Sequence Retrieval Research Articles

Genome-Wide Identification of WRKY Transcription Factors in Pea (Pisum sativum L.) and their Response to Sulfur and Water Stress

AbstractThe WRKY gene family is widely distributed in plants and plays essential roles in plant development and stress response. Despite extensive characterization of WRKY transcription factors in many plants, a genome-wide analysis of the WRKY gene family in Pisum sativum is still lacking. The central method of this study focused on the retrieval and characterization of WRKY gene sequences in the pea genome and further looking up for their modulation in response to sulfur and water stress. This involved a combination of bioinformatics tools and gene expression study to identify, characterize, and understand the properties of the WRKY gene family in pea. In this study, 86 PsWRKY genes were identified in the pea genome, categorized into five phylogenetic groups. They were dispersed across all seven chromosomes, with Chromosome 5 showing the highest enrichment. Synteny analysis revealed orthologs of 69 PsWRKY genes in Arabidopsis and Medicago. Transcriptome analysis identified 36 differentially expressed PsWRKY genes in response to sulfur (S) stress, water (W) stress, and their combination (W + S). Real-time PCR validation confirmed significant upregulation of PsWRKY23, PsWRKY58, PsWRKY64, and PsWRKY83 under water stress, PsWRKY58 and PsWRKY84 under sulfur stress, and PsWRKY03 under combined (W + S) stress. Overall, this study provides important insights on genetic information of the WRKY gene family in Pea and its possible role in water and sulfur stress, which will help further to study these candidate WRKY gene with a future objective of understanding their role in abiotic stress.

Quantum computing and tensor networks for laminate design: A novel approach to stacking sequence retrieval

As with many tasks in engineering, structural design frequently involves navigating complex and computationally expensive problems. A prime example is the weight optimization of laminated composite materials, which to this day remains a formidable task, due to an exponentially large configuration space and non-linear constraints. The rapidly developing field of quantum computation may offer novel approaches for addressing these intricate problems. However, before applying any quantum algorithm to a given problem, it must be translated into a form that is compatible with the underlying operations on a quantum computer. Our work specifically targets stacking sequence retrieval with lamination parameters, which is typically the second phase in a common bi-level optimization procedure for minimizing the weight of composite structures. To adapt stacking sequence retrieval for quantum computational methods, we map the possible stacking sequences onto a quantum state space. We further derive a linear operator, the Hamiltonian, within this state space that encapsulates the loss function inherent to the stacking sequence retrieval problem. Additionally, we demonstrate the incorporation of manufacturing constraints on stacking sequences as penalty terms in the Hamiltonian. This quantum representation is suitable for a variety of classical and quantum algorithms for finding the ground state of a quantum Hamiltonian. For a practical demonstration, we performed numerical state-vector simulations of two variational quantum algorithms and additionally chose a classical tensor network algorithm, the DMRG algorithm, to numerically validate our approach. For the DMRG algorithm, we derived a matrix product operator representation of the loss function Hamiltonian and the penalty terms. Although this work primarily concentrates on quantum computation, the application of tensor network algorithms presents a novel quantum-inspired approach for stacking sequence retrieval.

Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashing

Temporal sequences have become pervasive in various real-world applications such as finance, spatial mobility, health records, etc. Consequently, the volume of data generated in the form of continuous time-event sequence(s) or CTES(s) has increased exponentially in the past few years. Thus, a significant fraction of the ongoing research on CTES datasets involves designing models to address downstream tasks such as next-event prediction, long-term forecasting, sequence classification etc. The recent developments in predictive modeling using marked temporal point processes (MTPP) have enabled an accurate characterization of several real-world applications involving the CTESs. However, due to the complex nature of these CTES datasets, the task of large-scale retrieval of temporal sequences has been overlooked by the past literature. In detail, by CTES retrieval we mean that for an input query sequence, a retrieval system must return a ranked list of relevant sequences from a large corpus. To tackle this, we propose NeuroSeqRet , a first-of-its-kind framework designed specifically for end-to-end CTES retrieval. Specifically, NeuroSeqRet introduces multiple enhancements over standard retrieval frameworks and first applies a trainable unwarping function on the query sequence which makes it comparable with corpus sequences, especially when a relevant query-corpus pair has individually different attributes. Next, it feeds the unwarped query sequence and the corpus sequence into MTPP-guided neural relevance models. We develop four variants of the relevance model for different kinds of applications based on the trade-off between accuracy and efficiency. We also propose an optimization framework to learn binary sequence embeddings from the relevance scores, suitable for the locality-sensitive hashing leading to a significant speedup in returning top-K results for a given query sequence. Our experiments with several datasets show the significant accuracy boost of NeuroSeqRet beyond several baselines, as well as the efficacy of our hashing mechanism.

