Sample ID Research Articles

Viral coexistence and insertional mutations in the ORF8 region of SARS-CoV-2: A possible mechanism of nucleotide insertion

The virus obtained from a swab sample ID: S66 in Hiroshima was reported to have a single T-base insertion in the ORF8 coding region. However, no T insertion was observed when we determined the genomic sequence using another method. We then extracted RNA from the S66 swab sample and sequenced the insertion site using the Sanger method. The resulting waveform was disrupted beyond the insertion site, suggesting the presence of a mixed population of viruses with different sequences. Through plasmid cloning of RT-PCR amplification fragments and virus cloning by limiting dilution, along with TIDE analysis to determine the ratio of components from the Sanger sequencing waveform, it was confirmed that the sample contained a mixture of viruses with varying numbers of T-base insertions. The virus with one T insertion (T1+) was predominant in 70–75 % of the genomes, and genomes with T0, T2+, T3+, T4+, and T5+ were also detected. No T-base insertion mutations were observed in the ORF8 region in three other SARS-CoV-2 samples. In the S66 sample, a C27911T point mutation near the insertion site in the ORF8 region resulted in a sequence of seven or more consecutive T bases, which was the cause of the T-base insertion. When the cloned S66 virus (T1+) was passaged in cultured cells, there was a tendency for viruses with more insertion bases to become dominant with successive generations, suggesting that the T-base insertion was due to polymerase stuttering. The insertion of T bases resulted in synthesis of deletion mutants of the ORF8 protein, but no significant change was observed in the proliferation of the viruses in cultured cells. A search of the GenBank database using NCBI BLAST for viruses similar to S66 with T-base insertion mutations revealed hundreds of viruses widely distributed on the molecular phylogenetic tree. These base insertion viruses were thought to have occasionally arisen during the virus infection process. This study suggests one mechanism of insertion mutations in SARS-CoV-2, and it is important to consider the emergence of future mutant strains.

B-040 Direct Aspiration of Cryogenic Matrix Tubes on the Siemens Atellica System With Epic-Beaker Integration of External Barcode IDs for Analysis Small Volume Biobanked Specimens

Abstract Background Most core chemistry and immunochemistry clinical analyzers are designed for direct sampling of primary blood tubes with volumes ranging from 3.5-10 mL. This limits analysis of low volume pediatric specimens and biobanked aliquoted samples from clinical trials without manual technologist intervention. This work outlines a microsampling and data transmission scheme for analysis of 500 uL aliquots of biobanked serum collected for the Nutrition for Precision Health study powered by the NIH All of Us Research Program without relabeling secondary aliquot tubes after receipt. Methods Commercial plastic clip bearings sourced from IGUS (LCM-08-02) were used to stabilize the matrix tube in a standard 5 mL Sarstedt aliquot tube. 96-well format Matrix Tubes (ThermoFisher 3719-11) with pre-printed 10-digit side barcodes were programmed in the Atellica software as a custom false bottom tube with specifications height 75 mm, diameter 12 mm, bottom offset 41 mm and were loaded using a dedicated false bottom tray. Manifest files (.csv) containing the sample ID, clinical trials participant ID, collection date/time, age and sex were ingested into Epic via an Incoming Orders HL7 interface. Non-human patient records were generated using the participant ID as an external patient ID and patient name and calculating the Date of Birth based on the age in years given in the manifest file. Test orders were created based on assigned testing for the sample ID in the manifest. The sample ID corresponding the preprinted Matrix Tube barcode ID was inserted into the Epic container identifier field. Results Test orders were automatically transmitted to the Atellica Data Manger (ADM) allowing specimens to be run on the Atellica System (CH and IM modules) and results transmitted to Epic utilizing the established Data Innovations (DI) interface and processes used for patient samples without the need for Epic generated labels. Successful scanning and recognition of Matrix Tube barcodes was 100%. Complete aspiration of a Comprehensive Metabolic Panel, Lipid Panel, Iron, TIBC, hsCRP, insulin, and H, I, L indices occurred for 50% of samples tested without intervention. Approximately 250 uL of the 500 uL aliquot remained after aspiration of all tests (247.7 uL total volume) from the Matrix Tube. No probe crashes were observed despite the narrower 8 mm diameter of the Matrix Tubes versus the standard 12- or 13-mm diameter of primary blood collection tubes. Conclusions This Epic-DI-ADM data workflow and microsampling adaptation for Matrix Tube aspiration allows greater container type flexibility for the receipt and analysis of external samples delivered in non-primary blood tubes for clinical laboratories. Future work involving adjustment of the false bottom tube offset depth may reduce unusable sample volume allowing for a higher percentage of complete aspiration without intervention. This work is significant because it suggests existing clinical analyzers are easily adaptable to microsampling smaller volume tubes without significant effort.

