dataesr/scientific-paragraphs-categorization huggingface dataset:
For nuclear segmentation, a straightforward approach lies in intensity-based pixel classification, where each pixel is classified as being part of the nucleus according to some intensity threshold (either global or adapted to the local context  [30] ). This simplistic approach is fast and efficient on highly contrasted datasets, but quickly becomes error-prone as the SNR decreases. Alternative solutions have been proposed to combine intensity with size thresholds  [18] , or use multi-scale analysis  [28] , perhaps followed by region-growing approaches including watershed  [13]  and active contours  [34, 35]. Nevertheless, a common bottleneck arises in highly dense tissue, where nuclei that do not appear separated are either discarded or merged, leading to under-segmentation. Separating clustered objects is still an open problem with no universal solution, although several specialized methods have been proposed, notably based on watershed  [27] , gradient flow  [36, 37] , super-pixel grouping  [29]  or the concept of 'lines-of-sight'  [30].
While the watershed is particularly sensitive to noise, the final membranes are also not always optimally placed, notably on low-resolution datasets where considerable amounts of data are missing  [32]. A popular alternative for cell segmentation lies in deformable models (also known as active contours)  [18, 24, 31, 35] , where a contour is initialized around each seed (nucleus) and is attracted towards the cell membrane by forces resulting from the minimization of a cost functional with various parameters controlling the behaviour of the contour. Deformable models are more flexible in comparison to other approaches, at the expense of slightly increased computation times, although efficient implementations are available  [35, 42]. (2) Direct approaches rely primarily on the membrane signal, and rather consider the cell membranes as a network that is to be extracted from the image  [33, 43].
Such approaches are efficient on small datasets, but do not scale properly on conventional workstations. In the vast majority of cases, the efficacy of associationbased cell tracking methods is highly dependent on the previous (segmentation) step. A cell that is not properly detected can quickly add to the computational burden of the tracking algorithm, which must rely on more rules and strategies to determine whether the absence of a cell at a given location is due to erroneous segmentation, or rather due to a natural biological process (e. g. cell death, local change in tissue dynamics, etc. ). Eventually, excessively strict rules that produce a perfect tracking in a specific situation may inadvertently limit the interpretation of tissue dynamics to only a subset of the (biologically) plausible scenarios.
Towards reproducible research via open software resources Ingrained in the principles of reproducible research is the verification and reusability of experimental results, facilitating the conception of future experiments and preventing wasting time in reinventing the wheel and/or building upon false conclusions. While the importance of the availability of raw data is now openly recognized, such current practices in the bioimaging field are still far behind other scientific domains  [48] , while discussions between journal editors and publishers on developing best-practice guidelines to address this issue are still in their infancy  [49]. In recent years however, the bioimage informatics community has been actively pushing to promote reproducibility in both the software and hardware aspects, by releasing analysis algorithms and equipment blueprints online  [4, 50, 51] , as well as groundtruth data in the form of publicly available datasets and software for benchmarking purposes  [47].
Several such developments have directly targeted the developmental biology community, which we summarize in table  1. Each solution has its own strengths and weaknesses, which generally stem from the biological application driving its development. For instance, generalpurpose software platforms typically provide a wide range of analysis tools to analyse bioimaging data (including tissue), although they may exhibit lower efficacy in specific contexts where specialized (though less generic) solutions have been developed. While this review does not provide an exhaustive list of such solutions, we focus here on some of the most recent or active developments in the field of epithelial and embryonic development, most of which are available as open-source platforms: -Icy  [52]  and ImageJ/Fiji  [53]  are typical illustrations of such general-purpose, community-driven bioimaging platforms providing a large variety of tools and frameworks for data acquisition, visualization and analysis in biology, with a particular focus on extensibility via community-contributed plugins, scripts and protocols.
They have been successfully applied to developmental biology studies through various extensions  [14, 18, 20, 54]. For example, one such extension for the Icy platform studies the spatial organization of the embryonic mouse heart, by combining a filtering approach to increase membrane signal acquired in confocal microscopy, and a segmentation tool based on deformable models to extract both the nuclei and the cell membranes  [18, 55]. -MARS-ALT  [38]  is among the first reported freely available software dedicated for the segmentation and lineage tracing of cells in three-dimensional time-lapse imaging data for developmental biology studies, and more specifically in the context of plant growth in the Arabidopsis thaliana model plant. The pipeline starts with an algorithm to fuse multi-angle confocal threedimensional imaging stacks.
