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Background: Gene variants are responsible for more than half of hearing loss, particularly in nonsyndromic hearing loss (NSHL). The most common pathogenic variant in SLC26A4 gene found in East Asian populations is c.919-2A > G followed by c.2168A > G (p.H723R). This study was to evaluate their variant frequencies in patients with NSHL from special education schools in nine different areas of Southwest China's Yunnan. Methods: We performed molecular characterization by PCR-products directly Sanger sequencing of the SLC26A4 c.919-2AG and c.2168 A > G variants in 1167 patients with NSHL including 533 Han Chinese and 634 ethnic minorities. Results: The SLC26A4 c.919-2A > G variant was discovered in 8 patients with a homozygous state (0.69%) and twenty-five heterozygous (2.14%) in 1167 patients with NSHL. The total carrier rate of the c.919-2A > G variant was found in Han Chinese patients with 4.50% and ethnic minority patients with 1.42%. A significant difference existed between the two groups (P < 0.05). The c.919-2A > G allele variant frequency was ranged from 3.93% in Kunming to zero in Lincang and Nvjiang areas of Yunnan. We further detected the SLC26A4 c.2168 A > G variant in this cohort with one homozygotes (0.09%) and seven heterozygotes (0.60%), which was detected in Baoshan, Honghe, Licang and Pu'er areas. Between Han Chinese group (0.94%) and ethnic minority group (0.47%), there was no statistical significance (P > 0.05). Three Han Chinese patients (0.26%) carried compound heterozygosity for c.919-2A > G and c.2168 A > G. Conclusion: These data suggest that the variants in both SLC26A4 c.919-2A > G and c.2168 A > G were relatively less frequencies in this cohort compared to the average levels in most regions of China, as well as significantly lower than that in Han-Chinese patients. These results broadened Chinese population genetic information resources and provided more detailed information for regional genetic counselling for Yunnan. [ABSTRACT FROM AUTHOR]

Urban digitalization is a key driver of regional coordinated development in modern times due to its ubiquitous and significant impact on our production, way of life, and ecosystem. Most of the current research has focused on the economic and social impacts of digitalization, while little work has been done on the efficiency of digitalization. This study investigates urban digitalization efficiency (UDE) employing the super-efficient EBM model from a viewpoint of regional coordinated development. The dynamic evolution of UDE is revealed by the Markov chain method. The convergence properties of UDE in each region are examined using the β convergence models. The findings indicate that: (1) The UDE of each city and each of the eight integrated economic zones has increased to some extent. (2) The UDE is relatively likely to remain stable, with a greater probability of shifting to a neighbouring type than to a non-neighbouring type. (3) While UDE exhibits absolute and conditional β-convergence across all areas, the causes affecting UDE convergence in various economic zones vary. The results presented in this study can help clarify the laws and characteristics of UDE and serve as a reference for developing countries to promote coordinated regional development through urban digitalization. [ABSTRACT FROM AUTHOR]

Heterostructure and phase engineering are effective method to tune the electronic structure of MoS 2 , which can activate and boost its intrinsic catalytic activity. Herein, the theoretical calculations reveal that the free energy of H∗ adsorption (ΔG H∗) for Mo 2 C (−0.65 eV) is lower than MoS 2 (2.05 eV), which is favorable for hydrogen adsorption. Inspired by the theoretical predictions, we develop a facile strategy to prepare nanostructured Mo 2 C/MoS 2 dispersed on reduced graphene oxide (rGO) via a hydrothermal preparation and carburization reaction. Since the Mo 2 C/MoS 2 -rGO nanostructures with great deal of both MoS 2 and Mo 2 C edges by heterostructure and phase engineering, which can induce massive active sites for HER on the surface of rGO, the intimate interfacial coupling effect between Mo 2 C and MoS 2 can effectively improve the charge transfer rate. The Mo 2 C/MoS 2 -rGO catalyst exhibits exceptional HER properties, delivering a high turnover frequency (TOF) of 0.209 s−1 at an overpotential of 200 mV in 0.1 M KOH. This work may open a new window for designing NPM electrocatalysts with highly efficient HER property based on earth-abundant materials. [Display omitted] • A new preparation, it is first time to fabricate Mo 2 C/MoS 2 -rGO hybrid. • The DFT calculations reveal that the synergistic effect between Mo 2 C and MoS 2 can activate the HER efficiency. • The Mo 2 C/MoS 2 -rGO presents a high TOF of 0.209 s−1 at an overpotential of 200 mV in 0.1 M KOH. • The Mo 2 C/MoS 2 -rGO shows excellent cycle stability. [ABSTRACT FROM AUTHOR]

As a crucial component of the climate system, snow cover plays an important role on surface energy budgets, hydrological cycles and socioeconomic development. This paper investigated the spatiotemporal patterns of snow depth across the Qinghai Plateau (QP) during 1980–2018 based on passive microwave (PMW) satellite observation and reanalysis products. The study identified the relationships between the changes in PMW snow depth and topographic features and revealed the impact of climatic variables (air temperature, precipitation and wind speed) and large‐scale atmospheric circulations on observed snow depth variability. The results show that the spatial pattern of snow depth climatology is similar in all datasets, except in terms of magnitude, with high values in the southern and southeastern parts of the QP and low values in the eastern and northwestern parts. Average snow depth correlates positively with precipitation, elevation and slope, and negatively with air temperature and wind speed. The long‐term trends in snow depth vary with the season and the datasets. The PMW snow depth across the QP shows a significant annual (−0.125 cm·decades−1, p <.10) and spring (−0.184 cm·decades−1, p <.05) negative trend, while snow depth for ERA5 and MERRA2 does not present significant trends. Air temperature dominates total snow depth variation over the QP, explaining 43.85, 24.88 and 47.28% of annual, winter and spring PMW snow depth variations, which significantly affects snow depth variations in most parts of the QP, yet the effects of precipitation and wind speed on snow depth variation exhibit significant regional differences, and atmospheric circulations (e.g., AMO) also have a remarkable controlling effect on some localized areas. [ABSTRACT FROM AUTHOR]

Background: Carotid body tumors (CBTs) are relatively uncommon neoplasms that rarely have malignant potential. However, malignant CBTs (MCBTs) are still associated with a poor prognosis and the treatment is still challenging clinically. Therefore, we evaluated the necessity of intraoperative level IIA lymph node dissection in patients with CBT. Methods: The clinical characteristics, intraoperative details, and pathological diagnosis of 126 CBT patients who had undergone surgery were retrospectively reviewed. The patients were divided into 2 groups according to whether level IIA lymph node dissection was performed. The prognosis was analyzed using Kaplan-Meier curves and Cox model multivariate survival analysis. Results: Among the 126 patients, 7 patients (10.3%) in the selective lymph node dissection (SLND) group (68 patients) were diagnosed with MCBTs with evidence of lymph node metastasis. Two patients (3.4%) in the lymph node nondissection (LNND) group (58 patients) were diagnosed with MCBTs later after the second operation because they could not be diagnosed as malignant initially because of the lack of lymph node pathology results although the pathology of the primary lesion showed features of malignancy. The SLND group had a significantly higher relapse-free survival rate than the LNND group (94.1% vs. 79.3%, p = 0.021). Patients with a confirmed diagnosis had a better prognosis than those with insufficient evidence of a malignancy due to the lack of lymph node information. Twenty-nine patients in the SLND group and 26 patients in the LNND group had postoperative nerve injuries, with no significant difference between the groups (p = 0.879). Conclusion: Intraoperative dissection of level IIA lymph nodes around the tumor in CBT patients can help improve the diagnosis and prognosis of MCBTs without causing additional cranial nerve injury. [ABSTRACT FROM AUTHOR]

