Analytical quality by design-based development of a capillary electrophoresis method for Omeprazole impurity profiling

Omeprazole (OME) is a proton pump inhibitor used to treat gastroesophageal reflux disease associated conditions. The current study presents an Analytical Quality by Design-based approach for the development of a CE method for OME impurity profiling. The scouting experiments suggested the selection of solvent modified Micellar ElectroKinetic Chromatography operative mode using a pseudostationary phase composed of sodium dodecyl sulfate (SDS) micelles and n-butanol as organic modifier in borate buffer. A symmetric three-level screening matrix 37//16 was used to evaluate the effect of Critical Method Parameters, including Background Electrolyte composition and instrumental settings, on Critical Method Attributes (critical resolution values, OME peak width and analysis time). The analytical procedure was optimized using Response Surface Methodology through a Central Composite Orthogonal Design. Risk of failure maps made it possible to define the Method Operable Design Region, within which the following optimized conditions were selected: 72 mM borate buffer pH 10.0, 96 mM SDS, 1.45 %v/v n-butanol, capillary temperature 21 °C, applied voltage 25 kV. The method was validated according to ICH guidelines and robustness was evaluated using a Plackett-Burman design. The developed procedure enables the simultaneous determination of OME and seven related impurities, and has been successfully applied to the analysis of pharmaceutical formulations.

Dataset: Spatial visualization ability assessment among undergraduate students at Jiaying University

This dataset, collected through a comprehensive online survey and testing process, evaluates spatial visualization ability among undergraduate students at Jiaying University. Utilizing the Revised Purdue Spatial Visualization Test: Rotations (Revised PSVT: R), the dataset encompasses demographic information and responses to Likert-scale questions. With applications in experimental and cognitive psychology, the dataset offers valuable insights into spatial cognition and its implications for educational contexts. Researchers can utilize this dataset as a benchmark for comparative studies, explore correlations with demographic factors, and develop educational interventions to enhance spatial ability. The dataset, accessible on the repository, can be retrieved through the following citation [1].

Green method for 17-hydroxyprogesterone extraction and determination using PDMS stir bar sorptive extraction coupled with HPLC: optimization by response surface methodology

Quantifying small amounts of the 17-hydroxyprogesterone in various matrix is crucial for different purposes. In this study, a commercial polydimethylsiloxane stir bar was used to extract hormone from water and urine samples. Analysis was performed by high-performance liquid chromatography using a UV detector. The response surface methodology was used to optimize the desorption and extraction steps, with predicted optimal point relative errors of 1.25% and 6.40%, respectively. The optimized method was validated with a linear range of 1.21-1000.00 for aqueous and 2.43-2000.00 ng mL-1 for urine samples. The coefficient of determination was 0.9998 and 0.9967, and the detection limit of the proposed method was obtained to be 0.40 and 0.80 ng mL-1 for aqueous and urine samples, respectively. The recovery percentage and relative standard deviation within a day and between three days after the addition of three different concentration levels of the standard to the control sample were 87-103% and 0.4-3.6% for aqueous and 87.5-101% and 0.1-5.2% for urine samples, respectively. The results show that the proposed method can be appropriate and cost-effective for extracting and analyzing this hormone. In addition, using three different tools, the greenness of the proposed method was proven.

Development and characterization of curcumin nanosuspension-embedded genipin-crosslinked chitosan/polyvinylpyrrolidone hydrogel patch for effective wound healing

