Rich Samples Research Articles

An XAS study of Hg(II) sorption to Al-based drinking water treatment residuals

Drinking water treatment residuals (DWTRs) are produced from the coagulation and flocculation processes in conventional drinking water treatment. The abundant metal oxide content of these materials resulting from the use of coagulants, like alum and ferric chloride, has driven strong research interest into the reuse of DWTRs as sorptive materials. Using a suite of aluminum-based DWTRs, we provide new insights into Hg(II) sorption mechanisms. Experiments performed at circum-neutral pH show that sorption capacities are related to the amount of organic carbon/matter present in DWTRs. We found that carbon rich samples can scavenge about 9000 mg/kg of Hg, in contrast to 2000 mg/kg for lime based DWTRs. X-ray absorption spectroscopy (XAS) at the Hg L3 edge further characterizes mercury coordination. X-ray absorption near edge structure (XANES) and extended x-ray absorption fine structure (EXAFS) results point to a partial association of mercury with sulfur at low mass loadings, transitioning to a full association with oxygen/carbon at higher concentrations of sorbed Hg(II) and in DWTRs with limited sulfur content. These results suggest that sorption of Hg(II) is primarily controlled by the carbon/organic matter fraction of DWTRs, but not by the coagulants.

Multiple Chord Coding in China’s Minority Dance Music

Abstract In this paper, we first use CNNS to collect rich samples of folk dance music, establish a model framework and a functional system framework, and conduct a comprehensive process analysis of the music. Then, the instrumental features, frequency features, and timbre features are extracted to obtain the spectral information. In the stage of chord analysis and encoding, a multivariate chord encoding model is established based on the acquired spectral information, including two parts: chord representation preprocessing and chord encoding. By utilizing this model, the chord structure of music was successfully and accurately encoded, allowing for analysis with up to 98% accuracy. Furthermore, significant recall results were achieved, reaching over 0.9, which suggests that the extracted chord features are highly reliable and accurate in recognizing musical chord information.

Pharmacokinetics of oral spironolactone in infants up to 2years of age.

Spironolactone is a potassium sparing diuretic used for decades. Until now, pharmacokinetic (PK) studies of spironolactone have not been conducted in infants and therefore pediatric dosing is based on expert opinion. We aimed to describe the PK profiles of spironolactone and its main metabolites (7alpha-thiomethylspironolactone (TMS) and canrenone (CAN)) in infants up to two years of age. The PK of spironolactone and its main metabolites were evaluated following an oral administration of spironolactone (1mg/kg/dose) to pediatric patients with chronic heart failure, ascites, and/or oedema. The plasma concentration of spironolactone and metabolites (TMS and CAN) was determined using an ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). Based on rich sampling PK data, the estimation of population PK parameters was performed using nonlinear mixed-effects modelling software Monolix 2018R2. A total of 150 spironolactone, 158 TMS, and 158 CAN concentrations from 23 patients (ages: 3days-21months; median weight 4.3kg (2.2-12.6)) were available for PK analysis. A one-compartment model for spironolactone, TMS, and CAN best fitted the data. The median (range) of individual estimated apparent clearance values were 47.7 (11.9-138.1) L/h for spironolactone, 9.7 (1.5-66.9) L/h for TMS, and 1.0 (0.2-5.9) L/h for CAN. The disposition of spironolactone and metabolites was mainly affected by size of the patient: body weight explained 22% of inter-individual variability of spironolactone clearance. None of the undesirable effects of spironolactone was documented during the study period. The pharmacokinetics of spironolactone and its metabolites was highly variable between patients below 2years of age. Body weight explained a significant part of this variability; this highlights the need to take it into account for dosing prescription in this population. (Clinical trial Registration Number 2013-001189-40).

