Modified Fields-Backofen and Zerilli-Armstrong constitutive models to predict the hot deformation behavior in titanium-based alloys.

This work presents modifications for two constitutive models for the prediction of the flow behavior of titanium-based alloys during hot deformation. The modified models are the phenomenological-based Fields-Backofen and the physical-based Zerilli-Armstrong. The modifications are derived and suggested by studying the hot deformation of titanium-based alloy Ti55531. The predictability of the modified models along with the original Fields-Backofen and another modified Zerilli-Armstong models is assessed and evaluated using the well-known statistical parameters correlation coefficient (R), Average Absolute Relative Error (AARE), and Root Mean Square Error (RMSE), for the Ti55531 alloy, and validated with other two different titanium-based alloys SP700 and TC4. The results show that the modified Fields-Backofen gives the best performance with R value of 0.996, AARE value of 3.34%, and RMSE value of 5.64 MPa, and the improved version of the modified Zerilli-Armstrong model comes in the second-best place with R value of 0.992, AARE value of 3.52%, and RMSE value of 9.15 MPa for the Ti55531 alloy.

Transcarotid vascular access for transcatheter aortic valve implantation: is choosing the left side always right?

BACKGROUND: The transcarotid (TC) vascular access for transcatheter aortic valve implantation (TAVI) has emerged as the first-choice alternative to the transfemoral access, in patients unsuitable for the latter. The use of both the left and right common carotid arteries (CCAs) for TC-TAVI has been described, but the optimal side is subject to debate. We conducted this pilot study to compare the level of vessel tortuosity and plaque burden from either the left CCA to the aortic annulus, or the right CCA to the aortic annulus, considering them as surrogates for technical and procedural complexity. METHODS: Consecutive patients who underwent TC-TAVI between 2018 and 2021 in our institution were included. Using three-dimensional reconstruction, pre-TAVI neck and chest computed tomography angiography exams were reviewed to assess the tortuosity index (TI), sum of angles metric, as well as plaque burden, between each CCA and the aortic annulus. RESULTS: We included 46 patients who underwent TC-TAVI. No significant difference regarding the mean TIs between the left and right sides (respectively 1.20 and 1.19, p = 0.82), the mean sum of angles (left side: 396°, right side: 384°, p = 0.27), and arterial plaque burden (arterial plaque found in 30% of left CCAs and 45% of right CCAs, p = 0.19) was found. CONCLUSIONS: We found no convincing data favoring the use of one particular access side over the other one. The choice of the CCA side in TC-TAVI should to be made on a case-by-case basis, in a multidisciplinary fashion, and may also depend on the operators' experience.

Targeting Manganese Amidinates and ß-Ketoiminates Complexes as Precursors for Mn-Based Thin Film Vapor Deposition.

With a focus on Mn based organometallic compounds with suitable physico-chemical properties to serve as precursors for chemical vapor deposition (CVD) and atomic layer deposition (ALD) of Mn-containing materials, systematic synthetic approaches with ligand variation, detailed characterization, and theoretical input from density functional theory (DFT) studies are presented. A series of new homoleptic all-nitrogen and mixed oxygen/nitrogen-coordinated Mn(II) complexes bearing the acetamidinate, formamidinate, guanidinate and ß-ketoiminate ligands have been successfully synthesized for the first time. The specific choice of these ligand classes with changes in structure and coordination sphere and side chain variations result in significant structural differences whereby mononuclear and dinuclear complexes are formed. This was supported by density functional theory (DFT) studies. The compounds were thoroughly characterized by single crystal X-ray diffraction, magnetic measurements, mass spectrometry and elemental analysis. To evaluate their suitability as precursors for deposition of Mn-based materials, the thermal properties were investigated in detail. Mn(II) complexes possessing the most promising thermal properties, namely Bis(N,N´-ditertbutylformamidinato)manganese(II) (IV) and Bis(4-(isopropylamino)pent-3-en-2-onato)manganese(II) (ßIII) were used in reactivity studies with DFT to explore their interaction with oxidizing co-reactants such as oxygen and water which will guide future CVD and ALD process development.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

Antimony and arsenic migration in a heterogeneous subsurface at an abandoned antimony smelter under rainfall.

