dataesr/scientific-paragraphs-categorization huggingface dataset:
The mice were fed ad libitum and kept under a 12-h light-dark regimen (grey shading). h, ChIP-qPCR of Bmal1 and Vps15 enrichment on the Ppat promoter in the liver of 5-week-old WT male mice (ZT6; n = 6). i, Top: bioluminescence recordings of Nr1d1-Luc in the liver of a representative four-month-old male TtrCre + ;Vps15 f/f mouse kept in constant darkness (pretamoxifen, WT) for 9 days, treated with tamoxifen for 5 days and monitored for 14 days following tamoxifen-induced Vps15iLKO (post-tamoxifen; top). Bottom: locomotor activity is shown (bottom). j, Periodograms (FFT analysis) of the data in i. k, ChIP-qPCR of Bmal1 and Vps15 promoter enrichment in the liver of 5-weekold male mice (ZT6) that were fed ad libitum or fasted for 24 h (n = 3 for fast, n = 4 for fed and n = 6 for IgG). d-h,k, Data are the mean ± s.
The mass spectrometer (QExactive Orbitrap, Thermo Fisher Scientific) was operated in a polarity switching mode and metabolites were identified using TraceFinder Software (Thermo Fisher Scientific). For the analyses, the metabolomics data were normalized using the median normalization method. The MetaboAnalyst 3. 0 software was used to conduct statistical analyses and generate heatmaps, and an unpaired two-sample Student's t-test was chosen to perform the comparisons. The algorithm for heatmap clustering was based on the Pearson distance measure for similarity and the Ward linkage method for biotype clustering.
ChIP-seq data analysis Data filtering was performed with SOAPnuke to remove adaptor sequences and low-quality reads. The data filtering parameter was: SOAPnuke filter -l 5 -q 0. 5 -n 0. 1 -Q 2 -c 40. Clean reads were aligned to the mm10 mouse genome using Bowtie2 version 2. 4. 4 and SAMtools version 1. 13 was used to index and sort binary alignment map (BAM) files  70, 71. Heatmaps were generated using the deepTools2 plotHeatmap function  72. Peak calling was performed for all samples using MACS2 version 2. 2. 7. 1 (ref. 73) for ChIP-BMAL1 with the following parameter: callpeak -t -c -f BAM -g mm -p 0. 01; H3K27Ac peak calling was performed using: callpeak -t -c -f BAM -g mm -broad and ChIP-Vps15 was performed using: callpeak -t -c -f BAM -g mm -p 0. 001-broad -nomodel. Differential peak calling analysis was performed using DiffBind 3.
All normalized counts <20 were considered as background (flag 0) and ≥20 as signal (flag = 1). P50 lists used for the statistical analysis regroup the genes showing flag = 1 for at least half of the compared samples. Direct comparisons were made between WT and Vps15LKO animals to generate lists of differentially expressed genes (Supplementary Table  4 ). Pathway enrichment was performed using Gene Set Enrichment Analysis version 4. 2. 2 (refs. 78,79). Deep-sequencing (ChIP-seq and RNA-seq) data have been deposited in the Gene Expression Omnibus under accession code GSE229551.
The incubation with PLA probe PLUS and MINUS conjugated with oligonucleotides was performed for 1 h at 37 °C. The terminal steps of ligation and amplification were performed at 37 °C for 30 min and 90 min, respectively. Samples treated with PLA probes without primary antibodies served as controls for background binding (negative control). Images were acquired using an inverted microscope (Zeiss Axio Observer Z1 with Yokogawa CSU-X1 spinning disk) with a ×40 oil-immersion objective and Z-stack acquisition. Quantification of the PLA signal was performed on independent fields of cells (at least 300 cells per condition) either in all cells (proximity between endogenous proteins) or in GFP + cells (when at least one protein was transiently expressed). The data are presented as the number of puncta per field with the number of cells analysed specified in the figure legend.
The cells were synchronized by treatment with 100 nM dexamethasone in serum-free high-glucose DMEM (Gibco) medium for 1 h. After synchronization, the medium was changed to serum-free high-glucose DMEM (Gibco) complemented with the inhibitors SAR405, PIK-III and Vps34-IN1 at a dose of 2. 5 µM and 5 µM as well as 100 µM luciferin. The continuous bioluminescence recordings were performed using a LumiCycler 96 luminometer (Actimetrics), allowing the recording of 24-well plates simultaneously. To analyse the circadian parameters of time series without the variability of magnitudes, raw data were processed in parallel graphs by moving average with a window of 24 h (ref. 84).