Tuberculosis, caused by mycobacteria, continues to pose a substantial public health threat. Mycobacteria typically use cholesterol from the membranes of host macrophages as a carbon and energy source. Most genes that control cholesterol degradation are regulated by KstR, which is highly conserved in Mycobacterium tuberculosis and Mycobacterium smegmatis. Through bioinformatic analysis, we found a typical global nitrogen regulator (GlnR)‐binding motif (CCGAC‐AACAGT‐GACAC) in the promoter region of kstR of M. smegmatis, and we determined its binding activity in vitro using electrophoretic mobility shift assays. Using RT‐qPCR, we found that nine genes involved in side‐chain or sterol‐ring oxidation were upregulated in a ΔglnR M. smegmatis strain compared to the WT strain and glnR‐complemented strains under nitrogen limitation. ATP assays in macrophages revealed that coordinated GlnR–KstR regulation significantly reduced the viability of M. smegmatis in macrophages. Thus, we found that various genes involved in cholesterol catabolism are regulated by GlnR via KstR in response to environmental nitrogen, and that they further affect the invasive ability of M. smegmatis. These findings revealed a novel regulatory mechanism of cholesterol catabolism, which may be useful in the development of new strategies for controlling tuberculosis. [ABSTRACT FROM AUTHOR]

Background: The interaction between tumor microenvironment (TME) and tumors offers various targets in mounting anti-tumor immunotherapies. However, the prognostic biomarkers in endometrial carcinoma (EC) are still limited. Here, we aimed to analyze the TME features and identify novel prognostic biomarkers for EC. Methods: ESTIMATE, CIBERSORT, protein-protein interaction (PPI) network, univariate and multivariate Cox regression, and functional enrichment analysis were performed to identify immune- and survival-related hub genes as well as possible molecular mechanisms. The limma package and deconvolution algorithm were adopted to estimate the abundance of tumor-infiltrating immune cells (TICs) and their relationship with the target gene. In the validation section, tissue microarrays (TMAs) of EC and multiplex immunohistochemistry (m-IHC) were evaluated to validate the expression of TNFRSF4, and its correlation with immune markers, including CD4, CD8, and FOXP3. Besides, the receiver operating characteristic (ROC) curve was plotted to determine the diagnostic performance of TNFRSF4, CD4, CD8, and FOXP3 in EC. Results: Two genes, TNFRSF4 and S1PR4, were screened out from 386 intersection differential expression genes (DEGs) shared by ImmuneScore and StromalScore in EC. Highlighted by TNFRSF4, we found that it was not only positively correlated with the TICs (mainly CD4+ T cells, CD8+ T cells, and Tregs) but significantly related to the prognosis in patients of EC, both verified by data from The Cancer Genome Altas (TCGA)-EC database and clinical samples. At the same time, the expression trend of TNFRSF4 was further confirmed by an integrated meta-analysis based on six microarrays from the Gene Expression Omnibus database (GEO). Conclusions: Collectively, TNFRSF4, a previously unrecognized key player in EC, could serve as a potential biomarker for prognosis prediction and immunomodulation of EC. [ABSTRACT FROM AUTHOR]

Background: The interaction between tumor microenvironment (TME) and tumors offers various targets in mounting anti-tumor immunotherapies. However, the prognostic biomarkers in endometrial carcinoma (EC) are still limited. Here, we aimed to analyze the TME features and identify novel prognostic biomarkers for EC.Methods: ESTIMATE, CIBERSORT, protein-protein interaction (PPI) network, univariate and multivariate Cox regression, and functional enrichment analysis were performed to identify immune- and survival-related hub genes as well as possible molecular mechanisms. The limma package and deconvolution algorithm were adopted to estimate the abundance of tumor-infiltrating immune cells (TICs) and their relationship with the target gene. In the validation section, tissue microarrays (TMAs) of EC and multiplex immunohistochemistry (m-IHC) were evaluated to validate the expression of TNFRSF4, and its correlation with immune markers, including CD4, CD8, and FOXP3. Besides, the receiver operating characteristic (ROC) curve was plotted to determine the diagnostic performance of TNFRSF4, CD4, CD8, and FOXP3 in EC.Results: Two genes, TNFRSF4 and S1PR4, were screened out from 386 intersection differential expression genes (DEGs) shared by ImmuneScore and StromalScore in EC. Highlighted by TNFRSF4, we found that it was not only positively correlated with the TICs (mainly CD4+ T cells, CD8+ T cells, and Tregs) but significantly related to the prognosis in patients of EC, both verified by data from The Cancer Genome Altas (TCGA)-EC database and clinical samples. At the same time, the expression trend of TNFRSF4 was further confirmed by an integrated meta-analysis based on six microarrays from the Gene Expression Omnibus database (GEO).Conclusions: Collectively, TNFRSF4, a previously unrecognized key player in EC, could serve as a potential biomarker for prognosis prediction and immunomodulation of EC. [ABSTRACT FROM AUTHOR]

After long-term oxidation and energy storage, broken coal body borehole walls and drainage shaft walls may cause spontaneous combustion during gas extraction. The high-temperature thermal shock caused by the spontaneous combustion of coal incurs thermal damage on adjacent coal, which, in turn, causes changes in the mechanical properties of the coal. However, only a few studies have been conducted in this context, which has limited our understanding of the thermal damage characteristics of coal bodies in such situations. This study aimed to experimentally investigate the correlation between the crack evolution law and the mechanical properties of coal bodies at different temperatures (50–300°C) using heat-force loading considering Ping Mei No. 10 coal mine as the research object. The results suggest that the coal body experiences a large amount of visible damage, and becomes increasingly complex. At 50–300°C, some indexes (such as longitudinal wave velocity, Poisson's ratio, compressive strength, elastic modulus, impact energy index, and pre-peak strain) are positively correlated with temperature. In addition, the dynamic failure time and temperature show a negative correlation, and the overall change slope is small. The relationship between each index and temperature at 200–300°C is opposite to that at 50–200°C, and the overall change slope is larger. Moreover, when the oxidation temperature exceeds 200°C, the destruction of the coal body changes from elastic brittleness to ductility-plasticity. High-temperature oxidation incurs irreversible thermal damage of coal. Hence, it is necessary to focus on the changes in mechanical properties of coal after a spontaneous combustion process is extinguished. [ABSTRACT FROM AUTHOR]

The effect of porous flexible polyvinyl chloride (PVC) barriers on the kinetic properties of propane explosions is investigated based on fluid dynamics and combustion-related theory. Furthermore, using small-sized cylindrical pipes, separate experimental studies on propane deflagration are conducted with different types of flexible PVC barriers placed in the pipeline. The results show that when the conditions of the same nature as the obstacles appear at the outlet end of the pipeline and the blocking rate is low, the presence of porous flexible PVC barriers counteracts the effect of tail-end barriers on propane deflagration. In the tail-end opening experiment, the porous flexible PVC obstacle has a significant effect on the flame pattern passing through it but has a weaker effect on the flames before they pass through it. Furthermore, it only affects the degree of "fingertip" sharpening of finger-shaped flames; the fewer the number of holes in the obstacle, the higher the sharpness of the fingertip flame. The porous flexible PVC barriers can reduce the rate of flame propagation and creasing of propane deflagration. The porous flexible PVC barriers have a lifting effect on the overpressure of the blast and a delaying effect on the onset of peak overpressure; the larger the number of holes in the barrier, the stronger the delaying effect. [ABSTRACT FROM AUTHOR]

