DNA methylation haplotype block signatures responding to Staphylococcus aureus subclinical mastitis and association with production and health traits.

BACKGROUND: DNA methylation has been documented to play vital roles in diseases and biological processes. In bovine, little is known about the regulatory roles of DNA methylation alterations on production and health traits, including mastitis. RESULTS: Here, we employed whole-genome DNA methylation sequencing to profile the DNA methylation patterns of milk somatic cells from sixteen cows with naturally occurring Staphylococcus aureus (S. aureus) subclinical mastitis and ten healthy control cows. We observed abundant DNA methylation alterations, including 3,356,456 differentially methylated cytosines and 153,783 differential methylation haplotype blocks (dMHBs). The DNA methylation in regulatory regions, including promoters, first exons and first introns, showed global significant negative correlations with gene expression status. We identified 6435 dMHBs located in the regulatory regions of differentially expressed genes and significantly correlated with their corresponding genes, revealing their potential effects on transcriptional activities. Genes harboring DNA methylation alterations were significantly enriched in multiple immune- and disease-related pathways, suggesting the involvement of DNA methylation in regulating host responses to S. aureus subclinical mastitis. In addition, we found nine discriminant signatures (differentiates cows with S. aureus subclinical mastitis from healthy cows) representing the majority of the DNA methylation variations related to S. aureus subclinical mastitis. Validation of seven dMHBs in 200 cows indicated significant associations with mammary gland health (SCC and SCS) and milk production performance (milk yield). CONCLUSIONS: In conclusion, our findings revealed abundant DNA methylation alterations in milk somatic cells that may be involved in regulating mammary gland defense against S. aureus infection. Particularly noteworthy is the identification of seven dMHBs showing significant associations with mammary gland health, underscoring their potential as promising epigenetic biomarkers. Overall, our findings on DNA methylation alterations offer novel insights into the regulatory mechanisms of bovine subclinical mastitis, providing further avenues for the development of effective control measures.

Mimicking non-ideal instrument behavior for hologram processing using neural style translation.

Holographic cloud probes provide unprecedented information on cloud particle density, size and position. Each laser shot captures particles within a large volume, where images can be computationally refocused to determine particle size and location. However, processing these holograms with standard methods or machine learning (ML) models requires considerable computational resources, time and occasional human intervention. ML models are trained on simulated holograms obtained from the physical model of the probe since real holograms have no absolute truth labels. Using another processing method to produce labels would be subject to errors that the ML model would subsequently inherit. Models perform well on real holograms only when image corruption is performed on the simulated images during training, thereby mimicking non-ideal conditions in the actual probe. Optimizing image corruption requires a cumbersome manual labeling effort. Here we demonstrate the application of the neural style translation approach to the simulated holograms. With a pre-trained convolutional neural network, the simulated holograms are "stylized" to resemble the real ones obtained from the probe, while at the same time preserving the simulated image "content" (e.g. the particle locations and sizes). With an ML model trained to predict particle locations and shapes on the stylized data sets, we observed comparable performance on both simulated and real holograms, obviating the need to perform manual labeling. The described approach is not specific to holograms and could be applied in other domains for capturing noise and imperfections in observational instruments to make simulated data more like real world observations.

Gene co-expression in response to Staphylococcus aureus infection reveals networks of genes with specific functions during bovine subclinical mastitis.