Gluten-Free Cookies Enriched with Baobab Flour (Adansonia digitata L.) and Buckwheat Flour (Fagopyrum esculentum)

To provide people with celiac disease with nutrient-rich gluten-free foods, this study aimed to produce cookies based on buckwheat and baobab flours, which were then subjected to nutritional, phytochemical, and sensory analyses. Results demonstrate that baobab flour (BF) and buckwheat flour (BWF) work together to enhance the nutritional properties of the cookies, in that nutrients that BWF is deficient in, BF provides sufficiently, and vice versa. BF is rich in minerals and carbohydrates, while BWF contains comparatively higher fat and protein levels. As for macro- and micro-elements, potassium (K) is the predominant macro-element in BF and BWF, with 13,276.47 ± 174 mg/kg and 1255.35 ± 58.92 mg/kg, respectively. The polyphenol content is higher in BF than BWF, at 629.7 ± 0.35 mg/100 g as opposed to 283.87 ± 0.06 mg/100 g. Similarly, the total flavonoid content and antioxidant activity of BF was greater than that of BWF, while BF exhibited 213.13 ± 0.08 mg/100 g and 86.62 ± 0.04%, in contrast to BWF, which had 125.36 ± 1.12 mg/100 g and 79.72 ± 0.01%, respectively. BF significantly enhanced the phytochemical composition of the cookies, with the richest sample being BBC3 containing 30% baobab. Buckwheat and baobab have the most abundant phenolic compounds of rutin and epicatechin, respectively. About the analysis of sensory attributes of the cookies, the partial substitution of BWF by BF of up to 20% (BWF3) significantly increased the scores for all attributes. Indeed, the appearance (physical aspect of the cookie: whether it is firm or not) and color (influence of baobab addition on cookie coloration) of the cookies were significantly improved with the addition of BF of up to 20%, but above 20% they were less appreciated. Similarly, up to 20% BF, the texture, flavor, and overall acceptability of the cookies were significantly improved. Taste, on the other hand, was not significantly improved, maybe due to the acidic taste provided by the baobab.

Integrating population neuroscience and syndemics frameworks to better understand global sex and gender‐based risk in Alzheimer’s disease and related dementias

AbstractAging populations in low‐ and middle‐income countries (LMICs) are growing more rapidly than those in high income countries (HICs). Thus, globally, the burden of Alzheimer’s disease and related dementias (ADRD) profoundly impacts LMICs, with greater burden and risk for late‐life women than men. LMICs are unable to meet diagnostic challenges with underfunded healthcare infrastructure, low numbers of clinicians with training and expertise in ADRD, and a lack of culturally and linguistically sensitive screening assessments. Emerging social and societal factors that are important risk and protective factors for later life ADRD and are differentially experienced by gender/sex are relatively understudied, yet essential in LMICs. Typical samples in neuroscience studies have been small and highly selected. They have been biased toward Western, Educated, Industrialized, Rich, and Democratic (WEIRD) samples and do not apply well to target populations in LMICs. In addition, levels‐of‐analysis across social/societal and biological determinants of disease are not typically integrated, and social/societal factors such as sociocultural (e.g., culture, religion, gender norms, access to education, discrimination, social aspects of pandemics, disability), political (e.g., governments and policies; migrants and displaced persons; war and conflict; reproductive health, justice, and agency), environmental (e.g., climate change and pollution), and economic factors (e.g., poverty), and their co‐occurrence, have been minimally considered for their role and impact on ADRD. To better understand gender/sex differences in ADRD in LMICs, we review how these social and societal factors may be important to ADRD using an integrated Population Neuroscience and Syndemics framework. Population neuroscience considers the brain in context using multiple levels of analyses, a life course perspective, and tools to enhance internal and external validity, while Syndemics suggest that diseases and social conditions may cluster and interact in populations with syndemic risk factors—sociocultural, political, economic, and environmental factors which promote stress pathways. We lay out a model in which syndemic risk and protective factors contribute to or protect from stress and biological aging and ultimately ADRD risk, both directly and indirectly through physical and mental health conditions. We close with recommendations for future aging and ADRD research based on this integrated Population Neuroscience‐Syndemics of ADRD framework.

