dataesr/scientific-paragraphs-categorization huggingface dataset:
In both cohorts, the low-risk group had significantly longer OS than the high-risk group (HR 0. 26, 95% CI 0. 12 to 0. 56, p=0. 00025; HR 0. 30, 95% CI 0. 10 to 0. 91, p=0. 024, respectively). Regarding the whole cohort, LIPS also predicted progression-free survival (PFS). The identified LIPS was not affected by clinicopathological features with the exception of brain metastases. NKT cells and neutrophils of the LIPS can be used as dynamic predictive biomarkers for OS and PFS after first administration of the ICI. Conclusion Our study identified a predictive LIPS for survival of patients with cancer treated with PD-1/PD-L1 ICI, which is based on immune cell subsets in the peripheral whole blood. Trial registration number NCT03453892.
Multicolor flow cytometry Whole blood samples of the patients were collected and the fresh whole blood was analyzed by multicolor flow cytometry according to our previously published and optimized IPT protocols. 17 18 IPT was performed within 3 hours after the collection of whole blood. Data acquisition was performed on a Gallios Flow Cytometer (Beckman Coulter) in the standard filter configuration. The Kaluza Flow Analysis Software (Beckman Coulter) was used for data analysis. The immune cell subpopulations analyzed are visualized and specified in online supplemental sFigure 1.
20 21 The Cox-LASSO regression model was applied to develop a multi-immunophenotype-based predictive signature for OS in the training cohort. After 200,000time steps for LASSO, the best C-index related model was selected as LIPS. Subsequently, we analyzed the data in a validation cohort for this model, to assess its feasibility and reliability in patients treated with ICI. The optimal cut-off value for the risk score (figure  2A, D ) was determined by surv_cutpoint function in survminer package. All patients were divided into different groups (high-risk or low-risk) based on the cut-off of the risk score, which was calculated by considering the expression of immunophenotypes and the correlation coefficient. In order to validate the predictive ability of LIPS for PFS, these same coefficients of LIPS were calculated as risk score for survival analysis.
Results of Cox regression analysis are described by means of HRs, 95% CI of HR and p values (Wald test). The concordance index (C-index) and the time-dependent receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) values were calculated for different models as a measure of the discriminatory ability that allows comparison of signatures. A signature with a C-index of 0. 5 has no predictive value, but the signature with a C-index of 1 would allow a perfect prediction of the patient's outcome. 22 The C-index was analyzed using the survcomp (V. 1. 22. 0). 23 The ROC curve and AUC values were calculated with the timeROC package (V. 0. 3); for survival analyzes the survival package was used. All of the analyzes were carried out using R V. 3. 6. 1 (R Foundation for Statistical Computing) and related packages.
Development and definition of the LIPS After the data cleaning steps, 54 peripheral blood immune markers were included into a univariate cox survival analysis. The following 14 of them were associated with OS (p<0. 2): monocytes (CD14 high ), monocytes (CD14 low ), neutrophils, dendritic cells (DCs), myeloid (m)DCs-1, mDCs-2, plasmacytoid DCs (pDCs), natural killer (NK) cells (CD56 high /CD16 + ), NKT cells (CD3 + /CD56 + /CD16 + ), NKT cells (CD3 + / CD16 + ), CD8 + T cells (PD-1 + ), CD8 + T cells (CD25 + ), CD8 + T cells (CD69 + ), and regulatory T cells (T regs ) (table  2 ). The ST-ICI cohort was then randomly assigned to a training cohort (n=56) and a validation cohort (n=33) for further LASSO proportional hazards Cox regression analysis. After 200,000 times repeat LASSO regression, the highest C-index's model was chosen as the LIPS.
