecular insights of ICP development. The ICP rat models were constructed to validate pathological features. Results The heatmap of proteomics analysis showed the top 30 up-regulated and 30 down-regulated proteins. The metabolomic analysis revealed 20 richer and 4 less abundant metabolites in ICP samples compared with placenta specimens from patients without ICP, and enrichment pathways by these metabolites included primary bile acid biosynthesis, cholesterol metabolism, bile secretion, nicotinate and nicotinamide metabolism, purine metabolism and metabolic pathways. Combined analysis of multiple omics results demonstrated that bile acids such as Glycohyocholic acid, Glycine deoxycholic acid, beta-Muricholic acid, Noncholic acid, cholic acid, Gamma-Mercholic Acid, alpha-Muricholic acid and Glycochenodeoxycholic Aicd were significantly associated with the expression of GLRX3, MYL1, MYH7, PGGT1B, ACTG1, SP3, LACTB2, C2CD5, APBB2, IPO9, MYH2, PPP3CC, PIN1, BLOC1S1, DNAJC7, RASAL2 and ATCN3 etc. The core protein ACAT2 was involved in lipid metabolic process and animal model showed that ACAT2 was up-regulated in placenta and liver of pregnant rats and fetal rats. The neonates had low birth weight and Safranin O-Fast green FCF staining of animal models showed that poor osteogenic and chondrogenic differentiation of fetal rats. Conclusion Multiple metabolites-alpha-Muricholic acid, beta-Muricholic acid, Glycine deoxycholic acid and Glycochenodeoxycholic Acid etc. were perfect biomarkers to predict occurrence of ICP. Bile acids were significantly associated with varieties of protein expression and these proteins were differentially expressed in ICP samples. Our study provided several biomarkers for ICP detection and potential therapeutic targets for ICP development.

BackgroundPatients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients’ prognosis. ObjectiveThis study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients. MethodsIn this retrospective study, data from 958 patients who underwent LT between January 2015 and January 2020 were extracted from the Third Affiliated Hospital of Sun Yat-sen University. Six ML algorithms were used to predict post-LT PND, and model performance was evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, and F1-scores. The best-performing model was additionally validated using a temporal external dataset including 309 LT cases from February 2020 to August 2022, and an independent external dataset extracted from the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database including 325 patients. ResultsIn the development cohort, 201 out of 751 (33.5%) patients were diagnosed with PND. The logistic regression model achieved the highest AUC (0.799) in the internal validation set, with comparable AUC in the temporal external (0.826) and MIMIC-Ⅳ validation sets (0.72). The top 3 features contributing to post-LT PND diagnosis were the preoperative overt hepatic encephalopathy, platelet level, and postoperative sequential organ failure assessment score, as revealed by the Shapley additive explanations method. ConclusionsA real-time logistic regression model-based online predictor of post-LT PND was developed, providing a highly interoperable tool for use across medical institutions to support early risk stratification and decision making for the LT recipients.

ABSTRACT: Black soldier fly (BSF) larvae convert wastes into protein, playing a vital role in addressing the challenge of sustainable poultry production. These larvae accumulate toxic substances, posing a risk to feed and food safety. This study investigates the effects of substituting soybean meal with different levels of BSF larvae meal on laying performance, egg quality, serum analysis, and the deposition of various metals in eggs, meat, and excreta. A total of 1,008 Lohmann hens (age 48 wk) were randomly assigned to 4 treatments. The treatments consisted of corn-soybean meal (CK) diet replaced with 7% (BSF7), 14% (BSF14), and 21% (BSF21) BSF larvae meal. Each treatment consisted of 6 replicates with 42 hens each and the trial lasted for 8 wk. Dietary BSF larvae meal treatments increased (linear, P < 0.05) the laying rate (1.52 to 1.95%) and decreased (linear, P < 0.01) the feed intake (3.64–3.86 g) during the entire experiment. During 48 to 52 wk, egg weight was decreased (P < 0.001) 0.93 g for the BSF21 group compared to CK group, however, during 52-56 wk, no differences in egg weight were observed among treatments. The addition of BSF larvae meal enhanced the eggshell strength (linear, P < 0.05), with no effect on the albumen height and yolk weight (P > 0.05). Low transfer of arsenic, lead, and cadmium concentration was observed in the egg yolk and egg white across different treatments (P > 0.05). Conversely, the concentrations of these metals and iron showed an increase, while Zinc exhibited a decrease in excreta as the dietary intake of BSF larvae meal increased. The chromium and iron increased linearly (P < 0.001) in the egg white with the inclusion of BSF larvae meal. Egg white iron and chromium showed a strong positive correlation with the dietary zinc and copper. Taken together, BSF larvae can replace soybean meal completely in laying diet, however, careful attention requires to elevated metal levels in diet and excreta.

