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Abstract Aluminium matrix composites (AMCs) are well known for their excellent wear resistance and low weight. In the present work, in-situ synthesis of Al-Si-TiB2 composites with near eutectic and hypereutectic compositions of Al-Si alloys has been attempted through salt-metal reaction (K2TiF6 and KBF4 halide salts) by stir casting route. The fabricated composites were subjected to microstructure analysis, XRD study, sliding wear test, hardness and density measurements. The combined effect of Si and TiB2 is the novelty of this investigation to alter the structure–property correlation as well as hardness and tribological properties. Optical Emission Spectroscopy analysis indicated some amount of Si loss during stir casting and revealed the final composition of the cast composites. Though the increase in the density of the composite was not considerable due to incorporation of TiB2 particles, there was remarkable improvement in hardness and tribological properties attributed to clear interface between the matrix and the reinforcement as a result of in-situ process of fabrication. Wear resistance was found to be improved with increasing amount of Si content with a fixed TiB2 content in the composites under a constant load. TiB2 acts as a good grain refiner and improves the wear properties of the hypereutectic Al-Si alloy composites by decreasing the brittle primary silicon particle size.

Background: The weight gain that occurs in pregnancy has the potential to influence a woman’s long-term health by increasing the risk for weight retention and obesity, as well as related comorbidities such as chronic hypertension (HTN) or Type 2 diabetes mellitus. Aims and Objectives: The aim of the study was to study maternal and perinatal outcomes associated with excessive maternal gestational weight gain (GWG). Materials and Methods: The current study was a cross-sectional study conducted at the Department of Obstetrics and Gynecology, from October 2018 to October 2019. A total of 91 women attending the antenatal clinic of MR Bangur Hospital and getting admitted for delivery during the study period was considered as the study population. Medical records were maintained for variables of the mothers such as gestational diabetes mellitus (GDM), gestational HTN, pre-eclampsia, eclampsia, duration of labor, mode of delivery, indication of cesarean section, Postpartum hemorrhage, and perineal tears. coGuide v.0.01 used for statistical analysis. Results: There were majority of 55 (60.4%) participants reported 18.5–24.9 body mass index (BMI). The difference in the proportion of BMI across maternal weight gain was statistically significant. The difference in the proportion of GDM, duration of labor >18 h, and duration of the second stage of labor >2 h between maternal weight gain were statistically significant. The difference in the proportion of APGAR score at 1 min, and 5 min between groups of maternal weight gain was statistically significant. Conclusion: Our study suggested that GWG has to be achieved within the Institute of Medicine recommendation according to pre-pregnancy BMI to improve pregnancy outcomes and reduce maternal and perinatal adverse outcomes.

Agriculture sector is major sufferer of climate change both at a global level as well as at India level. Cereals account for about 92 % of India's total food grain output and climate change has a significant influence on the production of cereals. This study aimed to evaluate the long-term and short-term effects of climatic and non-climatic variables, specifically temperature, precipitation, cereal area, total cropped area, fertilizer consumption, and pesticide consumption, on cereal production in India. The study included annual time series data that covered the period from 1960 to 2018, covering a period of 58 years. Various econometric techniques were employed to examine these relationships. The validity of a long-term and short-term relationship among the relevant variables included in the study was validated by employing the Autoregressive Distributed Lag (ARDL) technique and the Johansen cointegration test. The ARDL model's estimation outcomes reveals that input factors such as cereal area became a key factor in rising cereal production, as evidenced by its positive coefficient. Similarly, fertilizer consumption and precipitation had positive effects on production in the long run whereas total cropped area and minimum temperature has little influence over the results of production both in short run as well as long run. Furthermore, the long-term findings were also supported using econometric tools like Canonical Cointegrating Regression (CCR) and Fully Modified Least Squares (FMOLS). These methods confirmed that variations in cereal production in India were significantly influenced by both climatic factors and agricultural inputs and factors. The study emphasizes the urgency for policymakers to prioritize proactive measures aimed at reducing the adverse impacts of climate change on cereal production in India. This necessitates a comprehensive strategy integrating sustainable practices, technological innovations, and robust policy frameworks to ensure resilient agricultural sectors and sustainable food production.

# Background Tripura is hyperendemic for malaria and is one of the few states in India that still fall under Category 3 for malaria elimination (i.e., state annual parasite incidence (API) > 1, i.e., number of malaria cases per thousand inhabitants in a year). Tribal communities in Tripura are largely dependent on Jhum cultivation for their sustenance, and several studies have highlighted Jhum as a major risk factor for malaria. # Methods To assess the relationship between Jhum distribution and malaria in Tripura, Jhum hotspots were identified from Landsat-8 OLI spectral images using the Normalized burn ratio index and compared with secondary data on malaria incidence and the state/district-wise total area under Jhum for the years 2017 and 2019. # Results The study's results show that between 2017 and 2019, the total area under Jhum reduced from 39.56 km2 to 27.4 km2, which was accompanied by a shift in several Jhum hotspots from Dhalai and North Tripura in 2017 to West Tripura in 2019. Malaria cases showed a strong correlation with Jhum production both at the district and state level, which was significant at the 95% confidence interval. Furthermore, a stronger correlation in 2019 indicated that the share of malaria cases in Jhum cultivators may be increasing. # Conclusions The present study presents preliminary evidence based on secondary data analysis on the possible implications of Jhum cultivation for malaria epidemiology in an endemic north-eastern state of India. It is suggested that real-time evidence should be generated on malaria prevalence among Jhum cultivators and their families. Based on the evidence, future intervention strategies for the state should focus on the elimination of malaria risk from Jhum cultivators by minimizing outdoor exposure to malaria vectors and improving access to diagnostics and treatment.