Human-machine cooperation: Optimizing layout design and drug retrieval sequencing in automated drug dispensing systems

Automated drug dispensing systems (ADDSs) are increasingly in demand in today’s pharmacies, primarily driven by the growing aging population. Recognizing the practical challenges faced by pharmacies implementing ADDSs, this study aims to optimize the layout design and retrieval sequencing issues to enhance the order picking efficiency of ADDSs within a human–machine cooperation environment. In ADDSs, the robotic arm retrieves the drug bin and after that pharmacists need to sort the drug at the Input/output (I/O) points for order picking, which is a human–machine cooperation way. We consider the pharmacists’ stochastic sorting time in the order picking problem. For the various layout designs in ADDSs, we develop dual command retrieval sequencing models with one I/O point layout design and two I/O points layout designs. The successive order arrival and the dual command mode operations make the process intertwined. The proposed models are tested using the pharmacy data. We incorporate the stochastic sorting time of pharmacists to analyze its impact on ADDS performance. Through experimental comparisons of average picking times for prescription orders under various parameters, we demonstrate that the system layout design with two I/O points significantly enhances the efficiency of prescription order fulfillment within a human–machine cooperation environment. Furthermore, our proposed retrieval sequencing method outperforms dynamic programming, greedy, and random strategies in terms of improving prescription order-picking efficiency. Specifically, a comparison is made with the current situation in hospitals as well. By addressing the layout design and sequencing challenges, our research contributes to the field of intelligent warehousing, particularly in smart pharmacies.

Recall tempo of Hebbian sequences depends on the interplay of Hebbian kernel with tutor signal timing

Understanding how neural circuits generate sequential activity is a longstanding challenge. While foundational theoretical models have shown how sequences can be stored as memories in neural networks with Hebbian plasticity rules, these models considered only a narrow range of Hebbian rules. Here, we introduce a model for arbitrary Hebbian plasticity rules, capturing the diversity of spike-timing-dependent synaptic plasticity seen in experiments, and show how the choice of these rules and of neural activity patterns influences sequence memory formation and retrieval. In particular, we derive a general theory that predicts the tempo of sequence replay. This theory lays a foundation for explaining how cortical tutor signals might give rise to motor actions that eventually become "automatic." Our theory also captures the impact of changing the tempo of the tutor signal. Beyond shedding light on biological circuits, this theory has relevance in artificial intelligence by laying a foundation for frameworks whereby slow and computationally expensive deliberation can be stored as memories and eventually replaced by inexpensive recall.

Design of an epitope-based peptide vaccine against Cryptococcus neoformans.

Cryptococcus neoformans is the highest-ranked fungal pathogen in the Fungal Priority Pathogens List (FPPL) released by the World Health Organization (WHO). In this study, through in silico simulations, a multi-epitope vaccine against Cryptococcus neoformans was developed using the mannoprotein antigen (MP88) as a vaccine candidate. Following the retrieval of the MP88 protein sequences, these were used to predict antigenic B-cell and T-cell epitopes via the bepipred tool and the artificial neural network, respectively. Conserved B-cell epitopes AYSTPA, AYSTPAS, PASSNCK, and DSAYPP were identified as the most promising B-cell epitopes. While YMAADQFCL, VSYEEWMNY, and FQQRYTGTF were identified as the best candidates for CD8+ T-cell epitopes; and YARLLSLNA, ISYGTAMAV, and INQTSYARL were identified as the most promising CD4+ T-cell epitopes. The vaccine construct was modeled along with adjuvant and peptide linkers and the expasy protparam tool was used to predict the physiochemical properties. According to this, the construct vaccine was predicted to be antigenic, nontoxic, nonallergenic, soluble, stable, hydrophilic, and thermostable. Furthermore, the three-dimensional structure was also used in docking analyses with Toll-like receptor (TLR4). Finally, the cDNA of vaccine was successfully cloned into the E. coli pET-28a (+) expression vector. The results presented here could contribute towards the design of an effective vaccine against Cryptococcus neoformans.

