Set Of Sequences Research Articles

Novel Constructions of Complementary Sets of Sequences of Lengths Non-Power-of-Two

Novel Constructions of Complementary Sets of Sequences of Lengths Non-Power-of-Two

High-Frequency Recombination of Human Adenovirus in Children with Acute Respiratory Tract Infections in Beijing, China.

Recombination events in human adenovirus (HAdV) have led to some new highly pathogenic or infectious types. It is vital to monitor recombinant HAdVs, especially in children with acute respiratory tract infections (ARIs). In the retrospective study, HAdV positive specimens were collected from pediatric patients with ARIs during 2015 to 2021, then typed by sequence analysis of the penton base, hexon and fiber gene sequence. For those with inconsistent typing results, a modified method with species-specific primer sets of a fiber gene sequence was developed to distinguish co-infections of different types from recombinant HAdV infections. Then, plaque assays combined with meta-genomic next-generation sequencing (mNGS) were used to reveal the HAdV genomic characteristics. There were 466 cases positive for HAdV DNA (2.89%, 466/16,097) and 350 (75.11%, 350/466) successfully typed with the most prevalent types HAdV-B3 (56.57%, 198/350) and HAdV-B7 (32.00%, 112/350), followed by HAdV-C1 (6.00%, 21/350). Among 35 cases (7.51%, 35/466) with inconsistent typing results, nine cases were confirmed as co-infections by different types of HAdVs, and 26 cases as recombinant HAdVs in six genetic patterns primarily clustered to species C (25 cases) in pattern 1-5, or species D (1 case) in pattern 6. The novel recombinant HAdV of species D was identified with multiple recombinant events among HAdV-D53, HAdV-D64, and HAdV-D8, and officially named as HAdV-D115. High-frequency recombination of HAdVs in six genetic recombination patterns were identified among children with ARIs in Beijing. Specifically, there is a novel Adenovirus D human/CHN/S8130/2023/115[P22H8F8] designed as HAdV D115.

Boundary dynamics for holomorphic sequences, non-autonomous dynamical systems and wandering domains

There are many classical results, related to the Denjoy–Wolff theorem, concerning the relationship between orbits of interior points and orbits of boundary points under iterates of holomorphic self-maps of the unit disc. Here, we address such questions in the very general setting of sequences (Fn) of holomorphic maps between simply connected domains. We show that, while some classical results can be generalised, with an interesting dependence on the geometry of the domains, a much richer variety of behaviours is possible. One of our main results is new even in the classical setting.Our methods apply in particular to non-autonomous dynamical systems, when (Fn) are forward compositions of holomorphic maps, and to the study of wandering domains in holomorphic dynamics.The proofs use techniques from geometric function theory, measure theory and ergodic theory, and the construction of examples involves a ‘weak independence’ version of the second Borel–Cantelli lemma and the concept from ergodic theory of ‘shrinking targets’.

Efficient Motif Discovery in Protein Sequences Using a Branch and Bound Algorithm.

Identifying motifs within sets of protein sequences constitutes a pivotal challenge in proteomics, imparting insights into protein evolution, function prediction, and structural attributes. Motifs hold the potential to unveil crucial protein aspects like transcription factor binding sites and protein-protein interaction regions. However, prevailing techniques for identifying motif sequences in extensive protein collections often entail significant time investments. Furthermore, ensuring the accuracy of obtained results remains a persistent motif discovery challenge. This paper introduces an innovative approach-a branch and bound algorithm-for exact motif identification across diverse lengths. This algorithm exhibits superior performance in terms of reduced runtime and enhanced result accuracy, as compared to existing methods. To achieve this objective, the study constructs a comprehensive tree structure encompassing potential motif evolution pathways. Subsequently, the tree is pruned based on motif length and targeted similarity thresholds. The proposed algorithm efficiently identifies all potential motif subsequences, characterized by maximal similarity, within expansive protein sequence datasets. Experimental findings affirm the algorithm's efficacy, highlighting its superior performance in terms of runtime, motif count, and accuracy, in comparison to prevalent practical techniques.

Classifying protein kinase conformations with machine learning.

