Evidential Bagging: Combining Heterogeneous Classifiers in the Belief Functions Framework

International audienceIn machine learning, Ensemble Learning methodologies are known to improve predictive accuracy and robustness. They consist in the learning of many classifiers that produce outputs which are finally combined according to different techniques. Bagging, or Bootstrap Aggre-gating, is one of the most famous Ensemble methodologies and is usually applied to the same classification base algorithm, i.e. the same type of classifier is learnt multiple times on bootstrapped versions of the initial learning dataset. In this paper, we propose a bagging methodology that involves different types of classifier. Classifiers' probabilist outputs are used to build mass functions which are further combined within the belief functions framework. Three different ways of building mass functions are proposed; preliminary experiments on benchmark datasets showing the relevancy of the approach are presented

Time Markers and Temporal Illusions

Peer reviewe

Non-Linear Population Firing Rates and Voltage Sensitive Dye Signals in Visual Areas 17 and 18 to Short Duration Stimuli

Visual stimuli of short duration seem to persist longer after the stimulus offset than stimuli of longer duration. This visual persistence must have a physiological explanation. In ferrets exposed to stimuli of different durations we measured the relative changes in the membrane potentials with a voltage sensitive dye and the action potentials of populations of neurons in the upper layers of areas 17 and 18. For durations less than 100 ms, the timing and amplitude of the firing and membrane potentials showed several non-linear effects. The ON response became truncated, the OFF response progressively reduced, and the timing of the OFF responses progressively delayed the shorter the stimulus duration. The offset of the stimulus elicited a sudden and strong negativity in the time derivative of the dye signal. All these non-linearities could be explained by the stimulus offset inducing a sudden inhibition in layers II–III as indicated by the strongly negative time derivative of the dye signal. Despite the non-linear behavior of the layer II–III neurons the sum of the action potentials, integrated from the peak of the ON response to the peak of the OFF response, was almost linearly related to the stimulus duration

Effect of sampling effort and sampling frequency on the composition of the planktonic crustacean assemblage: a case study of the river Danube

Although numerous studies have focused on the seasonal dynamics of riverine zooplankton, little is known about its short-term variation. In order to examine the effects of sampling frequency and sampling effort, microcrustacean samples were collected at daily intervals between 13 June and 21 July of 2007 in a parapotamal side arm of the river Danube, Hungary. Samples were also taken at biweekly intervals from November 2006 to May 2008. After presenting the community dynamics, the effect of sampling effort was evaluated with two different methods; the minimal sample size was also estimated. We introduced a single index (potential dynamic information loss; to determine the potential loss of information when sampling frequency is reduced. The formula was calculated for the total abundance, densities of the dominant taxa, adult/larva ratios of copepods and for two different diversity measures. Results suggest that abundances may experience notable fluctuations even within 1 week, as do diversities and adult/larva ratios

Gene-Based Tests of Association

Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal. Permutation tests provide p-values that correct for the number of independent tests genome-wide and within each genetic locus. When applied to a dataset comprising 2.5 million SNPs in up to 8,000 individuals measured for various electrocardiography (ECG) parameters, this method identifies more validated associations than conventional GWAS approaches. The method also provides, for the first time, systematic assessments of the number of independent effects within a gene and the fraction of disease-associated genes housing multiple independent effects, observed at 35%–50% of loci in our study. This method can be generalized to other study designs, retains power for low-frequency alleles, and provides gene-based p-values that are directly compatible for pathway-based meta-analysis

Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples

Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them

Sensitivity of Local Dynamic Stability of Over-Ground Walking to Balance Impairment Due to Galvanic Vestibular Stimulation

Impaired balance control during gait can be detected by local dynamic stability measures. For clinical applications, the use of a treadmill may be limiting. Therefore, the aim of this study was to test sensitivity of these stability measures collected during short episodes of over-ground walking by comparing normal to impaired balance control. Galvanic vestibular stimulation (GVS) was used to impair balance control in 12 healthy adults, while walking up and down a 10 m hallway. Trunk kinematics, collected by an inertial sensor, were divided into episodes of one stroll along the hallway. Local dynamic stability was quantified using short-term Lyapunov exponents (λs), and subjected to a bootstrap analysis to determine the effects of number of episodes analysed on precision and sensitivity of the measure. λs increased from 0.50 ± 0.06 to 0.56 ± 0.08 (p = 0.0045) when walking with GVS. With increasing number of episodes, coefficients of variation decreased from 10 ± 1.3% to 5 ± 0.7% and the number of p values >0.05 from 42 to 3.5%, indicating that both precision of estimates of λs and sensitivity to the effect of GVS increased. λs calculated over multiple episodes of over-ground walking appears to be a suitable measure to calculate local dynamic stability on group level

Peak bone mineral density in Vietnamese women

While the prevalence of osteoporosis and risk factors for low bone mineral density (BMD) has been well documented in Caucasian populations, there is a lack of data from Asia. This work was designed to clarify to what extent osteoporosis could be regarded as a major public health problem in Vietnam. Furthermore, to elucidate the prevalence of certain risk factors, such as vitamin D deficiency and other determinants of bone mass as a basis to indentify high-risk individuals among the Vietnamese women and men. The clinical studies were designed as cross-sectional investigations using a multistage sampling scheme. Within the setting of northern Vietnam (latitude 21°N), districts were selected to represent urban and rural areas. In total 612 healthy women and 222 men aged 13-83 years were investigated. BMD was measured at the lumbar spine, femoral neck and total hip in all qualified subjects with dual energy X-ray absortiometry. Serum concentrations of 25(OH)D, parathyroid hormone, estrogen and testosterone were quantified by electrochemiluminescence immunoassay. Data on clinical history and lifestyle were collected by individual face-to-face interviews. Reference values for peak BMD were defined. These data allowed the calculation of T-scores and thus for the first time, an accurate identification of osteoporosis in a Vietnamese population. As determined at the femoral neck, the prevalence of osteoporosis was 17-23% in women and 9% in men. The results clearly suggest that osteoporosis is an important public health problem. Postmenopausal women living in urban areas experienced osteoporosis more than rural residents. Serum levels of 25(OH)D and estrogen were significantly associated with bone mass in both women and men. The prevalence of vitamin D deficiency (<20 ng/mL) was very high, 30% in women and 16% in men. An experimental study on the isoflavone content of different soymilk preparations was performed by HPLC (high pressure liquid chromatography). Values of isoflavones were very low, around 60-80 mg/L, and there were only 10-20% of bioactive aglycones. This is far below the reported threshold levels to exert significant effects on bone. In the future these data will be useful as a valuable reference base to diagnose osteoporosis and for the clinical management of its consequences. The high prevalence of vitamin D deficiency should raise the awareness of potentially important health issues such as osteoporosis but also about other serious diseases within the Vietnamese society

G = MAT: Linking Transcription Factor Expression and DNA Binding Data

Transcription factors are proteins that bind to motifs on the DNA and thus affect gene expression regulation. The qualitative description of the corresponding processes is therefore important for a better understanding of essential biological mechanisms. However, wet lab experiments targeted at the discovery of the regulatory interplay between transcription factors and binding sites are expensive. We propose a new, purely computational method for finding putative associations between transcription factors and motifs. This method is based on a linear model that combines sequence information with expression data. We present various methods for model parameter estimation and show, via experiments on simulated data, that these methods are reliable. Finally, we examine the performance of this model on biological data and conclude that it can indeed be used to discover meaningful associations. The developed software is available as a web tool and Scilab source code at http://biit.cs.ut.ee/gmat/