The Developing Human Connectome Project Neonatal Data Release

The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed

Ratio selection for classification models

This paper is concerned with the selection of inputs for classification models based on ratios of measured quantities. For this purpose, all possible ratios are built from the quantities involved and variable selection techniques are used to choose a convenient subset of ratios. In this context, two selection techniques are proposed: one based on a pre-selection procedure and another based on a genetic algorithm. In an example involving the financial distress prediction of companies, the models obtained from ratios selected by the proposed techniques compare favorably to a model using ratios usually found in the financial distress literature

Variable selection for financial distress classification using a genetic algorithm

This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature

Giant mesenteric teratoma in a 6 month-old

Objective: We report a case of mesenteric teratoma in a healthy 6 months old baby girl. The purpose of this report is to familiarize the pediatric surgery doctors with the characteristic appearance of this tumor, which is rare. Method: Case report and review of the English-language literature (using PubMed, Ovid, and Proquest databases). Results: Case of mesenteric teratoma occurred in an infant girl to be reported in the English-language literature. Conclusion: Mesenteric teratoma is an uncommon tumor that included all three embryonic layers. Although the presurgical diagnosis might be difficult in some cases, mesenteric teratoma must be kept in the differential of abdominal masses in children. The present case highlights the importance of considering mesenteric teratoma in the clinical and pathological differential diagnosis of intra-abdominal/retroperitoneal masses. Complete Surgical excision is almost always curative

Improved Many-Objective Optimization Algorithms for the 3D Indoor Deployment Problem

International audienceCompared with the two-dimensional deployment, the three-dimensional deployment of sensor networks is more challenging. We studied the problem of 3D repositioning of sensor nodes in wireless sensor networks. We aim essentially to add a set of nodes to the initial architecture. The positions of the added nodes are determined by the proposed algorithms while optimizing a set of objectives. In this paper, we suggest two main contributions. The first one is an analysis contribution where the modelling of the problem is given and a set of modifications is incorporated on the tested multi-objective evolutionary algorithms to resolve the issues encountered when resolving many-objective problems. These modifications concern essentially an adaptive mutation and recombination operators with neighbourhood mating restrictions, the use of a multiple scalarizing functions concept and the incorporation of the reduction in dimensionality. The second contribution is an application one, where an experimental study on real testbeds is detailed to test the behaviour of the enhanced algorithms on a real-world context. Experimental tests followed by numerical results prove the efficiency of the proposed modifications against original algorithms

Optimal number of routing paths in multi-path routing to minimize energy consumption in wireless sensor networks

In wireless sensor networks, multi-path routing is proposed for energy balancing which prolongs the network lifetime as compared to single-path routing where utilization of a single route between a source node and the base station results in imbalanced energy dissipation. While it is evident that increasing the number of routing paths mitigates the problem of energy over-utilization in a subset of nodes acting as relays, the net effect of the proliferation of multiple routing paths on energy balancing remains unclear. It is imperative to keep the number of routing paths as low as possible without significantly deteriorating the network lifetime; therefore, determination of the optimal number of routing paths in multi-path routing by considering the tradeoff in routing complexity and network lifetime extension is an interesting research problem. In this study, to investigate the impact of the number of routing paths in multi-path routing on network-wide energy balancing under optimal operating conditions, we build a novel mixed integer programming framework. We explore the parameter space consisting of a number of paths, number of nodes, maximum transmission range, network area, and network topology. The results of the analysis show that by utilizing the optimization scheme proposed, it is possible to achieve near-optimal energy consumption (within 1.0% neighborhood of the case where no restrictions are imposed on the number of routing paths in multi-path routing) using at most two paths for each node.Publisher's Versio