DISCUSSION In recent years, ICI with or without chemotherapy were approved for multiple recurrent or metastatic cancer types. 6] [27]  However, different studies used different cut-off values based on independently companies' database or cancer types to define low and high TMB. 9] [30]  The gene methylation status may also serve to select patients who will experience a clinical benefit from the PD-1 blockade. 31 Nevertheless, there is still particular need of additional or complementary time saving, cost-effective, safe, and easy applicable tools to identify patients with cancer who could benefit from ICI. In this context, a detailed knowledge about the immune status, both in the tumor and in the periphery, is needed to judge about the potential of cancer immunotherapy and the efficacies of immunotherapies.
Acknowledgements The authors thank the patients who participated in the study, their supporters, the investigators, and clinical research staff from the  Department of Radiation Oncology. The present work was performed by  J-GZ  in (partial) fulfillment of the requirements for containing the degree "Dr. rer. biol. hum. ". We further acknowledge the support by the  German Research Foundation  and the  Friedrich-Alexander-Universität Erlangen-Nürnberg  within the  funding program Open Access Publishing. Contributors Conception and design: MH, UG, BF, RF, HM,  J-GZ ,  ME ,  A-JD. Development of methodology:  J-GZ ,  A-JD. Acquisition of data: A-JD, SR, IB, BF, ME, MH, UG. Analysis and interpretation of data: J-GZ, AD, ED, RS, PS, UG, MH. Writing, review, and/or revision of the manuscript:  J-GZ ,  A-JD , RF, MH, UG, ED, RS, CS, PS.
Constitutive model and experiment modelling In this section, we present the model representation of the compression experiments assuming either uniaxial or axisymmetric hypotheses. The latter assumption enables us to address the issue of an inhomogeneous distribution of strains in the pellet as a result of friction at the interface between the pellets and tungsten plates. The mtest  [23]  and licos  [22]  softwares are used depending upon whether a one or two-dimensional hypothesis is assumed. However, the material behaviour law is implemented independently of the representation through the use of the mfront code generator  [23]. This tool has been coupled to a number of different finite element solvers including Cast3M which we used in this study. It has the advantage of enabling complex behaviour laws to be implemented in a form which is straightforward and close to their mathematical formulation.
This relationship shows that in a near constant cross-head speed experiment, the pellet loading to consider at time t is that given by the difference between the two displacement values provided by the displacement sensors described in section 2. 2. We now determine which experimental data the model representation should be assessed against. Throughout the experiments, the load L z exerted on the pellet through the column of the compression machine is monitored. To determine its calculated counterpart, one expresses the fact that creep induces a deformation at constant volume (see equations (  2 ) and (  3 )). As a result, the product of the pellet surface and pellet height is assumed constant during the viscoplastic deformation. It follows that the calculated load is given by: L calc. z (t) =| σ calc.
This comparison is shown in Figure  3. The experimental data are obtained from a laser measurement of the pellet diameter along the length of the pellet height. Four generating lines are thus characterised and average values are represented in Figure  3. The calculated profile reproduces the experimental data quite well. A comparison between experimental and calculated diameters at the top and bottom of the pellet and the general shape of the pellet following the test, would seem to indicate that the contact conditions are probably closer to infinite friction than they are of no friction, although the former hypothesis does not quite capture the entire pellet profile. Finally, we have included in Figure  3  a calculation carried out for different values of n and K (6. 7 and 271 MPa respectively).
The oxygen pressure relative to sample 1 was not monitored but is considered to be the lowest achievable oxygen pressure as the corresponding test was carried out in a dry argon-hydrogen mixture. Samples 2 and 3 correspond to tests 1 and 4 in Table  1. Large scale SEM images were taken of all three pellet sections whence it was observed that ✄ p o2 1/3. 7 ✁ vm 6. 7 Figure  5 : Estimate of the stress exponent relative to equation  (10)  with data points plotted assuming a value of α n of 0. 04; under these conditions, α is estimated at ≈ 1 3. 7. the two pellets tested at the highest oxygen partial pressures presented regions in which a much greater than average porosity was present. It was noteworthy that some of these regions lay away from the mid-pellet plane. Sites were therefore chosen in which EBSD characterizations were carried out.
