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The evolution toward the so-called smart grid (SG) needs to be supported by robust and cost-efficient Advanced Metering Infrastructure (AMI). The focus of this paper is on technology selection and gateway placement in hybrid SGs, wherein each smart meter (SM) is equipped with either a Power Line Communication (PLC) or a LoRaWAN node to connect to its serving gateway. The latter is endowed with both technologies. In particular, we introduce an analytical framework to characterize the performance-cost trade-offs using a multi-criteria optimization approach. For each technology, a set of performance objective functions (i.e. sum-rate and max-min) has been derived based on the ergodic capacity, whereas the cost is represented by SG hardware expenses. Then, the NSGA-II algorithm has been exploited using an appropriate chromosome structure to provide the suitable access technology for each SM along with the optimal number and the placement of the serving GWs. Extensive numerical simulations have shown that hybrid solutions provide a good interplay between performance and investment costs, spurring the need to redefine the conventional SG designs traditionally operating in single communication technologies.

Quantum-inspired differential evolution (QDE) is an evolutionary algorithm, which can effectively solve complex optimization problems. However, sometimes, it easily leads to premature convergence and low search ability and falls to local optima. To overcome these problems, based on the MSIQDE (improved QDE with multistrategies) algorithm, an enhanced MSIQDE algorithm based on mixing multiple strategies, namely, EMMSIQDE is proposed in this article. In the EMMSIQDE, a new differential mutation strategy of a difference vector is proposed to enhance the search ability and descent ability. Then, a new multipopulation mutation evolution mechanism is designed to ensure the relative independence of each subpopulation and the population diversity. The feasible solution space transformation strategy is used to achieve the optimal solution by mapping the quantum chromosome from a unit space to solution space. Finally, some multidimensional unimodal and multimodal functions are selected to demonstrate the optimization performance of EMMSIQDE. The results demonstrate that the EMMSIQDE is significantly better than the DE, QDE, QGA, and MSIQDE, and has better optimization ability, scalability, efficiency, and stability.

Motivation Long-read phasing has been used for reconstructing diploid genomes, improving variant calling and resolving microbial strains in metagenomics. However, the phasing blocks of existing methods are broken by large Structural Variations (SVs), and the efficiency is unsatisfactory for population-scale phasing. Results This article presents a novel algorithm, LongPhase, which can simultaneously phase single nucleotide polymorphisms (SNPs) and SVs of a human genome in 10–20 min, 10× faster than the state-of-the-art WhatsHap, HapCUT2 and Margin. In particular, co-phasing SNPs and SVs produces much larger haplotype blocks (N50 = 25 Mbp) than those of existing methods (N50 = 10–15 Mbp). We show that LongPhase combined with Nanopore ultra-long reads is a cost-effective and highly contiguous solution, which can produce between one and 26 blocks per chromosome arm without the need for additional trios, chromosome-conformation and strand-seq data. Availabilityand implementation LongPhase is freely available at https://github.com/twolinin/LongPhase/. Supplementary information Supplementary data are available at Bioinformatics online.

We propose a method for human embryo grading with its images. This grading has been achieved by positive-negative classification (i.e., live birth or non-live birth). However, negative (non-live birth) labels collected in clinical practice are unreliable because the visual features of negative images are equal to those of positive (live birth) images if these non-live birth embryos have chromosome abnormalities. For alleviating an adverse effect of these unreliable labels, our method employs Positive-Unlabeled (PU) learning so that live birth and non-live birth are labeled as positive and unlabeled, respectively, where unlabeled samples contain both positive and negative samples. In our method, this PU learning on a deep CNN is improved by a learning-to-rank scheme. While the original learning-to-rank scheme is designed for positive-negative learning, it is extended to PU learning. Furthermore, overfitting in this PU learning is alleviated by regularization with mutual information. Experimental results with 643 time-lapse image sequences demonstrate the effectiveness of our framework in terms of the recognition accuracy and the interpretability. In quantitative comparison, the full version of our proposed method outperforms positive-negative classification in recall and F-measure by a wide margin (0.22 vs. 0.69 in recall and 0.27 vs. 0.42 in F-measure). In qualitative evaluation, visual attentions estimated by our method are interpretable in comparison with morphological assessments in clinical practice.

Optimizing all the objectives of the printed circuit board assembly (PCBA) optimization in a multifunctional placer remains a formidable challenge till now. This article converts the original PCBA optimization problem to a newly defined component allocation problem, which decides the component-type handled by each head per pickup-and-place (PAP) cycle. The component allocation problem is a quadratic 3-D assignment problem (Q3AP) and effectively combines the optimization of all the main objectives. It is possible that one head stays idle, so the assigning 2-D locations are uncertain. We propose the cell division genetic algorithm (CDGA) to solve such a complex Q3AP. The CDGA allocates a component cell as the basic unit. Each of the first-generation component cells contains the mounting points of the same type. A cell chromosome decoding heuristic is designed to determine the next assigning head. By doing so, the problem dimension is reduced, so the conventional GA can be used for searching the optimal component allocation formed by the current-generation cells. When a better allocation can no longer be found by allocating the current cells, the cell division operation is performed to divide each cell into two new cells. The new cells are used in the next round of GA searching, which further optimizes the allocation from two perspectives: better balancing the minimization of nozzle changes and PAP cycles, more flexibly maximizing the simultaneous pickups with the uncertain locations. The CDGA works continuously until the current cells cannot bring any improvement. In simulations and experiments using the industrial samples, the proposed algorithm significantly reduces the PCBA time compared to two recent studies and the built-in optimizer of the widely used multifunctional placer, Hanwha SM482 PLUS, which demonstrates its effectiveness and superiority.

Background Structural variation (SV), which ranges from 50 bp to $$\sim$$ ∼ 3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replication. Three types of signals, including discordant read-pairs, reads depth and split reads, are commonly used for SV detection from high-throughput sequence data. Many tools have been developed for detecting SVs by using one or multiple of these signals. Results In this paper, we develop a new method called EigenDel for detecting the germline submicroscopic genomic deletions. EigenDel first takes advantage of discordant read-pairs and clipped reads to get initial deletion candidates, and then it clusters similar candidates by using unsupervised learning methods. After that, EigenDel uses a carefully designed approach for calling true deletions from each cluster. We conduct various experiments to evaluate the performance of EigenDel on low coverage sequence data. Conclusions Our results show that EigenDel outperforms other major methods in terms of improving capability of balancing accuracy and sensitivity as well as reducing bias. EigenDel can be downloaded from https://github.com/lxwgcool/EigenDel.

PurposeThe best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS) algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm, the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this paper is to detect credit card fraud using ARO.Design/methodology/approachIn this paper, the authors used ARO algorithm to classify the bank transactions into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables. Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed that every generated answer exists in the environment, and because of limited resources, only the best solution can remain alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud. Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains alive. If the offspring has suitable performance, it will be the next parent, and the current parent becomes obsolete. Otherwise, the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs.FindingsResults showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC) curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS, respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a significant figure.Originality/valueIn this paper, the authors implemented the ARO in credit card fraud detection. The authors compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these demands.

Background: The open and accessible regions of the chromosome are more likely to be bound by transcription factors which are important for nuclear processes and biological functions. Studying the change of chromosome flexibility can help to discover and analyze disease markers and improve the efficiency of clinical diagnosis. Current methods for predicting chromosome flexibility based on Hi-C data include the Flexibility-Rigidity Index (FRI) and the Gaussian Network Model (GNM), which have been proposed to characterize chromosome flexibility. However, these methods require the chromosome structure data based on 3D biological experiments, which is time-consuming and expensive. Objective: Generally, the folding and curling of the double helix sequence of DNA have a great impact on chromosome flexibility and function. Motivated by the success of genomic sequence analysis in biomolecular function analysis, we hope to propose a method to predict chromosome flexibility only based on genomic sequence data. Methods: We propose a new method (named "DeepCFP") using deep learning models to predict chromosome flexibility based on only genomic sequence features. The model has been tested in the GM12878 cell line. Results: The maximum accuracy of our model has reached 91%. The performance of DeepCFP is close to FRI and GNM. Conclusion: The DeepCFP can achieve high performance only based on genomic sequence.

Background To select the most complete, continuous, and accurate assembly for an organism of interest, comprehensive quality assessment of assemblies is necessary. We present a novel tool, called Evaluation of De Novo Assemblies (EvalDNA), which uses supervised machine learning for the quality scoring of genome assemblies and does not require an existing reference genome for accuracy assessment. Results EvalDNA calculates a list of quality metrics from an assembled sequence and applies a model created from supervised machine learning methods to integrate various metrics into a comprehensive quality score. A well-tested, accurate model for scoring mammalian genome sequences is provided as part of EvalDNA. This random forest regression model evaluates an assembled sequence based on continuity, completeness, and accuracy, and was able to explain 86% of the variation in reference-based quality scores within the testing data. EvalDNA was applied to human chromosome 14 assemblies from the GAGE study to rank genome assemblers and to compare EvalDNA to two other quality evaluation tools. In addition, EvalDNA was used to evaluate several genome assemblies of the Chinese hamster genome to help establish a better reference genome for the biopharmaceutical manufacturing community. EvalDNA was also used to assess more recent human assemblies from the QUAST-LG study completed in 2018, and its ability to score bacterial genomes was examined through application on bacterial assemblies from the GAGE-B study. Conclusions EvalDNA enables scientists to easily identify the best available genome assembly for their organism of interest without requiring a reference assembly. EvalDNA sets itself apart from other quality assessment tools by producing a quality score that enables direct comparison among assemblies from different species.

Background and Aim Chronic hepatitis C virus (HCV) infection, long‐term alcohol use, cigarette smoking, and obesity are the major risk factors for hepatocellular carcinoma (HCC) in the United States, but the disease risk varies substantially among individuals with these factors, suggesting host susceptibility to and gene–environment interactions in HCC. To address genetic susceptibility to HCC, we conducted a genome‐wide association study (GWAS). Methods Two case‐control studies on HCC were conducted in the United States. DNA samples were genotyped using the Illumian microarray chip with over 710 000 single nucleotide polymorphisms (SNPs). We compared these SNPs between 705 HCC cases and 1455 population controls for their associations with HCC and verified our findings in additional studies. Results In this GWAS, we found that two SNPs were associated with HCC at P, Genome‐wide association study (GWAS) showed that five SNPs in 22q13.31, three in PNPLA3 (rs2281135, rs2896019, and 4823173) and two in SAMM50 (rs3761472 and rs3827385), were associated with HCC in the United States.

