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Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN – a multi-view, weakly-supervised deep clustering method – which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes. Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes. [ABSTRACT FROM AUTHOR]

In the abrasive belt grinding process, actual material removal is an important parameter that affects its accuracy. At present, for obtaining the actual material removal, offline measurements are required to establish the mathematical prediction model. To improve the accuracy and efficiency of abrasive belt machine grinding, this paper proposes a novel method for monitoring material removal using multiple sensors and a two-dimensional (2D) convolutional neural network (2D-CNN) learning algorithm. In this method, features of multiple types (color, texture, and shape) are extracted from vision signals, and that of multiple domains (time, frequency, and time–frequency domain) are extracted from sound and tactile signals. These features are constructed into a 2D feature matrix as the input model, and the 2D-CNN prediction model is established between the multisensor features and the material removal rate of the abrasive belt grinding process. An experimental dataset is used to train and verify the established model. The results show that the proposed method can identify that sensor signals are sensitive to the material removal rate. After optimizing and tuning the model parameters, the coefficient of determination of the prediction results is as high as 94.5% and the root mean square error is 0.017. Therefore, the proposed method can be employed for the prediction of material removal rate for different belt specifications and different grinding parameters. Compared to traditional machine learning methods, this method can yield better training results without feature selection and optimization. [ABSTRACT FROM AUTHOR]

Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment. Alzheimer's disease is heterogeneous in its neuroimaging and clinical phenotypes. Here the authors present a semi-supervised deep learning method, Smile-GAN, to show four neurodegenerative patterns and two progression pathways providing prognostic and clinical information. [ABSTRACT FROM AUTHOR]

Quantifying the overall magnitude of every single locus' genetic effect on the widely measured human phenome is of great challenge. We introduce a unified modelling technique that can consistently provide a total genetic contribution assessment (TGCA) of a gene or genetic variant without thresholding genetic association signals. Genome-wide TGCA in five UK Biobank phenotype domains highlights loci such as the HLA locus for medical conditions, the bone mineral density locus WNT16 for physical measures, and the skin tanning locus MC1R and smoking behaviour locus CHRNA3 for lifestyle. Tissue-specificity investigation reveals several tissues associated with total genetic contributions, including the brain tissues for mental health. Such associations are driven by tissue-specific gene expressions, which share genetic basis with the total genetic contributions. TGCA can provide a genome-wide atlas for the overall genetic contributions in each particular domain of human complex traits. Quantifying the effects of individual loci on the human phenome is a challenging task. Here, the authors introduce a modelling technique, TGCA, that assesses total genetic contribution per locus and apply this to UK Biobank phenotype domains, revealing top loci and links to tissue-specific gene expression. [ABSTRACT FROM AUTHOR]

TATB (1,3,5-triamino-2,4,6-trinitrobenzene) is a widely used insensitive high-energy explosive. It is significant to study its structural evolution in order to get a credible understand of its properties and performances. Direct mechanical tests such as hardness tests are useful approaches to get information about its mechanical properties and shed light upon the microstructures. However, due to the poor solubility of TATB to most solvents, it is hard to produce large crystals for such tests. Fortunately, microcosmic approaches such as powder diffraction can reinforce it and has low requirement on samples. In this study, series of in-situ neutron diffraction experiments under different pressures are performed to investigate the structural change and mechanical properties of TATB. The recently reported phase transition at 4 GPa is studied. The microstrains in normal TATB (12–17 µm) are analysed to reveal the information of slip system and transitional process. The lattice parameters and bulk moduli of TATB under different conditions are given. These neutron diffraction results are significant supplement to correlative studies and will help to understand the performances of TATB, such as deformation and phase transition. [ABSTRACT FROM AUTHOR]

The Fusarium wilt disease caused by Fusarium oxysporum f. sp. batatas (Fob) is one of the devastating diseases of sweetpotato. However, the molecular mechanisms of sweetpotato response to Fob is poorly understood. In the present study, comparative quantitative proteomic analysis was conducted to investigate the defense mechanisms involved. Two sweetpotato cultivars with differential Fob infection responses were inoculated with Fob spore suspensions and quantitatively analyzed by Tandem Mass Tags (TMT). 2267 proteins were identified and 1897 of them were quantified. There were 817 proteins with quantitative ratios of 1.2-fold change between Fob-inoculated and mock-treated samples. Further, nine differentially expressed proteins were validated by Parallel Reaction Monitoring (PRM). According to Gene Ontology (GO) annotation information, the proteins functioned in molecular metabolism, cellular component formation, and biological processes. Interestingly, the results showed that sweetpotato resistant response to Fob infection included many proteins associated with signaling transduction, plant resistance, chitinase and subtilisin-like protease. The functions and possible roles of those proteins were discussed. The results provides first insight into molecular mechanisms involved in sweetpotato defense responses to Fob. [ABSTRACT FROM AUTHOR]

Weak interfacial interaction and poor surface wettability always restrict and weaken the performances of energetic composites. Conventional surface modifications can hardly achieve a desired result, due to the inadequacy for solving the interfacial incompatibility. We here demonstrated bioinspired hierarchical-structured 2,6-diamino-3,5-dinitropyrazine-1-oxide particles through in situ "grafting from" polymerization of hyperbranched polyurethane (HBPU) on polydopamine surface. Benefiting from the enhanced interfacial interaction and surface wettability which facilitated mechanical performance, the final results revealed that a remarkable mechanical enhancement was successfully achieved through this way. Meanwhile, improved safety performance was gained, and the impact sensitivity was reduced to a large extent which was ascribed to the cushioning and protective effects of HBPU shells with soft segments. We believe this approach of constructing bioinspired hierarchical interface aiming at solving low surface wettability and weak interface will shed light on the design and preparation of high-performance composites through a novel and effective way. [ABSTRACT FROM AUTHOR]

