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Abstract Polyunsaturated fatty acids (PUFAs), including C18 PUFAs (linoleic [LA] and α-linolenic [ALA] acids) and their longer-chain derivatives (LC-PUFAs such as arachidonic [AA], eicosapentaenoic [EPA], and docosahexaenoic [DHA] acids), are essential nutrients for human health. As a dietary source, microbially derived PUFAs offer a vegan, scalable, and sustainable alternative to traditional fish, meat and other sources due to their higher purity, easier industrial production, and lower environmental impact. This review comprehensively analyzes preclinical safety and efficacy research findings on microbially derived PUFAs using various animal models. Studies have evaluated the toxicity and effects of single PUFA oils, their combinations, and even whole PUFA-rich microbial biomass on the health of various organs. While many studies suggest potential benefits, there are conflicting findings regarding gut health, brain function, reproductive organs, and inflammation regulation. These findings highlight the need for further research to understand the effects of microbial PUFAs on human health. Additionally, we propose Caenorhabditis elegans as a potential in vivo preclinical model for future PUFA studies. To facilitate the exploitation and commercial production of microbial PUFAs, we also highlight the importance of addressing the demand–supply landscape and current regulatory environment. In summary, this review provides a comprehensive overview of the research landscape, identifies knowledge gaps, and proposes areas for further investigation involving microbial PUFAs. We emphasize the need for a roadmap to foster self-sufficiency in PUFA production for import-dependent nations like India through indigenous research and commercial capabilities.

This paper presents a comprehensive seismic hazard assessment for Guwahati city, located in the northeast region of India. The earthquake data considered for the study is from 1762 to 2020, which was sourced from the published data of various seismological agencies. The fault information comprises of the latest studies and Seismotectonic Atlas (SEISAT) data. The density of earthquakes close to the faults was used to define the fault zones. The G-R relationship was utilized to calculate the seismicity parameters of the identified faults. We have used PSHA and DSHA to estimate the ground motion parameters such as peak ground acceleration and spectral acceleration at various probabilities of exceedance to calculate the level of seismic hazard. The results indicate that the seismic hazard in Guwahati is moderate to high, with several active faults in the vicinity and a high potential for strong ground motions. This study includes identifying the controlling source for Guwahati and plotting the seismic hazard curve and uniform hazard spectra for various probabilities of exceedance, which will give a basic idea of the seismic hazard of the city. The findings of this study can be used as a basis for earthquake risk reduction and mitigation strategies in Guwahati.

Abstract Background Ectrodactyly-ectodermal dysplasia-cleft lip/palate (EEC) syndrome mainly affects ectodermal and mesodermal tissues. It is usually manifested as split hands and feet, ectodermal dysplasia, and orofacial clefting, along with other signs and symptoms. A multidisciplinary approach to treatment is required, in which dentists play an important role in identifying and treating various oral conditions that may be genetically linked to or may be the result of EEC syndrome. Case presentation The present case describes the oral condition of a young child suffering from EEC syndrome and presenting with peripheral giant cell granuloma (PGCG) in the mandibular anterior region. After obtaining a thorough medical and family history and a clinical examination, the lesion was surgically excised under local anesthesia. The patient was followed up at periodic intervals for the next twenty four months, during which no recurrence of the lesion was observed. Conclusion This report highlights the role of a dentist in the management of the oral conditions of patients suffering from EEC syndrome.

Abstract Birdshot chorioretinopathy is an inflammatory eye condition strongly associated with MHC-I allele HLA-A29. The striking association with MHC-I suggests involvement of T cells, whereas natural killer (NK) cell involvement remains largely unstudied. Here we show that HLA-A29-positive birdshot chorioretinopathy patients have a skewed NK cell pool containing expanded CD16 positive NK cells which produce more proinflammatory cytokines. These NK cells contain populations that express CD8A which is involved in MHC-I recognition on target cells, display gene signatures indicative of high cytotoxic activity (GZMB, PRF1 and ISG15), and signaling through NK cell receptor CD244 (SH2D1B). Long-term monitoring of a cohort of birdshot chorioretinopathy patients with active disease identifies a population of CD8bright CD244bright NK cells, which rapidly declines to normal levels upon clinical remission following successful treatment. Collectively, these studies implicate CD8bright CD244bright NK cells in birdshot chorioretinopathy.

Abstract The primary cause behind the degradation of reinforced concrete (RC) structures is the propagation of corrosion in the steel-RC structures. Nowadays, numerous retrofitting techniques are available in the construction sector. Fiber-reinforced polymer (FRP) is one of the efficient rehabilitation measures that can be implemented on corroded structures to enhance structural capacities. However, the estimation of axial strength of FRP-strengthened columns affected by corrosion has been a challenging and tedious task in the laboratory as well as on the site. Considering such shortcomings, the prediction of axial capacity can be done using various analytical methods and artificial intelligence (AI) techniques. In this study, a comprehensive dataset of circular columns was extracted from the literature to predict the axial strength of FRP-wrapped and unstrengthened RC corroded columns. The laboratory results from the assembled dataset were compared to corresponding values estimated using relevant design codes provided by American Concrete Institute (ACI 440.2R-17 and ACI 318-19), and Bureau of Indian Standard (IS 456:2000). Five machine learning models were employed on columns to predict the axial load carrying capacity of FRP-strengthened and un-strengthened RC corroded columns. The results discovered that the extreme gradient boosting (XGBoost) model achieves superior accuracy with the least errors and could be used by the scientific community and FRP applicators to forecast the axial performance of corroded columns strengthened with and without FRP. The findings from the design codes revealed that prediction errors were available in high margins. Furthermore, feature importance analysis was conducted using the Shapley Additive exPlanation algorithm to know the contribution and influence of each input parameter on axial capacity. The feature analysis found that unconfined compressive strength of concrete plays an important role in deciding the axial capacity of columns. Moreover, to enhance the precision of axial capacity computation and improving the overall efficacy in engineering practice, a web-based user-friendly interface was developed for FRP applicators and engineers to simplify the process.

Abstract Health risks due to preventable infections such as human papillomavirus (HPV) are exacerbated by persistent vaccine hesitancy. Due to limited sample sizes and the time needed to roll out, traditional methodologies like surveys and interviews offer restricted insights into quickly evolving vaccine concerns. Social media platforms can serve as fertile ground for monitoring vaccine-related conversations and detecting emerging concerns in a scalable and dynamic manner. Using state-of-the-art large language models, we propose a minimally supervised end-to-end approach to identify concerns against HPV vaccination from social media posts. We detect and characterize the concerns against HPV vaccination pre- and post-2020 to understand the evolution of HPV vaccine discourse. Upon analyzing 653 k HPV-related post-2020 tweets, adverse effects, personal anecdotes, and vaccine mandates emerged as the dominant themes. Compared to pre-2020, there is a shift towards personal anecdotes of vaccine injury with a growing call for parental consent and transparency. The proposed approach provides an end-to-end system, i.e. given a collection of tweets, a list of prevalent concerns is returned, providing critical insights for crafting targeted interventions, debunking messages, and informing public health campaigns.

