dataesr/scientific-paragraphs-categorization huggingface dataset:
Haematoxylin and eosin hypercellularity less subjective and provide more reproducible results. 24 One method defines increased cellularity as the presence of more than 53 cells in 0. 058 mm 2 (corresponding to 217 cells in 0. 237 mm 2 ). 24, 32 This cell density seems to be unrealistic in canine meningioma, since in the present study the recorded cell number in 0. 237 mm 2 varied between 538 and 3567 (data not shown). Second, in the present study, hypercellularity could have been interpreted in relation to the mean cellularity of that specific tumour subtype and not as an absolute parameter. And finally, in daily routine grading of human meningioma, hypercellularity is semi-quantitatively evaluated by neuropathologists at low magnification. 24, 32 Semi-quantitative evaluation of increased cellularity remains subjective,  25  which should be taken into account when a grading system for canine meningioma is correlated to prognosis.
Spontaneous exploration in freely moving animals is defined by independent sets of postures and movements Mice were recorded by using four synchronized cameras (Figure  S1A ) during spontaneous exploration of an open field arena either in the dark or under steady illumination (respectively n = 11, 8 animals). To capture posture and movements in freely moving mice we first performed 3D reconstruction of head and body in animals implanted with multichannel microelectrodes (STAR Methods) in dorsal Lateral Geniculate Nucleus (dLGN). Eleven landmarks were identified on the mouse's body and microelectrode head-stage and were used for tracking the animals (Figure  1A ). The 2D tracking data were then triangulated to generate an initial 3D reconstruction of the animal (STAR Methods). Outliers and missing data in the 3D reconstruction were then corrected by extending a previously developed approach  34  (STAR Methods).
The corrected 3D data provided a robust estimate of the animal body over time (Video S1). From the 3D reconstruction, we quantified the postures and movements of the mouse in terms of fourteen behavioural variables. Hereafter we will define this set of variables as the behavioural state of the animal. Given biomechanical constraints we expect some postures and movements to correlate with each other. Therefore, we set out to identify relevant groupings among behavioural state variables. To do that we first applied Mutual Information to quantify linear and nonlinear correlations between all pairs. This analysis revealed two main groups that corresponded to postures and movements respectively (Figure  1C ). The existence of these two groups was confirmed by a hierarchical clustering analysis (Figure  1D , High HL partition).
Upward/downward facing postures and full body movements dominate firing rate modulation in visual thalamus in the dark We set out to determine the extent to which behavioural state variables involved in spontaneous exploratory behaviour were coupled to neuronal activity. In order to eliminate the possibility that such couplings could arise from associations between behaviour and visual experience, we first ran these recordings in the dark (see Figure  S3A ,B for mean and peak firing rates for single units in this dataset). From simple visual inspection we observed that some units exhibited an increase or decrease in firing that accompanied step changes in behavioural state variables (Figure  2A, B ). Joint visualization of behavioural time series and spike patterns also suggested an association between behavioural variables and spiking activity (Figure  2C, D ).
0001, n = 96 units from 11 mice, t-test). These results indicate that the modulation of dLGN neurons by posture mainly reflected sensitivity to body arch, which encompasses head elevation, while modulation by movements was dominated by locomotion. We then trained a model (based on XGBoost,  35, 36  , STAR Methods) to predict firing rate from individual behavioural variables. We evaluated prediction accuracy by calculating Pearson's linear correlation between recorded and predicted spike-counts on cross-validated data. Based on these results, for each unit we identified the best predictor, i. e. the variable associated with highest Pearson's correlation. Across our dataset the best predictors captured significant amounts of variability (ρ = 0. 16±0. 08, mean±; Figure  3C ). To exclude the possibility that this result was an artifact of our analyses, we shifted the behavioural variables by half a recording epoch in order to break their associations with neuronal activity while preserving their temporal structure (STAR Methods).
In order to avoid potential artifacts due to electrode movements across different days we analysed a dataset in which animals were recorded both in dark and light within the same experimental session (n = 58 units from 5 mice). We found that the tuning for the strongest predictors, body arch and overall movement, was significantly preserved (Figure  5F, G ). These results show that the coupling between behavioural variables and single unit activity described in the dark is maintained and amplified by visual stimulation.
Experimental Set-Up A detailed description of the experimental set-up for behavioural recordings is provided in  63. The animals were recorded in a square open field arena (dimensions: 30cm x 30 cm, Figure  S1A ). Behavioural recordings were acquired with 4 programmable cameras (Chamaleon 3 from Point Grey; frame rate = 15Hz) equipped with infrared cut-on filters (cut-on at 720 nm, Edmund Optics, #65-796) to isolate light in the infra-red range. Neuronal recordings were performed via 16 channel electrodes (Neuronexus; model: A4x4-3mm-50-125-177; package: CM16) with a TBSI W16 wireless acquisition system (Triangular BioSystems; sampling rate = 30 kHz). Frame acquisition, controlled via Psychopy (version 1. 82. 01,  64 ), was synchronized with acquisition of neuronal data via an Arduino Uno board (www.
Reconstruction of 3D Poses An initial 3D reconstruction of the mouse body was obtained by triangulating body landmarks from the four cameras (see body landmarks in Figure  1A ). The four-camera system was calibrated as previously described  34. Tracking of body landmarks from individual cameras was performed with DeepLabCut 68. The algorithm was trained with ~1000 manually annotated images and ran on a dedicated Ubuntu machine equipped with a Titan RTX GPU (Nvidia, Santa Clara, California, USA). The initial 3D reconstruction was contaminated with missing data and outliers, typically due to occluded body points. In order to correct the 3D reconstruction we modified a previously developed algorithm  34. We first estimated a Statistical Shape Model (SSM,  69  ) from a dataset of 350 manually re-annotated 3D poses.
The poses were initially aligned by used Procrustes Superimposition (with scale parameter = 1). The SSM was then estimated by applying Probabilistic Principal Component Analysis (PPCA,  70  ; MATLAB function: ppca). The SSM allowed us to express the 3D pose of the animal position as 𝑿 = (𝑿 ̅ + ∑ 𝑷 𝒊 𝑏 𝒊 𝑁 𝑒𝑖𝑔𝑒𝑛𝑝𝑜𝑠𝑒𝑠 𝑖=1 ) 𝑹 + 𝑻 (1) Where:  is an   3 matrix representing the 3D pose for given frame (  is the number of body landmarks);  ̅ is the mean pose;   and   represent respectively the  ℎ eigenpose obtained by training the SSM and its score (the shape parameter);  and  are respectively the 3  3 rotation and   3 translation matrices that map the animal's position in the Action potentials (typically >50 µV, see Figure  5A ) were extracted from the continuous, highpass filtered signals (low cut-off at 250 Hz) by using Offline Sorter (version 3).
Noise artifacts were then removed. Then individual units were first sorted by combining template sorting method (Valley Seek) with Principal Component Analysis (PCA) in Offline Sorter software (Plexon, USA) and next manually adjusted. Inter-spike interval histograms were inspected to make sure that no spike events occurred during the refractory period (defined at 1ms duration). When there was more than one cluster on the channel (15 channels with 1 cluster and 58 channels with >1 cluster were spike sorted), reliable single unit isolation was confirmed by a MANOVA test (Offline Sorter). Signal-to noise ratio (SNR) was then calculated for each unit and only units with SNR>1. 5 were kept for further analysis (Figure  S5B-C ). In order to avoid double counting the same unit from different channels we merged units that shared > 50% spikes timestamps from pairs of neighbouring channels (this only happened twice).
G) Percentage of units with a significant cross-correlation peak for both up/down postures (UD) and full body movements (FB) variables (striped bars), for UD only (orange) and FB only (purple). The remaining units (grey) have no significant peaks for any of the remaining variables. H) Average magnitude squared coherence (MSC, black lines) across the full dataset of recorded units (n = 96 units from 11 mice) for body left/right (Blr, left panel), body arch (Bar, middle panel) and overall.
Acknowledgements We wish to express our gratitude to  James Morizio  and  Harry Fu  for support with TBSI recording system and to  Annette Allen  for comments and advice on the manuscript. This study was funded by: a  Sir Henry Dale fellowship  from  Wellcome Trust  to  R. S. (Grant Code:  220163/Z/20/Z ); a  David Sainsbury Fellowship  from  National Centre for Replacement, Refinement and Reduction of Animals in Research (NC3Rs ) to R. S. (Grant Code:  NC/P001505/1 ); the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie grant  to P. O. F (Grant Code: 897951); a  Bekker Programme grant  implemented by the  Polish National Agency for Academic Exchange  to P. O. F. (Grant Code:  PPN/ BEK/2018/1/00192 ); a  Wellcome Trust   Investigator award  ( 210684/Z/18/Z ) to R.
