An evaluation of fish and invertebrate mercury concentrations in the Caribbean Region.

Mercury is a ubiquitous pollutant of global concern but the threat of exposure is not homogenously distributed at local, regional, or global scales. The primary route of human exposure to mercury is through consumption of aquatic foods, which are culturally and economically important in the wider Caribbean Region, especially for Small Island Developing States (SIDS). We compiled more than 1600 samples of 108 unique species of fish and aquatic invertebrates collected between 2005 and 2023 from eleven countries or territories in the wider Caribbean Region. There was wide variability in total mercury concentrations with 55% of samples below the 0.23 µg/g wet weight (ww) guideline from the U.S. FDA/EPA (2022) for 2 or 3 weekly servings and 26% exceeding the 0.46 µg/g ww guideline consistent with adverse effects on human health from continual consumption, particularly for sensitive populations. Significant relationships were found between total mercury concentrations and taxonomic family, sampling country, fish length, and trophic level. The data analyzed here support the need for further sampling with concrete geospatial data to better understand patterns and mechanisms in mercury concentrations and allow for more informed decision making on the consumption of fish and invertebrates from the wider Caribbean Region as well as supporting efforts to evaluate the effectiveness of national, regional, and international mercury policies.

DNA Barcoding Analysis of Trinidad Haemagogus Mosquitoes Reveals Evidence for Putative New Species.

Background: Haemagogus janthinomys is a primary sylvan vector of yellow fever virus and the emerging Mayaro virus. However, despite its medical importance, there is a dearth of data on the molecular taxonomy of this mosquito species. Methods: In this study, DNA barcoding analysis was performed on 64 adult female mosquitoes from Trinidad morphologically identified as Hg. janthinomys. The mitochondrial cytochrome c oxidase I (COI) gene and ribosomal DNA internal transcribed spacer 2 (ITS2) region of the mosquitoes were PCR amplified and sequenced, and molecular phylogenies inferred. Results: The BLASTN analysis showed that only 20% (n = 13/66) of COI sequences had high similarity (>99% identity) to Hg. janthinomys and the remaining sequences had low similarity (<90% identity) to reference GenBank sequences. Phylogenetic analysis of COI sequences revealed the presence of four strongly supported groups, with one distinct clade that did not align with any reference sequences. Corresponding ITS2 sequences for samples in this distinct COI group clustered into three clades. Conclusions: These molecular findings suggest the existence of a putative new Haemagogus mosquito species and underscore the need for further, more in-depth investigations into the taxonomy and classification of the Haemagogus genus.

Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete.

Tire rubber waste is globally accumulated every year. Therefore, a solution to this problem should be found since, if landfilled, it is not biodegradable and causes environmental issues. One of the most effective ways is recycling those wastes or using them as a replacement for normal aggregate in the concrete mixture, which has high impact resistance and toughness; thus, it will be a good choice. In this study, 135 data were collected from previous literature to develop a model for the prediction of rubberized concrete compressive strength; the database comprised different mixture proportions, the maximum size of the rubber (1-40 mm), and the rubber percentage (0-100%) replacing natural fine and coarse aggregates were among the input parameters in addition to cement content (380-500 kg/m3) water content (129-228 kg/m3), fine aggregate content (0-925 kg/m3), coarse aggregate content (0-1303 kg/m3), and curing time of the samples (1-96 Days); then the collected data were used in developing Multi Expression Programming (MEP), Artificial Neural Network (ANN), Multi Adaptive Regression Spline (MARS), and Nonlinear Regression (NLR) Models for predicting compressive strength (CS) of rubberized concrete. The parametric analysis reveals that as the maximum rubber size increases, the reduction in compressive strength becomes more pronounced. Notably, this strength decline is more significant when rubber replaces coarse aggregate than its replacement of fine aggregate. Among the input parameters considered, it is evident that the fine aggregate content exerts the most substantial influence on the compressive strength of rubberized concrete. Its impact on predicting compressive strength surpasses other factors, with the concrete samples' curing time ranking second in importance. According to the assessment tools, the ANN model performed better than other developed models, with high R2 and lower RMSE, MAE, SI, and MAPE. Additionally, ANN and MARS models predicted the CS of different sizes better than MEP and NLR models. Subsequently, we employed the collected data to develop predictive models using Multi Expression Programming (MEP), Artificial Neural Network (ANN), Multi Adaptive Regression Spline (MARS), and Nonlinear Regression (NLR) techniques to forecast the compressive strength (CS) of rubberized concrete. The statistical analysis tools assessed the performance of these developed models through various evaluation criteria, including the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), and Mean Absolute Percentage Error (MAPE). In summary, our study underscores the efficacy of recycling rubber materials in concrete production. It presents a powerful predictive model for assessing the compressive strength of rubberized concrete, with the ANN model standing out as the most accurate and reliable choice for this purpose.

