dataesr/scientific-paragraphs-categorization huggingface dataset:
SUMO Connection with TraCi Our study consisted of collecting traffic data, including distance, speed, flow, and density. TraCi (traffic control interface) allowed us to interact with SUMO, giving access to a road traffic simulation and also allowing us to retrieve and manipulate the data online. The connection with TraCi was performed using script written in Python, following a request-response model on a TPC/IP connection. Then, we connected SUMO with our script. In the first step, we imported TraCi in our script and added the SUMO HOME/Tools directory on the Python path to use the library, as shown in Figures  7  and 8. In the second step, we inserted some lines in our script to interface with SUMO using Python. Finally, we added functions that consisted of calculating the distance d (i, j, t), q (i, j, t), K jam (i, j, t), and V f (i, j, t) for a choice of 6 vehicles (n = 6) and 6 stations (m = 6).
SUMO Connection with TraCi Our study consisted of collecting traffic data, including distance, speed, flow, and density. TraCi (traffic control interface) allowed us to interact with SUMO, giving access to a road traffic simulation and also allowing us to retrieve and manipulate the data online. The connection with TraCi was performed using script written in Python, following a request-response model on a TPC/IP connection. Then, we connected SUMO with our script. In the first step, we imported TraCi in our script and added the SUMO HOME/Tools directory on the Python path to use the library, as shown in Figures  7  and 8. In the second step, we inserted some lines in our script to interface with SUMO using Python. Finally, we added functions that consisted of calculating the distance d (i, j, t), q (i, j, t), Kjam (i, j, t), and Vf (i, j, t) for a choice of 6 vehicles (n = 6) and 6 stations (m = 6).
72). Treatment-related grade 3/4 adverse events occurred in 10. 3% (nivolumab) and 2. 3% (placebo) of patients. One treatment-related death (0. 2%) occurred with nivolumab. Nivolumab is an effective and generally well-tolerated adjuvant treatment in patients with resected stage IIB/C melanoma. ClinicalTrials. gov identifier: NCT04099251. The incidence of melanoma is rising worldwide, and patients with node-negative stage IIB/C disease comprise a large population at significant risk of recurrence [1] [2] [3] [4]. Per the American Joint Committee on Cancer (AJCC) Cancer Staging Manual, 8th edition, patients with stage IIB disease have primary tumors that are >2 mm and ≤4 mm thick with ulceration (T3b) or >4 mm thick without ulceration (T4a), whereas patients with stage IIC disease have primary tumors >4 mm thick with ulceration (T4b) (ref.
Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41591-023-02583-2. The safety population included all patients who received at least one dose of trial drug. The investigators determined whether adverse events were related to a trial drug. The events listed here occurred between the first dose and either 30 d after the last dose (adverse events and treatment-related adverse events) or 100 d after the last dose (treatment-related deaths). The severity of adverse events was graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 5.
Trial design and treatment In this phase 3, double-blind trial (ClinicalTrials. gov identifier: NCT04099251), eligible patients were randomized 2:1 to receive nivolumab 480 mg or placebo every 4 weeks via intravenous infusion for 12 months or until disease recurrence, unacceptable toxicity or withdrawal of consent (Extended Data Fig. 3 ). Permuted block randomization, stratified according to AJCC 8th edition T category (T3b versus T4a versus T4b), was performed via interactive web response technology using a randomization schedule. At randomization, patients were assigned to the next available treatment arm in the schedule. Patients, investigators and site staff were blinded to treatment arm assignments during the study, with patients being assigned participant identification numbers to ensure that outcomes could be tracked while concealing treatment allocation.
Secondary efficacy endpoints included DMFS (presented here), defined as the time between randomization and first distant recurrence or death due to any cause, as well as OS and progression-free survival through next-line therapy (follow-up is ongoing, and those results are not presented here). The secondary endpoint of safety for the blinded phase of the trial is presented here for treatment-related adverse events ≤30 d after the last dose of study therapy as well as for immune-mediated adverse events (non-endocrine events requiring immunomodulators and endocrine events, regardless of immune-modulating treatment) ≤100 d after the last dose of study therapy. Time to onset and resolution data are also presented for immune-mediated adverse events. Occurrences and severity of adverse events were defined by the National Cancer Institute Common Terminology Criteria for Adverse Events, version 5.
