Adaptive Sequence Research Articles

Selective whole-genome sequencing of Plasmodium parasites directly from blood samples by nanopore adaptive sampling.

Malaria is caused by parasites of the genus Plasmodium, and reached a global disease burden of 247 million cases in 2021. To study drug resistance mutations and parasite population dynamics, whole-genome sequencing of patient blood samples is commonly performed. However, the predominance of human DNA in these samples imposes the need for time-consuming laboratory procedures to enrich Plasmodium DNA. We used the Oxford Nanopore Technologies' adaptive sampling feature to circumvent this problem and enrich Plasmodium reads directly during the sequencing run. We demonstrate that adaptive nanopore sequencing efficiently enriches Plasmodium reads, which simplifies and shortens the timeline from blood collection to parasite sequencing. In addition, we show that the obtained data can be used for monitoring genetic markers, or to generate nearly complete genomes. Finally, owing to its inherent mobility, this technology can be easily applied on-site in endemic areas where patients would benefit the most from genomic surveillance.

A Novel Nonlinear Filter-Based Robust Adaptive Control Method for a Class of Nonlinear Discrete-Time Systems

This paper introduces an innovative adaptive control approach utilizing a nonlinear filter for a specific subset of nonlinear discrete-time systems, considering the presence of both input and output noise. The system can be transformed into a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model. The concept of discrete Nussbaum gain is introduced to address the theoretical constraint associated with unknown directions of feed-forward or control gains, and the extended adaptive tuning sequence is introduced to facilitate the acceleration of parameter updating. In the case of no noise, asymptotical output tracking and global stability are achieved with the adaptive control. Further, in the presence of input noise and output noise, a novel nonlinear filter is designed to generate a more accurate filtered output, which improves the control system’s ability to adapt and track accurately. Finally, examples are provided to showcase the effectiveness and precision of the method.

Adaptive Long-Read Sequencing Reveals GGC Repeat Expansion in ZFHX3 Associated with Spinocerebellar Ataxia Type 4.

Spinocerebellar ataxia type 4 (SCA4) is an autosomal dominant ataxia with invariable sensory neuropathy originally described in a family with Swedish ancestry residing in Utah more than 25 years ago. Despite tight linkage to the 16q22 region, the molecular diagnosis has since remained elusive. Inspired by pathogenic structural variation implicated in other 16q-ataxias with linkage to the same locus, we revisited the index SCA4 cases from the Utah family using novel technologies to investigate structural variation within the candidate region. We adopted a targeted long-read sequencing approach with adaptive sampling on the Oxford Nanopore Technologies (ONT) platform that enables the detection of segregating structural variants within a genomic region without a priori assumptions about any variant features. Using this approach, we found a heterozygous (GGC)n repeat expansion in the last coding exon of the zinc finger homeobox 3 (ZFHX3) gene that segregates with disease, ranging between 48 and 57 GGC repeats in affected probands. This finding was replicated in a separate family with SCA4. Furthermore, the estimation of this GGC repeat size in short-read whole genome sequencing (WGS) data of 21,836 individuals recruited to the 100,000 Genomes Project in the UK and our in-house dataset of 11,258 exomes did not reveal any pathogenic repeats, indicating that the variant is ultrarare. These findings support the utility of adaptive long-read sequencing as a powerful tool to decipher causative structural variation in unsolved cases of inherited neurological disease. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Exploring the solution space for adaptive curriculum sequencing: Study of a multi-objective approach

Adaptive Curriculum Sequencing (ACS) is an important issue in personalized learning. In ACS problems, one desires the best sequence of learning materials that meet the profile of a given student. To do so, multiple features of the students and the materials used are necessary to generate good solutions. In fact, understanding the students’ goals, motivation, and preferences is not an easy task and, consequently, different Internet of Things (IoT) approaches to gather this information during the learning process have been proposed. Actually, some works from the literature consider five objectives and, in this case, one has a many-objective optimization problem. Instead of solving the optimization problem considering the multiple objectives individually, the usual approach is to obtain solutions for a weighted sum of the objective values using search approaches for mono-objective optimization problems. However, this kind of approach may bias the search and limits the capacity of finding good results. Here, we solve the multi-objective ACS problem considering five objective functions. NSGA-II, a well-known Genetic Algorithm for multi-objective optimization problems, was used. In addition, the aggregation trees were employed to reduce the number of objectives to two and three due to the large number of objectives in the original problem. ACS problems from the literature were used to comparatively evaluate the proposed methods and the results obtained were compared to those found by the traditional approach of summing the objective values. According to these results, the best curriculum sequences were reached when using the proposal.

