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The spread of a new coronavirus infection in the last two years together with HIV infection preserves and even increases the potential for the spread of tuberculosis in the world. Sverdlovsk oblast (SO) of Russian Federation is the region with high levels of HIV and tuberculosis (TB). The search for new methods of forecasting of the future epidemic situation for tuberculosis has become particularly relevant. The aim was to develop an effective method for predicting the epidemic situation of tuberculosis using an artificial intelligence (AI) method in the format of a dynamic simulation model based on AI technologies. Statistical data was loaded from the state statistical reporting on TB patients for the period 2007-2017. The parameters were controlled through a system of inequalities. The proposed SDM made it possible to identify and reliably calculate trends of TB epidemiological indicators. Comparison of the predicted values made in 2017 with the actual values of 2018-2021 revealed a reliable coincidence of the trend of movement of TB epidemiological indicators in the region, the maximum deviation was no more than 14.82%. The forecast results obtained with SDM are quite suitable for practical use. Especially, in operational resource planning of measures to counteract the spread of tuberculosis at the regional level.

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Recent advances in understanding of biological mechanisms and adverse outcome pathways for many exposure-related diseases show that certain common mechanisms involve thresholds and nonlinearities in biological exposure concentration-response (C-R) functions. These range from ultrasensitive molecular switches in signaling pathways, to assembly and activation of inflammasomes, to rupture of lysosomes and pyroptosis of cells. Realistic dose-response modeling and risk analysis must confront the reality of nonlinear C-R functions. This paper reviews several challenges for traditional statistical regression modeling of C-R functions with thresholds and nonlinearities, together with methods for overcoming them. Statistically significantly positive exposure-response regression coefficients can arise from many non-causal sources such as model specification errors, incompletely controlled confounding, exposure estimation errors, attribution of interactions to factors, associations among explanatory variables, or coincident historical trends. If so, the unadjusted regression coefficients do not necessarily predict how or whether reducing exposure would reduce risk. We discuss statistical options for controlling for such threats, and advocate causal Bayesian networks and dynamic simulation models as potentially valuable complements to nonparametric regression modeling for assessing causally interpretable nonlinear C-R functions and understanding how time patterns of exposures affect risk. We conclude that these approaches are promising for extending the great advances made in statistical C-R modeling methods in recent decades to clarify how to design regulations that are more causally effective in protecting human health.

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Fascicle length of m. vastus lateralis in cyclists has been shown to correlate positively with peak sprint cycling power normalized for lean body mass. We investigated whether vasti morphology affects sprint cycling power via force-length and force-velocity relationships. We simulated isokinetic sprint cycling at pedaling rates ranging from 40 to 150 rpm with a forward dynamic model of the human musculoskeletal system actuated by eight leg muscles. Input of the model was muscle stimulation over time, which was optimized to maximize the average power output over a pedal cycle. This was done for a reference model and for models in which the vasti had equal volume but different morphology. It was found that models with longer muscle fibers but a reduced physiological cross-sectional area of the vasti produced a higher sprint cycling power. This was partly explained by better alignment of the peak power-pedaling rate curve of the vasti with the corresponding curves of the other leg muscles. The highest sprint cycling power was achieved in a model in which the increase in muscle fiber length of the vasti was accompanied by a concomitant shift in optimum knee angle. It was concluded that muscle mechanics can partly explain the positive correlations between fascicle length of m. vastus lateralis and normalized peak sprint cycling power. It should be investigated whether muscle fiber length of the vasti and optimum knee angle are suitable training targets for athletes who want to concurrently improve their sprint and endurance cycling performance.NEW & NOTEWORTHY We simulated isokinetic sprint cycling at pedaling rates ranging from 40 to 150 rpm with a forward dynamic model of the human musculoskeletal system actuated by eight leg muscles. We selectively modified vasti morphology: we lengthened the muscle fibers and reduced the physiological cross-sectional area. The modified model was able to produce a higher sprint cycling power.

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The modeling of free-water surface constructed wetlands (FWS-CWs) provides an improved understanding of their processes and constitutes a useful tool for the design and management of these systems. In this work, a dynamic simulation model for FWS-CWs was developed and used to simulate the operation of a FWS-CW proposed for improving the treatment of sewage effluents entering the Tablas de Daimiel National Park in central Spain. The process-based model simulates carbon, nitrogen and phosphorus dynamics, including key hydrological processes for wetlands under a fluctuating Mediterranean semiarid climate. The model allows for the simulation of the operation of FWS-CWs with variable flooding regimes, relating the surface water level to the flooded area and the water outflow. Simulations of the proposed FWS-CW under different water management schemes and scenarios were run, and the consequences of those management strategies on the treatment efficiency were analyzed. Under the Mediterranean climate and geology of the study area, namely, high water losses through evapotranspiration and infiltration, the decrease in nutrient concentrations was higher when the flooded area was reduced in summer than when a constant flooded area was maintained. Moreover, the meteorological variability introduced in different scenarios produced different results in terms of water outflow, but differences in terms of nutrient concentrations were not significant. The ability of the model to simulate different hydrological scenarios and their consequences on water quality makes it a useful decision-support tool.