dataesr/scientific-paragraphs-categorization huggingface dataset:
Tumor Infiltrating Lymphocyte Inference A HoVerNet model trained on the PanNuke dataset was used for nuclei segmentation  17, 33. We leveraged the implementation and pretrained model from the PathML toolkit  34  for computational pathology (https://github. com/Dana-Farber-AIOS/pathml). While this pretrained model produces 10 accurate nucleus segmentation, its subtype classification notably fails on clear cell renal carcinoma, likely due to a near-absence of this histology within PanNuke. Consequently, we finetuned the classification head of the model to predict tumor-context vs stromal-context nuclei using a pseudolabeling scheme; nuclei in a tile were randomly assigned "tumor nuclei" labels proportionally to the predicted probability produced by the tumor classifier (ex. , 90% of nuclei randomly assigned "tumor nuclei" if tumor score == 0.
Survival Analysis Survival analysis in the TCGA-KIRC And CM-025 cohorts was performed using the python package Lifelines  35. Kaplan-Meier regression and plotting was performed using the KaplanMeierFitter function with default parameters, and multivariate Cox Proportional Hazards regression was performed using the CoxPHFitter function with moderate regularization (L1 ratio = 0. 1 [multivariate models], L1 ratio = 0 [univariate models], penalizer scale = 0. 1 [multivariate models], penalizer = 0. 0 [univariate models]). We also further excluded slide images with fewer than 200 tiles predicted to contain tumor tissue. We considered only slides obtained from primary biopsy sites, excluding metastatic biopsies to remain consistent between cohorts. When annotations were available, we excluded Grade 1 (G1) cases due to their rarity.
Image Registration We adapted PathFlow MixMatch, displacing an input H&E image against a fixed mIF image at 1. 25X, and using GPU acceleration (Tesla V100) when learning each case's alignment/displacement tensor. Learned displacement tensors were then used to shift H&E-based grade segmentation maps into the same coordinate space as mIF data. These aligned maps were then used in K-nearest neighbors regression to assign cell predictions in the mIF data to a grade segmentation label. Since the image pairs are not from the same exact tissue section, alignments were assessed visually via overlay to assess quality, resulting in 13 total passing cases. Within successful alignments, putative tumor regions were manually reviewed, resulting in omission of two false-positive regions (adjacent metastatic tissue misclassified as "tumor").
Immune Hotspot Analysis To select regions of interest with high tumor-immune activity, we split full resolution mIF WSI data into tiles 2000px (approx 1mm) wide, and further selected for regions with at least 50% area overlap with H&E-inferred tumor region predictions. We then filtered for regions with at least 50 CD8+ and 50 Tumor+ cells that were engaged in tumor-immune interactions. From each patient slide, we sampled up to 10 hotspots, selecting those with the most CD8+ density involved in tumor-immune interactions (min=2 samples, mean=9. 0 samples; 42 total microheterogeneous samples [n=5 slides], 57 total microhomogeneous samples [n=6 slides]) (See Extended Data Figures  28 29 ). Two patients lacked any hotspots and were excluded from this analysis.
Statistical Testing All statistical analysis was performed using python 3. For comparison of group counts, Fisher's Exact test was used via the scipy function fisher_exact  40. Other continuous, score-based comparisons were performed using a two-sided Wilcoxon rank-sum (Mann-Whitney U) test using the statannotations package to directly annotate Seaborn plots with p-value results  41, 42. For survival analyses, cohort subgroup survival distributions were compared using the log-rank test using the multivariate_logrank_test and pairwise_logrank_test functions in Lifelines. For Cox models, the concordance index (C-Index) and (Log) Likelihood Ratio Test (LLRT) were used to evaluate goodness of fit. When comparing continuous to categorical grade, the relative likelihood was estimated using the partial AIC produced for each Cox model, and interpreted as the probability one model minimizes the AIC of the other.
Background Learning by concordance (LbC) is a recent approach that introduces learners to the complexity and uncertainty of clinical practice. Some data on LbC suggest that it stimulates reflection in future clinicians. We developed an online LbC training program on electrocardiogram (ECG) interpretation in general practice at the University of Strasbourg, France, and conducted an exploratory qualitative study to document the impact of this ECG learning-by-concordance training program on reflection in participants.