While antimony (Sb) and arsenic (As) co-contamination in subsurface soil systems due to the legacy of Sb smelting wastes has been documented, the role of inherent heterogeneity on pollutant migration is largely overlooked. Herein this study investigated Sb and As migration in a slag impacted, vertically stratified subsurface at an abandoned Sb smelter. A 2-dimensional flume was assembled as a lab-scale analogue of the site and subject to rainfall and stop-rain events. Reactive transport modeling was then performed by matching the experimental observations to verify the key factors and processes controlling pollutant migration. Results showed that rainfall caused Sb and As release from the shallow slag layer and promoted their downward movement. Nevertheless, the less permeable deeper layers limited physical flow and transport, which led to Sb and As accumulation at the interface. The re-adsorption of Sb and As onto iron oxides in the deeper, more acidic layers further retarded their migration. Because of the large difference between Sb and As concentrations, Sb re-adsorption was much less effective, which led to higher mobility. Our findings overall highlight the necessity of understanding the degree and impacts of physicochemical heterogeneity for risk exposure assessment and remediation of abandoned Sb smelting sites.

Public Discourse, User Reactions, and Conspiracy Theories on the X Platform About HIV Vaccines: Data Mining and Content Analysis.

BACKGROUND: The initiation of clinical trials for messenger RNA (mRNA) HIV vaccines in early 2022 revived public discussion on HIV vaccines after 3 decades of unsuccessful research. These trials followed the success of mRNA technology in COVID-19 vaccines but unfolded amid intense vaccine debates during the COVID-19 pandemic. It is crucial to gain insights into public discourse and reactions about potential new vaccines, and social media platforms such as X (formerly known as Twitter) provide important channels. OBJECTIVE: Drawing from infodemiology and infoveillance research, this study investigated the patterns of public discourse and message-level drivers of user reactions on X regarding HIV vaccines by analyzing posts using machine learning algorithms. We examined how users used different post types to contribute to topics and valence and how these topics and valence influenced like and repost counts. In addition, the study identified salient aspects of HIV vaccines related to COVID-19 and prominent anti-HIV vaccine conspiracy theories through manual coding. METHODS: We collected 36,424 English-language original posts about HIV vaccines on the X platform from January 1, 2022, to December 31, 2022. We used topic modeling and sentiment analysis to uncover latent topics and valence, which were subsequently analyzed across post types in cross-tabulation analyses and integrated into linear regression models to predict user reactions, specifically likes and reposts. Furthermore, we manually coded the 1000 most engaged posts about HIV and COVID-19 to uncover salient aspects of HIV vaccines related to COVID-19 and the 1000 most engaged negative posts to identify prominent anti-HIV vaccine conspiracy theories. RESULTS: Topic modeling revealed 3 topics: HIV and COVID-19, mRNA HIV vaccine trials, and HIV vaccine and immunity. HIV and COVID-19 underscored the connections between HIV vaccines and COVID-19 vaccines, as evidenced by subtopics about their reciprocal impact on development and various comparisons. The overall valence of the posts was marginally positive. Compared to self-composed posts initiating new conversations, there was a higher proportion of HIV and COVID-19-related and negative posts among quote posts and replies, which contribute to existing conversations. The topic of mRNA HIV vaccine trials, most evident in self-composed posts, increased repost counts. Positive valence increased like and repost counts. Prominent anti-HIV vaccine conspiracy theories often falsely linked HIV vaccines to concurrent COVID-19 and other HIV-related events. CONCLUSIONS: The results highlight COVID-19 as a significant context for public discourse and reactions regarding HIV vaccines from both positive and negative perspectives. The success of mRNA COVID-19 vaccines shed a positive light on HIV vaccines. However, COVID-19 also situated HIV vaccines in a negative context, as observed in some anti-HIV vaccine conspiracy theories misleadingly connecting HIV vaccines with COVID-19. These findings have implications for public health communication strategies concerning HIV vaccines.

How will climate change and forest harvesting influence the habitat quality of two culturally salient species?

