dataesr/scientific-paragraphs-categorization huggingface dataset:
Settings and participants It is an hospital based registry established in hospitals with expertise in head and neck cancers. Only adult patients with epithelial tumours of nasopharynx; nasal cavity and paranasal sinuses; salivary gland cancer in large and small salivary glands; and middle ear will be included in the registry. This registry won't select a sample of patients. Each patient in the facility who meets the above mentioned inclusion criteria will be followed prospectively and longitudinally with follow-up at cancer progression and / or cancer relapse or patient death. It is a secondary use of data which will be collected from the clinical records. The data collected for the registry will not entail further examinations or admissions to the facility and/or additional appointments to those normally provided for the patient follow-up.
Design and setting The registry is designed to prospectively collect clinical data derived from diagnostic tests and treatments performed by the HCP as part of patient management. The data collected for the registry will not entail further examinations or admissions to the HCP and/or additional appointments to those normally provided. In other words, it will be an observational, registrybased cohort study including only people with rare head and neck cancers. The EURACAN Registry will include EURACAN HCPs (across 7 different EU member States) and will be open to other expert HCPs, not yet in EURACAN but with proven experience in rare head and neck cancers.
Sample size This is an observational clinical registry which implies a long-term data collection lasting until all the registry objectives are met. Only HCPs that treat at least 100 cases a year of all rare head and neck cancers are EURACAN member. As of April 2022, the registry is activating 10 HCPs and, with time, is envisioning to at least duplicate the number of data providers. Considering that we expected 6 centres in Italy, 2 centres in Germany, 1 centre in Czech Republic, 1 centre in Spain, 2 centres in France (400 cases per year)and the whole rare head and neck cases in The Netherlands (300 patients yearly, based on incidence estimations), we expect about 1700 patients with a rare head and neck cancer yearly. Due to the observational nature of the registry, sample size justification is based on the precision of the estimates presented in terms of width of two-sided 95% Confidence Interval (CI) for a single proportion using the Simple Asymptotic method (in case of categorical variables) and in form of normal distribution for means (in case of continuous variables).
Data collection and storage The registry will exploit data available from: • national or regional registries/databases (DBs) dedicated to rare head and neck cancers (i. e. nasopharynx; nasal cavity and paranasal sinuses; salivary gland; and middle ear cancers) • HCP registries/DBs; • ad hoc data collection by HCPs. The registry is federated thus, data are stored by the data provider (i. e. national/regional registries and/or HCP). At the local level, data are pseudonymised.
Data analyses The Personal Health Train (PHT) enables data from multiple organizations to be analysed without identifiable data leaving the organization. By keeping data at its source, no copies of datasets are generated and/or shared with third parties. Vantage6 is the open source implementation of the PHT (https://www. vantage6. ai). Vantage6 uses the mathematical principle of "federated learning", typically applied to horizontally partitioned data (i. e. organizations have data from different patient cohorts, but with similar characteristics/items). Federated learning is based on the mathematical principle of splitting computations into (a) parts at the station (local HCP or registry) and (b) a central part. The stations share sub-computations with the central server only. If federated learning does not work, the data, after quality validation, will be anonymised and sent to the coordination centre (i.
According to the specific research question the role of potential confounders and effect modifiers will be evaluated. Confounders will be included in the model and sensitivity analysis will be performed. Stratified analyses will be performed in the presence of effect modifiers that will be detected by testing the interaction between the predictors and the variable. To assess treatment effectiveness multivariable models with propensity score adjustment that can remove bias due to real world nature of the registry and progression-free survival will be performed. High proportion of missing data threaten the validity of the inferences/prognostic models. Thus, a maximum of 10% of missing data will be allowed and missing data will be imputed using strategy such as unconditional/conditional mean or expectation maximum.
Data quality checks Data quality checks aim to assess whether data value are present, valid and believable in terms of: • Validity: the data should be in the correct format (e. g. allowed only some values, data formats) • Plausibility: the data should be acceptable (eg, internal consistency; temporal and atemporal) • Completeness: the data should not have missing values or miss data records Validity and plausibility checks are embedded in the electronic case report form (CRF) in the form of alerts and errors during the data input. Additional checks are implemented in R. The R script, including the checks is downloaded locally from an online instruction repository (Fig 1  , step 1 ). The R script extracts from the Research Electronic Data Capture (RedCap) (the IT solution used for the registry CRF) all the completed cases, stores a copy of the DB in the dedicated local server and runs the checks locally (Fig 1  , step 2  and 3 ).
The results of these checks are summarized in two reports: a summary and an individual report (Fig 1  , step 4 ). The summary report collects: the number of patients with a complete and valid information on the core data elements; the distribution of treatment by stage for each cancer site included in the registry and the proportion of cases with missing information for each variable. The individual report includes a summary of error for each patient together with a specification of the variables with the errors to be corrected. Thanks to the Vantage6 software, the two reports (not the After the corrections made by the data providers, all checks will be re-run and quality reports reviewed by INT. Interaction with data providers will be reiterated until sufficient data quality is achieved. The DB with sufficient data quality will be saved and used for the federated learning analysis.
Data to be collected Core data elements will include the "Set of common data elements for Rare Disease Registration" developed by the Joint Research Centre (JRC) (https://eu-rd-platform. jrc. ec. europa. eu/ set-of-common-data-elements) to address specificities of rare cancers as compared to rare diseases. 1. Following the EURACAN registry objectives, data will be prospectively collected on patient characteristics, exposure and outcomes. Patient characteristics are descriptive patient data, such as patient demographics, including lifestyle, medical history, health status, etc. The registry will not collect genetic data. Exposure data focus on the disease, devices, procedures, treatments or services of interest. Outcome data describe patient outcomes (e. g. survival, progression, progression-free survival, death, etc.
Ethical considerations The registry was approved by the ethic committee of the Fondazione IRCCS Istituto Nazionale dei Tumori (INT) with protocol number (INT 43/21). INT is the coordinator of the EURACAN registry as well as a data provider. At the INT, and at each HCP involved, responsible investigators ensure that the EURACAN registry will be implemented in compliance with the protocol, following the instructions and procedures described herein. Each HCP is a controller and will identify a data processor. Patient protection Personal data will be recorded and stored in pseudonymised format in a DB installed at the HCP level. Either the PHT solution will enable data sharing among multiple organizations without identifiable data leaving the HCP, or anonymised data will be sent to the INT. All parties involved in registry development will maintain strict confidentiality to ensure that neither personal privacy nor the privacy of the families of patients participating in the registries will be violated.
Data will be processed exclusively by authorized personnel participating in the EURACAN registry (a data processor will be identified at each HCP involved). Access to computer systems and the premises where they are kept will be controlled by appropriate security measures which comply with privacy regulation requirements. The processing of patients' personal data taking part in the EURACAN registry, and specifically in relation to consent-related data, will comply with local privacy legislation and the General Data Protection Regulation 2016/679 (GDPR) of the European Union. The registry protocol is submitted to the ethics committees (EC) of the HCPs involved. Furthermore, the ECs of the HCP involved will authorize in advance any research carried out using EURACAN registry data. Informed consent Written informed consent is the legal basis of the EURACAN registry and so is required from all participants.
Limitations There is a risk of limited representativeness due to the hospital-based nature of the registry and to the fact that hospital contributing to the registry are expert centres for these rare cancers. Representativeness of the registry will be tested comparing the registry data with population-based data in terms of relevant variables (eg. age, stage, prognosis). Adequate statistical (eg. marginal structural model) methods will be used if time-varying treatments/confounders and confounding by indication (selective prescribing) will be present, not to raise methodological problems. Directed acyclic graphs can also be useful to identify the source of bias and will be utilized in the definitions of the path between covariates. Close collaboration with clinicians will be crucial also in this part to allow a proper interpretation of the registry data  [7].
