EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration.

Reference evapotranspiration (ET0 ) is a significant parameter for efficient irrigation scheduling and groundwater conservation. Different machine learning models have been designed for ET0 estimation for specific combinations of available meteorological parameters. However, no single model has been suggested so far that can handle diverse combinations of available meteorological parameters for the estimation of ET0. This article suggests a novel architecture of an improved hybrid quasi-fuzzy artificial neural network (ANN) model (EvatCrop) for this purpose. EvatCrop yielded superior results when compared with the other three popular models, decision trees, artificial neural networks, and adaptive neuro-fuzzy inference systems, irrespective of study locations and the combinations of input parameters. For real-field case studies, it was applied in the groundwater-stressed area of the Terai agro-climatic region of North Bengal, India, and trained and tested with the daily meteorological data available from the National Centres for Environmental Prediction from 2000 to 2014. The precision of the model was compared with the standard Penman-Monteith model (FAO56PM). Empirical results depicted that the model performances remarkably varied under different data-limited situations. When the complete set of input parameters was available, EvatCrop resulted in the best values of coefficient of determination (R2 = 0.988), degree of agreement (d = 0.997), root mean square error (RMSE = 0.183), and root mean square relative error (RMSRE = 0.034).

Peer support for people living with hepatitis B virus-A foundation for treatment expansion.

Chronic hepatitis B infection (CHB) affects 300 million people worldwide and is being targeted by the United Nations 2030 Sustainable Development Goals (SDGs) and the World Health Organisation (WHO), working towards elimination of hepatitis B virus (HBV) as a public health threat. In this piece, we explore the evidence and potential impact of peer support to enhance and promote interventions for people living with CHB. Peer support workers (PSWs) are those with lived experience of an infection, condition or situation who work to provide support for others, aiming to improve education, prevention, treatment and other clinical interventions and to reduce the physical, psychological and social impacts of disease. Peer support has been shown to be a valuable tool for improving health outcomes for people living with human immunodeficiency virus (HIV) and hepatitis C virus (HCV), but to date has not been widely available for communities affected by HBV. HBV disproportionately affects vulnerable and marginalised populations, who could benefit from PSWs to help them navigate complicated systems and provide advocacy, tackle stigma, improve education and representation, and optimise access to treatment and continuity of care. The scale up of peer support must provide structured and supportive career pathways for PSWs, account for social and cultural needs of different communities, adapt to differing healthcare systems and provide flexibility in approaches to care. Investment in peer support for people living with CHB could increase diagnosis, improve retention in care, and support design and roll out of interventions that can contribute to global elimination goals.

HIV/HBV coinfection remodels the immune landscape and natural killer cell ADCC functional responses.

BACKGROUND AND AIMS: HBV and HIV coinfection is a common occurrence globally, with significant morbidity and mortality. Both viruses lead to immune dysregulation including changes in natural killer (NK) cells, a key component of antiviral defense and a promising target for HBV cure strategies. Here we used high-throughput single-cell analysis to explore the immune cell landscape in people with HBV mono-infection and HIV/HBV coinfection, on antiviral therapy, with emphasis on identifying the distinctive characteristics of NK cell subsets that can be therapeutically harnessed. APPROACH AND RESULTS: Our data show striking differences in the transcriptional programs of NK cells. HIV/HBV coinfection was characterized by an over-representation of adaptive, KLRC2 -expressing NK cells, including a higher abundance of a chemokine-enriched ( CCL3/CCL4 ) adaptive cluster. The NK cell remodeling in HIV/HBV coinfection was reflected in enriched activation pathways, including CD3ζ phosphorylation and ZAP-70 translocation that can mediate stronger antibody-dependent cellular cytotoxicity responses and a bias toward chemokine/cytokine signaling. By contrast, HBV mono-infection imposed a stronger cytotoxic profile on NK cells and a more prominent signature of "exhaustion" with higher circulating levels of HBsAg. Phenotypic alterations in the NK cell pool in coinfection were consistent with increased "adaptiveness" and better capacity for antibody-dependent cellular cytotoxicity compared to HBV mono-infection. Overall, an adaptive NK cell signature correlated inversely with circulating levels of HBsAg and HBV-RNA in our cohort. CONCLUSIONS: This study provides new insights into the differential signature and functional profile of NK cells in HBV and HIV/HBV coinfection, highlighting pathways that can be manipulated to tailor NK cell-focused approaches to advance HBV cure strategies in different patient groups.

