Utilization of carbon and nitrogen sources by Streptomyces kanamyceticus M 27 for the production of an Anti bacterial antibiotic

We tested a number of carbon and nitrogen compounds for their effect on the production of an antibacterial antibiotic by Streptomyces kananmyceticus M27. Dextrose was found to be the most suitable carbon source, though maltose, sucrose, and soluble starch gave moderate yields. (NH4)H2PO4 and yeast extract were adequate nitrogen sources for antibiotic production. There was, however, no direct relation between the growth of the organism and antibiotic formation. The pH of the medium might be an important factor for antibiotic formation, as media giving high antibiotic yields showed an alkaline pH

Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh

© 2018 Elsevier Ltd Recently, big data (BD) has attracted researchers and practitioners due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular among manufacturing companies as it helps gain insights and make decisions based on BD. However, there many barriers to the adoption of BDA in manufacturing supply chains. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Previous studies have mostly built conceptual frameworks for BDA in a given situation and have ignored examining the nature of the barriers to BDA. Due to the significance of both BD and BDA, this research aims to identify and examine the critical barriers to the adoption of BDA in manufacturing supply chains in the context of Bangladesh. This research explores the existing body of knowledge by examining these barriers using a Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies. The findings of this research are as follows: (i) data-related barriers are most important, (ii) technology-related barriers are second, and (iii) the five most important components of these barriers are (a) lack of infrastructure, (b) complexity of data integration, (c) data privacy, (d) lack of availability of BDA tools and (e) high cost of investment. The findings can assist industrial managers to understand the actual nature of the barriers and potential benefits of using BDA and to make policy regarding BDA adoption in manufacturing supply chains. A sensitivity analysis was carried out to justify the robustness of the barrier rankings

Efficacy of anti-thrombotic treatment in thrombophilia patients with adverse pregnancy outcome

Background: Thrombophilia is a potentially treatable cause of adverse pregnancy outcome. The objective was to compare the fetomaternal outcome in thrombophilia patients with adverse pregnancy outcome after treating with low-molecular-weight (LMW)/ unfractionated heparin and aspirin.Methods: 54 antenatal women studied who had an earlier or presenting pregnancy complicated by adverse pregnancy outcome were included in this study. In the present pregnancy, therapy consisting of LMW heparin and aspirin was administered who were found to be thrombophilia positive. Patients also received folic acid supplementation throughout their pregnancy. The fetomaternal outcome is compared according to the time of initiation of treatment.Results: Low-molecular-weight heparin and aspirin was well tolerated and none of the women or the newborns developed any hemorrhagic complications.3 thrombophilia negative cases with history of recurrent pregnancy loss aborted even getting treatment from 1 trimester. 1 thrombophilia positive case with history of recurrent pregnancy loss aborted when received treatment from 2nd trimester. There is 25.8% increase in birth weight of neonate if thrombophilia positive cases were treated from 1st trimester. Whereas there was only 10.23% increase in birth weight in thrombophilia negative cases when treated from first trimester. We found, our treatment was significantly effective in preventing IUD, IUGR, abruption, abortion, eclampsia. Though prevention of PIH had no significant correlation with antithrombotic treatment, only 2 cases booked from 1st trimester developed PIH among thrombophilia positive cases. But neither of cases had suffered from any severe complication as compared to 81% of eclampsia cases, 16.67% of DVT cases, 1 case of mortality in cases treated after third trimester.Conclusions: This case control trial suggests that patients with adverse pregnancy outcome and thrombophilia may get benefit from treatment with combined LMW heparin and aspirin in subsequent pregnancies. We suggest all patients with adverse pregnancy outcome should be investigated for thrombophilia markers

Evidence of Coronavirus (CoV) Pathogenesis and Emerging Pathogen SARS-CoV-2 in the Nervous System: A Review on Neurological Impairments and Manifestations.

