Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of restricted marital practices on genetic variation in an endogamous Gujarati group.

Recent studies have examined the influence on patterns of human genetic variation of a variety of cultural practices. In India, centuries-old marriage customs have introduced extensive social structuring into the contemporary population, potentially with significant consequences for genetic variation. Social stratification in India is evident as social classes that are defined by endogamous groups known as castes. Within a caste, there exist endogamous groups known as gols (marriage circles), each of which comprises a small number of exogamous gotra (lineages). Thus, while consanguinity is strictly avoided and some randomness in mate selection occurs within the gol, gene flow is limited with groups outside the gol. Gujarati Patels practice this form of "exogamic endogamy. " We have analyzed genetic variation in one such group of Gujarati Patels, the Chha Gaam Patels (CGP), who comprise individuals from six villages. Population structure analysis of 1,200 autosomal loci offers support for the existence of distinctive multilocus genotypes in the CGP with respect to both non-Gujaratis and other Gujaratis, and indicates that CGP individuals are genetically very similar. Analysis of Y-chromosomal and mitochondrial haplotypes provides support for both patrilocal and patrilineal practices within the gol, and a low-level of female gene flow into the gol. Our study illustrates how the practice of gol endogamy has introduced fine-scale genetic structure into the population of India, and contributes more generally to an understanding of the way in which marriage practices affect patterns of genetic variation.
Dynamic Cache Splitting

A novel hardware for cache management that we call split cache is presented. With some help from the compiler, a program can dynamically split the cache in several caches of any size that is power of two, and choose in which cache each data element is mapped. When properly used, this scheme can reduce the connict misses near to zero. Moreover, as the total capacity of the original cache is used in a more optimal way, also the capacity misses can be reduced.
Macrophage ABCG1 Deletion Disrupts Lipid Homeostasis in Alveolar Macrophages and Moderately Influences Atherosclerotic Lesion Development in LDL Receptor-Deficient Mice

Objective—ABCG1 has recently been identified as a facilitator of cellular cholesterol and phospholipid efflux to high-density lipoprotein (HDL). Its expression in macrophages is induced during cholesterol uptake in macrophages and by liver X receptor (LXR). The role of macrophage ABCG1 in atherosclerotic lesion development is, however, still unknown. Methods and Results—To assess the role of macrophage ABCG1 in atherosclerosis, we generated low-density lipoprotein (LDL) receptor knockout (LDLr−/−) mice that are selectively deficient in macrophage ABCG1 by using bone marrow transfer (ABCG1−/− → LDLr−/−). Peritoneal macrophages isolated from donor ABCG1−/− mice exhibited a 22% (P=0.0007) decrease in cholesterol efflux to HDL. To induce atherosclerosis, transplanted mice were fed a high-cholesterol diet containing 0.25% cholesterol and 15% fat for 6 and 12 weeks. Serum lipid levels and lipoprotein profiles did not differ significantly between ABCG1−/− → LDLr−/− mice and controls. In lungs of ABCG1−/− → LDLr−/− mice a striking accumulation of lipids was observed in macrophages localized to the subpleural region. After 6 weeks of high-cholesterol diet feeding the atherosclerotic lesion size was 49±12×103 &mgr;m2 for ABCG1+/+ → LDLr−/− mice versus 65±15×103 &mgr;m2 for ABCG1−/− → LDLr−/− mice and after 12 weeks of high-cholesterol diet feeding 124±17×103 &mgr;m2 for ABCG1+/+ → LDLr−/− mice versus 168±17×103 &mgr;m2 for ABCG1−/− → LDLr−/− mice. Atherosclerotic lesion size depended on both time and the macrophage ABCG1 genotype (P=0.038 by 2-way ANOVA, n≥8), indicating a moderately 33% to 36% increase in lesion formation in the absence of macrophage ABCG1. Conclusions—Macrophage ABCG1 deficiency does lead to heavy lipid accumulation in macrophages of the lung, and also a moderately significant effect on atherosclerotic lesion development was observed.
iScope: personalized multi-modality image search for mobile devices