Characterization of a novel RNAi yeast insecticide that silences mosquito 5-HT1 receptor genes.

G protein-coupled receptors (GPCRs), which regulate numerous intracellular signaling cascades that mediate many essential physiological processes, are attractive yet underexploited insecticide targets. RNA interference (RNAi) technology could facilitate the custom design of environmentally safe pesticides that target GPCRs in select target pests yet are not toxic to non-target species. This study investigates the hypothesis that an RNAi yeast insecticide designed to silence mosquito serotonin receptor 1 (5-HTR1) genes can kill mosquitoes without harming non-target arthropods. 5-HTR.426, a Saccharomyces cerevisiae strain that expresses an shRNA targeting a site specifically conserved in mosquito 5-HTR1 genes, was generated. The yeast can be heat-inactivated and delivered to mosquito larvae as ready-to-use tablets or to adult mosquitoes using attractive targeted sugar baits (ATSBs). The results of laboratory and outdoor semi-field trials demonstrated that consumption of 5-HTR.426 yeast results in highly significant mortality rates in Aedes, Anopheles, and Culex mosquito larvae and adults. Yeast consumption resulted in significant 5-HTR1 silencing and severe neural defects in the mosquito brain but was not found to be toxic to non-target arthropods. These results indicate that RNAi insecticide technology can facilitate selective targeting of GPCRs in intended pests without impacting GPCR activity in non-targeted organisms. In future studies, scaled production of yeast expressing the 5-HTR.426 RNAi insecticide could facilitate field trials to further evaluate this promising new mosquito control intervention.

Demonstration of RNAi Yeast Insecticide Activity in Semi-Field Larvicide and Attractive Targeted Sugar Bait Trials Conducted on Aedes and Culex Mosquitoes.

Eco-friendly new mosquito control innovations are critical for the ongoing success of global mosquito control programs. In this study, Sh.463_56.10R, a robust RNA interference (RNAi) yeast insecticide strain that is suitable for scaled fermentation, was evaluated under semi-field conditions. Inactivated and dried Sh.463_56.10R yeast induced significant mortality of field strain Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus larvae in semi-field larvicide trials conducted outdoors in St. Augustine, Trinidad, where 100% of the larvae were dead within 24 h. The yeast was also stably suspended in commercial bait and deployed as an active ingredient in miniature attractive targeted sugar bait (ATSB) station sachets. The yeast ATSB induced high levels of Aedes and Culex mosquito morbidity in semi-field trials conducted in Trinidad, West Indies, as well as in Bangkok, Thailand, in which the consumption of the yeast resulted in adult female mosquito death within 48 h, faster than what was observed in laboratory trials. These findings support the pursuit of large-scale field trials to further evaluate the Sh.463_56.10R insecticide, a member of a promising new class of species-specific RNAi insecticides that could help combat insecticide resistance and support effective mosquito control programs worldwide.

Behaviour and associated determinants of COVID-19 vaccine acceptance and advocacy: a nationwide survey of pharmacy professionals in Qatar.

