dataesr/scientific-paragraphs-categorization huggingface dataset:
Data resources DPUK collaborates with 42 clinical and population cohorts (Table  1 ). Of these, 24 (n = 1 410 302) have either completed or are in the process of uploading full or partial datasets and 18 (n = 1 960 627) upload data on a per-project basis. The data are diverse. Clinical cohort studies include familial disease cohorts  [43, 55] , disease focused patient cohorts  [32, 36] , ageing focused population cohorts  [21, 24, 40, 57] , repurposed cardiovascular cohorts  [19, 37] , birth cohorts  [38, 49] , repurposed cancer cohorts  [15, 44] , and disease agnostic cohorts [45,  47, 54]. In terms of real world evidence, the Data Portal provides access to an e-cohort (SAIL-DeC) covering health records for 1. 2 m individuals including 130 k dementia cases  [34]. Data availability varies according to cohort but includes epidemiologic survey, imaging, genetics, and linked administrative data.
Of the 80 declined requests, 49 (61%) were due to the data requested either not existing or not being relevant for the analysis. The data requests were diverse. Projects include Mendelian randomisation, imaging, psychometric, and machine learning studies, alongside risk stratification studies and dementia prediction and diagnosis modelling. Other studies are less dementia specific including trajectories of longitudinal assessment of comorbidities between mental health, hormonal indicators and cognitive change; the impact of child adversity on adult outcomes; the longitudinal tracking and determinants of well-being and cognitive performance; and successful cognitive ageing in 90 + year olds. To facilitate innovative and exploratory analyses, the Data Portal can be configured as a 'sandbox' environment, without compromising data security or cohort governance principles.
An example of this was the hosting of a three-day multidisciplinary workshop (datathon) for 40 data scientists at the Alan Turing Institute utilising the Deep and Frequent Phenotyping pilot study data  [23]. These multi-modal data include Magneto-encephalography (MEG), Positron Emission Tomography (PET), structural and functional MRI, ophthalmology, gait, and serial cognitive and clinical assessment. The Data Portal was used to host 25 data scientists in a datathon at the University of Exeter and Swansea University, during which traditional regression and machine learning procedures were used to interrogate the data within the virtual desktop interface. A datathon at the University of East Anglia hosted 60 attendees, mostly early career researchers, using data from the Deep and Frequent Phenotyping Study  [23] , the English Longitudinal Study of Ageing  [24] , and the Caerphilly Prospective Study  [59].
Discussion Rapid data access continues to be a challenge for researchers, funders, and governments. The DPUK Data Portal is one of several initiatives to address this issue. In the dementia space, other data platforms are available. The JPND Global Cohort Directory  [60]  provides contact details for 175 cohorts (n = 3 586 109) whilst the IALSA Network  [61]  provides details for 110 cohorts (n = 1 485 410). More sophisticated and convenient data discovery tools are provided by GAAIN  [62]  with 47 cohorts (n = 480 020). GAAIN also offers centralised processing for selected datasets. EMIF-AD  [61]  offers a comprehensive data harmonisation programme for a selection of their 60 catalogued cohorts (n = 135 959) and 18 electronic health records datasets (n = 65 m). For selected datasets EMIF-AD provides centralised cohort data processing facilities through the TranSMART platform  [63].
Data Portal benefits However, in providing independent trusted third-party data management, the data repository approach has several benefits. Although the Data Portal is optimised for dementia, its remote access model provides a generic solution that can be adapted to any outcome for which relevant data are available. Specifically, by automating, streamlining and standardising procedures, decision-making can be more precise, quicker, and more transparent, and the practice of repeated data transfers avoided. For cohort research teams a repository substantially reduces the administrative burden of routine data management tasks, particularly avoiding repeated data transfers. This assists cohorts in meeting their data sharing obligations, and releases cohort-based data scientists to focus on increasing the scientific value of their data.
For researchers a repository provides a single point of contact and a single standard procedure for accessing multiple independent datasets. Within the Data Portal this supports data discovery, feasibility modelling, access management and analysis. The use of a common data model reduces data preparation times and reduces uncertainty due to unnecessary variation in data pre-processing. For the research community as a whole, a repository facilitates knowledge management including the linking of codeto-dataset through the use of persistent identifiers (Digital Object Identifiers: DOIs), the development and discoverability of APIs for rapid replication, and the improvement of the end-to-end user experience through adaptive learning. Repositories have an important role in the democratisation of science.
Data Portal challenges New infrastructure requires new working practice, which brings challenges to platform operators, data controllers and data users. Operational challenges for platform operators focus on improving the user experience and increasing the scientific opportunity. Data platforms are complex infrastructures that integrate the highest levels of security and governance requirements for accessing large, complex, and non-standard datasets. They are also integrated within larger computing facilities which require specific resource allocation for computer intensive operations. In this context, improving the user experience is an incremental process. For example, improving data discovery and visualisation tools to improve the precision of data access requests, streamlining decision making to reducing approval times, and developing links to similar resources globally.
