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The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted U-Net architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences into BRAC, BRAF, and KRAS categories. Our comprehensive methodology includes rigorous data preprocessing, model training, and a multi-faceted evaluation approach. The adapted U-Net model exhibited exceptional performance, achieving an overall accuracy of 0.96. The model also achieved high precision and recall rates across the classes, with precision ranging from 0.93 to 1.00 and recall between 0.95 and 0.97 for the key markers BRAC, BRAF, and KRAS. The F1-score for these critical markers ranged from 0.95 to 0.98. These empirical results substantiate the architecture's capability to capture local and global features in DNA sequences, affirming its applicability for critical, sequence-based bioinformatics challenges. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel face-based random walk method aimed at addressing the 3D semantic segmentation issue. Our method utilizes a one-dimensional convolutional neural network for detailed feature extraction from sequences of triangular faces and employs a stacked gated recurrent unit to gather information along the sequence during training. This approach allows us to effectively handle irregular meshes and utilize the inherent feature extraction potential present in mesh geometry. Our study's results show that the proposed method achieves competitive results compared to the state-of-the-art methods in mesh segmentation. Importantly, it requires fewer training iterations and demonstrates versatility by applying to a wide range of objects without the need for the mesh to adhere to manifold or watertight topology requirements. • Segment 3D meshes using a hybrid architecture, gated recurrent units, and 1D convolutional neural networks. • Extraction of geometric features from 3D mesh faces using mathematical algorithms; random walks. • Apply deep learning algorithms to irregular 3D data without considering whether each mesh is a manifold or closed. [ABSTRACT FROM AUTHOR]

We describe adult males and females and a nymph belonging to Linguatula serrata in Australia, based on light and scanning electron microscopies. In addition, 18S and Cox1 sequence data have also been provided and were compared with similar sequences in GenBank. Our specimens had identical 18S sequences and limited genetic distance in Cox1 region which fell within the intra-specific range observed for Linguatula spp. suggesting that they all belong to one species. Phylogenetic analyses showed that Australian specimens were grouped with L. serrata in Europe where the species was first found and described. A number of L. serrata from Iran and Bangladesh formed a distinct group. The genetic distance between these Linguatula and Australian/European L. serrata ranged from 0.46% to 2.21% which is larger than the genetic distance observed between L. arctica and Australian/European L. serrata (0.12%) suggesting that they belong to a different species. As pointed out previously by several other authors, L. serrata comprises more than one species and those from the Palearctic region (including Iran and Bangladesh) should not be automatically named L. serrata unless there is enough evidence for the identification. To accurately address the complex taxonomy of Linguatula spp. a detailed morphological and genetic characterisation of numerous developmental stages of the parasite is necessary, to ensure morphological differences are not due to development. This however may not be achievable in the near future due to significant reduction in expertise as well as research funding awarded in this area of research to understand the basics of our planet. Image 1 • Detailed description of Australian tongue worms. • First genetic characterization of Linguatula serrata in Australia. • Specific identification of Linguatula serrata in Australia. • Taxonomic status of L. dingophila could be valid but requires further investigation. [ABSTRACT FROM AUTHOR]