Staphylococcus aureus is one of the most prevalent contagious bacterial pathogen of bovine mastitis. The subclinical mastitis it causes has long-term economic implications and it is difficult to control. To further understanding of the genetic basis of mammary gland defense against S. aureus infection, the transcriptomes of milk somatic cells from 15 cows with persistent natural S. aureus infection (S. aureus-positive, SAP) and 10 healthy control cows (HC) were studied by deep RNA-sequencing technology. Comparing the transcriptomes of SAP to HC group revealed 4,077 differentially expressed genes (DEG; 1,616 up- and 2,461 downregulated). Functional annotation indicated enrichment of DEG in 94 Gene Ontology (GO) and 47 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Terms related to the immune response and disease processes were mostly enriched for by upregulated DEG, whereas biological process terms related to cell adhesion, cell movement and localization, and tissue development were mostly enriched for by downregulated DEG. Weighted gene co-expression network analysis grouped DEG into 7 modules, the most important module (colored turquoise by software and here referred to as Turquoise module) was positively significantly correlated with S. aureus subclinical mastitis. The 1,546 genes in the Turquoise module were significantly enriched in 48 GO terms and 72 KEGG pathways, with 80% of them being disease- and immune-related terms [e.g., immune system process (GO:0002376), cytokine-cytokine receptor interaction (bta04060) and S. aureus infection (bta05150)]. Some DEG such as IFNG, IL18, IL1B, NFKB1, CXCL8, and IL12B were enriched in immune and disease pathways suggesting their possible involvement in the regulation of the host response to S. aureus infection. Four modules (Yellow, Brown, Blue, and Red) were negatively correlated (significantly) with S. aureus subclinical mastitis, and were enriched in functional annotations involved in the regulation of cell migration, cell communication, metabolic process, and blood circulatory system development, respectively. Application of sparse partial least squares discriminant analysis to genes of the Turquoise module identified 5 genes (NR2F6, PDLIM5, RAB11FIP5, ACOT4, and TMEM53) capable of explaining the majority of the differences in the expression patterns between SAP and HC cows. In conclusion, this study has furthered understanding of the genetic changes in the mammary gland and the molecular mechanisms underlying S. aureus mastitis, as well as revealed a list of candidate discriminant genes with potential regulatory roles in response to S. aureus infection.

Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences.

Demands to manage the risks of artificial intelligence (AI) are growing. These demands and the government standards arising from them both call for trustworthy AI. In response, we adopt a convergent approach to review, evaluate, and synthesize research on the trust and trustworthiness of AI in the environmental sciences and propose a research agenda. Evidential and conceptual histories of research on trust and trustworthiness reveal persisting ambiguities and measurement shortcomings related to inconsistent attention to the contextual and social dependencies and dynamics of trust. Potentially underappreciated in the development of trustworthy AI for environmental sciences is the importance of engaging AI users and other stakeholders, which human-AI teaming perspectives on AI development similarly underscore. Co-development strategies may also help reconcile efforts to develop performance-based trustworthiness standards with dynamic and contextual notions of trust. We illustrate the importance of these themes with applied examples and show how insights from research on trust and the communication of risk and uncertainty can help advance the understanding of trust and trustworthiness of AI in the environmental sciences.

Genome-Wide DNA Methylation and Transcriptome Integration Associates DNA Methylation Changes with Bovine Subclinical Mastitis Caused by Staphylococcus chromogenes.

Staphylococcus chromogenes (SC) is a common coagulase-negative staphylococcus described as an emerging mastitis pathogen and commonly found in dairy farms. This study investigated the potential involvement of DNA methylation in subclinical mastitis caused by SC. The whole-genome DNA methylation patterns and transcriptome profiles of milk somatic cells from four cows with naturally occurring SC subclinical mastitis (SCM) and four healthy cows were characterized by next-generation sequencing, bioinformatics, and integration analyses. Comparisons revealed abundant DNA methylation changes related to SCM, including differentially methylated cytosine sites (DMCs, n = 2,163,976), regions (DMRs, n = 58,965), and methylation haplotype blocks (dMHBs, n = 53,098). Integration of methylome and transcriptome data indicated a negative global association between DNA methylation at regulatory regions (promoters, first exons, and first introns) and gene expression. A total of 1486 genes with significant changes in the methylation levels of their regulatory regions and corresponding gene expression showed significant enrichment in biological processes and pathways related to immune functions. Sixteen dMHBs were identified as candidate discriminant signatures, and validation of two signatures in more samples further revealed the association of dMHBs with mammary gland health and production. This study demonstrated abundant DNA methylation changes with possible involvement in regulating host responses and potential as biomarkers for SCM.

