dataesr/scientific-paragraphs-categorization huggingface dataset:
Narrowing the scope on alternative (immunological) targets As alternative targets among HLA ligands with potential therapeutic relevance, we screened our HCC dataset for proteins previously described as cancer-testis antigens (CTA) and found eight different HLA class I ligands mapping to six CTA. These few CTA encompass ARMC3 (Q5W041), ATAD2 (Q6PL18), MAEL (Q96JY0), PRAME (P78395), proteins of the SSX family, and TFDP3 (Q5H9I0) (Table  2 ). Further, we identified a limited number of CTA among different patients on shotgun proteome level (Additional file 1: Table  S12 ). Additionally, gene expression analysis revealed 213 differentially expressed (DE) genes, resulting from comparison of autologous tumor and non-malignant tissues. All but one DE gene showed downregulation when compared to matching non-malignant liver (Additional file 3: Figure  S3 ).
Respective results indicate apparent differences in the underlying gene expression patterns of tumor and non-malignant liver samples, corroborating the separation of the tumor and non-malignant liver samples in the PCA (on principal component one level; Additional file 3: Figure  S4 ). Visualization by heatmaps and accompanying dendrograms clearly support these observations and show  that tumor and non-malignant tissue samples mostly separate in two distinct clades. This separation is seen in most tumor samples except for six patients (HCC024/ 028/ 034/ 035/ 043/ 045), which rather group with the non-malignant tissue samples on the heatmap. To benchmark results from DE expression analysis to publicly available RNA-Seq datasets of HCC, we used recount2, a multi-experiment resource of analysis-ready RNA-Seq datasets with the R package recount.
We performed a simple pairwise comparison of the TCGA dataset between non-malignant (n = 50) and tumor (n = 374) samples using DESeq2. We identified 6044 genes that were DE, based on a statistical significance that was determined by a multiple-testing adjusted p value < 0. 05 and log2 fold-change > 1 or < -1. From the 213 DE genes observed in our HCC cohort, about half (n = 105) were also found differentially regulated in the TCGA dataset. Gene functional classification analysis (using DAVID  [69, 70] ) pointed to mono-oxygenases (CYP450 enzymes) as most prominently inhibited class among DE genes. This gene list was mapped to unique Entrez IDs (n = 115), which were mapped to 14 significantly enriched pathways in return (Additional file 1: Table  S13 ). Finally, we assessed mutations evidenced in our HCC cohort on transcriptome level (Var exp ) regarding their potential druggability.
Moreover, biomarkers predicting response to ICP inhibitors with higher precision than TMB  [62]  are sought-after  [77]. A respective biomarker might not only assess the odds for ICP therapy success but may simultaneously allow the development of tailored neoantigen-targeted immunotherapies. In contrast to the vast array of data available relating to PNE  [78, 79] , often derived from data of consortia like the International Cancer Genome Consortium (ICGC) or TCGA, current physical evidence for exome-derived mutated HLA ligands (NE lig ) seems anecdotal (reviewed in  [63] ) and positive examples for finding this proverbial needle in the haystack are scarce. Hence, to be able to benchmark our results obtained in HCC, we used the best evidence available to us, provided by a dataset from Bassani-Sternberg et al.
[24]. Even though this dataset from malignant melanoma (Mel) differs fundamentally from HCCs in a variety of aspects, including inter alia a tenfold increased average TMB and a sixfold higher PNE count, this approach enabled benchmarking our pipeline against a dataset containing the required targets (NE lig ). This notion was also confirmed on a larger scale by TCGA data, corroborating that the average mutation numbers were typically fivefold increased in Mel vs. HCC and the proportion of tumors with high TMB (< 100 Var ns ) was elevated from 1. 5% in HCC to 32% in Mel. Our HCC dataset is characterized by close to 70 amino acid-changing mutations (Var ns ) on average translating to a TMB of about two per megabase, numbers corresponding very well with data from a comprehensive set of resectable HCCs  [80].
