dataesr/scientific-paragraphs-categorization huggingface dataset:
Still, the metagenomics analyses do not exclude the possibility for bacterial biosynthesis of C-6-hydroxylated BAs in humans since there is a major lack of functional annotation of bacterial genes in currently available databases. Similarly, GSMM of the human gut microbiota based on available limited literature and database knowledge was unable to identify the microbial reactions or enzymes that can convert αMCA to HCA or HDCA, and their derivatives. Therefore, a further clarification of a putative gut microbiome contribution to the origin of C-6-hydroxylated BAs in human serum must await the development of more complete catalogs of functionally annotated bacterial genes. Our findings of inverse relationships between C-6-hydroxylated BAs and markers of dysmetabolism and adiposity in 283 nondiabetic Danes extend previous findings in both humans and pigs.
Conclusion Co-abundant species of Clostridia in the human gut microbiota are positively associated with fasting serum of C-6 hydroxylated BAs, which in turn are inversely associated with adiposity traits and markers of dysmetabolism. In perspective, our findings merit testing in future preclinical interventions and human feasibility studies exploring the potential therapeutic role of C-6 hydroxylated BAs in states of obesity and its metabolic co-morbidities. In addition, when human gut metagenome databases become considerably more complete, further search for a bacterial contribution to the biosynthesis of C-6-hydroxylated BAs merits consideration.
Acknowledgements The  Novo Nordisk Foundation Center for Basic Metabolic Research  is an independent research center at the  University of Copenhagen  partially funded by an unrestricted donation from the  Novo Nordisk Foundation. The present work was supported by  Metagenopolis  grant  ANR-11-DPBS-0001  (granted to S. D. E), and  Academy of Finland  (grant no. 333981  to M. O. ).
Image processing and quantification A filtered back-projection algorithm was used to reconstruct the 3-D tomographic datasets and remove ring artifacts  (Titarenko et al. , 2010). Each reconstructed 3-D dataset had dimensions of 800 × 600 × 600 voxels. Image processing and analysis was performed using Avizo 9. 5 (FEI, France) and ImageJ (US NIH, USA). Time-lapse images were registered, and filtered by a 3D non-local means filter algorithm (search window 21, local neighborhood 5, similarity 0. 6) to reduce noise  (Buades et al. , 2005). The fractures were then segmented on the basis of grey levels on the 3D images. 3D digital volume correlation (DVC) was applied to measure the displacement fields and calculate strain fields during the indentation using Davis 8. 3 software. Three-step window sizes were used (128 voxels; 64 voxels; 48 voxels; 2 passes and 50% overlap).
Acknowledgements This project was funded in part by the  UK-NERC  ( NE/M001458/1  and  NE/R013527/1 ), the  European Union 's  Horizon 2020 Program  under the  ShaleXenvironmenT  project (grant no. 640979 ). Facilities at the  Research Complex at Harwell  are gratefully acknowledged, supported in part by the  UK-EPSRC  ( EP/I02249X/1 ). Mr. Sebastian Marussi  is acknowledged for technical support.
, 2018; Alsema et al. , 2020). Recent work has demonstrated that single-nucleus RNA sequencing (RNA-seq) of stored human tissue fails to detect differences in microglia activation between AD and controls  (Thrupp et al. , 2020) , further complicating direct comparisons between humans and mouse models. The vast majority of mouse microglia gene datasets have been generated using the inbred laboratory strain, C57BL/6 (B6). Genetic complexity in the human population is expected to influence differences in, and the even presence of, microglia subtypes. However, inclusion of similar genetic diversity in mouse strains has not been explored. We have taken advantage of wild-derived mouse strains that exhibit natural genetic variation in AD risk genes  (Onos et al. , 2019). As the wild-derived strains CAST/EiJ (CAST), WSB/EiJ (WSB), and PWK/PhJ (PWK) were captured from the wild from different geographical regions for laboratory use, their genomes are closer to recapitulating the diversity of genetic variants that would exist in the natural world.