How to realize the ecological protection and high‐quality development (HQD) of the Yellow River Basin (YRB) has become a great challenge for China. Based on panel data from 100 prefecture‐level cities in the YRB from 2006 to 2018, this innovative paper employs the System Generalized Method of Moments (SYS‐GMM) panel model and takes environmental regulation as a situational factor to empirically examine the influence mechanism of industrial agglomeration on HQD in the YRB. Our findings reveal that: (i) overall, a "U‐shaped" relationship exists between industrial agglomeration and HQD in the YRB, and environmental regulation plays a vital role in augmenting their relationship. (ii) Presently, the industrial agglomeration degree prevails on the left side of the threshold value, and the upsurge of industrial agglomeration degree still limits HQD. (iii) From the perspective of river basins, the influence of industrial agglomeration on HQD in the upstream is not significant, and the strengthening effect of environmental regulation is not noticeable. We find a "U‐shaped" relationship between industrial agglomeration and HQD in the midstream, along with notable differences in the correlation between industrial agglomeration and different dimensions of HQD in downstream. This paper helps in further understanding the influence of industrial agglomeration, environmental regulation, and HQD, as well as establishes efficient policies to promote HQD in the YRB and other similar river basins through industrial agglomeration in the context of environmental constraints. [ABSTRACT FROM AUTHOR]

Lithium sulfur (Li-S) battery is considered as a promising alternative for the development of battery technologies. However, the shuttle effect seriously limits its practical use. Herein, hollow tubular graphene-like carbon nitride (Tg-C3N4) is synthesized and utilized as a functional interlayer to inhibit shuttling effect and promote catalytic kinetics. Both experiments and DFT calculations together suggest that N-doping enhances the electron transfers between Tg-C3N4 and LiPSs, leading to improved chemical adsorptions and catalytic effects towards the redox conversions of the active sulfur species. Besides, Tg-C3N4 delivers a unique hollow tubular architecture with massive ion transfer pathways and fully exposed active interfaces. In addition, the abundant C–N heteroatomic structures also impose strong chemical immobilization toward lithium polysulfides. Benefiting from these unique superiorities, the cell with the Tg-C3N4-modified separator exhibits a reversible capacity of 494 mAh g−1 after 500 cycles at 1 C with a negligible capacity decay of 0.085% per cycle, indicating an efficient strategy toward high-performance modified separators. [ABSTRACT FROM AUTHOR]

• PTPN1 inhibition improves LPS-induced myocardial damage and cardiac dysfunction through alleviating mitochondrial injury and dysfunction in the cardiomyocytes. • PTPN1 inhibits mitophagy in the cardiomyoblasts following LPS stimulation. • Dephosphorylation of STAT3 at Tyr705 is required for PTPN1 to inhibit mitophagy following LPS exposure. • STAT3 regulates the transcription of Pink1 and Prkn through binding to the SRE in their promoter regions. To investigate the role of protein tyrosine phosphatase non-receptor type 1 (PTPN1) in mitophagy during sepsis and its underlying mechanisms and determine the therapeutic potential of PTPN1 inhibitors in endotoxemia-induced cardiac dysfunction. A mouse model of endotoxemia was established by administering an intraperitoneal injection of lipopolysaccharide (LPS). The therapeutic effect of targeting PTPN1 was evaluated using its inhibitor Claramine (CLA). Mitochondrial structure and function as well as the expression of mitophagy-related proteins were evaluated. Rat H9c2 cardiomyocytes were exposed to mouse RAW264.7 macrophage-derived conditioned medium. Cryptotanshinone, a specific p-STAT3 (Y705) inhibitor, was used to confirm the role of STAT3 in PTPN1-mediated mitophagy following LPS exposure. Electrophoretic mobility shift and dual luciferase reporter assays were performed to discern the mechanisms by which STAT3 regulated the expression of PINK1 and PRKN. CLA alleviated LPS-induced myocardial damage, cardiac dysfunction, and mitochondrial injury and dysfunction in the mouse heart. PTPN1 upregulation exacerbated LPS-induced mitochondrial injury and dysfunction in H9c2 cardiomyocytes, but inhibited LPS-induced mitophagy. LPS promoted the interaction between PTPN1 and STAT3 and reduced STAT3 phosphorylation at Tyr705 (Y705), which was required to inhibit mitophagy by PTPN1. Upon LPS stimulation, PTPN1 negatively regulated the transcription of PINK1 and PRKN through dephosphorylation of STAT3 at Y705. STAT3 regulated the transcription of PINK1 and PRKN by binding to STAT3-responsive elements in their promoters. PTPN1 upregulation aggravates endotoxemia-induced cardiac dysfunction by impeding mitophagy through dephosphorylation of STAT3 at Y705 and negative regulation of PINK1 and PRKN transcription. [ABSTRACT FROM AUTHOR]

Chronic temporomandibular joint (TMJ) pain profoundly affects patients' quality of life. Trigeminal tumor necrosis factor-α (TNFα) plays a pivotal role in mediating TMJ pain in mice, yet the underlying epigenetic mechanisms remain enigmatic. To unravel these epigenetic intricacies, we employed a multifaceted approach. Hydroxymethylated DNA immunoprecipitation (hMeDIP) and chromatin immunoprecipitation (ChIP) followed by qPCR were employed to investigate the demethylation of TNFα gene (Tnfa) and its regulation by ten-eleven translocation methylcytosine dioxygenase 1 (TET1) in a chronic TMJ pain mouse model. The global levels of 5-hydroxymethylcytosine (5hmc) and percentage of 5hmc at the Tnfa promoter region were measured in the trigeminal ganglia (TG) and spinal trigeminal nucleus caudalis (Sp5C) following complete Freund's adjuvant (CFA) or saline treatment. TET1 knockdown and pain behavioral testing were conducted to ascertain the role of TET1-mediated epigenetic regulation of TNFα in the pathogenesis of chronic TMJ pain. Our finding revealed an increase in 5hmc at the Tnfa promoter region in both TG and Sp5C of CFA-treated mice. TET1 was upregulated in the mouse TG, and the ChIP result showed TET1 direct binding to the Tnfa promoter, with higher efficiency in the CFA-treated group. Immunofluorescence revealed the predominant expression of TET1 in trigeminal neurons. TET1 knockdown in the TG significantly reversed CFA-induced TNFα upregulation and alleviated chronic TMJ pain. In conclusion, our study implicates TET1 as a vital epigenetic regulator contributing to chronic inflammatory TMJ pain via trigeminal TNFα signaling. Targeting TET1 holds promise for epigenetic interventions in TMJ pain management. [ABSTRACT FROM AUTHOR]

Purpose: Prediction of Fuhrman nuclear grade is crucial for making informed herapeutic decisions in clear cell renal cell carcinoma (ccRCC). The current study aimed to develop a multi-information fusion model utilizing computed tomography (CT)-based features of tumors and preoperative biochemical parameters to predict the Fuhrman nuclear grade of ccRCC in a non-invasive manner. Methods: 218 ccRCC patients confirmed by histopathology were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed to identify independent predictors and establish a model for predicting the Fuhrman grade in ccRCC. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration, the 10-fold cross-validation method, bootstrapping, the Hosmer–Lemeshow test, and decision curve analysis (DCA). Results: R.E.N.A.L. Nephrometry Score (RNS) and serum tumor associated material (TAM) were identified as independent predictors for Fuhrman grade of ccRCC through multivariate logistic regression. The areas under the ROC curve (AUC) for the multi-information fusion model composed of the above two factors was 0.810, higher than that of the RNS (AUC 0.694) or TAM (AUC 0.764) alone. The calibration curve and Hosmer–Lemeshow test showed the integrated model had a good fitting degree. The 10-fold cross-validation method (AUC 0.806) and bootstrap test (AUC 0.811) showed the good stability of the model. DCA demonstrated that the model had superior clinical utility. Conclusion: A multi-information fusion model based on CT features of tumor and routine biochemical indicators, can predict the Fuhrman grade of ccRCC using a non-invasive approach. This model holds promise for assisting clinicians in devising personalized management strategies. [ABSTRACT FROM AUTHOR]