BACKGROUND: Vaccine hesitancy poses a global challenge and is acknowledged to be a complex, multifactorial phenomenon. Of particular concern is hesitancy among health professionals, as this may also impact their advocacy roles. There is a lack of theory-based investigations of pharmacy professionals. AIM: The study aims to determine the behaviour and associated determinants influencing pharmacy professionals' attitude towards vaccine acceptance and advocacy. METHODS: A cross-sectional survey of 2400 pharmacists and pharmacy technicians at government, semi-government, and private community pharmacies in Qatar. Questionnaire items captured perspectives on COVID vaccine acceptance, advocacy and associated determinants based on the domains and constructs of the Theoretical Domains Framework (TDF). Data were analysed by descriptive and inferential statistics, with TDF items subjected to principal components analysis (PCA). FINDINGS: The response rate was 38.6% (927/2400). Almost all (n = 825, 89.0%) were willing to receive the vaccine, which was higher for males (p < 0.001) and those in polyclinics (p < 0.05). PCA of acceptance items gave five components, with response to 'emotions' being most negative, associated with acceptance (p < 0.001) and more negative in females (p < 0.001). The majority (n = 799, 86.2%) agreed that it was their professional duty to advocate vaccines. PCA for advocacy items gave two components, with the most negative responses for 'professional role and identity', which were more negative for those working in hospitals (p < 0.05). CONCLUSION: Respondents were least positive regarding emotion-related behavioral determinants for acceptance and professional role and identity for advocacy. Behavior change technique interventions that target these issues have the potential to influence the vaccine hesitancy of pharmacy professionals and other individuals.

Soft computing techniques to predict the compressive strength of green self-compacting concrete incorporating recycled plastic aggregates and industrial waste ashes.

Abstract: Rapid urbanization and industrialization with corresponding economic growth have increased concrete production, leading to resource depletion and environmental pollution. The mentioned problems can be resolved by using recycled aggregates and industrial waste ashes as natural aggregate and cement replacement in concrete production. Incorporating different by-product ashes and recycled plastic (RP) aggregates are viable options to produce sustainable self-compacting concrete (SCC). On the other hand, compressive strength is an essential characteristic among other evaluated properties. As a result, establishing trustworthy models to forecast the compressive strength of SCC is critical to saving cost, time, and energy. Furthermore, it provides valuable instruction for planning building projects and determining the best time to remove the formwork. In this study, four alternative models were suggested to predict the compressive strength of SCC mixes produced by RP aggregates: the artificial neural network (ANN), nonlinear model, linear relationship model, and multi-logistic model. To do so, an extensive set of data consisting of 400 mixtures were extracted and analyzed to develop the models, various mixture proportions and curing times were considered as input variables. To test the effectiveness of the suggested models, several statistical evaluations, including coefficient of determination (R 2), scatter index, root mean squared error (RMSE), mean absolute error (MAE), and Objective (OBJ) value were utilized. Compared to other models, the ANN model performed better to forecast the compressive strength of SCC mixes incorporating RP aggregates. The RMSE, MAE, OBJ, and R 2 values for this model were 5.46 MPa, 2.31 MPa, 4.26 MPa, and 0.973, respectively.

Proposing several model techniques including ANN and M5P-tree to predict the compressive strength of geopolymer concretes incorporated with nano-silica.

Geopolymers are innovative cementitious materials that can completely replace traditional Portland cement composites and have a lower carbon footprint than Portland cement. Recent efforts have been made to incorporate various nanomaterials, most notably nano-silica (nS), into geopolymer concrete (GPC) to improve the composite's properties and performance. Compression strength (CS) is one of the essential properties of all types of concrete composites, including geopolymer concrete. As a result, creating a credible model for forecasting concrete CS is critical for saving time, energy, and money, as well as providing guidance for scheduling the construction process and removing formworks. This paper presents a large amount of mixed design data correlated to mechanical strength using empirical correlations and neural networks. Several models, including artificial neural network, M5P-tree, linear regression, nonlinear regression, and multi-logistic regression models, were utilized to create models for forecasting the CS of GPC incorporated with nS. In this case, about 207 tested CS values were collected from literature studies and then analyzed to promote the models. For the first time, eleven effective variables were employed as input model parameters during the modeling process, including the alkaline solution to binder ratio, binder content, fine and coarse aggregate content, NaOH and Na2SiO3 content, Na2SiO3/NaOH ratio, molarity, nS content, curing temperatures, and ages. The developed models were assessed using different statistical tools such as root mean squared error, mean absolute error, scatter index, objective function value, and coefficient of determination. Based on these statistical assessment tools, results revealed that the ANN model estimated the CS of GPC incorporated with nS more accurately than the other models. On the other hand, the alkaline solution to binder ratio, molarity, NaOH content, curing temperature, and ages were those parameters that have significant influences on the CS of GPC incorporated with nS.

Soft computing models to predict the compressive strength of GGBS/FA- geopolymer concrete.