Proteomics Profiling of Stool Samples from Preterm Neonates with SWATH/DIA Mass Spectrometry for Predicting Necrotizing Enterocolitis.

Necrotizing enterocolitis (NEC) is a life-threatening condition for premature infants in neonatal intensive care units. Finding indicators that can predict NEC development before symptoms appear would provide more time to apply targeted interventions. In this study, stools from 132 very-low-birth-weight (VLBW) infants were collected daily in the context of a multi-center prospective study aimed at investigating the potential of fecal biomarkers for NEC prediction using proteomics technology. Eight of the VLBW infants received a stage-3 NEC diagnosis. Stools collected from the NEC infants up to 10 days before their diagnosis were available for seven of them. Their samples were matched with those from seven pairs of non-NEC controls. The samples were processed for liquid chromatography-tandem mass spectrometry analysis using SWATH/DIA acquisition and cross-compatible proteomic software to perform label-free quantification. ROC curve and principal component analyses were used to explore discriminating information and to evaluate candidate protein markers. A series of 36 proteins showed the most efficient capacity with a signature that predicted all seven NEC infants at least a week in advance. Overall, our study demonstrates that multiplexed proteomic signature detection constitutes a promising approach for the early detection of NEC development in premature infants.

Multi-omics integration identifies regulatory factors underlying bovine subclinical mastitis.

BACKGROUND: Mastitis caused by multiple factors remains one of the most common and costly disease of the dairy industry. Multi-omics approaches enable the comprehensive investigation of the complex interactions between multiple layers of information to provide a more holistic view of disease pathogenesis. Therefore, this study investigated the genomic and epigenomic signatures and the possible regulatory mechanisms underlying subclinical mastitis by integrating RNA sequencing data (mRNA and lncRNA), small RNA sequencing data (miRNA) and DNA methylation sequencing data of milk somatic cells from 10 healthy cows and 20 cows with naturally occurring subclinical mastitis caused by Staphylococcus aureus or Staphylococcus chromogenes. RESULTS: Functional investigation of the data sets through gene set analysis uncovered 3458 biological process GO terms and 170 KEGG pathways with altered activities during subclinical mastitis, provided further insights into subclinical mastitis and revealed the involvement of multi-omics signatures in the altered immune responses and impaired mammary gland productivity during subclinical mastitis. The abundant genomic and epigenomic signatures with significant alterations related to subclinical mastitis were observed, including 30,846, 2552, 1276 and 57 differential methylation haplotype blocks (dMHBs), differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs), respectively. Next, 5 factors presenting the principal variation of differential multi-omics signatures were identified. The important roles of Factor 1 (DEG, DEM and DEL) and Factor 2 (dMHB and DEM), in the regulation of immune defense and impaired mammary gland functions during subclinical mastitis were revealed. Each of the omics within Factors 1 and 2 explained about 20% of the source of variation in subclinical mastitis. Also, networks of important functional gene sets with the involvement of multi-omics signatures were demonstrated, which contributed to a comprehensive view of the possible regulatory mechanisms underlying subclinical mastitis. Furthermore, multi-omics integration enabled the association of the epigenomic regulatory factors (dMHBs, DELs and DEMs) of altered genes in important pathways, such as 'Staphylococcus aureus infection pathway' and 'natural killer cell mediated cytotoxicity pathway', etc., which provides further insights into mastitis regulatory mechanisms. Moreover, few multi-omics signatures (14 dMHBs, 25 DEGs, 18 DELs and 5 DEMs) were identified as candidate discriminant signatures with capacity of distinguishing subclinical mastitis cows from healthy cows. CONCLUSION: The integration of genomic and epigenomic data by multi-omics approaches in this study provided a better understanding of the molecular mechanisms underlying subclinical mastitis and identified multi-omics candidate discriminant signatures for subclinical mastitis, which may ultimately lead to the development of more effective mastitis control and management strategies.

