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In the open-winding motor fed by a common DC bus, unbalanced inverter common-mode voltage (CMV), zero-sequence components of the permanent magnet flux linkage, and the PWM dead-time effect can induce a zero-sequence current (ZSC) through the inherent current path. For an open-winding five-phase permanent magnet synchronous motor (OW-FPPMSM) applied in an aerospace rocket starter-generator system, two ZSC suppression strategies based on zero-sequence voltage (ZSV) generation mechanisms are proposed in this paper, which improve motor performance in a simple and efficient manner. In the first strategy, the conventional method is modified to enable asynchronous operation of the two inverters, thereby generating the required ZSV pulses. The switching order and time offset between the two inverters are determined by the reference ZSV. The second strategy employs basic voltage vectors with larger magnitudes, resulting in higher DC bus voltage utilization. By adjusting the switching sequence of the second inverter, the ZSC components at the carrier frequency are eliminated. Both strategies also achieve the injection of the third-harmonic current. Finally, the two strategies are further analyzed in terms of the modulation index and ZSV modulation range. Simulation and experimental results verify the effectiveness of the ZSC suppression strategies.

Industrial sensor networks exhibit heterogeneous, federated, large-scale, and intelligent characteristics due to the increasing number of Internet of Things (IoT) devices and different types of sensors. Efficient and accurate anomaly detection of sensor data is essential for guaranteeing the system’s operational reliability and security. However, existing research on sensor data anomaly detection for industrial sensor networks still has several inherent limitations. First, most detection models usually consider centralized detection. Thus, all sensor data have to be uploaded to the control center for analysis, leading to a heavy traffic load. However, industrial sensor networks have high requirements for reliable and real-time communication. The heavy traffic load may cause communication delays or packets lost by corruption. Second, there are complex spatial and temporal features in industrial sensor data. The full extraction of such features plays a key role in improving detection performance. Nevertheless, the majority of existing methodologies face challenges in simultaneously and comprehensively analyzing both features. To solve the limitations above, this paper develops a cloud-edge collaborative data anomaly detection approach for industrial sensor networks that mainly consists of a sensor data detection model deployed at individual edges and a sensor data analysis model deployed in the cloud. The former is implemented using Gaussian and Bayesian algorithms, which effectively filter the substantial volume of sensor data generated during the normal operation of the industrial sensor network, thereby reducing traffic load. It only uploads all the sensor data to the sensor data analysis model for further analysis when the network is in an anomalous state. The latter based on GCRL is developed by inserting Long Short-Term Memory network (LSTM) into Graph Convolutional Network (GCN), which can effectively extract the spatial and temporal features of the sensor data for anomaly detection. The proposed approach is extensively assessed through experiments using two public industrial sensor network datasets compared with the baseline anomaly detection models. The numerical results demonstrate that the proposed approach outperforms the existing state-of-the-art models.

