dataesr/scientific-paragraphs-categorization huggingface dataset:
This can include analysis of their gene expression profiles by microarrays, focused qRT-PCR assays or RNA-Seq, comparing the isolated population with internal positive and negative control populations isolated simultaneously (e. g. other skin MP subsets as well as blood DC subsets), and/or in comparison with existing MP subset transcriptome datasets in publicly available repositories. The microarray transcriptome analysis performed in Artyomov et al. relied on a limited number of MP subsets as comparator populations, with major differences in MP subset composition between the human skin versus mouse datasets and between the human skin versus blood datasets. The mouse dataset used was restricted to mouse CDP-derived DCs and to LCs, without inclusion of monocytes, macrophages or monocytederived DCs.
In contrast, the human skin dataset used turned out to only encompass monocyte-derived cells and LCs, without inclusion of any CDP-derived DCs. Hence, the gene modules generated through the analyses of these mouse versus human datasets could not be rigorously mapped across species, including for LCs since the reference MP subset populations used to generate the LC-specific signatures are completely different between the two species studied. Specifically, the design of the compendia analyzed by  Artyomov et al. led  to biasing the human LC gene module for enrichment in genes highly expressed in DC subsets as compared to monocytes/macrophages, and conversely for the mouse LC gene module which was enriched in genes highly expressed in monocytes/macrophages as compared to DC subsets (Fig. S9 ).
A similar problem occurred for the comparison between the human blood versus skin datasets. Surprisingly, the authors of this study  (Artyomov et al. , 2015)  did not directly compare the gene expression profiles of their dermal 'CD141 + DCs' with the dermal CD141 high DC population reported previously  (Haniffa et al. , 2012). It is unclear why in their analysis  Artyomov et al. used  only the blood MP from Haniffa et al. but disregarded the skin MP transcriptome data from the same study. In our study, we have tried to match as well as possible the human skin, human blood and mouse compendia. In addition, because each method of gene expression profiling has its own drawbacks and biases, we used different complementary methods to answer our questions, not only module analysis using BubbleGUM but also PCA and hierarchical clustering.
However, our study illustrates the critical need for a rigorous design of gene expression compendia in order to accurately map cell populations or biological pathways across tissues or species, which requires using comprehensive datasets as tightly matched as possible across tissue and species for their cell type contents. In addition, our study also highlights the important need for accurate a priori definition of populations during sampling of biological material for RNA extraction, and the need for a posteriori analyses to authenticate the identity of the cell populations isolated such as to determine if the sampling procedure was accurate or needs to be changed  (Vu Manh et al. , 2015a). In our own studies of MP subsets from pig and sheep, the three-step strategy above allowed us to identify that the cells we had isolated as putative pig homolog of human CD1c + cDCs were not CDP-derived DCs but cells of the monocytic lineage and most likely pig ncMo  (Vu Manh et al.
Acknowledgments This work was supported by  CNRS ,  Inserm ,  European Research Council  ( FP7/2007-2013  grant agreement no. 281225  to  M. D. for the  Systems Dendritic  project ) , grant  ANR-11-DPBS-002  (S. C. ) and the  Wellcome Trust   WT088555  (M. H. ). We also acknowledge support from the  DCBIOL Labex  ( ANR-11-LABEX-0043 , grant  ANR-10-IDEX-0001-02 PSL ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the  French Government 's  "Investissements d'Avenir" program  managed by the  ANR.
cerium dioxide nanoparticles and gadolinium intraperitoneally; 8. gadolinium intraperitoneally; 9. gold nanoparticles intraperitoneally; 10. europium and cerium dioxide nanoparticles intraperitoneally. All mice underwent magnetic resonance imaging in T1-WI VIBE iso fat suppression, TR -18, TE -3. 8, Flip angle -10°, fat suppression -SPAIR, resolution: FoV 190 mm (phase 100 %), slice thickness -0. 5 mm, base resolution -256 mm, phase resolution -100 %, slice resolution -83 %. Signal intensity was measured at 30 and 60 min after injection in different organs and tissues three times and average value was calculated. Results: We obtained heterogenous data in all experiments, the highest signals were registered in kidneys of all mice. We revealed slightly superior magnetic signal intensity in brain and muscles in mice injected with gold nanoparticles and europium with cerium dioxide nanoparticles intraperitoneally vs controls and vs mice injected with gadolinium; data are presented on Table  1.
