Balancer genetic algorithm-a novel task scheduling optimization approach in cloud computing

Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing

Heartbeat classification and arrhythmia detection using a multi-model deep-learning technique

Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost

Disease Pattern and Outcome among Neonates in Pediatric Ward of POF Hospital, Wah

Background: Neonatal period is the duration between 0-28 days of birth and it is the most susceptible period of life because of the large number of problems and diseases which a neonate is likely to face. The objective of the study was to determine the disease pattern and outcome among neonates in Pediatric ward of POF Hospital.Material and Methods: A descriptive study was carried out at Pediatric ward of POF Hospital Wah Cantt. Retrospective data regarding age, sex, reasons for admission, outcome and mode of delivery (from hospital record) was collected for all neonates admitted during the year 2016 from 1st January to 31st December. The data was analyzed by using SPSS V-19.Results: Among total neonates (n=887) admitted during the year, 63.2% were males and 36.8% were females. Mean weight of neonates was 2.54 + 0.75 kg while mean age was 2.39 + 5.8 days. Most common diseases were Prematurity, Respiratory Distress Syndrome, Seizures and Sepsis. Overall, 82.64% recovered from their illness while 17.02% expired.Conclusions: Prematurity, respiratory distress syndrome, seizures and sepsis were the major causes of neonatal admission in this study.Key words: Diagnostic value, Immature-to-total neutrophil ratio, Neonatal sepsi

Impact of diabetes-related knowledge and medication adherence on quality of life among type 2 diabetes patients in a tertiary health facility in Multan, Pakistan

Purpose: To assess the impact of drug adherence and diabetes-related knowledge on the quality of life (QoL) of type 2 diabetes patients in a hospital in Pakistan.Methods: A cross-sectional study was conducted in City Hospital, Multan, Pakistan between March and September 2020. A total of 151 patients diagnosed with type 2 diabetes mellitus (T2DM) were recruited. Medication adherence, diabetes-related knowledge, and QoL were assessed by Drug attitude inventory-10 (DAI-10), the Michigan Diabetes Knowledge Test (MDKT), and EQ-5D-3L tools, respectively. The association between sociodemographic data and study variables was assessed by independent t-test and one-way ANOVA.Results: Among the 151 patients, 53 % were males. The mean MDKT score was 0.33 ± 0.18, indicating poor knowledge of diabetes. An overall moderate level of adherence was observed among the participants (mean adherence score, 6.14 ± 1.39). Mean QoL score was 1.31 ± 0.28, and the Visual Analog Scale score (VAS) was 59.6 ± 12.21, indicating a good to moderate QoL among the study participants. Study participants with a longer duration of diabetes and poor adherence to their medications showed poor QoL (p = 0.01, p = 0.004 respectively).Conclusion: Overall, the patients reported poor knowledge, moderate adherence, and good to moderate QoL. Moreover, patients with poor adherence to medication, longer duration of diabetes, and poorly controlled HbA1c showed poor QoL

Synthesis of Silver Nanoparticles from Extracts of Wild Ginger (Zingiber zerumbet) with Antibacterial Activity against Selective Multidrug Resistant Oral Bacteria

Antibiotic resistance rate is rising worldwide. Silver nanoparticles (AgNPs) are potent for fighting antimicrobial resistance (AMR), independently or synergistically. The purpose of this study was to prepare AgNPs using wild ginger extracts and to evaluate the antibacterial efficacy of these AgNPs against multidrug-resistant (MDR) Staphylococcus aureus, Streptococcus mutans, and Enterococcus faecalis. AgNPs were synthesized using wild ginger extracts at room temperature through different parameters for optimization, i.e., pH and variable molar concentration. Synthesis of AgNPs was confirmed by UV/visible spectroscopy and further characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy analysis (EDXA), and Fourier-transform infrared spectroscopy (FTIR). Disc and agar well diffusion techniques were utilized to determine the in vitro antibacterial activity of plant extracts and AgNPs. The surface plasmon resonance peaks in absorption spectra for silver suspension showed the absorption maxima in the range of 400–420 nm. Functional biomolecules such as N–H, C–H, O–H, C–O, and C–O–C were present in Zingiber zerumbet (Z. zerumbet) (aqueous and organic extracts) responsible for the AgNP formation characterized by FTIR. The crystalline structure of ZZAE-AgCl-NPs and ZZEE-AgCl-NPs was displayed in the XRD analysis. SEM analysis revealed the surface morphology. The EDXA analysis also confirmed the element of silver. It was revealed that AgNPs were seemingly spherical in morphology. The biosynthesized AgNPs exhibited complete antibacterial activity against the tested MDR bacterial strains. This study indicates that AgNPs of wild ginger extracts exhibit potent antibacterial activity against MDR bacterial strains

