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Abstract Textile wastewater poses a significant environmental challenge, primarily due to the presence of diverse contaminants, especially textile dyes. Untreated release of these effluents directly into aquatic systems can lead to esthetic degradation, eutrophication, reduced photosynthetic activity, and accumulation of hazardous substances. Although conventional treatment methods are employed for reducing the contaminant load in effluents, they often are less efficient, thus prompting the exploration of innovative alternatives. Current review highlights myco-remediation as an inexpensive, promising and environmentally sustainable solution. Fungi, with their diverse decontamination mechanisms such as biosorption, biotransformation, and immobilization, prove effective in reducing heavy metals, persistent organic pollutants, and emerging contaminant levels present in these effluents, However, more research effort is needed to apply the biodegradation strategy to decompose completely the “forever chemicals” per‐ and polyfluorinated alkyl substances. Fungi play a key role in degrading and decolorizing textile dyes due to their biocatalytic activity mediated by the production of oxidative enzymes, such as laccases, lignin peroxidases, and manganese peroxidases, as well as their dye adsorption capabilities. This comprehensive review concentrates on fungi-based remediation of textile wastewater effluents, including the mechanisms they employ. While most studies concentrate on effluent treatment, this review also explores the concurrent utilization of biomass and growth kinetics for efficient reduction in pollutant concentrations. Further, the current work showed data on optimization of growth conditions such as pH, temperature and nutrient requirements that lead to efficient effluent decontamination.

Abstract Active acoustic metamaterials (AAMM) have garnered special attention because of their potential as multi-function devices. In this direction, the present article demonstrates a novel AAMM that can be programmed as a multi-functional Active Acoustic Meta-device (AAMD) that can switch functionalities between Acoustic Switch (AS), Acoustic Lens (AL), and Acoustic Barrier (AB). Functionality: AL corresponds to the wave vector space, and AS and AB correspond to the frequency space of the proposed AAMM. Additional functionality, such as acoustic logic gates in phase space, is also envisaged. The proposed design is found to change the dispersion diagram by acquiring different configurations while keeping the basic design parameters constant. These design parameters include constituent elements, lattice constants, and filling fractions. Further, for the said functionalities, the proposed AAMM does not rely on the deformation characteristics of the constituents. It rather capitalises on the possible relative displacements of the scatterers. As an AL, AAMM demonstrates zero angle refraction, i.e., collimation, and negative refraction of the transmitted beam at a given angle of incidence over a frequency range of 200 kHz (22.22% of the applied frequency sweep, a.f.s.). AB is shown to attenuate acoustic energy over a frequency range of 700 kHz (77.78% of a.f.s.) compared to its reference and foundation design, a statically designed Phononic Crystal (PnC). Furthermore, as AS, it operates over the entire range of applied frequency sweep (100 kHz to 1000 kHz), i.e., over the frequency range of 900 kHz (100% of a.f.s.).

Background: Infertility is “failure to achieve a clinical pregnancy after >= 12 months of regular unprotected sexual intercourse”. Abnormal semen parameters are found in 50% of the infertile couples. Thus, Semen analysis is usually the first step in evaluating male partner in infertile couples. Material and methods: This is a 2 years retrospective study including 103 cases. Patient’s clinical history was taken and Semen sample was obtained by masturbation in sterile universal container following abstinence of 3-5 days. Using WHO standards, Samples were examined for various physical parameters and sperm motility. Sperms were counted with improved neubauer chamber and stained with Pap stain to observe morphology. Results: Total 103 male partners of infertile couples were investigated. Abnormal seminal parameters were found in 57.2% with maximum number in 4th decade (41.7%). Abnormal appearances included transparent (5.8%), opaque (1.9%) and reddish brown (1.9%). Hypospermia was seen in 23.3% and increased liquefaction time in 1.9% samples. Sperm concentration and total sperms/ejaculate were reduced in 30.7 % and 31.7% cases respectively. Total Sperm motility was below WHO lower reference range in 16.5% cases. However, progressive motility alone was below lower reference range in 25.8% cases. On morphology, maximum defects were seen in the head of spermatozoa. Leucocytospermia was seen in in 9.9 % . Abnormalities in sperm count included oligozoospermia (21.4%), Cryptozoospermia (2.9%) and azoospermia (3.9%). Oligozoospermia was the commonest microscopic abnormality followed by Asthenozoospermia (14.6%). Conclusion: Semen analysis is the cornerstone of investigating male infertility.