Protein kinases are key actors of signaling networks and important drug targets. They cycle between active and inactive conformations, distinguished by a few elements within the catalytic domain. One is the activation loop, whose conserved DFG motif can occupy DFG-in, DFG-out, and some rarer conformations. Annotation and classification of the structural kinome are important, as different conformations can be targeted by different inhibitors and activators. Valuable resources exist; however, large-scale applications will benefit from increased automation and interpretability of structural annotation. Interpretable machine learning models are described for this purpose, based on ensembles of decision trees. To train them, a set of catalytic domain sequences and structures was collected, somewhat larger and more diverse than existing resources. The structures were clustered based on the DFG conformation and manually annotated. They were then used as training input. Two main models were constructed, which distinguished active/inactive and in/out/other DFG conformations. They considered initially 1692 structural variables, spanning the whole catalytic domain, then identified ("learned") a small subset that sufficed for accurate classification. The first model correctly labeled all but 3 of 3289 structures as active or inactive, while the second assigned the correct DFG label to all but 17 of 8826 structures. The most potent classifying variables were all related to well-known structural elements in or near the activation loop and their ranking gives insights into the conformational preferences. The models were used to automatically annotate 3850 kinase structures predicted recently with the Alphafold2 tool, showing that Alphafold2 reproduced the active/inactive but not the DFG-in proportions seen in the Protein Data Bank. We expect the models will be useful for understanding and engineering kinases.

Deciding Irreducibility/Indecomposability of Feedback Shift Registers Is NP-Hard

Feedback shift registers (FSRs) are used as a fundamental component in electronics and confidential communication. A FSR f is said to be reducible if all the output sequences of another FSR g can also be generated by f and the FSR g costs less memory than f. A FSR is said to be decomposable if it has the same set of output sequences as a cascade connection of two FSRs. Two polynomial-time computable transformations from Boolean circuits to FSRs are proposed such that the output FSR of the first (resp. second) transformation is irreducible (resp. indecomposable) if and only if the input Boolean circuit is satisfiable. Through the two transformations, it is proved that deciding irreducibility (indecomposability) of FSRs is NP-hard. Additionally, FSRs are constructed to show that there exist infinitely many irreducible (resp. indecomposable) FSRs which are decomposable (resp. reducible).

Supervised learning study on ground classification and state recognition of agricultural robots based on multi-source vibration data fusion

In agricultural environments, recognizing the walking ground and state of tracked mobile robots is a complex and challenging task, influenced by clay conditions and other external environmental disturbances. Therefore, this paper proposes a novel data processing method and efficient classifier. Firstly, the noise signals on the left and right sides of the collected robot are averaged, and a time-wavelet-time domain transformation is performed using the Mallat algorithm to achieve nonlinear enhancement of data features and signal denoising. Secondly, the Gramian Angular Summation Fields (GASF) is introduced to transform sequence data into single-channel images, capturing the periodicity and similarity of time series. Next, the images of three sets of sequences are stacked in the channel dimension in RGB format, thus achieving feature fusion of multi-source data. Finally, a supervised learning classifier named Attention-fused Residual Convolutional Neural Network (ANR-CNN) is proposed. Here, the combination of channel and spatial attention mechanisms captures important features in the feature map in both channel and spatial dimensions. The convolutional residual structure enhances feature transmission, improving the model's classification accuracy. Experimental results demonstrate that the proposed data augmentation method effectively enhances model performance, and the classification accuracy of ANR-CNN reaches 92.35%. This implies accurate recognition of the walking ground and state of tracked mobile robots in agricultural environments.

Fruit recognition and classification based on tactile information of flexible hand

In the field of agricultural production, the sorting of agricultural products is a basic operation performed by robot manipulators. Agricultural products are fragile and easily damaged, and their shape, texture and size are different. In order to ensure grasping compliance and the integrity of agricultural products. In this study, firstly, a three-layer flexible tactile sensor was designed and fabricated. The performance of the sensor was tested. Three functions were used to fit the relationship between the sensor pressure and the output voltage, and the function with the best fitting degree was selected for sensor calibration. The sensor exhibits two kinds of sensitivity under the loading of 0 ∼ 4 N.When the applied pressure is 0 ∼ 2.75 N, the sensitivity is 1.21 V / N. When the applied pressure is 2.75 ∼ 4 N, the sensitivity is 0.24 V / N. Then, the sensor and the flexible gripper are combined to build a tactile information acquisition platform to collect the original data set of the tactile sequence generated when the flexible hand grabs the fruit, and the original data is preprocessed. Finally, we evaluated the performance of Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB) and Neural Network (NN) in fruit classification. The results show that the RF classifier achieves the highest accuracy in the training and testing stages, which are 98.64% and 93.06%, respectively, and the accuracy of other classifiers ranges from 66.43% to 95.83%. Our work is helpful to the realization of robotic tactile perception and non-invasive grasping in the field of agriculture, and can be helpful to fruit picking, handling, sorting and other related fields.