Enhancing Non-Small Cell Lung Cancer Survival Prediction through Multi-Omics Integration Using Graph Attention Network.

Background: Cancer survival prediction is vital in improving patients' prospects and recommending therapies. Understanding the molecular behavior of cancer can be enhanced through the integration of multi-omics data, including mRNA, miRNA, and DNA methylation data. In light of these multi-omics data, we proposed a graph attention network (GAT) model in this study to predict the survival of non-small cell lung cancer (NSCLC). Methods: The different omics data were obtained from The Cancer Genome Atlas (TCGA) and preprocessed and combined into a single dataset using the sample ID. We used the chi-square test to select the most significant features to be used in our model. We used the synthetic minority oversampling technique (SMOTE) to balance the dataset and the concordance index (C-index) to measure the performance of our model on different combinations of omics data. Results: Our model demonstrated superior performance, with the highest value of the C-index obtained when we used both mRNA and miRNA data. This demonstrates that the multi-omics approach could be effective in predicting survival. Further pathway analysis conducted with KEGG showed that our GAT model provided high weights to the features that are associated with the viral entry pathways, such as the Epstein-Barr virus and Influenza A pathways, which are involved in lung cancer development. From our findings, it can be observed that the proposed GAT model leads to a significantly improved prediction of survival by exploiting the strengths of multiple omics datasets and the findings from the enriched pathways. Our GAT model outperforms other state-of-the-art methods that are used for NSCLC prediction. Conclusions: In this study, we developed a new model for the survival prediction of NSCLC using the GAT based on multi-omics data. Our model showed outstanding predictive values, and the KEGG analysis of the selected significant features showed that they were implicated in pivotal biological processes underlying pathways such as Influenza A and the Epstein-Barr virus infection, which are linked to lung cancer progression.

Electrical characterization of lead-modified bismuth borovanadate glasses

Specifically, the study looks into dielectric characteristics (ε′andε″) and complex modulus formulation and AC conductivity of lead-modified bismuth borovanadate glasses (50-x) V2O5-40 B2O3-10 Bi2O3-xPbO, where x = 5,10, 15, 20 and 25 mol% with sample ID's VPb1, VPb2, VPb3, VPb4 & VPb5 according to different compositions of lead and vanadate. When the PbO content rises, there is a decreasing tendency in both the alternating current conductivity and dielectric constants. In order to fit AC conductivity data, Almond West equation is used to extract parameters, including crossover frequency (ωH), frequency exponent (s), and direct current conductivity (σdc). Direct current conduction mechanism in all glass samples except VPb5 [Correlated Barriers Hopping (CBH) conduction at all frequencies] at lower frequencies might potentially be attributed to large-polaron quantum mechanical tunneling (LQMT). Similar to this, at high frequencies, small polaron quantum mechanical tunnelling is followed by VPb2 & VPb3 while LQMT is followed by VPb1 & VPb4. Activation energy of dc conduction (Edc) at higher frequencies (0.373–0.476 eV) for samples having sample ID VPb1 to VPb4 and sample VPb5 at all frequencies with Edc 0.686 eV and modulus activation energy (ER) (0.382–0.534 eV) are found in good agreement. Dielectric studies reveal non-Debye-type behaviour.