Since we had shotgun proteomics data available, we also assessed whether we could establish any additional physical evidence for the respective source proteins (PNE prot ) constituting the immediate proteomic context of NE lig , which was the case in about one fifth of PNE exp and comprised about 10% of the initial PNE pool. Nevertheless, since this neither implies the actual detection of a mutation in the proteomics dataset (only the identification of at least one tryptic peptide matching the respective protein), nor the HLA presentation of a NE lig , we assessed the eluted HLA ligands and searched for any PNE with actual evidence for HLA presentation by LC-MS/MS. Although the ~1400 HLA-bound peptides detected on average in HCCs are generally comparable with the numbers previously published in solid cancers  [34, 82] , they do fall short of the considerable depth reached in Mel, particularly in one single exceptional case, for which more than 20,000 HLA-bound peptides were reported (Mel15;  [24] ).
Since this Mel dataset was available to us and could be processed by our pipeline, we can prove that we would be able to discover NE lig when MS/MS spectra are acquired. In this way, we corroborated all NE lig previously reported  [24] , as well as four additional NE lig previously unidentified, validated by matching spectra from synthetic peptides. However, in this direct comparison, it becomes particularly clear that Mel and HCC, despite both representing solid tumors, feature fundamental differences on a variety of biological levels. Those differences may imply disparities in antigenicity, determining the odds for immunotherapy success  [15]. This notion is confirmed by an extensive analysis of 30 cancer types using comprehensive sequencing data from ICGC and TCGA  [26] , with striking differences concerning the PNE pool between HCCs and Mel or lung and colorectal cancer  [79].
Conclusions We failed to confirm any exome-derived mutated HLA ligands with sophisticated (targeted) MS approaches in HCC, supporting the assumption that in malignancies with low TMB immunoediting may be a relevant driving force shaping the HLA ligand landscape  [84]. Certainly, LC-MS/MS comes with specific limitations that must be considered and HLA ligands may be missed, but it remains the best tool currently available. Paying close attention to the rules of HLA presentation is important and may support choosing suitable NE lig candidates. We therefore screened our HLA ligand database and found that respective knowledge may indeed guide selection. Undoubtedly, since the HLA ligandome is very complex and our data are limited, the required knowledge needs to be generated in a community effort  [85].
Acknowledgements The authors wish to thank  Claudia Falkenburger ,  Patricia Hrstić ,  Ulrich Wulle , and  Beate Pömmerl  for production of peptides, monoclonal antibodies, and excellent technical support as well as  Dr. Silvia Wagner  and all the staff of the study center of the  Department of General, Visceral and Transplant Surgery at the University Hospital Tübingen  for their support and all the participating patients. Further, we gratefully acknowledge the proficient technical support by  Silke Wahl  and helpful discussions with  Boumediene Soufi, PhD. Members  of the HEPAVAC Consortium are:  Andrea Mayer-Mokler ,  Toni Weinschenk ,  Christian Flohr ,  Carsten Reinhardt ,  Harpreet Singh-Jasuja ,  Immatics Biotechnologies GmbH, Tübingen, Germany ;  Roberto S. Accolla ,  Giovanna Tosi  and  Greta Forlani ,  Department of Medicine and Surgery, University of Insubria, Varese, Italy ;  Yuk T.
Funding This work was supported by the  European Research Council  (ERC; AdG 339842 MUTAEDITING) granted to  H. G. Rammensee  and by the  European Union   7th Framework Program  ( EU FP7 ) Project  Cancer Vaccine development for Hepatocellular Carcinoma -HEPAVAC  (Grant No. 602893 ), as well as the  German Consortium for Translational Cancer Research (DKTK). C. Mohr  and  O. Kohlbacher  acknowledge funding from the  European Union  (APERIM, contract no. 633592 ) and funding from the  German Ministry for Research and Education (BMBF)  as part of the  Medical Informatics Initiative  ( DIFUTURE, grant   01ZZ1804D ). L. Bichmann  and  O. Kohlbacher  acknowledge funding from  BMBF  as part of the  German Network for Bioinformatics Infrastructure (de. NBI , grant  031A535A ). S. Nahnsen  and  S.
Availability of data and materials The MS datasets, including proteome and ligandome data, generated and analyzed during the current study have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRoteomics IDEntifications (PRIDE) database partner repository  [92]  with the dataset identifier PXD013057  [93]. The MS data reanalyzed during the current study are also available online through the PRIDE database (with the dataset identifier PXD004894)  [94]. Somatic variant lists  [95]  and expression data  [96]  generated and analyzed during the current study are available from figshare. The whole exome and transcriptome sequencing raw data generated and analyzed for the current study are not publicly available, as participants did not provide respective informed consent for broad sharing of their data.
The brainstem, a structure of vital importance in mammals, is currently becoming a principal focus in cognitive, affective, and clinical neuroscience. Midbrain, pontine and medullary structures serve as the conduit for signals between the forebrain and spinal cord, are the epicenter of cranial nerve-circuits and systems, and subserve such integrative functions as consciousness, emotional processing, pain, and motivation. In this study, we parcellated the nuclear masses and the principal fiber pathways that were visible in a high-resolution T2-weighted MRI dataset of 50-micron isotropic voxels of a postmortem human brainstem. Based on this analysis, we generated a detailed map of the human brainstem. To assess the validity of our maps, we compared our observations with histological maps of traditional human brainstem atlases.
g. ,  Olszewksi and Baxter, 1982; Swanson, 2015). The atlases resulting from such investigations have enabled us to determine the location and cellular composition of individual nuclei as well as their fiber pathways in the human brainstem. These lines of investigation have been crucial in understanding normative neuroanatomy as well as neuropathology (e. g. ,  Olszewksi and Baxter, 1982). With the advent of MRI, studies of the brainstem have demonstrated the potential of neuroimaging for analyzing this brain component structurally, metabolically and functionally  (Toga and Mazziotta, 2002; Aggarwal et al. , 2013; Deistung et al. , 2013; Beissner, 2015; Sclocco et al. , 2018; Tang et al. , 2018; Edlow et al. , 2019). Currently, ex-vivo acquisitions of human brain tissue using high field MRI scanners have allowed for high spatial resolution brainstem datasets (e.
g. ,  Calabrese et al. , 2015; Edlow et al. , 2019). These datasets have enabled the visualization of gray and white matter structures to such a degree that the goal of matching morphologically histological preparations traditionally used in neuroanatomy with those of the MRI images has been met for certain nuclei as a whole, although not yet at the cellular level. Precise MRI-based 2D and 3D computational reconstructions of gray and white matter anatomical structures allow their integration into current multimodal neuroimaging, a unique feature of current morphometric analysis, with a significant advantage over traditional histological methodologies. Consequently, these data can be used in different fields of basic and clinical neuroscience in which knowledge of structure, function, and metabolism of the human brainstem is needed.
In the present study, we parcellated the anatomical gray matter structures and the fiber pathways of conduit and cranial nerve systems that were visible in an ultrahighresolution T2-weighted MRI dataset of a postmortem human brainstem. Based on our initial analyses, we combined the gray and white matter parcellation results to generate a detailed map of the human brainstem. To evaluate the anatomical accuracy and precision of our maps, we compared our observations with histological maps of classical human brainstem atlases. Furthermore, by assembling the different component structures, we reconstructed the motor, sensory, and integrative neural systems of the brainstem and rendered them in 3D representations. Given the computational nature of our maps in 2D and 3D spaces, we anticipate their utilization by the neuroimaging community at large for applications in basic and clinical neuroscience.
Image Acquisition and Protocol Parameters of an Ultrahigh-Resolution ex vivo T2-Weighted MRI Dataset Postmortem imaging was performed in a single human brainstem from an anonymous 65-year old male donor as described by  Calabrese et al. (2015) , following our institutional rules and guidelines. Briefly, the brain was extracted with a post-mortem interval of 24 h and the brainstem and thalamus were dissected. The vasculature was flushed with normal heparinized saline (100 UI/ml heparin) and the brain subsequently immersed in 10% neutral buffered formalin for 2 weeks. A week before scanning, the sample was placed in a 0. 1 M phosphate buffer containing 1% gadoteridol. Before imaging, the brain was immersed in liquid fluorocarbon (Galden PFPE, Solvay Plastics, Brussels, Belgium). RF transmission and reception were achieved with a 65 mm inner-diameter quadrature RF coil.
