Dynamic Impact Factor Determination of an Existing Pre-stressed Concrete I-Girder Bridge Using Vehicle-Bridge Interaction Modelling

The dynamic Impact Factor (IM) of a bridge is influenced by many factors, including Vehicle-Bridge Interaction (VBI), vehicle speed and road roughness. This paper represents the dynamic effects of moving vehicles and the determination of IM of an existing Pre-stressed concrete I-girder bridge utilizing VBI modeling. Evaluation of the IM is expected to provide valuable information for condition assessment and management of the existing bridge. The interaction problem between the vehicle and the bridge includes a dynamic model for the bridge structure subsystem, a dynamic model for the vehicle subsystem, interaction constraints, road roughness modelling and numerical solution techniques for the dynamic systems. The Half-car model is utilized for modelling of the vehicle dynamics and the bridge dynamic model is idealized according to Finite Element Method (FEM). Then FEM along with the mode superposition method are utilized for determining the Equation of Motion (EOM) for the bridge subsystem. D’Alembert’s principle is used for developing EOM for the vehicle subsystem. The interaction between vehicle vibration and bridge vibration is established through the contact forces between the wheels and the bridge by employing the compatibility relationship between the contact points and by applying the static equilibrium condition. Lastly, Newmark’s-β method is used for solving the coupled mathematical model of the vehicle and bridge interaction problem to determine the responses of the two sub-systems. The whole procedure is then performed for different vehicle speeds and various bridge deck surface roughness conditions to determine the dynamic impact on the existing I-girder bridge named Teesta Bridge located in Bangladesh

Screening of bacterial strains for pectate lyase production and detection of optimal growth conditions for enhanced enzyme activity

In the present study, the pectatelyase production by fifty two bacterial strains isolated from ramie grown soils were studied and the strain RDSM01 showed maximum pectate lyase activity. According to sequence homology of Genbank, the strain RDSM01 was identified as Bacillus subtilis (Genbank Accession No. KX035109). Maximum pectate lyase activity of the strain was observed when 1.5% (v/v) inoculum was added to the growth medium and was incubated for 48 hours at 34-370C and at pH 7.0. The relative activity of the strain was 19% higher when apple pectin was used as carbon source compared to citrus pectin. Maximum enzyme production (149.1 – 153.4 IU/ml) was recorded when ammonium chloride or ammonium sulphate at 0.4% concentration was used as nitrogen source. Thus, B. subtilis strain RDSM01 possessing high pectate lyase activity may be effectively utilized for removal of gum from ramie fibre, which is primarily made of pectin and hemicellulose

Impact of COVID-19 pandemic on safe abortion and family planning services at a tertiary care women’s hospital in Nepal

Background: The COVID-19pandemic emerged as a major public health crisis, which has affected all dimensions of the health care system. Sexual and reproductive health services were severely affected, leading to a decrease in access and service utilization, affecting the overall health of women.Methods: A two-year comparative study, before and during the COVID-19 pandemic, on safe abortion services and family planning, was conducted at Paropakar maternity and women's hospital to assess the impact of COVID-19 on service utilization.Results: Safe abortion services were decreased by 34.4%, and family planning services by 39%, in 2020 as compared to the previous year. Uptake of long-acting reversible contraceptives and permanent methods was most affected. Utilization of services was affected markedly during lockdown, and showed a persistent decline, even after the lockdown was lifted.Conclusions: The COVID-19 pandemic has seriously affected safe abortion and family planning services in Nepal due to lockdown, travel restriction, home isolation, resource reallocation, health facilities serving only emergencies and confusing messages about COVID-19 control. The decline in these services will create additional demand and pressure on the health care system, resulting from unplanned pregnancies and unsafe abortions. Health care staffs should be reoriented about the essential nature of safe abortion and family planning services during emergencies, and the implications of service disruption, on society and the country. Pragmatic and gender sensitive changes to national policies should be made, to ensure that women's health is safeguarded, and safe abortion and family planning included as essential health care services during emergencies.

Combination therapy with ampicillin and azithromycin in an experimental pneumococcal pneumonia is bactericidal and effective in down regulating inflammation in mice

OBJECTIVES: Emergence of multidrug resistance among Streptococcus pneumoniae (SP), has limited the available options used to treat infections caused by this organism. The objective of this study was to compare the role of monotherapy and combination therapy with ampicillin (AMP) and azithromycin (AZM) in eradicating bacterial burden and down regulating lung inflammation in a murine experimental pneumococcal infection model. METHODS: Balb/C mice were infected with 10(6) CFU of SP. Treatments with intravenous ampicillin (200 mg/kg) and azithromycin (50 mg/kg) either alone or in combination was initiated 18 h post infection, animals were sacrificed from 0 – 6 h after initiation of treatment. AMP and AZM were quantified in serum by microbiological assay. Levels of TNF-α, IFN-γ IL-6, and IL-10 in serum and in lungs, along with myeloperoxidase, inflammatory cell count in broncho alveolar lavage fluid, COX-2 and histopathological changes in lungs were estimated. RESULTS: Combination therapy down regulated lung inflammation and accelerated bacterial clearance. This approach also significantly decreased TNF-α, IFN-γ, IL-6 and increased IL-10 level in serum and lungs along with decreased myeloperoxidase, pulmonary vascular permeability, inflammatory cell numbers and COX-2 levels in lungs. CONCLUSIONS: Combinatorial therapy resulted in comparable bactericidal activity against the multi-drug resistant isolate and may represent an alternative dosing strategy, which may help to alleviate problems with pneumococcal pneumonia

Impact Learning: A Learning Method from Features Impact and Competition

Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of wellknown machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of the impact learning over the conventional machine learning algorithm

Impact learning : A learning method from feature’s impact and competition

Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of well-known machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of impact learning over the conventional machine learning algorithm

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Protective effects of methanolic extract of Adhatoda vasica Nees leaf in collagen-induced arthritis by modulation of synovial toll-like receptor-2 expression and release of pro-inflammatory mediators

RA associated with oxidative stress and chronic inflammation has been a major health problem among the population worldwide. In this study protective effect of methanolic extract of Adhatoda vasica leaf (AVE) was evaluated on Collagen-induced arthritis in male Swiss albino mice. Post oral administration of AVE at 50, 100 and 200 mg/kg body weight doses decreased the arthritic index and footpad swelling. AVE administration diminished pro-inflammatory cytokines in serum and synovial tissues. Reduced chemokines and neutrophil infiltration in synovial tissues after AVE administration dictated its protective effect against RA. Decreased LPO content and SOD activity along with concomitant rise in GSH and CAT activities from liver, spleen and synovial tissues indicated regulation of oxidative stress by AVE. In addition decreased CRP in serum along with suppressed TLR-2 expression in CIA mice after AVE treatment was also observed. Protective effect of AVE in RA is further supported from histopathological studies which showed improvement during bone damage. In conclusion this study demonstrated A. vasica is capable of regulating oxidative stress during CIA and therefore down regulated local and systemic release of pro-inflammatory mediators, which might be linked to mechanism of decreasing synovial TLR-2 expression via downregulating release of its regular endogenous ligands like CRP