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Variational auto-encoders (VAE) have been widely used in process modeling due to the ability of deep feature extraction and noise robustness. However, the construction of a supervised VAE model still faces huge challenges. The data generated by the existing supervised VAE models are unstable and uncontrollable due to random resampling in the latent subspace, meaning the performance of prediction is greatly weakened. In this paper, a new multi-layer conditional variational auto-encoder (M-CVAE) is constructed by injecting label information into the latent subspace to control the output data generated towards the direction of the actual value. Furthermore, the label information is also used as the input with process variables in order to strengthen the correlation between input and output. Finally, a neural network layer is embedded in the encoder of the model to achieve online quality prediction. The superiority and effectiveness of the proposed method are demonstrated by two real industrial process cases that are compared with other methods.

A novel online final product quality prediction scheme is proposed in this paper for the improvement of quality prediction in multi-phase batch processes. Phase cumulative product quality (PCPQ), which is quality cumulated from the beginning of the phase to the end, is introduced for quality prediction, and final product quality prediction offline is achieved by cumulating all the predicted PCPQ based on the corresponding PCPQ model. In this way, the quality prediction approach proposed not only explores the different effects of process variables in different phases on final product quality, but also takes the common effects of process variables in different phases into account. The PCPQ model and remained phase cumulative product quality (RPCPQ) model are combined to improve online prediction precision, without the missing observations estimation. The proposed approach is applied to a simulated penicillin fermentation process and the results of simulation demonstrate effectiveness and superiority.

Background & aims Centenarians represent a unique population where predictors of mortality are understudied.Studies have shown that sarcopenia is associated with adverse health outcomes and premature death in elderly patients. The aim of our study was to explore the association between sarcopenia and centenarian mortality. Methods Our study is a prospective cohort study. Muscle mass was measured using bioelectrical impedance analysis (BIA), and the relative skeletal muscle index (RSMI) was adjusted for height to reduce individual differences. Sarcopenia is defined according to the Asian Sarcopenia Working Group’s (AWGS)2019 updated diagnostic criteria for sarcopenia, it introduces the concept of “possible sarcopenia”, defined as the presence of only low muscular strength or low physical performance, specifically for primary health care or community health promotion to enable earlier lifestyle interventions. The survival time of centenarians with and without sarcopenia was compared using the Kaplan-Meier method, and the hazard ratio (HR) and 95% confidence interval (95% CI) for mortality were determined using the Cox proportional hazards regression model. Results A total of 98 centenarians were included in this study, with an average age of (102.2 ± 1.8) years. The prevalence of sarcopenia was 20.4%. The Cox proportional hazards model analysis showed that after adjusting for confounding factors, the risk of death was increased by 3.64 times in the sarcopenia group (HR = 4.64, 95% CI: 2.48–8.66). Conclusion Sarcopenia can serves as a significant predictor of long-term mortality in centenarians and functions as an important independent predictor. Trial registration The trial was registered at the Chinese Clinical Trial Center on 17-01-2019. Trial registration numbers 2018 − 463; ChiCTR1900020754.

In this study, the tribological properties of three AISI 1045 steel samples were investigated. Two samples were treated with ultrasonic shot peening (USP) and ultrasonic strengthening grinding process (USGP), respectively, while the other one was only treated with a polishing process. Sample properties, such as surface morphology, roughness, microhardness, elastic modulus, frictional coefficient, and phase structures were analyzed. Results show that the sample treated with USGP had the best tribological properties. It realized the highest surface roughness, microhardness, and elastic modulus. Compared with a polished sample, the roughness of the sample treated with USGP increased by 157%, and the microhardness and elastic modulus improved by 32.8% and 21.3%, respectively. Additionally, USGP provided an average frictional coefficient of 0.4, decreasing approximately 45% compared to polishing. The possible mechanisms of USGP surface texturing were discussed. The findings denote that USGP could be an efficient approach to improve the fatigue life of some mechanical components.

Alcohol use disorder (AUD) is a profound psychiatric condition marked by disrupted connectivity among distributed brain regions, indicating impaired functional integration. Previous connectome studies utilizing functional magnetic resonance imaging (fMRI) have predominantly focused on undirected functional connectivity, while the specific alterations in directed effective connectivity (EC) associated with AUD remain unclear. To address this issue, this study utilized multivariate pattern analysis (MVPA) and spectral dynamic causal modeling (DCM). We recruited 32 abstinent men with AUD and 30 healthy controls (HCs) men, and collected their resting-state fMRI data. A regional homogeneity (ReHo)-based MVPA method was employed to classify AUD and HC groups, as well as predict the severity of addiction in AUD individuals. The most informative brain regions identified by the MVPA were further investigated using spectral DCM. Our results indicated that the ReHo-based support vector classification (SVC) exhibits the highest accuracy in distinguishing individuals with AUD from HCs (classification accuracy: 98.57%). Additionally, our results demonstrated that ReHo-based support vector regression (SVR) could be utilized to predict the addiction severity (alcohol use disorders identification test, AUDIT, R 2  = 0.38; Michigan alcoholism screening test, MAST, R 2  = 0.29) of patients with AUD. The most informative brain regions for the prediction include left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. These findings were validated in an independent data set (35 patients with AUD and 36 HCs, Classification accuracy: 91.67%; AUDIT, R 2  = 0.17; MAST, R 2  = 0.20). The results of spectral DCM analysis indicated that individuals with AUD exhibited decreased EC from the left pre-SMA to the right putamen, from the right dACC to the right putamen, and from the right LOFC to the right NACC compared to HCs. Moreover, the EC strength from the right NACC to left pre-SMA and from the right dACC to right putamen mediated the relationship between addiction severity (MAST scores) and behavioral measures (impulsive and compulsive scores). These findings provide crucial evidence for the underlying mechanism of impaired self-control, risk assessment, and impulsive and compulsive alcohol consumption in individuals with AUD, providing novel causal insights into both diagnosis and treatment.

