Search

Abstract Rice (Oryza sativa L.) is a vital crop with enormous economic and nutritional importance in Nepal and worldwide. The yellow stem borer (YSB), Scirpophaga incertulas (Walker) (Lepidoptera: Crambidae), is a monophagous severe pest that poses a substantial threat to rice crops, causing significant yield losses by its larva feeding on stems and tillers. This review covers the life cycle, distribution, damage, and management and control strategies of YSB. YSB primarily targets rice plants, causing symptoms like dead hearts and whiteheads. Integrated pest management (IPM) strategies are crucial for effective YSB control. Cultural practices include selecting resistant rice varieties, using Bacillus thuringiensis (Bt) incorporated transgenic rice plants, synchronizing planting to reduce generations, and promoting natural enemies by conserving their habitats. Mechanical methods involve pheromone and light traps to disrupt mating and reduce adult populations. Biological control methods utilize natural predators like beetles and spiders and parasitoids such as Cotesia flavipes and Trichogramma spp. In contrast, chemical control involves targeted insecticide application when pest populations reach economic threshold levels that is when 5–10% dead hearts are seen. The article emphasizes the importance of IPM techniques in reducing YSB infestation and fostering sustainable rice production. These approaches not only help to lower the YSB population but also contribute to environmental preservation and long-term agricultural viability. As rice is a staple food for millions, effective management of YSB becomes crucial for guaranteeing food security and livelihoods across the globe.

The present work aimed to evaluate the effects of municipal solid waste (MSW) dumping on the quality of soil and groundwater at six specific sites (S1-S6) located in the Haridwar district of Uttarakhand, India. An analysis of selected physicochemical and heavy metal characteristics (Cu, Zn, Fe, Mn, Cd, and Cr) was conducted on groundwater and soil samples collected between July 2022 and June 2023 using standard techniques. Furthermore, the composition of municipal solid waste (MSW) was also analysed, revealing a significant proportion of biodegradable waste in comparison to non-biodegradable wastes. The areas affected by urban and industrial activity showed markedly higher concentrations of physicochemical and heavy metal parameters in both soil and groundwater samples (p < 0.05). An study of groundwater revealed concentrations from BDL to 0.170 ± 0.008 mg/L (S6) , 0.034 ± 0.004 mg/L (S4) to 1.565 ± 0.048 mg/L (S6), 1.786 ± 0.089 mg/L (S4) to 10.630 ± 0.279 mg/L (S6), 0.096 ± 0.006 mg/L (S4) to 0.321 ± 0.005 mg/L (S6), and 0.110 ± 0.005 mg/L (S6) for Cu, Zn, Fe, Mn, and Cd mg/kg , respectively. Cr was detected at the BDL level in all groundwater samples from all sites. Findings revealed that metals exceeded the BIS range, with Iron over the limit at all sites, Manganese at S1, S2, and S6 sites, and Cadmium at S1, S2, S3, and S6 sites. The examination of the soil samples revealed concentrations ranging from 0.399 ± 0.125 mg/kg (S5) to 2.806 ± 0.083 mg/kg (S6), 1.207 ± 0.044 mg/kg (S5) to 3.813 ± 0.238 mg/kg (S2), 24.972 ± 1.128 mg/kg (S5) to 47.417 ± 1.713 mg/kg (S3), 2.297 ± 0.43 mg/kg (S5) to 14.641 ± 0.229 mg/kg (S2), and BDL (S4, S5, and S6) to 0.194 ± 0.291 mg/kg (S2) for their respective elements. The Cr content was BDL in all soil samples from all sites. Several metals over the BIS range were detected, including Cu at site S6, Zn at sites S1, S2, S3, S4, and S6, Fe at all sites and Mn at site S2. In S1-S6, the total bacterial population in the soil ranged from 397 ± 67 CFU to 459±88 CFU, with values of 442 ± 86 CFU, 404 ± 78 CFU, 459 ± 88 CFU, 397 ± 67 CFU, 405 ± 67 CFU, and 451 ± 77 CFU, respectively. The findings revealed soil and groundwater pollution caused by the elevated levels of heavy metals in the groundwater, rendering the water unfit for drinking. The study underscores the need of adopting effective waste management strategies to reduce the adverse effects of solid waste disposal sites. The results can guide the formulation of development policies and laws aimed at enabling the implementation of suitable solid waste management techniques.

Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of predictive models. Algorithms based on a combination of wavelet with deep learning, machine learning, and stochastic model have been proposed. The denoised series are fitted with various benchmark models, including long short-term memory (LSTM), support vector regression (SVR), artificial neural network (ANN), and autoregressive integrated moving average (ARIMA) models. The effectiveness of a wavelet-based denoising approach was investigated on monthly wholesale price data for three major spices (turmeric, coriander, and cumin) for various markets in India. The predictive performance of these models is assessed using root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The wavelet LSTM model with Haar filter at level 6 emerged as a robust choice for accurate price predictions across all spices. It was found that the wavelet LSTM model had a significant gain in accuracy than the LSTM model by more than 30% across all accuracy metrics. The results clearly highlighted the efficacy of a wavelet-based denoising approach in enhancing the accuracy of price forecasting.

This study investigated the aerodynamic behavior and structural responses of prismatic and tapered high-rise buildings under extreme wind conditions, focusing on peak wind-induced forces and moments. Using Computational Fluid Dynamics (CFD) simulations with a hybrid RANS/LES approach, the analysis explored the effects of turbulent inflow on the mean pressure coefficients, vortex dynamics, and force coefficients at different wind incidence angles (0°, 30°, and 60°). The results revealed significant differences in peak aerodynamic loads between prismatic and tapered building shapes. The tapered models experienced larger vortex formations and greater suction effects, particularly at two-thirds of the building height, with peak across-wind forces occurring at a 30° wind incidence angle. In contrast, the prismatic model showed the highest peak in along-wind forces and base overturning moments at a 60° wind incidence angle, with Karman vortex shedding and horseshoe vortices prominently captured. The study also highlighted the importance of unsteady inflow conditions in accurately predicting peak dynamic responses, particularly in the wake flow, where vortices significantly influence aerodynamic loads.

Being a cheap, simple, and low-energy consumer, solar stills have been introduced by water and energy scientists as an alternative desalination method to fossil fuel-based ones. A wide variety of designs and modifications have been applied to enhance the solar stills' performance, which may be associated with experimental works that require time and cost. Therefore, coupling solar stills with state-of-the-art machine learning is expected to overcome these disadvantages of experimental work. Artificial intelligence models try to build relationships between the input and output data similar to the human brains depending on given dataset. In light of these, this paper carries out a literature review that considers the applications of artificial intelligence in solar stills’ performance prediction. It covers the most repeated machine learning methods employed for performance prediction, focusing on principles, advantages, limitations, and the mathematical description of each method; besides the models' evaluation criteria. Then, comprehensive discussions are performed on the solar stills models by classifying them according to the design. The work compares the previous studies within comprehensive analyses that give reasons for the authors' findings, highlighting the reasons for the variation between the models' prediction and experimental findings. Accordingly, models with root mean square errors close to zero are highlighted throughout the review.

Small and Medium Enterprises (SMEs) play a crucial role in the Indian economy. To remain competitive in the global market, application of Lean Manufacturing Techniques (LMT) helps SMEs to improve their processes in alignment with customer needs. The purpose of this study is to reduce overall lead time by identification and eliminating non-value added activities (NVA) from the manufacturing system. In this paper, the current manufacturing processes have been thoroughly studied using the principle of work study. Several lean approaches such as- TAKT time computation, Value stream Mapping (VSM), layout optimization, Kanban, Andon etc. have been used to make improvement in line efficiency and hence productivity. The study shows that use of line balancing technique, matches the cycle time with calculated TAKT time. And the efficiency of the line has increased approx 10% and hence balance delay got reduced. Again VSM helps to reduce the lead time of foundry shops by 51 minutes. It is one of the few studies that deal with productivity improvement in Indian SME.