Background N6-methyladenosine (m6A) is the most prevalent RNA modification. Although hnRNPA2B1, as a reader of m6A modification, has been reported to promote tumorigenesis in a few types of tumors, its role in hepatocellular carcinoma (HCC) and the underlying molecular mechanism remains unclear. Methods Multiple public databases were used to analyze the expression of hnRNPA2B1 in HCC and its correlation with survival prognosis. We employed a CRISPR-Cas9 sgRNA editing strategy to knockout hnRNPA2B1 expression in HCC cells. The biological function of hnRNPA2B1 in vitro in HCC cells was measured by CCK8, colony formation, migration, and invasion assay. The tumorigenic function of hnRNPA2B1 in vivo was determined by a subcutaneous tumor formation experiment and a HCC mouse model via tail injection of several plasmids into the mouse within 5s-7s. RNA binding protein immunoprecipitation (RIP) experiment using hnRNPA2B1 was performed to test the target genes of hnRNPA2B1 and methylated RNA immunoprecipitation (MeRIP) assay was performed to explore the m6A methylated mRNA of target genes. Results hnRNPA2B1 highly expressed in HCC tissues, correlated with high grades and poor prognosis. Its knockout reduced HCC cell proliferation, migration, and invasion in vitro, while overexpression promoted these processes. hnRNPA2B1-knockout cells inhibited tumor formation in graft experiments. In HCC mice, endogenous knockout attenuated hepatocarcinogenesis. RNA-seq showed downregulated gluconeogenesis with high hnRNPA2B1 expression. hnRNPA2B1 negatively correlated with PCK1, a key enzyme. RIP assay revealed hnRNPA2B1 binding to PCK1 mRNA. hnRNPA2B1 knockout increased m6A-methylation of PCK1 mRNA. Interestingly, PCK1 knockout partially counteracted tumor inhibition by hnRNPA2B1 knockout in mice. Conclusion Our study indicated that hnRNPA2B1 is highly expressed in HCC and correlated with a poor prognosis. hnRNPA2B1 promotes the tumorigenesis and progression of HCC both in vitro and in vivo. Moreover, hnRNPA2B1 downregulates the expression of PCK1 mRNA via a m6A methylation manner. More importantly, the ability of hnRNPA2B1 to induce tumorigenesis and progression in HCC is dependent on its ability to decrease the expression of PCK1. Therefore, this study suggested that hnRNPA2B1 might be a diagnostic marker of poor prognosis of HCC and a potential therapeutic target for HCC patients.

Introduction There was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning‐based prediction model in preterm birth. Material and methods We conducted a systematic review, searching the PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disk, VIP Database, and WanFang Data from inception to September 27, 2021. Studies that developed (validated) a prediction model using machine learning methods in preterm birth were included. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and Prediction model Risk of Bias Assessment Tool (PROBAST) to evaluate the reporting quality and the risk of bias of included studies, respectively. Findings were summarized using descriptive statistics and visual plots. The protocol was registered in PROSPERO (no. CRD 42022301623). Results Twenty‐nine studies met the inclusion criteria, with 24 development‐only studies and 5 development‐with‐validation studies. Overall, TRIPOD adherence per study ranged from 17% to 79%, with a median adherence of 49%. The reporting of title, , blinding of predictors, sample size justification, explanation of model, and model performance were mostly poor, with TRIPOD adherence ranging from 4% to 17%. For all included studies, 79% had a high overall risk of bias, and 21% had an unclear overall risk of bias. The analysis domain was most commonly rated as high risk of bias in included studies, mainly as a result of small effective sample size, selection of predictors based on univariable analysis, and lack of calibration evaluation. Conclusions Reporting and methodological quality of machine learning‐based prediction models in preterm birth were poor. It is urgent to improve the design, conduct, and reporting of such studies to boost the application of machine learning‐based prediction models in preterm birth in clinical practice. The reporting and methodological quality of machine learning‐based prediction models in preterm birth were poor. It is urgent to improve the design, conduct and reporting of such studies to boost the application of machine learning‐based prediction models in preterm birth in clinical practice.

Sphingosine-1-phosphate (S1P) is a crucial sphingolipid mediator in vasculature and neovascular eye diseases by controlling angiogenesis, inflammation and fibrosis. Five S1P receptors (S1PRs) are key therapeutic targets, with several S1PR-targeted drugs already in clinical use or trials. However, the vascular function of its major metabolic product, the reactive lipid aldehyde 2-hexadecenal (2-HD), remains unexplored. Here, we show that loss of the aldehyde dehydrogenase ALDH3B1 impairs 2-HD detoxification and leads to retinal vascular abnormalities in zebrafish, without affecting the trunk vasculature. Mechanistically, multi-omics analyses reveal that 2-HD accumulation disrupts iron homeostasis and induces ferroptosis by directly interacting with S1PR5. This finding is supported by integrative analyses of single-cell RNA sequencing and RNA sequencing from human neovascular retinal samples, identifying S1PR5 as a clinically relevant target. These findings uncover a previously unrecognized role of S1P derived 2-HD in vasculature and retinal vascular homeostasis, suggesting that targeting S1PR5 could offer a therapeutic strategy for diabetic retinopathy. Sphingolipids mediate inflammation, although the role of lipid aldehyde 2-HD is poorly understood. Here, the authors show ALDH3B1 detoxifies 2-HD to protect retinal vasculature, with 2HD accumulation causing vascular abnormalities.

