dataesr/scientific-paragraphs-categorization huggingface dataset:
All significant variables in the univariate analysis with a level set at p < 0. 1 were incorporated into multivariate models. Two sensitivity analyses were then performed, a Cox regression with clusters, a marginal approach that takes into account the correlation between each line of the same patient, and a frailty model, which allows the unexplained variability to be represented by a random variable. These last two models gave almost the same results. Only the model with clusters is presented here. Statistical significance was arbitrarily set at 5%. Analysis were performed by using SPSS software, version 23. 0 (IBM) and R software (v 4. 1. 0) with the package survival.
However, the effectiveness and the safety of long-term CST therapy still appears Note: Data shown as either n (%) or median (interquartile range) for continuous variables. Each factor was tested in univariate analysis by Cox regression. Recurrence was considered as a time-dependent variable in univariate and multivariate analyses. Two multivariate models were tested: with or without a cluster variance to account for the intra-class correlation (since each subject have multiple events). We present here the model with clusters. Significant variables (p < 0. 05) are highlighted in bold. Abbreviations: ACLF, acute-on-chronic liver failure; AKI, acute kidney injury; CLD, chronic liver disease; FH, fulminant hepatitis; IS, immunosuppression; LT, liver transplantation; ns, not significant. *Anti-HLA antibody testing was performed in 189 patients before LT, 50 of whom were positive.
Smartphone based systems With the development of Smartphones integrating accelerometers, gyroscopes, cameras and communication tools, these are also used in several research works  [17, 18]. In the study  [19] , the authors propose a fall detection system based on a Smartphone's accelerometer data from two publicly available databases. An eccentric approach with a one-class classification based on SVM was used, resulting in a sensitivity of 98. 81% and a specificity of 98. 65%. In this context, Hakim et al. [20]  detect a fall using the inertial sensors of a body-worn smartphone, with signals transmitted wirelessly to a remote PC for processing. Chen et al. [21]  proposed a fall detection system based on a wrist-worn accelerometer. Experiments conducted on simulated falls and real ADL data from two different groups of volunteers yielded an accuracy of 97.
Biomedical sensor based systems Continuous monitoring of heart rate can reduce the risk of accidents due to heart failure. To achieve this objective, several heart rate sensors have been developed. Mahmood et al. [29]  developed a system that monitors an athlete's heart rate during training or exercise using a finger pulse meter. The pulsometer sends the collected data to a wrist strap, and is used as a heart rate monitor so that athletes and coaches can monitor their fitness level. Biomedical sensors can also be integrated for fall detection; a wearable fall detection system using a triaxial accelerometer and pulsometer was developed in the study  [30]. Wang et al. [31]  proposed an improved fall detection system for monitoring the elderly, based on body-worn smart sensors and operating via consumer home networks.
-Location service This service detects a person's movement from one room to another, by merging data collected from photoelectric sensors placed around the home at door level. The transmitter of each sensor sends a light beam to the receiver, which sends a logic output equal to '1' if no object crosses the beam, and equal to '0' if the person passes through the door and cuts the beam. In our case, we use the detector in sampled mode, reading these values every 500 ms. We say that the person has passed through a room door on the way in if, and only if, the receiver on the external side has generated a logic output equal to '0' before the one on the internal side. Similarly, when the person has left the room through the door, the receiver on the internal side generates a '0' output first. After merging the outputs from the eight sensors, we can determine the room number (noted Rnb) where the person is located.
Modality 4: the emergency order classification ranges from a minimum value of '0' for normal situations to a maximum value of '1' for dangerous situations. Between the two values, the operator can classify the urgency with a percentage between 0% and 100%, after a conversation with the monitored person. In addition, our system records the history of any anomalies detected, and stores all data concerning the user's condition in a database so that the doctor can consult the file at any time.
IMPLEMENTATION For the architecture modeling, we use the Timed and Stochastic Colored Petri Nets (CPN), simulated with CPNTools version V4. 0. 0  [40]. The programming language used for this modeling is CPN ML which is an extension of the standard ML language. The architecture composed of the three modules, input modalities, merge system and output modalities, is shown in Figure  5. To model this architecture under CPNTools, we used the hierarchical network based on the call of "sub-networks" in order to present a simplified view of it. The double rectangle transitions named 'Fall detection Service', Heart disorderdetection Service' and 'Location Service' represent sub-networks that take as input the input modalities and generate as output the detected anomalies and the user location for the same sample.
The purpose of a population-based cancer registry is to provide information on the disease burden for cancer control planning and is essential in studies on assessing the effectiveness of prevention, early detection, screening and cancer care interventions, where implemented. Sri Lanka is one of the Member States of the World Health Organisation's South-East Asia Region and receives technical support for cancer registration from the International Agency for Research on Cancer (IARC), and the IARC Regional Hub based at the Tata Memorial Centre in Mumbai, India. For data management of cancer registry records, Sri Lanka National Cancer Registry (SLNCR) uses the open-source registry software tool, CanReg5, as developed by IARC. The SLNCR has received data from 25 centres located across the country.
Inputted data from the respective centres was then exported from various CanReg5 systems to the main centre in Colombo. As the import to the central CanReg5 system held in the capital is manual, the records were manually modified to avoid any duplicate entries, and the quality of data was compromised. To overcome this issue, a new software tool, Rupantaran, has been created and developed by IARC Regional Hub, Mumbai to help merge the records from different centres. Rupantaran was tested and implemented successfully at the SLNCR with 47,402 merged records. The Rupantaran software has proven beneficial in maintaining the quality of cancer registry data by avoiding manual errors, thus enabling rapid analysis and dissemination, a limiting factor previously.
There are specific challenges in running PBCR in LMICs, including poor medical records, lack of cooperation from data providers, poor death registration systems, lack of trained registry staff, and difficulties in data entry and management  [1]. In many LMICs, concerned ministries are often not in a position to provide the needed technical support and resources to cancer registries, including the provision of software for data management and statistical analysis. To overcome the specific challenges in South and South-East Asia, IARC partnered with the Tata Memorial Centre (TMC) to establish an IARC Regional Hub in Mumbai in 2012  [3] , with a remit to provide technical support for cancer registration to countries within the region and serve as the first point of contact to countries  [4]. The Hub supports the operation of CanReg5 software as the data management software used by many PBCRs and provides technical assistance in data analysis and report writing to PBCRs where it is needed.
The CanReg5 software is free, open-source and widely used by PBCR to input, manage, quality assure and analyse the data of cancer patients  [2]. The CanReg5 software also has features to validate the entry of the data which helps maintain the quality of the same. The Sri Lanka National Cancer Registry (SLNCR) regularly contacts the IARC Regional Hub, Mumbai for technical assistance and has installed the CanReg5 software in 25 centres across Sri Lanka to collect and manage the data of cancer patients. Data from these centres are regularly submitted to the central office in Colombo where these data are merged into the central CanReg5 database  [5]. In CanReg5, new patient, tumour and source Identification (ID) are assigned for each entry. However, these new IDs are assigned sequentially and are used by CanReg5 for identifying duplicates in case consolidation between and within files.
Thus, the same ID will appear for different patients in more than one centre. This results in incorrect data merging and has led to data-related problems which have impacted the functions of the SLNCR in recent years. The data cleaning process required significant time and effort, with merging the database a manual process and a major challenge to the registry staff. The implementation of a virtual private network (VPN) can be a practical solution in dealing with data merging problems. A VPN extends a private network across a public network and enables users to send and receive data across shared or public networks, as if their computing devices were directly connected to the private network  [6]. CanReg5's client-server architecture feature can perform data entry simultaneously from different CanReg5 systems (client) so that the data get saved in the main CanReg5 system (server) in a centralized database  [7].
If the VPN solution is implemented and used with the CanReg5 client-server architecture feature, the data merging problem can be resolved as data merging is no longer needed given the data has been saved centrally from the various centres. This solution could be replicated by other registries in the region where multiple centres are established. However, implementing VPN has some limitations. It is not cost-effective. It is difficult to obtain funding from the Ministry of Health due to their prioritization of other public health programmes. Implementing a VPN network may result in administrative issues from different centres/hospitals, resulting from a lack of IT personnel and resources. Therefore, implementing a VPN in most LMICs is not likely to be feasible. There has been no means to merge exported data from CanReg5 software to another system that renumbers IDs to avoid duplication whilst maintaining the series of the record.
The manual methods that have been developed proved complex, time-consuming and introduced errors. Following discussions between the SLNCR and the IARC Regional Hub, Mumbai, the new Rupantaran software was developed which allows automated merging of CanReg5 data collected from several centres. The main aim of the software is to merge CanReg5 data from various centres to the main centre. The software has been successfully installed at the central office of the SLNCR in Colombo. This article aims to provide technical details of the Rupantaran software and how its application resolved merging issues of CanReg5 data collected from different hospitals in Sri Lanka.
Method The word 'Rupantaran' is derived from the Marathi language, a language of Maharashtra state India, and signifies a 'process of modification or conversion'. The CanReg5 software system is based on three tables for the entering details of the patient, tumour and source. The patient The modifications done through Rupantaran are shown in Figure  2. The column names in the CanReg5 software can vary for different registries. The steps to merge the CanReg5 data received from different centres to the main centre CanReg5 software using Rupantaran are illustrated in Figure  3. The steps to be followed are provided in Table  1. During the conversion process, Rupantaran software takes the last unique ID of the CanReg5 software as input and later selects a patient record one at a time from the patient table selected by the user.