• Plasma boron-nitriding was primarily developed for 42CrMo4 steel. • Saw-tooth-shaped boron-iron compounds of FeB and Fe 2-3 B were formed. • The surface hardness was increased from 750HV0.05 to 1002HV0.05. • The effective diffusion layer was increased about 35%. • The wear rate is decreased from 3.06 mg/cm2 to 1.02 mg/cm2. In order to further enhance the performance of the components previously treated by plasma nitriding (PN) and meet the technical requirements proposed by the cooperative enterprise, plasma boron-nitriding (PNB) was primarily developed for 42CrMo4 steel at 520 °C for 6 h by adding a little Ferroboron around the samples during the process of plasma nitriding. The results show that plasma boron-nitriding (PNB) can significantly increase the surface hardness of the sample from 750HV 0.05 to 1002HV 0.05 due to the formation of very hard borides of FeB and Fe 2 B, and the thickness of compound layer is increased from 18.78um to 29.44um, the effective diffusion layer is increased from 265um to 355um, which is equivalent to an increase of nitriding efficiency by about 35%. At the same time, the wear resistance of the samples treated by PNB is significantly enhanced, the wear marks turns to be much shallower and narrower, and the wear rate is decreased from 3.06 mg/cm2 to 1.02 mg/cm2. [ABSTRACT FROM AUTHOR]

Transverse arrangement is one of the main methods used in the polar inertial navigation system (INS). In the traditional algorithm, the calculation of using the earth ellipsoid model is complex, while using the earth sphere model cannot satisfy a high-accuracy application. Therefore, an approach based on the virtual sphere model is proposed, which has been proved in simulation experiments to reduce the computational complexity and maintain the same accuracy as the ellipsoid algorithm, but its accuracy has not yet been proved in theory. Starting from the basic principles of the ellipsoid and virtual sphere model algorithm, this paper compares the key formulations of the two. Finally, it is proved that the two arrangements are actually the same. [ABSTRACT FROM AUTHOR]

Clear cell renal carcinoma (ccRCC) is the most common histological type of renal cancer and has the highest mortality. Several studies have been conducted on the relationship between adipose tissue and ccRCC prognosis, however, the results have been inconsistent to date. The current study aimed at establishing a link between abdominal fat composition and short-term prognosis in patients with ccRCC after T-stage stratification. We retrospectively analysed 250 patients with pathologically confirmed ccRCC (173 low T-stage and 77 high T-stage) in our hospital. The computed tomography (CT) images were evaluated using ImageJ. Then, subcutaneous and visceral fat areas (SFA and VFA), total fat areas (TFA) and the relative VFA (rVFA) were measured and computed. Meanwhile, biochemical indices of blood serum were analysed. The results showed that rVFA in low T-stage cohort who had a history of short-term postoperative complications were significantly lower than those who did not. No such association was observed in the high T-stage cohort. Further investigation revealed that the correlations between biochemical indexes and fat area-related variables varied across T-stage groups. As a result, rVFA is a reliable independent predictor of short-term prognosis in patients with low T-stage ccRCC but not in patients with high T-stage ccRCC. [ABSTRACT FROM AUTHOR]

In view of the low accuracy of the motion parameters generated by the typical ship trajectory generator, and the fact that the problem of wind, current and wave interference is not considered, this paper establishes a new ship trajectory generator by analyzing the changes in the ship's attitude and speed under different motion states. Through simulation, the accuracy of the main motion parameters is significantly improved compared with the typical trajectory generator; the time-varying non-uniform wind, current and wave fields are constructed, and the interference effect of wind, current and waves on ship motion is analyzed by combining the empirical formulas of force and moment; an adaptive neuro fuzzy inference system (ANFIS) based on wind, current and wave interference is designed, and the fuzzy rules of the fuzzy system are determined by training and testing the measured data; the motion parameters of superimposed wind, current and wave interference are compared with the measured data, and the accuracy is further improved after superimposing wind, current and wave interference. [ABSTRACT FROM AUTHOR]

Purpose: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit.166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000–1.000) in the training set and 0.927 (95%CI: 0.837–0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837–0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908.This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.Methods: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit.166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000–1.000) in the training set and 0.927 (95%CI: 0.837–0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837–0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908.This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.Results: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit.166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000–1.000) in the training set and 0.927 (95%CI: 0.837–0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837–0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908.This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.Conclusion: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit.166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000–1.000) in the training set and 0.927 (95%CI: 0.837–0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837–0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908.This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making. [ABSTRACT FROM AUTHOR]

To solve the problem of high-precision and fast initial alignment for the Strapdown Inertial Navigation System (SINS) under both dynamic and static conditions, the high-precision attitude measured by the celestial navigation system (CNS) is used as the reference information for the initial alignment. The alignment algorithm is derived in the Earth-centered inertial (ECI) frame. Compared with the alignment algorithm in the navigation frame, it is independent of position parameters and avoids the influence of the approximate error caused by the dynamic deflection angle. In addition, hull deformation is considered in attitude optimal estimation, which can realize initial the alignment of the SINS installed in various parts of the carrier. On this basis, the velocity measurement information is added to the alignment process, which further improves the accuracy and speed of the initial alignment under static conditions. The experimental results show that the algorithms proposed in this paper have better performance in alignment accuracy, speed, and stability. The attitude and velocity matching algorithm in the ECI frame can achieve alignment accuracy better than 0.6′. The attitude matching algorithm in the ECI frame has better robustness and can be used for both dynamic and static conditions, which can achieve alignment accuracy better than 1.3′. [ABSTRACT FROM AUTHOR]

This paper proposes a novel grey dynamic double incentive decision-making model to evaluate the high-quality development of manufacturing industry. First, we define the concepts of the improved grey incidence analysis and power weight Heronian aggregation (PWHA) operator. Then, we present the double incentive factors and determine incentive static evaluation values. In addition, we construct the weight vector of the time series. Guided by the incentive static evaluation values and weight vector of the time series, the dynamic evaluation values are produced. Finally, a practical example of the manufacturing industry in the Yangtze River Delta (YRD) demonstrates the effectiveness and application of the proposed model. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray‐matter has been reported in MDD, the alterations to IVFC within white‐matter (WM‐IVFC) remain unknown. Based on the resting‐state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM‐IVFC between the two groups. We further assessed the meta‐analytic cognitive functions related to the alterations. The discriminant WM‐IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome‐neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM‐IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM‐IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher‐order functions. The discriminant WM‐IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability‐related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD. [ABSTRACT FROM AUTHOR]

Clinical evidence shows that the mechanical stimulation obtained from occlusion could enhance periodontal ligament (PDL) remodeling. Mechano‐growth factor (MGF) is a growth factor produced specifically following mechanical stimulus Here, we aim to investigate the mechanical enhancement potential and mechanism of the MGF in PDL regeneration. In vivo study found that MGF produced from the PDL under occlusion force could strongly enhance PDL remodeling. In vitro experiments and mathematical modeling further confirmed the mechanical enhancement effect of MGF for PDLSC differentiation toward fibroblasts. A mechanochemical coupling effect of MGF mediated the enhancement of mechanical effect, which was modulated by Fyn‐FAK kinases signaling and subsequent MAPK pathway. Finally, enhanced PDL regeneration under the mechanochemical coupling of MGF and occlusal force was verified in vivo. There exists an additive mechanical effect of MGF mediated by Fyn‐FAK crosstalk and subsequent ERK1/2 and p38 phosphorylation, which could be developed as an MGF‐centered adjuvant treatment to optimize PDL remodeling, especially for patients with weakened bite force or destroyed periodontium. [ABSTRACT FROM AUTHOR]