Medians and rates were estimated, and corresponding 95% CIs were computed based on a log-log-transformed CI for the survivor function. The efficacy analysis endpoints included all randomized patients (defined as, the intention-to-treat population). Safety was assessed in patients who received at least one dose of study drug. Extended Data Fig. 3  | Trial design. RFS was defined as the time between randomization and first recurrence (recurrence events included local, regional, or distant recurrence, new primary melanomas [including in situ], and death [due to any cause]). Imaging assessments occurred every 26 weeks during years 1 through 3 and every 52 weeks in years 4 and 5. DMFS, distant metastasis-free survival (the time between randomization and first distant recurrence or death); FFR, freedom from relapse (with censoring patients who died from causes other than disease); IV, intravenous; OS, overall survival; PFS2, progression-free survival through next-line therapy; Q4W, every 4 weeks; RFS, recurrence-free survival; T, tumor.
Acknowledgements The CheckMate 76K trial was funded by  Bristol Myers Squibb. We thank the patients and investigators who participated in the CheckMate 76K trial. We acknowledge  Ono Pharmaceutical Company  for contributions to nivolumab development and Dako, an  Agilent Technologies company , for collaborative development of the PD-L1 immunohistochemistry 28-8 pharmDx assay. Findings from CheckMate 76K were reported, in part, at the  20th International Congress of the Society for Melanoma Research. Professional medical writing and editorial assistance were provided by  M. Kirk  and  M. Salernitano  of  Ashfield MedComms, Inizio Company , and funded by  Bristol Myers Squibb ; specifically,  M. Kirk  assisted with the development of the first draft and subsequent revisions, under the direction of the authors, and  M.
Data availability Qualified researchers may submit a proposal to access de-identified and anonymized datasets for this study to Bristol Myers Squibb. Data will be made available to researchers whose proposals are approved by the independent review committee (Duke University), with available information dependent upon the individual request. The option to submit data requests as well as review criteria for data requests are available at https://vivli. org/ourmember/bristol-myers-squibb/. Additional information on Bristol Myers Squibb's policy on data sharing may be found at https://www. bms. com/researchers-and-partners/ clinical-trials-and-research/disclosure-commitment. html. The study protocol of CheckMate 76K is provided in the Supplementary Information.
J. Page et al. [10]  used an inhomogeneous Markov chain to model the transitions between presence (1) and absence (0). The CDF inversion method is used to generate the occupancy profile. Shide Salim et al. [11]  use an inhomogeneous Markov chain to predict transitions from one area to another in a workplace. Data were collected using a real-time locating system (RTLS). Transition probabilities are a function of the occupant, weather, and day of the week. Zhaoxuan Li et al. [12]  also used Markovian modeling on the occupancy profile of a residential building. The transition matrices are estimated by maximum likelihood and the procedure is optimized using the Pearson divergence test to determine the best training window. The authors compare their method to different models (SVM, ANN, probability sampling) over different prediction horizons (15, 30 min, and 24 h).
et al. [17]  used LSTM networks combined with different classification algorithms (SVM, RF, MLP, KNN) to predict the number of occupants at three offices. Environmental data (CO 2 , temperature, etc. ), the number of occupants, and the consumption of certain appliances were collected. Their strategy was to predict environmental variables via LSTM networks (one for each office) and rank the predictions. Their strategy offers good results and the random forest shows better performance than other classification algorithms. In papers  [18, 19] , LSTM networks are also used to predict the occupancy state. Hamza Elkhoukhi et al. [20]  use an LSTM network to predict CO 2 concentration and merge this prediction with ventilation rate, normal CO 2 concentration in the air, and the rate of generated CO 2 /person through a steady-state model to determine the number of occupants.
Data Collection In this work, we use unlabeled environmental data from a residential building in Amiens in the region of Picardie (northern France). The raw data were obtained through sensors (Netatmo equipment from Boulogne-Billancourt in France) placed in 3 rooms (living room, bedroom on the floor, and office) (Figure  1 ) of the building. The data, ranging from 1 September 2018 at 0:00 to 8 October 2018 at 4:40 are recorded at 5 min intervals. In each room, we have the evolution of the CO 2 concentration in ppm, the living room is equipped in addition to the CO 2 sensor with the noise sensor in dB. In Table  1  below, we can observe statistics on our variables in the different rooms. We notice that the medians and averages of CO 2 are relatively close in the living room and the office which generally suggests a more or less symmetrical distribution.
