The simplest model of the evolution of agents with different energy strategies is considered. The model is based on the most general thermodynamic ideas and includes the procedures for selection, ...inheritance, and variability. The problem of finding a universal strategy (principle) as a selection of possible competing strategies is solved. It is shown that when there is non-equilibrium between the medium and agents, a direction in the evolution of agents arises, but at the same time, depending on the conditions of the evolution, different strategies can be successful. However, for this case, the simulation results reveal that in the presence of significant competition of agents, the strategy that has the maximum total energy dissipation of agents arising as a result of evolution turns out to be successful. Thus, it is not the specific strategy that is universal, but the maximization of dissipation. This result discovers an interesting connection between the basic principles of Darwin-Wallace evolution and the maximum entropy production principle.
We sought to identify susceptibility genes for high-grade serous ovarian cancer (HGSOC) by performing a transcriptome-wide association study of gene expression and splice junction usage in ...HGSOC-relevant tissue types (N = 2,169) and the largest genome-wide association study available for HGSOC (N = 13,037 cases and 40,941 controls). We identified 25 transcriptome-wide association study significant genes, 7 at the junction level only, including LRRC46 at 19q21.32, (P = 1 × 10
), CHMP4C at 8q21 (P = 2 × 10
) and a PRC1 junction at 15q26 (P = 7 × 10
). In vitro assays for CHMP4C showed that the associated variant induces allele-specific exon inclusion (P = 0.0024). Functional screens in HGSOC cell lines found evidence of essentiality for three of the new genes we identified: HAUS6, KANSL1 and PRC1, with the latter comparable to MYC. Our study implicates at least one target gene for 6 out of 13 distinct genome-wide association study regions, identifying 23 new candidate susceptibility genes for HGSOC.
Agricultural use of pesticides has greatly increased worldwide over the last several decades, affecting soil microorganisms. Microbial basal respiration and substrate-induced respiration rates are ...commonly used to assess the detrimental effects of pesticides on soil quality. The goal of the present study was (1) to compare the impact of different pesticides on soil microbial respiration under field conditions, and (2) to characterize the recovery time of soil microbial respiration after pesticide application. The following pesticides were used in the present study: chlorpyrifos, phosalone, dimethoate (organophosphorus insecticides), λ-cyhalothrin (pyrethroid insecticide), and kresoxim-methyl (fungicide). The application of all the pesticides at commercial doses led to a decrease in soil microbial respiration. The inhibition of basal respiration and substrate-induced respiration rate decreased in the following order: chlorpyrifos > phosalone > dimethoate > λ-cyhalothrin ≈ kresoxim-methyl. Among all the pesticides assessed, chlorpyrifos showed the highest toxicity as well as the highest persistence. Several of the observed results differed greatly from previous studies; thus, local assessments are highly advisable. Given that environmental concerns can be a key decision factor for pesticide selection, assessment of different pesticides—such as undertaken in this study—could help farmers to choose the most appropriate pesticide.
•Pesticides at commercial doses led to a decrease in soil microbial respiration.•Chlorpyrifos showed the highest toxicity as well as the highest persistence.•Local assessments of pesticides are highly advisable.•Pesticide assessment help farmers to choose the most appropriate pesticide.
•Machine learning was applied to predict avoidable hospitalizations of the coronary artery disease patients based on the electronic health records.•Dataset included records of more than 49 thousands ...patients.•CatBoost model showed superior performance and achieved AUROC value of 0.872 for the external validation dataset that was only slightly similar to the train data.
Improved survival of patients after acute coronary syndromes, population growth, and overall life expectancy rise have led to a significant increase in the proportion of patients with stable coronary artery disease (CAD), creating a significant load on the entire healthcare system. The disease often progresses with the development of many complications while significantly increasing the likelihood of hospitalization. Developing and applying a machine learning model for predicting hospitalizations of patients with CAD to an inpatient medical facility will allow for close monitoring of high-risk patients, early preventive interventions, and optimized medical care.