The coronavirus disease 2019 (COVID-19) pandemic is an issue of global significance that has taken the lives of many across the world. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for its pathogenesis. The pulmonary manifestations of COVID-19 have been well described in the literature. Initially, it was thought to be limited to the respiratory system; however, we now recognize that COVID-19 also affects several other organs, including the nervous system. Two similar human coronaviruses (CoV) that cause severe acute respiratory syndrome (SARS-CoV-1) and Middle East respiratory syndrome (MERS-CoV) are also known to cause disease in the nervous system. The neurological manifestations of SARS-CoV-2 infection are growing rapidly, as evidenced by several reports. There are several mechanisms responsible for such manifestations in the nervous system. For instance, post-infectious immune-mediated processes, direct virus infection of the central nervous system (CNS), and virus-induced hyperinflammatory and hypercoagulable states are commonly involved. Guillain-Barré syndrome (GBS) and its variants, dysfunction of taste and smell, and muscle injury are numerous examples of COVID-19 PNS (peripheral nervous system) disease. Likewise, hemorrhagic and ischemic stroke, encephalitis, meningitis, encephalopathy acute disseminated encephalomyelitis, endothelialitis, and venous sinus thrombosis are some instances of COVID-19 CNS disease. Due to multifactorial and complicated pathogenic mechanisms, COVID-19 poses a large-scale threat to the whole nervous system. A complete understanding of SARS-CoV-2 neurological impairments is still lacking, but our knowledge base is rapidly expanding. Therefore, we anticipate that this comprehensive review will provide valuable insights and facilitate the work of neuroscientists in unfolding different neurological dimensions of COVID-19 and other CoV associated abnormalities

Removal of phenol using sulphate radicals activated by natural zeolite-supported cobalt catalysts

Two Co oxide catalysts supported on natural zeolites from Indonesia (INZ) and Australia (ANZ) were prepared and used to activate peroxymonosulphate for degradation of aqueous phenol. The two catalysts were characterized by several techniques such as X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy (EDS) and N2 adsorption. It was found that Co/INZ and Co/ANZ are effective in activation of peroxymonosulphate to produce sulphate radicals for phenol degradation. Co/INZ and Co/ANZ could remove phenol up to 100 and 70 %, respectively, at the conditions of 25 ppm phenol (500 mL), 0.2 g catalyst, 1 g oxone and 25 °C. Several parameters such as amount of catalyst loading, phenol concentration, oxidant concentration and temperature were found to be the key factors influencing phenol degradation. A pseudo first order would fit to phenol degradation kinetics, and the activation energies on Co/INZ and Co/ANZ were obtained as 52.4 and 61.3 kJ/mol,respectively

Diagnostic and prognostic utility of an inexpensive rapid on site malaria diagnostic test (ParaHIT f) among ethnic tribal population in areas of high, low and no transmission in central India

BACKGROUND: Malaria presents a diagnostic challenge in most tropical countries. Rapid detection of the malaria parasite and early treatment of infection still remain the most important goals of disease management. Therefore, performance characteristics of the new indigenous ParaHIT f test (Span diagnostic Ltd, Surat, India) was determined among ethnic tribal population in four districts of different transmission potential in central India to assess whether this rapid diagnostic test (RDT) could be widely applied as a diagnostic tool to control malaria. Beyond diagnosis, the logical utilization of RDTs is to monitor treatment outcome. METHODS: A finger prick blood sample was collected from each clinically suspected case of malaria to prepare blood smear and for testing with the RDT after taking informed consent. The blood smears were read by an experienced technician blinded to the RDT results and clinical status of the subjects. The figures for specificity, sensitivity, accuracy and predictive values were calculated using microscopy as gold standard. RESULTS: The prevalence of malaria infection estimated by RDT in parallel with microscopy provide evidence of the type of high, low or no transmission in the study area. Analysis revealed (pooled data of all four epidemiological settings) that overall sensitivity, specificity and accuracy of the RDT were >90% in areas of different endemicity. While, RDT is useful to confirm the diagnosis of new symptomatic cases of suspected P. falciparum infection, the persistence of parasite antigen leading to false positives even after clearance of asexual parasitaemia has limited its utility as a prognostic tool. CONCLUSION: The study showed that the ParaHIT f test was easy to use, reliable and cheap. Thus this RDT is an appropriate test for the use in the field by paramedical staff when laboratory facilities are not available and thus likely to contribute greatly to an effective control of malaria in resource poor countries

Internet of Medical Things (IoMT) and Reflective Belief Design-Based Big Data Analytics with Convolution Neural Network-Metaheuristic Optimization Procedure (CNN-MOP)