This study investigated the development of a genipin-crosslinked chitosan (CS)-based polyvinylpyrrolidone (PVP) hydrogel containing curcumin nanosuspensions (Cur-NSs) to promote wound healing in an excisional wound model. Cur-NSs were prepared, and a simplex centroid mixture design was employed to optimize hydrogel properties for high water absorption, degree of crosslinking, and sufficient toughness. The in vivo wound healing effect was tested in Wistar rats. The optimized hydrogel consisted of a 70:30 ratio of CS:PVP, crosslinked with a 2 % w/w genipin solution. It exhibited high swelling capability (486 %) while maintaining solidity, robustness, and durability. Incorporating 5 % w/w Cur-NSs resulted in a more compact structure, although with a reduction in swelling properties. The release kinetics of Cur from the hydrogel followed the Korsmeyer-Peppas Fickian diffusion model. In vitro biocompatibility studies demonstrated that the hydrogel was non-toxic to skin fibroblast cells. The in vivo experiment revealed a desirable wound healing rate with over 80 % recovery by day 7. Cur-NSs likely aided wound healing by reducing the inflammatory response and stimulating fibroblast proliferation. Additionally, the CS-based hydrogel provided a moist wound environment with hydration and gas transfer, further accelerating wound closure. These findings suggest that the Cur-NS-embedded hydrogel shows promise as a wound dressing material.

Multilevel Intervention Stepped Wedge Designs (MLI-SWDs)

Multilevel interventions (MLIs) hold promise for reducing health inequities by intervening at multiple types of social determinants of health consistent with the socioecological model of health. In spite of their potential, methodological challenges related to study design compounded by a lack of tools for sample size calculation inhibit their development. We help address this gap by proposing the Multilevel Intervention Stepped Wedge Design (MLI-SWD), a hybrid experimental design which combines cluster-level (CL) randomization using a Stepped Wedge design (SWD) with independent individual-level (IL) randomization. The MLI-SWD is suitable for MLIs where the IL intervention has a low risk of interference between individuals in the same cluster, and it enables estimation of the component IL and CL treatment effects, their interaction, and the combined intervention effect. The MLI-SWD accommodates cross-sectional and cohort designs as well as both incomplete (clusters are not observed in every study period) and complete observation patterns. We adapt recent work using generalized estimating equations for SWD sample size calculation to the multilevel setting and provide an R package for power and sample size calculation. Furthermore, motivated by our experiences with the ongoing NC Works 4 Health study, we consider how to apply the MLI-SWD when individuals join clusters over the course of the study. This situation arises when unemployment MLIs include IL interventions that are delivered while the individual is unemployed. This extension requires carefully considering whether the study interventions will satisfy additional causal assumptions but could permit randomization in new settings.

Insights on the role of blocking agent on the properties of the lipase from Thermomyces lanuginosus immobilized on heterofunctional support for hydroesterification reactions

Here, we report a study of the effect of the blocking agent on the properties of the lipase from Thermomyces lanuginosus (TLL) immobilized on a heterofunctional support (Purolite C18-ethylnediamina (EDA)- vinyl sulfone (VS)-TLL-blocking agent) in different reactions. The performance of the biocatalysts was compared to those immobilized on standard hydrophobic support (Purolite C18-TLL) and the commercial one (TLL-IM). The nature of the blocking agent (Cys, Gly and Asp) altered the enzyme features. TLL-IM always gave a comparatively worse performance, with its specificity for the oil being very different to the Purolite biocatalysts. Under optimized conditions, Purolite C18-TLL yielded 97 % of hydrolysis conversion after 4 h using a water/waste cooking soybean oil (WCSO) mass ratio of 4.3, biocatalyst load of 6.5 wt% and a temperature of 44.2 °C (without buffer or emulsification agent). In esterification reactions of the purified free fatty acids (FFAs) obtained from WCSO, the best TLL biocatalysts depended on the utilized alcohol: linear amyl alcohol was preferred by Purolite C18-TLL and Purolite C18-EDA-VS-TLL-Gly, while higher activity was achieved utilizing isoamyl alcohol as nucleophile by Purolite C18-EDA-VS-TLL-Cys, Purolite C18-EDA-VS-TLL-Asp and IM-TLL as catalysts. All the results indicate the influence of the blocking step on the final biocatalyst features.

Artificial intelligence for neuro MRI acquisition: a review

OBJECT

To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts.

MATERIALS AND METHODS

A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods.

RESULTS

The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency.

