Protein binding affinity prediction using support vector regression and interfecial features

In understanding biology at the molecular level, analysis of protein interactions and protein binding affinity is a challenge. It is an important problem in computational and structural biology. Experimental measurement of binding affinity in the wet-lab is expensive and time consuming. Therefore, machine learning approaches are widely used to predict protein interactions and binding affinities by learning from specific properties of existing complexes. In this work, we propose an innovative computational model to predict binding affinities and interaction based on sequence, structural and interface features of the interacting proteins that are robust to binding associated conformational changes. We modeled the prediction of binding affinity as classification and regression problem with least-squared and support vector regression models using structure and sequence features of proteins. Specifically, we have used the number and composition of interacting residues at protein complexes interface as features and sequence features. We evaluated the performance of our prediction models using Affinity Benchmark Dataset version 2.0 which contains a diverse set of both bound and unbound protein complex structures with known binding affinities. We evaluated our regression performance results with root mean square error (RMSE) as well as Spearman and Pearson's correlation coefficients using a leave-one-out cross-validation protocol. We evaluate classification results with AUC-ROC and AUC-PR Our results show that Support Vector Regression performs significantly better than other models with a Spearman Correlation coefficient of 0.58, Pearson Correlation score of 0.55 and RMSE of 2.41 using 3-mer and sequence feature. It is interesting to note that simple features based on 3-mer features and the properties of the interface of a protein complex are predictive of its binding affinity. These features, together with support vector regression achieve higher accuracy than existing sequence based methods

A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial setting.Comment: 9 pages, 3 figure

Internet of Things (IoT) Enabled Smart Indoor Air Quality Monitoring System

This article introduces development of a system that monitors indoor air quality by using Internet of Things (IoT) technology. The objective of this system is to monitor and improve indoor air quality automatically, i.e. with minimum human intervention. The system contains physical circuit and an interactive platform. Main components used in physical circuit are Arduino Leonardo, Dust Sensor, Temperature and Humidity Sensor, LCD Display and Fan. Interactive platforms involved are The Things Network and Ubidots. Principal parameters of interest are sensed by physical circuit and converted into Air Quality Index (AQI), which is then sent to an interactive platform via gateway. After estimating AQI, the Interactive platform triggers events based on certain predetermined conditions to improve air quality through SMS alerts and circuit actuators

Artificial Intelligence enabled Smart Refrigeration Management System using Internet of Things Framework

Design of an intelligent refrigeration management system using artificial intelligence and Internet of Things (IoT) technology is presented in this paper. This system collects the real-time temperature inside the refrigeration implement, record the information of products and enhance function of refrigerators through the application of Internet of Things technology to facilitate people in managing their refrigerated and frozen groceries smartly. The proposed system is divided into two parts, On-board sub-system and Internet based sub-system. An Arduino Leonardo board is used in onboard sub-system to control other components including low power machine vision OpenMV module, temperature & Humidity sensor, and GY-302 light intensity sensor. OpenMV camera module is used for recognizing types of food, reading barcodes and OCR (optical character recognition) through convolution neural network (CNN) algorithm and tesseract-ocr. The food type identification model is trained by the deep learning framework Caffe. GY-302 light intensity sensor works as a switch of camera module. DHT11 sensor is used to monitor the environmental information inside the freezer. The internet based sub-system works on the things network. It saves the information and uploads it from onboard sub-system and works as an interface to food suppliers. The system demonstrates that the combination of existing everyday utility systems and latest Artificial Intelligence (AI) and Internet of Things (IoT) technologies could help develop smarter applications and devices

Human intronic enhancers control distinct sub-domains of Gli3 expression during mouse CNS and limb development