Since 2017, we have used IonTorrent NGS platform in our hospital to diagnose and treat cancer. Analyzing variants at each run requires considerable time, and we are still struggling with some variants that appear correct on the metrics at first, but are found to be negative upon further investigation. Can any machine learning algorithm (ML) help us classify NGS variants? This has led us to investigate which ML can fit our NGS data and to develop a tool that can be routinely implemented to help biologists. Currently, one of the greatest challenges in medicine is processing a significant quantity of data. This is particularly true in molecular biology with the advantage of next-generation sequencing (NGS) for profiling and identifying molecular tumors and their treatment. In addition to bioinformatics pipelines, artificial intelligence (AI) can be valuable in helping to analyze mutation variants. Generating sequencing data from patient DNA samples has become easy to perform in clinical trials. However, analyzing the massive quantities of genomic or transcriptomic data and extracting the key biomarkers associated with a clinical response to a specific therapy requires a formidable combination of scientific expertise, biomolecular skills and a panel of bioinformatic and biostatistic tools, in which artificial intelligence is now successful in developing future routine diagnostics. However, cancer genome complexity and technical artifacts make identifying real variants challenging. We present a machine learning method for classifying pathogenic single nucleotide variants (SNVs), single nucleotide polymorphisms (SNPs), multiple nucleotide variants (MNVs), insertions, and deletions detected by NGS from different types of tumor specimens, such as: colorectal, melanoma, lung and glioma cancer. We compared our NGS data to different machine learning algorithms using the k-fold cross-validation method and to neural networks (deep learning) to measure the performance of the different ML algorithms and determine which one is a valid model for confirming NGS variant calls in cancer diagnosis. We trained our machine learning with 70% of our data samples, extracted from our local database (our data structure had 7 parameters: chromosome, position, exon, variant allele frequency, minor allele frequency, coverage and protein description) and validated it with the 30% remaining data. The model offering the best accuracy was chosen and implemented in the NGS analysis routine. Artificial intelligence was developed with the R script language version 3.6.0. We trained our model on 70% of 102,011 variants. Our best error rate (0.22%) was found with random forest machine learning (ntree = 500 and mtry = 4), with an AUC of 0.99. Neural networks achieved some good scores. The final trained model with the neural network achieved an accuracy of 98% and an ROC-AUC of 0.99 with validation data. We tested our RF model to interpret more than 2000 variants from our NGS database: 20 variants were misclassified (error rate

This paper proposes an integrated job shop scheduling and assembly sequence planning (IJSSASP) approach for discrete manufacturing, enabling the part processing sequence and assembly sequence to be optimized simultaneously. The optimization objectives are to minimize the total production completion time and the total inventory time of parts during production. The interaction effects between the job shop schedule and the assembly sequence plan in discrete manufacturing are analyzed, and the mathematical models including the objective functions and the constraints are established for IJSSASP. Based on the above, a non-dominated sorting genetic algorithm-II (NSGA-Ⅱ) with a hybrid chromosome coding mechanism is applied to solve the IJSSASP problem. Through the case studies and comparison tests for different scale problems, the proposed IJSSASP approach is verified to be able to improve the production efficiency and save the manufacturing cost of the discrete manufacturing enterprise more effectively.

Ongoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of variants that contribute to the genetic component of complex trait variability, these data have brought forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Optimal realization of these objectives requires ongoing methodological developments, designed to identify true trait-associated variants and accurately predict phenotype from genotype. In addition to optimal accuracy, these methods must also be computationally efficient, in order to remain tractable in the context of high variant densities and very large sample sizes. Here we show that the power of linear mixed models that are in widespread use for GWAS can be increased significantly by modeling off target genetic effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Applying this method to simulated and real data we found that it can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data.

Construction of DNA-encoded programs is central to synthetic biology and the chosen method often determines the time required to design and build constructs for testing. Here we describe and summarise key features of the available toolkits for DNA construction for mammalian cells. We compare the different cloning strategies based on their complexity and the time needed to generate constructs of different sizes, and we reflect on why Golden Gate toolkits now dominate due to their modular design. We look forward to future advances, including accessory packs to cloning toolkits that can facilitating editing, orthogonality, advanced regulation, and integration into synthetic chromosome construction.

Originally relatively ignored in forensic investigations because its genetic analysis lacks inference of individual identification, the value of Y chromosome analysis has been proven in cases of sexual assault, particularly where the amount of material left by a male assailant is limited in comparison with female DNA. All routine analysis of autosomal DNA, however, targets a gene (AMELY) on the Y chromosome in order to identify the sex of the DNA source and this is discussed in the context of the genetic structure of this male-specific chromosome. Short-tandem repeat markers on the chromosome are tested in dedicated multiplexes that have developed over time and these are described alongside international guidance as to their use in a forensic setting. As a marker of lineage, the Y chromosome provides additional tools to assist in the inference of ancestry, both geographical and familial and the value of Y chromosome testing is illustrated through descriptions of cases of criminal and historical interest. A decision to analyse the Y chromosome has to be considered in the context, not only of the circumstances of the case, but also with regard to the ethical questions it might raise, and these are discussed in relation to the cases that have been described in more detail in the accompanying online supplementary material.