Objective: To characterize changes in the retinal microvasculature in eyes with birdshot chorioretinopathy (BCR) using OCT angiography (OCTA). Design: Retrospective, observational, single center. Subjects: Twenty-eight patients (53 eyes) with BCR and 59 age-matched controls (110 eyes). Methods: En face OCTA images of the superficial capillary plexus (SCP) and deep capillary plexus (DCP) of each eye were assessed for the presence of microvascular abnormalities and used to measure the vessel and foveal avascular zone (FAZ) areas. A longitudinal analysis was performed with a representative cohort of 23 BCR eyes (16 patients) at baseline and at a 2-year time point. Main Outcome Measures: Whole-image vessel density (VD, %), extrafoveal avascular zone (extra-FAZ) VD (%), and FAZ area (%) were calculated and compared between control and BCR eyes. The frequency of microvascular abnormalities in BCR eyes was recorded. Results: In the SCP, increased intercapillary space and capillary loops were common features present on OCTA images. Whole-image and extra-FAZ VD were lower in the BCR group compared with controls (P < 0.0001 [SCP and DCP]). Foveal avascular zone area was enlarged in BCR eyes (P = 0.0008 [DCP]). Worsening best-corrected visual acuity was associated with a decrease in whole-image and extra-FAZ VD in the SCP (P

Background: Vitello-intestinal duct (VID) is found in approximately 2% of newborns, with patency in about 6%. Failure of obliteration leads to some anomalies, most commonly Meckel’s diverticulum. Protrusion of the small bowel is one of the rare presentations. Case Presentation: A 10-month-old male infant presented with a prolapsed, irreducible polypoid-shaped loop of intestine protruding from the anterior abdomen. On exploratory laparotomy, patent VID was confirmed with appendicular agenesis and reverse intussusception. Gangrenous bowel was resected, followed by end-to-end ileo-ileal anastomosis, with a favorable outcome. Conclusion: Patent VID with ileal intussusception and prolapse is a rare entity, which can be easily diagnosed clinically. A high index of suspicion is needed for any associated gut anomaly in these cases, like appendicular agenesis.

We report a rare case of heparin-induced thrombocytopenia with thrombosis (HITT) following treatment for May-Thurner syndrome complicated by deep vein thrombosis (DVT), which resulted in venous stent thrombosis. A 27-year-old male with acute left lower-limb DVT successfully underwent thrombolysis and stenting for May-Thurner syndrome. However, the patient developed recurrent thrombosis and thrombocytopenia 3 days post-procedure. HITT was confirmed by a positive antiplatelet factor 4-heparin antibody test. After discontinuing heparin, the patient was successfully treated with fondaparinux, followed by repeat thrombectomy and thrombolysis, and then transitioned to warfarin. This is the second reported case of venous stent thrombosis due to HITT in May-Thurner syndrome. This case underscores the importance of early recognition and prompt management of HITT using alternative anticoagulants like fondaparinux to prevent complications such as venous limb gangrene. Further randomized controlled trials are required to evaluate the safety and efficacy of fondaparinux in HITT.

Mechanism of action and molecular basis of positive growth effects including yield increase due to carbon nanoparticle (CNP) treatment in rice plants is dissected here. CNP at 500 -750 µg/mL were found to be the optimum dosages showing best seedling growth. CNP treatment resulted increase in stomata size, stomatal conductance and water use efficiency along with low relative humidity, decrease in stomata frequency and internal CO2 concentration. CNP-coupled with water uptake was found to be endocytosis mediated, although CNP uptake was not affected by endocytosis inhibitor application in roots. Genomic analysis resulted major involvement of ABA pathway and stomata size and frequency genes in Arabidopsis and rice. Elevated endogenous ABA in rice seedlings and flag leaves along with increased expression of ABA biosynthetic genes in Arabidopsis and rice AtNCED3, AtNCED6, OsNCED1 confirmed increased ABA synthesis. Negative regulators of ABA pathway, OsSNRK2 down-regulation and up-regulation of stomagen (OsEPFL9) reconfirmed ABA's involvement. CNP treatment exerted cold tolerance in seedlings and water stress tolerance in reproductive stage. CNP treatment under water withheld condition resulted water stress tolerance by maintaining lower stomatal conductance, transpiration rate and higher relative water content. Increased ABA (OsSNRK1, OsSNRK2) and aquaporin (OsPIP2–5 and OsPIP1–3) genes’ expressions could explain the better water stress tolerance in rice plants treated with CNP. Altogether, due to thermomorphogenesis, down-regulation of Phytochrome B transcript resulted altered the ABA pathway and stomatal distribution with size (Graphical Abstract). These changes resulted improved water relation parameters and water use efficiency (WUE) showing improvement in yield. Detailed mechanism of action of CNP in abiotic stress tolerance can be exploited using nano-agriculture at field level.

Abstract Chickpea is a highly nutritious protein-rich source and one of the major crops to alleviate global malnutrition, but poor seed quality affects its productivity. Seed quality is essential for better crop establishment and higher yields, particularly in the uncertain climate change. The present study investigated the impact of botanical priming versus hydropriming and bavistin seed treatment on chickpea seeds. A detailed physiological (germination percentage, root and shoot length, vigour index) and biochemical (amylase, protease, dehydrogenase, phytase, and lipid peroxidation) analysis was carried out in order to assess the effect of priming treatments. Turmeric-primed seeds showed better germination rate (94.5%), seedling length, enzyme activity, and lower malondialdehyde (MDA) content. Sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis revealed the expression of minor polypeptides of albumin and globulin in the primed seeds. Moreover, field experiments indicated increased crop growth, vigour, days to 50% flowering, yield and its attributing traits in turmeric-primed seeds. Botanical priming can increase chickpea yield by up to 16% over the control group. This low-cost and eco-friendly technique enhances seed and crop performance, making it a powerful tool for augmenting chickpea growth. Therefore, chickpea growers must adopt botanical priming techniques to enhance the quality of seed and crop performance. Moreover, this approach is environmentally sustainable and can help conserve natural resources in the long term. Therefore, this new approach must be widely adopted across the agricultural industry to ensure sustainable and profitable farming practices.

Fetus-in-fetu (FIF) is a rare congenital anomaly in which a malformed parasitic twin develops within the body of a live fetus or child. Abdominal teratoma, a type of germ cell tumor, can be a great imaging mimicker of FIF and vice-versa, as they both can present as a heterogeneous mass with calcifications and a fat component. Radiological differentiation of these 2 entities should be made because of the difference in surgical planning and treatment options. Features such as visualization of distinct bony vertebral elements and encysted cystic components are the specific features of Fetus in fetu [1]. In contrast, the presence of elevated serum markers can help diagnose teratoma. Here, we report a case of a 5-month-old girl presented with progressive distension of the upper abdomen for the last 2 months, noticed by her mother. Her initial imaging with abdominal X-ray and ultrasonography showed the presence of a large heterogenous solid-cystic mass in the upper abdomen with large elongated calcifications. A provisional diagnosis of teratoma vs FIF was considered. CECT abdomen showed clear identification of osseous structures of the axial and appendicular skeleton within a fat density mass, along with an encapsulated cystic component, strongly suggestive of FIF. Her serum tumor markers were within normal limits. The final diagnosis of FIF was confirmed on Laparotomy and postoperative specimens.

In this report, we have investigated the CsMgCl3 compound with the help of the WIEN2K software package. The structural and electronic properties are performed using the full potential augmented plane wave (FP-LAPW) method with the generalised gradient approximation (GGA) approximation as exchange correlation potentials. We used the Birch-Murnaghan equation (BME) to find the structural properties of the material. These include the lattice parameter, the bulk modulus, the first derivative of the bulk modulus, the minimum energy, and the volume. The structural properties match up with the experimental data. Electronic properties in terms of the band structure (BS) and total and partial density of state (T-DOS and P-DOS) profiles of CsMgCl3 using GGA potentials exhibit an indirect wide energy band gap of 5.35 eV. All these properties show that the CsMgCl3 compound is used as a perovskite in solar cells.

