Sequence Of Segments Research Articles

Sequence-based identification of amyloidogenic β-hairpins reveals a prostatic acid phosphatase fragment promoting semen amyloid formation

β-Structure-rich amyloid fibrils are hallmarks of several diseases, including Alzheimer’s (AD), Parkinson’s (PD), and type 2 diabetes (T2D). While amyloid fibrils typically consist of parallel β-sheets, the anti-parallel β-hairpin is a structural motif accessible to amyloidogenic proteins in their monomeric and oligomeric states. Here, to investigate implications of β-hairpins in amyloid formation, potential β-hairpin-forming amyloidogenic segments in the human proteome were predicted based on sequence similarity with β-hairpins previously observed in Aβ, α-synuclein, and islet amyloid polypeptide, amyloidogenic proteins associated with AD, PD, and T2D, respectively. These three β-hairpins, established upon binding to the engineered binding protein β-wrapin AS10, are characterized by proximity of two sequence segments rich in hydrophobic and aromatic amino acids, with high β-aggregation scores according to the TANGO algorithm. Using these criteria, 2505 potential β-hairpin-forming amyloidogenic segments in 2098 human proteins were identified. Characterization of a test set of eight protein segments showed that seven assembled into Thioflavin T-positive aggregates and four formed β-hairpins in complex with AS10 according to NMR. One of those is a segment of prostatic acid phosphatase (PAP) comprising amino acids 185–208. PAP is naturally cleaved into fragments, including PAP(248−286) which forms functional amyloid in semen. We find that PAP(185−208) strongly decreases the protein concentrations required for fibril formation of PAP(248−286) and of another semen amyloid peptide, SEM1(86−107), indicating that it promotes nucleation of semen amyloids. In conclusion, β-hairpin-forming amyloidogenic protein segments could be identified in the human proteome with potential roles in functional or disease-related amyloid formation.

Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.

Dynamic functional connectivity (dFC), which can capture the abnormality of brain activity over time in resting-state functional magnetic resonance imaging (rs-fMRI) data, has a natural advantage in revealing the abnormal mechanism of brain activity in patients with Attention Deficit/Hyperactivity Disorder (ADHD). Several deep learning methods have been proposed to learn dynamic changes from rs-fMRI for FC analysis, and achieved superior performance than those using static FC. However, most existing methods only consider dependencies of two adjacent timestamps, which is limited when the change is related to the course of many timestamps. In this paper, we propose a novel Temporal Dependence neural Network (TDNet) for FC representation learning and temporal-dependence relationship tracking from rs-fMRI time series for automated ADHD identification. Specifically, we first partition rs-fMRI time series into a sequence of consecutive and non-overlapping segments. For each segment, we design an FC generation module to learn more discriminative representations to construct dynamic FCs. Then, we employ the Temporal Convolutional Network (TCN) to efficiently capture long-range temporal patterns with dilated convolutions, followed by three fully connected layers for disease prediction. As the results, we found that considering the dynamic characteristics of rs-fMRI time series data is beneficial to obtain better diagnostic performance. In addition, dynamic FC networks generated in a data-driven manner are more informative than those constructed by Pearson correlation coefficients. We validate the effectiveness of the proposed approach through extensive experiments on the public ADHD-200 database, and the results demonstrate the superiority of the proposed model over state-of-the-art methods in ADHD identification.

Assessing cerebrovascular reactivity (CVR) in rhesus macaques (Macaca mulatta) using a hypercapnic challenge and pseudo-continuous arterial spin labeling (pCASL)

