dataesr/scientific-paragraphs-categorization huggingface dataset:
Rhabdoid tumors predominantly occur in children < 3 years affecting brain (atypical teratoid rhabdoid tumor, (ATRT)) and kidney but can occur virtually in any anatomic location; 6-year overall and event-free survival is around 45% with maximally intensive treatments  [5]. Shared hallmark finding of both malignancies is inactivation of SMARCB1 by mutation and/or deletion  [6, 7]. INI1, core component of the SWI/SNF chromatin remodeling complex whose role as bona fide tumor suppressor has been validated, regulates gene expression of several genes by acting on chromatin conformation in an ATP-dependent manner  [8, 9]. Correspondent loss of INI1 expression is present in rhabdoid tumor and 80-90% of epithelioid sarcoma  [1, 7]. INI1 inactivation deregulates multiple axes maintaining physiologic cell regulation promoting dedifferentiation, tumor cell growth, and progression, which led to the clinical evaluation of histone methyltransferase Enhancer of zeste homolog 2 (EZH2) inhibitor tazemetostat in INI-negative tumors (NCT02601937).
Rhabdoid tumor cells exhibit sensitivity towards multityrosine kinase inhibition whereas oncogenic signaling is activated in epithelioid sarcoma Based on the overexpression of growth factor receptors in both tumor types, we selected multi-tyrosine kinase inhibitors (TKI), comprising FGFR which were either approved for the treatment of advanced soft tissue sarcoma (pazopanib) or in clinical investigation (regorafenib, NCT02085148), and erlotinib for EGFR inhibition. mTOR inhibitor everolimus was also evaluated. A204 proved to be the most sensitive cell line towards multityrosine kinase inhibition of cell proliferation, but was resistant to erlotinib and everolimus (Fig. 2A ). VAESBJ exhibited low sensitivity to regorafenib and resistance to pazopanib, but it was highly sensitive to everolimus; it was the only cell line in which panobinostat did not succeed to impair AKT phosphorylation (see also Fig.
Regorafenib treatment induced significant tumor growth inhibition of VAESBJ xenografts which could likely be assigned to the antiangiogenic effect of regorafenib, as described by our group previously  [22, 26]. These observations are aligned with the report from a recent retrospective trial evaluating pazopanib in 18 patients with epithelioid sarcoma which reported no objective responses but an inferior outcome as compared to patients that had received chemotherapy  [40]. Remarkably however, our own clinical experience includes a young adult patient with metastatic disease who the primary cells used in this study were derived from. This patient underwent complete remission by pazopanib, ongoing for 3 years  [27]. She had received multiple preceding treatments including surgery, cryotherapy, conventional chemotherapy, erlotinib in combination with rapamycin, and lastly EZH2 inhibitor tazemetostat (NCT02601937).
For rhabdoid tumors in particular, models should be chosen which comprise both also ATRT and rhabdoid tumors of the kidney, and also taking into account the molecular dissection of ATRT into three distinct epigenetic subgroups  [48]. From a clinical point of view, there is a precarious gap between promising preclinical data and disappointing lack of single agent efficacy in patients with solid cancers, an example of which is diffuse intrinsic pontine glioma (DIPG), a pediatric tumor with dismal outcome. Panobinostat alone (NCT02717455) or in combination with a proteasome inhibitor (NCT04341311) has entered clinical investigation for this entity based on promising preclinical data  [49]. However, later studies yielded discordant results in vivo despite the fact that in a genetically-engineered mouse model supposedly active concentrations could be achieved in the brainstem tumor, as opposed to the normal cortex  [49, 50].
The relatively intact blood-brain barrier (BBB) that characterizes DIPG might be a main factor that up to present, preclinically effective treatments could not be translated into clinics. To bypass the BBB, currently convectionenhanced delivery (CED) for panobinostat has been brought forward  [49, 51]. The resulting agent MTX110 is currently under clinical investigation for both DIPG (NCT03566199) and medulloblastoma (NCT04315065). Various HDAC inhibitors like entinostat alone (NCT02780804) and in combination (INFORM 2; NCT03838042) are now being explored in pediatric cancers and data are awaited for these. Furthermore, regorafenib as various other multi-tyrosine kinase inhibitors are currently introduced into sarcoma treatment combined with chemotherapy whereas selective FGFR inhibitor have only started recently in phase 1 trials (i.