There were no exclusion criteria. The investigators presented the research by explaining the qualitative method and the preservation of the anonymity of participants; they also secured participants' oral consent for the audio recording and transcription of the focus group and individual interviews. Students who were available to participate during the proposed dates were contacted via email. Data were collected from the focus group in a restaurant in April 2018 (first focus group) and in the University of Besançon's Faculty of Medicine in May 2018 (second focus group). Data were collected from the individual interviews in the Faculty of Medicine of the University of Strasbourg between March and September 2022. The interview guides were prepared beforehand and submitted to a group of qualitative research experts before the data collection phase; however, they were not tested.
These guides were refined after the first focus group and the first individual interview. Additional file 2 presents the interview guides. During the individual interviews, AL projected the LbC program on a wide screen. Each participant could progressively review the training, including their own answers. Vignettes were picked randomly, and the participant was invited to comment on how they progressed through the training program. The investigators used an audio recording to transcribe the discussions extensively. Field notes were taken before the interviews to describe the participants. The participants were notified that they could ask for the transcripts to make changes or comments, but no participants took advantage of this offer. All the verbatim interviews were anonymized. EH and AL coded the entirety of the data using a thematic approach with the help of NVivoâ software.
However, prefetchers can fail to capture the specifics of more dynamic memory access patterns, causing interference, spurious cache evictions and general performance degradation. Thus, prefetchers can be disabled using privileged, administrator-level means. In the case of the Intel processor used in our study, each core supports four independently controlled prefetchers (PREF): the DCU IP-correlated prefetcher brings data from L2 to L1 based on a stride computed using the instruction pointer; the DCU prefetcher brings the next line to the L1 cache; the L2 Adjacent Cache Line prefetcher brings the previous or next cache line that completes a block aligned to 128 Bytes; and the L2 Streamer prefetcher tries to identify data streams and to bring the next predicted line to the L2 cache.
We also assess the quality of our measurements as they condition both our analysis and the prediction capabilities of our Machine Learning models. Section IV presents the actual results analysis. A. Experimental Setup 1) Hardware and Software: All experiments were performed on an Intel Xeon Gold 6130, with two NUMA nodes and 16 physical cores (32 virtual cores with Intel HT) per node, and 192 GB of DDR4 memory at 2666 MHz. To increase results stability, we used the base CPU frequency 2. 1 GHz. We compiled all the applications with clang-6. 0, -O3, and executed them on Linux 5. 10. 40-1. All applications used in the experiments are codes written in C/C++ and annotated with OpenMP directives. They include established HPC benchmarks from the Rodinia Benchmark Suite  [11]  (v. 3. 1), the NAS Parallel Benchmarks (NPB v.
This profiling also returns the memory footprint for each case. We avoided recalculating offsets across executions by disabling the Address Space Layout Randomization. Finally, the OS migrates pages to maximize locality. We disabled it because we already implemented a data policy that maximizes locality with Numalize and we do not want the automatic migration to disturb our decisions during the search space exploration. 3) Setting Varied Problem Sizes: Problem sizes are selected differently depending on the benchmark suite. The NAS Parallel Benchmarks are compiled with a selection of problem sizes (i. e. , classes) that affect the size of data structures as well as the number of iterations to compute. The other benchmarks rely either on generated inputs or arguments set at launch time. We present the commands to replicate our experiments in Table  II.
[6]  and  [7]  used the method at reservoir conditions with x-ray saturation monitoring for estimating oil recovery from transition zones. More recently,  [8]  developed a full petrophysical rock characterization workflow similar to  [3]  but included the full relative permeability curve, the produced phase relative permeability being derived from the electrical properties or using history match process. Few other studies can be found in the literature, there is not one comparing the semi-dynamic capillary pressure Pc and relative permeability Kr to those obtained from the standard methods, centrifuge and steady-state methods. The aim of the study is to compare data results from all these methods, performed at true reservoir conditions with live fluids (except for centrifuge tests). A 1D linear x-ray scanner was used to monitor the saturation profiles obtained at capillary equilibrium.
