dataesr/scientific-paragraphs-categorization huggingface dataset:
7 and the area co v ered was increased to ∼3. 1 arcmin in radius. The tiling pattern of the two observations was determined using the software MOSAIC 5  (Chen et al. 2021 ) and is shown in Fig. 1. In both cases, the observation length was 4 h. The first observation was already described in  Ridolfi et al. ( 2021 )  and was used to localize the known pulsars. The total intensity data from each coherent beam was recorded in filterbank mode with a time resolution of 76 μs and a bandwidth of 856 MHz, centred around 1284 MHz and divided into 4096 channels. After the observation was concluded, the data were incoherently de-dispersed at the dispersion measure (DM) of 86. 88 pc cm -3 , corresponding to the DM of J1823 -3021A located near the centre of the cluster as measured by MeerKAT observations in the same band  (Abbate et al.
3 ms for a DM of 80 pc cm -3 to 0. 4 ms for a DM of 95 pc cm -3. This effect will reduce the sensitivity of our search for rapidly spinning pulsars if the DM is far from the value chosen before reducing the number of channels. The searching procedure was carried out using PULSAR MINER 7  based around the PRESTO pulsar searching package  (Ransom, Eikenberry & Middleditch 2002 )  and described in detail in  Ridolfi et al. ( 2021 ). The beams were searched for both isolated and binary pulsars by performing a search in Fourier space. In the case of binary pulsars, the acceleration along the line of sight, a l , shifts the observed spin frequency of a potential pulsar o v er the length of the observation, t obs , by a number of z = t 2 obs a l /cP Fourier bins, where c is the speed of light and P is the period of the potential pulsar.
Additionally, for the most interesting candidates, we folded the corresponding beam of the other observation at the period and DM of the candidate for further confirmation. After a candidate has been confirmed, we derived an approximate ephemeris based on the detection, folded the observation using the DSPSR 8  pulsar package (van Straten & Bailes 2011 ) and run the PDMP routine of PSRCHIVE 9   (Hotan, van Straten & Manchester 2004 ; van Straten, Demorest & Oslowski 2012 )  to impro v e the quality of the detection. To obtain the best value of DM we first averaged the frequency channels of the best detection from 256 to 8 and extracted times-of-arri v al (ToAs) at each channel with the PAT routine of PSRCHIVE using a single template. We used TEMPO2 10   (Hobbs, Edwards & Manchester 2006 )  to fit these ToAs leaving the DM as the only free parameter.
J1823 -3022 PSR J1823 -3022 is an isolated bright pulsar with a period of 2. 4978 s and a DM of 96. 7 pc cm -3. It has a single component with equi v alent Downloaded from https://academic. oup. com/mnras/article/513/2/2292/6568555 by guest on 14 September 2022 Table  1. Properties of the newly disco v ered pulsars in the observations of NGC 6624. We report the barycentric period, DM, position, and flux density derived from the radiometer equation. For pulsars I and K, we report the position found using the SEEKAT software. For pulsar J the software could not be used and we report the position of the beam at the centre of the cluster were the pulsar was found. For pulsar J1823 -3022 we report the position derived by timing. Numbers in parentheses represent 1-σ uncertainties in the last digit.
L O C A L I Z AT I O N The small size of the beams in the TRAPUM observations can determine the position of the newly disco v ered pulsars with a precision of 10-20 arcsec. If the new disco v ery has been detected in more than one beam, it is possible to impro v e on the localization using the SEEKAT mulitbeam localizer 14 (Bezuidenhout et al. , in  preparation). This code compares the S/N of the detections of the various beams with the Point Spread Function (PSF) of the beam calculated using the MOSAIC software. The PSF will be dependent on the specific antennas used in the array during the observations, the position in the sky, and the frequency. We obtain the best impro v ement on the localization when we compare detections from dif ferent observ ations with dif ferent PSFs. To facilitate the localization of the pulsars close to the centre of the cluster, we performed another observation on 2021 June 18 in L -band using 60 antennas with an average overlap of ∼ 0.
