Discussing Issues of Information Technology in Country Development

The development of countries with laggard economies is becoming a global concern. The harmful complications of poverty and illiteracy are beginning to show up outside those countries in violent forms. The precautions against this migration of harm are splitting the world apart at the same time as technology may become able to bring it together. Over the last few years, information technology (IT) has grown very fast and is still spreading into all aspects of life in developed countries. The recent research in those countries is bringing more evidence that IT is a leveraging tool for business and not a luxury. This paper discusses the potential role IT could play in developing a country. We start by raising the awareness about the major factors of development of relevance to the thesis topic: natural resources, location, industrialization and education. We identify the two main approaches to development and point out the potential of each using cases of two exemplary countries. Finally, we discuss the literature surveyed and the conclusions one could derive about the role of IT in country development

Multivariate characterization of white matter heterogeneity in autism spectrum disorder

The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying neuroimaging-based phenotypes, as well as help elucidate underlying neural mechanisms of brain variation in autism spectrum disorders

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Modified mallampati classification as a clinical predictor of peroral esophagogastroduodenoscopy tolerance

<p>Abstract</p> <p>Background</p> <p>Unsedated esophagogastroduodenoscopy (EGD) is simpler and safer than sedated EGD; however, approximately 40% of patients cannot tolerate it. Early identification of patients likely to poorly tolerate unsedated EGD is valuable for improving compliance. The modified Mallampati classification (MMC) has been used to evaluate difficult tracheal intubation and laryngoscope insertion. We tried to assess the efficacy of MMC to predict the tolerance of EGD in unsedated patients.</p> <p>Methods</p> <p>Two hundred patients who underwent an unsedated diagnostic EGD were recruited. They were stratified according to the view of the oropharynx as either MMC class I + II (good view) or class III + IV (poor view). EGD tolerance was assessed in three ways: gag reflex by endoscopist assessment, patient satisfaction by interview, and the degree of change in vital signs.</p> <p>Results</p> <p>MMC was significantly correlated to gag reflex (<it>P </it>< 0.001), patient satisfaction (<it>P </it>= 0.028), and a change of vital signs (<it>P </it>= 0.024). Patients in the poor view group had a 3.87-fold increased risk of gag reflex (<it>P </it>< 0.001), a 1.78-fold increased risk of unsatisfaction (<it>P </it>= 0.067), and a 1.96-fold increased risk of a change in vital signs (<it>P </it>= 0.025) compared to those in the good view group.</p> <p>Conclusions</p> <p>MMC appears to be a clinically useful predictor of EGD tolerance. Patients with poor view of oropharynx by MMC criteria may be candidates for sedated or transnasal EGD.</p

Improving a gold standard: treating human relevance judgments of MEDLINE document pairs

Given prior human judgments of the condition of an object it is possible to use these judgments to make a maximal likelihood estimate of what future human judgments of the condition of that object will be. However, if one has a reasonably large collection of similar objects and the prior human judgments of a number of judges regarding the condition of each object in the collection, then it is possible to make predictions of future human judgments for the whole collection that are superior to the simple maximal likelihood estimate for each object in isolation. This is possible because the multiple judgments over the collection allow an analysis to determine the relative value of a judge as compared with the other judges in the group and this value can be used to augment or diminish a particular judge’s influence in predicting future judgments. Here we study and compare five different methods for making such improved predictions and show that each is superior to simple maximal likelihood estimates

The Novel μ-Opioid Receptor Antagonist GSK1521498 Decreases Both Alcohol Seeking and Drinking: Evidence from a New Preclinical Model of Alcohol Seeking.

Distinct environmental and conditioned stimuli influencing ethanol-associated appetitive and consummatory behaviors may jointly contribute to alcohol addiction. To develop an effective translational animal model that illuminates this interaction, daily seeking responses, maintained by alcohol-associated conditioned stimuli (CSs), need to be dissociated from alcohol drinking behavior. For this, we established a procedure whereby alcohol seeking maintained by alcohol-associated CSs is followed by a period during which rats have the opportunity to drink alcohol. This cue-controlled alcohol-seeking procedure was used to compare the effects of naltrexone and GSK1521498, a novel selective μ-opioid receptor antagonist, on both voluntary alcohol-intake and alcohol-seeking behaviors. Rederived alcohol-preferring, alcohol-nonpreferring, and high-alcohol-drinking replicate 1 line of rats (Indiana University) first received 18 sessions of 24 h home cage access to 10% alcohol and water under a 2-bottle choice procedure. They were trained subsequently to respond instrumentally for access to 15% alcohol under a second-order schedule of reinforcement, in which a prolonged period of alcohol-seeking behavior was maintained by contingent presentations of an alcohol-associated CS acting as a conditioned reinforcer. This seeking period was terminated by 20 min of free alcohol drinking access that achieved significant blood alcohol concentrations. The influence of pretreatment with either naltrexone (0.1-1-3 mg/kg) or GSK1521498 (0.1-1-3 mg/kg) before instrumental sessions was measured on both seeking and drinking behaviors, as well as on drinking in the 2-bottle choice procedure. Naltrexone and GSK1521498 dose-dependently reduced both cue-controlled alcohol seeking and alcohol intake in the instrumental context as well as alcohol intake in the choice procedure. However, GSK1521498 showed significantly greater effectiveness than naltrexone, supporting its potential use for promoting abstinence and preventing relapse in alcohol addiction.The present study was funded by Medical Research Council Programme Grant (no. G1002231) and by GlaxoSmithKline (GSK), which has a commercial interest in GSK1521498. Charles R. Goodlett was funded by a grant from the IUPUI International Development Fund, which supported his sabbatical leave at the University of Cambridge. Maria Pilar Garcia-Pardo was funded by Val+id para investigadores en formación (Conselleria de educacion, Generalitat Valenciana), which also supported her stay at the University of Cambridge (January-April 2014) as a Visiting Student.This is the accepted manuscript. The final version is available from NPG at http://dx.doi.org/10.1038/npp.2015.15

