Substructure Analysis Research Articles

Infrared Spectral Analysis for Prediction of Functional Groups Based on Feature-Aggregated Deep Learning.

Infrared (IR) spectroscopy is a powerful and versatile tool for analyzing functional groups in organic compounds. A complex and time-consuming interpretation of massive unknown spectra usually requires knowledge of chemistry and spectroscopy. This paper presents a new deep learning method for transforming IR spectral features into intuitive imagelike feature maps and prediction of major functional groups. We obtained 8272 gas-phase IR spectra from the NIST Chemistry WebBook. Feature maps are constructed using the intrinsic correlation of spectral data, and prediction models are developed based on convolutional neural networks. Twenty-one major functional groups for each molecule are successfully identified using binary and multilabel models without expert guidance and feature selection. The multilabel classification model can produce all prediction results simultaneously for rapid characterization. Further analysis of the detailed substructures indicates that our model is capable of obtaining abundant structural information from IR spectra for a comprehensive investigation. The interpretation of our model reveals that the peaks of most interest are similar to those often considered by spectroscopists. In addition to demonstrating great potential for spectral identification, our method may contribute to the development of automated analyses in many fields.

Machine Learning Models to Predict Cytochrome P450 2B6 Inhibitors and Substrates.

Cytochrome P450 2B6 (CYP2B6) is responsible for the metabolism of ∼7% of marketed drugs. The in vitro drug interaction studies guidance for industry issued by the FDA stipulates that drug sponsors need to evaluate whether the investigated drugs interact with the major drug-metabolizing P450s including CYP2B6. Therefore, there has been greater attention to the development of predictive models for CYP2B6 inhibitors and substrates. In this study, conventional machine learning and deep learning models were developed to predict CYP2B6 inhibitors and substrates. Our results showed that the best CYP2B6 inhibitor model yielded the AUC values of 0.95 and 0.75 with the 10-fold cross-validation and the test set, respectively, and the best CYP2B6 substrate model produced the AUC values of 0.93 and 0.90 with the 10-fold cross-validation and the test set, respectively. The generalization ability of the CYP2B6 inhibitor and substrate models was assessed by using the external validation sets. Several significant substructural fragments relevant to CYP2B6 inhibitors and substrates were detected via frequency substructure analysis and information gain. In addition, the applicability domain of the models was defined by employing a nonparametric method based on the probability density distribution. We anticipate that our results would be useful for the prediction of potential CYP2B6 inhibitors and substrates in the early stage of drug discovery.

Receptance coupling substructure analysis based FRF modeling for multi-segment shell with nonlinear joint interface

Multi-segment shell typical of underwater vehicles is currently important underwater equipment. Stealth performance is a key indicator to improve the working capacity of underwater vehicles. Low-noise design is an important means to improve stealthy performance. Accurate and effective identification of its structure Frequency Response Function (FRF) is the premise of low-noise design. However, the multi-section shell joint interface has many connection points, complex transmission path and typical nonlinear characteristics, which challenges the acquisition of its structural FRF. The Receptance Coupling Substructure Analysis (RCSA) is used to segment the multi-stage shell, which can realize the integration of finite element method and experimental method, and provide an effective way to obtain the FRF of multi-segment shell. At the same time, the multi-segment shell is a typical thin-wall structure, and its actual buckling process under impact is a nonlinear (large deformation) process. Therefore, the nonlinear joint interface is constructed based on linear connection, and the RCSA for nonlinear joint interface is studied. On the basis of accurately obtaining the FRF, it is of theoretical and application significance to study the low noise design of shell.

Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations

BackgroundYungui Plateau in Southwest China is characterized by multi-language and multi-ethnic communities and is one of the regions with the wealthiest ethnolinguistic, cultural and genetic diversity in East Asia. There are numerous Tai-Kadai (TK)-speaking populations, but their detailed evolutionary history and biological adaptations are still unclear.ResultsHere, we genotyped genome-wide SNP data of 77 unrelated TK-speaking Zhuang and Dong individuals from the Yungui Plateau and explored their detailed admixture history and adaptive features using clustering patterns, allele frequency differentiation and sharing haplotype patterns. TK-speaking Zhuang and Dong people in Guizhou are closely related to geographically close TK and Hmong-Mien (HM)-speaking populations. Besides, we identified that Guizhou TK-speaking people have a close genetic relationship with Austronesian (AN)-speaking Atayal and Paiwan people, which is supported by the common origin of the ancient Baiyue tribe. We additionally found subtle genetic differences among the newly studied TK people and previously reported Dais via the fine-scale genetic substructure analysis based on the shared haplotype chunks. Finally, we identified specific selection candidate signatures associated with several essential human immune systems and neurological disorders, which could provide evolutionary evidence for the allele frequency distribution pattern of genetic risk loci.ConclusionsOur comprehensive genetic characterization of TK people suggested the strong genetic affinity within TK groups and extensive gene flow with geographically close HM and Han people. We also provided genetic evidence that supported the common origin hypothesis of TK and AN people. The best-fitted admixture models further suggested that ancestral sources from northern millet farmers and southern inland and coastal people contributed to the formation of the gene pool of the Zhuang and Dong people.

Prediction of the Posture-Dependent Tool Tip Dynamics in Robotic Milling Based on Multi-Task Gaussian Process Regressions

Chatter vibration is one of the main factors that limit the productivity and quality of the robotic milling process. To predict the robotic milling stability, it is essential to obtain the tool tip frequency response function (FRF). The tool tip dynamics of a robot heavily depend on its postures and used tools. A state-of-art methodology of combining the regression model with the Receptance Coupling Substructure Analysis (RCSA) method is proved to be effective in predicting tool tip FRFs of machine tools for different positions and tools. However, for the milling robot, the cross coupling FRFs have an obvious influence on the dynamic property of the milling robot, thereby greatly affecting the milling stability boundary. It is of great challenge to directly integrate the effect of the cross coupling FRFs into the state-of-art approach to predict the tool tip dynamics. To tackle this challenge, in this paper, we propose an approach to predict the posture-dependent tool tip dynamics for different tools in robotic milling considering the cross coupling FRFs. First, a more comprehensive RCSA procedure is adopted to include the cross coupling FRFs. Then, the impact test is designed to measure the required FRF matrix. By fitting the measured FRF matrix with the multiple-degree-of-freedom (MDOF) model, the number of modal parameters is significantly reduced. Next, the Multi-Task Gaussian Process (MTGP) regression model is employed to mine the physical correlations between different modal parameters. Compared to the ordinary Gaussian Process regression model, the number of required regression models in MTGP is reduced and the prediction performance is improved in terms of accuracy and robustness. Furthermore, the effectiveness of the proposed approach is validated by the impact test and milling experiment on an industrial robot.

The modal behaviour of a violin corpus

Violins incorporate numerous interfaces and anisotropic behaviour due to natural materials. Hence, the modelling of these instruments is extremely complex. The present work investigates the structural dynamics of a specific violin corpus (violin resonant body) with the aim of using numerical modelling to accurately understand the modal behaviour of the violin using a novel stepwise approach. In the first step, an inverse material parameter identification via experimental and numerical modal analysis of the raw woods is performed to obtain the material parameters of the individual woods. In the second step, an experimental modal analysis of the violin substructures is performed using 2D scanning laser Doppler vibrometry and building finite element models based on accurate 3D scans of the parts manufactured by a luthier. The experimentally obtained modal parameters of the individual parts are compared with the numerical results. In the third step, the whole violin corpus, i.e. the resonant body of the instrument, is assessed. The good agreement between the numerical and the experimental results demonstrates that the application of standard techniques of modal analysis on a very complex mechanical system, the violin body, is possible. Our methodology offers a novel approach to analysing individual components of bowed string instruments, facilitating a comprehensive vibrational analysis with implications for broader musical research, including luthier optimisation tools, digital reconstruction of historical violins, and advancements in instrument design for improved playability and tonal quality.

A robust phenomenological modeling framework based on cross-slip propensity factor for capturing the effect of dynamic strain aging on work hardening behavior of an Al-Mg alloy

In the present work, a new modeling framework for analyzing flow behavior of Al-Mg alloys is proposed by considering both direct and indirect effect of solutes on flow stress. While direct effect addresses the increased slip resistance due to dynamic strain aging (DSA), the indirect effect represents the role of solutes on cross-slip probability. This affects the dislocation annihilation propensity and consequently the work hardening behavior. The dislocation density evolution equation is suitably modified by including a factor based on activation volume and DSA-induced strengthening (σdsa) to account for the effect of solutes on dislocation annihilation propensity and develop a new phenomenological modeling framework for predicting flow curves spanning over a wide range of strain rate (0.00002–0.2 s−1). The proposed modeling framework is shown to be performing better than the existing models which consider only the direct effect of solutes on flow stress. Experimental validation of the proposed concept is shown via dislocation density measurements using X-ray line profile analysis, dislocation substructure analysis via transmission electron microscopy and fractography studies. The new modeling framework is also extended to predict flow curves of several Al-Mg alloys with varying Mg concentration to establish the robustness of the proposed concept.

