An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

Pathology of subacute hepatic failure

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Multicamera Action Recognition with Canonical Correlation Analysis and Discriminative Sequence Classification

Proceedings of: 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, La Palma, Canary Islands, Spain, May 30 - June 3, 2011.This paper presents a feature fusion approach to the recognition of human actions from multiple cameras that avoids the computation of the 3D visual hull. Action descriptors are extracted for each one of the camera views available and projected into a common subspace that maximizes the correlation between each one of the components of the projections. That common subspace is learned using Probabilistic Canonical Correlation Analysis. The action classification is made in that subspace using a discriminative classifier. Results of the proposed method are shown for the classification of the IXMAS dataset.Publicad

A North American Expert Opinion Statement on Sarcopenia in Liver Transplantation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151960/1/hep30828_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151960/2/hep30828.pd

Frailty in liver transplantation: An expert opinion statement from the American Society of Transplantation Liver and Intestinal Community of Practice

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149699/1/ajt15392_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149699/2/ajt15392.pd

ESPEN Practical Guideline: clinical nutrition in liver disease

Desnutrición; Insuficiencia hepática aguda grave; CirrosisMalnutrition; Acute liver failure; CirrhosisDesnutrició; Insuficiència hepàtica aguda greu; CirrosiIntroducción: la Guía Práctica se basa en la actual guía científica de la ESPEN sobre nutrición clínica en las enfermedades hepáticas. Métodos: se ha reducido y transformado en diagramas de flujo para facilitar su uso en la práctica clínica. La guía está dedicada a todos los profesionales, incluidos médicos, dietistas, nutricionistas y enfermeras, que trabajan con pacientes con enfermedad hepática crónica. Resultados: la guía presenta un total de 103 pronunciamientos y recomendaciones con breves comentarios para el manejo nutricional y metabólico de pacientes con (i) insuficiencia hepática aguda grave, (ii) esteatohepatitis alcohólica, (iii) enfermedad hepática grasa no alcohólica, (iv) cirrosis hepática, y (v) cirugía o trasplante de hígado. Conclusión: las recomendaciones relacionadas con enfermedades están precedidas por recomendaciones generales sobre el diagnóstico del estado nutricional en los pacientes hepáticos y sobre las complicaciones hepáticas asociadas a la nutrición médica.Background: the Practical Guideline is based on the current scientifi c ESPEN guide on Clinical Nutrition in Liver Disease. Methods: it has been shortened and transformed into fl ow charts for easier use in clinical practice. The guideline is dedicated to all professionals including physicians, dieticians, nutritionists and nurses working with patients with chronic liver disease. Results: a total of 103 statements and recommendations are presented with short commentaries for the nutritional and metabolic management of patients with (i) acute liver failure, (ii) alcoholic steatohepatitis, (iii) non-alcoholic fatty liver disease, (iv) liver cirrhosis, and (v) liver surgery/ transplantation. Disease-related recommendations are preceded by general recommendations on the diagnosis of nutritional status in liver patients and on liver complications associated with medical nutrition. Conclusion: this Practical Guideline gives guidance to health care providers involved in the management of liver disease on how to offer optimal nutritional care

Application of two machine learning algorithms to genetic association studies in the presence of covariates

BACKGROUND: Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML) algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized. METHODS AND RESULTS: In this manuscript, we investigate two approaches: Random Forests (RFs) and Multivariate Adaptive Regression Splines (MARS). Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is also provided. CONCLUSION: Consistent with more traditional regression modeling theory, our findings highlight the importance of considering the nature of underlying gene-covariate-trait relationships before applying ML algorithms, particularly when there is potential confounding or effect mediation