ABSTRACT: The excessive usage and limited availability of fossil fuels have put enough impetus on researchers to find alternative energy sources to control the energy crisis and reduce climate change. To mitigate environmental impact while generating clean energy, researchers and energy experts are particularly focused on harnessing energy from bioresources and waste materials. This review article gives insight into various type of alternative fuels, their production strategies, and applications. Further, it explores the availability of domestic carbon resources like agroforestry, nonfood energy crops, municipal solid waste, agro-industry waste, food waste, wastewater, and anthropogenic-generated wastes from various industries. Furthermore, the potential for making alternative fuels like biodiesel and bioethanol adopts sustainable biochemical processes like aerobic and anaerobic digestion, fermentation, and methanation. Landfill processes and thermal processes like gasification, and pyrolysis are also explored to harness the waste streams into alternative energy sources, promising environmental benefits.

Double perovskites have garnered significant attention for use in renewable energy applications, particularly solar cells. The development of organic-inorganic hybrid halide perovskite solar cells has advanced rapidly over the last ten years. In this article, fundamental aspects of transport properties of Cs2AgGaCl6 have been illustrated using DFT-based simulation. Constantly is covered that the calculated lattice constant agreed with the available experimental data. With an indirect band gap, the electronic profile demonstrates a semiconducting nature. Reporting bandgap of 1.20 eV for Cs2AgGaCl6. This bandgap remains suitable for solar cell semiconductors. The computed values of effective masses reveal that effective masses of holes are greater than those of electrons. Further various optical parameters have also been examined. The absorption of light in the visible and ultraviolet spectrums is guaranteed by the absorption bands 2.7–4.2 eV and 3.5–4.5 eV. We believe that such insights offer a new starting point and a framework for future research. So, knowledge and drive gained from these studies will stimulate more theoretical investigation into novel perovskite-based materials with novel applications.

Key Clinical Message Sarcoidosis‐induced LETM represents a rare but life‐threatening neurological manifestation of sarcoidosis, characterized by spinal cord inflammation, and associated neurological deficits. Sarcoidosis should be included in the differential diagnosis of LETM, particularly in patients with no lung involvement. Prompt recognition and management are obligatory to optimize outcomes and prevent long‐term disability. Abstract Sarcoidosis is a multisystem inflammatory granulomatous disorder characterized by the formation of noncaseating granulomas. Although sarcoidosis commonly affects the skin, lymph nodes, and lungs, neurological involvement of sarcoidosis has also been reported. Longitudinally extensive transverse myelitis (LETM) is a rare but well‐documented serious manifestation of neuroscoidosis. We report a case of LETM caused by sarcoidosis in a 53‐year‐old male who presented with progressive bilateral lower extremity weakness, urinary retention, and paresthesia. Laboratory evaluations revealed elevated inflammatory markers. Magnetic resonance imaging of the spine showed hyperintense signals consistent with transverse myelitis. Cerebrospinal fluid analysis revealed lymphocytic pleocytosis and elevated protein levels. Chest computed tomography showed hilar lymphadenopathy. A biopsy of the intrathoracic lymph node showed noncaseating granulomas consistent with sarcoidosis. A diagnosis of sarcoidosis‐induced LETM was made after ruling out all other possible etiologies. His condition improved gradually after starting high‐dose prednisone, mycophenolate, and rehabilitation strategies. Our case underscores the importance of prompt diagnosis and management of sarcoidosis‐induced LETM and highlights that sarcoidosis must be included among differential diagnoses of LETM, especially in cases with no lung involvement.

The optimization and prediction of thermodynamic parameters including synergistic effects, and kinetic analysis in co-pyrolysis of banana peel (BP) and waste polystyrene (PS) plastic at different heating rates using ANN and AIC models has been performed. Thermogravimetric analysis was performed to determine the initial, maximum, and final degradation temperatures. The synergistic effect was studied using additive formula to determine the theoretical thermal behavior and compared with experimental TGA data. Kinetic parameters were determined by using the advanced isoconversional (AIC) model for estimation of activation energy (Eα), Criado master plot for reaction mechanism (f(α)), and compensation method for frequency factor (Aα). The analysis showed that the average activation energy values were 182.5, 140.6, and 161.8 kJ mol−1 for PS, BP, and PS+BP, respectively. It also clearly shows positive synergy in co-pyrolysis of PS and BP by reducing 11.3 % activation energy compared to that of PS alone. The frequency factor was found to be 1.0 × 1014, 1.0 × 1015, and 1.0 × 1023 s−1 for PS, BP, and PS+BP, respectively. The reaction mechanism was identified as R3, D4, and D4+R3 for PS, BP, and PS+BP, respectively. Further, the obtained kinetic parameters were used to determine the thermodynamic parameters such as enthalpy (ΔH), Gibbs energy (ΔG), and Entropy (ΔS). Finally, ANN was designed to address the co-pyrolysis behavior subjected to various heating rates. Subsequently, the trained ANN model (5 × 4×4 × 4) was employed to forecast thermal degradation behavior. Impressively, the model yielded highly accurate results with a correlation coefficient R2 > 0.998 in each case. The optimized model was further used to predict TGA data and activation energy for unknown mixtures of PS and BP. The suggested ANN model showed a great advantage in optimizing to avoid extensive experiments at various heating rates to achieve the goal.

Chromium (Cr) stress is a prevalent abiotic stress that alters various physiological, growth and metabolic parameters in plants. The toxicity of chromium at concentrations up to 100 µM were studied in two common Indian Oryza sativa genotypes, Lalat upland and Swarnaprabha. More chromium accumulated in roots than in leaves in both genotypes, with Lalat upland taking up more chromium than Swarnaprabha. Higher chromium concentrations reduced various physiological traits including stomatal conductance, transpiration rate, photosynthetic water use efficiency, photosynthetic pigments content in both the genotypes, and this decline was more pronounced in Lalat upland. Increase in vapour pressure deficit, sub stomatal carbon dioxide concentration and malondialdehyde content was detected at 100 µM chromium treated genotypes, however the effects were higher in Lalat upland. Confocal microscopy showed stomatal guard cell deformation, greater stomatal area and closed stomatal aperture in the leaves of 100 µM chromium treated Lalat upland plants. Abscisic acid and Salicylic acid content and expression of NCED1 and NCED2 genes involved in stomata aperture regulation were enhanced in Lalat upland and down regulated in Swarnaprabha under chromium stress. Variation in glandular and non-glandular trichome density and length was found in the leaves of chromium stressed plants in both the genotypes. The results suggest that Lalat upland is more sensitive to chromium toxicity than Swarnaprabha at higher chromium concentrations.

Abstract This study conducts an extensive comparative analysis of computational intelligence approaches aimed at predicting the compressive strength (CS) of concrete, utilizing two non-destructive testing (NDT) methods: the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) test. In the ensemble learning approach, the six most popular algorithms (Adaboost, CatBoost, gradient boosting tree (GBT), random forest (RF), stacking, and extreme gradient boosting (XGB)) have been used to develop the prediction models of CS of concrete based on NDT. The ML models have been developed using a total of 721 samples, of which 111 were cast in the laboratory, 134 were obtained from in-situ testing, and the other samples were gathered from the literature. Among the three categories of analytical models—RH models, UPV models, and combined RH and UPV models; seven, ten, and thirteen models have been used respectively. AdaBoost, CatBoost, GBT, RF, Stacking, and XGB models have been used to improve the accuracy and dependability of the analytical models. The RH-M5, UPV-M6, and C-M6 (combined UPV and RH model) models were found with highest performance level amongst all the analytical models. The MAPE value of XGB was observed to be 84.37%, 83.24%, 77.33%, 59.46%, and 81.08% lower than AdaBoost, CatBoost, GBT, RF, and stacking, respectively. The performance of XGB model has been found best than other soft computing techniques and existing traditional predictive models.