with US, Rare Cancers Asia, etc. ), in other words ensure that the EURACAN Registry will be FAIR (findable, accessible, interoperable, reusable) • to review the EURACAN registry governance • to identify opportunities for financial support to maintain the registry. Data access rules If the research question is relevant but does not require a study protocol, a quick review will be provided by the SC and a written report including results of data analyses will be shared with the third party 5. Commercial companies, depending on the study, may be asked to contribute funding. Data do not move from the HCPs or national registries and the commercial companies will not access the EURACAN registry federated database. Funding will be used to support the registry maintenance and/or specific studies proposed by the EURACAN members and based on the registry data 6.
In the event the HCP or national registry does not confirm whether it wishes to share its data within 2 weeks from the scientific secretariat request, its data will not be used for the study 8. the scientific secretariat will contact the national registry coordinator, not each centre contributing to the national registry (e. g. NETSARC, etc. ). This is in line with the vision of EURACAN being a network of networks and therefore also a registry network 9. the scientific secretariat will inform the principal investigator (PI) of the proposed study about the SC decision 10. the PI of the proposed study will be responsible for arranging a preliminary ethical review that will be shared with the participating centres. The participating centres should provide their institutional ethical review response to the PI within 60 days.
1. data remain the property of the contributing HCPs or registries 2. each HCP or national registry is free to access and use its own data for research purposes 3. each HCP or national registry including data in the EURACAN Registry can request access to the EURACAN Registry federated database upon the presentation of a study protocol that has to be approved by the SC 4. third parties (e. g. , pharmaceutical companies, patient organisations, competent authorities etc. ) can request use of the registry data. However, data should be used for projects that include EURACAN members. Thus, third parties proposing a research question, should work with a EURACAN Principal Investigator (PI) and should present a study protocol to be reviewed by the SC.
This paper deals with investigating various techniques of segmentation for detecting the cervical cancer. It proposes a novel method to develop an assistance system for the detection diagnosis of cervical cancer, based on work of Martin, Byriel and Norup. The analysis is focused on Pap smear pictures of single cells. Smear testing is a method of detecting abnormalities in the blood. Image processing is an effective method for extracting data. It is used to determine the size of cervical carcinoma and the length of the uterus. Martin's database, which is open source and utilised for analysis and validation, is obtainable for research purposes. Cervical malignancy information utilizing three grouping strategies to anticipate the disease and afterward analyzed the outcomes showed that choice tree is the best classifier indicator with the test dataset.
We propose a novel method to develop an assistance system for the detection diagnosis of cervical cancer, based on work of Martin, Byriel and Norup. The analysis is focused on Pap smear  [9]  pictures of single cells. Martin's database, which is open source and utilised for analysis and validation, is obtainable for research purposes. The cervix, or cervix uteri, is the bottom portion of the uterus. In less developed nations, around 14,000 women have a serious health problem such as cervical cancer as a result of inadequate immunization efforts. It is a serious illness that affects women. It's a volumetric and anthropomorphic radiation  [10]  treatment technique. Smear testing is a method of detecting abnormalities in the blood. Image processing is an effective method for extracting data. It is used to determine the size of cervical carcinoma  [11]  and the length of the uterus.
88 percent', '99. 28 percent' and '97. 47 percent' were achieved for each dataset, respectively. Defuzzification and classifications performance were enhanced due to a rigorous features extraction approach that properly picked cell characteristics that increased segmentation results and the amount of groups employed. Once implemented to the Herlev benchmark Pap smear dataset, the approach surpasses several current schemes in terms of sensitivity (99. 28 percent), specificity (97. 47 percent) and accuracy (98. 88 percent). These results were achieved when applied to Pap-smear slides from the clinical laboratory. During the year 2018, B Karthiga Jaya, S Senthil Kumar, we provide a cervical cancer identification and classification approach that uses an ANFIS (Adaptive Network-based Fuzzy Inference System) classifier.
