Miracle-dz/UniFAR-FTUs huggingface dataset:
Supracommissural replacement of the ascending aorta and the aortic valve via partial versus full sternotomy-a propensity-matched comparison in a high-volume centre.

OBJECTIVES Full sternotomy (FS) is the common surgical access for patients undergoing open aortic valve replacement (AVR) with concomitant supracommissural replacement of the tubular ascending aorta. Since minimally invasive approaches are being used with increasing frequency in cardiac surgery, the aim of this study was to compare outcomes of patients undergoing AVR with supracommissural replacement of the tubular ascending aorta via FS versus partial upper sternotomy (PS). METHODS We included all patients who underwent elective AVR with concomitant supracommissural replacement of the tubular ascending aorta at our institution between 2000 and 2015. Exclusion criteria were emergency surgery, other major concomitant procedures and reoperations. After 2:1 propensity score matching, outcomes of patients with PS and FS were compared. RESULTS A total of 652 consecutive patients were included, 117 patients operated via PS and 234 patients operated via FS. Cardiopulmonary bypass time and aortic cross-clamp time of the PS and FS groups were 89 vs 92 min (P = 0.2) and 65 vs 70 min (P = 0.3), respectively. Postoperative morbidity was low and there were no significant differences in postoperative outcomes between patient groups. In-hospital mortality was 1.7% in the PS vs 0.4% in the FS group (P = 0.3). Kaplan-Meier analysis revealed no difference in mid-term survival (P = 0.3). Reoperation rates for valve or aortic complications were very low with no significant difference between groups. CONCLUSIONS In a high-volume centre with extensive experience in minimally invasive cardiac surgery, AVR with concomitant supracommissural replacement of the tubular ascending aorta via PS results in similar outcomes with regard to safety and longevity when compared to conventional FS.
Cellular immunity predominates over humoral immunity after homologous and heterologous mRNA and vector-based COVID-19 vaccine regimens in solid organ transplant recipients

Knowledge on the immunogenicity of vector‐based and mRNA‐vaccines in solid organ transplant recipients is limited. Therefore, SARS‐CoV‐2–specific T cells and antibodies were analyzed in 40 transplant recipients and 70 controls after homologous or heterologous vaccine‐regimens. Plasmablasts and SARS‐CoV‐2–specific CD4 and CD8 T cells were quantified using flow cytometry. Specific antibodies were analyzed by ELISA and neutralization assay. The two vaccine types differed after the first vaccination, as IgG and neutralizing activity were more pronounced after mRNA priming (p = .0001 each), whereas CD4 and CD8 T cell levels were higher after vector priming (p = .009; p = .0001). All regimens were well tolerated, and SARS‐CoV‐2–specific antibodies and/or T cells after second vaccination were induced in 100% of controls and 70.6% of transplant recipients. Although antibody and T cell levels were lower in patients, heterologous vaccination led to the most pronounced induction of antibodies and CD4 T cells. Plasmablast numbers were significantly higher in controls and correlated with SARS‐CoV‐2–specific IgG‐ and T cell levels. While antibodies were only detected in 35.3% of patients, cellular immunity was more frequently found (64.7%) indicating that assessment of antibodies is insufficient to identify COVID‐19‐vaccine responders. In conclusion, heterologous vaccination seems promising in transplant recipients, and combined analysis of humoral and cellular immunity improves the identification of responders among immunocompromised individuals.
Testing the performance of field calibration techniques for low-cost gas sensors in new deployment locations: across a county line and across Colorado