Virtual Discovery of Immune-Stimulating Epitopes in Chikungunya Virus for Vaccine Design

Epitope identification is a key step in vaccine development, and this can be achieved much faster and less expensively with in silico methods, compared to traditional methods for vaccine production. In silico methods applied in this research utilised both bioinformatics and immunoinformatics approaches for chikungunya virus vaccine design, which involved the retrieval of sequences from databases, and identification of conserved regions within the sequences by multiple sequence alignment on the MEGA X software (Pennsylvania State University, State College, USA). The epitopes in the conserved regions were selected, and various immunological predictions and screenings were carried out by employing immunological databases and tools. This process identifies epitopes such as conservation of cytotoxic T lymphocyte, helper T lymphocytes, and B cell epitopes. The primary, secondary, and tertiary structure of the vaccine was also predicted using structure predicting servers, and finally, the vaccine candidate was docked to toll-like receptor 4 to study its binding affinity and configuration. A total of 125 conserved antigenic epitopes were selected from capsid, 6K, and E1 proteins, which were found to be non-allergens and conform to acceptable physicochemical standards, as reported by other authors with similar work. The epitopes were predicted to be capable of inducing cytotoxic T lymphocytes, helper T lymphocytes, and B cell production. Construction of secondary structure was done using the Self-Optimized Prediction Method with Alignment (SOPMA), which predicted 17.96% α-helices, and 4.69% β-turns, among others. Predicting the tertiary structure provided five models, of which Model 1 was selected on the bases of its confidential score of 0.59, estimated TM-score of 0.79±0.09, and root mean square deviation of 8.0±4.4Å. Validity analysis revealed a Ramachandran plot where 97.2% of the vaccine residue was within the favoured region, and the peptide showed a Z-score of -1.52. The predicted peptide effectively docked with toll-like receptor 4 with a binding energy of -1,072.8. From the data obtained, it was revealed that the selected epitopes are highly immunogenic, non-allergenic, conform to native protein, and form a peptide capable of vaccine application. The authors can conclude this is a promising candidate for vaccine design and development.

Performance Analysis of Multi-Tote Storage and Retrieval Autonomous Mobile Robot Systems

Multi-tote storage and retrieval (MTSR) autonomous mobile robots can carry multiple product totes, store and retrieve them from different shelf rack tiers, and transport them to a workstation where the products are picked to fulfill customer orders. In each robot trip, totes retrieved during the previous trip must be stored. This leads to a mixed storage and retrieval route. We analyze this mixed storage and retrieval route problem and derive the optimal travel route for a multiblock warehouse by a layered graph algorithm, based on storage first-retrieval second and mixed storage and retrieval policies. We also propose an effective heuristic routing policy, the closest retrieval (CR) sequence policy, based on a local shortest path. Numerical results show that the CR policy leads to shorter travel times than the well-known S-shape policy, whereas the gap with the optimal mixed storage and retrieval policy in practical scenarios is small. Based on the CR policy, we model the stochastic behavior of the system using a semiopen queuing network (SOQN). This model can accurately estimate average tote throughput time and system throughput capacity as a function of the number of robots in the system. We use the SOQN and corresponding closed queuing network models to optimize the total annual cost as a function of the warehouse shape, the number of robots, and tote buffer positions on the robots for a given average tote throughput time and throughput capacity. Compared with robots that retrieve a single tote per trip, an MTSR system with at least five buffer positions can achieve lower operational costs while meeting given average tote throughput time and tote throughput capacity constraints. Funding: This work was supported by National Natural Science Foundation of China [Grant 72372088] and the Shenzhen Science and Technology Program [Grant GJHZ20220913143003006]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0397 .

