Increasing Efficiency of Recommendation System using Big Data Analysis

In the present Digital Space a lot of Internet users try to come up with solutions to a particular problem by suggesting solutions that are pre-existing on the Internet. This brings down the originality of posts and we are able to overcome this problem by applying prediction models on data sets. It is important for a user to come up with original ideas to gain up votes which in turn represent the quality of a post. Due to the huge influx of data at every moment, the need for big data analytics becomes essential and hence the use of an open source framework like Hadoop is imperative so as to increase effectiveness of recommender system built on these prediction models

Factors affecting stillbirth: prospective study

Background: Stillbirth is defined by WHO as the birth of a baby with a birth weight of 500 gm or more, 22 or more completed weeks of gestation or a body length of 25 cm or more, who died before or during labour and birth.Methods: This was prospective observational study of factors affecting stillbirth was conducted in tertiary hospital for a period of 1 year from 1st June 2014 to 31st May 2015. During the study period, 200 parturient of gestational age 28 weeks or more and fetal weight 1000 gm or more with or without medical disorders were included.Results: The total number of births during study period was 11,951. Stillbirth rate in the present study was 16.73 per 1000 births. Most of stillbirths were seen in the antepartum period (76%) when compared to intrapartum period (24%). Maximum stillbirths occurred in gestational age of 36 weeks and above (52%) and fetal weight between 2001-2500 gm (27.50%). Patients with inadequate antenatal care, less than three visits had 86% stillbirths.Conclusions: Proper antenatal care, prompt referral services and availability of emergency obstetric care will provide a pivotal role for reduction of stillbirths

A Flexible Crypto-system Based upon the REDEFINE Polymorphic ASIC Architecture

The highest levels of security can be achieved through the use of more than one type of cryptographic algorithm for each security function. In this paper, the REDEFINE polymorphic architecture is presented as an architecture framework that can optimally support a varied set of crypto algorithms without losing high performance. The presented solution is capable of accelerating the advanced encryption standard (AES) and elliptic curve cryptography (ECC) cryptographic protocols, while still supporting different flavors of these algorithms as well as different underlying finite field sizes. The compelling feature of this cryptosystem is the ability to provide acceleration support for new field sizes as well as new (possibly proprietary) cryptographic algorithms decided upon after the cryptosystem is deployed.Defence Science Journal, 2012, 62(1), pp.25-31, DOI:http://dx.doi.org/10.14429/dsj.62.143

QS9: Host Biofilm Interaction In Breast Implant Illness

Purpose: Breast Implant Illness (BII) is patient-described constellation of symptoms that are believed to be related to their breast implant. The symptoms described include fibromyalgia, chronic fatigue and a host of other symptoms that are often associated with autoimmune illnesses. In this work, we report that bacterial biofilm associated with breast implant, metabolize fatty acid oleic acid present in the breast tissue milieu to oxylipins, one such oxylipin identified from this study is (10S)-hydroxy-(8E)-octadecenoic acid (10-HOME). We hypothesize that immunomodulatory effects of oxylipin 10-HOME produced by biofilm present on the implant could be correlated with BII pathogenesis. Methods: Capsulectomy and breast implants from clinically indicated procedures for patients requesting prosthetic removal were collected using clinical parameters outlined in previous studies, and questionnaire screened for the commonly reported symptoms associated with BII. Predictive variables included age, diabetes status, co-morbidities, nature and duration of implant. Scanning electron microscopy (SEM), Wheat Germ Agglutinin (WGA) and 16SrRNA sequencing were used for bacterial biofilm bacterial identification. 10-HOME was quantitated through targeted and untargeted lipidomic analyses using LC-MS-MS. Results: Sixty eight Implant, associated capsules and breast tissue specimen were collected for BII (n=46) and two control groups, group I, (non-BII, n=14) patients with breast implants, no BII symptoms. Group II (normal tissue, n = 8), patients without an implant, whose breast tissue was removed due to surgical procedures. Bacterial biofilm was detected through SEM in both BII and non BII cohorts. However, WGA analysis (quantitative analysis) indicated increased abundance of biofilm in the BII cohort (n=7, p=0.0036). 16SrRNA (genomic) sequencing identified increased abundance of Staphylococcus epidermidis (Fisher’s exact test, p<0.001) in the BII group (63.04%) compared to non-BII group (14.3%) and the normal group. The BII group was 9.8 times significantly more likely to have Staphylococcus epidermidis colonization compared to the non-BII group (p=0.003, logistic regression), compared to normal, it is 17.4 times significantly more likely to have Staphylococcus epidermidis (p=0.0021). Elevated levels of 10-HOME BII compared to non-BII samples, (p < 0.0001) were observed through mass spectrometry. Positive correlation was observed between bacterial abundance and concentration of 10-HOME in BII subjects (R2=0.88). Similar correlation was observed in BII subjects with Staphylococcus epidermidis (R2=0.77). Conclusion: This study investigated the biofilm hypothesis of breast implant illness through a host-pathogen interaction. The breast microenvironment led to formation of biofilm derived 10-HOME from host oleic acid. The study provides the first evidence of a possible correlation between bacterial biofilm and biofilm derived 10-HOME in the context of 10-HOME. In consideration of reports of biofilm association with other metal implants, the findings of this study can possibly explain autoimmune response associated with those implants

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Build your own closed loop: Graph-based proof of concept in closed loop for autonomous networks

Next Generation Networks (NGNs) are expected to handle heterogeneous technologies, services, verticals and devices of increasing complexity. It is essential to fathom an innovative approach to automatically and efficiently manage NGNs to deliver an adequate end-to-end Quality of Experience (QoE) while reducing operational expenses. An Autonomous Network (AN) using a closed loop can self-monitor, self-evaluate and self-heal, making it a potential solution for managing the NGN dynamically. This study describes the major results of building a closed-loop Proof of Concept (PoC) for various AN use cases organized by the International Telecommunication Union Focus Group on Autonomous Networks (ITU FG-AN). The scope of this PoC includes the representation of closed-loop use cases in a graph format, the development of evolution/exploration mechanisms to create new closed loops based on the graph representations, and the implementation of a reference orchestrator to demonstrate the parsing and validation of the closed loops. The main conclusions and future directions are summarized here, including observations and limitations of the PoC

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions