Detection, Isolation and Characterization of Principal Synthetic Route Indicative Impurities in Verapamil Hydrochloride

Two unknown impurities were detected in verapamil hydrochloride bulk drug using isocratic reversed-phase high performance liquid chromatography (HPLC). These impurities were isolated by preparative HPLC. Spectral data for the isolated impurities were collected. Based on the spectral data derived from two-dimensional nuclear magnetic resonance (2D-NMR) spectroscopy and mass spectrometry (MS), impurity-1 and impurity-2 were characterized as 2-(3,4-dimethoxyphenyl)-3-methylbut-2-enenitrile and 2-(3,4-dimethoxyphenyl)-2-isopropyl-3-methylbutanenitrile, respectively

Carbon Nanotube from Unconventional Precursor-Optimization of Synthesis Parameters

Carbon is a versatile element of distinctive properties and has been described as the key element of living substance. Carbon nanostructures have attracted lots of interest, due their prominent properties. Spray pyrolysis method is adopted for synthesis of carbon nanotubes (CNTs). Contrast to any petroleum product, there is no fear of its ultimate shortage as it is a renewable source and can be obtained easily by cultivating as much quantity as required. Synthesize well crystalline multiwalled carbon nanotubes (MWNTs) from unconventional precursor of methyl ester of Helianthus annuus oil by optimize the parameters such as reaction temperature, catalyst composition and feed rate of carbon precursor in order to obtain good yield with desirable morphology

Improving safety, efficiency and efficacy of neuraxial blockade through enhanced operator peformance

The objective of this thesis was to enhance operator performance to improve efficiency, safety and efficacy of neuraxial blocks. Methodology Efficiency In study 1 and 1a, a metric based assessment tool for labour epidural catheter placement was developed and validated. The effect of proficiency based progression training (PBP) in provision of labour epidural analgesia was then studied. Safety Study 2 was undertaken to improve the accuracy of palpated landmarks for administering spinal anaesthesia in patients undergoing caesarean section. Efficacy In study 3, clinically relevant association and correlation(s) between i. ultrasound images and ii. Magnetic Resonance Imaging (MRI) of lumbar spine was studied. In study 4a and 4b, landmark-guided midline approach was compared to pre-procedure ultrasound guided paramedian techniques in spinal anaesthesia. Results PBP (study 1, 1a, n=17) group had a significantly (p= 0.04) lower failure rate (13.3%) compared to simulation only group (28.7%). Study 2 (n=112) showed that inserting the spinal needle below the intercristal line significantly reduces the incidence of spinal anaesthesia performed at or above L2-3 interspace compared to at or above intercristal line (absolute risk reduction of 38.2%, p<0.001). In study 3 it was observed that the odds of obtaining a poor view in neuraxial ultrasound was seven times higher in the presence of facet joint degeneration (95% CI 1.7-28.9, p=0.007). Study 4a (n=100), it was observed that the number of passes to achieve successful dural tap was significantly lower in the ultrasound group (mean 4, SD 4) compared to the conventional group (mean 8.2, SD 12.3).In study 4b (n=120), we found no difference between groups in the number of passes or attempts to achieve successful dural puncture when L5S1 paramedian space was selectively used in the ultrasound group. Conclusion Significant improvements in safety, efficiency and efficacy of neuraxial blocks were demonstrated

Investigating self-efficacy and behavioural bias on investment decisions

The determinants of irrational decisions on the stock market are found in numerous empirical studies. However, self-efficacy and behavioural biases have a sturdy influence on stock market investment decisions. Behavioural biases are formed with heuristics, prospect theory and herding effect concerning stock market investments. Self-efficacy is independent of behavioural biases but is closely connected with controlling behavioural intentions in decision-making. The research was conducted to find the influence of self-efficacy and behavioural biases in the decision of stock market investment. The study was conducted with 250 individual investors and applied the SEM technique. Findings indicated that heuristics had a positive relationship with behavioural biases, but behavioural biases reported a negative relationship with the herding effect and prospect theory. Heuristics were mostly developed on the intrinsic strength of individual investors; therefore, investors believe heuristics will be a better decision-making tool than prospect theory or the herding effect. Prospect theory is shaped and influenced by regret aversion, loss aversion, self-control and mental accounting. Financial literacy, risk tolerance, and peer support profoundly develop the self-efficacy of investors to make profitable investment decisions. Self-efficacy is formed by risk tolerance, financial literacy and peer support in the stock market investment decision and identified the evidence of individual investors not making rational decisions and facing one or more behavioural biases and self-efficacy factors. The study finds the combined effect of behavioural biases and self-efficacy in stock market investment decisions, which have significant implications among individual investors, particularly in emerging markets

Cloud based multicasting using fat tree data confidential recurrent neural network