Conflict-Based safety evaluations at unsignalized intersections using surrogate safety measures.

Conflict-based road safety assessments may provide a deeper insight into the processes leading to crashes compared to assessments solely based on field crash data. The evaluation of road safety is conducted on specific road segments using different surrogate measure of safety indicators, such as temporal, spatial, and kinematic proximity measures, depending on the relevant context and applicability of these measures. Therefore, this study endeavored to develop a methodology by adopting safety measures such as post encroachment time (PET) and conflicting speeds of through vehicles for crossing maneuvers and time to collision (TTC) for rear-end collisions at five unsignalized intersections in urban mixed traffic conditions. Critical conflicts are calculated by calculating a speed variable known as the critical speed, which is based on the braking distance. A study found that the motorized two wheeler (MTW) categories involve the highest proportion of critical conflict with right-turning vehicles, followed by cars, autos, and light commercial vehicle (LCVs). Furthermore, crossing conflicts were modeled as a function using the generalized linear regression approach. The findings revealed that the most significant factors were traffic volume and vehicular composition in a conflicting stream. The unsupervised classification technique k-mean clustering was used to determine the defined severity level threshold for rear-end maneuvers. The result observed was that a TTC threshold of less than 1.15 s was identified as high-risk vehicular interaction. Additional investigation indicated that presence of certain moving vehicle categories, including MTWs and cars, led to a higher proportion of critical crossing conflicts. The conceptualized safety framework can be applied to evaluate safety at unsignalized intersections in the mixed traffic scenarios.

Examining factors influencing the severity of motorized two-wheeler crashes in Delhi.

Failure to meet road safety targets has necessitated urgent actions from stakeholders worldwide, especially in developing countries like India. Road safety of motorized two-wheelers (MTWs), one of India's most preferred travel modes for urban commutes, is in danger and witnessing threatening figures of fatalities and injuries. Most of the studies in the domain of MTW safety were conducted in developed countries, with very limited research in countries having a significant proportion of MTWs. The present work investigates police-reported crash data to identify the contributory factors of motorized two-wheeler crash severity. Data from MTW crash-prone areas were selected from Delhi, which is leading in road traffic fatalities among the million-plus urban cities in India. A binary logistic regression model was developed using the data for 2016-2018 period. The model results show that the odds of fatal motorized two-wheeler crashes increase when the following circumstances apply: crash occurs on underpasses; involves bus, truck, heavy motor vehicle (lorry, crane) as the striking vehicle; when hit-and-run type of crash occurs and when older age-group (> = 55) riders are involved. Finally, based on the findings, countermeasures were suggested to facilitate policymakers and traffic enforcement agencies, in improving the road safety situation of MTW users.

Hepatitis B virus (HBV) screening, linkage and retention-in-care in inclusion health populations: Evaluation of an outreach screening programme in London.

