Are phenome-wide association studies feasible in a developing country?

Phenome-wide association studies (PheWASs), a powerful approach that examines phenotypes associated with a genetic marker, have been used extensively in highly developed countries. Although there may be a clear need for PheWAS in a developing country such as the Philippines, limitations related to resources and practicality would make conducting them a challenge.

The Isorhamnetin-Containing Fraction of Philippine Honey Produced by the Stingless Bee Tetragonula biroi Is an Antibiotic against Multidrug-Resistant Staphylococcus aureus.

Honey exhibits antibacterial and antioxidant activities that are ascribed to its diverse secondary metabolites. In the Philippines, the antibacterial and antioxidant activities, as well as the bioactive metabolite contents of the honey, have not been thoroughly described. In this report, we investigated the in vitro antibacterial and antioxidant activities of honey from Apis mellifera and Tetragonula biroi, identified the compound responsible for the antibacterial activity, and compared the observed bioactivities and metabolite profiles to that of Manuka honey, which is recognized for its antibacterial and antioxidant properties. The secondary metabolite contents of honey were extracted using a nonionic polymeric resin followed by antibacterial and antioxidant assays, and then spectroscopic analyses of the phenolic and flavonoid contents. Results showed that honey extracts produced by T. biroi exhibits antibiotic activity against Staphylococcal pathogens as well as high antioxidant activity, which are correlated to its high flavonoid and phenolic content as compared to honey produced by A. mellifera. The bioassay-guided fractionation paired with Liquid Chromatography Mass Spectrometry (LCMS) and tandem MS analyses found the presence of the flavonoid isorhamnetin (3-methylquercetin) in T. biroi honey extract, which was demonstrated as one of the compounds with inhibitory activity against multidrug-resistant Staphylococcus aureus ATCC BAA-44. Our findings suggest that Philippine honey produced by T. biroi is a potential nutraceutical that possesses antibiotic and antioxidant activities.

Development of High-Performance Coconut Oil-Based Rigid Polyurethane-Urea Foam: A Novel Sequential Amidation and Prepolymerization Process.

The utilization of coconut diethanolamide (p-CDEA) as a substitute polyol for petroleum-based polyol in fully biobased rigid polyurethane-urea foam (RPUAF) faces challenges due to its short chain and limited cross-linking capability. This leads to compromised cell wall resistance during foam expansion, resulting in significant ruptured cells and adverse effects on mechanical and thermal properties. To address this, a novel sequential amidation-prepolymerization route was employed on coconut oil, yielding a hydroxyl-terminated poly(urethane-urea) prepolymer polyol (COPUAP). Compared to p-CDEA, COPUAP exhibited a decreased hydroxyl value (496.3-473.2 mg KOH/g), an increase in amine value (13.464-24.561 mg KOH/g), and an increase in viscosity (472.4-755.8 mPa·s), indicating enhanced functionality of 34.3 mgKOH/g and chain lengthening. Further, COPUAP was utilized as the sole B-side polyol in the production of RPUAF (PU-COPUAP). The improved functionality of COPUAP and its improved cross-linking capability during foaming have significantly improved cell morphology, resulting in a remarkable 4.7-fold increase in compressive strength (132-628 kPa), a 3.5-fold increase in flexural strength (232-828 kPa), and improved insulation properties with a notable decrease in thermal conductivity (48.02-34.52 mW/m·K) compared to PU-CDEA in the literature. Additionally, PU-COPUAP exhibited a 16.5% increase in the water contact angle (114.93° to 133.87°), attributing to the formation of hydrophobic biuret segments and a tightly packed, highly cross-linked structure inhibiting water penetration. This innovative approach sets a new benchmark for fully biobased rigid foam production, delivering high load-bearing capacity, exceptional insulation, and significantly improved hydrophobicity.

Synergistic effect of phytic acid and eggshell bio-fillers on the dual-phase fire-retardancy of intumescent coatings applied on cellulosic substrates.

