Alloy information helps prioritize material criticality lists.
Ontology highlight
ABSTRACT:
SUBMITTER: Graedel TE
PROVIDER: S-EPMC8748655 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In this work, a systematic study of Cu(NO3)2·2.5 H2O (copper nitrate hemipentahydrate, CN), an alternating Heisenberg antiferromagnetic chain model material, is performed with multi-technique approach including thermal tensor network (TTN) simulations, first-principles calculations, as well as magnetization measurements. Employing a cutting-edge TTN method developed in the present work, we verify the couplings J = 5.13 K, α = 0.23(1) and Landé factors g∥= 2.31, g⊥ = 2.14 in CN, with which the magnetothermal properties have been fitted strikingly well. Based on first-principles calculations, we reveal explicitly the spin chain scenario in CN by displaying the calculated electron density distributions, from which the distinct superexchange paths are visualized. On top of that, we investigated the magnetocaloric effect (MCE) in CN by calculating its isentropes and magnetic Grüneisen parameter. Prominent quantum criticality-enhanced MCE was uncovered near both critical fields of intermediate strengths as 2.87 and 4.08 T, respectively. We propose that CN is potentially a very promising quantum critical coolant.
| S-EPMC5353727 | biostudies-literature
Project description:ObjectivePatient information can be retrieved more efficiently in electronic medical record (EMR) systems by using machine learning models that predict which information a physician will seek in a clinical context. However, information-seeking behavior varies across EMR users. To explicitly account for this variability, we derived hierarchical models and compared their performance to nonhierarchical models in identifying relevant patient information in intensive care unit (ICU) cases.Materials and methodsCritical care physicians reviewed ICU patient cases and selected data items relevant for presenting at morning rounds. Using patient EMR data as predictors, we derived hierarchical logistic regression (HLR) and standard logistic regression (LR) models to predict their relevance.ResultsIn 73 pairs of HLR and LR models, the HLR models achieved an area under the receiver operating characteristic curve of 0.81, 95% confidence interval (CI) [0.80-0.82], which was statistically significantly higher than that of LR models (0.75, 95% CI [0.74-0.76]). Further, the HLR models achieved statistically significantly lower expected calibration error (0.07, 95% CI [0.06-0.08]) than LR models (0.16, 95% CI [0.14-0.17]).DiscussionThe physician reviewers demonstrated variability in selecting relevant data. Our results show that HLR models perform significantly better than LR models with respect to both discrimination and calibration. This is likely due to explicitly modeling physician-related variability.ConclusionHierarchical models can yield better performance when there is physician-related variability as in the case of identifying relevant information in the EMR.
| S-EPMC7886572 | biostudies-literature
Project description:Integrated information theory (IIT) describes consciousness as information integrated across highly differentiated but irreducible constituent parts in a system. However, in a complex dynamic system such as the brain, the optimal conditions for large integrated information systems have not been elucidated. In this study, we hypothesized that network criticality, a balanced state between a large variation in functional network configuration and a large constraint on structural network configuration, may be the basis of the emergence of a large
| S-EPMC7514311 | biostudies-literature
Project description:We implement the Ising model on a structural connectivity matrix describing the brain at two different resolutions. Tuning the model temperature to its critical value, i.e. at the susceptibility peak, we find a maximal amount of total information transfer between the spin variables. At this point the amount of information that can be redistributed by some nodes reaches a limit and the net dynamics exhibits signature of the law of diminishing marginal returns, a fundamental principle connected to saturated levels of production. Our results extend the recent analysis of dynamical oscillators models on the connectome structure, taking into account lagged and directional influences, focusing only on the nodes that are more prone to became bottlenecks of information. The ratio between the outgoing and the incoming information at each node is related to the the sum of the weights to that node and to the average time between consecutive time flips of spins. The results for the connectome of 66 nodes and for that of 998 nodes are similar, thus suggesting that these properties are scale-independent. Finally, we also find that the brain dynamics at criticality is organized maximally to a rich-club w.r.t. the network of information flows.
| S-EPMC3976308 | biostudies-literature
Project description:Low-coking reactor material technologies are key for improving the performance and sustainability of steam crackers. In an attempt to appraise the coking performance of an alternative Ti-base alloy during ethane steam cracking, an experimental study was performed in a jet stirred reactor under industrially relevant conditions using thermogravimetry (Tgasphase = 1173 K, Ptot = 0.1 MPa, XC2H6 = 70%, and dilution δ = 0.33 kgH2O/kgHC). Initially, a typical pretreatment used for Fe-Ni-Cr alloys was utilized and compared with a pretreatment at increased temperature, aiming at better surface oxidation and thus suppressing coke formation. The results revealed a decrease in coking rates upon high temperature pretreatment of the Ti-base alloy, however, its coking performance was significantly worse compared to the typically used Fe-Ni-Cr alloys, and carbon oxides formation increased by a factor of 30 or more. Moreover, the analyzed coupons showed crack propagation after coking/decoking and cooling down to ambient temperature. Scanning electron microscopy combined with energy-dispersive X-ray spectroscopy indicated that the prompt and unsystematic oxidation of the surface and bulk caused observable crack initiation and propagation due to alloy brittleness. Hence, the tested Ti-base alloy cannot be considered an industrially noteworthy steam cracking reactor alloy.