Transcriptome analysis and identification of differentially expressed genes between early and mature ovarian stages in the female mantis shrimp (Harpiosquilla raphidea) using RNA-Seq

Harpiosquilla raphidea, the largest of the mantis shrimps, is a commercially important crustacean species widely distributed in many countries of the Pacific Ocean. However, no data are currently available regarding the molecular mechanisms that regulate reproduction in this species. To address this knowledge gap, we performed transcriptome sequencing (RNA-Seq) of previtellogenic and vitellogenic ovaries of female H. raphidea, and compared the expression patterns of transcripts from the two resulting libraries to identify genes involved in ovarian development. A total of 418,635,748 clean reads were retrieved after removing adapter sequences and filtering out low-quality data. The reads were assembled into 242,861 unigenes with an average length of 585.27 base pairs (bp) and an N50 of 829 bp. A search of all unigenes against the NR (non-redundant protein), SwissProt, KEGG, GO, and COG databases yielded 53,111; 25,460; 27,255; 26,793; and 3838 unigene matches, respectively). Of the 53,111 unigenes identified in the NR database, 30,441 could be functionally annotated, 59.49% of which were significantly matched with the transcripts of L. vannamei. A total of 13,867 full-length transcripts and 11,441 partial transcripts were retrieved. In addition, using all-unigenes as a reference, a total of 12,765 simple sequence repeats (SSRs) were identified. DESeq2 analysis identified a total of 962 differentially expressed genes (DEGs) between previtellogenic and vitellogenic ovaries, 456 of which were upregulated and 506 downregulated. Five unigenes were validated by quantitative real-time PCR (qPCR). The results of both the RNA-Seq and qPCR analysis revealed that the H. raphidea vitellogenin gene (HrVtg) was upregulated in vitellogenic ovaries. The open reading frame of HrVtg was found to be 7455 bp long, corresponding to a polypeptide of 2485 amino acids. The important functional genes and pathways identified here provide a valuable dataset for understanding the molecular mechanisms controlling ovarian development in H. raphidea.

Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models

Motion sequence data comprise a chronologically organized recording of a series of movements or actions carried out by a human being. Motion patterns found in such data holds significance for research and applications across multiple fields. In recent years, various feature representation techniques have been proposed to carry out sequence analysis. However, many of these methods have not fully uncovered the correlations between elements in sequences nor the internal interrelated structures among different dimensions, which are crucial to the recognition of motion patterns. This study proposes a novel Adaptive Sequence Coding (ASC) feature representation with ensemble hidden Markov models for motion sequence analysis. The ASC adopts the dual symbolization integrating first-order differential symbolization and event sequence encoding to effectively represent individual motion sequences. Subsequently, an adaptive boost algorithm based on a hidden Markov model is presented to distinguish the coded sequence data into different motion patterns. The experimental results on several publicly available datasets demonstrate that the proposed methodology outperforms other competing techniques. Meanwhile, ablation studies conducted on ASC and the adaptive boost approach further verify their significant potential in motion sequence analysis.

Data and knowledge collaborative-driven fault identification and self-healing control action inference framework for blast furnace

The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. Considering the small samples of fault data and non-stationarity of the production process with a considerable time lag, it is difficult to diagnose and eliminate the faults timely. This paper proposes for the first time a data and knowledge collaborative-driven framework to identify fault and perform self-healing control. Following the adaptive two-scale sequence division method, the prior knowledge matrix refined K2 algorithm is used to construct Bayesian networks to model the control process. When a new abnormal condition arises, the most similar historical sequence will be found, and inference on the corresponding Bayesian network can lead to fault identification results and self-healing control action. The proposed framework has achieved satisfactory results in simulation experiments and real data experiments. In the simulation data, the Bayesian network (BN) modeling achieved an accuracy of 98.75%, and the time required for structure learning of the BN was reduced by over 75.69%. In real data, the accuracy of identifying blast furnace faults reached 97.1%, and the results of self-healing control reasoning surpassed those of traditional BN reasoning methods in terms of correctness, order, completeness, and necessity.