Cerebrovascular reactivity (CVR) is a measure of cerebral small vessels’ ability to respond to changes in metabolic demand and can be quantified using magnetic resonance imaging (MRI) coupled with a vasoactive stimulus. Reduced CVR occurs with neurodegeneration and is associated with cognitive decline. While commonly measured in humans, few studies have evaluated CVR in animal models. Herein, we describe methods to induce hypercapnia in rhesus macaques (Macaca mulatta) under gas anesthesia to measure cerebral blood flow (CBF) and CVR using pseudo-continuous arterial spin labeling (pCASL). Fifteen (13 M, 2 F) adult rhesus macaques underwent pCASL imaging that included a baseline segment (100% O2) followed by a hypercapnic challenge (isoflurane anesthesia with 5% CO2, 95% O2 mixed gas). Relative hypercapnia was defined as an end-tidal CO2 (ETCO2) ≥5 mmHg above baseline ETCO2. The mean ETCO2 during the baseline segment of the pCASL sequence was 34 mmHg (range: 23–48 mmHg). During this segment, mean whole-brain CBF was 51.48 ml/100g/min (range: 21.47–77.23 ml/100g/min). Significant increases (p<0.0001) in ETCO2 were seen upon inspiration of the mixed gas (5% CO2, 95% O2). The mean increase in ETCO2 was 8.5 mmHg and corresponded with a mean increase in CBF of 37.1% (p<0.0001). The mean CVR measured was 4.3%/mmHg. No anesthetic complications occurred as a result of the CO2 challenge. Our methods were effective at inducing a state of relative hypercapnia that corresponds with a detectable increase in whole brain CBF using pCASL MRI. Using these methods, a CO2 challenge can be performed in conjunction with pCASL imaging to evaluate CBF and CVR in rhesus macaques. The measured CVR in rhesus macaques is comparable to human CVR highlighting the translational utility of rhesus macaques in neuroscience research. These methods present a feasible means to measure CVR in comparative models of neurodegeneration and cerebrovascular dysfunction.

Applications of DNA Barcoding in Fisheries: A Review

The review article focuses on the widespread application of DNA barcoding in the fishery background. With the advancement in science and technology, there is a dire necessity for upgradation in approaches for taxonomy, wildlife conservation, and health management in the fishery industry. To reduce invasiveness, illegal fishing, and health complications in fish in both marine as well aquaculture ecosystems different required mitigative measures need to be enforced. DNA barcoding is one important tool in biological science. It involves the sequencing of a small DNA segment called a ‘barcode’ of 648 base pairs. In animals and protists, the specific barcode is the mitochondrial gene cytochrome oxidase Ⅰ (COⅠ or COX 1) and it has proven to be extremely effective in identifying metazoans such as birds, butterflies, fish, flies, and many other animal groups. The use of DNA Barcoding has been proven to be effective in taxonomical classification of every group of organisms. In fishery, the application of barcoding created an avenue in marine conservation and aquaculture caretaking.

How chromosomal inversions reorient the evolutionary process.

Inversions are structural mutations that reverse the sequence of a chromosome segment and reduce the effective rate of recombination in the heterozygous state. They play a major role in adaptation, as well as in other evolutionary processes such as speciation. Although inversions have been studied since the 1920s, they remain difficult to investigate because the reduced recombination conferred by them strengthens the effects of drift and hitchhiking, which in turn can obscure signatures of selection. Nonetheless, numerous inversions have been found to be under selection. Given recent advances in population genetic theory and empirical study, here we review how different mechanisms of selection affect the evolution of inversions. A key difference between inversions and other mutations, such as single nucleotide variants, is that the fitness of an inversion may be affected by a larger number of frequently interacting processes. This considerably complicates the analysis of the causes underlying the evolution of inversions. We discuss the extent to which these mechanisms can be disentangled, and by which approach.

Semi-Supervised Representation Learning for Segmentation on Medical Volumes and Sequences.

Benefiting from the massive labeled samples, deep learning-based segmentation methods have achieved great success for two dimensional natural images. However, it is still a challenging task to segment high dimensional medical volumes and sequences, due to the considerable efforts for clinical expertise to make large scale annotations. Self/semi-supervised learning methods have been shown to improve the performance by exploiting unlabeled data. However, they are still lack of mining local semantic discrimination and exploitation of volume/sequence structures. In this work, we propose a semi-supervised representation learning method with two novel modules to enhance the features in the encoder and decoder, respectively. For the encoder, based on the continuity between slices/frames and the common spatial layout of organs across subjects, we propose an asymmetric network with an attention-guided predictor to enable prediction between feature maps of different slices of unlabeled data. For the decoder, based on the semantic consistency between labeled data and unlabeled data, we introduce a novel semantic contrastive learning to regularize the feature maps in the decoder. The two parts are trained jointly with both labeled and unlabeled volumes/sequences in a semi-supervised manner. When evaluated on three benchmark datasets of medical volumes and sequences, our model outperforms existing methods with a large margin of 7.3% DSC on ACDC, 6.5% on Prostate, and 3.2% on CAMUS when only a few labeled data is available. Further, results on the M&M dataset show that the proposed method yields improvement without using any domain adaption techniques for data from unknown domain. Intensive evaluations reveal the effectiveness of representation mining, and superiority on performance of our method. The code is available at https://github.com/CcchenzJ/BootstrapRepresentation.

Topic Integrated Opinion-Based Drug Recommendation With Transformers

Information from online platforms is vast, with health related data remaining largely unexplored for the purpose of developing a sentiment-based recommendation model. Though state-of-the-art models such as transformers are being researched in this domain, the model configuration has not been diligently investigated, particularly for deriving quality input for sentiment classification by inlaying contextual embeddings and significant sequence segments. A topic modeling and transformer-based model ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">topicT-AttNN</i> ) with LSTM and attention mechanism is proposed in this study for classifying sentiments from drug reviews on three aspects and overall opinion. The sentiment score thus obtained is used as a measure for identifying user-advocated drugs for a condition. The proposed model outperforms baselines for all the aspects with higher test accuracy and F1-scores, with the highest F1-score recorded as 0.9585. The results indicate the significance of LSTM and attention layers for identifying words in documents based on the dominance and the competence of the transformer unit in extracting specific context of words in reviews. With this work, we propose that the transformer architecture can be further enhanced with deep learning techniques by contriving potent layers to form the most optimal framework.

Relationship Between Arm Path, Ball Velocity, and Elbow Varus Torque in Professional Baseball Pitchers.

Currently, most pitching instructors suggest a shorter arm path-the total distance the arm travels during pitching. Theoretically, this combination allows for better body segment sequencing, a more efficient energy transfer through the kinetic chain, and increased ball velocity, while limiting elbow varus torque. Shorter arm paths would be associated with increased ball velocity and decreased elbow varus torque. Descriptive laboratory study. A total of 182 professional pitchers threw 8 to 12 fastball pitches while evaluated by 3-dimensional motion capture (480 Hz). The arm path was calculated as the total distance the hand marker traveled during the pitch. The pitch was divided into early, late, and total arm paths. A linear regression model assessed the interpitcher relationship between arm path, elbow varus torque, and ball velocity. A linear mixed-effects model with random intercepts assessed intrapitcher relationships. Interpitcher comparison showed that total arm path weakly correlated with greater elbow varus torque (P = .025). Strong correlations were found between ball velocity and early (R2 = 0.788; P < .001), late (R2 = 0.787; P = .024), and total arm paths (R2 = 0.792; P < .001). Strong positive intrapitcher correlations were found between elbow varus torque and early (R2 = 0.962; P < .001) and total arm path (R2 = 0.964; P < .001). For individual pitchers, there was a large variation in the early (30.1 ± 15.7 cm) and late (21.4 ± 12.1 cm) arm path. For every 30-cm (11.8-inch) increase in early arm path (the mean range for an individual pitcher), there was a 1.29-N (β = 0.0429) increase in elbow varus torque and a 0.354 m/s (0.79 mph) (β = 0.0118) increase in ball velocity. A shorter arm path correlated with decreased elbow varus torque and decreased ball velocity in intrapitcher comparisons. Determining the individual mechanics that decrease elbow varus torque may help coaches and trainers correct these patterns. A shorter arm path during the pitch can decrease elbow varus torque, which limits the load on the medial elbow but also has a detrimental effect on ball velocity. An improved understanding of the impact of shortening arm paths on stresses on the throwing arm may help minimize injury risk.

RCHOP Plus High-Dose Methotrexate As First-Line Therapy in Large B-Cell Lymphoma with Testis Involvement

Primary testicular lymphoma (PTL) and diffuse large B-cell lymphoma with secondary testicular involvement (DLBCL-T) are rare subtypes of lymphomas associated with poor prognosis and a high risk of central nervous system (CNS) relapse. The optimal CNS prophylaxis strategy for these subtypes remains uncertain. High-dose methotrexate (HD-MTX) is debated for CNS prophylaxis, but its efficacy in this setting is unknown. This study aimed to retrospectively analyze the use of HD-MTX in combination with chemotherapy (R HD-MTX CHOP) as frontline treatment for PTL and DLBCL-T and to compare the biological characteristics of these two forms of testicular involvement. Data from patients who received R HD-MTX CHOP as first-line treatment for DLBCL-T or PTL between 2010 and 2020 were collected retrospectively. The study included 15 patients, 5 with PTL and 10 with DLBCL-T. The overall response rate was 86%, with 73% of patients achieving complete remission. Treatment-related adverse events were generally manageable, with febrile neutropenia being the most common. Two patients experienced CNS relapse, both from the DLBCL-T subgroup, with a median time of 10.7 months from diagnosis to CNS relapse. None of the PTL patients receiving HD-MTX experienced relapse during the median follow-up of 6.08 years (Figure 1A), compared to 8/10 DLBCL-T (p=0.007). All five PTL cases had a non-germinal center phenotype, while 7 out of 10 DLBCL-T cases were non-germinal center. One DLBCL-T case exhibited high-grade lymphoma. Myc expression was positive in 3 out of 10 patients assessed, of whom all were double expressors. Bcl-2 expression was positive in all 13 assessable patients, while Bcl-6 expression was positive in 6 patients. Myc, Bcl-2, and Bcl-6 rearrangements were assessed in 10 patients and were present in 1 patient who was found to have triple-hit lymphoma. No other patient had double-hit lymphoma or any significant rearrangement. Polysomy of Myc, Bcl-2, and/or Bcl-6 was found in 2 patients. PDL1 expression was observed in one out of six patients analyzed, while no PD1 expression was detected. In 8 out of 15 patients with available tissue material, next-generation sequencing using a panel targeting 43 lymphoid genes was performed. Among these 8 patients, 87% exhibited gain-of-function mutations in MYD88 (MYD88 L265P), and 62% had missense mutations in CD79B at position Y196. Multiple mutations in the PIM1 gene, a known target of aberrant somatic hypermutation, were also observed in 7 patients. Non-recurrent mutations were detected in various genes, including PRDM1, CD58, B2M, CREBBP, CIITA, CARD11, CDKN2A, EP300, CCDND3, FOXO1, IRF4, NFKBIE, and TCF3 (Figure 1B). Ig rearrangement was analyzed in 4/9 patients with available DNA. All 4 were clonal, with distinct but mutated VH segments (sequence identity between 81 and 89%). This study suggests that HD-MTX in combination with RCHOP chemotherapy is highly effective in preventing CNS relapse in PTL but less so in DLBCL-T. DLBCL-T had a higher risk of CNS relapse and poorer outcomes compared to PTL. Further studies are needed to determine the optimal timing and sequence of HD-MTX in DLBCL-T, as well as alternative treatment approaches. PTL and DLBCL-T have distinct clinical presentations and responses to frontline chemotherapy with HD-MTX prophylaxis. PTL may benefit from HD-MTX in preventing relapse, while alternative treatments should be explored for DLBCL-T. Future studies should consider PTL and DLBCL-T as separate entities to better understand their characteristics and optimize treatment strategies.

Putative novel outer membrane antigens multi-epitope DNA vaccine candidates identified by Immunoinformatic approaches to control Acinetobacter baumannii

Multi-epitope polypeptide vaccines, a fusion protein, often have a string-of-beads system composed of various specific peptide epitopes, potential adjuvants, and linkers. When choosing the sequence of various segments and linkers, many alternatives are available. These variables can influence the vaccine’s effectiveness through their effects on physicochemical properties and polypeptide tertiary structure.The most conserved antigens were discovered using BLASTn. To forecast the proteins’ subcellular distribution, PSORTb 3.0.2 was used. Vaxign was used for the preliminary screening and antigenicity assessment. Protein solubility was also predicted using the ccSOL omics. Using PRED-TMBB, it was anticipated that the protein would localize across membranes. The IEDB and BepiPred-2.0 databases were used to predict the immunogenicity of B cell epitopes. A multi-epitope construct was developed and analyzed to evaluate. Twenty epitopes from A. baumannii’s outer membrane protein (omp) were included in the vaccination. TLR4 agonist explosibility was investigated. The physicochemical characteristics, secondary and tertiary structures, and B-cell epitopes of vaccine constructs were assessed. Additionally, docking and MD experiments were used to examine the relationship between TLR4 and its agonist.Thirteen antigens were discovered, and eight of the 13 chosen proteins were predicted to be surface proteins. The 34 kDa outer membrane protein, Omp38, Omp W, CarO, putative porin, OmpA, were chosen as having the right antigenicity (≥0.5). FhuE and CdiA were eliminated from further study because of their low antigenicity. The vaccine design was developed by combining the most effective 10 B-cell and 10 MHC-I/MHCII combined coverage epitopes. The molecular formula of the vaccine was determined to be C1718H2615N507O630S17. The vaccine form has a molecular weight of 40,996.70 Da and 47 negatively charged residues (Asp + Glu), whereas 28 positively charged residues (Arg + Lys). The estimated half-life was 7.2 hours (mammalian reticulocytes, in vitro), > 20 hours (yeast, in vivo) and > 10 hours (Escherichia coli, in vivo) for the vaccine. The multi-epitope vaccine insertion is carried via the expression vector pcDNA3.1 (+).The multi-epitope vaccine may stimulate humoral and cellular immune responses, according to our findings, and it may be a candidate for an A. baumannii vaccine.

Parametric Doppler correction for wayside array acoustic signal via short-time reconstruction

The trackside acoustic detection system (TADS) plays a vital role in vehicle bearings condition monitoring. However, the inevitable Doppler distortion embedded in collected data will bring considerable difficulties to defect inspection and diagnosis in the system. The distortion elimination approaches mainly achieve spectrum recovery based on time-domain interpolation resampling (TIR) in a data-driven way. These will be further limited by the requirement to signal to noise ratio (SNR) or calculation speed. Considering the principle of wayside acoustic physical model and the benefits of array acoustic in practice, this study proposes an adaptive parametric Doppler correction (PDC) scheme based on short-time reconstruction (STR), including perception modeling, inverse perception optimization and Doppler correction. Firstly, mathematical modeling with multiple perception operators, including acoustic amplitude modulation operator, frequency modulation operator and array time delay operator, a wayside acoustic physical model based on array sensing is established. Secondly, through the corresponding inverse multiple perception operators, a transition signal with frequency-domain energy concentrated is obtained for the optimal moving parameters searching. Thirdly, within short-time sequence segments of the sound source, the real-time position recovery is realized via the proposed STR with formulaic propagation delay compensation. Theoretically, it can be seen that the proposed PDC scheme can simultaneously recover the amplitude, frequency distribution and phase of the Doppler signal, which can be applicable to all wayside acoustic diagnosis scenarios. With the correction analysis of simulation and experimental array defect signals, it can be confirmed that the proposed STR-based PDC scheme is effective for Doppler signal recovery in high fidelity, and has great potential to the real-time diagnosis of TADS.

A probabilistic generative model to discover the treatments of coexisting diseases with missing data

Background and ObjectiveComorbidities, defined as the presence of co-existing diseases, progress through complex temporal patterns among patients. Learning such dynamics from electronic health records is crucial for understanding the coevolution of diseases. In general, medical records are represented through temporal sequences of clinical variables together with their diagnosis. However, we consider the specific problem where most of the diagnoses are missing. We present a novel probabilistic generative model with a three-fold objective: (i) identify and segment the medical history of patients into treatments associated with comorbidities; (ii) learn the model associated with each identified disease treatment; and (iii) discover subtypes of patients with similar coevolution of comorbidities. MethodsTo this end, the model considers a latent structure for the sequences, where patients are modeled by a latent class defined by the evolution of their comorbidities, and each observed medical event of their clinical history is associated with a latent disease. The learning process is performed using an Expectation-Maximization algorithm that considers the exponential number of configurations of the latent variables and is efficiently solved with dynamic programming. ResultsThe evaluation of the method is carried out both on synthetic and real world data: the experiments on synthetic data show that the learning procedure allows the generative model underlying the data to be recovered; the experiments on real medical data show accurate results in the segmentation of sequences into different treatments, subtyping of patients and diagnosis imputation. ConclusionWe present an interpretable generative model that handles the incompleteness of EHRs and describes the different joint evolution of coexisting diseases depending on the active comorbidities of the patient at each moment.