DISCUSSION

The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.

Data processing for predicting DNA damaging properties of complex UV sources

A growing number of experimental evidence emphasizes that photobiological phenomena are not always the sum of the effect of individual wavelengths present in the emission spectrum of light sources. Unfortunately, tools are missing to identify such non-additive effects and predict effects of various exposure conditions. In the present work, we addressed these points for the formation of pyrimidine dimers in DNA upon co-exposure to UVC, UVB and UVA radiation. We first applied a combination index approach to determine whether mixtures of theses UV ranges exhibited additive, inhibitory or synergistic effects on the formation of cyclobutane pyrimidine dimers, (6-4) photoproducts and Dewar isomers. A predictive approach based on an experiments plan strategy was then used to quantify the contribution of each wavelength range to the formation of DNA photoproducts. The obtained models allowed us to accurately predict the level of pyrimidine dimers in DNA irradiated under different conditions. The data were found to be more accurate than those obtained with the simple additive approach underlying the use of action spectra. Experiment plans thus appear as an attractive concept that could be widely applied in photobiology even for cellular experiments.

Optimization of lignin precipitation from black liquor using organic acids and its valorization by preparing lignin nanoparticles

This work focuses on the precipitation of lignin from kraft black liquor (BL) along with its valorization into lignin nanoparticles (LNP). Two organic acids namely, acetic acid, and lactic acid were used for the precipitation of lignin as an alternative to sulfuric acid. An optimization study was carried out to determine the effect of three key variables, namely acid type, temperature, and pH, on the isolation yield and purity of lignin. The study showed that all factors primarily influenced the lignin yield, while the purity of precipitated lignin varied only around 1 % between minimum to maximum purity. Further, the acid precipitation method was selected for the preparation of LNP. The study aimed to observe the effect of pH, lignin concentration, and surfactant concentration over the properties of the prepared nanoparticles. The results showed that a smaller nanoparticle size and maximization of phenolic content was achieved with a lignin concentration of 35 mg/mL, a surfactant concentration of 10 % (w/w lignin), and a pH of 5. Additionally, the antibacterial activity of LNPs against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa bacteria was evaluated. The results showed only minor activity against Staphylococcus aureus. Overall, the study demonstrates the potential method for precipitation and valorization of lignin through the production of LNP with desirable properties.

Hope messages influence health behavior intentions more than fear messages: An experimental study during COVID-19

BACKGROUND

Fear tactics were used in the pandemic of Coronavirus Disease 2019.

PURPOSE

We tested how messaging style, fear vs. hope, and differences in age and political affiliation related to intentions to engage in preventive behaviors during Coronavirus Disease 2019.

METHODS

Participants (N = 606) aged 18 to 94 were randomly assigned to receive health messages that emphasized the dangers of the virus (fear messages) or the ability of health behaviors to mitigate the impact of the virus (hope messages). The primary outcome was health behavior intentions.

DISCUSSION

Hope messaging rather than fear messaging promoted health behavior intentions with no moderation by age or political affiliation. Older and Democratic-identified adults had higher health behavior intentions. Health behaviors were mediated by death anxiety and perceived credibility of the messages.

CONCLUSION

Tailored hope messaging may improve health behaviors by increasing the credibility of messages.

Response surface methodology for process optimization in livestock wastewater treatment: A review

With increasing demand for meat and dairy products, the volume of wastewater generated from the livestock industry has become a significant environmental concern. The treatment of livestock wastewater (LWW) is a challenging process that involves removing nutrients, organic matter, pathogens, and other pollutants from livestock manure and urine. In response to this challenge, researchers have developed and investigated different biological, physical, and chemical treatment technologies that perform better upon optimization. Optimization of LWW handling processes can help improve the efficacy and sustainability of treatment systems as well as minimize environmental impacts and associated costs. Response surface methodology (RSM) as an optimization approach can effectively optimize operational parameters that affect process performance. This review article summarizes the main steps of RSM, recent applications of RSM in LWW treatment, highlights the advantages and limitations of this technique, and provides recommendations for future research and practice, including its cost-effectiveness, accuracy, and ability to improve treatment efficiency.

