Parametric Study on Dynamic Response of Fiber Reinforced Polymer Composite Bridges

Because of high strength and stiffness to low self-weight ratio and ease of field installation, fiber reinforced polymer (FRP) composite materials are gaining popularity as the materials of choice to replace deteriorated concrete bridge decks. FRP bridge deck systems with lower damping compared to conventional bridge decks can lead to higher amplitudes of vibration causing dynamically active bridge deck leading serviceability problems. The FRP bridge models with different bridge configurations and loading patterns were simulated using finite element method. The dynamic response results under varying FRP deck system parameters were discussed and compared with standard specifications of bridge deck designs under dynamic loads. In addition, the dynamic load allowance equation as a function of natural frequency, span length, and vehicle speed was proposed in this study. The proposed dynamic load allowance related to the first flexural frequency was presented herein. The upper and lower bounds’ limits were established to provide design guidance in selecting suitable dynamic load allowance for FRP bridge systems

The diagnosis of dengue in patients presenting with acute febrile illness using supervised machine learning and impact of seasonality

Background: Symptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined. Methods: We analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of 90%). Conclusion: Supervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account—this is of significant importance given unpredictable effects of human-induced climate change and the impact on health

Understanding the Potential Impact of Different Drug Properties On SARS-CoV-2 Transmission and Disease Burden: A Modelling Analysis

Background The public health impact of the COVID-19 pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear. Methods Using a mathematical model of SARS-CoV-2 transmission, COVID-19 disease and clinical care, we explore the public-health impact of different potential therapeutics, under a range of scenarios varying healthcare capacity, epidemic trajectories; and drug efficacy in the absence of supportive care. Results The impact of drugs like dexamethasone (delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R=1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalisation) could have much greater benefits, particularly in resource-poor settings facing large epidemics. Conclusions Advances in the treatment of COVID-19 to date have been focussed on hospitalised-patients and predicated on an assumption of adequate access to supportive care. Therapeutics delivered earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority

Understanding the potential impact of different drug properties on SARS-CoV-2 transmission and disease burden : a modelling analysis

Q1Q1Background The unprecedented public health impact of the COVID-19 pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear. Methods and Findings develop a mathematical model of SARS-CoV-2 transmission, COVID-19 disease and clinical care to explore the potential public-health impact of a range of different potential therapeutics, under a range of different scenarios varying: i) healthcare capacity, ii) epidemic trajectories; and iii) drug efficacy in the absence of supportive care. In each case, the outcome of interest was the number of COVID-19 deaths averted in scenarios with the therapeutic compared to scenarios without. We find the impact of drugs like dexamethasone (which are delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in highincome countries but only 8% in low-income countries (assuming R=1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalisation) could have much greater benefits, particularly in resource-poor settings facing large epidemics. Conclusions There is a global asymmetry in who is likely to benefit from advances in the treatment of COVID-19 to date, which have been focussed on hospitalised-patients and predicated on an assumption of adequate access to supportive care. Therapeutics that can feasibly be delivered to those earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priorityRevista Internacional - Indexad

Microbiology of traditional fermented soybean curd (Sufu)

Microorganisms in traditional fermenting soybean curd (Sufu) were quantitated. Total microbial populations of bacteria, molds and yeasts were 1.6×101 to 4.0×105, 2.4×101 to 3.9×105 and 4.4×103 to 8.0×105 CFU/g, respectively. Aspergillus, Penicillium, Rhizopus and Bacillus were dominantly found in koji inoculum. Bacillus, Pediococcus and Saccharomyces were mainly detected throughout the fermentation process. The other microorganisms were Staphylococcus, Pichia and Debaryomyces. All isolated microorganisms were halotolerant at salt concentrations between 5 to 20%. Aspergillus, Penicillium and Bacillus could produce potential proteolytic and amylolytic enzymes, implying that these microorganisms may play significant roles in the fermentation of tofu substrate. The nutritional evaluation of fermenting Sufu had protein content between 16.09-21.91%, sugar 4.23- 9.14%, lipid 7.20- 12.76%, salt 10.06-11.26%, humidity 47.55-57.97%, ash 9.24-15.63%, fibre 0.10-0.16%, pH 4.99-5.75 and fermenting temperature at 29-31ºC. Additionally, aflatoxin B1 at the concentration of 10.8- 22.8 ppb could be detected in the fermenting Sufu by ELISA methods whereas the final product of Sufu remained 18.4 ppb. Additionally, the commercial Sufu in the markets had aflatoxin in the range of 1.5-15.2 ppb which is in the control of FDA (U.S.A.) standard that aflatoxin in food and peanut products should be less than 20 ppb