Introduction: The most crucial factor in improving animal reproduction efficiency is early pregnancy diagnosis. Early diagnosis not only reduces the time interval between two calvings but also aids farmers in identifying open animals, thereby preventing significant milk production losses. Therefore, the objective of this study was to discover circulatory miRNAs that would be useful for early pregnancy diagnosis in buffalo.Material and methods: Blood samples were taken on 0, 6th, 12th, and 18th day after artificial insemination from pregnant animals (n = 30) and non-pregnant animals (n = 20). During these stages of pregnancy, total RNA was extracted, and a small RNA library was subsequently generated and sequenced on the Illumina platform. Subsequently, Real-time PCR was used to validate the findings.Results and discussion: There were 4,022 miRNAs found during the pregnancy, with 15 of those lacking sequences and 4,007 having sequences already in the database. From the beginning of pregnancy until the 18th day, 25 of these miRNAs showed a substantial shift in expression levels in the maternal blood, with a change more than two logs. Furthermore, based on qPCR results, 19 miRNAs were found to be more abundant in pregnant animals than in non-pregnant animals. We used target prediction analysis to learn how maternally expressed miRNAs relate to fetal-maternal communication. In conclusion, miRNA based biomarkers that could be associated with the diagnosis of pregnancy were identified including miR-181a and miR-486 highly upregulated on the 18th day of pregnancy. This study also provides a comprehensive profile of the entire miRNA population in maternal buffalo blood during the early stages of pregnancy.

Polyelectrolyte complexes (PECs) formed by the interaction between oppositely charged polymers have emerged as promising carriers for accomplishing colon-specific release. In this study, we have explored the potential of polyelectrolyte complexes between a succinate derivative of Leucaena leucocephala galactomannan and cationic guar gum for colon delivery of synbiotic. The PECs were prepared using a polyelectrolyte complexation method and characterized. The PECs exhibited excellent stability, with high encapsulation efficiency for both probiotics (95.53 %) and prebiotics (83.33 %). In vitro studies demonstrated enhanced survivability and proliferation of the encapsulated probiotics in the presence of prebiotics (93.29 %). The SEM images revealed a smooth and firm structure with reduced number of pores when both prebiotic and probiotic were encapsulated together. The treatment with synbiotic PECs in acetic acid induced IBD rats significantly relieves colitis symptoms as was evident from colon/body ratio, DAI score and histopathology studies. An increase in the protein and reduced glutathione levels and reduction in superoxide dismutase activity was observed in colitic rats that received synbiotic treatment as compared to colitic rats. Overall, this study highlights the potential of Leucaena leucocephala succinate-cationic guar gum PECs as a promising system for colon-specific synbiotic delivery, with implications for improved gut health and the treatment of various gastrointestinal disorders.

Bovine herpesvirus-1 is highly contagious virus of cattle and buffaloes all over the world. It establishes lifelong latency in ganglionic neurons of the peripheral nervous system. Since, trigeminal ganglia are the main sites of latency, therefore, it is challenging to detect BoHV-1 in latently infected live animals. No research work has been done to correlate the sero-prevalence and latency in peripheral blood mononuclear cells (PBMC). The present study was designed to detect BoHV-1 latency related transcript or microRNA in peripheral blood mononuclear cells of sero-positive animals. The highly sensitive RT-qPCR assays based on TaqMan chemistry have been developed for the detection of transcripts of BoHV-1 latency. The limit of detection (LOD) of the assays for ORF-1 specific RT-qPCR and miRNA specific RT-qPCR was 460 copies and 117 copies respectively. The efficiency of the developed assays was 93.5% for ORF-1 and 97.93% for miRNA transcript. None of the PBMC samples of seropositive animals found positive for ORF-1 and miRNA transcripts in developed assays.The absence of latency specific transcripts in PBMC might be due to very low expression i.e. beyond the LOD of newly developed assays or absence of latency in PBMC of seropositive animal. However, further studies are required to establish the fact. To the best of authors knowledge, this is the first report of latency-specific RT-qPCR assay development and its application in PBMC of BoHV-1 seropositive cattle.

The chemical industry is directly or indirectly connected with almost every industrial process and holds a crucial place in the economic and social advancement of India. At the same time it is however, the second highest energy intensive industry in the country. Thus, we attempt to look into the factors that affect the energy intensity of Indian chemical industry. We collect a panel dataset including a total of 2613 chemical firms for the period 2010 to 2021 for our analysis. We carry out our empirical testing under both linear and non-linear framework by employing fixed effect regression and panel quantile regression methods respectively.We find that energy intensity for the sample chemical firms increases due to poor labour productivity, huge plant and machinery, outsourcing operations, and more use of IT-enabled services. On the other hand, use of better quality raw material and advanced technology can help optimising the energy intensity. We also discover that profitable firms and firms larger in size are energy efficient. Based on the findings, we suggest professional training programs and vocational skill development programs for enhancing labour productivity. Profitable firms are suggested to invest in technological up-gradation and energy saving technology. We believe the findings of the study can provide a portfolio of variables to the managers and policy makers in order to diversify their attention to find all possible ways to reduce energy intensity of the Indian chemical firms.

Predicting FRP-to-concrete bond strength (FRP-CBS) under diverse exposure conditions is an intricate task influenced by multiple variables. Yet, existing pertinent models have several limitations. Accordingly, this study proposes a novel data driven machine learning (ML) methodology to predict the FRP-CBS considering various exposure conditions. A comprehensive database on single and double lap-shear strength tests on concrete specimens was meticulously compiled. Twenty-seven analytical models were used to appraise the developed ML models. Feature importance analysis was conducted to ascertain the influence of input parameters on bond strength. The proposed data-driven ML models attained exceptional accuracy and superior performance compared to existing analytical models. To enhance the accuracy of bond strength estimation and simplify the process for practicing engineers and FRP applicators, a user-friendly graphical interface was developed. It could eliminate the need for complex design procedures, making it easier to accurately estimate the FRP-CBS, thus improving overall efficiency in engineering practice.

Abstract Background Ongoing large-scale shift towards direct seeded rice (DSR) necessitates a convergence of breeding and genetic approaches for its sustenance and harnessing natural resources and environmental benefits. Improving seedling vigour remains key objective for breeders working with DSR. The present study aims to understand the genetic control of seedling vigour in deep sown DSR. Combined genome-wide association mapping, linkage mapping, fine mapping, RNA-sequencing to identify candidate genes and validation of putative candidate genes were performed in the present study. Results Significant phenotypic variations were observed among genotypes in both F3:4:5 and BC2F2:3 populations. The mesocotyl length showed significant positive correlation with %germination, root and shoot length. The 881 kb region on chromosome 7 reported to be associated with mesocotyl elongation. RNA-seq data and RT-PCR results identified and validated seven potential candidate genes. The four promising introgression lines free from linkage drag and with longer mesocotyl length, longer root length, semi-dwarf plant height have been identified. Conclusion The study will provide rice breeders (1) the pre breeding material in the form of anticipated DSR adapted introgression lines possessing useful traits and alleles improving germination under deep sown DSR field conditions (2) the base for the studies involving functional characterization of candidate genes. The development and utilization of improved introgression lines and molecular markers may play an important role in genomics-assisted breeding (GAB) during the pyramiding of valuable genes providing adaptation to rice under DSR. Our results offer a robust and reliable package that can contribute towards enhancing genetic gains in direct seeded rice breeding programs.