A variety of ashes used as the binder in geopolymer concrete such as fly ash (FA), ground granulated blast furnace slag (GGBS), rice husk ash (RHA), metakaolin (MK), palm oil fuel ash (POFA), and so on, among of them the FA was commonly used to produce geopolymer concrete. However, one of the drawbacks of using FA as a main binder in geopolymer concrete is that it needs heat curing to cure the concrete specimens, which lead to restriction of using geopolymer concrete in site projects; therefore, GGBS was used as a replacement for FA with different percentages to tackle this problem. In this study, Artificial Neural Network (ANN), M5P-Tree (M5P), Linear Regression (LR), and Multi-logistic regression (MLR) models were used to develop the predictive models for predicting the compressive strength of blended ground granulated blast furnace slag and fly ash based-geopolymer concrete (GGBS/FA-GPC). A comprehensive dataset consists of 220 samples collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, eleven effective variable parameters on the compressive strength of the GGBS/FA-GPC, including the Activated alkaline solution to binder ratio (l/b), FA content, SiO2/Al2O3 (Si/Al) of FA, GGBS content, SiO2/CaO (Si/Ca) of GGBS, fine (F) and coarse (C) aggregate content, sodium hydroxide (SH) content, sodium silicate (SS) content, (SS/SH) and molarity (M) were considered as the modeling input parameters. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2) were used to evaluate the efficiency of the developed models. The results indicated that the ANN model better predicted the compressive strength of GGBS/FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the alkaline liquid to binder ratio, fly ash content, molarity, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the GGBS/FA-GPC.

Modeling the compressive strength of eco-friendly self-compacting concrete incorporating ground granulated blast furnace slag using soft computing techniques.

Concern regarding global climate change and its detrimental effects on society demands the building sector, one of the major contributors to global warming. Reducing cement usage is a significant challenge for the concrete industry; achieving this objective can help reduce global carbon dioxide emissions. Replacing the cement in concrete with by-product ashes is a promising approach for reducing the embodied carbon in concrete and improving some of its properties. Among different by-product ashes, ground granulated blast furnace slag (GGBFS) is a viable option to produce sustainable self-compacting concrete (SCC). Compressive strength (CS), on the other hand, is an essential characteristic among other evaluated properties. As a result, establishing trustworthy models to forecast the CS of SCC is critical to saving cost, time, and energy. Furthermore, it provides helpful instruction for planning building projects and determining the best time to remove the formwork. In this study, four alternative models were suggested to predict the CS of SCC mixes produced by GGBFS: the artificial neural network (ANN), nonlinear model (NLR), linear relationship model (LR), and multi-logistic model (MLR). To do so, an extensive set of data consisting of about 200 mixtures were extracted and analyzed to develop the models, and various mixture proportions and curing times were considered input variables. To test the effectiveness of the suggested models, several statistical evaluations including determination coefficient (R2), mean absolute error (MAE), scatter index (SI), root mean squared error (RMSE), and objective (OBJ) value were utilized. In comparison to other models, the ANN model performed better to forecast the CS of SCC mixes incorporating GGBFS. The RMSE, MAE, OBJ, and R2 values for this model were 4.73 MPa, 2.3 MPa, 3.4 MPa, and 0.955, respectively.

Field trials reveal the complexities of deploying and evaluating the impacts of yeast-baited ovitraps on Aedes mosquito densities in Trinidad, West Indies.

The use of lure-and-kill, large-volume ovitraps to control Aedes aegypti and Aedes albopictus populations has shown promise across multiple designs that target gravid females (adulticidal) or larvae post-oviposition (larvicidal). Here we report on a pilot trial to deploy 10 L yeast-baited ovitraps at select sites in Curepe, Trinidad, West Indies during July to December, 2019. Oviposition rates among ovitraps placed in three Treatment sites were compared to a limited number of traps placed in three Control areas (no Aedes management performed), and three Vector areas (subjected to standard Ministry of Health, Insect Vector Control efforts). Our goal was to gain baseline information on efforts to saturate the Treatment sites with ovitraps within 20-25 m of each other and compare oviposition rates at these sites with background oviposition rates in Control and Vector Areas. Although yeast-baited ovitraps were highly attractive to gravid Aedes females, a primary limitation encountered within the Treatment sites was the inability to gain access to residential compounds for trap placement, primarily due to residents being absent during the day. This severely limited our intent to saturate these areas with ovitraps, indicating that future studies must include plans to account for these inaccessible zones during trap placement.