Gut metagenomic analysis of gastric cancer patients reveals Akkermansia, Gammaproteobacteria, and Veillonella microbiota as potential non-invasive biomarkers

The goal of the study was to investigate the changes in the gut microbiota during the advancement of gastric cancer (GC) and identify pertinent taxa associated with the disease. We used a public fecal amplicon gastric cancer dataset from the Sequence Retrieval Archive (SRA), of patients with GC, gastritis, and healthy individuals. We did sequence pre-processing, including quality filtering of the sequences. Then, we performed a diversity analysis, evaluating α- and β-diversity. Next, taxonomic composition analysis was performed and the relative abundances of different taxa at the phylum and genus levels were compared between GC, gastritis, and healthy controls. The obtained results were subsequently subjected to statistical validation. To conclude, metagenomic function prediction was carried out, followed by correlation analysis between the microbiota and KEGG pathways. α analysis revealed a significant difference between male and female categories, while β analysis demonstrated significant distinctions between GC, gastritis, and healthy controls, as well as between sexes within the GC and gastritis groups. The statistically confirmed taxonomic composition analysis highlighted the presence of the microbes Bacteroides and Veillonella. Furthermore, through metagenomic prediction analysis and correlation analysis with pathways, three taxa, namely Akkermansia, Gammaproteobacteria, and Veillonella, were identified as potential biomarkers for GC. Additionally, this study reports, for the first time, the presence of two bacteria, Desulfobacteriota and Synergistota, in GC, necessitating further investigation. Overall, this research sheds light on the potential involvement of gut microbiota in GC pathophysiology; however, additional studies are warranted to explore its functional significance.

Computational analysis of 16D10 effector peptides from root-knot nematodes and their interaction with Mi resistance (R) protein through molecular docking

Plant-parasitic nematodes, especially root-knot nematodes (RKNs), pose significant threats to global agriculture, affecting both the quantity and quality of crop yields. Central to the parasitism strategy of RKNs is the deployment of effector proteins, such as the 16D10 peptide, which interact with host plant resistance mechanisms. This study focuses on the computational analysis and molecular docking of 16D10 effector peptides derived from major RKN species (M. incognita, M. arenaria, M. hapla, M. javanica, and M. chitwoodi) to explore their interaction with the Mi resistance protein in tomatoes. Initial bioinformatics assessments included sequence retrieval, multiple sequence alignment, and phylogenetic analysis, utilizing tools such as CLUSTALW and the MEME Suite. Notably, our phylogenetic analysis revealed a close evolutionary relationship between M. incognita and M. chitwoodi, contrasting with other RKN species based on both DNA and protein data. Subsequent, in silico 3D modeling and docking studies, conducted using Chem-Sketch and MOE software, provided insights into the structural basis of the interaction between 16D10 peptides and the Mi protein. Our findings indicate that these interactions could play a crucial role in the modulation of plant defense mechanisms, potentially leading to effector-triggered susceptibility (ETS). This study not only enhances our understanding of nematode-host interactions but also aids in the development of novel strategies for managing RKN infections in economically important crops.

VIRA: An exoplanet atmospheric retrieval framework for JWST transmission spectroscopy

Abstract JWST observations are leading to important new insights into exoplanetary atmospheres through transmission spectroscopy. In order to harness the full potential of the broad spectral range and high sensitivity of JWST, atmospheric retrievals of exoplanets require a high level of robustness and accuracy in the underlying models. We present the VIRA retrieval framework which implements a range of modelling and inference capabilities motivated by early JWST observations of exoplanet transmission spectra. This includes three complementary approaches to modelling atmospheric composition, three atmospheric aerosol models, including a physically-motivated Mie scattering approach, and consideration of correlated noise. VIRA enables a cascading retrieval architecture involving a sequence of retrievals with increasing sophistication. We demonstrate VIRA using a JWST transmission spectrum of the hot Saturn WASP-39 b in the ∼1–5 μm range. In addition to confirming prior chemical inferences, we retrieve molecular abundances for H2O, CO, CO2, SO2 and H2S, resulting in super-solar elemental abundances of log(O/H) = −2.0 ± 0.2, log(C/H) = −2.1 ± 0.2 and log(S/H) = −3.6 ± 0.2, along with C/O and S/O ratios of $0.83^{+0.05}_{-0.07}$ and $0.029^{+0.012}_{-0.009}$, respectively, in the free chemistry case. The abundances correspond to $20.1^{+10.5}_{-8.1}\times$, $28.2^{+16.3}_{-12.1}\times$ and $20.8^{+10.3}_{-7.5}\times$ solar values for O/H, C/H and S/H, respectively, compared to C/H =8.67 ± 0.35 × solar for Saturn. Our results demonstrate how JWST transmission spectroscopy combined with retrieval frameworks like VIRA can measure multi-elemental abundances for giant exoplanets and enable comparative characterisation with solar system planets.