Early January 2021, vaccination was made available in France. To increase first vaccine dose availability, French Health authorities recommended a 28-day interval for both mRNA vaccines, with a 42-day interval for healthy people, this delayed second dosing being debatable  25, 26. Due to uncertainties concerning vaccination of cancer patients, the observational COVIDVAC-OH study (clinical. trials. gov NCT04776005) was launched, sponsored by Paris University Hospitals. This study sought to investigate SARS-CoV-2 vaccination's (mainly mRNA-based vaccines) effectiveness in over 1100 consecutive patients with solid cancers or hematological malignancies, at North-Paris University Cancer center. This report concerns 306 thoracic cancer patients.
They were given a written information leaflet on COVID19 mRNA BNT162b2 vaccine, and on serological and hematological blood tests to be performed at first dose (Day 0), at second dose (Day 28), and at least 2 weeks post-booster dose (Day 42). All patients could oppose blood samplings and still undergo vaccine injections. Recommendations to keep facial masks and social distancing were given. All patients were registered into National Health Insurance computed COVID-19 vaccine database, with national identification number, complete identity, main underlying conditions, and vaccine batch number. Following blood sampling and vaccine injection, patients were followed-up for 15 minutes under medical surveillance  27. This study was approved by the Comité d'Evaluation de l'Ethique des projets de Recherche Biomedicale (CEERB, IRB00006477) of University Hospitals Paris-North Val-de-Seine, Paris 7 University, Assistance Publique-Hôpitaux de Paris (AP-HP) upon the approval number N°CER-2021-72.
Statistical analysis All samples were de-identified and assigned an ID number, with the sampling date. Sample processing and data analyses were performed, with all study personal blinded to information concerning patients and samples. De-identified data were exported from Microsoft Excel Version 2013 for Windows (Microsoft Corporation, 2013) to IBM® SPSS® Statistics for Windows, Version 25. 0 (IBM Corp. , Armonk, N. Y. , USA) for statistical analysis. Between-group comparisons were performed using Pearson's chi-squared or Fisher's exact tests for discrete variables, and Student's-T or Mann-Whitney U tests for continuous variables. Odds ratios (OR) and respective 95% confidence intervals (95%CI) were calculated using binary logistic regression. Hypothesis testing was two-tailed, with p values<0.
received research grants from  Roche-France ,  BMS ,  Takeda  outside of the submitted work, perceived fees from  BMS ,  Astra-Zeneca ,  Pfizer ,  Borhinger-Ingelheim  outside the submitted work and reimbursement for international meetings assistance support from  Astra Zeneca ,  BMS ,  Takeda , and  Pfizer ; grants, personal fees, and non-financial support from  Roche , all outside the submitted work;.
Data Collection Epidemiological and clinical data were collected using the electronic medical records applications Clinicom R and ORBIS R for COVID-19 patients and the self-questionnaire for HCWs. This self-administered questionnaire collected information on demographic factors, medical history, previous or present treatments, hospital function, known risk factors for COVID-19, and symptoms that may have occurred in the 3 months preceding the blood sample. HCWs were also asked if they had already been tested for COVID-19 RT-PCR and what were the results. When available, the time delays (in days) between the onset of the first symptoms of COVID-19 and inclusion, i. e. , the day of the first blood sampling, were recording. For asymptomatic IgA-positive HCWs without anti-SARS-CoV-2 IgG antibodies, we estimated this time to be between 7 and 10 days.
and muscle model-based controllers Muscle model-based controllers predict the muscle forces as a function of muscle neural activities and joint kinematics by using either the Hill muscle model  15  or directly computing the torques from the EMG signals  16  ,  17. This work describes the implementation of an EMG-based control system dedicated for an upper limb exoskeleton for children suffering from neuromuscular diseases. This paper is structured in the following way: the present section introduces the work and its outline, section 2 addresses the problem statement, section 3 analyzes the biomechanics of the upper limb, section 4 presents the used dataset, section 5 describes the proposed control system concept, section 6 illustrates the obtained results and analyzes the error in estimating both angular trajectories and torques.