A novel nanocomposite for photocatalytic rhodamine B dye removal from wastewater using visible light

Providing safe access to water and addressing the impact of waterborne diseases, which claim over two million lives annually, is a major contribution to water purification. The study introduces a novel nanocomposite, Ch/Fe3O4/α-MoO3, which exhibits outstanding photocatalytic efficacy under visible light. An in-depth investigation of the nanocomposite's synthesis, characterization, and photodegradation mechanisms reveals its outstanding capabilities. Photocatalytic activity is influenced by the catalytic dose, pH, dye concentration, and reaction time, according to the study. A response surface method is used to determine the optimal conditions for Rhodamine B degradation, which results in 96.3% removal efficiency at pH 8.5, dye concentration 25 mg/L, nanocomposite dose at 22 mg/L, and reaction time 50 min. As a result of its high surface area, biocompatibility, availability, and magnetization with iron compounds, Chitosan is an excellent substrate for enhancing the photocatalytic properties of MoO3 nanoparticles. A nanocomposite with an energy band of 3.18 eV exhibits improved visible light absorption. This study confirms the nanocomposite's recyclability and stability, affirming its practicality. Besides dye removal, it offers hope for the global quest for clean water sources by addressing a broader range of waterborne contaminants. By combining molybdenum and magnetite, nanocomposite materials facilitate the degradation of pollutant and bacteria, contributing positively to society's quest for clean and safe water. It emphasizes the role nanotechnology plays in preserving human health and well-being in combating waterborne diseases.

Advanced oxidation processes for water and wastewater treatment - Guidance for systematic future research

Advanced oxidation processes (AOPs) are a growing research field with a large variety of different process variants and materials being tested at laboratory scale. However, despite extensive research in recent years and decades, many variants have not been transitioned to pilot- and full-scale operation. One major concern are the inconsistent experimental approaches applied across different studies that impede identification, comparison, and upscaling of the most promising AOPs. The aim of this tutorial review is to streamline future studies on the development of new solutions and materials for advanced oxidation by providing guidance for comparable and scalable oxidation experiments. We discuss recent developments in catalytic, ozone-based, radiation-driven, and other AOPs, and outline future perspectives and research needs. Since standardized experimental procedures are not available for most AOPs, we propose basic rules and key parameters for lab-scale evaluation of new AOPs including selection of suitable probe compounds and scavengers for the measurement of (major) reactive species. A two-phase approach to assess new AOP concepts is proposed, consisting of (i) basic research and proof-of-concept (technology readiness levels (TRL) 1-3), followed by (ii) process development in the intended water matrix including a cost comparison with an established process, applying comparable and scalable parameters such as UV fluence or ozone consumption (TRL 3-5). Subsequent demonstration of the new process (TRL 6-7) is briefly discussed, too. Finally, we highlight important research tools for a thorough mechanistic process evaluation and risk assessment including screening for transformation products that should be based on chemical logic and combined with complementary tools (mass balance, chemical calculations).

Utilizing experimental design and desirability function in optimizing RP-HPLC method for simultaneous determination of some skeletal muscle relaxants and analgesics

An experimental design and response surface methodologies using Plackett-Burman and Box-Behnken designs were applied for selecting and optimizing the most appropriate parameters which significantly affect the separation and quantitative estimation of five skeletal muscle relaxants and four analgesic drugs (baclofen, methocarbamol, dantrolene sodium, orphenadrine citrate, cyclobenzaprine hydrochloride, ketoprofen, etoricoxib, ibuprofen, and mefenamic acid) with a relatively short duration of analysis in a single run. For the separation of the nine drugs, an INERTSIL ODS-V3-5 µm C18 column (250 × 4.6 mm I.D.) was used with the optimum mobile phase conditions (45.15 mM ammonium acetate buffer pH 5.56 adjusted with acetic acid, acetonitrile, and methanol in a ratio of 30.5:29.5:40, v/v/v with a flow rate of 1.5 mL/min) and UV-detection at 220 nm. The optimized method was successfully subjected to the validation steps as described in ICH guidelines for linearity, precision, accuracy, robustness, and sensitivity. The optimized and validated method was effectively applied to determine the content of the studied drugs in their pharmaceutical preparations and to expand its applicability to the counterfeit estimation of etoricoxib in different brands of tablet dosage forms.

