Rich Samples Research Articles

Identifying genetic differences between bipolar disorder and major depression through multiple genome-wide association analyses.

Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD). We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis. Based on individual genotypes from case-control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses. Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case-case GWAS and that of case-control BPD. We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

Virgo Filaments. IV. Using WISE to Measure the Modification of Star-forming Disks in the Extended Regions Around the Virgo Cluster

Abstract Recent theoretical work and targeted observational studies suggest that filaments are sites of galaxy preprocessing. The aim of the WISESize project is to directly probe galaxies over the full range of environments to quantify and characterize extrinsic galaxy quenching in the local universe. In this paper, we use GALFIT to measure the IR 12 μm (R 12) and 3.4 μm (R 3.4) effective radii of 603 late-type galaxies in and surrounding the Virgo cluster. We find that Virgo cluster galaxies show smaller star-forming disks relative to their field counterparts at the 2.5σ level, while filament galaxies show smaller star-forming disks to almost 1.5σ. Our data, therefore, show that cluster galaxies experience significant effects on their star-forming disks prior to their final quenching period. There is also tentative support for the hypothesis that galaxies are preprocessed in filamentary regions surrounding clusters. On the other hand, galaxies belonging to rich groups and poor groups do not differ significantly from those in the field. We additionally find hints of a positive correlation between stellar mass and size ratio for both rich group and filament galaxies, though the uncertainties on these data are consistent with no correlation. We compare our size measurements with the predictions from two variants of a state-of-the-art semi-analytic model (SAM), one which includes starvation and the other incorporating both starvation and ram pressure stripping (RPS). Our data appear to disfavor the SAM, which includes RPS for the rich group, filament, and cluster samples, which contributes to improved constraints for general models of galaxy quenching.

Language exposure and use in study abroad versus migration contexts: modelling activity and learner profiles with ESM data

Abstract Language exposure and use (LEU) are widely viewed as key factors in multilingual development, and research highlights the importance of considering not just the frequency and quantity of LEU, but also contextual factors such as when and where a language is used, with whom and why. In this study, we illustrate the complexity of LEU in two contexts (study abroad and migration) by applying sequential mixture modelling to rich Experience Sampling Method data, considering combinations of various aspects of LEU such as language choice, type of activity, quantity, interlocutor characteristics and learner engagement. We argue that together, these methods for data collection and analysis have the potential to significantly strengthen research into LEU and broader language-related development. By uncovering distinct classes of language-related activities and language user profiles, we gain deeper insight into the nature of situated LEU and its relationship to language development among migrants and in study abroad.

Meaning-making with romantic partners: Shared reality promotes meaning in life by reducing uncertainty.

We propose that, although deeply personal, meaning is facilitated by interpersonal processes. Namely, we theorize that experiencing a sense of shared reality with a close partner (i.e., perceiving an overlap in inner states about the world in general) reduces uncertainty about one's environment, which in turn promotes meaning in work and life. In the current research, we test this hypothesis across five mixed-method studies (e.g., longitudinal, experimental). We find cross-sectional evidence for this association in a couples' study (Study 1: N = 103 romantic dyads) and in ecologically rich samples of people experiencing highly uncertain situations, specifically Black people consistently facing racism in the United States (Study 2: N = 190 participants) and frontline health care workers directly treating COVID-19 patients during the height of the pandemic (Study 3: N = 139 participants). Further, we provide causal evidence for this association in two experiments (Studies 4 and 5: N₄ = 364 participants, N₅ = 389 participants). Taken together, this work suggests that shared reality with close partners has real-world benefits, reducing uncertainty and promoting meaning. In addition, we show that experimentally heightening shared reality, by reducing uncertainty, can promote a greater sense of meaning in life. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Anidulafungin exposure and population pharmacokinetics in critically ill patients with invasive candidiasis.