The dataset is divided into sections based on the values of all the characteristics. An excellent attribute is one that separates information data in such a way that each succeeding node is as good as it looks, that is, each node's distribution of dataset instances must contain only instances of a single class. To choose the splitting standard that "best" differentiates a given description of the various tests, R, of a C class for turning data points into singleton classes. It determines how the item sets at a specific hub will be combined. These measures beautifully depict the higher-positioning for each quality of the provided tuples. At each hub in the tree, the information gain metric is used to determine the test quality. The attribute with most noteworthy data gain is picked as the test quality for the hub.
Decision Tree Approach A supervised classifier with a tree structure made up of nodes is known as a decision tree. Internal nodes and leaves are the two types of nodes,  [17]. Internal nodes are also known as decision nodes since they test qualities before making a decision and partitioning the data set. Predicted or decision classes are represented The dataset is divided into training and testing datasets using decision tree techniques. To build trees, it employs a recursive technique. It creates a trained classifier by recursively partitioning a training set. When the subset at a node has the same prediction value as the target variable, the recursion process is complete. A training dataset is made up of a set of instances, each of which has attributes and a class label,  [18]. An attribute's values can be ordinal, real, or Boolean.
Results and Discussion On a Windows 7 Intel Core i7 CPU (Central processing unit) with a 2. 53 GHz operating frequency and 8GB of accessible memory, the implementation is carried out in MATLAB (MATrix LABoratory) R2018. In past findings, single cells did the most of the job, while the Pap smear slides received many and overlapping cells. We obtained cytological pictures from pathology labs in Jaipur. We have 50 shots in total; all of them are in JPEG format. There are 15 normal cells, 20 CIN1 cells, and the rest are CIN2/CIN3. The image is made up of numerous cells that are overlapping. The attribute is labeled on a node, branches are formed for each value of the attribute and the data are subdivided properly and the predictor variables are tabulated in Tab. 3. Fig. 6  shows the comparison of different phases of processing.
The error rate of the decision tree and random tree algorithms of sizes 7 and 13 is shown in Fig. 7. The decision tree algorithm has a minimal error rate. Fig. 8  shows the performance data for the texturing feature. It is evident that the accuracy of the proposed method has shown a tremendous improvement regarding detection of cervical cancer based on GLCM features. Fig. 9  depicts the accuracy measurements for the various categorization techniques presented. The goal of this study is to take a comprehensive and in-depth look at the latest state of the art in automated cervical cytopathology cell segmentation and categorization. We searched databases such as PubMed, arXiv, Google Scholar, IEEE (Institute of Electrical and Electronics Engineers), ACM (Association for Computing Machinery), Springer and Elsevier to create this complete review.
Dysregulated molecular signaling pathways during neurodevelopment To further characterize dysregulated molecular processes in the absence of maternal auts2a expression, we investigated the enrichment of human orthologs of the identified DEGs in signaling pathways using KEGG database (https://www. genome. jp/kegg/pathway. html). When comparing all single-nuclei DEGs between heterozygous individuals lacking (MCH-) or not (MCH+) maternal auts2a expression, we observed an enrichment for "ribosome", "axon guidance", "cell adhesion molecules" and "Wnt signaling" pathways. Similar enrichment patterns emerged when examining neural DEGs (i. e. , with an adjusted P-value <0. 05 in at least one neural population), displaying an over enrichment of the Wnt signaling pathway and the appearance of a significant enrichment of the "signaling pathways regulating pluripotency of stem cells".
Dysregulated genes associated with NDDs To explore links between maternal auts2a expression and molecular mechanisms linked to NDDs, we used the DisGeNET database that covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants  33. From the single-nuclei analysis, at stage 18, a highly significant proportion (48. 7%, P=1,47E-06) of the 328 found human orthologs were associated with NDDs, in comparison to 30. 5% for a random selection of 328 genes in the genome (Fig. 3h , Supplementary Table  5 ). This strong association with NDDs was also observed when looking at the neural (48. 5%, P=2. 19E-6) and exclusively neural DEGs (52. 5%, P=0. 0087) at stage 18 (Fig. 3h ). Among the genes identified, some were associated with various pathologies including autism spectrum disorders (ASDs), behavioral abnormalities (BH), epilepsy (EP), hypotonia (HP), intellectual disability (ID), microcephaly (µC) and schizophrenia (SCZ) (Fig.