Gestational age at birth influences protein and RNA content in human milk extracellular vesicles.

Human milk extracellular vesicles (HM EVs) are proposed to protect against disease development in infants. This protection could in part be facilitated by the bioactive EV cargo of proteins and RNA. Notably, mothers birth infants of different gestational ages with unique needs, wherein the EV cargo of HM may diverge. We collected HM from lactating mothers within two weeks of a term or preterm birth. Following purification of EVs, proteins and mRNA were extracted for proteomics and sequencing analyses, respectively. Over 2000 protein groups were identified, and over 8000 genes were quantified. The total number of proteins and mRNA did not differ significantly between the two conditions, while functional bioinformatics of differentially expressed cargo indicated enrichment in immunoregulatory cargo for preterm HM EVs. In term HM EVs, significantly upregulated cargo was enriched in metabolism-related functions. Based on gene expression signatures from HM-contained single cell sequencing data, we proposed that a larger portion of preterm HM EVs are secreted by immune cells, whereas term HM EVs contain more signatures of lactocyte epithelial cells. Proposed differences in EV cargo could indicate variation in mother's milk based on infants' gestational age and provide basis for further functional characterisation.

Application of SWATH Mass Spectrometry and Machine Learning in the Diagnosis of Inflammatory Bowel Disease Based on the Stool Proteome.

Inflammatory bowel disease (IBD) flare-ups exhibit symptoms that are similar to other diseases and conditions, making diagnosis and treatment complicated. Currently, the gold standard for diagnosing and monitoring IBD is colonoscopy and biopsy, which are invasive and uncomfortable procedures, and the fecal calprotectin test, which is not sufficiently accurate. Therefore, it is necessary to develop an alternative method. In this study, our aim was to provide proof of concept for the application of Sequential Window Acquisition of All Theoretical Mass Spectra-Mass spectrometry (SWATH-MS) and machine learning to develop a non-invasive and accurate predictive model using the stool proteome to distinguish between active IBD patients and symptomatic non-IBD patients. Proteome profiles of 123 samples were obtained and data processing procedures were optimized to select an appropriate pipeline. The differentially abundant analysis identified 48 proteins. Utilizing correlation-based feature selection (Cfs), 7 proteins were selected for proceeding steps. To identify the most appropriate predictive machine learning model, five of the most popular methods, including support vector machines (SVMs), random forests, logistic regression, naive Bayes, and k-nearest neighbors (KNN), were assessed. The generated model was validated by implementing the algorithm on 45 prospective unseen datasets; the results showed a sensitivity of 96% and a specificity of 76%, indicating its performance. In conclusion, this study illustrates the effectiveness of utilizing the stool proteome obtained through SWATH-MS in accurately diagnosing active IBD via a machine learning model.

Small molecule activators of SIRT1 as therapeutics for the treatment of type 2 diabetes.

Calorie restriction extends lifespan and produces a metabolic profile desirable for treating diseases of ageing such as type 2 diabetes. SIRT1, an NAD+-dependent deacetylase, is a principal modulator of pathways downstream of calorie restriction that produce beneficial effects on glucose homeostasis and insulin sensitivity. Resveratrol, a polyphenolic SIRT1 activator, mimics the anti-ageing effects of calorie restriction in lower organisms and in mice fed a high-fat diet ameliorates insulin resistance, increases mitochondrial content, and prolongs survival. Here we describe the identification and characterization of small molecule activators of SIRT1 that are structurally unrelated to, and 1,000-fold more potent than, resveratrol. These compounds bind to the SIRT1 enzyme-peptide substrate complex at an allosteric site amino-terminal to the catalytic domain and lower the Michaelis constant for acetylated substrates. In diet-induced obese and genetically obese mice, these compounds improve insulin sensitivity, lower plasma glucose, and increase mitochondrial capacity. In Zucker fa/fa rats, hyperinsulinaemic-euglycaemic clamp studies demonstrate that SIRT1 activators improve whole-body glucose homeostasis and insulin sensitivity in adipose tissue, skeletal muscle and liver. Thus, SIRT1 activation is a promising new therapeutic approach for treating diseases of ageing such as type 2 diabetes.