OBJECTIVES: We evaluated a hepatitis B virus (HBV) screening programme, delivered by a specialist pan-London multidisciplinary outreach team, to understand population characteristics and care cascade among people who experience extreme social exclusion (Inclusion Health (IH) groups). METHODS: Point-of-care HBV screening was performed in temporary accommodation for people experiencing homelessness (PEH) and people seeking asylum (initial accommodation centres, IACs) via a mobile unit staffed by peers with lived experience, nurses, and doctors. We analysed demographics and HBV characteristics of adults screened between May 2020 and January 2022. We ascertained linkage-to-care (LTC), retention-in-care (RIC) and loss-to-follow-up (LTFU). People LTFU were contacted by peers to re-engage in care. RESULTS: 2473 people were screened: 809 in IACs, 1664 in other temporary accommodation. Overall hepatitis B surface antigen (HBsAg) prevalence was 1.7% (43/2473), highest in IACs (3.5%, 28/809). LTC within 3 months was 56% (24/43) and RIC, 87% (26/30). LTC was higher when referred to a local IH-specialist hepatitis service, compared to other services (77%, 17/22 vs 33%, 7/21; p = 0.006). LTFU was 30% (13/43), reduced to 21% (9/43) after intervention by peers. CONCLUSION: Our findings support outreach screening among IH populations and peer-supported linkage to IH-specialist hepatitis services. We recommend increased HBV testing and HBV-specific IH specialist services.

General cross-coupling reactions with adaptive dynamic homogeneous catalysis.

Cross-coupling reactions are among the most important transformations in modern organic synthesis1-3. Although the range of reported (het)aryl halides and nucleophile coupling partners is very large considering various protocols, the reaction conditions vary considerably between compound classes, necessitating renewed case-by-case optimization of the reaction conditions4. Here we introduce adaptive dynamic homogeneous catalysis (AD-HoC) with nickel under visible-light-driven redox reaction conditions for general C(sp2)-(hetero)atom coupling reactions. The self-adjustive nature of the catalytic system allowed the simple classification of dozens of various classes of nucleophiles in cross-coupling reactions. This is synthetically demonstrated in nine different bond-forming reactions (in this case, C(sp2)-S, Se, N, P, B, O, C(sp3, sp2, sp), Si, Cl) with hundreds of synthetic examples under predictable reaction conditions. The catalytic reaction centre(s) and conditions differ from one another by the added nucleophile, or if required, a commercially available inexpensive amine base.

A systematic review of commercial high concentration antibody drug products approved in the US: formulation composition, dosage form design and primary packaging considerations.

Three critical aspects that define high concentration antibody products (HCAPs) are as follows: 1) formulation composition, 2) dosage form, and 3) primary packaging configuration. HCAPs have become successful in the therapeutic sector due to their unique advantage of allowing subcutaneous self-administration. Technical challenges, such as physical and chemical instability, viscosity, delivery volume limitations, and product immunogenicity, can hinder successful development and commercialization of HCAPs. Such challenges can be overcome by robust formulation and process development strategies, as well as rational selection of excipients and packaging components. We compiled and analyzed data from US Food and Drug Administration-approved and marketed HCAPs that are ≥100 mg/mL to identify trends in formulation composition and quality target product profile. This review presents our findings and discusses novel formulation and processing technologies that enable the development of improved HCAPs at ≥200 mg/mL. The observed trends can be used as a guide for further advancements in the development of HCAPs as more complex antibody-based modalities enter biologics product development.

Model-Based Estimation of Expected Time to Cholera Extinction in Lusaka, Zambia.

The developing world has been facing a significant health issue due to cholera as an endemic communicable disease. Lusaka was Zambia's worst affected province, with 5414 reported cases of cholera during the outbreak from late October 2017 to May 12, 2018. To explore the epidemiological characteristics associated with the outbreak, we fitted weekly reported cholera cases with a compartmental disease model that incorporates two transmission routes, namely environment-to-human and human-to-human. Estimates of the basic reproduction number show that both transmission modes contributed almost equally during the first wave. In contrast, the environment-to-human transmission appears to be mostly dominating factor for the second wave. Our study finds that a massive abundance of environmental vibrio's with a huge reduction in water sanitation efficacy triggered the secondary wave. To estimate the expected time to extinction (ETE) of cholera, we formulate the stochastic version of our model and find that cholera can last up to 6.5-7 years in Lusaka if any further outbreak occurs at a later time. Results indicate that a considerable amount of attention is to be paid to sanitation and vaccination programs in order to reduce the severity of the disease and to eradicate cholera from the community in Lusaka.

