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

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

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

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

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

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