Abstract Background Goat milk is gaining popularity as a superior alternative to bovine milk due to its closer resemblance to human milk. Understanding the molecular processes underlying lactation is crucial for improving milk quality and production in goats. However, the genetic mechanisms governing lactation in goats, particularly in indigenous breeds like the Jakhrana, remain largely unexplored. Results In this study, we performed a comprehensive transcriptomic analysis of Jakhrana goat mammary glands during early and late lactation stages. We isolated milk somatic cells and conducted RNA sequencing, followed by transcript quantification and mapping against the ARS1.2 Capra hircus reference assembly. Our analysis identified differentially expressed genes (DEGs) and commonly expressed genes (CEGs) across the lactation phases. Early lactation showed enrichment of genes encoding antimicrobial peptides and lubrication proteins, while late lactation exhibited heightened expression of genes encoding major milk proteins. Additionally, DEG analysis revealed upregulation of pivotal genes, such as the ABC transporter gene MRP4, implicated in modulating milk composition and quality. Conclusion Our findings provide insights into the genetic mechanisms underlying lactation dynamics in the Jakhrana goat. Understanding these mechanisms could help in improving milk production and quality in goats, benefiting both the dairy industry and consumers.

Abstract Salinization is an ever-increasing problem that severely affects crop performance and finally agricultural production worldwide. This adverse effect of salinity on crop plants occurs in two phase i.e. ionic and osmotic phase. Plants accumulate osmolytes to combat with the osmotic phase of salinity. Osmolytes are polar, non-toxic, uncharged organic compatible solutes that are easily soluble in nature and do not cause any disturbance in normal cellular functions. Osmotic adjustment is crucial to maintain cell turgidity and membrane stability. Osmolytes also regulates the protein folding essential for mediating stress signalling mechanisms or pathways responsible for tolerance. Additionally, they help maintain thylakoid membrane stability, keeping the plants in photosynthetically active phase under stressed conditions. Osmolytes are broadly classified into three groups: ammonium containing compounds (polyamines and betaines), sugars, amino acids and sugar alcohols. In consideration of immense potential of plant osmolytes under salinity, the present review emphasized on the biosynthesis of various reported osmotically active substances and their involvement in different mechanisms or pathways responsible for salinity tolerance in crop plants.

Introduction: Intestinal obstruction is the impairment or arrest of the passage of contents through the intestine. Tubercular aetiology is the commonest cause of small intestinal obstruction in developing countries, while postoperative adhesions are common in developed countries. Neoplastic aetiology is more common in Large Bowel Obstruction (LBO). Clinical and radiological findings are non specific, and histopathology is required for a definitive diagnosis of the aetiology. Aim: To study histopathological examination of obstructive lesions of the small and large intestine. Materials and Methods: The present cross-sectional study was conducted from January 2020 to September 2021, including a total of 77 cases of intestinal obstruction due to mechanical causes. Detailed gross and microscopic examinations were performed, along with routine and special stains (wherever required), in the Department of Pathology at Government Medical College, Haldwani, Nainital, Uttarakhand. Data were presented in numbers and percentages. Results: The present study showed a male predominance 46 (59.8%) with a male to female ratio of 1.5:1. The most commonly affected age group was 31-40 years 20 (25.9%). Constipation (76; 98.7%) was the most common clinical symptom, followed by abdominal pain 72 (93.5%). The small intestine was more commonly involved 35 (45.5%), with the ileum being the most commonly affected site 40 (53.2%). Among cases of large intestine obstruction, the caecum was the most common site 10 (14.2%). Perforation was the most common gross finding 25 (32.9%). Non specific inflammatory pathology was the most common cause of obstruction (23.5%), followed by adenocarcinoma (20.8%). Tubercular pathology was the third most common cause (11.9%). Conclusion: The present study highlights the important pathologies in the aetiology of obstruction. Prevention and early intervention can help control some of the infective diseases and avoid such emergencies. Early diagnosis and treatment of neoplastic pathology, which is a leading cause of LBO, are beneficial for the better management of patients. A detailed histopathological study of small intestinal specimens should be conducted in constant correlation with clinical and radiological findings for an accurate diagnosis.

Abstract Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people’s security and promote their well-being.

The increasing use of Automatic Dependent Surveillance-Broadcast (ADS-B) technology in flight control systems has created many serious concerns.These weaknesses threaten the security and safety of our aviation industry. Therefore, to enhance aviation control security and better deal with these problems, this research focuses on developing a strong ADS-B injection detection system. It combines XGBoost and Random Forest with Logistic Regression in an Ensemble Learning Meta-Learning Model to identify ADS-B injection risks and categorise them. Ensemble methods, which combine several models can increase the detection accuracy and robustness of the model used to identify the threat. In addition, Explainable AI (XAI) methods are employed to enhance the process of explaining how the model reaches its decisions and building trust in aviation security systems. The system’s training, testing, and evaluation are conducted with ADS-B data. This result indicates that the Stacked Random Forest and XGBoost with Logistic Regression Meta-Learner with 99.60% accuracy, along with good recall rates (99.49%) and precision (99.41%). Also, aviation control authorities are reassured by the model’s transparent and applicable decision logic through the application of XAI techniques. This research contributes to enhanced aviation security by proposing a new, highly accurate ADS-B injection detection system with explainable outcomes. A strategy like this can help flight control systems maintain integrity amidst an ever-digitising aviation reality.

