Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly Polygenic Variation in Environmental Perception Determines Dauer Larvae Formation in Growing Populations of Caenorhabditis elegans

Background Determining how complex traits are genetically controlled is a requirement if we are to predict how they evolve and how they might respond to selection. This requires understanding how distinct, and often more simple, life history traits interact and change in response to environmental conditions. In order to begin addressing such issues, we have been analyzing the formation of the developmentally arrested dauer larvae of Caenorhabditis elegans under different conditions. Results We find that 18 of 22 previously identified quantitative trait loci (QTLs) affecting dauer larvae formation in growing populations, assayed by determining the number of dauer larvae present at food patch exhaustion, can be recovered under various environmental conditions. We also show that food patch size affects both the ability to detect QTLs and estimates of effect size, and demonstrate that an allele of nath-10 affects dauer larvae formation in growing populations. To investigate the component traits that affect dauer larvae formation in growing populations we map, using the same introgression lines, QTLs that affect dauer larvae formation in response to defined amounts of pheromone. This identifies 36 QTLs, again demonstrating the highly polygenic nature of the genetic variation underlying dauer larvae formation. Conclusions These data indicate that QTLs affecting the number of dauer larvae at food exhaustion in growing populations of C. elegans are highly reproducible, and that nearly all can be explained by variation affecting dauer larvae formation in response to defined amounts of pheromone. This suggests that most variation in dauer larvae formation in growing populations is a consequence of variation in the perception of the food and pheromone environment (i.e. chemosensory variation) and in the integration of these cues.
Genetic Variations Associated with Postoperative Recurrence in Stage I Non–Small Cell Lung Cancer

Purpose: Postoperative recurrence in stage I non–small cell lung cancer (NSCLC) is the major cause of a poor prognosis. This study aims to identify genetic variants that are associated with the prognosis of early-stage NSCLCs. Experimental Design: A genome-wide association study (GWAS) was conducted in 250 patients in stage I NSCLCs and the results were replicated in additional 308 patients. Results: Results from an Affymetrix Genome-wide Human SNP array in 250 patients identified 94 SNPs with significant associations (P < 2 × 10−4), which were selected for replication in 308 additional patients. Pooled analysis of the 558 patients determined that rs1454694 in chromosome 4q34 was the most significant marker of lung cancer prognosis in the stage I patients (adjusted HR = 2.81; P = 5.91 × 10−8). After the candidate loci were mapped, an additional four markers at chromosome 4q34.3 were significantly associated with recurrence-free survival (RFS; P < 5 × 10−5). A haplotype of five SNPs in 4q34 also showed significant association with RFS (P = 4.29 × 10−6). Conclusions: A genetic polymorphism rs1454694 was identified as a novel genetic risk factor for RFS of stage I NSCLCs. This genome-wide study suggests that genetic markers in 4q34.3 contribute to predict the prognosis of Korean patients with stage I NSCLCs. Clin Cancer Res; 20(12); 3272–9. ©2014 AACR.
DIGITAL INCLUSION PROJECTS IN DEVELOPING COUNTRIES: PROCESSES OF INSTITUTIONALISATION

This paper concerns digital inclusion projects in developing countries and, in particular, focuses on processes of institutionalisation of such projects. Three case studies are described and analysed. The first is the Akshaya telecentre project in the state of Kerala in India. The second is a community-based ICT project in a town in a rural area of South Africa. The third is the efforts of various agencies on telecentre projects in the mega-city of São Paulo in Brazil. The cases are analysed through a simple theoretical schema derived from institutional theory. The analyses are used to derive four key processes of institutionalisation which are argued to be of relevance to all digital inclusion projects: getting symbolic acceptance by the community; stimulating valuable social activity in relevant social groups; generating linkage to viable revenue streams; and enrolling government support. The paper concludes with some theoretical, methodological and policy implications.
Optimizing ZigBee Security using Stochastic Model Checking