Boreal landscapes face increasing disturbances which can affect cultural keystone species, i.e. culturally salient species that shape in a major way the cultural identity of a people. Given their importance, the fate of such species should be assessed to be able to act to ensure their perennity. We assessed how climate change and forest harvesting will affect the habitat quality of Rhododendron groenlandicum and Vaccinium angustifolium, two cultural keystone species for many Indigenous peoples in eastern Canada. We used the forest landscape model LANDIS-II in combination with species distribution models to simulate the habitat quality of these two species on the territories of three Indigenous communities according to different climate change and forest harvesting scenarios. Climate-sensitive parameters included wildfire regimes as well as tree growth. Moderate climate change scenarios were associated with an increased proportion of R. groenlandicum and V. angustifolium in the landscape, the latter species also responding positively to severe climate change scenarios. Harvesting had a minimal effect, but slightly decreased the probability of presence of both species where it occurred. According to the modeling results, neither species is at risk under moderate climate change scenarios. However, under severe climate change, R. groenlandicum could decline as the proportion of deciduous trees would increase in the landscape. Climate change mitigation strategies, such as prescribed fires, may be necessary to limit this increase. This would prevent the decrease of R. groenlandicum, as well as contribute to preserve biodiversity and harvestable volumes.

Comparing Sensitivity of Different Bee Species to Pesticides: A TKTD modeling approach.

Risk assessment for bees is mainly based on data for honey bees; however, risk assessment is intended to protect all bee species. This raises the question of whether data for honey bees are a good proxy for other bee species. This issue is not new and has resulted in several publications in which the sensitivity of bee species is compared based on the values of the 48-h median lethal dose (LD50) from acute test results. When this approach is used, observed differences in sensitivity may result both from differences in kinetics and from inherent differences in species sensitivity. In addition, the physiology of the bee, like its overall size, the size of the honey stomach (for acute oral tests), and the physical appearance (for acute contact tests) also influences the sensitivity of the bee. The recently introduced Toxicokinetic-Toxicodynamic (TKTD) model that was developed for the interpretation of honey bee tests (Bee General Uniform Threshold Model for Survival [BeeGUTS]) could integrate the results of acute oral tests, acute contact tests, and chronic tests within one consistent framework. We show that the BeeGUTS model can be calibrated and validated for other bee species and also that the honey bee is among the more sensitive bee species. In addition, we found that differences in sensitivity between species are smaller than previously published comparisons based on 48-h LD50 values. The time-dependency of the LD50 and the specifics of the bee physiology are the main causes of the wider variation found in the published literature. Environ Toxicol Chem 2024;00:1-11. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Physical habitat modeling for river macroinvertebrate communities.

Habitat models rarely consider macroinvertebrate communities as ecological targets in rivers. Available approaches mainly focus on single macroinvertebrate species, not addressing the ecological needs and functionality of the whole community. This research aimed at providing an approach to model the habitat of the macroinvertebrate communities. The study was carried out in three rivers, located in Italy and characterized by a braiding morphology, gravel riverbeds, and low flows during the summer period. The approach is based on the recently developed Flow-T index, together with a Random Forest (RF) regression, which is employed to apply the Flow-T index at the mesohabitat scale. Using different datasets gathered from field data collection and 2D hydrodynamic simulations, the model was calibrated in the Trebbia River (2019 field campaign) and validated in the Trebbia, Taro, and Enza rivers (2020 field campaign). The RF model selected 12 mesohabitat descriptors as important for the macroinvertebrate community. These descriptors belong to different frequency classes of water depth, flow velocity, substrate grain size, and connectivity to the main river channel. The cross-validation R2 coefficient (R2cv) of the training dataset was 0.71, whereas the R2 coefficient (R2test) for the validation dataset was 0.63. The agreement between the simulated results and the experimental data shows sufficient accuracy and reliability. The outcomes of the study reveal that the model can identify the ecological response of the macroinvertebrate community to possible flow regime alterations and river morphological modifications. Lastly, the proposed approach allowed to extend the MesoHABSIM methodology, widely used for the fish habitat assessment, to a different ecological target community. Further applications of the approach can be related to ecological flows design in both perennial and non-perennial rivers, including river reaches in which fish fauna is absent.

Investigating cryopreserved PBMC functionality in an antigen-induced model of sarcoidosis granuloma formation.

Sarcoidosis, a systemic inflammatory disease, poses challenges in understanding its etiology and variable clinical courses. Despite ongoing uncertainty about causative agents and genetic predisposition, granuloma formation remains its hallmark feature. To address this, we developed a validated in vitro human granuloma model using patient-derived peripheral blood mononuclear cells (PBMCs), offering a dynamic platform for studying early granuloma formation and sarcoidosis pathogenesis. However, a current limitation of this model is its dependence on freshly isolated PBMCs obtained from whole blood. While cryopreservation is a common method for long-term sample preservation, the biological effects of freezing and thawing PBMCs on granuloma formation remain unclear. This study aimed to assess the viability and functionality of cryopreserved sarcoidosis PBMCs within the granuloma model, revealing similar granulomatous responses to fresh cells and highlighting the potential of cryopreserved PBMCs as a valuable tool for studying sarcoidosis and related diseases.