Integrated study of water levels and water storage variations using GNSS-MR and remote Sensing: A case study of Sarez Lake, the world's Highest-Altitude dammed lake

Sarez Lake, recognized as the world's highest-altitude dammed lake, necessitates meticulous monitoring of dam deformation, lake water levels, and water storage fluctuations to ensure its safety. In pursuit of this critical objective, a Global Navigation Satellite System (GNSS) monitoring network has been established around the lake to compute real-time station coordinates for deformation monitoring. GNSS Multipath Reflectometry (GNSS-MR) technology has been introduced as an innovative approach, enabling water level retrieval through conventional GNSS geodetic receivers. However, the complex and rugged terrain surrounding Sarez Lake poses challenges to the application of GNSS-MR in this region. This research is dedicated to addressing the challenges posed by the limited available elevation range selection and the retrieval of inter-site water level combinations in such demanding terrain. It proposes a novel method for effectively discerning valid Signal-to-Noise Ratio (SNR) arcs and an inter-site inter-signal water level combination retrieval approach, resulting in a continuous sequence of water level retrievals for this region. The incorporation of remote sensing techniques further facilitates the estimation of water storage fluctuations in the area. The outcomes of this study demonstrate the efficacy of GNSS-MR in capturing the lake's seasonal water-level variations, with retrieval uncertainties ranging from centimeters to low decimeters. The fusion of GNSS-MR with remote sensing data offers a comprehensive approach for monitoring the water levels and water storage fluctuations of Sarez Lake within its challenging environmental conditions. This research provides a valuable methodology applicable to the study and monitoring of other dammed lakes.

A Systematic Review on the Evolution of Natural Mutation-dependent Antiviral Drug Resistance in Hepatitis C Virus (HCV) Genotypes Prevalent in Saudi Arabia: A Genomic and Therapeutic Perspective

Background: The characterization of resistance-associated amino acid substitution (RAAS) in direct-acting antivirals (DAA) molecular targets could reshape the treatment strategy for HCV infection. Hence, this review aimed to provide evidence on the impact of the RAASs on DAA treatment for HCV. <P> Methods: PRISMA model was followed for the literature survey and evaluation (sources: PubMed, Sci-Hub, and Google Scholar). RAASs in DAA target proteins and their locations were mapped on 3-D proteins using two reference protein sequences: NS3/ID: AAA72945.1 and NS5A/NS5B/ID: NP_671491.1. NCBI-Basic Local Alignment Tool, UniProt and AlphaFold2/MMSeqs2, PyMol, and R-base/R-studio were used for alignment, retrieval of protein sequences, 3-D protein structure modeling, visualization of locations of RAASs, and diagrammatic representations, respectively. <P> Results: Genotype 4 afterwards genotype 1 was the most prevalent in Saudi Arabia. L2003M, Y2065N, M2000T, L2003V, and Q2002H RAASs were anti-NS5A inhibitor. NS5A-Q2002H was anti-daclatasvir in genotype 4. NS5A-Y2065H and NS5B-S2702T in genotype 1 were antisofosbuvir- resistant. NS3-A1182V, NS3-Q1106K/R, and NS3-T1080S RAASs exhibited resistance to double antivirals. NS3-D1194A RAAS was a multi-drug resistant variant (against 3 DAAs). NS3- V1062A, NS3-D1194G, NS3-D1194E/T, NS3- S1148R, NS3-V1196A, NS3-V1062LNS3-S1148A, and NS3-S1148G RAASs in genotype 1 were single drug-resistant variants. NS3- S1148R and NS3- S1148A in genotypes 2 and 5 were anti-simeprevir-resistant variants. <P> Conclusions: An array of identified RAASs, RAAS-dependent DAA treatment failure, and recommended combination DAA therapy in such clinical scenarios of RAASs are the significant outcomes of this research. RAAS-linked in vitro and in vivo resistance profiling at genotype/sub-genotype level will be crucial in treatment choice and for future DAA design strategies.

