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Hierarchical porous activated carbon (KHBC) with honeycomb structure was obtained by one-step slow pyrolysis using industrial waste cork particles as raw materials and KHC2O4 as activator, and it was applied to adsorb tetracycline hydrochloride (TC) in water. The relationship between activated carbon pore structure and adsorption performance was analyzed by fitting pore structure parameters with tetracycline adsorption capacity. Static adsorption experiments were conducted to investigate the effects of initial pH, temperature, initial concentration, and ionic strength on adsorption performance. The physicochemical structures of KHBC before and after adsorption were characterized using SEM and FT-IR techniques, establishing the correlation between the activation process, cork activated carbon pore structure, and adsorption performance. The results show that increasing the activation temperature and KHC2O4 addition ratio positively affects the specific surface area and total pore volume, promoting the formation of 2−5 nm mesopores. The specific surface area and total pore volume exhibit a strong linear relationship with TC adsorption capacity, with R2 values of 0.9852 and 0.958, respectively, and R2 of 0.8388 between 2−5 nm mesopore volume and TC adsorption capacity. At an activation temperature of 900 ℃ and an addition ratio of 5∶1, the resulting KHBC5-900 exhibits a specific surface area of 2447 m2/g, with a significant increase in 2−5 nm mesopores, showing the best adsorption effect on TC in water. It presents a micropore-mesopore pore size structure, with adsorption capacities of 1113.45 mg/g at 45 ℃ and 961.54 mg/g at 25 ℃, reaching 85% of the total adsorption in 5 minutes and equilibrium in 1 hour, which is 46 times that of un-activated BC-900 (21.25 mg/g). Also, the adsorption capacity is almost unaffected by environmental pH and ionic strength. The adsorption of TC by KHBC5-900 fits the quasi-second-order kinetic equation and Langmuir isotherm adsorption model, and the adsorption process is spontaneous and endothermic. The adsorption mechanism indicates that pore filling is the main adsorption mechanism, with the coexistence of π-π interactions and hydrogen bond interactions.

IntroductionPremature ovarian insufficiency (POI) is a condition characterized by ovarian dysfunction occurring before the age of 40, and its etiology is multifactorial, including genetic, immunological, infectious, environmental, and iatrogenic factors, with over half of the cases remaining unexplained. Whether the microbial communities and metabolites in follicular fluid, which is the direct microenvironment for oocyte survival, are related to POI has not been reported.MethodsIn this study, Follicular fluid samples of 26 patients with POI and 27 controls with a normal ovarian reserve were collected and analyzed using 16S rDNA sequencing and untargeted metabolomics. Conjoint analysis was performed to identify key microbial communities and metabolites that might be involved in POI.ResultsPatients with POI exhibited significant alterations in microbial richness and diversity and metabolic profile in their follicular fluid. The downregulation of ABC transporters and upregulation of the citrate cycle (TCA cycle) might be critical for the development and progression of POI. G-Rhodopseudomonas and g-Caulobacter were identified as key microbial genera, while L-aspartic acid, citrate, isoleucine, and cytidine were identified as key metabolites.DiscussionThese findings offer novel insights into the pathogenesis of POI and might pave the way for improved clinical outcomes for individuals with POI.

In the nature scene, because of the high degree of similarity between the background and the tea buds, as well as the different growth postures of the tea buds, finding and precisely identifying the picking point is challenging. To solve these issues, this paper proposes a precise way to find the best picking point for tea buds by combining traditional algorithms with Swin-Transformer-based target detection and semantic segmentation algorithms, namely SORC-SFT. Firstly, an improved target detection algorithm, Swin-Oriented R-CNN (SORC), is used to realize the recognition of four types of high-quality tea. The mean Average Precision (mAP) of the four categories was 82.3% after replacing the feature fusion network FPN with PAFPN and adding the Coordinate Attention (CA) mechanism. Secondly, the corresponding segmentation mask of the four recognized categories is obtained by adding Semask, Feature Alignment Module (FAM), and Feature Selection Module (FSM) to the improved semantic segmentation algorithm Semask-Fa-Transformer (SFT). The mean Intersection over Union (mIoU) of the semantic segmentation algorithm for each category is 89.83%, 91.97%, 88.85%, and 89.68%, respectively. Finally, the morphology of different categories of tea buds is analyzed, and the traditional algorithm is used to realize the accurate localization of the identified tea buds. For the four tested categories, the proportion of correct samples in locating picking points is 96.18%, 91.28%, 93.85%, and 90.58%, respectively. The experimental results show that, out of all the algorithms, the proposed picking point identification and localization approach has the best performance and will make a strong contribution to the accurate identification of tea leaves during the intelligent picking process.