All steps were performed on ice or at 4 C to minimize tissue dissociation-related microglia activation  (Wu et al. , 2017). Single myeloid cell RNA libraries were generated using 10x Genomics v3 chemistry and sequenced by Illumina Nova-seq S2 sequencer (see method details). Fastq files were aligned to customized strain-specific genomes using scBASE pipeline  (Choi et al. , 2019) , and gene counts were estimated by emaze-zero software  (Raghupathy et al. , 2018)  (Figure  1A ). Gene count matrix and downstream clustering analysis was processed using the Seurat package after removing low-quality cells and contaminating non-myeloid cells  (Figures S1A and S1B). Overall, 91,201 myeloid cells were integrated across the strains (Figure  S1C ), with 87,746 identified as microglia (Figure  S1D ). No significant differences in yield of myeloid cells or microglia between the strains were observed (Figures  S1C  and S1D ), despite differences in Itgam (Cd11b) gene expression determined from our previous bulk RNA-seq study  (Onos et al.
Thirteen microglia clusters were annotated based on relative expression levels of marker genes such as Tmem119,  Cx3cr1, Cst7, Clec7a, Apoe, Ifitm3, Hexb, C3ar1, ; Table  S1 ). Cluster number was assigned and ordered based on overall cell abundance. Gene expression for our clusters was then compared with microglia subtypes from previous scRNA-seq studies  (Keren-Shaul et al. , 2017; Hammond et al. , 2019; Sala Frigerio et al. , 2019)  (Figure  1E ) by evaluating enrichment scores of marker genes from reported microglia subtype in our clusters. In our dataset, clusters 0-5 were the most abundant and were (B) UMAP plot showed 87,746 strain-integrated microglia from all 29 mice (20,732 from B6, 24,124 from CAST, 19,702 from PWK, and 23,188 from WSB), reflecting diverse microglia subtypes including homeostatic (clusters 0-5), disease-associated (clusters 6 and 12), interferon-responding (cluster 7), Hexb high /Cd81 high (cluster 8), ribosomal gene-enriched (cluster 9), Ccl3 high /Ccl4 high (cluster 10), and proliferative microglia (cluster 11).
, 2017; Hammond et al. , 2019; Roy et al. , 2020). As our study is the first to use genetically diverse mouse strains, we sought to understand the similarities and differences between these microglia subtypes with previous mouse datasets. We first compared the marker genes (Table  S4 ) defining cluster 6 with the top marker genes from the Amit study (DAM)  (Keren-Shaul et al. , 2017)  and the de Strooper study (ARM)  (Sala Frigerio et al. , 2019)  (Figure  4A ; Table  S5 ). This identified a core set of 20 genes conserved across all datasets and included Cst7, Clec7a, Tyrobp, and Lpl. Genes that were present only in our dataset were primarily ribosomal and, thus, may be reflective of differences in metabolic status of the cells at the time of sample collection, the mouse models used, and/or the library generation and sequencing platforms.
Core genes shared by all datasets are highlighted in the orange box; genes shared only in our B6 and wild-derived strains are colored in gray. (B) Violin boxplots showing the enrichment Z score of the core DAM genes in cluster H (homeostatic) and cluster 6 for each strain and genotype. Significant strain and genotype effects were detected (p z 0, two-way ANOVA). (C) Violin plots showing the expression of selected DAM core genes in cluster 6 for each strain and genotype. (D) Heatmap summarizing top 20 significantly enriched diseases and functions (IPA) terms based on DE genes from comparisons of wild-derived versus B6 mice (pval-BH < 0. 05, |Z score| R 2). The dot indicates the enrichment of diseases and functions term is not significant for a given comparison (pval-BH R 0. 05). (E) Example of an RE for WSB versus B6 highlighting the of ''binding of endothelial cells.
Comparison of genetically diverse mouse microglia with human microglia A critical and active area of investigation is the comparison between human and mouse microglia. Mouse models will likely play a major role in identifying potential microglia-based therapies to treat AD. Here, we chose four studies  (Zhou et al. , 2020; Johnson et al. , 2020; Mathys et al. , 2019; Olah et al. , 2018)  that surveyed human microglia to compare to our (A) Upset plot illustrating the intersection of the top IRM marker genes in B6 and wild-derived strains integrated with Aging_OA3 cluster from the Stevens study  (Hammond et al. , 2019)  and IRM from the de Strooper study  (Sala Frigerio et al. , 2019). The genes in selected intersections are detailed in the plot. Core genes shared by all datasets are highlighted in the orange box.