The Swift Deep Galactic Plane Survey (DGPS) Phase I Catalog

The Swift Deep Galactic Plane Survey (DGPS) is a Swift Key Project consisting of 380 tiled pointings covering ∼40 deg2 of the Galactic plane between longitude 10 < ∣l∣ < 30 deg and latitude ∣b∣ < 0.5 deg. Each pointing has a 5 ks exposure, yielding a total of 1.9 Ms spread across the entire survey footprint. Phase I observations were carried out between 2017 March and 2021 May. The survey is complete to depth L X > 1034 erg s−1 to the edge of the Galaxy. The main survey goal is to produce a rich sample of new X-ray sources and transients, while also covering a broad discovery space. Here, we introduce the survey strategy and present a catalog of sources detected during Phase I observations. In total, we identify 928 X-ray sources, of which 348 are unique to our X-ray catalog. We report on the characteristics of sources in our catalog and highlight sources newly classified and published by the DGPS team.

DMMP: A distillation-based multi-task multi-tower learning model for personalized recommendation

Recommendation systems involve the matching and ranking stages. The click-through rate (CTR) and the conversion rate (CVR) predictions are two fundamental modules in recommendation systems. Most candidate generation matching models leverage a two-tower architecture to model the CTR prediction task. However, items with low-quality but attractive titles, i.e., click baits, may be recommended to the user, which worsens the user’s experience. Therefore, both click and conversion tasks should be modeled during the matching stage to improve user engagement. An intuitive way is to model these two tasks with a three-tower matching model. However, its efficiency and effectiveness are limited. By inheriting the merits of knowledge distillation and multi-task learning, we propose a distillation-based multi-task multi-tower model (DMMP) for personalized recommendation. Specifically, the MTL-based teacher network builds the task-shared and task-specific expert networks, and employs a customized multi-gate control network to merge these expert networks adaptively within the task embedding layer. Two auxiliary pCTR and pCTCVR tasks are incorporated to model CVR directly across the whole space. By sharing the feature representation parameters with the CTR modeling, the CVR modeling can be trained with richer samples. Three-tower-based candidate generation student network incorporates a user preference gating network to learn the task scores so that personalized candidates can be produced. Furthermore, we design an adaptive weighting strategy for total loss to eventually adjust the importance and relevance of different networks. Extensive experiments on public and industrial datasets validate the effectiveness of DMMP.

Fault anomaly detection method of aero-engine rolling bearing based on distillation learning

In this study, we address the issue of limited generalization capabilities in intelligent diagnosis models caused by the lack of high-quality fault data samples for aero-engine rolling bearings. We provide a fault anomaly detection technique based on distillation learning to address this issue. Two Vision Transformer (ViT) models are specifically used in the distillation learning process, one of which serves as the teacher network and the other as the student network. By using a small-scale student network model, the computational efficiency of the model is increased without sacrificing model accuracy. For feature-centered representation, new loss and anomaly score functions are created, and an enhanced Transformer encoder with the residual block is proposed. Then, a rolling bearing dynamics simulation method is used to obtain rich fault sample data, and the pre-training of the teacher network is completed. For anomaly detection, the training of the student network is completed based on the proposed loss function and the pre-trained teacher network, using only the vibration acceleration samples obtained from the normal state. Finally, the trained completed network and the designed anomaly score function are used to achieve the anomaly detection of rolling bearing faults. The experimental validation was carried out on two sets of test data and one set of real vibration data of a whole aero-engine, and the detection accuracy reached 100 %. The results show that the proposed method has a high capability of rolling bearing fault anomaly detection.

Phenolic Composition of Red and White Wine Byproducts from Different Grapevine Cultivars from La Rioja (Spain) and How This Is Affected by the Winemaking Process.

The recovery of raw materials offers an opportunity for applying the principles of circular bioeconomy. The phenolic composition of three underused wine byproducts (skin, seed, and bunch stem) was analyzed through UHPLC-QqQ-MS/MS to evaluate the intercultivar variability comparing red and white grape cultivars from La Rioja (Spain) and the influence of the winemaking, comparing conventional fermentation and carbonic maceration. We observed that the red skin, especially from Graciano, is rich in anthocyanins, whereas the white skin contains mainly phenolic acids, flavonols, and flavan-3-ols, with Maturana Blanca being the richest variety. Seeds are rich in flavan-3-ols and lignans with Maturana Blanca and Viura, respectively, the richest cultivars. Stems contain high amounts of flavan-3-ols, lignans, and stilbenes, with the red cultivars of Garnacha and Tempranillo being the richest samples. Carbonic maceration has a negative effect on the phenolic amount compared to conventional fermentation. In synthesis, we observed that each type of byproduct from red or white grape cultivars has a particular phenolic composition that can result in obtaining different ingredients with particular phenolic composition for target applications.