Background: Globally, the burden of malnutrition, especially undernutrition, as well as the consequences of malnutrition is of the rise which is especially of significant concern for underdeveloped countries. Countries like Nepal are also facing a similar problem of malnutrition. In Madhesh province, among the under 5 children the incidence of chronic malnutrition is 29.3%. Our aim is to assess the malnourishment in the children of Madhesh province in Nepal. Methods: This cross-sectional quantitative study was carried out among 409 malnourished children who were admitted from July 17, 2018 to July 16, 2022 at NRH in Gajendra Narayan Singh Hospital. Collected data were fed into Microsoft-excel and analyzed using SPSS software, version-2016. As data were normally distributed, frequency, percentage, mean and standard deviation were calculated for descriptive analysis. To find out the association of categorical variables, the Chi-square test or Fisher’s Exact test was used where appropriate. P-value

Traditional techniques to evaluate the complexity of biological signals forgo the numerous time scales present in such data. When these algorithms were applied to real-world datasets gathered in health and illness states, they produced inconsistent results. Sample entropy (SampEn) has been utilized extensively to evaluate the complexity of RR-interval time series. Using multi-scale Entropy analysis incorporating Sample Entropy, several research on the complexity of physiological signals have been conducted. The primary disadvantage of MSE is that coarse-graining results in a noticeable and considerable loss of information. For small scales, the method proves to be ineffective. So, for the MSE analysis of physiological data, we have employed the FDM rather than coarse-graining. This FDM, which is based on the Fourier theory, is excellent for studying nonlinear and nonstationary time series. This approach generates a TFE distribution that displays the data’s fundamental premise. This will provide a number of frequency bands with varying cut-off frequencies but consistent data across all of them. To further validate our suggested technique, we used “ANOVA analysis” to compare our results to the entropy analysis of a synthetic simulated database containing white noise (WN) and power noise (PN) signals.

Polysaccharides have attracted considerable attention as potential vehicles for drug delivery because of their unique and versatile characteristics. The biodegradability, biocompatibility, mucoadhesive characteristics, and tunable properties of polysaccharide make them attractive carriers for formulating of colon-targeted drug delivery systems. This review article encompasses the research on nanoparticles (NPs) based on polysaccharides (e. g. pectin, sodium alginate and chitosan) for targeted drug delivery to the colon. Various strategies such as microbially triggered, enzymatic degradation, and pH responsive systems have been discussed. Furthermore, ligand-based active targeting of therapeutics to colon has also been highlighted. The review also covers the various characterization and evaluation techniques used in the development of polysaccharide-based NPs. The importance of morphology, particle size (PS), surface charge of the NPs on performance of the delivery system has also been given due consideration. The challenges and future perspectives associated with colon drug delivery have been summarized. The information presented will be helpful to the scientific community when working with polysaccharides and will pave the way for improved therapeutic outcomes.

AbstractNorth East India is a highly seismically active zone of India due to the collision tectonics between the Indian and the Eurasian plate in the north and subduction tectonics along the Indo-Myanmar range (IMR) in the east. It is utmost important to investigate the seismicity of this region along with uniform hazard spectra (UHS). In this paper, seismic hazard study has been performed at the Silchar district headquarters of Assam state, located in North East India. The earthquake data have been collected from various agencies from 1762 to 2020 and the fault data has been collected from seismotectonic atlas (SEISAT). Earthquake and fault data are superimposed on the Northeast India map using MapInfo software. The fault zone is defined based upon the earthquake’s density near the fault. Seismicity parameters and maximum magnitude have been estimated using Gutenberg-Richter (G-R) relationship. The best-fitted ground motion equation is considered. The outcome is represented in terms of controlling source, seismic hazard curve and UHS for Silchar city with varying ground motions. Finally, the results are presented as PGA based upon DSHA and PSHA methods. The effect of Importance Factor (I) and Response reduction factor (R) have been investigated by comparing UHS with IS: 1893-2016 code response spectra.

This article uses the FP-LAPW approach within the DFT method, and the quasi-harmonic Debye model to investigate the electronic and thermodynamic properties of intermetallic rare earth materials (such as SmInZn, SmInCd, and SmTlZn). Thermodynamic properties were determined by the quasi-harmonic Debye model, whereas the FP-LAPW approaches within DFT method were utilized to derive electronic properties. The calculated structural parameters and the available experimental data have been examined, and it was observed that there was a good agreement between available experimental and calculated values of structural parameters. The electronic behavior of SmInZn, SmInCd and SmTlZn compounds shows the metallic character. We have examined a few thermodynamic characteristics. All calculated characteristics were found to match experimental or theoretical calculations.

Early pregnancy diagnosis is vital for economic sustainability of dairy farms and maintaining the reproductive efficiency of the herd. There are many techniques including progesterone assay, pregnancy specific proteins and interferon stimulated genes have been explored for early pregnancy diagnosis but, they are associated with varying level of efficacy. In the present experiment, interferon stimulated gene (Myxovirus resistance gene 2/MX2) expression pattern was used as a potential biomarker for early pregnancy in cattle. The association of MX2 gene expression in relation to progesterone assay was studied to explore its potential use as biomarker of early pregnancy. The plasma progesterone concentration in conceived animals on day 7 (2.26±0.19 ng/ml), 17 (5.42±0.35 ng/ml) and 21(6.38±0.39 ng/ml) was recorded to be significantly higher as compared to respective values in non-conceived animals, i.e. 1.55±0.09 ng/ml, 4.14±0.14 ng/ml and 0.81±0.06 ng/ml. The sudden decrement in plasma progesterone concentration after day 17th discriminates conceived and non-conceived animals. MX2 expression levels were observed to spike in blood due to release of interferon tau (τ) after implantation of embryo. The relative mRNA expression of MX2 gene showed a 9.5 to 28.64-fold higher expression on 17 days post insemination in pregnant animals as compared to non-pregnant animals. Thus, MX2 gene can be used as a reliable biomarker for the early detection of pregnancy.

Abstract The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.

BACKGROUND: Students make their decision on a future medical career while experiencing different areas of medical specialties based on their perceptions and preferences. OBJECTIVE: The objective of the study is to explore the factors and perceptions of undergraduate medical students which influence their choice of postgraduate specialization. MATERIALS AND METHODS: This cross-sectional study was conducted in the year 2022 among 257 2nd year undergraduate medical students who were queried about possible factors that might influence their choice of specialization. The factors were grouped as intrinsic factors (personal attributes and preferences); and extrinsic factors (nature of work and working environment). Data entry and analysis were done in SPSS version 23. RESULTS: Final analysis was done on 235 students out of a total of 257 students (response rate of 91.43%). Majority (86.4%) were 19–20 years of age, with mean (±standard deviation) age of 19.8 (±0.9) years. Gender preference was observed to affect choices of a few specialties such as orthopedics and obstetrics/gynecology. Motivating factors for choosing a certain specialization were the perception of a better quality of life (84.7%); financial rewards (26.8%); and less work pressure (23.4%). Most of our medical students claimed that the selection of their specialty was based on prior clinical posting (65.5%) and influenced by teaching faculty and hospital consultants (59.6%). 8.1% of the students reported that COVID affected their choice of specialty. Specialization in pre- and para-clinical subjects was preferred by 3.4% (n = 8) of the participants. CONCLUSIONS: The study identified a mix of both intrinsic and extrinsic factors influencing, motivating, and guiding their choice. Specialized careers support programs may be integrated into the curriculum following an analysis of students' perceptions in different disciplines.