ZigBee is a fairly new but promising wireless sensor network standard that offers the advantages of simple and low resource communication. Nevertheless, security is of great concern to ZigBee, and enhancements are prescribed in the latest ZigBee specication: ZigBee-2007. In this technical report, we identify an important gap in the specification on key updates, and present a methodology for determining optimal key update policies and security parameters. We exploit the stochastic model checking approach using the probabilistic model checker PRISM, and assess the security needs for realistic application scenarios.
Incorporating technology acceptance into composite IT BSC for IT investment evaluation: An exploratory study in Egypt

This Paper proposes a theoretical framework to evaluate IT Investments based on Composite IT Balanced Scorecard (IT BSC) which takes into consideration Behavioral Dimension represented by Unified Theory of Acceptance and Use of Technology (UTAUT). Clearly, the proposed framework extends the IT BSC to account for technology acceptance as a behavioral dimension along with other dimensions of IT BSC. Furthermore, UTAUT is updated with relevant variables and beliefs that are not considered by the theory, rather than inclusion of emerging trends proposed by other models and theories and subsequent studies (e.g. proposed framework take Attitude into consideration as a multi-dimension construct). Contributions of this paper are not limited to theory development, it is extended to practice as well. Not only does the study examine the extent that decision makers account for Technology Acceptance in IT investment decisions in Egypt, but also it explores the extent that users accept IT based information systems in Egypt. Results are in general so far consistent with what is proposed by theory and literature in the field. However, with regard to IT investment decisions in Egypt, results reflect lack of awareness of decision makers of Technology Acceptance in such decisions contexts.
Dimensionality reduction for visual exploration of similarity structures

Visualizations of similarity relationships between data points are commonly used in exploratory data analysis to gain insight on new data sets. Answers are searched for questions like: Does the data consist of separate groups of points? What is the relationship of the previously known interesting data points to other data points? Which points are similar to the points known to be of interest? Visualizations can be used both to amplify the cognition of the analyst and to help in communicating interesting similarity structures found in the data to other people. One of the main problems faced in information visualization is that while the data is typically very high-dimensional, the display is limited to only two or at most three dimensions. Thus, for visualization, the dimensionality of the data has to be reduced. In general, it is not possible to preserve all pairwise relationships between data points in the dimensionality reduction process. This has lead to the development of a large number of dimensionality reduction methods that focus on preserving different aspects of the data. Most of these methods were not developed to be visualization methods, which makes it hard to assess their suitability for the task of visualizing similarity structures. This problem is made more severe by the lack of suitable quality measures in the information visualization field. In this thesis a new visualization task, visual neighbor retrieval, is introduced. It formulates information visualization as an information retrieval task. To assess the performance of dimensionality reduction methods in this task two pairs of new quality measures are introduced and the performance of several dimensionality reduction methods are analyzed. Based on the insight gained on the existing methods, three new dimensionality reduction methods (NeRV, fNeRV and LocalMDS) aimed for the visual neighbor retrieval task, are introduced. All three new methods outperform other methods in numerical experiments; they vary in their speed and accuracy. A new color coding scheme, similarity-based color coding, is introduced in this thesis for visualization of similarity structures, and the applicability of the new methods in the task of creating graph layouts is studied. Finally, new approaches to visually studying the results and convergence of Markov Chain Monte Carlo methods are introduced. Venna, J. (2007): Dimensionalisuuden pienentäminen samankaltaisuuksien visuaalista tarkastelua varten. Väitöskirja, Teknillinen korkeakoulu, Dissertations in Computer and Information Science, Raportti D20, Espoo, Suomi. Avainsanat: Dimensionaalisuuden pienentäminen, eksploratiivinen dataanalyysi, informaation visualisointi, Markov-ketju Monte Carlo, monistojen oppiminen, tiedonhaku.
Learning touch preferences with a tactile robot using dopamine modulated STDP in a model of insular cortex