Assessment of the Phytochemical Analysis and Antimicrobial Potentials of Zingiber zerumbet

Antimicrobial resistance (AMR) has arisen as a global concern in recent decades. Plant extracts used in combination with antibiotics are promising against AMR, synergistically. The purpose of this study was to evaluate the component of the bitter ginger (Zingiber zerumbet) extract in different solvents using high-performance liquid chromatography (HPLC), in addition to evaluate the antibacterial activity of these extracts, in combination with their antibiotic potential against four multi-drug resistant (MDR) bacterial strains (Lactobacillus acidophilus, Streptococcus mutans, Enterococcus faecalis and Staphylococcus aureus). Ethanol and the aqueous extracts of bitter ginger were prepared using a conventional solvent extraction method and were evaluated for their phytochemistry using HPLC, qualitatively and quantitatively. Moreover, the antibiotic susceptibility of the pathogenic isolates was determined. A disc diffusion assay was used to obtain the antimicrobial potential of the extracts alone and with antibiotics. Eight components were identified from the separation of the bitter ginger extract by HPLC. For AMR bacteria, the combination of the antibiotic solution with the bitter ginger crude extracts could improve its susceptibility of these antibiotics. This study indicates that the combination of an antibiotic solution with the bitter ginger crude extract exhibits potent antibacterial activities against MDR bacterial strains. Therefore, they can be used for the treatment of various diseases against the microbial pathogen and can be incorporated into medication for antibacterial therapy

Homozygosity mapping and whole exome sequencing provide exact diagnosis of Cohen syndrome in a Saudi family

Background: Cohen syndrome (CS) is a rare multi-system autosomal recessive disorder with a high prevalence in the Finnish population. Clinical features of Finnish-type CS are homogeneous, however, in non-Finnish populations, CS diagnosis is challenging due to broad phenotypic variability. Methods: We studied a consanguineous family having three affected individuals with clinical features of severe intellectual disability and global developmental delay. Clinical diagnosis of the phenotype could not be established based on the features. Therefore, whole genome SNP genotyping and whole exome sequencing (WES) were performed on DNA samples from affected and unaffected family members. Results: Homozygosity mapping identified a shared loss of heterozygosity region on chromosome 8q22.1-q22.3 and WES data analysis revealed an insertion-deletion (indel) mutation (c.11519_11521delCAAinsT) in the VPS13B gene. The indel is predicted to cause a frameshift resulting in a premature termination of the VPS13B protein (NP_060360.3:p.Pro3840Leufs*2). Conclusion: VPS13B encodes a giant transmembrane protein called vacuolar protein sorting 13 homolog B. VPS13B is known to play a role in the glycosylation of Golgi proteins and in endosomal-lysosomal trafficking. Moreover, it is thought to function in vesicle mediated transport and sorting of proteins within the cell. The mechanism by which abnormalities of the VPS13B protein lead to the phenotype of CS is currently unknown. Here, in this study, we successfully established a clinical diagnosis of CS cases from a family using genomic analyses. Clinical re-examination of the patients revealed characteristic ocular abnormalities. (C) 2020 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved

Exome sequencing reveals MCM8 mutation underlies ovarian failure and chromosomal instability

Premature ovarian failure (POF) is a genetically and phenotypically heterogeneous disorder that includes individuals with manifestations ranging from primary amenorrhea to loss of menstrual function prior to age 40. POF presents as hypergonadotropic hypogonadism and can be part of a syndrome or occur in isolation. Here, we studied 3 sisters with primary amenorrhea, hypothyroidism, and hypergonadotropic hypogonadism. The sisters were born to parents who are first cousins. SNP analysis and whole-exome sequencing revealed the presence of a pathogenic variant of the minichromosome maintenance 8 gene (MCM8, c.446C>G; p.P149R) located within a region of homozygosity that was present in the affected daughters but not in their unaffected sisters. Because MCM8 participates in homologous recombination and dsDNA break repair, we tested fibroblasts from the affected sisters for hypersensitivity to chromosomal breaks. Compared with fibroblasts from unaffected daughters, chromosomal break repair was deficient in fibroblasts from the affected individuals, likely due to inhibited recruitment of MCM8 p.P149R to sites of DNA damage. Our study identifies an autosomal recessive disorder caused by an MCM8 mutation that manifests with endocrine dysfunction and genomic instability