Experimental and Mathematical Study of Flexible–Rigid Rail Vehicle Riding Comfort and Safety

This paper analyses the dynamic behavior of a rail vehicle using experimental and simulation analysis on a multi-rigid–flex body model. The mathematical models are developed considering the car body, bogie frame, and wheel axle for rail vehicles of rigid–flexible and multi-rigid formulations, taking the car body as rigid for the rigid body analysis and the flexible car body for flex–rigid analysis. A finite element model of the car body was developed in ANSYS, and substructure and modal analyses were performed. The mathematical model is validated through an experiment conducted by the Research Design and Standards Organization. Then, the validated model is further analyzed to evaluate the running comfort, using the Sperling ride index and the running safety, by investigating the derailment coefficient and wheel load reduction rate. The impact of flexibility on the vehicle’s running stability is investigated using the rigid body dynamics model and experimental data. Compared to experimental data, the simulation results reveal that elastic vibration cannot be neglected in vehicle dynamics, since the rigid–flexible coupling model is slightly more significant than the rigid-body model for ride comfort and safety.

Integration of Building Information Modeling Interoperability into Nonlinear Finite Element Analysis of Bridge Substructures

Acceptance of building information modeling (BIM) throughout the bridge industry is widely recognized as inevitable. However, while adoption of BIM technologies has proliferated throughout the building industry, advances pertaining to BIM for bridges and other structures remain underway. The present study aims to increase the potential for integrating BIM processes into software packages that possess capabilities for nonlinear finite element analysis (FEA) of bridge substructures. Moreover, the present study is intended to galvanize stakeholders within the area of soil–structure interaction (SSI) modeling and analysis to more effectively participate in interoperability workflows as part of bridge design. A proof-of-concept exporter tool was formulated in a modular manner to robustly export finite element model data stored in a selected SSI analysis package into the Industry Foundation Classes (IFC) standard, with the IFC standard chosen as the target schema owing to its widespread usage in the construction industry. The ability of the exporter tool to—with fidelity—convert a model from the selected analysis software to the IFC standard is showcased through an illustrative case study. In carrying out the case study, collections of geotechnical modeling parameters were identified that, while being common to numerous nonlinear SSI analysis packages, are not present in the existing IFC schema. Inclusion of these parameters in the IFC schema is proposed, serving to: 1) lower the barrier for entry of SSI modeling and analysis stakeholders to take part in interoperability processes and 2) encourage adoption of novel BIM processes in the bridge industry.

Calibration rod selection strategy in RCSA-based method for reliable calculation of milling tool-tip FRFs in rotating conditions

The measurement of milling tool-tip frequency response functions (FRFs) in rotating conditions is challenging in practice. Methods based on the receptance coupling substructure analysis (RCSA) can obtain rotating tool-tip FRFs using normal modal test devices; thus, they have received extensive attention in the research community. The typical RCSA framework first adopts a calibration rod for measuring rotating FRFs. Then, it analytically calculates the desired tool-tip FRFs through the RCSA theory. As the calculation process involves matrix inversion, high-quality FRF data is required. However, experimentally measured FRFs in rotating structures contain severe noise, leading to an unreliable calculation. This paper presents a novel error analysis model to investigate the propagation mechanism of measurement errors in the typical RCSA framework. Results show that measurement errors would cause errors in the length of the coupled substructure while introducing scaling effects. The calibration rod is found to be vital for RCSA calculation reliability. The patterns of the calculation error are opposed when adopting a short or long calibration rod. Then, a calibration rod selection strategy is proposed. The strategy makes full use of the high measurement quality near the resonance in rotating FRFs and achieves the dominant mode frequency matching between the clamped rod and the clamped tool by adjusting the rod length. Simulations validate the error analysis model and the calibration rod selection strategy. Experimental results also show that the optimal selection of the calibration rod could improve the calculation reliability of rotating tool-tip FRFs in the typical RCSA framework.