This work is supported by the  Max-Planck Society  as part of the 'LEGACY' collaboration on low-frequency gravitational wave astronomy. AR MBu and AP gratefully acknowledge financial support by the research grant 'iPeska' (P. I. Andrea Possenti ) funded under the  Istituto Nazionale di Astrofisica (INAF)  national call Prin-SKA/CTA appro v ed with the Presidential Decree 70/2016. AR, MBu, and AP also acknowledge support from the  Ministero degli Affari Esteri e della Cooperazione Internazionale -Direzione Generale per la Promozione del Sistema Paese -Progetto di Grande Rile  v anza ZA18GR02. BWS acknowledges funding from the  ERC  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 694745 ). RPB acknowledges support  ERC Starter Grant  'Spiders', Grant Agreement Number  715051.
The resulting cDNA was used as the template for real-time amplification (LightCycler 2. 0; Roche, Bâle, Suisse) using the specific primers (Table  2 ). The relative amounts of toxin gene amplicons (psma1, hld in RNAIII), for a part of Pmt, pmtB, for Pmt regulator, pmtR, were determined relative to an internal standard (gyrB) as described elsewhere  (35). The expression levels of the investigated genes were therefore expressed as n-fold ratios compared to the standard in the presence of antibiotics relative to the same ratios in the absence of antibiotic (growth control). All quantitative PCR data were analyzed using the relative expression software tool REST 2009, version 2. 0. 13.
Ethics This is an ancillary study of a prospective cohort study of patients with COVID-19 infection hospitalized in the ICU at Amiens University Hospital (NCT04354558). This study was approved by the Amiens University Hospital IRB (Comité de Protection des Personnes Nord-Ouest II CHU-Place V. Pauchet, 80054 AMIENS Cedex 1, CNIL Number: PI2020_843_0026). In accordance with French law on clinical research for non-interventional studies, informed consent was waived but oral and written information was provided whenever possible to the patients and their families, specifying that they could oppose using their data  (19).
Data Electronic data, medical reports, and biological values were collected prospectively. SARS-CoV-2 infection was confirmed by a positive reverse transcription-polymerase chain reaction on a nasopharyngeal swab or bronchoalveolar lavage on ICU admission. The severity of illness at the time of the TTE exam was evaluated by the simplified acute physiology score (SAPS) II  (20). The severity of COVID-19-related pneumonia was defined according to the World Health Organization (WHO) case definition  (21). The critical group included patients with respiratory failure requiring mechanical ventilation and shock or organ failure  (21). Vasopressor use was evaluated by the SOFA cardiovascular (SOFA cv) score  (22). The ACP risk score was defined by four variables, namely, pneumonia as cause of ARDS, driving pressure >18 cmH 2 O, PaO 2 /FiO 2 ratio <150 mmHg, and PaCO 2 level > 48 mmHg  (1).
Variables with a probability of <0. 10 were integrated into the multivariate analysis. The Kaplan-Meier method was used to plot the survival curves compared with the log-rank test. A statistical test was significant when the P-value was less than 0. 05. All P-values are the results of 2-tailed tests. Statistical analyses were performed using the SPSS software version 24 (IBM Corp, Armonk, NY).
Acute cor pulmonale in non-intubated patient and right ventricular dilatation In our study, 38% (n = 20/52) of non-intubated patients with ACP were diagnosed. There are very less data on ACP in non-intubated patients without thrombotic event. Several studies have described RV dilatation and dysfunction (assessed by echocardiography) in COVID-19, but ACP is rarely reported. For example, in a large prospective international survey including 1,272 patients with COVID-19 infection, Dweck et al. reported 33% of RV abnormalities, including 15% of RV dilatation. However, the presence of ACP was not reported  (16). In the study by Soulat-Dufour et al. , RV dilatation (defined by an RV/LV ratio of >0. 6) in non-intubated patients with COVID-19 occurred in 12% of cases (n = 47/407). It was independently associated with death or ICU transfer (14).
Timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology. However, microscopes could become much more powerful investigation systems if they were endowed with simple unsupervised decision-making algorithms to transform them into fully responsive and automated measurement devices. Here, we report CyberSco. Py, Python software for advanced automated timelapse experiments. We provide proof-of-principle of a user-friendly framework that increases the tunability and flexibility when setting up and running fluorescence timelapse microscopy experiments. Importantly, CyberSco. Py combines real-time image analysis with automation capability, which allows users to create conditional, event-based experiments in which the imaging acquisition parameters and the status of various devices can be changed automatically based on the image analysis.