Mobile devices are becoming a primary medium for personal information gathering, management, and sharing. Managing personal image data on mobile platforms is a difficult problem due to large data set size, content diversity, heterogeneous individual usage patterns, and resource constraints. This article presents a user-centric system, called iScope, for personal image management and sharing on mobile devices. iScope uses multi-modality clustering of both content and context information for efficient image management and search, and online learning techniques for predicting images of interest. It also supports distributed content-based search among networked devices while maintaining the same intuitive interface, enabling efficient information sharing among people. We have implemented iScope and conducted in-field experiments using networked Nokia N810 portable Internet tablets. Energy efficiency was a primary design focus during the design and implementation of the iScope search algorithms. Experimental results indicate that iScope improves search time and search energy by 4.1X and 3.8X on average, relative to browsing.
Management of Chronic Obstructive Pulmonary Disease Patients after Hospitalization for Acute Exacerbation

The objective of this review is to sum up the literature regarding the management of patients with chronic obstructive pulmonary disease (COPD) after hospitalization for an acute exacerbation. Guidelines recommend a follow-up 4–6 weeks after hospitalization to assess coping strategies, inhaler technique, the need for long-term oxygen therapy and the measurement of FEV1. This review discusses the follow-up of patients with exacerbations of COPD, the use and value of spirometry in their further management, the potential benefit of home monitoring, the value of long-term oxygen therapy, the value of self-management programs including the use of action plans, the potential benefit of noninvasive ventilation as well as the value of early rehabilitation. There is not enough literature to allow specific recommendations and to define components of a care plan after hospitalization for an acute exacerbation; however, early rehabilitation should be included.
Gene-Network Analysis Identifies Susceptibility Genes Related to Glycobiology in Autism

The recent identification of copy-number variation in the human genome has opened up new avenues for the discovery of positional candidate genes underlying complex genetic disorders, especially in the field of psychiatric disease. One major challenge that remains is pinpointing the susceptibility genes in the multitude of disease-associated loci. This challenge may be tackled by reconstruction of functional gene-networks from the genes residing in these loci. We applied this approach to autism spectrum disorder (ASD), and identified the copy-number changes in the DNA of 105 ASD patients and 267 healthy individuals with Illumina Humanhap300 Beadchips. Subsequently, we used a human reconstructed gene-network, Prioritizer, to rank candidate genes in the segmental gains and losses in our autism cohort. This analysis highlighted several candidate genes already known to be mutated in cognitive and neuropsychiatric disorders, including RAI1, BRD1, and LARGE. In addition, the LARGE gene was part of a sub-network of seven genes functioning in glycobiology, present in seven copy-number changes specifically identified in autism patients with limited co-morbidity. Three of these seven copy-number changes were de novo in the patients. In autism patients with a complex phenotype and healthy controls no such sub-network was identified. An independent systematic analysis of 13 published autism susceptibility loci supports the involvement of genes related to glycobiology as we also identified the same or similar genes from those loci. Our findings suggest that the occurrence of genomic gains and losses of genes associated with glycobiology are important contributors to the development of ASD.
The Value of Forest Conservation for Water Quality Protection

Forests protect water quality by reducing soil erosion, sedimentation, and pollution; yet there is little information about the economic value of conserving forests for water quality protection in much of the United States. To assess this value, we conducted a meta-analysis of willingness-to-pay (WTP) for protecting unimpaired waters, and econometrically determined several significant drivers of WTP: type of conservation instrument (tool), aquatic resource type, geographic context, spatial scale, time, and household income. Using a benefit transfer to two highly forested sites, we illustrate the importance of these factors on WTP for water quality protection programs, forest conservation and policy design.
Diversified Microbiota of Meconium Is Affected by Maternal Diabetes Status