Assessing the Fatigue Stress Behavior of Starch Biodegradable Films with Nanoclay Using Accelerated Survival Test Methods

A destructive degradation model was applied on films made from different concentrations of starch, glycerol and nanoclay using various elongation levels as a stress variable at different stress times and stretch cycles. The log tensile quotient (logarithm of the tensile strength to the corresponding break cycle) was recorded as the response variable. The log tensile quotient increased, and the log exact break time decreased, as the elongation level increased. The treatment containing the highest starch and nanoclay and lowest glycerol content proved to be the most resistant to stress conditions and the most versatile in relation to the varying log tensile quotients, while the treatments containing the lowest nanoclay and highest glycerol contents, regardless of the starch concentration, manifested the lowest log tensile quotient at higher levels of log exact break time. According to multiple regression findings, the break cycle governed mostly the stress conditions in the degradation model, followed by the sample ID and the log exact break time. The term log tensile quotient, attempted for the first time on data concerning biodegradable films enhanced with nanoclay, seems very promising for deeper research due to its ability to retrieve predictive information from survival equations and to discriminate the difference between film structures.

Sentimental Analysis for the Improved User Experience in the E-Commerce Platform with the Fuzzy Model

This paper investigates the integration of the Mamdani Fuzzy Regression for User Experience (MFR-UE) model with deep learning techniques to enhance predictive analytics in e-commerce platforms. Through empirical analysis, the study evaluates the effectiveness of this hybrid approach in classifying customer preferences and sentiments based on complex feature sets. Results from demonstrate the model’s capability: Sample ID 1, characterized by Feature 1: 0.8, Feature 2: 0.6, Feature 3: 0.4, and Feature 4: 0.2, accurately predicts Class A as both the Predicted Class and Actual Class. Similarly, Sample ID 4, with Feature 1: 0.1, Feature 2: 0.4, Feature 3: 0.3, and Feature 4: 0.8, correctly identifies Class C. The integration leverages deep learning’s capacity to discern intricate data patterns alongside MFR-UE’s fuzzy logic for nuanced decision-making, optimizing business strategies and enhancing user experience. Practical implications include refined customer segmentation, personalized marketing strategies, and improved service delivery, emphasizing the model’s potential for driving competitive advantage in e-commerce.

Label-guided seed-chain-extend alignment on annotated De Bruijn graphs.

Exponential growth in sequencing databases has motivated scalable De Bruijn graph-based (DBG) indexing for searching these data, using annotations to label nodes with sample IDs. Low-depth sequencing samples correspond to fragmented subgraphs, complicating finding the long contiguous walks required for alignment queries. Aligners that target single-labelled subgraphs reduce alignment lengths due to fragmentation, leading to low recall for long reads. While some (e.g. label-free) aligners partially overcome fragmentation by combining information from multiple samples, biologically irrelevant combinations in such approaches can inflate the search space or reduce accuracy. We introduce a new scoring model, 'multi-label alignment' (MLA), for annotated DBGs. MLA leverages two new operations: To promote biologically relevant sample combinations, 'Label Change' incorporates more informative global sample similarity into local scores. To improve connectivity, 'Node Length Change' dynamically adjusts the DBG node length during traversal. Our fast, approximate, yet accurate MLA implementation has two key steps: a single-label seed-chain-extend aligner (SCA) and a multi-label chainer (MLC). SCA uses a traditional scoring model adapting recent chaining improvements to assembly graphs and provides a curated pool of alignments. MLC extracts seed anchors from SCAs alignments, produces multi-label chains using MLA scoring, then finally forms multi-label alignments. We show via substantial improvements in taxonomic classification accuracy that MLA produces biologically relevant alignments, decreasing average weighted UniFrac errors by 63.1%-66.8% and covering 45.5%-47.4% (median) more long-read query characters than state-of-the-art aligners. MLAs runtimes are competitive with label-combining alignment and substantially faster than single-label alignment. The data, scripts, and instructions for generating our results are available at https://github.com/ratschlab/mla.