Retrieval-based concept mapping makes a difference as a retrieval practice activity: a study with high school students

IntroductionWhile specific sequences of retrieval-based activities have been shown to have a powerful effect on learning, no previous study has examined this issue in children or adolescents. Here, we aimed to determine whether the benefit of concept mapping as an initial retrieval activity observed previously with college students in a lab setting may also be found in younger and less experienced students in a more naturalistic school setting.MethodsAfter a short training in concept mapping, participants (N = 60) read an educational text and then engaged in a sequence of two learning activities that required them to retrieve relevant ideas from the text. One of the activities involved free recall by writing down as many ideas as possible and the other involved creating a concept map, both of them in the absence of educational texts. Critically, we manipulated the order in which the activities were performed.ResultsA mediation analysis with success during retrieval practice as the mediator revealed both a direct effect of retrieval sequence and an indirect effect. Creating a concept map first and then freely recalling by writing paragraphs significantly improved performance on a 2-week delayed learning test, as compared to performing the same activities in the inverse order, even when doing concept mapping first led to lower success rates during practice.DiscussionThese results support the idea that concept learning from instructional texts can be modulated by the sequence of retrieval activities performed and point to the educational value of retrieval-based concept mapping as a first learning activity within a series across different learners and educational settings.

In-silico Analysis of Adenosine Deaminase: From Gene Sequence Retrieval to Clinical Implications and Drug Targets

Background: Adenosine Deaminase (ADA) plays a critical role in purine metabolism and immune function. Its genetic and molecular characterization is essential for understanding its involvement in various physiological and pathological processes. The ADA gene has been implicated in immune system disorders and offers a potential target for therapeutic intervention. Objective: This study aims to comprehensively analyze the ADA gene and its protein product, detailing the genetic structure, physiochemical properties, subcellular localization, expression patterns, clinical variations, functional domains, splice variants, and potential as a drug target, to enhance the understanding of its function in human health. Methods: We utilized an array of in silico tools for gene sequence retrieval, including analysis of genomic databases for ADA's chromosomal location and gene structure. Physiochemical properties were determined using ProtParam, and subcellular localization was predicted with CELLO v.2.5. Gene expression levels across various tissues were ascertained using the Archive Ensemble TOOL. Clinical variations were categorized based on their potential pathogenicity, while splice variants and functional domains were predicted using domain-specific databases. Post-translational modifications were analyzed with prediction tools from the CBS server, and the STRING database facilitated protein interaction network analysis. Additionally, the DoG-Site Scorer predicted ADA's druggability profile. Results: ADA was localized to chromosome 12 (20q13.12), comprising 45940 base pairs encoding 363 amino acids. The protein's molecular weight was calculated as 37948.09, with a total atom count of 5396. Predictive localization indicated periplasmic and cytoplasmic presence with reliability scores of 1.437*, 1.210*, and 1.058*. Expression profiling revealed high ADA levels in the duodenum, with a notable expression in the lymphatic node. Clinical variations identified included 21 insertions and 23 inversions, with two pathogenic instances. Splice variant analysis predicted nine sites, and drug-binding potential was identified with scores as high as 0.81. Conclusion: Our study provides a detailed analysis of ADA, highlighting its genetic complexity and biological significance. The identification of clinically relevant gene variations and drug-binding pockets offers potential pathways for therapeutic intervention, emphasizing the importance of ADA in medical research and treatment strategies.

Privacy-Enhanced Frequent Sequence Mining and Retrieval for Personalized Behavior Prediction

Privacy-Enhanced Frequent Sequence Mining and Retrieval for Personalized Behavior Prediction

A neural network model for online one-shot storage of pattern sequences.

Based on the CRISP theory (Content Representation, Intrinsic Sequences, and Pattern completion), we present a computational model of the hippocampus that allows for online one-shot storage of pattern sequences without the need for a consolidation process. In our model, CA3 provides a pre-trained sequence that is hetero-associated with the input sequence, rather than storing a sequence in CA3. That is, plasticity on a short timescale only occurs in the incoming and outgoing connections of CA3, not in its recurrent connections. We use a single learning rule named Hebbian descent to train all plastic synapses in the network. A forgetting mechanism in the learning rule allows the network to continuously store new patterns while forgetting those stored earlier. We find that a single cue pattern can reliably trigger the retrieval of sequences, even when cues are noisy or missing information. Furthermore, pattern separation in subregion DG is necessary when sequences contain correlated patterns. Besides artificially generated input sequences, the model works with sequences of handwritten digits and natural images. Notably, our model is capable of improving itself without external input, in a process that can be referred to as 'replay' or 'offline-learning', which helps in improving the associations and consolidating the learned patterns.

