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Background: Intraoperative physiologic parameters could offer predictive utility in evaluating risk of adverse postoperative events yet are not included in current standard risk models. This study examined whether the inclusion of continuous intraoperative data improved machine learning model predictions for multiple outcomes after coronary artery bypass grafting, including 30-day mortality, renal failure, reoperation, prolonged ventilation, and combined morbidity and mortality (MM). Methods: The Society of Thoracic Surgeons (STS) database features and risk scores were combined with retrospectively gathered continuous intraoperative data from patients. Risk models were developed for each outcome by training a logistic regression classifier on intraoperative data using 5-fold cross-validation. STS risk scores were included as offset terms in the models. Results: Compared with the STS Risk Calculator, models developed using a combination of the intraoperative features and the STS preoperative risk score had improved mean area under the receiver operating characteristic curve for prolonged ventilation (0.750 [95% CI, 0.690-0.809] vs 0.800 [95% CI, 0.750-0.851]) and MM (0.695 [95% CI, 0.644-0.746] vs 0.724 [95% CI, 0.673-0.775]). Additionally, models developed using intraoperative features had improved calibration, measured with Brier score, for prolonged ventilation (0.060 [95% CI, 0.050-0.070] vs 0.055 [95% CI, 0.045-0.065]) and MM (0.092 [95% CI, 0.081-0.103] vs 0.087 [95% CI, 0.075-0.098]). Conclusions: The inclusion of time series intraoperative data in risk models may improve early postoperative care by identifying patients who require closer monitoring postoperatively.

Abstract The structural and magnetic properties of the ThCr2Si2-type solid solution LaMn2(Ge1−x Si x )2 (x = 0.0 to 1.0) have been investigated employing a combination of X-ray diffraction, magnetization and neutron diffraction measurements, which allowed establishing a magnetic composition-temperature phase diagram. Substitution of Ge by Si leads to a compression of the unit cell, which affects the magnetic exchange interactions. In particular, the magnetic structure of LaMn2(Ge1−x Si x )2 is strongly affected by the unit cell parameter c, which is related to the distance between adjacent Mn layers. Commensurate antiferromagnetic layers and a canted ferromagnetic structure dominate the Si-rich part of the solid solution, whilst an incommensurate antiferromagnetic flat spiral and a conical magnetic structure are observed in the Si-poor part.

In this paper, we investigate the solubility effects of Co in ZnO (Zn1−xCoxO, where x = 0, 0.03, 0.05, 0.1, 0.2, 0.25, 0.4, 0.8, and 1) by combining the results of perturbed angular correlation (PAC) spectroscopy using highly diluted 111Cd as probe nuclei and ab initio calculations based on spin-density functional theory (SDFT). This combined approach enables us to characterize the local structure around Cd ions, where, through PAC technique, it was possible to measure the EFG as a function of temperature and Co concentration and thereby monitor the changes in the structure and the Co solubility threshold in ZnO and the ZnO/CoO/Co3O4 mixed phase. The full-potential linear augmented plane wave plus local orbital (APW+lo) formalism were used here to describe the electronic structure of the supercells, including the atomic relaxations. These Ab initio calculations show an interesting behavior of the Cd and Co impurity levels in the band structure of ZnO, which explains the experimental results in terms of the origin of EFG and the evidence of ferromagnetic response.

Abstract We herein report a comprehensive investigation on the magnetic, structural, and electric properties of CoO nanoparticles with different sizes by local inspection through hyperfine interactions measured in a wide range of temperatures (10–670 K) by using radioactive $$^{111}$$ 111 In( $$^{111}$$ 111 Cd) tracers with the perturbed angular correlations technique. Small cobalt oxide nanoparticles with the characteristic size of 6.5 nm have been prepared by the wet chemical route that turned out to be essential to incorporate radioactivity tracers during nucleation and growth of the particles. Nanocrystalline samples with 22.1 nm size were obtained by thermal treatments under low pressure of helium at 670 K. The hyperfine data were correlated with X-ray diffraction, ZFC–FC magnetic measurements, and transmission electron microscopy to describe the structure, magnetic properties, size, and shape of samples. An analysis of the temperature evolution of hyperfine parameters revealed that the structural distortion and the magnetic disorder in the core and on the surface layer play an important role in the magnetic behavior of CoO nanoparticles.