With the progress of cloud computing, more users are attracted by its strong and cost-effective computation potentiality. Nevertheless, whether Cloud Service Providers can efficiently protect Cloud Users data confidentiality (DC) remains a demanding issue. The CU may execute several applications with multicast needs. In Cloud different techniques were used to provide DC with multicast necessities. In this work, we aim at ensuring DC in the cloud. This is achieved using a two-step technique, called Fat Tree Data Confidential Recurrent Neural Network (FT-DCRNN) in a cloud environment. The first step performs the construction of Fat Tree based on Multicast model. The aim to use Fat Tree with Multicast model is that the multicast model propagates traffic on multiple links. With the Degree Restrict Multicast Fat Tree construction algorithm using a reference function, the minimum average between two links is measured. With these measured links, multicast is said to be performed that in turn improves the throughput and efficiency of cloud service. Then, with the objective of providing DC for the multi-casted data or messages, DCRNN model is applied. With the Non-linear Recurrent Neural Network using Logistic Activation Function, by handling complex non-linear relationships, average response time is said to be reduced

Sleep duration in school-age children with epilepsy: A cross-sectional study

Background: Normal sleep is required for the optimal growth and development of the children. Ineffective or inadequate sleep is common in children with epilepsy. Objectives: The objectives of this study were to study the sleep duration and describe the factors affecting it in school-aged children with epilepsy attending the seizure clinic of a pediatric tertiary care hospital. Materials and Methods: 6–12-year-old children with epilepsy, attending the seizure clinic formed the study subjects. They were assessed for inclusion in the study using INCLEN diagnostic tool for epilepsy (INDT-Epi) to achieve a sample size of 139. Informed written consent was obtained from parents. Background sociodemographic information, seizure type and treatment details, and duration of sleep of the child were collected from the parents. The proportion of children with epilepsy who had sleep problems were expressed as percentage. Results: The mean age of study population was 9.07±2.09 years. The average sleep duration of the study population was 9.41±1.41 h. The mean nap time of the study population was 68.51±33.88 min. No significant association was seen among the factors that determine sleep duration. Conclusion: Children with epilepsy tend to sleep for lesser hours when compared to historic controls of normal school-age children reported in literature

Towards AI enabled automated tracking of multiple boxers

Continuous tracking of boxers across multiple training sessions helps quantify traits required for the well-known ten-point-must system. However, continuous tracking of multiple athletes across multiple training sessions remains a challenge, because it is difficult to precisely segment bout boundaries in a recorded video stream. Furthermore, re-identification of the same athlete over different period or even within the same bout remains a challenge. Difficulties are further compounded when a single fixed view video is captured in top-view. This work summarizes our progress in creating a system in an economically single fixed top-view camera. Specifically, we describe improved algorithm for bout transition detection and in-bout continuous player identification without erroneous ID updation or ID switching. From our custom collected data of ~11 hours (athlete count: 45, bouts: 189), our transition detection algorithm achieves 90% accuracy and continuous ID tracking achieves IDU=0, IDS=0

Zinc finger nucleases: custom-designed molecular scissors for genome engineering of plant and mammalian cells

Custom-designed zinc finger nucleases (ZFNs), proteins designed to cut at specific DNA sequences, are becoming powerful tools in gene targeting—the process of replacing a gene within a genome by homologous recombination (HR). ZFNs that combine the non-specific cleavage domain (N) of FokI endonuclease with zinc finger proteins (ZFPs) offer a general way to deliver a site-specific double-strand break (DSB) to the genome. The development of ZFN-mediated gene targeting provides molecular biologists with the ability to site-specifically and permanently modify plant and mammalian genomes including the human genome via homology-directed repair of a targeted genomic DSB. The creation of designer ZFNs that cleave DNA at a pre-determined site depends on the reliable creation of ZFPs that can specifically recognize the chosen target site within a genome. The (Cys(2)His(2)) ZFPs offer the best framework for developing custom ZFN molecules with new sequence-specificities. Here, we explore the different approaches for generating the desired custom ZFNs with high sequence-specificity and affinity. We also discuss the potential of ZFN-mediated gene targeting for ‘directed mutagenesis’ and targeted ‘gene editing’ of the plant and mammalian genome as well as the potential of ZFN-based strategies as a form of gene therapy for human therapeutics in the future

Hydrological Modeling of Highly Glacierized Basins (Andes, Alps, and Central Asia)

The Soil and Water Assessment Tool (SWAT) was used to simulate five glacierized river basins that are global in coverage and vary in climate. The river basins included the Narayani (Nepal), Vakhsh (Central Asia), Rhone (Switzerland), Mendoza (Central Andes, Argentina), and Central Dry Andes (Chile), with a total area of 85,000 km2. A modified SWAT snow algorithm was applied in order to consider spatial variation of associated snowmelt/accumulation by elevation band across each subbasin. In previous studies, melt rates varied as a function of elevation because of an air temperature gradient while the snow parameters were constant throughout the entire basin. A major improvement of the new snow algorithm is the separation of the glaciers from seasonal snow based on their characteristics. Two SWAT snow algorithms were evaluated in simulation of monthly runoff from the glaciered watersheds: (1) the snow parameters are lumped (constant throughout the entire basin) and (2) the snow parameters are spatially variable based on elevation bands of a subbasin (modified snow algorithm). Applying the distributed SWAT snow algorithm improved the model performance in simulation of monthly runoff with snow-glacial regime, so that mean RSR decreased to 0.49 from 0.55 and NSE increased to 0.75 from 0.69. Improvement of model performance was negligible in simulations of monthly runoff from the basins with a monsoon runoff regime