ObjectiveThis study aims to evaluate the utility of polygenic risk scores (PRS) in women with early menopause (EM) and to investigate the clinical characteristics and risk factors associated with EM based on genetic risk.Study designGenotyping data and clinical data from women with EM and women with normal age of menopause retrieved from UK Biobank were used for early menopause risk prediction model establishment. Subsequently, 99 women diagnosed with EM and 1027 control women underwent PGT-M were recruited for model validation from across eight hospitals in China. According to PRS percentiles, these participants were further classified into high risk and intermediate risk groups. Characteristics among women at different risk levels were compared, and risk factors with early menopause were also statistical analyzed.Main outcome measuresThe proportion of women at high risk in EM and control groups; Characteristics with significant difference among women at different risk levels; risk factors associated with EM.ResultsThe proportion of high-risk women in the EM group was significantly higher than that in control women underwent PGT-M (Group PGT-M) (OR = 3.78), and that in women with normal age menopause from UK Biobank (Group UKB) (OR = 5.11). Notably, the women with high risk of EM exhibited distinct characteristics compared to women with the intermediate-risk of EM, and identified several risk factors associated with EM.ConclusionsWe established a PRS model to serves as a valuable instrument for EM risk prediction. The exploratory analysis revealed that women with high risk of EM exhibited a higher height, suggesting EM related genetic loci may also influence growth and development level. Several risk factors were found to be potentially associated with EM, such as excessive familial contentment, COVID-19 vaccination, staying up late, and the husband’s engagement in smoking and alcohol abuse.

Abstract The complement system, comprising over 30 proteins, is integral to the immune system, and the coagulation system is critical for vascular homeostasis. The activation of the complement and coagulation systems involves an organized proteolytic cascade, and the overactivation of these systems is a central pathogenic mechanism in several diseases. This review describes the role of complement and coagulation system activation in critical illness, particularly sepsis. The complexities of sepsis reveal significant knowledge gaps that can be compared to a profound abyss, highlighting the urgent need for further investigation and exploration. It is well recognized that the inflammatory network, coagulation, and complement systems are integral mechanisms through which multiple factors contribute to increased susceptibility to infection and may result in a disordered immune response during septic events in patients. Given the overlapping pathogenic mechanisms in sepsis, immunomodulatory therapies currently under development may be particularly beneficial for patients with sepsis who have concurrent infections. Herein, we present recent findings regarding the molecular relationships between the coagulation and complement pathways in the advancement of sepsis, and propose potential intervention targets related to the crosstalk between coagulation and complement, aiming to provide more valuable treatment of sepsis.

Abstract Inflammasome activation and pyroptotic cell death are known to contribute to the pathogenesis of cardiovascular diseases, such as myocardial ischemia-reperfusion (I/R) injury, although the underlying regulatory mechanisms remain poorly understood. Here we report that expression levels of the E3 ubiquitin ligase membrane-associated RING finger protein 2 (MARCH2) were elevated in ischemic human hearts or mouse hearts upon I/R injury. Genetic ablation of MARCH2 aggravated myocardial infarction and cardiac dysfunction upon myocardial I/R injury. Single-cell RNA-seq analysis suggested that loss of MARCH2 prompted activation of NLRP3 inflammasome in cardiomyocytes. Mechanistically, phosphoglycerate mutase 5 (PGAM5) was found to act as a novel regulator of MAVS-NLRP3 signaling by forming liquid-liquid phase separation condensates with MAVS and fostering the recruitment of NLRP3. MARCH2 directly interacts with PGAM5 to promote its K48-linked polyubiquitination and proteasomal degradation, resulting in reduced PGAM5–MAVS co-condensation, and consequently inhibition of NLRP3 inflammasome activation and cardiomyocyte pyroptosis. AAV-based re-introduction of MARCH2 significantly ameliorated I/R-induced mouse heart dysfunction. Altogether, our findings reveal a novel mechanism where MARCH2-mediated ubiquitination negatively regulates the PGAM5/MAVS/NLRP3 axis to protect against cardiomyocyte pyroptosis and myocardial I/R injury.