Predictive Modelling Analyses Prediction of spike counts based on behavioural state variables was performed with eXtreme Gradient Boosting (XGBoost,  35, 67  ; parameters: learning rate = 0. 025; number of boosting iterations = 500; evaluation metric: log-likelihood loss; subsample = 1; maximum depth = 3; gamma = 1; tree method: gpu_hist). This method has been shown to outperform spike count predictions obtained with more standard approaches based on Generalized Linear Models  36. Prediction of behavioural state variables from spike counts was also based on the same XGB models. All datasets were bisected into two equal parts that were used for training and cross-validation. Prediction performance on the cross-validation set were measured as Pearson's linear correlation between predicted and original firing rate.
We overlaid each movement track on a raster consisting of equal amounts of two habitat types (hereafter, Habitat 1 and Habitat 2) that each take up half of the raster surface. All simulations were performed using the ctmm R package (v0. 6. 2; Calabrese et al. , 2016)  in the R statistical software environment (v3. 6. 2; R Core Team, 2020). Habitat rasters were created using the raster R package (v3. 4-10; Hijmans, 2021).
, 2018] ; conventional KDE for IID RSFs  [Worton, 1989] ), and fit RSFs using the ctmm R package (v0. 6. 2; Calabrese et al. , 2016)  in the R statistical software environment (v3. 6. 2; R Core Team, 2020). For the caracal, we then examined how using IID RSFs on thinned datasets would alter inferences gained from this dataset. To do this, we created 27 subsampled movement tracks (all possible regular subsamples of this data in which the absolute sample size is roughly equal to the effective sample size, rendering locations independent from one another). We used a weighted RSF to estimate strength of selection for built-up areas (accounting for selection for plantation), and the confidence intervals around this parameter estimate, for the full movement track. We used IID RSFs to estimate strength of selection for built-up areas (accounting for plantation), and the confidence intervals around this parameter estimate, for each subsampled movement track.
| Scenario 2: Data thinning In our simulations of data thinning, we found that RSFs performed as expected, with the parameter estimates of the weighted RSF very close to the expected parameter (ln(3)) while the IID RSFs parameterized using thinned datasets exhibited substantial variation in parameter estimates (range: [-1. 94, -0. 44]; Figure  2 ). The expected parameter fell within 48 out of 50 (96%) of the IID RSF 95% confidence intervals, indicating that our measure of effective sample size is a reasonable measure of independence. One IID RSF failed to identify statistically significant habitat selection.
| Empirical examples When analysing the water mongoose data (Figure  4a ), we found that both IID and weighted RSFs identified statistically significant When analysing how data thinning would influence inferences of habitat selection by the caracal, we found that IID RSFs parameterized using subsets of the caracal movement track exhibited substantial variation around the parameter estimate provided by the weighted RSF (Figure  5 ). The parameter identified by the weighted RSF fell within 27 out of 27 (100%) of the IID RSF 95% confidence intervals, but 7 out of 27 (26%) of the IID RSFs did not identify statistically significant habitat selection for this individual. F I G U R Our empirical analyses demonstrate that inferences gained from weighted and IID RSFs can diverge substantially. Weighted RSFs produce wider confidence intervals than conventional RSFs and meaningfully different parameter estimates when missing data are common, indicating that the results of our simulations translate to real-world data, even at absolute sample sizes that are relatively small for modern GPS datasets.
Although applied here to tracking data of individual animals, the statistical principles underlying our method are also applicable at the species and population levels, which would be of great Although ecologists and conservation biologists have access to better statistical and computational tools than ever before, new methods are still required to maximize the value of animal tracking data for informing our understanding of the natural world. In this study, we introduced a method of autocorrelation-informed weighting to reduce pseudoreplication and bias when parameterizing RSFs with autocorrelated and irregular animal tracking data, which are pervasive in studies of animal movement. Our method is easily implemented using the ctmm R package  (Calabrese et al. , 2016) , and we provide an annotated workflow in our supplementary materials (Appendix S1) so that other researchers can implement weighted RSFs on their own animal location data.