Analyses of circadian rhythmicity, plots and schema For datasets of time series, rhythmic metabolites, transcripts and proteins were detected by implementing JTK_CYCLE  85. The period length, phase and amplitude were detected with the non-parametric 'MetaCycle' ( JTK_CYCLE) algorithm implemented in R  86. No modifications to the code were made for analysis. The selected parameters were evaluated as rhythmic with P < 0. 05. Metabolite classes and pathways were identified using the R package 'MetaboAnalystR3. 0' and crossed with published lists  87. For data presentation, all data plots were made with ggplot2 using R studio. Where indicated, the transcript analysis around the clock was plotted using a LOESS regression curve fitted to the log 2 -normalized data. The summary schemas were created with BioRender.
001, ZT 6 P = 0. 001, ZT 18 P = 0. 001; xanthine: ZT 0 P < 0. 000001, ZT 6 P < 0. 000001, ZT 12 P = 0. 02, ZT 18 P = 0. 0001); two-tailed unpaired Student's t-test. d, Ppat transcript levels in primary hepatocytes transduced with the indicated adenoviral vectors. Data are the mean ± s. e. m. (n = 7 independent experiments). #P < 0. 05 versus GFP; two-tailed unpaired Student's t-test. e, Ppat transcript levels in Vps15 f/f primary hepatocytes transduced with GFP or CRE adenoviral vectors. Data are the mean ± s. e. m. (independent experiments, n = 8 GFP and n = 9 CRE). #P = 0. 00001 versus GFP; two-tailed unpaired Student's t-test. f, Bmal1 recruitment, determined by ChIP-qPCR, to the promoter of Ppat in pooled liver tissue samples collected at ZT 6-12 and ZT 18-24 of five-week-old ad libitum-fed Vps15LKO and control male mice.
Acknowledgements We thank all the members of the  Panasyuk laboratory ,  M. Pende  and members of his laboratory,  F. Terzi ,  J. -C. Weil ,  G. Asher  and  D. Jacobi  as well as the members of  INSERM -U1151 for their support and insightful discussions. We thank  B. Lemesre  and  E. Chalouhi  for their technical help with cell culture. We thank  M. Pontoglio ,  A. Bagattin ,  S. Garbay  and  K. Shostak  for technical help with the set-up of the ChIP method. We thank  S. Berissi  ( SFR Small Animal Histology and Morphology Platform ) and  S. Fabrega  ( SFR Viral Vector and Gene Transfer Platform ) for technical support. We thank  L. Fajas  for the gift of plasmid vectors (β-Gal, p300 and CBP) and  Y. Liu  for the gift of Flag-BMAL1-expressing plasmid;  M. Vasseur-Cognet  and  A. -F. Burnol  for TtrCre deleter line.
This work was supported by grants from:  Agence National de la Recherche  (grant no. ANR-JCJC-NUTRISENSPIK-16-CE14-0029 ), the  European Research Council  (grant no. ERC-CoG-MetaboSENS-819543 ), an  INSERM  trempling grant ( PINUT) ,  Campus France PHC GERMAINE DE STAEL and Campus France PHC PROCOPE  to G. P. ; C. A. was supported by the  Boulos Foundation and Fondation pour la Recherche Médicale ; N. F. H. was supported by the  French Ministry for Education  and the  Fondation pour la Recherche Médicale ; Y. S. was supported by  JSPS  and the  Uehara Foundation ; C. D. was supported by the  Swiss National Science Foundation  (grant no. 310030_184708/1 ),  Vontobel Foundation ,  Novartis Consumer Health Foundation ,  EFSD/Novo Nordisk Programme for Diabetes Research in Europe ,  Swiss Life Foundation ,  Olga Mayenfisch Foundation ,  Velux Foundation ,  Swiss Cancer League ,  Ligue pulmonaire Genevoise and Fondation pour l 'innovation sur le cancer et la biologie; V.
P. was supported by the  Bo  and  Kerstin Hjelt Foundation  for diabetes type 2, a  Young Independent Investigator Grant SGED/SSED  and the  Gertrude von Meissner Foundation ; D. L. and A. W. were supported by a  German Center for Diabetes Research (DZD)   Cell free DNA biomarkers grant  (grant no. 82DZD07C1G ) and the  DAAD/PROCOPE  grant ( 57658289 ); and K. H. was supported by the  AFM  (association Française contre les myopathies, grant no. 23101 ),  ANR-PRC  (grant no. ANR-21-CE14-0056-02-MuscLY  to K. H. and G. P. ) and  ANR France Relance program  (grant no. ANR_21_PRRD-0002-01 ).