Purpose: To compare the outcomes of papillomacular bundle (PMB) sparing internal limiting membrane (ILM) peeling (group LP) and conventional ILM peeling (group CP) for treatment of idiopathic macular hole (MH) of ≤400 μm. Methods: Fifteen eyes were included in each group. In group CP, conventional 360° peeling was done, while in group LP, ILM was spared over PMB. The changes in peripapillary retinal nerve fiber layer (pRNFL) thickness and ganglion cell-inner plexiform layer (GC-IPL) thickness were analyzed at 3 months. Results: MH was closed in all with comparable visual improvement. Postoperatively, retinal nerve fiber layer (RNFL) was significantly thinner in the temporal quadrant in group CP. GC-IPL was significantly thinner in the temporal quadrants in group LP, whereas it was comparable in group CP. Conclusion: PMB sparing ILM peeling is comparable to conventional ILM peeling in terms of closure rate and visual gain, with the advantage of less retinal damage at 3 months.

India is the 7th largest country by area and 2nd most populated country in the world. The reports prepared by IQAir revels that India is 3rd most polluted country after Bangladesh and Pakistan, on the basis of fine particulates (PM2.5) concentration for the year 2020. In this article, the quality of air in six Indian cities is predicted using data-driven Artificial Neural Network. The data was taken from the 'Kaggle' online source. For six Indian cities, 6139 data sets for ten contaminants (PM2.5, PM10, NO, NO2, NH3, CO, SO2, O3, C6H6 and C7H8) were chosen. The datasets were collected throughout the last five years, from 2016 to 2020, and were used to develop the predictive model. Two machine learning model are proposing in this study namely Artificial Intelligence (AI) and Gaussian Process Regression (GPR) The R-value of ANN and GPR models are 0.9611 and 0.9843 sequentially. The other performance indices such as RMSE, MAPE, MAE of the GPR model are 21.4079, 7.8945% and 13.5884, respectively. The developed model is quite useful to update citizens about the predicted air quality of the urban spaces and protect them from getting affected by the poor ambient air quality. It can also be used to find the proper abatement strategies as well as operational measures.

Objectives Pulmonary tuberculosis (TB) is chronic communicable bacterial disease caused by Mycobacterium tuberculosis. The objective was to assess the performance in terms of knowledge and practice of pulmonary tuberculosis (PTB) program among directly observed treatment short course (DOTS) provider, lab technician, senior treatment supervisor (STS), senior tuberculosis laboratory supervisor (STLS), factors affecting knowledge and practice of PTB program, rate of detection, cure rate and to identify the gaps and underlying contributing factors in terms of supply, manpower, and infrastructure. Materials and Methods A cross-sectional study was conducted on knowledge and practice of PTB program among DOTS Provider, lab technician, STS, STLS in ten designated tuberculosis center of West Tripura from May 2018 to April 2019. Simple random sampling method was followed for selecting DOTS provider and sample size of 50 was taken. Results Majority (40.0%) of patients were in the age group of 31 to 40 years and mean age of 40.98 ± 10.13 years. Females and males were 68.0 and 32.0%, respectively. Among DOTS provider, 66.0% had served for more than 5 years of service and 84.0% of them had verified address before starting the treatment. Conclusion Majority of the DOTS provider had satisfactory knowledge and practice about PTB program. Proportion of the medical officers attending DOTS training was inadequate and there were adequate number of the health care workers who had attended DOTS training. There were no gaps found in proportion of the cases receiving the supervised treatment.

Telephonic survey was done with randomly sampled 570 farmers spread across 57 districts representing eastern (16 districts), western (17 districts), central (16 districts) and Bundelkhand (7 districts) zones of Uttar Pradesh during the period of 20 April–25 June, 2020 to ascertain the effect of COVID-19 lockdown on the investment and profitability of the summer crops grown by them. Farmers’ responses were compared with their observations on the same variable during the same period of previous year, i.e. 2019. Suitable descriptive and inferential statistical treatments were given to collected data. Results established that there was maximum increase in all cost components for summer maize (8.74–16.21%) followed by okra (7.40–16.24%) and bottle gourd (7–16%). Summer greengram also had similar trends of cost increase (8–17%). Summer groundnut (3.55–5.54%) witnessed a relatively lesser increase for all costs except the plant protection cost (20.19%). Thus, compared to last year (2019), the cost components significantly increased for all the cultivated crops in summer. However, the government facilitation coupled with favourable climatic conditions helped the summer crop growers to harvest better yield advantage (4–10%) and accordingly the enhanced market price (5.02–8.52% more) increased profitability index (1.02–14.44%). The parameters like per unit production and plant protection cost emerged as the most prominent determinants of the net return of most of the summer crops during COVID-19 lockdown.

Understanding the molecular mechanisms of environmental salinity stress tolerance and acclimation strategies by photosynthetic organisms facilitates accelerating the genetic improvement of tolerant economically important crops. In this study, we have chosen the marine algae Dunaliella (D.) salina, a high-potential and unique organism that shows superior tolerance against abiotic stresses, especially hypersaline conditions. We have grown the cells in three different salt concentrations 1.5M NaCl (control), 2M NaCl, and 3M NaCl (hypersaline). Fast chlorophyll fluorescence analysis showed increased initial fluorescence (Fo) and decreased photosynthetic efficiency, indicating hampered photosystem II utilization capacity under hypersaline conditions. Also, the reactive oxygen species (ROS) localization studies and quantification revealed elevated accumulation of ROS was observed in the chloroplast in the 3M condition. Pigment analysis shows a deficit in chlorophyll content and increased carotenoid accumulation, especially lutein and zeaxanthin content. This study majorly explored the chloroplast transcripts of the D. salina cell as it is the major environmental sensor. Even though most of the photosystem transcripts showed moderate upregulation in hypersaline conditions in the transcriptome study, the western blot analysis showed degradation of the core as well as antenna proteins of both the photosystems. Among the upregulated chloroplast transcripts, chloroplast Tidi, flavodoxin IsiB, and carotenoid biosynthesis-related protein transcripts strongly proposed photosynthetic apparatus remodeling. Also, the transcriptomic study revealed the upregulation of the tetrapyrrole biosynthesis pathway (TPB) and identified the presence of a negative regulator of this pathway, called the s-FLP splicing variant. These observations point towards the accumulation of TPB pathway intermediates PROTO-IX, Mg-PROTO-IX, and P-Chlide, those earlier reported as retrograde signaling molecules. Our comparative transcriptomic approach along with biophysical and biochemical studies in D. salina grown under control (1.5 M NaCl) and hypersaline (3M NaCl) conditions, unveil an efficient retrograde signaling mechanism mediated remodeling of photosynthetic apparatus.