The light blue box outlines the confidence interval of the weighted RSF, while the darker blue horizontal line identifies the expected parameter (ln(3)). As expected, IID RSFs parameterized using thinned datasets exhibit substantial variation in their estimated parameters. F I G U R E 3 Mean parameter estimates and confidence intervals for weighted resource selection functions (RSFs) vs. independent and identically distributed (IID) RSFs for 400 simulations of habitat-specific Global Positioning System (GPS) triangulation failure. Row 1 (panels a and b) shows the performance of RSFs in a scenario of highly clustered habitat, while row 2 (panels c and d) shows the performance of RSFs in a scenario of unclustered habitat. Black points in column 1 (panels a and c) indicate mean parameter estimates for weighted RSFs performed on 400 simulated movement tracks, while white points in column 2 (panels b and d) indicate mean parameter estimates for IID RSFs.
The larger point and bar on the far left represent the weighted RSF, while the smaller points and bars represent individual subsets. The light blue box outlines the confidence interval of the weighted RSF, while the darker blue horizontal line identifies the selection parameter identified by the weighted RSF. As expected, IID RSFs parameterized using thinned datasets exhibit substantial variation in their estimated parameters. structured populations (e. g. repeated observations from individuals within the same herd or pack). Incorporating random effects into RSFs and SSFs has since become standard practice (Fieberg et al. , 2010; Muff et al. , 2020) , but remains an imperfect solution for accounting for pseudoreplication. Random effects generate confidence intervals that depend on variation in the point estimates of RSF parameters between individuals, which may or may not reflect true parameter uncertainty.
Yfiler alleles diversity The majority of the haplotypes obtained in this study occur only once. A total number of 157 haplotypes were observed out of 162 samples; only five were shared among 5 individuals. Haplotype data is presented in Table  S1 , and has also been uploaded to the Y-chromosome STR haplotype reference database (YHRD), release R66 (accession number YA005683). Allele frequencies of the 17-STR loci were calculated using Genetic Analysis in Excel (GenAIEx 6. 5) and presented in Table  S2 , while their distribution is shown in Fig. 2  below. The number of different alleles at each locus varied from 12 for DYS385a/b to 4 for DYS389I, DYS437 and DYS438. Gene diversity (GD), polymorphism information content (PIC), match probability (PM) and power of discrimination (PD) were calculated using the STR Analysis for Forensics (STRAF) online tool; the values for each locus are presented in Table  S3.
7%); three samples were also observed at the locus DYS385a/b (1. 9%) and one at the locus DYS19. Most of the microvariant samples (20/24) are predicted to belong to haplogroup J (J1 = 17 and J2 = 3). Twelve samples (7. 6%) were observed as mono-allele for the bi-allelic DYS385a/b locus. Several variant alleles were observed in our dataset that migrated outside the range of the allelic ladder of the AmpFLSTR ™ Yfiler ™ PCR Amplification Kit. The off-ladder alleles were at locus DYS635 allele 27 (three samples), predicted to belong to haplogroup R2-M124; and at locus Y GATA H4 allele 14 (two samples) predicted to belong to haplogroup E1b1b-M35.
Population comparison The haplotypes of the Sorani Kurd were compared to fifteen other population haplotypes using YHRD databases: Iraqi (Arabs), Iran, Qatar, Saudi Arabia, Lebanon, Yemen, Israel, Turkey, Afghanistan, Azerbaijan, Egypt, Ethiopia, Greece, Denmark and Sweden. The population pairwise genetic distances (R st ) were calculated between the studied population and the previously reported populations (Table  S5 ); the results are shown in Fig. 4. A multidimensional scaling (MDS) plot was performed from R st distances (Fig. S4 ), and the result showed that the Sorani Kurdish population clustered with Qatar, Lebanon, Iraq (Arab), Iran, Greece, Azerbaijan and Turkey. The closest populations to the Sorani Kurds were Qatar (R st = 0. 0042), Lebanon (R st = 0. 0078), Iraqi Arabs (R st = 0.
