Graph-guided joint prediction of class label and clinical scores for the Alzheimer’s disease

Accurate diagnosis of Alzheimer’s disease and its prodromal stage, i.e., mild cognitive impairment, is very important for early treatment. Over the last decade, various machine learning methods have been proposed to predict disease status and clinical scores from brain images. It is worth noting that many features extracted from brain images are correlated significantly. In this case, feature selection combined with the additional correlation information among features can effectively improve classification/regression performance. Typically, the correlation information among features can be modeled by the connectivity of an undirected graph, where each node represents one feature and each edge indicates that the two involved features are correlated significantly. In this paper, we propose a new graph-guided multi-task learning method incorporating this undirected graph information to predict multiple response variables (i.e., class label and clinical scores) jointly. Specifically, based on the sparse undirected feature graph, we utilize a new latent group Lasso penalty to encourage the correlated features to be selected together. Furthermore, this new penalty also encourages the intrinsic correlated tasks to share a common feature subset. To validate our method, we have performed many numerical studies using simulated datasets and the Alzheimer’s Disease Neuroimaging Initiative dataset. Compared with the other methods, our proposed method has very promising performance

Higher-order multi-scale method for high-accuracy nonlinear thermo-mechanical simulation of heterogeneous shells

In the present work, we consider multi-scale computation and convergence for nonlinear time-dependent thermo-mechanical equations of inhomogeneous shells possessing temperature-dependent material properties and orthogonal periodic configurations. The first contribution is that a novel higher-order macro-micro coupled computational model is rigorously devised via multi-scale asymptotic technique and Taylor series approach for high-accuracy simulation of heterogeneous shells. Benefitting from the higher-order corrected terms, the higher-order multi-scale computational model keeps the conservation of local energy and momentum for nonlinear thermo-mechanical simulation. Moreover, a global error estimation with explicit rate of higher-order multi-scale solutions is first derived in the energy norm sense. Furthermore, an efficient space-time numerical algorithm with off-line and on-line stages is presented in detail. Adequate numerical experiments are conducted to confirm the competitive advantages of the presented multi-scale approach, exhibiting not only the exceptional numerical accuracy, but also the less computational expense for heterogeneous shells

Insights into the expression of DNA (de)methylation genes responsive to nitric oxide signaling in potato resistance to late blight disease

Our previous study concerning the pathogen-induced biphasic pattern of nitric oxide (NO) burst revealed that the decline phase and a low level of NO, due to S-nitrosoglutathione reductase (GSNOR) activity, might be decisive in the upregulation of stress-sensitive genes via histone H3/H4 methylation in potato leaves inoculated with avr P. infestans. The present study refers to the NO-related impact on genes regulating DNA (de)methylation, being in dialog with histone methylation. The excessive amounts of NO after the pathogen or GSNO treatment forced the transient upregulation of histone SUVH4 methylation and DNA hypermethylation. Then the diminished NO bioavailability reduced the SUVH4-mediated suppressive H3K9me2 mark on the R3a gene promoter and enhanced its transcription. However, we found that the R3a gene is likely to be controlled by the RdDM methylation pathway. The data revealed the time-dependent downregulation of the DCL3, AGO4, and miR482e genes, exerting upregulation of the targeted R3a gene correlated with ROS1 overexpression. Based on these results, we postulate that the biphasic waves of NO burst in response to the pathogen appear crucial in establishing potato resistance to late blight through the RdDM pathway controlling R gene expression

Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method

Independent markers of nonalcoholic fatty liver disease in a gentrifying population‐based Chinese cohort

BackgroundPrevalence of nonalcoholic fatty liver disease (NAFLD) is increasing in developing countries, but its causes are not known. We aimed to ascertain the prevalence and determinants of NAFLD in a new largely unmedicated population‐based cohort from the rapidly gentrifying region of Pinggu, China.MethodsWe randomized cluster sampled 4002 Pinggu residents aged 26 to 76 years. Data from 1238 men and 1928 women without significant alcohol drinking or hepatitis virus B or C infection were analysed. NAFLD was defined using a liver‐spleen ratio (L/S ratio) ≤1.1 on unenhanced abdominal computed tomography (CT) scanning.ResultsOf men and women, 26.5% and 20.1%, respectively, had NAFLD. NAFLD prevalence was highest in younger men and older women. In multivariate logistic regression models, higher body mass index, waist circumference, serum triglyceride, alanine transaminase, and haemoglobin A1c independently increased the odds of NAFLD in both men and women separately. Higher annual household income and systolic blood pressure for men and higher serum uric acid and red meat intake and lower physical activity levels for women also independently associated with higher odds of NAFLD. Individuals with L/S ratio ≤1.1 had linearly increasing rates of obesity, diabetes, and metabolic syndrome that paralleled fatty liver increase.ConclusionsNAFLD is common in a gentrifying Chinese population particularly in younger men of high socioeconomic status and older women with sedentary behaviour who eat red meat. Demographic factors add independent risk of NAFLD above traditional metabolic risk factors. A CT L/S ratio of ≤1.1 identifies individuals at high risk of metabolic disease.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149708/1/dmrr3156_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149708/2/dmrr3156.pd

Genetic portrait of polyamine transporters in barley: insights in the regulation of leaf senescence

Nitrogen (N) is one of the most expensive nutrients to supply, therefore, improving the efficiency of N use is essential to reduce the cost of commercial fertilization in plant production. Since cells cannot store reduced N as NH3 or NH4+, polyamines (PAs), the low molecular weight aliphatic nitrogenous bases, are important N storage compounds in plants. Manipulating polyamines may provide a method to increase nitrogen remobilization efficiency. Homeostasis of PAs is maintained by intricate multiple feedback mechanisms at the level of biosynthesis, catabolism, efflux, and uptake. The molecular characterization of the PA uptake transporter (PUT) in most crop plants remains largely unknown, and knowledge of polyamine exporters in plants is lacking. Bi-directional amino acid transporters (BATs) have been recently suggested as possible PAs exporters for Arabidopsis and rice, however, detailed characterization of these genes in crops is missing. This report describes the first systematic study to comprehensively analyze PA transporters in barley (Hordeum vulgare, Hv), specifically the PUT and BAT gene families. Here, seven PUTs (HvPUT1-7) and six BATs (HvBAT1-6) genes were identified as PA transporters in the barley genome and the detailed characterization of these HvPUT and HvBAT genes and proteins is provided. Homology modeling of all studied PA transporters provided 3D structures prediction of the proteins of interest with high accuracy. Moreover, molecular docking studies provided insights into the PA-binding pockets of HvPUTs and HvBATs facilitating improved understanding of the mechanisms and interactions involved in HvPUT/HvBAT-mediated transport of PAs. We also examined the physiochemical characteristics of PA transporters and discuss the function of PA transporters in barley development, and how they help barley respond to stress, with a particular emphasis on leaf senescence. Insights gained here could lead to improved barley production via modulation of polyamine homeostasis

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe