dataesr/scientific-paragraphs-categorization huggingface dataset:
From this list, the 67 satellites in Vibrionaceae genomes have a significantly smaller size (average 6. 7 kbp, unpaired t test P<0. 0001) (Fig. S13 ). We thus extended our search for such elements to a much larger albeit specific dataset of 19185 Vibrionaceae genomes (NCBI Assembly database, 02/16/2023). We identified a total of 97 elements, broadly distributed in diverse Vibrionaceae species (Fig. 4 , Table  S3 ). We never detected more than one PICMI-like element in these genomes, contrasting with P4-like satellites in E. coli genomes, that can contain up to three P4-like satellites  10. Pairwise alignment of the DNA sequences of PICMIlike satellites permitted grouping them into 35 distinct PICMI subfamilies (>90% global pairwise nucleotide identity) (Fig. S14 , Table  S3 ). Up to seven subfamilies could be detected in one species (V.
Data and code availability Accession numbers of vibrio and phage genomes isolated and sequenced in  22  are listed in the Key Resources table. This paper does not report original code. All programs used to analyze genomes were previously reported and are freely available online (see key resources table ). The MacSyFinder models used to identify PICMI are available upon request and can be used with the MacSyFinder to make novel analysis. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
In silico prediction and analysis of PICMI-like element The PICMI-like elements were searched using two datasets:   43  , and each PICMI subfamily was defined by a pairwise nucleic identity ≥90% (Table  S3  and Fig. S14 ). The PICMI genes were clustered in families using mmseqs2 (v14. 7e284) reciprocal best-hit  44  with 20% identity and 50% coverage thresholds (Table  S3  and Fig. S14 ). Phage defense systems were annotated using Defense-Finder (v1. 0. 8 and models v1. 1. 0)  33  and phage structural genes were annotated using PhANNs (v1. 0. 0)  45    Fold change   i.
ACKNOWLEDGEMENTS We thank  Agnes Thierry ,  Céline Loot ,  Francois-Xavier Barre  and  Mélanie Blokesch  for valuable suggestions. We thank  Sophie Le Panse  ( MERIMAGE, Roscoff ),  Gwen Tanguy ,  Erwan Legeay  ( GENOMER, Roscoff ),  Karine Cahier  and  Yannick Labreuche  for technical assistance. We thank  Jenna Sternberg  from  Life Science Editors  for help with the Manuscript.
This work was supported by fundings from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 884988, Advanced ERC Dynamic) to FLR, from the Agence Nationale de la Recherche (ANR-20-CE35-0014 « RESISTE ») to EPCR and FLR. R. B. -C. acknowledges the Spanish Ministerio de Ciencia e Innovación for his FPI predoctoral contract (BES-2017-079730). EPCR lab was funded by Equipe FRM (Fondation pour la Recherche Médicale): EQU201903007835. This work used the computational and storage services (TARS cluster) provided by the IT department at Institut Pasteur, Paris.
13  was used on both datasets with a dedicated PICMI model defined by four mandatory genes encoding: the integrase (PF00589. 25, PF00239. 24, PF07508. 16), AlpA (PF05930. 15, PF12728. 10), the primase (DUF3987 with PF13148. 9 and DUF5906 with PF19263. 2) and the Fis regulator (PF02954. 22). The resulting elements were then filtered by excluding those with int, alpA, and fis localized in different contigs, those predicted to belong to other families (PICI, cf-PICI, P4 and PLE), and those integrated into a gene showing a lower identity with fis. The genomic region starting with the fis gene and ending with the direct repeat upstream the int gene was extracted, aligned with FAMSA (v1. 6. 2)  1) the bacterial division of GenBank release v243 (5/26/2021) that contains 24,243 complete genomes, including 456 genomes of the Vibrionaceae family and 2) the NCBI Assembly database (2/16/2023) with 19,185 Vibrionaceae genomes available but with variable assembly quality.
Efficacy of cancer vaccines remains low and mechanistic understanding of antigen presenting cell function in cancer may improve vaccine design and outcomes. Here, we analyze the transcriptomic and immune-metabolic profiles of Dendritic Cells (DCs) from 35 subjects enrolled in a trial of DC vaccines in late-stage melanoma (NCT01622933). Multiple platforms identify metabolism as an important biomarker of DC function and patient overall survival (OS). We demonstrate multiple immune and metabolic gene expression pathway alterations, a functional decrease in OCR/OXPHOS and increase in ECAR/glycolysis in patient vaccines. To dissect molecular mechanisms, we utilize single cell SCENITH functional profiling and show patient clinical outcomes (OS) correlate with DC metabolic profile, and that metabolism is linked to immune phenotype.
Differential expression of 82 genes enriched in hypoxia-related pathways and biosynthetic processes was further detected in adenovirally engineered (post-maturation) DC (Supplementary Fig. 1B ). We focused our analysis on mDC and not the adenovirally engineered DC because most transcriptional changes occurred with maturation, the data would be more broadly applicable, and to compare our melanoma data to the healthy donor (HD) dataset. Differences between healthy donors (3 day) and melanoma patient mDC (5 days) iDC cultures do not impact our results as studies examining differences in immature iDC generation revealed that monocytes cultured in the presence of IL-4 + GM-CSF within 48 h exhibit iDC characteristics and upon maturation these cells displayed a fully mature mDC immunophenotype  38.
Methods Specimens were obtained from a Phase I, single site study (NCT01622933) designed to evaluate the toxicity and immunologic and clinical responses from autologous DC transduced with the tyrosinase, MART-1 and MAGE-A6 genes in 35 subjects with recurrent, unresectable stage III or IV melanoma (M1a, b, or c), or resected stage IIIB-C or IV melanoma. The endpoints were local and systemic toxicity, generation of immunity to immunizing antigens and epitope spreading, and clinical response. Enrollment occurred from 9/2012-11/2015, after institutional scientific and IRB approvals (UPCI #09-021) and with informed consent. We confirm that our research complies with all relevant ethical regulations; the clinical trial was conducted with full IRB approval at the University of Pittsburgh. We also confirm that the study design and conduct complied with all relevant regulations regarding the use of human study participants and was conducted in accordance with the criteria set by the Declaration of Helsinki.
Microarray and Gene Expression Analysis (GSE157738) and (GSE111581) Total RNA from 5 × 10e6 iDC, mDC and vaccine DC was isolated using RNAlater (Qiagen). HUGENE 2. 0 ST arrays (Affymetrix) was used for gene expression analyses. Differential gene expression was analyzed using limma (Version 3. 38. 3) with weights generated by the voom function  70, 71. A log2 fold change of 2 and FDR-adj. p-value threshold of 0. 05 was used to determine statistical significance. Web-based tool gProfiler  72  was used for pathway analysis of significantly up and downregulated gene sets. Gene set enrichment analysis (GSEA) was conducted using gene sets from the Molecular Signature Database (MSigDB, Version 6. 2) in the C2 curated gene category (2005, PNAS 102, 15545-15550). Plots were generated using the R package ggplot2 (Version 3.
After puromycin treatment, cells were detached from wells using TypLE Select (Fisher Scientific, 505914419), washed in cold PBS and stained with a combination of Human TureStain FcX (Biolegend, 422301) and fluorescent cell viability dye (Biolegend, 423105) for 10 min 4 °C in PBS. Following PBS wash step, primary antibodies against surface markers were incubated for 25 min at 4 °C in Brilliant Stain Buffer (BD Biosciences, 563794). Cells were fixed and permeabilized using True-Nuclear Transcription Factor Buffer Set (Biolegend, 424401) as per manufacturer instructions. Intracellular staining of puromycin and protein targets was performed for 1 h in diluted (10x) permeabilization buffer at 4 °C. Finally, data acquisition was performed using the Cytek Aurora flow cytometer. Primary conjugated antibody information used in SCENITH panels are listed in Supplementary Tables  1  and 3.