<p>Abstract</p> <p>Background</p> <p>The zinc-finger transcription factor GLI3 is an important mediator of Sonic hedgehog signaling and crucial for patterning of many aspects of the vertebrate body plan. In vertebrates, the mechanism of SHH signal transduction and its action on target genes by means of activating or repressing forms of GLI3 have been studied most extensively during limb development and the specification of the central nervous system. From these studies it has emerged, that <it>Gli3 </it>expression must be subject to a tight spatiotemporal regulation. However, the genetic mechanisms and the cis-acting elements controlling the expression of <it>Gli3 </it>remained largely unknown.</p> <p>Results</p> <p>Here, we demonstrate in chicken and mouse transgenic embryos that human <it>GLI3</it>-intronic conserved non-coding sequence elements (CNEs) autonomously control individual aspects of <it>Gli3 </it>expression. Their combined action shows many aspects of a <it>Gli3</it>-specific pattern of transcriptional activity. In the mouse limb bud, different CNEs enhance <it>Gli3</it>-specific expression in evolutionary ancient stylopod and zeugopod versus modern skeletal structures of the autopod. Limb bud specificity is also found in chicken but had not been detected in zebrafish embryos. Three of these elements govern central nervous system specific gene expression during mouse embryogenesis, each targeting a subset of endogenous <it>Gli3 </it>transcription sites. Even though fish, birds, and mammals share an ancient repertoire of gene regulatory elements within <it>Gli3</it>, the functions of individual enhancers from this catalog have diverged significantly. During evolution, ancient broad-range regulatory elements within <it>Gli3 </it>attained higher specificity, critical for patterning of more specialized structures, by abolishing the potential for redundant expression control.</p> <p>Conclusion</p> <p>These results not only demonstrate the high level of complexity in the genetic mechanisms controlling <it>Gli3 </it>expression, but also reveal the evolutionary significance of <it>cis</it>-acting regulatory networks of early developmental regulators in vertebrates.</p

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection

With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm which uses sensor data integration and data classification for recognizing the fall. By adopting the Hebbian learning method for training neural networks, weights of human activity features are obtained and implemented/embedded into the hardware design. Here, the neural network weight of fall activity is achieved through data preprocessing, and then the weight is mapped to the amplification factor setting in the hardware. The designs are checked with validation scenarios, and the experiment is completed with a Hopfield neural network in the analog module. Through simulations, the classification accuracy of the fall data reached 88.9% which compares well with some other results achieved by the software-based machine-learning algorithms, which verify the feasibility of our hardware design. The designed system performs the complex signal calculations of the hardware’s feedback signal, replacing the software-based method. A straightforward circuit design is used to meet the weight setting from the Hopfield neural network, which is maximizing the reusability and flexibility of the circuit design

The Effects of Body Acupuncture on Obesity: Anthropometric Parameters, Lipid Profile, and Inflammatory and Immunologic Markers

A randomized controlled clinical trial in 196 obese subjects was performed to examine the effectiveness of body acupuncture on body weight loss, lipid profile and immunogenic and inflammatory markers. Subjects received authentic (cases) or sham (controls) acupuncture for 6 weeks in combination with a low-calorie diet. In the following 6 weeks, they received the low-calorie diet alone. Subjects were assessed at the beginning, 6 and 12 weeks later. Heat shock protein (Hsps)-27, 60, 65, 70 antibody titers and high sensitivity C-reactive protein (hs-CRP) levels were also assessed. A significant reduction in measures of adiposity and improvement in lipid profile were observed in both groups, but the levels of anti-Hsp-antibodies decreased in cases only. A reduction in anthropometric and lipid profile in cases were sustained in the second period, however, only changes in lipid profile were observed in the control group. Anti-Hsp-antibodies and hs-CRP levels continued to be reduced in cases but in controls only the reduction in hs-CRP remained. Changes in anthropometric parameters, lipid profile, and anti-Hsp-antibodies were more evident in cases. Body acupuncture in combination with diet restriction was effective in enhancing weight loss and improving dyslipidemia

The global burden of adolescent and young adult cancer in 2019:a systematic analysis for the Global Burden of Disease Study 2019