Abstract Human papillomaviruses (HPVs) are circular, nonenveloped small double-stranded DNA viruses that infect stratified epithelium and can cause a number of life-threatening diseases. HPV is the central risk factor for developing cervical cancer and is estimated that approximately 98% of this disease is associated with oncogenic types of HPV. HPV infection leads to an estimated 266,000 cervical cancer deaths annually. Therefore, the objective of this study was to determine the prevalence of HPV infection and risk factors associated with cervical lesion among women attending the cervical cancer screening clinic at the Ethiopian Family Guidance Association, Addis Ababa. A cross-sectional study was conducted to determine the prevalence of HPV infection. Data were collected using a questionnaire and samples leftover from cervical screening were taken. The leftover swab was air dried and DNA was extracted and amplified by using a PCR. A total of 247 women were included in the study. The prevalence of HPV was 9.72% among the population studied. Of all participants, 27.13% were positive for cervical intraepithelial neoplasia-1 (CIN1). CIN1 positivity was found in half of HPV positive women. Among HPV positive women, half of them had started sexual intercourse at ages 12–17 years and 41.66% were women who gave birth at ages 12–17 years. The high prevalence of HPV and the CIN1 positive group were ages 36–57 and women with multiple sexual partners. The other groups with the highest CIN1 positive were 22.39% grade (9–12) and 20.9% primary (1–8) and uneducated women. Among HPV positive women, 83.33% had an abortion history and 80% miscarried in the first trimester. Among the CIN1 positives, 53.73% had more than two sexual partners. Among HPV positive women, half of them were users of contraception methods. In conclusion, the highest prevalence of HPV is among women who began sexual intercourse earlier and who gave birth at 12–17 years of age, have an abortion history, with MSP and oral contraceptive methods users. In addition to HPV, early pregnancy and sexual intercourse at 12–17 years of age, abortion, MSP, and oral hormonal contraceptives are factors in cervical cancer. Finally, most women do not have enough knowledge and awareness about cervical cancer and the risk factor.

Abstract Chocolate is a delectable sweet enjoyed all over the world. Made from dried cocoa beans, through the process of roasting, grinding, and conching, chocolate is known for its taste and flavor. Although the sugar content of chocolate is limiting its reach to the health-conscious population, chocolate is endowed with immense medicinal benefits. The major ingredient of chocolate, cocoa, Theobroma cacao L., is a plant known as the “Food of the Gods” due to the abundance of polyphenols present in it. Processing conditions and parameters affect the polyphenol stability. Roasting process conditions affect the polyphenols of cocoa beans, especially the epicatechin and procyanidin. The variety of cocoa beans, quality and presence of fat, sugar, emulsifiers, drying time, and temperature are the important influencing factors affecting the polyphenol content of chocolate. Chocolate possesses antioxidant, antidiabetic, anticancer, anti–inflammatory, and good mood food properties but it is also shown to be prebiotic and helps in alleviating the symptoms of Alzheimer’s and heart disease. The functional properties of chocolate can be enhanced by the addition of certain foods/ functional ingredients. This paper reviews the role of chocolate as a functional food, the effect of processing on polyphenols, and the recent developments in improving the functional properties of chocolates through fortification.

Abstract Groundnuts are mostly contaminated with the mold Aspergillus flavus which produces a carcinogenic mycotoxin called as aflatoxin. It is very important to understand the genetic factors underlying its pathogenicity, regulation, and biosynthesis of secondary metabolites and animal toxicities, but it still lacks useful information due to certain gaps in the era of modern technology. Therefore, the present study was considered to determine the key genes and metabolites involved in the biosynthesis of aflatoxin by using a molecular approach in a virulent strain of Aspergillus. The whole genome sequence of highly toxic and virulent Aspergillus isolates JAM-JKB-B HA-GG20 revealed 3,73,54,834 bp genome size, 2, 26, 257 number of contigs with N50 value of 49,272 bp, 12,400 genes and 48.1% of GC contained respectively. The genome sequence was compared with other known aflatoxin producing and non-producing genome of Aspergillus spp. and 61 secondary metabolite (SM) gene clusters were annotated with the toxic strain JAM-JKB-BHA-GG20 which showed similarity with other Aspergillus spp. A total number of eight genes (ver-1, AflR, pksA, uvm8, omt1, nor-1, Vha and aflP) were identified related to biosynthesis of aflatoxin and ochratoxin. Also, 69 SSR with forward and reverse primers and 137 di and tri nucleotide motifs were identified in the nucleotide sequence region related to aflatoxin gene pathway. The genes and putative metabolites identified in this study are potentially involved in host invasion and pathogenicity. As such, the genomic information obtained in this study is helpful in understanding aflatoxin gene producing pathway in comparison to other Aspergillus spp. and predicted presence of other secondary metabolites clusters viz. Nrps, T1pks etc. genes associated with a biosynthesis of OTA mycotoxin.

The power system network is moving towards a smarter grid with increasing deployment of distributed generators, and prosumers, embedded with distributed control. An evolving active distribution network will require a distribution system operator (DSO) which will utilize information and communication technology to perform optimization and control of distributed energy resources (DERs). There are two major challenges in performing simulation and analysis of a distribution system. The first one is the detailed modelling and integration of a large number of distributed generators and the second one is the integration of communication and power layers in real-time simulation. In this paper, a co-simulation framework is proposed, which facilitates the performance monitoring of both layers simultaneously. A CIGRE benchmark system is used to investigate the performance of the communication layer along with the detailed modelling of distributed generators. A network topology with different network scenarios is used to analyze the impact of the network performance. This analysis helps in determining network scenarios for the optimal operation of the distribution system. The simulation study has utilized Real-Time Digital Simulator (RTDS), Typhoon, and OpalRT real-time simulators for the power layer, and NetSim for designing a communication layer between all the simulators and emulating the actual communication.