| S-EPMC6719091 | biostudies-literature
Project description:Genome-wide association studies (GWAS) evaluate associations between genetic variants and a trait or disease of interest free of prior biological hypotheses. GWAS require stringent correction for multiple testing, with genome-wide significance typically defined as association p-value <5*10-8. This study presents a new tool that uses external information about genes to prioritize SNP associations (GenToS). For a given list of candidate genes, GenToS calculates an appropriate statistical significance threshold and then searches for trait-associated variants in summary statistics from human GWAS. It thereby allows for identifying trait-associated genetic variants that do not meet genome-wide significance. The program additionally tests for enrichment of significant candidate gene associations in the human GWAS data compared to the number expected by chance. As proof of principle, this report used external information from a comprehensive resource of genetically manipulated and systematically phenotyped mice. Based on selected murine phenotypes for which human GWAS data for corresponding traits were publicly available, several candidate gene input lists were derived. Using GenToS for the investigation of candidate genes underlying murine skeletal phenotypes in data from a large human discovery GWAS meta-analysis of bone mineral density resulted in the identification of significantly associated variants in 29 genes. Index variants in 28 of these loci were subsequently replicated in an independent GWAS replication step, highlighting that they are true positive associations. One signal, COL11A1, has not been discovered through GWAS so far and represents a novel human candidate gene for altered bone mineral density. The number of observed genes that contained significant SNP associations in human GWAS based on murine candidate gene input lists was much greater than the number expected by chance across several complex human traits (enrichment p-value as low as 10-10). GenToS can be used with any candidate gene list, any GWAS summary file, runs on a desktop computer and is freely available.
| S-EPMC5017755 | biostudies-literature
Project description:Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.
| S-EPMC7180982 | biostudies-literature
Project description:BackgroundShort tandem repeats (STRs) were recently found to have significant impacts on gene expression and diseases in humans, but their roles on gene expression and complex traits in pigs remain unexplored. This study investigates the effects of STRs on gene expression in liver tissues based on the whole-genome sequences and RNA-Seq data of a discovery cohort of 260 F6 individuals and a validation population of 296 F7 individuals from a heterogeneous population generated from crosses among eight pig breeds.ResultsWe identified 5203 and 5868 significantly expression STRs (eSTRs, FDR < 1%) in the F6 and F7 populations, respectively, most of which could be reciprocally validated (π1 = 0.92). The eSTRs explained 27.5% of the cis-heritability of gene expression traits on average. We further identified 235 and 298 fine-mapped STRs through the Bayesian fine-mapping approach in the F6 and F7 pigs, respectively, which were significantly enriched in intron, ATAC peak, compartment A and H3K4me3 regions. We identified 20 fine-mapped STRs located in 100 kb windows upstream and downstream of published complex trait-associated SNPs, which colocalized with epigenetic markers such as H3K27ac and ATAC peaks. These included eSTR of the CLPB, PGLS, PSMD6 and DHDH genes, which are linked with genome-wide association study (GWAS) SNPs for blood-related traits, leg conformation, growth-related traits, and meat quality traits, respectively.ConclusionsThis study provides insights into the effects of STRs on gene expression traits. The identified eSTRs are valuable resources for prioritizing causal STRs for complex traits in pigs.
| S-EPMC8762894 | biostudies-literature
Project description:Empirical evidence suggesting that living systems might operate in the vicinity of critical points, at the borderline between order and disorder, has proliferated in recent years, with examples ranging from spontaneous brain activity to flock dynamics. However, a well-founded theory for understanding how and why interacting living systems could dynamically tune themselves to be poised in the vicinity of a critical point is lacking. Here we use tools from statistical mechanics and information theory to show that complex adaptive or evolutionary systems can be much more efficient in coping with diverse heterogeneous environmental conditions when operating at criticality. Analytical as well as computational evolutionary and adaptive models vividly illustrate that a community of such systems dynamically self-tunes close to a critical state as the complexity of the environment increases while they remain noncritical for simple and predictable environments. A more robust convergence to criticality emerges in coevolutionary and coadaptive setups in which individuals aim to represent other agents in the community with fidelity, thereby creating a collective critical ensemble and providing the best possible tradeoff between accuracy and flexibility. Our approach provides a parsimonious and general mechanism for the emergence of critical-like behavior in living systems needing to cope with complex environments or trying to efficiently coordinate themselves as an ensemble.
| S-EPMC4104871 | biostudies-other
Project description:An increasing number of disorders have been identified for which two or more distinct alleles in two or more genes are required to either cause the disease or to significantly modify its onset, severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of alleles underlying digenic and oligogenic diseases in individual whole exome or whole genome sequences. Information that links patient phenotypes to databases of gene-phenotype associations observed in clinical or non-human model organism research can provide useful information and improve variant prioritization for genetic diseases. Additional background knowledge about interactions between genes can be utilized to identify sets of variants in different genes in the same individual which may then contribute to the overall disease phenotype. We have developed OligoPVP, an algorithm that can be used to prioritize causative combinations of variants in digenic and oligogenic diseases, using whole exome or whole genome sequences together with patient phenotypes as input. We demonstrate that OligoPVP has significantly improved performance when compared to state of the art pathogenicity detection methods in the case of digenic diseases. Our results show that OligoPVP can efficiently prioritize sets of variants in digenic diseases using a phenotype-driven approach and identify etiologically important variants in whole genomes. OligoPVP naturally extends to oligogenic disease involving interactions between variants in two or more genes. It can be applied to the identification of multiple interacting candidate variants contributing to phenotype, where the action of modifier genes is suspected from pedigree analysis or failure of traditional causative variant identification.
| S-EPMC6168481 | biostudies-literature