Morphological and molecular characterisation of Longidorus patuxentensis n. sp. (Nematoda: Longidoridae) from Maryland and California, USA

Summary A new needle nematode species, Longidorus patuxentensis n. sp., was collected along the banks of the Western Branch Patuxent River from Upper Marlboro, MD, USA, and described herein. Female body length of the new species ranges from 3.8 to 5.2 mm, with a set off lip region by depression, 77-92 μm long odontostyle, 40-53 μm long odontophore, vulva located at 46.3-50.1% and tail conoid with bluntly rounded tip. The new species has four juvenile developmental stages and no males. It looks morphologically similar to L. breviannulatus, L. elongatus, L. martini, L. americanus, L. grandis, L. sabalanicus and L. sturhani by having a ventrally curved to spiral body, generally similar lip region and conoid tail with a rounded terminus, but differs from these species by the odontostyle, odontophore, total stylet length and a few other characters. Phylogenetic analysis of the D2-D3 expansion segments of 28S rRNA gene sequences placed L. patuxentensis n. sp. in a clade with L. litchi, L. fangi, L. jonesi, L. diadecturus and Longidorus sp. The D2-D3 sequence of L. patuxentensis n. sp. was identical to that of Longidorus sp.5 collected from Juglans sp. growing in Butte County, California, USA. The D2-D3 of 28S and ITS1 rRNA and COI gene sequences indicated that the Maryland and California populations belong to the same species, described herein as L. patuxentensis n. sp.

A malicious network traffic detection model based on bidirectional temporal convolutional network with multi-head self-attention mechanism

The increasingly frequent network intrusions have brought serious impacts to the production and life, thus malicious network traffic detection has received more and more attention in recent years. However, the traditional rule matching-based and machine learning-based malicious network traffic detection methods have the problems of relying on human experience as well as low detection efficiency. The continuous development of deep learning technology provides new ideas to solve malicious network traffic detection, and the deep learning models are also widely used in the field of malicious network traffic detection. Compared with other deep learning models, bidirectional temporal convolutional network (BiTCN) has achieved better detection results due to its ability to obtain bidirectional semantic features of network traffic, but it does not consider the different meanings as well as different importance of different subsequence segments in network traffic sequences; In addition, the loss function used in BiTCN is the negative log likelihood function, which may lead to overfitting problems when facing multi-classification problems and data imbalance problems. To solve these problems, this paper proposes a malicious network traffic detection model based on BiTCN and multi-head self-attention (MHSA) mechanism, namely BiTCN_MHSA, it innovatively uses the MHSA mechanism to assign different weights to different subsequences of network traffic, thus making the model more focused on the characteristics of malicious network traffic as well as improving the efficiency of processing global network traffic; Moreover, it also changes its loss function to a cross-entropy loss function to penalize misclassification more severely, thereby speeding up the convergence. Finally, extensive experiments are conduced to evaluate the efficiency of proposed BiTCN_MHSA model on two public network traffic, the experimental results verify that the proposed BiTCN_MHSA model outperforms six state-of-the-arts in precision, recall, F1-measure and accuracy.

Physiological constraints and cognitive chunking: Sequence organization in the songs of zebra finches (Taeniopygia guttata).

