A NiO-nanostructure-based electrochemical sensor functionalized with supramolecular structures for the ultra-sensitive detection of the endocrine disruptor bisphenol S in an aquatic environment.

Herein, NiO nanoparticles (NPs) functionalized with a para-hexanitrocalix[6]arene derivative (p-HNC6/NiO) were synthesized by using a facile method and applied as a selective electrochemical sensor for the determination of bisphenol S (BPS) in real samples. Moreover, the functional interactions, phase purities, surface morphologies and elemental compositions of the synthesized p-HNC6/NiO NPs were investigated via advanced analytical tools, such as Fourier-transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and energy dispersive X-ray spectroscopy (EDX). Additionally, the synthesized p-HNC6/NiO NPs were cast on the surface of a bare glassy carbon electrode (GCE) via a drop casting method, which resulted in uniform deposition of p-HNC6/NiO/GCE over the surface of the GCE. Additionally, the developed p-HNC6/NiO/GCE sensor demonstrated an outstanding electrochemical response to BPS under optimized conditions, including a supporting electrolyte, a Briton-Robinson buffer electrolyte at pH 4, a scan rate of 110 mV s-1 and a potential window of between -0.2 and 1.0 V. The wide linear dynamic range was optimized to 0.8-70 µM to obtain a brilliant linear calibration curve for BPS. The limit of detection (LOD) and limit of quantification (LOQ) of the developed sensor were estimated to be 0.0059 and 0.019 µM, respectively, which are lower than those of reported sensors for BPS. The feasibility of the developed method was successfully assessed by analyzing the content of BPS in waste water samples, and good recoveries were achieved.

Synthesis of PVP-capped trimetallic nanoparticles and their efficient catalytic degradation of organic dyes.

The study proposes a simple and efficient way to synthesize a heterogeneous catalyst that can be used for the degradation of organic dyes. A simple and fast chemical process was employed to synthesize Au: Ni: Co tri-metal nanohybrid structures, which were used as a catalyst to eliminate toxic organic dye contamination from wastewater in textile industries. The catalyst's performance was tested by degrading individual dyes as well as mixtures of dyes such as methylene blue (MB), methyl orange (MO), methyl red (MR), and Rose Bengal (RB) at various time intervals. The experimental results show the catalytic high degradation efficiency of different dyes achieving 72-90% rates in 29 s. Moreover, the material displayed excellent recycling stability, maintaining its degradation efficiency over four consecutive runs without any degradation in performance. Overall, the findings of the study suggest that these materials possess efficient catalytic properties, opening avenues toward their use in clean energy alternatives, environmental remediation, and other biological applications.

Fabrication of para-dimethylamine calix[4]arene functionalized self-assembled graphene oxide composite material for effective removal of 2, 4, 6-tri-Cholorphenol from aqueous environment.

Water pollution caused by the release of organic pollutants is a major environmental concern worldwide. These pollutants can have harmful effects on aquatic ecosystems and the organisms living within them, as well as on human health when contaminated water is consumed. It is essential to implement proper treatment and management strategies to prevent and mitigate water pollution. Moreover, the major untreated industrial effluents are synthetic organic compounds especially 2,4,6-trichlorophenol (TCP) which cause several environmental issues and heath related problems in humans. To cope with this problem, an excellent 2D porous material based on p-DMAC4/GO composite has been synthesized as adsorbent material for the effective removal of 2,4,6-trichlorophenol pollutant from wastewater. In this regard, the advanced analytical tools such as Fourier-Transform infrared (FT-IR), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray spectroscopy (EDS) were used for its characterization. The results justified the chemical composition, excellent crystalline nature, surface morphology and elemental composition of the synthesized composite material. The synthesized adsorbent material showed 95% adsorption of TCP from wastewater system at optimal conditions i.e., pH (6), adsorbent dosage (30 mg) and shaking time (60 min). The mathematical models such as isotherms, thermodynamics and kinetics studies validate the nature of adsorption process of TCP pollutant. The adsorption data found to be best fitted with Langmuir isotherms (R2 = 0.99); whereas kinetic study suggested the pseudo-second-order nature of reaction with R2 = 0.99. The thermodynamics study confirmed the spontaneous and endothermic nature of the TCP pollutant onto the surface of p-DMAC4/GO material. Moreover, the results of current work were also compared with existing reported adsorbents and data suggested the higher efficiency, feasibility, and reusability of p-DMAC4/GO material to remove the TCP pollutant from the wastewater system.

