The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.

• MeSH
• algoritmy * MeSH
• biologická evoluce * MeSH
• datové soubory jako téma * MeSH
• interpretace obrazu počítačem * MeSH
• rozpoznávání automatizované * MeSH
• Publikační typ
• časopisecké články MeSH

Evolutionary technique differential evolution (DE) is used for the evolutionary tuning of controller parameters for the stabilization of set of different chaotic systems. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used also as the chaotic pseudorandom number generator to drive the mutation and crossover process in the DE. The idea was to utilize the hidden chaotic dynamics in pseudorandom sequences given by chaotic map to help differential evolution algorithm search for the best controller settings for the very same chaotic system. The optimizations were performed for three different chaotic systems, two types of case studies and developed cost functions.

• MeSH
• algoritmy * MeSH
• nelineární dynamika * MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Nárůst poznatků o buněčné povaze, patogenezi a genetických změnách chronické lymfocytární leukemie (CLL) spolu s novými možnostmi terapie purinovými analogy a monoklonálními protilátkami v 90. letech minulého století vedly k dalšímu zvýšení zájmu o výzkum této chorobné jednotky. Dosažené poznatky z počátku tohoto století významně pomohly ve vývoji nových cílených léků, které se dnes stávají páteří léčby CLL a zásadním způsobem budou měnit terapeutické algoritmy v budoucích několika příštích letech. Pro většinu pacientů bude zřejmě optimální léčebná strategie založena na režimech bez použití cytostatik. Při kombinaci či sekvenční aplikaci monoklonálních protilátek a cílených molekul se můžeme v blízké budoucnosti dočkat kurabilního efektu takto vedené terapie u významného počtu pacientů s CLL.

Advances in understanding the cell origin, pathogenesis, genetic and molecular biology together with introduction of novel agents – purine nucleoside analogues and monoclonal antibodies – led to dynamic changes in the field of chronic lymphocytic leukaemia (CLL) in the 1990s. In the 2000s and 2010s, important biological insights helped develop small targeted drugs that now form the backbone of CLL therapy and will dramatically change therapeutic algorithms in the near future. Modern treatment approaches will most probably be chemotherapy-free for a majority of CLL patients. Optimal combination or sequencing of monoclonal antibodies and targeted molecules may cure a substantial proportion of CLL patients in the next years.

• MeSH
• alkylační látky farmakologie   terapeutické užití   MeSH
• chronická lymfatická leukemie * farmakoterapie   radioterapie   MeSH
• glukokortikoidy terapeutické užití   MeSH
• lidé MeSH
• monoklonální protilátky terapeutické užití   MeSH
• protokoly protinádorové kombinované chemoterapie MeSH
• purinové nukleosidy farmakologie   terapeutické užití   MeSH
• pyrazoly terapeutické užití   MeSH
• sulfonamidy terapeutické užití   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• práce podpořená grantem MeSH

The Roma represents a transnational ethnic group, with a current European population of 8-10 million. The evolutionary process that had the greatest impact on the gene pool of the Roma population is called the founder effect. Renal hypouricemia (RHUC) is a rare heterogenous inherited disorder characterized by impaired renal urate reabsorption. The affected individuals are predisposed to recurrent episodes of exercise-induced nonmyoglobinuric acute kidney injury and nephrolithiasis. To date, more than 150 patients with a loss-of-function mutation for the SLC22A12 (URAT1) gene have been found, most of whom are Asians. However, RHUC 1 patients have been described in a variety of ethnic groups (e.g., Arab Israelis, Iraqi Jews, Caucasians, and Roma) and in geographically noncontiguous countries. This study confirms our previous findings regarding the high frequency of SLC22A12 variants observed. Frequencies of the c.1245_1253del and c.1400C>T variants were found to be 1.92% and 5.56%, respectively, in a subgroup of the Roma population from five regions in three countries: Slovakia, Czech Republic, and Spain. Our findings suggested that the common dysfunction allelic variants of URAT1 exist in the general Roma population and thus renal hypouricemia should be kept in differential diagnostic algorithm on Roma patients with defect in renal tubular urate transport. This leads to confirm that the genetic drift in the Roma have increased the prevalence of hereditary disorders caused by very rare variants in major population.

