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Introduction: High-altitude pulmonary edema (HAPE) is a severe and potentially fatal condition with limited treatment options. Although ceramide kinase (CERK)-derived ceramide-1-phosphate (C1P) has been demonstrated to offer protection against various pulmonary diseases, its effects on HAPE remain unclear. Objectives: Our study aimed to investigate the potential role of CERK-derived C1P in the development of HAPE and to reveal the molecular mechanisms underlying its protective effects. We hypothesized that CERK-derived C1P could protect against HAPE by stabilizing circadian rhythms and maintaining mitochondrial dynamics. Methods: To test our hypothesis, we used CERK-knockout mice and established HAPE mouse models using a FLYDWC50-1C hypobaric hypoxic cabin. We utilized a range of methods, including lipidomics, transcriptomics, immunofluorescence, Western blotting, and transmission electron microscopy, to identify the mechanisms of regulation. Results: Our findings demonstrated that CERK-derived C1P played a protective role against HAPE. Inhibition of CERK exacerbated HAPE induced by the hypobaric hypoxic environment. Specifically, we identified a novel mechanism in which CERK inhibition induced aryl hydrocarbon receptor nuclear translocator-like (ARNTL) autophagic degradation, inducing the circadian rhythm and triggering mitochondrial damage by controlling the expression of proteins required for mitochondrial fission and fusion. The decreased ARNTL caused by CERK inhibition impaired mitochondrial dynamics, induced oxidative stress damage, and resulted in defects in mitophagy, particularly under hypoxia. Exogenous C1P prevented ARNTL degradation, alleviated mitochondrial damage, neutralized oxidative stress induced by CERK inhibition, and ultimately relieved HAPE. Conclusions: This study provides evidence for the protective effect of C1P against HAPE, specifically, through stabilizing circadian rhythms and maintaining mitochondrial dynamics. Exogenous C1P therapy may be a promising strategy for treating HAPE. Our findings also highlight the importance of the circadian rhythm and mitochondrial dynamics in the pathogenesis of HAPE, suggesting that targeting these pathways may be a potential therapeutic approach for this condition.

Background: Blood metabolites serve as pivotal indicators in identifying and predicting the course of rheumatoid arthritis (RA). However, empirical substantiation of a direct causal link between these serum biomarkers and the development of RA is still lacking comprehensive support. Method: In pursuit of a thorough exploration of the causal links between circulating blood metabolites and RA, we deployed a two-sample Mendelian randomization (MR) approach during our initial investigative phase. This method was utilized to examine the potential connections between 249 distinct circulating metabolites and the prevalence of RA. In the validation phase, we conducted replication analyses with a new metabolic dataset consisting of 123 metabolites. Furthermore, we employed the Mendelian randomization based on Bayesian model averaging (MR-BMA) technique to pinpoint key metabolic characteristics that have significant causal implications. Results: In our primary analysis, we found that acetate, acetoacetate and pyruvate exhibited a consistent protective causal association with rheumatoid arthritis, while lactate demonstrated a positive correlation with rheumatoid arthritis risk. It is also noteworthy that a substantial subset of traits related to both saturated and unsaturated fatty acids showed causal influences. Subsequent secondary analyses substantiated these observations, revealing that traits associated with the average number of methylene groups in a fatty acid chain exhibited protective effects. Ultimately, our MR-BMA analyses unveiled that the ratio of polyunsaturated fatty acids (PUFAs) to total fatty acids assumes a paramount role in increasing the susceptibility to rheumatoid arthritis. Conclusions: By employing systemic MR analyses, our study has successfully generated an all-encompassing atlas elucidating the intricate connections between circulating metabolites and the susceptibility to rheumatoid arthritis. Our results indicate the high unsaturation degree is a dominant risk factors correlated with rheumatoid arthritis.

Abstract Structural variants (SVs), accounting for a larger fraction of the genome than SNPs/InDels, are an important pool of genetic variation, enabling environmental adaptations. Here, we perform long-read sequencing data of 320 Tibetan and Han samples and show that SVs are highly involved in high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict enhancers, target genes and biological functions. We reveal diverse Tibetan-specific SVs affecting the regulatory circuitry of biological functions, including the hypoxia response, energy metabolism and pulmonary function. We find a Tibetan-specific deletion disrupts a super-enhancer and downregulates EPAS1 using enhancer reporter, cellular knock-out and DNA pull-down assays. Our study expands the global SV landscape, reveals the role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles of SVs in human biology.

Infertility is a significant challenge in modern society, and observed studies have reported the association between telomere length and infertility. Whether this relationship is causal remains controversial.We employed two-sample mendelian randomization (MR) to investigate the causal relationship between leukocyte telomere length (LTL) and major causes of infertility, including male and female infertility, sperm abnormalities, and endometriosis. MR analyses were mainly performed using the inverse variance weighted (IVW) method and complemented with other MR methods.Our findings demonstrate a causal association between LTL and endometriosis (OR1.304, 95% CI (1.122,1.517), p = 0.001), suggesting its potential as a biomarker for this condition. However, we did not observe a significant causal relationship between LTL and other infertility causes.Our study presents compelling evidence on the relationship between LTL and endometriosis. Meanwhile, our study demonstrates that there is no causal relationship between LTL and infertility. This research contributes to the field by shedding light on the importance of LTL in the early diagnosis and intervention of endometriosis.