Learned bird songs often have a hierarchical organization. In the case of zebra finches, each bird's song is made up of a string of notes delivered in a stereotyped sequence to form a "motif," and motifs are repeated to form a song bout. During song learning, young males copy "chunks" of two or more consecutive notes from their tutors' songs. These chunks are represented as distinct units within memory (during learning) and within motor systems (during song production). During song performance, motifs may deviate from the learned sequence by stopping short, starting late, or by skipping, inserting, or repeating notes. We measured acoustic and temporal variables related to the respiratory and vocal physiology of song production and asked how they related to deviations from each bird's "canonical" sequence. The best predictor of deviations from that sequence was the duration of the silent interval between notes, when inspiration normally occurs. Deviations from the canonical motif occurred less often after higher-pitched notes, perhaps because a high-low sequence forms a prosodic unit. Premature stops often followed louder and longer notes, suggesting that respiratory and muscular physiology influence the location of such stops. Boundaries between the learned chunks of a male's motif predicted where and how often noncanonical starts occurred. Physiological and cognitive elements also interacted to define the segmentation of zebra finch song sequences. Long silent intervals between notes were associated both with physiology (inspirations) and with the cognitive boundaries of learned chunks-and hence with deviations from the canonical motif. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Sequence signal reconstruction based multi-task deep learning for sleep staging on single-channel EEG

The temporal context information between sleep stage sequence contains sleep transition rules, which is important for improving sleep staging performance. Existing multi-task learning methods reconstruct EEG signals from one sleep stage, ignoring the importance of sequential temporal context in capturing long-term dependencies to enhance representation learning. To address these issues, we propose a multi-task deep learning model to jointly reconstruct sequence signal and segment time series. The model enhances the ability of time series segmentation task to capture sequential temporal context and improves the performance of single-channel EEG by optimizing the common encoder for sequence signal reconstruction task. In addition, we design a one-dimensional channel attention module to enhance the feature representation extracted for sleep sequence signal. The experimental results on four datasets show that the multi-task deep learning model can improve the generalization using sequence signal reconstruction. Compared with other state-of-the-art methods, the method proposed in this study obtained competitive performance in terms of metrics such as accuracy, which is 85.6% on the 2013 version of Sleep-EDF Database Expanded, 83.4% on the 2018 version of Sleep-EDF Database Expanded, 85.6% on Sleep Heart Health Study, and 77.4% on CAP Sleep Database.

Recovery of complete genome sequences of Crimean-Congo haemorrhagic fever virus (CCHFV) directly from clinical samples: A comparative study between targeted enrichment and metagenomic approaches

Crimean-Congo haemorrhagic fever (CCHF) is the most prevalent human tick-borne viral disease, endemic to the Balkans, Africa, Middle East and Asia. There are currently no licensed vaccines or effective antivirals against CCHF. CCHF virus (CCHFV) has a negative sense segmented tripartite RNA genome consisting of the small (S), medium (M) and large (L) segments. Depending on the segment utilised for genetic affiliation, there are up to 7 circulating lineages of CCHFV. The current lack of geographical representation of CCHFV sequences in various repositories highlights a requirement for increased CCHFV sequencing capabilities in endemic regions. We have optimised and established a multiplex PCR tiling methodology for the targeted enrichment of complete genomes of Europe 1 CCHFV lineage directly from clinical samples and compared its performance to a non-targeted enrichment approach on both short-read and long-read sequencing platforms. We have found a statistically significant increase in mapped viral sequencing reads produced with our targeted enrichment approach. This has allowed us to recover near complete S segment sequences and above 90% of the M and L segment sequences for samples with Ct values as high as 31.3. This study demonstrates the superiority of a targeted enrichment approach for recovery of CCHFV genomic sequences from samples with low virus titre. CCHFV is an important vector-borne human pathogen with wide geographical distribution. The validated methodology reported here adds value to front-line public health laboratories employing genomic sequencing for CCHFV Europe 1 lineage surveillance, particularly in the Balkan and Middle Eastern territories currently monitoring the spread of the pathogen. Tracking the genomic evolution of the virus across regions improves risk assessment and directly informs the development of diagnostics, therapeutics, and vaccines.