Causal variants inferred from sequence data analysis are expected to increase accuracy of genomic selection. In this work we evaluated the gain in reliability of genomic predictions, for stature in US Holsteins, when adding selected sequence variants to a pre-existent SNP chip. Two prediction methods were tested: de-regressed proofs assuming heterogeneous (genomic BLUP; GBLUP) residual variances and by single-step GBLUP (ssGBLUP) using actual phenotypes. Phenotypic data included 3,999,631 records for stature on 3,027,304 Holstein cows. Genotypes on 54,087 SNP markers (54k) were available for 26,877 bulls. Additionally, 16,648 selected sequence variants were combined with the 54k markers, for a total of 70,735 (70k) markers. In all methods, SNP in the genomic relationship matrix (G) were unweighted or weighted iteratively, with weights derived either by SNP effects squared or by a nonlinear method that resembles BayesA (nonlinear A). Reliability of genomic predictions were obtained by cross validation. With unweighted G derived from 54k markers, the reliabilities (× 100) were 72.4 for GBLUP and 75.3 for ssGBLUP. With unweighted G derived from 70k markers, the reliabilities were 73.4 and 76.0, respectively. Weighting by nonlinear A changed reliabilities to 73.3, and 75.9, respectively. Addition of selected sequence variants had a small effect on reliabilities. Weighting by quadratic functions reduced reliabilities. Weighting by nonlinear A increased reliabilities for GBLUP but had only a small effect in ssGBLUP. Reliabilities for direct genomic values extracted from ssGBLUP using unweighted G with 54k were higher than reliabilities by any GBLUP. Thus, ssGBLUP seems to capture more information than GBLUP and there is less room for extra reliability. Improvements in GBLUP may be because the weights in G change the covariance structure, which can explain a proportion of the variance that is accounted for when a heterogeneous residual variance is assumed by considering a different number of daughters per bull. [ABSTRACT FROM AUTHOR]

The surface electromyography (sEMG) signal is a complex interference pattern resulting from the electrical activity of contracting muscles, which directly correlates with muscle activity and exercise level. Patellofemoral osteoarthritis (PF OA) refers to the softening and destruction of the articular cartilage of the knee cap, which is important to diagnose in the early phase. The aim of this study is to develop a predictive model, called the Enhanced Sequencing Deep Learning (PESDL) Algorithm, to detect PF OA from the sEMG signal. The algorithm consists of four main modules: (i) Data Acquisition Module (DAM), (ii) Signal Preprocessing Module (SPM), (iii) Data Augmentation and Concatenation Module (DACM), and (iv) Lower Limb Classification (LLC). The sEMG signals from five core muscles of the lower limb were acquired during stepping stairs up and down, and machine learning was used to obtain the muscle activation data signals. The acquired data was preprocessed, augmented, concatenated, and shuffled. Three feedforward deep networks (RNN, LSTM, and GRU) were used to classify the lower limb sEMG time-sequence data, with the GRU network showing better performance than the other two. The model's performance was evaluated using seven performance measures and 10-fold cross-validation. The maximum values of feedforward deep networks (RNN), LSTM, and GRU for the five muscles (RF, BF, VM, ST, and FX) using seven performance measures were as follows: RNN (0.961, 0.154, 0.943, 0.244, 1.0, 1.0, 0.899), LSTM (0.967, 0.147, 0.945, 0.217, 0.997, 1.0, 0.927), and GRU (0.976, 0.111, 0.949, 0.212, 0.996, 0.998, 0.938), respectively, listed in the order of accuracy, loss, validation accuracy, validation loss, recall, precision, and F1-score. Comparisons with other state-of-the-art models using the same datasets demonstrated the effective performance of the predictive model. The results suggest that RNN models, particularly GRU and LSTM, can be effective for detecting PF OA, and the specific RNN architecture used can have a significant impact on performance. [ABSTRACT FROM AUTHOR]

In preparation for the growing use of massively parallel sequencing (MPS) technology to genotype forensic STRs, a comprehensive genomic audit of 73 STRs was made in 2016 [Parson et al., Forensic Sci. Int. Genet. 22, 54–63]. The loci examined included miniSTRs that were not in widespread use, but had been incorporated into MPS kits or were under consideration for this purpose. The current study expands the genomic analysis of autosomal STRs that are not commonly used, to include the full set of developed miniSTRs and an additional 24 STRs, most of which have been recently included in several supplementary forensic multiplex kits for capillary electrophoresis. The genomic audit of these 47 newly-adopted STRs examined the linkage status of new loci on the same chromosome as established forensic STRs; analyzed world-wide population variation of the newly-adopted STRs using published data; assessed their forensic informativeness; and compiled the sequence characteristics, repeat structures and flanking regions of each STR. A further 44 autosomal STRs developed for forensic analyses but not incorporated into commercial kits, are also briefly described. [ABSTRACT FROM AUTHOR]