Impact of financial literacy and financial confidence on the savings behaviour of the farmers: the Indian scene

PurposeThe study aims to explore the savings behaviour of Indian farmers. An attempt is also made to inspect the effect of financial literacy (FL) and financial confidence (FC) on the savings behaviour of the farmers in India.Design/methodology/approach This study used secondary data on 10,263 Indian farmers from Financial Inclusion Insights, 2017 database. Relevant statistical techniques and ordered probit regression were used to unfold the effect of FL and FC on the savings behaviour of farmers.Findings The outcome of the study revealed that the majority of the Indian farmers exhibited poor levels of FL and FC. Of the total, 42.99% were found to save regularly. FL and FC were observed to play instrumental roles in steering the savings behaviour of the Indian farmers. Household size, financial shocks, gender, farm ownership, income, household financial decision-making process, religion and educational attainment have emerged to be significant predictors of the savings behaviour of Indian farmers.Originality/value The present study makes an original contribution to the extant literature by unfolding the savings behaviour of Indian farmers and the effect of FL and FC on such behaviour using a rich sample of 10,263 farmers for the first time.

A mechanism-based pathway toward administering highly active N-phage cocktails.

Bacteriophage (phage) therapy is being explored as a possible response to the antimicrobial resistance public health emergency. Administering a mixture of different phage types as a cocktail is one proposed strategy for therapeutic applications, but the optimal method for formulating phage cocktails remains a major challenge. Each phage strain has complex pharmacokinetic/pharmacodynamic (PK/PD) properties which depend on the nano-scale size, target-mediated, self-dosing nature of each phage strain, and rapid selection of resistant subpopulations. The objective of this study was to explore the pharmacodynamics (PD) of three unique and clinically relevant anti-Pseudomonas phages after simulation of dynamic dosing strategies. The Hollow Fiber Infection Model (HFIM) is an in vitro system that mimics in vivo pharmacokinetics (PK) with high fidelity, providing an opportunity to quantify phage and bacteria concentration profiles over clinical time scales with rich sampling. Exogenous monotherapy-bolus (producing max concentrations of Cmax = 7 log10 PFU/mL) regimens of phages LUZ19, PYO2, and E215 produced Pseudomonas aeruginosa nadirs of 0, 2.14, or 2.99 log10 CFU/mL after 6 h of treatment, respectively. Exogenous combination therapy bolus regimens (LUZ19 + PYO2 or LUZ19 + E215) resulted in bacterial reduction to <2 log10 CFU/mL. In contrast, monotherapy as a continuous infusion (producing a steady-state concentration of Css,avg = 2 log10PFU/mL) was less effective at reducing bacterial densities. Specifically, PYO2 failed to reduce bacterial density. Next, a mechanism-based mathematical model was developed to describe phage pharmacodynamics, phage-phage competition, and phage-dependent adaptive phage resistance. Monte Carlo simulations supported bolus dose regimens, predicting lower bacterial counts with bolus dosing as compared to prolonged phage infusions. Together, in vitro and in silico evaluation of the time course of phage pharmacodynamics will better guide optimal patterns of administration of individual phages as a cocktail.

Miniaturized paper-based analytical device for the portable analysis of phyto-cannabinoids in plant and oral fluids

In this work, a low-cost and eco-friendly paper-based analytical device (PAD) method is described for the determination of phyto-cannabinoids in cannabis and oral fluids based on a simple colorimetric reaction. The PAD was able to distinguish tetrahydrocannabinol (THC)- and cannabidiol (CBD)-rich plant samples by using 4-aminophenol (4-AP) and later on to quantify total phyto-cannabinoid content (THC + CBD + CBN) in plant and oral fluids by using the Fast Corinth V reagent. The chemical and physical properties regarding paper type and reagent concentration in the PAD were optimized to achieve the best analytical performance. After that, analytical features were obtained, including a linear range of 0.01–0.1 mg mL−1, a limit of detection (LOD) of 0.003 mg mL−1, and a suitable precision, expressed as relative standard deviation (RSD) lower than 10%. Furthermore, no significant interferences were observed in colorimetric reactions when tea, herbs, and drug samples were analyzed. Additionally, the PAD proved color stability up to 1 month after the sampling at 25 °C. The developed PAD was suitable for determining total phyto-cannabinoid content in plants and oral fluids, obtaining good results compared to GC–MS. Overall, this method showed good reliability resulting in an operational on-site device for drug monitoring.Graphical