Cellulosic substrates, including wood and thatch, have become icons for sustainable architecture and construction, however, they suffer from high flammability because of their inherent cellulosic composition. Current control measures for such hazards include applying intumescent fire-retardant (IFR) coatings that swell and form a char layer upon ignition, protecting the underlying substrate from burning. Typically, conventional IFR coatings are opaque and are made of halogenated compounds that release toxic fumes when ignited, compromising the roofing's aesthetic value and sustainability. In this work, phytic acid, a naturally occurring phosphorus source extracted from rice bran, was used to synthesize phytic acid-based fire-retardants (PFR) via esterification under reflux, along with powdered chicken eggshells (CES) as calcium carbonate (CaCO3) bio-filler. These components were incorporated into melamine formaldehyde resin to produce the transparent IFR coating. It was revealed that the developed IFR coatings achieved the highest fire protection rating based on UL94 flammability standards compared to the control. The coatings also yielded increased LOI values, indicative of self-extinguishing properties. A 17 °C elevation of the IFR coating's melting temperature and a significant ∼172% increase in enthalpy change from the control were observed, indicating enhanced fire-retardancy. The thermal stability of the coatings was improved, denoted by reduced mass losses, and increased residual masses after thermal degradation. As validated by microscopy and spectroscopy, the abundance of phosphorus and carbon groups in the coatings' condensed phase after combustion indicates enhanced char formation. In the gas phase, TG-FTIR showed the evolution of non-flammable CO2, and fire-retardant PO and P-O-C. Mechanical property testing confirmed no reduction in the adhesion strength of the IFR coating. With these results, the developed IFR coating exhibited enhanced fire-retardancy whilst remaining optically transparent, suggestive of a dual-phase IFR protective mechanism involving the release of gaseous combustion diluents and the formation of a thermally insulating char layer.

Valorization of Agricultural Rice Straw as a Sustainable Feedstock for Rigid Polyurethane/Polyisocyanurate Foam Production.

Agricultural rice straw (RS), often discarded as waste in farmlands, represents a vast and underutilized resource. This study explores the valorization of RS as a potential feedstock for rigid polyurethane/polyisocyanurate foam (RPUF) production. The process begins with the liquefaction of RS to create an RS-based polyol, which is then used in a modified foam formulation to prepare RPUFs. The resulting RPUF samples were comprehensively characterized according to their physical, mechanical, and thermal properties. The results demonstrated that up to 50% by weight of petroleum-based polyol can be substituted with RS-based polyol to produce a highly functional RPUF. The obtained foams exhibited a notably low apparent density of 18-24 kg/m3, exceptional thermal conductivity ranging from 0.031-0.041 W/m-K, and a high compressive strength exceeding 250 kPa. This study underlines the potential of the undervalued agricultural RS as a green alternative to petroleum-based feedstocks to produce a high-value RPUF. Additionally, the findings contribute to the sustainable utilization of abundant agricultural waste while offering an eco-friendly option for various applications, including construction materials and insulation.

Facile Synthesis of Band Gap-Tunable Kappa-Carrageenan-Mediated C,S-Doped TiO2 Nanoparticles for Enhanced Dye Degradation.

Semiconducting nanoparticles (SNPs) have garnered significant attention for their role in photocatalysis technology, offering a cost-effective and highly efficient method for breaking down organic dyes. Of particular significance within SNP-based photocatalysis are tunable band gap TiO2 nanoparticles (NPs), which demonstrate remarkable enhancement in photocatalytic efficiency. In the present work, we introduce an approach for the synthesis of TiO2 NPs using kappa-carrageenan (κ-carrageenan), not just as a reducing and stabilizing agent but as a dopant for the resulting TiO2 NPs. During the synthesis of TiO2 NPs in the presence of sulfate-rich carrageenan, the process predominantly leaves residual sulfur and carbon. The presence of residual carbon, in conjunction with sulfur doping, as indicated by fast FTIR spectra, XPS, and EDX, leads to a significant reduction in the band gap of the resulting composite to 2.71 eV. The reduction of composite band gap yields remarkable degradation of methylene blue (99.97%) and methyl orange (97.84%). This work presents an eco-friendly and highly effective solution for the swift removal of environmentally harmful organic dyes.

Quantification of Lactobacillus reuteri ProTectis in MRS Broth Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy and Chemometrics.