We seek to transform how new and emergent variants of pandemic-causing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pre-training on over 110 million prokaryotic gene sequences and fine-tuning a SARS-CoV-2-specific model on 1.5 million genomes, we show that GenSLMs can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLMs represents one of the first whole-genome scale foundation models which can generalize to other prediction tasks. We demonstrate scaling of GenSLMs on GPU-based supercomputers and AI-hardware accelerators utilizing 1.63 Zettaflops in training runs with a sustained performance of 121 PFLOPS in mixed precision and peak of 850 PFLOPS. We present initial scientific insights from examining GenSLMs in tracking evolutionary dynamics of SARS-CoV-2, paving the path to realizing this on large biological data. [ABSTRACT FROM AUTHOR]

Adult granulosa cell tumors (AGCTs) are rare malignancies that accounts for approximately 1% of ovarian neoplasms. As there are currently no well-recognized models for predicting relapse-free survival (RFS), we performed a clinicopathological analysis to identify risk factors for AGCT recurrence. We investigated 130 patients with pathologically diagnosed AGCT as confirmed by the presence of the characteristic FOXL2 C402G mutation. Most patients had International Federation of Gynecology and Obstetrics stage I disease (n = 122, 95.3%). The 10-year RFS rate was 31.4% (22/70) and mean 10-year RFS was 74.4 (95% CI, 65.2–83.7) months. Ten patients experienced recurrence beyond the 10-year follow-up period. Undergoing fertility sparing surgery, an estrogen receptor-α (ERα) score (>0.25), and a Ki-67 index >15% were independent risk factors for recurrence in patients with stage I disease (bias-corrected C-index: 0.776). We constructed a nomogram with well-fitting calibration plots; the areas under the curve (AUCs) for 5-, and 10-year RFS prediction were 0.883 and 0.906 respectively. A simplified model with 3 predictive factors (ERα score, Ki-67 index, and primary surgical procedure) and 2 risk stratification subgroups (low- and high-risk) was constructed; its AUCs for 5-, and 10-year RFS prediction were 0.825 and 0.850 respectively. Kaplan-Meier survival curves showed significant differences in 10-year RFS between the low- and high-risk groups (p < 0.001). The type of primary surgical procedure, ERα score, and Ki-67 index are independent predictors of recurrence for patients with stage I AGCT. Our predictive model based on these factors showed good performance. • We evaluated recurrence risk in patients with adult granulosa cell tumors (AGCTs). • We focused on factors predicting recurrence in patients with stage I AGCT. • Independent predictors were the ERα score, Ki-67 index, and primary surgical procedure. • The histopathological characteristics were not associated with recurrence. • Our predictive model showed good reliability for predicting the risk of recurrence. [ABSTRACT FROM AUTHOR]

[Display omitted] Myocardial fibrosis is the villain of sudden cardiac death. Myocardial ischemia/reperfusion (MI/R) injury induces cardiomyocyte damage or even death, which in turn stimulates fibroblast activation and fibrosis, but the intercellular communication mechanism remains unknown. Recent studies have shown that small extracellular vesicles (sEVs) significantly contribute to intercellular communication. Whether and how sEV might mediate post-MI/R cardiomyocyte/fibroblasts communication remain unknown. Here, in vivo and in vitro MI/R models were established. We demonstrate that sEVs derived from cardiomyocyte (Myo-sEVs) carry mitochondrial components, which enter fibroblasts to initiate myocardial fibrosis. Based on bioinformatics screening and experimental verification, the activating molecule in Beclin1-regulated autophagy protein 1 (autophagy/beclin-1 regulator 1, Ambra1) was found to be a critical component of these sEV and might be a new marker for Myo-sEVs. Interestingly, release of Ambra1+-Myo-sEVs was caused by secretory rather than canonical autophagy after MI/R injury and thereby escaped degradation. In ischemic and peripheral areas, Ambra1+-Myo-sEVs were internalized by fibroblasts, and the delivered mtDNA components to activate the fibroblast cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway to promote fibroblast activation and proliferation. In addition, our data show that Ambra1 is expressed on the EV surface and cardiac-specific Ambra1 down regulation inhibits the Ambra1+-Myo-sEVs release and fibroblast uptake, effectively inhibiting ischemic myocardial fibrosis. This finding newly provides the evidence that myocardial secretory autophagy plays a role in intercellular communication during cardiac fibrosis. Ambra1 is a newly characterized molecule with bioactivity and might be a marker for Myo-sEVs, providing new therapeutic targets for cardiac remodeling. [ABSTRACT FROM AUTHOR]

Myocardial ischemia/reperfusion (I/R) injury is characterized by cell death via various cellular mechanisms upon reperfusion. As a new type of cell death, ferroptosis provides new opportunities to reduce myocardial cell death. Ferroptosis is known to be more active during reperfusion than ischemia. However, the mechanisms regulating ferroptosis during ischemia and reperfusion remain largely unknown. The contribution of ferroptosis in ischemic and reperfused myocardium were detected by administered of Fer-1, a ferroptosis inhibitor to C57BL/6 mice, followed by left anterior descending (LAD) ligation surgery. Ferroptosis was evaluated by measurement of cell viability, ptgs2 mRNA level, iron production, malondialdehyde (MDA) and 4-hydroxynonenal (4-HNE) levels. H9C2 cells were exposed to hypoxia/reoxygenation to mimic in vivo I/R. We used LC-MS/MS to identify potential E3 ligases that interacted with frataxin in heart tissue. Cardiac-specific overexpression of frataxin in whole heart was achieved by intracardiac injection of frataxin, carried by adeno-associated virus serotype 9 (AAV9) containing cardiac troponin T (cTnT) promoter. We showed that regulators of iron metabolism, especially iron regulatory protein activity, were increased in the ischemic myocardium or hypoxia cardiomyocytes. In addition, we found that frataxin, which is involved in iron metabolism, is differentially expressed in the ischemic and reperfused myocardium and involved in the regulation of cardiomyocytes ferroptosis. Furthermore, we identified an E3 ligase, NHL repeat-containing 1 (NHLRC1), that mediates frataxin ubiquitination degradation. Cardiac-specific overexpression of frataxin ameliorated myocardial I/R injury through ferroptosis inhibition. Through a multi-level study from molecule to animal model, these findings uncover the key role of frataxin in inhibiting cardiomyocyte ferroptosis and provide new strategies and perspectives for the treatment of myocardial I/R injury. [Display omitted] • IRP activity was enhanced in ischemic hearts and weakened in reperfused hearts. • NHLRC1 promoted frataxin ubiquitination and degradation. • Cardiac-specific frataxin overexpression protected against myocardial I/R injury through ferroptosis inhibition. [ABSTRACT FROM AUTHOR]

Rolling treatments have been identified as a promising fabrication and deformation processing technique for graphene/metal composites with high performance. However, it is still a challenge to choose appropriate rolling parameters to achieve high strength, ductility and electrical conductivity of the composite simultaneously. In this study, graphene/Cu composites were prepared with an in situ growth method and rolling treatment. The effects of rolling deformation and temperature on the microstructural evolution of graphene and Cu grains, interface bonding between graphene and the matrix, mechanical and electrical properties were systemically investigated. The cold-rolled composite with 85% deformation displayed a maximum ultimate strength of 548 MPa, a high elongation of 8.8% and a good electrical conductivity of 86.2% IACS. This is attributed to oriented graphene arrangement and matrix grain refinement. Our research provides a comprehensive understanding for the rolling behavior of graphene/Cu composites, and can promote the development of graphene-based composites with high performance. [ABSTRACT FROM AUTHOR]