Seismic Stratigraphy of Contourite Drift Deposits Associated With the Loop Current on the Eastern Campeche Bank, Gulf of Mexico

AbstractThe Loop Current is a key component of global circulation via the northward transport of warm, salty water, and an important influence on Gulf of Mexico hydrography. Understanding how the Loop Current will respond to ongoing anthropogenic warming is critically important, but the history of the Loop Current is poorly known. Here, we present the results of a high resolution (3–8 m) multichannel seismic survey of pelagic carbonate sediment drifts on the eastern Campeche Bank associated with the Loop Current. We identify three seismic megasequences: Megasequence A is a Lower Cretaceous carbonate platform, Megasequence B comprises Cretaceous to lower Cenozoic pelagic carbonates with weak/no contour current flow, and Megasequence C comprises a series of large (100s of m thick) contourite drifts representing the inception and history of the Loop Current. The base of the contourites is marked by a regionally mappable unconformity eroding underling strata, sometimes incising hundreds of meters. The drifts contain a succession of sequence sets separated from each other by regional unconformities and comprising plastered drifts and massive mounded drifts, which characterize modern deposition with active moats on the seafloor. A lack of sediment cores in the study area precludes age determination of these drifts, except for the youngest (Late Pleistocene). Comparison to legacy seismic lines across Deep Sea Drilling Project Site 95, outside our study area, implies that the base of Megasequence C is Oligocene in age, and that the Loop Current developed during the global reorganization of ocean circulation around the Eocene‐Oligocene Transition.

New Correlation Bound and Construction of Quasi-Complementary Sequence Sets

New Correlation Bound and Construction of Quasi-Complementary Sequence Sets

The finite sequences and the partitions whose members are finite of a set

Abstract In this paper, we investigate relationships between $|\text{{seq}}(A)|$ and $|\text{{Part}}_{\text{{fin}}}(A)|$ in the absence of the Axiom of Choice, where $\text{{seq}}(A)$ is the set of finite sequences of elements in a set $A$ and $\text{{Part}}_{\text{{fin}}}(A)$ is the set of partitions of $A$ whose members are finite. We show that $|\text{{seq}}(A)|<|\text{{Part}}_{\text{{fin}}}(A)|$ if $A$ is Dedekind-infinite and the condition cannot be removed. Moreover, this relationship holds for an arbitrary infinite set $A$ if we restrict $\text{{seq}}(A)$ to the set of finite sequences with a bounded length.

Multi-temporal change detection of asbestos roofing: A hybrid object-based deep learning framework with post-classification structure

The detection of changes in asbestos roofing is vital due to the significant health risks associated with asbestos exposure. Utilising remote sensing for this task is promising, yet it encounters challenges like misregistration and variations in illumination within temporal images. This study presents a novel object-based deep learning framework with a post-classification structure designed specifically to detect changes in asbestos roofing. The method employs a hybrid deep learning model that utilises DenseNet121 to extract spatial features and integrates with either LSTM (DenseNet-LSTM) or conv1D (DenseNet-Conv1D) to capture temporal features. Using VHR aerial imagery, image patch sequences were generated for each roofing object across seven temporal points. After training the model, labels were assigned to each image patch in the sequence through binary classification. In post-processing, sequence alignment based on Hamming distance was used to enhance label assignments by aligning them with a pre-determined set of valid label sequences. Finally, the temporal change map was generated by comparing label transitions across temporal points. The results highlighted the model's robust capability to address inconsistencies across temporal points without relying on preprocessing techniques to manage such temporal discrepancies. The DenseNet-LSTM and DenseNet-Conv1D models demonstrated comparable classification performance in asbestos roofing detection, achieving average overall accuracies of 95.8% and 96.0%, respectively. Furthermore, the model's generalisability was underscored by a test on an independent dataset, which produced up to 94% detection accuracy for asbestos class. While the framework has limitation in detecting painted asbestos roofing and partial changes, it offers a reproducible approach for change detection tasks that target a specific roofing type, rather than detecting changes in all roofing types within remote sensing imagery.