Development and external validation of personalized models for predicting the preventable hospitalizations of patients with stable CAD and its complications using ML algorithms and data of real-world clinical practice.
135,873 depersonalized electronic health records of 49,103 patients with stable CAD were included in the study. Anthropometric measurements, physical examination results, laboratory, instrumental, anamnestic, and socio-demographic data, widely used in routine medical practice, were considered as potential predictors, a total of 73 features. Logistic regression, decision tree-based methods including gradient boosting (AdaBoost, LightGBM, XGBoost, CatBoost) and bagging (RandomForest and ExtraTrees), discriminant analysis (LinearDiscriminant, QuadraticDiscriminant), and naive Bayes classifier were compared. External validation was performed on the data of a separate region.
The best results and stability to external validation data were shown by the CatBoost model with an AUC of 0.875 (95% CI 0.865–0.885) for the internal testing and 0.872 (95% CI 0.856–0.886) for the external validation. The best model showed good performance evaluated through AUROC, Brier score and standardized net benefit (for the target NPV threshold) for the validation dataset that was only slightly similar to the train data.
The metrics of the best model were superior to previously published studies. The results of external validation demonstrated the relative stability of the model to new data from another region that confirms the possibility of the model’s application in real clinical practice.
We present GERMLINE, a robust algorithm for identifying segmental sharing indicative of recent common ancestry between pairs of individuals. Unlike methods with comparable objectives, GERMLINE scales ...linearly with the number of samples, enabling analysis of whole-genome data in large cohorts. Our approach is based on a dictionary of haplotypes that is used to efficiently discover short exact matches between individuals. We then expand these matches using dynamic programming to identify long, nearly identical segmental sharing that is indicative of relatedness. We use GERMLINE to comprehensively survey hidden relatedness both in the HapMap as well as in a densely typed island population of 3000 individuals. We verify that GERMLINE is in concordance with other methods when they can process the data, and also facilitates analysis of larger scale studies. We bolster these results by demonstrating novel applications of precise analysis of hidden relatedness for (1) identification and resolution of phasing errors and (2) exposing polymorphic deletions that are otherwise challenging to detect. This finding is supported by concordance of detected deletions with other evidence from independent databases and statistical analyses of fluorescence intensity not used by GERMLINE.
To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health records at scale. Here, we train deep natural ...language processing (NLP) models to extract outcomes for participants with any of 7 solid tumors in a precision oncology study. Outcomes are extracted from 305,151 imaging reports for 13,130 patients and 233,517 oncologist notes for 13,511 patients, including patients with 6 additional cancer types. NLP models recapitulate outcome annotation from these documents, including the presence of cancer, progression/worsening, response/improvement, and metastases, with excellent discrimination (AUROC > 0.90). Models generalize to cancers excluded from training and yield outcomes correlated with survival. Among patients receiving checkpoint inhibitors, we confirm that high tumor mutation burden is associated with superior progression-free survival ascertained using NLP. Here, we show that deep NLP can accelerate annotation of molecular cancer datasets with clinically meaningful endpoints to facilitate discovery.
Recent studies have identified thousands of regions in the genome associated with chromatin modifications, which may in turn be affecting gene expression. Existing works have used heuristic methods ...to investigate the relationships between genome, epigenome, and gene expression, but, to our knowledge, none have explicitly modeled the chain of causality whereby genetic variants impact chromatin, which impacts gene expression. In this work we introduce a new hierarchical fine-mapping framework that integrates information across all three levels of data to better identify the causal variant and chromatin mark that are concordantly influencing gene expression. In simulations we show that our method is more accurate than existing approaches at identifying the causal mark influencing expression. We analyze empirical genetic, chromatin, and gene expression data from 65 African-ancestry and 47 European-ancestry individuals and show that many of the paths prioritized by our method are consistent with the proposed causal model and often lie in likely functional regions.