Review on carbon-based adsorbents from organic feedstocks for removal of organic contaminants from oil and gas industry process water: Production, adsorption performance and research gaps.

Large amounts of process water with considerable concentrations of recalcitrant organic contaminants, such as polycyclic aromatic hydrocarbon (PAHs), phenolic compounds (PCs), and benzene, toluene, ethylbenzene, and xylene (BTEX), are generated by several segments of oil and gas industries. These segments include refineries, hydraulic fracturing (HF), and produced waters from the extraction of shale gas (SGPW), coalbed methane (CBMPW) and oil sands (OSPW). In fact, the concentration of PCs and PAHs in process water from refinery can reach 855 and 742 mg L-1, respectively. SGPW can contain BTEX at concentrations as high as 778 mg L-1. Adsorption can effectively target those organic compounds for the remediation of the process water by applying carbon-based adsorbents generated from organic feedstocks. Such organic feedstocks usually come from organic waste materials that would otherwise be conventionally disposed of. The objective of this review paper is to cover the scientific progress in the studies of carbon-based adsorbents from organic feedstocks that were successfully applied for the removal of organic contaminants PAHs, PCs, and BTEX. The contributions of this review paper include the important aspects of (i) production and characterization of carbon-based adsorbents to enhance the efficiency of organic contaminant adsorption, (ii) adsorption properties and mechanisms associated with the engineered adsorbent and expected for certain pollutants, and (iii) research gaps in the field, which could be a guidance for future studies. In terms of production and characterization of materials, standalone pyrolysis or hybrid procedures (pyrolysis associated with chemical activation methods) are the most applied techniques, yielding high surface area and other surface properties that are crucial to the adsorption of organic contaminants. The adsorption of organic compounds on carbonaceous materials performed well at wide range of pH and temperatures and this is desirable considering the pH of process waters. The mechanisms are frequently pore filling, hydrogen bonding, π-π, hydrophobic and electrostatic interactions, and same precursor material can present more than one adsorption mechanism, which can be beneficial to target more than one organic contaminant. Research gaps include the evaluation of engineered adsorbents in terms of competitive adsorption, application of adsorbents in oil and gas industry process water, adsorbent regeneration and reuse studies, and pilot or full-scale applications.

Predicting and explaining the impact of genetic disruptions and interactions on organismal viability.

MOTIVATION: Existing computational models can predict single- and double-mutant fitness but they do have limitations. First, they are often tested via evaluation metrics that are inappropriate for imbalanced datasets. Second, all of them only predict a binary outcome (viable or not, and negatively interacting or not). Third, most are uninterpretable black box machine learning models. RESULTS: Budding yeast datasets were used to develop high-performance Multinomial Regression (MN) models capable of predicting the impact of single, double and triple genetic disruptions on viability. These models are interpretable and give realistic non-binary predictions and can predict negative genetic interactions (GIs) in triple-gene knockouts. They are based on a limited set of gene features and their predictions are influenced by the probability of target gene participating in molecular complexes or pathways. Furthermore, the MN models have utility in other organisms such as fission yeast, fruit flies and humans, with the single gene fitness MN model being able to distinguish essential genes necessary for cell-autonomous viability from those required for multicellular survival. Finally, our models exceed the performance of previous models, without sacrificing interpretability. AVAILABILITY AND IMPLEMENTATION: All code and processed datasets used to generate results and figures in this manuscript are available at our Github repository at https://github.com/KISRDevelopment/cell_viability_paper. The repository also contains a link to the GI prediction website that lets users search for GIs using the MN models. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Imperative Need of Metal Salt for the Treatment of Industrial Wastewater via the Synergic Coagulation-Flocculation Method.