Anidulafungin is recommended as a first-line treatment for invasive Candida infections in critically ill patients. Pharmacokinetic (PK) variability is large in critically ill patients, potentially compromising pharmacokinetic-pharmacodynamic (PKPD) target attainment under standard dosing. We aimed to assess anidulafungin exposure, PKPD target attainment, and population (pop)PK in critically ill patients. Adult ICU patients receiving standard anidulafungin dosing [200mg on day 1, then 100mg daily] were included (NCT04045366). We performed rich blood sampling on an early (day 2 ± 1) and/or late (day 5 ± 1) treatment day. Using total anidulafungin plasma concentrations, we developed a popPK model (NONMEM7.5) and conducted Monte Carlo simulations (n = 1,000 per virtual patient) to evaluate the impact of patient factors on PKPD target attainment (AUC24h target 83.5mg×h/L). Twenty patients contributed 188 anidulafungin concentrations. PKPD target attainment was 45% and 65% on early and late sampling days, respectively. A two-compartment popPK model with first-order elimination described the data. Anidulafungin clearance increased with bodyweight and central volume of distribution increased as serum albumin decreased. Both bodyweight and serum albumin had a clinically relevant impact on PKPD target attainment at day 1 (area under the ROC curve; AUROC 0.82 and 0.62, respectively), and bodyweight on PKPD target attainment at day 14 (AUROC 0.94). Standard anidulafungin dosing regimen fails to achieve adequate target attainment throughout the treatment period. Standard anidulafungin dosing is insufficient for achieving adequate exposure in critically ill patients. An interactive simulation tool is provided to aid dose-finding research and explore different dosing strategies and targets.

A multi modal fusion coal gangue recognition method based on IBWO-CNN-LSTM

Accurate identification of coal and gangue is a crucial guarantee for efficient and safe mining of top coal caving face. This article proposes a coal-gangue recognition method based on an improved beluga whale optimization algorithm (IBWO), convolutional neural network, and long short-term memory network (CNN-LSTM) multi-modal fusion model. First, the mutation and memory library mechanisms are introduced into the beluga whale optimization to explore the solution space fully, prevent falling into local optimum, and accelerate the convergence process. Subsequently, the image mapping of the audio signal and vibration signal is performed to extract Mel-Frequency Cepstral Coefficients (MFCC) features, generating rich sample data for CNN-LSTM. Then the multi-head attention mechanism is introduced into CNN-LSTM to speed up the training speed and improve the classification accuracy. Finally, the IBWO-CNN-LSTM coal-gangue recognition model is constructed by the optimal hyperparameter combination obtained by IBWO to realize the automatic recognition of coal-gangue. The benchmark function proves that IBWO is superior to other optimization algorithms. By building an experimental platform for the impact of coal and gangue falling on the tail beam of hydraulic support, multiple experimental data collection is carried out. The experimental results show that the proposed coal-gangue recognition model has better performance than other recognition models, and the accuracy rate reaches 95.238%. The multi-modal fusion strategy helps to improve the accuracy and robustness of coal-gangue recognition.

Understanding multilevel organizing: a focus of customer satisfaction on firm performance

ABSTRACT Customer satisfaction (CSAT) with a firm’s products and/or services is one of the most fundamental strategic assets a firm can nurture to achieve superior performance. Unfortunately, the effects of CSAT on firm-level performance have been examined almost exclusively by focusing on either consumer transactions and/or firm-level actions toward achieving performance. From the lens of understanding the organized ecosystem involving firms, we expand theorizing to the multilevel effects of CSAT as a strategic firm asset on performance (1) within firms over time, (2) between firms within industries, and (3) between industries. We compiled a rich sample of 1.2 million customer responses and 2,460 firm-year observations spanning 2008 to 2019 (pre-COVID-19 repeated observations for 306 firms across 38 industries). We include five performance metrics – three that reflect the firm’s ability to convert financial inputs and assets into profitability (ROA, ROE, and ROCE) and two direct measures of profitability (PM, EPS). Broadly, the results show that a firm’s performance varies (1) with differences in CSAT levels within firms over time, (2) relative to CSAT performance between firms within industries, and (3) relative to CSAT performance between industries. Also observed are combinative CSAT effects across levels for ROE and profit margin (PM).

Preparation of calcium-based phosphate adsorbent and mineral-rich humic acid fertilizer from biomass ash and bamboo by hydrothermal-pyrolysis: Performance and mechanism

Biomass ash (BA) contains alkaline cations such as K, Ca, and Mg. Due to its high pH, direct application to the soil may result in soil salinization. Composting of BA with organic matter is an effective strategy, but the composting cycle is long and there is a large amount of insoluble residue in the product. Therefore, this research proposed for the first time using the hydrothermal method to rapidly convert BA and bamboo powder (BP) into water - soluble fertilizer (WSF) within 4 h. The insoluble hydrothermal residue was further converted into calcium - rich biochar phosphorus adsorption material by a simple pyrolysis process. WSF was neutral and contained humic acid and elements like K, Ca, Mg, and Si. A 14 - day wheat hydroponic experiment showed that the addition of 0.0125% WSF increased the fresh weight of wheat by 18.77% compared with deionized water. The calcium - based biochar adsorbent produced by pyrolysis had an ideal adsorption capacity of up to 113.6 mg P g−1 for phosphate in water, higher than many existing reports. The adsorption mechanisms mainly included surface precipitation, ion exchange, and electrostatic attraction. Moreover, the calcium - rich biochar sample slowly released phosphorus into water after adsorbing phosphate. When the pH was 3 or 4, the removal rate of Pb2+, Cd2+, and Cu2+ at 15 – 20 mg L−1 was as high as 99%. This indicated its potential as a slow - release fertilizer and heavy metal remediation agent. This research provided a new way of thinking for the treatment and disposal of BA.

Combining style generative adversarial networks with particle swarm optimisation-support vector regression to design affective social robot for public health intervention

In order to improve the attractiveness of social robot serving health interventions in public workspace, we propose a product design method with the combination of Style-generative adversarial network (StyleGAN) model and particle swarm optimisation-support vector regression (PSO-SVR). This paper aims to explore the modelling generation method of social robots for health intervention based on artificial intelligence generated content (AIGC) and a mapping model between the shape characteristics and users’ visual perception based on Kansei Engineering (KE). Firstly, to address the defects of typical KE over-reliance on existing samples, we introduce the StyleGAN model in AIGC to learn and train existing robots’ shape samples and generate new robots’ shape sample images. Secondly, the morphological deconstruction method is used to deconstruct the shape features of the new robot sample. Factor analysis (FA) is also used to reduce dimension and cluster emotional words and establish a Likert scale between the shape features of robots and users’ emotional vocabulary. Finally, particle swarm optimisation-support vector regression (PSO-SVR) is used to establish a mapping model between the shape features of social robots and the emotional images of users, thus obtaining the most attractive shape combination scheme. The research results showed that the StyleGAN model in AIGC can be used to assist industrial designers in creative expression and provide rich sample sources for KE; and the PSO-SVR of machine learning can be used to build a mapping model among users’ visual feelings, emotional images and shape features. In the end, we designed an attractive social robot for public health intervention.

Model of oil pipeline tiny defects detection based on DDPM gated parallel convolutional swin transformer

Abstract To address the challenges of difficult detection of minute magnetic flux leakage (MFL) defects, insufficient inspection data, and low detection accuracy, the denoising diffusion probabilistic model (DDPM) gate dilated parallel convolution swin transformer (DGPST) is proposed. First, we introduce a DDPM-based data generation model, successfully generating a large quantity of diverse and rich MFL defect samples. Second, a gated parallel convolution layer is introduced into the backbone network. This strategy uses the characteristics of dilated convolution to broaden the receptive field of the model, thus enhancing the integration ability of global information. The addition of gating mechanism enables the model to adjust the calculation of attention weight based on broader context information in advance, which not only complicates the shortcomings of window self-attention in global dependence understanding, but also effectively suppress irrelevant calculation. Finally, the loss function of H Intersection over Union is introduced to improve the mean average precision. Following these enhancements, DGPST attains a satisfactory outcome in detecting tiny defects within the MFL problem. Experimental data indicates the accuracy of the algorithm reaches 95.6% and the delay is reduced to 7.6 ms.

Inferring in-home/out-of-home situations unreported in time-use surveys using supervised machine learning

Time-use surveys provide useful data for travel analyses. However, the survey on time use and leisure activities (TULA) Questionnaire A, a representative time-use survey in Japan, does not include questions related to the locations of activities, thus making it difficult to use for travel analyses. This study proposes machine-learning methods to determine the in-home/out-of-home situations of TULA Questionnaire A using TULA Questionnaire B with activity locations as the training data. Random forest performs better than logistic regression and decision trees in the inference. The activity was the most important factor in determining the in-home/out-of-home situations, followed by the accompanying person and time of day. The inferred outputs in the TULA Questionnaire A included the individual-based out-of-home rate profiles and the proportions of mobile persons from 1996 to 2016. Using these outputs, we analyzed trip misreporting in household travel surveys. Comparisons with nationwide and Tokyo person trip (PT) surveys implied soft refusals and trip misreporting in travel surveys. The comparison with the nationwide PT surveys suggested higher soft refusals on weekends than on weekdays. The comparison with the 1998, 2008, and 2018 Tokyo PT surveys implied the increased soft refusal in PT surveys, particularly among the male group aged between 20 and 39 and the female group aged between 35 and 49 during 1998–2018. These results suggest that careful handling of recent household travel survey data may be required. In addition, the proposed machine-learning-based method enables us to utilize the rich sample of Questionnaire A for activity-based travel analysis in future studies.

Morality through the lens of Confucian heritage countries: collective self variations and moral worldviews.

The issue of Western, Educated, Industrialized, Rich, and Democratic (WEIRD) samples dominating research has been ongoing for decades, and now the emerging trend is to turn to theoretical perspectives from the Majority World. Adopting Western-centric methods based on reductionism can overlook important details and differences between similar cultures, particularly in East Asian cultures, where the Confucian values of relational harmony take many forms. We discuss a novel theoretical perspective on moral constitutions in Confucian heritage cultures. Our central tenet is that divergent moral concepts and ideals are present in Confucian cultures because each culture emphasizes a specific pillar and the self is situated differently in the social relationships that define the scope of interpersonal moral obligations. We consider three Confucian countries: China, Japan, and South Korea as examples. Despite geographical proximity and conventional categorization in cultural psychology, each Confucian country manifests distinct patterns of the self, moral ideals, and behavior in socio-moral contexts. To understand how and why moral worldviews vary within a region, we need to examine how the self in socio-cultural contexts differs and guides interpersonal norms and behaviors across sociocultural contexts. We conclude this paper by offering methodological recommendations for including indigenous moral concepts outside the WEIRD context.

B-229 Extraction-free Nucleic Acid Sample Preparation from Inhibitor Rich Samples via Hyperbaric Heating