8 mm in first group (p < 0. 01), and 1. 1 mm in second and 0. 9 in control group (in 83 % patients). Indirect US signs of neuropathy are registered as follows: reducing number of nerve bundles, increase in the fibrous component, inequality nerve path, thinning of the muscles and reducing the amplitude of movements. Conclusion: Ultrasonography is an effective diagnostic tool for diabetic neuropathy, in particular for early diagnosis in patients with DM type 1 in young age. Outlook and Expert recommendations: According to obtained results, ultrasonography has strong diagnostic predictive potential for neuropathy, much expressed in patients with DM type 1. Further studies for correlation imaging-neurophysiological molecular data and validation are needed. Current diagnostic imaging information should impact on the diagnostic thinking of the clinician, effect on the patient management plan, improving the therapeutic outcome and have essential societal benefits of implementing this diagnostic imaging technology.
And the main idea that in work on this dictionary were included our school students and they did a big part of this dictionary. We can give the name of our system, for example, "From young professional to mentor". Our analysis shows that SF-36 was employed most frequently and ranged between 60 % and 100 % in all of the 130 papers that were evaluated. At 20-50 %, the second most frequently employed questionnaire was EQ-5D. Overall, scientific development shows that there is an increasing number of studies on the quality of life. As and example until 1995, 512 questionnaire studies on the quality of life were recorded in the PubMed database, were us until 2014, this figure rose to 6325. Outlook and Expert recommendations: As a result of the growing interest on the quality of life and the quite inexpensive data collection that requires little effort, a further rise in the number of studies on the quality of life using questionnaires can be expected.
Results interpretation and Outlook: The results comprise the prototype of a portable meteo-station for self-monitoring and integrated care. The developed device is applicable to several patient cohorts such as those suffering from cardio-vascular and psychoneurological disorders. Classification, ADNI database. The goal to provide individualized tailored treatment to patients in terms of efficacy and adverse events depends on the ability to define the "disease signatures". A term that became a premise in the therapeutic targets, precision medicine and disease prediction processes, but depends heavily on the comprising, analysis and translation of all relevant personal data. It may include all the micro-and macrofactors of the human body functioning in its physical, mental and community environment, such as age, gender, clinical tests, biological markers, lifestyle, medical history, genetics, imaging etc.
The new sophisticated and advanced technologies are enabling the capturing of these parameters through profound insights on the human body functioning, in various levels and degrees of sensitivity, providing "Big Data" initiated from various sources. The goal is to analyze simultaneously all these identified and captured factors, including their interplay, translated to "disease signatures" towards responsible personalized, preventive and predictive medicine applied in the clinics. Thus, it creates the need to bridge barriers between "Big Data" and "Small Data" such as the simultaneous analysis of different types of data, sometimes related to a few number of people (many parameters, few records) tackling over fitting as well as database leakage  [1] , replicability  [2]  and reproducibility barriers.
For example, hospital databases with limited availability of healthy records and different diagnostic tests undertaken by healthy and sick people, suffer from leakage and replicability barriers. Furthermore, other challenges are: -Expert knowledge is valuable but current diagnosis might be misleading. -Compensatory mechanisms, obscuring the linkage between biological markers (i. e. imaging, pathology and genetics) and disease manifestation, are difficult to discover. -Big Data: Big potential but increased chances of capturing irrelevant markers. Thus to address these challenges new technologies and targeted tools are being developed, among them the "3C" strategy developed in our group. The goal is to meet the growing sensitivity and specificity needs in representing the medical data variance and its relevance and contribution to personalized medicine.
Technological approaches: Our study is devoted to the validation of the use of infrared thermal imaging for the assessment of cellulite and to development of the objective method to evaluate the severity of cellulite. Thermal images of the thigh were recorded and further processed to quantify the temperature distribution. 12 young women (20-24 years old) with various degrees of cellulite were enrolled in to the study. We developed our own application based on Java Technology, for importing and analyzing data from infra-red images. Our new software (ThermaAnalyzer 2. 0) is designed for assessment of the degree of cellulite. It is based on an algorithm, which can search for irregularities (blemishes) on thermal image. By analyzing the temperature distribution of the examined part of body, it is possible to diagnose the cellulite stage and thus, the influence of possible anticellulite therapy.