Technical note: Darkroom lighting for luminescence dating laboratory

Abstract. An optimal lighting setting for the darkroom laboratory is fundamental for the accuracy of luminescence dating results. Here, we present the lighting setting implemented in the new Luminescence Dating Research Laboratory at Stony Brook University, USA. In this study, we performed spectral measurements on different light sources and filters. Then, we measured the optically stimulated luminescence (OSL) signal of quartz and the infrared stimulated luminescence (IRSL) at 50 ∘C (IR50) as well as post-IR IRSL at 290 ∘C (pIR-IR290) signal of potassium (K)-rich feldspar samples exposed to various light sources and durations. Our ambient lighting is provided by ceiling fixtures, each equipped with a single orange light-emitted diode (LED). In addition, our task-oriented lighting, mounted below each wall-mounted cabinet and inside the fume hoods, is equipped with a dimmable orange LED stripline. The ambient lighting, delivering 0.4 lx at the sample position, induced a loss of less than 5 % (on average) in the quartz OSL dose after 24 h of exposure and up to 5 % (on average) in the IR50 dose for the K-rich feldspar samples, with no measurable effect on their pIR-IR290 dose. The fume hood lighting, delivering 1.1 lx at the sample position, induced a dose loss of less than 5 % in quartz OSL and K-rich feldspar IR50 doses after 24 h of exposure, with no measurable effect on their pIR-IR290 dose. As light exposure during sample preparation is usually less than 24 h, we conclude that our lighting setting is suitable for luminescence dating darkrooms; it is simple, inexpensive to build, and durable.

The MILK study: Investigating intergenerational transmission of low-calorie sweeteners in breast milk

IntroductionForty-four percent of lactating women in the United States consume beverages containing low calorie sweeteners (LCS), and the presence of LCS in the food supply has continued to increase in recent years. While LCS are approved by the United States Food and Drug Administration (FDA) and are believed to be safe for human consumption, intergenerational LCS transmission and the health impacts of early life LCS exposure are severely understudied. Methods and analysisIn a tightly controlled, single site, prospective interventional study, mothers' plasma and breast milk, and infants’ plasma will be collected from 40 mother-infant dyads over the course of 72 h, with rich sampling following maternal ingestion of a LCS sweetened beverage containing sucralose and acesulfame potassium (ace-K). Concentration-time data will be used to build maternal and infant pharmacokinetic models for future simulations and analysis. ConclusionThis study aims to measure LCS concentrations in breast milk, maternal plasma, and infant plasma, to gain insight into infant exposure and inform recommendations for LCS consumption during breastfeeding.

The New Swift/UVOT+MaNGA (SwiM) Value-added Catalog

We present the the new Swift/UVOT+MaNGA (SwiM) catalog (SwiM_v4.1). SwiM_v4.1 is designed to study star formation and dust attenuation within nearby galaxies given the unique overlap of Swift/UVOT near-ultraviolet (NUV) imaging and MaNGA integral field optical spectroscopy. SwiM_v4.1 comprises 559 objects, ∼4 times more than the original SwiM catalog (SwiM_v3.1), spans the redshift range z ≈ 0.0002–0.1482, and provides a more diverse and rich sample. Approximately 5% of the final MaNGA sample is included in SwiM_v4.1, and 42% of the SwiM_v4.1 galaxies are cross-listed with other well-known catalogs. We present the same data as SwiM_v3.1, including UVOT images, Sloan Digital Sky Survey (SDSS) images, and MaNGA emission-line and spectral index maps with the same pixel size and angular resolution for each galaxy, and a file containing galaxy and observational properties. We designed SwiM_v4.1 to be unbiased, which resulted in some objects having low signal-to-noise ratios in their MaNGA or Swift data. We addressed this by providing a new file containing the fraction of science-ready pixels in each MaNGA emission-line map, and the integrated flux and inverse variance for all three NUV filters. The uniform angular resolution and sampling in SwiM_v4.1 will help answer a number of scientific questions, including constraining quenching and attenuation in the local Universe and studying the effects of black hole feedback. The galaxy maps, catalog files, and their associated data models are publicly released on the SDSS website (a description of the SwiM VAC is provided at https://www.sdss4.org/dr17/data_access/value-added-catalogs/?vac_id=swift-manga-value-added-catalog, and the data are stored on the SDSS Science Archive Server at https://data.sdss.org/sas/dr17/manga/swim/v4.1/).