Tile industry wastewater is known to contain a high concentration of TSS and turbidity resulting from various raw materials. In the present study, the effectiveness of the coagulation process on turbidity and TSS removal from Kuwait ceramic tile industry wastewater was investigated using ferric chloride as a coagulant. The experiments were conducted using jar tests to determine the optimum operating conditions of coagulant dosages, pH, and settling time. It was found that the coagulant dosage and medium pH greatly affect the efficiency of the coagulation process. A gradual increase in coagulant dosage from 10 to 50 mg/L increased the efficiency of turbidity removal from 95.6% to 99.5%. The efficiency of the coagulation process was also found to be dependent on pH values, where higher pH improved the efficiency of turbidity removal. It was found that a medium pH of 10, 1 h settling time, and 50 mg/L of coagulant dosage are the optimum process conditions to achieve almost complete removal of turbidity (99.5%) and TSS (99.8%). This study concluded that coagulation might be useful as a primary wastewater treatment process for tile industry wastewater.

Potential Antiviral Action of Alkaloids.

Viral infections and outbreaks have become a major concern and are one of the main causes of morbidity and mortality worldwide. The development of successful antiviral therapeutics and vaccines remains a daunting challenge. The discovery of novel antiviral agents is a public health emergency, and extraordinary efforts are underway globally to identify safe and effective treatments for different viral diseases. Alkaloids are natural phytochemicals known for their biological activities, many of which have been intensively studied for their broad-spectrum of antiviral activities against different DNA and RNA viruses. The purpose of this review was to summarize the evidence supporting the efficacy of the antiviral activity of plant alkaloids at half-maximum effective concentration (EC50) or half-maximum inhibitory concentration (IC50) below 10 µM and describe the molecular sites most often targeted by natural alkaloids acting against different virus families. This review highlights that considering the devastating effects of virus pandemics on humans, plants, and animals, the development of high efficiency and low-toxicity antiviral drugs targeting these viruses need to be developed. Furthermore, it summarizes the current research status of alkaloids as the source of antiviral drug development, their structural characteristics, and antiviral targets. Overall, the influence of alkaloids at the molecular level suggests a high degree of specificity which means they could serve as potent and safe antiviral agents waiting for evaluation and exploitation.

Revegetation of native desert plants enhances food security and water sustainability in arid regions: Integrated modeling assessment.

Food security and water sustainability in arid and semiarid regions are threatened by rapid population growth, declining natural resources, and global climate change. Countries in the arid regions compensate meat import by raising domestic livestock with cultivated green fodder, which diminishes lands for other crops and depletes precious water resources. This study presents for the first time an in-depth integrated food water ecosystem (FWEco) nexus modeling on the feasibility of restoring 10% of Kuwait's desert as grazing rangeland to alleviate water consumption from fodder production. Our results showed that revegetating 10% of the country's land with native species could support up to 23% of domestic livestock through natural grazing at optimal coverage (70%) and high productivity, and decrease water consumption by up to 90%. However, depending solely on natural rainfall is unlikely to achieve the optimal coverage. Strategic supplemental irrigation in the fall season (e.g., October and November) is required to maximize vegetation coverage and enhance food security and water sustainability. Significantly, strategic irrigation results in much lower net water consumption because irrigating native species requires much less water than green fodder cultivation. Therefore, revegetating desert lands with native species to restore their natural grazing service can be a sustainable approach to simultaneously improve food security and water sustainability in arid landscapes.

The Use of Very-High-Resolution Aerial Imagery to Estimate the Structure and Distribution of the Rhanterium epapposum Community for Long-Term Monitoring in Desert Ecosystems.