In Situ Transglutaminase Cross-Linking Improves Mechanical Properties of Self-Assembling Peptides for Biomedical Applications.

The development of three-dimensional (3D) biomaterials that mimic natural tissues is required for efficiently restoring physiological functions of injured tissues and organs. In the field of soft hydrogels, self-assembled peptides (SAPs) stand out as distinctive biomimetic scaffolds, offering tunable properties. They have garnered significant attention in nanomedicine due to their innate ability to self-assemble, resulting in the creation of fibrous nanostructures that closely mimic the microenvironment of the extracellular matrix (ECM). This unique feature ensures their biocompatibility and bioactivity, making them a compelling area of study over the past few decades. As they are soft hydrogels, approaches are necessary to enhance the stiffness and resilience of the SAP materials. This work shows an enzymatic strategy to selectively increase the stiffness and resiliency of functionalized SAPs using transglutaminase (TGase) type 2, an enzyme capable of triggering the formation of isopeptide bonds. To this aim, we synthesized a set of SAP sequences and characterized their cross-linking via rheological experiments, atomic force microscopy (AFM), thioflavin-T binding assay, and infrared spectroscopy (ATR-FTIR) tests. The results showed an improvement of the storage modulus of cross-linked SAPs at no cost of the maximum stress-at-failure. Further, in in vitro tests, we examined and validated the TGase capability to cross-link SAPs without hampering seeded neural stem cells (hNSCs) viability and differentiation, potentially leaving the door open for safe in situ cross-linking reactions in vivo.

A direct construction of cross z-complementary sequence sets with large set size

A direct construction of cross z-complementary sequence sets with large set size

A Construction of an Asynchronous Channel Hopping Sequence Sets for Cognitive Radio Networks

A Construction of an Asynchronous Channel Hopping Sequence Sets for Cognitive Radio Networks

Ras-related associated with diabetes genes for biomarker-based therapeutics in cancer: A comparative evolutionary genomic study.

To compare Ras-related associated with diabetes (RRAD) across different species and to identify specific biomarkers for cancer therapy. The study involves comparing the coding sequences, genes, messenger ribonucleic acid (RNA), non-coding RNA, open reading frame, short- and long-sequence repeats, and transcription factors of RRAD genes from 82 species. Various tools and software are employed for these comparisons, and evolutionary analysis was carried out to understand the gene's evolutionary history. The data are classified based on forward and reverse sequences. Our analysis indicates that ACTG1 may function as a downstream effector of RRAD, offering potential avenues for diabetes and cancer treatments. By collecting RRAD sequences from 82 species and carrying out comparative genomics, this study provides diverse strategies for developing biomarker-based therapeutics. Furthermore, it suggests using RRAD in other organisms as a model for studying the knockdown effects of specific sequence sets. The study presents RRAD sequences from 82 organisms across different families, contributing to a diverse knowledge base for identifying drug-designing biomarkers. This research offers insights into the potential of RRAD as a therapeutic target in various organisms and highlights the importance of biomarker identification in drug development.

PERFUMES: pipeline to extract RNA functional motifs and exposed structures.

Up to 75% of the human genome encodes RNAs. The function of many non-coding RNAs relies on their ability to fold into 3D structures. Specifically, nucleotides inside secondary structure loops form non-canonical base pairs that help stabilize complex local 3D structures. These RNA 3D motifs can promote specific interactions with other molecules or serve as catalytic sites. We introduce PERFUMES, a computational pipeline to identify 3D motifs that can be associated with observable features. Given a set of RNA sequences with associated binary experimental measurements, PERFUMES searches for RNA 3D motifs using BayesPairing2 and extracts those that are over-represented in the set of positive sequences. It also conducts a thermodynamics analysis of the structural context that can support the interpretation of the predictions. We illustrate PERFUMES' usage on the SNRPA protein binding site, for which the tool retrieved both previously known binder motifs and new ones. PERFUMES is an open-source Python package (https://jwgitlab.cs.mcgill.ca/arnaud_chol/perfumes).

Phospholysine phosphohistidine inorganic pyrophosphate phosphatase suppresses insulin-like growth factor 1 receptor expression to inhibit cell adhesion and proliferation in gastric cancer.