Abstract:: Grain size plays a significant role in rice, starting from affecting yield to consumer preference, which is the driving force for deep investigation and improvement of grain size characters. Quantitative inheritance makes these traits complex to breed on account of several alleles contributing to the complete trait expression. We employed genome-wide association study in an association panel of 88 rice genotypes using 142 new candidate gene based SSR (cgSSR) markers, derived from yield-related candidate genes, with the efficient mixed-model association coupled mixed linear model for dissecting complete genetic control of grain size traits. A total of 10 significant associations were identified for four grain size-related characters (grain weight, grain length, grain width, and length-width ratio). Among the identified associations, seven marker trait associations explain more than 10% of the phenotypic variation, indicating major putative QTLs for respective traits. The allelic variations at genes OsBC1L4, SHO1 and OsD2 showed association between 1000-grain weight and grain width, 1000-grain weight and grain length, and grain width and length-width ratio, respectively. The cgSSR markers, associated with corresponding traits, can be utilized for direct allelic selection, while other significantly associated cgSSRs may be utilized for allelic accumulation in the breeding programs or grain size improvement. The new cgSSR markers associated with grain size related characters have a significant impact on practical plant breeding to increase the number of causative alleles for these traits through marker aided rice breeding programs.

Interrupted lung angiogenesis is a hallmark of bronchopulmonary dysplasia (BPD); however, druggable targets that can rescue this phenotype remain elusive. Thus, our investigation focused on amphiregulin (Areg), a growth factor that mediates cellular proliferation, differentiation, migration, survival, and repair. While Areg promotes lung branching morphogenesis, its effect on endothelial cell (EC) homeostasis in developing lungs is understudied. Therefore, we hypothesized that Areg promotes the proangiogenic ability of the ECs in developing murine lungs exposed to hyperoxia. Lung tissues were harvested from neonatal mice exposed to normoxia or hyperoxia to determine Areg expression. Next, we performed genetic loss-of-function and pharmacological gain-of-function studies in normoxia- and hyperoxia-exposed fetal murine lung ECs. Hyperoxia increased Areg mRNA levels and Areg+ cells in whole lungs. While Areg expression was increased in lung ECs exposed to hyperoxia, the expression of its signaling receptor, epidermal growth factor receptor, was decreased, indicating that hyperoxia reduces Areg signaling in lung ECs. Areg deficiency potentiated hyperoxia-mediated anti-angiogenic effects. In contrast, Areg treatment increased extracellular signal-regulated kinase activation and exerted proangiogenic effects. In conclusion, Areg promotes EC tubule formation in developing murine lungs exposed to hyperoxia.

A field experiment was conducted to evaluate the impacts of a 12-year old conservation agriculture (CA)- based pigeon pea-wheat system on weeds, wheat crop, and resource use during winter (rabi) 2021–22. Results indicated that surface retention of residue irrespective of ZT permanent bed and N dose led to significant reduction in weed interference at 60 DAS. CA-based systems reduced weed density and dry weight considerably than CT. CA- based systems led to significantly higher wheat grain yield (by 11.6–14.9%) and net B:C (by 24.0 –28.0%) than CT, and PFBR100N and PBBR100N were slightly superior to others. PBBR100N and PBBR75N had lower irrigation water use and significantly higher irrigation water productivity than CT. Contrast analysis showed that wheat yield and water productivity were comparable between 75% N and 100% N in CA, indicating a saving of 25% N under CA.

The roof shape and the slope of the roof are both significant constraints for the protection of the structure against wind load. The present study aims to investigate the variation of wind pressure coefficients on the conical roof and to observe the wind behavior around the building. CFD (Computational Fluid Dynamics) software ANSYS has been used for modeling and simulation. It is found that the 35° roof slope is the optimum roof angle for the present study, as there is the least area-weighted average pressure coefficient for this roof angle. While the roof slope 30° is found the most critical roof angle in terms of wind load resistance among all the roof angles of the present study.