Methylome and transcriptome data integration reveals potential roles of DNA methylation and candidate biomarkers of cow Streptococcus uberis subclinical mastitis.

BACKGROUND: Mastitis caused by different pathogens including Streptococcus uberis (S. uberis) is responsible for huge economic losses to the dairy industry. In order to investigate the potential genetic and epigenetic regulatory mechanisms of subclinical mastitis due to S. uberis, the DNA methylome (whole genome DNA methylation sequencing) and transcriptome (RNA sequencing) of milk somatic cells from cows with naturally occurring S. uberis subclinical mastitis and healthy control cows (n = 3/group) were studied. RESULTS: Globally, the DNA methylation levels of CpG sites were low in the promoters and first exons but high in inner exons and introns. The DNA methylation levels at the promoter, first exon and first intron regions were negatively correlated with the expression level of genes at a whole-genome-wide scale. In general, DNA methylation level was lower in S. uberis-positive group (SUG) than in the control group (CTG). A total of 174,342 differentially methylated cytosines (DMCs) (FDR < 0.05) were identified between SUG and CTG, including 132,237, 7412 and 34,693 DMCs in the context of CpG, CHG and CHH (H = A or T or C), respectively. Besides, 101,612 methylation haplotype blocks (MHBs) were identified, including 451 MHBs that were significantly different (dMHB) between the two groups. A total of 2130 differentially expressed (DE) genes (1378 with up-regulated and 752 with down-regulated expression) were found in SUG. Integration of methylome and transcriptome data with MethGET program revealed 1623 genes with significant changes in their methylation levels and/or gene expression changes (MetGDE genes, MethGET P-value < 0.001). Functional enrichment of genes harboring ≥ 15 DMCs, DE genes and MetGDE genes suggest significant involvement of DNA methylation changes in the regulation of the host immune response to S. uberis infection, especially cytokine activities. Furthermore, discriminant correlation analysis with DIABLO method identified 26 candidate biomarkers, including 6 DE genes, 15 CpG-DMCs and 5 dMHBs that discriminated between SUG and CTG. CONCLUSION: The integration of methylome and transcriptome of milk somatic cells suggests the possible involvement of DNA methylation changes in the regulation of the host immune response to subclinical mastitis due to S. uberis. The presented genetic and epigenetic biomarkers could contribute to the design of management strategies of subclinical mastitis and breeding for mastitis resistance.

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth From Dense Satellite and Sparse In Situ Observations.

The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal ( < 90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.

Integrin-linked kinase regulates migration and proliferation of human intestinal cells under a fibronectin-dependent mechanism.

Integrin-linked kinase (ILK) plays a role in integrin signaling-mediated extracellular matrix (ECM)-cell interactions and also acts as a scaffold protein in functional focal adhesion points. In the present study, we investigated the expression and roles of ILK in human intestinal epithelial cells (IECs) in vivo and in vitro. Herein, we report that ILK and its scaffold-function interacting partners, PINCH-1, alpha-parvin, and beta-parvin, are expressed according to a decreasing gradient from the bottom of the crypt (proliferative/undifferentiated) compartment to the tip of the villus (non-proliferative/differentiated) compartment, closely following the expression pattern of the ECM/basement membrane component fibronectin. The siRNA knockdown of ILK in human IECs caused a loss of PINCH-1, alpha-parvin, and beta-parvin expression, along with a significant decrease in cell proliferation via a loss of cyclin D1 and an increase in p27 and hypophosphorylated pRb expression levels. ILK knockdown severely affected cell spreading, migration, and restitution abilities, which were shown to be directly related to a decrease in fibronectin deposition. All ILK knockdown-induced defects were rescued with exogenously deposited fibronectin. Altogether, our results indicate that ILK performs crucial roles in the control of human intestinal cell and crypt-villus axis homeostasis-especially with regard to basement membrane fibronectin deposition-as well as cell proliferation, spreading, and migration.