Neurorobots enable researchers to study how behaviors are produced by neural mechanisms in an uncertain, noisy, real-world environment. To investigate how the somatosensory system processes noisy, real-world touch inputs, we introduce a neurorobot called CARL-SJR, which has a full-body tactile sensory area. The design of CARL-SJR is such that it encourages people to communicate with it through gentle touch. CARL-SJR provides feedback to users by displaying bright colors on its surface. In the present study, we show that CARL-SJR is capable of learning associations between conditioned stimuli (CS; a color pattern on its surface) and unconditioned stimuli (US; a preferred touch pattern) by applying a spiking neural network (SNN) with neurobiologically inspired plasticity. Specifically, we modeled the primary somatosensory cortex, prefrontal cortex, striatum, and the insular cortex, which is important for hedonic touch, to process noisy data generated directly from CARL-SJR's tactile sensory area. To facilitate learning, we applied dopamine-modulated Spike Timing Dependent Plasticity (STDP) to our simulated prefrontal cortex, striatum, and insular cortex. To cope with noisy, varying inputs, the SNN was tuned to produce traveling waves of activity that carried spatiotemporal information. Despite the noisy tactile sensors, spike trains, and variations in subject hand swipes, the learning was quite robust. Further, insular cortex activities in the incremental pathway of dopaminergic reward system allowed us to control CARL-SJR's preference for touch direction without heavily pre-processed inputs. The emerged behaviors we found in this model match animal's behaviors wherein they prefer touch in particular areas and directions. Thus, the results in this paper could serve as an explanation on the underlying neural mechanisms for developing tactile preferences and hedonic touch.
Region-based Image Retrieval Using Semantic Mining

In this paper, a multi-level image representation model is developed and used to mine semantic feature hidden in the original remote sensing image. This model is consisted of three levels : region level, region feature level and semantic level. The first two levels aim at represent image content by using region feature. Semantic level aims at extracting hidden semantic feature. At last, interested part and uninterested part method is used to improve the retrieval precision. Experiment shows that this method can well improve the accuracy of the retrieval result.
Association of the Transcription Factor 7 Like 2 (TCF7L2) Polymorphism With Diabetic Nephropathy Risk

AbstractIt is assumed that genetic factors may participate in the development of diabetic nephropathy (DN). The association between TCF7L2 gene polymorphism and DN risk is still unclear. To evaluate the relationship, we performed this meta-analysis.Eligible relevant studies were searched and selected from PubMed, Embase, and ISI Web of Science. Summary effect estimates were derived using a random effects model, with attention to study quality and publication bias. Ethnical approval was not necessary, because this meta-analysis was based on published articles, and did not involve patient consent.A total of 7 studies were identified. Analysis of all studies indicated significant association between TCF7L2 gene polymorphism and DN risk (odds ratio [OR] = 1.31, 95% confidence interval (CI) = 1.10–1.56, Pheterogeneity < 0.00001, P = 0.002). Subgroup analysis showed similar results in Asian (OR = 1.33, 95% CI = 1.10–1.62, Pheterogeneity = 0.03, P = 0.004), in Caucasian (OR = 2.27, 95% CI = 1.78–2.90, Pheterogeneity = 0.17, P < 0.00001), in rs7903146 mutation (OR = 1.61, 95% CI = 1.25–2.07, Pheterogeneity < 0.00001, P = 0.0002), However, no association was observed in Negroid (OR = 1.10, 95% CI = 0.90–1.35, Pheterogeneity < 00001, P = 0.36). Our results suggest that TCF7L2 gene polymorphism may contribute to the risk of DN. However, more studies should be launched in the future.
Simultaneous EEG-fMRI data acquisition during cognitive task