The forgotten people: Hepatitis B virus (HBV) infection as a priority for the inclusion health agenda.

Hepatitis B virus (HBV) infection represents a significant global health threat, accounting for 300 million chronic infections and up to 1 million deaths each year. HBV disproportionately affects people who are under-served by health systems due to social exclusion, and can further amplify inequities through its impact on physical and mental health, relationship with stigma and discrimination, and economic costs. The 'inclusion health' agenda focuses on excluded and vulnerable populations, who often experience barriers to accessing healthcare, and are under-represented by research, resources, interventions, advocacy, and policy. In this article, we assimilate evidence to establish HBV on the inclusion health agenda, and consider how this view can inform provision of better approaches to diagnosis, treatment, and prevention. We suggest approaches to redress the unmet need for HBV interventions among excluded populations as an imperative to progress the global goal for the elimination of viral hepatitis as a public health threat.

A mechanistic model for airborne and direct human-to-human transmission of COVID-19: effect of mitigation strategies and immigration of infectious persons.

The COVID-19 pandemic is the most significant global crisis since World War II that affected almost all the countries of our planet. To control the COVID-19 pandemic outbreak, it is necessary to understand how the virus is transmitted to a susceptible individual and eventually spread in the community. The primary transmission pathway of COVID-19 is human-to-human transmission through infectious droplets. However, a recent study by Greenhalgh et al. (Lancet 397:1603-1605, 2021) demonstrates 10 scientific reasons behind the airborne transmission of SARS-COV-2. In the present study, we introduce a novel mathematical model of COVID-19 that considers the transmission of free viruses in the air beside the transmission of direct contact with an infected person. The basic reproduction number of the epidemic model is calculated using the next-generation operator method and observed that it depends on both the transmission rate of direct contact and free virus contact. The local and global stability of disease-free equilibrium (DFE) is well established. Analytically it is found that there is a forward bifurcation between the DFE and an endemic equilibrium using central manifold theory. Next, we used the nonlinear least-squares technique to identify the best-fitted parameter values in the model from the observed COVID-19 mortality data of two major districts of India. Using estimated parameters for Bangalore urban and Chennai, different control scenarios for mitigation of the disease are investigated. Results indicate that the vaccination of susceptible individuals and treatment of hospitalized patients are very crucial to curtailing the disease in the two locations. It is also found that when a vaccine crisis is there, the public health authorities should prefer to vaccinate the susceptible people compared to the recovered persons who are now healthy. Along with face mask use, treatment of hospitalized patients, and vaccination of susceptibles, immigration should be allowed in a supervised manner so that economy of the overall society remains healthy.

Within Host Dynamics of SARS-CoV-2 in Humans: Modeling Immune Responses and Antiviral Treatments.

In December 2019, a newly discovered SARS-CoV-2 virus was emerged from China and propagated worldwide as a pandemic, resulting in about 3-5% mortality. Mathematical models can provide useful scientific insights about transmission patterns and targets for drug development. In this study, we propose a within-host mathematical model of SARS-CoV-2 infection considering innate and adaptive immune responses. We analyze the equilibrium points of the proposed model and obtain an expression of the basic reproduction number. We then numerically show the existence of a transcritical bifurcation. The proposed model is calibrated to real viral load data of two COVID-19 patients. Using the estimated parameters, we perform global sensitivity analysis with respect to the peak of viral load. Finally, we study the efficacy of antiviral drugs and vaccination on the dynamics of SARS-CoV-2 infection. Results suggest that blocking the virus production from infected cells can be an effective target for antiviral drug development. Finally, it is found that vaccination is more effective intervention as compared to the antiviral treatments.

Modeling the effects of prosocial awareness on COVID-19 dynamics: Case studies on Colombia and India.