SIDA

The coronavirus disease 2019 (COVID-19) pneumonia still persists and its chief complaint is dry cough. Physicians design wireless stethoscopes to facilitate diagnosis, however, lung sounds could be easily interfered with by external noises. To achieve lung sound enhancement, prior researches mostly assume the amount of clean and noisy data are the same. This assumption is hardly met due to extensive labor effort for data collection and annotation. The data imbalance across domains widely happens in real-world IoT systems, e.g. sound enhancement and WiFi-based human sensing. In this paper, we propose SIDA, a self-supervised imbalanced domain adaptation framework for sound enhancement and WiFi sensing, which makes it a generic time series domain adaptation solution for IoT systems. SIDA proposes a self-supervised imbalanced domain adaptation model that separately learns the representation of time series signals in a minority domain with limited samples, a majority domain with rich samples, and their mapping relations. For lung sound enhancement, we further proposes a phase correction model to sanitize the phase and a SNR prediction algorithm to recursively perform domain adaptation in an imbalanced noisy and clean lung sound dataset. Extensive experiments demonstrate SIDA increases noisy samples' SNR by 16.49dB and 4.06dB on a synthetic and a realistic imbalanced lung sound dataset, respectively. For WiFi-based human sensing, SIDA designs a cross-domain WiFi-based human identification model irrespective of walking trajectory. A specific trajectory where a group of people walks along in a realistic testing environment is considered the minority domain, and several other trajectories are stored at a server as the majority domain. Extensive experiments show SIDA could recognize individuals with an average accuracy of 94.72% and significantly outperform baselines on highly imbalanced WiFi dataset in cross-domain human identification tasks.

Single-Nucleus RNA-Seq: Open the Era of Great Navigation for FFPE Tissue.

Single-cell sequencing (scRNA-seq) has revolutionized our ability to explore heterogeneity and genetic variations at the single-cell level, opening up new avenues for understanding disease mechanisms and cell-cell interactions. Single-nucleus RNA-sequencing (snRNA-seq) is emerging as a promising solution to scRNA-seq due to its reduced ionized transcription bias and compatibility with richer samples. This approach will provide an exciting opportunity for in-depth exploration of billions of formalin-fixed paraffin-embedded (FFPE) tissues. Recent advancements in single-cell/nucleus gene expression workflows tailored for FFPE tissues have demonstrated their feasibility and provided crucial guidance for future studies utilizing FFPE specimens. In this review, we provide a broad overview of the nuclear preparation strategies, the latest technologies of snRNA-seq applicable to FFPE samples. Finally, the limitations and potential technical developments of snRNA-seq in FFPE samples are summarized. The development of snRNA-seq technologies for FFPE samples will lay a foundation for transcriptomic studies of valuable samples in clinical medicine and human sample banks.

Development and Nutritional Analysis of Taro Powder [Colocasia esculenta (L.) Schott.] Enriched with Natural Colorants