Background This study evaluated the role of supplementing Society of Thoracic Surgeons (STS) risk models for surgical aortic valve replacement with machine learning (ML). Methods and Results Adults undergoing isolated surgical aortic valve replacement in the STS National Database between 2007 and 2017 were included. ML models for operative mortality and major morbidity were previously developed using extreme gradient boosting. Concordance and discordance in predicted risk between ML and STS models were defined using equal‐size tertile‐based thresholds of risk. Calibration metrics and discriminatory capability were compared between concordant and discordant patients. A total of 243 142 patients were included. Nearly all calibration metrics were improved in cases of concordance. Similarly, concordance indices improved substantially in cases of concordance for all models with the exception of deep sternal wound infection. The greatest improvements in concordant versus discordant cases were in renal failure: ML model (concordance index, 0.660 [95% CI, 0.632–0.687] discordant versus 0.808 [95% CI, 0.794–0.822] concordant) and STS model (concordance index, 0.573 [95% CI, 0.549–0.576] discordant versus 0.797 [95% CI, 0.782–0.811] concordant) (each P

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The importance of education for balanced development in integrated primary school is really vital. School is one of the institutions which must supply its students with certain knowledge about the natural environment, teach them responsibility towards the natural environment and themeselves, make them aware of the dangers created by modern civilization and teach, how to use the natural resources in proper way. In the year 2004 was carried out research checking the efficiency of natural science in grade I-III of primary school teaching, the material presented below shows achieved results and the influence of natural science on young students.

In this paper, we investigate the solubility effects of Co in ZnO (Zn1−xCoxO, where x = 0, 0.03, 0.05, 0.1, 0.2, 0.25, 0.4, 0.8, and 1) by combining the results of perturbed angular correlation (PAC) spectroscopy using highly diluted 111Cd as probe nuclei and ab initio calculations based on spin-density functional theory (SDFT). This combined approach enables us to characterize the local structure around Cd ions, where, through PAC technique, it was possible to measure the EFG as a function of temperature and Co concentration and thereby monitor the changes in the structure and the Co solubility threshold in ZnO and the ZnO/CoO/Co3O4 mixed phase. The full-potential linear augmented plane wave plus local orbital (APW+lo) formalism were used here to describe the electronic structure of the supercells, including the atomic relaxations. These Ab initio calculations show an interesting behavior of the Cd and Co impurity levels in the band structure of ZnO, which explains the experimental results in terms of the origin of EFG and the evidence of ferromagnetic response. [ABSTRACT FROM AUTHOR]

The synthesis of functional Ni/Ni3C nanoparticles has attracted significant interest, especially in the field of electrocatalysis, where these promising nanoparticles are employed to develop sophisticated electrocatalysts, particularly for hydrogen production through the hydrogen evolution reaction. However, the significant reactivity of these systems makes them susceptible to degradation, compromising their catalyst performance. One solution explored to mitigate this problem involves the catalytic growth of carbon nanostructures to encapsulate and protect these nanoparticles. The mechanisms for the formation of carbon nanostructures from nanoparticles remain the subject of this study. Among the reported processes, the annealing of nanocatalysts has been described as a highly effective method for producing such systems. This process is influenced by parameters, such as the temperature, atmosphere, and structural and morphological characteristics of the nanocatalysts. In the work reported here, we evaluated the influence of different ligand pairs (oleylamine/oleic acid and oleylamine/palm kernel oil) on the structural, morphological, and magnetic properties of Ni/Ni3C nanoparticles obtained through thermal decomposition at 240 °C for 3 h. Additionally, we investigated the impact of annealing in a nitrogen atmosphere on the structural properties of these nanoparticles and the growth of carbon nanostructures as a protective mechanism. The analyses include conventional techniques such as X-ray diffraction, transmission electron microscopy (TEM), magnetization measurements, and thermogravimetric analysis with differential scanning calorimetry. Additionally, local analysis was conducted using perturbed angular correlation spectroscopy (PAC) across a broad temperature range (30–693 K), utilizing the radioactive tracer 111In-(111Cd) for these measurements. The characterizations revealed that palm kernel oil contributes to the formation of nanoparticles with a higher Ni3C content, a broader size distribution, and a lower saturation magnetization. The PAC measurements in the range of 30–50 K, along with density functional theory calculations, indicated the absence of the Ni-hcp phase in the nanoparticles, a topic frequently discussed in the literature. Moreover, the presence of Ni3C regions with carbon deficiency was identified, characterized by a quadrupole frequency (νQ) of 23 MHz and a hyperfine field (Bhf) of 1 T. The temperature-dependent local analysis, combined with thermal analysis and TEM measurements, confirmed the development of carbon nano-onions around the nanoparticles during thermal treatment above 695 K in a nitrogen atmosphere. This observation demonstrates that nanoparticles obtained with palm kernel oil, which has the highest Ni3C content, offer superior encapsulation of Ni nuclei through these graphitic nanostructures. [ABSTRACT FROM AUTHOR]