All antibodies were titrated to reduce spillover and increase resolution using single stained DC (generated as described above) samples. Unstained cell controls used for autofluorescence extraction were generated with additions of respective metabolic inhibitors (C, 2DG, O, DGO). Samples were unmixed using reference controls generated in combination with stained Ultracomp beads (Fisher Scientific, 01-2222-41) and stained cells using the SpectroFlo Software v2. 2. 0. 1. The unmixed FCS files were used for data processing and analysis using CellEngine (CellCarta). For in-vitro cultured mDC, manually gated CD14 -HLA-DR + CD86 + cells were used for downstream analysis. For median expression analyses MFI expression values from respective mDC and circulating cell populations from CellEngine were imported into R environment for correlation and heatmap clustering analyses using the below described R packages.
Statistical analysis Clinical outcomes for analysis were described in detail previously, briefly: "good" outcomes were PR + SD > 6 mo. + non-recurrent NED that was high risk at study entry (or NED1); "bad" outcomes were PD + SD ≤ 6 mo. + recurrent high risk NED  34, 40, 41. Multi-group comparisons were tested by one-way ANOVA with Tukey's post-hoc test. The Shapiro-Wilk test was used to asses data normality, and statistical tests were performed using R (version 3. 6. 1). Two-tailed Wilcoxon signed-rank test (non-normal data) and Two-tailed Student's ttest (normal data) was used for statistical analysis between 2 groups. Figure graphs were generated using the R package ggplot2 (version 3. 1. 1). Kaplan-Meier survival curve analysis and Cox proportionalhazards modeling were carried out using the R packages survival (version 3.
Acknowledgements We acknowledge the financial support from the  Parker Institute for Cancer Immunotherapy. We acknowledge the  UCSF Parnassus Flow Cytometry Core Facility  (RRID: SCR_018206 ) supported in part by Grant  NIH   P30 DK063720  and by the  NIH   S10 Instrumentation  Grant  S10 1S10OD026940-01. We thank both  Ashley Menk  and  Dr. Greg Delgoffe  for assistance with the Seahorse assays (Univ. Pittsburgh). We also acknowledge  ANR  and  Inserm Transfert  for the  ANR-20-CE-CE14-0028-01  and  CoPoC MAT-PI-17493-A-04  grants to R. J. A.
Data availability Data files and workflows generated for these analyses are available in a GitHub repository at https://github. com/ButterfieldLab/09-021-DC-Metabolism-Manuscript. The healthy donor microarray publicly available data used in this study are available in the GEO database under accession code GSE111581. The melanoma patient microarray data generated in this study have been deposited in the GEO database under accession code GSE157738. The remaining data are available within the article, Supplementary Information or Source Data file. Source data are provided with this paper.
i. Formulation of research questions and keywords query The primary goal of this step is to comprehensively search different databases to identify the relevant scientific publications that assist in answering the formulated research questions. First, journal articles, conference papers, and book chapters were gathered from the Scopus, Science Direct, and Google Scholar 1 3 Queueing network models for the analysis and optimisation… databases. The next part of this step is to identify relevant keywords for the search. Subsequently, appropriate Boolean operators were identified and coupled with keywords to carry out a search query (Table  1 ). Finally, the papers containing keyword combinations in the title, abstract, and keywords were selected. The two critical areas of the study, "queueing network" and "material handling system", are included in the search query with the selected domain areas.
Co-citation network of publications The co-citation network provides a visual image of the importance of reviewed publications. A connected line between two circles indicates that another publication has cited these two publications together. The sizes of frames and thickness of lines imply stronger co-citations of publications. Figure  9  shows the co-citation network of the reviewed publications based on the authors in the reviewed studies. The cocitation network visualisation map was created using VOSviewer software with the LinLog/modularity as the method option configuration. The layout was created using attraction and repulsion values of 2 and -1, respectively. The minimum cluster size was set to 1, and the layout was allowed to merge small clusters.
High-frequency keywords Authors use keywords to convey the primary focus of studies. Figure  10  shows a word tree map that summarises the occurrence of such words in the reviewed articles. The map was created using VOSviewer software and MS Excel. The co-occurrences of keywords are extracted using VOS viewer software. Extracted data was used to create the word tree map in MS Excel. The area of the rectangles is directly proportional to the occurrence of keywords used in the reviewed studies. In the following sub section, brief explanations of selected keywords were provided. Queueing network models for the analysis and optimisation….