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar.

In recent years, geopolymer has been developed as an alternative to Portland cement (PC) because of the significant carbon dioxide emissions produced by the cement manufacturing industry. A wide range of source binder materials has been used to prepare geopolymers; however, fly ash (FA) is the most used binder material for creating geopolymer concrete due to its low cost, wide availability, and increased potential for geopolymer preparation. In this paper, 247 experimental datasets were obtained from the literature to develop multiscale models to predict fly-ash-based geopolymer mortar compressive strength (CS). In the modeling process, thirteen different input model parameters were considered to estimate the CS of fly-ash-based geopolymer mortar. The collected data contained various mix proportions and different curing ages (1 to 28 days), as well as different curing temperatures. The CS of all types of cementitious composites, including geopolymer mortars, is one of the most important properties; thus, developing a credible model for forecasting CS has become a priority. Therefore, in this study, three different models, namely, linear regression (LR), multinominal logistic regression (MLR), and nonlinear regression (NLR) were developed to predict the CS of geopolymer mortar. The proposed models were then evaluated using different statistical assessments, including the coefficient of determination (R2), root mean squared error (RMSE), scatter index (SI), objective function value (OBJ), and mean absolute error (MAE). It was found that the NLR model performed better than the LR and MLR models. For the NLR model, R2, RMSE, SI, and OBJ were 0.933, 4.294 MPa, 0.138, 4.209, respectively. The SI value of NLR was 44 and 41% lower than the LR and MLR models' SI values, respectively. From the sensitivity analysis result, the most effective parameters for predicting CS of geopolymer mortar were the SiO2 percentage of the FA and the alkaline liquid-to-binder ratio of the mixture.

SGLT2 Inhibitors in Type 2 Diabetes Mellitus and Heart Failure-A Concise Review.

The incidence of both diabetes mellitus type 2 and heart failure is rapidly growing, and the diseases often coexist. Sodium-glucose co-transporter 2 inhibitors (SGLT2i) are a new antidiabetic drug class that mediates epithelial glucose transport at the renal proximal tubules, inhibiting glucose absorption-resulting in glycosuria-and therefore improving glycemic control. Recent trials have proven that SGLT2i also improve cardiovascular and renal outcomes, including reduced cardiovascular mortality and fewer hospitalizations for heart failure. Reduced preload and afterload, improved vascular function, and changes in tissue sodium and calcium handling may also play a role. The expected paradigm shift in treatment strategies was reflected in the most recent 2021 guidelines published by the European Society of Cardiology, recommending dapagliflozin and empagliflozin as first-line treatment for heart failure patients with reduced ejection fraction. Moreover, the recent results of the EMPEROR-Preserved trial regarding empagliflozin give us hope that there is finally an effective treatment for patients with heart failure with preserved ejection fraction. This review aims to assess the efficacy and safety of these new anti-glycemic oral agents in the management of diabetic and heart failure patients.

Relapsing polychondritis: state-of-the-art review with three case presentations.

BACKGROUND: Relapsing polychondritis (RPC) is a complex immune-mediated systemic disease affecting cartilaginous tissue and proteoglycan-rich organs. The most common and earliest clinical features are intermittent inflammation involving the auricular and nasal regions, although all cartilage types can be potentially affected. The life-threatening effects of rpc involve the tracheobronchial tree and cardiac connective components. Rpc is difficult to identify among other autoimmune comorbidities; diagnosis is usually delayed and based on nonspecific clinical symptoms with limited laboratory aid and investigations. Medications can vary, from steroids, immunosuppressants, and biologics, including anti-tnf alpha antagonist drugs. METHOD: Information on updated etiology, clinical symptoms, diagnosis, and treatment of rpc has been obtained via extensive research of electronic literature published between 1976 and 2019 using PubMed and medline databases. English was the language of use. Search inputs included 'relapsing polychondritis,' 'polychondritis,' 'relapsing polychondritis symptoms,' and 'treatment of relapsing polychondritis.' Published articles in English that outlined and reported rpc's clinical manifestations and treatment ultimately met the inclusion criteria. Articles that failed to report the above and reported on other cartilaginous diseases met the exclusion criteria. RESULT: Utilizing an extensive overview of work undertaken in critical areas of RPC research, this review intends to further explore and educate the approach to this disease in all dimensions from pathophysiology, diagnosis, and management. CONCLUSION: RPC is a rare multi-systemic autoimmune disease and possibly fatal. The management remains empiric and is identified based on the severity of the disease per case. The optimal way to advance is to continue sharing data on RPC from reference centers; furthermore, clinical trials in randomized control groups must provide evidence-based treatment and management. Acquiring such information will refine the current knowledge of RPC, which will improve not only treatment but also diagnostic methods, including imaging and biological markers.