Robust Retrieval of Dynamic Sequences through Interaction Modulation

Robust Retrieval of Dynamic Sequences through Interaction Modulation

Drying without dying: A genome database for desiccation-tolerant plants and evolution of desiccation tolerance.

Desiccation is typically fatal, but a small number of land plants have evolved vegetative desiccation tolerance (VDT), allowing them to dry without dying through a process called anhydrobiosis. Advances in sequencing technologies have enabled the investigation of genomes for desiccation-tolerant plants over the past decade. However, a dedicated and integrated database for these valuable genomic resources has been lacking. Our prolonged interest in VDT plant genomes motivated us to create the "Drying without Dying" database, which contains a total of 16 VDT-related plant genomes (including 10 mosses) and incorporates 10 genomes that are closely related to VDT plants. The database features bioinformatic tools, such as blast and homologous cluster search, sequence retrieval, Gene Ontology term and metabolic pathway enrichment statistics, expression profiling, co-expression network extraction, and JBrowser exploration for each genome. To demonstrate its utility, we conducted tailored PFAM family statistical analyses, and we discovered that the drought-responsive ABA transporter AWPM-19 family is significantly tandemly duplicated in all bryophytes but rarely so in tracheophytes. Transcriptomic investigations also revealed that response patterns following desiccation diverged between bryophytes and angiosperms. Combined, the analyses provided genomic and transcriptomic evidence supporting a possible divergence and lineage-specific evolution of VDT in plants. The database can be accessed at http://desiccation.novogene.com. We expect this initial release of the "Drying without Dying" plant genome database will facilitate future discovery of VDT genetic resources.

Molecular Mechanisms of Resistance to Direct-Acting Antiviral (DAA) Drugs for the Treatment of Hepatitis C Virus Infections.

Hepatitis C virus (HCV) is a hepatotropic virus that affects millions of human lives worldwide. Direct-acting antiviral (DAA) regimens are the most effective HCV treatment option. However, amino acid substitution-dependent resistance to DAAs has been a major challenge. This study aimed to determine the increasing risk of DAA resistance due to substitutions in DAA target non-structural proteins (NS3/4A, NS5A, and NS5B). Using a Sequence Retrieval System (SRS) at the virus pathogen resource (ViPR/BV-BRC), n = 32763 target protein sequences were retrieved and analyzed for resistance-associated amino acid substitutions (RAASs) by the Sequence Feature Variant Type (SFVT) antiviral-resistance assessment modeling tool. Reference target protein sequences with 100% identity were retried from UniProt following NCBI BLAST. The types and locations of RAASs were identified and visualized by AlphaFold and PyMol. Linux-r-base/R-studio was used for the data presentation. Multi-drug-resistant variants of NS3/4A in genotype 1 (n = 9) and genotype 5 (n = 5) along with DAA-specific NS3/4A, NS5A, and NS5B variants were identified pan-genotypically. A total of 27 variants (RAASs) of all the targets were identified. Fourteen genotype 1-specific substitutions: V1196A, V1158I, D1194A/T/G, R1181K, T1080S, Q1106R, V1062A, S1148G, A1182V, Y2065N, M2000T, and L2003V were identified. The most frequent substitutions were V1062L and L2003M, followed by Q2002H. L2003V, Q2002H, M2000T, Y2065N, and NL2003M of NS5A and L2003M of NS5B conferred resistance to daclatasvir. S2702T NS5B was the sofosbuvir-resistant variant. D1194A NS3/4A was triple DAA (simeprevir, faldaprevir, and asunaprevir) resistant. The double-drug resistant variants R1181K (faldaprevir and asunaprevir), A1182V and Q1106K/R (faldaprevir and simeprevir), T1080S (faldaprevir and telaprevir), and single drug-resistant variants V1062L (telaprevir), D1194E/T (simeprevir), D1194G (asunaprevir), S1148A/G (simeprevir), and Q1106L (Boceprevir) of NS3/4A were determined. The molecular phenomenon of DAA resistance is paramount in the development of HCV drug candidates. RAASs in NS3, NS5A, and NS5B reduce the susceptibility to DAAs; therefore, continuous RAAS-dependent resistance profiling in HCV is recommended to minimize the probability of DAA therapeutic failure.