Decreased Brain Volume in Adults with Childhood Lead Exposure

Using magnetic resonance imaging to assess brain volumes, Kim Cecil and colleagues find that inner-city children with higher blood lead levels showed regions of decreased gray matter as adults

The effects of patient characteristics on ADHD diagnosis and treatment: a factorial study of family physicians

<p>Abstract</p> <p>Background</p> <p>Attention Deficit Hyperactivity Disorder (ADHD) is a costly and prevalent disorder in the U.S., especially among youth. However, significant disparities in diagnosis and treatment appear to be predicted by the race and insurance status of patients.</p> <p>Methods</p> <p>This study employed a web-based factorial survey with four ADHD cases derived from an ADHD clinic, two diagnosed with ADHD in actual evaluation, and two not. Randomized measures included race and insurance status of the patients. Participants N = (187) included clinician members of regional and national practice-based research networks and the U.S. clinical membership of the Society of Teachers of Family Medicine. The main outcomes were decisions to 1) diagnose and 2) treat the cases, based upon the information presented, analyzed via binary logistic regression of the randomized factors and case indicators on diagnosis and treatment.</p> <p>Results</p> <p>ADHD-positive cases were 8 times more likely to be diagnosed and 12 times more likely to be treated, and the male ADHD positive case was more likely to be diagnosed and treated than the female ADHD positive case. Uninsured cases were significantly more likely to be treated overall, but male cases that were uninsured were about half as likely to be diagnosed and treated with ADHD. Additionally, African-American race appears to increase the likelihood of medicinal treatment for ADHD and being both African-American and uninsured appears to cut the odds of medicinal treatment in half, but not significantly.</p> <p>Conclusions</p> <p>Family physicians were competent at discerning between near-threshold ADHD-negative and ADHD positive cases. However, insurance status and race, as well as gender, appear to affect the likelihood of diagnosis and treatment for ADHD in Family Medicine settings.</p

Simulation and background characterisation of the SABRE South experiment

Published online: 28 September 2023SABRE(Sodium iodide with Active Background REjection) is a direct detection darkmatter experiment based on arrays of radio-pureNaI(Tl) crystals.The experiment aims at achieving an ultra-low background rate and its primary goal is to confirm or refute the results from the DAMA/LIBRA experiment. The SABRE Proof-of-Principle phase was carried out in 2020–2021 at the Gran Sasso National Laboratory (LNGS), in Italy. The next phase consists of two full-scale experiments: SABRE South at the Stawell Underground Physics Laboratory, in Australia, and SABRE North at LNGS. This paper focuses on SABRE South and presents a detailed simulation of the detector, which is used to characterise the background for darkmatter searches includingDAMA/ LIBRA-like modulation. We estimate an overall background of 0.72 cpd/kg/keVee in the energy range 1–6 keVee primarily due to radioactive contamination in the crystals. Given this level of background and considering that the SABRE South has a target mass of 50 kg, we expect to exclude (confirm) DAMA/LIBRA modulation at 4 (5)σ within 2.5 years of data taking.E. Barberio ... I. Bolognino ... G. C. Hill ... K. T. Leaver ... P. McGee ... A. G. Williams ... et al. (SABRE South Collaboration

A Combination of Independent Transcriptional Regulators Shapes Bacterial Virulence Gene Expression during Infection

Transcriptional regulatory networks are fundamental to how microbes alter gene expression in response to environmental stimuli, thereby playing a critical role in bacterial pathogenesis. However, understanding how bacterial transcriptional regulatory networks function during host-pathogen interaction is limited. Recent studies in group A Streptococcus (GAS) suggested that the transcriptional regulator catabolite control protein A (CcpA) influences many of the same genes as the control of virulence (CovRS) two-component gene regulatory system. To provide new information about the CcpA and CovRS networks, we compared the CcpA and CovR transcriptomes in a serotype M1 GAS strain. The transcript levels of several of the same genes encoding virulence factors and proteins involved in basic metabolic processes were affected in both ΔccpA and ΔcovR isogenic mutant strains. Recombinant CcpA and CovR bound with high-affinity to the promoter regions of several co-regulated genes, including those encoding proteins involved in carbohydrate and amino acid metabolism. Compared to the wild-type parental strain, ΔccpA and ΔcovRΔccpA isogenic mutant strains were significantly less virulent in a mouse myositis model. Inactivation of CcpA and CovR alone and in combination led to significant alterations in the transcript levels of several key GAS virulence factor encoding genes during infection. Importantly, the transcript level alterations in the ΔccpA and ΔcovRΔccpA isogenic mutant strains observed during infection were distinct from those occurring during growth in laboratory medium. These data provide new knowledge regarding the molecular mechanisms by which pathogenic bacteria respond to environmental signals to regulate virulence factor production and basic metabolic processes during infection