Functional Magnetic Resonance Imaging (fMRI) has very high spatial resolution as compared to the Electroencephalography (EEG) which on other hand has very high temporal resolution. The pros and cons of the EEG and fMRI are complementary to each other. Simultaneous EEG-fMRI data recording solve the problem to get high spatial and temporal resolution at the same time to study the brain dynamics in efficient manner. EEG-fMRI integration is a new approach to study human brain activity. Recent developments in MRI compatible EEG equipment made this integration more easy and attractive in cognitive neuroscience. The simultaneous EEG-fMRI data acquisition gives us the better information for all activated areas of the brain to understand the cognitive processes. We developed data acquisition setup for simultaneous EEG-fMRI for cognitive tasks. Also data recording has been done for two healthy participants as a pilot study which will be further continued on other healthy participants as well as on patients. The EEG and fMRI data is pre-processed and artefacts are removed. This combined EEG-fMRI data can be further used in multimodal data integration or data fusion which can give the better results and understanding of the cognitive processes of human brain as compared to analysing EEG and fMRI data separately.
PKA Enhances the Acute Insulin Response Leading to the Restoration of Glucose Control

Diabetes arises from insufficient insulin secretion and failure of the β-cell mass to persist and expand. These deficits can be treated with ligands to Gs-coupled G-protein-coupled receptors that raise β-cell cAMP. Here we studied the therapeutic potential of β-cell cAMP-dependent protein kinase (PKA) activity in restoring glucose control using β-caPKA mice. PKA activity enhanced the acute insulin response (AIR) to glucose, which is a primary determinant of the efficacy of glucose clearance. Enhanced AIR improved peripheral insulin action, leading to more rapid muscle glucose uptake. In the setting of pre-established glucose intolerance caused by diet-induced insulin resistance or streptozotocin-mediated β-cell mass depletion, PKA activation enhanced β-cell secretory function to restore glucose control, primarily through augmentation of the AIR. Enhanced AIR and improved glucose control were maintained through 16 weeks of a high-fat diet and aging to 1 year. Importantly, improved glucose tolerance did not increase the risk for hypoglycemia, nor did it rely upon hyperinsulinemia or β-cell hyperplasia, although PKA activity was protective for β-cell mass. These data highlight that improving β-cell function through the activation of PKA has a large and underappreciated capacity to restore glucose control with minimal risk for adverse side effects.
Do financial networks matter in retirement investment decisions?

Using experimental survey data collected from a sample of Generation Yers, we examine the joint influence of financial literacy and financial networks on individual retirement investment decisions. We find, first, that financial literacy and financial network intensity (the network strength with the financially literate) are positively related to stock allocation. Second, the positive relationship between financial literacy and stock allocation, however, is significant only among those having high financial network intensity. This finding suggests that the positive effects of financial literacy documented in the literature can be limited to only those who have strong networks with the financially literate.
Characterization of White and Gray Matters in healthy brain: An in-vivo Diffusion Kurtosis Imaging study

The extensions of diffusion weighted magnetic resonance imaging (DW-MRI) technique such as Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI) are useful in understanding the complex cellular micro-structures non-invasively. DTI is used to quantify the three-dimensional water diffusion in biological tissues by assuming the diffusion to be Gaussian, whereas DKI which is an extension of DTI, quantifies the deviation of water diffusion from Gaussian model. In this study, we focus on finding the correlation between DTI and DKI parameters for characterizing the brain regions such as White Matter (WM) and Gray Matter (GM) of healthy human brain. The data obtained from DKI scans on nine healthy volunteers are used in this study. Parameters such as Mean Kurtosis (MK) and Fractional Anisotropy (FA) were estimated for the two brain regions, WM and GM. Correlation analysis was performed between MK and FA in WM and GM. Significant differences in correlation results were observed for WM and GM. These preliminary findings indicate that this correlation measure could be used to characterize WM and GM regions in normal human brain. This could be used in applications such as segmentation of WM and GM and additionally bear potential for discriminating pathologies.
Bioinformatics Discovery Note Duf283 Domain of Dicer Proteins Has a Double-stranded Rna-binding Fold