PvFed: Personalized Vertical Federated learning for Client‐Specific Tasks

Federated Learning (FL) is a distributed machine learning paradigm that enables multiple data holders to collaborate on building machine learning models while preserving the privacy of their data. FL can be categorized as horizontal or vertical, depending on the distribution characteristics of the data. Specifically, horizontal FL uses data partitioned in the sample space, whereas vertical FL uses data partitioned in the feature space. Traditional vertical FL methods aim to facilitate collaboration among clients to infer a single global target. However, these methods may be impractical because each client often has a unique target to be inferred. In this paper, we propose a novel vertical FL method, called personalized vertical federated learning (pvFed), which addresses this limitation by allowing each client to perform inferences specific to their individual task. To the best of our knowledge, no existing method currently addresses this limitation. The objective of pvFed is to construct a global model that generates a representation vector to support client inference. The global model, constructed using distillation and dimensionality reduction, takes a sample ID common to all clients as input and outputs a sample‐specific representation vector. Clients utilize the intermediate representation of their own model and the representation vectors output by the global model for inference. Because these vectors are not dependent on client‐specific tasks, clients can repurpose them for any additional tasks. Our experiments, conducted on two distinct data types—image and tabular data sets, under a vertical partitioning where each client had its own specific task, demonstrated the efficacy of vectors generated by the global model in pvFed for client inference. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Epidemiology, Phylogeny and Drug Resistance against Human Immuno- Deficiency Virus in Pakistan

Background: The human immunodeficiency virus is categorised in the genus Lentivirus, subfamily Orthoretrovirinae of the Retroviridae family. Methodology: 40 specimens in total were included in the research to conduct the whole experimentation. The viral RNA extraction was performed by using the Qiagen viral RNA extraction kit. After the extraction of RNA, the cDNA was synthesized. Qiagen One step rt-PCR kit was used for amplification by using pair of primers forward primers (A) & reverse primer (D) 10uM/ug. Sequencing and capillary electrophoresis were performed. The phylogenetic tree of HIV was done by using mega. Results: Out of 40, 10 samples are sequenced 4 of them are resistance against drugs. Sample Ids 226 and 229 are resistance to Rilpivirine (RPV) and Etravirine(ETR) also has 138A mutation in HIV-1. Sample Id 231 have resistance to Lamivudine (3TC), emtricitabine (FTC) and abacavir (ABC) also has M184V mutation in HIV-1. Sample Id 232 have resistance to Lamivudine (3TC), emtricitabine (FTC) and abacavir (ABC) also has M184V Nevirapine (NVP) and efavirenz(EFV) mutation in HIV-1.

MMVFL: A Simple Vertical Federated Learning Framework for Multi-Class Multi-Participant Scenarios.

Federated learning (FL) is a privacy-preserving collective machine learning paradigm. Vertical federated learning (VFL) deals with the case where participants share the same sample ID space but have different feature spaces, while label information is owned by one participant. Early studies of VFL supported two participants and focused on binary-class logistic regression problems, while recent studies have put more attention on specific aspects such as communication efficiency and data security. In this paper, we propose the multi-participant multi-class vertical federated learning (MMVFL) framework for multi-class VFL problems involving multiple parties. By extending the idea of multi-view learning (MVL), MMVFL enables label sharing from its owner to other VFL participants in a privacy-preserving manner. To demonstrate the effectiveness of MMVFL, a feature selection scheme is incorporated into MMVFL to compare its performance against supervised feature selection and MVL-based approaches. The proposed framework is capable of quantifying feature importance and measuring participant contributions. It is also simple and easy to combine with other communication and security techniques. The experiment results on feature selection for classification tasks on real-world datasets show that MMVFL can effectively share label information among multiple VFL participants and match the multi-class classification performance of existing approaches.

Flexible Vertical Federated Learning With Heterogeneous Parties.