EFFECT LEADERSHIP STYLE AND ORGANIZATIONAL CULTURE ON PERFORMANCE WITH WORK MOTIVATION AS INTERVENING VARIABLE

With work motivation as an auxiliary variable, this study seeks to ascertain the impact of leadership style and organizational culture on the performance of specialist doctors. This study is a cross sectional study with 65 specialist doctors as respondents. Collecting data for the four variables using a linkert scale of 1-5 points and as a test tool descriptive statistical analysis is used with a three box method approach which is divided into three scale ranges, namely H (High), M (Medium), and L (Low). The results of the analysis of sub-structure 1 and the probability value simultaneously and partially indicating that the leadership style, organizational culture, and work motivation simultaneously and partially have a positive and substantial impact on performance. Sub structure 2 and the comparison of probability values simultaneously and partially shows that simultaneously and partially leadership style and organizational culture have an effect positive and significant effect on work motivation. The equation of path of total influence shows that job motivation influences leadership style and organizational culture in a way that enhances the performance of specialist doctors.

Why the clock ticks differently in Parkinson's disease: Insights from motor imagery and resting-state functional magnetic resonance imaging

In Parkinson's disease (PD), an impaired perception of suprasecond time intervals has been reported. From a neurobiological perspective, dopamine is thought to be an important mediator of timing. Nevertheless, it is still unclear whether timing deficits in PD occur mainly in the motor context and are associated with corresponding striatocortical loops. This study attempted to fill this gap by investigating time reproduction in the context of a motor imagery task, and its neurobiological correlates in resting-state networks of basal ganglia substructures in PD.Nineteen PD patients and 10 healthy controls therefore underwent two time reproduction tasks. In a motor imagery task, subjects were asked to walk down a corridor for 10 s and reproduce the time spent walking during motor imagery afterwards. In an auditory task, the subjects had to reproduce an acoustically presented time interval of 10 s. Subsequently, resting-state functional magnetic resonance imaging was performed and voxel-wise regressions were conducted between striatal functional connectivity and performance in the individual task at group level and compared between groups.Patients significantly misjudged the time interval in the motor imagery task and an auditory task in comparison to controls. Seed-to-voxel functional connectivity analysis of basal ganglia substructures revealed a significant association between striatocortical connectivity and motor imagery performance. PD patients showed a different pattern of associated striatocortical connections as indicated by significantly different regression slopes for connections of the right putamen and left caudate nucleus.In accordance with previous findings, our data confirm an impaired time reproduction of suprasecond time intervals in PD patients. Our data imply that deficits in time reproduction tasks are not specific to motor context but reflect a general time reproduction deficit. According to our findings, impaired performance in context of motor imagery is accompanied by a different configuration of striatocortical resting-state networks responsible for timing.

Tether response of offshore Triceratops under hurricane conditions

The form-dominant approach is increasingly adopted in offshore platform design; triceratops is one of the very recent examples. These platforms are position-restrained by taut-moored tethers, which govern the dynamic response under extreme environmental loads. Due to the partial isolation of the deck from the buoyant legs in the presence of ball joints, sub-structure analysis is more critical. The present study examines the tether response of triceratops in 2400 m water-depth under hurricane conditions caused by 10-yr, 100-yr, and 1000-yr Gulf of Mexico (GoM) storms. The numerical studies show that the Peak-Wave case governs the dynamic tension variation of tethers for the chosen hurricane conditions. For the 100-yr and 1000-yr storms, they are about 15% and 45% higher than 10-yr storms, respectively. However, the maximum axial tensile stresses are about 8% of the yield, ensuring a no-rupture condition of the tethers. The service lives of the tethers for 10-yr storms are about one-third that of 100-yr and 1000-yr storms. A positive minimum tension for all load cases confirms no slackening of the tethers.