For the selected patient record, the corresponding tumour record is searched in the tumour table. If the corresponding tumour record is found, then the corresponding source record is searched in the source table. When Rupantaran locates the tumour and source records for a given patient ID, a unique ID for each record is created. These unique IDs are the updated ID sequences. After the unique ID conversion of the selected patient record, the updated patient, tumour and source table data are saved in a new table. When the data conversion process is completed, the new tables which contain converted data are exported as tabseparated value (TSV) files. This results in the creation of new patient, tumour and source files. The name of those files has a format as (Table  Name ) _year (yyyy) -month (MM) -day (dd) hour (hh) _minutes (mm) _seconds (ss), e.
g. , 'Patient_2022-10-15 17_32_18'. Initially, the software was tested by the technical team at the IARC Regional Hub, Mumbai with minor errors observed and modified accordingly. The installation process and its application were demonstrated to the Sri Lanka Team. Following a period of testing by the SLNCR, the performance of the software was considered acceptable to address the issue of duplicate IDs. Subsequently, the software has been installed to merge the various cancer patient data files from several centres to the main database. The technical details and installation procedure are available as video clips online  [8].
Results The Rupantaran software was introduced in SLNCR on 16th September 2021. The SLNCR has successfully merged 47,402 records using Rupantaran received from different hospitals. Moreover, as Rupantaran software creates a unique ID for each case, there is a complete elimination of concern of duplicates at the outset. The hospitals along with the number of records received centrally are provided in Table  2. The differences between the manual method and the use of Rupantaran software in merging and uploading the data are described in Table  3. Earlier 2 weeks were required for the data merging process along with four human resources. The same amount of data was merged in approximately 1 hour using Rupantaran software. Due to the software human hours, time was saved and the process was fast and more accurate.
The SLNCR staff has been trained by the IARC Regional Hub, Mumbai, in CanReg5 as well as the merging of data via the Rupantaran software. The registry team has started working effectively on the merging of the database and are in the process of analysing the data for publication in a timely report to following previous publications 2012-2019 available on the website [9]. The Rupantaran software complements CanReg5 by allowing the merging of data received from different sources and can also be used in office environments where local area network connectivity is not established. Registries can have different lengths of unique ID for patient, tumour and source tables within CanReg5, and hence, Rupantaran needs to be specifically designed for a cancer registry with this in mind. The software can be updated as per specific requirements by the particular registries and the update is free of cost.
The cancer registries from South-East Asia which are interested in implementing Rupantaran software should write an email to IARC Regional Hub, Mumbai (email id: budukham@tmc. gov. in and atul. budukh@gmail. com). The software is freely available and technical support will be provided by the Hub. The Rupantaran software works only on the Windows operating system. During the CanReg5 virtual workshop organized by the IARC Regional Hub, Mumbai; Rupantaran was demonstrated to PBCR in Nepal, Bhutan, Afghanistan, and Timor Leste. As of now, these countries have their registry at one centre only. Later when these countries expand their registries, the option of using Rupantaran software should be considered for efficient and valid data merging. The software can be made available to the centres.
Conclusion The Rupantaran software has proven beneficial in maintaining the quality of cancer registry data by ensuring manual errors are avoided and, hence, enabling the SLNCR registry to work efficiently and progress with data collection, analysis and dissemination. Countries from the South-East Asian region are encouraged to contact the IARC Regional Hub in Mumbai for technical assistance in CanReg5 software, cancer registry training, and in collaboration in developing registry reports and scientific publications based on the collected data.
Steps needed to merge the CanReg5 data received from different centres to the centralized CanReg5 database using Rupantaran. StepsAction required1Receive the CanReg5 data tables -patient, tumour and source from each centre in TSV format. 2In Rupantaran, browse and select the received patient, tumour and source TSV files. 3 Click on the convert files button. The Rupantaran software will ask you to enter a unique ID. 4Enter the last unique ID from the centralized CanReg5 database and click start conversion. 5Rupantaran will convert the selected files with the required unique ID sequence and will export those files in TSV format 6 Import those exported files into the main CanReg5 software.