All involve engagement with different stakeholders, each of which have different expectations and requirements. For some data controllers the repository 'trusted thirdparty' approach to data management involves uncertainty. Whilst the practice of managing access through repeated data transfer may be labour intensive, it is familiar. Uncertainty surrounds the legal status of Electronic Health Records (EHRs) data accessed via the platform, quality of the proposed science, the relevance (and existence) of the requested data, and potential conflicts of scientific interest. The legal status of accessing EHR data linked to research data using third party platforms is a grey area in the UK. Although there is no change in data controller, and no forward sharing to a third-party data controller, current UK data sharing licenses have not anticipated the Data Portal solution.
Discussions are underway in the UK data community to resolve this issue. Wider access also brings reduced control, and data controllers can be concerned over the reputation of their datasets if used for egregious science. In response DPUK strongly advises a collaboration with cohort research groups as this is the quickest way to understand a dataset and receive expert advice on how to approach its analysis. To facilitate good science it is not unreasonable for cohort research teams to insist on a collaborative approach. That 61% of declined access requests (see section: Data Resource Use) were due to the data requested either not existing or not being relevant for the analysis is to an extent a matter of due diligence on the part of the applicant, but it can also be a matter of judgement. The impact of conflict of interest depends largely on the perspective of the DAC.
By streamlining and standardising the application process DPUK encourages coalescence around the development of common standards that are widely accepted. The informatics infrastructure required by these groups also varies in terms of operating systems, compute power, and software. Our response is to provide flexibility. Standard environments are available that suit the requirements of most users, but bespoke environments are built on request including access to high performance computing. In the pursuit of scientific rigour, increased data access is only part of the equation. Also required is a commensurate development in analytic competence. That 40 project proposals failed a not very demanding triage process, suggests that some analysts are unaware of the value and complexity of the data resources that are available.
Looking ahead The mission of the Data Portal is to increase the realised scientific value of cohort data. Future developments include developing linkage to electronic health records. In partnership with Health Data Research UK, DPUK is developing a centralised system enabling DPUK cohorts to link research data to routinely collected data. In partnership with Gates Ventures, DPUK along with other platforms is developing global connectivity. DPUK is also working with academic and industry partners to provide digital phenotyping using devices such as smart phones, for collaborating cohorts. A third development is the use of cohort data to risk-stratify for trials recruitment. For dementia, there is a growing need for risk stratification by phenotype according to genotype in order to recruit to highly targeted experimental medicine studies.
Conclusions The DPUK Data Portal was established by the MRC to support the development of new treatments for dementia by using cohort data to inform experimental medicine. It is recognition of the unique value of cohort data and a contribution to the wider debate on how best to support cohort studies and facilitate their contribution to the wider research environment. By streamlining procedures for cohort research teams, increasing data accessibility for researchers, reducing costs, and adding value, the Data Portal is an investment in the future of cohort studies. Applications for access can be made through the Data Portal: https ://porta l. demen tiasp latfo rm. uk/.
Simulation of absorption systems Single effect The work done in this section, is based on the modeling and simulation of the refrigeration machine using the Aspen-Hysys software which is an advanced flowsheeting tool for process simulation. It has a rich library of model calculations of thermodynamic properties. Figure  5  illustrates the absorption machine model in Aspen-Hysys. The temperatures obtained by the Oldham Diagram (Te= -1°C, Tc =30°C) are used for the simulation of the machine. Stated below in table  2  are the characteristics of the generator's inlet in 1. x H2O (%) 48 The results of the Aspen-Hysys simulation are shown in Tables  3  and 4. The absorption refrigerating machine works correctly according to the operating parameters presented in the following table. Upon comparing the pressure and fractions results given by the Oldham diagram and the results obtained using Hysys (Table  3 ), we notice that we get almost the same values of temperature, pressure and composition.
In  [14] , an IoU-adaptive deformable R-CNN was developed with the goal of adapting IoU threshold according to the object size, to make dealing with small objects whose loss, according to the authors, would be absorbed during training phase easier. In  [15] , UAV-YOLO was proposed to adapt the YOLOv3 to detect small objects from unmanned aerial vehicle (UAV) data. Slight modification from YOLOv3 was done by concatenating two residual blocks of the network backbone having the same size. By focusing more on the training optimization of their dataset with UAV-viewed perspectives, the authors reported superior performance of UAV-YOLO compared to YOLOv3 and SSD. Another enhancement of YOLOv3 was done in  [16]  where the proposed YOLO-fine is able to better deal with small and very small objects thanks to its finer detection grids.