Video Semantic Segmentation Network with Low Latency Based on Deep Learning

Recently, new advances in deep learning algorithms have yielded some fascinating results in the field of computer vision technology. As a result, it can now perform activities that formerly required the use of human vision and the brain. Classification, object identification, and semantic segmentation have all seen substantial advancements in deep learning architecture in the last few years. For still images and movies, there has been a major advancement in the field of semantic segmentation. In practical uses like autonomous vehicles, segmenting semantic video continues to be difficult due to high-performance standards, the high cost of convolutional neural networks (CNNs), and the significant need for low latency. An effective machine-learning environment will be developed to meet the performance and latency challenges outlined above. The use of deep learning architectures like SegNet and FlowNet2.0 on the CamVid dataset enables this environment to conduct pixel-wise semantic segmentation of video properties while maintaining low latency. As a result, it is ideally suited for real-world applications since it takes advantage of both SegNet and FlowNet topologies. The decision network determines whether an image frame should be processed by a segmentation network or an optical flow network based on the expected confidence score. In conjunction with adaptive scheduling of the key frame approach, this technique for decision-making can help to speed up the procedure. Using the ResNet50 SegNet model, a mean Intersection on Union (IoU) of "54.27 percent" and an average frame per second of "19.57" were observed. Aside from decision network and adaptive key frame sequencing, it was discovered that FlowNet2.0 increased the frames processed per second9(fps) to "30.19" on GPU with a mean IoU of "47.65%". Because the GPU was utilized "47.65%" of the time, this resulted. There has been an increase in the speed of the Video semantic segmentation network without sacrificing quality, as demonstrated by this improvement in performance.

Turbofan Engine Health Assessment Based on Spatial-Temporal Similarity Calculation.

Aiming at the problem of the remaining useful life prediction accuracy being too low due to the complex operating conditions of the aviation turbofan engine data set and the original noise of the sensor, a residual useful life prediction method based on spatial-temporal similarity calculation is proposed. The first stage is adaptive sequence matching, which uses the constructed spatial-temporal trajectory sequence to match the sequence to find the optimal matching sample and calculate the similarity between the two spatial-temporal trajectory sequences. In the second stage, the weights of each part are assigned by the two weight allocation algorithms of the weight training module, and then the final similarity is calculated by the similarity calculation formula of the life prediction module, and the final predicted remaining useful life is determined according to the size of the similarity and the corresponding remaining life. Compared with a single model, the proposed method emphasizes the consistency of the test set and the training set, increases the similarity between samples by sequence matching with other spatial-temporal trajectories, and further calculates the final similarity and predicts the remaining use through the weight allocation module and the life prediction module. The experimental results show that compared with other methods, the root mean square error (RMSE) index and the remaining useful life health score (Score) index are reduced by 12.6% and 14.8%, respectively, on the FD004 dataset, and the RMSE index is similar to that in other datasets; the Score index is reduced by about 10%, which improves the prediction accuracy of the remaining useful life and can provide favorable support for the operation and maintenance decision of turbofan engines.

Vehicle Detection and Speed Estimation Using Semantic Segmentation with Low Latency

Computer vision researchers are actively studying the use of video in traffic monitoring. TrafficMonitor uses a stationary calibrated camera to automatically track and classify vehicles on roadways. In practical uses like autonomous vehicles, segmenting semantic video continues to be difficult due to high-performance standards, the high cost of convolutional neural networks (CNNs), and the significant need for low latency. An effective machine learning environment will be developed to meet the performance and latency challenges outlined above. The use of deep learning architectures like SegNet and Flownet2.0 on the CamVid dataset enables this environment to conduct pixel-wise semantic segmentation of video properties while maintaining low latency. In this work, we discuss some state-of-the-art ways to estimating the speed of vehicles, locating vehicles, and tracking objects. As a result, it is ideally suited for real-world applications since it takes advantage of both SegNet and Flownet topologies. The decision network determines whether an image frame should be processed by a segmentation network or an optical flow network based on the expected confidence score. In conjunction with adaptive scheduling of the key frame approach, this technique for decision-making can help to speed up the procedure. Using the ResNet50 SegNet model, a mean IoU of "54.27 per cent" and an average fps of "19.57" were observed. Aside from decision network and adaptive key frame sequencing, it was discovered that FlowNet2.0 increased the frames processed per second to "30.19" on GPU with such a mean IoU of "47.65%". Because the GPU was utilised "47.65%" of the time, this resulted. There has been an increase in the speed of the Video semantic segmentation network without sacrificing quality, as demonstrated by this improvement in performance.