Solving Integer Linear Programming (ILP) models generally lies in the category of NP-hard problems and finding the optimal answer for large models is a computational challenge. Genetic algorithms are a family of metaheuristic algorithms capable of adjusting and redesigning parameters and operations according to the characteristics of ILP models. On the other hand, still the genetic algorithm performs a lot of operations to solve large models, and parallel processing is a suitable technique to tackle this problem. This paper introduces an LP-Relaxation based parallel genetic algorithm that uses a population-based incremental learning technique to presents an expandable solver for large ILP models derived from a behavioral synthesis of digital circuits. In the proposed algorithm, each chromosome provides a state subspace of possible solutions, and each generation is produced based on a probability vector as well as elitism. Our experiments verify the efficiency of the proposed algorithm on multicore platforms, as it outperformed four previous genetic algorithms for solving mixed integer programming problems. The proposed genetic algorithm solved 20 ILP models include up to 5183 int / binary decision variables in less than 20 min using four 16-core AMD Opteron 6386 SE processors. Also, the results indicate that for models with more than 4000 variables, the speedup and the efficiency of the proposed parallel genetic algorithm on 60 CPU cores is more than 18X and $$30\%$$ , respectively.

Chromosome 1q duplication is one of the rare congenital anomalies accompanied by numerous visceral organ anomalies, dysmorphism, and psychomotor retardation. Our case is an 8-year-old male patient with 1q31.3q42.12 trisomy. The patient was brought to our rehabilitation center because he couldn't able to go up and down stairs and slopes without support also he couldn’t run and jump. The patient receives physical therapy service for kyphotic posture, speech therapy for speech and drooling problems, and special education support for mild cognitive impairment. Our aim is to describe the dysmorphic features and posture examination of an 8-year-old male patient with chromosome 1q31.3q42.12 trisomy. In lateral, anterior and posterior posture examination, various anatomic and dysmorphic problems were observed on the patient. To improve the life quality and comfort of the patient, it should be prepared necessary treatment plans for this patients.

This article aims to mitigate the imbalance of capacity and flow in airspace. Targeting to minimize the total delay time, taking the real-life sector operation and aircraft flight rules into consideration, with constraints of sector capacity limit and minimal time interval, the principle to prioritize aircraft when the delay occurred was raised. A trajectory planning model with the priority of aircraft was then established in the model of trajectory based on operation in a continuous period. The traditional genetic algorithm was also improved through the strategy of enhanced elitism preserving and double-stranded chromosome structure. Case studies indicated that the proposed trajectory planning model and solution algorithm have contributed to, over the two periods, an average reduction of 71.77 % in the delay time on optimization effect, and an increase of 19.48 % in the calculation speed. In this case, the model appears to, in a relatively short time, provide a trajectory allocation strategy with security and timeliness, for aircraft operated in consecutive periods. As a result, the sectors can operate without any conflicts while effectively reducing flight delays, minimizing the traffic congestion and potential accidents, so as to take the most advantages of sector resources allocation.

The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements.

We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.

Information on the karyotypes of Turkish species of Cerambycidae is scanty. Our study contributes to the knowledge of the karyological data (chromosomal number and mechanism of sex determination) of five Turkish longicorn beetles; karyotypes of four taxa, one endemic, are described for the first time and for the remaining one, Purpuricenus budensis (Götz, 1783), the previously published chromosome count is confirmed. The chromosome number of Purpuricenus desfontainii inhumeralis Pic, 1891 and Purpuricenus budensis (Götz, 1783) (Cerambycinae, Trachyderini) was found to be 2n = 28 (13 + Xyp); Clytus rhamni Germar, 1817 and Plagionotus floralis (Pallas, 1773) (Cerambycinae, Clytini) 2n = 20 (9 + Xyp); and the endemic Dorcadion triste phrygicum Peks, 1993 (Lamiinae, Dorcadionini) 2n = 24 (11 + Xyp). In view of the paucity of data available until now, our study is important for both to improve the poor karyological knowledge of Turkish Cerambycidae and to provide an incentive for other researchers.

One of the most important issues in cloud manufacturing involves obtaining an optimal manufacturing service composition solution. However, traditional manufacturing service composition methods either focused on single-task-oriented service composition or optimized solutions under a deterministic environment. In the study, a multitask-oriented manufacturing service composition (MMSC) model with two stages in uncertain environment is proposed. It handles the problem of multitask scheduling and also deals with the inherent uncertainty and ambiguity in cloud manufacturing including the occurrence of urgent task requests and the delayed delivery time of raw materials. In order to solve the MMSC model, a new genetic based hyper-heuristic algorithm (GA-HH) with adjustable length of chromosome is proposed. The GA-HH contains a set of low-level heuristics that directly operate on the solution domain that are organized by the high-level heuristic (i.e., genetic algorithm). Finally, the proposed GA-HH is proved as an efficient, effective, and robust algorithm to solve the MMSC model with considerations of multitask and uncertainty, by comparing it with other well-known meta-heuristic algorithms such as the genetic algorithm and particle swarm optimization.

