Modulation of the peripheral blood transcriptome by the ingestion of probiotic yoghurt and acidified milk in healthy, young men

The metabolic health benefits of fermented milks have already been investigated using clinical biomarkers but the development of transcriptomic analytics in blood offers an alternative approach that may help to sensitively characterise such effects. We aimed to assess the effects of probiotic yoghurt intake, compared to non-fermented, acidified milk intake, on clinical biomarkers and gene expression in peripheral blood. To this end, a randomised, crossover study was conducted in fourteen healthy, young men to test the two dairy products. For a subset of seven subjects, RNA sequencing was used to measure gene expression in blood collected during postprandial tests and after two weeks daily intake. We found that the postprandial response in insulin was different for probiotic yoghurt as compared to that of acidified milk. Moreover changes in several clinical biomarkers were associated with changes in the expression of genes representing six metabolic genesets. Assessment of the postprandial effects of each dairy product on gene expression by geneset enrichment analysis revealed significant, similar modulation of inflammatory and glycolytic genes after both probiotic yoghurt and acidified milk intake, although distinct kinetic characteristics of the modulation differentiated the dairy products. The aryl hydrocarbon receptor was a major contributor to the down-regulation of the inflammatory genesets and was also positively associated with changes in circulating insulin at 2h after yoghurt intake (p = 0.05). Daily intake of the dairy products showed little effect on the fasting blood transcriptome. Probiotic yoghurt and acidified milk appear to affect similar gene pathways during the postprandial phase but differences in the timing and the extent of this modulation may lead to different physiological consequences. The functional relevance of these differences in gene expression is supported by their associations with circulating biomarkers

Analytical model for the calculation of lateral velocity distributions in potential cross-sections

[EN] The hydraulic modeling of water depth and flow velocities in open channel flows that were fitted by power-law cross-section stand out for their versatility, allowing their use in numerous practical applications, both in natural and artificial channels. The determination of the hydraulic variables of depth and average velocity has been widely studied in potential cross-sections; however, the variation seen in these variables along the cross-section was not found in the literature. Knowledge of this variation allows the development of studies (e.g. to know the approximate damage in different areas of the cross-section, to analyse sediment transport, or other applications in river hydraulics). This paper presents a methodology which allows calculation of the hydraulic variables in any area of a power-law cross-section. The methodology is applied to symmetrical cross-sections, comparing its generated results with the obtained values by different computational hydraulic codes, which are thoroughly accepted by scientific community, such as CES, HEC-RAS and IBER. The obtained predictions of hydraulic parameters (using the explicit formulation described in this research) present very low errors when compared with results of other models, with great computational cost. These errors reach a root mean square error (RMSE) of 0.13 and 0.05 in the determination of velocities' lateral distribution and the ratio between velocity and average velocity. These values indicate a very successful validation for the analysed symmetrical sections.[ES] La modelización hidráulica de calados y velocidades de flujo, en cauces con secciones que admiten una representación de tipo potencial, se destaca por su versatilidad, permitiendo su utilización en numerosas aplicaciones prácticas tanto en canales naturales como artificiales. El cálculo de las variables hidráulicas (calado y velocidad media) ha sido ampliamente estudiado para este tipo de secciones. Sin embargo, en la literatura técnica no se han encontrado estudios que muestren la variación de estas magnitudes a lo largo de la sección transversal. El conocimiento de esta variación permite desarrollar estudios (ejemplo: conocer de manera aproximada los daños en diferentes zonas de la sección, analizar el transporte de sedimentos, estudiar los procesos de erosión u otras aplicaciones en hidráulica fluvial). Presentamos una metodología que permite el cálculo de las variables hidráulicas en cualquier zona de una sección tipo potencial. La metodología es aplicada a secciones simétricas, comparando los resultados generados con los obtenidos por diferentes códigos hidráulicos computacionales ampliamente aceptados por la comunidad científica (p-e- CES, HECRAS e IBER). Las predicciones de los parámetros hidráulicos obtenidas (usando la formulación explícita descrita en este artículo) presentan errores muy bajos, en comparación con otros modelos con mayor costo computacional. Estos errores alcanzan un valor promedio para la raíz del error cuadrático medio (RMSE) en el cálculo de la distribución lateral de velocidades de 0.13 y de 0.05, en el cálculo de la relación de velocidades respecto a la velocidad media. Estos valores indican una validación muy satisfactoria para las secciones simétricas analizadas.Sánchez-Romero, F.; Pérez-Sánchez, M.; López Jiménez, PA. (2018). Modelo analítico para el cálculo de distribuciones de velocidad laterales en secciones tipo potencial-ley. RIBAGUA - Revista Iberoamericana del Agua. 5(1):29-47. doi:10.1080/23863781.2018.1442189S29475

In Vitro Phenotypic, Genomic and Proteomic Characterization of a Cytokine-Resistant Murine β-TC3 Cell Line

Type 1 diabetes mellitus (T1DM) is caused by the selective destruction of insulin-producing β-cells. This process is mediated by cells of the immune system through release of nitric oxide, free radicals and pro-inflammatory cytokines, which induce a complex network of intracellular signalling cascades, eventually affecting the expression of genes involved in β-cell survival

Insulin signalling and the regulation of glucose and lipid metabolism

The epidemic of type 2 diabetes and impaired glucose tolerance is one of the main causes of morbidity and mortality worldwide. In both disorders, tissues such as muscle, fat and liver become less responsive or resistant to insulin. This state is also linked to other common health problems, such as obesity, polycystic ovarian disease, hyperlipidaemia, hypertension and atherosclerosis. The pathophysiology of insulin resistance involves a complex network of signalling pathways, activated by the insulin receptor, which regulates intermediary metabolism and its organization in cells. But recent studies have shown that numerous other hormones and signalling events attenuate insulin action, and are important in type 2 diabetes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62568/1/414799a.pd

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues

Aplicaci\uf3n de un m\ue9todo autom\ue1tico para la separaci\uf3n de las componentes del hidrograma

Data-centric and service-oriented workflows are commonly used in scientific research to enable the composition and execution of complex analysis on distributed resources. Although there are a plethora of orchestration frameworks to implement workflows, most of them are not suitable to execute data-centric workflows. The main issue is transferring output of service invocations through a centralized orchestration engine to the next service in the workflow, which can be a bottleneck for the performance of a data-centric workflow. In this paper, we propose a flexible and lightweight workflow framework based on the Object Modeling Systems (OMS). Moreover, we take advantage of the OMS architecture to deploy and execute data-centric workflows in a decentralized manner to avoid passing through the centralized engine. The proposed framework is implemented in context of the Australian Urban Research Infrastructure Network (AURIN) project which is an initiative aiming to develop an e-Infrastructure supporting research in the urban and built environment research disciplines. Performance evaluation results using spatial data-centric workflows show that we can reduce 20% of the workflows execution time while using Cloud resources in the same network domain