Observations of the sub-haplogroups E1b1b-M35 and G2a-P15 were also significant from the earlier study on the paternal lineage of Northern Iraqi Kurds, E1b1b-M35 (13/104) (12. 5%) and G2a-P15 (8/104) (7. 69%)  [19]. However, these slight differences in the genetic parameters were expected. The current study, the first to our knowledge, focuses on one group of the Kurdish population (Sorani Kurds) in Iraq, separated from the Kurmanji Kurdish group in the northwest of Iraq on the border with Turkey. In addition, a higher number of population samples were collected than in the previous paternal lineage studies of the Kurdish population  [19, 20, 47] , which is also an important consideration in obtaining more reliable results with greater precision and power. The Sorani Kurdish population was compared with 15 other populations in the YHRD database.
Conclusions Human Y-STR markers provide powerful results for haplotype analysis and haplogroup prediction, which lead to individual geographical origins. In this study, a database of 17 Y-STR loci for the Sorani Kurdish population was established. The highest and lowest gene diversities were found at the loci DYS385a/b and DYS392, respectively, and the J2 haplogroup predominated. The findings also show that the Sorani Kurdish population is genetically more similar to populations in Western and Central Asia than to populations in Europe and Africa. Because genetic data on the diversity of the Kurdish population is limited, providing Sorani Kurd paternal lineage data may aid in developing a better understanding of this Kurdish ethnic group's bio-geographic ancestry.
Data analysis The haplotype frequencies were calculated using GenAIEx 6. 5  [49]. The forensics statistics including gene diversity (GD), polymorphism information content (PIC), match probability (PM) and power of discrimination (PD) were calculated using the STRAF online tool  [50]. Haplogroup predictions from Y-STR values of the Sorani Kurdish population were inferred using Athey's Haplogroup Predictor  [51]. Median-joining networks were constructed by the software NETWORK v5. 0. 1. 0 and NETWORK Publisher v2. 1. 2. 5 (Fluxus Technology Ltd. ) following the author's recommendations  [52, 53]. Population comparisons were performed using the YHRD database (https:// yhrd. org (accessed on 17/01/2022))  [54]. The online AMOVA program was used to calculate the R st among the populations and the multidimensional scaling (MDS) was performed based on Kruskal's non-metric MDS algorithm  [55].
3 was considered as a benefit of treatment 2 for the patient, as defined by Von Hoff et al. [32]. Patients were eligible if they had a solid tumor and underwent comprehensive cfDNA genomic profiling during routine care in our centre (AP-HM), between February 2019 and March 2021. No restriction on tumor type, pathology, previous treatments, or other factor were imposed. cfDNA genomic profiling was performed in patients selected at the physician's discretion. For each patient, the following clinical data at the time of the molecular profiling were retrospectively collected in electronic medical records: demographic data (age, sex, smoking status), biological data (tumor type, molecular status), radiological data (tumor stage, response to treatments according to RECIST 1. 1), treatment data (previous and subsequent treatment regimen, time on treatment(s)) and outcome data (progressionfree survival, overall survival).
Actionable molecular alterations (AMA) were defined as molecular alteration for whom a therapy targeting either the molecular alteration or the pathway activated by the molecular alteration was available, using the OncoKB database (v3. 4; 17 June 2021)  [34]. All evidence levels from 1 to 4 were considered (level 1: FDA-recognized biomarker predictive of response to an FDA-approved drug in a specific cancer type; level 2A: standard care biomarkers predictive of response to an FDA-approved drug in a specific cancer type; level 2B: standard care biomarkers predictive of response to an FDA-approved drug in another cancer type; level 3A: compelling clinical evidence in a specific cancer type supporting the biomarker as predictive of response to a drug that is not yet approved in the standard of care; level 3B: compelling clinical evidence in another cancer type supporting the biomarker as predictive of response to a drug that is not approved in the standard of care; and level 4: compelling biological evidence supporting the non-FDA-recognized biomarker as predictive of response to a drug).
32 (95%CI: 0. 12-0. 84; p = 0. 02) (Figure  S4 , Supplementary Materials). In an additional subgroup analysis, in the cohort (n = 50) of patients with a level 1-2 AMA (according to the OncoKB database) that received treatment after F1LCDx testing (treatment 2), no clinical benefit of the MMT strategy was found compared to non-MMT in terms of PFS2 (2. 7 months vs. 2. 8 months, p = 0. 87), OS (4. 7 months vs. 7. 2 months, p = 0. 6), the PFS2/PFS1 ratio ≥ 1. 3 (19% vs. 27%, p = 0. 73), or tumor responses (Table  S2 , Supplementary Materials).