dataesr/scientific-paragraphs-categorization huggingface dataset:
We used k-means unsupervised clustering, as detailed in the STAR Methods, to group these genes based on their expression profiles into 6 clusters (Figures  2A  and 2B ). Clusters A1, A2, and A3 contain genes that were up-regulated upon altitude elevation, while cluster A6 contains down-regulated genes. Unexpectedly, clusters A4 and A5 show a more complex behavior, in which the response to altitude is non-monotonic: in cluster A4, there is up-regulation in PU followed by down-regulation in RI, and in cluster A5, there is down-regulation in PU followed by up-regulation in RI. To identify related biological functions for these different expression profiles, we performed pathway enrichment analysis using the GO Biological Processes (BP) database and upstream transcriptional regulators enrichment analysis using the ChEA 2016 (ChEA) database (Figures  3  and S3 ; Table  S3 ).
We first examined the clusters that showed monotonic behavior, namely clusters A1-A3 and A6. The up-regulated cluster A3 was mainly enriched for protein-biosynthesis-related processes (Figure  3A ). The other up-regulated clusters (A1 and A2) showed no significant enrichment within the selected cutoffs. Subsequent analysis on the up-regulated clusters based on ChEA revealed enrichment for target genes of transcription fac- (A) The study design. Data were collected from 8 male subjects at three different altitudes (SL, sea level, 200 m; PU, Puno, 3,800 m; RI, La Rinconada, 5,100 m) according to the scheme. In each 24-h sampling round, blood samples were collected from each subject every 4 h for subsequent transcriptomic analysis. In addition, subjects were continuously monitored for activity and heart rate (HR).
Whole-blood gene expression shows altitude-dependent daily variance In recent years, a connection between hypoxia and circadian rhythms was elucidated, mainly in animal models  (Adamovich et al. , 2017 (Adamovich et al. , , 2019 (Adamovich et al. , , 2022;; Liu et al. , 2022; Manella et al. , 2020)  and to some extent in humans  (Bosco et al. , 2003). This prompted us to ask whether daily variance in gene expression changes between the different altitudes, characterized by varying oxygen availability. As described above, we collected the blood samples at 4-h intervals over 24 h, which enabled assessment of the time-of-day effect in addition to the overall effect of altitude. We first analyzed the temporal transcriptomic data using several well-established algorithms for analyzing circadian rhythms, namely JTK, Lomb-Scargle periodogram, ARSER  (Wu et al.
Whole-blood samples contain a mixture of transcripts from multiple cell types. Hence, it is conceivable that changes Resource in the transcriptome are partially due to differences in the cytological composition of the blood. This is especially true for the immune response, which involves cell infiltration, proliferation, and death of specific cell types. To gain insights regarding the differences in cytological composition, we employed CIBERSORTx, a digital cytometry approach that assesses the cell-type proportions in mixed transcriptomes using expression signatures unique to each cell type in the sample  (Newman et al. , 2019). As a signature dataset, we used the LM22, which contains signatures of 22 different leukocyte cell types and was previously used for assessing cell-type proportions in whole-blood samples  (Newman et al.
METHOD DETAILS Accelerometer and heart rate data collection and analysis During each 24-h sampling round, physical activity and heart rate (HR) were monitored using a wrist-worn triaxial accelerometer (Actigraph GT9X, ActiGraph Corp, Pensacola, FL, USA) associated with a Bluetooth HR transmitter chest belt (Polar H10 Heart Rate Monitor, Polar Electro Oy, Kempele, Finland). From activity counts registered per minute, data were aggregated per hour to determine fraction of time spent in sedentary, light or moderate-to-vigorous activity.
ACKNOWLEDGMENTS We are grateful to all the members of Expedition 5300 for their efforts and hard work. G. A. is supported by a grant from the  European Research Council  ( ERC-2017 CIRCOMMUNICATION 770869 ),  Abisch Frenkel Foundation for the Promotion of Life Sciences, Adelis Foundation , and  Susan  and  Michael Stern. S. V. is supported by the  Grenoble Alpes University Foundation , the  ''Fonds de dotation AGIR pour les maladies chroniques ,'' and by the  French National Research Agency  ( ANR-12-TECS-0010 ) in the framework of the '' Investissements d'avenir'' program  ( ANR-15-IDEX-02 ). J. G. is partially supported by the  French National Research Agency  ( ANR-19-CE14-0037-01 ) and the  Grenoble-Alpes University  ( ANR-UGA-15-IDEX-0002 ).
Background The SARS-CoV-2 pandemic is a worldwide challenge. The CRIT-CoV-U pilot study generated a urinary proteomic biomarker consisting of 50 peptides (COV50), which predicted death and disease progression from SARS-CoV-2. After the interim analysis presented for the German Government, here, we aimed to analyse the full dataset to consolidate the findings and propose potential clinical applications of this biomarker.
Procedures All participants were followed up until recovery, hospital discharge, or death. On days 0-3, 4-7, and 10-21 after diagnosis, participants who were alive were staged according to the 8-point WHO Clinical Progression Scale. 7 Electronic case report forms (MARVIN EDC; XClinical, Munich, Germany) were used for data compilation. For UPP, 10-mL urine samples were collected in borated test tubes (ExactoBac-U; Sarstedt, Nümbrecht, Germany) and kept at -20°C or less until assayed. The methods for capillary electrophoresis coupled with mass spectrometry, for peptide sequencing, and for the evaluation, calibration, and quality control of the mass spectrometric data are described in detail in the appendix (pp 2-5). For identification of the urinary biomarker, 196 urine samples were randomly selected from those available from participants at days 4-7 and days 10-21 after diagnosis, excluding participants at WHO stages 4-5, allowing for a comparison of the UPP profiles at WHO stages 1-3 (n=116) and 6-8 (n=80).
Statistical analysis and outcomes The initial sample size calculations, informed by a proofof-concept study,  8  required 212 participants with lifethreatening COVID-19 (WHO stage ≥6) to be compared with 271 participants with mild symptoms (WHO stage <4) to identify and validate a UPP biomarker with 75% sensitivity and 80% specificity. Given the 33% progression rate from mild to severe disease in the pilot study,  8  and accounting for a 15% rate of missing data, the sample size for the full study was initially set at 645 participants. On the basis of the interim study,  6  in which the mortality rate was 10% (23/228), the sample size for the full study was revised to 1000 participants. For database management and statistical analysis, SAS software (version 9•4) was used. Significance was a two-tailed p value of 0•05 or less.
Using SAS interactive matrix language (version 9. 4), a Markov chain simulation  11  was bootstrapped 1000 times to generate the transition probabilities from the entry WHO score to the maximum WHO score during follow-up (appendix p 16). Transition probabilities were computed for the whole cohort and for various risk strata, which were defined by the entry COV50 score and age (<65 vs ≥65 years). The transition probabilities allowed extrapolating the number of participants reaching follow-up WHO scores of 3-4, 5, and 6-8, and therefore requiring regular (score 3-4), intermediate (score 5), or intensive (score 6-8) care. Point estimates and uncertainty limits were derived from the median and from the 5th to the 95th percentile interval of the bootstrapped distributions. Next, the expected hospitalisation costs were computed in three steps.
Results From June 30 to Nov 19, 2020, 228 participants were recruited, and from April 30, 2020, to April 14, 2021, 784 participants were recruited. The 1012 participants making up the full dataset were on average aged 62•3 years, included 447 (44%) women, 557 (55%) participants with hypertension, 154 (15%) with heart failure, 257 (25%) with diabetes, and 106 (11%) participants with cancer (appendix p 22). The WHO score at enrolment (table  1 ) was 1-3 in 445 (44%) participants, 4-5 in 529 (52%) participants, and 6 in 38 (4%) participants. The mean (IQR) COV50 score at baseline was -0•23 (-1•27 to 0•80; appendix p 23). Compared with the initially enrolled participants, those recruited later scored lower on the WHO scale (table 1; p<0•0001), included more participants with a history of cancer (6% with the initial phase vs 12% with the continued phase; p=0•012), but fewer participants on inhibitors of the renin-angiotensin system (54% with the initial phase vs 39% with the continued phase; p<0•0001).