Abstract. We assessed the performance of ambient ozone (O3) and carbon dioxide (CO2) sensor field calibration techniques when they were generated using data from one location and then applied to data collected at a new location. This was motivated by a previous study (Casey et al., 2018), which highlighted the importance of determining the extent to which field calibration regression models could be aided by relationships among atmospheric trace gases at a given training location, which may not hold if a model is applied to data collected in a new location. We also explored the sensitivity of these methods in response to the timing of field calibrations relative to deployment periods. Employing data from a number of field deployments in Colorado and New Mexico that spanned several years, we tested and compared the performance of field-calibrated sensors using both linear models (LMs) and artificial neural networks (ANNs) for regression. Sampling sites covered urban and rural–peri-urban areas and environments influenced by oil and gas production. We found that the best-performing model inputs and model type depended on circumstances associated with individual case studies, such as differing characteristics of local dominant emissions sources, relative timing of model training and application, and the extent of extrapolation outside of parameter space encompassed by model training. In agreement with findings from our previous study that was focused on data from a single location (Casey et al., 2018), ANNs remained more effective than LMs for a number of these case studies but there were some exceptions. For CO2 models, exceptions included case studies in which training data collection took place more than several months subsequent to the test data period. For O3 models, exceptions included case studies in which the characteristics of dominant local emissions sources (oil and gas vs. urban) were significantly different at model training and testing locations. Among models that were tailored to case studies on an individual basis, O3 ANNs performed better than O3 LMs in six out of seven case studies, while CO2 ANNs performed better than CO2 LMs in three out of five case studies. The performance of O3 models tended to be more sensitive to deployment location than to extrapolation in time, while the performance of CO2 models tended to be more sensitive to extrapolation in time than to deployment location. The performance of O3 ANN models benefited from the inclusion of several secondary metal-oxide-type sensors as inputs in five of seven case studies.
Dispositional Learning Analytics for Supporting Individualized Learning Feedback

An important goal of learning analytics (LA) is to improve learning by providing students with meaningful feedback. Feedback is often generated by prediction models of student success using data about students and their learning processes based on digital traces of learning activities. However, early in the learning process, when feedback is most fruitful, trace-data-based prediction models often have limited information about the initial ability of students, making it difficult to produce accurate prediction and personalized feedback to individual students. Furthermore, feedback generated from trace data without appropriate consideration of learners’ dispositions might hamper effective interventions. By providing an example of the role of learning dispositions in an LA application directed at predictive modeling in an introductory mathematics and statistics module, we make a plea for applying dispositional learning analytics (DLA) to make LA precise and actionable. DLA combines learning data with learners’ disposition data measured through for example self-report surveys. The advantage of DLA is twofold: first, to improve the accuracy of early predictions; and second, to link LA predictions with meaningful learning interventions that focus on addressing less developed learning dispositions. Dispositions in our DLA example include students’ mindsets, operationalized as entity and incremental theories of intelligence, and corresponding effort beliefs. These dispositions were inputs for a cluster analysis generating different learning profiles. These profiles were compared for other dispositions and module performance. The finding of profile differences suggests that the inclusion of disposition data and mindset data, in particular, adds predictive power to LA applications.
Magnetic Nanoscale Zerovalent Iron Assisted Biochar: Interfacial Chemical Behaviors and Heavy Metals Remediation Performance

It has been reported that zerovalent iron can help biochar improve efficiency in heavy metal (HM) absorption, but the surface chemical behaviors and HM removal mechanisms remain unclear. We successfully synthesized the magnetic nanoscale zerovalent iron assisted biochar (nZVI-BC). The porosity, crystal structure, surface carbon/iron atom state, and element distribution were comprehensively investigated to understand nZVI-BC’s interfacial chemical behaviors and HM removal mechanisms. We clearly revealed the formation of a nanoscale Fe0 core–Fe3O4 shell on the surface/pores/channels of biochar. With the combination of iron nanoparticles and biochar, C–O/COOH groups were cracked with the formation of C═O/C═C, indicating the C–O–Fe acted as an electron acceptor during the reduction reaction. We also demonstrated that the stabilization was dramatically improved in the nZVI-BC, while more reduced iron and better homogeneity were observed. These results, showing the surface chemical behaviors of nZVI-BC, would h.
Application of ethylenediamine hydroxypropyl tamarind fruit shell as adsorbent to remove Eriochrome black T from aqueous solutions – Kinetic and equilibrium studies

ABSTRACT Ethylenediamine Hydroxypropyl Tamarind Fruit Shell (EDAHP-TFS) was synthesized to remove Eriochrome Black T (EBT) from aqueous solutions. Maximum adsorption occurred at pH 2.0 and equilibrium time 2 h. Adsorption increased with increase in initial concentration and temperature. Adsorption kinetics obeyed the pseudo-second-order model and indicates a partial ion exchange, followed by the complexation mechanism. Lower E# (4.48 kJ/mol) represents film diffusion as the rate-limiting step. EBT removal followed the Sips isotherm model with a maximum adsorption capacity of 0.237 mmol/g at 30ºC. Isosteric heat of adsorption (ΔHi) decreased with increase in surface loading, indicating heterogeneous adsorption sites. Desorption studies were carried out and 96.4% recovery was achieved at pH 11.0. GRAPHICAL ABSTRACT
Entrepreneurs, Managers, and the Firm Size Distribution