Dental plaque microbiota sequence counts for microbial profiling and resistance genes detection

Shotgun metagenomics sequencing experiments are finding a wide range of applications. Nonetheless, there are still limited guidelines regarding the number of sequences needed to acquire meaningful information for taxonomic profiling and antimicrobial resistance gene (ARG) identification. In this study, we explored this issue in the context of oral microbiota by sequencing with a very high number of sequences (~ 100 million), four human plaque samples, and one microbial community standard and by evaluating the performance of microbial identification and ARGs detection through a downsampling procedure. When investigating the impact of a decreasing number of sequences on quantitative taxonomic profiling in the microbial community standard datasets, we found some discrepancies in the identified microbial species and their abundances when compared to the expected ones. Such differences were consistent throughout downsampling, suggesting their link to taxonomic profiling methods limitations. Overall, results showed that the number of sequences has a great impact on metagenomic samples at the qualitative (i.e., presence/absence) level in terms of loss of information, especially in experiments having less than 40 million reads, whereas abundance estimation was minimally affected, with only slight variations observed in low-abundance species. The presence of ARGs was also assessed: a total of 133 ARGs were identified. Notably, 23% of them inconsistently resulted as present or absent across downsampling datasets of the same sample. Moreover, over half of ARGs were lost in datasets having less than 20 million reads. This study highlights the importance of carefully considering sequencing aspects and suggests some guidelines for designing shotgun metagenomics experiments with the final goal of maximizing oral microbiome analyses. Our findings suggest varying optimized sequence numbers according to different study aims: 40 million for microbiota profiling, 50 million for low-abundance species detection, and 20 million for ARG identification. KEY POINTS: • Forty million sequences are a cost-efficient solution for microbiota profiling • Fifty million sequences allow low-abundance species detection • Twenty million sequences are recommended for ARG identification.

Adapting vector surveillance using Bayesian experimental design: An application to an ongoing tick monitoring program in the southeastern United States

Maps of the distribution of medically-important ticks throughout the US remain lacking in spatial and temporal resolution in many areas, leading to holes in our understanding of where and when people are at risk of tick encounters, an important baseline for informing public health response. In this work, we demonstrate the use of Bayesian Experimental Design (BED) in planning spatiotemporal surveillance of disease vectors. We frame survey planning as an optimization problem with the objective of identifying a calendar of sampling locations that maximizes the expected information regarding some goal. Here we consider the goals of understanding associations between environmental factors and tick presence and minimizing uncertainty in high risk areas. We illustrate our proposed BED workflow using an ongoing tick surveillance study in South Carolina parks. Following a model comparison study based on two years of initial data, several techniques for finding optimal surveys were compared to random sampling. Two optimization algorithms found surveys better than all replications of random sampling, while a space-filling heuristic performed favorably as well. Further, optimal surveys of just 20 visits were more effective than repeating the schedule of 111 visits used in 2021. We conclude that BED shows promise as a flexible and rigorous means of survey design for vector control, and could help alleviate pressure on local agencies by limiting the resources necessary for accurate information on arthropod distributions. We have made the code for our BED workflow publicly available on Zenodo to help promote the application of these methods to future surveillance efforts.

Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning

Molecularly imprinted polymers (MIPs) rely on synthetic engineered materials able to selectively bind and intimately recognise a target molecule through its size and functionalities. The way in which MIPs interact with their targets, and the magnitude of this interaction, is closely linked to the chemical properties derived during the polymerisation stages, which tailor them to their specific target. Hence, MIPs are in-deep studied in terms of their sensitivity and cross-reactivity, further being used for monitoring purposes of analytes in complex analytical samples. As MIPs are involved in sensor development within different approaches, a systematic optimisation and rational data-driven sensing is fundamental to obtaining a best-performant MIP sensor. In addition, the closer integration of MIPs in sensor development requires that the inner properties of the materials in terms of sensitivity and selectivity are maintained in the presence of competitive molecules, which focus is currently opened. Identifying computational models capable of predicting and reporting the best-performant configuration of electrochemical sensors based on MIPs is of immense importance. The application of chemometrics using design of experiments (DoE) is nowadays increasingly adopted during optimisation problems, which largely reduce the number of experimental trials. These approaches, together with the emergent machine learning (ML) tool in sensor data processing, represent the future trend in design and management of point-of-care configurations based on MIP sensing. This review provides an overview on the recent application of chemometrics tools in optimisation problems during development and analytical assessment of electrochemical sensors based on MIP receptors. A comprehensive discussion is first presented to cover the recent advancements on response surface methodologies (RSM) in optimisation studies of MIPs design. Therefore, the recent advent of machine learning in sensor data processing will be focused on MIPs development and analytical detection in sensors.

Enhanced benzene vapor adsorption through microwave-assisted fabrication of activated carbon from peanut shells using ZnCl2 as an activating agent

Herein, microwave-assisted activated carbon (MW-AC) was fabricated from peanut shells using a ZnCl2 activator and utilized for the first time to eliminate benzene vapor as a volatile organic compound (VOC). During the MW-AC production process, which involved two steps-microwave treatment and muffle furnace heating-we investigated the effects of various factors and achieved the highest iodine number of 1250 mg/g. This was achieved under optimal operating conditions, which included a 100% impregnation ratio, CO2 as the gas in the microwave environment, a microwave power set at 500 W, a microwave duration of 10 min, an activation temperature of 500 °C and an activation time of 45 min. The structural and morphological properties of the optimized MW-AC were assessed through SEM, FTIR, and BET analysis. The dynamic adsorption process of benzene on the optimized MW-AC adsorbent, which has a significant BET surface area of 1204.90 m2/g, was designed using the Box-Behnken approach within the response surface methodology. Under optimal experimental conditions, including a contact duration of 80 min, an inlet concentration of 18 ppm, and a temperature of 26 °C, the maximum adsorption capacity reached was 568.34 mg/g. The experimental data are better described by the pseudo-second-order kinetic model, while it is concluded that the equilibrium data are better described by the Langmuir isotherm model. MW-AC exhibited a reuse efficiency of 86.54% for benzene vapor after five consecutive recycling processes. The motivation of the study highlights the high adsorption capacity and superior reuse efficiency of MW-AC adsorbent with high BET surface area against benzene pollutant. According to our results, the developed MW-AC presents itself as a promising adsorbent candidate for the treatment of VOCs in various industrial applications.

Correctly establishing evidence for cue combination via gains in sensory precision: Why the choice of comparator matters

Studying how sensory signals from different sources (sensory cues) are integrated within or across multiple senses allows us to better understand the perceptual computations that lie at the foundation of adaptive behaviour. As such, determining the presence of precision gains - the classic hallmark of cue combination - is important for characterising perceptual systems, their development and functioning in clinical conditions. However, empirically measuring precision gains to distinguish cue combination from alternative perceptual strategies requires careful methodological considerations. Here, we note that the majority of existing studies that tested for cue combination either omitted this important contrast, or used an analysis approach that, unknowingly, strongly inflated false positives. Using simulations, we demonstrate that this approach enhances the chances of finding significant cue combination effects in up to 100% of cases, even when cues are not combined. We establish how this error arises when the wrong cue comparator is chosen and recommend an alternative analysis that is easy to implement but has only been adopted by relatively few studies. By comparing combined-cue perceptual precision with the best single-cue precision, determined for each observer individually rather than at the group level, researchers can enhance the credibility of their reported effects. We also note that testing for deviations from optimal predictions alone is not sufficient to ascertain whether cues are combined. Taken together, to correctly test for perceptual precision gains, we advocate for a careful comparator selection and task design to ensure that cue combination is tested with maximum power, while reducing the inflation of false positives.