Ethics approvals allowing access to claims databases were not requested. 4] [25]  Because a high UPP risk profile justifies an earlier intervention rather than later treatment guided by clinical deterioration, presumably applying the test will not affect drug costs. One possible limitation of the Markov chain simulation is that the WHO scores at presentation of the participants in CRIT-CoV-U were used as the initial distribution vector, so the medians of the simulated patient distributions, as presented in the appendix (p 17), closely reflect the baseline distribution vector. However, the main objective of running the Markov chain analysis was to generate uncertainty intervals as captured by the 5th and 95th percentiles of the simulated number of participants with possible outcomes. •• •• Cost of intermediate care, M€ •• 0•988 (0•819-1•156) 0•592 (0•337-0•807) •• •• Days in intensive care •• •• 6 (4-11) •• •• Cost of intensive care, M€ •• •• 0•505 (0•321-0•689) •• •• Days in all care facilities •• •• •• 8 (4-13) •• Cost of all care, M€ •• •• •• 6•208 (5•110-7•273) 0•887 (0•730- •• •• Days in intensive care •• •• 5 (4-14) •• •• Cost of intensive care, M€ •• •• 3•200 (2•626-3•856) •• •• Days in all care facilities •• •• •• 11 (5-17) •• Cost of all care, M€ •• •• •• 15•032 ( Among the strong points of our study is the high consistency in the discriminatory performance of COV50 among participants recruited both initially and later (table 2; appendix pp 13-15), which shows a high degree of cohesion in the results within the CRIT-CoV-U cohort.
Third, CRIT-CoV-U enrolled adults, who were predominantly white Europeans. How ethnicity might affect the UPP is currently under investigation in the Urinary Proteomics Combined with Home Blood Pressure Telemonitoring for Health Care Reform trial (NCT04299529). 26 Along similar lines, the recruitment of participants into the present study involved 19 centres in eight countries and was driven by the temporal and geographical spread of the successive waves of the pandemic. Therefore, it is difficult to assess to what extent the study participants were representative of the wider population, in which the COV50 test might be used in clinical practice. Fourth, one limitation of the capillary electrophoresis combined with mass spectrometry approach is the application of ultrafiltration with the threshold set at 20 kDa, so that larger proteins escape analysis.
Figure: COV50 performance adjusted for baseline risk factors in the full dataset for mortality and worsening WHO score Figure shows the sensitivity and specificity of the urinary marker COV50 for mortality versus survival (panels A-C) and for progression versus non-progression in the baseline WHO score (panels D-F) during follow-up. The base model included sex, age, BMI, and the presence of comorbidities (hypertension, heart failure, diabetes, or cancer). In subsequent steps, the baseline WHO score was added and then COV50 score as a continuously distributed variable (panels B and E) or as a categorised variable based on an optimised threshold of 0•47 for mortality (panel C) or 0•04 for a worsening WHO score (panel F). At each step, the p values are for the comparison with the preceding model.
In this paper, we introduce the Obstacle Detection & Avoidance (ODA) Dataset for Drones, aiming at providing raw data obtained in a real indoor environment with sensors adapted for aerial robotics in the context of obstacle detection and avoidance. Our micro air vehicle (MAV) is equipped with the following sensors: (i) an event-based camera, the performance of which makes it optimized for drone applications; (ii) a standard RGB camera; (iii) a 24-GHz radar sensor to enhance multi-sensory solutions; and (iv) a 6-axes IMU. The ground truth position and attitude are provided by an OptiTrack motion capture system. The resulting dataset consists of more than 1350 sequences obtained in four distinct conditions (one or two obstacles, full or dim light). It is intended for benchmarking algorithmic and neural solutions for obstacle detection and avoidance with UAVs, but also course estimation and in general autonomous navigation.
In recent years, a series of datasets have been collected for indoor navigation tasks. In 2015,  [20]  acquired a collection of more than 10k RGB-D images provided by multiple sensors such as the Kinect v1 and v2, as well as the Intel RealSense depth sensor. By doing so, they extended the NYU Depth v2 dataset which contains 1449 RGB-D images from the Kinect v1  [19]. Similarly, the ESAT dataset was released for the purpose of indoor visual-based obstacle detection and avoidance with a drone using a single RGB monocular camera  [2]. It consists of 20k RGB-D images collected from a Kinect sensor by walking. The ESAT and the NYU Depth v2 datasets are mostly used in the context of scene understanding (e. g. , including semantic segmentation, object detection and classification, context reasoning, etc.