ABSTRACT Small firms can contribute to job creation and aggregate income. However, small firms are volatile and only a fraction of those can transition into larger firms, which create high-paying jobs. Entrepreneurs self-select for the transition. This study examines the pattern of entrepreneur self-selection. The main determinants of the self-selection are ability distribution of entrepreneurs and business environment, which represents skill distribution of laborers and social capital. This study predicts that firm-size distribution is truncated with the entrepreneur self-selection and aggregate income is larger when the business environment is better. This study contributes to the literature on firm-size distribution.
A Phenomenological Approach to Epistemic Interpretations of Quantum Mechanics

ABSTRACT Generally, there are two interpretative approaches to quantum theory: psi-ontic and psi-epistemic. According to the psi-ontic interpretations, quantum theory does/should describe or represent what the world is like. In contrast, according to the psi-epistemic interpretations, rather than representing the way the world is, quantum theory advises us on what to expect from the world and so helps us better navigate our surrounding world. In this paper, we will argue that Edmund Husserl’s philosophy of science especially his notion of lifeworld provides an appropriate framework for better understanding this shift from the psi-ontic to psi-epistemic interpretations of quantum theory. In particular, we analyse Richard Healey’s pragmatic approach to quantum mechanics from a phenomenological point of view.
Linear and brute force stability of orthogonal moment multiple-relaxation-time lattice Boltzmann methods applied to homogeneous isotropic turbulence

Multiple-relaxation-time (MRT) lattice Boltzmann methods (LBM) based on orthogonal moments exhibit lattice Mach number dependent instabilities in diffusive scaling. The present work renders an explicit formulation of stability sets for orthogonal moment MRT LBM. The stability sets are defined via the spectral radius of linearized amplification matrices of the MRT collision operator with variable relaxation frequencies. Numerical investigations are carried out for the three-dimensional Taylor–Green vortex benchmark at Reynolds number 1600. Extensive brute force computations of specific relaxation frequency ranges for the full test case are opposed to the von Neumann stability set prediction. Based on that, we prove numerically that a scan over the full wave space, including scaled mean flow variations, is required to draw conclusions on the overall stability of LBM in turbulent flow simulations. Furthermore, the von Neumann results show that a grid dependence is hardly possible to include in the notion of linear stability for LBM. Lastly, via brute force stability investigations based on empirical data from a total number of 22 696 simulations, the existence of a deterministic influence of the grid resolution is deduced. This article is part of the theme issue ‘Progress in mesoscale methods for fluid dynamics simulation’.
Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.
Conformational decoupling in acid-sensing ion channels uncovers mechanism and stoichiometry of PcTx1-mediated inhibition

Acid-sensing ion channels (ASICs) are trimeric proton-gated cation channels involved in fast synaptic transmission. Pharmacological inhibition of ASIC1a reduces neurotoxicity and stroke infarct volumes, with the cysteine knot toxin Psalmotoxin-1 (PcTx1) being one of the most potent and selective inhibitors. PcTx1 binds at the subunit interface in the extracellular domain (ECD), but the mechanism and conformational consequences of the interaction, as well as the number of toxin molecules required for inhibition remain unknown. Here we use voltage-clamp fluorometry and subunit concatenation to decipher the mechanism and stoichiometry of PcTx1 inhibition of ASIC1a. Besides the known inhibitory binding mode, we propose PcTx1 to have at least two additional binding modes that are decoupled from the pore. One of these modes induces a long-lived ECD conformation that reduces the activity of an endogenous neuropeptide. This long-lived conformational state is proton-dependent and can be destabilized by a mutation that decreases PcTx1 sensitivity. Lastly, the use of concatemeric channel constructs reveal that disruption of a single PcTx1 binding site is sufficient to destabilize the toxin-induced conformation, while functional inhibition is not impaired until two or more binding sites are mutated. Together, our work provides insight into the mechanism of PcTx1 inhibition of ASICs and uncovers a prolonged conformational change with possible pharmacological implications.
Hierarchical resampling for bagging in multistudy prediction with applications to human neurochemical sensing.

