Transport system modelling based on analogies between road networks and electrical circuits

This article describes a probabilistic mathematical model which can be used to analyse traffic flows in a road network. This model allows us to calculate the probability of distribution of vehicles in a regional road network or an urban street network. In the model, the movement of cars is treated as a Markov process. This makes it possible to formulate an equation determining the probability of finding cars at key points of the road network such as street intersections, parking lots or other places where cars concentrate. For a regional road network, we can use cities as such key points. This model enables us, for instance, to use the analogues of Kirchhoff First Law (Ohm's Law) for calculation of traffic flows. This calculation is based on the similarity of a real road network and resistance in an electrical circuit. The traffic flow is an analogue of the electric current, the resistance of the section between the control points is the time required to move from one key point to another, and the voltage is the difference in the number of cars at these points. In this case, well-known methods for calculating complex electrical circuits can be used to calculate traffic flows in a real road network. The proposed model was used to calculate the critical load for a road network and compare road networks in various regions of the Ural Federal District

Body Fat Parameters, Glucose and Lipid Profiles, and Thyroid Hormone Levels in Schizophrenia Patients with or without Metabolic Syndrome

In this study, we aim to investigate associations between body fat parameters, glucose and lipid profiles, thyroid-stimulating hormone (TSH), and thyroid hormones (THs) levels in Tomsk-region schizophrenia patients depending upon the presence or absence of metabolic syndrome (MetS). A total of 156 psychiatric inpatients with schizophrenia who had been treated with antipsychotics for at least six months before entry were studied: 56 with and 100 without MetS. Reference groups consisted of general hospital inpatients with MetS and without schizophrenia (n = 35) and healthy individuals (n = 35). Statistical analyses were performed using the Mann-Whitney U-test, chi-square test, Spearman's rank correlation coefficient, multiple regression analyses, and descriptive statistics. Patients with schizophrenia and MetS had significantly higher levels of free triiodothyronine (FT3) and thyroxine (FT4) compared to schizophrenia patients without MetS (3.68 [3.25; 5.50] vs. 3.24 [2.81; 3.66], p = 0.0001, and 12.68 [10.73; 15.54] vs. 10.81 [9.76; 12.3], p = 0.0001, in pmol/L, respectively). FT3 maintained an association with MetS (p = 0.0001), sex (p = 0.0001), age (p = 0.022), and high-density lipoproteins (p = 0.033). FT4 maintained an association with MetS (p = 0.0001), sex (p = 0.001), age (p = 0.014), and glucose (p = 0.009). The data obtained showed body fat parameters, glucose and lipid profiles, and THs levels in Western-Siberian schizophrenia patients depending on MetS presence or absence

Association of the HTR2A T102C SNP with Weight Gain and Changes in Biochemical Markers in Patients Receiving Antipsychotics

The purpose of our research was to study the association of the HTR2A T102C (rs6313) SNP with anthropometric and biochemical markers in patients treated with typical and atypical antipsychotics in monotherapy mode. Materials and methods: One hundred and seventeen white inpatients (95 men and 22 women) with F2 disorders (ICD-10, 1995) were enrolled in the study. All patients were divided into two groups by the antipsychotic class with which they were treated (Group 1 included 40 patients treated with typical antipsychotics; Group 2 included 77 patients treated with atypical antipsychotics) and two subgroups by weight change criteria during the study (Subgroup 1 included patients with weight change >6%; Subgroup 2 included patients with weight change <6%). The following examinations were performed: physical examination, anthropometric measurements (BMI. WC, TC), clinical examination, blood test (ALT, AST, FPG, VLDL-C, LDL-C, HDL-C, total cholesterol, triglycerides, total protein, albumin, creatinine, uric acid, carbamide), and genotyping for the HTR2A T102C (rs6313) SNP. Results: There were no statistically significant differences in the distribution of genotypes of the HTR2A T102C (rs6313) SNP between Group 1 and Group 2 (P>0.05). Kruskal-Wallis one-way analysis of variance between subgroups showed statistically significant differences between carbamide levels in the second visit in Group 2 (P=0.02). A Dunn post hoc test with Bonferroni adjustment showed statistically significant differences between TT and CT genotypes of the HTR2A T102C SNP: carbamide level was greater in TT carriers (P=0.02). The strength of associations and risks between alleles of the HTR2A T102C SNP and antipsychotic-induced weight change were as follows: ORC=0.49; CIC [0.25; 0.95]; RRC=0.58 CIC [0.35; 0.97]; ORT=2.03; CIT [1.05; 3.94]; RRT=1.7 CIT [1.02; 2.81]. Conclusion: Our results of the pilot pharmacogenetic studies show an association of the T allele carriage of the HTR2A T102C SNP with risk of antipsychotic-induced weight gain. The continuation of this study and an increase in the sample size will allow establishing valid pharmacogenetic markers for the risk of antipsychotic-induced weight gain

Phenological shifts of abiotic events, producers and consumers across a continent

Ongoing climate change can shift organism phenology in ways that vary depending on species, habitats and climate factors studied. To probe for large-scale patterns in associated phenological change, we use 70,709 observations from six decades of systematic monitoring across the former Union of Soviet Socialist Republics. Among 110 phenological events related to plants, birds, insects, amphibians and fungi, we find a mosaic of change, defying simple predictions of earlier springs, later autumns and stronger changes at higher latitudes and elevations. Site mean temperature emerged as a strong predictor of local phenology, but the magnitude and direction of change varied with trophic level and the relative timing of an event. Beyond temperature-associated variation, we uncover high variation among both sites and years, with some sites being characterized by disproportionately long seasons and others by short ones. Our findings emphasize concerns regarding ecosystem integrity and highlight the difficulty of predicting climate change outcomes. The authors use systematic monitoring across the former USSR to investigate phenological changes across taxa. The long-term mean temperature of a site emerged as a strong predictor of phenological change, with further imprints of trophic level, event timing, site, year and biotic interactions.Peer reviewe

Chronicles of nature calendar, a long-term and large-scale multitaxon database on phenology

We present an extensive, large-scale, long-term and multitaxon database on phenological and climatic variation, involving 506,186 observation dates acquired in 471 localities in Russian Federation, Ukraine, Uzbekistan, Belarus and Kyrgyzstan. The data cover the period 1890-2018, with 96% of the data being from 1960 onwards. The database is rich in plants, birds and climatic events, but also includes insects, amphibians, reptiles and fungi. The database includes multiple events per species, such as the onset days of leaf unfolding and leaf fall for plants, and the days for first spring and last autumn occurrences for birds. The data were acquired using standardized methods by permanent staff of national parks and nature reserves (87% of the data) and members of a phenological observation network (13% of the data). The database is valuable for exploring how species respond in their phenology to climate change. Large-scale analyses of spatial variation in phenological response can help to better predict the consequences of species and community responses to climate change.Peer reviewe