Background: Abnormal uterine bleeding [AUB] is one of the most common presentations among women of all age groups. It is ‘Bleeding from the uterine corpus that is abnormal in volume, regularity or timing for the majority of last 6 months. Common pathologies include hormonal imbalance patterns, atrophic endometrium, endometritis, endometrial polyp, endometrial hyperplasia and endometrial carcinoma. Material & Methods: All specimens of endometrial curettage, biopsy and hysterectomy received in department of pathology GMC, Haldwani from January 2020 to September 2021 , have been included in the study. Specimens were fixed in 10% formalin and processed routinely. Clinical details and endometrial thickness by ultrasonography were recorded and analysed. Microscopic examination was performed after routine and special stains. Results: Out of a total of 103 cases, abnormal uterine bleeding was frequently observed in the age group 40 – 49 years, followed by 50 – 59 years. Most common presenting complaint was menorrhagia followed by post menopausal bleeding. Most common histopathological finding was proliferative phase endometrium followed by secretory phase endometrium. The range of endometrial thickness recorded in the proliferative phase was mostly 4-9 mm followed by the secretory phase with 9 – 15 mm. Two cases of endometrial carcinoma were seen after 5th decade onward. All cases of endometrial hyperplasia and carcinoma had an endometrial thickness of more than 15 mm. Conclusion: AUB may be the only presenting complaint in patients with malignant or pre-malignant endometrial lesions. Histopathology helps to reach a definitive diagnosis crucial for management. Keywords: Abnormal uterine bleeding, Endometrial sampling, Endometrial thickness, Endometrial hyperplasia, Endometrial carcinoma

Abstract Background The World Health Organization (WHO) reports that Asia and Africa have the highest Chronic Liver Disease (CLD) mortality rate. Cirrhosis, responsible for 22.2 fatalities per 100,000 people, is India’s 10th most common cause of mortality. The increasing prevalence of chronic liver disease necessitates a study to identify predictive factors for patients who visit the emergency department. Identifying elements that enhance the predictive value of mortality in unstable patients with CLD complications is important in emergency departments. This study aims to determine Clinical and Laboratory Parameters as mortality predictors in adult chronic liver disease patients. Methodology The study was conducted at the emergency department of a tertiary healthcare center in Northern India. Patients with chronic liver disease above 18 years of age who satisfied the inclusion criteria were clinically evaluated. Clinical and demographic details were collected, and data was analyzed. Results Two hundred thirty-six patients were enrolled. The mean age was 50.77 ± 14.26 years. 78.4% of the participants were men. Abdominal distension, affecting 59.7% of patients, was the most common presenting ailment, followed by melena and hematemesis, affecting 41.9% and 32.6%, respectively. The mean stay in the emergency department was 10.29 ± 8.10 h. Refractory septic shock, the leading cause of mortality, accounts for 69.2% of all deaths, alongside grade 4 hepatic encephalopathy and massive Upper Gastrointestinal (UGI) bleeding, as identified in our study. Factors such as altered mental sensorium, high respiratory rate, low SpO2, increased heart rate, low systolic blood pressure, low diastolic blood pressure, and low Glasgow Coma Scale (GCS) on Emergency Department (ED) arrival are significantly associated with mortality. Conclusions Chronic liver disease, a prevalent condition in India, most commonly seen in middle aged men and lower socioeconomic groups. The parameters independently associated with mortality in our study were presence of altered mental sensorium, Glasgow coma scale, Child Pugh class and need for ICU admission. Understanding the presentation pattern, and mortality predictors can help ED physicians in managing acute events and follow-ups.

IntroductionThere is a lack of both data and, more significantly, computational analyses of the linguistic behavior near an individual's final moments of life. The present study is aimed to reduce human bias and save time in psycholinguistic studies by providing data-backed insights.MethodsA novel machine learning based pipeline proposed, using elements of semantic similarity (BERT and transformers) and emotion extraction in collaboration, to analyze the final statements of death row inmates to understand the consistency in their verbal expression moments before their death. A new method of analysis was proposed in this study to explore the notions inherent in the statements. A large database of 466 final statements from death row inmates in Texas was utilized in this study. Manual notion analysis was validated by a computational method of notion inferencing.ResultsBasic emotions of Anger and Fear majorly dominated the statements, constituting 54% of the whole, while 21% of all statements were of emotional states of Happiness and Serenity.DiscussionThe outcomes of this study are expected to contribute to psychological analyses of humans, moments before death, and provide insights to criminology researchers to formulate better strategies of rehabilitation and debate the death penalty.