We propose flexible vertical federated learning (Flex-VFL), a distributed machine algorithm that trains a smooth, nonconvex function in a distributed system with vertically partitioned data. We consider a system with several parties that wish to collaboratively learn a global function. Each party holds a local dataset; the datasets have different features but share the same sample ID space. The parties are heterogeneous in nature: the parties' operating speeds, local model architectures, and optimizers may be different from one another and, further, they may change over time. To train a global model in such a system, Flex-VFL utilizes a form of parallel block coordinate descent (P-BCD), where parties train a partition of the global model via stochastic coordinate descent. We provide theoretical convergence analysis for Flex-VFL and show that the convergence rate is constrained by the party speeds and local optimizer parameters. We apply this analysis and extend our algorithm to adapt party learning rates in response to changing speeds and local optimizer parameters. Finally, we compare the convergence time of Flex-VFL against synchronous and asynchronous VFL algorithms, as well as illustrate the effectiveness of our adaptive extension.

Homology Identification and Cross-Contamination Analysis: A Method for Evaluating the Quality of Biological Samples Stored in a Biobank Using the Advanta Sample ID Genotyping Panel.

Biological samples are important resources for scientific research. These samples are stored in biobanks over years until needed, and some of them can never be retrieved if they are improperly stored, causing them to be wasted. Thus, they are priceless, and they should be used correctly and effectively. Sample quality substantially affects biomedical research results. However, sample misidentification or mix-up is common. It is necessary to establish quality standards for sample identification. In this study, we used the Advanta Sample ID genotyping panel to detect homology identification and cross-contamination. We compared the single-nucleotide polymorphism (SNP) typing results of two different samples and calculated the similarity score of homologous sample pairs and nonhomologous sample pairs. Through analysis, we obtained a similarity score cutoff point of 0.8620, which was an effective way to distinguish homology and nonhomology. Cross-contamination was detected in two sets of mixtures (STD8:STD6 and jj3:1-P) mixed at a series of special ratios. Sensitivity was dependent on the sample characteristics and mixing ratios. Finally, we assessed the effect of sample degradation degree on SNP genotyping and found that degraded samples with a minimal DNA integrity number of 1.9 had complete genotyping results. On the whole, this study shows that the Sample ID panel is reliable for homology identification and cross-contamination analysis. Moreover, this technology has promising further applications in biological sample quality control.

A-188 Managing a Fully-automated Paperless Laboratory During a Cyber-attack

Abstract Background On 23rd Nov, 2022 a tertiary care apex medical institute in India was subject to cyberattack which disrupted the Hospital Information system (HIS) and the Laboratory Information system (LIS) servers. The services from the SmartLab, an end-to-end integrated total-lab-automation system, designed to run on paperless mode got disrupted. This is the main service laboratory with an average daily sample load of approx. 5000 including hematology, coagulation, clinical chemistry, immunoassays and serology services. Methods All lab operations were immediately shifted to manual mode. Standard operating protocols (SOPs) were designed for manual processing and circulated to OPD and IPD staff through WhatsApp. Instructions were issued to use previously used paper test-request-forms (TRFs) for sending samples. Previous stock of TRFs were retrieved from Stores and distributed to the IPD and OPDs. Staff from other areas of the hospital were mobilized.In OPD sample collection area, patients’ registrations were done by writing Patient-Id, Name, Age, Sex, Sample-Id, Treating department and Test requests in registers. A small sticker was introduced and pasted on the blood collection tubes capturing minimal relevant patient details, treating department codes and sample Id. Duplicates were pasted on OPD cards of respective patients. Traceability of patient Ids and sample Ids were maintained through data entry in registers. In the lab, the LIS were shut down on a precautionary note. Test requests corresponding to each sample Id were programmed directly on the analyzers. When done, print-outs of test results were obtained from the analyzers and hand-written results were entered by data entry operators on the TRFs. Reports were then segregated as per the treating department codes and dispatched to respective places. This continued till partial restoration of HIS services on and from 12th December, 2023. Results Despite best efforts, turn-around-time (TAT) for reports got compromised. IPD reports were prioritized and about 60% of the same could be dispatched by 9 PM same day, rest being delivered by 9 AM, next morning. TAT for OPD reports stretched up to 36 h. Inspite of introducing separate numbering systems with codes for different OPDs and IPDs, about 40% reports could not be traced to the respective OPDs. These reports were collected by the patients’ attendants from the lab itself. Manual report retrieval counters were set up outside the lab. At least 20% reports of the approx. 3000 reports generated per day remained unclaimed even after 30 days. The utilization of lab services also came down by 20%–25% during this period. About 5% reports had to be repeated with fresh samples due to inability to retrieve previous reports. The data generated during this 20-days period could not be stored electronically and hence are not available in our database now. Conclusion Cyberattacks may happen to any institution. Having a strict cyber-security protocol is a must for all institutions. However, having a back-up plan for handling such a crisis should be also in place in case of any eventuality. Sharing our experience is an endeavour to create awareness regarding the prerequisites of such a plan.