Characterization of the virome associated with Haemagogus mosquitoes in Trinidad, West Indies.

Currently, there are increasing concerns about the possibility of a new epidemic due to emerging reports of Mayaro virus (MAYV) fever outbreaks in areas of South and Central America. Haemagogus mosquitoes, the primary sylvan vectors of MAYV are poorly characterized and a better understanding of the mosquito's viral transmission dynamics and interactions with MAYV and other microorganisms would be important in devising effective control strategies. In this study, a metatranscriptomic based approach was utilized to determine the prevalence of RNA viruses in field-caught mosquitoes morphologically identified as Haemagogus janthinomys from twelve (12) forest locations in Trinidad, West Indies. Known insect specific viruses including the Phasi Charoen-like and Humaiata-Tubiacanga virus dominated the virome of the mosquitoes throughout sampling locations while other viruses such as the avian leukosis virus, MAYV and several unclassified viruses had a narrower distribution. Additionally, assembled contigs from the Ecclesville location suggests the presence of a unique uncharacterized picorna-like virus. Mapping of RNA sequencing reads to reference mitochondrial sequences of potential feeding host animals showed hits against avian and rodent sequences, which putatively adds to the growing body of evidence of a potentially wide feeding host-range for the Haemagogus mosquito vector.

Enhanced digestion of complex cosmetic matrices for analysis of As, Hg, Cd, Cr, Ni, and Pb using triton X-100.

A suitable optimized digestion method for lipsticks and powders for the analysis of As, Hg, Cd, Cr, Ni and Pb by Hydride Generation Atomic Absorption Spectrophotometry (HG-AAS), Cold Vapor Atomic Absorption Spectrophotometry (CV-AAS) and Flame Atomic Absorption Spectrophotometry (FAAS) was developed using common acid digestion methods enhanced by the use of Triton X-100. The three acid digestion methods used in this study were Method A (nitric acid and hydrogen peroxide), Method B (nitric acid and hydrochloric acid) and Method C (nitric acid, hydrochloric acid and hydrogen peroxide). Triton X-100 was added to each of these and the effects were studied. The acid digestion method that was determined to be the most suitable and efficient for lipsticks and powders was Method A-1 (nitric acid and hydrogen peroxide with 5% Triton X-100 at 95 °C for 3 h). The range of percentage recoveries obtained were; powders (98.50% to 92. 61%) and lipsticks (100.96% to 99.41%) for As, Cd, Cr, Pb, Hg and Ni. The addition of Triton X-100 significantly improved the efficiency of the method.•Triton X-100 improves the efficiency of acid digestion of fatty hydrophobic samples by dispersing the sample throughout the acid digestant.

Survey of Mechanical Properties of Geopolymer Concrete: A Comprehensive Review and Data Analysis.

Mechanical properties and data analysis for the prediction of different mechanical properties of geopolymer concrete (GPC) were investigated. A relatively large amount of test data from 126 past works was collected, analyzed, and correlation between different mechanical properties and compressive strength was investigated. Equations were proposed for the properties of splitting tensile strength, flexural strength, modulus of elasticity, Poisson's ratio, and strain corresponding to peak compressive strength. The proposed equations were found accurate and can be used to prepare a state-of-art report on GPC. Based on data analysis, it was found that there is a chance to apply some past proposed equations for predicting different mechanical properties. CEB-FIP equations for the prediction of splitting tensile strength and strain corresponding to peak compressive stress were found to be accurate, while ACI 318 equations for splitting tensile and elastic modulus overestimates test data for GPC of low compressive strength.

Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes.

