dataesr/scientific-paragraphs-categorization huggingface dataset:
8 rensen et al. (2014), however, who systematically evaluated a large, unselected cohort of 375 patients who discontinued natalizumab therapy, highlighted some of the issues associated with the study of ''rebounds. ''  7 Their results show that, although the average relapse rate increased at Month 3 after discontinuation of natalizumab, it never reached the same magnitude as before natalizumab therapy. In addition, these authors pointed out that the relapse activity observed after natalizumab discontinuation includes a period in which the patients were essentially untreated, whereas the pre-natalizumab activity data were obtained for a period in which the vast majority of patients were on an active treatment. 7 cently, individual case reports of an unexpectedly high level of disease activity have been reported in relation to the withdrawal of fingolimod, a DMT that significantly reduces the frequency of relapses and of magnetic resonance imaging (MRI) lesion activity in patients with RRMS.
Materials and methods FREEDOMS and FREEDOMS II had very similar placebo-controlled study designs and inclusion criteria. This allowed the exploration of two aspects of disease activity following treatment withdrawal: (a) the frequency and severity of higher-than-expected disease activity within each treatment group of FREEDOMS and FREEDOMS II, and (b) the consistency of the pattern of any higher-than-expected disease activity across the two studies. Both studies were conducted in compliance with the tenets of the Declaration of Helsinki. Approvals were obtained from the independent ethics committees or institutional review boards and all patients provided written informed consent before enrollment into the trials. In both FREEDOMS and FREEDOMS II, disease activity data, including relapses and MRI scans, were collected for at least three months after withdrawal (follow-up data at three-month follow-up visit, with a visit window of Day 46 to Day 104) and were collected beyond this point in time and up to seven months (210 days) after SDD when available.
MRI analysis To analyze and compare MRI disease activity following SDD at the individual patient level, baseline gadolinium (Gd)-enhancing T1-lesion volume data from each study were used to generate prediction intervals assuming a normal distribution, and were derived as: mean±z(a/2) Â standard deviation (SD). An outlier was defined as a patient with an increase in Gd-enhancing T1-lesion volume outside the upper boundary of the modeled 95% (two-sided) prediction limits. In this way, individual patients with MRI disease activity after SDD that exceeded that predicted from (unbiased) baseline data and the degree to which disease activity exceeded that predicted within each study could be identified. Outliers' rates and characteristics were also assessed at baseline and at end of treatment as a comparison.
Results The outlier analysis for both the clinical and MRI outcomes was based on the data from the intentionto-treat (ITT) populations of FREEDOMS (n ¼ 1272) and FREEDOMS II (n ¼ 1083), which were identical to the respective safety populations. In FREEDOMS, the number of patients in the placebo, fingolimod 0. 5 mg, and fingolimod 1. 25 mg was 418, 425, and 429, respectively. 17 The corresponding numbers for FREEDOMS II were 355, 358, and 370. 17, 18 For reference, the group-level baseline disease characteristics relevant to these studies are presented in Table  1  (overall ITT population) and Table  2  (patients who discontinued treatment, by treatment group).
Comparison with group-level data The potential risk of rebound effect following withdrawal of fingolimod has also been assessed at the treatment-group level using pooled data from the Phase II and Phase III clinical development program. 20 Annual relapse rates (ARRs) in patients prematurely discontinuing study drug were similar across groups: 0. 23 for placebo, 0. 18 for fingolimod 0. 5 mg (p ¼ 0. 9366 vs placebo) and 0. 21 for fingolimod 1. 25 mg (p ¼ 0. 7280 vs placebo), respectively (Supplementary Table  e4 ). Additionally, mean Gdenhancing lesion counts recorded 1590 days after SDD were similar to baseline counts in both fingolimod groups. 20.
25 mg group were smaller for both instances as compared to placebo. This absence of any dose-dependent pattern across the two similar studies suggests that these observations are rather reflective of the underlying variability of disease activity, as opposed to being reflective of any ''rebound effect. '' Such variability is further illustrated by the similar range of disease activity observed after drug cessation compared to the existing range at baseline. The dotted line depicts the 95% prediction limit ((a) 1253. 2 mm 3 ; (b) 818. 8 mm 3 ). n: number of patients with Gd-enhancing T1-lesion volume above the 95% upper limit (i. e. magnetic resonance imaging outliers); N: total number of patients included in the analysis. All data up to 30 days after study drug discontinuation (SDD) are considered to be on treatment.