Parametric Study on Dynamic Response of Fiber Reinforced Polymer Composite Bridges

Because of high strength and stiffness to low self-weight ratio and ease of field installation, fiber reinforced polymer (FRP) composite materials are gaining popularity as the materials of choice to replace deteriorated concrete bridge decks. FRP bridge deck systems with lower damping compared to conventional bridge decks can lead to higher amplitudes of vibration causing dynamically active bridge deck leading serviceability problems. The FRP bridge models with different bridge configurations and loading patterns were simulated using finite element method. The dynamic response results under varying FRP deck system parameters were discussed and compared with standard specifications of bridge deck designs under dynamic loads. In addition, the dynamic load allowance equation as a function of natural frequency, span length, and vehicle speed was proposed in this study. The proposed dynamic load allowance related to the first flexural frequency was presented herein. The upper and lower bounds’ limits were established to provide design guidance in selecting suitable dynamic load allowance for FRP bridge systems

Using cluster analysis to reconstruct dengue exposure patterns from cross-sectional serological studies in Singapore

Background Dengue is a mosquito-borne viral disease caused by one of four serotypes (DENV1-4). Infection provides long-term homologous immunity against reinfection with the same serotype. Plaque reduction neutralization test (PRNT) is the gold standard to assess serotype-specific antibody levels. We analysed serotype-specific antibody levels obtained by PRNT in two serological surveys conducted in Singapore in 2009 and 2013 using cluster analysis, a machine learning technique that was used to identify the most common histories of DENV exposure. Methods We explored the use of five distinct clustering methods (i.e. agglomerative hierarchical, divisive hierarchical, K-means, K-medoids and model-based clustering) with varying number (from 4 to 10) of clusters for each method. Weighted rank aggregation, an evaluating technique for a set of internal validity metrics, was adopted to determine the optimal algorithm, comprising the optimal clustering method and the optimal number of clusters. Results The K-means algorithm with six clusters was selected as the algorithm with the highest weighted rank aggregation. The six clusters were characterised by (i) dominant DENV2 PRNT titres; (ii) co-dominant DENV1 and DENV2 titres with average DENV2 titre > average DENV1 titre; (iii) co-dominant DENV1 and DENV2 titres with average DENV1 titre > average DENV2 titre; (iv) low PRNT titres against DENV1-4; (v) intermediate PRNT titres against DENV1-4; and (vi) dominant DENV1-3 titres. Analyses of the relative size and age-stratification of the clusters by year of sample collection and the application of cluster analysis to the 2009 and 2013 datasets considered separately revealed the epidemic circulation of DENV2 and DENV3 between 2009 and 2013. Conclusion Cluster analysis is an unsupervised machine learning technique that can be applied to analyse PRNT antibody titres (without pre-established cut-off thresholds to indicate protection) to explore common patterns of DENV infection and infer the likely history of dengue exposure in a population

Connectivity of rapid-testing diagnostics and surveillance of infectious diseases

The World Health Organization (WHO) developed the ASSURED criteria to describe the ideal characteristics for point-of-care testing in low-resource settings: affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free and deliverable.1 These standards describe. Over the last decade, widespread adoption of point-of-care testing has led to significant changes in clinical decision-making processes. The development of compact molecular diagnostics, such as the GeneXpert® platform, have enabled short turnaround times and allowed profiling of antimicrobial resistance. Although modern assays have increased operational requirements, many devices are robust and can be operated within communities with minimal training. These new generation of rapid tests have bypassed barriers to care and enabled treatment to take place independently from central facilities. Here we describe the importance of connectivity, the automatic capture and sharing of patient healthcare data from testing, in the adoption and roll-out of rapid testing