Abstract Background Extraction-based nucleic acid sample preparation for PCR is limited by long processing times, liquid transfers, cost, and storage requirements. Extraction-free approaches enable streamlined sample prep but are severely impacted by inhibitors present in biological sample matrices. We previously reported the use of Hyperbaric Heating (HBH) for effective organism lysis and demonstrate here its effectiveness to prepare nucleic acids for PCR from various inhibitor-rich sample matrix types. Methods HBH exposes the sample to high temperature/high pressure environment for a short duration. This process effectively lyses organisms and denatures inhibitors within seconds. Samples are sealed within a pressure-tight metallic vessel, then rapidly heated using magnetic induction. Within the sealed metallic vessel, vaporization of the aqueous sample raises the internal pressure, enabling an internal temperature higher than 100°C. Proprietary sample processing beads containing chelating agents and other proteins protect nucleic acids from fragmentation during heating. Results The efficacy of HBH for PCR amplification was tested against commercial kits and extraction-free methods. HBH treatment of nasal swab samples spiked with inactivated SARS-CoV-2 elicited detection down to 125 viral copies/mL. Viruses such as RSV and Influenza spiked in nasal swab samples demonstrated comparable PCR results as extraction prepped samples. HBH treatment of microbe samples, including Candida albicans spiked in urine and Bacillus subtilis spiked in serum, had comparable PCR threshold cycles as microbe specific DNA extraction methods (Table 1). Conclusions HBH is a generalizable extraction-free nucleic acid sample prep technology affording detection capabilities comparable to commercial sample prep methods. We demonstrated the use of HBH sample prep for detection of several infectious microorganisms in common biological matrices (nasal swab, serum, urine) without additional sample purification. With its ease of use, speed, and lower cost, HBH will be a key tool in applications where nucleic acid amplification is pivotal for diagnostic and research use.

Fatty Acid Composition Analysis of Some Apiaceae Plants Using Gas Chromatography-Flame Ionization Detector (GC-FID)

Background/aim: The Apiaceae family, also referred to as the parsley or carrot family, comprises a diverse group of plants with significant ecological and economic importance. The fundamental building blocks of plant lipids, fatty acids are involved in a number of critical biological functions. Flame ionization detection in gas chromatography (GC-FID) is a potent analytical method widely used for the qualitative and quantitative analysis of fatty acid composition in plant samples. Materials and methods: In this study, we investigated the fatty acid profiles of several Apiaceae plants using GC-FID to elucidate their lipid composition and potential applications. Results: A wide spectrum of fatty acids was confirmed, ranging from C6:0 to C22:6. Astrodaucus orientalis leaf and fruit were found rich in palmitic acid (C16:0; 35.96% and 37.65%, respectively). Ferulago asparagifolia was determined as the richest sample in terms of poly-unsaturated fatty acids such as linoleic acid (C18:2 cis, 25.65%), alpha linoleic acid (C18:3n3, 7.42%) and eicosadienoic acid (C20:2, 32.66%). Ferulago syriaca ethanol and hexane extracts contained considerable amount of oleic acid (C 18:1 n9, 40. 37% and 49.19%, respectively). Conclusion: Our review of the literature revealed that no prior reports have been made about the fatty acid compositions of Astrodaucus orientalis, Ferulago asparagifolia and Ferulago syriaca. As a result, the information provided here may be the first to describe the fatty acid contents of these species. Exploring the lipid profiles of these plants can enhance their potential applications in the food, pharmaceutical, and cosmetic industries.

Pharmacokinetics of Ganaplacide and Lumefantrine in Adults, Adolescents, and Children with Plasmodium falciparum Malaria Treated with Ganaplacide Plus Lumefantrine Solid Dispersion Formulation: Analysis of Data from a Multinational Phase 2 Study.