Objectives This study was aimed to assess the diversity of the meconium microbiome and determine if the bacterial community is affected by maternal diabetes status. Methods The first intestinal discharge (meconium) was collected from 23 newborns stratified by maternal diabetes status: 4 mothers had pre-gestational type 2 diabetes mellitus (DM) including one mother with dizygotic twins, 5 developed gestational diabetes mellitus (GDM) and 13 had no diabetes. The meconium microbiome was profiled using multi-barcode 16S rRNA sequencing followed by taxonomic assignment and diversity analysis. Results All meconium samples were not sterile and contained diversified microbiota. Compared with adult feces, the meconium showed a lower species diversity, higher sample-to-sample variation, and enrichment of Proteobacteria and reduction of Bacteroidetes. Among the meconium samples, the taxonomy analyses suggested that the overall bacterial content significantly differed by maternal diabetes status, with the microbiome of the DM group showing higher alpha-diversity than that of no-diabetes or GDM groups. No global difference was found between babies delivered vaginally versus via Cesarean-section. Regression analysis showed that the most robust predictor for the meconium microbiota composition was the maternal diabetes status that preceded pregnancy. Specifically, Bacteroidetes (phyla) and Parabacteriodes (genus) were enriched in the meconium in the DM group compared to the no-diabetes group. Conclusions Our study provides evidence that meconium contains diversified microbiota and is not affected by the mode of delivery. It also suggests that the meconium microbiome of infants born to mothers with DM is enriched for the same bacterial taxa as those reported in the fecal microbiome of adult DM patients.
1 ECONOMIC CAPITAL ASSESSMENT VIA COPULAS : AGGREGATION AND ALLOCATION OF DIFFERENT RISK TYPES 1

1 We are grateful to Silvio Cuneo, who supervised the whole project, giving precious comments and suggestions. We also thank Eric M. Czervionke for the revision of this first draft and Sorin Talamba for sharing his insights with us. The views and opinions expressed in the document are those of the authors and not necessarily those of Intesa-Sanpaolo bank. The most common approach in estimating the inter-risk diversification effect is to aggregate the stand alone economic capital for each risk type through a variance/covariance Markowitz approach. In this paper we propose an alternative framework, still " ex-post " in the sense that marginal models for the loss distribution of each risk are independently developed and then merged to a joint distribution, but based on the simulation of a t-copula dependence structure; the Kendall's τs measure is used to correlate the risks. No assumption is then required on the marginal distributions, which depend in fact on the specific models used to describe each risk. The method is applied to calculate the economic capital for an hypothetical portfolio of risks typically faced by a commercial bank. A Window Conditional Expectation approach for allocating back the diversified capital has also been experimented.
Can't Touch This: Using Hover to Compromise the Confidentiality of User Input on Android

We show that the new hover (floating touch) technology, available in a number of today's smartphone models, can be abused by any Android application running with a common SYSTEM_ALERT_WINDOW permission to record all touchscreen input into other applications. Leveraging this attack, a malicious application running on the system is therefore able to profile user's behavior, capture sensitive input such as passwords and PINs as well as record all user's social interactions. To evaluate our attack we implemented Hoover, a proof-of-concept malicious application that runs in the system background and records all input to foreground applications. We evaluated Hoover with 40 users, across two different Android devices and two input methods, stylus and finger. In the case of touchscreen input by finger, Hoover estimated the positions of users' clicks within an error of 100 pixels and keyboard input with an accuracy of 79%. Hoover captured users' input by stylus even more accurately, estimating users' clicks within 2 pixels and keyboard input with an accuracy of 98%. We discuss ways of mitigating this attack and show that this cannot be done by simply restricting access to permissions or imposing additional cognitive load on the users since this would significantly constrain the intended use of the hover technology.
Maybe Poor Johnny Really Cannot Encrypt: The Case for a Complexity Theory for Usable Security