Enhancing sensitivity and versatility of Tn5-based single cell omics

The analysis of chromatin features in single cells centers around Tn5 transposase and exploits its activity to simultaneously fragment target DNA and integrate adapter sequences of choice. This reaction provides a direct readout in the assay for transposase-accessible chromatin in single cells (scATAC-seq) to map open chromatin loci. However, a current limitation is the sparse coverage of these open sites in a given single cell by droplet-based methods. Thus, enhancing Tn5 activity to improve genomic coverage of scATAC-seq or facilitating multi-omics readouts of chromatin features via Tn5 together with the transcriptome is of great interest. Here, we address these issues by optimizing scATAC-seq for an increased number of integrations per cell. In addition, we provide a protocol that combines mapping of histone modification with scRNA-seq from the same cell by targeting Tn5 to antibody-bound chromatin epitopes. Our experimental workflows improve the results obtained from the downstream data analysis and serve to better resolve epigenetic heterogeneity and transcription regulation in single cells.

CSIG-21. TITLE: INTEGRATIVE TRANSCRIPTOME PROFILING OF EXOSOME AND TISSUE IDENTIFIES PROGNOSTIC GENES AS THERAPEUTIC TARGETS OF GLIOBLASTOMA

Abstract For glioma, molecular genetic analysis has grown in significant amounts for disease diagnosis and pathogenesis research. Due to limitations of tissue samples, liquid biopsy is being considered as a substitute for tissue biopsy which can be developed as a diagnostic technique. Studies have demonstrated that exosome-carrying molecules can foster a milieu that is favorable to cancer growth and therapy resistance. METHODS: In the present study the tumor tissue and serum of the same 80 patients were collected with a confirmed diagnosis of glioma with prior consent. Demographic details, pathology reports and survival data were also collected. RNA-seq analysis was performed in both tumor tissue and exosomal RNA. Fastq file with 100bp paired end reads were generated. Raw reads were trimmed using Trimmomatic (Version 0.39) to remove adapter sequences and low-quality reads. Reads mapping to ribosomal RNA were removed using SortMeRNA tool. Trimmed reads were then mapped to the human reference genome GRCh38.d1.vd1.fa using a two-pass method with STAR (2.7.10a). Differential gene expression was performed using DESeq2. The gene ontology and pathway analysis were performed using open source tool PANTHER. RESULTS: The differentially expressed genes shared between tumour tissue RNA and exosomal RNA experiments were selected for further analysis. Genes such as EGFR (tissue: Fold change (FC)[6.1], exosome: FC[7.2]; p-value< 0.001), PDFGRA (tissue: FC[3.7], exosome: FC[5.2]; p-value< 0.001), TTN (tissue: FC[12.6], exosome: FC[11.3]; p-value< 0.001) were found to be differentially regulated and were prominently associated with Integrin signaling pathway, Synaptic vesicle trafficking pathway, Wnt signaling and cellular proliferation. As per literature and clustering analysis, we found that these pathways and genes play an important role in the progression of tumors which can be further integrated into diagnostic platforms. CONCLUSION: This study will provide a reliable and effective way of diagnosis and management of glioma in patients with minimal invasion.

RAMP up: Risk Adapted Post-Autologous Transplant Maintenance Therapy in MRD Positive Myeloma Patients