The rapid assessment and monitoring of native desert plants are essential in restoration and revegetation projects to track the changes in vegetation patterns in terms of vegetation coverage and structure. This work investigated advanced vegetation monitoring methods utilizing UAVs and remote sensing techniques at the Al Abdali protected site in Kuwait. The study examined the effectiveness of using UAV techniques to assess the structure of desert plants. We specifically examined the use of very-high-resolution aerial imagery to estimate the vegetation structure of Rhanterium epapposum (perennial desert shrub), assess the vegetation cover density changes in desert plants after rainfall events, and investigate the relationship between the distribution of perennial shrub structure and vegetation cover density of annual plants. The images were classified using supervised classification techniques (the SVM method) to assess the changes in desert plants after extreme rainfall events. A digital terrain model (DTM) and a digital surface model (DSM) were also generated to estimate the maximum shrub heights. The classified imagery results show that a significant increase in vegetation coverage occurred in the annual plants after rainfall events. The results also show a reasonable correlation between the shrub heights estimated using UAVs and the ground-truth measurements (R2 = 0.66, p < 0.01). The shrub heights were higher in the high-cover-density plots, with coverage >30% and an average height of 77 cm. However, in the medium-cover-density (MD) plots, the coverage was <30%, and the average height was 52 cm. Our study suggests that utilizing UAVs can provide several advantages to critically support future ecological studies and revegetation and restoration programs in desert ecosystems.

An Overview of the World Current and Future Assessment of Novel COVID-19 Trajectory, Impact, and Potential Preventive Strategies at Healthcare Settings.

This study is an overview of the current and future trajectory, as well as the impact of the novel Coronavirus (COVID-19) in the world and selected countries including the state of Kuwait. The selected countries were divided into two groups: Group A (China, Switzerland, and Ireland) and Group B (USA, Brazil, and India) based on their outbreak containment of this virus. Then, the actual data for each country were fitted to a regression model utilizing the excel solver software to assess the current and future trajectory of novel COVID-19 and its impact. In addition, the data were fitted using the Susceptible-Infected-Recovered (SIR) Model. The Group A trajectory showed an "S" shape trend that suited a logistic function with r2 > 0.97, which is an indication of the outbreak control. The SIR models for the countries in this group showed that they passed the expected 99% end of pandemic dates. Group B, however, exhibited a continuous increase of the total COVID-19 new cases, that best suited an exponential growth model with r2 > 0.97, which meant that the outbreak is still uncontrolled. The SIR models for the countries in this group showed that they are still relatively far away from reaching the expected 97% end of pandemic dates. The maximum death percentage varied from 3.3% (India) to 7.2% with USA recording the highest death percentage, which is virtually equal to the maximum death percentage of the world (7.3%). The power of the exponential model determines the severity of the country's trajectory that ranged from 11 to 19 with the USA and Brazil having the highest values. The maximum impact of this COVID-19 pandemic occurred during the uncontrolled stage (2), which mainly depended on the deceptive stage (1). Further, some novel potential containment strategies are discussed. Results from both models showed that the Group A countries contained the outbreak, whereas the Group B countries still have not reached this stage yet. Early measures and containment strategies are imperative in suppressing the spread of COVID-19.

Adequacy of Logistic models for describing the dynamics of COVID-19 pandemic.

Logistic models have been widely used for modelling the ongoing COVID-19 pandemic. This study used the data for Kuwait to assess the adequacy of the two most commonly used logistic models (Verhulst and Richards models) for describing the dynamics COVID-19. Specifically, the study assessed the predictive performance of these two models and the practical identifiability of their parameters. Two model calibration approaches were adopted. In the first approach, all the data was used to fit the models as per the heuristic model fitting method. In the second approach, only the first half of the data was used for calibrating the models, while the other half was left for validating the models. Analysis of the obtained calibration and validation results have indicated that parameters of the two models cannot be identified with high certainty from COVID-19 data. Further, the models shown to have structural problems as they could not predict reasonably the validation data. Therefore, they should not be used for long-term predictions of COVID-19. Suggestion have been made for improving the performances of the models.

Techno-economic analysis of multi-stage ion concentration polarization with recirculation for treatment of oil produced water.

The concept of recirculation of diluate/concentrate stream is implemented in multi-stage ion concentration polarization (ICP) desalination to deal with the issue of uncontrolled concentrate streams and deteriorated overall recovery rate to treat highly concentrated oil produce water from refineries. An improved empirical optimization model was established to calculate total energy consumption for operating cost and required membrane area for capital cost for a given set of operating parameters, feed salinity, salt removal ratio, and flow velocity. Using the empirical optimization model, a techno-economic analysis is performed to evaluate the feasibility of two-stage ICP system with recirculation loops. Brine of 160 g/kg is set as the system feed stream, whereas other operating conditions such as dilaute and concentrate streams are being controlled/fixed with 20 g/kg and ~250 g/kg respectively. Also, the system can be flexibly controlled to produce a specific concentration of product water and a recovery ratio with a corresponding water cost. With careful choices of recirculation rates, one can significantly increase the recovery ratio of two-stage ICP brine treatment process (from 25% to 39%) with only minor increase in overall cost (from $16.4-25.9/m3 to $20.6-22.54/m3), which is favourable for brine waste treatment application.