Background Globally ovarian tumour is one of the leading causes of cancer death among women. The ovarian tumor has varied histogenesis, clinical behavior, and malignant potential.The aims and objectives of the present study is to study the histopathological pattern and age distribution of ovarian neoplasms. Methods A total of 68 cases were studied for a period of 3 years in a tertiary care center of Garhwal region. They were reviewed and analyzed for age, histopathological findings, and clinical presentations. The classification was done according to the WHO histologic classification of ovarian tumors,2020. Results Of the 68 ovarian tumors studied, 80.80% were benign, 7.35% cases were borderline, and 11.76% cases were malignant. Among the WHO 2020 morphological classification, surface epithelial tumors comprised 60.30%, followed by germ cell tumors (27.94%). Serous cystadenoma (23.52%) was the most typical benign neoplasm, followed by mature cystic teratoma (19.11%). Mucinous cyst adenocarcinoma (2.90%) was the most common malignant neoplasm. Tumors were seen over an age range of 18-78 years, and a maximum number of cases presented in the 2nd to 3rd decade of life. The younger age group primarily presented with benign tumors, whereas malignant tumors were common in the elderly age group. Conclusions Surface epithelial tumors were the most common ovarian tumors. Maximum numbers of ovarian tumors were in the age range of 20-39 years.

ABSTRACT: Purpose: The impact of the COVID-19 pandemic on antimicrobial resistance (AMR) is largely studied in healthcare settings. There is a need to understand the fluctuations in AMR during pandemic at the community level. With urinary tract infection (UTI) being one of the most common infections in the community, the AMR profile of community-acquired UTI (CA-UTI) is considered representative AMR at the community level. Methods: The study was taken in a cohort of patients with a clinical diagnosis of CA-UTI. The four study sites represented different community health centres in India. Escherichia coli isolates were analysed phenotypically and genotypically for AMR pre-COVID (October 2019–February 2020) and in the first (March 2020–February 2021) and second waves of COVID-19 (March 2021–December 2021). Results: E. coli was the predominant uropathogen (229, 82%). Increased susceptibility to nitrofurantoin was observed during the pandemic. Reduced susceptibility to first-line oral antibiotics and carbapenems was seen during the second wave, and an increased minimum inhibitory concentration (MIC50) to beta-lactams and fluoroquinolones was seen during the pandemic. Genomic analysis of E. coli isolates showed some AMR genes (aacC1, aacC4, SHV, QepA) only during the second wave. Conclusion: One good outcome of the pandemic was increased susceptibility to nitrofurantoin, while drawback was a significant decrease in susceptibility to oral antibiotics during the second wave and increased MIC50 of some antibiotics. Decreased susceptibility to last-resort carbapenems and the occurrence of various AMR genes during the second wave of the pandemic are of great concern.

Methanotrophy is a biological process that effectively reduces global methane emissions by utilizing microorganisms that can utilize methane as a source of energy under both oxic and anoxic conditions, using a variety of different electron acceptors. Methanotrophic microbes, which utilize methane as their primary source of carbon and energy, are microorganisms found in various environments, such as soil, sediments, freshwater, and marine ecosystems. These microbes play a significant role in the global carbon cycle by consuming methane, a potent greenhouse gas, and converting it into carbon dioxide, which is less harmful. However, methane is known to be the primary contributor to ozone formation and is considered a major greenhouse gas. Methane alone contributes to 30% of global warming; its emissions increased by over 32% over the last three decades and thus affect humans, animals, and vegetation adversely. There are different sources of methane emissions, like agricultural activities, wastewater management, landfills, coal mining, wetlands, and certain industrial processes. In view of the adverse effects of methane, urgent measures are required to reduce emissions. Methanotrophs have attracted attention as multifunctional bacteria with potential applications in biological methane mitigation and environmental bioremediation. Methanotrophs utilize methane as a carbon and energy source and play significant roles in biogeochemical cycles by oxidizing methane, which is coupled to the reduction of various electron acceptors. Methanotrophy, a natural process that converts methane into carbon dioxide, presents a promising solution to mitigate global methane emissions and reduce their impact on climate change. Nonetheless, additional research is necessary to enhance and expand these approaches for extensive use. In this review, we summarize the key sources of methane, mitigation strategies, microbial aspects, and the application of methanotrophs in global methane sinks with increasing anthropogenic methane emissions.