An oxidant, detergent and salt stable alkaline protease from Bacillus cereus SIU1

A novel soil bacterium, Bacillus cereus SIU1 was earlier isolated from non-saline, slightly alkaline soil of Eastern Uttar Pradesh, India. The isolate B. cereus SIU1 was grown in modified glucose yeast extract (modified GYE) medium at pH 9.0 and 45°C. It produced maximum protease at 20 h incubation. The enzyme was stable at pH 9.0 and 55°C. It was fully stable at 0.0 to 3.0% and moderately stable at 4.0 to 10.0% (w/v) NaCl concentrations. Whereas PMSF, EDTA and ascorbic acid were inhibitory, cysteine and β-mercaptoethanol enhanced protease activity. Calcium, magnesium, manganese and copper at 1 mM concentration increased the enzyme activity. Hydrogen peroxide, sodium perborate, sodium lauryl sulphate, Triton X100 and Tween 80 significantly increased the activity, while protease remained fairly stable (52 to 98%) at 0.1 and 1.0% concentrations of commercial detergents. The halotolerant thermoalkaline protease of B. cereus SIU1 was highly active and stable in the presence of several modulators, oxidants and detergents, revealing its possible use in several commercial and biological applications.Key words: Bacillus cereus SIU1, thermoalkaline protease, PMSF, EDTA, Hydrogen peroxide, Triton X100, Tween 80

Role of Negative Pressure Wound Therapy in Healing of Diabetic Foot Ulcers

Introduction: Foot disorders such as ulceration, infection and gangrene are the most common, complex and costly sequelae of diabetes mellitus.[1-3] Even for the most superficial wounds, treatment is often difficult with poor healing responses and high rates of complications. The purpose of this study is to compare the rate of ulcer healing with the negative pressure dressing technique to conventional moist dressings in the treatment of diabetic foot ulcers. Materials and Methods: The study was conducted on 30 patients, which were divided into two groups. One group received negative pressure dressing while other group received conventional saline moistened gauze dressing. Results were compared for rate of wound healing. Results: There was a statistically significant difference in the rate of appearance of granulation tissue between the two groups; with granulation tissue appearing earlier in the study group. The study group promised a better outcome (80% complete responders) as compared to the control group (60% complete responders). Conclusions: Negative pressure wound therapy has a definitive role in healing of diabetic foot ulcers

Potential of Mimulas glabratus in removal of Fe and Cu from the aqueous solutions containing Nitrate and Phosphate and its growth responses

The metal bioabsorption potential and survival efficiency of aquatic macrophyte M. glabratus was examined for the removal of Fe and Cu in presence of nitrate and Phosphates. M.glabratus removes Fe 10% more than Cu in  case of bio-chemical and physical responses the increment in fresh weight found 0.74% more in Fe treated plants than Cu treated plants and in photosynthetic pigments there was 10% more increment was noted in the plants treated with Fe. Bioabsorption of Fe was noted 18.9% more than Cu by M. glabratus. The results demonstrate that M.glabratus can be utilized in the remediation operations of aquatic systems Keywords: Bioabsorption, M. glabratus, Photosynthetic pigments, Biomas

Predictive approach for simultaneous biosorption of hexavalent chromium and pentachlorophenol degradation by Bacillus cereus RMLAU1

Chromium and pentachlorophenol are the major environmental pollutants emanating from tannery effluent. Indigenous Bacillus cereus isolate was employed for biosorption and PCP degradation studies under varied environmental conditions such as pH, temperature, contact time, presence of other heavy metals, initial biosorbent and Cr6+ concentrations. Best results for Cr6+ biosorption (% removal) by living and dead biomass at 2.0 g l-1 were found to be 35.2 mg Cr g-1 dry wt (63%) at pH 5.0, and 42.5 mg Cr g-1 dry wt (70.5%) at pH 4.0, respectively at 35ºC (150 rpm) during 120 min at an initial concentration of 200 mg Cr6+ l-1 and 500 mg PCP l-1. Among various factors, pH had profound effect on Cr6+ biosorption and PCP degradation. Maximum 7.5 % (w/v) PCP degradation ensued in 2 h only by live cells in the presence of 0.4 % (w/v) cometabolite glucose. Presumably, this is the first report on simultaneous biosorption of chromium and pentachlorophenol remediation by native Bacillus cereus isolate from tannery effluent. Statistical regressional analysis suitably validated the experimental findings. This strain would be helpful in eco-friendly simultaneous bioremediation allied with a predictive computational approach.Key words: Bacillus cereus, Biosorption, Chromium, Heavy metals, Pentachlorophenol

The Partial Visibility Representation Extension Problem

For a graph $G$, a function $\psi$ is called a \emph{bar visibility representation} of $G$ when for each vertex $v \in V(G)$, $\psi(v)$ is a horizontal line segment (\emph{bar}) and $uv \in E(G)$ iff there is an unobstructed, vertical, $\varepsilon$-wide line of sight between $\psi(u)$ and $\psi(v)$. Graphs admitting such representations are well understood (via simple characterizations) and recognizable in linear time. For a directed graph $G$, a bar visibility representation $\psi$ of $G$, additionally, puts the bar $\psi(u)$ strictly below the bar $\psi(v)$ for each directed edge $(u,v)$ of $G$. We study a generalization of the recognition problem where a function $\psi'$ defined on a subset $V'$ of $V(G)$ is given and the question is whether there is a bar visibility representation $\psi$ of $G$ with $\psi(v) = \psi'(v)$ for every $v \in V'$. We show that for undirected graphs this problem together with closely related problems are \NP-complete, but for certain cases involving directed graphs it is solvable in polynomial time.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

Point Prevalence Surveys of Antimicrobial Use among Hospitalized Children in Six Hospitals in India in 2016.

The prevalence of antimicrobial resistance in India is among the highest in the world. Antimicrobial use in inpatient settings is an important driver of resistance, but is poorly characterized, particularly in hospitalized children. In this study, conducted as part of the Global Antimicrobial Resistance, Prescribing, and Efficacy in Neonates and Children (GARPEC) project, we examined the prevalence of and indications of antimicrobial use, as well as antimicrobial agents used among hospitalized children by conducting four point prevalence surveys in six hospitals between February 2016 and February 2017. A total of 681 children were hospitalized in six hospitals across all survey days, and 419 (61.5%) were prescribed one or more antimicrobials (antibacterials, antivirals, antifungals). Antibacterial agents accounted for 90.8% (547/602) of the total antimicrobial prescriptions, of which third-generation cephalosporins (3GCs) accounted for 38.9% (213/547) and penicillin plus enzyme inhibitor combinations accounted for 14.4% (79/547). Lower respiratory tract infection (LRTI) was the most common indication for prescribing antimicrobials (149 prescriptions; 24.8%). Although national guidelines recommend the use of penicillin and combinations as first-line agents for LRTI, 3GCs were the most commonly prescribed antibacterial agents (55/149 LRTI prescriptions; 36.9%). In conclusion, 61.5% of hospitalized children were on at least one antimicrobial agent, with excessive use of 3GCs. Hence there is an opportunity to limit their inappropriate use

A contemporary review on drought modeling using machine learning approaches

Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the minimum temperature using backpropagation algorithms. The favourite ML techniques for weather forecasting include singular vector machines (SVM), support vector regression, random forest, decision tree, logistic regression, Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzy inference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models, and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presents a recent review of the literature using ML in drought prediction, the drought indices, dataset, and performance metrics