Background. Post-autologous stem cell transplant (ASCT) maintenance therapy for patients with newly diagnosed multiple myeloma (MM) continues to evolve. Currently, single agent lenalidomide maintenance remains standard of care but outcomes for patients with high-risk cytogenetics are inferior to those with standard risk disease; optimal maintenance strategies for these patients need to be developed. Minimal residual disease (MRD) has proven to be a powerful prognostic factor for PFS and OS but is not yet used to guide treatment. Isatuximab is a cytolytic CD38-directed, immunoglobulin G1 (IgG1) chimeric monoclonal antibody FDA-approved for the treatment of relapsed or refractory MM that is overall well tolerated and efficacious when used in combination regimens. Our upcoming trial, RAMP UP (NCT05344833), investigates the safety and efficacy of isatuximab combined with lenalidomide as a maintenance regimen in patients who are MRD-positive after autologous transplant. Study Design and Methods. This prospective, multicenter single-arm phase 2 (Figure 1) trial will enroll patients that are MRD-positive (10 -5) by Adaptive next generation sequencing within 90 days (+/- 30 days) post-autologous transplant. We plan to enroll: R-ISS stage 1, 2 or 3; <2 lines of induction therapy prior to transplant; > PR per IMWG criteria post-transplant; and ECOG Performance Status <2. Patients will be treated with isatuximab (10 mg/kg weekly for cycle 1 and every other week thereafter) and lenalidomide (10 mg daily on days 1-21) on 28-day cycles for a total of 3 years. Patients will be monitored for disease response every 3 months and for marrow MRD every 12 months. The primary endpoint is the 1-year rate of CR/MRD negativity (10 -5); secondary endpoints include 1-year rate of CR/MRD negativity (10 -6), IMWG-defined response rates, 3-year overall CR/MRD-negativity, PFS, OS, treatment-related adverse events, and health related quality of life (HRQoL) measured by EORTC-QLQ-C30, EQ-5D-5L, EORTC QLQ-MY20. We propose enrollment of 50 patients to allow for approximately 10% dropout rate in order to have 43 patients analyzed for the primary endpoint; for 50 patients to be treated, we estimate that 75-100 patients may have to be enrolled. Correlative objectives include correlation of isatuximab pharmacokinetics and receptor occupancy with MRD-negative response and time to progression, characterization of differences in the BM and peripheral blood T, B, and myeloid cell compartments using cellular indexing of transcriptomes and epitopes (CITE-Seq) between responders and nonresponders, and concurrent assessment of other disease biomarkers, including peripheral blood circulating multiple myeloma cells (CELLSEARCH), with MRD testing. Discussion. Our study started June 2023. One patient has been enrolled, and two have signed informed consent for enrollment. Ultimately, our study aims to inform MRD risk-adapted approaches for maintenance therapy in myeloma.

Guardrails for trust, safety, and ethical development and deployment of Large Language Models (LLM)

The AI era has ushered in Large Language Models (LLM) to the technological forefront, which has been much of the talk in 2023, and is likely to remain as such for many years to come. LLMs are the AI models that are the power house behind generative AI applications such as ChatGPT. These AI models, fueled by vast amounts of data and computational prowess, have unlocked remarkable capabilities, from human-like text generation to assisting with natural language understanding (NLU) tasks. They have quickly become the foundation upon which countless applications and software services are being built, or at least being augmented with. However, as with any groundbreaking innovations, the rise of LLMs brings forth critical safety, privacy, and ethical concerns. These models are found to have a propensity to leak private information, produce false information, and can be coerced into generating content that can be used for nefarious purposes by bad actors, or even by regular users unknowingly. Implementing safeguards and guardrailing techniques is imperative for applications to ensure that the content generated by LLMs are safe, secure, and ethical. Thus, frameworks to deploy mechanisms that prevent misuse of these models via application implementations is imperative. In this study, we propose a Flexible Adaptive Sequencing mechanism with trust and safety modules, that can be used to implement safety guardrails for the development and deployment of LLMs.

Genomic vulnerability of a freshwater salmonid under climate change.

Understanding the adaptive potential of populations and species is pivotal for minimizing the loss of biodiversity in this era of rapid climate change. Adaptive potential has been estimated in various ways, including based on levels of standing genetic variation, presence of potentially beneficial alleles, and/or the severity of environmental change. Kokanee salmon, the non-migratory ecotype of sockeye salmon (Oncorhynchus nerka), is culturally and economically important and has already been impacted by the effects of climate change. To assess its climate vulnerability moving forward, we integrated analyses of standing genetic variation, genotype-environment associations, and climate modeling based on sequence and structural genomic variation from 224 whole genomes sampled from 22 lakes in British Columbia and Yukon (Canada). We found that variables for extreme temperatures, particularly warmer temperatures, had the most pervasive signature of selection in the genome and were the strongest predictors of levels of standing variation and of putatively adaptive genomic variation, both sequence and structural. Genomic offset estimates, a measure of climate vulnerability, were significantly correlated with higher increases in extreme warm temperatures, further highlighting the risk of summer heat waves that are predicted to increase in frequency in the future. Levels of standing genetic variation, an important metric for population viability and resilience, were not correlated with genomic offset. Nonetheless, our combined approach highlights the importance of integrating different sources of information and genomic data to formulate more comprehensive and accurate predictions on the vulnerability of populations and species to future climate change.