Data after SDD are considered up to seven months (210 days). Data during steroid use are not presented. In fact, the highest activity levels for patients in the fingolimod 1. 25 mg or 0. 5 mg group were observed at baseline, both in FREEDOMS and FREEDOMS II (which was actually not always the case in the placebo group, since in FREEDOMS the highest level of disease activity was observed after placebo discontinuation). Altogether these findings support the notion that individual cases of ''high'' disease activity are to be expected as part of the natural unpredictability of the disease course regardless of treatment history. Finally, the individual-patient-level data presented here are in line and complement results from the group-level analysis, which demonstrate that annual relapse rates and Gd-enhancing lesion counts were similar in post-SDD patients from the fingolimod groups compared to those from the placebo group.
Results and discussion To highlight the contribution of our compression approach REPro. JPEG, we will compare its performances with those of the JPEG in terms of NBpP and in terms of preserving the image content quantified by PSNR and SSIM. At first, it is necessary to define the REPro parameters. These parameters are the reduction and expansion techniques and the starting colour space adopted in the proposed compression scheme. During this assessment, we will use image colour image database compounded by 30 medical dermatological images. We began by the determination of the best reduction/expansion image combination for each colour space. Tables 1-3 summarise the evaluation results of this study in terms of PSNR averages, SSIM averages and selection rates for the image database. In these illustrations, we named R the square-square mesh decimation used for image reduction, A1, A2, A3 and A4, respectively, the zero padding, nearest neighbour, cubic interpolation and B-Spline interpolation techniques used for image enlargement.
The application of REPro on these images reduces the image pixel number to the quarter which involves a decrease in the storage image file volume in bitmap format to almost the quarter of the original image volume. Thus, the preservation of the image content becomes the determining factor in the selection of the reduction/enlargement combination. Based on the established results in Tables  1 2 3 ,w e note that the enlargement by nearest neighbour technique of the reduced image guarantees the best fidelity to the original image in terms of PSNR for all images of the image database (PSNR selection rate 100%) and RGB, HSV and YCbCr colour spaces. This same decimation/expansion combination seems the most adequate for the REPro applied in the HSV and YCbCr colour spaces according to the SSIM. However, the RGB space colour promotes the use of the cubic interpolation to enlarge image according to the SSIM metric (Table  1 ) for all images (SSIM selection rate = 100%).
JPEG) combines a reduction of the image pixel number followed by the JPEG compression. In what follows, we will apply this new compression approach on our dermatological image database transformed on the RGB, HSV and YCbCr colour spaces. The best combination of each colour space will be used for the REPro. JPEG and will be compared to the standard JPEG compression. Thus, the REPro protocol will be represented by the couple RoA 2 (square-square mesh decimation and interpolation to the nearest neighbour) in RGB, HSV and YCbCr spaces. This assessment will highlight the compression performances in terms of NBpP versus the image alteration quantified by PSNR and SSIM. The figure below illustrates the results of this comparison that evaluates the standard JPEG (--purple colour), REPro. JPEG RGB (--green colour), the REPro.
Conclusion Image compression is still one of the busiest areas of research especially in the telemedicine domain. Indeed, medical images are known for their large data volume which can slow their transmissions and hinder their storages. Thus, compression is a necessity for the exploitation of these images. However, a high compression can result in a loss of information and leads to a false diagnosis. In this context, the JPEG is seen as the most common compression standard. In this paper, we propose a new approach that combines the encoding aspect of the JPEG compression to the image resizing aspect of the REPro (reduction/expansion protocol). Thus, our new approach, which we named REPro. JPEG, consists in reducing the number of image pixel before its storage or its transmission and then its compression through JPEG.
At the reception or when it is displayed, the image is decompressed and expanded. The reduction is provided by the square-square mesh decimation. To enlarge the image, we proposed four techniques namely zero padding, interpolation to the nearest neighbour, cubic interpolation and interpolation by the transformed B-Spline. In addition, we studied the impact of the choice of the starting colour space in the proposed compression scheme. Indeed, the REPro and REPro. JPEG were tested on a database including 30 dermatological images which could be coded in the RGB, HSV and YCbCr colour spaces. The preliminary study on REPro demonstrated an advantage of the combination square-square mesh decimation, followed by an expansion based on the  nearest neighbour interpolation. Moreover, we noted that the HSV space is the most adequate image representation to the REPro.
Additional challenges with genomics research in Africa include the inability to transfer, store, Mulder et al: Genomic Research Data Generation, Analysis and Sharing -Challenges in the African Setting Art. 49, page 2 of 15 process and analyze large-scale genomics data on the continent, because this requires highly specialized skills and expensive computing infrastructure which are often unavailable. Recently initiatives such as H3Africa and H3ABioNet which aim to build capacity for large-scale genomics projects in Africa have emerged. Here we describe such initiatives, including the challenges faced in the generation, analysis and sharing of genomic data and how these challenges are being overcome.
