U(IV) Complexes with 8-Hydroxyquinoline 8-Hydroxy-7-iodoquinoline-5-sulphonic Acid Quinaldinic Acid & Pyridine-2,6-dicarboxylic Acid

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Genetic variation, linkage mapping of QTL and correlation studies for yield, root, and agronomic traits for aerobic adaptation

BACKGROUND: Water scarcity and drought have seriously threatened traditional rice cultivation practices in several parts of the world, including India. Aerobic rice that uses significantly less water than traditional flooded systems has emerged as a promising water-saving technology. The identification of QTL conferring improved aerobic adaptation may facilitate the development of high-yielding aerobic rice varieties. In this study, experiments were conducted for mapping QTL for yield, root-related traits, and agronomic traits under aerobic conditions using HKR47 × MAS26 and MASARB25 × Pusa Basmati 1460 F(2:3) mapping populations. RESULTS: A total of 35 QTL associated with 14 traits were mapped on chromosomes 1, 2, 5, 6, 8, 9, and 11 in MASARB25 x Pusa Basmati 1460 and 14 QTL associated with 9 traits were mapped on chromosomes 1, 2, 8, 9, 10, 11, and 12 in HKR47 × MAS26. Two QTL (qGY(8.1) with an R(2) value of 34.0% and qGY(2.1) with an R(2) value of 22.8%) and one QTL (qGY(2.2) with an R(2) value of 43.2%) were identified for grain yield under aerobic conditions in the mapping populations MASARB25 × Pusa Basmati 1460 and HKR47 × MAS26, respectively. A number of breeding lines with higher yield per plant, root length, dry biomass, length-breadth ratio, and with Pusa Basmati 1460-specific alleles in a homozygous or heterozygous condition at the BAD2 locus were identified that will serve as novel material for the selection of stable aerobic Basmati rice breeding lines. CONCLUSIONS: Our results identified positive correlation between some of the root traits and yield under aerobic conditions, indicating the role of root traits for improving yield under aerobic situations possibly through improved water and nutrient uptake. Co-localization of QTL for yield, root traits, and yield-related agronomic traits indicates that the identified QTL may be immediately exploited in marker-assisted-breeding to develop novel high-yielding aerobic rice varieties

Cost-Effective Scheduling in Fog Computing: An Environment Based on Modified PROMETHEE Technique

With the rising use of Internet of Things (IoT)-enabled devices, there is a significant increase in the use of smart applications that provide their response in real time. This rising demand imposes many issues such as scheduling, cost, overloading of servers, etc. To overcome these, a cost-effective scheduling technique has been proposed for the allocation of smart applications. The aim of this paper is to provide better profit by the Fog environment and minimize the cost of smart applications from the user end. The proposed framework has been evaluated with the help of a test bed containing four analysis phases and is compared on the basis of five metrics- average allocation time, average profit by the Fog environment, average cost of smart applications, resource utilization and number of applications run within given latency. The proposed framework performs better under all the provided metrics.&nbsp

Patohistološke promjene kod subakutnoga otrovanja nakon oralne primjene tiakloprida u kokoši (Gallus domesticus)

Repeated oral administration of 10 mg/kg/day thiacloprid, a neonicotinoid insecticide, for 28 consecutive days in Gallus domesticus, resulted in significant changes in the gross morphology of liver, lungs and intestine but no alterations in the kidneys, brain, heart and ovaries. Histopathologically significant alterations in the liver were observed, such as mild fatty changes, congestion and degeneration of hepatocytes. Alterations in the histoarchitecture of the kidneys included marked congestion, tubular cell degeneration and sloughing of epithelial cells. The cerebral hemisphere revealed changes comprising of mild neuronal degeneration with surrounding glial cells, satellitosis and vacuolation. Mild congestion and haemorrhage was observed in the lungs and myocardial tissues following oral administration of thiacloprid. No adverse effect on the ovarian histoarchitecture and thus the reproductive performance of Gallus domesticus was seen. The oral sub-acute toxicity study of thiacloprid revealed that this neonicotinoid insecticide is of moderate risk in Gallus domesticusPonovljena oralna primjena neonikotinoidnog insekticida tiakloprida u količini od 10 mg/kg/dan uzastopno tijekom 28 dana u kokoši je dovela do značajnih patomorfoloških promjena u jetrima, plućima i crijevima, ali neznatnih u bubrezima, mozgu, srcu i jajnicima. U jetrima su ustanovljene znatne patohistološke promjene poput blage masne degeneracije, kongestije i degeneracije hepatocita. Promjene u bubrezima uključivale su znatnu kongestiju, degeneraciju tubularnih stanica i ljuštenje epitelnih stanica. U mozgu su ustanovljene promjene u smislu blage degeneracije živčanih stanica okruženih glijalnim stanicama te satelitoza i vakuolacija. Blaga kongestija i krvarenje ustanovljeni su u plućima i u srčanom mišiću nakon oralne primjene tiakloprida. Nije ustanovljen štetan učinak na histološku građu jajnika i time na reprodukcijsku sposobnost kokoši. Subakutno otrovanje kokoši tiaklopridom pokazalo je da je taj neonikotinoidni insekticid umjereno štetan za koko

A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing

A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID-19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time-sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID-19