Aims: To prepare an anthocyanin rich food product from Indigenous plant species.&#x0D; Study Design: Lab experiments based on CRD statistical analysis.&#x0D; Place and Duration of Study: Department biochemistry and agricultural chemistry, Assam Agricultural university, Jorhat, Assam, India between June 2018 and January 2020.&#x0D; Methodology: We collected anthocyanin rich plant samples viz. fruits of jamun (S. cumini L. and S. fruticosum Roxb. DC.), full-grown flowers of rose (R. indica L.) and studied the stability of total anthocyanin under pH, temperature and light. Taro powder was selected with low oxalic acid content to prepare an anthocyanin rich food product. The storage duration was also study biochemically for its commercial application.&#x0D; Results: total anthocyanin content as 124.87±0.07 mg/100g (S. cumini L.) followed by 115.26±0.09 mg/100g (R. indica L.) and 91.41±0.09 mg/100 g (S. fruticosum Roxb. DC.). The anthocyanin pigments were found to be stable at an acidic pH (up to pH 5) and below 40º C temperature and activity was maintained under a light of 2500 lux for 6 hours. A novel food product was developed by immobilizing pigments extracted from S. cumini L., S. fruticosum Roxb. DC. and R. indica L. on Colocasia esculenta (L.) Schott. (variety: Ahina) powder.&#x0D; Conclusion: Anthcyanins act as potent antioxidant but its stability after extraction is a major concern. By applying different storage condition and suitable carrier material selection it can be stored for long.

An LC-ESI-MS/MS method for determination of ondansetron in low-volume plasma and cerebrospinal fluid: Method development, validation, and clinical application

Ondansetron is used in clinical settings as an antiemetic drug. Although the animal studies showed its potential effectiveness also in treating neuropathic pain, the results from humans are inconclusive. The lack of efficacy of ondansetron in a subset of patients might be due to the overexpression of P-glycoprotein, which could result in low concentrations of ondansetron in the central nervous system (CNS). A surrogate of the CNS exposure might be drug concentration in the cerebrospinal fluid (CSF), especially in humans, as assessing the drug disposition directly in the patient’s brain would be challenging. The study aimed to develop a sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to determine concentrations of ondansetron in human K3EDTA plasma and CSF. Ondansetron was extracted from biological matrices by liquid-liquid extraction. The quantification was performed on a Sciex QTRAP 6500+ mass spectrometer with labeled ondansetron as an internal standard. The calibration range was 0.25–350 ng/mL in plasma and 0.025–100 ng/mL in CSF; for both matrices, 25 µL of samples was required for the assays. The method was validated according to the FDA and EMA guidelines and showed acceptable results. A pilot study confirmed its suitability for clinical samples: after 4–16 mg of intravenous ondansetron, the determined concentrations in plasma were 1.22–235.90 ng/mL, while in CSF - 0.018–11.93 ng/mL. In conclusion, the developed method fulfilled all validation requirements and can be applied to pharmacokinetic studies assessing the CNS ondansetron exposure in humans. The method’s advantages, such as a low volume of matrix and a wide calibration range, support its use in a study in which rich sampling and various drug doses are expected.

Mastery-approach goals: A large-scale cross-cultural analysis of antecedents and consequences.

Mastery-approach (MAP) goals, focusing on developing competence and acquiring task mastery, are posited to be the most optimal, beneficial type of achievement goal for academic and life outcomes. Although there is meta-analytic evidence supporting this finding, such evidence does not allow us to conclude that the extant MAP goal findings generalize across cultures. Meta-analyses have often suffered from overrepresentation of Western, educated, industrialized, rich, and democratic (WEIRD) samples; reliance on bivariate correlations; and lack the ability to directly control individual-level background variables. To address these limitations, this study used nationally representative data from 77 countries/regions (N = 595,444 adolescents) to examine the relations of MAP goals to four antecedents (workmastery, competitiveness, fear of failure, fixed mindset) and 16 consequences (task-specific motivational, achievement-related, and well-being outcomes), and tested the cross-cultural generalizability of these relations. Results showed that MAP goals were: (a) grounded primarily in positive but not negative achievement motives/beliefs; (b) most strongly predictive of well-being outcomes, followed by adaptive motivation; (c) positively but consistently weakly associated with achievement-related outcomes, particularly for academic performance (β = .069); (d) negatively and weakly associated with maladaptive outcomes; and (e) uniquely predictive of various consequences, controlling for the antecedents and covariates. Further, the MAP goal predictions were generalizable across countries/regions for 13 of 16 consequences. While directions of effect sizes were slightly mixed for academic performance, perceived reading, and PISA test difficulty, the effect sizes were consistently small for most countries/regions. This generalizability points to quite strong cross-cultural support for the observed patterns. (PsycInfo Database Record (c) 2022 APA, all rights reserved).