• MeSH
• efekt zakladatele MeSH
• frekvence genu MeSH
• genetické asociační studie MeSH
• heterozygot MeSH
• lidé MeSH
• močové kameny epidemiologie   genetika   MeSH
• molekulární evoluce MeSH
• přenašeče organických aniontů genetika   MeSH
• prevalence MeSH
• proteiny přenášející organické kationty genetika   MeSH
• Romové genetika   MeSH
• sekvenční delece MeSH
• vrozené poruchy tubulárního transportu epidemiologie   genetika   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND AND OBJECTIVE: Diabetes mellitus manifests as prolonged elevated blood glucose levels resulting from impaired insulin production. Such high glucose levels over a long period of time damage multiple internal organs. To mitigate this condition, researchers and engineers have developed the closed loop artificial pancreas consisting of a continuous glucose monitor and an insulin pump connected via a microcontroller or smartphone. A problem, however, is how to accurately predict short term future glucose levels in order to exert efficient glucose-level control. Much work in the literature focuses on least prediction error as a key metric and therefore pursues complex prediction methods such a deep learning. Such an approach neglects other important and significant design issues such as method complexity (impacting interpretability and safety), hardware requirements for low-power devices such as the insulin pump, the required amount of input data for training (potentially rendering the method infeasible for new patients), and the fact that very small improvements in accuracy may not have significant clinical benefit. METHODS: We propose a novel low-complexity, explainable blood glucose prediction method derived from the Intel P6 branch predictor algorithm. We use Meta-Differential Evolution to determine predictor parameters on training data splits of the benchmark datasets we use. A comparison is made between our new algorithm and a state-of-the-art deep-learning method for blood glucose level prediction. RESULTS: To evaluate the new method, the Blood Glucose Level Prediction Challenge benchmark dataset is utilised. On the official test data split after training, the state-of-the-art deep learning method predicted glucose levels 30 min ahead of current time with 96.3% of predicted glucose levels having relative error less than 30% (which is equivalent to the safe zone of the Surveillance Error Grid). Our simpler, interpretable approach prolonged the prediction horizon by another 5 min with 95.8% of predicted glucose levels of all patients having relative error less than 30%. CONCLUSIONS: When considering predictive performance as assessed using the Blood Glucose Level Prediction Challenge benchmark dataset and Surveillance Error Grid metrics, we found that the new algorithm delivered comparable predictive accuracy performance, while operating only on the glucose-level signal with considerably less computational complexity.

• MeSH
• algoritmy MeSH
• diabetes mellitus 1. typu * MeSH
• inzulin MeSH
• krevní glukóza MeSH
• lidé MeSH
• selfmonitoring glykemie * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Mantle cell lymphoma (MCL) is a heterogeneous malignancy with a broad spectrum of clinical behavior from indolent to highly aggressive cases. Despite the fact that MCL remains in most cases incurable by currently applied immunochemotherapy, our increasing knowledge on the biology of MCL in the last two decades has led to the design, testing, and approval of several innovative agents that dramatically changed the treatment landscape for MCL patients. Most importantly, the implementation of new drugs and novel treatment algorithms into clinical practice has successfully translated into improved outcomes of MCL patients not only in the clinical trials, but also in real life. This review focuses on recent advances in our understanding of the pathogenesis of MCL, and provides a brief survey of currently used treatment options with special focus on mode of action of selected innovative anti-lymphoma molecules. Finally, it outlines future perspectives of patient management with progressive shift from generally applied immunotherapy toward risk-stratified, patient-tailored protocols that would implement innovative agents and/or procedures with the ultimate goal to eradicate the lymphoma and cure the patient.

• MeSH
• buněčný cyklus účinky léků   genetika   MeSH
• chemorezistence MeSH
• cílená molekulární terapie * metody   MeSH
• genetická variace MeSH
• klonální evoluce účinky léků   genetika   MeSH
• kombinovaná terapie MeSH
• lidé MeSH
• lymfom z plášťových buněk farmakoterapie   etiologie   metabolismus   mortalita   MeSH
• náchylnost k nemoci MeSH
• nádorové biomarkery antagonisté a inhibitory   MeSH
• prognóza MeSH
• protinádorové látky farmakologie   terapeutické užití   MeSH
• receptory antigenů B-buněk metabolismus   MeSH
• recidiva MeSH
• regulace genové exprese u nádorů účinky léků   MeSH
• signální transdukce účinky léků   MeSH
• výsledek terapie MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

