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

Clinical and Genomic Risk to Guide the Use of Adjuvant Therapy for Breast Cancer

BACKGROUND The use of adjuvant chemotherapy in patients with breast cancer may be guided by clinicopathological factors and a score based on a 21-gene assay to determine the risk of recurrence. Whether the level of clinical risk of breast cancer recurrence adds prognostic information to the recurrence score is not known. METHODS We performed a prospective trial involving 9427 women with hormone-receptor–positive, human epidermal growth factor receptor 2–negative, axillary node–negative breast cancer, in whom an assay of 21 genes had been performed, and we classified the clinical risk of recurrence of breast cancer as low or high on the basis of the tumor size and histologic grade. The effect of clinical risk was evaluated by calculating hazard ratios for distant recurrence with the use of Cox proportional-hazards models. The initial endocrine therapy was tamoxifen alone in the majority of the premenopausal women who were 50 years of age or younger. RESULTS The level of clinical risk was prognostic of distant recurrence in women with an intermediate 21-gene recurrence score of 11 to 25 (on a scale of 0 to 100, with higher scores indicating a worse prognosis or a greater potential benefit from chemotherapy) who were randomly assigned to endocrine therapy (hazard ratio for the comparison of high vs. low clinical risk, 2.73; 95% confidence interval [CI], 1.93 to 3.87) or to chemotherapy plus endocrine (chemoendocrine) therapy (hazard ratio, 2.41; 95% CI, 1.66 to 3.48) and in women with a high recurrence score (a score of 26 to 100), all of whom were assigned to chemoendocrine therapy (hazard ratio, 3.17; 95% CI, 1.94 to 5.19). Among women who were 50 years of age or younger who had received endocrine therapy alone, the estimated (±SE) rate of distant recurrence at 9 years was less than 5% (≤1.8±0.9%) with a low recurrence score (a score of 0 to 10), irrespective of clinical risk, and 4.7±1.0% with an intermediate recurrence score and low clinical risk. In this age group, the estimated distant recurrence at 9 years exceeded 10% among women with a high clinical risk and an intermediate recurrence score who received endocrine therapy alone (12.3±2.4%) and among those with a high recurrence score who received chemoendocrine therapy (15.2±3.3%). CONCLUSIONS Clinical-risk stratification provided prognostic information that, when added to the 21-gene recurrence score, could be used to identify premenopausal women who could benefit from more effective therapy