Abstract In this study, six isolates of Chryseobacterium balustinum were characterized from diseased rainbow trout fingerlings. The virulence characteristics, pathogenicity, and antimicrobial susceptibility pattern of these isolates were investigated. The bacterium showed positive results for catalase, cytochrome oxidase, and aesculin hydrolysis, while negative results were obtained for DNase, gelatinase, methyl red, Voges-Proskauer's reaction, Simon citrate, Hydrogen sulphide, and starch hydrolysis. Amino acid metabolism analysis revealed the inability to metabolize arginine, lysine, and ornithine decarboxylase. Molecular characterization (16S rRNA) and phylogenetic analysis revealed the test isolates as C. balustinum, closely related to strain WLT (99.85% similarity) and C. balustinum P-27 (99.77%). Virulence assay indicated haemolytic activity and biofilm formation by the test bacterium. The challenge test confirmed moderate pathogenicity in rainbow trout and established Koch's postulates. The clinical manifestations of infection included fin erosion, eye and body surface haemorrhage, exophthalmia, and organ liquefaction. The minimum inhibitory concentrations of various antimicrobials ranged from 1 to > 256 µg mL−1. The novel synthetic antimicrobial peptides exhibited MICs of 8 to > 256 µg mL−1, suggesting a potential control method. These findings suggest that C. balustinum is an opportunistic pathogen with moderate pathogenicity in rainbow trout. Further research on the host–pathogen relationship is necessary to understand virulence characteristics and pathogenicity in aquaculture.

Rapid, inexpensive, and low-power/solar light-driven photocatalytic degradation of organic pollutants to deal with annually produced trillion tons of synthetic dye wastewater to prevent water scarcity issues, ecotoxicological risks, and human health has always been challenging. To overcome this limitation, the present study synthesized earth-abundant, inexpensive copper oxide nanosheets using a simple single-step hydrothermal route. The structural, physicochemical, and functional properties of the nanosheets have been characterized using several characterization techniques. The photocatalytic activity was studied for two commonly industrially used organic dyes, Methylene Blue (MB) and Rhodamine B (RhB). The importance of this work is the usage of a cheap commercially available Phillips UV light (11 W) as well as direct sunlight. With several optimized conditions, almost complete degradation of both dyes was achieved within 35 minutes under low-power UV light and within 70 minutes by the direct illumination of natural sunlight. The enhanced photocatalytic performance can be correlated to the synergetic effect of a higher charge transfer mechanism, good catalytic ‘active surface area’ availability (13.2 m2/g), and several optimized parameters that affect the reaction efficacy. Additionally, five repeated uses of nanosheets without sacrificing performance confirmed their stability and sustainability as a promising candidate for large-scale industrial textile wastewater remedies.

Rationale & Objective: The incidence of arrhythmia varies by time of day. How this affects individuals on maintenance dialysis is uncertain. Our objective was to quantify the relationship of arrhythmia with the time of day and timing of dialysis. Study Design: Secondary analysis of the Monitoring in Dialysis study, a multicenter prospective cohort study. Settings & Participants: Loop recorders were implanted for continuous cardiac monitoring in 66 participants on maintenance dialysis with a follow up of 6 months. Exposure: Time of day based on 6-hour intervals. Outcomes: Event rates of clinically significant arrhythmia. Analytical Approach: Negative binomial mixed effects regression models for repeated measures were used to evaluate data from the Monitoring in Dialysis study for differences in diurnal patterns of clinically significant arrhythmia among those with end-stage kidney disease with heart failure and end-stage kidney disease alone. We additionally analyzed rates according to presence of heart failure, time of dialysis shift, and dialysis versus nondialysis day. Results: Rates of clinically significant arrhythmia peaked between 12:00 AM and 5:59 AM and were more than 1.5-fold as frequent during this interval than the rest of the day. In contrast, variations in atrial fibrillation peaked between 6:00 AM and 11:59 AM, but variations across the day were qualitatively small. Clinically significant arrhythmia occurred at numerically higher rate in individuals with end-stage kidney disease and heart failure (5.9 events/mo; 95% CI, 1.3-26.8) than those without heart failure (4.0 events/mo; 95% CI, 0.9-17.9). Although differences in overall rate were not significant, their periodicity was significantly different (P

The best biocontroller Bacillus subtilis produced silver nanoparticles (AgNPs) with a spherical form and a 62 nm size through green synthesis. Using UV-vis spectroscopy, PSA, and zeta potential analysis, scanning electron microscopy, and Fourier transform infrared spectroscopy, the properties of synthesized silver nanoparticles were determined. Silver nanoparticles were tested for their antifungicidal efficacy against the most virulent isolate of the Aspergillus flavus fungus, JAM-JKB-BHA-GG20, and among the 10 different treatments, the treatment T6 [PDA + 1 ml of NP (19: 1)] + Pathogen was shown to be extremely significant (82.53%). TG-51 and GG-22 were found to be the most sensitive groundnut varieties after 5 and 10 days of LC-MS QTOF infection when 25 different groundnut varieties were screened using the most toxic Aspergillus flavus isolate JAM- JKB-BHA-GG20, respectively. In this research, the most susceptible groundnut cultivar, designated GG-22, was tested. Because less aflatoxin (1651.15 g.kg-1) was observed, treatment T8 (Seed + Pathogen + 2 ml silver nanoparticles) was determined to be much more effective. The treated samples were examined by Inductively Coupled Plasma Mass Spectrometry for the detection of metal ions and the fungicide carbendazim. Ag particles (0.8 g/g-1) and the fungicide carbendazim (0.025 g/g-1) were found during Inductively Coupled Plasma Mass Spectrometry analysis below detectable levels. To protect plants against the invasion of fungal pathogens, environmentally friendly green silver nanoparticle antagonists with antifungal properties were able to prevent the synthesis of mycotoxin by up to 82.53%.