Social network analysis (SNA) of queueing network models of MHS applications SNA is well-suited for bibliometric clustering and conceptual model development based on the co-occurrences of keywords in the reviewed studies  (Cho 2011; Jaewoo and Woonsun 2014). This tool was used to identify the current trends in the investigated study area. In this study, Mendeley and VOSviewer software are used to extract the keywords, to develop the SNA model. The frequency of the appearance and co-existence of keywords is depicted by the circle's size and links between circles. The same colours indicate that the clustering of keywords falls under similar areas of interest. Figure  12  shows the SNA of queueing network models for the MHS applications in the reviewed publications.
Simulation of escalators Usually, experimental and numerical studies of pedestrian dynamics serve as validation ground for mathematical models that aim to describe them. Concerning escalators, however, this approach is only partially practical as there is only little empirical data to compare with and due to limited possibilities to perform experimental work under extreme conditions and over an extended period of time. In this work, we therefore follow a different approach by developing a space-continuous model that describes the entering behavior of pedestrians at escalators. We implement the model in the open-source software JuPedSim  [21]  and capitalize on the obtained numerical results to formulate a theoretical concept of the escalator capacity. For the simulations, we use the geometry shown in Fig 2.
This work was supported by the  German Federal Ministry of Education and Research (BMBF)  within the framework of the project  KapaKrit  under the grant number  13N14620. MC and AT acknowledge the Franco-German research project  MADRAS  funded in France by the  Agence Nationale de la Recherche (ANR ,  French National Research Agency)  under the grant number  ANR-20-CE92-0033 , and in Germany by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under the grant number  446168800. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Forest fires often have a devastating effect on the planet's ecology. Accurate and rapid monitoring of forest fires has therefore become a major focus of current research. Considering that manual monitoring is often inefficient, UAV-based remote sensing fire monitoring algorithms based on deep learning are widely studied and used. In UAV monitoring, the size of the flames is very small and potentially heavily obscured by trees, so the algorithm is limited in the amount of valid information it can extract. If we were to increase the ability of the algorithm to extract valid information simply by increasing the complexity of the algorithm, then the algorithm would run much slower, ultimately reducing the value of the algorithm to the application. To achieve a breakthrough in both algorithm speed and accuracy, this manuscript proposes a two-stage recognition method that combines the novel YOLO algorithm (FireYOLO) with Real-ESRGAN.
Firstly, as regards the structure of the FireYOLO algorithm, "the backbone part adopts GhostNet and introduces a dynamic convolutional structure, which improves the information extraction capability of the morphologically variable flame while greatly reducing the computational effort; the neck part introduces a novel cross-layer connected, two-branch Feature Pyramid Networks (FPN) structure, which greatly improves the information extraction capability of small targets and reduces the loss in the information transmission process; the head embeds the attention-guided module (ESNet) proposed in this paper, which enhances the attention capability of small targets". Secondly, the flame region recognized by FireYOLO is input into Real-ESRGAN after a series of cropping and stitching operations to enhance the clarity, and then the enhanced image is recognized for the second time with FireYOLO, and, finally, the recognition result is overwritten back into the original image.
Xue et al. (2022)  proposed an improved forest fire small-target detection model based on YOLOv5 with an improved backbone layer and embedded SPPFP module and added CBAM Net in YOLOv5 to improve the recognition of forest fire small-targets. These algorithms have some advantages in terms of accuracy or inference speed, but they mostly do not work perfectly on small embedded devices and these algorithms are not wellsolved for problems such as illumination changes and foreign object occlusion. Therefore, the aim of this paper is to propose a remote sensing fire detection algorithm with low computational complexity and high detection accuracy. Considering the obvious advantages of the YOLO family of algorithms  (Redmon et al. , 2016; Redmon and Farhadi, 2017, 2018; Bochkovskiy et al. , 2020)  in terms of structural plasticity and good adaptability to small embedded and other hardware devices, this paper proposes a new target detection algorithm, FireYOLO, by mimicking the YOLOv4 structure.