e. erdafitinib NCT04083976 or futibatinib in our AcSe-ESMART NCT02813135). This study was dedicated to decipher cellular and regulatory effects of HDAC inhibitors in two aggressive sarcoma types. Next to the great cytotoxic potential of panobinostat at low nanomolar concentrations in these tumors, we show to the best of our knowledge for the first time that HDAC-mediated growth factor receptor regulation, EGFR in particular, is not an exclusive feature to epithelial-derived tumors. We delineate that EGFR has important implications in cellular dedifferentiation rather than proliferation in both genetically linked tumor types. Intrinsic EGFR overexpression in epithelioid sarcoma maintains the cell's mesenchymal traits which are in part reversible by panobinostat. In rhabdoid tumor cell line A204, the induced EGFR upregulation serves to resist the differentiating potential of HDAC inhibitors.
Relationship between Green Status of Species outputs and IUCN Red List category To investigate the potential of Green Status of Species assessments to provide novel conservation insights, we evaluated whether species recovery scores were predicted by IUCN Red List category by performing beta regression in the R package betareg (Cribari-Neto & Zeileis, 2010) in R version 4. 0. 0 (R Core Team 2020). We excluded species considered extinct in the wild (EW) on the IUCN Red List because by definition their species recovery score is 0. Our data set included 0s (pre-exclusion of EW) and 1s, but we did not use zero-one inflated beta regression because it assumes that the 0s and 1s are special cases generated under different processes than other data points  (Buis, 2010) , which was not true for our data set.
To allow for regular beta regression (where the data set cannot contain 0s or 1s), we used the rescaling method recommended by  Smithson and Verkuilen (2006) : y' = [y(N -1) * 1/2]/ N, where N is the total sample size. No transformation was greater than adding or subtracting 0. 003 from the original data point. Model terms were evaluated using the function joint_tests (package "emmeans  [Lenth, 2020] ) and the pseudo R 2 obtained using R base package summary. Pairwise comparison of estimated marginal means (a. k. a. least-squares means) was performed using the function cld (package multcomp  [Hothorn et al. , 2008] ); estimated marginal means were compared rather than observed means to account for unbalanced sampling. Unlike species recovery score, the 4 conservation impact metrics can take negative values.
Future directions Although our tests covered a diversity of species, geographies, and biomes, we did not sample in a systematic or representative way (which is why we do not, e. g. , report statistics by taxon). Our data set was biased toward threatened species (67%), so our figures of extinctions prevented by past conservation or likely to be prevented by future conservation cannot be generalized. Comprehensive evaluation of counterfactual status for all species within a taxon yielded lower rates of extinctions prevented  (Butchart et al. , 2006; Hoffmann et al. , 2015). Although Green Status of Species assessments could eventually improve understanding of the global impact of conservation, the values reported here are not representative. Understanding the recovery status and trajectories of a systematic sample of the world's major species groups would provide valuable information for conservation planning.
ACKNOWLEDGMENTS This work would not have been possible without the countless hours volunteered by species experts around the globe, including many assessors who did not choose to be listed as authors. We thank the  IUCN Species Survival Commission and Red List Unit  for their help coordinating this input and to the  National Geographic Society  for supporting a suite of test assessments. Workshops were funded by the  Prince Albert of Monaco Foundation  (administered via the  Cambridge Conservation Initiative Collaborative Fund)  and by Fondation Franklinia. We thank  Global Wildlife Conservation  for their help coordinating the workshops. Special thanks are due to  A. Rodrigues  for her active involvement in every stage of developing the  IUCN Green Status of Species. We thank  T. Kuiper  for help with data analyses and  M.
The resulting dataset is a spatially and spectrally resolved three-dimensional set of information, called a hypercube (spatial coordinates: x, y; spectral coordinate: z). Given its ability to rapidly discriminate and quantify chemical composition over large areas, HSI has been successfully used within the fields of remote sensing  [5] , food quality control  [6] , vegetation and water resource control  [7] , forensic medicine  [8] , recycling industry  [9] , analysis/restoration of old paintings or manuscripts  [10]. HSI can distinguish the biochemical composition of healthy and pathological biological tissue  [11]  in a non-invasive fashion, and for this reason, it has been increasingly used in the medical field  [12, 13]. Previously, other authors have written comprehensive overviews concerning the application of HSI in gastroenterology  [14] , surgery  [15]  or surgical clinical studies  [16].