AbstractAgricultural and food system transformation helps increase farm productivity and encourages farmers to participate in updated value chains, adopt newer technologies, thereby helping farmers transform their livelihoods in a sustainable manner. Relatedly, value chain innovations depend on multiple decisions farmers make at various stages of the value chain, adequate participation being a primary factor. In this paper, we integrate farmers’ adoption decision of a new variety of chilli crop (“Teja”) along with their electronic market participation decision and e-market participation intensity, based on data from the chilli farming sector in India, where agricultural markets have been modernized through digitization (Kalgudi e-Market). Thus, the employed Triple-Hurdle Model (THM) integrates adoption decision of “Teja” variety of chilli, e-Market Participation Decision and e-market participation intensity thereby, allowing us to make inferences relating to chilli farmers in Andhra Pradesh, India. Our results, showed that the drivers of “Teja” variety adoption, e-market participation, and e-participation intensity include education, reliable extension services, access to seeds of high yielding varieties, market information, and membership in farmer-producer organizations. Added to these, personnel training visits, prompt deliveries of inputs, and prompt payment of sales proceeds are also important in influencing participation and intensities. Results show that the three stochastic decisions of THM are strongly correlated implying that the adoption decision of “Teja” variety of chilli by the farmers influences the e-market participation decision and consequently, e-market participation intensity and these three decisions are sequential. On the contrary, the decisions viz., e-market participation decision and e-market participation intensity as input buyers and consequent adoption of “Teja” variety of chilli are simultaneous. So, the policy measures that promote production technology interventions (say, “Teja” variety of chilli) will definitely enhance better e-market access of chilli farmers. Accordingly, the breeding programs of the agricultural research stations should enhance the uptake of improved varieties in tune with modern marketing (e-market) technologies. Future farm policy and agricultural-research and innovations must recognize the potential that the digital marketing systems have to offer. Such considerations coupled with the provision of market infrastructure including assaying, grading, storage, and market information will promote digital transformation in agricultural value chains in developing countries like India.

A critical global policy question is how the environmental management interventions could be repurposed to meet the sustainable development goals and their target for food security, climate protection, and environmental sustainability. A common challenge facing food systems in developing countries is to improve agricultural productivity to ensure food security for all without increasing the emission of greenhouse gases (GHGs) from agriculture. Climate-smart agriculture (CSA) approaches help to reduce GHG emissions from agriculture and address the challenges of climate change (CC) and food insecurity. Yet, CSA lack understanding of the institutional arrangements and policy processes. This paper examines 38 aspects to assess the institutional and policy status for CC mitigation and adaptation and CSA in Nagaland, India. Furthermore, we use these aspects to develop a scale to measure the policy and institutional environment for mitigation and adaptation of CC and implementation of CSA. Nagaland is relatively in a better position in nine aspects, although it can improve. Methodologically, the scale developed in this paper and the identified factors can help study the institutional and policy status of a country, state, or region. We identify several implications for understanding CC and CSA institutions and policies for informing policy research and practice. HIGHLIGHTS This paper examines 38 different institutional and policy variables affecting the mitigation and adaptation of climate change (CC) strategies supporting the implementation of climate-smart agriculture (CSA).; Evidence-based strategies are identified for scaling up the CSA interventions in the Indian context.; The proposed scale is consistent to measure the institutional and policy status concerning CC mitigation and adaptation and implementation of CSA.;

Background: Pregnancy is considered as the most precious gift for any woman. She should have a safe pregnancy and delivery. Unfortunately, many women are deprived from this right due to poor knowledge or awareness. Birth preparedness and complication readiness (BPACR) have proven to be effective strategy to reduce maternal mortality and morbidity. This study was done with an objective to assess the knowledge and practices of BPACR among third trimester women attending antenatal clinic. Methods: The present study was a cross sectional study conducted in antenatal clinic of Howrah District Hospital West between 15th December 2020 to 20th January 2021. Study participants were 200 Primigravida mothers who were in third trimester of pregnancy attending the antenatal clinic. Semi- structured questionnaire was used to collect demographic data and obstetrical history; structured knowledge questionnaire and checklist was used to asses’ knowledge and practice on BPACR. Chi-square test or Fischer’s exact test was used as test of significance for qualitative data. Correlations were performed with Pearson Correlation coefficient. coGuide software was used for statistical analysis. Results: Out of 200 pregnant women, 169(84.5%) have done higher secondary education and 176(88%) were home makers. Majority (62.5%) were residing in urban area. Among participants, 138 (69%) primigravida mothers had inadequate levels of knowledge about BPACR and 140(70%) had inadequate practice. Conclusion: In this study, the BPACR knowledge and practice of the participants were inadequate. Ideal BPACR given through education can help in healthy practice during antenatal and postnatal period.

This paper documents the food and agricultural policy process in the context of agrarian reforms in Tajikistan. It uses the case study of Tajikistan and applies a recently developed conceptual framework for understanding the drivers of policy change. It undertakes a historical review of agrarian reforms and appraises current policy challenges within the food and agricultural sector. Using specific tools to study power relations, financing, and information flows in the policy process, it maps institutional architecture and key stakeholders in the pre- and post-soviet era. Information gathered through focused group discussions, key informant interviews, and recent field research on food and agricultural policy issues is used to analyze factors that drive different stages of the policy making process. We find that understanding the political economy and policy process interface in Tajikistan is key for designing and implementing successful policy interventions. While progress has been made, agrarian reforms towards improving land tenure rights, strengthening WUAs, providing crop insurance against drought, are the necessary steps in a larger policy discussion. Ensuring the effectiveness of land reforms, building agricultural extension system, and supporting agricultural research systems are examples of some key initiatives that the government should focus on. Investments in transportation, storage, credit facilities, and markets involving private sector will speed up the reform process.