From pregnancy to the postpartum: Unraveling the complexities of symptom profiles among trauma-exposed women.

BACKGROUND: Many women experience new onset or worsening of existing posttraumatic stress disorder (PTSD) symptoms during pregnancy and the early postpartum period. However, perinatal PTSD symptom profiles and their predictors are not well understood. METHODS: Participants (N = 614 community adults) completed self-report measures across three methodologically similar longitudinal studies. Mixture modeling was used to identify latent subgroups of trauma-exposed women with distinct patterns of symptoms at pregnancy, 1-month, and 3-month postpartum. RESULTS: Mixture modeling demonstrated two classes of women with relatively homogenous profiles (i.e., low vs. high symptoms) during pregnancy (n = 237). At 1-month postpartum (n = 391), results suggested a five-class solution: low symptoms, PTSD only, depression with primary appetite loss, depression, and comorbid PTSD and depression. At 3-months postpartum (n = 488), three classes were identified: low symptoms, elevated symptoms, and primary PTSD. Greater degree of exposure to interpersonal trauma and reproductive trauma, younger age, and minoritized racial/ethnic identity were associated with increased risk for elevated symptoms across the perinatal period. LIMITATIONS: Only a subset of potential predictors of PTSD symptoms were examined. Replication with a larger and more racially and ethnically diverse sample of pregnant women is needed. CONCLUSIONS: Results highlight limitations of current perinatal mental health screening practices, which could overlook women with elevations in symptoms (e.g., intrusions) that are not routinely assessed relative to others (e.g., depressed mood), and identify important risk factors for perinatal PTSD symptoms to inform screening and referral.

Daily stress reactivity and risk appraisal mediates childhood parental abuse predicting adulthood psychopathology severity: An 18-year longitudinal mediation analysis.

Identifying mechanisms of childhood abuse-adulthood psychopathology relations could facilitate preventive efforts, but most prior studies used cross-sectional or two-wave designs and did not test the effects of childhood maternal and paternal abuse separately. Our 18-year three-wave study thus determined if Wave 2 daily stress reactivity and risk appraisal severity mediated Wave 1 retrospectively-reported childhood maternal and paternal abuse on Wave 3 generalized anxiety disorder (GAD), major depressive disorder (MDD), panic disorder (PD), alcohol (AUD), and substance use disorder (SUD) self-rated symptom severity. Longitudinal structural equation modeling was employed, adjusting for Wave 1 psychopathology severity. Higher childhood maternal and paternal abuse consistently predicted greater future daily stress reactivity and risk appraisal, and these mediators subsequently predicted increased GAD, MDD, and PD, but not AUD and SUD severity. Daily stress reactivity and risk appraisal consistently mediated the pathways between childhood maternal and paternal abuse predicting heightened adulthood GAD, MDD, and PD (Cohen's d = 0.333-0.888) but not AUD and SUD severity. Mediation effect sizes were stronger for childhood maternal (24.5-83.0 %) than paternal (19.5-56.0 %) abuse as the predictor. The latent interaction between Wave 1 childhood maternal and paternal abuse did not moderate the effect of Wave 1 maternal or paternal abuse on any Wave 3 adulthood psychopathology severity through Wave 2 daily stress reactivity and risk appraisal. Our research emphasizes the urgent requirement for continuous evaluation and intervention initiatives in trauma-informed care, both in inpatient and outpatient treatment settings.

Protective effects of antibiotic resistant bacteria on susceptibles in biofilm: Influential factors, mechanism, and modeling.

