Deciphering Microbiome, Transcriptome, and Metabolic Interactions in the Presence of Probiotic Lactobacillus acidophilus against Salmonella Typhimurium in a Murine Model.

Salmonella enterica serovar Typhimurium (S. Typhimurium), a foodborne pathogen that poses significant public health risks to humans and animals, presents a formidable challenge due to its antibiotic resistance. This study explores the potential of Lactobacillus acidophilus (L. acidophilus 1.3251) probiotics as an alternative strategy to combat antibiotic resistance associated with S. Typhimurium infection. In this investigation, twenty-four BALB/c mice were assigned to four groups: a non-infected, non-treated group (CNG); an infected, non-treated group (CPG); a group fed with L. acidophilus but not infected (LAG); and a group fed with L. acidophilus and challenged with Salmonella (LAST). The results revealed a reduction in Salmonella levels in the feces of mice, along with restored weight and improved overall health in the LAST compared to the CPG. The feeding of L. acidophilus was found to downregulate pro-inflammatory cytokine mRNA induced by Salmonella while upregulating anti-inflammatory cytokines. Additionally, it influenced the expression of mRNA transcript, encoding tight junction protein, oxidative stress-induced enzymes, and apoptosis-related mRNA expression. Furthermore, the LEfSe analysis demonstrated a significant shift in the abundance of critical commensal genera in the LAST, essential for maintaining gut homeostasis, metabolic reactions, anti-inflammatory responses, and butyrate production. Transcriptomic analysis revealed 2173 upregulated and 506 downregulated differentially expressed genes (DEGs) in the LAST vs. the CPG. Functional analysis of these DEGs highlighted their involvement in immunity, metabolism, and cellular development. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis indicated their role in tumor necrosis factor (TNF), mitogen-activated protein kinase (MAPK), chemokine, Forkhead box O (FOXO), and transforming growth factor (TGF-ß) signaling pathway. Moreover, the fecal metabolomic analysis identified 929 differential metabolites, with enrichment observed in valine, leucine, isoleucine, taurine, glycine, and other metabolites. These findings suggest that supplementation with L. acidophilus promotes the growth of beneficial commensal genera while mitigating Salmonella-induced intestinal disruption by modulating immunity, gut homeostasis, gut barrier integrity, and metabolism.

Interfacial engineering of POM-stabilized Ni quantum dots on porous titanium mesh for high-rate and stable alkaline hydrogen production.

The development of low-cost, high-efficiency, and stable electrocatalysts for the alkaline hydrogen evolution reaction (HER) is a key challenge because the alkaline HER kinetics is slowed by an additional water dissociation step. Herein, we report an interfacial engineering strategy for polyoxometalate (POM)-stabilized nickel (Ni) quantum dots decorated on the surface of porous titanium mesh (POMs-Ni@PTM) for high-rate and stable alkaline hydrogen production. Benefiting from the strong interfacial interactions among POMs, Ni atoms, and PTM substrates, as well as unique POM-Ni quantum dot structures, the optimized POMs-Ni@PTM electrocatalyst exhibits a remarkable alkaline HER performance with an overpotential (η10) of 30.1 mV to reach a current density of 10 mA cm-2, which is much better than those of bare Ni decorated porous titanium mesh (Ni@PTM) (η10 = 171.1 mV) and POM decorated porous titanium mesh (POMs@PTM) electrocatalysts (η10 = 493.6 mV), comparable to that of the commercial 20 wt% platinum/carbon (20% Pt/C) electrocatalyst (η10 = 20 mV). Moreover, the optimized POMs-Ni@PTM electrocatalyst demonstrates excellent stability under continuous alkaline water-splitting at a current density of ∼100 mA cm-2 for 100 h, demonstrating great potential for its practical application.

A State-of-the-Art Review of Organic Polymer Modifiers for Slope Eco-Engineering.

In slope ecological restoration projects, reinforcing soil and promoting vegetation growth are essential measures. Guest soil spraying technology can be used to backfill modified soil and vegetation seeds onto the slope surface, resulting in successful ecological restoration. The use of organic polymer modifiers to reinforce soil has several benefits, such as high strength, effective results, and low pollution levels. Organic polymer soil modifiers can be divided into two categories: synthetic polymer modifiers and biopolymer modifiers. This paper provides a thorough review of the properties and interaction mechanisms of two types of polymer modifiers in soil consolidation. The properties of organic polymer modifiers make them applicable in soil and vegetation engineering on slopes. These modifiers can enhance soil mechanics, infiltration, and erosion resistance and promote vegetation growth. Therefore, the suitability of organic polymer modifiers for soil and vegetation engineering on slopes is demonstrated by their properties and potential for improvement in key areas. Furthermore, challenges and future prospects for slope protection technology using organic polymer modifiers are suggested.

Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease.

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

Measuring the suicidal mind: The 'open source' Suicidality Scale, for adolescents and adults.

Clinicians are expected to provide accurate and useful mental health assessments, sometimes in emergency settings. The most urgent challenge may be in calculating suicide risk. Unfortunately, existing instruments often fail to meet requirements. To address this situation, we used a sustainable scale development approach to create a publicly available Suicidality Scale (SS). Following a critical review of current measures, community input, and panel discussions, an international item pool survey included 5,115 English-speaking participants aged 13-82 years. Revisions were tested with two follow-up cross-sectional surveys (Ns = 814 and 626). Pool items and SS versions were critically examined through item response theory, hierarchical cluster, factor and bifactor analyses, resulting in a unidimensional eight-item scale. Psychometric properties were high (loadings > .77; discrimination > 2.2; test-retest r = .87; internal consistency, ω = .96). Invariance checks were satisfied for age, gender, ethnicity, rural/urban residence, first language, self-reported psychiatric diagnosis and suicide attempt history. The SS showed stronger psychometric properties, and significant differences in bivariate associations with depressive symptoms, compared with included suicide measures. The 'open source' Suicidality Scale represents a significant step forward in accurate assessment for people aged 13+, and diverse populations. This study provides an example of sustainable scale development utilizing community input, emphasis on strong psychometric evidence from diverse samples, and a free-to-use license allowing instrument revisions. These methods can be used to develop a wide variety of psychosocial instruments that can benefit clinicians, researchers, and the public.

Laboratory Investigation of the Low-Temperature Crack Resistance of Wood Tar-Based Rejuvenated Asphalt Mixture Based on the Semi-Circular Bend and Trabecular Bending Test.

Low-temperature crack resistance is the core issue affecting the promotion of rejuvenated asphalt, but most current studies do not consider the creep relaxation characteristics of rejuvenated asphalt mixture at low temperatures, which is inconsistent with the actual situation. To explore the low-temperature crack resistance of a wood tar-based rejuvenated asphalt mixture, we observed the low-temperature crack resistance of styrene butadiene styrene (SBS) modified asphalt, wood tar-based rejuvenated asphalt, and RA-102 rejuvenated asphalt and their mixtures using laboratory tests. Our results showed that the low temperature crack resistance of the wood tar-based rejuvenated asphalt mixture was better than that of the RA-102 rejuvenated asphalt mixture, but slightly worse than that of the original SBS asphalt mixture. After the synergistic action of wood tar and biomass fiber, wood tar can be fully mixed into the new asphalt, effectively alleviating the bonding failure between asphalt and aggregate and improving the stiffness of the mixture, so that the toughness and crack resistance of rejuvenated asphalt mixture at low temperatures are evidently improved. Wood tar-based rejuvenated asphalt mixture has a good creep deformation ability at low temperatures. The established creep damage model can better describe the flexural creep performance of rejuvenated asphalt mixtures at low temperatures, and can be used to infer the deformation characteristics at other low temperatures.

Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment.

BACKGROUND: In Alzheimer's Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. RESULTS: DGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. CONCLUSION: Overall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI.

Study on the Durability of Acid Rain Erosion-Resistant Asphalt Mixtures.

Acid erosion can accelerate the process of early damage of asphalt pavement and decrease the durability of asphalt pavement. However, there are limited research results for asphalt mixtures that can resist acid rain erosion. To systematically evaluate the impact and action law of acid rain erosion on the durability of asphalt mixtures, three gradation schemes were used: periodic dry-wet cycle immersion test, contact angle test and road performance test. The acid rain erosion resistance of epoxy asphalt mixture, SBS-modified asphalt mixture and 70# matrix asphalt mixture were tested from three aspects of anti-aging performance, freeze-thaw cycle performance and fatigue performance. The results show that the erosion of acid rain can significantly decrease the adhesion between asphalt and aggregate, and affects the road performance of the asphalt mixture. Acid rain erosion can significantly decrease the mechanical properties, adhesion and durability of asphalt mixtures. Epoxy asphalt has better physical properties, adhesion and acid rain erosion resistance than 70# matrix asphalt and SBS-modified asphalt. Epoxy asphalt has excellent adhesion due to its polar group, high cohesion and thermosetting resin with low shrinkage, which can effectively resist moisture erosion, spalling and temperature stress cracking, thereby effectively resisting the erosion of acid rain. Epoxy asphalt mixture has the strongest acid rain erosion resistance, which can be further enhanced when used together with waste rubber powder and modified bamboo fiber. On the whole, asphalt mixture with high-density structure and thicker asphalt film can effectively resist acid rain erosion. The durability of asphalt concrete (AC)-type gradation mixture and stone mastic asphalt (SMA)-type gradation mixture are equivalent, and both are superior to open-graded friction courses (OGFC)-type gradation mixture. The gradation of asphalt mixtures and the type of asphalt binder have great influence on their acid rain erosion resistance and durability. In order to realize the directional control of the acid rain erosion resistance and durability of different asphalt mixtures, a multi-parameter comprehensive assessment indicator system between the type and property of asphalt, the gradation of asphalt mixture, and the acid rain resistance and durability of the mixture need to be established in the future.

