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Abstract Abscisic acid (ABA) is the primary preventing factor of seed germination, which is crucial to plant survival and propagation. ABA-induced seed germination inhibition is mainly mediated by the dimeric PYR/PYL/RCAR (PYLs) family members. However, little is known about the relevance between dimeric stability of PYLs and seed germination. Here, we reveal that stabilization of PYL dimer can relieve ABA-induced inhibition of seed germination using chemical genetic approaches. Di-nitrobensulfamide (DBSA), a computationally designed chemical probe, yields around ten-fold improvement in receptor affinity relative to ABA. DBSA reverses ABA-induced inhibition of seed germination mainly through dimeric receptors and recovers the expression of ABA-responsive genes. DBSA maintains PYR1 in dimeric state during protein oligomeric state experiment. X-ray crystallography shows that DBSA targets a pocket in PYL dimer interface and may stabilize PYL dimer by forming hydrogen networks. Our results illustrate the potential of PYL dimer stabilization in preventing ABA-induced seed germination inhibition.

Abstract. Background:. Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors. Methods:. The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25–70 years old from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020. Results:. The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy. Conclusions:. We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.

Abstract Microflow catalysis is a cutting-edge approach to advancing chemical synthesis and manufacturing, but the challenge lies in developing efficient and stable multiphase catalysts. Here we showcase incorporating amine-containing metal-organic cages into automated microfluidic reactors through covalent bonds, enabling highly continuous flow catalysis. Two Fe4L4 tetrahedral cages bearing four uncoordinated amines were designed and synthesized. Post-synthetic modifications of the amine groups with 3-isocyanatopropyltriethoxysilane, introducing silane chains immobilized on the inner walls of the microfluidic reactor. The immobilized cages prove highly efficient for the reaction of anthranilamide with aldehydes, showing superior reactivity and recyclability relative to free cages. This superiority arises from the large cavity, facilitating substrate accommodation and conversion, a high mass transfer rate and stable covalent bonds between cage and microreactor. This study exemplifies the synergy of cages with microreactor technology, highlighting the benefits of heterogenous cages and the potential for future automated synthesis processes

Abstract Background Ferroptosis is associated with the pathological progression of hemorrhagic injury and ischemia–reperfusion injury. According to our previous study, exosomes formed through bone marrow mesenchymal stem cells modified with miR-340-3p (MB-exos) can restore damaged endometrium. However, the involvement of ferroptosis in endometrial injury and the effect of MB-exos on ferroptosis remain elusive. Methods The endometrial injury rat model was developed. Exosomes were obtained from the supernatants of bone marrow mesenchymal stromal cells (BMSCs) and miR-340/BMSCs through differential centrifugation. We conducted RNA-seq analysis on endometrial tissues obtained from the PBS and MB-exos groups. Ferroptosis was induced in endometrial stromal cells (ESCs) by treating them with erastin or RSL3, followed by treatment with B-exos or MB-exos. We assessed the endometrial total m6A modification level after injury and subsequent treatment with B-exos or MB-exos by methylation quantification assay. We performed meRIP-qPCR to analyze m6A modification-regulated endogenous mRNAs. Results We reveal that MB-exos facilitate the injured endometrium to recover by suppressing ferroptosis in endometrial stromal cells. The injured endometrium showed significantly upregulated N 6-methyladenosine (m6A) modification levels; these levels were attenuated by MB-exos through downregulation of the methylase METTL3. Intriguingly, METTL3 downregulation appears to repress ferroptosis by stabilizing HMOX1 mRNA, thereby potentially elucidating the mechanism through which MB-exos inhibit ferroptosis in ESCs. We identified YTHDF2 as a critical m6A reader protein that contributes to HMOX1 mRNA degradation. YTHDF2 facilitates HMOX1 mRNA degradation by identifying the m6A binding site in the 3′-untranslated regions of HMOX1. In a rat model, treatment with MB-exos ameliorated endometrial injury-induced fibrosis by inhibiting ferroptosis in ESCs. Moreover, METTL3 short hairpin RNA-mediated inhibition of m6A modification enhanced the inhibitory effect of MB-exos on ferroptosis in endometrial injury. Conclusions Thus, these observations provide new insights regarding the molecular mechanisms responsible for endometrial recovery promotion by MB-exos and highlight m6A modification-dependent ferroptosis inhibition as a prospective therapeutic target to attenuate endometrial injury.