Timely and accurate translation of maternal mRNA is essential for oocyte maturation and early embryonic development. Previous studies have highlighted the importance of Primordial Germ cell 7 (PGC7) as a maternal factor in maintaining DNA methylation of maternally imprinted loci in zygotes. However, it is still unknown whether PGC7 is involved in the regulation of Maternal mRNA Translation. In this study, we have identified that PGC7-AKT1-YBX1 axis is involved in promoting the translation of maternal mRNAs. PGC7 not only sustains AKT1 activity by counteracting PP2A dephosphorylation and facilitating PDK1-AKT1 binding but also assists AKT1 in phosphorylating the translation inhibitor YBX1. In the absence of PGC7, despite increased PIK3CA expression and AKT1 phosphorylation, AKT1 is unable to phosphorylate YBX1. PGC7 facilitates the interaction between AKT1 and YBX1, enhancing YBX1-Serine 100 phosphorylation, which leads to YBX1 dissociation from eIF4E, thereby activating the translation of maternal Cyclin B1 and YAP1. The findings demonstrate the indispensability of PGC7 for translation activation in mammalian oocytes and provide a potential network regulated by PGC7 in early oogenesis.

Excessive activation of the NOD-like receptor pyrin domain–containing protein 3 (NLRP3) inflammasome plays a key role in the pathogenesis of various inflammatory diseases. Despite the development of several NLRP3 inhibitors, no specific therapy has been approved for clinical use, underscoring the urgent need for safe and effective agents. Here, we demonstrate that ML345 acts as a highly potent and selective NLRP3 inhibitor with strong therapeutic potential for NLRP3-driven inflammation. ML345 effectively suppresses canonical, noncanonical, and alternative NLRP3 inflammasome activation pathways, without affecting other inflammasomes. Mechanistically, ML345 blocks NLRP3 inflammasome activation independently of its intrinsic insulin-degrading enzyme (IDE) inhibitory activity. ML345 binds to NLRP3 in a non-covalent manner and directly targets tyrosine 381 (Y381), disrupting its essential interaction with NIMA-related kinase 7 (NEK7), consequently preventing inflammasome complex formation. In vivo, ML345 is well tolerated and markedly alleviates inflammatory responses and pathology in mouse models of NLRP3-associated disorders, including systemic inflammation and miscarriage triggered by lipopolysaccharide (LPS). Compared with several previously reported NLRP3 inhibitors, ML345 exhibits superior selectivity and comparable or greater inhibitory potency. These findings establish ML345 as a safe and selective NLRP3 inhibitor with robust anti-inflammasome effects and highlight its potential as a promising therapeutic candidate for NLRP3-driven diseases.

The technological advances of intelligent electric substations have significantly improved the operational performance of power utilities by incorporating advanced monitoring and control functionalities. The data traffic patterns in substation communication network (SCN) need to be better understood to improve the SCN performance against different forms of cyber-attacks. To this end, this study presents a fractional auto-regressive integrated moving average (FARIMA)-based threshold model to characterise the SCN traffic flow based on the IEC 61850 protocol and carry out anomaly detection. The performance of the proposed anomaly detection solution is assessed and validated through numerical analysis under the condition of the cyber storm based on the collected SCN data traffic from a real 110 kV substation, and the numerical results clearly confirmed its effectiveness.