Human thymic putative CD8αα precursors exhibit a biased TCR repertoire in single cell AIRR-seq

Thymic T cell development comprises T cell receptor (TCR) recombination and assessment of TCR avidity towards self-peptide-MHC complexes presented by antigen-presenting cells. Self-reactivity may lead to negative selection, or to agonist selection and differentiation into unconventional lineages such as regulatory T cells and CD8mathrm{alpha alpha } T cells. To explore the effect of the adaptive immune receptor repertoire on thymocyte developmental decisions, we performed single cell adaptive immune receptor repertoire sequencing (scAIRR-seq) of thymocytes from human young paediatric thymi and blood. Thymic PDCD1+ cells, a putative CD8mathrm{alpha alpha } T cell precursor population, exhibited several TCR features previously associated with thymic and peripheral ZNF683+ CD8mathrm{alpha alpha } T cells, including enrichment of large and positively charged complementarity-determining region 3 (CDR3) amino acids. Thus, the TCR repertoire may partially explain the decision between conventional vs. agonist selected thymocyte differentiation, an aspect of importance for the development of therapies for patients with immune-mediated diseases.

SAT432 Interactions Of Endocrine-disrupting Chemical (EDC) Exposure And Fear Recall On Small Noncoding RNA (sncRNA) Contents Of Rat Caput Extracellular Vesicles

Abstract Disclosure: R. Gillette: None. M. Aguirre: None. K. Bell: None. Y. Nadeem: None. L. Thompson: None. A.C. Gore: None. Extracellular vesicles (EVs) transfer a complex repertoire of small non-coding RNAs (sncRNA) to sperm as they transit the caput of the epididymis. These molecules are essential for the functional competence of sperm and are directly influenced by environmental factors such as stress, diet, and drug use. We hypothesized that prenatal exposure to EDCs would alter the sncRNA contents of caput extracellular vesicles, and that an exposure to a previously established contextual fear cue would exacerbate this effect. Pregnant dams were fed Nilla Wafers with either 3% DMSO (vehicle control) or Aroclor 1221 (1 mg/kg; a weakly estrogenic EDC) from embryonic day (E)8 until E18 and post-natal day (PND)1 to P21. In adulthood male rat offspring were subjected to a contextual fear conditioning paradigm in which each rat was exposed to an odorant paired with a shock (four 500 ms shocks at 0.7 mA) for three consecutive days. Two weeks later, all rats were returned to the conditioning chambers and exposed to the odorant for 5 minutes (no shocks). The next day, male rats were euthanized and the epididymis was dissected. EVs and total RNA isolated from the caput portion were sequenced on a NovaSeq 6000 SP in a single-end 75 bp format. Reads were checked for quality, trimmed for adapter sequences, aligned to the rat genome, and assigned an annotation designation across 10 different categories of sncRNA. We found that the majority of caput EV sncRNA were either tRNA fragments or piRNA and that unlike mice, miRNA accounted for a small portion of the total reads. The third largest category of aligned reads was in intergenic space and not associated with canonical small non-coding (snc) RNA loci. In-depth investigation determined these reads (∼19 nt) aligned strictly within the boundaries of known CpG islands (CpGi), which have not previously been reported to express any form of sncRNA. Re-exposure to a fear-associated scent 24 hours prior to sample collection substantially reduced the proportion tRNAs in EVs, which were supplanted by an increase in piRNA, CpGi sRNA, and lncRNA. This effect was exacerbated by prenatal EDC exposure and was particularly prevalent in the lncRNA and CpGi sRNA categories. Taken together our data show that the small RNA contents of caput epididymosomes of male rats is substantially different from previous observations reported in mice. We demonstrate that exposure to a contextual fear cue is a powerful influence on sncRNA composition of caput extracellular vesicles and that it is uniquely altered by prenatal EDC exposure. Finally, we provide evidence for what we believe may be a novel category of sncRNA. Presentation: Saturday, June 17, 2023

