Do mutual funds have consistency in their performance?

Using a comprehensive data set of 714 Chinese mutual funds from 2004 to 2015, the study investigates these funds’ performance persistence by using the Capital Asset Pricing model, the Fama-French three-factor model and the Carhart Four-factor model. For persistence analysis, we categorize mutual funds into eight octiles based on their one year lagged performance and then observe their performance for the subsequent 12 months. We also apply Cross-Product Ratio technique to assess the performance persistence in these Chinese funds. The study finds no significant evidence of persis- tence in the performance of the mutual funds. Winner (loser) funds do not continue to be winner (loser) funds in the subsequent time period. These findings suggest that future performance of funds cannot be predicted based on their past performance.info:eu-repo/semantics/publishedVersio

Modelling of sPn phases for reliable estimation of focal depths in northeastern India

In North East India, reliable estimation of earthquake focal depths has always been a problem, owing to paucity of permanent regional seismic stations, constraints on good quality data and lack of crustal models in a complex tectonic terrane. The depth estimations by international agencies, mostly based on teleseismic data, vary from very shallow to even 60 km, suggestive of earthquake occurrence in the upper mantle region. However, modelling of sPn phases in earthquake waveforms that are highly sensitive to focal depths indicates that the earthquake locations are probably well within the crustal layer. The current method has the advantage that dt, the travel time difference between sPn and Pn, remains constant for a wide range of source-station distances, and hence enables easy identification of the sPn phase, while providing direct and accurate estimate of the focal depth. The approach is also insensitive to location errors and variations in crustal models, a useful feature especially while dealing with sparse data with high location errors. In the present study, earthquakes in the magnitude range 3.0-4.0 recorded by a network of nine broadband stations in NE India have been analysed. Consistent dt values at different stations were observed for each event, enabling precise depth estimation mostly within ±1 or 2 km. In general, depths of 15-20 km in the Shillong plateau region, >20 km in the Sylhet basin region and shallow <10 km in the eastern Himalayan foothills are confirmed, that correlate well with the local tectonics. Further, we propose that the flat characteristics of the dt curve that begin to change for earthquakes below the Moho, can be a potential tool for discriminating between crustal and sub-crustal earthquakes, as well as for delineation of the Moho using dense regional seismic networks in future

A Novel Method for Classification and Modelling of Underwater Acoustic Communication through Machine Learning and Image Processing Technique

The increasing prevalence of underwater activities has highlighted the urgent need for reliable underwater acoustic communication systems. However, the challenging nature of the underwater environment poses significant obstacles to the implementation of conventional voice communication methods. To better understand and improve upon these systems, simulations of the underwater audio channel have been developed using mathematical models and assumptions. In this study, we utilize real-world information gathered from both a measured water reservoir and Lake to evaluate the ability of machine learning and machine learning methods, specifically Long Short-Term Memory (LSTM) and Deep Neural Network (DNN), to accurately reconstruct the underwater audio channel. The outcomes validate the efficiency of machine learning methods, particularly LSTM, in accurately simulating the underwater acoustic communication channel with low mean absolute percentage error. Additionally, this research also includes an image processing to identify the objects present the in the acoustic environmen

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD Neurocognitive Prediction Challenge at MICCAI 201

Interaction of C-Terminal Truncated Human αA-Crystallins with Target Proteins

Significant portion of alphaA-crystallin in human lenses exists as C-terminal residues cleaved at residues 172, 168, and 162. Chaperone activity, determined with alcohol dehydrogenase (ADH) and betaL-crystallin as target proteins, was increased in alphaA(1-172) and decreased in alphaA(1-168) and alphaA(1-162). The purpose of this study was to show whether the absence of the C-terminal residues influences protein-protein interactions with target proteins.Our hypothesis is that the chaperone-target protein binding kinetics, otherwise termed subunit exchange rates, are expected to reflect the changes in chaperone activity. To study this, we have relied on fluorescence resonance energy transfer (FRET) utilizing amine specific and cysteine specific fluorescent probes. The subunit exchange rate (k) for ADH and alphaA(1-172) was nearly the same as that of ADH and alphaA-wt, alphaA(1-168) had lower and alphaA(1-162) had the lowest k values. When betaL-crystallin was used as the target protein, alphaA(1-172) had slightly higher k value than alphaA-wt and alphaA(1-168) and alphaA(1-162) had lower k values. As expected from earlier studies, the chaperone activity of alphaA(1-172) was slightly better than that of alphaA-wt, the chaperone activity of alphaA(1-168) was similar to that of alphaA-wt and alphaA(1-162) had substantially decreased chaperone activity.Cleavage of eleven C-terminal residues including Arg-163 and the C-terminal flexible arm significantly affects the interaction with target proteins. The predominantly hydrophilic flexible arm appears to be needed to keep the chaperone-target protein complex soluble