'' The color code is the same as described in Figures  3D  and 3E. genetically diverse mouse microglia dataset. Two datasets were generated via single-nucleus RNA-seq (Mic1 from Tsai study  [Mathys et al. , 2019]  and Micro0 from the Colonna study  [Zhou et al. , 2020] ), one was generated via bulk RNA-seq of isolated aged microglia by Bradshaw and colleagues  (Olah et al. , 2018) , and one (module 4, enriched for microglial genes) was obtained from the proteomics study performed by Seyfried and colleagues  (Johnson et al. , 2020). The marker gene set for Mic1 (Tsai) showed the highest enrichment values compared to the other three datasets, with clusters 6, 9, and 12 showing the   (Zhou et al. , 2020) ; aged microglia from Bradshaw  (Olah et al. , 2018) ; and M4 microglia module from Seyfried  (Johnson et al.
, 2020). (B) Violin boxplots showing the enrichment Z score of the top marker genes of the Mic1 cluster from the Tsai study for each strain and genotype. Significant strain and genotype effects were detected for each cluster (p z 0, two-way ANOVA). (C) Upset plot illustrating the intersection of the top DAM marker genes in B6 and wild-derived strains integrated with top marker genes associated with human Mic1 cluster (Tsai)  (Mathys et al. , 2019). The genes in selected intersections are detailed in the plot. Core genes shared by all datasets are highlighted in the orange box; genes shared in our B6 and wild-derived strains but not Mic1 are colored in gray. (D) Violin plots showing the expression of selected genes for each strain and genotype. greatest alignment (Figure  6A ). The poor alignment with Micro0 (Colonna) may be because the AD patients were enriched from common or rare variants in TREM2.
The poor alignment with the Bradshaw and Seyfried datasets may be due to differences in sample type, preparation, and sequencing technologies. Given the greatest similarity across the human datasets was observed from the Tsai study  (Mathys et al. , 2019) , enrichment scores for our clusters were calculated using the Mic1 marker genes (method details). The resulting enrichment score was significantly affected by strain-, genotype-, and strain-by-genotype (Figure  6B ; p z 0, two-way ANOVA). For instance, CAST and PWK displayed slightly yet significantly higher enrichment scores than B6 and WSB for clusters 6, 9, and 12 (Figure  6B ). Comparisons between Mic1 and cluster 6 across all strains identified 18 core signature genes that included the classical DAM genes such as Apoe and Tyrobp, but also the ribosomal genes (Figures  6C  and 6D ; Table  S5 ) that had been absent in mouse DAM datasets from previous studies  (Keren-Shaul et al.
, 2017; Sala Frigerio et al. , 2019)  (Figure  4A ). Interestingly, 19 of the 77 Mic1 marker genes were identified in only one or some, but not all, of the mouse strains. For instance, ribosomal proteins Rps3, Rps16, Rpl26, and Rpl27a were common between Mic1 and all wild-derived strains (CAST, WSB, and PWK) but not B6. Tmem163 was common between Mic1 and PWK. Spp1 was common among Mic1, PWK, and B6. These data support the use of genetically diverse mouse strains to improve the alignment to human studies. However, the Mic1 dataset lacks other classic mouse DAM genes-including Cst7, Clec7a, and Lpl (Figure  6C )-that have been identified in multiple mouse studies including our own (Figure  4A ), suggesting further work is required to elucidate apparent species-specific differences in microglial subtypes such as DAM.
Further, in this study, microglia from female mice at one age (9 months) were profiled. In addition to all the strain-and strain-by-genotype-specific changes observed in this dataset, sex-, brain-region-and/or age-specific changes are still to be determined. Differences across wild-derived strains in microglia subtypes often showed downregulation of specific biological pathways in comparison to B6. While B6 has been used across biomedical research for practical and historical reasons, such work may be inherently biased to neuroimmune responses driven by a singular genetic context, with limited translation to humans. For example, B6 (as well as other commonly used strains such as DBA/2) carries a mutation in the P2rx7 locus that severely impairs important functions of this receptor. This is thought to influence critical steps relating to induction of apoptosis and cytokine secretion.