There is an increasing global demand for probiotics because of their numerous health benefits. However, a significant percentage of commercially available probiotic products have microbial quantities that are not in accordance with their product labels. In quantifying bacteria, the viable plate count is the standard method but is considered laborious and time-consuming. We demonstrate the use of an attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometrics to quantify a pure culture of Lactobacillus reuteri (L. reuteri) ProTectis grown in deMan, Rogosa, and Sharpe broth. The chemometric partial least-squares model generated was able to accurately quantify viable plate count (VPC) (root-mean-square error of cross-validation (RMSECV) = 0.115 log CFU mL-1, root-mean-square error of prediction (RMSEP) = 0.145 log CFU mL-1, R2 = 0.982). These results provide proof of concept for this quantification technique and can potentially be developed and applied for the quantification of L. reuteri ProTectis in food products.

Producing polyglycerol polyester polyol for thermoplastic polyurethane application: A novel valorization of glycerol, a by-product of biodiesel production.

The production of biodiesel generates glycerol as a by-product that needs valorization. Glycerol, when converted to polyglycerol, is a potential polyol for bio-based thermoplastic polyurethane (TPU) production. In this study, a novel polyglycerol polyester polyol (PPP) was developed from refined glycerol and coconut oil-based polyester polyol. Glycerol was first converted to glycerol acetate and then polymerized with coconut oil-based polyester polyol (CPP) as secondary polyol and phthalic anhydride. The resulting PPP polymerized at 220 °C and OH:COOH molar ratio of 2.5 exhibited an OH number of <100 mg KOH·g sample-1, an acid number of <10 mg KOH·g sample-1, and a molecular weight (MW) of 3697 g mol-1 meeting the polyol requirement properties for TPU (Handlin et al., 2001; Parcheta et al., 2020) [1-2]. Fourier-transform infrared (FTIR) spectroscopic characterization determined that higher reaction temperatures increase the polymerization rate and decrease the OH and acid numbers. Further, higher OH:COOH molar ratios decrease the polymerization rate and acid number, and increase the OH number. Gel permeation chromatography determined the molecular weight of PPP and suggested two distinct molecular structures which differ only in the number of moles of CPP in the structure. A differential scanning calorimetric (DSC) experiment on a sample of PPP-based polyurethane revealed that it was able to melt and remelt after 3 heating cycles which demonstrates its thermoplastic ability. The novel PPP derived from the glycerol by-product of biodiesel industries can potentially replace petroleum-derived polyols for TPU production.

A novel reaction mechanism for the synthesis of coconut oil-derived biopolyol for rigid poly(urethane-urea) hybrid foam application.

Coconut oil (CO) has become one of the most important renewable raw materials for polyol synthesis due to its abundance and low price. However, the saturated chemical structure of CO limits its capability for functionalization. In this study, a novel reaction mechanism via the sequential glycerolysis and amidation of CO triglycerides produced an amine-based polyol (p-CDEA). The synthesized biopolyol has a relatively higher hydroxyl value of 361 mg KOH per g relative to previously reported CO-based polyols with values ranging from 270-333 mg KOH per g. This primary hydroxyl-rich p-CDEA was used directly as a sole B-side polyol component in a polyurethane-forming reaction, without further purification. Results showed that a high-performance poly(urethane-urea) (PUA) hybrid foam was successfully produced. It has a compressive strength of 226 kPa and thermal conductivity of 23.2 mW (m-1 K-1), classified as type 1 for a rigid structural sandwich panel core and type 2 for rigid thermal insulation foam applications according to ASTM standards. Fourier-transform infrared (FTIR) spectroscopy was performed to characterize the chemical features of the polyols and foams. Scanning electron microscopy (SEM) analysis was also performed to evaluate the morphological structures of the synthesized foams. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were conducted to investigate the foam's thermal characteristics. Thus far, this work is the first to report a novel and effective reaction mechanism for the synthesis of a highly functional CO-derived polyol and the first CO-based polyol with no petroleum-based replacement that may serve as raw material for rigid PUA foam production. PUA hybrid foams are potential insulation and structural materials. This study further provided a compelling case for enhanced sustainability of p-CDEA PUA hybrid foam against petroleum-based polyurethane.

Coconut power: a sustainable approach for the removal of Cr6+ ions using a new coconut-based polyurethane foam/activated carbon composite in a fixed-bed column.