Abstract Background Adiposity profoundly impacts reproductive health in both humans and animals. However, the precise subpopulations contributing to infertility under obese conditions remain elusive. Results In this study, we established an obese mouse model through an eighteen-week high-fat diet regimen in adult female mice. Employing single-cell RNA sequencing (scRNA-seq), we constructed a comprehensive single-cell atlas of ovarian tissues from these mice to scrutinize the impact of obesity on the ovarian microenvironment. ScRNA-seq revealed notable alterations in the microenvironment of ovarian tissues in obese mice. Granulosa cells, stromal cells, T cells, and macrophages exhibited functional imbalances compared to the control group. We observed heightened interaction strength in the SPP1-CD44 pairing within lgfbp7+ granulosa cell subtypes and Il1bhigh monocyte subtypes in the ovarian tissues of obese mice. Moreover, the interaction strength between Il1bhigh monocyte subtypes and Pdgfrb+ stromal cell subtypes in the form of TNF − TNFrsf1α interaction was also enhanced subsequently to obesity, potentially contributing to ovarian fibrosis pathogenesis. Conclusions We propose a model wherein granulosa cells secrete SPP1 to activate monocytes, subsequently triggering TNF-α secretion by monocytes, thereby activating stromal cells and ultimately leading to the development of ovarian fibrosis. Intervening in this process may represent a promising avenue for improving clinical outcomes in fertility treatments for obese women.

Abstract Without intervention, a considerable proportion of patients with metabolism‐associated fatty liver disease (MAFLD) will progress from simple steatosis to metabolism‐associated steatohepatitis (MASH), liver fibrosis, and even hepatocellular carcinoma. However, the molecular mechanisms that control progressive MAFLD have yet to be fully determined. Here, we unraveled that the expression of the N6-methyladenosine (m6A) methyltransferase METTL14 is remarkably downregulated in the livers of both patients and several murine models of MAFLD, whereas hepatocyte-specific depletion of this methyltransferase aggravated lipid accumulation, liver injury, and fibrosis. Conversely, hepatic Mettl14 overexpression alleviated the above pathophysiological changes in mice fed on a high-fat diet (HFD). Notably, in vivo and in vitro mechanistic studies indicated that METTL14 downregulation decreased the level of GLS2 by affecting the translation efficiency mediated by YTHDF1 in an m6A-depedent manner, which might help to form an oxidative stress microenvironment and accordingly recruit Cx3cr1 + Ccr2 + monocyte-derived macrophages (Mo-macs). In detail, Cx3cr1 + Ccr2 + Mo-macs can be categorized into M1-like macrophages and S100A4-positive macrophages and then further activate hepatic stellate cells (HSCs) to promote liver fibrosis. Further experiments revealed that CX3CR1 can activate the transcription of S100A4 via CX3CR1/MyD88/NF-κB signaling pathway in Cx3cr1 + Ccr2 + Mo-macs. Restoration of METTL14 or GLS2, or interfering with this signal transduction pathway such as inhibiting MyD88 could ameliorate liver injuries and fibrosis. Taken together, these findings indicate potential therapies for the treatment of MAFLD progression.