Anatomical Image Analysis: Segmentation and Labeling of Regions of Interest Identification and delineation of anatomical regions of interest (ROI) were carried out on the T2-weighted MRI dataset as follows. Every 5th axial slice was manually segmented, using the 3D Slicer software analysis platform to perform segmentation and labeling of visualized brainstem anatomical structures  (Fedorov et al. , 2012). All the tools used are publicly available and can be downloaded from the official 3D Slicer website 2. The segmentation of structures was done as follows. The brainstem was segmented as a whole and then subdivided into three component parts, namely midbrain or mesencephalon, pons, and medulla. Following traditional anatomical conventions,   Nolte et al. (2016)  and  Vanderah (2018)  with the present study and with the terminologia neuroanatomica  (ten Donkelaar et al.
This was performed using the Segment Editor module of 3D Slicer, which allows segmentation operations. To ensure anatomical accuracy in the identification of gray and white matter structures we were also guided by anatomical textbooks and atlases of the brainstem. These included  Olszewski-Baxter (Olszewksi and Baxter, 1954, 1982; Büttner-Ennever and Horn, 2014)  Paxinos and Huang  (Paxinos and Huang, 1995) , Haines  (Haines, 1991) ,  Carpenter and Sutin (Carpenter and Sutin, 1983) , Mai and Paxinos  (Mai and Paxinos, 2012) , and Nolte  (Nolte et al. , 2016; Vanderah, 2018). Furthermore, we assessed our results by matching the extent of each structure of our analysis with those of  Paxinos and Huang (1995). To address neural systems analysis in the brainstem, we assembled individual structures into the motor, sensory and integrative (or neuromodulatory) neural systems, a topic of great relevance in current basic and clinical neuroscience  (Dahlström and Fuxe, 1964; Mesulam, 2000; Solms and Turnbull, 2002; Panksepp, 2004; Damasio, 2010; Blessing and Benarroch, 2012).
, 2016; Vanderah, 2018). The latter was done for didactic purposes to aid the student of brainstem anatomy who is not sufficiently familiar with this complex structure. The results observed in our analysis matched the topography reported by classical atlases, namely those of  Haines (1991) ,  Paxinos and Huang (1995), and Tona et al. (2017). The size of our brainstem dataset (i. e. , 54. 2 mm in length) was comparable to the sample used by  Tona et al. (2017; i. e. , 54. 02 mm) by 99. 7%, and with the brainstem analyzed by  Paxinos and Huang (1995;  i. e. , 59 mm) by 91. 6%. The topographical relationships among the various gray and white matter structures and the ventricular complex also met expectations based on traditional neuroanatomy. More specifically, a comparison between left and right structures in our analysis showed an overall 99% correspondence and a spatial overlap of 97%, as shown in Figure  2A.
DISCUSSION In this study, we localized and delineated the principal structures of the human brainstem using an MRI ex  vivo ultrahigh-resolution dataset with properties largely comparable to histological representation. More specifically, we volumetrically identified 47 gray (viz. , 25) and white matter (viz. , 22) structures in the brainstem based on an ex vivo human T2-weighted MRI dataset of 50-micron spatial resolution. Furthermore, we were able to delineate morphometrically all 25 gray matter structures and 16 of the 22 white matter structures. To our knowledge, this has not been previously achieved in MRI-based morphometric analysis. Moreover, we assembled individual structures into neural systems specifically serving cranial nerve, conduit, and integrative functions. The brainstem is becoming a major focus in clinical and basic neuroscience due to our current ability to image it volumetrically at resolutions previously inaccessible to MRI.
, 2011; Meola et al. , 2016) , spinothalamic tract, medial longitudinal fasciculus, dorsal longitudinal fasciculus  (Meola et al. , 2016)  and central tegmental tract  (Kamali et al. , 2009; Meola et al. , 2016). In the context of an increasing body of information in the anatomical brainstem imaging field, to our knowledge the present study takes a step forward in identifying and delineating the greatest number of nuclear structures to date. This was achieved principally because of the high spatial resolution, and the very high signal quality of the dataset we analyzed. Importantly, the anatomical analysis was done by expert neuroanatomists using 3D Slicer segmentation and visualization tools. Finally, comparisons of the delineated nuclear structures were done with guidance from classical brainstem textbooks and atlases portraying the precise cytoarchitecture and topography of the human brainstem nuclei.
