Central Asian populations remain underrepresented in pharmacogenomic research, limiting the availability of population-specific data for genotype-informed prescribing and precision medicine. This study analyzed clinically relevant pharmacogenomic variant frequencies in Kazakh, Russian, and Uzbek population groups residing in Kazakhstan using genome-wide genotype data from 1301 individuals: Kazakh (n = 1111), Russian (n = 156), and Uzbek (n = 34). ClinPGx, a PharmGKB-based clinical annotation framework that prioritizes variant–drug associations according to levels of evidence, was used to select variants with evidence levels 1A, 1B, and 2A. In total, 112 directly genotyped variants were retained for population-specific allele and genotype frequency analysis. All 112 variants were queried against the gnomAD v4.1 genome and exome reference datasets. Of these, matching allele-frequency data for the predefined reported allele were available in at least one of the two gnomAD datasets for 103 variants, whereas for 9 variants the VEP-based query did not return a matching gnomAD frequency for that allele. Frequencies were reported for the same predefined reported allele across all groups, and differences between the study groups were assessed using 95% confidence intervals, Fisher’s exact tests, and false discovery rate correction. Genotype counts and the proportions of individuals carrying at least one copy of the reported allele were also summarized for all selected variants. Several pharmacogenomic variants showed population-specific frequency patterns, including NUDT15 rs116855232, SLCO1B1 rs4149056, VKORC1 rs9934438, and UGT1A1 rs10929302. Comparison with gnomAD showed that the observed frequencies were variant-specific and could not be consistently approximated by a single broad genetic ancestry group. Reference-based population structure analysis provided additional ancestry context and supported separate reporting by population group. The study did not evaluate clinical outcomes or make individual prescribing recommendations, and the small Uzbek sample size limits the precision of frequency estimates for this group, particularly for rare variants. Overall, this study provides a clinically prioritized pharmacogenomic frequency resource for underrepresented population groups in Kazakhstan and supports broader Central Asian representation in pharmacogenomic implementation research.

Physics-informed neural networks (PINNs) are difficult to train on strongly coupled, multi-objective systems: with fixed loss weights an otherwise identical run succeeds or diverges with the random seed, and a leading explanation attributes the failures to conflict between the loss-term gradients. We test that explanation on a coupled non-isothermal Buckley–Leverett displacement with a passive tracer, a three-front hot-water flood with an exact analytical solution as ground truth. The test produces an exactly verified counterexample: the conflict-free update ConFIG attains its alignment objective yet converges to a stable solution that violates the equations, while a quasi-second-order optimizer (SOAP) and per-term gradient surgery (PCGrad) train the network reliably with no loss-weight search. The most aligned update is thus the one that fails: gradient alignment is neither necessary nor sufficient for trainability, and the conflict is a diagnostic of the loss rather than its cause. The same formulation serves the forward, parametric, and inverse problems: it predicts the movable-oil recovery with a relative error of about 5%, returns the solution for any oil viscosity in a trained range from one network, and, from sparse near-injector monitoring, recovers the thermal front speed and one transport coefficient when the other is known. To our knowledge this is the first PINN solution of this coupled displacement, which is central to thermal oil recovery.

We analyse inversion of the viscosity ratio M in the Buckley–Leverett equation, distinguishing structural identifiability, observation-operator conditioning, and numerical estimation error. The Riemann solution gives $M=4s^2-4s$ from the shock speed s, with relative condition number $\kappa \left(M\right)=2\sqrt{1+M}/(\sqrt{1+M}-1)$, which diverges as $M\downarrow 0$. The continuum physics objective admits exact solution–parameter pairs for every $M>0$, so identifying the true ratio requires observations. We examine numerical recovery using a viscous physics-informed neural network (PINN) with positive parameterisation and 30 paired repetitions per setting. At $M=3$, the median parameter error decreases from $514.25\%$ without observations to $6.13\%$ with 25 noisy observations. Additional data decrease the median field error, while median parameter errors are nonmonotone. Over $M\in [0.25,10]$, $\kappa$ decreases from $18.94$ to $2.86$, whereas the smooth-branch Cramér–Rao relative scale for 50 scattered observations with $5\%$ Gaussian noise levels off near $7\%$. Shock crossings supply additional nonregular information. Finite-network physics losses remain small across the tested ratios, with different orderings on training and held-out points. Scalar controls assess finite-resolution viscous-model discrepancy; complementary PINN tests show poorer recovery as the front sharpens. Recovery accuracy therefore depends jointly on the observation operator, regularisation, and numerical estimator.