Dataset The dataset used in this work was provided be the Warsaw Children Memorial Hospital and consists of twelve trials in which a healthy male adult subject performs the flexion/extension motion of the elbow holding different loads in the hand (0, 1, 2, and 5 kG). The trials are arranged in three groups of four trials each, depending on the position of the subject. In the first group, the subject stands upon one force platform during the recording of the motion. In the second group, he stands upon two platforms, while in the third group, he is seated. The dataset includes the EMG data recorded from 16 channels, attached to the main muscles of the upper limb, the positions and the angles of the whole body acquired by using capture motion device, the moments and the forces of several parts of the body.
Moreover, the provided EMG data were already processed: the signals were amplified to a range of 0 -5V and filtered to reduce the noise. In our research, two features were extracted from the EMG signal recorded from channels number 10 and 14 and used as inputs for the fuzzy neural network. It was chosen to use these channels as their electrodes are above the main muscle group that contracts more intensively during the performance of the designed movements. The data were recorded using VICON system with the dedicated software. Besides of the research purposes, the system is used by hospital for diagnosing the children's neural diseases, and for testing the rehabilitation and medication processes. The selected features were the root mean square (RMS) and the maximum fractal length (MFL), obtained using a software package, developed with Matlab by T.
The fuzzy NN is a hybrid system of fuzzy logic and NN technique. In fact, while fuzzy logic deals with the imprecision and the uncertainty of the system, the NN gives it a sense of adaptability. The model scheme of a fuzzy NN is shown Fig. 5. The fuzzy NN is governed with the following IF-THEN rules 24 : The IF-THEN rules define the state of the output depending on the state of the inputs, following such principle: IF the input variable is in a certain state, THEN the output will have that specific state. Available in Matlab Mandani type 25 of fuzzy NN was applied. • If x is A 1 and y is B 1 , then f = C 1 ; • If x is A 2 and y is B 2 , then f = C 2 ; The architecture of the fuzzy NN consists of five layers. The fuzzification layer takes the input values and determines the membership functions belonging to them.
Firstly the comparison was made for the shoulder (Fig. 13 ). For each sampling time the difference between the fuzzy NN output and the actual angular trajectories is plotted against the average. It was detected that 93% of the differences between the results from the fuzzy NN and the actual angular trajectories lie between the limits of agreement, represented by red dotted horizontal lines. It means that there is a certain degree of similarity between the two methods, however the level of agreement is  For the elbow, 95% of the difference lies between the limits of agreement, which proves that there is strong correlation and agreement between the results delivered by the the fuzzy NN and the real angular trajectories , Fig. 14. As the proposed control system is predictive for both shoulder and elbow, it is expected that torques, which are computed using the inverse dynamic model (1), are earlier in time towards the actual torques provided by the dataset.
Final validation using OpenSim To validate the proposed control strategy, it was chosen to simulate the motion using OpenSim 27 , which is a software system dedicated for building and analyzing computer models of the musculoskeletal system and for dynamic simulation of the movement. The movement analyzed in this work is the flexion/extension of the shoulder and the elbow joints, therefore it was possible to use the OpenSim model called "Arm26", which is a right upper extremity model with two DoF (flexion/extension of the shoulder and the elbow) and with three main muscle groups (biceps, triceps, and brachialis). After scaling the physical parameters of the model according to the subject characteristic, the motion was simulated using the angular trajectories delivered by the fuzzy NN. The torques were calculated using the simplified dynamic model developed for controller.
Next, for the real motion trajectories recorded by the motion capture system the motion was simulated using OpenSim and Arm26 and the corresponding torques were obtained. Both torque trajectories obtained using simplified model presented some noise and were not as smooth as the ones delivered by the OpenSim. This difference is a consequence of the numerical schemes used for calculating the velocities and accelerations. OpenSim has its efficient procedures for it, in which the data is filtered with a lowpass filter with a cut-off frequency of 6Hz. In this work, the velocities and the accelerations were computed using linear approximation and then smoothed with a moving average. Without considering the discrepancies due to numerical schemes, the torques delivered by the control system represented in Fig.