In general, statistical models for estimation of heritability follow certain assumptions, i.e. random components including the error follow a normal distribution and are identically independently distributed. But in the practical situation, sometimes these assumptions are violated. Thus, from the perspective of plant and animal breeding programs, estimating various genetic variances and inferring their inheritance based on estimations of various genetic parameters is studied. In the present study, estimation of heritability for the half-sib model is considered with correlated error, and sire and error follow a range of different distributions like normal, Cauchy, beta, and t- distribution. Two error structures AR(1) and AR(2) was considered and observations for correlated and uncorrelated cases were generated using a one-way classification model. The developed procedure was applied using the generated observations using simulation. Various heritability ranges, such as high and low (0.5, 0.1), Half-sib AR(1), varied sample sizes (100 and 500), and various correlations of errors between AR(1) and AR, were used to obtain the data (2). ρ= -1 to +1. It was noticed that correlated errors a significant effect on heritability estimation and are highly affected by the distribution it follows.

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.

Inflammation causes bronchopulmonary dysplasia (BPD), a common lung disease of preterm infants. One reason this disease lacks specific therapies is the paucity of information on the mechanisms regulating inflammation in developing lungs. We address this gap by characterizing the lymphatic phenotype in an experimental BPD model because lymphatics are major regulators of immune homeostasis. We hypothesized that hyperoxia (HO), a major risk factor for experimental and human BPD, disrupts lymphatic endothelial homeostasis using neonatal mice and human dermal lymphatic endothelial cells (HDLECs). Exposure to 70% O2 for 24–72 h decreased the expression of prospero homeobox 1 (Prox1) and vascular endothelial growth factor c (Vegf-c) and increased the expression of heme oxygenase 1 and NAD(P)H dehydrogenase [quinone]1 in HDLECs, and reduced their tubule formation ability. Next, we determined Prox1 and Vegf-c mRNA levels on postnatal days (P) 7 and 14 in neonatal murine lungs. The mRNA levels of these genes increased from P7 to P14, and 70% O2 exposure for 14 d (HO) attenuated this physiological increase in pro-lymphatic factors. Further, HO exposure decreased VEGFR3+ and podoplanin+ lymphatic vessel density and lymphatic function in neonatal murine lungs. Collectively, our results validate the hypothesis that HO disrupts lymphatic endothelial homeostasis.

Context: The pandemic of COVID-19, with its high rates of infectivity, unpredictable nature, and measures taken to deal with it such as extended periods of lockdown, has had an adverse impact on the psychological status of individuals affected by it directly or indirectly. To the best of our knowledge, this is the first report from India on the psychological status of COVID-19-positive individuals. Aim: The aim of the study was to assess the impact of COVID-19 pandemic on the psychological status of persons tested positive for COVID-19. Setting and Design: This was a case–control study in a tertiary care hospital setting at Mumbai. Materials and Methods: A total of 104 individuals detected to have positive COVID-19 status and admitted to the hospital from May 1, 2020 to May 30, 2020, were compared with 106 age- and gender-matched controls from the general population for the psychological impact of COVID-19 as measured by Perceived Stress Scale-10, Patient Health Questionnaire-9, Generalized Anxiety Disorder-7 Questionnaire, Insomnia Severity Index, and World Health Organization Well-Being Index-5. Statistical Analysis Used: Group comparisons on nominal variables were analyzed by Chi-square test. P < 0.05 was taken as statistically significant. Results: About 58.6% from COVID-19-positive group and 76.4% from control group reported moderate-to-high perceived stress. Moderate-to-severe depressive symptoms were reported in 6.7% versus 15%, moderate-to-severe anxiety symptoms in 1.9% versus 14.1%, clinical insomnia in 3.8% versus 14.1%, and poor quality of well-being in 22.1% versus 35.8%, in cases versus controls, respectively. Control group reported significantly higher levels of perceived stress (P = 0.020), depressive symptoms (P = 0.021), anxiety symptoms (P = 0.013), insomnia severity (P = 0.045), and poorer well-being index (P = 0.018) compared to COVID-19-positive group. Conclusions: Despite limitations, study findings, if replicated, highlight the urgent need for incorporating psychological screening and interventions into protocols for dealing with ongoing COVID-19 pandemic not only for infected individuals but also for the community as a whole.

