Screening and Optimization of Physical Parameters for Enhanced Alkaline Protease Production by Alkaliphilic Bacillus Subtilis SH2 Isolate

The present investigations dealt with the optimization of the physical parameters for production of alkaline protease by alkaliphilic Bacillus subtilis SH-2 isolated from slaughter house soil of Warangal, Telangana State, India. Primary screening of four different samples revealed one potent isolate. Morphological and Biochemical characterization followed by Molecular signature of 16s rRNA homology confirmed that the isolate SH-2 belongs to Bacillus subtilis. Bacillus subtilis SH-2 was screened on four different reported mediums (M1213, M660, Horikoshi and Halophilic Bacillus medium) under shake culture conditions. Maximum alkaline protease production (500 EU/ml) obtained on M1213 and Horikoshi mediums. Further optimization of physical parameters by OVAT method revealed that mean generation time (41.18 min), 4% level inoculum, incubation time 72 hrs, pH 10, temperature 350C and agitation 150 rpm are ideal for enzyme production. OVAT method resulted in 2.2 fold increased production of alkaline protease production (1100 EU/ml)

Development and validation of new analytical method for the simultaneous estimation of omeprazole and domperidone in pharmaceutical dosage form by UV spectrophotometry

A simple, rapid and precise method was developed for the quantitative simultaneous determination of Omeprazole and Domperidone in combined pharmaceutical-dosage forms. The method was based on UV-Spectrophotometric determination of two drugs, using simultaneous equation method. It involves absorbance measurement at 291 nm (λmax of Omeprazole) and 289 nm (λmax of Domperidone) in Methanol: Acetonitrile (30:70 v/v). For UV Spectrophotometric method, linearity was obtained in concentration range of 1-15 µg/ml for Domperidone and 1-50 µg/ml for Omeprazole respectively, with regression 0.999 and 0.999 for Domperidone and Omeprazole respectively. Recovery was in the range of 99 -103%; the value of standard deviation and %R.S.D were found to be < 2 %; shows the high precision of the method., in accordance with ICH guidelines. The method has been successively applied to pharmaceutical formulation and was validated according to ICH guidelines

Synthesis of Persea Americana Bio-Oil and Its Spectroscopic Characterization Studies

The present investigation aims to evaluate the feasibility of using Persea americana (Avocado) biodiesel in compression ignition engines. Persea americana bio-oil was extracted through a soxhlet extraction process using n-hexane solvent after careful pre-processing of the feedstocks. Since the Free Fatty Acid content was 1.78% estimated through titration, single stage base-catalyzed transesterification technique was adopted using methanol and sodium hydroxide as catalysts in the molar ratio of 1:6. Gas Chromatography-Mass Spectrometry analysis revealed the presence of Oleic acid in major proportions. The Fourier transform Infra-Red analysis confirmed the presence of carbonyl group ester ions between 722.19 cm-1 and 1460 cm-1. The 13C NMR and 1H NMR studies supported the successful transformation of triglycerides into Fatty Acid Methyl Esters with distinct peaks at 3.369 ppm and 48.147 ppm, respectively

Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The modern-day urban energy sector possesses the integrated operation of various microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility grid burden. However, these cluster microgrids require a precise electric load projection to manage the operations, as the integrated operation of multiple microgrids leads to dynamic load demand. Thus, load forecasting is a complicated operation that requires more than statistical methods. There are different machine learning methods available in the literature that are applied to single microgrid cases. In this line, the cluster microgrids concept is a new application, which is very limitedly discussed in the literature. Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines, long short-term memory, and artificial neural networks (ANN) to forecast the load demand in the short term. The effectiveness of these methods is analyzed by computing various factors such as root mean square error, R-square, mean square error, mean absolute error, mean absolute percentage error, and time of computation. From this, it is observed that the ANN provides effective forecasting results. In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg−Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The effectiveness of these optimization algorithms is verified in terms of training, test, validation, and error analysis. The proposed system simulation is carried out using the MATLAB/Simulink-2021a® software. From the results, it is found that the Levenberg−Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.Peer reviewe

Radiographic Findings and Association With Clinical Severity and Outcomes in Critically Ill Patients With COVID-19

PURPOSE: To describe evolution and severity of radiographic findings and assess association with disease severity and outcomes in critically ill COVID-19 patients. MATERIALS AND METHODS: This retrospective study included 62 COVID-19 patients admitted to the intensive care unit (ICU). Clinical data was obtained from electronic medical records. A total of 270 chest radiographs were reviewed and qualitatively scored (CXR score) using a severity scale of 0-30. Radiographic findings were correlated with clinical severity and outcome. RESULTS: The CXR score increases from a median initial score of 10 at hospital presentation to the median peak CXR score of 18 within a median time of 4 days after hospitalization, and then slowly decreases to a median last CXR score of 15 in a median time of 12 days after hospitalization. The initial and peak CXR score was independently associated with invasive MV after adjusting for age, gender, body mass index, smoking, and comorbidities (Initial, odds ratio [OR]: 2.11 per 5-point increase, confidence interval [CI] 1.35-3.32, P= 0.001; Peak, OR: 2.50 per 5-point increase, CI 1.48-4.22, P= 0.001). Peak CXR scores were also independently associated with vasopressor usage (OR: 2.28 per 5-point increase, CI 1.30-3.98, P= 0.004). Peak CXR scores strongly correlated with the duration of invasive MV (Rho = 0.62, P\u3c 0.001), while the initial CXR score (Rho = 0.26) and the peak CXR score (Rho = 0.27) correlated weakly with the sequential organ failure assessment score. No statistically significant associations were found between radiographic findings and mortality. CONCLUSIONS: Evolution of radiographic features indicates rapid disease progression and correlate with requirement for invasive MV or vasopressors but not mortality, which suggests potential nonpulmonary pathways to death in COVID-19