ILK supports RhoA/ROCK-mediated contractility of human intestinal epithelial crypt cells by inducing the fibrillogenesis of endogenous soluble fibronectin during the spreading process.

BACKGROUND: Fibronectin (FN) assembly into an insoluble fibrillar matrix is a crucial step in many cell responses to extracellular matrix (ECM) properties, especially with regards to the integrin-related mechanosensitive signaling pathway. We have previously reported that the silencing of expression of integrin-linked kinase (ILK) in human intestinal epithelial crypt (HIEC) cells causes significant reductions in proliferation and spreading through concomitantly acquired impairment of soluble FN deposition. These defects in ILK-depleted cells are rescued by growth on exogenous FN. In the present study we investigated the contribution of ILK in the fibrillogenesis of FN and its relation to integrin-actin axis signaling and organization. RESULTS: We show that de novo fibrillogenesis of endogenous soluble FN is ILK-dependent. This function seemingly induces the assembly of an ECM that supports increased cytoskeletal tension and the development of a fully spread contractile cell phenotype. We observed that HIEC cell adhesion to exogenous FN or collagen-I (Col-I) is sufficient to restore fibrillogenesis of endogenous FN in ILK-depleted cells. We also found that optimal engagement of the Ras homolog gene family member A (RhoA) GTPase/Rho-associated kinase (ROCK-1, ROCK-2)/myosin light chain (MLC) pathway, actin ventral stress fiber formation, and integrin adhesion complex (IAC) maturation rely primarily upon the cell's capacity to execute FN fibrillogenesis, independent of any significant ILK input. Lastly, we confirm the integrin α5ß1 as the main integrin responsible for FN assembly, although in ILK-depleted cells αV-class integrins expression is needed to allow the rescue of FN fibrillogenesis on exogenous substrate. CONCLUSION: Our study demonstrates that ILK specifically induces the initiation of FN fibrillogenesis during cell spreading, which promotes RhoA/ROCK-dependent cell contractility and maturation of the integrin-actin axis structures. However, the fibrillogenesis process and its downstream effect on RhoA signaling, cell contractility and spreading are ILK-independent in human intestinal epithelial crypt cells.

Discovery of benzothiazole derivatives as efficacious and enterocyte-specific MTP inhibitors.

A series of triamide derivatives bearing a benzothiazole core is shown to be potent microsomal triglyceride transfer protein (MTP) inhibitors. In order to minimize liver toxicity, these compounds have been optimized to have activity only in the enterocytes and have limited systemic bioavailability. Upon oral administration, selected analogs within this series have been further demonstrated to reduce food intake along with body weight and thereby improve glucose homeostasis and insulin sensitivity in a 28-day mice diet-induced obesity (DIO) model.

Upregulation of a functional form of the beta4 integrin subunit in colorectal cancers correlates with c-Myc expression.

The integrin beta4 subunit has been shown to be involved in various aspects of cancer progression. The aim of the present work was to evaluate the expression of beta4 in primary colon cancers and to investigate the occurrence of a previously identified intestinal nonfunctional variant of beta4 (beta4ctd-) for adhesion to laminin. Immunodetection of beta4 using a panel of antibodies and RT-PCR analyses were performed on series of paired primary colon tumors and corresponding resection margins. The beta4 subunit was found to be significantly overexpressed in cancer specimens at both the protein and transcript levels. Surprisingly, beta4 levels of expression were closely correlated with those of the oncogene c-Myc in individual specimens. In vitro studies of c-Myc overexpression showed an upregulation of beta4 promoter activity. Finally, the beta4ctd- form was identified in the normal proliferative colonic cells but was found to be predominantly absent in colon cancer cells, both in situ and in vitro. We concluded that the beta4ctd- form is lost from colon cancer cells, while the level of the wild-type form of beta4, which is functional for adhesion to laminin, is increased in primary tumors in relation with the expression of c-Myc.

Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning.

Severe weather, including tornadoes, thunderstorms, wind, and hail annually cause significant loss of life and property. We are developing spatiotemporal machine learning techniques that will enable meteorologists to improve the prediction of these events by improving their understanding of the fundamental causes of the phenomena and by building skillful empirical predictive models. In this paper, we present significant enhancements of our Spatiotemporal Relational Probability Trees that enable autonomous discovery of spatiotemporal relationships as well as learning with arbitrary shapes. We focus our evaluation on two real-world case studies using our technique: predicting tornadoes in Oklahoma and predicting aircraft turbulence in the United States. We also discuss how to evaluate success for a machine learning algorithm in the severe weather domain, which will enable new methods such as ours to transfer from research to operations, provide a set of lessons learned for embedded machine learning applications, and discuss how to field our technique.

RGD-Dependent Epithelial Cell-Matrix Interactions in the Human Intestinal Crypt.

Interactions between the extracellular matrix (ECM) and integrin receptors trigger structural and functional bonds between the cell microenvironment and the cytoskeleton. Such connections are essential for adhesion structure integrity and are key players in regulating transduction of specific intracellular signals, which in turn regulate the organization of the cell microenvironment and, consequently, cell function. The RGD peptide-dependent integrins represent a key subgroup of ECM receptors involved in the maintenance of epithelial homeostasis. Here we review recent findings on RGD-dependent ECM-integrin interactions and their roles in human intestinal epithelial crypt cells.

Collagen VI is a basement membrane component that regulates epithelial cell-fibronectin interactions.

Collagen VI is a heterotrimer composed of three α chains (α1, α2, α3) widely expressed throughout various interstitial matrices. Collagen VI is also found near the basement membranes of many tissues where it serves as an anchoring meshwork. The aim of this study was to investigate the distribution and role of collagen VI at the epithelial-stromal interface in the intestine. Results showed that collagen VI is a bona fide epithelial basal lamina component and constitutes the major collagen type of epithelial origin in this organ. In vitro, collagen VI co-distributes with fibronectin. Targeted knockdown of collagen VI expression in intestinal epithelial cells was used to investigate its function. Depletion of collagen VI from the matrix led to a significant increase in cell spreading and fibrillar adhesion formation coinciding with an upregulation of fibronectin expression, deposition and organization as well as activation of myosin light chain phosphorylation by the myosin light chain kinase and Rho kinase dependent mechanisms. Plating cells deficient for collagen VI on collagen VI rescued the phenotype. Taken together, these data demonstrate that collagen VI is an important basal lamina component involved in the regulation of epithelial cell behavior most notably as a regulator of epithelial cell-fibronectin interactions.

Discovery of imidazo[1,2-b]thiazole derivatives as novel SIRT1 activators.

A series of imidazo[1,2-b]thiazole derivatives is shown to activate the NAD(+)-dependent deacetylase SIRT1, a potential new therapeutic target to treat various metabolic disorders. This series of compounds was derived from a high throughput screening hit bearing an oxazolopyridine core. Water-solubilizing groups could be installed conveniently at either the C-2 or C-3 position of the imidazo[1,2-b]thiazole ring. The SIRT1 enzyme activity could be adjusted by modifying the amide portion of these imidazo[1,2-b]thiazole derivatives. The most potent analogue within this series, namely, compound 29, has demonstrated oral antidiabetic activity in the ob/ob mouse model, the diet-induced obesity (DIO) mouse model, and the Zucker fa/fa rat model.