257 research outputs found

    Short communication: An association analysis between one missense polymorphism at the SREBF1 gene and milk yield and composition traits in goats

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    Sterol regulatory element binding transcription factor 1 (SREBF1) regulates the expression of genes involved in the biosynthesis of fatty acids and cholesterol. Herewith, we have sequenced the near-complete coding region and part of the 3?UTR of the goat SREBF1 gene. In doing so, we have detected a missense c.353CT polymorphism causing a proline to leucine substitution at position 118 (P118L). An association analysis with milk composition traits recorded in MurcianoGranadina goats only revealed a statistical tendency linking SREBF1 genotype and milk omega-3 fatty acid content. The lack of significant associations suggests that the P118L substitution does not involve a functional change.Le facteur de transcription de´nomme´ Sterol regulatory element binding transcription factor 1 (SREBF1) re´gule l’expression des ge`nes implique´s dans la biosynthe`se des acides gras et du choleste´rol. Dans cette e´tude, nous avons se´quence´ la quasi-totalite´ de la re´gion codante et une partie du la re´gion 3?UTR du ge`ne SREBF1 de la che`vre. Ce travail, nous a permis d’identifier un polymorphisme non-synonyme c.353CT causant la substitution d’une Proline en Leucine a` la position 118. L’e´tude d’association avec la composition du lait enregistre´e en che`vres Murciano-Granadina, a re´ve´le´ seulement une tendance statistique reliant SREBF1 ge´notype et l’acide gras ome´ga-3 du lait. L’absence d’associations significatives sugge`re que la substitution P118L n’implique pas un changement fonctionnel

    The while of participation: A systematic review of participatory research involving people with sensory impairments and/or intellectual impairments.

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    This paper reports on the first systematic review of literature associated with participatory research involving people identified with sensory impairments and/or intel- lectual impairments. It was initiated as part of ARCHES, an European Union-funded heritage project. The review sought to examine processes and activities used for organising participatory research involving people identified with sensory and/or intellectual impairments. 54 papers were included, involving studies from 14 countries and varying numbers of participants across different time scales. Insights were gained into use of advisory groups, organisation and support, collecting and analysing data, sharing findings and activity types. Emergent themes enabled an identification of the while of participation. The while represents the tensions, outcomes and component parts which are evident within the multiple moments that span an experience of participatory research. Participation is not about types of activity but how any activity is undertaken

    Assessment of an Average Controller for a DC/DC Converter via Either a PWM or a Sigma-Delta-Modulator

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    Sliding mode control is a discontinuous control technique that is, by its nature, appropriate for controlling variable structure systems, such as the switch regulated systems employed in power electronics. However, when designing control laws based on the average models of these systems a modulator is necessary for their experimental implementation. Among the most widely used modulators in power electronics are the pulse width modulation (PWM) and, more recently, the sigma-delta-modulator (Σ-Δ-modulator). Based on the importance of achieving an appropriate implementation of average control laws and the relevance of the trajectory tracking task in DC/DC power converters, for the first time, this research presents the assessment of the experimental results obtained when one of these controllers is implemented through either a PWM or a Σ-Δ-modulator to perform such a task. A comparative assessment based on the integral square error (ISE) index shows that, at frequencies with similar efficiency, the Σ-Δ-modulator provides a better tracking performance for the DC/DC Buck converter. In this paper, an average control based on differential flatness was used to perform the experiments. It is worth mentioning that a different trajectory tracking controller could have been selected for this research

    Challenges and Opportunities in Cancer Immunotherapy: A Society for Immunotherapy of Cancer (SITC) Strategic Vision

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    Cancer immunotherapy has flourished over the last 10-15 years, transforming the practice of oncology and providing long-term clinical benefit to some patients. During this time, three distinct classes of immune checkpoint inhibitors, chimeric antigen receptor-T cell therapies specific for two targets, and two distinct classes of bispecific T cell engagers, a vaccine, and an oncolytic virus have joined cytokines as a standard of cancer care. At the same time, scientific progress has delivered vast amounts of new knowledge. For example, advances in technologies such as single-cell sequencing and spatial transcriptomics have provided deep insights into the immunobiology of the tumor microenvironment. With this rapid clinical and scientific progress, the field of cancer immunotherapy is currently at a critical inflection point, with potential for exponential growth over the next decade. Recognizing this, the Society for Immunotherapy of Cancer convened a diverse group of experts in cancer immunotherapy representing academia, the pharmaceutical and biotechnology industries, patient advocacy, and the regulatory community to identify current opportunities and challenges with the goal of prioritizing areas with the highest potential for clinical impact. The consensus group identified seven high-priority areas of current opportunity for the field: mechanisms of antitumor activity and toxicity; mechanisms of drug resistance; biomarkers and biospecimens; unique aspects of novel therapeutics; host and environmental interactions; premalignant immunity, immune interception, and immunoprevention; and clinical trial design, endpoints, and conduct. Additionally, potential roadblocks to progress were discussed, and several topics were identified as cross-cutting tools for optimization, each with potential to impact multiple scientific priority areas. These cross-cutting tools include preclinical models, data curation and sharing, biopsies and biospecimens, diversification of funding sources, definitions and standards, and patient engagement. Finally, three key guiding principles were identified that will both optimize and maximize progress in the field. These include engaging the patient community; cultivating diversity, equity, inclusion, and accessibility; and leveraging the power of artificial intelligence to accelerate progress. Here, we present the outcomes of these discussions as a strategic vision to galvanize the field for the next decade of exponential progress in cancer immunotherapy

    Actualización del Factor de Convectividad para la Cuenca del Valle de México

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    Uno de los problemas principales en México es la escasez de información pluviográfica, por lo que para obtener la relación entre las lluvias diarias máximas anuales y su evolución durante el día es necesario recurrir a métodos que describan el comportamiento de la lluvia en la CVM, para esto se recurrió a la obtención del factor de convectividad también conocido como Factor R, el cual determina la relación de las precipitaciones acumuladas de un día con las de una hora. Para esto se utilizó la información de 49 pluviógrafos dentro de la Ciudad de México, obteniendo así la información de 143 de las tormentas más importantes ocurridas entre 1988 y 2008. Con esta información se obtuvieron 3 factores R de los cuales se encontró que guardan relación con la topografía de la zona

    Employment and SMEs during crises

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    The persistent increasing duration of unemployment has become an issue during economic crises. Although lay-offs at large firms normally make headlines during crises, we still know little about the potential impact of firm size on adjustment behavior in a crisis. We studied effects of firm size on employment growth during economic slowdowns using a rich microeconomic database for the 1988-2007 period in Portuguese manufacturing industry. The results show that economic downturns affect firm growth negatively. This negative effect is found to be higher for larger firms, both during and immediately following crisis periods. Small and medium-sized enterprises (SMEs) emerge as potential stabilizers in downturn periods. However, larger firms seem to be able to quickly recover from downturn periods. Our results contribute to the scarce literature and to the understanding of the Portuguese case, where many SMEs secure most jobs. These first results may be useful, because SMEs play a determinant role in other European Union economies

    Binarized Support Vector Machines

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    The widely used support vector machine (SVM) method has shown to yield very good results in supervised classification problems. Other methods such as classification trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in data mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals that are critical for the classification. The method involves the optimization of a linear programming problem in the spirit of the Lasso method with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard column generation strategy leads to a classification method that, in terms of classification ability, is competitive against the standard linear SVM and classification trees. Moreover, the proposed method is robust; i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler but still competitive classifiers
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