Psychology and neuroscience literature shows the existance of upper bounds on the human capacity for executing cognitive tasks and for information processing. These bounds are where, demonstrably, people start experiencing cognitive strain and consequently committing errors in the tasks execution. We argue that the usable security discipline should scientifically understand such bounds in order to have realistic expectations about what people can or cannot attain when coping with security tasks. This may shed light on whether Johnny will be ever be able to encrypt. We propose a conceptual framework for evaluation of human capacities in security that also assigns systems to complexity categories according to their security and usability. From what we have initiated in this paper, we ultimately aim at providing designers of security mechanisms and policies with the ability to say: "This feature of the security mechanism X or this security policy element Y is inappropriate, because this evidence shows that it is beyond the capacity of its target community".
Socio-economic, demographic, lifestyle and health characteristics associated with consumption of fatty-sweetened and fatty-salted foods in middle-aged French adults

Few studies have specifically focused on characteristics associated with consumption of combined fatty-salted and fatty-sweetened foods, whereas their identification could be useful for defining effective public health measures. The aim of the present study was to investigate the association between demographic, socio-economic, lifestyle and health characteristics and consumption of these types of food in a general sample of French adults. Dietary intake was assessed using a minimum of six 24 h dietary records collected over a 2-year period in 6240 subjects aged 35–60 years who participated in the Supplémentation en VItamines Minéraux et AntioXydants cohort study. Associations of individual characteristics with high and intermediate consumption of fatty-sweetened and fatty-salted foods were assessed using multivariate polytomic logistic regression models. Risk of moderate or high consumption of fatty-salted foods decreased with increasing age. Current smokers, drinkers, individuals with overweight and with hypertension were more likely to consume moderate or high amounts of such foods. Risk of moderate or high consumption of fatty-sweetened foods decreased with increasing age. Women, individuals living as a couple, moderate drinkers and persons with low or medium physical activity level were more likely to consume moderate or high amounts of such foods. Lower educated subjects, current smokers, heavy drinkers and individuals with severe hypertriacylglycerolaemia were less likely to have moderate or high consumption. Consumption of fatty-sweetened and fatty-salted foods varied according to demographic, lifestyle and health characteristics. Common unhealthy behaviours such as smoking, low physical activity and alcohol drinking, associated with high consumption of these food groups, may help to effectively target public health efforts.
Analytical model for anycast service provisioning in data center interconnections

This paper presents an analytical model to evaluate the performance of an optical wavelength-routed network for inter- and intra-data center interconnection, designed to work according to the cloud computing paradigm. Such a scenario calls for anycast routing of service requests to find a network path that best suits both connectivity and IT resource requirements. The analytical model is based on conventional queuing theory for loss systems combined with an ad-hoc combinatorial analysis of the anycast service alternatives. The model presented here is meant to provide engineering guidelines for data center interconnection networks supporting the cloud paradigm, in particular in terms of performance and cost of resource distribution.
A Data Mining Framework for Adaptive Intrusion Detection ∗

In this paper we describe a data mining framework for constructing intrusion dete ction models. The key ideas are to mine system audit data for consistent and useful patterns of pr ogram and user behavior, and use the set of relevant system features presented in the patterns to compute (inductively learned) classifiers that can recognize anomalies and known intrusi ons. Our experiments on audit data of system programs and network activities showed that classificat ion models can detect intrusions, provided that sufficient audit data is available for training and the r ig t set of system features are selected. We propose to use the association rules and frequent epi sodes computed from audit data as the basis for guiding the audit data gathering and feature selecti on processes. This research is supported in part by grants from DARPA (F30602-96-1 0311) and NSF (IRI-96-32225 and CDA96-25374)
Search Mechanism Using Distributed Metadata Caching in P2P Networks

A new mechanism for P2P-based metadata discovery in a community dealing with high-mix low-volume content is proposed. The basic idea is to narrow down candidates for favourite content by using metadata elements, not by using so-called genres Evaluation results obtained from a simulation indicate that the proposed mechanism achieves the high efficiency from the view point of the amount of cache and the number of discovery hops.
Female teachers’ math anxiety affects girls’ math achievement