To attain efficient removal of hexavalent chromium (Cr6+) from aqueous solutions, a novel polyurethane foam-activated carbon (PUAC) adsorbent composite was developed. The composite material was synthesized by the binding of coconut shell-based activated carbon (AC) onto a coconut oil-based polyurethane matrix. To thoroughly characterize the physicochemical properties of the newly developed material, various analytical techniques including FTIR spectroscopy, SEM, XRD, BET, and TGA analyses were conducted. The removal efficiency of the PUAC composite in removing Cr6+ ions from aqueous solutions was evaluated through column experiments with the highest adsorption capacity of 28.41 mg g-1 while taking into account variables such as bed height, flow rate, initial Cr6+ ion concentration, and pH. Experimental data were fitted using Thomas, Yoon-Nelson, and Adams-Bohart models to predict the column profiles and the results demonstrate high breakthrough and exhaustion time dependence on these variables. Among the obtained R2 values of the models, a better fit was observed using the Thomas and Yoon-Nelson models, indicating their ability to effectively predict the adsorption of Cr6+ ions in a fixed bed column. Significantly, the exhausted adsorbent can be conveniently regenerated without any noteworthy loss of adsorption capability. Based on these findings, it can be concluded that this new PUAC composite material holds significant promise as a potent sorbent for wastewater treatment backed by its excellent performance, cost-effectiveness, biodegradability, and outstanding reusability.

Production of Bio-Based Polyol from Coconut Fatty Acid Distillate (CFAD) and Crude Glycerol for Rigid Polyurethane Foam Applications.

This study propounds a sustainable alternative to petroleum-based polyurethane (PU) foams, aiming to curtail this nonrenewable resource's continued and uncontrolled use. Coconut fatty acid distillate (CFAD) and crude glycerol (CG), both wastes generated from vegetable oil processes, were utilized for bio-based polyol production for rigid PU foam application. The raw materials were subjected to catalyzed glycerolysis with alkaline-alcohol neutralization and bleaching. The resulting polyol possessed properties suitable for rigid foam application, with an average OH number of 215 mg KOH/g, an acid number of 7.2983 mg KOH/g, and a Gardner color value of 18. The polyol was used to prepare rigid PU foam, and its properties were determined using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis/derivative thermogravimetric (TGA/DTA), and universal testing machine (UTM). Additionally, the cell foam morphology was investigated by scanning electron microscope (SEM), in which most of its structure revealed an open-celled network and quantified at 92.71% open-cell content using pycnometric testing. The PU foam thermal and mechanical analyses results showed an average compressive strength of 210.43 kPa, a thermal conductivity of 32.10 mW·m-1K-1, and a density of 44.65 kg·m-3. These properties showed its applicability as a type I structural sandwich panel core material, thus demonstrating the potential use of CFAD and CG in commercial polyol and PU foam production.

Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory.

Facile, robust, and accurate analyses of honey adulterants are required in the honey industry to assess its purity for commercialization purposes. A stacked regression ensemble approach using Fourier transform infrared spectroscopic method was developed for the quantitative determination of corn, cane, beet, and rice syrup adulterants in honey. A training set (n=81) was used to predict the percent adulterant composition of the aforementioned constituents in an independent test set (n=32). A comprehensive comparison of the performance of various machine learning techniques including support vector regression using linear function, least absolute shrinkage and selection operator, ride regression, elastic net, partial least squares, random forests, recursive partitioning and regression trees, gradient boosting, and gaussian process regression was assessed. The predictive performance of the aforementioned machine learning approaches was then compared with stacked regression, an ensemble learning technique which collates the performance of the various abovementioned techniques. Results show that stacked regression did not primarily outperform other techniques across all four syrup adulterant constituents in the testing set data. Further, elastic net generalized linear model generated the optimum results (Rootmeansquareerrorofprediction(RMSEP)average=0.0107,Raverage2=0.809) across all four honey adulterant constituents. Elastic net coupled with Fourier transform infrared spectroscopy may offer a novel, direct, and accurate method of simultaneously quantifying corn, cane, beet, and rice syrup adulterants in honey.

Biomarker Discovery for Meta-Classification of Melanoma Metastatic Progression Using Transfer Learning.