Three-dimensional porous network encapsulation strategy is an effective means to obtain composite phase-change materials (PCMs) with high heat storage capacity and enhanced thermal conductivity. Herein, macroporous reduced graphene oxide (rGO) aerogels with adjustable pore size are prepared by the emulsion template method and hydrothermal reduction process. Further, the shape-stabilized rGO-aerogel-based composite PCMs are constructed after the combination of 3D porous rGO supports and paraffin wax (PW) through vacuum melting infiltration. By regulating the pore structure of the rGO aerogel network, the rGO-based composite PCMs achieve excellent energy storage properties with a phase-change enthalpy of 179.94 J/g for the loading amount of 95.61 wt% and an obvious enhancement in thermal conductivity of 0.412 W/m−1·K−1, which is 54.89% higher than pristine PW and enduring thermal cycling stability. The obtained macroporous rGO-aerogel-based composite PCMs with high thermal storage and heat transfer performance effectively broaden the application of PCMs in the field of thermal energy storage. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is the leading cause of noncommunicable disease-related death worldwide, and effective therapeutic strategies against CVD are urgently needed. Mitochondria dysfunction involves in the onset and development of CVD. Nowadays, mitochondrial transplantation, an alternative treatment aimed at increasing mitochondrial number and improving mitochondrial function, has been emerged with great therapeutic potential. Substantial evidence indicates that mitochondrial transplantation improves cardiac function and outcomes in patients with CVD. Therefore, mitochondrial transplantation has profound implications in the prevention and treatment of CVD. Here, we review the mitochondrial abnormalities that occur in CVD and summarize the therapeutic strategies of mitochondrial transplantation for CVD. [ABSTRACT FROM AUTHOR]

• Setting the acquisition rate of the photodiode sensor can avoid photodiode signals aliasing. • Robust correlation can be established between photodiode signal and average melt pool temperature through the neural network. • Time series model is suitable for flaw detection based on the melt pool photodiode signal. The continuous online flaws detection of the melt pool plays a significant role in assessing the quality of the printed sample in metal additive manufacturing (AM). However, the continuous detection of melt pool flaws faces great challenges because the suitable continuous acquisition system and real-time processing algorithm for melt pool feature extraction are difficult to establish. In this work, a continuous online flaws detection method combining the photodiode signal and melt pool temperature based on deep learning algorithms in the Laser Powder Bed Fusion (LPBF) is proposed. A multi-signal fusion system is designed to synchronously obtain the photodiode signal and temperature signal of the melt pool. The characteristics of the melt pool photodiode signal under the action of pulsed laser and the characteristics of the melt pool temperature are explored. The aliasing of photodiode signals is found due to the different settings of pulse laser frequency and photodiode sensor acquisition rate in AM. According to the two principles that the melt pool temperature is related to the flaw formation, and accurate flaws identification can be carried out based on the melt pool temperature field, the Back Propagation Neural Network (BPNN), Stacked Sparse AutoEncoder (SSAE), and Long Short-Term Memory (LSTM) are used to build the correlation model between the photodiode signal and the average melt pool temperature, and the flaw detection is carried out through the average melt pool temperature error. Therefore, once the correlation model is established, continuous online flaws detection can be realized only by using photodiode signals. The results demonstrated that a robust correlation can be established between the photodiode signal and the average melt pool temperature through the neural network, and the correlation error can be as low as 2.2 %. The detection of flaws is simplified into a binary classification problem through a reasonable threshold setting, and the LSTM with 74.39 % detection accuracy is more suitable for flaws detection based on the photodiode signal of the melt pool. [ABSTRACT FROM AUTHOR]

Background: Ferroptosis, which is characterized by lipid peroxidation and iron accumulation, is closely associated with the pathogenesis of acute renal injury (AKI). Cyanidin-3-glucoside (C3G), a typical flavonoid that has anti-inflammatory and antioxidant effects on ischemia‒reperfusion (I/R) injury, can induce AMP-activated protein kinase (AMPK) activation. This study aimed to show that C3G exerts nephroprotective effects against I/R-AKI related ferroptosis by regulating the AMPK pathway. Methods: Hypoxia/reoxygenation (H/R)-induced HK-2 cells and I/R-AKI mice were treated with C3G with or without inhibiting AMPK. The level of intracellular free iron, the expression of the ferroptosis-related proteins acyl-CoA synthetase long chain family member 4 (ACSL4) and glutathione peroxidase 4 (GPX4), and the levels of the lipid peroxidation markers 4-hydroxynonenal (4-HNE), lipid reactive oxygen species (ROS) and malondialdehyde (MDA) were examined. Results: We observed the inhibitory effect of C3G on ferroptosis in vitro and in vivo, which was characterized by the reversion of excessive intracellular free iron accumulation, a decrease in 4-HNE, lipid ROS, MDA levels and ACSL4 expression, and an increase in GPX4 expression and glutathione (GSH) levels. Notably, the inhibition of AMPK by CC significantly abrogated the nephroprotective effect of C3G on I/R-AKI models in vivo and in vitro. Conclusion: Our results provide new insight into the nephroprotective effect of C3G on acute I/R-AKI by inhibiting ferroptosis by activating the AMPK pathway. [ABSTRACT FROM AUTHOR]

Background: This study aims to develop an attention-based deep learning model for distinguishing benign from malignant breast lesions on CESM. Methods: Preoperative CESM images of 1239 patients, which were definitely diagnosed on pathology in a multicentre cohort, were divided into training and validation sets, internal and external test sets. The regions of interest of the breast lesions were outlined manually by a senior radiologist. We adopted three conventional convolutional neural networks (CNNs), namely, DenseNet 121, Xception, and ResNet 50, as the backbone architectures and incorporated the convolutional block attention module (CBAM) into them for classification. The performance of the models was analysed in terms of the receiver operating characteristic (ROC) curve, accuracy, the positive predictive value (PPV), the negative predictive value (NPV), the F1 score, the precision recall curve (PRC), and heat maps. The final models were compared with the diagnostic performance of conventional CNNs, radiomics models, and two radiologists with specialised breast imaging experience. Results: The best-performing deep learning model, that is, the CBAM-based Xception, achieved an area under the ROC curve (AUC) of 0.970, a sensitivity of 0.848, a specificity of 1.000, and an accuracy of 0.891 on the external test set, which was higher than those of other CNNs, radiomics models, and radiologists. The PRC and the heat maps also indicated the favourable predictive performance of the attention-based CNN model. The diagnostic performance of two radiologists improved with deep learning assistance. Conclusions: Using an attention-based deep learning model based on CESM images can help to distinguishing benign from malignant breast lesions, and the diagnostic performance of radiologists improved with deep learning assistance. [ABSTRACT FROM AUTHOR]

Background: Major depressive disorder (MDD) is an overbroad and heterogeneous diagnosis with no reliable or quantifiable markers. We aim to combine machine-learning techniques with the individual minimum spanning tree of the morphological brain network (MST-MBN) to determine whether the network properties can provide neuroimaging biomarkers to identify patients with MDD.Method: Eight morphometric features of each region of interest (ROI) were extracted from 3D T1 structural images of 106 patients with MDD and 97 healthy controls. Six feature distances of the eight morphometric features were calculated to generate a feature distance matrix, which was defined as low-order MBN. Further linear correlations of feature distances between ROIs were calculated on the basis of low-order MBN to generate individual high-order MBN. The Kruskal's algorithm was used to generate the MST to obtain the core framework of individual low-order and high-order MBN. The regional and global properties of the individual MSTs were defined as the feature. The support vector machine and back-propagation neural network was used to diagnose MDD and assess its severity, respectively.Result: The low-order and high-order MST-MBN constructed by cityblock distance had the excellent classification performance. The high-order MST-MBN significantly improved almost 20 % diagnostic accuracy compared with the low-order MST-MBN, and had a maximum R2 value of 0.939 between the predictive and true Hamilton Depression Scale score. The different group-level connectivity strength mainly involves the central executive network and default mode network (no statistical significance after FDR correction).Conclusion: We proposed an innovative individual high-order MST-MBN to capture the cortical high-order morphological correlation and make an excellent performance for individualized diagnosis and assessment of MDD. [ABSTRACT FROM AUTHOR]