Response Surface Methodology Optimization of an Acidic Protease Produced by Penicillium bilaiae Isolate TDPEF30, a Newly Recovered Endophytic Fungus from Healthy Roots of Date Palm Trees (Phoenix dactylifera L.).

To explore proteolytic activity of endophytic fungi inhabiting date palm roots, a Penicillium bilaiae isolate, displaying the highest level of protease production, has been recovered. Response surface methodology (RSM) was applied to optimize culture conditions for protease production by the fungus. Plackett-Burman design allowed for screening of variables effective in protease production. Results indicated that temperature, initial pH and glucose concentration dramatically affect protease yield. These factors were further optimized using a Box-Behnken design and RSM. A combination of initial pH (6.26), temperature (24.5 °C), glucose (13.75 g/L), NaNO3 (1.5 g/L), MgSO4 (0.2 g/L), KH2PO4 (0.5 g/L) and KCl (0.5 g/L) were optimum for maximum production of protease. A 1086-fold enhancement of protease production was gained after optimization. Biochemical properties of fungal protease including the effect of pH and temperature on the activity and the stability of proteolytic enzyme were determined. Moreover, the influence of carbon and nitrogen sources, metal ions, detergents as well as enzyme inhibitors was investigated. Our results highlighted that protease of Penicillium bilaiae isolate TDPEF30 could be considered as a promising candidate for industrial applications.

Techno-economic analysis of ion concentration polarization desalination for high salinity desalination applications.

A techno-economic analysis is used to evaluate the economic feasibility of ion concentration polarization (ICP) desalination for seawater desalination and brine management. An empirical optimization model based on a limited set of experimental data, which was obtained from a lab-scale ICP desalination prototype, was established to calculate the required energy and membrane area for a given set of operating parameters. By calculating operating and capital expenses in various feed and product cases, the optimal levelized cost of water is determined over a range of feed salinities, mostly above seawater salinity (35 g/kg). Through these analyses, we study the economic feasibility of three applications: 1) partial desalination of brine discharge by ICP (feed varied from 35 to 75 g/kg) to common seawater RO feed level (35 g/kg) in a hybrid ICP-RO system; 2) the concentration of seawater desalination brine for salt production, and 3) partial desalination of oilfield wastewater. The economic feasibility of ICP desalination processes has been evaluated and the rough cost of treatment has been generated for several relevant applications. The approach taken in this work could be employed for other new and existing desalination processes, where a priori process modeling and optimization is scientifically and/or numerically challenging.

Identification and characterization of mushroom body neurons that regulate fat storage in Drosophila.

BACKGROUND: In an earlier study, we identified two neuronal populations, c673a and Fru-GAL4, that regulate fat storage in fruit flies. Both populations partially overlap with a structure in the insect brain known as the mushroom body (MB), which plays a critical role in memory formation. This overlap prompted us to examine whether the MB is also involved in fat storage homeostasis. METHODS: Using a variety of transgenic agents, we selectively manipulated the neural activity of different portions of the MB and associated neurons to decipher their roles in fat storage regulation. RESULTS: Our data show that silencing of MB neurons that project into the α'ß' lobes decreases de novo fatty acid synthesis and causes leanness, while sustained hyperactivation of the same neurons causes overfeeding and produces obesity. The α'ß' neurons oppose and dominate the fat regulating functions of the c673a and Fru-GAL4 neurons. We also show that MB neurons that project into the γ lobe also regulate fat storage, probably because they are a subset of the Fru neurons. We were able to identify input and output neurons whose activity affects fat storage, feeding, and metabolism. The activity of cholinergic output neurons that innervating the ß'2 compartment (MBON-ß'2mp and MBON-γ5ß'2a) regulates food consumption, while glutamatergic output neurons innervating α' compartments (MBON-γ2α'1 and MBON-α'2) control fat metabolism. CONCLUSIONS: We identified a new fat storage regulating center, the α'ß' lobes of the MB. We also delineated the neuronal circuits involved in the actions of the α'ß' lobes, and showed that food intake and fat metabolism are controlled by separate sets of postsynaptic neurons that are segregated into different output pathways.