Melanoma is considered to be the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in its prognosis. Herein, we developed a transfer learning-based biomarker discovery model that could aid in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results revealed that the genes found were consistent with those found using other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set. Further novel biomarkers were also found. Our ensemble model achieved an AUC of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB) in melanoma. The results show the utility of a transfer learning approach for biomarker discovery in melanoma.

Development of a Direct Spectrophotometric and Chemometric Method for Determining Food Dye Concentrations.

An ultraviolet visible (UV-Vis) spectrophotometric and partial least squares (PLS) chemometric method was developed for the simultaneous determination of erythrosine B (red), Brilliant Blue, and tartrazine (yellow) dyes. A training set (n = 64) was generated using a full factorial design and its accuracy was tested in a test set (n = 13) using a Box-Behnken design. The test set garnered a root mean square error (RMSE) of 1.79 × 10-7 for blue, 4.59 × 10-7 for red, and 1.13 × 10-6 for yellow dyes. The relatively small RMSE suggests only a small difference between predicted versus measured concentrations, demonstrating the accuracy of our model. The relative error of prediction (REP) for the test set were 11.73%, 19.52%, 19.38%, for blue, red, and yellow dyes, respectively. A comparable overlay between the actual candy samples and their replicated synthetic spectra were also obtained indicating the model as a potentially accurate method for determining concentrations of dyes in food samples.

Current Developments in Machine Learning Techniques in Biological Data Mining.

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

Castor Oil: Properties, Uses, and Optimization of Processing Parameters in Commercial Production.

Castor oil, produced from castor beans, has long been considered to be of important commercial value primarily for the manufacturing of soaps, lubricants, and coatings, among others. Global castor oil production is concentrated primarily in a small geographic region of Gujarat in Western India. This region is favorable due to its labor-intensive cultivation method and subtropical climate conditions. Entrepreneurs and castor processors in the United States and South America also cultivate castor beans but are faced with the challenge of achieving high castor oil production efficiency, as well as obtaining the desired oil quality. In this manuscript, we provide a detailed analysis of novel processing methods involved in castor oil production. We discuss novel processing methods by explaining specific processing parameters involved in castor oil production.

Development and Application of a Genetic Algorithm for Variable Optimization and Predictive Modeling of Five-Year Mortality Using Questionnaire Data.

The problem of selecting important variables for predictive modeling of a specific outcome of interest using questionnaire data has rarely been addressed in clinical settings. In this study, we implemented a genetic algorithm (GA) technique to select optimal variables from questionnaire data for predicting a five-year mortality. We examined 123 questions (variables) answered by 5,444 individuals in the National Health and Nutrition Examination Survey. The GA iterations selected the top 24 variables, including questions related to stroke, emphysema, and general health problems requiring the use of special equipment, for use in predictive modeling by various parametric and nonparametric machine learning techniques. Using these top 24 variables, gradient boosting yielded the nominally highest performance (area under curve [AUC] = 0.7654), although there were other techniques with lower but not significantly different AUC. This study shows how GA in conjunction with various machine learning techniques could be used to examine questionnaire data to predict a binary outcome.

Development and Validation of a Clinical Risk-Assessment Tool Predictive of All-Cause Mortality.

In clinical settings, the diagnosis of medical conditions is often aided by measurement of various serum biomarkers through the use of laboratory tests. These biomarkers provide information about different aspects of a patient's health and overall function of multiple organ systems. We have developed a statistical procedure that condenses the information from a variety of health biomarkers into a composite index, which could be used as a risk score for predicting all-cause mortality. It could also be viewed as a holistic measure of overall physiological health status. This health status metric is computed as a function of standardized values of each biomarker measurement, weighted according to their empirically determined relative strength of association with mortality. The underlying risk model was developed using the biomonitoring and mortality data of a large sample of US residents obtained from the National Health and Nutrition Examination Survey (NHANES) and the National Death Index (NDI). Biomarker concentration levels were standardized using spline-based Cox regression models, and optimization algorithms were used to estimate the weights. The predictive accuracy of the tool was optimized by bootstrap aggregation. We also demonstrate how stacked generalization, a machine learning technique, can be used for further enhancement of the prediction power. The index was shown to be highly predictive of all-cause mortality and long-term outcomes for specific health conditions. It also exhibited a robust association with concurrent chronic conditions, recent hospital utilization, and current health status as assessed by self-rated health.