), making them less relevant for the context of obstacle detection and avoidance per se, particularly in case the obstacle is located in the course of the drone/robot. In the meantime,  [22]  proposed a dataset for obstacle avoidance onboard ground robots. The dataset consists of 10k RGB images used to estimate the depth and therefore allow safe navigation of a ground robot. More recently,  [15]  released a collision avoidance video dataset collected by means of a drone equipped with a frontward RGB camera. A distinctive feature of this dataset is that it includes 100 samples of actual collisions with static and moving obstacles. Despite being very popular, the aforementioned datasets have limited applications to robotics in general, and for MAVs in particular: (i) obstacles are highly textured and outside of the course of the moving entity (robot/human), (ii) only a limited number of sensors (i.
In light of these advantages, DVS cameras represent a powerful alternative to conventional cameras in the field of visualbased obstacle detection and avoidance, where motion perception is crucial to achieve robust and accurate performances. This is further supported by the growing number of datasets now available with applications for Visual Inertial Odometry (VIO,  [4, 12] ), depthestimation  [8] , and image reconstruction  [14, 18]. However, and to the best of our knowledge, none of the available datasets addresses the problem of obstacle detection when the obstacle is located in the drone's course, thus implying avoidance maneuvers. Visual-based detection of obstacles in dim light represents a very challenging task, even for DVS cameras, particularly when exploration is performed in the total darkness.
Alternatively, millimeter wave (MW) radars could help in obstacle detection regardless of the lighting conditions. Recent work showed the growing interest for radar applications to MAVs for autonomous exploration of unknown environments  [10, 23]. MW radar sensors also have the advantage of being able to detect the Doppler signature of moving objects, from which the radial velocity can be extracted. Nowadays, the form factor of radars is small enough to be used onboard MAVs: typical MW radars weigh 10-30 д, for a total surface in the range of 25 -30 cm 2. So far, MW radars remain quite unused in drones applications, where embedded vision remains the major type of sensing for robots. The side effect of this under-representation of radars in obstacle detection and avoidance onboard MAVs is a complete lack of datasets including MW radar sensors.
MATERIAL AND METHODS 2. 1 The hardware architecture During the dataset collection, we used the Parrot Bebop-2 drone equipped with additional sensors and embedded CPU for data acquisition. More precisely, the drone embeds an Odroid XU4 board running the Linux Kernel 4. 14 LTS. For the purpose of obstacle detection and avoidance in MAVs flying through challenging environments, the following sensors have been integrated on the drone: (i) the DVS240 event-based camera  [1] ; (ii) a 6-axes IMU, integrated with the DVS240 camera; and (iii) the Infineon XENSIV 24GHz Position2Go radar sensor. Integrated to the Bebop 2 drone, a full HD RGB camera is mounted at the front and provides wide-angle video recordings on flight. The hardware architecture is detailed in Table  1  and Fig  1. The DVS240 1  is an event-based camera with a frame resolution of 240 × 180 pixels.
The experimental setup The obstacle avoidance dataset has been collected in the Cyber Zoo, the flying arena of the Delft University of Technology (10 × 10 × 7 meters). The arena is equipped with the OptiTrack motion capture system, featuring a total of 16 Prime X-13 cameras, allowing to realtime track drones (position and attitude) with millimeter precision. The MAV was equipped with IR LEDs and the OptiTrack cameras lights were turned off to avoid visual disturbances caused by the IR flickering from the cameras. For each trial, a pilot flies the MAV towards the center of the Cyber Zoo where one or two obstacles (i. e. 2 https://github. com/uzh-rpg/rpg_dvs_ros poles) are placed (Fig. 2. ) Depending on the position of the MAV, the pilot chooses to either turn left or right (avoidance), or continue straight-forward.
Dataset overview The dataset contains a total of 1369 samples combining the data collected from the sensors embedded onboard the MAV, for a total of 92 GB after data compression. An overview of the files enclosed in the dataset is proposed in Table  2  (Extensions 1-14). All the data provided in the dataset are time-synchronized by means of the internal clock of the Odroid XU4 on which our ROS node for data acquisition is executed. First, the data for all sensors and all trials are provided in both ROS bag (Extension 3) and CSV (Extensions 4-7) formats, with the exception of the video recorded from the RGB camera, available under AVI format (Extension 8). A Python script is made available for conversion between ROS bags and CSV files (Extension 13). While ROS-based formatting is convenient for direct use in ROS, all data are also available in CSV format, making them easily used in standard frameworks (MATLAB, Python, C/C++, etc.