Widely used mobile devices equipped with microphones can be also used for early speech-based digital diagnosis. Implementation of speech analysis algorithms with adaptation function for individual user can be a part of personalized digital medical assistant. Proposed solutions: This proposal is a description of our planned participation as a technical partner in H2020 project named iManage-Prenatal for speech/voice related human machine interaction (HCI) components. The existing server based speech recognition module will be used as starting point  [1]. We aim to create a new acoustic multilingual drug name database providing speech input for speech based management of drug prescription recommendation during pregnancy  [2, 3]. We also plan to create a new multilingual speech command interface providing eHealth mobile application functionalities for visually impaired end-users.
The last proposal is to provide multilingualism for automatic transcription of speech search query to text for smart search in the knowledge base of the eHealth portal. The last proposal includes the design and implementation of a Natural Language Processing (NLP) Text Analyser of textual queries semantics expressed by the end-user  [4]. The NLP module will benefit from semantic knowledge extracted and stored in the drug databases provided by the project partners. The database analysis should go beyond the simple syntactical parsing of grammatical structures. The advantage of the proposed system is that the language resources used for decoding the audio stream on the server side can be modified by the provider without any changes on the mobile application, so the feedback gathered during the application lifetime can be used to improve the accuracy of the components in a very short time, and without necessary end-user application upgrade.
Given the significance of early detection of such-like diseases for further effective treatment, it is impossible not to note a high diagnostic potential of the gingival crevicular fluid (GCF) in detection of various periodontal diseases. Our investigation has summed up the available data on using the GCF markers to detect periodontal diseases at early stages. We examined 120 individuals of both sexes aged 18 to 25 and analyzed the cytobacterioscopic, hygienic and clinical findings as well as different GCF markers. The results have shown that the early stages of the inflammatory process are characterized by the growth in the number of cocci forming a kind of clumps, the formation of pseudomycelium of yeast cells chains up to 8-10 per high power field as well as not quite mature epithelial cells with a nucleocytoplasmic ratio of 1:2.
Clinical application of the pre-polymerised forms of composite was the qualitative breakthrough in this issue  [3, 4]. In this study, 274 restorations were carried out. 130 restorations were carried out by using the standard form of composite and 144 restorationsby using the pre-polymerised form of composite. The following personality parameters of each restoration were taken into account in this research: Ryge criteria, hygienic index, index of the hard tissues destruction, intensity of the caries, the age, the vitality of the teeth, and the number of previous invasions. During the analysis of the results of statistic proceeding by the method of cluster analysis the following data were discovered: the quality of treatment immediately after the restoration in the group of pre-polymerised composite was 5.
A Scientific Society for Individualised medicine (ESPT). MP, IB, PV, ET, CDR are members of the  Working Group Personalized Laboratory Medicine (WG-PLM) in the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM). Acknowledgements This publication is supported by the Project implementation:  University Science Park TECHNICOM for Innovation Applications Supported by Knowledge Technology , ITMS:  26220220182 , supported by the  Research & Development Operational Programme  funded by the  ERDF. Acknowledgements This publication is supported by the Project implementation:  University Science Park TECHNICOM for Innovation Applications Supported by Knowledge Technology , ITMS:  26220220182 , supported by the  Research & Development Operational Programme  funded by the  ERDF.
Body weight and height were measured, and BMI calculated; salt intake was categorized as <3. 0 g/day, >3. 0 < 6. 0 g. /day and ≥ 6. 0 g/day. Cigarette smoking, alcohol habits (at least twice a week > 1 base as the understanding of modern medicine is impossible without understanding the terminology. Scientific objectives: Innovations in science and advanced technolo- gies are accompanied by the burst of novel terms. They undergo nu- merous modifications, improve and become more complicated for being understood. The extent of this renewal is not in line with men- tal status of specialists thus obstructing the understanding of current progress. This determines the need of the adequate representation in ethnical languages. ). Factors to be considered for individualized In Russian scientific community, English glossaries are amongst the patient profiles providing personalized therapy in HCC patient cohort main sources to renew medical vocabulary (more than 90 % of scien- tific datasets is published in English).