As for S5, the effective oil permeability Keo at Swi on the two stacks (stack 1 for SD and stack 2 for SS tests) was measured prior water flooding. The design of the SD and SS Kr coreflooding tests and multistep Pc and single-step Kro centrifuge tests was performed using the core analysis software CYDAR, using a first guess of Kr/Pc anticipating a slightly waterwet behaviour based on previous works.
The quasifree p-p scattering cross section deduced from the 2 H(p, pp)n data using the THM, is indeed not normalized  13, 14. An overall normalization was done in  13, 14  by matching to the theoretical on-shell p-p cross-section above the Coulomb barrier after being averaged out at the same energy bin of THM data. Here, a Bayesian approach (see Methods section "Bayesian analysis approach") was used to fit the data, in order to better sample the parameters of the cross section model (Eq. (  3 )) according to our data. A Markov Chain Monte Carlo (MCMC) was implemented using the emcee Python library  23  , which is based on the algorithm described in  24. A flat prior distribution was chosen for a, sampled in the interval (-25 fm, -15 fm) to account for the large historical dispersion of this model parameter.
To sample the posterior distributions in real situations where probabilistic inference is used, Markov Chain Monte Carlo (MCMC) methods are usually applied. Indeed, MCMC methods can be used even without a full analytic description of the aforementioned probability distributions and do not necessarily require computing any process-intensive task such as calculating derivatives or integrals of the function. Many of the MCMC applications use the Metropolis-Hasting (M-H) method  47  to generate the chain, however, this algorithm is very sensitive to a set of "tuning" parameters, the number of which scales with the square of the dimension of the parameters space. Several heuristic methods have been developed to estimate these tuning parameters in a data-driven fashion. In particular, we decided to use the emcee  23  Python library which is based on the algorithm described in  24  and has been thoroughly used in astrophysics.
Acknowledgements The authors wish to thank prof. R. Schiavilla  for fruitful discussions. A. O. acknowledges the support of dr. R. J. deBoer for Baeysian fitting analysis. C. A. B. was supported as a  HFHF Visiting Professor of the German Helmholtz Research Academy  and by the  U. S. DOE  grant  DE-FG02-08ER41533. L. L. , S. R. and M. L. S. acknowledge " Programma ricerca di ateneo UNICT 2020-22 linea2 " and  "Starting  grant  2020 " of  University of Catania. A. O. acknowledges support by the  National Science Foundation  under Grant No. OISE-1927130  (IReNA).
Coding Three types of food refusals were coded (refusal-to-participate, refusal-to-take and refusal-toconsume a food item) and pooled during analysis to create a binary response variable (refusal = yes/no). In addition to food refusals, we coded pull-latency behaviour. These pull-latency data were used in an exploratory analysis. 50 test trials and 43 control trials were excluded from analysis at the coding stage (documented in ESM, Tables  S2  and S3 ). In total 1066 test trials and 437 control trials were analysed. E2 coded all videos and a second coder who was blind to the hypothesis of the study coded 20% of the videos. Inter-observer agreement scores were determined using the icc and the maxwell functions of the irr R package  (Gamer et al. , 2019). Observer agreement on refusal behaviour was very good (Maxwell's correlation coefficient (RE) = 0.
Subject ID and subject-session ID were included as random effects. The latter was nested within subject and accounted for potential session to session variation in refusal probabilities. The model included random slopes  (Schielzeth & Forstmeier, 2009; Barr et al. , 2013)  of partner presence, trial number and session number within subject, and trial number within subject-session-ID. We included estimates of the correlations between the random intercept and slopes in the model. For Model 1A we utilised the full dataset; 1066 trials collected in 93 sessions. The data was from 12 subjects tested with 2 partners. To ascertain the (potential interactive) effect of our two factors of interest, we used a likelihood ratio test to compare the full model outlined above with a null model lacking the interaction term and their main effects.