  The Bayesian model was applied for analyzing the first lactation in Karan Fries cattle. First lactation data of production (305-day or less milk yield and daily milk yield) were collected from the history-cum pedigree sheet and daily milk yield registers of the division of Dairy Cattle Breeding (DCB), National Dairy Research Institute (NDRI), Karnal. In the Bayesian paradigm, MCMC methods are applied to solve complex mathematical problems to estimate a large number of unknown parameters. Assuming linear mixed model and using the different prior set up, diagnostic of MCMC (Markov Chain Monte Carlo) was carried out graphically as well as by Heidelberg stationarity test. Variance estimates of the random effects (VA) and residual variance estimation (VR) and Variance estimate location effects i.e. fixed effects were calculated along with effective sample size. Finally, heritability (h2) estimates for First lactation 305 days or less milk yield (FL305DMY) was estimated along with its credible interval.

Bronchopulmonary dysplasia (BPD) is a morbid lung disease distinguished by lung alveolar and vascular simplification. Hyperoxia, an important BPD causative factor, increases extracellular signal-regulated kinases (ERK)-1/2 expression, whereas decreased lung endothelial cell ERK2 expression reduces angiogenesis and potentiates hyperoxia-mediated BPD in mice. However, ERK1′s role in experimental BPD is unclear. Thus, we hypothesized that hyperoxia-induced experimental BPD would be more severe in global ERK1-knockout (ERK1-/-) mice than their wild-type (ERK1+/+ mice) littermates. We determined the extent of lung development, ERK1/2 expression, inflammation, and oxidative stress in ERK1-/- and ERK1+/+ mice exposed to normoxia (FiO2 21%) or hyperoxia (FiO2 70%). We also quantified the extent of angiogenesis and hydrogen peroxide (H2O2) production in hyperoxia-exposed neonatal human pulmonary microvascular endothelial cells (HPMECs) with normal and decreased ERK1 signaling. Compared with ERK1+/+ mice, ERK1-/- mice displayed increased pulmonary ERK2 activation upon hyperoxia exposure. However, the extent of hyperoxia-induced inflammation, oxidative stress, and interrupted lung development was similar in ERK1-/- and ERK1+/+ mice. ERK1 knockdown in HPMECs increased ERK2 activation at baseline, but did not affect in vitro angiogenesis and hyperoxia-induced H2O2 production. Thus, we conclude ERK1 is dispensable for hyperoxia-induced experimental BPD due to compensatory ERK2 activation.

Influence of weather variables on occurrence of spiders in pigeon pea across locations of seven agro-climatic zones of India was studied in addition to development of forecast models with their comparisons on performance. Considering the non-normal and nonlinear nature of time series data of spiders, non-parametric techniques were applied with developed algorithm based on combinations of wavelet–regression and wavelet–artificial neural network (ANN) models. Haar wavelet filter decomposed each of the series to extract the actual signal from the noisy data. Prediction accuracy of developed models, viz., multiple regression, wavelet–regression, and wavelet–ANN, tested using root mean square error (RMSE) and mean absolute percentage error (MAPE), indicated better performance of wavelet–ANN model. Diebold Mariano (DM) test also confirmed that the prediction accuracy of wavelet–ANN model, and hence its use to forecast spiders in conjunction with the values of pest–defender ratios, would not only reduce insecticidal sprays, but also add ecological and economic value to the integrated pest management of insects of pigeon pea.