In this paper, we explore the enhancement of a 4.6 km dual-polarization 2 × 2 MIMO D-band photonic-assisted terahertz communication system using iterative pruning-based deep neural network (DNN) nonlinear equalization techniques. The system employs advanced digital signal processing (DSP) methods, including down-conversion, resampling, matched filtering, and various equalization algorithms to combat signal distortions. We demonstrate the effectiveness of DNN and iterative pruning techniques in significantly reducing bit error rates (BERs) across a range of symbol rates (10 Gbaud to 30 Gbaud) and polarization states (vertical and horizontal). Before pruning, at 10 GBaud transmission, the lowest BER was 0.0362, and at 30 GBaud transmission, the lowest BER was 0.1826, both of which did not meet the 20% soft-decision forward error correction (SD-FEC) threshold. After pruning, the BER at different transmission rates was reduced to below the hard decision forward error correction (HD-FEC) threshold, indicating a substantial improvement in signal quality. Additionally, the pruning process contributed to a decrease in network complexity, with a maximum reduction of 85.9% for 10 GBaud signals and 63.0% for 30 GBaud signals. These findings indicate the potential of DNN and pruning techniques to enhance the performance and efficiency of terahertz communication systems, providing valuable insights for future high-capacity, long-distance wireless networks.

Muscles play an indispensable role in human life. Surface electromyography (sEMG), as a non-invasive method, is crucial for monitoring muscle status. It is characterized by its real-time, portable nature and is extensively utilized in sports and rehabilitation sciences. This study proposed a wireless acquisition system based on multi-channel sEMG for objective monitoring of grip force. The system consists of an sEMG acquisition module containing four-channel discrete terminals and a host computer receiver module, using Bluetooth wireless transmission. The system is portable, wearable, low-cost, and easy to operate. Leveraging the system, an experiment for grip force prediction was designed, employing the bald eagle search (BES) algorithm to enhance the Random Forest (RF) algorithm. This approach established a grip force prediction model based on dual-channel sEMG signals. As tested, the performance of acquisition terminal proceeded as follows: the gain was up to 1125 times, and the common mode rejection ratio (CMRR) remained high in the sEMG signal band range (96.94 dB (100 Hz), 84.12 dB (500 Hz)), while the performance of the grip force prediction algorithm had an R2 of 0.9215, an MAE of 1.0637, and an MSE of 1.7479. The proposed system demonstrates excellent performance in real-time signal acquisition and grip force prediction, proving to be an effective muscle status monitoring tool for rehabilitation, training, disease condition surveillance and scientific fitness applications.

Abstract The degradation of photovoltaic modules has an impact on various parameters of photovoltaic modules. Ignoring the degradation of photovoltaic modules or inaccurate estimation of the degradation will lead to wrong power dispatching strategies and lead to economic losses. For PV module life estimation or reliability estimation, it is necessary to first establish an accurate statistical degradation model of PV module. The main goal of this paper is to analyze a selection of explicit PV module degradation model based on distribution. Since the degradation is related to time, the study assumed that those parameters in Gamma or Gaussian distributions are related to time. Five models are calculated based on maximum likelihood estimation and particle swarm optimization. Through verification and comparison on the measured PV module degradation data, the performance of these models in four cases: long‐term data fitting, long‐term data prediction, single‐module short‐term data fitting, and multimodule short‐term data fitting are evaluated. The results show that the model proposed in this paper has a great improvement over the original model, and the constant‐σ Gaussian distribution degradation model achieves the best performance.

Abstract Neuronal voltage changes which are dependent on chloride transporters and channels are involved in forming neural functions during early development and maintaining their stability until adulthood. The intracellular chloride concentration maintains a steady state, which is delicately regulated by various genes coding for chloride transporters and channels (GClTC) on the plasmalemma; however, the synergistic effect of these genes in central nervous system disorders remains unclear. In this study, we first defined 10 gene clusters with similar temporal expression patterns, and identified 41 GClTC related to brain developmental process. Then, we found 4 clusters containing 22 GClTC were enriched for the neuronal functions. The GClTC from different clusters presented distinct cell type preferences and anatomical heterogeneity. We also observed strong correlations between clustered genes and diseases, most of which were nervous system disorders. Finally, we found that one of the most well-known GClTC, SLC12A2, had a more profound effect on glial cell-related diseases than on neuron-related diseases, which was in accordance with our observation that SLC12A2 was mainly expressed in oligodendrocytes during brain development. Our findings provide a more comprehensive understanding of the temporal and spatial expression characteristics of GClTC, which can help us understand the complex roles of GClTC in the development of the healthy human brain and the etiology of brain disorders.

