Characterization of the fertilization independent endosperm (FIE) gene from soybean

Reproduction of angiosperm plants initiates from two fertilization events: an egg fusing with a sperm to form an embryo and a second sperm fusing with the central cell to generate an endosperm. The tryptophan-aspartate (WD) domain polycomb protein encoded by fertilization independent endosperm (FIE) gene, has been known as a repressor of hemeotic genes by interacting with other polycomb proteins, and suppresses endosperm development until fertilization. In this study, one Glycine max FIE (GmFIE) gene was cloned and its expression in different tissues, under cold and drought treatments, was analyzed using both bioinformatics and experimental methods. GmFIE showed high expression in reproductive tissues and was responsive to stress treatments, especially induced by cold. GmFIE overexpression lines of transgenic Arabidopsis were generated and analyzed. Delayed flowering was observed from most transgenic lines compared to that of wild type. Overexpression of GmFIE in Arabidopsis also leads to semi-fertile of the plants.Keywords: Polycomb proteins, fertilization independent endosperm (FIE), Glycine max, Arabidopsis thalian

Estimation of the directions for unknown parameters in semiparametric models

Semiparametric models are useful in econometrics, social sciences and medicine application. In this paper, a new estimator based on least square methods is proposed to estimate the direction of unknown parameters in semi-parametric models. The proposed estimator is consistent and has asymptotic distribution under mild conditions without the knowledge of the form of link function. simulations show that the proposed estimator is significantly superior to maximum score estimator given by Manski (1975) for binary response variables. When the error term is long-tailed distributions or distribution with no moments, the proposed estimator perform well. Its application is illustrated with data of exportibg participation of manufactures in Guangdon

Signaling from the plasma-membrane localized plant immune receptor RPM1 requires self-association of the full-length protein

Pathogen recognition first occurs at the plasma membrane, where receptor-like kinases perceive pathogen-derived molecules and initiate immune responses. To abrogate this immune response, pathogens evolved effector proteins that act as virulence factors, often following delivery to the host cell. Plants evolved intracellular receptors, known as NOD-like receptors (NLRs), to detect effectors, thereby ensuring activation of effector-triggered immunity. However, despite their importance in immunity, the molecular mechanisms underlying effector recognition and subsequent immune activation by membrane-localized NLRs remain to be fully elucidated. Our analyses reveal the importance of and need for self-association and the coordinated interplay of specific domains and conserved residues for NLR activity. This could provide strategies for crop improvement, contributing to effective, environmentally friendly, and sustainable solutions for future agriculture

Unveiling the microbiota-metabolite-myocardium axis: a novel perspective on cardiovascular health

IntroductionCardiovascular diseases, including myocardial infarction, remain a leading cause of death globally. Emerging evidence suggests the gut microbiota plays a crucial role in cardiovascular health. This study aims to explore the impact of gut microbiota on myocardial infarction using a mouse model.MethodsThe research utilizes a multi-omics approach, including 16S rDNA sequencing and LC-MS-based metabolomics to analyze fecal and serum samples from mice modeled to mimic myocardial infarction. This methodology allows for a comprehensive analysis of microbial populations and their metabolic output.ResultsThe findings reveal a significant reduction in gut microbiota α-diversity in mice with induced myocardial infarction compared to healthy controls. Notably, there is an increase in populations of Fusobacteria and Clostridia. Metabolomic analysis indicates disruptions in amino acid and energy metabolism, suggesting a metabolic dysregulation linked to myocardial health.DiscussionThe study proposes a novel microbiota-metabolite-myocardium axis, where specific microbial metabolites may directly affect heart health. This connection points to the gut microbiota as a potential player in the pathogenesis of myocardial infarction and may open new therapeutic avenues targeting the gut microbiome to combat cardiovascular diseases

CO2 balances and mitigation costs of advanced CHP systems with CO2 capture in pulp and paper mills

This paper investigates the energy efficiency, CO2 mitigation potential and CO2 mitigation cost of biomass-based combined heat and power production (CHP) with a CO2 capture option in Kraft pulp and paper mills. CHP systems based on black liquor and biomass gasifier with combined cycle technology and biomass boiler with steam turbine technology are considered. The study shows that steep CO2 reductions can be achieved through CO2 capture and storage regardless of CHP technology used although alternatives based on black liquor and biomass gasification provide the most efficient systems from a resource and energy point of view. Moreover, using black liquor and biomass gasification technology both pulp mills and integrated pulp and paper mills can potentially become net electricity exporters while at the same time removing CO2 from the atmosphere on a net basis. Furthermore, cost curves are constructed, which show how the cost of CO2 capture and storage in pulp and paper mills depends on system configuration and the distance that the CO2 must be transported to injection sites. The assessment shows that systems based on black liquor and biomass gasification with CO2 capture in integrated pulp and paper mills remove CO2 most cost-effectively.QCR 20231113</p

CO2 balances and mitigation costs of advanced CHP systems with CO2 capture in pulp and paper mills