Given that we utilised two partner monkeys in the course of the experiment, we fitted a second, identical model -Model 1B -using just the data obtained with partner identity 1, in order to check whether partner identity had an obvious effect on the outcome. For Model 1B we utilised 597 test trials collected in 52 sessions. The data came from 12 subjects tested with 1 partner. For more detail and to see the output of Model 1B see ESM Tables  S7  and S8. In a second analysis we considered the control condition data. Here we tested whether the value of the partner's reward had an effect on monkeys' food refusal behaviour in the two distributor conditions. More specifically, we tested whether the subjects were more or less motivated to accept the LV reward in the human distributor as opposed to the machine distributor condition.
To this end we constructed another GLMM -Model 2 (output in ESM Table  S9 ). The response variable was food refusal (yes/no). The model comprised the fixed effects of distributor, trial number, session number, and sex. Subject ID and subject-session ID were included as random effects and random slopes of trial number and session number within subject and trial number within subject-session ID were included. The same monkey was the partner in all control conditions and we therefore did not need to consider partner ID in the model. For Model 2 we used 437 control condition trials. The trials were collected in 40 sessions and the data came from 10 subjects with 1 partner. To ascertain the effect of our factor of interest (distributor), we used a likelihood ratio test to compare the full model outlined above with a null model lacking the term.
In a third analysis we explored the pull-latency data to establish whether the time it took a monkey to pull the lever differed as a function of distributor and partner presence. Here we fitted a survival analysis -Model 3 (documented in ESM, Tables  S10  and S11 ). The response variable was time in seconds, determined as when the subject pulled the lever or when the trial ended. Distributor and partner presence were included as an interaction term (in addition to their main effects), and trial number, session number, sex, and counterbalance were included as fixed effects. Subject ID and subject-session ID were included as random effects. Random slopes of partner presence, trial number and session number within subject were included, in addition to the random slope of trial number within subject-session ID.
Model 3 used the full dataset of 1066 trials; of these, 119 trials were refusal-to-participate (i. e. "timeout") events. To ascertain the potential interactive effect of distributor and partner presence on pulllatency, we conducted a likelihood ratio test, comparing the full model with a null model lacking the interaction term and their main effects. Where appropriate, we checked model stability and collinearity (inspected Variance Inflation Factors;  Field, 2005). Confidence intervals, where reported, were derived using the bootMer function of the lme4 package. More details on the statistical analyses can be found in the ESM (section 7).
Model 1B Our motivation in fitting Model 1B was to account for possible partner ID effects. Model 1B used a subset of the data to estimate the effect of distributor and partner presence on monkeys' food refusal behaviour. This model utilised only the data collected with the first partner (using the subset collected with the second partner would have resulted in order effects unaccounted for by the model). The sample size was 597 trials collected in 52 sessions and the data came from 12 subjects with 1 partner. As explained in the previous sub-section, we initially intended that only one monkey (Ilja) would partner all subjects. Ill-health meant Ilja had to be replaced part-way through the study. Ilja's replacement (Linus) was male and similar in age. It is likely that subjects respond to particular partner identities differently, however with only two partner identities it was not possible to include partner ID or dyad as random effects in Model 1A.
This test revealed the two models were borderline in terms of differing from one another (likelihood ratio test: χ 2 3 = 6. 871, p = 0. 076) and as the output from the full model revealed the distributor and partner presence interaction term was not significant (p = 0. 392), we created a reduced model, dropping the interaction and looking at the fixed effects of distributor and partner presence. The output from the full model is reported in Table  S10  and the output from the reduced model in Table  S11. In the paper we discuss the output of the reduced model. We estimated model stability by dropping the levels of the random effects (one at a time) and comparing the estimates derived from each reduced dataset with those obtained from the full dataset. This check revealed model stability was fine (see columns min.