Introduction: Atrial fibrillation (AF) is common in patients with kidney failure on hemodialysis (KF-HD). We determined both AF incidence and burden in patients with KF-HD using implantable loop recorder (ILR) monitoring. Methods: Patients with KF-HD were enrolled and received an ILR. In 6 monitoring months, the incidence of AF events lasting ≥6 minutes was captured. Demographic, clinical, and dialysis characteristics were collected, and associations with incident AF were estimated using negative binomial regression models and expressed as incidence rate ratios and 95% CIs. Results: We enrolled 66 patients with KF-HD (mean age = 56 years, 70% male); 59 (90%) were without previously diagnosed AF. AF lasting ≥6 minutes was detected in 18 of 59 subjects (31%) without previously diagnosed AF and in 5 of 7 subjects (71%) with a previous AF diagnosis. Among the 23 with detected AF, episodes were present on 16% of patient days. Although 14 of 23 patients (61%) had AF on 2.5 vs. 2.5 mEq/l: incidence rate ratio = 0.62; 95% CI, 0.48–0.80) was associated with lower AF risk whereas higher dialysate bicarbonate concentrations (>35 vs. 35 mEq/l: incidence rate ratio = 3.18; 95% CI, 1.13–8.94) were associated with higher AF risk. Conclusion: New AF was detected in approximately one-third of patients with KF-HD. AF affects a substantial proportion of patient days and may be an underappreciated cause of stroke in KF-HD.

In the present experiment, the attempt has been made to study the biosafety, toxicity, residue depletion and drug tolerance of graded doses of emamectin benzoate (EB) in juveniles of golden mahseer, Tor putitora as a model candidate fish for sport fishery and conservation in temperate waters through an extended medicated feeding. The graded doses of EB viz., 1× (50 μg/kg fish/day), 2 × (100 μg/kg fish/day), 5 × (250 μg/kg fish/day) and 10 × (500 μg/kg fish/day) were administered to golden mahseer juveniles through medicated diet for 21 days at water temperature of 18.6°C. The higher doses of EB did not cause any mortality during and 30 days after the end of medication period, but considerable variations in feeding and behavior were observed. Severe histological alterations observed after EB-diets (5 × and 10×) were vacuolation, pyknotic nuclei, melanomacrophage centre and necrosis in liver; Bowman’s capsule dilation, degenerated renal tubules in kidney; myofibril disintegration, muscle oedema, splitting of muscle fibres, migration of inflammatory cells in muscle; and abundant goblet cells, dilated lamina propria and disarrangement of mucosa in intestine tissues. The residual concentrations of EB metabolites Emamectin B1a and B1b were analyzed using muscle extracts and were found to be peaked during medication period followed by gradual depletion in post-medication period. The outcome of this study showed that the Emamectin B1a residual concentration in fish muscle in 1×, 2×, 5×, and 10× EB treatment groups were 1.41 ± 0.49, 1.2 ± 0.7, 9.7 ± 3.3, and 37.4 ± 8.2 μg/kg at 30 days of post-medication period, respectively, which falls under the maximum residue limits (MRLs) of 100 μg/kg. The results support the biosafety of EB at recommended dose of 50 μg/kg fish/day for 7 days. As residue of EB is recorded falling within the MRL, no withdrawal period is recommended for golden mahseer.

This paper examines the preconditions for collective action in the context of Water User Associations (WUAs) in Tajikistan. The paper uses qualitative case studies of five WUAs from three cotton-producing districts of Khatlon region in Tajikistan. Findings suggest that the ability of WUAs to generate collective action for the benefit of the rural community depends on factors internal and external to WUAs. The WUAs in our study tended to exhibit top-down organizational forms which might not bode well for their sustainability. WUAs were set up by donors in a setting where large, collectivized farms and vestiges of command agriculture persist alongside small individual farms. The presence of large farms among smaller holdings makes it difficult for WUAs to distribute water equitably among its members. Local government officials tend to favor the interest of big planters over small independent farms and can exert pressure on WUAs to skew water distribution. By studying the institutional challenges facing water resource management in Tajikistan, a major natural resource challenge facing all the Central Asian economies, the paper identifies opportunities for speeding up the process of agrarian reforms currently underway in all the transition economies.

Wildlife rescue and rehabilitation centers offer important insights regarding the threats and attributes associated with raptor casualties. We utilized a data set on raptors admitted to a single wildlife rescue center (WRC) in the Mid-Hills region of Nepal by applying two approaches. First, we explored the overview of species admitted and the cause of admission at WRC, and modeled the outcome (survival, mortality) of frequently admitted raptor species (FARS) by analyzing the cause of admittance, the season of admittance, and age category through binary logistic regression. Second, we identified the high-risk zone for raptors through an ensemble approach using multiple species distribution modeling algorithms in BIOMOD2. A total of 17 species of raptors with 830 cases of admittance; Black Kite (Milvus migrans) 47%, Barn Owl (Tyto alba) 24%, Eurasian Eagle-Owl (Bubo bubo) 12%, and other species (17%) were recorded at the WRC over the course of five years (2017–2021). Unknown causes (39%) comprised the largest proportion of admittance, followed by illegal captivity (17%), injured by dogs (10%), and injured by humans (10%). Among the three FARS, the mortality rate was highest in the Barn Owl (62%), followed by the Eurasian Eagle-Owl (55%) and the Black Kite (47%). The model with an additive effect of species, cause, and age category was the best fit model to predict the outcome for FARS. Out of the total study area (705 km2), 19% was under high-risk, 41% was under medium-risk, and 40% was under low-risk. Most of the high-risk zones were incorporated within agricultural areas (57%) and built-up areas (31%). The variation in mortality rate among the FARS indicates that species-specific conservation strategies need to be developed to reduce casualty risk. Identified potential anthropogenic threats and their impact on the survivability and mortality of raptors in this study could help to mitigate casualties. Moreover, the high-risk areas associated with raptor casualties in this study need to be prioritized to implement the conservation endeavors for the raptors.

Abstract To assess threats to the population of native mammals and their survival, we performed a retrospective study utilizing a rescue and rehabilitation database from Nepal. Based on a total number of 1013 cases involving six taxonomic families of mammals admitted over five years, the outcome (mortality and survivability) differed significantly among six taxa (χ2[df = 5, N = 1013] = 65.08, p

Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research.

Abstract Lane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements.