Corrigendum: Purification of High Salinity Brine by Multi-Stage Ion Concentration Polarization Desalination.

This corrects the article DOI: 10.1038/srep31850.

Assessment and modeling of E-waste generation based on growth rate from different telecom companies in the State of Kuwait.

The present work assesses the production rate of cell phone e-waste in Kuwait by comparing the number of clients in three telecommunication service providers like Zain, Ooredoo, and Viva in the state of Kuwait over a period of 7 years from 2008 to 2015. An online survey was conducted to evaluate the growth in the number of clients in three cell phone companies, and the data analysis was carried out using statistical package for the social sciences (SPSS) software. The prediction of the growth percentage of the number of clients in each telecommunication company was analyzed using analysis of variance (ANOVA) test and followed by the regression model. The study shows that there is an increase in the number of clients in all three companies (Zain, Ooredoo, and Viva) between year 2008 and 2015, and it was estimated that approximately 7.9 million cell phone users would be achieved in the first quarter of 2015. Based on this predicted number of cell phone users, the production of e-waste would be 3 kt per year with an average growth of 12.7%.

Anti-Fouling Double-Skinned Forward Osmosis Membrane with Zwitterionic Brush for Oily Wastewater Treatment.

Despite its attractive features for energy saving separation, the performance of forward osmosis (FO) has been restricted by internal concentration polarization and fast fouling propensity that occur in the membrane sublayer. These problems have significantly affected the membrane performance when treating highly contaminated oily wastewater. In this study, a novel double-skinned FO membrane with excellent anti-fouling properties has been developed for emulsified oil-water treatment. The double-skinned FO membrane comprises a fully porous sublayer sandwiched between a highly dense polyamide (PA) layer for salt rejection and a fairly loose dense bottom zwitterionic layer for emulsified oil particle removal. The top dense PA layer was synthesized via interfacial polymerization meanwhile the bottom layer was made up of a zwitterionic polyelectrolyte brush - (poly(3-(N-2-methacryloxyethyl-N,N-dimethyl) ammonatopropanesultone), abbreviated as PMAPS layer. The resultant double-skinned membrane exhibited a high water flux of 13.7 ± 0.3 L/m2.h and reverse salt transport of 1.6 ± 0.2 g/m2.h under FO mode using 2 M NaCl as the draw solution and emulsified oily solution as the feed. The double-skinned membrane outperforms the single-skinned membrane with much lower fouling propensity for emulsified oil-water separation.

Modeling and analysis of modular structure in diverse biological networks.

Biological networks, like most engineered networks, are not the product of a singular design but rather are the result of a long process of refinement and optimization. Many large real-world networks are comprised of well-defined and meaningful smaller modules. While engineered networks are designed and refined by humans with particular goals in mind, biological networks are created by the selective pressures of evolution. In this paper, we seek to define aspects of network architecture that are shared among different types of evolved biological networks. First, we developed a new mathematical model, the Stochastic Block Model with Path Selection (SBM-PS) that simulates biological network formation based on the selection of edges that increase clustering. SBM-PS can produce modular networks whose properties resemble those of real networks. Second, we analyzed three real networks of very different types, and showed that all three can be fit well by the SBM-PS model. Third, we showed that modular elements within the three networks correspond to meaningful biological structures. The networks chosen for analysis were a proteomic network composed of all proteins required for mitochondrial function in budding yeast, a mesoscale anatomical network composed of axonal connections among regions of the mouse brain, and the connectome of individual neurons in the nematode C. elegans. We find that the three networks have common architectural features, and each can be divided into subnetworks with characteristic topologies that control specific phenotypic outputs.