In 2011, MicroPilot  5  used LabVIEW (a proprietary systems engineering automation software) to interface µManager  6, 7  (the most popular open source and cross platform software used to pilot a large variety of microscopy devices) and other commercial vendor automation software with a machine learning algorithm to identify and only focus on cells in a specific phase of mitosis. This strategy increased the throughput and decreased the time required to screen the desired cells. Since then, the rise of machine learning and the popularization of simple automation strategies-using low cost prototyping microcontroller boards (Arduino 8 ) or compact single-board computers (Raspberry Pi 9 ) and Python programming-have made it easier to build simple open-source solutions to achieve the same goals.
For example, µMagellan  4  and NanoJFluidics 1 were built directly on µManager to achieve some level of feedback loop control and automation of image acquisition. µMagellan focuses on creating content-aware maps to adapt imaging modalities to a 3D biological sample. NanoJFluidics 1 homemade array of Arduino-controlled syringe pumps combined with µManager can perform automated fixation, labeling and imaging of cells; notably, this system could be triggered by real-time detection of the rounding of mitotic cells through a basic image analysis algorithm. More recently, Pinkard et al. 3  established PycroManager, a Python library that can interact with µManager to program a microscope in a very flexible way, though at the expense of a prerequisite for expert-level Python coding skills and the knowledge of additional software or programing language.
This novel and active field of research, called cybergenetics, harnesses the possibility of creating interactions between cells and a numerical model in real-time, and thus opens novel areas of both applied and fundamental research. To demonstrate the power of cybergenetics, we and others have developed various software to implement feedback loop-controlled microscopy systems. These solutions combine µManager and/or MATLAB with dedicated image analysis and control algorithms to close the feedback loop  14, 15. However, in practice, these solutions are difficult for non-experts to implement and cannot be easily transposed to a broad range of biological problems. More recently, several groups proposed Python and µManager-based software to develop cybergenetic experiments  19, 20  , though these approaches remain specific to the control of gene expression in cells over time and do not meet all of the varied needs of cell biologists.
Here, we present CyberSco. Py software, which is a follow-up to our contributions to piloting gene expression in real-time in yeast and bacteria  14, 15. CyberSco. Py is written in Python, has been designed with automated, realtime feedback loops in mind, and includes deep learning image analysis methods as an integral part of image acquisition. We focused on achieving a proof-of-concept software with a simple, robust, user-friendly interface that can be deployed as a web application. Importantly, CyberSco. Py natively includes the ability to control basic microfluidic devices through an Arduino board that drives electro-fluidic valves (see "Materials and methods"). CyberSco. Py is, by design, oriented towards advanced timelapse experiments that include triggered events and routed tree scenarios-rather than preprogrammed sequences of image acquisition.
CyberSco. py is an open-source web application for timelapse microscopy and microfluidics automation. It is written in Python (Fig. 1 ) and employs real-time image analysis and decision-making algorithms to trigger changes in the imaging parameters in real-time during the experiment. At present, this proof-of-concept is operational on a fully automated Olympus microscope (IX81) equipped with brightfield and epifluorescence illumination and linked to an Arduino-based homemade microfluidic control device (Supplementary Figs. S1  and S2 ). A web interface allows the user to easily setup an experimental plan and/or to select pre-configured conditional experimental scenarios, together with the corresponding image analysis solutions. A local server receives these parameters and launches the experiment.
During the experiment, the acquired images are constantly analyzed and used to trigger events according to the chosen scenario. In particular, the detected events can feedback on the microscopy settings and the microfluidic settings to adapt the experimental plan during the experiment (Fig. 1 ). Thus, CyberSco. Py transforms microscopes and their related devices into an advanced imaging automaton capable of performing unsupervised time-dependent tasks with the capacity to handle various user-defined triggers. More details of the CyberSco. Py source code and its documentation are available on the GitHub page of the project (Supplementary Information). Although the software has been developed for a given microscopy setup, it can be extended to any equipment providing there is a driver and/or Figure 1.