Materials Our experiments were carried out with a DELL brand laptop from Montpellier in France with an Intel Core i. 7 CPU architecture and equipped with an nVidia GM108M (GeForce 930MX) graphics card. We used the Miro (Amsterdam/The Netherlands) collaborative platform to design the diagrams. The implementation codes were written in Python (version 3. 10. 6), and the neural networks were implemented using the tensorflow library (2. 9. 2). The Table  2  summarizes the calculation times as well as the parameters trained following two scenarios studied in this work, namely prediction over 30 min and prediction over 1 h.
PURPOSE An international meta-analysis identified a group of patients with advanced epithelial ovarian cancer (EOC) with a very poor survival because of two unfavorable features: (1) a poor chemosensitivity defined by an unfavorable modeled CA-125 ELIMination rate constant K (KELIM) score ,1. 0 with the online calculator CA-125-Biomarker Kinetics, and (2) an incomplete debulking surgery. We assumed that patients belonging to this poor prognostic group would benefit from a fractionated densified chemotherapy regimen. METHODS The data set of ICON-8 phase III trial (ClinicalTrials. gov identifier: NCT01654146), where patients with EOC were treated with the standard three-weekly, or the weekly dose-dense, carboplatin-paclitaxel regimens and debulking primary surgery (immediate primary surgery [IPS] or delayed primary [or interval] surgery [DPS]), was investigated.
17  In that context, the ICON-8 trial was designed to compare the efficacy of the standard three-weekly carboplatin-paclitaxel regimen, with that of two fractionated dose-dense regimens in a predominantly European population. 15 However, the trial was negative with no benefit in PFS or OS found with the fractionated dose-dense arms in the whole population. 18 e objective of the present retrospective analysis of ICON-8 trial data set was to assess if the weekly dose-dense chemotherapy was associated with longer PFS and OS compared with the standard three-weekly regimen in patients with ovarian cancer belonging to the poor prognostic group identified in the GCIG meta-analysis database (defined as the patients with poorly chemosensitive disease with KELIM score ,1. 0, and who could not benefit from complete surgical cytoreduction).
Patients and Data In this international multicenter trial, patients with FIGO stage IC-IV EOC were randomly assigned (1:1:1) to one of three treatment arms: arm 1 (control) to three-weekly carboplatin AUC5 or AUC6 and three-weekly paclitaxel 175 mg/m 2 ; arm 2 to three-weekly carboplatin AUC5 or AUC6 and weekly paclitaxel 80 mg/m 2 ; and arm 3 to weekly carboplatin AUC2 and weekly paclitaxel 80 mg/m 2 , given in all arms for six cycles. Patients could have been operated with upfront (immediate) primary cytoreductive surgery (IPS, equivalent to primary debulking surgery), or could have received neoadjuvant chemotherapy for three cycles with a plan for delayed primary cytoreductive surgery (DPS, equivalent to interval debulking surgery), followed by three postoperative cycles. The concentrations of CA-125 were measured at baseline, on day 1 of each treatment cycle, and at each follow-up visit.
Relevance The utility of chemotherapy densification in patients with a poor prognostic ovarian cancer, characterized by a lower chemosensitivity (unfavorable KELIM score. 1. 0, calculable on CA-125-Biomarker Kinetics) and a disease not amenable to complete interval debulking surgery, after three cycles of neoadjuvant chemotherapy, will be assessed in the future European SALVOVAR trial. [22] [23]  At least three available CA-125 values within the first 100 days of neoadjuvant or adjuvant chemotherapy were required to ensure an accurate assessment of KELIM by the model. The mathematical modeling of early CA-125 kinetics with a nonlinear mixed-effect model was described previously. 20, 22 Basic details about the semimechanistic kinetic-pharmacodynamic (K-PD) model adjustment and qualification are presented in the Data Supplement.