Grassland which covers 40% of terrestrial land is an important ecosystem having a multitude of functions, which has suffered various degrees of degradation with the interaction between global climate change and unreasonable human utilization (e.g., grazing and reclamation). Improved understanding of soil and microbial community diversity during meadow steppe degradation is crucial for predicting degradation mechanisms and restoration strategies. Here, we used Illumina sequencing technology to investigate the patterns of soil microbial community structure and the driving factors of its change across different degradation degrees of meadow steppe [i.e., non-degraded grasslands (NDG), lightly degraded grasslands (LDG), moderately degraded grasslands (MDG), and severely degraded grasslands (SDG)] south of the Greater Khingan Mountains. Our results showed a significant variation in soil properties, enzyme activity, and soil metal elements across the degraded meadows. Soil available phosphorus (AP), urease (UE), and cellulase (CL) in soils increased with the intensity of grassland degradation. Grassland degradation significantly decreased soil bacterial and fungal richness. In addition, grassland degradation significantly increased the relative abundance of Firmicutes (from 1.65% to 5.38%) and Myxococcota (from 2.13% to 3.13%). Degradation considerably increased the relative abundance of Ascomycota (from 66.54% to 75.05%), but decreased Basidiomycota (from 18.33% to 9.92%). The relative abundance of nitrogen fixation and cellulolysis decreased significantly due to grassland degradation. For fungal functional guilds, the relative abundance of pathotrophs increased while saprotrophs decreased significantly with increasing severity of degradation. Total nitrogen (TP), AP, available potassium (AK), manganese (Mn), lead (Pb), UE, sucrase (SC), and alcalase protease (ALPT) were the main drivers of soil bacterial community composition, while TP, AP, AK, Pb, UE, and SC were the main drivers of soil fungal community composition in the degraded grassland. Our findings demonstrated that severe grassland degradation has an enormous effect on soil microbial communities and soil physicochemical dynamics. These findings improve our theoretical understanding of the interactions between soil microbial populations and soil environmental variables in degraded grassland.

The growing development of the Internet of Things (IoT) is accelerating the emergence and growth of new IoT services and applications, which will result in massive amounts of data being generated, transmitted and processed in wireless communication networks. Mobile Edge Computing (MEC) is a desired paradigm to timely process the data from IoT for value maximization. In MEC, a number of computing-capable devices are deployed at the network edge near data sources to support edge computing, such that the long network transmission delay in cloud computing paradigm could be avoided. Since an edge device might not always have sufficient resources to process the massive amount of data, computation offloading is significantly important considering the cooperation among edge devices. However, the dynamic traffic characteristics and heterogeneous computing capabilities of edge devices challenge the offloading. In addition, different scheduling schemes might provide different computation delays to the offloaded tasks. Thus, offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay. This paper seeks to guarantee low delay for computation intensive applications by jointly optimizing the offloading and scheduling in such an MEC system. We propose a Delay-Greedy Computation Offloading (DGCO) algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices. A Reinforcement Learning-based Parallel Scheduling (RLPS) algorithm is further designed to schedule offloaded tasks in the multi-core MEC server. With an offloading delay broadcast mechanism, the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization. Finally, the simulation results show that our proposal can bound the end-to-end delay of various tasks. Even under slightly heavy task load, the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%, while that given by benchmarked algorithms is reduced to intolerable value. The simulation results are demonstrated the effectiveness of DGCO-RLPS for delay guarantee in MEC.