The ongoing COVID-19 pandemic has affected most of the countries on Earth. It has become a pandemic outbreak with more than 50 million confirmed infections and above 1 million deaths worldwide. In this study, we consider a mathematical model on COVID-19 transmission with the prosocial awareness effect. The proposed model can have four equilibrium states based on different parametric conditions. The local and global stability conditions for awareness-free, disease-free equilibrium are studied. Using Lyapunov function theory and LaSalle invariance principle, the disease-free equilibrium is shown globally asymptotically stable under some parametric constraints. The existence of unique awareness-free, endemic equilibrium and unique endemic equilibrium is presented. We calibrate our proposed model parameters to fit daily cases and deaths from Colombia and India. Sensitivity analysis indicates that the transmission rate and the learning factor related to awareness of susceptibles are very crucial for reduction in disease-related deaths. Finally, we assess the impact of prosocial awareness during the outbreak and compare this strategy with popular control measures. Results indicate that prosocial awareness has competitive potential to flatten the COVID-19 prevalence curve.

Short-term predictions and prevention strategies for COVID-19: A model-based study.

An outbreak of respiratory disease caused by a novel coronavirus is ongoing from December 2019. As of December 14, 2020, it has caused an epidemic outbreak with more than 73 million confirmed infections and above 1.5 million reported deaths worldwide. During this period of an epidemic when human-to-human transmission is established and reported cases of coronavirus disease 2019 (COVID-19) are rising worldwide, investigation of control strategies and forecasting are necessary for health care planning. In this study, we propose and analyze a compartmental epidemic model of COVID-19 to predict and control the outbreak. The basic reproduction number and the control reproduction number are calculated analytically. A detailed stability analysis of the model is performed to observe the dynamics of the system. We calibrated the proposed model to fit daily data from the United Kingdom (UK) where the situation is still alarming. Our findings suggest that independent self-sustaining human-to-human spread ( R 0 > 1 , R c > 1 ) is already present. Short-term predictions show that the decreasing trend of new COVID-19 cases is well captured by the model. Further, we found that effective management of quarantined individuals is more effective than management of isolated individuals to reduce the disease burden. Thus, if limited resources are available, then investing on the quarantined individuals will be more fruitful in terms of reduction of cases.

Zoonotic MERS-CoV transmission: modeling, backward bifurcation and optimal control analysis.

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) can cause mild to severe acute respiratory illness with a high mortality rate. As of January 2020, more than 2500 cases of MERS-CoV resulting in around 860 deaths were reported globally. In the absence of neither effective treatment nor a ready-to-use vaccine, control measures can be derived from mathematical models of disease epidemiology. In this manuscript, we propose and analyze a compartmental model of zoonotic MERS-CoV transmission with two co-circulating strains. The human population is considered with eight compartments while the zoonotic camel population consist of two compartments. The expression of basic reproduction numbers are obtained for both single strain and two strain version of the proposed model. We show that the disease-free equilibrium of the system with single stain is globally asymptotically stable under some parametric conditions. We also demonstrate that both models undergo backward bifurcation phenomenon, which in turn indicates that only keeping R 0 below unity may not ensure eradication. To the best of the authors knowledge, backward bifurcation was not shown in a MERS-CoV transmission model previously. Further, we perform normalized sensitivity analysis of important model parameters with respect to basic reproduction number of the proposed model. Furthermore, we perform optimal control analysis on different combination interventions with four components namely preventive measures such as use of masks, isolation of strain-1 infected people, strain-2 infected people and infected camels. Optimal control analysis suggests that combination of preventive measures and isolation of infected camels will eventually eradicate the disease from the community.

Decline in Hepatitis C Virus (HCV) Incidence in Men Who Have Sex With Men Living With Human Immunodeficiency Virus: Progress to HCV Microelimination in the United Kingdom?