Background Adipose-derived stem cells (ADSCs) have been extensively used in preclinical and clinical trials for treating various diseases. However, the differences between ADSCs from lean individuals (L-ADSCs) and those from obese individuals (O-ADSCs) have not been thoroughly investigated, particularly regarding their mitochondrial and lysosomal functions. Therefore, this study aims to evaluate the differences between L-ADSCs and O-ADSCs in terms of cell biological activity, mitochondria, and lysosomes. Methods We first isolated and cultured L-ADSCs and O-ADSCs. We then compared the differences between the two groups in terms of biological activity, including cell proliferation, differentiation potential, and their effect on the polarization of macrophages. Additionally, we observed the mitochondrial and lysosomal morphology of ADSCs using an electronic microscope, MitoTracker Red, and lysotracker Red dyes. We assessed mitochondrial function by examining mitochondrial membrane potential and membrane fluidity, antioxidative ability, and cell energy metabolism. Lysosomal function was evaluated by measuring autophagy and phagocytosis. Finally, we performed transcriptome analysis of the ADSCs using RNA sequencing. Results The biological activities of O-ADSCs were decreased, including cell immunophenotypic profiles, cell proliferation, and differentiation potential. Furthermore, compared to L-ADSCs, O-ADSCs promoted M1-type macrophage polarization and inhibited M2-type macrophage polarization. Additionally, the mitochondrial morphology of O-ADSCs was altered, with the size of the cells becoming smaller and mitochondrial fragments increasing. O-ADSCs also exhibited decreased mitochondrial membrane potential and membrane fluidity, antioxidative ability, and energy metabolism. With respect to lysosomes, O-ADSCs contained ungraded materials in their lysosomes, enhanced lysosomal permeability, and reduced autophagy and phagocytosis ability. RNA sequence analysis indicated that the signalling pathways related to cell senescence, cancer, and inflammation were upregulated, whereas the signalling pathways associated with stemness, cell differentiation, metabolism, and response to stress and stimuli were downregulated. Conclusions This study indicates that ADSCs from individuals (BMI > 30 kg/m 2 ) exhibit impaired mitochondrial and lysosomal function with decreased biological activity.

Background Psoriasis is a chronic inflammatory skin disease. Tissue stem cells have exhibited a therapeutic effect on psoriatic mice. However, the therapeutic effect of topical administration of the secretome derived from tissue stem cells on psoriasis has not been reported. Methods The secretome from human amniotic epithelial cells (AEC-SC) and human umbilical cord mesenchymal stem cells (UMSC-SC) was topically administrated on the back of imiquimod-induced psoriasis-like mice. Subsequently, we observed the skin lesions and skin inflammation of psoriasis-like mice. Next, we further analyzed the paracrine factors in AEC-SC and UMSC-SC by protein chips. Lastly, the effect of the crucial paracrine factor was investigated by imiquimod-induced psoriasis-like mice. Results We found that AEC-SC had a better therapeutic effect on attenuating psoriasis-like skin lesions including skin scales, skin redness and skin thickness than UMSC-SC, and it had a better regulatory effect on keratinocyte hyperproliferation and altered differentiation. Thus, we focused on AEC-SC. Further study showed that AEC-SC reduced the infiltration of neutrophils and interleukin-17-producing T cells. Next, the analysis of AEC-SC with protein chip revealed that the levels of anti-inflammatory factor interleukin-1 receptor antagonist (IL-1ra) were much higher in AEC-SC compared to that in UMSC-SC. More importantly, the beneficial effect of AEC-SC on psoriasis-like skin lesions and skin inflammation of mice were significantly impaired when neutralizing with IL-1ra antibody, while the recombinant human IL-1ra showed a less protective effect than AEC-SC. Conclusions The present study demonstrated that AEC-SC could efficiently ameliorate psoriasis-like skin lesions and skin inflammation and IL-1ra plays an essential role. Therefore, topical administration of AEC-SC may provide a novel strategy for treating psoriasis-like inflammatory skin diseases.