Abstract Actomyosin networks constrict cell area and junctions to alter cell and tissue shape. However, during cell expansion under mechanical stress, actomyosin networks are strengthened and polarized to relax stress. Thus, cells face a conflicting situation between the enhanced actomyosin contractile properties and the expansion behaviour of the cell or tissue. To address this paradoxical situation, we study late Drosophila oogenesis and reveal an unusual epithelial expansion wave behaviour. Mechanistically, Rac1 and Rho1 integrate basal pulsatile actomyosin networks with ruffles and focal adhesions to increase and then stabilize basal area of epithelial cells allowing their flattening and elongation. This epithelial expansion behaviour bridges cell changes to oocyte growth and extension, while oocyte growth in turn deforms the epithelium to drive cell spreading. Basal pulsatile actomyosin networks exhibit non-contractile mechanics, non-linear structures and F-actin/Myosin-II spatiotemporal signal separation, implicating unreported expanding properties. Biophysical modelling incorporating these expanding properties well simulates epithelial cell expansion waves. Our work thus highlights actomyosin expanding properties as a key mechanism driving tissue morphogenesis.

Soil-derived CO2 flux (SCF) plays a key role in mitigating global warming, and the accurate prediction of SCF is the basis for the accurate prediction of climate change. To explore the changes in SCF, discover soil respiration patterns, and accurately predict soil respiration CO2 flux, we used a self-developed soil respiration monitoring device to collect soil CO2 flux data, and proposed a Dish-ECA-Adain-Autoformer (DEAF) combination model with adaptive feature aggregation and bias correction of the Autoformer improvement network to accurately predict the changes in soil respiration. To explore the potential feature patterns in the CO2 flux time series data, the trend and seasonal features of the data were enhanced using inverse contrast based on Fourier continuous transform. The irregular features of the local segments in the unit time were eliminated by adaptive aggregation convolution, and the residual linkage was applied to obtain the potential paradigm of the sequence changes. In the self-constructed CO2 flux dataset, MSE and MAE of the proposed model reached 0.296 and 0.328, respectively, with an average improvement of 34.4% compared to the unimproved model. In the experiments on the public time series dataset, the average improvement was approximately 20.2%, indicating that the model can effectively mine the time series information of the data and the correlation information of the feature dimensions to improve the prediction performance and verify the accuracy and universality of the proposed model. Additionally, the short-, medium-, and long-term prediction results showed that the DEAF model fitted the soil CO2 flux data well, providing a reference basis for the accurate prediction of soil CO2 flux.

This study designed an asymmetric, interference-free and excellent heat dissipation performance CPU cooler. Due to the increasing size of the motherboard cooler, the heat pipes or aluminum fins of the CPU cooler may interfere with the motherboard or the graphics card, thus preventing smooth installation. The proposed tower-type cooler avoids this problem by offsetting the aluminum fins and bending the heat pipes away from the motherboard, ensuring compatibility with the motherboard, memory, graphics card, and other components. Combining the heat pipes, copper base, and aluminum base requires careful consideration of tolerance. The heat pipes are composed of copper tubes, copper powder, and pure water, then vacuum treated. The copper tubes are filled with copper powder and sintered (900 °C–1000 °C) to inject pure water and vacuum seal once more, then bent according to the design. The aluminum fins are stamped from sheet metal. After nickel plating, they facilitate welding between copper and aluminum materials. The CPU cooler developed in this study includes the design, drawing, fabrication, and assembly of each component (finning, tubing, soldering). It demonstrates successful installation on most commercial motherboards. Compared with commercially available coolers, the temperature of the cooler developed in this study is reduced by about 1∼2 °C.

BackgroundTraditional studies of health behaviors are typically conducted using one-shot, cross-sectional surveys. Thus, participants’ recall bias may undermine the reliability and validity of the data. To capture mood changes and health behaviors in everyday life, we designed an online survey platform, ClickDiary, which helped collect more complete information for comprehensive data analyses. ObjectiveWe aim to understand whether daily mood changes are related to one’s personal characteristics, demographic factors, and daily health behaviors. MethodsThe ClickDiary program uses a Web-based platform to collect data on participants’ health behaviors and their social-contact networks. The name ClickDiary comes from the platform’s interface, which is designed to allow the users to respond to most of the survey questions simply by clicking on the options provided. Participants were recruited from the general population and came from various backgrounds. To keep the participants motivated and interested, the ClickDiary program included a random drawing for rewards. We used descriptive statistics and the multilevel proportional-odds mixed model for our analysis. ResultsWe selected 130 participants who had completed at least 30 days of ClickDiary entries from May 1 to October 31, 2014 as our sample for the study. According to the results of the multilevel proportional-odds mixed model, a person tended to be in a better mood on a given day if he or she ate more fruits and vegetables, took in more sugary drinks, ate more fried foods, showed no cold symptoms, slept better, exercised longer, and traveled farther away from home. In addition, participants were generally in a better mood during the weekend than on weekdays. ConclusionsSleeping well, eating more fruits and vegetables, and exercising longer each day all appear to put one in a better mood. With the online ClickDiary survey, which reduces the recall biases that are common in traditional one-shot surveys, we were able to collect and analyze the daily variations of each subject’s health behaviors and mood status.