Two RNases, Dicer and Argonaute, are at the heart of the RNA interference (RNAi) molecular machinery responsible for gene silencing. Both RNases contain multiple domains, most of which have been characterized or have functions that can be predicted based on sequence comparisons. However, Dicers of higher eukaryotes contain the domain known as DUF283 which at present has no assigned role. Using sensitive profile–profile comparisons, we detected a divergent double-stranded RNA-binding domain coinciding with the DUF283 of Dicer. This finding has potential implications regarding the mechanistic role of Dicer in RNAi. Supplementary information: Supplementary images are available at Bioinformatics online.
Authorization in distributed systems: a formal approach

It is argued that authorization is an independent semantic concept that must be separated from implementation mechanisms and given a precise semantics. A logical approach to representing and evaluating authorization is proposed. Specifically, a language for specifying policy bases is introduced. A policy base encodes a set of authorization requirements and is given a precise semantics based on a formal notion of authorization policy. The semantics is computable, thus providing a basis for authorization evaluation. Two composition operators for policy bases which are appropriate for modeling distributed systems with multiple administrative domains are introduced. <<ETX>>
Treemaps and Choropleth Maps Applied to Regional Hierarchical Statistical Data

An emerging and challenging Geovisual Analytics application domain is visualization of hierarchical regional (sub-national) statistics. The OECD regional database is a potential treasure chest for policy-makers, researchers and citizens to gain a better understanding of a region’s structure and performance and to carry out analysis of territorial trends and disparities based on sound information comparable across countries. New methods are needed to visually make comparisons between groups on different hierarchical levels, such as cities with countries or parts of countries. In this context, we demonstrate and reflect upon the potential synergy between information and geovisualization through the use of a squarified treemap dynamically linked to a choropleth map to facilitate visualization of complex hierarchical social science data. We exemplify and evaluate our approach with three usage scenarios that explore population change in the OECD countries using 1) squarified treemap, 2) regional choropleth map and 3) combined treemap and choropleth map.
A Window Based Approach To Novelty

This paper discusses and explains the window based technique towards novelty. The scores we have obtained are relatively high compared to other approaches in the novelty task. High Correlation between relevance scores and novelty scores will be found because the latter depends on the former. Due to this fact an important part of this paper is focused on relevance. Chapter
Cloning and Genetic Characterization of dca Genes Required for β-Oxidation of Straight-Chain Dicarboxylic Acids in Acinetobacter sp. Strain ADP1

ABSTRACT A previous study of deletions in the protocatechuate (pca) region of the Acinetobacter sp. strain ADP1 chromosome revealed that genes required for utilization of the six-carbon dicarboxylic acid, adipic acid, are linked to thepca structural genes. To investigate the genes involved in adipate catabolism, a 33.8-kb SacI fragment, which corrects a deletion spanning this region, was cloned. In addition to containing known pca, qui, and pob genes (for protocatechuate, quinate, and 4-hydroxybenzoate dissimilation), clone pZR8000 contained 10 kb of DNA which was the subject of this investigation. A mutant strain of Escherichia coli DH5α, strain EDP1, was isolated that was able to utilize protocatechuate and 4-hydroxybenzoate as growth substrates when EDP1 cells contained pZR8000. Sequence analysis of the new region of DNA on pZR8000 revealed open reading frames predicted to be involved in β-oxidation. Knockouts of three genes implicated in β-oxidation steps were introduced into the chromosome of Acinetobacter sp. strain ADP1. Each of the mutants was unable to grow with adipate. Because the mutants were affected in their ability to utilize additional saturated, straight-chain dicarboxylic acids, the newly discovered 10 kb of DNA was termed the dca(dicarboxylic acid) region. Mutant strains included one with a deletion in dcaA (encoding an acyl coenzyme A [acyl-CoA] dehydrogenase homolog), one with a deletion in dcaE(encoding an enoyl-CoA hydratase homolog), and one with a deletion indcaH (encoding a hydroxyacyl-CoA dehydrogenase homolog). Data on the dca region should help us probe the functional significance and interrelationships of clustered genetic elements in this section of the Acinetobacterchromosome.