Abstract Immune‐chromatographic kits are being used since several years in the rapid detection of infectious diseases. It is also called the lateral flow technique, and is used for antigen or antibody detection. There are a series of steps involved in the development of these immune‐chromatographic test kits. Still, the preparation of gold nanoparticles (AuNPs) is an important quality variable for the immune‐chromatographic test kit sensitivity. The immune chromatographic test must be specific in detection for specific antigen and antibody; this implies that the test kit should not show a false result. Secondly, the test kit should be sensitive enough to give a readable result, and the intensity of the test line should increase or decrease with the concentration of an analytic sample. Various factors can influence the performance of a test. Temperature differences in AuNPs preparation can alter the assay kinetics and contribute to assay variability. Other factors such as assay components, manufacturing processes and reagent variation also contribute to assay precision and accuracy. It is important to note that assay reproducibility is the combined effect of individual sources of variability. The authors have synthesized AuNPs by immediately controlling the reaction temperature. Different batches of Malaria rapid test kit were developed and the test kit sensitivity was analysed. It was found that test kits designed with temperature‐controlled AuNPs sensor had reproducible uniformity in terms of batch to batch sensitivity than AuNPs synthesized by conventional Turkevich and Fern process.

To meet the augmented load power demand, the doubly-fed induction generator (DFIG) based wind electrical power conversion system (WECS) is a better alternative. Further, to enhance the power flow capability and raise security margin in the power system, the STATCOM type FACTS devices can be adopted as an external reactive power source. In this paper, a three-level STATCOM coordinates the system with its dc terminal voltage is connected to the common back-to-back converters. Hence, a lookup table-based control scheme in the outer control loops is adopted in the Rotor Side Converter (RSC) and the grid side converter (GSC) of DFIG to improve power flow transfer and better dynamic as well as transient stability. Moreover, the DC capacitor bank of the STATCOM and DFIG converters connected to a common dc point. The main objectives of the work are to improve voltage mitigation, operation of DFIG during symmetrical and asymmetrical faults, and limit surge currents. The DFIG parameters like winding currents, torque, rotor speed are examined at 50%, 80% and 100% comparing with earlier works. Further, we studied the DFIG system performance at 30%, 60%, and 80% symmetrical voltage dip. Zero-voltage fault ride through is investigated with proposed technique under symmetrical and asymmetrical LG fault for super-synchronous (1.2 p.u.) speed and sub-synchronous (0.8 p.u.) rotor speed. Finally, the DFIG system performance is studied with different phases to ground faults with and without a three-level STATCOM.

Onchocerciasis is a neglected tropical disease that is prevalent throughout Africa, including developing countries such as Ethiopia. It affects around 37 million people, the majority of whom are from Africa. As a result, the study was designed to look into the community’s knowledge, attitude, and practice about the onchocerciasis elimination campaign. Four communities from Gesha town, Southwest Ethiopia, were chosen. The population was selected using a basic random selection procedure, and 312 people were identified for the study based on the eligibility requirements, with 302 (96.79%) of them responding correctly. The data were analyzed using the descriptive method with the SPSS program version 20. It was discovered that the majority of communities (89.4%) are aware of onchocerciasis. They also have a good awareness of the severity, preventability, therapy, and mode of transmission, yet they have certain misunderstandings. The communities attitude towards community directed therapy (CDT) using Ivermectin is positive (68.5%). According to 56% of the community, offering incentives for community drug distributors (CDD) has the potential to make the elimination campaign more successful. Different measures, such as avoiding any activities near the river, are important in the process of eradicating this disease at the community level. As a result, the respondent demonstrates that covering the lower body part lessens the black fly’s vulnerability because they may attack below the knee. In general, community awareness and attitude are required to eradicate this disease from the district. In addition, increased stakeholder participation and offering motivating rewards for CDT are required to make the elimination program a success.

The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.

The majority of people in the modern biosphere struggle with depression as a result of the coronavirus pandemic’s impact, which has adversely impacted mental health without warning. Even though the majority of individuals are still protected, it is crucial to check for post-corona virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, and if they are not recognised at an early stage, the risks will be increased. Additionally, if the post-symptoms are severe and go untreated, it might harm one’s mental health. In order to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person’s mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%.

Brain tumors are among the leading human killers. There are over 120 different types of brain tumors, but they mainly fall into two groups: primary brain tumors and metastatic brain tumors. Primary brain tumors develop from normal brain cells. Early and accurate detection of primary brain tumors is vital for the treatment of this disease. Magnetic resonance imaging is the most common method to diagnose brain diseases, but the manual interpretation of the images suffers from high inter-observer variance. In this paper, we presented a new computer-aided diagnosis system named PBTNet for detecting primary brain tumors in magnetic resonance images. A pre-trained ResNet-18 was selected as the backbone model in our PBTNet, but it was fine-tuned only for feature extraction. Then, three randomized neural networks, Schmidt neural network, random vector functional-link, and extreme learning machine served as the classifiers in the PBTNet, which were trained with the features and their labels. The final predictions of the PBTNet were generated by the ensemble of the outputs from the three classifiers. 5-fold cross-validation was employed to evaluate the classification performance of the PBTNet, and experimental results demonstrated that the proposed PBTNet was an effective tool for the diagnosis of primary brain tumors.

Medical equipment highly contributes to the effectiveness of healthcare services quality. Generally, healthcare institutions experience malfunctioning and unavailability of medical equipment that affects the healthcare services delivery to the public. The problems are frequently due to a deficiency in managing and maintaining the medical equipment condition by the responsible party. The assessment of the medical equipment condition is an important activity during the maintenance and management of the equipment life cycle to increase availability, performance, and safety. The study aimed to perform a systematic review in extracting and categorising the input parameters applied in assessing the medical equipment condition. A systematic searching was undertaken in several databases, including Web of Science, Scopus, PubMed, Science Direct, IEEE Xplore, Emerald, Springer, Medline, and Dimensions, from 2000 to 2020. The searching processes were conducted in January 2020. A total of 16 articles were included in this study by adopting Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). The review managed to classify eight categories of medical equipment reliability attributes, namely equipment features, function, maintenance requirement, performance, risk and safety, availability and readiness, utilisation, and cost. Applying the eight attributes extracted from computerised asset maintenance management system will assist the clinical engineers in assessing the reliability of medical equipment utilised in healthcare institution. The reliability assessment done in these eight attributes will aid clinical engineers in executing a strategic maintenance action, which can increase the equipment's availability, upkeep the performance, optimise the resources, and eventually contributes in providing effective healthcare service to the community. Finally, the recommendations for future works are presented at the end of this study.