Fluorescence in situ hybridization (FISH) evaluation is essential for initial risk stratification in multiple myeloma (MM). The presence of specific cytogenetic abnormalities (CA) confers a heterogeneity impact on prognosis. However, the cutoff values among different centers are not uniform. Therefore, we conduct this study to better predict the prognosis of newly diagnosed MM patients based on FISH results. The Kaps method was used to calculate the chromosomal abnormal cutoff values. A total of 533 participants were included in the study. The best cutoff value of overall survival were as follows: 17p− 20.1%, 13q− 85%, 1q21+ 39%, t(11;14) 55.5%, t(14;16) 87%, and t(4;14) 53.5%. The survival analysis showed that 17p− and 1q21+ were the independent factors affecting both OS and progress free survival (PFS) among CA. The analysis based on the cutoff value obtained by Kaps suggested that 13q−, t(14;16), 17p−, and 1q21+ were independent factors affecting OS among CA; t(14;16), 17p−, and 1q21+ were independent factors affecting PFS among CA. The prognostic model was constructed by the Kaps method with the Harrell concordance index (c-index) at 0.719 (95% CI, 0.683–0.756; corrected 0.707), which was higher than that calculated by the European Myeloma Network criteria (0.714; 95% CI, 0.678–0.751; corrected 0.696). In conclusion, chromosomal abnormalities in different proportions and combinations can affect the prognosis of MM patients. Therefore, effective criteria should be formulated to evaluate the prognosis of MM patients better.

The proposed article presents the research of decision-making processes for control system of transport robot, acting inside warehouse. The analysis shows the growing importance of warehouse systems for flexible integrated system. Such warehouse operations, like loading/uploading pallets and sending goods to distribution center can be fully automated by applying robots and some examples are known. Simultaneously, the wide introduction of transport robots with extended possibilities is still limited. Application of intelligent robot can significantly improve properties of warehouse systems, making them closer to Industry 4.0 standards. The functioning of a transport robot can be described as a Travelling Salesmen Problem with numerous solutions that must be effectively limited. Genetic algorithms are proposed as a basis for path generation. The algorithms and software are implemented to model warehouse transfer robots using an Arduino-robot. The results of testing show the effectiveness of the developed algorithms, software and hardware system.

Tugas Akhir merupakan salah satu persyaratan bagi mahasiswa STMIK STIKOM Indonesia untuk menyelesaikan studinya. Tugas Akhir memiliki dua tahapan yaitu proses seminar proposal dan sidang tugas akhir, untuk menyelesaikan tahapan tersebut tentunya pihak program studi harus membuat jadwal berlangsungnya tahapan tersebut. Permasalahan yang sering terjadi dalam kegiatan penjadwalan adalah terjadinya bentrok antara jadwal yang satu dengan yang lain, jadwal bentrok dengan kegiatan mengajar dosen sebagai pembimbing dan penguji . dan adanya permintaan waktu larangan dosen untuk menguji. Salah satu metode untuk menyelesaikan permasalahan tersebut dengan menggunakan algoritma genetika yang bekerja melalui seleksi alam dan genetika. Terdapat 8 prosedur algoritma genetika, Prosedur teknik pengkodean, populasi awal dan kromosom secara acak (random), fungsi fitness untuk meminimalkan jumlah bentrok antar jadwal, metode seleksi roulette-wheel, pindah silang, mutasi genetik, elitisme dan kondisi selesai bila iterasi maksimum telah tercapai. Hasil output dari sistem berupa susunan penjadwalan perkuliahan dan ujian akhir semester dalam format file PDF

In chromosome analysis, centromere is an essential component. By analyzing centromere, genetic disorder can be identified easily. In this paper, automatic classification and centromere detection in human chromosome image using Band Distance Feature is proposed. Initially the microscopic image of G-band chromosome is preprocessed in order to remove the blobs. Then, the image is segmented using labelling algorithm and the endpoints are calculated. Now, the overlapping chromosomes are removed when the number of end points is greater than two. The non-overlapped chromosomes are straightened using Reversible Projection algorithm. From the straightened chromosome band distance feature is calculated. The extracted features are given to the ANN classifier to identify the class of chromosome and to calculate the centromere. From the experimental results, it is observed that the proposed method is superior to the traditional method.

Aiming at the complex multiproduct scheduling problem with 0-wait constraint, a hybrid algorithm based on genetic algorithm (GA) and simulated annealing (SA) algorithm was studied. Based on the results of pruning and grading to the operation tree of complex multiproduct, the design structure matrix (DSM) with precedence constraints was established. Then, an initial population coding method based on DSM was proposed and three strategies to optimize the initial population were proposed to improve the quality of the initial population for the situation of multiple operations in the same grade which need to be processed on the same machine. The specific process flow and the setting method of related parameters for the hybrid algorithm were given out. For the infeasible solution produced in the crossover operation, the repair method was proposed. In the decoding process with makespan as the optimization objective, the chromosome genes were classified and the decoding for complex multiproduct scheduling problem with 0-wait constraint was realized through the analysis of its characteristics. The effectiveness of the proposed algorithm for complex multiproduct scheduling problem with 0-wait constraint is verified by the test of related examples in the existing literature.

Congenital malformations (congenital malformations) of the fetus are considered the most important medical and social problem, since they occupy a leading place in the structure of the causes of perinatal, neonatal and infant morbidity, mortality and disability. The study is devoted to the analysis of this problem in Mariupol.