Time series analysis and forecasting is one of the challenging issues of statistical modelling. Modelling of price and forecasting is a vital matter of concern for both the farming community and policy makers, especially in agriculture. Many practical agricultural data, principally commodity price data shows the typical feature of long memory process or long range dependency. For capturing the long memory behavior of the data Autoregressive Fractionally Integrated Moving Average (ARFIMA) model is generally used. Sometimes, in time series data besides the original series, data on some auxiliary or exogenous variables may be available or can be made available with a lower cost; like besides the market prices of commodities, market arrivals for that commodity may be available and it affects the market price of commodities. This type of exogenous variable may be incorporated in existing model to improve the model performance and forecasting accuracy, like Autoregressive Fractionally Integrated Moving Average with exogenous variables (ARFIMAX) model. In the present study undertaken at ICAR-IASRI, New Delhi during 2019, daily maximum and modal price of potato of Agra market of UP, India are taken along with daily market arrival. Both the ARFIMA and ARFIMAX model with market arrival as exogenous variable are applied for the data under study. Comparative studies of the fitted models are employed by using the Relative Mean Absolute Percentage Error (RMAPE) and Root Mean Square Error (RMSE) criteria. We could establish superiority of the ARFIMAX model over the ARFIMA model in terms of modeling and forecasting efficiency.

This study analyzes whether the four components of subjective well-being—satisfaction with life, flourishing, positive affect, and negative affect—are independent, form a hierarchical structure, a composite structure, or a causal structure. It also examines the social and personality factors that predict subjective well-being among millennials in India. One thousand seven hundred and seventy-nine millennials, aged 16–27, attending technical educational institutions in India, participated. Four components assessed subjective well-being. Factual and subjective indicators of income, education, health, and relationships were measured. An inventory assessed Big-Five personality factors. The hierarchical structure, having interrelated components, formed subjective well-being better than other structures. The most important positive social predictor of the hierarchical structure of subjective well-being was satisfaction with personal relationships and the personality predictor was emotional stability. Activities which promote personal relationships and emotional stability can improve the subjective well-being among millennials.

Background: The rising geriatric population is facing significant health and social problems in the developing world that are impacting the quality of their lives. Objective: The study describes the general health status and the socioeconomic characteristics of the elderly people of Pakhribas village development committee (VDC) of Eastern Nepal. Materials and Methods: Descriptive cross-sectional study was carried out targeting the people aged 60 years and above in the sample area using a semi-structured questionnaire and convenient sampling to get the required sample size. A total of 189 elderly people who consented were interviewed in May 2010. Result: The major part of the elderly population was in the age group of 60-69 years. Seventy-two percent were illiterate and 75% were still earning with the majority involved in farming. Nine out of ten were living with their families and still made decisions for the household, Eighty-two percent did not feel lonely and 88.4% did not report any misbehavior by the house members. Regarding health, 69% had some diagnosed health issue with 36% suffering from gastritis and 20.4% from chronic lung disease. Using the ICD 10 criteria, depression was found among 18%. The most prevalent geriatric problem was a dental problem as found in 61% even though 87% claimed to brush their teeth regularly. More than half were suffering from visual difficulty and the proportions suffering from memory and hearing issues were also substantive. Smoking habit was found in 60%, and the relation between smoking and chronic respiratory diseases had a statistical association (P =