A process that involves the registration of two brain Magnetic Resonance Imaging (MRI) acquisitions is proposed for the subtraction between previous and current images at two different follow-up (FU) time points. Brain tumours can be non-cancerous (benign) or cancerous (malignant). Treatment choices for these conditions rely on the type of brain tumour as well as its size and location. Brain cancer is a fast-spreading tumour that must be treated in time. MRI is commonly used in the detection of early signs of abnormality in the brain area because it provides clear details. Abnormalities include the presence of cysts, haematomas or tumour cells. A sequence of images can be used to detect the progression of such abnormalities. A previous study on conventional (CONV) visual reading reported low accuracy and speed in the early detection of abnormalities, specifically in brain images. It can affect the proper diagnosis and treatment of the patient. A digital subtraction technique that involves two images acquired at two interval time points and their subtraction for the detection of the progression of abnormalities in the brain image was proposed in this study. MRI datasets of five patients, including a series of brain images, were retrieved retrospectively in this study. All methods were carried out using the MATLAB programming platform. ROI volume and diameter for both regions were recorded to analyse progression details, location, shape variations and size alteration of tumours. This study promotes the use of digital subtraction techniques on brain MRIs to track any abnormality and achieve early diagnosis and accuracy whilst reducing reading time. Thus, improving the diagnostic information for physicians can enhance the treatment plan for patients.

Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently.Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called “fractional max-pooling” (FMP). In addition, multiple-way data augmentation (DA) is employed to reduce overfitting. Model averaging (MA) is used to reduce randomness.Results: We ran our algorithm on a four-category dataset that contained COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy control (HC). The 10 runs on the test set show that the micro-averaged F1 (MAF) score of our DFMPNN is 95.88%.Discussions: This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).

Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow.

The Yamuna river has become one of the most polluted rivers in India as well as in the world because of the high-density population growth and speedy industrialization. The Yamuna river is severely polluted and needs urgent revival. The Yamuna river in Dehradun is polluted due to exceptional tourist activity, poor sewage facilities, and insufficient wastewater management amenities. The measurement of the quality can be done by water quality assessment. In this study, the water quality index has been calculated for the Yamuna river at Dehradun using monthly measurements of 12 physicochemical parameters. Trend forecasting for river water pollution has been performed using different parameters for the years 2020–2024 at Dehradun. The study shows that the values of four parameters namely, Temperature, Total Coliform, TDS, and Hardness are increasing yearly, whereas the values of pH and DO are not rising heavily. The considered physicochemical parameters for the study are TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium. As per the results and trend analysis, the value of total coliform, temperature, and hardness are rising year by year, which is a matter of concern. The values of the considered physicochemical parameters have been monitored using various monitoring stations installed by the Central Pollution Control Board (CPCB), India.

Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.

Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important tool for effective diagnosis which is requisite to replace the existing manual detection system where patients rely on the skills and expertise of a human. In order to solve this problem, a brain tumor segmentation & detection system is proposed where experiments are tested on the collected BraTS 2018 dataset. This dataset contains four different MRI modalities for each patient as T1, T2, T1Gd, and FLAIR, and as an outcome, a segmented image and ground truth of tumor segmentation, i.e., class label, is provided. A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model. The first step is to transform input image data, which is further processed through various techniques—subset division, narrow object region, category brain slicing, watershed algorithm, and feature scaling was done. All these steps are implied before entering data into the U-Net Deep learning model. The U-Net Deep learning model is used to perform pixel label segmentation on the segment tumor region. The algorithm reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. The proposed model achieved a dice coefficient of 0.9815, 0.9844, 0.9804, and 0.9954 on the testing dataset for sets HGG-1, HGG-2, HGG-3, and LGG-1, respectively.

Objective: Although till date no management protocol for esthesioneuroblastoma (ENB) has been standardized due to tumor rarity, still multimodality approach shows better treatment outcomes as compared to surgery alone. The objective of this study was to analyze the clinicopathological spectrum of ENB and to correlate treatment response with tumor staging, histopathological grading, and various treatment modalities. Materials and Methods: Twenty-one consecutive patients with biopsy-proven ENB were studied and evaluated for response to treatment in the form of complete tumor resolution. Results were analyzed and correlated with stage and grade of tumor and form of therapy received. Results: There was male preponderance (3.2:1) with age ranging between 7 and 63 years (median of 25 years). Survival rates significantly dropped with increasing tumor stage (63.6% in stages A and B vs. 30% in stages C and D) and grade (100% in Grades 1 and 2 vs. 31.25% in Grades 3 and 4). The recurrence rate was 80% in surgery alone group, which came down to 43.7% if surgery was supplemented with other modalities. In cases where multimodality treatment plan was used, endoscopic procedures fared equally as open surgical procedures. Conclusion: Hyam's grade and Kadish stage are important prognostic indicators of treatment outcome, with survival rates dropping with increasing tumor stage and grade. Multimodality treatment protocols have improved the disease outcome, making endoscopic surgery equivalent to radical surgeries regarding result outcomes and giving other advantages such as better cosmesis, less treatment-related morbidities, decreased hospital stay, and better cost-effectiveness.

Anaplastic thyroid carcinoma (ATC), one of the most aggressive malignancies, is extremely rare in childhood. We present a case of 5-yearold child who presented with rapidly progressing thyroid swelling and stridor, for which she underwent emergency tracheostomy and biopsy. Histopathological features were suggestive of ATC and the patient died within two months after diagnosis. ATC, though very rare in childhood, should be kept in the differential diagnoses of rapidly enlarging neck masses in children. To the best of our knowledge, this is the youngest case of ATC reported in literature.