The novel antimalarial ganaplacide combined with lumefantrine solid dispersion formulation (LUM-SDF) was effective and well tolerated in the treatment of uncomplicated falciparum malaria in adults, adolescents, and children in a multinational, prospective, randomized, active-controlled Phase II study conducted between August 2017 and June 2021 (EudraCT 2020-003284-25, Clinicaltrials.gov NCT03167242). Pharmacokinetic data from that study are reported here. The trial comprised three parts: a run-in part in 12 adult/adolescent patients treated with a single dose of ganaplacide 200 mg plus LUM-SDF 960 mg assessed potential pharmacokinetic (PK) interactions between ganaplacide and lumefantrine; in Part A, adult/adolescent patients received one of the six ganaplacide-LUM-SDF regimens or artemether-lumefantrine; and in Part B, three dose regimens identified in Part A, and artemether-lumefantrine, were assessed in children aged 2 to <12 years, with body weight ≥10 kg. A rich blood sampling schedule was used for all 12 patients in the PK run-in part and a subset of patients (N = 32) in Part A, with sparse sampling for remaining patients in Parts A (N = 275) and B (N = 159). Drug concentrations were determined by a validated protein precipitation and reverse phase liquid chromatography with tandem mass spectrometry detection method. Parameters including AUCinf, AUClast, AUC0-t, Cmax, and tmax were reported where possible, using non-compartmental analysis. In the PK run-in part, there was no notable increase in ganaplacide or lumefantrine exposure when co-administered. In Parts A and B, ganaplacide exposures increased with dose, but lumefantrine exposure was numerically under dose-proportional. Lumefantrine exposure was higher with ganaplacide-LUM-SDF than with artemether-lumefantrine, although high variability was observed. Ganaplacide and lumefantrine exposures (Cmax and AUC0-24 h) were comparable across age and body weight groups. Drug exposures needed for efficacy were achieved using the dose regimen 400 mg ganaplacide plus lumefantrine 960 mg once daily for 3 days under fasted conditions.

ATLAS upgrade for the HL-LHC

While the on-going Run-3 data-taking campaign will provide twice the integrated proton–proton luminosity currently available at the LHC, most of the data expected for the full LHC physics program will only be delivered during the HL-LHC phase. For this, the LHC will undergo an ambitious upgrade program to be able to deliver an instantaneous luminosity of 7.5⋅1034cm−2s−1, allowing the collection of more than 3 ab−1 of data at s=13.6(14)TeV. This unprecedented data sample will allow the ATLAS experiment to perform several precision measurements to constrain the Standard Model (SM) in yet unexplored regions of phase space, in particular in the Higgs sector, only accessible at the LHC. To benefit from such a rich data sample, it is fundamental to upgrade the detector to cope with the challenging experimental conditions that include radiation doses 10 times higher than what ATLAS experienced since its construction and increase in the number of interactions per bunch crossing from the current 50-60 to 200. The upgrade of the ATLAS detector comprises a completely new all-silicon tracker with extended rapidity coverage that will replace the current inner tracker detector; a redesigned trigger and data acquisition system for the calorimeters and muon systems, allowing the implementation of a readout system that can operate complex trigger algorithms at 40 MHz. Finally, a new subsystem called High Granularity Timing Detector will aid the track-vertex association in the forward region by providing timing information to the reconstructed tracks. An important ingredient, relevant to almost all measurements, is the precise determination of the delivered luminosity with systematic uncertainties below the percent level. This challenging task will be achieved by collecting the information from several detector systems using different and complementary techniques. This document will describe the ongoing ATLAS detector upgrade status and the main results obtained with the prototypes, giving a synthetic view of the whole upgrade project.

The art of research cohort construction using administrative datasets

ObjectivesUsing research case studies, I will give an overview as to how researchers can experience problems translating great research ideas, grounded in the relevant literature, into a robust research design. Assuming that the question cannot be reliably researched using small but rich sample surveys, I will present ways in which routine admin data can help in this context, along with the additional challenges posed when creating the correct cohort to address the research question. MethodsThe examples given are from a study which links together routinely collected administrative data for a representative sample of the population. It includes a wealth of information from census, civil registration data, education data, and with further permission health data can also be linked. The study in question is provided deidentified, and accessed within a data enclave. The size and scope of the study make it an unparalleled resource for analysing a range of socio-economic, demographic and health questions. ResultsI will demonstrate how despite the large number of study members, these cohorts must be carefully considered in order to research outcomes (events/results) due to inconsistency in the years that different admin data are available centrally in admin systems (health/education data). Examples include: life-course events for a cohort of women followed up to mid-life, and a complete sub-cohort followed up into later-life. ConclusionsTo showcase exciting ways in which linked admin data can be operationalised to take a longitudinal/life-course approach to address research questions, and to emphasise cohort construction is complex.