The head network is embedded with a novel attention-guided module (ESNet), designed in this project to enhance the ability to focus on small targets. Next, the locations of suspected small fires initially identified by FireYOLO are cropped out and fed into the enhanced super-resolution-generative adversarial Network (Real-ESRGAN) to enhance the clarity of small fires, and then the clarity-enhanced images are identified a second time with FireYOLO, and, finally, more accurate small target identification results are output. Our algorithm are able to run stably and quickly on high performance devices. Finally, to verify the suitability of the algorithm, we installed it on a Jetson Xavier NX (a small embedded device with less computing power) and the overall recognition speed was about 20. 67 FPS (real-time inference), which is 21.
Introducing dynamic convolution at GhostNet GhostNet is used as the backbone extraction network for FireYOLO, considering the dual balance of real time and accuracy, GhostNet can reasonably utilize the redundancy of feature maps and obtain better performance in terms of accuracy and latency than other lightweight networks. It can achieve excellent algorithm performance on embedded devices with ARM architecture such as Jeston Xavier NX. GhostNet is composed of the Ghost module, which consists of ordinary convolution and cheap operations. The m original feature maps are generated by one convolution, and these original feature maps are transformed in two parts: one part uses 1 × 1 ordinary convolution for identity to generate m necessary feature concentrations, and the other part uses depthseparable convolution blocks for layer-by-layer convolution to linearly transform and stack the m original feature maps to generate s Ghost feature maps.
The trained weight file (. pt) is converted into an engine file (. engine) and dynamic library (. dll) via C language, which are deployed in the network to give the model accelerated inference. The common data structures that TensorRT can transform are INT8, INT16, INT32, FP16, FP32. The final data format chosen for this paper is the highest precision FP32. a GhsotNet is a lightweight neural network framework that was proposed in the literature16 and a detailed description of it in this paper can be found in 3. 1. b FPN stands for Feature Pyramid Networks, which was first proposed in the literature  (Shen et al. , 2018)  and which is also an important component in the YOLOv4 neck network. c This paper proposes a new attention-guiding module, the principle of which is described in 3. 3 and the structure of which is shown in Figure  1C.
Training dataset Several datasets related to flames [which includes FLAME  (Shamsoshoara et al. , 2021) ] were collected and divided into two parts. The datasets used for training the model include singleflame, multi-flame, indoor fire, forest fire, and complex background fire scenarios, with a total of 23,982 images. During the training of the model, 23,982 images were divided into 18,653 training image sets and 5,329 validation image sets in a ratio of 7:2. Before input to the training framework, there was no imposition on the image size, and after input to the training framework, the images of various sizes were scaled uniformly to 416 × 416 by the algorithm. while the datasets used for the comparison experiments in the subsequent experiments of the paper were all UAV remote sensing images of high altitude areas, 7,752 images were used in this dataset, which were divided into two categories according to their types: The first category is the images containing fire, named FIRE 1  type, with 6,331 images; and the second category is the images without fire, named NOFIRE 2  type, with 1,421 images.
Model building and training Considering that the subsequent verification process of the algorithm in this paper involves many comparison experiments, some complex algorithms will be applied in these comparison experiments, and most of these algorithms cannot be run on embedded devices. In the principle of controlling variables, the platform of the pre-contrast validation experiments in this paper is unified with the model training platform. The platform used for training and experiments is CUDA 11. 2 CUDNN v8. 2. 1, the deep learning environment is Tensorflow 2. 5, the programming language is Python 3. 9, and the system is Ubuntu 18. 04. There is no pre-training process for the FireYOLO model, and the model is trained directly from scratch with the following training hyperparameters settings: epochs for training is set to 1,000; batchsize is set to 64 and subdivisions is set to 1.
Figures  7A-C  represented the trends of the seven algorithms regarding the three evaluation metrics mentioned above under the three lighting conditions, and On the confusion matrix of the detection results of the six algorithms (A) shows the results of the six algorithms for the FIRE 1 class of images, where the horizontal coordinate FIRE indicates that the algorithm correctly identified the flame and NOFIRE indicates that the algorithm did not correctly identify the flame, possibly because the algorithm missed or misidentified it; (B) shows the results of the six algorithms for the NOFIRE 2 class of images, where the horizontal coordinate FIRE indicates that the algorithm misidentified the flame and NOFIRE indicates that the algorithm did not identify the flame; (C) shows the confusion matrix of the six algorithms for the test dataset regarding the distribution of the number of attributes TP, FN, FP, and TN.