Statistics and Computing Process All survival analyses were implemented with a landmark time point set at 100 days after the start of neoadjuvant chemotherapy or at the surgery date, since CA-125 was modeled from day 0 to 100. 27 All tests were implemented using a two-sided. 05 α risk. NONMEM 7. 5 (ICON Development Solutions, Ellicott City, MD) software program was used to fit the semimechanistic model to CA-125 kinetic data. 28 POSE4 program was used for graphical evaluation of model fits. 29 Logistic analyses, survival analyses, and concordance probability (C-index) were obtained using R version 4. 1. 1.
If no effort is made to improve the chemosensitivity of this poor prognostic group, the OS gap between the patients with highly chemosensitive disease and those with poorly chemosensitive disease will increase, thereby contributing to exacerbating this inequity. The present evaluation of ICON-8 trial suggests that weekly dose-dense carboplatin and paclitaxel might produce improved PFS and OS compared with the standard threeweekly regimen in patients with poorly chemosensitive disease characterized by unfavorable KELIM ,1. 0. A recent post hoc analysis of VELIA trial (ClinicalTrials. gov identifier: NCT02470585), where patients with EOC were treated with carboplatin-paclitaxel 6 veliparib in first-line setting, suggested that those with homologous recombination proficient disease (known to be less platinum-sensitive) treated with the weekly dose-dense regimen experienced better PFS compared with those treated with the three-weekly standard regimen.
5(a). Each of the maps obtained for each dataset has the same size and maximum angle of incidence as the ones shown in the previous section. The case of zero-noise maps is first considered, i. e. , computed directly from the intensity model. When fitting the maps associated to a particular set of material parameters, the fit variables are initialized as completely random before applying the least-squares minimizer, as would be done experimentally where no prior knowledge about the sample' s magnetic configuration or optical and magneto-optical properties would exist. For further details on the algorithm, refer to Sec. SM3 of the Supplemental Material  [37]. By applying this procedure to each dataset, it is found that the algorithm succeeds in retrieving all the parameter values with precision up to the numerical computer tolerance.
The dynamic range is modeled by imposing a finite full well capacity (FWC) for the generated photoelectrons combined with a given read noise, and the ADC resolution is modeled by registering the intensity values into a total number of discrete levels given in bits, i. e. , the number of levels is 2 bits. Photon shot noise due to the discrete nature of photon emission from the light source, as well as the photoelectron generation process described by the quantum efficiency (QE), have also been modeled, following a similar approach as the one described in the PYTHON Pyxel package  [38]. The values for these parameters have been selected based on a typical 16 bit CMOS commercial midrange scientific camera: QE = 61%, FWC = 40000e -, and read noise of 1. 5e -rms. Figure  5 (b) shows the nonmagnetic and MOKE maps for a particular set of parameters, dataset no.
1, marked by the black dashed vertical line in Fig. 5 (a), as a function of the image average number after having included these sources of noise and discretization (the zero-noise maps are also included for direct comparison). As expected, the single-shot (1 average) images are highly affected by noise, with the magneto-optical intensity map being more affected by Poisson shot noise than the nonmagnetic. An identical fitting procedure as in the zero-noise case is followed for these datasets, obtaining for each file and number of image averages a set of best-fit parameters. The relative error for the optical and magneto-optical constants is determined by comparing those obtained from the fitting with their input values. The error in the determination of the magnetization vector is given by the angular separation between input ( m I ) and fitted ( m F ) vectors.
ACKNOWLEDGMENTS This work was supported by  UKRI  through an  EP-SRC studentship , Grants No. EP/N509668/1  and No. EP/R513222/1 , the  European Community  under the  Horizon 2020 Program , Contract No. 101001290  ( 3DNANOMAG ), the  MCIN  with funding from  European Union NextGen-erationEU  ( PRTR-C17. I1 ), and the  Aragon Government  through the Project Q-MAD. A. H. -R. acknowledges the support by  Spanish MICIN  under Grant No. PID2019-104604RB/AEI/10. 13039/501100011033  and by  Asturias FICYT  under Grant No. AYUD/2021/51185  with the support of  FEDER  funds. D. S. -H. acknowledges funding from  ANR/CNRS  under the  French "Plan Relance de l' etat " for the preservation of R&D. L. S. acknowledges support from the  EPSRC Cambridge NanoDTC  Grant No. EP/L015978/1. C. N. C. acknowledges the  UK EPSRC Centre  for Doctoral  Training in Sensor Technologies for a Healthy and Sustainable Future.