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g. cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale. [ABSTRACT FROM AUTHOR]

In this work, unique Co3O4/N‐doped reduced graphene oxide (Co3O4/N‐rGO) composites as favorable sulfur immobilizers and promoters for lithium–sulfur (Li–S) batteries are developed. The prepared Co3O4 nanopolyhedrons (Co3O4‐NP) and Co3O4 nanocubes mainly expose (112) and (001) surfaces, respectively, with different atomic configurations of Co2+/Co3+ sites. Experiments and theoretical calculations confirm that the octahedral coordination Co3+ (Co3+Oh) sites with different oxidation states from tetrahedral coordination Co2+ sites optimize the adsorption and catalytic conversion of lithium polysulfides. Specially, the Co3O4‐NP crystals loaded on N‐rGO expose (112) planes with ample Co3+Oh active sites, exhibiting stronger adsorbability and superior catalytic activity for polysulfides, thus inhibiting the shuttle effect. Therefore, the S@Co3O4‐NP/N‐rGO cathodes deliver excellent electrochemical properties, for example, stable cyclability at 1 C with a low capacity decay rate of 0.058% over 500 cycles, superb rate capability up to 3 C, and high areal capacity of 4.1 mAh cm−2. This catalyst's design incorporating crystal surface engineering and oxidation state regulation strategies also provides new approaches for addressing the complicated issues of Li–S batteries. [ABSTRACT FROM AUTHOR]

The effect of electrolyte presence form on the corrosion evolution and stress corrosion cracking (SCC) behavior of E690 steel in the simulated marine environment is investigated. The steel exhibits the highest and lowest corrosion rate in splash and immersion environment, respectively, attributed to the differences in oxygen availability, rust layer property, and scour stress. γ-FeOOH dominates the rust formed in atmospheric environment, while Fe 3 O 4 prevails in the splash environment. The steel corroded in splash environment exhibits the high pit number density, high maximum pit depth, and high cathodic reaction activity, resulting in the most severe SCC degradation. • Effect of electrolyte presence form on the corrosion and SCC of E690 steel in marine environment is studied. • Oxygen diffusion, drying period, scour stress, and water holding ability determines the corrosion rate. • γ-FeOOH dominates the rust in atmosphere, while Fe 3 O 4 prevails in splash environment. • Corrosion pits with high number density and depth are formed in splash environment. • E690 steel exhibits the highest SCC sensitivity in the splash environment. [ABSTRACT FROM AUTHOR]

Mitochondria-induced cell death is a vital mechanism of heart failure (HF). Thus, identification of mitochondria-related genes (Mito-RGs) based on transcriptome sequencing data of HF might provide novel diagnostic markers and therapeutic targets for HF. First, bioinformatics analysis was conducted on the GSE57338, GSE76701, GSE136547, and GSE77399 datasets in the Gene Expression Omnibus. Next, we analyzed HF-Mito differentially expressed genes (DEGs) using the protein-protein interaction (PPI) network for obtaining critical genes and exploring their functions. Subsequently, immune cell scores of the HF and normal groups were compared. The potential alteration mechanisms of the key genes were investigated by constructing a competing endogenous RNA network. Finally, we predicted potential therapeutic agents and validated the expression levels of the key genes. Twenty-three HF-Mito DEGs were acquired in the GSE57338 dataset, and the PPI network obtained four key genes, including IFIT3, XAF1, RSAD2, and MX1. According to gene set enrichment analysis, the key genes showed high enrichment in myogenesis and hypoxia. Immune cell analysis demonstrated that aDCs, B cells, and 20 other immune cell types varied between the HF and normal groups. Moreover, we observed that H19 might affect the expression of IFIT3, AXF1, and RSAD2. PCGEM1 might regulate RSAD2 expression. A total of 515 potential therapeutic drugs targeting the key genes, such as tretinoin, silicon dioxide, and bisphenol A, were acquired. Finally, IFIT3, RSAD2, and MX1 expression increased in HF samples compared with normal samples in the GSE76701 dataset, conforming to the GSE57338 dataset analysis. This work screened four key genes, namely, IFIT3, XAF1, RSAD2, and MX1, which can be further explored in subsequent studies for their specific molecular mechanisms in HF. [ABSTRACT FROM AUTHOR]

For solving the problem of polar performance of the inertial navigation system (INS) at mid-low latitudes, the simulation test system constructed by the "attitude and velocity invariant method of trajectory transfer rule based on the transverse coordinate system (T-AVIM)" of the Earth sphere model is used. The test system structure, especially the IMU conversion formula from mid-low latitudes to polar region simulation test, is introduced, and it is proved that the IMU conversion error can be equivalently superimposed on the bias error of the polar simulated IMU. According to the marine estimation formula for the effect of the reference error on the IMU conversion error, the specific influence of the constant error component and the random error component of the reference system on the simulated IMU is analyzed. The calculation method of the simulated IMU error is given with examples and intuitively explained, and the correctness of the theory is verified through simulation experiments. [ABSTRACT FROM AUTHOR]

The Central African Copperbelt is a globally important sediment-hosted stratiform copper cluster, with regional Cu–Co deposits controlled by Neoproterozoic rift basin. Ore formation in the Copperbelt involves multiple stages of mineralization during diagenesis, basin inversion, and collisional orogeny. Understanding the depositional environment of ore-hosting rocks is crucial for revealing the geological settings and pre-orogenic ore genesis. This paper presents a biomarker study focusing on the ore-bearing black shale in the Luishia Cu–Co deposit, Democratic Republic of Congo. The research investigates thermal maturity, source, and depositional environment of organic matter through rock organic carbon and pyrolysis analysis, chloroform bitumen "A" group component analysis, as well as saturated hydrocarbon gas chromatography-mass spectrometry (GC-MS) analysis. The results indicate significant thermal maturation of organic matter in Luishia black shale. Bacteria are identified as the primary source of organic matter followed by red algae and green algae. The sedimentary environment is characterized by a restricted rift basin under shallow marine conditions with a stratified water column. The processes of bacterial sulphate reduction (BSR) and thermochemical sulphate reduction (TSR) significantly influence the mineralization during diagenesis and basin inversion, respectively. Enhanced thermal maturity promotes methane generation which plays a role in reducing anhydrite and subsequently precipitating ore minerals. The findings have important implications for regional exploration strategies. • Organic matter has a mixed origin and exhibits a high-level thermal maturity. • Black shale was formed in a restricted rift basin with stratified water column. • Organic matter contributes to ore precipitation in two mineralization phases. • Regional exploration strategies were recommended based on the geological model. [ABSTRACT FROM AUTHOR]