The Electromagnetic Spectrum and the Hypercube The human eye only detects a narrow portion of the electromagnetic (EM) spectrum. In fact, human sight can only perceive light in the visible (VIS) range (400-780 nm) (Figure  1A ). Light in the VIS range exhibits only minimal tissue penetration (1-2 mm), while in the near-infrared (NIR) range (780-2500 nm) it can pass up to several millimeters through tissue, hence having a higher diagnostic value than VIS  [12]. HSI is a hybrid imaging modality which combines digital photograph and spectrometry, generating a three-dimensional dataset, namely the hypercube. This is composed of a stack of two-dimensional images (spatial coordinates) across a wide and generally contiguous range of the EM spectrum (spectral coordinate), hence augmenting the human vision far beyond its natural capabilities (Figure  1B ).
Cancer Recognition As previously mentioned, the hypercube is a large dataset, and information that is required to discriminate between different tissue types must be extracted. As a result, machine learning algorithms are used to perform this task automatically. Ideally, to assist the surgeon, an HSI imaging system should automatically detect the different tissue types immediately or shortly after the acquisition and provide the operator with simple visual feedback. Deep learning algorithms, in particular neural networks, can promptly differentiate between different tissues, based on their spectral characteristics, either completely automatically (unsupervised learning) or after learning from previously annotated images (supervised learning). Although many previous works have focused on the detection of cancerous tissue using HSI coupled with various data processing algorithms  [67] , most of them were performed ex vivo (in the histopathological lab) or in vivo, but using small animal models.
92 for colorectal and of 0. 93 for esophagogastric cancer, respectively)  [28]. Interestingly, the authors noticed a significant improvement in the automatic classification by combining both cancer types within the learning datasets. Although this study involved a rather small number of patients (namely 22), the interesting novel approach of combinations for the algorithm training phase of different histological tumor types proved able to improve the automatic classification accuracy. This method could be useful when dealing with limited datasets (e. g. , small tumors, small patient cohorts, rare tumor types) in order to maximize the machine learning algorithm prediction efficacy. Research in the field of brain cancer detection using HSI has made considerable improvements towards the intraoperative automatic recognition of pathological tissue, due to the introduction of complex deep learning algorithms and the discovery of tumor-specific spectral bands.
The authors then successfully analyzed the spectral characteristics of several human tissue types in the different wavelengths using a sophisticated post-processing set-up. This work was propaedeutic to future research aimed to identify human tissue types using the same custom-made camera combined with further ML algorithms. In a pilot study in 9 human patients undergoing endocrine neck procedures, Barberio et al. acquired intraoperative HSI images and could find the specific spectral features of thyroid and parathyroid glands  [32]. This preliminary work was the precursor of a second one  [33] , in which a similar dataset was analyzed with a supervised classifier and automatic recognition of the parathyroid gland was achieved (Figure  2A ) against the surrounding tissue types. However, the accuracy of the automatic detection was rather low to be acceptable within a clinical setting.
As a result, by customizing the commercially available HSI system, with the addition of a high-resolution video camera, and thanks to an in-house software solution, the system could provide a live video, displaying the quantitative perfusion information obtained with HSI in augmented reality. The solution was named HYPerspectral-based Enhanced Reality (HYPER)  [38]  and it allowed accurate intraoperative quantification of bowel perfusion. The perfusion quantification accuracy was tested in a complex experimental set-up involving a small bowel ischemia model in 6 pigs, with an ischemia time ranging from 5 min to 6 h. HYPER's perfusion quantification was validated through a good correlation with robust ischemia biomarkers and it allowed for the precise localization of the HSI information on the surgical scene (Figure  2C ).
Cases and Controls Phenotype and genotype data of 58,181 entries of the data repository of ILCCO were extracted. Details of the repository is described previously. [1, 25]  QC control samples, individuals without information on smoking status or age, and samples of poor genotyping quality or sex discrepancies, were excluded, To avoid population stratification, this analysis is focused on European-ancestry population (defined as more than 95% probability of being of European descent). 14,068 incident LC-cases and 12,390 cancer-free controls of European descent remained for analysis. Those genotyped with other genome-wide array in addition to OncoArray were separated to form an independent validation set (2 nd validation set) of size (n=4,359, including 2,360 LC-cases and 1,999 controls).