CyberSco. Py framework. (A) Architecture. CyberSco. Py is built in Python and uses the web application library Flask to create a web user interface. Microscopy protocols are written into a YAML (human readable data serialization language) file, which can be interpreted by the Python core module of CyberSco. Py, which drives the various components of a IX81 fully automated microscope. The core module also drives a set of fluidic valves that can be used to switch the media flowing into a microfluidic device. A class in Python is associated to each device. Images obtained from the camera are analyzed in real-time by a U-NET deep learning model to segment yeast cells and/or detect specific events, depending on the pre-trained model selected by the user. The result of the analysis is used by the core module to update the current state of any devices under its control (see "Materials and methods" for more information).
g. , number of cells, size of cells, etc. ). The structure of a scenario consists of a list of instructions for the microscope ("make the autofocus", "take a picture", etc. ) to be serially executed at each iteration, a conditional block, and an initialization block. Each scenario corresponds to a unique Python file with the same consistent structure. The user can also enter information about the projected experiment, as well as selecting modalities for monitoring the experiment remotely via email (selecting where to send the emails and at which frequency) and/or through a discussion channel (e. g. , Microsoft Teams or Slack). a documented communication protocol (see Supplementary Information and the GitHub page of the project on how to proceed).
CyberSco. Py allows to automate complex multidimensional acquisitions. CyberSco. Py can obviously be used to build classic MDA experiments. A web interface enables the imaging acquisition settings to be easily defined through a drag and drop interface (Fig. 1  and Supplementary Information). The user can define X-Y positions and the corresponding focal planes and set the illumination parameters, as in conventional microscopy software. CyberSco. Py makes it easy to perform a classic MDA experiment that follows, for example, the proliferation of a population of yeast cells in a microfluidic device (Fig. 2A ). Crucially, the user can define specific imaging settings for each position (Fig. 2B ). This modification of how MDA is defined through the user interface is simple but powerful: by design, the user has full control over the acquisition settings without having to follow the classic MDA patterns of nested loops, which by default impose the same imaging acquisition parameters on all time points and positions.
CyberSco. py also offers the possibility of operating the microscope and the attached devices in real-time based on events detected during unsupervised analysis of the cell sample. The central idea is to let the microscope focus on "interesting" events through adjustment of the imaging acquisition parameters without supervision. This task requires efficient image analysis to segment cells, measure their properties and detect cellular events of interest. Image analysis is conveniently achieved in Python using the U-NET convolutional neural network  23  ("Materials and methods" and Supplementary Information), which is trained on a set of images. Once the training is complete, CyberSco. Py can use the resulting model to segment cells, display the segmentation in the user interface, compute cellular features and trigger user-defined events.
Discussion The main goal of CyberSco. Py is to enable the design of augmented MDA experiments in which the imaging settings can be changed in real-time as a function of unsupervised image analysis conducted during the time course of the experiment. CyberSco. Py has been built as a modular web application; in addition to the modularity of the device management, both the image analysis and decision-making algorithm are separate modules that can be adjusted individually and plugged into the communication modules that drive the microscope and its associated components. In that respect, CyberSco. Py aims to push forward "low code" or "no code" strategies, which are becoming increasingly popular, and enable biologists without coding expertise to create complex, event-based routines and workflows using cloud-based web applications.
Creation of automation protocols that contain simple logical, conditional statements such as "IF this THEN that" or "WHILE this DO that" are within the reach of CyberSco. Py and may have an impact for researchers in biology who do not want, nor have the time or expertise, to dive into programming. This goal could be reached progressively by building on the examples proposed here and adding generic scenarios that are driven through the results of real-time image analysis. For example, the scenario developed in this work that acts on a microfluidic chip based on the number of cells could be directly reprogrammed by changing the trigger condition. Image analysis remains the bottleneck of automation and will continue to require expertise in machine learning and coding. However, the pace at which deep learning is being adopted in laboratories and adapted into user-friendly software solutions and online tools  25, 26  suggests that an increasing number of easy-to-use methods to create UNET-trained networks will be developed in the near future and could subsequently be incorporated into CyberSco.