Conclusion In this paper, an EMG-based control system was developed as a part of the design of an upper limb exoskeleton to assist the flexion/extension motion of the shoulder and the elbow. The proposed control system was able to correctly estimate the angular trajectories and the torques needed for actuating the exoskeleton, especially for the elbow. However, due to the limited motion records, the difference between the shoulder torques obtained from the control system and that one given in the dataset is little more significant than that for the elbow. Future developments should include: • the improvement of the results by optimizing the parameters of the membership functions of the fuzzy NN, • testing the actions involving more DoF; extending the limb motion from planar to three-dimensional with hands orientation changes as well, • analyzing stability of the human/exoskeleton behavior for avoiding unexpected motions and stresses.
Acknowledgments We would like to express our gratitude to the Cluster Frame (https://next-isite. fr/frame) for supporting the project and to  Professor Malgorzata Syczewska  and the  Kinesiology Laboratory, Department of Rehabilitation of The Children's Memorial Health Institute in Warsaw  for sharing the motion recording infrastructure and professional support offered during data collecting. The collected data were made available by  Warsaw University of Technology , Faculty of Power and Aeronautical Engineering - Dr. Marek Surowiec.
right). The resulting CVT-MAP-Elites algorithm  [78] , requires a first, offline step (before QD optimization) to compute an approximate CVT based on a user-provided number  which defines the capacity of the container. The approximate CVT computation typically involves creating a dataset of  >>  uniformly-distributed random points in the bounded feature space, and using the -means clustering algorithm  [50]  on this dataset to find  centroids  [38]  (Algo. 3). If  is large enough, the  centroids become wellspread in the bounding volume. Typically as the number of dimensions increases so should , however, the approximate nature of the algorithm, as well as the large number of solutions requested () by QD algorithms makes the algorithm perform well. For instance, for finding 10,000 effective gaits for a 6-legged robot, Vassiliades et al.
This is, for instance, the case when one wishes to generate solutions resembling a set of examples that has been obtained from a different process. A solution to address this problem is to collect all the possible features of each example into a dataset which is then used to train a dimensionality reduction algorithm, such as an auto-encoder or principal component analysis. The outcome of this training is a low-dimensional latent space that captures the relations and similarities of the different examples and that can serve as a behavioral descriptor space. The encoder in the case of the auto-encoder (or the projector for the PCA) algorithm can project any new solution in the descriptor space. With this learned behavioral descriptor space, a QD algorithm produces a collection of solution that covers the latent space and that maximize a fitness function.
The fitness function can be totally unrelated to the dimensionality reduction algorithm, or it can be defined as the reconstruction error of the algorithm, to encourage the QD algorithm to generate solutions that look similar to the example set. A schematic illustration of this approach is provided in Figure  3-a. For example, this approach has been used to teach a robot to execute trajectories that resemble handwritten digits. Finding a way to characterise an arbitrary trajectory into a low-dimensional behavioral descriptor capturing the diversity of hand-written digit is particularly challenging. To side-step this challenge, Cully and Demiris  [14]  employed an existing dataset of handwritten digits (the well-known MNIST dataset  [44] ) and train an auto-encoder to find a low-dimensional latent space that captures the most prominent features of the different digits.
This latent space can then be used as the behavioral descriptor space and QD algorithms produce a collection of solutions that covers the space of the learned features. As a result, this enabled a robotic arm to learn how to draw digits without having to manually define what are the main features of the different digits. Instead of using an existing dataset, an alternative is to directly use the solutions generated by the QD algorithm. In this case, the randomly generated samples used in the initialisation of the QD algorithm are used to train the dimensionality reduction algorithm. The corresponding latent space definition becomes the behavioral descriptor space for the execution of a few steps of the QD process. during these steps, the number of solutions in the collection grows, which has the direct effect of accumulating new samples that can be used to extend the training of the dimensionality reduction algorithm and thus change the definition of the latent space.