In environmental biofilms, antibiotic-resistant bacteria facilitate the persistence of susceptible counterparts under antibiotic stresses, contributing to increased community-level resistance. However, there is a lack of quantitative understanding of this protective effect and its influential factors, hindering accurate risk assessment of biofilm resistance in diverse environment. This study isolated an opportunistic Escherichia coli pathogen from soil, and engineered it with plasmids conferring antibiotic resistance. Protective effects of the ampicillin resistant strain (AmpR) on their susceptible counterparts (AmpS) were observed in ampicillin-stress colony biofilms. The concentration of ampicillin delineated protective effects into 3 zones: continuous protection (<1 MIC of AmpS), initial AmpS/R dependent (1-8 MIC of AmpS), and ineffective (>8 MIC of AmpS). Intriguingly, Zone 2 exhibited a surprising "less is more" phenomenon tuned by the initial AmpS/R ratio, where biofilm with an initially lower AmpR (1:50 vs 50:1) harbored 30-90 % more AmpR after 24 h growth under antibiotic stress. Compared to AmpS, AmpR displayed superiority in adhesion, antibiotic degradation, motility, and quorum sensing, allowing them to preferentially colonize biofilm edge and areas with higher ampicillin. An agent-based model incorporating protective effects successfully simulated tempo-spatial dynamics of AmpR and AmpS influenced by antibiotic stress and initial AmpS/R. This study provides a holistic view on the pervasive but poorly understood protective effects in biofilm, enabling development of better risk assessment and precisely targeted control strategies of biofilm resistance in diverse environment.

Optimizing lyophilization primary Drying: A vaccine case study with experimental and modeling techniques.

In this study, we present the lyophilization process development efforts for a vaccine formulation aimed at optimizing the primary drying time (hence, the total cycle length) through comprehensive evaluation of its thermal characteristics, temperature profile, and critical quality attributes (CQAs). Differential scanning calorimetry (DSC) and freeze-drying microscopy (FDM) were used to experimentally determine the product-critical temperatures, viz., the glass transition temperature (Tg') and the collapse temperature (Tc). Initial lyophilization studies indicated that the conventional approach of targeting product temperature (Tp) below the Tc (determined from FDM) resulted in long and sub-optimal drying times. Interestingly, aggressive drying conditions where the product temperature reached the total collapse temperature did not result in macroscopic collapse but, instead, reduced the drying time by ∼ 45 % while maintaining product quality requirements. This observation suggests the need for a more reliable measurement of the macroscopic collapse temperature for product in vials. The temperature profiles from different lyophilization runs showed a drop in product temperature following the primary drying ramp, of which the magnitude was correlated to the degree of macroscopic collapse. The batch-average product resistance, Rp, determined using the manometric temperature measurement (MTM), decreased with increasing dried layer thickness for aggressive primary drying conditions. A quantitative analysis of the product temperature and resistance profiles combined with qualitative assessment of cake appearance attributes was used to determine a more representative macro-collapse temperature, Tcm, for this vaccine product. A primary drying design space was generated using first principles modeling of heat and mass transfer to enable selection of optimum process parameters and reduce the number of exploratory lyophilization runs. Overall, the study highlights the importance of accurate determination of macroscopic collapse in vials, pursuing aggressive drying based on individual product characteristics, and leveraging experimental and modeling techniques for process optimization.

Challenges faced with 3D-printed electrochemical sensors in analytical applications.

Prototyping analytical devices with three-dimensional (3D) printing techniques is becoming common in research laboratories. The attractiveness is associated with printers' price reduction and the possibility of creating customized objects that could form complete analytical systems. Even though 3D printing enables the rapid fabrication of electrochemical sensors, its wider adoption by research laboratories is hindered by the lack of reference material and the high "entry barrier" to the field, manifested by the need to learn how to use 3D design software and operate the printers. This review article provides insights into fused deposition modeling 3D printing, discussing key challenges in producing electrochemical sensors using currently available extrusion tools, which include desktop 3D printers and 3D printing pens. Further, we discuss the electrode processing steps, including designing, printing conditions, and post-treatment steps. Finally, this work shed some light on the current applications of such electrochemical devices that can be a reference material for new research involving 3D printing.

Optimizing the releasing strategy used for the biological control of the sugarcane borer Diatraea saccharalis by Trichogramma galloi with computer modeling and simulation.