The role of Vibrio parahaemolyticus in causing diarrhoeal disease is well known. However, phenotypic and genetic traits of this pathogen isolated from diverse sources have not been investigated in detail. In this study, we have screened samples from diarrhoeal cases (2603), brackish water fish (301) and aquatic environments (115) and identified V. parahaemolyticus in 29 (1.1%), 171 (56.8%) and 43 (37.4%) samples, respectively. Incidence of pathogenic V. parahaemolyticuswith virulence encoding thermostable-direct haemolysin gene (tdh) was detected mostly in fishes (19.3%) and waters (15.6%) than clinical samples (1.04%). The pandemic strain marker genes (toxRS and PGS-sequences) have been detected relatively more in water (6%) and fish (5%) samples than in clinical samples (0.7%). Majority of the V. parahaemolyticus isolates from clinical cases and fish samples (26.3%) belonged to classical pandemic serovars (O3:K6). In addition, several newly recognised pandemic serovars have also been identified. Pulsed field-gel electrophoresis (PFGE) analysis showed clonal relatedness (60–85%) of V. parahaemolyticus from different sources. The study observation revealed that the brackish water fishes and water bodies may act as a reservoir of pathogenic V. parahaemolyticus. Emergence of several new serovars of pandemic V. parahaemolyticussignifies the changing phenotypic characteristics of the pathogen.

Background: General anesthesia (GA) is the conventional norm whenever major breast surgeries are thought of. However, in recent years, thoracic paravertebral block (TPVB) has emerged as a potential alternative to GA. Previously, studies have compared analgesic efficacy between TPVB administered before GA and GA alone. Aims and Objectives: we aimed to compare analgesic efficacy and safety between multi-level TPVB (MPVB) and single-level TPVB (SPVB) in modified radical mastectomy (MRM). Main objective was to assess the duration of postoperative analgesia provided by MPVB and SPVB. Materials and Methods: In this prospective, randomized, double-blind study, we compared MPVB (Group M) with SPVB (Group S) using 0.3 ml/kg of 0.5% bupivacaine with 0.5 μg/kg dexmedetomidine in 60 American Society of Anesthesiologists I and II female patients who were posted for MRM. Patients were randomly allotted into Group M (n = 30) or S (n = 30). Results: Mean time to primary rescue analgesia (RA) administration was significantly longer in Group M than Group S (736.90 min vs. 581.57 min, P < 0.001). Frequency of primary RA (Group M 1.17 vs. Group S 1.87, P < 0.001) and primary RA consumption (Group M 101.17 mg vs. Group S 166.20 mg, P < 0.001) in the first 24 h of the postoperative period was significantly less in Group M than Group S. Postoperative nausea and vomiting occurred in 3.34% (n = 1) and 6.67% (n = 2) of Group M and Group S, respectively. Conclusion: Multi-level technique of administering TPVB is a better choice for providing stand-alone intraoperative anesthesia and postoperative analgesia in patients undergoing MRM.

Abstract Aim: The objective of the study was to isolate and characterize pathogenic Vibrio parahaemolyticus from crustacean shellfishes (crab and shrimp) commonly retailed in coastal parts of eastern India. Materials and Methods: Samples were processed by bacteriological isolation followed by biochemical characterization in Kaper’s medium. Presumptively identified isolates were confirmed by species-specific Vp-toxR polymerase chain reaction (PCR) assay. Virulence and pandemic property of the confirmed V. parahaemolyticus isolates were determined by specific PCR assays. Results: On screening of 167 samples comprising crabs (n=82) and shrimps (n=85) by the standard bacteriological cultural method, V. parahaemolyticus was presumptively identified in 86.6% (71/82) and 82.3% (70/85) of respective samples. Of these, 46 (56%) and 66 (77.6%) isolates from crab and shrimp, respectively, were confirmed as V. parahaemolyticus by biochemical characterization (Kaper’s reaction) followed by specific Vp-toxR PCR assay. About 10 isolates each from crab and shrimp was found to carry the virulence gene (tdh). It denotes that 12.2% of crab and 11.7% of shrimp in the study area are harboring the pathogenic V. parahaemolyticus. Such tdh+ isolates (n=20) were subjected for screening of pandemic genotype by pandemic group specific (PGS) - PCR (PGS-PCR) and GS-PCR (toxRS gene) where 11 (6.5%) isolates revealed the pandemic determining amplicon (235 bp) in PGS-PCR and belonged to crab (7.3%) and shrimp (6%) samples; however, 2 (2.4%) isolates were positive in GS-PCR and belonged to crab samples only. These two GS-PCR+ isolates from crab were also positive in PGS-PCR. Conclusion: The findings of the study conclusively indicated that a considerable percentage of crab and shrimp in these areas were harboring pathogenic and pandemic V. parahaemolyticus posing a public health risk in consumption of improperly processed such shellfishes. Cross contamination of other marine and fresh water market fishes by such shellfishes in these areas may provide scope for spreading this pathogen in community food chain.

This research represents a detailed insight into the modern and popular hybridization of deterministic chaotic dynamics and evolutionary computation. It is aimed at the influence of chaotic sequences on the performance of four selected Differential Evolution (DE) variants. The variants of interest were: original DE/Rand/1/ and DE/Best/1/ mutation schemes, simple parameter adaptive jDE, and the recent state of the art version SHADE. Experiments are focused on the extensive investigation of the different randomization schemes for the selection of individuals in DE algorithm driven by the nine different two-dimensional discrete deterministic chaotic systems, as the chaotic pseudorandom number generators. The performances of DE variants and their chaotic/non-chaotic versions are recorded in the one-dimensional settings of 10D and 15 test functions from the CEC 2015 benchmark, further statistically analyzed.