BACKGROUND: Modeling of the London hepatitis C virus (HCV) epidemic in men who have sex with men (MSM) and are living with human immunodeficiency virus (HIV) suggested that early access to direct-acting antiviral (DAA) treatment may reduce incidence. With high rates of linkage to care, microelimination of HCV within MSM living with HIV may be realistic ahead of 2030 World Health Organization targets. We examined trends in HCV incidence in the pre- and post-DAA eras for MSM living with HIV in London and Brighton, United Kingdom. METHODS: A retrospective cohort study was conducted at 5 HIV clinics in London and Brighton between 2013 and 2018. Each site reported all acute HCV episodes during the study period. Treatment timing data were collected. Incidence rates and reinfection proportion were calculated. RESULTS: A total of.378 acute HCV infections were identified, comprising 292 first infections and 86 reinfections. Incidence rates of acute HCV in MSM living with HIV peaked at 14.57/1000 person-years of follow-up (PYFU; 95% confidence interval [CI], 10.95-18.20) in 2015. Rates fell to 4.63/1000 PYFU (95% CI, 2.60 to 6.67) by 2018. Time from diagnosis to starting treatment declined from 29.8 (2013) to 3.7 months (2018). CONCLUSIONS: We observed a 78% reduction in the incidence of first HCV episode and a 68% reduction in overall HCV incidence since the epidemic peak in 2015, which coincides with wider access to DAAs in England. Further interventions to reduce transmission, including earlier access to treatment and for reinfection, are likely needed for microelimination to be achieved in this population.

Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis.

The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting 201 countries and territories around the globe. As of April 4, 2020, it has caused a pandemic outbreak with more than 11,16,643 confirmed infections and more than 59,170 reported deaths worldwide. The main focus of this paper is two-fold: (a) generating short term (real-time) forecasts of the future COVID-19 cases for multiple countries; (b) risk assessment (in terms of case fatality rate) of the novel COVID-19 for some profoundly affected countries by finding various important demographic characteristics of the countries along with some disease characteristics. To solve the first problem, we presented a hybrid approach based on autoregressive integrated moving average model and Wavelet-based forecasting model that can generate short-term (ten days ahead) forecasts of the number of daily confirmed cases for Canada, France, India, South Korea, and the UK. The predictions of the future outbreak for different countries will be useful for the effective allocation of health care resources and will act as an early-warning system for government policymakers. In the second problem, we applied an optimal regression tree algorithm to find essential causal variables that significantly affect the case fatality rates for different countries. This data-driven analysis will necessarily provide deep insights into the study of early risk assessments for 50 immensely affected countries.

Renewable Aromatics from Tree-Borne Oils over Zeolite Catalysts Promoted by Transition Metals.

Despite the ever-growing demand for benzene-toluene-xylene (BTX), the alternative route of production from tree-borne oils is rarely investigated and poorly understood. Here, we have synthesized a Zn-loaded Y-zeolite catalyst for the continuous production of bio-BTX from tree-borne oils (nonedible seed oil), e.g., neem oil. Our approach involves low-temperature selective cracking-dehydrogenation-aromatization of neem oil over metal-supported catalysts to xylene-rich aromatics. The physicochemical properties of the prepared catalyst were characterized using powder XRD, N2 physisorption, TEM, NH3-TPD, XPS, Py-FTIR, solid-NMR, and TG analyses. Mesoporous Y-zeolites with a pore diameter of 7.4 Šshowed better selectivity toward aromatics and were found to be the most effective catalyst for the aromatization process, especially for BTX. The aromatic yield was found to increase with the addition of Zn, and the highest conversion of 90-94% with an ∼75% BTX yield was achieved with the ZnY catalyst. During aromatization, a sizable number of short alkanes and olefins were also obtained on acidic Y-zeolites. The off-gas composition shows the presence of ∼45% C2-C4 olefins with 8.9% H2. The incorporation of Zn species can promote the dehydrogenation activity, and the subsequent aromatization required a suitable pore network. The optimized ZnY catalyst inspires the formation of toluene and xylenes, inhibiting the formation of benzene and gaseous alkanes.