Multi-task learning based structured sparse canonical correlation analysis for brain imaging genetics.

The advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data. Although the sparse canonical correlation analysis is a powerful bi-multivariate association analysis technique for feature selection, we are still facing major challenges in integrating multi-modal imaging genetic data and yielding biologically meaningful interpretation of imaging genetic findings. In this study, we propose a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) to deliver interpretable results and improve integration in imaging genetics studies. We perform comparative studies with state-of-the-art competing methods on both simulation and real imaging genetic data. On the simulation data, our proposed model has achieved the best performance in terms of canonical correlation coefficients, estimation accuracy, and feature selection accuracy. On the real imaging genetic data, our proposed model has revealed promising features of single-nucleotide polymorphisms and brain regions related to sleep. The identified features can be used to improve clinical score prediction using promising imaging genetic biomarkers. An interesting future direction is to apply our model to additional neurological or psychiatric cohorts such as patients with Alzheimer's or Parkinson's disease to demonstrate the generalizability of our method.

Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer's Disease.

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware.

Artificial neural networks (ANNs), like convolutional neural networks (CNNs), have achieved the state-of-the-art results for many machine learning tasks. However, inference with large-scale full-precision CNNs must cause substantial energy consumption and memory occupation, which seriously hinders their deployment on mobile and embedded systems. Highly inspired from biological brain, spiking neural networks (SNNs) are emerging as new solutions because of natural superiority in brain-like learning and great energy efficiency with event-driven communication and computation. Nevertheless, training a deep SNN remains a main challenge and there is usually a big accuracy gap between ANNs and SNNs. In this paper, we introduce a hardware-friendly conversion algorithm called "scatter-and-gather" to convert quantized ANNs to lossless SNNs, where neurons are connected with ternary {-1,0,1} synaptic weights. Each spiking neuron is stateless and more like original McCulloch and Pitts model, because it fires at most one spike and need be reset at each time step. Furthermore, we develop an incremental mapping framework to demonstrate efficient network deployments on a reconfigurable neuromorphic chip. Experimental results show our spiking LeNet on MNIST and VGG-Net on CIFAR-10 datasetobtain 99.37% and 91.91% classification accuracy, respectively. Besides, the presented mapping algorithm manages network deployment on our neuromorphic chip with maximum resource efficiency and excellent flexibility. Our four-spike LeNet and VGG-Net on chip can achieve respective real-time inference speed of 0.38 ms/image, 3.24 ms/image, and an average power consumption of 0.28 mJ/image and 2.3 mJ/image at 0.9 V, 252 MHz, which is nearly two orders of magnitude more efficient than traditional GPUs.

Orientation selectivity enhances context generalization and generative predictive coding in the hippocampus.

We learn and remember multiple new experiences throughout the day. The neural principles enabling continuous rapid learning and formation of distinct representations of numerous sequential experiences without major interference are not understood. To understand this process, here we interrogated ensembles of hippocampal place cells as rats explored 15 novel linear environments interleaved with sleep sessions over continuous 16 h periods. Remarkably, we found that a population of place cells were selective to environment orientation and topology. This orientation selectivity property biased the network-level discrimination and re/mapping between multiple environments. Novel environmental representations emerged rapidly as more generic, but repeated experience within the environments subsequently enhanced their discriminability. Generalization of prior experience with different environments consequently improved network predictability of future novel environmental representations via strengthened generative predictive codes. These coding schemes reveal a high-capacity, high-efficiency neuronal framework for rapid representation of numerous sequential experiences with optimal discrimination-generalization balance and reduced interference.

Study on the Durability of Bamboo Fiber Asphalt Mixture.