Geopolymer concrete is an inorganic concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to the geopolymer concrete being an eco-efficient and environmentally friendly construction material. A variety of ashes used as the binder in geopolymer concrete such as fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin ash, and Palm oil fuel ash, fly ash was commonly consumed to prepare geopolymer concrete composites. The most important mechanical property for all types of concrete composites, including geopolymer concrete, is the compressive strength. However, in the structural design and construction field, the compressive strength of the concrete at 28 days is essential. Therefore, achieving an authoritative model for predicting the compressive strength of geopolymer concrete is necessary regarding saving time, energy, and cost-effectiveness. It gives guidance regarding scheduling the construction process and removal of formworks. In this study, Linear (LR), Non-Linear (NLR), and Multi-logistic (MLR) regression models were used to develop the predictive models for estimating the compressive strength of fly ash-based geopolymer concrete (FA-GPC). In this regard, a comprehensive dataset consists of 510 samples were collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, twelve effective variable parameters on the compressive strength of the FA-GPC, including SiO2/Al2O3 (Si/Al) of fly ash binder, alkaline liquid to binder ratio (l/b), fly ash (FA) content, fine aggregate (F) content, coarse aggregate (C) content, sodium hydroxide (SH)content, sodium silicate (SS) content, (SS/SH), molarity (M), curing temperature (T), curing duration inside ovens (CD) and specimen ages (A) were considered as the modeling input parameters. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2) were used to evaluate the efficiency of the developed models. The results indicated that the NLR model performed better for predicting the compressive strength of FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the curing temperature, alkaline liquid to binder ratio, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the FA-GPC.

Assessment of Trinidad community stakeholder perspectives on the use of yeast interfering RNA-baited ovitraps for biorational control of Aedes mosquitoes.

Dengue, Zika, chikungunya and yellow fever viruses continue to be a major public health burden. Aedes mosquitoes, the primary vectors responsible for transmitting these viral pathogens, continue to flourish due to local challenges in vector control management. Yeast interfering RNA-baited larval lethal ovitraps are being developed as a novel biorational control tool for Aedes mosquitoes. This intervention circumvents increasing issues with insecticide resistance and poses no known threat to non-target organisms. In an effort to create public awareness of this alternative vector control strategy, gain stakeholder feedback regarding product design and acceptance of the new intervention, and build capacity for its potential integration into existing mosquito control programs, this investigation pursued community stakeholder engagement activities, which were undertaken in Trinidad and Tobago. Three forms of assessment, including paper surveys, community forums, and household interviews, were used with the goal of evaluating local community stakeholders' knowledge of mosquitoes, vector control practices, and perceptions of the new technology. These activities facilitated evaluation of the hypothesis that the ovitraps would be broadly accepted by community stakeholders as a means of biorational control for Aedes mosquitoes. A comparison of the types of stakeholder input communicated through use of the three assessment tools highlighted the utility and merit of using each tool for assessing new global health interventions. Most study participants reported a general willingness to purchase an ovitrap on condition that it would be affordable and safe for human health and the environment. Stakeholders provided valuable input on product design, distribution, and operation. A need for educational campaigns that provide a mechanism for educating stakeholders about vector ecology and management was highlighted. The results of the investigation, which are likely applicable to many other Caribbean nations and other countries with heavy arboviral disease burdens, were supportive of supplementation of existing vector control strategies through the use of the yeast RNAi-based ovitraps.

A comparison of risk factors for osteo- and rheumatoid arthritis using NHANES data.

Osteoarthritis and rheumatoid arthritis are both diseases of joints, but they have very different etiologies. Osteoarthritis is a disease assumed to result from wear and tear over time, whereas rheumatoid arthritis is an autoimmune disease where the body's immune system attacks joint tissues. Using NHANES data (1999-2015), we have compared the influence of age, sex, ethnicity, body mass index and smoking on these two very different forms of arthritis. Incidence of both increases with age and are more frequent in females than males. There is little apparent difference between osteoarthritis and rheumatoid arthritis in women of normal as comparted to overweight, but both are more frequent in obese women, especially those over the age of 60. While osteoarthritis is more frequent in whites, blacks have more rheumatoid arthritis, and Hispanics show an intermediate prevalence. Smoking significantly increased the incidence of both osteoarthritis and rheumatoid arthritis in women, but increased prevalence of only RA in men. There was no effect of smoking on OA prevalence in males. It is remarkable that two diseases of joints, which have quite different causes, should have so many commonalities. The differences that exist appear to be due to a combination of inflammatory markers and access to health care.