In this work, we present an analysis of the antibacterial activity of TiS
nanostructures in water and 0.9% NaCl solution suspensions. TiS
nanoribbons 1-10 µm long, 100-300 nm wide, and less than 100 ...nm thick were produced by the direct reaction of pure titanium powder with elemental sulphur in a quartz tube sealed under vacuum. For the toxicity test of a bioluminescent strain of
we used concentrations from 1 to 0.0001 g L
and also studied fresh suspensions and suspensions left for 24 h. The strongest toxic effect was observed in freshly prepared water solutions where the luminescence of bacteria decreased by more than 75%. When saline solution was substituted for water or when the solutions were stored for 24 h it resulted in a considerable decrease in the TiS
antibacterial effect. The toxicity of TiS
in water exceeded the toxicity of the reference TiO
nanoparticles, though when saline solution was used instead of water the opposite results were observed. In addition, we did not find a relationship between the antibacterial activity of water suspensions of nanoribbons and the stability of their colloidal systems, which indicates an insignificant contribution to the toxicity of aggregation processes. In 0.9% NaCl solution suspensions, toxicity increased in proportion to the increase in the zeta potential. We suppose that the noted specificity of toxicity is associated with the emission of hydrogen sulphide molecules from the surface of nanoribbons, which, depending on the concentration, can either decrease or increase oxidative stress, which is considered the key mechanism of nanomaterial cytotoxicity. However, the exact underlying mechanisms need further investigation. Thus, we have shown an important role of the dispersion medium and the period of storage in the antibacterial activity of TiS
nanoribbons. Our results could be used in nanotoxicological studies of other two-dimensional nanomaterials, and for the development of novel antibacterial substances and other biomedical applications of this two-dimensional material.
Polarized Raman-scattering spectra of superconducting, single-crystalline FeSe evidence pronounced phonon anomalies with temperature reduction. A large (~ 6.5%) hardening of the B sub(1)g(Fe) phonon ...mode is attributed to the suppression of local fluctuations of the iron spin state with a gradual decrease of the iron paramagnetic moment. Ab initio lattice dynamic calculations support this conclusion. The enhancement of the low-frequency spectral weight above the structural phase transition temperature Ts and its change below Ts is discussed in relation with the opening of an energy gap between low (S = 0) and higher spin states which prevents magnetic order in FeSe. The very narrow phonon linewidths compared to observations in FeTe suggest the absence of intermediate spin states in the fluctuating spin state manifold in FeSe. In the tetragonal phase under approaching Ts we observe the development of a quasielastic Raman response that is typical for the presence of nematic fluctuations.
Many time series forecasting applications use ranges rather than point forecasts. Producing forecasts in the form of Prediction Intervals (PIs) is natural, since intervals are an important component ...of many mathematical models. The LUBE (Lower Upper Bound Estimation) method is aimed at finding ranges based on solving optimization problems taking into account interval width and coverage. Using the Particle Swarm Training of simple neural network, we look for a solution to the optimization problem of the Coverage Width-Based Criterion (CWC), which is the exponential convolution of conflicting criteria PICP (Prediction Interval Coverage Probability) and PINRW (Prediction Interval Normalized Root-mean-square Width). Based on the concept of the Pareto compromise, it is introduced as a Pareto front in the space of specified criteria. The Pareto compromise is constructed as a relationship between conflicting criteria based on the found solution to the optimization problem. The data under consideration are the financial time series of the MOEX closing prices. Our findings reveal that a relatively simple neural network, comprising eight neurons and their corresponding 26 parameters (weights of neuron connections and neuron signal biases), is sufficient to yield reliable PIs for the investigated financial time series. The novelty of our approach lies in the use of a simple network structure (containing fewer than 100 parameters) to construct PIs for a financial time series. Additionally, we offer an experimental construction of the Pareto frontier, formed by the PICP and PINRW criteria.
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