In this experiment, an intermediate alloy (Al-3Sr-8Y) that can refine and modify A356 alloy simultaneously was developed, and the synergistic effect of Sr and Y on the microstructure and mechanical properties of A356 alloy was investigated. The results show that when the content of Al-3Sr-8Y intermediate alloy reaches 0.3 wt%, the morphology of α -Al dendrites is significantly refined and the secondary dendrite arm spacing (SDAS) was significantly reduced. Moreover, the morphology of eutectic Si transforms from acicular to small fibrous, and the average area and aspect ratio of eutectic Si decrease to 0.81 μ m ^2 , and 2.01. This change is caused by the twin plane re-entrant edge (TPRE) poisoning mechanism, where the addition of Al-3Sr-8Y can poison the intrinsic growth position of the Si phase, reduce the growth rate of the Si phase, promote isotropic growth, and achieve a highly branched morphology of the Si phase to form a fibrous structure. Due to the excellent synergistic effect of Sr and Y, the tensile strength and elongation of the alloy reached the maximum values of 303.5 MPa and 9.5% when 0.3 wt% Al-3Sr-8Y was added after heat treatment, which is an increase of 18.2% and 86.3% compared with the untreated A356 alloy, respectively.

Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in different ways. However, the performance of conventional machine learning models on EEG data is unsatisfactory due to the low Signal to Noise Ratio (SNR) in the EEG signals, especially in the office-based anesthesia EEG setting. Deep learning models have been used widely in the field of Brain Computer Interface (BCI) to perform classification and pattern recognition tasks due to their capability of good generalization and handling noises. Compared to other BCI applications, where deep learning has demonstrated encouraging results, the deep learning approach for classifying different brain consciousness states under anesthesia has been much less investigated. In this paper, we propose a new framework based on meta-learning using deep neural networks, named Anes-MetaNet, to classify brain states under anesthetics. The Anes-MetaNet is composed of Convolutional Neural Networks (CNN) to extract power spectrum features, and a time consequence model based on Long Short-Term Memory (LSTM) networks to capture the temporal dependencies, and a meta-learning framework to handle large cross-subject variability. We use a multi-stage training paradigm to improve the performance, which is justified by visualizing the high-level feature mapping. Experiments on the office-based anesthesia EEG dataset demonstrate the effectiveness of our proposed Anes-MetaNet by comparison of existing methods.

ObjectiveTo study patients’ new treatment methods and mechanisms of repeated implantation failure.DesignA retrospective study.SettingIn vitro fertilization (IVF) unit in a Three-A hospital.Patient(s)Ninety-three patients with repeated implantation failure in IVF and embryo transfer.Intervention(s)the luteal phase support.Main outcome measure(s)According to whether human chorionic gonadotropin(HCG) was added, the two groups were divided into an observation group and a control group, and the clinical outcomes of the two groups were compared. Furthermore, 20 patients were selected for whole exome sequencing to investigate the mechanism.ResultsThe observation group’s clinical pregnancy rate and live birth rate were significantly higher than those in the control group (P=0.004). Functional enrichment analysis showed that these genes were significantly enriched in embryo implantation or endometrial receptivity processes, such as microtubule-based movement, NABA CORE MATRISOME, superoxide anion generation, protein localization to vacuole, extracellular matrix organization, fertilization, microtubule-based transport, cell junction organization, microtubule cytoskeleton organization. Furthermore, variants detected in these pathway genes were missense mutations that affect the protein’s biological activity but do not effectuate its inactivation.ConclusionsAdding HCG in the luteal phase might improve the clinical pregnancy and live birth rates in RIF patients. The potential pathogenesis of RIF genetic level may be caused by microtubule-based movement, extracellular matrix organization, and the Superoxide Anion generation pathway.

Mitochondrial DNA (mtDNA) mutations are often associated with incurable diseases and lead to detectable pathogenic variants in 1 out of 200 babies. Uncoupling of the inheritance of mtDNA and the nuclear genome by spindle transfer (ST) can potentially prevent the transmission of mtDNA mutations from mother to offspring. However, no well-established studies have critically assessed the safety of this technique. Here, using single-cell triple omics sequencing method, we systematically analyzed the genome (copy number variation), DNA methylome, and transcriptome of ST and control blastocysts. The results showed that, compared to that in control embryos, the percentage of aneuploid cells in ST embryos did not significantly change. The epiblast, primitive endoderm, and trophectoderm (TE) of ST blastocysts presented RNA expression profiles that were comparable to those of control blastocysts. However, the DNA demethylation process in TE cells of ST blastocysts was slightly slower than that in the control blastocysts. Collectively, our results suggest that ST seems generally safe for embryonic development, with a relatively minor delay in the DNA demethylation process at the blastocyst stage. Uncoupling of the inheritance of mtDNA and the nuclear genome by spindle transfer could prevent the transmission of mtDNA mutations. Systematic single-cell multiomic analyses of spindle transferred human embryos suggest this technique seems generally safe for human embryonic development and deserves further scientific evaluation and clinical testing.