One such effort is the Human Heredity and Health in Africa initiative (H3Africa: www. h3africa. org) designed to generate large and complex genomics datasets from multiple ethnic groups across Africa (H3Africa consortium 2014). At the core of the informatics activities of H3Africa is the H3ABioNet (www. h3abionet. org) (Mulder et al. 2015). This Pan African bioinformatics network is building bioinformatics capacity to enable genomics data analysis on the continent. Here, as members of H3Africa projects and the H3ABioNet network, we describe the challenges associated with data generation, analysis and sharing in Africa, and how these initiatives and networks are working towards overcoming these challenges.
There is a need to systematically organize this data and to disseminate the resulting information through technically sound means to provide opportunities to academics and researchers globally. This information dissemination can facilitate advances in biomedical research for the improvement of health  (Stephens et al. 2015). Much of the data generated by NGS applications and other omics (including genomics, transcriptomics, proteomics and other high-throughput methods) technologies are housed in public databases (Rung and Brazma 2013). These databases are either general (e. g. Sequence Read Archive, Array express, Gene Expression Omnibus) or dedicated to one research area (e. g. Oncomine for cancer research, plexDB for plant research). They are openly accessible for use by any researcher, though some, notably those containing human data, require access approval from specialist committees.
Genomic technologies are still relatively unaffordable in Africa. Generation of genomic datasets for human health and populations research has been limited, with South Africa, Ghana and Kenya being the most studied  (Adedokun et al. 2016; HuGE database). However, more recently, various African institutions are equipping themselves with next generation sequencing platforms and setting up systems for genomic data generation. H3Africa is the largest project to date with a focus on generating large scale genomic sequence data by scientists in Africa for human health research. There are different data types being generated by H3Africa projects, including genotyping by arrays, whole human genome or exome sequencing, targeted sequencing of human or pathogen genes, and 16S rRNA sequencing for microbiome analysis.
Data transfer and storage Until recently, African institutions were not generating large genomics datasets alone, due to high cost and unavailability of equipment. Most were not involved in genomics research at all and the few that were generally outsourced their sequencing work or downloaded data from publicly available datasets. In both cases, a new challenge arose with transfer of large datasets in environments with limited infrastructure. Big data transfer is an essential service particularly when collaborating across multiple sites. For many African countries, data transfer is expensive, unreliable, insecure, and difficult to monitor. For example, it took 5 months to transfer 140TB of sequence data from the USA to South Africa using currently available transfer resources. With reliable internet, sufficient storage in place and constant monitoring, this should have taken 2 months, but delays were caused by low bandwidth and internet down times.
H3ABioNet has invested a lot of effort in infrastructure and skills development to enable big data transfer in Africa. There were challenges in trying to identify a single data transfer solution that can be used regardless of the operating system and the networking status of the institution. H3ABioNet started the NetMap project to map the internet speeds between its nodes (member institutions located throughout the continent), and implemented a unified transfer solution using Globus Online  (Foster 2011). Technical staff at nodes were trained to maintain their computing infrastructure and support their researchers with data transfer needs. How-To guides were developed to help technical staff and ensure the sustainability of the infrastructure. Genomic data storage has also been a challenge.
This includes the primary data, temporary files created during analysis, and the final dataset generated from research projects. Storing this large amount of data in federated but connected resources is a challenge in its own right. Many African and other Low and Middle Income Country (LMIC) institutions lack a well maintained and organized storage unit with built in redundancy that can ensure the security of research data. Apart from technical challenges, data organization and tracking is also a challenge. Genomic data can vary between several huge files and thousands of small files. Organizing them in ways that make them easily accessible and identifiable to researchers can be difficult and requires careful curation of the metadata. There is enormous potential for scientific discovery when datasets from multiple projects are merged, particularly with genomic research that requires large sample sizes.
In order to facilitate this, datasets need to be harmonized to ensure the metadata is consistent (same terms mean the same things), and data within files are easily searchable and findable. Within the H3Africa consortium alone, the clinical data has proven to be quite heterogeneous. Even when similar clinical observations have been captured, they are often not comparable due to differences in phrasing of questions or measurements. Therefore special effort is being invested in harmonizing data by mapping them to existing biomedical ontologies, which is essential for efficient data sharing. For example, curators have assessed case report forms for H3Africa projects to determine compatibility of data collected with similar but non-identical questions. The data is then being mapped to the phenotype, disease and experimental factor ontologies.