Abstract We aimed to investigate the association between air pollution and advanced fibrosis among patients with metabolic associated fatty liver disease (MAFLD) and chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections. A total of 1376 participants who were seropositive for HBV surface antigen (HBsAg) or antibodies to HCV (anti‐HCV) or had abnormal liver function in a community screening program from 2019 to 2021 were enrolled for the assessment of liver fibrosis using transient elastography. Daily estimates of air pollutants (particulate matter ≤2.5 μm in diameter [PM2.5], nitrogen dioxide [NO2], ozone [O3] and benzene) were aggregated into mean estimates for the previous year based on the date of enrolment. Of the 1376 participants, 767 (52.8%) and 187 (13.6) had MAFLD and advanced fibrosis, respectively. A logistic regression analysis revealed that the factors associated with advanced liver fibrosis were HCV viremia (odds ratio [OR], 3.13; 95% confidence interval [CI], 2.05–4.77; p

The lung nodule detection technology plays a vital role in the diagnosis and treatment of early lung cancer. Deep learning is currently one of the main technologies applied in computer vision related fields. Therefore, this topic is to combine deep learning and lung nodule detection technology. Different from some research methods, we improved from two-dimensional space to three- dimensional space and applied it to lung nodule detection. After statistically analyzing the lung CT data, we find that the lung CT data has inconsistent scales on the vertical axis. Aiming at the problem of lung CT data, this article is based on the deep learning method. First, the lung interpolation network based on voxel flow is used to achieve the same scale, and then the lung nodule detection network based on three-dimensional convolution is used to complete the detection of lung nodules. The entire network combines U- Net-like and RPN-like network structures. Through data slice input, it avoids the limitation of the display memory of the computing platform. The network structure also introduces prior knowledge of the coordinate information of the input slices to improve the classification accuracy of lung nodules. The experimental results show that the lung slice data of consistent scale is achieved through interpolation, and then through the three-dimensional convolution of the lung nodule detection network, the state- of-the-art detection effect is achieved. Because of the introduction of the interpolation network, the time- consuming has increased. Of course, the overall speed stillmeets the actual use value.

The utilization of target detection algorithms for counting edible centipedes represents a novel endeavor in the field of traditional Chinese medicine materials using deep learning. However, the accuracy of current target detection algorithms is relatively low due to the complexity of the centipede background and the density of the detection targets, making them poorly suited for practical application scenarios. To address this, this study proposes a centipede target detection algorithm based on an improved Your Only Look Once V5 (YOLOv5) model, termed FD-YOLO. This algorithm enhances the original model by incorporating the CBAM attention mechanism and the BiFormer universal visual transformer to suppress irrelevant information and intensify focus on the desired detection targets, thereby improving the precision of the algorithm. Additionally, the FD-YOLO algorithm enhances the model’s generalizability and robustness by improving the existing SPPF module. Experimental results demonstrate that compared to the original YOLOv5 prototype network, the improved YOLOv5 model has increased the AP@0.5 by 3.5%, reaching 97.2%, and raised the recall rate from 86.2% to 92.9%. Therefore, the enhanced YOLOv5 algorithm can effectively detect and count centipedes, making it more suitable for current practical application scenarios.