One of the challenges in augmentative biological control programs is the definition of releasing strategy for natural enemies, especially when macro-organisms are involved. Important information about the density of insects to be released and frequency of releases usually requires a great number of experiments, which implies time and space that are not always readily available. In order to provide science-based responses for these questions, computational models offer an in silico option to simulate different biocontrol agent releasing scenarios. This allows decision-makers to focus their efforts to more feasible options. The major insect pest in sugarcane crops is the sugarcane borer Diatraea saccharalis, which can be managed using the egg parasitoid Trichogramma galloi. The current strategy consists in releasing 50,000 insects per hectare for each release, in three weekly releases. Here, we present a simulation model to check whether this releasing strategy is optimal against the sugarcane borer. A sensitive analysis revealed that the population of the pest is more affected by the number of releases rather than by the density of parasitoids released. Only the number of releases demonstrated an ability to drive the population curve of the pest towards a negative growth. For example, releasing a total of 600,000 insects per hectare in three releases led to a lower pest control efficacy that releasing only 250,000 insects per hectare in five releases. A higher number of releases covers a wider range of time, increasing the likelihood of releasing parasitoids at the correct time given that the egg stage is short. Based on these results, it is suggested that, if modifications to the releasing strategy are desired, increasing the number of releases from 3 to 5 at weekly intervals is most likely preferable.

Integrating decision modeling and machine learning to inform treatment stratification.

There is increasing interest in moving away from "one size fits all (OSFA)" approaches toward stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules ('policies') that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modeling framework in order to reflect long-term policy-relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, when individual patient data is available to estimate treatment-specific survival time. We also propose a novel implementation of policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for "standard" and "intensive" blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB.

Transcutaneous auricular vagus nerve stimulation enhanced emotional inhibitory control via increasing intrinsic prefrontal couplings.

Background: Inhibitory control represents a core executive function that critically facilitates adaptive behavior and survival in an ever-changing environment. Non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) has been hypothesized to improve behavioral inhibition performance, however the neurocomputational mechanism of taVNS-induced neuroenhancement remains elusive. Method: In the current study, we investigated the efficacy of taVNS in a sham-controlled between-subject functional near infrared spectroscopy (fNIRS) experiment with an emotional face Go/No-Go paradigm in ninety healthy young adults. Results: After a data quality check, eighty-two subjects were included in the final data analysis. Behaviorally, the taVNS improved No-Go response accuracy, together with computational modeling using Hierarchical Bayesian estimation of the Drift Diffusion Model (HDDM) indicating that it specifically reduced the information accumulation rate for Go responses, and this was negatively associated with increased accuracy of No-Go responses. On the neural level, taVNS enhanced engagement of the bilateral inferior frontal gyrus (IFG) during inhibition of angry expression faces and modulated functional couplings (FCs) within the prefrontal inhibitory control network. Mediation models revealed that taVNS-induced facilitation of inhibitory control was critically mediated by a decreased information accumulation for Go responses and concomitantly enhanced neurofunctional coupling between the inferior and orbital frontal cortex. Discussion: Our findings demonstrate a potential for taVNS to improve emotional inhibitory control via reducing pre-potent responses and enhancing FCs within prefrontal inhibitory control networks, suggesting a promising therapeutic role in treating specific disorders characterized by inhibitory control deficits.

Seasonal extreme temperatures and short-term fine particulate matter increases pediatric respiratory healthcare encounters in a sparsely populated region of the intermountain western United States.

BACKGROUND: Western Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Evaluating while accounting for these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health is becoming more important. METHODS: We explored short-term exposure to air pollution on children's respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated healthcare events. The main outcome measure included individual-based address located respiratory-related healthcare visits for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for ages 0-17 from 2017-2020. We used a time-stratified, case-crossover analysis with distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 14 prior-days modified by temperature or season. RESULTS: For asthma, increases of 1 µg/m3 in PM2.5 exposure 7-13 days prior a healthcare visit date was associated with increased odds that were magnified during median to colder temperatures and winter periods. For LRTIs, 1 µg/m3 increases during 12 days of cumulative PM2.5 with peak exposure periods between 6-12 days before healthcare visit date was associated with elevated LRTI events, also heightened in median to colder temperatures but no seasonal effect was observed. For URTIs, 1 unit increases during 13 days of cumulative PM2.5 with peak exposure periods between 4-10 days prior event date was associated with greater risk for URTIs visits that were intensified during median to hotter temperatures and spring to summer periods. CONCLUSIONS: Delayed, short-term exposure increases of PM2.5 were associated with elevated odds of all three pediatric respiratory healthcare visit categories in a sparsely population area of the inter-Rocky Mountains, USA. PM2.5 in colder temperatures tended to increase instances of asthma and LRTIs, while PM2.5 during hotter periods increased URTIs.

Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends.

Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field. Methods: The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test. Results: The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning". Conclusion: Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.