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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-β) 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.

Abstract 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.

The Fourier transform method is adopted to analyze random quasi-phase matched (RQPM) nonlinear frequency conversion problems in polycrystalline materials. By treating the material as random signals of effective nonlinear coefficient (deff) along the beam path, fast Fourier transform (FFT) operations directly produce the generated field amplitude versus spatial frequency mismatch (Δk/2π), which relates to all the possible RQPM nonlinear interactions. Thus, the RQPM bandwidth is intuitive from the wavelength-dependent results. More importantly, the Fourier transform treatment greatly decreases the computational amount by orders compared with the traditional step-by-step integration method. The quantitative simulation of RQPM difference frequency generation (DFG) with three variable wavelengths is realized for the first time.

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.

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.

Monte-Carlo simulations were performed on the efficiency of random quasi-phase matching (RQPM) nonlinear optical frequency conversion in polycrystalline materials to study the detailed effects of the statistical grain morphology. The second harmonic generation (SHG) process was taken for example based on random structures generated by a realistic grain growth model, and the effective nonlinear coefficient was rigorously derived by tensor transformation in randomly rotated coordinates. The effects of average grain size, standard deviation, and sphericity on SHG efficiency were figured out to conclude that the grain-size deviation plays the most important role in RQPM efficiency and the optimal grain size is smaller than the coherence length. The impacts of fundamental wavelength variation were also investigated. These results reveal how the RQPM process is affected by statistical and nonlinear optical laws, which enrich the RQPM theory and provide criteria for polycrystalline material processing in specific nonlinear applications.

Abstract Background A fundamental problem in RNA-seq data analysis is to identify genes or exons that are differentially expressed with varying experimental conditions based on the read counts. The relativeness of RNA-seq measurements makes the between-sample normalization of read counts an essential step in differential expression (DE) analysis. In most existing methods, the normalization step is performed prior to the DE analysis. Recently, Jiang and Zhan proposed a statistical method which introduces sample-specific normalization parameters into a joint model, which allows for simultaneous normalization and differential expression analysis from log-transformed RNA-seq data. Furthermore, an ℓ 0 penalty is used to yield a sparse solution which selects a subset of DE genes. The experimental conditions are restricted to be categorical in their work. Results In this paper, we generalize Jiang and Zhan’s method to handle experimental conditions that are measured in continuous variables. As a result, genes with expression levels associated with a single or multiple covariates can be detected. As the problem being high-dimensional, non-differentiable and non-convex, we develop an efficient algorithm for model fitting. Conclusions Experiments on synthetic data demonstrate that the proposed method outperforms existing methods in terms of detection accuracy when a large fraction of genes are differentially expressed in an asymmetric manner, and the performance gain becomes more substantial for larger sample sizes. We also apply our method to a real prostate cancer RNA-seq dataset to identify genes associated with pre-operative prostate-specific antigen (PSA) levels in patients.

The sustainable performance of asphalt pavement depends on the quality and mix design of road aggregates. Identifying aggregate morphology and size is a prerequisite step for material design and numerical modeling of asphalt mixtures. The paper aims to review the morphometric measurement, characteristic parameters and visualization technique of road aggregates. Types, calculation methods and advantages of aggregate morphological characteristics are highlighted. The applications of aggregate morphological features on the volumetric design, compaction processes, mechanical properties and size effect of asphalt mixtures are summarized. Although digital image processing technology has been studied for years, aggregates in the complex accumulation are still difficult to measure accurately. In the current research, the morphological parameters of aggregates remain diverse without a standard protocol. Compared to theoretical models, numerical models have more difficulties establishing irregular morphology features in the simulated specimens but provide a volume parameter closer to the real value. The future investigation of road performance under dynamic loading should account for the microscopic evolution of shape, orientation and distribution of aggregates over time.

Simulating human brain with hardware has been an attractive project for many years, since memory is one of the fundamental functions of our brains. Several memory models have been proposed up to now in order to unveil how the memory is organized in the brain. In this paper, we adopt spatio-temporal memory (STM) model, in which both associative memory and episodic memory are analyzed and emulated, as the reference of our hardware network architecture. Furthermore, some reasonable adaptations are carried out for the hardware implementation. We finally implement this memory model on FPGA, and additional experiments are performed to fine tune the parameters of our network deployed on FPGA.