Physical and infrastructural challenges of large scale genomic studies in Africa There are few high performance computing centers in Africa outside of South Africa, although this is slowly changing. Additionally, the use of Cloud computing has not been practical for large datasets located on the continent due to slow internet speeds. Data security is also an issue for human data. Another challenge is the heterogeneous nature of the multitude of bioinformatics tools available. Many bioinformatics algorithms and software have been written by students who move on to other projects, so they remain unmaintained and unsupported. The field moves so rapidly that tools and pipelines constantly need to be updated. One advantage though is that most bioinformatics developers keep their code open source and available in github for the community to use and develop further.
Nevertheless, using these tools for big data analysis in biomedical research requires familiarity with computer programming for data manipulation and bug fixing, biostatistics and use of analysis software. There is currently limited availability of skills to process and interpret big data in Africa. In anticipation of the development of an African Research Cloud, H3ABioNet has attempted to ease the barrier to accessing tools and computing by developing workflows for common data types that can be deployed on local high performance computing clusters or clouds. The network is also using courses and hackathons to build skills in the development and running of these workflows.
Sharing of genomic data An ongoing challenge in the context of genomics research, is the relative novelty of data sharing practices in Africa, partly because African scientists have not been large generators of genomic data in the past. This novelty frequently translates into considerable concerns by ethics committees to approve genomics research protocols  (Ramsay et al. 2014; De Vries et al. 2015a; De Vries et al. 2016 ) -although admittedly this is something that is more pertinent to the sharing of genomic samples than it seems to be the case for genomic data. Data sharing with a wide range of users is increasingly required by funding agencies, to maximize the use of data generated from individual research institutions or consortia. Sharing genomics data provides an opportunity to verify original analyses, improve reproducibility, test new hypotheses and combine datasets from different sources to achieve higher statistical power.
Regardless of these potential benefits, sharing genomic data has provoked concerns for research participants and researchers. These concerns include endangering the privacy of the data subjects  (Gymrek et al. 2013; Homer et al. 2008 ) and downstream uses of data  (Shabani et al. 2016) , which were not addressed by the initial informed consent process. To address some of these concerns, several resources have been put in place, including setting up Data Access Committees (DACs), which are responsible for data release to external requestors based on legal, ethical and scientific eligibility. In a survey of DAC members involved in reviewing access requests for genomic data available in databases of Genotypes and Phenotypes (dbGaP) and the European Genome-phenome Archive (EGA) on their experiences and attitudes to the tools and mechanisms for access review and its adequacy in fulfilling the goals of controlled-access model of data sharing, the researchers concluded that DAC members and experts were ambivalent about the effectiveness and consistency of the review procedures and oversight process  (Shabani et al.
2011; Gaheen et al. 2013) ; implementing meta-analysis of data with the flexibility to include data and samples from different platforms (Rung and Brazma 2012) for making new observations that could not be detected using individual data sets; and integrating and analyzing several primary datasets in order to acquire new knowledge and/or build a new data resource (Rung and Brazma 2012). However, the reuse of data requires availability of sufficient information on the data generation process and experimental design to ensure its use in a scientifically appropriate way, as well as careful interpretation of the results. Data repositories provide infrastructural solutions that enable scientists to share and reuse public data. The Wellcome Trust and the National Institutes for Health (NIH) have made large investments in sustainable infrastructures for genomic data (Pisani and AbouZahr 2010).
New bioinformatics curricula can prepare students to address challenges raised by big data in the area of data unification, computational and storage limitations and multiple hypothesis testing  (Greene et al. 2016). Data unification refers to the challenges of addressing the inconsistency in data to obtain the necessary data in the appropriate format, as well as normalizing them to make them comparable across sources. This is essential for effective data sharing. H3ABioNet has run short courses on data management and analysis and has developed a recommended curriculum and guidelines for developing new degree programs in bioinformatics (https://training. h3abionet. org/curriculum_development_wg/). These were used for establishing a new Masters degree at the University of Bamako, Mali, which has completed its first successful program this year (2017).
The field is moving rapidly, and the challenges and thus the solutions will keep changing. Scientists with skills in big data will need to be able to understand the current computing environment (processor, storage, memory and network costs) and how to, within that environment, most effectively mine the large-scale data to derive interesting insights. Significant resources are being allocated for training scientists in the analysis of large-scale data in the US (United States) and worldwide. In Africa, various training courses on big data have been provided to scientists and students under both the Square Kilometer Array Africa (www. ska. ac. za/) and IBM MEA (Middle East and Africa) initiatives (www. ibm. com/services/weblectures/meapwww. ibm. com/services/weblectures/meap). IBM has partnered with academia in Africa to promote big data and analytics training programs through its MEA program.