Application of a magnetically separable Zr-MOF for fast extraction of palladium before its spectrophotometric detection

In this research, a novel magnetic zirconium-based metal-organic framework (Fe3O4@SiO2@MIP-202, MMOF), was fabricated, fully characterized, and applied for the batch-mode solid phase extraction of trace amounts of Pd2+ ions from water and wastewater samples before its spectrophotometric detection. Pd2+ ions were desorbed from MMOF by nitric acid and were complexed by treating with KI solution to have a maximum absorbance at 410 nm. The synthesized MMOF composite showed a very large surface area (65 m2.g- 1), good magnetization (1.7 emu.g- 1) and a large pore volume (0.059 cm3.g- 1) with adsorption capacity of 194.5 mg of Pd2+ ions/g of the adsorbent. This nanosorbent boasts chemo-mechanical stability, high adsorption capacity due to its vast active sites, and facile recovery facilitated by its magnetic properties. Parameters affecting the extraction efficiency of the method were optimized as pH of the sample 7.4, volume of the sample 25 mL, 15 mg adsorbent, 1 mL of 0.1 M HNO3 eluent, with 10 and 15 min as the extraction and desorption times, respectively. The calibration curve was found to be linear across the 10.0-1500.0 µg.L- 1 range with a limit of detection of 1.05 µg.L- 1. The obtained extraction efficiency and enrichment were 98% and 245, respectively. The total analysis time was less than 30 min. This MMOF has never been used for the extraction of Pd2+ ions before.

Highly efficient ceramic membrane synthesized from sugar scum and fly ash as sustainable precursors for dyes removal

Recycling solid industrial wastes into valuable materials is always the priority solution in waste management. In this perspective, sugar scum and fly ash were used to produce an effective low-cost porous ceramic membrane. The impacts of the sintering temperature, amount of sugar scum, and sintering time on the properties of the prepared ceramic membrane were investigated and optimized using experimental design. A simultaneous rise in both the sintering temperature and the amount of sugar scum leads to a notable increase in porosity. Moreover, the simultaneous increase or decrease in the time and the amount of sugar scum causes a significant decrease in the compressive strength. The optimal conditions have been determined as a sintering temperature of 1197 °C, a sugar scum amount of 12.06 %, and a sintering time of 253 min. Under these conditions, the density, porosity, and compressive strength were found to be 2.16 g/cm³, 34.66 %, and 28.24 MPa, respectively. In addition, the obtained ceramic membrane has a water permeability of 2356.68 L/h m2 bar, a pore size in the range 0-4.5 μm, and excellent chemical resistance in both acidic and basic media. Finally, the performance of the prepared ceramic membrane was evaluated by the filtration of methylene blue. The results indicate that sugar scum and fly ash are suitable precursors to manufacture an effective ceramic membrane for the treatment of wastewater.

UV-light-responsive Ag/TiO2/PVA nanocomposite for photocatalytic degradation of Cr, Ni, Zn, and Cu heavy metal ions