Abstract Nitrogen transport is one of the most important processes in plants mediated by specialized transmembrane proteins. Plants have two main systems for nitrogen uptake from soil and its transport within the system—a low-affinity transport system and a high-affinity transport system. Nitrate transporters are of special interest in cereal crops because large amount of money is spent on N fertilizers every year to enhance the crop productivity. Till date four gene families of nitrate transporter proteins; NPF (nitrate transporter 1/peptide transporter family), NRT2 (nitrate transporter 2 family), the CLC (chloride channel family), and the SLAC/SLAH (slow anion channel-associated homologues) have been reported in plants. In our study, in silico mining of nitrate transporter genes along with their detailed structure, phylogenetic and expression analysis was carried out. A total of 412 nitrate transporter genes were identified in hexaploid wheat genome using HMMER based homology searches in IWGSC Refseq v2.0. Out of those twenty genes were root specific, 11 leaf/shoot specific and 17 genes were grain/spike specific. The identification of nitrate transporter genes in the close proximity to the previously identified 67 marker-traits associations associated with the nitrogen use efficiency related traits in nested synthetic hexaploid wheat introgression library indicated the robustness of the reported transporter genes. The detailed crosstalk between the genome and proteome and the validation of identified putative candidate genes through expression and gene editing studies may lay down the foundation to improve nitrogen use efficiency of cereal crops.

The paucity of novel antibiotics for treating human and animal infections has heightened global fears about growing drug-resistant superbugs. Antibiotic replacement approaches for food-producing animals, particularly poultry and livestock, are urgently needed by the agricultural business. The need of the hour is to develop and exploit promising antibiotic-alternative phytochemicals in order to minimize Antibiotic Growth Promoters and develop an antibiotic-free animal production system. Phytogenic plant dietary supplements have positive effects on poultry performance and immunity. Toll-like receptors (TLRs) play a key role in the innate immune system. TLRs recognize microbes that have broken through physical barriers such as the skin or the mucosa of the digestive tract, triggering immune cell responses. Supplementation of herbs in the dietary regimen of poultry had shown stimulatory effects on the immune system. Herbs such as Amla, Ashwagandha, Garlic powder, Giloy, Turmeric, Shatavari, Aloe vera, Moringa and holy basil have been shown to increase the relative mRNA expression of different toll-like receptors, thereby enhancing broiler immunological status by modulating their immune response.

GIGANTEA (GI) is a plant-specific nuclear protein that plays a pleiotropic role in the growth and development of plants. GI’s involvement in circadian clock function, flowering time regulation, and various types of abiotic stress tolerance has been well documented in recent years. Here, the role of GI in response to Fusarium oxysporum (F. oxysporum) infection is investigated at the molecular level comparing Col-0 WT with the gi-100 mutant in Arabidopsis thaliana. Disease progression, photosynthetic parameters, and comparative anatomy confirmed that the spread and damage caused by pathogen infection were less severe in gi-100 than in Col-0 WT plants. F. oxysporum infection induces a remarkable accumulation of GI protein. Our report showed that it is not involved in flowering time regulation during F. oxysporum infection. Estimation of defense hormone after infection showed that jasmonic acid (JA) level is higher and salicylic acid (SA) level is lower in gi-100 compared to Col-0 WT. Here, we show that the relative transcript expression of CORONATINE INSENSITIVE1 (COI1) and PLANT DEFENSIN1.2 (PDF1.2) as a marker of the JA pathway is significantly higher while ISOCHORISMATE SYNTHASE1 (ICS1) and NON-EXPRESSOR OF PATHOGENESIS-RELATED GENES1 (NPR1), the markers of the SA pathway, are downregulated in the gi-100 mutants compared to Col-0 plants. The present study convincingly suggests that the GI module promotes susceptibility to F. oxysporum infection by inducing the SA pathway and inhibiting JA signaling in A. thaliana.

Over the world, there is growing worry about the corrosion of reinforced concrete structures. Structure repair, rehabilitation, replacement, and new structures all require cost-effective and long-lasting technologies. Fiber Reinforced Polymer (FRP) has been widely employed in both retrofitting existing structures and building new ones. Due to its varied qualities in reinforced concrete and masonry constructions as a repair composite material, FRP have seen a rise in use over the last decade. This material have several advantages such as high stiffness-to-weight and strength-to-weight ratios, light weight, possibly high longevity, and relative ease of usage in the field. Among all the parameters the bond between concrete and FRP composite play an important role in the strengthening of structures. However, the bond behaviour of the FRP-concrete interface is complex, with several failure modes, making the bond strength difficult to forecast, resulting in the FRP strengthened concrete structure. To overcome such kind of issues machine learning models are sufficient to forecast the bond strength of FRP-concrete. In this article Artificial Neural Network (ANN), optimized Artificial Bee Colony (ABC)-ANN and Gaussian Process Regression (GPR) algorithms are deployed to predict the bond strength. The R-value of ABC-ANN and GPR models are 0.9514 and 0.9618 respectively. This research aids researchers in estimating bond strength in less time, at a lower cost, and with less experimental work.

Cement manufacturing and utilization is one of the majorly responsible factors for global CO2 emissions. In light of sustainability and climate change concerns, it is essential to find alternative solutions to reduce the carbon footprint of cement. Secondary cementitious materials (SCMs) are helpful in reducing carbon emissions from concrete. One such solution is the use of agricultural waste as SCMs to reduce carbon emissions from concrete. Especially rice husk ash (RHA) is a silica-rich, globally available agricultural waste material. The compressive strength (CS) of concrete is important and is used to evaluate the material’s strength and durability. Predicting CS using a laboratory method is a costly, time-consuming, and complex process. ML-based prediction models are the modern solution to these problems. In this study, a total of 407 datasets are used to develop an ML-based model by using the ANN algorithm to predict the CS of concrete containing RHA. Cement, coarse aggregates, fine aggregates, water, rice husk ash, superplasticizer, and type of sample are used as input parameters to predict CS at 28 days. Various statistical parameters including correlation coefficient (R), root means square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe (NS), and the a20-index have been used to assess the performance of the developed ANN model. The R and RMSE values of training, validation, and testing samples are 0.9928, 0.9864, and 0.9545, and 1.6471 MPa, 2.7149 MPa, and 4.4334 MPa, respectively. The results obtained from this study have been found to be promising and enrich the available literature. This work will nudge civil engineering and material science researchers toward opting for sustainable computing techniques. However, the study’s limitations include the need for additional research into the material’s long-term behaviour as well as the consideration of other characteristics that may affect its strength, such as environmental conditions like temperature and humidity.

Corrosion of embedded steel reinforcement is the prime influencing factor that deteriorates the structural performance and reduces the serviceability of reinforced concrete (RC) structures, especially during earthquakes. In structural elements, RC columns play a vital role in transferring the superstructure’s load to the substructure. The deterioration of RC columns can affect the structures’ overall performance. Hence, it becomes essential to estimate the remaining life of deteriorated RC columns. In the literature, only limited analytical models are available to calculate the remaining life of corroded and eccentrically loaded RC columns. As the number of dependent parameters increases, assessing the residual life of the structural elements and providing a practically applicable suitable model become very complex. Machine learning (ML)-based prediction models are beneficial in dealing with such complex databases. In this article, an ML-based artificial neural network (ANN), Gaussian process regression (GPR), and support vector machine (SVM) algorithms have been applied to estimate the residual strength of corroded and eccentrically loaded RC columns. The performance of the analytical and ML models is accessed using commonly used performance indices, namely, the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), a-20 index, and Nash–Sutcliffe (NS). The results of the proposed ANN model have been compared with the existing analytical model to identify the suitability of the best model. Based on performance analysis, the precision of the GPR and SVM models is lower than that of the ANN model. The processed results revealed that the R2 value of the ANN model for training, testing, and validation datasets is 0.9908, 0.9757, and 0.9855, respectively. The MAPE, MAE, RMSE, NS, and a-20 index for all the datasets are 8.31%, 48.35 kN, 72.53 kN, 0.9886, and 0.8978, respectively. The precision of the ANN model in terms of the coefficient of determination is 225.77% higher than that of the analytical model. The sensitivity analysis demonstrates that the compressive strength of concrete plays the most significant role in the load-carrying capacity of corroded and eccentrically loaded RC columns. The proposed ANN model is reliable, accurate, fast, and cost effective. This model can also be used as a structural health-monitoring tool to detect the early damages in the RC columns.