-Focusing more particularly on the dynamics of epithelial tissue during development, the EDGE4D software  [43]  has been proposed to segment and track cells across the complex process of epithelial folding during Drosophila gastrulation. The algorithm combines nuclear and membrane signals to provide superior segmentation to its aforementioned counterparts in the challenging context of multi-photon microscopy. -MorphoGraphX  [41]  is a platform developed for the quantification of four-dimensional (three-dimensional time-lapse) analysis of growth dynamics in plant morphogenesis, focused on computational efficiency and realistic visualization through the use of graphics processing units (GPU). This software provides specific routines to segment and.
Discussion Joint developments in both the bioimaging and bioimage informatics fields are progressively revolutionizing how developmental biologists apprehend fundamental questions related to morphogenesis and tissue dynamics. High-quality imaging data can now be acquired from a wide range of experimental models and conditions, with remarkable precision, longevity and reproducibility, slowly leading us into the era of Big (Imaging) Data. Concomitantly, computerized algorithms and dedicated software able to handle, visualize and analyse such colossal amounts of data have rapidly flourished, bringing an ever more quantitative insight into the numerous processes underlying morphogenesis. These developments (and the new to come) are dramatically accelerating the pace at which new biological hypotheses can be tested and validated, and are therefore becoming an essential part of the standard toolkit in developmental biology.
It is frequently argued that the exponential increase in imaging data produced routinely (notably via multi-view SPIM devices, but not only) is raising the issue (both technical and financial) of data storage, management and curating. Meanwhile, the development of open-hardware imaging solutions is progressively reducing the overall cost of imaging systems and thereby of data production. Without giving up on reproducible research, it is becoming reasonable to question whether long-term storage of terabyte-sized datasets is effectively worth the effort, since new imaging data can be produced rapidly and reliably in reasonable time (while benefiting from latest advances in, for example, imaging techniques and fluorescent probes). Potentially, the progressive integration of imaging hardware and software through engineering will allow us to speculate on the future emergence of task-focused, bench-top equipment able to perform imaging and quantitative analysis in a single and transparent step, thereby circumventing the issue altogether.
[52]57]ocietypublishing. org Phil. Trans. R. Soc. B 372: 20150512 track the outer cell layer of the embryo (represented as an unstructured graph), by leveraging the power of existing open-source packages including CImg (www. cimg. eu)andITK(www. itk. org). It has mainly been applied in the context of Arabidopsis[56,57], but has been shown to work with other models, notably Drosophila[41]. -TissueMiner is a framework to quantify epithelial cell dynamics in living tissues over prolonged acquisitions[19]. It specifically includes tools to generate a database from cell segmentation and tracking results, and provides a number of quantitative spatio-temporal descriptors of epithelial morphogenesis, and was applied to study the Drosophila pupil wing. The interface is based on the R platform and provides utilities to query and visualize the database in an efficient manner.
-Real-timeaccuratecellshapeextractor(RACE)[28]isahighperformance image analysis framework for automated three-dimensional cell segmentation, with a particular focus on massive SPIM datasets. It provides the necessary software to extract nuclei and cell shapes from terabytesized images, and therefore constitutes a step towards scalability and high-throughput studies, as illustrated by examples on Drosophila, zebrafish and mouse embryos. Additionally, a companion tracking software was developed to study large-scale embryonic development[29]. -EpiTools is another open-source toolkit for the study of developing epithelial tissue[20]. Based on Matlab, it provides graphical interfaces to segment and track the contour of cells in 3Dþtime confocal series of membrane-labelled cells. It also closely integrates with the Icy platform[52]by providing specific extensions to open and visualize the segmentation and tracking results within Icy.
Method Covariate data for type 2 diabetes patients were generated with the Observational Medical Dataset Simulator Generation 2 3 (698 features for 50,000 patients). The 20 densest features were used to define hypothetical models for a binary treatment assignment and a binary outcome. The performance of the three methods in estimating the in-sample average treatment effect (ATE) and predicting out-of-sample individual treatment effects (ITEs) (not applicable for PSM) was compared across three confounding scenarios (low, moderate and high) with highly HTE. results Compared to PSM, BART produced less accurate ATE estimates in the moderate and high confounding scenarios and significantly worse coverage rates for the corresponding 95% intervals (in the high confounding scenario, the relative bias Introduction Acute respiratory infections diagnosed in primary care are associated with increased risk of subsequent myocardial infarction (MI).
Future studies using individual level linked data will provide further insights into the timing of exposure and nature of the outcome. Introduction Routinely collected electronic health records (EHRs) have been widely used in studies of drug effects. It is necessary to clean and transform prescription data held in EHR databases before they can be used in analyses. Research groups Introduction Gout is a form of chronic arthritis caused by elevated serum uric acid (SUA). Gout culminates in severely painful acute attacks and can cause permanent joint damage if left untreated. Very effective uric acid-lowering therapies (ULTs) for gout exist; however, adherence to ULTs is extremely low, and gout prevalence is increasing. Mobile health applications (apps) have been shown to be useful in self-management of chronic conditions.
Introduction The grand challenge of identifying personalised healthcare strategies relies on the discovery of subtypes of complex diseases that typical statistical methods might not uncover. Current clinical practice defines asthma as a single diagnosis characterised by impaired lung function and wheezing. We hypothesised that distinct syndromes with heterogeneous profiles of lung function from childhood to adolescence might be resolved by using advanced statistical learning methods and that the different trajectories indicate biological sub-types (endotypes) of asthma. Method Data source: population-based birth cohorts: the Manchester Asthma and Allergy Study (MAAS) (n=1184 participants recruited in 1995) and the Avon Longitudinal Study of Parents and Children (ALSPAC) (n=14,541 participants recruited in 1991).
Lung function was assessed using forced expiratory volume (FEV1) collected at ages 5, 8, 11 and 16 years in the MAAS and at ages 8, 15 and 24 years in the ALSPAC, and data were expressed as percent predicted FEV1 using standardised spirometry centile charts. We used a latent profile model to assign children to one of N latent trajectories (profiles) of FEV1, with the number or size of profiles not known a priori. The models were compared for goodness-of-fit. For each child, the posterior probability of belonging to a given class was calculated using Empirical Bayes and children were assigned to the latent profile with the largest conditional posterior probability. We then constructed a multivariable model to identify a set of early-life predictors of subsequent latent profiles of FEV1. We used receiver operating characteristic (ROC) curves to evaluate the model predictive ability.
Analysis was repeated independently using data from ALSPAC. results The optimal model was a 4-class random intercept latent profile model. We labelled classes as:  Class   Multinomial regression analysis revealed that children who wheeze have severe exacerbations by age 3 years, have at least one positive skin test by age 3 years and have been exposed to smoking by age 3 years were more likely to belong to Class 4. This set of variables provided a good predictive ability for discriminating between children in Class 4 from children in Class 1 (area under ROC curve = 90. 7%, sensitivity=81. 8% specificity=80. 95%). Similar profiles were identified in ALSPAC.
Discussion This study demonstrates that advanced statistical learning methods can uncover plausible, distinct profiles of lung function over time and we have identified conditionally independent predictors at age three years, which can distinguish children with poor prognosis in terms of lung function from ages 5 to 24 years. These profiles of lung function persist into adulthood, remaining a stable feature throughout life. conclusion Longitudinal clinical datasets contain statistically 'learnable' structure reflecting the heterogeneous profiles of disease that are key to advancing personalized medicine.
However, there are no culturally appropriate psychoeducation programmes for African-Caribbeans in the UK. Our study aims to inform development of a culturally appropriate e-learning resource with African-Caribbean stakeholders. Method Collecting qualitative data was essential as no information of African-Caribbean stakeholders' views of learning resources for schizophrenia is currently available. We conducted focus groups comprising i) persons diagnosed with schizophrenia (n = 7), ii) carers and family members (n = 6), iii) community members (n = 6) and iv) young (x̄ = 25 years) mixed group (n = 6). Participants were identified as African-Caribbean, Black African, Black British or 'Mixed' heritage, or cared for a person with schizophrenia of African-Caribbean descent. Participants were community members and people recruited via third sector organisations and local universities.
Framework Analysis was conducted with NVivo (v11). The framework was constructed by identifying participants' responses to a priori themes. Emergent themes and sub-themes were coded to inform resource development. results Data yielded ten a priori themes including perceptions of the 'need for the resource' and 'culturally sensitive issues' such as spirituality. Five other themes 'self-acceptance' and 'police involvement' emerged spontaneously. Findings showed that all groups perceived a strong need for a culturally appropriate learning resource. There were similarities and differences in the groups' responses. All groups prioritised learning about symptoms and treatment. However, whereas patients considered 'relationships' important theme, others highlighted the need for community-level psychoeducation.
Methods Utilising the MIQUEST search methodology, we developed IMPAKT, an automated data extraction tool compatible with any electronic primary care clinical systems. Data extracted include demographic, co-morbidity and clinical investigation data. The tool has evolved from a quality improvement(QI) tool to a bespoke research tool for two large pragmatic clinical trials, in CKD (PSP-CKD) and AKI(IMPAKT-EVOLVE). All research data are anonymised via one-way hashing of NHS numbers. In addition to gathering data for epidemiological analysis, the IMPAKT tool can be used to identify patients at risk of CKD/AKI in an automated fashion and provide primary care practitioners with a precise list of patients who would benefit from intervention. results We have recruited over 70 primary care practices to participate in CKD/AKI QI and research projects.