Acknowledgments and funding We gratefully acknowledge  Dr. R. A. Badwe  (Director, TMC) and  Dr. Pankaj Chaturvedi  (Deputy Director, CCE) for their guidance. We value the cooperation and support from  IARC, Lyon, France. We acknowledge  SEARO/WHO, New Delhi -India  office for providing financial support to IARC regional hub, Mumbai (APW No:  202829565 ). We appreciate the  Sri Lanka Cancer Registry  and the  Ministry of Health  for their collaborative efforts.
a b s t r a c t This paper outlines the initial development of a novel magnetohydrodynamic (MHD) plasma control system which aims at mitigating shock-induced heating and the radio-frequency communication blackout typically encountered during (re-)entry into planetary atmospheres. An international consortium comprising universities, SMEs, research institutions, and industry has been formed in order to develop this technology within the MEESST project. The latter is funded by the Future and Emerging Technologies (FET) program of the European Commission's Horizon 2020 scheme (grant no. 899298). Atmospheric entry imposes one of the harshest environments which a spacecraft can experience. The combination of hypersonic velocities and the rapid compression of atmospheric particles by the spacecraft leads to highenthalpy, partially ionised gases forming around the vehicle.
Although this method is effective in some real and simulated data  6;29  , its application is limited to datasets which can be aligned to such a global template, which excludes oscillating local field potentials and spiking data which is highly nonstationary or features drift which is large relative to the length of the probe or the spatial extent of the blocks used to account for nonrigidity. Further, KS' motion estimate is limited in its temporal resolution by the noise characteristics of spiking data, leading to the development of algorithms to assist manual tracing at higher temporal resolution like MTracer  16  , which in addition to relying on manual annotations is also limited in its application to rigid drift (i. e. , motion which does not vary along the depth of the probe). In this work, we introduce DREDge (Decentralized Registration of Electrophysiology Data).
DREDge further extends this method through computational and algorithmic improvements which enable scaling to both longer and more rapidly sampled data, in particular by implementing an online algorithm that enables the inference of motion at hundreds of hertz from the local field potential band (Fig. 1 ). DREDge's motion estimation runs in a small fraction of real time in the action potential band after spike detection and localization, and at around a quarter of real time when estimating nonrigid motion at high temporal resolution (⇠250Hz) from local field potentials (Supp. Fig. 1 ). We applied DREDge to in vivo datasets from a variety of species and MEA types, including human Neuropixels recordings  15;16  , recordings in mice from the International Brain Lab's large-scale reproducible electrophysiology experiment  32  , non-human primate recordings during probe insertion  33  , and mouse recordings using the experimental ultra high-density Neuropixels probe  34  , among others.
Through these experiments, we demonstrate the usage and utility of DREDge along with some of the novel downstream analyses that it enables. These include extending motion tracking to smaller and denser probes, leveraging local field potential-based motion estimation to improve local field potential event tracking and spike sorting in human datasets, tracking electrophysiological properties of cells across tens of millimeters of brain tissue during a deep probe insertion in rhesus macaque, and enabling stable motion correction in chronic recordings with sessions separated by days or weeks. We also include detailed comparisons to current methods (i. e. , Kilosort), introducing DREDge as a leading algorithm for this task. The processing pipeline of DREDge motion estimation and analysis. Electrophysiology recordings are first preprocessed into spike rasters (here, extracted from a recording in mouse  32  ; see see Section 4.
DREDge rescues spike sorting and LFP features in human intraoperative patient brain activity. A major motivation for this work was the significant motion observed while recording human brain activity using Neuropixels probes (Figs. 2  and 3 ; Supplementary Video 1). As reported by two separate groups  15;16  , the brain movements during open craniotomy and deep brain stimulation surgeries are substantial, ranging up to millimeters (Fig. 2. a; Supplementary Video 1). Previous approaches to combat and correct for this motion signal primarily involved manual tracking in the local field potential  15  or action potential bands  16  or semi-automated tracking  16  (MTracer, https://github. com/yaxigeigei/MTracer). In a collection of both openly shared deidentified data sets and newly collected data sets, we demonstrate the capability of DREDge to automatically track this movement within the neural signal both in the LFP band and the spiking activity or AP band (Fig.
Fig. 2 ). To further validate this cross-band registration procedure, we examined another human recording  15  with fast drift. In this dataset, DREDge's AP-based motion estimation was able to capture the slow trend of the true motion, but not the faster motion due to heartbeats and other sub-second brain motions. Since this recording featured prominent and well-isolated spiking activity traces from probable single units, it was possible to estimate the trajectories of these point clouds in order to refine the motion estimate at higher temporal resolution. To do so, we used a rough clustering to isolate each of these units' traces, and used the spike positions within these clusters to fit a spline (Fig. 2. b, Supp. Fig. 3 ; see also Section 4. 8). This sub-second AP-based motion correction procedure was able to track the fast (< 1Hz period) heartbeat-induced motion visible in the modeled spike positions and LFP raster (Fig.