EFFECTIVE INTENSITY OF EXERCISE FOR IMPROVING THE MODIFIABLE RISK FACTORS OF CVD IN OVERWEIGHT ADULT MALES

The leading cause of death is CVD worldwide. Physical activity has been labeled as the single most important modifiable risk factor that alters majority of the other risk factors. However, the existing literature about the effective intensity of exercise to influence other modifiable risk factors is obscure and contradictory. Therefore, this study is aimed to find out the effective exercise intensity beneficial enough to influence the other modifiable risk factors. Methodology:After ethical approval and written informed consent 20 male overweight and apparently healthy participants, (age = 31 ± 6.1 years) were recruited. Participants attended the lab for 4 days one week apart. On day 1, height, weight,  blood pressure,  heart rate,  waist and hip circumference, and body composition was measured using Tinnita body analyzer was measured. Based on submaximal exercise testing the intensity for 50%, 60% and 70% of the predicted maximum heart rates were calculated. On subsequent visits, blood samples for fasting sugar, fasting lipid and insulin were taken. The participants performed exercise test on the treadmill as per calculated intensity for 30mins. Exercise induced thermogenesis and substrate metabolism was calculated using breath by breath analyzer. Post exercise blood sampling for lipid, blood sugar and insulin were taken immediately after intervention. Results: A dose response relationship of exercise with majority of the parameters was found. Exercise intensity of 60% and above were found to be significantly influencing the other modifiable risk factors including cholesterol (0.04 & <0.001), HDL (0.03), Triglyceride (0.023 & <0.01), insulin (0.12 & 0.05) and blood sugar level (0.02 & 0.001). Conclusion: Exercise intensity need to be in the influential range for affecting the other modifiable risk factors. If well planned even a single bout of exercise can be proved beneficial and add towards prevention of CVDs

Evaluating Cross- feature Trained Machine Learning Models for Estimating QoT of Unestablished Lightpaths

The rapid increase in bandwidth-driven applications has resulted in exponential internet traffic growth, especially in the backbone networks. To address this growth of internet traffic, operators always demand the total capacity utilization of underlying infrastructure. In this perspective, precise estimation of the quality of transmission (QoT) of the lightpaths (LPs) is vital for reducing the margins provisioned by uncertainty in network equipment's working point. This article proposes and compares several data-driven Machine learning (ML) based models to estimate QoT of unestablished LP before its deployment in the future deploying network. The proposed models are cross-trained on the data acquired from an already established LP of an entirely different in-service network. The metric considered to evaluate the QoT of LP is the Generalized Signal-to-Noise Ratio (GSNR). The dataset is generated synthetically using well tested GNPy simulation tool. Promising results are achieved to reduce the GSNR uncertainty and, consequently, the provisioning margin

Self-Configurable Current-Mirror Technique for Parallel RGB Light-Emitting Diodes (LEDs) Strings

Traditional current-mirror circuits require buck converter to deal with one fixed current load. This paper deals with improved self-adjustable current-mirror methods that can address different LED loads under different conditions with the help of one buck converter. The operating principle revolves around a dynamic and self-configurable combinational circuit of transistor and op-amp based current balancing circuit, along with their op-amp based dimming circuits. The proposed circuit guarantees uniformity in the outputs of the circuit. This scheme of current-balancing circuits omitted the need for separate power supply to control the load currents through different kinds of LEDs, i.e. RGB LEDs. The proposed methods are identical and modular, which can be scaled to any number of parallel current sources. The principle methodology has been successfully tested in Simulink environment to verify the current balancing of parallel LED strings

Successful DNA Profiling for Identification of burnt Families from their bones using AmpFℓSTR Identifiler® Plus Kit

Background: DNA profiling plays a vital role in the identification of dead bodies during mass disasters. Severe fragmentation, decomposition, burning and intermixing of the remains can occur in the mass disasters. DNA analysis faces many challenges especially when the dead bodies are completely decomposed or burnt. This report presents the identification of 32 completely burnt individuals including three families from their remains in a bus using AmpFlSTR Identifiler Plus® Kit and AmpFlSTR Y-filer® Kit. Methods: DNA was extracted from provided remains of burnt bodies and reference samples by organic extraction procedure. The extracted quantity of DNA was calculated on ABI SDS7500 real time PCR with Quantifiler® Human DNA Quantification Kit (Applied Biosystems). DNA samples of 32 completely burnt individuals including three families were amplified using AmpFlSTR Identifiler Plus® Kit and AmpFlSTR Y-filer® Kit. The genotyping of these amplified samples was performed on ABI 3130xl Genetic Analyzer. Results: The resulting data obtained from Genetic Analyzer was analyzed using GeneMapper ID software version 3.2 (Applied Biosystems). Seventeen burnt individuals including 3 burnt families were identified with the help of 16 autosomal STRs and 6 were identified through Y-STR analysis by allele sharing of their provided reference samples of parents and brothers respectively. Conclusion: For the identification of unknown individuals particularly burnt deceased victims, STR analysis has become the gold standard in forensic science. Successful DNA profiling through the amplification of STR markers of AmpFlSTR Identifiler Plus® Kit proved to be very helpful in identifying the remains of burnt individuals even in the presence of inhibition observed in the Real Time PCR

Accuracy of Community Informant Led Detection of Maternal Depression in Rural Pakistan.

Maternal depression is a global mental health and a public health priority. Despite the priority its active detection is still a challenge. We tested the accuracy of an adapted version of Community Informant Detection Tool for Maternal Depression (CIDT-MD) in rural settings of Pakistan. Using a single stage design, trained community informants (lady health workers and lay peers) identified women (pregnant and/or with children) with symptoms of probable depression using CIDT-MD. This was immediately followed by diagnostic interviews of all the women using the Structured Clinical Interview for the Diagnostic and Statistical Manual (SCID-V) for current major depressive episode by trained assessors, blinded to the outcome of CIDT-MD. Data were analyzed using Statistical Package for Social Sciences (Version 25.0, IBM Corp., Armonk, NY, USA) and FACTOR software (Version. 10.3.01, Virgili University, Tarragona, Spain). Descriptive statistics, factor analysis, validity, reliability and known group validity was conducted to evaluate the psychometric properties of the adapted CIDT-MD. In all, 425 women, with mean age of 28 years (SD = 4.7), participated. Nearly 10% were illiterate, while the rest (90%) had an education ranging from eight to 15 years of schooling. The majority (73.2%) of the participants had 1-3 children while only 17.4% had >3 children. The sensitivity and specificity of CIDT-MD in detecting depressive symptoms was 97.5% (95% CI: 94.2-99.1) and 82.4% (95% CI: 77.8-86.4) respectively. It's positive predictive value (PPV), 77.3% (95% CI: 72.9-81.2) and the negative predictive value (NPV) was 98.17% (95% CI: 95.7-99.2). While factor analysis revealed high inter-item correlation for most items (0.62-0.77) with an adequately fair Kaiser-Meyer-Olkin (KMO) sampling adequacy (0.73), significant Bartlett's test of sphericity (p p < 0.001). The adapted version of the Community Informant Detection Tool for Maternal Depression is a valid and a reliable tool for active case detection of maternal depression in rural settings of Pakistan