To evaluate the durability of bamboo fiber asphalt mixture using four gradation schemes, the durability of the bamboo fiber asphalt mixture is studied considering three aspects: ageing durability, freeze-thaw cycle durability and fatigue durability through the Marshall test, indoor ageing test, uniaxial compression test, low-temperature bending test, immersion Marshall test, freeze-thaw splitting test and four-point bending fatigue test. Nonfiber asphalt mixture and lignin fiber asphalt mixture were used as control groups. The results show that the addition of plant fiber can effectively improve the durability of asphalt mixture. Bamboo fiber modified asphalt mastic has good ductility and adhesion due to its rough surface and good oil absorption performance. Bamboo fiber asphalt mixture has better and more stable low-temperature ageing durability and moisture ageing durability than lignin fiber asphalt mixture, but its mechanical property is weaker than the latter. The improvement effect of the two fibers on the freeze-thaw cycle durability of asphalt mixture is basically the same. Bamboo fiber can improve the flexibility of the mixture and delay the development of cracks so that the mixture has good fatigue durability. The smaller the void ratio, the thicker the asphalt film, and the denser the structure of the mixture, the better the durability. The durability of the stone mastic asphalt (SMA) gradation mixture is better than that of asphalt concrete (AC) gradation. The material composition and aggregate gradation of plant fiber asphalt mixture have a great influence on its durability. In the future, it is necessary to establish a multiparameter comprehensive evaluation index system among fiber type and properties, mixture gradation and durability so as to realize the directional regulation of the durability of different fiber asphalt mixtures. Bamboo fiber is a reliable substitute for lignin fiber, and further research on improving its surface properties and dispersion uniformity can be carried out in the future.

A structural enriched functional network: An application to predict brain cognitive performance.

The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models.

Multi-Task Sparse Canonical Correlation Analysis with Application to Multi-Modal Brain Imaging Genetics.

Brain imaging genetics studies the genetic basis of brain structures and functionalities via integrating genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). In this area, both multi-task learning (MTL) and sparse canonical correlation analysis (SCCA) methods are widely used since they are superior to those independent and pairwise univariate analysis. MTL methods generally incorporate a few of QTs and could not select features from multiple QTs; while SCCA methods typically employ one modality of QTs to study its association with SNPs. Both MTL and SCCA are computational expensive as the number of SNPs increases. In this paper, we propose a novel multi-task SCCA (MTSCCA) method to identify bi-multivariate associations between SNPs and multi-modal imaging QTs. MTSCCA could make use of the complementary information carried by different imaging modalities. MTSCCA enforces sparsity at the group level via the G2,1-norm, and jointly selects features across multiple tasks for SNPs and QTs via the l2,1-norm. A fast optimization algorithm is proposed using the grouping information of SNPs. Compared with conventional SCCA methods, MTSCCA obtains better correlation coefficients and canonical weights patterns. In addition, MTSCCA runs very fast and easy-to-implement, indicating its potential power in genome-wide brain-wide imaging genetics.

Accelerated age-related decline in hippocampal neurogenesis in mice with noise-induced hearing loss is associated with hippocampal microglial degeneration.

Large-scale epidemiological surveys suggest that hearing loss (HL) is a significant risk factor for dementia. We previously showed that noise-induced HL (NIHL) impairs hippocampal cognitive function and decreases hippocampal neurogenesis and neuronal complexity, suggesting a causal role of HL in dementia. To further investigate the influence of acquired peripheral HL on hippocampal neurogenesis with the aging process as well as the underlying mechanism, we produced NIHL in male CBA/J mice and assessed hippocampal neurogenesis and microglial morphology in the auditory brain and hippocampus at 4 days post-noise exposure (DPN) or 1, 3, 6, or 12 months post-noise exposure (MPN) by immunofluorescence labeling. We found that the age-related decline in hippocampal neurogenesis was accelerated in mice with NIHL. Furthermore, in mice with NIHL, prolonged microglial activation occurred from 1 MPN to 12 MPN across multiple auditory nuclei, while aggravated microglial deterioration occurred in the hippocampus and correlated with the age-related decline in hippocampal neurogenesis. These results suggest that acquired peripheral HL accelerates the age-related decline in hippocampal neurogenesis and that hippocampal microglial degeneration may contribute to the development of neurodegeneration following acquired peripheral HL.

Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method.

Brain imaging genetics becomes more and more important in brain science, which integrates genetic variations and brain structures or functions to study the genetic basis of brain disorders. The multi-modal imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary information. Unfortunately, we do not know the extent to which the phenotypic variance is shared among multiple imaging modalities, which further might trace back to the complex genetic mechanism. In this paper, we propose a novel dirty multi-task sparse canonical correlation analysis (SCCA) to study imaging genetic problems with multi-modal brain imaging quantitative traits (QTs) involved. The proposed method takes advantages of the multi-task learning and parameter decomposition. It can not only identify the shared imaging QTs and genetic loci across multiple modalities, but also identify the modality-specific imaging QTs and genetic loci, exhibiting a flexible capability of identifying complex multi-SNP-multi-QT associations. Using the state-of-the-art multi-view SCCA and multi-task SCCA, the proposed method shows better or comparable canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. In addition, the identified modality-consistent biomarkers, as well as the modality-specific biomarkers, provide meaningful and interesting information, demonstrating the dirty multi-task SCCA could be a powerful alternative method in multi-modal brain imaging genetics.