Data availability All data are available in the main text or the supplementary materials. Deep-sequencing (ChIP-seq and RNA-seq) data that support the findings of this study have been deposited in the Gene Expression Omnibus under the accession code GSE229551. Mass spectrometry metabolomics data are available as Supplementary Table  6. Source data are provided within this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request. that it is independent of the Vps34 role and showed its binding on chromatin. N. F. H. characterized the nuclear pool of Vps15 and Vps34, and performed the luciferase assays, cell transcription assays, RNA Pol2 and Bmal1 interaction studies with Vps15 and Vps34, ChIP-qPCR with Bmal1 and RNA Pol2, and ChIP-Seq/RNA-Seq data bioinformatic analyses.
The main focus of the OHEJP is to enhance harmonization of methodologies, databases and procedures for the assessment and management of FBZ, AMR, and ET across Europe. Surveillance of zoonoses and investigations of foodborne and zoonotic outbreaks are examples of OH activities requiring correct diagnostics and sensitive and specific analytical methods across sectors and disciplines. National, regional, and local authorities, physicians, veterinarians, food business operators and laboratories within animal health, food safety, and public health sectors may have different approaches on when and how to analyze a sample from animals, food, or humans for enteropathogenic Campylobacter, Salmonella and/or Yersinia. The samples may, for instance, originate from official control or surveillance programmes on animal health or food safety, or from Hazard Analysis Critical Control Point (HACCP) samplings at food companies or be taken from patients in hospitals or from outpatients for determination of an illness or be part of an outbreak investigation  (4, 5).
According to Zoonoses Directive 2003/99, all Member States in the European Union (EU) shall collect relevant data on zoonoses and zoonotic agents in primary production and/or at other stages in the food chain. Campylobacter and Salmonella are among the zoonotic agents to be included in monitoring, whereas Yersinia is to be covered according to the epidemiological situation. Also, Member States shall investigate foodborne outbreaks. Data collected within the monitoring programmes and investigations of foodborne outbreaks shall be reported to the European Food Safety Authority (EFSA) but data from other samplings may not be collected. However, as only part of the monitoring is harmonized, results from the national monitoring programmes are difficult to compare  (4). On EU level within the public health sector, notifications of campylobacteriosis, salmonellosis and yersiniosis are mandatory in most Member States  (4).
Quality trimming and assembly of the genome were performed with CLC Assembly Cell software (version 5. 2. 0. ; Qiagen, Denmark) using the settings (clc_quality_trim -c-25 and clc_assembler -v -q -o). Species was identified by BLAST toward an in-house database with reference sequences  (22)  and sequence type (ST) was determined using the Multi Locus Sequence Typing (MLST) scheme from PubMLST for Campylobacter (23), the MLST scheme from Enterobase for Salmonella (24-26) and the Enterobase McNally MLST scheme for Yersinia  (26). In silico serovar prediction for Salmonella was performed with an in-house database of STs and corresponding serovars in combination with SeqSero  (27).
Quality control Sequencing using the IonTorrent and Illumina platforms yielded the same result except for one sample (Table  2 ). Analysis of the Illumina sequence data of Y. enterocolitica O:3/BT4 of C2 showed that the virulence factors YadA (Yersinia adhesin A), VirF, and the Yops (Yersinia outer proteins) were missing, while being present in the Ion Torrent sequence data, suggesting that the Yersinia ∼70-kb virulence plasmid (pYV), encoding the virulence factors, may have been lost. The genome size for the Illumina sequence data showed a smaller genome compared to the Ion Torrent genome size, indicating a plasmid loss. The extracted DNA for the Illumina sequencing were from an additional cultivation cycling.
Acknowledgments The authors would like to express their gratitude to  Ségolène Quesne ,  Sandra Rouxel  and  Emmanuelle Houard  ( Anses, France ),  Yngve Dahlborg  ( Gävle Region, Sweden ),  Wendy van Overbeek  and  Lucas Wijnands  ( RIVM, the Netherlands ),  Jonas Ilbäck och Marianne Törnquist (Swedish Food Agency, Sweden) ,  María García ,  Estefanía Rivero , and  Nisrin Maasoumi  ( VISAVET-UCM, Spain ) for their technical skills in performing the pilot study and other contributions.
Funding This work was supported by funding from the  European Union 's  Horizon 2020 Research and Innovation programme  under grant agreement No 773830: One Health European Joint Programme and was part of the  OH EJP  project  CARE. Research at the  National Veterinary Research Institute (PIWet ), Poland, was also partially supported by the  Polish Ministry of Education and Science  from the funds for science in the years  2018-2022  allocated for the implementation of and co-financed international project.