The rapid growth of industrialization has led to the uncontrolled pollution of the environment, and rapid action is needed. This study synthesized Ag/TiO2/polyvinyl alcohol (PVA) nano photocatalyst for promising light-derived photocatalytic removal of heavy metal ions. The design of experiment (DOE) was used to study the effect of important factors (pH, reaction time, and photocatalyst dosage) to maximize the final performance of the photocatalyst. In the optimized condition, the Ag/TiO2/PVA nano-photocatalyst removed more than 94% of Cr6+ in 180 min, and the efficiency was more than 70% for Cu2+, Zn2+, and Ni2+ metal ions. The adsorption of the heavy metal ions on the photocatalyst was described well with the Langmuir isotherm, while the pseudo-second-order linear kinetic model fitted with the experimental data. The nano-photocatalyst's stability was confirmed after maintaining its performance for five successive runs. The enhanced photocatalytic activity for the heavy metal ions removal can be attributed to the presence of metallic silver nanoparticles (electron transfer and plasmonic fields mechanisms) and PVA, which delayed the recombination of electron-hole. The synthesized ternary Ag/TiO2/PVA nano-photocatalyst showed promising performance for the elimination of heavy metal ions and can be used for environmental remediation purposes.

Chronic brain functional ultrasound imaging in freely moving rodents performing cognitive tasks

BACKGROUND

Functional ultrasound imaging (fUS) is an emerging imaging technique that indirectly measures neural activity via changes in blood volume. Chronic fUS imaging during cognitive tasks in freely moving animals faces multiple exceptional challenges: performing large durable craniotomies with chronic implants, designing behavioral experiments matching the hemodynamic timescale, stabilizing the ultrasound probe during freely moving behavior, accurately assessing motion artifacts, and validating that the animal can perform cognitive tasks while tethered.

NEW METHOD

We provide validated solutions for those technical challenges. In addition, we present standardized step-by-step reproducible protocols, procedures, and data processing pipelines. Finally, we present proof-of-concept analysis of brain dynamics during a decision making task.

RESULTS

We obtain stable recordings from which we can robustly decode task variables from fUS data over multiple months. Moreover, we find that brain wide imaging through hemodynamic response is nonlinearly related to cognitive variables, such as task difficulty, as compared to sensory responses previously explored.

COMPARISON WITH EXISTING METHODS

Computational pipelines in fUS are nascent and we present an initial development of a full processing pathway to correct and segment fUS data.

CONCLUSIONS

Our methods provide stable imaging and analysis of behavior with fUS that will enable new experimental paradigms in understanding brain-wide dynamics in naturalistic behaviors.

Beyond single paradigms, pipelines, and outcomes: Embracing multiverse analyses in psychophysiology

Psychophysiological research is an inherently complex undertaking due to the nature of the data, and its analysis is characterized by many decision points that shape the final dataset and a study's findings. These decisions create a "multiverse" of possible outcomes, and each decision from study conceptualization to statistical analysis can lead to different results and interpretations. This review describes the concept of multiverse analyses, a methodological approach designed to understand the impact of different decisions on the robustness of a study's findings and interpretation. The emphasis is on transparently showcasing different reasonable approaches for constructing a final dataset and on highlighting the influence of various decision points, from experimental design to data processing and outcome selection. For example, the choice of an experimental task can significantly impact event-related brain potential (ERP) scores or skin conductance responses (SCRs), and different tasks might elicit unique variances in each measure. This review underscores the importance of transparently embracing the flexibility inherent in psychophysiological research and the potential consequences of not understanding the fragility or robustness of experimental findings. By navigating the intricate terrain of the psychophysiological multiverse, this review serves as an introduction, helping researchers to make informed decisions, improve the collective understanding of psychophysiological findings, and push the boundaries of the field.

Science of forests and floods: The quantum leap forward needed, literally and metaphorically

A century of research has generated considerable disagreement on the effect of forests on floods. Here we call for a causal inference framework to advance the science and management of the effect of any forest or its removal on flood severity and frequency. The causes of floods are multiple and chancy and, hence, can only be investigated via a probabilistic approach. We use the stochastic hydrology literature to infer a blueprint framework which could guide future research on the understanding and prediction of the effects of forests on floods in environments where rain is the dominant form of precipitation. Drawing parallels from other disciplines, we show that the introduction of probability in forest hydrology could stimulate a gestalt switch in the science of forests and floods. In light of increasing flood risk caused by climate change, this probabilistic framework can help policymakers develop robust forest and water management plans based on a defensible and clear understanding of floods.