Résumé: L’étude que nous envisageons s’intéresse à l’instance de réception dans le système de communication. Dans le champ qui est le nôtre, l’analyse du discours, l’accent est plus mis sur l’instance émettrice et sa production. Même lorsqu’on s’occupe du récepteur, c’est dans le discours du locuteur qu’on va chercher ses traces. Il nous semble qu’il est tout aussi important de voir dans quelle mesure et selon quelles modalités le discours du locuteur impacte réellement l’auditoire auquel il est adressé. Nous postulons que cette analyse est possible en nous penchant sur les retours de l’auditoire par rapport au discours du locuteur. Dans cette contribution, nous verrons comment le feedback du discours de candidature pour un troisième mandat de Ouattara en 2020 se structure en deux catégories quasi antagoniques : l’une validant l’argumentation et l’autre se posant en contre argumentation. Mots-clés : Argumentation ; Feedback ; Réfutation.

This study investigates the effect of financial inclusion (FI), considering micro and macro indicators as well as micro- and macro-FI separately, on the stability of Asian banks and examines the moderating effect of bank competition (BC) on this relationship. Using data from 2011 to 2021, this study examines the relationship between FI, BC, and bank stability (BS). The hypotheses were tested using a “two-step system-GMM framework”. The findings were also authenticated using the panel OLS approach. The results indicate that FI (considering micro- and macro-indicators) and micro- and macroFI have significant positive effects on the stability of Asian banks. However, the impact of micro-FI is greater than that of macro-FI on the BS in Asia. Furthermore, the results manifest that BC has a significant positive impact on BS and positively moderates the relationship between micro-FI and BS, whereas it negatively moderates the relationship between macro-FI and BS. The findings of this study have practical implications for regulators, bankers, and policymakers involved in formulating strategies to enhance Asian banks’ stability.

The active share of mutual funds drops significantly when investor sentiment is high, indicating that fund managers reduce their active stock selection and stay closer to their benchmarks during such periods. Our evidence is consistent with fund managers being sentiment-prone - challenging the conventional view that it is only the preponderance of retail investors during high sentiment periods that allows sentiment to influence asset prices. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jfineco.2005.03.009 Byline: Roger K. Loh (a)(b), G. Mujtaba Mian (c) Keywords: Earnings-based valuation models; Earnings forecasts; Stock recommendations; Security analysts Abstract: We find that analysts who issue more accurate earnings forecasts also issue more profitable stock recommendations. The average factor-adjusted return associated with the recommendations of analysts in the highest accuracy quintile exceeds the corresponding return for analysts in the lowest accuracy quintile by 1.27% per month. Our findings provide indirect empirical support for valuation models in the accounting and finance literatures (e.g., Ohlson, 1995) that emphasize the role of future earnings in predicting stock price movements. Our results also suggest that imperfectly efficient markets reward information gatherers, such as security analysts, for their costly activities in generating superior earnings forecasts. Author Affiliation: (a) Fisher College of Business, Ohio State University, Columbus, OH43210,USA (b) Singapore Management University, Lee Kong Chian School of Business, Singapore 178899 (c) The NUS Business School, National University of Singapore, BIZ1, 1 Business Link, Singapore 117592 Article History: Received 7 April 2004; Revised 29 October 2004; Accepted 22 March 2005 Article Note: (footnote) [star] We thank an anonymous referee, Kalok Chan, Louis Ederington, Christie Gleason, Allaudeen Hameed, Andrew Karolyi, Charles Lee, Inmoo Lee, Guan Hua Lim, John Sequeira, Alvaro Taboada, Melvyn Teo, and especially Srinivasan Sankaraguruswamy for their comments. This paper has also benefited from seminars at Ohio State University, Singapore Management University, and University of New South Wales. Any remaining errors are our responsibility. The second author would like to acknowledge the financial support from a research grant by the NUS Business School. Discussions with various people at Thomson Financial helped clarify certain critical data-related issues.

Abstract High‐valent iron‐oxo species are one of the common intermediates in both biological and biomimetic catalytic oxidation reactions. Recently, hydrogen‐bonding (H‐bonding) has been proved to be critical in determining the selectivity and reactivity. However, few examples have been established for mechanistic insights into the H‐bonding effect. Moreover, intramolecular H‐bonding effect on both C‐H activation and oxygen atom transfer (OAT) reactions in synthetic porphyrin model system has not been investigated yet. In this study, a series of heme‐containing iron(IV)‐oxo porphyrin species with or without intramolecular H‐bonding are synthesized and characterized. Kinetic studies revealed that intramolecular H‐bonding can significantly enhance the reactivity of iron(IV)‐oxo species in OAT, C‐H activation, and electron‐transfer reactions. This unprecedented unified H‐bonding effect is elucidated by theoretical calculations, which showed that intramolecular H‐bonding interactions lower the energy of the anti‐bonding orbital of iron(IV)‐oxo porphyrin species, resulting in the enhanced reactivities in oxidation reactions irrespective of the reaction type. To the best of the knowledge, this is the first extensive investigation on the intramolecular H‐bonding effect in heme system. The results show that H‐bonding interactions have a unified effect with iron(IV)‐oxo porphyrin species in all three investigated reactions.