Flow cytometry FACS buffer (phosphate-buffered saline with 2% fetal bovine serum and 2 mM EDTA) was used for staining and washing cells. For platelet immunostaining, anti-CD62P-FITC (Beckman Coulter ref: A07790) and anti-CD41/61-PE  antibodies were used. To determine platelet effects on EMT induction in the three colon CTC lines, cells were fixed before permeabilization with the IntraPerp Permeabilization kit (Beckman Coulter ref: A07803). Then, they were incubated with anti-cytokeratin 19 (CK19) Alexa Fluor ® 488 (Exbio ref: A4-120-C100) and anti-EpCAM-APC  antibodies followed by analysis using a Gallios flow cytometer (Beckman Coulter, Brea, CA, United States) at the Rio-Imaging Cytometry Core facility. Flow cytometry data were analyzed with the Kaluza software (Beckman Coulter Life Sciences).
Platelet RNA-sequencing data processing in Galaxy FASTQ files obtained by RNA-seq were analyzed with Galaxy Docker Image (version 20. 09) and R (version 4. 0. 1). Pre-processing, trimming, mapping, and mRNA count quantification were performed with tools available via Galaxy Docker Image  (Afgan et al. , 2018). The pre-processing step included the concatenation of the technical replicates for each sample and was performed with the Concatenate datasets tail-to-head tool (version 0. 1. 1). The read quality of FASTQ files was checked with the FastQC (version 0. 72) tool and adapters were removed with TrimGalore (version 0. 6. 7). Reads that passed the quality filtering procedure were mapped to the Homo sapiens genome assembly (GRCh38. 103) with the Hisat2 (version 2. 1. 0) alignment tool  (Kim, Langmead and Salzberg, 2015)  and transcript counts were extracted with FeatureCounts (version 2.
Data processing in R Transcript with low count number were filtered out with the filterByExpr-function from the EdgeR-package (version 3. 38. 1)  (Robinson, McCarthy and Smyth, 2010). Differential expression analysis was performed with the EdgeR package using Relative Log Expression as normalization method in R (version 4. 0. 1). The most significant differentially expressed transcripts were filtered based on the following criteria: |log2FC| > 1 and p-value <0. 05. Volcano transcripts plots and heatmaps were generated with the EnhancedVolcano package  (Blighe, Rana and Lewis, 2019)  and the Pheatmap function  (Kolde, 2012) , respectively.
Functional pathway enrichment To investigate the role of the differentially expressed mRNAs, a gene set enrichment analysis (GSEA) was performed with the GSEA software using the "hallmark" gene set collection in the Molecular Signature Database (MSigDB)  (Subramanian et al. , 2005 ) (version 7. 5. 1). A compendium of Gene Ontology terms (biological processes, molecular functions and cellular components) and a KEGG pathway analysis were carried out with the goseq tool  (Young, Wakefield, Smyth and Oshlack, 2010)  (version 1. 44. 0) using Galaxy.
Moreover, among the significant transcripts, PTGES expression was increased upon indirect co-culture with the three CTC lines [CTC41 (log FC = 3. 920256295, p = 0. 0005, CTC41. 4 (Log FC = 3. 268002339 p = 0. 0026), and CTC41. 5G (Log FC = 3. 034793859, p = 0. 0013)] compared with control. PTGES is involved in modulating vascular tone and platelet reactivity  (Friedman, Ogletree, Haddad and Boutaud, 2015). GSEA revealed that RNAs were significantly enriched in genes related to EMT, hypoxia, inflammatory response, and apoptosis. A Venn diagram showed for each of these pathways, the transcripts that were enriched in all three experimental conditions (i. e. , platelets indirectly co-cultured with the CTC41, CTC41. 4, and CTC41. 5G line). For instance, vascular endothelial growth factor A (VEGFA), tropomyosin 4 (TPM4), secreted protein acidic and cysteine rich (SPARC), secreted phosphoprotein 1 (SPP1), and transforming growth factor beta 1 (TGFß1) were significantly enriched in the EMT pathway in all three experimental conditions [CTC41 (p = 0.