). The ground truth position and orientation of the MAV w. r. t. time is provided by the OptiTrack motion capture system, and is available both in the ROS bags (Extension 3, under the topic /optitrack/pose) and in the dedicated CSV files (Extension 7). The Cartesian coordinates of the MAV are given in meters, while the orientations are given in quarternions (a, b, c, d), with the following representation: a + i • b + j • c + k • d, where (i, j, k) are the fundamental quaternion units. Lastly, an overview of the dataset is available in the trial_overview. csv file (Extension 2). This file contains information about the position of the obstacle in the flying arena for each sample, along with the lighting conditions (100 Lux vs. 1 -3 Lux). The first column provides the ID of the sample test, which can appear twice (in two consecutive rows) in case there are two obstacles in the arena.
For each CSV file, the first column provides the timestamp (expressed in nanoseconds). The next columns contain the sensor data: (a) optitrack. csv: first the x, y, and z positions (in meters), followed by the a, b, c, d quaternions; (b) dvs. csv: first the x and y pixel coordinates, then the polarity p ∈ {-1, +1}; (c) imu. csv: first the linear accelerations (in m/s 2 ) and then followed by the angular velocities (in rad/s) along the x, y, and z axes; (d) radar. csv: the first 2048 columns contain real values from the first antenna, followed by the 2048 next columns containing the imaginary values for the same antenna, and so on with the second antenna. An overview of the sensors' outputs for a given dataset sample is provided in Fig. 4  (sample no. 10). This trial has been performed under full light conditions and with one unique obstacle.
Parsing and indexing In order to help users handling the dataset, we provide a series of Python scripts for data loading and visualization (Extensions 11, 12). In particular, for each type of sensor except for the RGB camera video, a Python script allows the user to load and plot the result of a specific sample for both ROS bags and CSV files formats. Similarly, another Python script automatically loads and displays all sensors data (including the RGB camera) in a convenient way, again for both ROS bags and CSV files.
Calibration of the visual sensors A set of calibration files (Extensions 9, 10) are provided to the users for both cameras. The calibrations were performed using a checkerboard. In the case of the standard RGB camera, the checkerboard (8 × 5 squares, length: 2. 4 cm) was printed and presented in front of the camera. We then use a Python script to apply the pinhole camera model and the calibration tools available in OpenCV to determine the calibration parameters. Due to the neuromorphic nature of the visual information, DVS cameras require the checkerboard to move over time. A checkerboard is automatically rendered on a computer screen using a MATLAB script (adapted from  [16]  and  [13] ). While the DVS is recording events, the checkerboard flickers at a predetermined rate. Several recordings are made from distinct positions and orientations of the sensor, while the checkerboard dimensions are kept constant.
CONCLUSION The Obstacle Detection and Avoidance (ODA) dataset for drones has been introduced  [6]. Including raw data from a relevant range of sensors for obstacle detection, it aims at helping investigations on obstacle detection and avoidance in a challenging context (dim light condition, low-textured environment, obstacle within the drone's course), as demonstrated in recent work on radar-based obstacle avoidance  [21]. In addition, the visual information available in the dataset makes it possible to use it for visual inertial odometry (VIO) and course estimation as shown in  [5] , thus opening a wide range of applications in autonomous navigation of drones in complex environments.
General overview of the hardware required for the drone setup. Property Description Details Dimensions 382 mm x 328 mm x 140 mm Weight 600 g Battery included Flight performance 15 minutes 11. 1 V 3100 mAh LiPo battery Onboard processing -Parrot P7 dual-core CPU Cortex 9 (for autopilot) -Quad-core GPU Odroid XU4 ft. : Embedded CPU -Samsung Exynos5422 Cortex A15 2Ghz (for dataset collection) -Cortex A7 octa-core CPUs -Linux Kernel 4. 14 LTS -DVS240 event-based camera (iniVation) Resolution: 240×180 pixels Embedded sensors -Full HD wide-angle RGB camera Resolution: 1920×1080 pixels -24 GHz radar sensor Position2Go (Infineon) FOV: 76 • ×19 • -6-axes IMU sensor Integrated to DVS240 camera frequency-modulated continuous wave (FMCW) radar transmitter and two receiver antennas to determine the range, bearing and radial velocity of obstacles and direction of motion.