Association analysis We first performed association analysis for each marker separately using the program PLINK. [27, 28]  Crude (model 1) and adjusted odds ratios (ORs) were estimated along with 95%-confidence intervals within log-additive models. Sex, age and smoking status and the first 3 principal components (PCs) to adjust for population stratification (model 2); and in addition the 6 most significantly associated LC-markers (rs55781567, 15q25. 1 CHRNA5; rs11780471, 8p21. 2 CHRNA2; rs7705526, 5p15. 33 TERT; rs56113850, 19q13. 2 CYP2A6; rs71658797, 1p31. 1 AK5; rs11571833, 13q13. 1 BRCA2) (model 3) were included in adjusted models. ORs were estimated for overall LC, small cell LC (SCLC), squamous cell LC (SqCLC), adenocarcinoma LC (adenoLC), ever smokers, never smokers and young individuals (21 to 55 years of age) as subgroups.
This was particularly noticeable for SCLC, since AhR/Wnt-markers contribute more than twice as much to the prediction score as LC-markers. It is known, that within current smokers, tobacco consumption is strongest associated to SCLC. [42]  Moreover, within never smokers, a stringed defined score is made up from only two AhR/Wnt-markers, assigned to Axin2 and SFRP4. However, the discriminative ability of PRSs for LC, contributing markers with significance for main effect at different levels, is in general poor. The AUC of the BIC LC score for overall LC (0. 58 in the test set and 0. 55 in the 2 nd validation set) corresponds to the AUC=0. 54 based on four top LC-genes in a simulated population, as given by the GWAS-ROCS Database (https://gwasrocs. ca/). This may be due to other overpowering risk factors, since models including e.
Indeed, recent data showed the ability of MET of targeting the inflammatory tumor microenvironment, contributing to reduction of tumor mass and of cancer related M2 macrophage polarization  (22). For the reasons above, the primary objective of our study was to evaluate the effect of MET used in combination with TMZ on EGFR mutation (d2-7) carrying GBM models sensitive and resistant to TMZ and on patient-derived EGFR amplified Cancer Stem Cell line. Furthermore, we aimed to evaluate the potential use of in vivo Positron Emission Tomography (PET) molecular imaging to predict drug effects. For this purpose we measured at early time after treatment the uptake of [ 18 F]FLT and [ 18 F]VC701 radiopharmaceuticals targeting thymidine kinase 1 (TK1) and Translocator Protein 18 kDa (TSPO) which are receptors associated with glioma malignancy.
RNA Extraction and Real-Time PCR RNA was extracted using the commercially available illustra RNAspin Mini Isolation Kit (GE Healthcare, Italy), according to manufacturer's instructions. Total RNA was reversetranscribed to cDNA using the High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientifics, USA). Real-time PCR was performed in duplicate for each data point by using the Sybr Green technique and the oligonucleotides used were: bactin (FRW: TCAAGATCATTGCTCCTCCTG, REV: CCAGA GGCGTACAGGGATAG); bax (FRW: ATG GAC GGG TCC GGG GAG; REV: ATCCAGCCCAACAGCCGC); bad (FRW: C C C A G A G T T T G A G C C G A G T G ; R E V : C C C A T CCCTTCGTCGTCCT) ; bcl-2 (FRW: GATTGTGGCC TTCTTTGAG, REV: CAAACTGAGCAGAGTCTTC); sox2 (FRW: GCACATGAACGGCTGGAGCAACG; REV: TGCT GCGAGTAGGACATGCTGTAGG). Changes in the target mRNA content relative to housekeeping (b-actin) were determined with the DDct method.
Immunohistochemical Analysis Mice brains were collected at sacrifice and fixed in 10% buffered formalin (Sigma-Aldrich) as described  (34). After standard histological samples processing, serial 3 um-thick brain sections were cut and stained with hematoxylin and eosin (H&E) for morphological evaluation or probed with the following primary antibodies: Ki67 (Agilent Technologies, Santa Clara, US. ), Sox2 (Cell signaling technologies Leiden, The Netherlands), cleaved-Caspase 3 (Cell Signaling Technologies, Palo Alto, CA, USA), and Iba1 (Wako pure Chemical Ind. Ltd. ). Afterword, slides were incubated with Rodent Block R immunohistochemical reagent (Biocare Medical) before secondary antibody addition. Finally, slides were revealed using DAB as chromogen and counterstained with hematoxylin. Stained slides were scanned using Aperio digital scanner instrument (Leica Microsystems) and Iba1 expression was analyzed using the cytoplasmic algorithm available within ImageScope software (Leica Microsystems) after optimization of cell recognition parameters  (12).