We propose the "study strap ensemble", which combines advantages of two common approaches to fitting prediction models when multiple training datasets ("studies") are available: pooling studies and fitting one model versus averaging predictions from multiple models each fit to individual studies. The study strap ensemble fits models to bootstrapped datasets, or "pseudo-studies. " These are generated by resampling from multiple studies with a hierarchical resampling scheme that generalizes the randomized cluster bootstrap. The study strap is controlled by a tuning parameter that determines the proportion of observations to draw from each study. When the parameter is set to its lowest value, each pseudo-study is resampled from only a single study. When it is high, the study strap ignores the multi-study structure and generates pseudo-studies by merging the datasets and drawing observations like a standard bootstrap. We empirically show the optimal tuning value often lies in between, and prove that special cases of the study strap draw the merged dataset and the set of original studies as pseudo-studies. We extend the study strap approach with an ensemble weighting scheme that utilizes information in the distribution of the covariates of the test dataset. Our work is motivated by neuroscience experiments using real-time neurochemical sensing during awake behavior in humans. Current techniques to perform this kind of research require measurements from an electrode placed in the brain during awake neurosurgery and rely on prediction models to estimate neurotransmitter concentrations from the electrical measurements recorded by the electrode. These models are trained by combining multiple datasets that are collected in vitro under heterogeneous conditions in order to promote accuracy of the models when applied to data collected in the brain. A prevailing challenge is deciding how to combine studies or ensemble models trained on different studies to enhance model generalizability. Our methods produce marked improvements in simulations and in this application. All methods are available in the studyStrap CRAN package.
Phospholipase Dα1 Acts as a Negative Regulator of High Mg2+-Induced Leaf Senescence in Arabidopsis

Magnesium is a macronutrient involved in essential cellular processes. Its deficiency or excess is a stress factor for plants, seriously affecting their growth and development and therefore, its accurate regulation is essential. Recently, we discovered that phospholipase Dα1 (PLDα1) activity is vital in the stress response to high-magnesium conditions in Arabidopsis roots. This study shows that PLDα1 acts as a negative regulator of high-Mg2+-induced leaf senescence in Arabidopsis. The level of phosphatidic acid produced by PLDα1 and the amount of PLDα1 in the leaves increase in plants treated with high Mg2+. A knockout mutant of PLDα1 (plda1-1), exhibits premature leaf senescence under high-Mg2+ conditions. In pldα1-1 plants, higher accumulation of abscisic and jasmonic acid and impaired magnesium, potassium and phosphate homeostasis were observed under high-Mg2+ conditions. High Mg2+ also led to an increase of starch and proline content in Arabidopsis plants. While the starch content was higher in plda1-1 plants, proline content was significantly lower in plda1-1 compared with WT. Our results show that PLDα1 is essential for Arabidopsis plants to cope with the pleiotropic effects of high-Mg2+ stress and delay the leaf senescence.
Production and composition of group B streptococcal membrane vesicles varies across diverse lineages

Although the neonatal and fetal pathogen Group B Streptococcus (GBS) asymptomatically colonizes the vaginal tract of ∼30% of pregnant women, only a fraction of their offspring develops invasive disease. We and others have postulated that these dimorphic clinical phenotypes are driven by strain variability; however, the bacterial factors that promote these divergent clinical phenotypes remain unclear. It was previously shown that GBS produces membrane vesicles (MVs) that contain active virulence factors capable of inducing adverse pregnancy outcomes. Because the relationship between strain variation and vesicle composition or production is unknown, we sought to quantify MV production and examine the protein composition, using label-free proteomics on MVs produced by diverse clinical GBS strains representing three phylogenetically distinct lineages. We found that MV production varied across strains, with certain strains displaying nearly two-fold increases in production relative to others. Hierarchical clustering and principal component analysis of the proteomes revealed that MV composition is lineage-dependent but independent of clinical phenotype. Multiple proteins that contribute to virulence or immunomodulation, including hyaluronidase, C5a peptidase, and sialidases, were differentially abundant in MVs, and were partially responsible for this divergence. Together, these data indicate that production and composition of GBS MVs vary in a strain-dependent manner, suggesting that MVs have lineage-specific functions relating to virulence. Such differences may contribute to variation in clinical phenotypes observed among individuals infected with GBS strains representing distinct lineages.