Based on the analysis of global experience and our own preliminary studies aimed at identifying associations between athletic performance potential and individual genotypes across 97 SNP polymorphisms (75 candidate genes), interpretations of possible genotype variants for an athlete’s genetic passport were developed. A software program and several auxiliary scripts were created using the RStudio development environment and the Python programming language. The developed tools efficiently create and update databases containing the required genetic information, reducing the time needed to develop a genetic passport while minimizing the risk of errors. These tools significantly improved the efficiency of storing, sorting, and processing primary data obtained from genome-wide SNP microarray genotyping. The reduction of human involvement in the repetitive process of collecting and comparing tables minimized the risk of errors and facilitated the analysis of genotyping data. Automation of primary and secondary processing of genome-wide SNP microarray genotyping data, genotype interpretation, database updating, and generation of completed genetic passport reduced data processing time from several months to 20-30 minutes.

Physics-informed neural networks (PINNs) remain difficult to train for hyperbolic conservation laws. Artificial viscosity is a common remedy, and several methods reduce its coefficient during training. We examine adaptive moment estimation (Adam) followed by limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimisation. The schedule followed during Adam, the viscosity used during L-BFGS, and the checkpoint supplied between them can change together. We separate these factors for the one-dimensional Buckley–Leverett equation using a fixed multilayer perceptron with hyperbolic-tangent activations and 15 paired seeds per configuration. The central experiments impose a zero-diffusive-flux outlet condition. Lowering the L-BFGS viscosity from $3.5\times {10}^{-3}$ to $1.67\times {10}^{-4}$ increased full-grid relative $L^2$ error by $0.0413\pm 0.0069$. The schedule–viscosity interaction was $-0.0067\pm 0.0075$ and was not detected after multiplicity adjustment. A decay ending at $3.5\times {10}^{-3}$ improved on the tested low-endpoint decay before high-viscosity polishing, while its difference from constant high viscosity remained inconclusive. The higher of the two tested constant viscosities improved aggregate and pre-breakthrough accuracy but increased post-breakthrough error. Stage-matched curricula showed no detected accuracy advantage; their greater executed cost arose from intermediate diagnostic L-BFGS phases whose states were discarded. These results identify the terminal viscosity, checkpoint rule, outlet treatment, and optimiser exposure as controls needed to interpret schedule comparisons in this pipeline.

This article discusses the problems of studying the issue of joint motion of liquids in the porous space. The article provides the construction of a mathematical model of the theory of ltration, which describes phase transitions. The main diculty in constructing this model is associated with the fact that free interphase boundaries create regions that change over time, and it is required to nd the temperature or concentration elds of substances in them. In this case, the coordinates of the considered phase boundaries are not initially specied and must be calculated already in the process of solving. For this, a derivation of the averaged equation for the problem of nding the rupture surface during the movement of two incompressible viscous liquids in the pores of the soil skeleton was proposed. The article deals with the case when the skeleton is an absolutely rigid body. The rationale was given for the choice of an averaged ltration model instead of a microscopic one. The main research methods are classical methods of mathematical physics, functional analysis and computation methods of the theory of partial dierential equations, as well as dierence methods. The formulation of the problem is given, and the denition of a generalized solution for solving the problem is provided. Next, an averaged model is derived and the existence of at least one generalized solution to the problem is proved.