Children with sex chromosome aneuploidies (SCAs) are often characterized in the literature by limitations and pathologies related to the genetic diagnosis. This study aimed to broaden the SCA phenotype by describing parent reported character and academic strengths. Parents of children with SCAs ages 3-21 (N=377) responded to an electronic survey asking them to describe their child’s strengths in academic settings. Responses were coded for strengths-based content and analyzed using a mixed-methods content analysis approach. We identified overarching qualitative themes of Social Strengths and Assets for Learning. Quantitative results showed a pattern of overlapping strengths among the trisomy SCAs (perseverance and love of learning), with some significant differences between children with supernumerary X chromosomes (strengths in kindness) and those with an additional Y chromosome (strengths in curiosity, humor, and teamwork). Suggestions for future strengths-based research and educational practices to address academic, developmental, and psychosocial risks are explored.

Translocations arise when an end of one chromosome break is mistakenly joined to an end from a different chromosome break. Since translocations can lead to developmental disease and cancer, it is important to understand the mechanisms leading to these chromosome rearrangements. We review how characteristics of the sources and the cellular responses to chromosome breaks contribute to the accumulation of multiple chromosome breaks at the same moment in time. We also discuss the important role for chromosome break location; how translocation potential is impacted by the location of chromosome breaks both within chromatin and within the nucleus, as well as the effect of altered mobility of chromosome breaks. A common theme in work addressing both temporal and spatial contributions to translocation is that there is no shortage of examples of factors that promote translocation in one context, but have no impact or the opposite impact in another. Accordingly, a clear message for future work on translocation mechanism is that unlike normal DNA metabolic pathways, it isn't easily modeled as a simple, linear pathway that is uniformly followed regardless of differing cellular contexts.

Progress in plant breeding depends on the development of genetic resources, genetic knowledge, and breeding techniques. The core of plant breeding is the use of naturally occurring variation. At the beginning of the post-genomic era, a new concept of “breeding by design” was proposed, which aims to control all allelic variation for all genes of agronomic importance. In the past two decades, we have applied a three-step strategy for research on rice breeding by design. In the first step, we constructed a single-segment substitution line (SSSL) library using Huajingxian 74 (HJX74), an elite xian (indica) rice cultivar, as the recipient in which to assemble genes from the rice AA genome. In the second step, we identified a series of desirable genes in the SSSL library. In the third step, we designed new rice lines, and achieved the breeding goals by pyramiding target genes in the HJX74-SSSL library. This review introduces the background, concept, and strategy of breeding by design, as well as our achievements in rice breeding by design using the HJX74-SSSL platform. Our practice shows that target chromosome-segment substitution is a way to breeding by design.

In all forms of confidential communication, the most significant element is security. Cryptography can be used to secure the information, but it discloses the presence of covert communication. Hence, steganography was invented; steganography is an art of concealed communication. Steganography is performed by inserting the secret information in a cover media to camouflage the presence of covert communication. In image steganography, an image is used as the cover media for secure communication. There are various image steganography techniques invented by many researchers, but a small number of them focus on improving visual quality and increasing payload capacity simultaneously. This paper presents a secret data modification based high capacity image steganography technique using genetic algorithm (GA). This new technique uses the least significant bit (LSB) replacement steganography, to embed the secret data. However, the secret data is rearranged and modified before embedding it in the LSBs of the cover image. The parameters used to rearrange and modify the secret data are controlled by GA. A unique concept called flexible chromosome is introduced which allows GA to interpret the chromosome value in different ways. GA attempts to find the best possible parameter value that yields high visual quality of the stego images. The stego images produced by the proposed technique have an average PSNR value of 46.41 dB and 40.83 dB for 2 bit per pixel (bpp) and 3 bpp data hiding capacity respectively.

How genetic variations affect gene expression dynamics of field-grown plants remains unclear. Expression quantitative trait loci (eQTL) analysis is frequently used to find genomic regions underlying gene expression polymorphisms. This approach requires transcriptome data for the complete set of the QTL mapping population under the given conditions. Therefore, only a limited range of environmental conditions is covered by a conventional eQTL analysis. We sampled sparse time series of field-grown rice from chromosome segment substitution lines (CSSLs) and conducted RNA sequencing (RNA-Seq). Then, by using statistical analysis integrating meteorological data and the RNA-Seq data, we identified 1,675 eQTLs leading to polymorphisms in expression dynamics under field conditions. A genomic region on chromosome 11 influences the expression of several defense-related genes in a time-of-day- and scaled-age-dependent manner. This includes the eQTLs that possibly influence the time-of-day- and scaled-age-dependent differences in the innate immunity between Koshihikari and Takanari. Based on the eQTL and meteorological data, we successfully predicted gene expression under environments different from training environments and in rice cultivars with more complex genotypes than the CSSLs. Our novel approach of eQTL identification facilitated the understanding of the genetic architecture of expression dynamics under field conditions, which is difficult to assess by conventional eQTL studies. The prediction of expression based on eQTLs and environmental information could contribute to the understanding of plant traits under diverse field conditions.