Gene expression quantification from scRNA-seq data The analysis pipeline of scBASE  (Choi et al. , 2019)  was used in order to avoid alignment bias due to differences in genetic background of mouse strains. First, we built the read alignment index by combining the custom strain-specific transcriptomes of CAST/EiJ, PWK/ PhJ, WSB/EiJ, and C57BL/6J, created with g2gtools (http://churchill-lab. github. io/g2gtools). We removed PCR duplicates from the raw scRNA-seq data, and then aligned the remaining reads to the pooled transcriptome of the four strains using bowtie  (Langmead et al. , 2009)  with '-all', '-best', and '-strata' options. We processed the resulting bam files into an alignment incidence matrix (emase format) using alntools (https://churchill-lab. github. io/alntools) and quantified gene expression for each cell with emasezero  (Raghupathy et al.
, 2018)  (https://github. com/churchill-lab/emase-zero). We collated the estimated UMI counts into a loom formatted file (http://loompy. org) for downstream analysis. A docker container in which all the above-mentioned software tools are pre-installed is freely available at https://hub. docker. com/r/kbchoi/asesuite-sc. Identification of brain myeloid cell types and microglia subtypes First, we identified myeloid cell types by filtering out non-myeloid cells using a standard Seurat (v3. 1. 2)  (Butler et al. , 2018; Stuart Satija, 2019)  clustering pipeline for each strain, respectively. Cells with fewer than 600 detected genes or higher than 8% of mitochondrial genes were removed before initial analysis. We performed dimension reduction using PCA followed by UMAP using 3,000 most variable genes after normalizing the UMI counts.
We identified marker genes for each clusters (FindAllMarkers) with default setting, and then annotated each cluster using enrichCellMarkers package  (Zhang et al. , 2019). We repeated the same clustering analysis after filtering out non-myeloid cells to refine PCA projection of myeloid cell types for each strain. We integrated myeloid cell clusters across the mouse strains (IntegrateData) and repeated the same clustering analysis. We identified a total of 91,201 myeloid cells including microglia, perivascular macrophage, monocytes and neutrophil  (22, 212 from B6, 24, 976 from CAST; 20, 192  from PWK and 23,821 from WSB, Figure  S1 ). Next, for microglia sub-clustering, we selected only those cells defined as microglia e3 Cell Reports 34, 108739, February 9, 2021.
ACKNOWLEDGMENTS We gratefully acknowledge the contribution of  Sandy Daigle ,  Michael Samuels , and  Paul Robson  at the  Single Cell Biology Laboratory at The Jackson Laboratory (JAX ) for expert assistance with this publication. We thank  Duy Pham  from the  Churchill lab at JAX  for advice on pre-processing of scRNA-seq data on high-performance computing cluster. We thank  Christoph Preuss  from the  Carter lab at JAX  for advice on analysis using human AD-relevant GWAS genes. We thank  Sandeep Namburi  at  JAX research IT  for installing essential packages and troubleshooting on RStudio server. We thank  Jane Cha  from  JAX Creative  for the design and creation of our graphical abstract. This study was supported in part by the  National Institute of Aging   RF1AG051496  (G. R. H. , H.
Deposited data Raw and processed data and metadata spreadsheet This paper https://doi. org/10. 7303/syn23763409 Shiny app for searching B6 and wild microglia database This paper https://wild_microglia_scrna-seq. jax. org/ Code for reproducing the analysis This paper https://github. com/TheJacksonLaboratory/wild_AD_ mic_scRNA scRNA-seq data of microglia from 5xFAD mice (C57BL/6J and SJL/J background) Keren-Shaul et al. , 2017 https://www. cell. com/fulltext/S0092-8674(17)30578-0 scRNA-seq data of microglia from App NL-G-F.
A millimeter-wave (mmW) classifier system applied to images synthesized from a coded-aperture based computational imaging (CI) radar is presented. A developed physical model of a CI system is used to generate the image dataset for the classification algorithm. A convolutional neural network (CNN) is integrated with the physical model and trained using the dataset comprising of synthesized mmW images obtained directly from the developed CI physical model. A k-fold cross validation technique is applied during the training process to validate the classification model. The coded-aperture CI concept enables image reconstruction from a significantly reduced number of back-scattered measurements by facilitating physical layer compression. This physical layer compression can substantially simplify the data acquisition layer of imaging radars, which is realized using only two channels in this article.