Subject-specific timing adaption in time-encoded arterial spin labeling imaging

ObjectivesOne challenge in arterial spin labeling (ASL) is the high variability of arterial transit times (ATT), which causes associated arterial transit delay (ATD) artifacts. In patients with pathological changes, these artifacts occur when post-labeling delay (PLD) and bolus durations are not optimally matched to the subject, resulting in difficult quantification of cerebral blood flow (CBF) and ATT. This is also true for the free lunch approach in Hadamard-encoded pseudocontinuous ASL (H-pCASL).Material and methodsFive healthy volunteers were scanned with a 3 T MR-system. pCASL-subbolus timing was adjusted individually by the developed adaptive Walsh-ordered pCASL sequence and an automatic feedback algorithm. The quantification results for CBF and ATT and the respective standard deviations were compared with results obtained using recommended timings and intentionally suboptimal timings.ResultsThe algorithm individually adjusted the pCASL-subbolus PLD for each subject within the range of recommended timing for healthy subjects, with a mean intra-subject adjustment deviation of 47.15 ms for single-shot and 44.5 ms for segmented acquisition in three repetitions.DiscussionA first positive assessment of the results was performed on healthy volunteers. The extent to which the results can be transferred to patients and are of benefit must be investigated in follow-up studies.

Temperature-aware charging strategy for lithium-ion batteries with adaptive current sequences in cold environments

This paper proposes a temperature-aware charging strategy with adaptive current sequences for lithium-ion batteries to improve their charging performance in cold environments, trading off between charging speed and the thus-caused capacity degradation. Specifically, a curved surface of the maximal allowed charging currents with different battery temperatures and states of charge (SoCs) is experimentally generated by using an integrated battery model that thoroughly describes temperature’s impacts on battery properties. Then, the commonly used multistage constant current charging scheme is improved by adapting the number of stages and associated transition conditions to battery temperature and SoC. The adaptive charging process enables batteries to self-heat quickly by applying a fast-increasing charging current sequence. Moreover, a balanced contour interval size of the curved surface is selected considering the fitness and computation time of the particle swarm optimization algorithm. Cycle charging tests are conducted with NCR18650B batteries. The results show that the proposed strategy can reduce the average charging time by 207–757 s, slow down the total capacity decay by 63–143 mAh over 20 charging cycles, and reduce the time for batteries to self-heat from −10 to 0 °C by over 500 s when compared with other existing charging methods.

LogBASA: Log Anomaly Detection Based on System Behavior Analysis and Global Semantic Awareness

System log anomaly detection is important for ensuring stable system operation and achieving rapid fault diagnosis. System log sequences include data on the execution paths and time stamps of system tasks in addition to a large amount of semantic information, which enhances the reliability and effectiveness of anomaly detection. At the same time, considering the correlation between system log sequences can effectively improve fault diagnosis efficiency. However, the existing system log anomaly detection methods mostly consider only the sequence patterns or semantic information on the logs, so their anomaly detection results show a high rate of missed and false alarms. To solve these problems, this paper proposed an unsupervised log anomaly detection model (LogBASA) based on the system behavior analysis and global semantic awareness, aiming to decrease the leakage rate and increase the log sequence anomaly detection accuracy. First, a system log knowledge graph was constructed based on massive, unstructured, and multilevel system log data to represent log sequence patterns, which facilitates subsequent anomaly detection and localization. Then, a self-attention encoder-decoder transformer model was developed for log spatiotemporal association analysis. This model combines semantic mapping and spatiotemporal features of log sequences to analyze system behavior and log semantics in multiple dimensions. Furthermore, a system log anomaly detection method that combines adaptive spatial boundary delineation and sequence reconstruction objective functions was proposed. This method uses special words to characterize the log sequence states, delineates anomaly boundaries automatically, and reconstructs log sequences through unsupervised training for anomaly detection. Finally, the proposed method was verified by numerous experiments on three real datasets. The results indicate that the proposed method can achieve an accuracy rate of 99.3%, 95.1%, and 97.2% on HDFS, BGL, and Thunderbird datasets, which proves the effectiveness and superiority of the LogBASA model.