This paper investigates the energy efficiency, CO2 mitigation potential and CO2 mitigation cost of biomass-based combined heat and power production (CHP) with a CO2 capture option in Kraft pulp and paper mills. CHP systems based on black liquor and biomass gasifier with combined cycle technology and biomass boiler with steam turbine technology are considered. The study shows that steep CO2 reductions can be achieved through CO2 capture and storage regardless of CHP technology used although alternatives based on black liquor and biomass gasification provide the most efficient systems from a resource and energy point of view. Moreover, using black liquor and biomass gasification technology both pulp mills and integrated pulp and paper mills can potentially become net electricity exporters while at the same time removing CO2 from the atmosphere on a net basis. Furthermore, cost curves are constructed, which show how the cost of CO2 capture and storage in pulp and paper mills depends on system configuration and the distance that the CO2 must be transported to injection sites. The assessment shows that systems based on black liquor and biomass gasification with CO2 capture in integrated pulp and paper mills remove CO2 most cost-effectively.QCR 20231113</p

Effects of resonant absorption and inhomogeneous broadening on reflection and absorption spectra of optical lattices diamond NV centers

Using the transfer-matrix method, the effects of absorption and inhomogeneous broadening, in one-dimensional optical lattice constructed from inhomogeneously broadened spin transitions of nitrogen-vacancy color centers in single crystal diamond (NV diamond), on the reflection and absorption spectrum are presented. Further analysis show that, in realistic periodic stacks of the NV diamond, modulating the geometrical configuration of the external optical potential, the absorption lineshape scale, and the inhomogeneous broadening, one could easily access the diverse gap structures and a high band-gap reflectivity. These pretty useful calculations hold more potential for effective control of the light-matter interaction and realization in practice

Secular trends of hypertension prevalence based on 2017 ACC/AHA and 2018 Chinese hypertension guidelines: Results from CHNS data (1991‐2015)

Abstract This study aimed to assess the impact of the 2017 American College of Cardiology and American Heart Association (ACC/AHA) guideline and the 2018 Chinese hypertension guidelines on the different secular trends for hypertension prevalence. A total of 82 665 eligible individuals aged ≥20 years were selected from nine cross‐sectional study periods (1991‐2015) from the China Health and Nutrition Survey (CHNS). Over the 24‐year period, the long‐term trend for the prevalence of the 2017 ACC/AHA‐defined age‐adjusted hypertension showed an increase from 32.2% (95% confidence interval (CI): 31.0%‐33.3%) in 1991 to 60.0% (95% CI: 58.6%‐61.3%) in 2015 (Ptrend < 0.001). According to the 2018 Chinese guideline for hypertension, the weighted hypertension prevalence increased from 10.0% (95% CI: 9.4%‐10.5%) in 1991 to 28.7% (95% CI: 27.9%‐29.6%) in 2015 (Ptrend < 0.001). However, slopes of increasing prevalence of hypertension were significantly greater according to the 2017 ACC/AHA guideline than that based on Joint National Committee (JNC 7) report (β = 1.00% vs β = 0.67% per year, respectively, P = 0.041). Based on the 2017 ACC/AHA definition, the prevalence of stage 1 hypertension and elevated blood pressure significantly increase from 22.3% and 6.9% in 1991 to 31.2% and 10.1% in 2015 (all P < 0.05), respectively. The secular trend for the prevalence of hypertension according to the 2017 ACC/AHA guideline showed a greater rate of increase compared with the prevalence based on the 2018 Chinese hypertension guidelines. Public health initiatives should focus on the current status of hypertension in China because of the possible high prevalence of hypertension and concomitant vascular risks

Crystal Morphology Prediction Models and Regulating Methods

Growing high-quality crystals with ideal properties is of great importance. The morphology of crystal is one key factor reflecting product quality, as it can affect the performance of products and downstream operations. In this work, the current state of crystal morphology modification is reviewed from different perspectives. First, the most widely used crystal growth models are discussed. Then, a variety of crystal morphology control methods, which include adjustment of crystallization operation parameters, addition of foreign molecules, change of different solvents, membrane assistance, the addition of external physical fields and the use of ball milling are summarized. As for applications, the control of crystal morphology has application potential in pharmaceutical and material fields, for example, energetic materials and semiconductor materials. Finally, the future development direction of crystal morphology regulation is discussed

MIntRec2.0: A Novel Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations

Multimodal intent recognition poses significant challenges, requiring the incorporation of non-verbal modalities from real-world contexts to enhance the comprehension of human intentions. However, most existing multimodal intent benchmark datasets are limited in scale and suffer from handling out-of-scope samples that arise in multi-turn conversational interactions. In this paper, we introduce MIntRec2.0, a novel benchmark dataset for multimodal intent recognition in multi-party conversations. It comprises of 1,245 dialogues collected from three TV series, encompassing 15,040 high-quality samples with text, video, and audio information. We expand the existing intent taxonomy to 30 fine-grained intent classes and annotate over 9,300 in-scope and 5,700 out-of-scope instances. This allows for evaluating the effectiveness of both in-scope intent recognition as well as robustness in detecting out-of-scope samples. Moreover, the dataset provides information about the different speakers involved in each dialogue, enabling both single-turn and multi-turn conversational multimodal intent recognition. To demonstrate the efficacy of utilizing multimodal information in conversational intent recognition, we employ classic multimodal fusion methods as benchmark methods. Furthermore, evaluation benchmarks are built with ChatGPT and humans, revealing a substantial performance gap between large language models and humans. To the best of our knowledge, MIntRec2.0 is the first large-scale multimodal dataset for intent recognition and out-of-scope detection, providing a pioneering foundation for further research in this field. The dataset and codes can be accessed through the links provided in the supplementary materials