Elastic founder graphs improved and enhanced

Indexing labeled graphs for pattern matching is a central challenge of pangenomics. Equi et al. (2022) [14] developed the Elastic Founder Graph (EFG) representing an alignment of m sequences of length n, drawn from alphabet Σ plus the special gap character: the paths spell the original sequences or their recombination. By enforcing the semi-repeat-free property, the EFG admits a polynomial-space index for linear-time pattern matching, breaking through the conditional lower bounds on indexing labeled graphs (Equi et al. [13]). In this work, we improve the space of the EFG index answering pattern matching queries in linear time, from linear in the length of all strings spelled by three consecutive node labels, to linear in the size of the edge labels. Then, we develop linear-time construction algorithms optimizing for different metrics: we improve the existing linearithmic construction algorithms to O(mn), by solving the novel exclusive ancestor set problem on trees; we propose, for the simplified gapless setting, an O(mn)-time solution minimizing the maximum block height, that we generalize by substituting block height with prefix-aware height. Finally, to show the versatility of the framework, we develop a BWT-based EFG index and study how to encode and perform document listing queries on a set of paths of the graphs, reporting which paths present a given pattern as a substring. We propose the EFG framework as an improved and enhanced version of the framework for the gapless setting, along with construction methods that are valid in any setting concerned with the segmentation of aligned sequences.

Exploring virus presence in field-collected potato leaf samples using RNA sequencing

BackgroundThe quick and accurate identification of viruses is essential for plant disease management. Next-generation sequencing (NGS) technology may allow the discovery, detection, and identification of plant pathogens. This study adopted RNA-sequencing (RNA-Seq) technology to explore the viruses in three potato plants (S3, S4, and S6) growing under field conditions. ResultsPotato-known infecting viruses, such as alfalfa mosaic virus (AMV), potato leafroll virus (PLRV), and potato virus Y (PVY), were identified using bioinformatics programs and validated using RT-PCR. The presence of these potato viruses was also confirmed by visual inspection of host symptoms. In addition, the nearly complete genome of PLRV and the complete or partial genome sequence of multipartite virus segments have been identified. Besides the three major potato viruses that BLASTn analysis revealed were present in our samples, BLASTx analysis revealed some reads are derived from other potato viruses, such as potato virus V (PVV), Andean potato latent virus (APLV), and tomato chlorosis virus (ToCV), which are not frequently reported in potato field screenings in Egypt. Other microbial agents, such as bacteria and fungi, were also identified in the examined sample sequences. Some mycovirus sequences derived from ourmia-like viruses and Alternaria alternata chrysovirus were also identified in sample S4, confirming the complexity of the potato microbiome under field conditions. ConclusionNGS quickly and accurately identifies potato plant viruses under field conditions. Implementing this technology on a larger scale is recommended to explore potato fields and imported plants, where symptoms may be absent, unspecific, or only triggered under certain conditions.

Targeted mining of contiguous sequential patterns

In recent years, sequential pattern mining (SPM) has been applied in many domains, including recommender systems, fraud detection, and other related industries. Target-oriented SPM (TSPM) has been proposed to improve the interpretability of SPM by addressing the issue that too many irrelevant or meaningless sequential patterns are often discovered, which facilitates data analysis. Nevertheless, current TSPM research does not consider contiguity to identify sequential patterns, despite the fact that it is an important feature of sequence data. Contiguity has significant implications in applications such as bioinformatics and network intrusion detection, as it can aid the detection of periodic patterns and anomalous behaviors. Therefore, this paper proposes a novel problem of target-oriented contiguous sequential pattern mining (TCSPM) and introduces two algorithms, called TCSPM and TCSPM+. TCSPM utilizes a compact data structure called sequence chain and relies on a sequence chain pruning strategy to reduce the search space. In addition, TCSPM applies a reverse matching technique to quickly search for targeted contiguous sequences. TCSPM+ is an optimized version of TCSPM that utilizes sequence segmentation operations and a query sequence pruning strategy to further improve performance. Extensive experiments on both real and synthetic datasets demonstrate that TCSPM is an efficient algorithm for discovering targeted contiguous sequential patterns (TCSPs). TCSPM+ substantially enhances the performance of TCSPM in terms of runtime, memory usage, and scalability, outperforming TCSPM by an order of magnitude on some datasets.