Virus and cells Human lung epithelial A549 cells were used for the preparation of virus stocks and end-point dilution assays (EPDA) and cultured in HEPES-buffered Dulbecco's modified Eagle medium (DMEM; Gibco, Carlsbad, CA, USA) supplemented with 10% fetal bovine serum (FBS; Gibco) and 1% penicillin/streptomycin (Gibco) at 37 °C with 5% CO 2. A549 cells used for virus isolation from brain samples (see below) were cultured as described but with the addition of Fungizone (2. 5 μg/ml of amphotericin B and 2. 1 μg/ml of sodium deoxycholate; Gibco) and Gentamycin (50 μg/ml; Gibco), subsequently named DMEM+. The absence of Mycoplasma infection in the A549 cell culture was verified using the MycoStrip™ detection kit (InvivoGen). A P2 stock of TBEV-NL (GenBank accession no. ON502378. 1) and a P4 stock of TBEV-Neudoerfl (GenBank accession no.
Statistical analyses A linear model (LM) with a log-link function was used to test the associations of sex and mouse species with the weight of the mouse, including an interaction term between mouse and sex. A generalized linear model (GLM) with a log-link function was used to test for an effect of sex and mouse species on hindfoot length of the mouse. GLMs with a binomial distribution and logit-link function were used to evaluate the effects of virus strain, mouse species, time after infection, mouse sex and mouse weight on the presence/absence of TBEV RNA. GLMs with a negative binomial distribution and log-link function were used to test the effect of virus strain, mouse species, time after infection, mouse sex and mouse weight on TBEV RNA copy numbers. Model diagnostics were performed using the DHARMa package 60.
Acknowledgements We would like to thank  Overijssels landschap  and  Staatsbosbeheer  for access to their lands,  Wilma Blauw  for help with serum collection,  Dennis Bente  for advice on experimental set-up,  Johan Meijer ,  Albertjan ter Heide ,  Arnold van Zoelen  and  Antonique Spitshoven  for their help in the rodent infection experiments.
Methods The STOPCAP M1 collaboration conducted a systematic review and meta-analysis of individual participant data. We searched MEDLINE (from database inception to March 31, 2022), Embase (from database inception to March 31, 2022), the Cochrane Central Register of Controlled Trials (from database inception to March 31, 2022), proceedings of relevant conferences (from Jan 1, 1990, to Dec 31, 2022), and ClinicalTrials. gov (from database inception to March 28, 2023) to identify eligible randomised trials that assessed docetaxel plus ADT compared with ADT alone in patients with metastatic, hormone-sensitive prostate cancer. Detailed and updated individual participant data were requested directly from study investigators or through relevant repositories. The primary outcome was overall survival.
Findings We obtained individual participant data from 2261 patients (98% of those randomised) from three eligible trials (GETUG-AFU15, CHAARTED, and STAMPEDE trials), with a median follow-up of 72 months (IQR 55-85). Individual participant data were not obtained from two additional small trials. Based on all included trials and patients, there were clear benefits of docetaxel on overall survival (hazard ratio [HR] 0•79, 95% CI 0•70 to 0•88; p<0•0001), progression-free survival (0•70, 0•63 to 0•77; p<0•0001), and failure-free survival (0•64, 0•58 to 0•71; p<0•0001), representing 5-year absolute improvements of around 9-11%. The overall risk of bias was assessed to be low, and there was no strong evidence of differences in effect between trials for all three main outcomes. The relative effect of docetaxel on progression-free survival appeared to be greater with increasing clinical T stage (p interaction =0•0019), higher volume of metastases (p interaction =0•020), and, to a lesser extent, synchronous diagnosis of metastatic disease (p interaction =0•077).
Research in context Evidence before this study To identify randomised trials that assessed addition of docetaxel to standard androgen deprivation therapy for patients with metastatic hormone-sensitive prostate cancer, we used highly sensitive search strategies for MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials; and relevant search terms (including "metastatic hormone-sensitive prostate cancer", "chemotherapy", and "docetaxel") for ClinicalTrials. gov and proceedings and abstracts of relevant conferences. Five eligible trials were identified, two of which are unpublished, with the remaining three representing an estimated 98% of eligible participants and each considered as being at low risk of bias. Results of these trials, and meta-analyses using aggregate data from them show a survival benefit of adding docetaxel to androgen depravation therapy in people with metastatic, hormone-sensitive prostate cancer.