In recent times, the Internet of Medical Things (IoMT) is a new loomed technology, which has been deliberated as a promising technology designed for various and broadly connected networks. In an intelligent healthcare system, the framework of IoMT observes the health circumstances of the patients dynamically and responds to backings their needs, which helps detect the symptoms of critical rare body conditions based on the data collected. Metaheuristic algorithms have proven effective, robust, and efficient in deciphering real-world optimization, clustering, forecasting, classification, and other engineering problems. The emergence of extraordinary, very large-scale data being generated from various sources such as the web, sensors, and social media has led the world to the era of big data. Big data poses a new contest to metaheuristic algorithms. So, this research work presents the metaheuristic optimization algorithm for big data analysis in the IoMT using gravitational search optimization algorithm (GSOA) and reflective belief network with convolutional neural networks (DBN-CNNs). Here the data optimization has been carried out using GSOA for the collected input data. The input data were collected for the diabetes prediction with cardiac risk prediction based on the damage in blood vessels and cardiac nerves. Collected data have been classified to predict abnormal and normal diabetes range, and based on this range, the risk for a cardiac attack has been predicted using SVM. The performance analysis is made to reveal that GSOA-DBN_CNN performs well in predicting diseases. The simulation results illustrate that the GSOA-DBN_CNN model used for prediction improves accuracy, precision, recall, F1-score, and PSNR

Multiple order-up-to policy for mitigating bullwhip effect in supply chain network

This paper proposes a multiple order-up-to policy based inventory replenishment scheme to mitigate the bullwhip effect in a multi-stage supply chain scenario, where various transportation modes are available between the supply chain (SC) participants. The proposed policy is similar to the fixed order-up-to policy approach where replenishment decision “how much to order” is made periodically on the basis of the predecided order-up-to inventory level. In the proposed policy, optimal multiple order-up-to levels are assigned to each SC participants, which provides decision making reference point for deciding the transportation related order quantity. Subsequently, a mathematical model is established to define optimal multiple order-up-to levels for each SC participants that aims to maximize overall profit from the SC network. In parallel, the model ensures the control over supply chain pipeline inventory, high satisfaction of customer demand and enables timely utilization of available transportation modes. Findings from the various numerical datasets including stochastic customer demand and lead times validate that—the proposed optimal multiple order-up-to policy based inventory replenishment scheme can be a viable alternative for mitigating the bullwhip effect and well-coordinated SC. Moreover, determining the multiple order-up-to levels is a NP hard combinatorial optimization problem. It is found that the implementation of new emerging optimization algorithm named bacterial foraging algorithm (BFA) has presented superior optimization performances. The robustness and applicability of the BFA algorithm are further validated statistically by employing the percentage heuristic gap and two-way ANOVA analysis

QTL Mapping of Combining Ability and Heterosis of Agronomic Traits in Rice Backcross Recombinant Inbred Lines and Hybrid Crosses

BACKGROUND: Combining ability effects are very effective genetic parameters in deciding the next phase of breeding programs. Although some breeding strategies on the basis of evaluating combining ability have been utilized extensively in hybrid breeding, little is known about the genetic basis of combining ability. Combining ability is a complex trait that is controlled by polygenes. With the advent and development of molecular markers, it is feasible to evaluate the genetic bases of combining ability and heterosis of elite rice hybrids through QTL analysis. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we first developed a QTL-mapping method for dissecting combining ability and heterosis of agronomic traits. With three testcross populations and a BCRIL population in rice, biometric and QTL analyses were conducted for ten agronomic traits. The significance of general combining ability and special combining ability for most of the traits indicated the importance of both additive and non-additive effects on expression levels. A large number of additive effect QTLs associated with performance per se of BCRIL and general combining ability, and dominant effect QTLs associated with special combining ability and heterosis were identified for the ten traits. CONCLUSIONS/SIGNIFICANCE: The combining ability of agronomic traits could be analyzed by the QTL mapping method. The characteristics revealed by the QTLs for combining ability of agronomic traits were similar with those by multitudinous QTLs for agronomic traits with performance per se of BCRIL. Several QTLs (1-6 in this study) were identified for each trait for combining ability. It demonstrated that some of the QTLs were pleiotropic or linked tightly with each other. The identification of QTLs responsible for combining ability and heterosis in the present study provides valuable information for dissecting genetic basis of combining ability