Loop-extrusion and phase-separation have been proposed as mechanisms that shape chromosome large-scale spatial organization. It is unclear, however, how they perform relative to each other in explaining chromatin architecture data and whether they compete or co-exist at the single-molecule level. Here, we compare models of polymer physics based on loop-extrusion and phase-separation, as well as models where both mechanisms act simultaneously in a single molecule, against multiplexed FISH data available in human loci in IMR90 and HCT116 cells. We find that the different models recapitulate bulk Hi-C and average microscopy data. Single-molecule chromatin conformations are also well captured, especially by phase-separation based models that better reflect the experimentally reported segregation in globules of the considered genomic loci and their cell-to-cell structural variability. Such a variability is consistent with two main concurrent causes: single-cell epigenetic heterogeneity and an intrinsic thermodynamic conformational degeneracy of folding. Overall, the model combining loop-extrusion and polymer phase-separation provides a very good description of the data, particularly higher-order contacts, showing that the two mechanisms can co-exist in shaping chromatin architecture in single cells.

Since Hirschhorn's description in 1961, the history and chronology of the clinical, cytogenetic, and molecular characterization of Wolf-Hirschhorn syndrome (WHS) elegantly demonstrates the remarkable advances in genetic technology over the last six decades that have paralleled the delineation of the phenotype. After mention in the Human Chromosome Newsletter of a child with a visible deletion of the top of a B chromosome group, 4-5, Hirschhorn and colleagues companioned their report with that of Wolf et al. in Humangenetik in 1965, and the condition was recognized and named. The 1960-1970s witnessed the description of many of the now classic chromosome disorders, including WHS, while HRB allowed for the recognition of chromosome syndromes with smaller deletions/duplications. FISH probes, developed in the next two decades, enabled the characterization of the critical region of WHS and improved clinical diagnosis with subtelomeric probes. Cytogenomic microarray in the early-mid 2000s led to both improved diagnosis of WHS patients and documentation of microdeletions of

In this paper, a mathematical formulation is presented for fire station’s locating and facilities allocating to stations in different periods and emergency situations (wars and natural disasters). This model is designed, considering amount of demands and facilities coverage radius, being dynamic based on traffic and type region and fuzzy in different periods. According to fact, in the model, amount of demand for each demand point depends on number of coverage and the location. In this model, location of stations is positioned once in different periods. The number of facilities which are allocated to stations are located dynamically and can be relocated in different periods. Since the proposed model is NP-hard, particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms by considering an efficient combinatorial chromosome are presented to solve the problem at hand. In the PSO, way of making chromosome is such that locating chromosome, early and final allocation are presented in a novel approach. The results demonstrated that the presented PSO are better than ABC in terms of quality of solutions and computational time.

Planning driving routes is a common yet challenging task. Previous solutions that often suggest the shortest or time-dependent fastest paths for drivers are incompatible with the diversity of user requirements. Recent years are witnessing an increasing need of utility gained along the driving path and more advanced pathplanners are developed correspondingly, such as the safest and the most beautiful paths. Similar to the travel time on the edge, the utility score actually changes with the time as well. In this paper, targeting a more realistic path planning system, we allow both time cost and utility on the edge to vary from time to time. Over such two-fold time-dependent road network, we aim to discover the optimal driving path for the given user query. To address this NP-hard problem, we propose an approximated algorithm called 2TD Path-Planner, which mainly consists of three components, i.e., edge reachability computing, chromosome encoding, and chromosome decoding.

Aims/hypothesisGiven the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal colocalises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently.MethodsUsing publicly available type 2 diabetes summary statistics from a genomewide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7,467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery ratecoloc. Any association with a colocalisation posterior probability of ≥0.9 was considered a genuine shared association with both diseases.ResultsOf the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals colocalised between both diseases (posterior probability ≥0.9): (i) chromosome 16q23.1, near Chymotripsinogen B1 (CTRB1) / Breast Cancer Anti-Estrogen Resistance Protein 1 (BCAR1), which has been previously identified; (ii) chromosome 11p15.5, near the Insulin (INS) gene; (iii) chromosome 4p16.3, near Transmembrane protein 129 (TMEM129), and (iv) chromosome 1p31.3, near Phosphoglucomutase 1 (PGM1). In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified colocalisation on chromosome 9p24.2, near the GLIS Family Zinc Finger Protein 3 (GLIS3) gene, in this case with a concordant direction of effect.Conclusions/interpretationThat four of five association signals that colocalise between type 1 diabetes and type 2 diabetes are in opposite directions suggests a complex genetic relationship between the two diseases.Research in ContextWhat is already known about this subject?Other than insulin, there are currently no treatments for both type 1 and type 2 diabetes.Findings that genetic variants near the GLIS3 gene increase risk of both type 1 and type 2 diabetes have indicated shared genetic mechanisms at the level of the pancreatic β cell.What is the key question?By examining chromosome regions associated with both diseases, are there any more variants that affect risk of both diseases and could support common mechanisms and repositioning of therapeutics between the diseases?What are the new findings?At current sample sizes, there is evidence that five genetic variants in different chromosome regions impact risk of developing both diseases.However, four of these variants have the opposite direction of effect in type 1 diabetes compared to type 2 diabetes, with only one, near GLIS3, having a concordant direction of effect.How might this impact on clinical practise in the foreseeable future?Genetic findings have furthered research in type 1 and type 2 diabetes independently, and suggest therapeutic strategies. However, our current investigation into their shared genetics suggests that repositioning of current type 2 diabetes treatments into type 1 diabetes may not be straightforward.

Rationale: Birth cohort studies have identified several temporal patterns of wheezing, only some of which are associated with asthma. Whether 17q12-21 genetic variants, which are closely associated...