Identifying diagnosis-specific genotype-phenotype associations via joint multitask sparse canonical correlation analysis and classification.

MOTIVATION: Brain imaging genetics studies the complex associations between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). The neurodegenerative disorders usually exhibit the diversity and heterogeneity, originating from which different diagnostic groups might carry distinct imaging QTs, SNPs and their interactions. Sparse canonical correlation analysis (SCCA) is widely used to identify bi-multivariate genotype-phenotype associations. However, most existing SCCA methods are unsupervised, leading to an inability to identify diagnosis-specific genotype-phenotype associations. RESULTS: In this article, we propose a new joint multitask learning method, named MT-SCCALR, which absorbs the merits of both SCCA and logistic regression. MT-SCCALR learns genotype-phenotype associations of multiple tasks jointly, with each task focusing on identifying one diagnosis-specific genotype-phenotype pattern. Meanwhile, MT-SCCALR cannot only select relevant SNPs and imaging QTs for each diagnostic group alone, but also allows the selection of those shared by multiple diagnostic groups. We derive an efficient optimization algorithm whose convergence to a local optimum is guaranteed. Compared with two state-of-the-art methods, MT-SCCALR yields better or similar canonical correlation coefficients and classification performances. In addition, it owns much better discriminative canonical weight patterns of great interest than competitors. This demonstrates the power and capability of MTSCCAR in identifying diagnostically heterogeneous genotype-phenotype patterns, which would be helpful to understand the pathophysiology of brain disorders. AVAILABILITY AND IMPLEMENTATION: The software is publicly available at https://github.com/dulei323/MTSCCALR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Preparation and Properties of Wood Tar-based Rejuvenated Asphalt.

In order to explore the applicability of the rejuvenated asphalt with wood tar as the main raw material, the orthogonal test was used to determine the optimal ratio of wood tar-based rejuvenator. The physical properties, rheological properties and components of matrix asphalt, aged asphalt, wood tar-based rejuvenated asphalt and commercial RA-102# rejuvenated asphalt were tested and compared. The results show that the optimal ratio of wood tar-based rejuvenator is 15wt% wood tar, 0.3wt% biomass fiber, 5wt% plasticizer, 0.3wt% compatibilizer, and 1wt% stabilizer of the mass of aged asphalt. Wood tar-based rejuvenator can restore the physical properties of the aged asphalt to meet the specification requirements. The synergistic effect of biomass fiber and plasticizer make the wood tar-based rejuvenated asphalt has good resistance to accumulated permanent deformation, but its low-temperature cracking resistance needs to be further improved. During the rejuvenation process of aged asphalt, the colloidal state changes from gel-state to sol-state, characterizing that the viscosity of asphalt decreased and the fluidity increased. Wood tar-based rejuvenator can react with aged asphalt to weaken the vibration strength of carbonyl and sulfoxide groups, so as to realize the recovery of service performance. Wood tar-based rejuvenator has better environmental protection and applicability, which is worthy of further study and promotion.

Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured SCCA approach.

Brain imaging genetics becomes an important research topic since it can reveal complex associations between genetic factors and the structures or functions of the human brain. Sparse canonical correlation analysis (SCCA) is a popular bi-multivariate association identification method. To mine the complex genetic basis of brain imaging phenotypes, there arise many SCCA methods with a variety of norms for incorporating different structures of interest. They often use the group lasso penalty, the fused lasso or the graph/network guided fused lasso ones. However, the group lasso methods have limited capability because of the incomplete or unavailable prior knowledge in real applications. The fused lasso and graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. In this paper, we introduce two new penalties to improve the fused lasso and the graph/network guided lasso penalties in structured sparse learning. We impose both penalties to the SCCA model and propose an optimization algorithm to solve it. The proposed SCCA method has a strong upper bound of grouping effects for both positively and negatively highly correlated variables. We show that, on both synthetic and real neuroimaging genetics data, the proposed SCCA method performs better than or equally to the conventional methods using fused lasso or graph/network guided fused lasso. In particular, the proposed method identifies higher canonical correlation coefficients and captures clearer canonical weight patterns, demonstrating its promising capability in revealing biologically meaningful imaging genetic associations.