A database of detrital zircon U–Pb ages and Hf isotope of Precambrian strata in South China

AbstractDetrital zircon U–Pb chronology database of Precambrian deposits provides a context for the interpretation of the origin and evolution of ancient crust. Here, we tried to summarize the published literature containing detrital zircon U–Pb ages and Hf isotope data of Precambrian deposits in South China and then established a database, which contains details of information, such as reference, sample ID, locality, rock type, research institution, GPS coordinates, U–Pb ages and εHf(t) values. The data statistics of this paper rely on the OneSediment Working Group of The Deep‐time Digital Earth program (DDE). By November 2022, 610 samples with 38,278 U–Pb ages and 8,798 Lu‐Hf isotope data were collected from 136 papers, and these data can be downloaded from DDE Data Publish & Repository website, https://repository.deep‐time.org/. The purpose of the establishment of the dataset is to provide guidance and convenience for the research direction of future generations in South China and to improve the previous studies through the integrated data to avoid the waste of resources caused by a large number of repeated studies.

A database of detrital zircon geochronology ages of Cambrian to Paleogene deposits in South China

AbstractComplications of detrital zircons databases provide a means for statistically analysing a variety of significant geological problems. In this work, we tried to collect a database about the South China Phanerozoic detrital zircon geochronology data. The data statistics of this paper rely on the OneSediment Working Group of The Deep‐time Digital Earth program (DDE). By November 2022, the database contains a total of 699 samples with 55,532 U–Pb ages and 3,770 effective Hf isotope data, from 130 papers. Abundant information including reference title, sample ID, locality, rock type, research institution, GPS coordinates, U–Pb ages, εHf(t) values, etc., have been involved in our database, and all data can be downloaded from DDE Data Publish & Repository website, https://repository.deep‐time.org/. Through the integrated data, we can improve the previous studies and avoid the waste of resources caused by a large number of repeated studies.

Blood donor genotyping for prediction of blood group antigens: Results from 5 years' experience (2017-2022).

For 5 years, routine genotyping has been performed for selected blood groups of blood donors in the Copenhagen Capital Region, Denmark. The result is summarized in the following. Genotyping was carried out by an external service provider using the competitive allele specific PCR (KASP) technology. The genotypes were returned to the blood bank and translated into phenotypes by a proprietary IT application. In total, 65 alleles from 16 blood group systems (ABO, MNS, Rh, Lutheran, Kell, Duffy, Kidd, Diego, Yt, Dombrock, Colton, Landsteiner-Wiener, Cromer, Knops, Vel, secretor status) and the HPA1, HPA5 and HPA15 antigens were interrogated. After translation, phenotypes were imported into the laboratory information management system of the blood bank. The results from 31,538 genotyped blood donors were used to calculate the allele frequencies for a Danish blood donor population. ABO genotyping was done for sample ID purposes. Determination of the 1061delC single nucleotide polymorphism (SNP) (NM_020469.2), most frequently characteristic of ABO*A2, was validated against a series of 1287 samples with Dolichos biflorus lectin determination of the A1 phenotype. We report allele frequencies and phenotype frequencies for 16 blood groups from a total of 31,538 genotyped blood donors. Blood products were supplied from a total of 64,312 active blood donors, and of these active blood donors 25,396 (39.5%) were genotyped. These donors represent a valuable resource for patient treatment. This genotyping has enabled the provision of rare genotyped donor blood for patients with alloantibodies and rare reagent cells for serology.