In addition to measuring the strain, stress, and Young’s modulus of materials through tension and compression, in-plane shear modulus measurement is also an important part of parameter testing of composites. Tensile testing of ±45° composite laminates is an economical and effective method for measuring in-plane shear strength. In this paper, the in-plane shear modulus of T800 carbon fiber/epoxy composites were measured through tensile tests of ±45° composite laminates, and acoustic emission (AE) was used to characterize the damage of laminates under in-plane shear loading. Factor analysis (FA) on acoustic emission parameters was performed and the reconstructed factor scores were clustered to obtain three damage patterns. Finally, the development and evolution of the three damage patterns were characterized based on the cumulative hits of acoustic emission. The maximum bearing capacity of the laminated plate is about 17.54 kN, and the average in-plane shear modulus is 5.42 GPa. The damage modes of laminates under in-plane shear behavior were divided into three types: matrix cracking, delamination and fiber/matrix interface debonding, and fiber fracture. The characteristic parameter analysis of AE showed that the damage energy under in-plane shear is relatively low, mostly below 2000 mV × ms, and the frequency is dispersed between 150–350 kHz.

The brainstem houses numerous nuclei and tracts that serve vital functions. Genome-wide associations with brainstem substructure volumes have been explored in European individuals, yet other ancestries remain under-represented. Here, we conduct cross-ancestry genome-wide association meta-analyses in 103,098 individuals for brainstem and 78,062 individuals for eight substructure volumes, including 7094 Chinese Han individuals. We identify 713 locus-trait associations with brainstem and substructure volumes at P  < 5.56 ×10 −9 , comprising 569 new associations. Two associations show different effect sizes, while 496 associations have similar effect sizes between ancestries. We prioritize 186 genes associated with brainstem volumetric traits. We find both shared and distinct genetic loci, genes, and pathways for midbrain, pons, and medulla volumes, along with the shared genetic architectures related to disease phenotypes and physiological functions. The results provide new insights into the genetic architectures of brainstem and substructure volumes and their genetic associations with brainstem physiologies and pathologies. A cross-ancestry GWAS meta-analyses of brainstem structures identify 713 associations. It reveals shared/distinct genetic architectures across ancestries/substructures and overlaps with neuropsychiatric disorders and physiological functions.

Selective laser melted (SLM) 316L stainless steel (SS) has been widely employed in the fields of designing and manufacturing components with complex shapes and sizes. However, the low yield strength, low ultimate tensile stress, and low hardness of SLM 316L SS components hinder its further application. In this work, the strengthening grinding process (SGP) was used to enhance the mechanical properties of SLM 316L SS. The microhardness, residual stress, microstructure, and tensile properties of all the samples were analyzed. The results demonstrate that the SGP induced higher compressive residual stress and microhardness, as well as higher tensile properties. The maximum hardness and residual stress reached 354.5 HV and −446 MPa, respectively, indicating that the SGP resulted in a plastic deformation layer over 150 μm. The possible mechanisms have been discussed in further detail. Compared to the untreated sample, the SGP sample shows a significant improvement in yield strength (YS), ultimate tensile stress (UTS), and elongation (EL), increasing 30%, 25.5%, and 99.1%, respectively. This work demonstrates that SGP treatment could be an efficient approach to simultaneously improving the strength and ductility of the SLM 316L SS, which makes it more suitable for engineering applications.

Promising biomarkers would be used to improve the determination of diagnosis and severity, as well as the prediction of symptomatic and functional outcomes of schizophrenia. In this study, we used three different mouse models induced by a genetic factor (PV-Cre; ErbB4−/−, G group), an environmental stressor (adolescent social isolation, G group), and a combination of genetic factor and environmental stressor (PV-Cre; ErbB4−/− mice with isolation, G × E group). Attenuated PPI (%) confirmed the successful establishment of three schizophrenia-like mouse models. To evaluate whether neuropeptide levels in plasma would be potential biomarkers of different schizophrenia models in our work, we used MILLIPLEX® MAP method to simultaneously measure 6 critical neuropeptides in plasma. Among the evaluated neuropeptides, increased neurotensin tends to be associated with genetic factors of schizophrenia, increased orexin A seems to be a biomarker of an interplay between genetic and social isolation, while higher plasma oxytocin might be more apt to be responsive to social isolation. The potential biomarkers are mostly independent of sex. This research would provide novel clues to develop circulating biomarkers of plasma neuropeptides for multifactorial schizophrenia. •Both genetic and environmental factors were considered for the schizophrenia models.•Six neuropeptides were simultaneously measured using only 250 μL sample.•Different neuropeptides might be responsive to specific pathological factors.•Novel clues to develop circulating biomarkers for multifactorial schizophrenia

[This corrects the article DOI: 10.3389/fnhum.2017.00095.].[This corrects the article DOI: 10.3389/fnhum.2017.00095.].