Introduction: Whether metformin reduces all-cause cardiovascular mortality and the incidence of cardiovascular events in patients with pre-existing cardiovascular diseases (CVD) remains inconclusive. Some randomised controlled trials (RCTs) and cohort studies have shown that metformin is associated with an increased risk of mortality and cardiovascular events. Methods: We conducted a pooling synthesis to assess the effects of metformin in all-cause cardiovascular mortality and incidence of cardiovascular events in patients with CVD. Studies published up to October 2021 in PubMed or Embase with a registration in PROSPERO (CRD42020189905) were collected. Both RCT and cohort studies were included. Hazard ratios (HR) with 95% CI were pooled across various trials using the random-effects model. Results: This study enrolled 35 published studies (in 14 publications) for qualitative synthesis and identified 33 studies (published in 26 publications) for quantitative analysis. We analysed a total of 61,704 patients, among them 58,271 patients were used to calculate all-cause mortality while 12,814 patients were used to calculate cardiovascular mortality. Compared with non-metformin control, metformin usage is associated with a reduction in all-cause mortality (HR: 0.90; 95% CI 0.83, 0.98; p = 0.01), cardiovascular mortality (HR: 0.89; 95% CI 0.85, 0.94; p < 0.0001), incidence of coronary revascularisation (HR: 0.79; 95% CI 0.64, 0.98; p = 0.03), and heart failure (HR: 0.90; 95% CI 0.87, 0.94; p < 0.0001) in patients with pre-existing cardiovascular diseases. Conclusion: Metformin use is associated with a reduction in all-cause mortality, cardiovascular mortality, incidence of coronary revascularisation, and heart failure in patients with CVD; however, metformin usage was not associated with reduction in the incidence of myocardial infarction, angina, or stroke. [ABSTRACT FROM AUTHOR]

The traditional error model for strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated navigation is establish for special orthogonal group Kalman filter (SOKF), which is only suitable for the case of small misalignment angle. In this article, the Lie group based nonlinear error model is investigated for SINS/DVL in inertial frame, this model is no longer limited by the size of misalignment angle like traditional SOKF, so initial alignment and integrated navigation could be combined into one stage, and the navigation solution is more accurate and faster. First, a thorough theoretical analysis is presented for state transformation extended Kalman filter (STEKF) modeling in both special orthogonal group (SO(3)) and special euclidean group (SE(3)). Then, the nonlinear error models (both left and right invariant error models) in inertial frame are derived in extended special euclidean group (SE 2 (3)). On this basis, SINS/DVL is verified as a type of left invariant observation system which is more applicable to the left invariant error model. Finally, both simulation and field test for SINS/DVL are conducted to evaluate the actual performance of the investigated methods. The results show that the proposed left invariant error model in inertial frame performs better for SINS/DVL than right invariant error model, and it also has more excellent performance than other existing integrated navigation methods, such as SOKF and STEKF. Especially compared with the current advanced invariant error model defined in earth frame, the model we investigated is more concise, efficient and accurate. • A thorough theoretical analysis is presented for STEKF modeling in both special orthogonal group (SO(3)) and special euclidean group (SE(3)). The inherent defects of STEKF are discussed from linearized modeling and Lie group theory. • A new navigation state is defined in inertial frame extended special euclidean group (SE 2 (3)), which is proved to satisfy the group affine property. The left and right invariant error models corresponding to the aforementioned state are derived for SINS/DVL integrated navigation. SINS/DVL is discussed as a type of left invariant observation system based on the analysis of transition matrices. • The simulation and field test are conducted to evaluate the actual performance of the investigated nonlinear error models. The results are explicitly presented with thorough discussion, which are consistent with theoretical analysis. The proposed left invariant error model in inertial frame outperform the common models (such as SOKF and STEKF) on the state estimation. Especially compared with the current advanced invariant error model defined in earth frame, the model we investigated is more concise, efficient, and accurate. [ABSTRACT FROM AUTHOR]

Although much is known about how adipose tissue affects the development of clear cell renal carcinoma (ccRCC), little information is available for the utility of sex-specific abdominal visceral fat composition as a predictor of clear cell renal carcinoma (ccRCC) T stage. We conducted CT-based sex-specific abdominal fat measurements in ccRCC patients to assess whether VFA distribution could predict the ccRCC T stage. In total, 253 patients (182 males and 71 females) from our hospital with pathologically confirmed ccRCC (178 low T-stage and 75 high T-stage) were retrospectively reviewed for the present study. Computed tomography (CT) scans were assessed using ImageJ to differentiate between the visceral and subcutaneous fat areas (VFA and SFA), after which the relative VFA (rVFA) and total fat area (TFA) were computed. The relationships between these fat area-related variables, patient age, sex, and BMI, and ccRCC T stage were then evaluated through univariate and multivariate logistic regression analysis to clarify the association between general or sex-specific abdominal visceral fat and T stage. Following adjustment for age, males with high T stage ccRCC exhibited an increased rVFA as compared to males with low T stage ccRCC, with the same relationship being observed among females. This association between rVFA and high T stage was confirmed through both univariate and multivariate models. As thus, sex-specific visceral fat composition is a reliable independent predictor that can identify both male and female patients with high T stage ccRCC. [ABSTRACT FROM AUTHOR]

The poor cycle performance caused by the shuttle effect of polysulfides is the main challenge that affects the practical implementation for lithium-sulfur (Li-S) batteries. Here, a unique long-range and phosphorus-deficient phosphorene (BP 4-x) framework featured with 3D ordered sponge (3DOS) architecture and carbon nanotubes (CNTs) embedding is developed, which serves as a high-performance sulfur immobilizer and catalytic promoter for polysulfide conversion. The 3DOS architecture comprise ultrathin and large BP nanosheets and the semi-connected nanosheets afford open channels for fast ion/mass transfer. Simultaneously, CNTs embedded within the BP 4-x skeleton as a conductive network can improve the conductivity of the materials. The Li-S batteries constructed with these advanced features exhibits an enhanced capacity of 1120.1 mAh g−1 with a high coulombic efficiency of similar to 99.4%, and great rate capability of 660.1 mAh g−1 at 4 C. Our study illustrates potential application of a metal-free phosphorene catalyst for high energy storage devices. [ABSTRACT FROM AUTHOR]

Background: Postpartum depression (PPD) is a serious postpartum mental health problem worldwide. To date, minimal is known about the alteration of topographical organization in the brain structural covariance network of patients with PPD. This study investigates the brain structural covariance networks of patients with PPD by using graph theoretical analysis.Methods: High-resolution 3D T1 structural images were acquired from 21 drug-naive patients with PPD and 18 healthy postpartum women. Cortical thickness was extracted from 64 brain regions to construct the whole-brain structural covariance networks by calculating the Pearson correlation coefficients, and their topological properties (e.g., small-worldness, efficiency, and nodal centrality) were analyzed by using graph theory. Nonparametric permutation tests were further used for group comparisons of topological metrics. A node was set as a hub if its betweenness centrality (BC) was at least two standard deviations higher than the mean nodal centrality. Network-based statistic (NBS) was used to determine the connected subnetwork.Results: The PPD and control groups showed small-worldness of group networks, but the small-world network was more evidently in the PPD group. Moreover, the PPD group showed increased network local efficiency and almost similar network global efficiency. However, the difference of the network metrics was not significant across the range of network densities. The hub nodes of the patients with PPD were right inferior parietal lobule (BC = 13.69) and right supramarginal gyrus (BC = 13.15), whereas those for the HCs were left cuneus (BC = 14.96), right caudal anterior-cingulate cortex (BC = 15.51), and right precuneus gyrus (BC = 15.74). NBS demonstrated two disrupted subnetworks that are present in PPD: the first subnetwork with decreased internodal connections is mainly involved in the cognitive-control network and visual network, and the second subnetwork with increased internodal connections is mainly involved in the default mode network, cognitive-control network and visual network.Conclusions: This study demonstrates the alteration of topographical organization in the brain structural covariance network of patients with PPD, providing in sight on the notion that PPD could be characterized as a systems-level disorder. [ABSTRACT FROM AUTHOR]

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems. [ABSTRACT FROM AUTHOR]