Определены числа хромосом (2n) для 13 видов (19 популяций) из семейств Asparagaceae, Asteraceae, Boraginaceae, Brassicaceae, Caryophillaceae, Euphorbiaceae, Plantaginaceae из Монголии. Впервые исследованы виды Euphorbia potaninii Prokh. (2n = 20) и Smelowskiaaltaica (Pobed.) Botsch. (2n = 14). Впервые с территории Монголии определены хромосомные числа для 7 видов: Askellia flexuosa (Ledeb.) W. A. Weber (2n = 42), Cirsium arvense var. vestitum Wimm. et Grab. (2n = 34), Crepis crocea (Lam.) Babc. (2n = 16), Eremogone meyerii (Fenzl) Ikonn. (2n = 44), Heterochroa desertorum Bunge (2n = 34), Plantago komarovii Pavl. (2n = 10, 12), Rhinactinidia eremophila (Bunge) Novopokr. exBotsch. (2n = 36). Обнаружены новые цитотипы для видов Anoplocaryum compressum (Turcz.) Ledeb. (2х) и Eremogone meyerii (4x), для Plantago komarovii впервые описано явление анеуплоидии (2x = 10). Для каждого вида указан ареал и приведены литературные данные по числам хромосом., Chromosome numbers (2n) for 13 rare, endemic plant species (19 populations) from the families: Asparagaceae, Asteraceae, Boraginaceae, Brassicaceae, Caryophillaceae, Euphorbiaceae, Plantaginaceae – are reported on the material from Mongolia. For Euphorbia potaninii Prokh. (2n = 20), Smelowskia altaica (Pobed.) Botsch. (2n = 14) chromosome complements were examined for the first time. Chromosome numbers of Askellia flexuosa (Ledeb.) W. A. Weber (2n = 42), Cirsium arvense var. vestitum Wimm. et Grab. (2n = 34), Crepis crocea (Lam.) Babc. (2n = 16), Eremogone meyerii (Fenzl) Ikonn. (2n = 44), Heterochroa desertorum Bunge (2n = 34), Plantago komarovii Pavl. (2n= 10, 12), Rhinactinidia eremophila (Bunge) Novopokr. ex Botsch. (2n = 36) were determined from Mongolia for the first time. New cytotypes for Anoplocaryum compressum (Turcz.) Ledeb. (2х) and Eremogone meyerii (4x) were studied. For the species of Plantago komarovii, the phenomenon of aneuploidy (2x = 10) was described for the first time. For each species the area and published data on karyology are given.

Inferring the frequency and mode of hybridization among closely related organisms is an important step for understanding the process of speciation, and can help to uncover reticulated patterns of phylogeny more generally. Phylogenomic methods to test for the presence of hybridization come in many varieties, and typically operate by leveraging expected patterns of genealogical discordance in the absence of hybridization. An important assumption made by these tests is that the data (genes or SNPs) are independent given the species tree. However, when the data are closely linked, it is especially important to consider their non-independence. Recently, deep learning techniques such as convolutional neural networks (CNNs) have been used to perform population genetic inferences with linked SNPs coded as binary images. Here we use CNNs for selecting among candidate hybridization scenarios using the tree topology (((P(1), P(2)), P(3)), Out) and a matrix of pairwise nucleotide divergence (d(XY)) calculated in windows across the genome. Using coalescent simulations to train and independently test a neural network showed that our method, HyDe-CNN, was able to accurately perform model selection for hybridization scenarios across a wide-breath of parameter space. We then used HyDe-CNN to test models of admixture in Heliconius butterflies, as well as comparing it to phylogeny-based introgression statistics. Given the flexibility of our approach, the dropping cost of long-read sequencing, and the continued improvement of CNN architectures, we anticipate that inferences of hybridization using deep learning methods like ours will help researchers to better understand patterns of admixture in their study organisms.

Chromosome visualization has been used in human chromosome analysis and is a crucial step in clinical diagnosis and drug development. An important step in chromosome visualization is the extraction of chromosomes from chromosome images obtained by light microscopy. Chromosomes often overlap in a complex and variable manner, resulting in significant challenges in chromosome segmentation. The process of chromosome visualization requires manual intervention and is tedious. A method based on a neural network is proposed for the automatic segmentation of overlapping chromosome images to speed up the workflow of visualizing chromosomes. Three improved dilated convolutions are used in the chromosome image segmentation models based on U-Net. The proposed models successfully segment overlapping chromosomes in two publicly available overlapping chromosome data sets. Our models have better performance than existing overlapping chromosome segmentation methods based on U-Net. In summary, it is demonstrated that the improved dilated convolutions can be used for the automatic segmentation of overlapping chromosome images. The proposed improved dilated convolutions have a stable performance improvement, can be easily extended to the segmentation of multiple overlapping chromosomes, and are suitable as general neural network operations to replace standard convolutions in any network.

Objective: We present molecular cytogenetic characterization of a de novo chromosome 1q41-q42.11 microdeletion of paternal origin in a mentally retarded child of a family requesting for genetic counseling of the future pregnancy. Case report: A 43-year-old, gravida 1, para 1, woman, who had a 15-year-old son with mental retardation, planned to have another normal child and requested for genetic counseling of the future pregnancy. Her husband was 48 years old. The 15-year-old boy had a body height of 148 cm (