First report of lettuce ring necrosis virus in chili pepper and tomato in Belgium and The Netherlands.

Lettuce ring necrosis virus (LRNV), genus Ophiovirus, was detected by the Netherlands Institute for Vectors, Invasive plants and Plant health (NIVIP) in June and November of 2021 in two samples of chili pepper fruits (Capsicum spp.), both in mixed infection with other viruses. The first sample originated from a production site in Belgium (Sample ID: 40009704) and the second from a production site in the Netherlands (Sample ID: 41115269). One of the fruits of 40009704 showed a light purple circular pattern, while fruits from 41115269 showed colored (ring)spots. The samples were analyzed using Illumina sequencing on a NovaSeq 6000 platform (PE 150) as described previously (Hammond et al., 2021), obtaining 39.9M and 22.8M total reads for 40009704 and 41115269. The corresponding sequence read archives (SRA) were deposited in the NCBI SRA database under BioProject accession number PRJNA917231. From both samples, the nearly complete genome of LRNV (RNA1-4) was obtained and deposited in GenBank (40009704, OQ160823- OQ160826 (7616, 1799, 1502, 1382 nt, mapped reads: 40K, 12K, 114K, 12K , average read coverage (ARC): 0.8K, 0.9K, 11.3K and 1.1K); 41115269, OQ160827- OQ160830 (7616, 1801, 1518, 1389 nt, mapped reads: 112K, 7K, 357K, 55K reads, ARC: 2.2K, 0.6K, 34K and 5.8K)). The shared sequence identities with the Genbank reference sequence of LRNV (NC_006051-NC_006051) were 99.2 and 99.2% (RNA1), 99.1 and 99.1% (RNA2), 98.3 and 98.8% (RNA3), 99.0 and 98.9% (RNA4) for 40009704 and 41115269 respectively. The shared sequence identities between 40009704 and 41115269 were 99.9 (RNA1), 99.0 (RNA2), 99.1 (RNA3) and 99.5% (RNA4). In addition to LRNV, the ophiovirus ranunculus white mottle virus (RWMV) was detected in both samples (OQ160831-OQ160834; OQ160835-OQ160838), while the tobamovirus pepper mild mottle virus (PMMoV) was present in the fruits of 41115269 (OQ160839). Since RWMV has been associated with leaf symptoms in pepper (Gambley et al., 2019; Rivarez et al., 2022) and the colored (ring)spots of 41115269 were very similar to reported symptoms of PMMoV-infected pepper fruits (Martínez-Ochoa et al., 2003), it remains unclear whether LRNV contributed to the observed symptoms. Additionally, LRNV was detected in tomato (Solanum lycopersicum) in Belgium in 2020. In the frame of a metagenomic survey using Virion-Associated Nucleic Acids (VANA)-based protocol (Maclot et al., 2021) on a Nextseq 500 platform (PE 150), partial genome sequences of LRNV were detected in two pools of tomato plants. One pool was made of 44 asymptomatic cultivars from a non-commercial grower (one sample per cultivar) yielding 118K total reads of which 84, 59, 335, and 18 reads mapped on RNA1, 2, 3, and 4, covering 35%, 69%, 100% and 55% of the genome, respectively. The other pool consisted of 15 plants from one cultivar from a production site yielding 3.1M total reads of which 6 and 5 reads mapped on RNA3 and 4, respectively. The detection of LRNV was confirmed for both pooled samples using the real-time RT-PCR method, targeting the CP gene, as described by Maachi et al. (2021). To our knowledge this is the first report of LRNV in pepper anywhere in the world. Additionally, although the disease lettuce ring necrosis in lettuce (Lactuca sativa) has been described in Belgium and the Netherlands before the causal agent was identified (Bos & Huijberts, 1996), this is the first official report of this virus in Belgium and the Netherlands. This publication resulted from pre-publication data sharing of sequences and biological data among plant virologists to provide more context to two independent findings (Hammond et al., 2021).

