DeepPlace: Learning to Place Applications in Multi-Tenant Clusters

Large multi-tenant production clusters often have to handle a variety of jobs and applications with a variety of complex resource usage characteristics. It is non-trivial and non-optimal to manually create placement rules for scheduling that would decide which applications should co-locate. In this paper, we present DeepPlace, a scheduler that learns to exploits various temporal resource usage patterns of applications using Deep Reinforcement Learning (Deep RL) to reduce resource competition across jobs running in the same machine while at the same time optimizing for overall cluster utilization.Comment: APSys 201

Similarity analysis of <i>Spirulina/Arthrospira</i> strains on the basis of phycocyanin operon locus (cpcB-IGS-cpcA) and 16S rRNA gene sequences

84-90Spirulina/Arthrospira is a species of cyanobacteria used in health foods, animal feed, food additives and fine chemicals. The present study conducted a comparison of the 16S rRNA and cpcBA-intergenic spacer (cpcBA-IGS) gene sequences in Spirulina/Arthrospira strains from culture collection of CCUBGA, IARI, New Delhi. All the strains of Spirulina used in this study had shown nearly 99% similarity amongst them. About fifty sequences (cpcBA-IGS) of Spirulin a strains taken from NCBI with ten from the present strains of Spirulina, a neighbour-joing (NJ) tree was constructed with the help of MEGA 5.0. The tree showed 99% similarity. All the sequences were put to Multiple Sequence Alignment with the help of T-Coffee (version 7.38) and BioEdit (version 7.38) software. Similarity studies undertaken based upon 16S rRNA and cpcBA-IGS genes sequence analysis indicated similarity coefficient of 0.84. S. platensis and Arthrospira sp. showed 100 percent similarity. Therefore, the current study supports some previous conclusions based on 16S rRNA gene and cpcBA-IGS sequences, which found that Arthrospira taxa are monophyletic. However, compared to 16S rRNA sequences, cpcBA-IGS sequences might be better suited to resolve close relationships and interspecies variability

Current Scenario of Pathogen Detection Techniques in Agro-Food Sector

Over the past-decade, agricultural products (such as vegetables and fruits) have been reported as the major vehicles for foodborne diseases, which are limiting food resources. The spread of infectious diseases due to foodborne pathogens poses a global threat to human health and the economy. The accurate and timely detection of infectious disease and of causative pathogens is crucial in the prevention and treatment of disease. Negligence in the detection of pathogenic substances can be catastrophic and lead to a pandemic. Despite the revolution in health diagnostics, much attention has been paid to the agro-food sector regarding the detection of food contaminants (such as pathogens). The conventional analytical techniques for pathogen detection are reliable and still in operation. However, laborious procedures and time-consuming detection via these approaches emphasize the need for simple, easy-to-use, and affordable detection techniques. The rapid detection of pathogens from food is essential to avoid the morbidity and mortality originating from the suboptimal nature of empiric pathogen treatment. This review critically discusses both the conventional and emerging bio-molecular approaches for pathogen detection in agro-food

Nano-Biosensing Platforms for Detection of Cow’s Milk Allergens: An Overview

Among prevalent food allergies, cow milk allergy (CMA) is most common and may persist throughout the life. The allergic individuals are exposed to a constant threat due to milk proteins&rsquo; presence in uncounted food products like yogurt, cheese, and bakery items. The problem can be more severe due to cross-reactivity of the milk allergens in the food products due to homologous milk proteins of diverse species. This problem can be overcome by proper and reliable food labeling in order to ensure the life quality of allergic persons. Therefore, highly sensitive and accurate analytical techniques should be developed to detect the food allergens. Here, significant research advances in biosensors (specifically immunosensors and aptasensors) are reviewed for detection of the milk allergens. Different allergic proteins of cow milk are described here along with the analytical standard methods for their detection. Additionally, the commercial status of biosensors is also discussed in comparison to conventional techniques like enzyme-linked immunosorbent assay (ELISA). The development of novel biosensing mechanisms/kits for milk allergens detection is imperative from the perspective of enforcement of labeling regulations and directives keeping in view the sensitive individuals