An integrated analysis of RNA editing events in human embryo development suggests that recurrent RNA edits might enhance maternal mRNA clearance.

Abstract The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

Aiming at the problem of fixed candidate voltage vector (VV) in conventional finite-control-set (FCS) model predictive torque control (MPTC) of permanent magnet synchronous motor (PMSM), fuzzy control is use to determine dynamic candidate VVs based on effects of the value and angle of applying VV on stator flux and torque. The inputs of fuzzy controller are errors of stator flux and torque and outputs are 3 candidate VVs. Simulation results show compared with conventional FCS-MPTC, the proposed strategy can decrease stator flux and torque’s ripples and calculation burden of MPTC, but it will increase average switching frequency. In order to decrease average switching frequency, zero VV is also used as candidate VV, but it will decrease dynamic torque response. Thus the method that zero VV is used only at steady state is given. Simulation results show the control performance of the system remains basically unchanged and the average switching frequency is reduced.

Aiming at the problem of bad real-time performance caused by the large calculation of the finite control set model predictive torque control(MPTC), a data-driven method for MPTC of the permanent magnet synchronous motor(PMSM) is proposed. The data produced by PMSM-MPTC system are used to train DNN to learn its selective laws and then replace it to select the optimal voltage vector. This paper also analyzes the data-driven out-of-control problems caused by the difference between the steady data and dynamic data of the MPTC and solves the above issue by extending and balancing the data set, which further proves the feasibility of the data-driven MPTC. The simulation experiments show that the torque ripple, stator flux ripple and the system switching frequency of the data-driven MPTC are further reduced, indicating has certain superiority.

Simplified models to calculate the change of stator flux and torque of permanent magnet synchronous motor (PMSM) on stator flux coordinate system are proposed. In order to solve high torque ripples of PMSM direct torque control (DTC) system, dead-beat (DB) control of stator flux and torque is proposed. The calculation of ideal voltage vector (VV) for the DB control of stator flux and torque using simplified models are given, and a simplified VV selecting method is used to implement the DB control. In order to improve control performances, a novel way to expand VVs based on both angle and amplitude is proposed. The angle and amplitude of ideal VV are used to determine the angle and amplitude of output VV and the look-up table is used to generate output VVs. Simulation results show this novel method can decrease stator flux and torque ripples dramatically, but increase switching frequency.

Aiming at reducing computational complexity in the multi-step model predictive torque control (MPTC), a novel simplified multi-step MPTC algorithm (P-MPTC) was proposed. Firstly, a multi-step MPTC model was established, and the computational burden of the traditional MPTC (T-MPTC) was discussed. Secondly, based on the MPTC with simplified voltage set (S-MPTC), a simplified MPTC with voltage holding approach (P-MPTC) was proposed, which avoided enumerating all the voltage vectors in candidate set to simplify the computation. Finally, the proposed MPTC algorithm was compared with T-MPTC and S-MPTC to analyze the differences of control performance and computational burden. The result shows that, the proposed simplified algorithm can greatly reduce the computational complexity of multi-step MPTC while the control performance almost not change. For example, in the six-step MPTC, the computational burden in the proposed simplified algorithm is only 0.02% of that in the traditional algorithm, which verifies the effectiveness of the proposed algorithm.

Purpose: We evaluated the long-term effect of a smartphone-facilitated home-based cardiac rehabilitation (HBCR) model in revascularized patients with coronary heart disease (CHD) on major adverse cardiac events (MACE), and secondary outcomes, including safety, quality of life, and physical capacity.Methods: It was a prospective observational cohort study including a total of 335 CHD patients after successful percutaneous coronary intervention (PCI) referred to the CR clinic in China between July 23, 2015 and March 1, 2018. Patients were assigned to two groups: HBCR tailored by monitoring and telecommunication via smartphone app (WeChat) (HBCR group, n = 170) or usual care (control group, n = 165), with follow-up for up to 42 months. Propensity score matching was conducted to match patients in the HBCR group with those in the control group. The patients in the HBCR group received educational materials weekly and individualized exercise prescription monthly, and the control group only received 20-min education at baseline in the CR clinic. The primary outcome was MACE, analyzed by Cox regression models. The changes in the secondary outcomes were analyzed by paired t-test among the matched cohort.Results: One hundred thirty-five HBCR patients were matched with the same number of control patients. Compared to the control group, the HBCR group had a much lower incidence of MACE (1.5 vs. 8.9%, p = 0.002), with adjusted HR = 0.21, 95% CI 0.07–0.85, and also had reduced unscheduled readmission (9.7 vs. 23.0%, p = 0.002), improved exercise capacity [maximal METs (6.2 vs. 5.1, p = 0.002)], higher Seattle Angina Questionnaire score, and better control of risk factors.Conclusions: The Chinese HBCR model using smartphone interaction is a safe and effective approach to decrease cardiovascular risks of patients with CHD and improve patients' wellness.Clinical Trial Registration:http://www.chictr.org.cn, identifier: ChiCTR1800015042.