Abstract Background The triglyceride-glucose (TyG) index, identified as a reliable indicator of insulin resistance (IR), was reported to be associated with stroke recurrence and morbidity in the general population and critically ill patients. However, the relationship in liver transplantation (LT) recipients remains unknown. This study aimed to investigate the correlation between the TyG index and post-LT stroke along with all-cause mortality and further assess the influence of IR on the LT recipients’ prognosis. Methods The retrospective cohort study enrolled 959 patients who underwent LT at a university-based medical centre between January 2015 and January 2021. The participants were divided into three groups according to their TyG index tertiles. The primary outcome was post-LT stroke. Multivariate logistic regression, COX proportional hazards regression, and restricted cubic spline RCS were used to examine the association between the TyG index and outcomes in LT recipients. Results With a median TyG index of 8.23 (7.78–8.72), 780 (87.18% males) patients were eventually included. The incidence of post-LT stroke was 5.38%, and the in-hospital, 1-year, and 3-year mortality rates were 5.54%, 13.21%, and 15.77%, respectively. Multivariate regression analysis showed an independent association between the TyG index and an increased risk of post-LT stroke [adjusted odds ratio (aOR), 3.398 (95% confidence interval [CI]: 1.371–8.426) P = 0. 008], in-hospital mortality [adjusted hazard ratio (aHR), 2.326 (95% CI: 1.089–4.931) P = 0.025], 1-year mortality [aHR, 1.668 (95% CI: 1.024–2.717) P = 0.039], and 3-year mortality [aHR, 1.837 (95% CI: 1.445–2.950) P = 0.012]. Additional RCS analysis also suggested a linear increase in the risk of postoperative stroke with elevated TyG index (P for nonlinearity = 0.480). Conclusions The TyG index may be a valuable and reliable indicator for assessing stroke risk and all-cause mortality in patients undergoing LT, suggesting its potential relevance in improving risk stratification during the peri-LT period.

Abstract Background To analyze the expression of TXLNA in brain gliomas and its clinical significance. Methods Gene Expression Profiling Interactive Analysis（GEPIA）and Chinese Glioma Genome Atlas（CGGA）databases were retrieved as the methods. To assess the disparity between TXLNA expression in glioma and normal brain tissue. The Kaplan-Meier survival curve was employed to preliminarily evaluate the survival curves of the high and low expression groups, this was done for investigate the correlation between TXLNA expression level and the survival and prognosis of glioma. A Cox proportional regression risk model of multivariate nature was employed to evaluate the elements impacting the survival and prognosis of glioma. Gene pool enrichment analysis（GSEA）was used to investigate the related function of TXLNA in glioma. A Pearson correlation test and co-expression analysis were employed to identify the genes most associated with TXLNA expression. Result The enrichment analysis results were observably enriched in signal pathways for instance the cell cycle and completion and coordination cascade pathways, and it is evident that high expression of TXLNA in gliomas is related to a poor survival and a bad patient prognosis, thus making it an independent prognostic factor for gliomas. Genes such as STK40 and R1MS1 are significantly correlated with TXLNA, playing a synergistic or antagonistic role. Conclusions The prognosis of GBM patients is strongly linked to the high expression of TXLNA, which may be a viable therapeutic target for curbing cancer progression and creating new immunotherapies for GBM.