In the context of mmW security-screening and classification for threat detection, this requirement translates into the need for a sufficient number of mmW images of threat objects to successfully train the CNN model. Moreover, given the demand of the application to produce a fast and accurate classification system and keeping in mind the quality of mmW images, training the neural network model for mmW classification typically requires a large dataset. In light of these challenges, employing the traditional raster-scanning based methods to generate such large datasets and train the classification system can be counterproductive. This is because raster-scanning based imaging modalities conventionally suffer from slow data acquisition speeds and exhibit significant computational demands for image reconstruction due to the need to process large datasets caused by the dense sampling of the aperture layer, typically at the Nyquist limit.
• Third, and finally, we provide a systematic, quantitative analysis of the robustness of CI based radars to system noise and its effect to the classification accuracy -both numerically and experimentally. In order for CI based radars to be considered a realistic technology for security-screening applications, they must be able to achieve classification under various signalto-noise ratio (SNR) conditions. Currently, the robustness of CI based radars to system noise for classification is unknown. As well as answering this question (Section VI), we prove that the capability to generate our own mmW imaging dataset is vital to ensure optimum classification performance under various system noise levels. This is of significant importance in radar imaging because the developed physical model gives us the ability to alter the system noise level within the practical imaging environment.
II. RELATED WORK Object detection or classification of images using machine learning techniques has received substantial interest in the literature, particularly at optical frequencies  [36] -  [43]. However, mmW image classification, including data acquisition, to facilitate real-time ATD for security-screening is vastly understudied. In  [44] , classification was carried out based on a spectral analysis of mmW images using support vector machine (SVM). Despite encouraging results, it is important to emphasize that SVM is a more suitable technique for binary classification problems. As a result, using SVM for multi-class classification requires an additional pre-processing of dividing the problem into several binary classification problems. Also, when the size of the dataset is large, as is the case in this paper, research suggest that deep learning networks are known to perform better in image classification problems.
In  [52]  and  [53] , the use of CNN was shown for target detection in airborne SAR images. In these works, instead of fully connected layers at the end, sparsely connected layers were used. However, the classification was shown on an already available dataset that is a collection of thousands of SAR images. This approach is not ideal for classification studies because such datasets do not necessarily exhibit relevant system metrics in terms of operating frequency, bandwidth, aperture size, SNR, imaging distance and resolution. It is therefore vital to generate the training data using the actual physical model of a radar system under its relevant system parameters. An interesting work was presented in  [54]  where deep learning was facilitated as an enabling technique to solve the image reconstruction problem at terahertz (THz) frequencies.
It should be mentioned that this is a fundamentally different concept from the classification problem studied in this paper. Moreover, because the work presented in  [54]  was not experimentally tested, it is not possible assess the accuracy and applicability of  [54]  for practical applications. For the security-screening application we consider, this poses a significant disadvantage. From the available research on classification of mmW images, it can be concluded that most of the works were carried out on already available datasets consisting of SAR images or depend upon approaches that heavily rely on an experimental set-up to generate the images. Our system has an advantage over such approaches in the sense that we propose an end-to-end mmW CI model for ATD in real-time.
A. IMAGE DATASET In this work, we make use of CAD models of several threat objects such as guns, grenades, knives and scissors to form the dataset for training. Fig. 3  shows CAD models of a gun and a grenade and their respective mmW images generated by the developed CI model outlined in Section III. Since the dataset comprises of four types of threat objects, this classification problem will be a multi-class one, comprising of four classes: • Class '0' or Grenade, • Class '1' or Gun, • Class '2' or Knife, • Class '3' or Scissor. Using this method, four transformed copies of a single image are generated and this process is repeated for every image in the existing dataset, and used for training. Hence, the size of the dataset is increased from 531 to 2655 images.
B. CNN ARCHITECTURE The developed CNN architecture is shown in Fig. 4. As mentioned in Section V-A, the training dataset comprises of images reconstructed from the developed CI model. The properties of these images, such as colour space or pixel dimensions, are similar to the ones shown in Fig. 3  insets. Each input image for the network is of dimension 150 × 150. The CNN layer comprises of five convolution layers, layer 1 through layer 5. Each convolutional layer uses multiple filters to extract the useful features from the images. The size of each convolutional layer is specified in Fig. 4. For example, for the first convolution layer (layer 1), the size specified is 150 × 150 × 32. It means that the width and height of this particular 2D convolutional window is 150 and 150, respectively, whereas the number 32 signifies the number of filters that the convolutional layer will learn.