In  [17] , the authors exploited and adapted the YOLOv3 detector for the detection of vehicles in Pleiades images at 50 cm/pixel. For this purpose, a dataset of 88 k vehicles was manually annotated to train the network. This approach, therefore, has the drawback of costly manual annotation and, in the case of application to images from another satellite sensor, a new annotation phase would be necessary. Moreover, by further reducing the resolution of these images (1 m/pixel), we may reach the limits of the detection capacity of those detectors (shown later through our experimental study). An alternative approach that draws attention of researchers is to perform super-resolution (SR) to increase the spatial resolution of the images (and thus the size and details of the objects) before performing the detection task.
The rearrangement layer with an upscaling factor of 2 (i. e. , pixel shuffle operation) is shown in orange. We refer readers to the original paper  [22]  for further details about this approach. As previously mentioned, we evaluate the behavior and the performance of the approaches discussed here using the ISPRS 2D Semantic Labeling Contest dataset  [31]  (http://www2. isprs. org/ commissions/comm3/wg4/2d-sem-label-potsdam. html). More specifically, we are interested in aerial images acquired over the city of Potsdam which are provided with a spatial resolution of 5 cm/pixel. This dataset was initially designed to evaluate semantic segmentation methods, considering six classes: impervious surfaces, buildings, low vegetation, trees, vehicles, and other/background). It can, however, be exploited for vehicle detection tasks as done in  [34]  by retaining only the related components of pixels belonging to the vehicle class.
The generated dataset thus contains nearly 10,000 vehicles. We considered the RGB bands from these images to conduct experiments with different spatial resolutions artificially fixed at 12. 5 cm/pixel, 50 cm/pixel and 1 m/pixel, and to train the super-resolution network. The 12. 5 cm/pixel resolution is our reference high resolution (HR) version. We exploited an EDSR architecture with 16 residual blocks of size 64 × 64. To optimize the results, EDSR additionally performs a normalization of the image pixels on the three bands during the inference phase, based on the average pixel values of the images used for training. For this network, the calculation of the error was performed by the cost function L 1 and the optimizer used is Adam  [35]. Figure  2  shows the effect of super-resolution by a factor of 4, which increases the resolution from 50 cm/pixel to 12.
We show in Figure  9  the results obtained in super-resolution and object detection with the different improved architectures presented in this study for another sample image extracted from the studied dataset. For quantitative comparison, one can observe from Table  5  the detection results yielded by YOLOv3 detector performed on different SR methods. Here, three IoU-thresholds of 0. 05, 0. 25, and 0. 5 were set for our experiments. From the table, we observe a clear increase in the detection rate provided by the SR-WGAN, the SR-CWGAN and more significantly by the SR-CWGAN-Yolo compared to the baseline EDSR method. For low IoU thresholds of 0. 05 and 0. 25 (which are relevant to detect small objects), SR-CWGAN gained approximately 20% of mAP compared to EDSR (i. e. , 66. 72% compared to 47.
Evaluation with Various Object Detectors In this section, we assess the performance of our final super-resolution network w. r. t. the main object detectors, including Faster R-CNN (with VGG-16 backbone)  [1] , EfficientDet (D0)  [6, 40] , RetinaNet-50  [39] , and YOLOv3. To do so, we first trained these detectors on the HR version of ISPRS Potsdam dataset (256 × 256 pixels), before evaluating them on the super-resolved images produced by the proposed SR-CWGAN-Yolo model with an SR factor of 8 reconstructed from the LR images (32 × 32 pixels). We report the results in Table  7 , compared with those obtained by the HR version, the simple bicubic interpolation as well as the initial EDSR model. Interestingly, we can observe that the detection performance is actually independent of the detector used for the auxiliary network (which is YOLOv3 in our work as described in the previous section) when training the super-resolution network.
Electronic Health Records (EHR) is a digital collection of patient health information. EHR is increasingly being implemented in many developing countries. These records can be shared through well connected network across different health care settings. EHR includes demographic and personal statistics like age, weight, and billing information as well as vital signs, family history, medication and allergies history, immunization status, laboratory test results, and radiology images. EHR systems are designed in such a way to reduce paper medical records by storing data accurately and legible in a digital format. However digitally health records reduce the risk of data replication as file can be shared across the different health care systems and can be easy updated which reduce the risk of lost paperwork.