The Mascarene ridged frog, Ptychadena mascareniensis, is a species complex that includes numerous lineages occurring mostly in humid savannas and open forests of mainland Africa, Madagascar, the Seychelles, and the Mascarene Islands. Sampling across this broad distribution presents an opportunity to examine the genetic differentiation within this complex and to investigate how the evolution of bioclimatic niches may have shaped current biogeographic patterns. Using model-based phylogenetic methods and molecular-clock dating, we constructed a time-calibrated molecular phylogenetic hypothesis for the group based on mitochondrial 16S rRNA and cytochrome b (cytb) genes and the nuclear RAG1 gene from 173 individuals. Haplotype networks were reconstructed and species boundaries were investigated using three species-delimitation approaches: Bayesian generalized mixed Yule-coalescent model (bGMYC), the Poisson Tree Process model (PTP) and a cluster algorithm (SpeciesIdentifier). Estimates of similarity in bioclimatic niche were calculated from species-distribution models (maxent) and multivariate statistics (Principal Component Analysis, Discriminant Function Analysis). Ancestral-area reconstructions were performed on the phylogeny using probabilistic approaches implemented in BioGeoBEARS. We detected high levels of genetic differentiation yielding ten distinct lineages or operational taxonomic units, and Central Africa was found to be a diversity hotspot for these frogs. Most speciation events took place throughout the Miocene, including "out-of-Africa" overseas dispersal events to Madagascar in the East and to São Tomé in the West. Bioclimatic niche was remarkably well conserved, with most species tolerating similar temperature and rainfall conditions common to the Central African region. The P. mascareniensis complex provides insights into how bioclimatic niche shaped the current biogeographic patterns with niche conservatism being exhibited by the Central African radiation and niche divergence shaping populations in West Africa and Madagascar. Central Africa, including the Albertine Rift region, has been an important center of diversification for this species complex.

• MeSH
• analýza hlavních komponent MeSH
• Bayesova věta MeSH
• cytochromy b klasifikace   genetika   metabolismus   MeSH
• DNA chemie   izolace a purifikace   metabolismus   MeSH
• ekologie MeSH
• fylogeneze MeSH
• fylogeografie MeSH
• haplotypy MeSH
• homeodoménové proteiny klasifikace   genetika   metabolismus   MeSH
• Ranidae klasifikace   genetika   MeSH
• RNA ribozomální 16S klasifikace   genetika   metabolismus   MeSH
• sekvenční analýza DNA MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, U.S. Gov't, Non-P.H.S. MeSH
• Geografické názvy
• Afrika MeSH
• Madagaskar MeSH

MOTIVATION: Optical mapping is a complementary technology to traditional DNA sequencing technologies, such as next-generation sequencing (NGS). It provides genome-wide, high-resolution restriction maps from single, stained molecules of DNA. It can be used to detect large and small structural variants, copy number variations and complex rearrangements. Optical mapping is affected by different kinds of errors in comparison with traditional DNA sequencing technologies. It is important to understand the source of these errors and how they affect the obtained data. This article proposes a novel approach to modeling errors in the data obtained from the Bionano Genomics Inc. Saphyr system with Direct Label and Stain (DLS) chemistry. Some studies have already addressed this issue for older instruments with nicking enzymes, but we are unaware of a study that addresses this new system. RESULTS: The main result is a framework for studying errors in the data obtained from the Saphyr instrument with DLS chemistry. The framework's main component is a simulation that computes how major sources of errors for this instrument (a false site, a missing site and resolution errors) affect the distribution of fragment lengths in optical maps. The simulation is parametrized by variables describing these errors and we are using a differential evolution algorithm to evaluate parameters that best fit the data from the instrument. Results of the experiments manifest that this approach can be used to study errors in the optical mapping data analysis. AVAILABILITY AND IMPLEMENTATION: Source codes supporting the presented results are available at: https://github.com/mvasinek/olgen-om-error-prediction. The data underlying this article are available on the Bionano Genomics Inc. website, at: https://bionanogenomics.com/library/datasets/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

• Publikační typ
• časopisecké články MeSH

Abstract Motivation Optical mapping is a complementary technology to traditional DNA sequencing technologies, such as next-generation sequencing (NGS). It provides genome-wide, high-resolution restriction maps from single, stained molecules of DNA. It can be used to detect large and small structural variants, copy number variations and complex rearrangements. Optical mapping is affected by different kinds of errors in comparison with traditional DNA sequencing technologies. It is important to understand the source of these errors and how they affect the obtained data. This article proposes a novel approach to modeling errors in the data obtained from the Bionano Genomics Inc. Saphyr system with Direct Label and Stain (DLS) chemistry. Some studies have already addressed this issue for older instruments with nicking enzymes, but we are unaware of a study that addresses this new system. Results The main result is a framework for studying errors in the data obtained from the Saphyr instrument with DLS chemistry. The framework’s main component is a simulation that computes how major sources of errors for this instrument (a false site, a missing site and resolution errors) affect the distribution of fragment lengths in optical maps. The simulation is parametrized by variables describing these errors and we are using a differential evolution algorithm to evaluate parameters that best fit the data from the instrument. Results of the experiments manifest that this approach can be used to study errors in the optical mapping data analysis. Availability and implementation Source codes supporting the presented results are available at: https://github.com/mvasinek/olgen-om-error-prediction. The data underlying this article are available on the Bionano Genomics Inc. website, at: https://bionanogenomics.com/library/datasets/. Supplementary information Supplementary data are available at Bioinformatics online.

• Publikační typ
• časopisecké články MeSH