CyberSco. Py software. CyberSco. Py is written in Python for the backend and HTML/CSS/ JS for the frontend, connected by a WebSocket channel. Communication to the different devices is made directly through serial communication and whenever necessary, the drivers provided by the vendors or a generic version from the µManager community. CyberSco. Py is installed on computer software that must be equipped with a recent GPU to benefit from U-NET deep learning segmentation of cells. A server can interface several microscopes running CyberSco. Py and be extended with a user manager database and image database management program such as OMERO. The current open-source release of CyberSco. Py can be found on GitHub (https:// github. com/ Lab513/ Cyber Sco. Py). Acquisition, segmentation and tracking.
Image acquisition is preceded by an autofocus algorithm that optimizes the quality of the segmentation, as well as the sharpness of the object under scrutiny. Cell segmenta-tion is achieved via a machine-learning algorithm based on the U-NET architecture. Twenty images of cells at different positions and containing different numbers of cells were taken to produce the training set. This dataset was then augmented as it is classically done (see Supplementary Information). For the main model for yeast segmentation, the neural network was trained on five periods using a GPU NVIDIA GeForce GTX 1080; this training only took 5 min. Predictions with this model are obtained in around 0. 2 s. Cell tracking is performed using both image correlation in real-time and using a simple proximity relationship between the predicted contours.
HLA-Typing HLA typing was performed using PCR-SSO hybridization methods in a Luminex FLEXMAP-3D ® platform and analyzed on the HLA-FusionTM software (LABtype SSO class I/II tests: LABtype XR Class I locus A, B, C and Labtype SSO class II DRB1, DQA1/B1, OneLambda Inc. , West Hills, CA, USA).
Acknowledgments: We thank  Joséphine-Anna-Tine  and  Michèle Génin  for their work in the INSERM-UMRS-1136. We thank  Ali Boudifa  for HLA typing. We thank  T4-T8  laboratory,  Sophie Even  and  Christophe Parizot  for cytometer support. We thank  Elodie Couret  and  Basile Moambi  for the technical support. We thank  ANRS/Emerging infectious diseases , the  IFCT group  (in particular  Franck Morin  and  Elodie Amour ) and  Bristol-Myers Squibb  for their funding and their support. We thank the  CancerVIH group  for promoting the study and recruiting patients. Finally, we also deeply thank all the  French clinicians  for patients' inclusions in the study. We thank  Cecile Moins  and  Ventzislava Petrov-Sanchez  from the  ANRS/ Emerging infectious diseases  for their support.
Dataset To test UGDR, we downloaded publicly available Illumina paired-end reads sequencing data of recombined and potentially recombined strains from the NCBI Sequence Read Archive (SRA) (Additional file 1: Table  S1 ). The downloaded set includes: fourspore tetrads of haploid strains and two diploid mother and daughter RTG strains (RTG17M & RTG17D,) issued from a SK1/S288C hybrid background  [11] , and two clinical triploids strains (YJM1138 and YJM114) and two clinical tetraploids strains (YJM958, TJM959)  [13]. It is important to note that the triploids and tetraploids are sequenced by Zhu et al. without sporulation or induced recombination. The S288C fasta sequence was downloaded from the Saccharomyces Genome Database and the novel SK1 fasta assemble was provided by Liti  [5, 14, 15].
Genome alignment and variants calling (Galocal) The key steps for studying large-scale dataset are reads mapping and identification of potential polymorphic sites (SNP calling). To manage the alignment of the reads and call the variants, we assembled the necessary open source tools in a custom bash script named Galocal (Fig. 1a ). First, we mapped all the reads to the reference using the gapped Burrows-wheeler Aligner (BWA) v. 0. 7. 15  [14]. Next, to achieve an accurate variant detection, we conducted a different post-alignment analysis that included elimination of unmapped reads using SAMtools v. 1. 3 [14]  and removal of PCR duplicate using Picard tools v. 2. 4. 1 (https:// github. com/ broad insti tute/ picard)  [16]. After that, we used the Bayesian algorithms: FreeBayes framework-1. 0.