Background. Sudden cardiac death is a leading cause of death from coronary heart disease (CHD). The risk of sudden cardiac death (SCD) increases with age, and sudden arrhythmic death remains a major cause of mortality in elderly individuals, especially ventricular arrhythmias (VA). We developed a risk prediction model by combining ECG and other clinical noninvasive indexes including biomarkers and echocardiology for VA in elderly patients with CHD. Method. In the retrospective study, a total of 2231 consecutive elderly patients (≥60 years old) with CHD hospitalized were investigated, and finally 1983 patients were enrolled as the model group. The occurrence of VA within 12 months was mainly collected. Study parameters included clinical characteristics (age, gender, height, weight, BMI, and past medical history), ECG indexes (QTcd, Tp-e/QT, and HRV indexes), biomarker indexes (NT-proBNP, Myo, cTnT, CK-MB, CRP, K+, and Ca2+), and echocardiology indexes. In the respective study, 406 elderly patients (≥60 years old) with CHD were included as the verification group to verify the model in terms of differentiation and calibration. Results. In the multiparameter model, seven independent predictors were selected: LVEF, LAV, HLP, QTcd, sex, Tp-e/QT, and age. Increased HLP, Tp-e/QT, QTcd, age, and LAV were risk factors (RR > 1), while female and increased LVEF were protective factors (RR

ABSTRACT The intestinal microbiota is significantly affected by the external environment, but our understanding of the effects of extreme environments such as plateaus is far from adequate. In this study, we systematically analyzed the variation in the intestinal microbiota and 76 blood clinical indexes among 393 healthy adults with different plateau living durations (Han individuals with no plateau living, with plateau living for 4 to 6 days, with plateau living for >3 months, and who returned to the plain for 3 months, as well as plateau-living Tibetans). The results showed that the high-altitude environment rapidly (4 days) and continually (more than 3 months) shaped both the intestinal microbiota and clinical indexes of the Han population. With prolongation of plateau living, the general characteristics of the intestinal microbiota and clinical indexes of the Han population were increasingly similar to those of the Tibetan population. The intestinal microbiota of the Han population that returned to the plain area for 3 months still resembled that of the plateau-living Han population rather than that of the Han population on the plain. Moreover, clinical indexes such as blood glucose were significantly lower in the plateau groups than in the nonplateau groups, while the opposite result was obtained for testosterone. Interestingly, there were Tibetan-specific correlations between glucose levels and Succinivibrio and Sarcina abundance in the intestine. The results of this study suggest that a hypoxic environment could rapidly and lastingly affect both the human intestinal microbiota and blood clinical indexes, providing new insights for the study of plateau adaptability. IMPORTANCE The data presented in the present study demonstrate that the hypoxic plateau environment has a profound impact on the gut microbiota and blood clinical indexes in Han and Tibetan individuals. The plateau-changed signatures of the gut microbiota and blood clinical indexes were not restored to the nonplateau status in the Han cohorts, even when the individuals returned to the plain from the plateau for several months. Our study will improve the understanding of the great impact of hypoxic environments on the gut microbiota and blood clinical indexes as well as the adaptation mechanism and intervention targets for plateau adaptation.

Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our method can restore fine-grained details similar to those in high-resolution Hi-C matrices, boosting accuracy in chromatin loops identification and TADs detection, and outperforms the state-of-the-art methods in accuracy of prediction. Finally, application of DeepHiC to Hi-C data on mouse embryonic development can facilitate chromatin loop detection. We develop a web-based tool (DeepHiC, http://sysomics.com/deephic) that allows researchers to enhance their own Hi-C data with just a few clicks.

Transcription factors (TFs) and microRNAs (miRNAs) are well-characterized trans-acting essential players in gene expression regulation. Growing evidence indicates that TFs and miRNAs can work cooperatively, and their dysregulation has been associated with many diseases including cancer. A unified picture of regulatory interactions of these regulators and their joint target genes would shed light on cancer studies. Although online resources developed to support probing of TF-gene and miRNA-gene interactions are available, online applications for miRNA-TF co-regulatory analysis, especially with a focus on cancers, are lacking. In light of this, we developed a web tool, namely CMTCN (freely available at http://www.cbportal.org/CMTCN), which constructs miRNA-TF co-regulatory networks and conducts comprehensive analyses within the context of particular cancer types. With its user-friendly provision of topological and functional analyses, CMTCN promises to be a reliable and indispensable web tool for biomedical studies.