The procedure was as follows. The samples were held at 94 • C for 5 min, 30 × 30s cycles at 94, 52 and 72 • C, respectively, followed by 72 • C for 10 min. The PCR products were detected by 1% agarose gel electrophoresis for fragmentation. The average length and concentration of the PCR products were checked by 1% agarose gel electrophoresis. After comparing the concentrations of the PCR products using GeneTools Analysis Software (Version 4. 03. 05. 0, SynGene), the volume required for each sample was calculated according to the equal mass principle, and each PCR product was mixed. The PCR product mix was recovered using the E. Z. N. A. ® Gel Extraction Kit (Omega, USA), and the target DNA fragments were recovered by elution in TE buffer and sequenced on the NovaSeq platform.
Molecular docking analysis Molecular structures of catechins and anthocyanins were downloaded from PubChem (https://pubchem. ncbi. nlm. nih. gov). Pseudomonas putida (Arthrobacter siderocapsulatus) is the main dominant spoilage bacteria in meat, so protein sequences of DNA gyrase subunit A (Uniprot ID: A0A126S1Q8) and DNA gyrase subunit B (UniProt ID: P13364) from Pseudomonas putida (Arthrobacter siderocapsulatus) were submitted to Swiss Model to construct homologous protein models (https://swissmodel. expasy. org). The structures of polyphenols and enzymes were imported into SYBYL, and then the Surflex-Dock (SFIC) standard mode was used in the SYBYL-X 2. 1. 1 software  (Luo et al. , 2022). The original ligand molecule was separated from the receptor pocket, and hydrogenated and water molecules and other ligands were removed.
The processed protein was saved in PDB format as receptor protein. Catechins and anthocyanins were molecularly docked with the processed protein using the Surflex-Dock program in SYBYL-X 2. 1. 1 software. The affinity between polyphenols and receptor protein was analyzed by scoring function, and the interaction force was visualized by LigPlus to reveal the interaction mechanism. The molecular docking technology was used to analyze the affinity of polyphenols and receptor proteins. The Total-Score function T-Score is a function-derived SYBYL-X 2. 1. 1 software and consists mainly of Crash and Polar. C-Score is a function to evaluate the reliability of docking results by combining conformational prediction function, binding free energy function, common evaluation function, etc. , which is based on the calculation of four values, D-SCORE, PMF-SCORE, G-SCORE, and CHEM-SCORE.
Statistical analysis The data were expressed as mean ± standard error (SE). The results were analyzed by two-way analysis of variance (ANOVA) with storage time and biopreservative treatment as factors. Three replicates (muscle cube samples) in two groups (the CTG treatment and the control groups) were prepared for each day. Unless otherwise stated, each sample was measured in triplicate (a total of 6 data was collected for each test). In the case of sensory evaluation, the data were averaged over the 10 panelists to minimize the effects of pallenlist differences. Tukey's test was employed to determine the discrepancy between different storage times and between different biopreservative treatments. Pearson correlation analysis was applied to analyze the relationship between meat indices. The significance level was established at P < 0.
Acknowledgments The authors gratefully acknowledge the financial support by the  National Natural Science Foundation of China  ( 32001766 ),  Natural Science Foundation of Chongqing  ( cstc2021jcyj-msxmX1014 ),  Fundamental Research Funds for the Central Universities of China  ( SWU-KQ22023 ;  SWU 019009 ),  Chongqing's Modern Agricultural Industry Technology System Program for Herbivore  ( 2023[14 ]), and the  Collection, Utilization and Innovation of Germplasm Resources by Research Institutes and Enterprises of Chongqing, China (cqnyncwkqlhtxm).
Body size and longevity assessment Pupae were removed from pans daily, gently blotted to remove surface moisture, and weighed (Mettler Toledo XPR2U Ultra-Micro Balance, Columbus, OH, USA). After measuring pupal wet weight, pupae were transferred to individual screen-covered 50-ml conical centrifuge tubes containing 10 ml of deionized water. Water was removed from tubes immediately after emergence and adult females from all treatment groups were transferred to 25 °C and 81 ± 4% RH. Females were housed individually (unmated) and provided a cotton ball saturated with a 10% sucrose solution (replaced daily). Adult female survival was monitored at 24-h intervals to measure adult longevity. Wing length of all adult female specimens was measured post-mortem, from the axial incision to the wing tip, excluding the scale fringe (cellSens software ver.