Identifying a complete genome sequencing of canine parvovirus from red-collared doves in Guangxi, Southern China

Birds are the crucial hosts for viruses since they are able to carry and spread infections that may prove harmful to humans, domestic animals, and other wild animals. Finding a new host can aid in understanding the virus's dynamics of transmission, including how it spreads and potential sources of origin. In this study, 40 fecal samples from birds were collected from various regions of Guangxi province, China. The CPV-2 was found in the sample collected in the group 2019, sample pool 21H with sample ID numbers (BJF-5, BJF-6, BJF-7, BJF-8, BJF-9) by High-throughput sequences. PCR investigation found BJF-6 was positive, and the complete genome, namely, RCD-CPV NA, was found to be 5322 bp in length with typical open reading frames. BLASTn analysis showed that the genome sequence was closely identical to the CPV strain N with 100 % query coverage and 99.04 % identity. After BLAST comparison of NCBI nucleotide, the similarity of BJF6 nucleotide sequence to the first 100 CPVs sequences is 99.60 % - 97.24 %. Genome sequence comparisons and phylogenetic analysis with NS1 and VP2 amino acid sequences suggested that those sequences shared the highest similarity with the Pangolin protoparvovirus strain PPrV, a CPV-2 variant. It's significant to remember that this is a novel instance of CPV being found in an avian species. This study improves knowledge of the variety of avian viruses and provides new light on the factors that contribute to their pathogenicity.

Secure vertical federated learning based on feature disentanglement

Federated learning (FL) faces many security threats. Although multiple robust FL frameworks have been proposed to defend against these malicious attacks in horizontal federated learning (HFL), security issues in vertical federated learning (VFL) have not been adequately studied. Recent studies show that VFL is vulnerable to inference attacks (e.g., label inference attacks), which puts VFL at risk. To solve this problem, we propose a new VFL framework SVFL (Secure Vertical Federated Learning) to defend against privacy breaches inspired by feature disentanglement. Specifically, in SVFL, the bottom models are feature extractors to extract samples’ features in the high-dimensional space, and the top model sews samples’ features of the same sample ID. Then, disentangling the samples’ features into the class-relevant feature and class-irrelevant one via two classifiers: one is to recognize the class-relevant feature by regular training, and another is to recognize the class-irrelevant feature by adversarial training. Our experiments show that SVFL not only defends against label inference attacks, no matter how many samples features a malicious participant occupies, but also improves the global model’s accuracy. Therefore, SVFL provides a privacy security guarantee for the vertical federated learning system.

Characterization of two novel HIV-1 second-generation recombinants (CRF01_AE/CRF07_BC) identified in Hebei Province, China.

The unique recombinant forms (URFs) of HIV-1 consist of a mixture of subtypes, and each URF has a unique breakpoint. In this study, we identified the near fulllength genome (NFLG) sequences of two novel HIV-1 URFs (Sample ID: BDD034A and BDL060) isolated during HIV-1 molecular surveillance in 2022 in Baoding city, Hebei Province, China. The two sequences were aligned with subtype reference sequences and CRFs from China using MAFFT v7.0, and the alignments were adjusted manually using BioEdit (v7.2.5.0). Phylogenetic and subregion trees were constructed using MEGA11 with the neighbor-joining (N-J) method. Recombination breakpoints were identified by SimPlot (v3.5.1) based on Bootscan analyses. Recombinant breakpoint analysis revealed that the NFLGs of BDD034A and BDL060 were composed of CRF01_AE and CRF07_BC, containing seven segments, respectively. For BDD034A, three CRF01_AE fragments were inserted into the CRF07_BC main framework, whereas for BDL060, three CRF07_BC fragments were inserted into the CRF01_AE main framework. The emergence of the CRF01_AE/CRF07_BC recombinant strains indicates that HIV-1 co-infection is common. The increasing genetic complexity of the HIV-1 epidemic in China warrants continued investigation.