People’s fear and anxiety about doing math—over and above actual math ability—can be an impediment to their math achievement. We show that when the math-anxious individuals are female elementary school teachers, their math anxiety carries negative consequences for the math achievement of their female students. Early elementary school teachers in the United States are almost exclusively female (>90%), and we provide evidence that these female teachers’ anxieties relate to girls’ math achievement via girls’ beliefs about who is good at math. First- and second-grade female teachers completed measures of math anxiety. The math achievement of the students in these teachers’ classrooms was also assessed. There was no relation between a teacher’s math anxiety and her students’ math achievement at the beginning of the school year. By the school year’s end, however, the more anxious teachers were about math, the more likely girls (but not boys) were to endorse the commonly held stereotype that “boys are good at math, and girls are good at reading” and the lower these girls’ math achievement. Indeed, by the end of the school year, girls who endorsed this stereotype had significantly worse math achievement than girls who did not and than boys overall. In early elementary school, where the teachers are almost all female, teachers’ math anxiety carries consequences for girls’ math achievement by influencing girls’ beliefs about who is good at math.
Cyclic Reduction Tridiagonal Solvers on GPUs Applied to Mixed-Precision Multigrid

We have previously suggested mixed precision iterative solvers specifically tailored to the iterative solution of sparse linear equation systems as they typically arise in the finite element discretization of partial differential equations. These schemes have been evaluated for a number of hardware platforms, in particular, single-precision GPUs as accelerators to the general purpose CPU. This paper reevaluates the situation with new mixed precision solvers that run entirely on the GPU: We demonstrate that mixed precision schemes constitute a significant performance gain over native double precision. Moreover, we present a new implementation of cyclic reduction for the parallel solution of tridiagonal systems and employ this scheme as a line relaxation smoother in our GPU-based multigrid solver. With an alternating direction implicit variant of this advanced smoother, we can extend the applicability of the GPU multigrid solvers to very ill-conditioned systems arising from the discretization on anisotropic meshes, that previously had to be solved on the CPU. The resulting mixed-precision schemes are always faster than double precision alone, and outperform tuned CPU solvers consistently by almost an order of magnitude.
Structural Relationships between Spiking Neural Networks and Functional Samples

Models of spiking neural networks have a great potential to become a crucial tool in the development of complex network theory. Of particular interest, these models can be used to better understand the important class of brain functional networks, which are frequently studied in the context of computational network analysis. A fundamental question is whether functional connectivity sampling via surface multichannel recordings is able to reproduce the main connectivity features of the underlying spatial neural network. In this work we address this problem through computational modeling using the integrate-and-fire spiking neuron model, which enabled us to relate neural connectivity and the respective mesoscopic dynamics. Functional samples were then compared to an idealized spatial neural network model in terms of established topological network measurements. Results show that some measurements (e.g., betweenness centrality) are able to fairly approximate functional and spatial networks. Therefore, under specific circumstances of sampling size and simulation approach, it is possible to say that functional networks are able to reproduce connectivity features of the underlying neural network.
Sisyphus and prediction of protein structure

The problem of predicting protein structure from the sequence remains fundamentally unsolved despite more than three decades of intensive research effort. However, new and promising methods in three-dimensional (3D), 2D and 1D prediction have reopened the field. Mean-force-potentials derived from the protein databases can distinguish between correct and incorrect models (3D). Inter-residue contacts (2D) can be detected by analysis of correlated mutations, albeit with low accuracy. Secondary structure, solvent accessibility and transmembrane helices (1D) can be predicted with significantly improved accuracy using multiple sequence alignments. Some of these new prediction methods have proven accurate and reliable enough to be useful in genome analysis, and in experimental structure determination. Moreover, the new generation of theoretical methods is increasingly influencing experiments in molecular biology.