Modeling and prediction for frequency response functions of parameter-varying mechanical systems based on generalized receptance coupling substructure analysis

Most machining systems such as machine tools and robots are parameter-varying mechanical systems, which show different dynamic characteristics under different parameters. Generally, a large number of modal tests are required for parameter-varying mechanical systems to obtain frequency response functions (FRFs) under different system parameters, which reduces efficiency. Receptance coupling substructure analysis (RCSA) provides ideas for solving such problems. In this study, the generalized RCSA (GRCSA) was proposed. The coupling method of two substructures at arbitrary pairs of nodes was derived, and a more general coupling method of multiple substructures at arbitrary pairs of nodes was further elaborated, which provides a complete set of theories and methods for the modeling and prediction of FRF of general parameter-varying mechanical systems. Based on the derivation process and results of the GRCSA, the parameter-varying mechanical systems are classified into three basic categories: variable interface systems, variable spatial attitude systems, and variable substructure systems. Typical cases of three basic categories in engineering practice were selected for studying, and the prediction models for FRFs of various parameter-varying mechanical systems were derived in detail. The calculation methods for FRF matrices of various substructures and the calibration methods for interface parameters are given. The validity of the prediction models was verified by experiments and simulations. The relative errors of the established prediction models under most system variables are less than 1% for the natural frequency, and the maximum relative error of the prediction models is 3.775% for the natural frequency. Finally, based on the prediction models, the FRFs under different system variables were predicted, and the variation laws of the natural frequencies with the system variables were analyzed. The modeling processes based on the GRCSA for the FRFs of typical parameter-varying mechanical systems are general, and can provide references and ideas for the study of frequency response characteristics of other complex parameter-varying mechanical systems.

Criminological analysis of the identity of a fraudster in the field of misuse of electronic means of payment

The processes of digitalization and technologization developed the social relations, but also acted as a catalyst for the development of illegal activities. A typical example of a crime of this group is fraud using electronic means of payment. Based on the low detection rate of this crime, about 30 %, according to the Ministry of Internal Affairs of Russia, as well as the high degree of latency of illegal banking transactions, the optimization of preventive activities is of particular relevance, which requires research the personality of the offender. Within the framework of this article, the socio-demographic, socio-role and socio-psychological substructures of the offender’s personality are analyzed. The official statistics for the period 2013–2020 were studied, as well as the judicial practice in the amount of 115 verdicts in criminal cases for the same period. The typical gender of the offender, his age, education and territory of residence, his professional affiliation were established. The analysis of the socio-psychological substructure of the personality made it possible to produce a conclusion about the depth and stability of the criminalization of the offender, as well as his criminal motivation. As a result of a comprehensive study, a criminological portrait of the personality of a fraudster in the field of misuse of electronic means of payment was compiled, contradictions were revealed between the portraits of the personality of a general criminal fraudster and a cybercriminal formed in the doctrine of criminology with the data obtained. The results of the study can be used in the framework of improving the prevention of crimes committed using information technology in general, and the misuse of electronic means of payment, in particular.

Simulation tool for chatter prediction of varying tool–machine configurations based on dynamic substructuring

Cutting tools of different type, shape and size are used in milling processes. Every individual tool changes the dynamic behavior of the system and results in varying occurrences of chatter vibrations. This paper presents a simulation tool using a frequency based substructuring method for predicting the process stability without separate measurements of the tool-machine configuration. In particular, frequency response functions of any combined tool-machine configuration is synthesized by the receptance coupling substructure analysis. It enables the calculation of critical machining parameters and maximizing material removal rates by avoiding unstable cutting conditions. Verification is done via finite element analysis of exemplary tool and machine structures.

Effects of circular plates on forced vibration of orthogonally stiffened cylindrical shell in wavenumber-frequency domain

A modified variational method for orthogonally stiffened cylindrical shell coupled with multiple circular plates is proposed in this paper. A unified variational modelling procedure is developed to accommodate three dimensional deformation restrains between the shell and substructures, including circular plates, ring and axial stiffeners. The vibration displacements are analytically expanded with Fourier series in the circumferential direction. The input energies due to external loads and the responses of the shell in circumferential wavenumber domain can be naturally obtained, which significantly facilitate the effect analysis of substructures. Close orthogonal independent polynomials are employed for the displacements in the axial direction to achieve satisfactory solutions. The convergence and accuracy of the present method are validated by comparing the analytical and numerical results. The effects of circular plates on vibration responses under different external forces are investigated. The contributions of different circumferential modes to the forced responses are analyzed, and reasons for the effects of the circular plates are then revealed in the circumferential wavenumber-frequency domain. The results indicate that the circular plates not only lead to frequency shift, but also coupling of the circumferential modes of the shell, which will respectively account for frequency shifts and increase of the resonance peaks.