UMAP constructed in dimensionality-reduced PCA defined by highly variable genes from integrated stage 18 MCH+ and MCH-datasets (see methods). Cells sharing a same color have the same cellular identity determined from expressed marker genes. Altogether, our analysis highlighted neural DEGs of particular interest. Sets of these genes have been identified as transcription factors, are parts of enriched pathways of interest (i. e. , axon guidance, Wnt signaling pathway and/or regulating stem cell pluripotency), and/or have been associated with relevant NDDs (ASD, behavior, ID, SCZ, microcephaly, hypotonia and/or epilepsy) (Fig. 4 , Supplementary Table  5 ). We identified highly significant neural DEGs (with an adjusted Pvalue <0. 01 and an absolute fold change >2 or an adjusted P-value <0. 001 and an absolute fold change >1.
Evolutionary conserved genes linked to behavior and nervous system development The present study is a unique vertebrate example demonstrating that maternal, but not paternal, LOF of a gene linked to numerous NDDs (i. e. , 149 according to the DisGeNET database) can trigger major neurodevelopmental and long-term behavioral differences in the progeny. We therefore aimed at identifying other genes tightly associated with NDDs that could exhibit similar features. We reasoned that relevant genes had to be expressed in the oocyte and used this criterion to search publicly available gene expression data. We used the mouse and the zebrafish, two popular model vertebrate species that are heavily used for biomedical purposes. We identified 11,521 and 22,738 transcripts expressed in mice and zebrafish, respectively.
To subsequently link candidate genes to human pathologies, zebrafish and mouse lists were filtered for common orthologs (Supplementary table  7 ), which led to the identification of a list of evolutionarily conserved maternally-expressed mRNAs. These conserved oocyte genes were subsequently filtered for genes that could be associated with either "Behavior" (GO:0007610) or "Nervous system development (NSD)" (GO:0007399) in either humans, mice (Mus musculus), rat (Rattus norvegicus), or zebrafish (Danio rerio) using the gene ontology database (http://geneontology. org/ , see methods for details). We were able to identify 107 oocyte genes associated with "Behavior" and "NSD" GO terms in both mice and zebrafish (Fig. 6a ). We then used the DisGeNET database to identify genes linked to human pathologies.
We also used the Disease Pleiotropry Index (DPI) that ranges between 0 and 1 and reflects the diversity of associated diseases; a DPI of 1 corresponding to the highest diversity. Among the 107 identified genes, 92 had a human ortholog present in DisGeNET database (Supplementary table  8 ). The DPI of these genes (green box plot, Fig. 6b ) was significantly higher than the DPI of genes not associated with "Behavior" or "NSD" GO terms (grey box plot, Fig. 6b ). Similarly, the DPI of the genes with "Behavior" and "NSD" GO terms (green box plot, Fig. 6b ) was significantly higher than the DPI of the genes associated only with "NSD" (yellow box plot, Fig. 6b ). We then focused on the genes associated with a higher diversity of diseases (i. e. , DPI > 0. 75). Among the evolutionarily conserved oocyte genes associated with "Behavior" and "NSD" GO terms, 49% (45/92) had a DPI above 0.
KEGG pathway enrichment analysis Human ortholog of differentially expressed genes were found using getBM() function from the biomaRt package (version 2. 58. 0). Enrichment analysis was performed on these human orthologs using enrichKEGG() function from the KEGGREST package (version 1. 42. 0). Covid19 disease was deliberately excluded from the enriched KEGG pathways due to its overrepresentation in recent studies.