Abstract This study evaluates the use of simple linear or piecewise linear predictive models to predict extreme performance metrics in soccer matches, based on historical training and to match data of soccer players from RKS Raków Częstochowa football club. The data were collected from January to June 2023. The collected training and matched data average is 9000 records per month. A standard workweek at the RKS Academy consisted of 5 training units and at least 1 match. The best individual models found predict selected game performance metrics with a relative error of 2.3%, suggesting an excellent model fit between prediction and the actual value. This is illustrated by input data metric called “Metabolic Time Zone 5 and 6 Per Distance”, and output data by “Decelarations Total Distance in Zone 5 and 6 Per Distance”—calculated for in 3 min sliding window and characterized by the highest value of the generated parameter based on High Metabolic Load Distance (HMLD). The result concerns models run on aggregated performance metrics developed in APEX-PRO system using expert knowledge in soccer training, while raw GPS location-based models performing worse but still acceptably. Although we believe that the accuracy of the models still has limited reliability, their clarity and up-to-date quality make them useful in the daily planning of training activities and the management of workloads that affect player performance in the upcoming match, as well as the tactical decisions of the coach. More accurate predictions are given by individual models compared to aggregated models (player position), but there are exceptions where group models also perform very well. Adding a second metric to the input did not show a significant difference in the analyzed examples (the results are very similar). Our findings indicate that the model based on metrics from the last match also effectively predict extreme motor performances occurring in the game. In the case of the analyzed player, it was at the input “Accelerations Total Time Per Distance in Zone 6” at the output “Distance in Zone 6”. Specific training or match parameters can be key in predicting exceptional soccer performance, but they can also vary depending on the analyzed player. This confirms the need for further analysis of this issue.

Abstract The microscopic pair structure of superfluids has profound consequences on their properties. Delocalized pairs are predicted to be less affected by static disorder than localized pairs. Ultracold gases allow tuning the pair size via interactions, where for resonant interaction superfluids show largest critical velocity, i.e., stability against perturbations. The sensitivity of such fluids to strong, time-dependent disorder is less explored. Here, we investigate ultracold, interacting Fermi gases across various interaction regimes after rapid switching optical disorder potentials. We record the ability for quantum hydrodynamic expansion of the gas to quantify its long-range phase coherence. Contrary to static expectations, the Bose-Einstein condensate (BEC) exhibits significant resilience against disorder quenches, while the resonantly interacting Fermi gas permanently loses quantum hydrodynamics. Our findings suggest an additional absorption channel perturbing the resonantly interacting gas as pairs can be directly affected by the disorder quench.

The majority of mitochondrial proteins are encoded in the nucleus, translated on cytosolic ribosomes, and subsequently targeted to the mitochondrial surface. Their further import into the organelle is facilitated by highly specialized protein translocases. Mitochondrial precursor proteins that are destined to the mitochondrial matrix and, to some extent, the inner membrane, utilize translocase of the inner membrane (TIM23). This indispensable import machinery has been extensively studied in yeast. The translocating unit of the TIM23 complex in yeast consists of two membrane proteins, Tim17 and Tim23. In contrast to previous findings, recent reports demonstrate the primary role of Tim17, rather than Tim23, in the translocation of newly synthesized proteins. Very little is known about human TIM23 translocase. Human cells have two orthologs of yeast Tim17, TIMM17A and TIMM17B. Here, using computational tools, we present the architecture of human core TIM23 variants with either TIMM17A or TIMM17B, forming two populations of highly similar complexes. The structures reveal high conservation of the core TIM23 complex between human and yeast. Interestingly, both TIMM17A and TIMM17B variants interact with TIMM23 and reactive oxygen species modulator 1 (ROMO1); a homolog of yeast Mgr2, a protein that can create a channel‐like structure with Tim17. The high structural conservation of proteins that form the core TIM23 complex in yeast and humans raises an interesting question about mechanistic and functional differences that justify existence of the two variants of TIM23 in higher eukaryotes.