A binomial GLM was also used to test for the influence of temperature and diet and their interaction on the sex ratio of individuals surviving to adult eclosion (i. e. , proportion of newly emerged adults that were female). Main and interactive effects of diet and temperature on pupal wet weight and female wing length were examined in linear mixedeffects models (LLMs) with a normal error distribution and identity link function, and replicate as a random effect. Relationships between both female wing length and pupal wet weight were assessed using an analysis of covariance (ANCOVA). To evaluate the direct effects of the main factors (larval diet and temperature) and their interaction on adult female longevity (i. e. , days surviving post-eclosion), data were initially fit to a Cox proportional hazards (PH) model  [50].
The development of PBPK models in poultry species has long been hampered by the lack of a comprehensive database for physiological, anatomical, and biochemical parameters in these species. Interestingly,  Wang et al. (2021)  have now published physiological parameter values for PBPK modeling in chickens and turkeys which they extracted from the existing literature. Concerning the prediction of residue concentration in eggs,  Lautz et al. (2020)  have developed a generic open-source PBPK model in chicken through the integration of meta-analyzed physiological parameters into an R-based algorithm. The model was calibrated and validated for 7 chemical substances that are relevant to food and feed safety, and predictions of plasma, tissue, and egg concentrations were made, which are in excellent agreement with published data  (Lautz et al.
It then ensures compliance with data privacy regulations, such as GDPR, and furthermore alleviates pressure on device resources. Each participant in the federated learning process ultimately benefits from a more robust and efficient model trained on a richer and wider range of data, regardless of the distribution and size of each participant's dataset  [11] [12] [13]. Despite the many advantages of federated learning, a number of challenges remain, including complexity of coordination, data heterogeneity, effective communication, and prevention of malicious attacks. In this paper, we evaluated and studied convolutional network models and models based on attention mechanisms (in particular Vision Transformers, ViT) using federated learning for disease detection. We have used four open source image datasets from the "PlantVillage" platform for our experiments.
Federated learning (FL) Federated learning was introduced in  22  as a distributed machine learning approach. Its techniques aim to train algorithms on decentralized devices while keeping data private at the local level. This approach contrasts with traditional machine learning techniques that are centralized in nature, but also with techniques based on parallel computing. The latter are designed to optimize computation for machine learning on multiple processors using a centralized dataset. Although powerful, current machine learning techniques have data privacy shortcomings because they require sharing training data with a central server, which can pose privacy, security, and regulatory issues related to data collection and sharing  12, 13. In federated learning, local participants (devices) train their local models based on their own private data.
They highlight the value of merging federated learning and IoT in smart hospitals, suggesting the training of local models on IoT devices to avoid data transfers to the cloud. Federated learning, widely used in networks, distributed systems, and IoT due to vast data and security vulnerabilities, aims to ensure system functionality while minimizing attack risks. Paper 25 introduces "FedProx", a framework for network heterogeneity. Article 30 reveals the superiority of "model poisoning" over "training data poisoning". Study 31 suggests methods to cut communication costs, ideal for bandwidth-limited devices. Lastly 32 , delves into federated learning's impact on IoT, especially in smart health and transportation. Agricultural data, dispersed over many devices, poses privacy and security challenges.
Federated learning offers solutions to these challenges, although it's an emerging area in agriculture. In  33  , a Privacy-Encoding-Based Federated Learning (PEFL) framework is introduced to enhance security in smart farming, proving efficient in identifying normal and attack patterns from IoT data  35. Examines federated learning for corn disease prediction, indicating the AlexNet CNN model as efficient, with a direct relation between CNN parameters and data exchange volume during training. Meanwhile, in  34  a study of federated learning based on drones (or UAV: Unmanned Aerial Vehicle) for plant disease diagnosis, including classification of different types of insect pests, is conducted. The authors performed preprocessing and data augmentation to improve the classification accuracy, achieving 99.
Methodology Federated learning framework Federated learning is a machine learning framework in which multiple workers or clients contribute to the training of a more robust and efficient global model that they will later share while keeping their data decentralized  11. Figure  1  shows an overview of the deployed FL architecture. We used open source datasets from the "PlantVillage" platform, and then simulate a collaborative set of clients who contribute to the improvement of a global model. The adopted methodology, detailed in Fig. 1 , is built around five essential phases. Phase 1, initialization, involves setting up a pre-trained model on a server that is then distributed to various pre-defined clients in the environment. During This five-phase cycle is repeated until the predefined number of communication rounds between the central server and the individual clients participating in the process is reached.