Background In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15-39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. Methods Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15-39 years to define adolescents and young adults. Findings There were 1.19 million (95% UI 1.11-1.28) incident cancer cases and 396 000 (370 000-425 000) deaths due to cancer among people aged 15-39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59.6 [54.5-65.7] per 100 000 person-years) and high-middle SDI countries (53.2 [48.8-57.9] per 100 000 person-years), while the highest age-standardised mortality rates were in low-middle SDI (14.2 [12.9-15.6] per 100 000 person-years) and middle SDI (13.6 [12.6-14.8] per 100 000 person-years) countries. In 2019, adolescent and young adult cancers contributed 23.5 million (21.9-25.2) DALYs to the global burden of disease, of which 2.7% (1.9-3.6) came from YLDs and 97.3% (96.4-98.1) from YLLs. Cancer was the fourth leading cause of death and tenth leading cause of DALYs in adolescents and young adults globally. Interpretation Adolescent and young adult cancers contributed substantially to the overall adolescent and young adult disease burden globally in 2019. These results provide new insights into the distribution and magnitude of the adolescent and young adult cancer burden around the world. With notable differences observed across SDI settings, these estimates can inform global and country-level cancer control efforts. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Anemia prevalence in women of reproductive age in low- and middle-income countries between 2000 and 2018

Anemia is a globally widespread condition in women and is associated with reduced economic productivity and increased mortality worldwide. Here we map annual 2000–2018 geospatial estimates of anemia prevalence in women of reproductive age (15–49 years) across 82 low- and middle-income countries (LMICs), stratify anemia by severity and aggregate results to policy-relevant administrative and national levels. Additionally, we provide subnational disparity analyses to provide a comprehensive overview of anemia prevalence inequalities within these countries and predict progress toward the World Health Organization’s Global Nutrition Target (WHO GNT) to reduce anemia by half by 2030. Our results demonstrate widespread moderate improvements in overall anemia prevalence but identify only three LMICs with a high probability of achieving the WHO GNT by 2030 at a national scale, and no LMIC is expected to achieve the target in all their subnational administrative units. Our maps show where large within-country disparities occur, as well as areas likely to fall short of the WHO GNT, offering precision public health tools so that adequate resource allocation and subsequent interventions can be targeted to the most vulnerable populations

Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019

Background Updated data on chronic respiratory diseases (CRDs) are vital in their prevention, control, and treatment in the path to achieving the third UN Sustainable Development Goals (SDGs), a one-third reduction in premature mortality from non-communicable diseases by 2030. We provided global, regional, and national estimates of the burden of CRDs and their attributable risks from 1990 to 2019. Methods Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we estimated mortality, years lived with disability, years of life lost, disability-adjusted life years (DALYs), prevalence, and incidence of CRDs, i.e. chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis, and other CRDs, from 1990 to 2019 by sex, age, region, and Socio-demographic Index (SDI) in 204 countries and territories. Deaths and DALYs from CRDs attributable to each risk factor were estimated according to relative risks, risk exposure, and the theoretical minimum risk exposure level input. Findings In 2019, CRDs were the third leading cause of death responsible for 4.0 million deaths (95% uncertainty interval 3.6–4.3) with a prevalence of 454.6 million cases (417.4–499.1) globally. While the total deaths and prevalence of CRDs have increased by 28.5% and 39.8%, the age-standardised rates have dropped by 41.7% and 16.9% from 1990 to 2019, respectively. COPD, with 212.3 million (200.4–225.1) prevalent cases, was the primary cause of deaths from CRDs, accounting for 3.3 million (2.9–3.6) deaths. With 262.4 million (224.1–309.5) prevalent cases, asthma had the highest prevalence among CRDs. The age-standardised rates of all burden measures of COPD, asthma, and pneumoconiosis have reduced globally from 1990 to 2019. Nevertheless, the age-standardised rates of incidence and prevalence of interstitial lung disease and pulmonary sarcoidosis have increased throughout this period. Low- and low-middle SDI countries had the highest age-standardised death and DALYs rates while the high SDI quintile had the highest prevalence rate of CRDs. The highest deaths and DALYs from CRDs were attributed to smoking globally, followed by air pollution and occupational risks. Non-optimal temperature and high body-mass index were additional risk factors for COPD and asthma, respectively. Interpretation Albeit the age-standardised prevalence, death, and DALYs rates of CRDs have decreased, they still cause a substantial burden and deaths worldwide. The high death and DALYs rates in low and low-middle SDI countries highlights the urgent need for improved preventive, diagnostic, and therapeutic measures. Global strategies for tobacco control, enhancing air quality, reducing occupational hazards, and fostering clean cooking fuels are crucial steps in reducing the burden of CRDs, especially in low- and lower-middle income countries. Funding Bill & Melinda Gates Foundation