Phospholysine phosphohistidine inorganic pyrophosphate phosphatase (LHPP) has recently emerged as a novel tumor suppressor. Researchers have observed that LHPP plays a crucial role in inhibiting proliferation, growth, migration, invasion, and cell metabolism across various cancers. Nevertheless, the specific functions and underlying mechanisms of LHPP as a tumor suppressor in gastric cancer (GC) require further exploration. The expression of LHPP was assessed in human GC specimens and cell lines. Various assays were employed to evaluate the impact of LHPP on GC cells. RNA sequencing and Gene Set Enrichment Analysis were conducted to unravel the mechanism through which LHPP regulates GC cell behavior. Additionally, xenograft nude mouse models were utilized to investigate the in vivo effects of LHPP. The findings indicate that LHPP, functioning as a tumor suppressor, is downregulated in both GC tissues and cells. LHPP emerges as an independent risk factor for GC patients, and its expression level exhibits a positive correlation with patient prognosis. LHPP exerts inhibitory effects on the adhesion and proliferation of GC cells by suppressing the expression of insulin-like growth factor 1 receptor (IGF1R) and modulating downstream signaling pathways. Consequently, LHPP holds potential as a biomarker for targeted therapy involving IGF1R inhibition in GC patients.

Molecular characterization and phylogenetic position of the giant deep-sea oyster Neopycnodonte zibrowii Gofas, Salas & Taviani, 2009

The giant deep-sea oyster Neopycnodonte zibrowii Gofas, C. Salas & Taviani, 2009 is a keystone deep-sea habitat builder species. Discovered about fifteen years ago in the Azores, it has been described and assigned to the genus Neopycnodonte Fischer von Waldheim, 1835 based on morphological features. In this study, we generated DNA sequence data for both mitochondrial (COI and 16S) and nuclear (ITS2 and 28S) markers based on the holotype specimen of N. zibrowii to establish a molecular phylogenetic framework for the systematic assessment of this species and to provide a reliable (i.e., holotype-based) reference sequence set for multilocus DNA barcoding approaches. Molecular data provide compelling evidence that the giant deep-sea oyster is a distinct species, rather than a deep-water ecophenotype of Neopycnodonte cochlear (Poli, 1795), with extremely high genetic divergence from any other gryphaeid. Multilocus phylogenetic analyses place the giant deep-sea oyster within the clade “Neopycnodonte/Pycnodonte” with closer affinity to N. cochlear rather than to P. taniguchii Hayami & Kase, 1992, thus supporting its assignment to the genus Neopycnodonte. Relationships within this clade are not well supported because mitochondrial variation is inflated by saturation that eroded phylogenetic signal, implying an old split between taxa within this clade. Finally, the set of reference barcode sequences of N. zibrowii generated in this study will be useful for a wide plethora of barcoding applications in deep-sea biodiversity surveys. Molecular validation of recent records of deep-sea oysters from the Atlantic Ocean and the Mediterranean Sea will be crucial to clarify the distribution of N. zibrowii and assess the phenotypic variation and ecology of this enigmatic species.

PandoGen: Generating complete instances of future SARS-CoV-2 sequences using Deep Learning.

One of the challenges in a viral pandemic is the emergence of novel variants with different phenotypical characteristics. An ability to forecast future viral individuals at the sequence level enables advance preparation by characterizing the sequences and closing vulnerabilities in current preventative and therapeutic methods. In this article, we explore, in the context of a viral pandemic, the problem of generating complete instances of undiscovered viral protein sequences, which have a high likelihood of being discovered in the future using protein language models. Current approaches to training these models fit model parameters to a known sequence set, which does not suit pandemic forecasting as future sequences differ from known sequences in some respects. To address this, we develop a novel method, called PandoGen, to train protein language models towards the pandemic protein forecasting task. PandoGen combines techniques such as synthetic data generation, conditional sequence generation, and reward-based learning, enabling the model to forecast future sequences, with a high propensity to spread. Applying our method to modeling the SARS-CoV-2 Spike protein sequence, we find empirically that our model forecasts twice as many novel sequences with five times the case counts compared to a model that is 30× larger. Our method forecasts unseen lineages months in advance, whereas models 4× and 30× larger forecast almost no new lineages. When trained on data available up to a month before the onset of important Variants of Concern, our method consistently forecasts sequences belonging to those variants within tight sequence budgets.