The total number of anonymised records extracted is over 250,000 combining 30 million data points. For an average sized primary care practice of 7,000 patients, the IMPAKT tool extracts over 400,000 data points. In total, 30,000 individuals, including approximately 6,000 individuals not previously correctly coded as having a kidney condition, are included in the CKD database. Linkage of these databases to secondary care has highlighted unidentified conditions in primary care records. Introduction Patient-level primary care data provide a rich source of information for addressing health inequalities. In Lambeth, an innercity borough of south London, the Lambeth DataNet (LDN) database contains pseudonymised data from all 360,000 patients registered at 47 general practices. LDN is a rich source of demographic data: 87% of patients have a record of ethnicity coding 51% have a record of 'country of birth' 74% have a record of 'language preference'.
Hypertension (HT) is the single largest cause, after smoking, of avoidable cardiovascular morbidity and mortality. Population studies demonstrate that target treatment levels fail to be achieved by a substantial minority of patients. We aimed to identify mean levels of blood pressure (BP) control in patients diagnosed with HT and to determine the relationship with ethnicity and social deprivation. Method LDN is based on patient-level data extracted from the clinical software system used by general practitioners. Read and EMIS codes are extracted and mapped to SNOMED-CT freetext data cannot be extracted. Access to data is controlled by the clinical commissioning group, the nominated 'data processor'. We conducted a cross-sectional case-control study of patients diagnosed with HT and recorded using validated clinical Read codes.
Firstly, we determined HT control status, based on the achievement of a target BP of ≤150/90mm Hg. We then identified risk factors in those who achieved or failed to achieve the BP target, including age, gender, social deprivation (IMD-2015) and ethnicity (2011 Census '5+1' categories). We used regression modelling to determine the proportion of patients from each ethnic group achieving the BP target, adjusted for age, gender, deprivation and ethnicity. Ethnic groups were further refined using data from recording of language preference and country of origin. results A total of 292,385 patients were ≥18 years 32,452 (11. 1%) had a recorded diagnosis of HT. 79. 9% of patients with HT achieved the BP control target. The adjusted proportions of patients who achieved the BP target were White British, 84.
Method The new 'unscheduled care datamart' developed by NHS Scotland Information Services Division comprises linked anonymised data for SAS, EDs, acute and mental health hospital episodes and Registrar General Scotland (RGS) recorded deaths. SAS attendances for 'psychiatric emergencies' or 'self-harm' were extracted for 2011 and 2012. People were followed up from their first contact with the SAS in 2011 for one year. results Nationwide during 2011, there were 9,014 calls attended by the SAS. These were made by 6,802 people (52% males, 48% females). Most people (n=5,624, 83%) made one call, with the remaining (n=1178, n=17%) making two or more calls, including 21 people who made 9 or more calls. The most common four care pathways were: 1. conveyance to and discharge from ED (4,566 calls 51%), 2.
Discussion RGS summary statistics for 2011 and 2012 recorded 772 and 762 deaths by suicide. There were 59 RGS recorded deaths by suicide in our data extraction, implying that 8% (59/762) had been in contact with SAS in the preceding year. The SAS had also been in contact with 102 people who were either left at home or transported to ED and discharged with no known follow-up. There is potential important benefit from implementing new suicide prevention interventions, and building resilience in people who habitually self-harm and call SAS, and are coded by paramedics as 'psychiatric emergency' or 'self-harm'. conclusion Only very recently has ambulance service become routinely linked to other datasets, including mortality outcomes. This study highlights the enormous potential of linked data and provides completely new insights into the behaviour of people who self-harm and are suicidal.
The use of electronic medication reconciliation tools that automate data retrieval and facilitate medication reconciliation may overcome many of these challenges and thus improve efficiency and hospital adherence. The aim of this study was to measure the adoption of an electronic medication reconciliation tool and its impact on reducing adjudication failures and medication discrepancies. Methods A clustered randomized control trial was conducted at three hospital sites in Quebec from October 2014 to October 2016. Adult participants were eligible for inclusion if they were using prescription medication at admission, were covered by Quebec drug insurance plan, admitted from the community, admitted to a medical or surgical unit, and discharged alive. The intervention comprises the electronic retrieval of the community drug list, combined with an electronic discharge reconciliation module and a discharge communication module.
Items were added, modified and deleted based on the Content Validity Index method. The final version of the modified questionnaire included 114 items, which were divided into six dimensions. Second, the modified questionnaire was tested in two hospitals (n=258 professionals). Data were collected in 2014. Information was considered important when 70% of respondents rated it nine or ten on a scale from 0 (completely unnecessary information) to 10 (absolutely necessary information). The questionnaires psychometric properties were analyzed with item-total correlations, a split-half analysis and Cronbach's α values. results The response rate was 26% (n=67). Staff members' information needs were more varied compared to those of the managers. All respondents shared 31% (35 items) of the important information needs.