Highly siderophile element and Re-Os isotope systematics of a Neoproterozoic Iron Formation and its temporal relation to glaciation events

The Urucum iron- and manganese formation (IF-MnF) in the Santa Cruz Formation, Brazil, was deposited in a glacially influenced, late Neoproterozoic depositional environment. It has proven to be a reliable and robust archive for the late Neoproterozoic, allowing unique insights into the composition of seawater during an interval that is characterized by dramatic changes in the Earth’s litho-, hydros-, atmo-, and biosphere, including several episodes of low-latitude glaciations. Here we present highly siderophile element data of the Urucum IF-MnF to evaluate elemental sources that affected the Neoproterozoic seawater from which the Urucum IF-MnF precipitated. High uncertainties associated with current dating attempts of this formation overlapped with the Marinoan glaciation (∼650–635 Ma) as well as the Gaskiers glaciation event (∼580 Ma). A Re-Os regression line defined by iron-, manganese-, and chert-rich samples of the Urucum formation is interpreted as an isochron, yielding a refined age estimate of 577 ± 38 Ma and an initial 187Os/188Os ratio of 0.122 ± 0.003. The proposed depositional age overlaps with previous published age data of the Urucum IF-MnF, but relates the Urucum IF-MnF to the Ediacaran Gaskiers glaciation and overlaps with the age of the Shuram CIE (∼567 Ma). An initial 187Os/188Os ratio of 0.122 ± 0.003 is interpreted to represent pristine Neoproterozoic seawater and supports strong hydrothermal input, probably related to rifting, that promoted ferruginous conditions in the Urucum seawater.In an alternative scenario, Re-Os isotope data for the Urucum IF-MnF could also be interpreted in a multi-component mixing scenario, defined by hydrothermally influenced water masses that sourced most metals within the Urucum basin and a crustal component that entered the Urucum seawater either as detrital admixture or via riverine loads. Although none of the scenarios can be ruled out, we consider the isochron scenario as more plausible.

Expanding the Scope of Diverse Contexts in Affective Science.

The Future of Affective Science special issue covered a broad range of topics, ranging from the valuation and experience of emotions to their expression and regulation. Despite the breadth of these topics, consideration of diverse contexts was limited in that most studies relied on Western, Educated, Industrialized, Rich, and Democratic (WEIRD) samples and/or failed to explore the rich diversity present in those countries with vast population heterogeneity. We argue that a more systematic approach is essential to advance theory and research on how diverse contexts impact and interact with different components of emotion processes. Specifically, future research should compare a wider range of cultures; examine the interplay between culture, individual, and situation; and transcend the traditional Eastern/Western or WEIRD/non-WEIRD dichotomy and other group comparisons by embracing a dimensional approach to diverse contexts. We provide concrete examples of how leveraging these three approaches to expand the scope of diverse contexts can benefit the future development of affective science, a field heavily impacted by sociocultural context.

Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methods.

Conventional approaches for establishing bioequivalence (BE) between test and reference formulations using non-compartmental analysis (NCA) may demonstrate low power in pharmacokinetic (PK) studies with sparse sampling. In this case, model-integrated evidence (MIE) approaches for BE assessment have been shown to increase power, but may suffer from selection bias problems if models are built on the same data used for BE assessment. This work presents model averaging methods for BE evaluation and compares the power and type I error of these methods to conventional BE approaches for simulated studies of oral and ophthalmic formulations. Two model averaging methods were examined: bootstrap model selection and weight-based model averaging with parameter uncertainty from three different sources, either from a sandwich covariance matrix, a bootstrap, or from sampling importance resampling (SIR). The proposed approaches increased power compared with conventional NCA-based BE approaches, especially for the ophthalmic formulation scenarios, and were simultaneously able to adequately control type I error. In the rich sampling scenario considered for oral formulation, the weight-based model averaging method with SIR uncertainty provided controlled type I error, that was closest to the target of 5%. In sparse-sampling designs, especially the single sample ophthalmic scenarios, the type I error was best controlled by the bootstrap model selection method.