Gd-T1-weighted MRI was conducted to verify the presence of lesion before the beginning of treatment and for manual co-registration with PET images performed using PMOD 3. 2 software. PET imaging was performed with 3′-deoxy-3′-[ 18 F] fluorothymidine ([ 18 F]FLT) and [ 18 F]VC701 to assess proliferation related to TK1 and TSPO receptor expression, respectively. [ 18 F]FLT and [ 18 F]VC701 uptake was evaluated by PET in distinct groups of Gli36DEGFR-1, Gli36DEGFR-2 and L0627 mice during control condition ([ 18 F]FLT: n = 11 for R-1, n = 11 for R-2 and n = 9 for L0627; [ 18 F]VC701: n = 9 for R-1, 11 for R-2 and n = 11 for L0627) and 1 week after the beginning of treatment (vehicle, TMZ, and TMZ plus MET). MET treated animals did not perform imaging. The sample size of each group is indicated in the figures.
Mice were injected via the tail vein with 4. 18 ± 0. 28 MBq of [ 18 F]FLT and 4. 79 ± 0. 91 MBq of [ 18 F]VC701. PET acquisitions were performed at 60 min ([ 18 F]FLT) or 120 min ([ 18 F]VC701) after tracer injection using the YAP-(S)-PET II small animal tomograph (ISE s. r. l. , Pisa, Italy) or X-ß-CUBE (Molecubes, Gent, Belgium) as already described  (36, 37). All the radiopharmaceuticals injected had a radiochemical purity greater than 99%. PET images were acquired in 3D mode. All the images were co-registered to MRI and quantified with PMOD 3. 2 software (Zurich, Switzerland). The quality of coregistration was judged and confirmed by two independent experts in the field (S. V. and I. R. ). Two different volumes of interest (VOI) were defined: (i) a control VOI covering the left striatum (volume 7 mm 3 ) was drawn on the axial MR images, adjusted on the other imaging planes and then copied on the PET images of each mouse; (ii) a second glioma-covering VOI was drawn in the tumor-affected brain hemisphere and centered on mice lesions.
On the other areas data are very heterogeneous. For the L0627-generated tumors we scored the mitotic count in three high power fields per mouse and tumor cell viability using an antibody toward cleaved-caspase 3, because Ki67 staining was surprisingly absent. This analysis showed that tumor proliferation was decreased by TMZ or MET individual treatments (20. 88 for vehicle, 14. 38 for TMZ, 13. 90 for MET), but only TMZ marginally increased glioma cells apoptosis (0. 68% for vehicle, 4. 35% for TMZ and 2. 56% for MET) (Figures  S7A, C ). Again, the combinatorial treatment significantly impaired the ability of the glioma cells to in vivo grow, one mouse negative-defined at MRI showed a small tumor mass at sacrifice. In regards of the glioma stem cell markers, we observed a high Nestin staining in all L0627 tumor samples and positive staining for Iba1 within and in the peripheral areas of the tumor and in cerebral hemisphere contralateral to tumor implantation (Figure  S7B ).
ACKNOWLEDGMENTS The authors thank  P. Simonelli ,  Dr. E. Turolla ,  S. Stucchi  and  Dr. S. Battaglia  of  University of Milano-Bicocca  for their technical support in FLT synthesis and animal handling,  T. Canu  of  CIS-San Raffaele Scientific Institute  for MRI acquisition and  Prof. A. Cappelli  of  University of Siena  for VC701 chemical synthesis.
Biomarkers The Olink Cardiovascular III (CVD III) panel includes 92 biomarkers from different pathophysiological domains. The Proseek Multiplex 96*96 kit of Olink Bioscience (Uppsala, Sweden) analysis service was used, which measured the 92 biomarkers in 1µl plasma samples. The reagents are based on the Proximity Extension Assay (PEA) technology, which binds 92 oligonucleotide-labeled antibody probe pairs to the target biomarker. 15 For further quantification, real-time PCR was performed. Olink wizard and GenEx software were used for further data analysis. Proseek® data are presented as arbitrary units (AU) on a log2 scale (Supplementary Table  1  and 2 ). Complete biomarker data was available in 87% of the patients under study.