• Temporal multi-head attention model to obtain global and local spatiotemporal features. • Unsupervised outlier detection algorithm for abnormal blood glucose without prior knowledge. • Imputation algorithm based on feature engineering to fill missing blood glucose values. • The proposed method can attain comparatively good performance. The hyperglycemic state of people with diabetes can lead to metabolic and healthy disturbances in the body. Diabetes is mainly treated clinically by conservative treatment, which requires frequent and continuous measurement of blood glucose concentration. Accurate blood glucose prediction plays an important role in the future blood glucose management of patients. To improve the accuracy of blood glucose prediction, this paper proposes a short-term prediction method of blood glucose based on temporal multi-head attention mechanism for diabetic patients (PBGTAM). Firstly, a detection algorithm of abnormal blood glucose based on Adaptive Density-Based Spatial Clustering of Applications with Noise is proposed by using an autonomous neighborhood parameter selection method. Secondly, the imputation algorithm based on feature engineering is proposed to fill the missing blood glucose values. Thirdly, we propose temporal multi-head attention model to obtain global and local spatiotemporal features from sequence data, in which a temporal series features module is designed to insight the detail feature so as to keep the useful information contained in the details, and an intensity correlation module is used to obtain global features of sequence data and then to achieve strong correlation between blood glucose data, and a sequence features module is employed to learn temporal sequence features between sequence data to guarantee continuous prediction of future blood glucose values. Finally, the comparative results show that PBGTAM outperforms six state-of-the-art methods in terms of the overall prediction root mean square error, forecasting accuracy for warning, and clinical consistency with 20.57, 84.35% and 85.18%, respectively. [ABSTRACT FROM AUTHOR]

Negative sequential pattern (NSP) discovery is crucial, and sometimes it carries more enlightening information than positive sequential pattern (PSP) mining in data science. Owing to its computational complexity and exponential search space, the task of discovering NSPs is often more difficult and challenging than that for PSPs. To date, a few NSP mining algorithms have been proposed. Particularly, most algorithms only consider a single support for mining, thus they cannot present good results in many special real-world applications. To solve this problem and achieve better efficiency on a long sequence database or a large-scale database, we propose a novel algorithm called Negative Sequential Patterns with Individual Support (NSPIS) in this paper. The projection mechanism is adopted to NSPIS, which allows greatly reduce the search space and simultaneously improve the efficiency. Finally, detailed results of the experiments show that NSPIS can achieve better performance, and it costs less memory on large-scale datasets compared to the state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

Abstract: The cougar (Puma concolor) is a large predatory feline found widely in the Americas that is susceptible to feline immunodeficiency virus (FIV), a fast-evolving lentivirus found in wild feline species that is analogous to simian immunodeficiency viruses in wild primates and belongs to the same family of viruses as human immunodeficiency virus. FIV infection in cougars can lead to a weakened immune system that creates opportunities for other infecting agents. FIV prevalence and lineages have been studied previously in several areas in the western United States, but typically without spatially explicit statistical techniques. To describe the distribution of FIV in a sample of cougars located in the northern Rocky Mountain region of North America, we first used kernel density ratio estimation to map the log relative risk of FIV. The risk surface showed a significant cluster of FIV in northwestern Montana. We also used Bayesian cluster models for genetic data to investigate the spatial structure of the feline immunodeficiency virus with virus genetic sequence data. A result of the models was two spatially distinct FIV lineages that aligned considerably with an interstate highway in Montana. Our results suggest that the use of spatial information and models adds novel insight when investigating an infectious animal disease. The results also suggest that the influence of landscape features likely plays an important role in the spatiotemporal spread of an infectious disease within wildlife populations. [Copyright &y& Elsevier]

Abstract: Under the framework of max margin method, this work proposes a model for training sequence data, which can be solved as a binary classification. However, there are too many samples in the auxiliary classification problem to make the model efficient enough for median to large scale data sets in practice. Therefore, under the additive assumption for the feature mapping and loss function, a simplified model is introduced in order to speed up training. The major advantage of our method is that the new model does not share slack variable for a sequence. This provides the ability to utilize the discriminate information within the sequence and select the discriminative patterns more precisely. Experiment on the task of named entity recognition validates the effectiveness of the new method. [ABSTRACT FROM AUTHOR]