This study analyzed the influence of sustainable livelihood and environmental cognition on herdsmen’s production behaviors on basis of logistic model. The consequences illustrated that the social capital followed by material capital, financial capital, and human capital has the greatest impact on herdsmen’s production behaviors, but the natural capital played a vital role in promoting herdsmen’s choice of raising breeding scale. The area and quality of grassland in herdsman families’ sustainable livelihood could encourage herdsmen to increase breeding scale, while the quantity of livestock, structure of house, the number of labor, the level of education, the status of health, and the income of families could promote herdsmen to restrain the breeding scale. The cognition of policy and ecological environment also had a significant positive effect on herdsmen restraining breeding scale.

Oocytes reconstructed by spindle transfer (ST) are prone to chromosome abnormality, which is speculated to be caused by mechanical interference or premature activation, the mechanism is controversial. In this study, C57BL/6N oocytes were used as the model, and electrofusion ST was performed under normal conditions, Ca2+ free, and at room temperature, respectively. The effect of enucleation and electrofusion stimulation on MPF activity, spindle morphology, γ-tubulin localization and chromosome arrangement was compared. We found that electrofusion stimulation could induce premature chromosome separation and abnormal spindle morphology and assembly by decreasing the MPF activity, leading to premature activation, and thus resulting in chromosome abnormality in oocytes reconstructed via ST. Electrofusion stimulation was an independent factor of chromosome abnormality in oocytes reconstructed via ST, and was not related to enucleation, fusion status, temperature, or Ca2+. The electrofusion stimulation number should be minimized, with no more than 2 times being appropriate. As the electrofusion stimulation number increased, several typical abnormalities in chromosome arrangement and spindle assembly occurred. Although blastocyst culture could eliminate embryos with chromosomal abnormalities, it would significantly decrease the number of normal embryos and reduce the availability of embryos. The optimum operating condition for electrofusion ST was the 37°C group without Ca2+.

Abstract A dispersion‐free scheme for stable multi‐access radio frequency dissemination in a fibre loop link is proposed and demonstrated. The phase jitter and dispersion‐induced power fading are suppressed simultaneously by transmitting different single‐band signals in opposite propagating directions. Experimentally, a 2.5 GHz radio frequency signal is steadily transmitted to different remote sites, with phase deviations less than 0.6 ps over 15,000 s.

This study developed a multi-classification model for vehicle interior noise from the subway system, collected on smartphones. The proposed model has the potential to be used to analyze the causes of abnormal noise using statistical methods and evaluate the effect of rail maintenance work. To this end, first, we developed a multi-source data (audio, acceleration, and angle rate) collection framework via smartphone built-in sensors. Then, considering the Shannon entropy, a 1-second window was selected to segment the time-series signals. This study extracted 45 features from the time- and frequency-domains to establish the classifier. Next, we investigated the effects of balancing the training dataset with the Synthetic Minority Oversampling Technique (SMOTE). By comparing and analyzing the classification results of importance-based and mutual information-based feature selection methods, the study employed a feature set consisting of the top 10 features by importance score. Comparisons with other classifiers indicated that the proposed XGBoost-based classifier runs fast while maintaining good accuracy. Finally, case studies were provided to extend the applications of this classifier to the analysis of abnormal vehicle interior noise events and evaluate the effects of rail grinding.

In this article, an improved particle filter with electromagnetism-like mechanism algorithm is proposed for aircraft engine gas-path component abrupt fault diagnosis. In order to avoid the particle degeneracy and sample impoverishment of normal particle filter, the electromagnetism-like mechanism optimization algorithm is introduced into resampling procedure, which adjusts the position of the particles through simulating attraction–repulsion mechanism between charged particles of the electromagnetism theory. The improved particle filter can solve the particle degradation problem and ensure the diversity of the particle set. Meanwhile, it enhances the ability of tracking abrupt fault due to considering the latest measurement information. Comparison of the proposed method with three different filter algorithms is carried out on a univariate nonstationary growth model. Simulations on a turbofan engine model indicate that compared to the normal particle filter, the improved particle filter can ensure the completion of the fault diagnosis within less sampling period and the root mean square error of parameters estimation is reduced.