The application of aluminum nanoparticle is limited due to its passivation Al2O3 layer even though it owns extreme specific surface and high reaction activity. In this work, the aluminum nanoparticles were modified via in situ grafting onto energetic glycidyl azide polymer (GAP) to improve the stability and energy-releasing performance. The results showed that GAP was grafted on the nano-aluminum surface with chemical bonds of -O-(CO-NH)- formed, and the thickness of shell layer of GAP could be tuned by changing the relative ratio of reactants. Furthermore, modified nanoparticles show hydrophobicity with static water contact angle changing from 20.2° to 142.4°. Significant increasing stability of aluminum nanoparticles is obtained in hot water, which is evidenced by that around 10 wt% of modified aluminum is reacted after 210 min. Based on the core-shell configurations, (Al@GAP)/fluorine composites were prepared. The violent combustion phenomenon and high release rate profiles revealed high energy-releasing performance with the assistance of GAP. This synthetic strategy may provide an effective approach to prepare other metal nanoparticles, and possess potential application value in the fields of metallized explosives and high-energy structure with energy release. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most common neurodegenerative disorder and has an elusive etiology. It is likely multifactorial, and genetic defects contribute to its pathogenesis. At least 25 genetic loci and 20 monogenic genes have been identified in monogenic PD. Recessive F-box protein 7 gene ( FBXO7) mutations reportedly cause hereditary parkinsonism. To explore the roles of four paralogs ( FBXO2, FBXO6, FBXO12, and FBXO41) in PD development, their variants (rs9614, rs28924120, rs6442117, and rs61733550, respectively) were analyzed in 502 Han Chinese patients with PD and 556 age, gender, and ethnicity-matched normal participants in mainland China. Statistically significant differences in genotypic and allelic frequencies were detected only in the FBXO2 variant rs9614 ( P = 0.001 and 0.023, respectively; odds ratio 0.819, 95% confidence interval 0.690-0.973) between patients and controls. These results suggest that the FBXO2 variant rs9614 C allele may decrease the PD risk in mainland Han Chinese and may be a biomarker for PD. [ABSTRACT FROM AUTHOR]

A novel carbon nanotube-patterned sapphire substrate (CPSS) has been utilized for the growth of GaN material and fabrication of a InGaN/GaN light emitting diode (LED) by metal-organic vapor phase epitaxy. Different lateral strain distributions and stress reductions were observed in a GaN thin film on CPSS compared with those on a conventional sapphire substrate. Nanoheteroepitaxy induced by small size nucleation islands of about 50 nm is ascribed to this significant strain modulation. The crystalline quality of the GaN thin film was also improved, as illustrated by X-ray diffraction. Performances of 1 mm × 1 mm LEDs on CPSS were also enhanced, with an operational power increase of 37.5% and higher saturation current. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

We aim to study the amelioration effect of adenovirus5-mediated human hepatocyte growth factor gene transfer on postinfarction heart failure in swine model. Twelve Suzhong young swine were randomly divided into 2 groups of 6 pigs each: Ad5-HGF group and mock-vector Ad5 group. Four weeks after ligation of the left anterior descending coronary artery, Ad5-HGF was intracoronarily transferred into the myocardium. Simultaneously, gate cardiac perfusion imaging was performed to evaluate the heart function. Three weeks later, gate cardiac perfusion imaging was performed again, then the hearts were removed and sectioned for immunohistochemical examination to illustrate the effects of Ad5-HGF on infarcted myocardium. The expression of HGF was examined by ELISA. The results were: (1) compared with the mock-vector Ad5 group, high expression of human HGF was observed in the myocardium of Ad5-HGF group; (2) in the Ad5-HGF group, the number of CD117+ cells co-expressing c-Met per mm2 was significantly larger; (3) the improvement in LVEF was greater in the Ad5-HGF group than in the mock-vector Ad5 group. We concluded that: (1) high expression of human HGF was observed in the myocardium through intracoronary gene transfection; (2) HGF can improve the mobilization of CD117+/c-Met+ stem cells into ischemic myocardium. The amelioration effect of HGF on postinfarction heart failure could not be limited to stimulating angiogenesis, anti-apoptosis, anti-fibrosis, but was also involved in the recruitment of stem cells into myocardium. [ABSTRACT FROM AUTHOR]

The continuous administration of compound danshen dripping pills (CDDP) showed good efficacy in relieving myocardial ischemia clinically. To probe the underlying mechanism, metabolic features were evaluated in a rat model of acute myocardial ischemia induced by isoproterenol (ISO) and administrated with CDDP using a metabolomics platform. Our data revealed that the ISO-induced animal model showed obvious myocardial injury, decreased energy production, and a marked change in metabolomic patterns in plasma and heart tissue. CDDP pretreatment increased energy production, ameliorated biochemical indices, modulated the changes and metabolomic pattern induced by ISO, especially in heart tissue. For the first time, we found that ISO induced myocardial ischemia was accomplished with a reduced fatty acids metabolism and an elevated glycolysis for energy supply upon the ischemic stress; while CDDP pretreatment prevented the tendency induced by ISO and enhanced a metabolic shift towards fatty acids metabolism that conventionally dominates energy supply to cardiac muscle cells. These data suggested that the underlying mechanism of CDDP involved regulating the dominant energy production mode and enhancing a metabolic shift toward fatty acids metabolism in ischemic heart. It was further indicated that CDDP had the potential to prevent myocardial ischemia in clinic. [ABSTRACT FROM AUTHOR]