Abstract: Items sold in a store can usually be organized into a concept hierarchy according to a taxonomy. Based on the hierarchy, sequential patterns can be found not only at the leaf nodes (individual items) of the hierarchy, but also at higher levels of the hierarchy; this is called multiple-level sequential pattern mining. In previous research, taxonomies had crisp relationships between the categories in one level and the categories in another level. In real life, however, crisp taxonomies cannot handle the uncertainties and fuzziness inherent in the relationships among items and categories. For example, the book Alice’s Adventures in Wonderland can be classified into the Children’s Literature category, but can also be related to the Action &Adventure category. To deal with the fuzzy nature of taxonomy, we apply fuzzy set techniques to concept taxonomies so that the relationships from one level to another can be represented by a value between 0 and 1. Accordingly, a fuzzy multiple-level mining algorithm, the fuzzy multi-level sequential mining algorithm (FMSM), is proposed to extract fuzzy multiple-level sequential patterns from databases. In addition, another algorithm, named the CROSS-FMSM algorithm, is developed to discover fuzzy cross-level sequential patterns. Experiments using synthetic datasets show the algorithms’ computational efficiency and scalability, and a real dataset is used to prove the patterns’ effectiveness. [Copyright &y& Elsevier]

Abstract: Given a sequence database and minimum support threshold, the goal of mining quantitative sequential patterns is to discover the complete set of sequential patterns with purchased quantities in databases. Although this type of pattern can provide more information than the traditional sequential pattern, it also causes a sharp boundary problem. This means that when an item''s quantity is close to the boundary of two adjacent quantity intervals, it is either ignored or overemphasized. In view of this weakness, a recent paper from Hong, Kuo, and Chi proposed a new kind of extended patterns, called fuzzy quantitative sequential patterns (FQSP), where an item''s quantity in the pattern is represented by a fuzzy term rather than a quantity interval. In their work an Apriori-like algorithm was developed to mine all FQSP. In this paper, we propose a new and novel algorithm to mine FQSP based on the divide-and-conquer strategy. Since the proposed algorithm greatly reduces the candidate subsequence generation efforts, the performance is improved significantly. Experiments show that the proposed algorithm is much more efficient and scalable than the previous algorithm. [Copyright &y& Elsevier]

Abstract: Routine STR-typing of 10,293 buccal swabs using different multiplex kits presented discordant D8S1179 profiles in four cases. Sequencing analysis identified a G-to-A transition upstream to the repeat, and an A-to-T transversion and a G-to-A transition downstream to the repeat. In the fourth case a four-base pair deletion downstream resulted in altered genotypes using different primer pairs. Current searching algorithms of the German DNA database are not capable of matching profiles that are divergent in only one STR-locus. Thus, to accommodate matching requirements and to avoid errors in individual genetic characterization for D8S1179, as described here, it is suggested that alternative primer pairs be used for routine genotyping as a matter of course. [Copyright &y& Elsevier]

Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, in an important data-mining problem with broad applications. Although conventional sequential patterns can reveal the order of items, the time between items is not determined; that is, a sequential pattern does not include time intervals between successive items. Accordingly, this work addresses sequential patterns that include time intervals, called time-interval sequential patterns. This work develops two efficient algorithms for mining time-interval sequential patterns. The first algorithm is based on the conventional Apriori algorithm, while the second one is based on the PrefixSpan algorithm. The latter algorithm outperforms the former, not only in computing time but also in scalability with respect to various parameters. [Copyright &y& Elsevier]