Furthermore, cell groups that are sources of major neurotransmission systems in the brain are localized in the brainstem, namely the substantia nigra and ventral tegmental area (VTA; dopamine), locus coeruleus (noradrenaline), raphe nuclei (serotonin) and reticular formation (acetylcholine; e. g. ,  Nieuwenhuys, 2012). All structures involved in the aforementioned functions were identified in the present study. It should be noted that the reticular formation and periaqueductal gray were labeled as single regions of interest, given the limitations in spatial resolution and contrast characteristics of our dataset for visualizing individual cell groups.
Limitations and Future Studies Given the quality of the imaging dataset used herein, a cogent way of viewing limitations is by comparing these data with histological observations at the microscopic level. A critical student of neuroanatomy always has as the gold standard of analysis a microscopic level of explanation. Neuroimaging is still a few steps away, perhaps by a factor of 10 in spatial resolution, from reaching this level of visualization, although it seems this is rapidly approaching. In this study, we were able to delineate 25 nuclear masses and 16 fiber pathways in the human brainstem using a 50-micron spatial resolution in a postmortem human brainstem. This is still far from the detail provided by microscopic histological examination, where brainstem nuclei and fiber tracts have been identified and delineated using such techniques.
It should be noted that in the present study the reticular formation was delineated as a region of interest by the exclusion of other structures and guided by histological atlases; the reticular formation per se could not be visualized with certainty using the current imaging methods. This limitation also applies to the description of the systems, such as the vestibuloocular system, which has more connections than were visualized in the current dataset. We recognize as well that certain boundaries of the ROIs segmented in this investigation may change as the state of MRI technology and neuroanatomical knowledge grows. We will publish updates in an on-line repository as the present structural borders may be modified as we receive feedback. Future studies using combined high-resolution MRI datasets with their histological sections for comparison  (Makris et al.
, 2013)  should provide further validation and accuracy of anatomical delineations in brainstem anatomy. These approaches will also permit a formal evaluation of the variability in volume and extent of regions of the human brainstem. To date, such variability in the human brainstem has been incompletely studied  (Andrew and Watkins, 1969; Afshar, 1978; Blood et al. , 2012) ; more detailed knowledge will be a critical step in understanding normal and abnormal brainstem anatomy. As MRI technology advances it is expected that higher resolution datasets than the one used in the present study will become available. Thus, histological analyses should be done in these samples as well in a co-registered fashion with the MRI datasets for precise and valid comparisons. In addition to the greater level of structural detail that these types of comparison will produce, they are also expected to generate a more complete description of brainstem systems.
21 MCP 1. 19 Dorsal Cochlear Nucleus 1. 40 Nucleus LC 0. 81 Solitary Complex 0. 58 Mesencephalic Complex 0. 44 Dorsal Motor Nucleus of X 0. 60 Median Raphe 0. 62 Hypoglossal 0. 74 PVG 2. 16 External Cuneate Nucleus 0. 67 Trigeminal Root 1. 03 Nucleus Cuneatus 0. 84 Superior Olivary Nucleus 1. 68 Nucleus Gracilis 0. 90 Motor Nucleus of V 0. 61 Note that some regions that were identified in the present dataset (i. e. , reticular formation, pontine nuclei, corticospinal tract, cerebral peduncle) were not identified in the Paxinos and Huang atlas per se and were removed for purposes of clarity. Abbreviations: CTT, central tegmental tract; IC, inferior colliculus; ICP, inferior cerebellar peduncle; LC, locus coeruleus; MCP, middle cerebral peduncle; MLF, medial longitudinal fasciculus; PAG, periaqueductal gray; PVG, periventricular gray; SCP, superior cerebellar peduncle; SpNV, the spinal nucleus of the trigeminal; SPTrV, spinal tract of the trigeminal; X, vagus.
The optimal exploitation of the oceanic information provided by recent high spatial resolution sensors such as Landsat 8-OLI is strongly conditioned by the quality of the water reflectance signal retrieval. One main issue stands in the ability to correct water pixels for the contamination of the sun glint, which might induce a seasonal or permanent loss of data according to the latitude. The SWIR information now provided for the most recent high spatial resolution sensors was used for evaluating the sun glint level and correcting the radiative signal for its effect. This has been performed transposing historical empirical formalisms based on the NIR signal. An automated SWIR-based sun glint correction procedure was then developed using a 4-year OLI archive gathered over very turbid waters of French Guiana (227 scenes).