Follow checkups are often an integral part of better patient care; with EHR, automated checkups are easily scheduled by the electronic program. Since the program is integrated with test reports and images, timely ISSN: 2252-8814  An immune memory and negative selection to visualizing clinical pathways from… (Mouna Berquedich) 45 access is readily enabled. Moreover, such test can be ordered through this multipurpose EHR system. Moreover, such system prevents unnecessary duplication of medical testing. The best part of such electronic records is that they are integrated with national and international disease database and registries. Thus, it helps the physicians track the epidemiological status of the current disease under treatment and be prepared for emergencies.
b. Step 1: Learning This step aims to produce sets of non-self cells using periodic comparisons between normal situations (self cells) and the state of the new EHR. We highlight matching ratios to quantify existing distances in given situations, using data collected from the facts. These measures are designated by SE and the elements of the set S with respect to testing their similarities. The matching rate is calculated mathematically by adapting  (3). Mat (Si,SE) = ×∑ 𝛼𝑖 (3) The «Mat (Si, SE)» represents the corresponding percentage (%). The "Si" indicates a normal situation among the set "S". SE determines a situation. Attribute values are extracted from the database. αi is calculated using the following method (4). αi= 1 𝑖𝑓 𝑆 = 𝑆 0 𝑒𝑙𝑠𝑒 (4) The "SEj" is the "j" th attribute of an "SE" situation captured from the database (look (1)).
The «» is the "j" th attribute of an "SE" situation captured from the database (look (1)). The «» is the "j" th attribute of the "i" th situation extracted from the set S. When the adequacy rate is lower than the coverage already fixed "1" e; therefore, the situation is classified as abnormal and will then be added to the R set of patterns. To close this process, the set R, this includes various types of deviant pathway, displays the most significant abnormal pathway. In fact, it is used in the next steps to identify the disturbances that have occurred. Figure  1  draws the components of the set R. The given R which represents all the patterns is constructed in the step indicated above. The PS is designed to progressively read the values from the databases and measure the similarities with the situation of abnormalities of the R-set, non-auto-cellular cells.
RESULTS AND ANALYSIS This section presents the implementation of the EHR management system and discusses the results obtained. The proposed decision support system is implemented with the JAVA programming language. To evaluate the system, a hospital database with a history of four years with more than 100,000 EHR patients. We have simulated the different cases presented below. To construct the S set, we used a real patient records database (EHR). The illustrations of an example sample EHR on our system is shown as: The results above show the phase of the regrouping of the relevant information in the form of a vector of five components, thereafter an analysis is conducted according to two immune processes to communicate to the hospital decision maker the path closest to the path of the patient entered.
The execution phase included a grouping and vector standardization compatible with the input patient information. Subsequently two immune algorithms as previously explained the negative selection and immune memory for the display of the optimal solution that responds to the patient input. The goal has been achieved, by offering the hospital decision maker a tool for detecting the patient's journey, in broad and unstructured information. The most important is the fact of constantly feeding the database R, containing the erroneous solutions in order to avoid the medical errors of the old patients during their medical journey to the recent patients who share the same pathology and symptoms with them. Figure  2  shows the result of HER selection. As illustrated in Figure  2 , we realized the efficient and effective decision support system, allowing the filtering and the analysis of the data of the EHRs of the different patients while adopting the immune principles of the memory cells and of negative selection.
This method is non-parametric, does not require a known null distribution, and is fast to compute. We apply multiple variants of this protocol to a GWAS inflammatory bowel disease (IBD) dataset. Different configurations produced different results, highlighting that various mechanisms are implicated in IBD, while at the same time, results overlapped with known disease biology. Importantly, the proposed pipeline also differs from a conventional approach were no network is used, showing the potential for additional discoveries when prior biological knowledge is incorporated into epistasis detection.
To tackle that issue, Pendergrass et al. [34]  developed Biofilter, a gene-gene co-function network, which aggregates multiple databases. Additionally, such approaches often require as well a proper mapping of SNP to genes. In this article, we propose guiding statistical epistasis using plausible biological epistasis. Taking exclusively interactions reported from at least 2 different sources in Biofilter, we compile a subset of gene-gene interactions that are biologically plausible. Then, we exclusively search for those interactions in a GWAIS dataset, reducing the multiple test burden and improving the interpretability. We investigate different ways of mapping SNPs to genes and use the adaptive truncated product method to estimate the association of gene pairs. Network and pathway analyses are used to further assist in the interpretation of epistasis findings.
Dataset and initial quality control We investigated the IIBDGC dataset, produced by the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC). The large sample size of this dataset helps overcoming the issue of reduced statistical power that is common in GWAIS. This dataset was genotyped on the Immunochip SNP array  [8]. We performed quality control as in Ellinghaus et al. [12] , hereby reducing the number of SNPs from 196 524 to 130 071. The final dataset contains 66 280 samples, out of which 32 622 are cases (individuals with IBD) and 33 658 are controls. The IIBDGC dataset aggregates different cohorts, and contains potentially confounding population structure. As in Ellinghaus et al. [12] , we used the first 7 principal components to model population stratification. Because several epistasis detection methods, such as those implemented in PLINK  [37] , cannot include covariates in their logistic regression models, we instead adjusted the phenotypes by regressing out those principal components.