Eleven Y specific microsatellites, previously studied in humans, were typed for fragment length and sequenced in chimpanzees (Pan troglodytes).The primers described by Ayub et al. (Nucleic Acids Res. 28, 2000, 2) for amplifying DYS434, DYS435, DYS436, DYS437, DYS438, DYS439 and those described by White et al. (Genomics, 57, 1999, 433) for GATA A10, A7.1, A7.2, C4, and H4, were used to amplify DNA samples from chimpanzees.Primers described for Y GATA A4 were found to amplify the same region as reported for DYS439. Moreover, the GATA A4 forward primer only matches the repeat flanking region in 14 of the 28 bp, being responsible for a very weak amplification. Therefore, this system was not included in this study.The analysis of the repeat and sequence structure observed in chimpanzee and human Y chromosomes allowed evolutionary comparisons as well as the basis for improving Y STR nomenclature and therefore, a unified nomenclature for these novel STRs is proposed to the scientific community following ISFG recommendations. [Copyright &y& Elsevier]

• Proposed model that use normal data training are more suitable for practical applications. • Apply adversarial learning and LSTM structure to anomaly detection. • Multi-distribution features are utilized to construct an anomaly index. • DSMDA can prove sufficient robustness for anomalies detection of low SNR signals. Time series anomaly detection is one of the key challenges in the field of condition monitoring. Many anomaly detection methods are inefficient and easy to lose effective information due to manual features extracting. Deep learning-based methods can solve the problem effectively, but the detection accuracy is still not satisfactory. In addition, most of the methods cannot take the time-ordered specialty into account which is significant for time-series-based anomaly detection. To address these issues, a novel method named deep sequence multi-distribution adversarial model (DSMDA) is proposed to improve the accuracy of anomaly detection in bearing condition monitoring. The proposed model utilizes the data reconstruction capability of the Variational Autoencoder (VAE) under the framework of generative adversarial network (GAN) to make full use of information. The feedforward neural network layer of VAE is replaced by the long-term and short-term memory (LSTM) layer, which uses the forgetting mechanism of LSTM to effectively avoid the false alarms caused by the excessive influence of the old sequences. Additionally, the fault-attention abnormal state index can be constructed by the real-time spatial distribution and latent spatial distribution features learned by the double discriminators. To verify the effectiveness of the proposed approach, experiments on two public datasets are carried out with only healthy data in training stage that is more suitable for practical industrial applications. The results show that the proposed method is superior to GANomaly and other advanced methods. Furthermore, the 2-D visualization results can indicate the level of fault while the last feature space of the two discriminators is combined and embedded into the visualization, and the fault-attention abnormal state indictor constructed on these features can indicate abnormalities well. [ABSTRACT FROM AUTHOR]

• Introduce a new method to identify the wrong filter assembly of pyrometer. • Reveal the relationship between radiation energy equivalence and wavelength. • The method is more effective for optical fiber type multi-wavelength pyrometers. Multi-wavelength pyrometer needs temperature calibration by blackbody before application. Aiming at multi-target multi-wavelength pyrometer with optical fiber for beam-splitting and filter for limiting wavelength, there are a large number of filters. Any error in the assembly of a filter will directly affect the accuracy of the measurement results. In this paper, an error correction method based on abnormal trend of temperature calibration sequence data is proposed for identifying whether the filter assembly is wrong or not. The intrinsic relationship between radiation energy equivalence and wavelength is revealed for the first time, providing theoretical basis for the method. And the simulation experiment and actual experiment are carried out to verify the feasibility of the method. [ABSTRACT FROM AUTHOR]

There is often a delay between completion of a genome sequence and its publication, mainly because of the lengthy process of annotation. For most researchers, the raw sequence alone does not easily yield the rich information it contains. An online tool (Virulence Searcher) has been designed that enables scientists interested in bacterial pathogenesis to search sequences from unannotated bacterial genomes for putative genes encoding virulence factors. This will facilitate an immediate start on important research into bacterial disease without having to wait for the annotated sequence to be published. [ABSTRACT FROM AUTHOR]

On the occasion of a study on mutation rates of human STR loci, numerous alleles have been sequenced. Several new sequence or length variant alleles were found. [Copyright &y& Elsevier]

Abstract: ACTBP2 (SE33), D3S1358, D8S1132, D18S51 and D21S11 are frequently used STR-loci in the forensic field. This study reports sequence data of further new or rare alleles at these loci, varying in length or in sequence, which were detected in course of investigations for various purposes. [Copyright &y& Elsevier]