Coordinated optimization of visual cortical maps (I) Symmetry-based analysis

In the primary visual cortex of primates and carnivores, functional architecture can be characterized by maps of various stimulus features such as orientation preference (OP), ocular dominance (OD), and spatial frequency. It is a long-standing question in theoretical neuroscience whether the observed maps should be interpreted as optima of a specific energy functional that summarizes the design principles of cortical functional architecture. A rigorous evaluation of this optimization hypothesis is particularly demanded by recent evidence that the functional architecture of OP columns precisely follows species invariant quantitative laws. Because it would be desirable to infer the form of such an optimization principle from the biological data, the optimization approach to explain cortical functional architecture raises the following questions: i) What are the genuine ground states of candidate energy functionals and how can they be calculated with precision and rigor? ii) How do differences in candidate optimization principles impact on the predicted map structure and conversely what can be learned about an hypothetical underlying optimization principle from observations on map structure? iii) Is there a way to analyze the coordinated organization of cortical maps predicted by optimization principles in general? To answer these questions we developed a general dynamical systems approach to the combined optimization of visual cortical maps of OP and another scalar feature such as OD or spatial frequency preference.Comment: 90 pages, 16 figure

Investigation of attentional bias in obsessive compulsive disorder with and without depression in visual search

Copyright: © 2013 Morein-Zamir et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedWhether Obsessive Compulsive Disorder (OCD) is associated with an increased attentional bias to emotive stimuli remains controversial. Additionally, it is unclear whether comorbid depression modulates abnormal emotional processing in OCD. This study examined attentional bias to OC-relevant scenes using a visual search task. Controls, non-depressed and depressed OCD patients searched for their personally selected positive images amongst their negative distractors, and vice versa. Whilst the OCD groups were slower than healthy individuals in rating the images, there were no group differences in the magnitude of negative bias to concern-related scenes. A second experiment employing a common set of images replicated the results on an additional sample of OCD patients. Although there was a larger bias to negative OC-related images without pre-exposure overall, no group differences in attentional bias were observed. However, OCD patients subsequently rated the images more slowly and more negatively, again suggesting post-attentional processing abnormalities. The results argue against a robust attentional bias in OCD patients, regardless of their depression status and speak to generalized difficulties disengaging from negative valence stimuli. Rather, post-attentional processing abnormalities may account for differences in emotional processing in OCD.Peer reviewedFinal Published versio

Invited commentary to the paper ‘Zinc status and its association with the health of adolescents: a review of studies in India’

We are pleased to view the article based on Dr. Rama Kawade's thesis illustrating the importance of micronutrient adequacy, especially zinc, and associated health implications in Indian adolescent girls. This brief commentary addresses three major aspects in which Kawade's work has made a significant contribution; nutrition and health issues of adolescents, rising importance of zinc in terms of deficiency problems being addressed, and development of dietary interventions to alleviate micronutrient deficiencies

Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression

Coordinated optimization of visual cortical maps (II) Numerical studies

It is an attractive hypothesis that the spatial structure of visual cortical architecture can be explained by the coordinated optimization of multiple visual cortical maps representing orientation preference (OP), ocular dominance (OD), spatial frequency, or direction preference. In part (I) of this study we defined a class of analytically tractable coordinated optimization models and solved representative examples in which a spatially complex organization of the orientation preference map is induced by inter-map interactions. We found that attractor solutions near symmetry breaking threshold predict a highly ordered map layout and require a substantial OD bias for OP pinwheel stabilization. Here we examine in numerical simulations whether such models exhibit biologically more realistic spatially irregular solutions at a finite distance from threshold and when transients towards attractor states are considered. We also examine whether model behavior qualitatively changes when the spatial periodicities of the two maps are detuned and when considering more than 2 feature dimensions. Our numerical results support the view that neither minimal energy states nor intermediate transient states of our coordinated optimization models successfully explain the spatially irregular architecture of the visual cortex. We discuss several alternative scenarios and additional factors that may improve the agreement between model solutions and biological observations.Comment: 55 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:1102.335