Physics-informed Bayesian machine learning case study: Integral blade rotors

This paper provides a physics-informed Bayesian machine learning (PIBML) description and case study. The PIBML approach applies three physics-based models to establish the initial beliefs before testing to determine the probability of milling stability (or prior). These include: receptance coupling substructure analysis (RCSA) prediction for the tool tip frequency response functions; finite element software prediction of the mechanistic force model coefficients; and a spindle speed-dependent power law model for process damping. Testing was then performed to identify optimal stable machining conditions using an expected improvement in material removal rate criterion. The prior probability of stability was updated using the test results to determine the posterior probability of stability. The test results were compared to the parameter recommendations provided by the endmill manufacturer. A demonstration integral blade rotor was machined at the optimal stable machining conditions for 304 stainless steel and 6061-T6 aluminum. The disagreement between manufacturer recommendations and milling performance in both materials tested emphasizes the need for broad implementation of PIBML approaches to increase machining productivity and efficiency.

Systematic quark/gluon identification with ratios of likelihoods

Discriminating between quark- and gluon-initiated jets has long been a central focus of jet substructure, leading to the introduction of numerous observables and calculations to high perturbative accuracy. At the same time, there have been many attempts to fully exploit the jet radiation pattern using tools from statistics and machine learning. We propose a new approach that combines a deep analytic understanding of jet substructure with the optimality promised by machine learning and statistics. After specifying an approximation to the full emission phase space, we show how to construct the optimal observable for a given classification task. This procedure is demonstrated for the case of quark and gluons jets, where we show how to systematically capture sub-eikonal corrections in the splitting functions, and prove that linear combinations of weighted multiplicity is the optimal observable. In addition to providing a new and powerful framework for systematically improving jet substructure observables, we demonstrate the performance of several quark versus gluon jet tagging observables in parton-level Monte Carlo simulations, and find that they perform at or near the level of a deep neural network classifier. Combined with the rapid recent progress in the development of higher order parton showers, we believe that our approach provides a basis for systematically exploiting subleading effects in jet substructure analyses at the Large Hadron Collider (LHC) and beyond.

Genetic diversity of two native sheep breeds by genome-wide analysis of single nucleotide polymorphisms

Wallachian and Sumava sheep are autochthonous breeds that have undergone a significant bottleneck effect and subsequent restoration efforts. The first objective of this study was to evaluate the degree of genetic variability of both breeds and, therefore, the current management of the breeding. The second was to determine whether these two breeds still retain their genetic uniqueness in relation to each other and other breeds, despite regenerative interventions. Our data consisted of 48 individuals of Sumava and 37 individuals of Wallachian sheep. The comparison data contained 25 other breeds (primarily European) from the HapMap dataset generated by the International Sheep Genomics Consortium. When comparing all 27 breeds, the Czech breeds clustered with 15 other breeds and formed a single branch with them according to Nei's distances. At the same time, however, the clusters of both breeds were integral and easily distinguishable from the others when displayed with principal component analysis (PCA). Population substructure analysis did not show any common genetic ancestry of the Czech national breeds and breeds used for regeneration or, eventually, breeds whose ancestral population was used for regeneration. The average values of FST were higher in Wallachian sheep (FST = 0.14) than in Sumava sheep (FST = 0.08). The linkage disequilibrium (LD) extension per autosome was higher in Wallachian than in Sumava sheep. Consequently, the Ne estimates five generations ago were 68 for Sumava versus 34 for Wallachian sheep. Both native Czech breeds exhibit a wide range of inbreeding based on the excess of homozygosity (FHOM) among individuals, from −0.04 to 0.16 in Sumava and from −0.13 to 0.12 in Wallachian. Average inbreeding based on runs of homozygosity was 0.21 in Sumava and 0.27 in Wallachian. Most detected runs of homozygosity (ROH) were less than 5 Mb long for both breeds. ROH segments longer than 15 Mb were absent in Wallachian sheep. Concerning putative selection signatures, a total of 471 candidate genes in Wallachian sheep within 11 hotspots and 653 genes within 13 hotspots in Sumava sheep were identified. Czech breeds appear to be well differentiated from each other and other European breeds. Their genetic diversity is low, especially in the case of the Wallachian breed. Sumava is not so threatened by low diversity but has a larger share of the non-native gene pool.