Abstract Low‐coverage whole genome sequencing (lcWGS) has great potential to effectively genotype large‐scale population and to provide solid data for imputation; however, the time for imputation needs to be optimized. There is also no publicly available reference panel for whole genome selection in cattle. Here, we proposed a combination of Beagle v5.4 for phasing and GLIMPSE2 for imputation, which is fast and accurate for cattle lcWGS data. Furthermore, we established a multi‐breed reference panel with 61.8 million SNPs based on 2976 worldwide cattle, of which 1766 were bulls, by evaluating diversity and the size of the reference panel. The evaluation of imputation accuracy was conducted using new reference panel for both lcWGS and Bovine BeadChip data. The average concordance rate in Holstein was 99.6%, 99.6%, and 99.5% for 1X, 0.5X, and 0.1X lcWGS data, 99.5% and 99.0% for 777K and 50K chip data, and it was 98.8% for 1X lcWGS data in Simmental. We further investigated the factors affecting the imputation accuracy of lcWGS data and discovered that segmental duplication, structural variant, and guanine‐cytosine content were the top three factors. Interestingly, we found that 10 regions longer than 0.5 Mb showed low imputation accuracy enriched with immune function, such as 96.1% characterized genes in regions of chromosome 10, with more attention being paid on downstream immune‐related analysis. Our study provides the workflow of imputing lcWGS data and establishes the first high‐quality cattle reference panel with free access, which provides a resource to conduct subsequent large‐scale genome‐wide association studies and genomic selection.

Résumé : Le Forum pour la Réconciliation Nationale en Côte d’Ivoire visait, comme stipulé par le triptyque qui le sous-tendait, la Vérité, la Repentance et le Pardon, afin d’aboutir à la Réconciliation. La question du pardon est donc centrale. Notre contribution ambitionne de montrer que par une étude des différentes sémiotisations du pardon, il est possible d’entrevoir que cette grande rencontre était vouée à l’échec. Mots-clés : Acte illocutionnaire, Pardon, Postures énonciatives, Stratégies argumentatives, Victimisation

Background: We aimed to explore the potential risk factors and short-term prognosis for SIRS after thermal ablation of hepatocellular carcinoma (HCC). Methods: Data from patients with HCC who underwent thermal ablation in the Third Affiliated Hospital of Sun Yat-sen University between January 2015 and August 2021 were retrieved from the perioperative database. Pre-, intra- and postoperative data between SIRS group and non-SIRS group were compared and multivariate logistic regression analysis was performed to identify the risk factors for SIRS after thermal ablation. Results: A total of 1491 patients were enrolled and 234 (15.7 %) patients developed SIRS after thermal ablation. Compared with those without SIRS, patients with SIRS had a longer hospital stay, higher hospitalization costs and higher risk of more severe postoperative complications. In the multivariate logistic regression analysis, current smoking (OR 1.58, 95 %CI 1.09–2.29), decreased HCT (OR 1.51,95 %CI 1.11–2.04), NEUT < 1.5 × 109/L(OR 1.74, 95 %CI 1.14–2.65), NEUT% < 0.5 or > 0.7 (OR 1.36, 95 %CI 1.01–1.83) and PT > 16.3s (OR 2.42, 95 %CI 1.57–3.74) were significantly associated with postoperative SIRS. Conclusions: Current smoking, decreased HCT, neutropenia, abnormal percentage of neutrophils and prolonged PT are the independent risk factors for SIRS after thermal ablation of HCC, which worsens outcomes of patients. This study can help identify high-risk population and guide appropriate care so as to reduce the incidence of postoperative SIRS.

Summary: Omicron, as the emerging variant with enhanced vaccine tolerance, has sharply disrupted most therapeutic antibodies. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the subgenus Sarbecovirus, members of which share high sequence similarity. Herein, we report one sarbecovirus antibody, 5817, which has broad-spectrum neutralization capacity against SARS-CoV-2 variants of concern (VOCs) and SARS-CoV, as well as related bat and pangolin viruses. 5817 can hardly compete with six classes of receptor-binding-domain-targeted antibodies grouped by structural classifications. No obvious impairment in the potency is detected against SARS-CoV-2 Omicron and subvariants. The cryoelectron microscopy (cryo-EM) structure of neutralizing antibody 5817 in complex with Omicron spike reveals a highly conserved epitope, only existing at the receptor-binding domain (RBD) open state. Prophylactic and therapeutic administration of 5817 potently protects mice from SARS-CoV-2 Beta, Delta, Omicron, and SARS-CoV infection. This study reveals a highly conserved cryptic epitope targeted by a broad sarbecovirus neutralizing antibody, which would be beneficial to meet the potential threat of pre-emergent SARS-CoV-2 VOCs.