Bronchopulmonary dysplasia (BPD)-associated pulmonary hypertension (PH) is a significant lung morbidity of infants, and disrupted lung angiogenesis is a hallmark of this disease. We observed that extracellular signal-regulated kinases (ERK) 1/2 support angiogenesis in vitro, and hyperoxia activates ERK1/2 in fetal human pulmonary microvascular endothelial cells (HPMECs) and in neonatal murine lungs; however, their role in experimental BPD and PH is unknown. Therefore, we hypothesized that Tie2 Cre-mediated deficiency of ERK2 in the endothelial cells of neonatal murine lungs would potentiate hyperoxia-induced BPD and PH. We initially determined the role of ERK2 in in vitro angiogenesis using fetal HPMECs. To disrupt endothelial ERK2 signaling in the lungs, we decreased ERK2 expression by breeding ERK2flox/flox mice with Tie-Cre mice. One-day-old endothelial ERK2-sufficient (eERK2+/+) or –deficient (eERK2+/−) mice were exposed to normoxia or hyperoxia (FiO2 70%) for 14 d. We then performed lung morphometry, gene and protein expression studies, and echocardiography to determine the extent of inflammation, oxidative stress, and development of lungs and PH. The knockdown of ERK2 in HPMECs decreased in vitro angiogenesis. Hyperoxia increased lung inflammation and oxidative stress, decreased lung angiogenesis and alveolarization, and induced PH in neonatal mice; however, these effects were augmented in the presence of Tie2-Cre mediated endothelial ERK2 deficiency. Therefore, we conclude that endothelial ERK2 signaling is necessary to mitigate hyperoxia-induced experimental BPD and PH in neonatal mice. Our results indicate that endothelial ERK2 is a potential therapeutic target for the management of BPD and PH in infants.

This paper reports a preliminary study of the evaluation and forecast of transport-related air pollution dispersion in urban areas with the help of Geographic Information System (GIS) platform and a simulative system with graphical interface. The urban population growth, economic development, energy consumption, growing transportation demand and living standards play major role in pollution exposure in atmosphere. A lot of research has already been done to investigate the functional relationship between air quality and air pollution from transport. This study is an effort to develop a more flexible framework of model to find the exposure of the air pollution in the atmosphere. This review article describes the development of framework of different GIS inputs that help to find the exposure of vehicular pollution in megacity.

Background: Dengue infection is endemic in most regions of India and is a major cause of morbidity and mortality. There is paucity of data from the Armed Forces. This report on 52 children is from a Zonal Hospital in a dengue-naïve area. Methods: A prospective, observational study of clinical features and laboratory parameters in children up to 15 years with dengue infection confirmed by ELISA for NS1 antigen. Results: Mean age was 9.9 years, male to female ratio 1.08:1. All 52 had Dengue Fever with no hemorrhagic manifestations and all recovered. Leading symptoms were nausea (53.85%), myalgia (51.92%) and vomiting (50%). Significant laboratory findings included leucopenia (

Vehicles are recognized as one of the major sources of air pollution. Delhi having a very large number of vehicles produces a lot of air pollution. There is an utter need to ascertain the concentration of the pollutants and their impact on the health of the people of the concerned region. This paper makes an attempt towards such an examination. The method employed here is based on Epidemiology. The assessment of health risks due to increased levels of various pollutants on people represented in this paper is based on the spreadsheet model, Risk of Mortality/Morbidity due to Air Pollution (Ri–MAP). This paper presents the findings of the study that evaluates the Impact of increasing air pollution on human health, especially vehicular pollution, in the monitored region, i.e., National Capital Territory of Delhi (NCT Delhi) during the year 2015 by choosing fifteen study sites from different parts of the capital city. The findings are in terms of excess number of cases of cardiovascular and respiratory mortality, and cardiovascular and respiratory morbidity for all fifteen transport corridors. Among others, Noida link road recorded highest excess number of cases of cardiovascular mortality and morbidity and respiratory Morbidity. In case of respiratory mortality, Wazirabad road recorded maximum excess number of cases. With respect to least excess number of cases, it was Connaught Place (Outer circle). The excess number of cases are directly proportional to the population density of the respective region. Also, the regions showing smaller excess number of cases are the planned, less crowded and posh regions of the national capital territory of Delhi.

Transitional cell carcinoma of urinary bladder (TCCUB) usually presents with hematuria. Cerebral and leptomeningeal metastases as a first manifestation of TCCUB have been occasionally reported in the medical literature. Hence, we report a case of an elderly man who presented with localizing neurological signs and whose magnetic resonance imaging brain showed multifocal cerebral and leptomeningeal lesions suspected to be metastatic deposits. Thorough search for the primary tumor revealed TCCUB.