Elevated intraocular pressure (IOP) is influenced by environmental and genetic factors. Increased IOP is a major risk factor for most types of glaucoma, including primary open angle glaucoma (POAG). Investigating the genetic basis of IOP may lead to a better understanding of the molecular mechanisms of POAG. The goal of this study was to identify genetic loci involved in regulating IOP using outbred heterogeneous stock (HS) rats. HS rats are a multigenerational outbred population derived from eight inbred strains that have been fully sequenced. This population is ideal for a genome-wide association study (GWAS) owing to the accumulated recombinations among well-defined haplotypes, the relatively high allele frequencies, the accessibility to a large collection of tissue samples, and the large allelic effect size compared to human studies. Both male and female HS rats (N = 1,812) were used in the study. Genotyping-by-sequencing was used to obtain ∼3.5 million single nucleotide polymorphisms (SNP) from each individual. SNP heritability for IOP in HS rats was 0.32, which agrees with other studies. We performed a GWAS for the IOP phenotype using a linear mixed model and used permutation to determine a genome-wide significance threshold. We identified three genome-wide significant loci for IOP on chromosomes 1, 5, and 16. Next, we sequenced the mRNA of 51 whole eye samples to find cis-eQTLs to aid in identification of candidate genes. We report 5 candidate genes within those loci: Tyr, Ctsc, Plekhf2, Ndufaf6 and Angpt2. Tyr, Ndufaf6 and Angpt2 genes have been previously implicated by human GWAS of IOP-related conditions. Ctsc and Plekhf2 genes represent novel findings that may provide new insight into the molecular basis of IOP. This study highlights the efficacy of HS rats for investigating the genetics of elevated IOP and identifying potential candidate genes for future functional testing.

Although many endocrine diseases can be associated with acquired cardiomyopathy and heart failure, conditions except hypothyroidism, hyperthyroidism, phaeochromocytoma-paraganglioma (PPGL), and primary hyperaldosteronism are rare. PPGL is a rare catecholamine-secreting neuroendocrine tumour arising from the adrenal gland in 80–85% or extra-adrenal chromaffin cells of the autonomic neural ganglia in the remainder. The annual incidence of PPGL is 3–8 cases per million per year in the general population. Catecholamine-induced cardiomyopathy (CICMP) has got a prevalence of 8–11% among patients with PPGL. Hypertension, either sustained or episodic, is present in the vast majority (95%) of PPGL patients. However, among patients with CICMP, hypertension is present only in 65% of cases and the classical triad of paroxysmal headache, sweating, and palpitation is present only in 4%. Based on the cardiac remodelling in response to endogenous catecholamine excess, PPGL patients might present with one of the three CICMPs, including dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), or Takotsubo cardiomyopathy (TCM). Regardless of the subtypes, all CICMPs have many features in common — a dramatic clinical presentation, reversible cardiomyopathy, similar repolarisation electrocardiography changes, mild-moderate cardiac biomarker elevation, and normal coronary arteries on coronary angiography. CICMP should be suspected in patients with non-ischaemic, non-valvular forms of cardiomyopathy, even in those without definite features of catecholamine excess. PPGL associated TCM should be suspected in all acute coronary syndrome (ACS) patients exhibiting pronounced blood pressure variability with no culprit lesions on coronary angiography. This article will provide a review of the various CICMPs, their pathophysiology, clinical features, and the management options.

Chronic myelogenous leukemia (CML), also known as chronic myeloid leukemia, is a myeloproliferative disorder characterized by increased proliferation of the granulocytic cell line without the loss of their capacity to differentiate. Consequently, the peripheral blood cell profile shows an increased number of granulocytes and their immature precursors, including occasional blast cells. CML accounts for 20% of all leukemias affecting adults. Retinal lesions are the most common ocular manifestation of leukemia. They are found most often in adults and in patients with myeloid leukemia. Despite the significant efforts made by different groups to optimize treatment and outcome, there are still unmet needs and unanswered questions. Ophthalmologic manifestations are among the therapeutic challenge. Here we present a case of CML (chronic phase) with ophthalmologic manifestations as initial presentation, trying to shed light on this important type of presentation.

Abstract Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (

Poly- and perfluoroalkyl substances (PFASs) are a group of anthropogenic chemicals with an aliphatic fluorinated carbon chain. Due to their durability, bioaccumulation potential, and negative impacts on living organisms, these compounds have drawn lots of attention across the world. The negative impacts of PFASs on aquatic ecosystems are becoming a major concern due to their widespread use in increasing concentrations and constant leakage into the aquatic environment. Furthermore, by acting as agonists or antagonists, PFASs may alter the bioaccumulation and toxicity of certain substances. In many species, particularly aquatic organisms, PFASs can stay in the body and induce a variety of negative consequences, such as reproductive toxicity, oxidative stress, metabolic disruption, immunological toxicity, developmental toxicity, cellular damage and necrosis. PFAS bioaccumulation plays a significant role and has an impact on the composition of the intestinal microbiota, which is influenced by the kind of diet and is directly related to the host’s well-being. PFASs also act as endocrine disruptor chemicals (EDCs) which can change the endocrine system and result in dysbiosis of gut microbes and other health repercussions. In silico investigation and analysis also shows that PFASs are incorporated into the maturing oocytes during vitellogenesis and are bound to vitellogenin and other yolk proteins. The present review reveals that aquatic species, especially fishes, are negatively affected by exposure to emerging PFASs. Additionally, the effects of PFAS pollution on aquatic ecosystems were investigated by evaluating a number of characteristics, including extracellular polymeric substances (EPSs) and chlorophyll content as well as the diversity of the microorganisms in the biofilms. Therefore, this review will provide crucial information on the possible adverse effects of PFASs on fish growth, reproduction, gut microbial dysbiosis, and its potential endocrine disruption. This information aims to help the researchers and academicians work and come up with possible remedial measures to protect aquatic ecosystems as future works need to be focus on techno-economic assessment, life cycle assessment, and multi criteria decision analysis systems that screen PFAS-containing samples. New innovative methods requires further development to reach detection at the permissible regulatory limits.

Background: Infantile esotropia is misalignment of eyes from bifoveal fixation where surgical correction at an early age usually presents with good outcomes. Aims and Objective: To compare the surgical outcome between bilateral medial rectus recession (BMR) and recession- resection (RR) surgery in infantile esotropia. Materials and Methods: The study was conducted in Lumbini Eye Institute, Bhairahawa, from December 2017 to December 2018. Patients with infantile esotropia who underwent surgical correction were included in the study. Patients were examined pre operatively, and post operatively at day one and third month, where the angle of deviation was measured and the level of binocular single vision (BSV) was assessed. Data was entered and analyzed using the SPSS program. Results: Total 79 patients were included in the study out of which 33 underwent BMR and 46 underwent RR. The mean pre-operative angle by PBCT was 52.58 PD (SD ±15.468) in the BMR group and 47.83 PD (SD ±12.140) in the RR group. Post-operative deviation in the BMR group had angle