a decision-making protocol combines outcomes obtained from multiple models, 3. a multi-criteria optimization algorithm finds the most likely protein architecture. The method is evaluated for domain and architecture prediction over several datasets and statistical testing hypotheses. Its performance is compared against HMMScan and HHblits, two widely used search methods based on sequence-profile and profile-profile comparison. Due to their closeness to actual protein sequences, clade-centered models are shown to be more specific and functionally predictive than the broadly used consensus models. Based on them, we improved annotation of Plasmodium falciparum protein sequences on a scale not previously possible. We successfully predict at least one domain for 72% of P. falciparum proteins against 63% achieved previously, corresponding to 30% of improvement over the total number of Pfam domain predictions on the whole genome.
Introduction The evolutionary history of eukaryotic proteins involves rapid sequence divergence, addition and deletion of protein domains, fusion and fission of genes. This implies that protein repertoires of distantly related species differ greatly (new architectures, that is combinations of domains, are many), while domain repertoires do not (new domains are few)  [1]. To account for the great diversity of domain contexts in eukaryotes, an effort was made to categorize coding regions into protein domains and domain families. An important contribution issued by this effort is the Pfam database  [2] , a large collection of protein families, each represented by multiple sequence alignments (MSAs) and hidden Markov models (HMMs). Pfam also provides protein domain architectures and higher-level groupings of related domains (clans).
Another reason is that proteins without predicted domains might belong to novel families completely missing in Pfam and other databases. Based on the observation that structural and functional constraints might not be globally conserved through all species, we propose a novel pipeline, called CLADE (CLoser sequences for Annotations Directed by Evolution), that identifies domains in proteins by using all known Pfam domains and the large quantity of available genomic data spanning through a large panel of species. The idea is to "decompose" the signal of consensus shared by homologous sequences, collected at the scale of the entire phylogenetic tree, into several consensus signals coming from homologous sequences collected at the scale of species within clades (we shall speak of clade-centered models, in short CCM), and possibly of species that are phylogenetically distant from the genome considered.
Results Our main claim is that protein domains are subject to heterogeneous evolutionary signals, possibly due to multiple evolutionary pressures of structural and functional origins. To demonstrate that this observation greatly helps to improve annotation, we first present how CLADE exploits the large quantity of available genomic information to provide protein sequence annotation. Then, we validate CLADE approach on several datasets of sequences by demonstrating that "multi-source" domain modeling is more appropriate than "mono-source" domain modeling for capturing remote homology. We compare CLADE to widely used domain prediction tools based on the mono-source approach. Finally, we report the improved annotation realized by CLADE of all P. falciparum proteins, known to be difficult to annotate.
The CLADE approach To predict domains in protein sequences, CLADE pipeline is organized in three main steps (Fig  1 ). The input is a set of protein sequences coming from the same genome or from different ones. In the latter case, each sequence is accompanied by its NCBI taxon code. CLADE's first step: Construction of protein profiles to identify potential domains. In this step, CLADE uses information coming from the Pfam v27 database (Pfam 27 ) and constructs probabilistic models by automatically sampling reference sequences through a large panel of species representing the whole tree of eukaryotic, archaeal, bacterial, viral worlds, and from metagenomes. If a user wants to use a preferred set of species, CLADE accepts the specified set, representing phylogenetic variability in the tree of life, as optional input.
We hypothesize that some species share their evolutionary solutions with the species to annotate, even if they are phylogenetically distant from it. The construction of CCMs constitutes a basic difference between CLADE approach and existing methods: CLADE uses SCMs like other methods and goes beyond them by exploiting extra information present in our databases. Namely, it constructs CCMs describing the multiple, possibly divergent, evolutionary solutions that are present in nature and uses CCMs to annotate. CLADE's second step: Multiple criteria support predictions. CLADE exploits all models produced in its first step to predict domains in the set of protein sequences to be annotated (Fig 1, middle; see section "Combining Model Predictions" in Methods and S2 Fig). It uses PSI-BLAST and HMMer (and their respective models) to screen each query sequence and identify potential domains.
This step of merging together the results produced by different profiles for the same protein sequence, distinguishes CLADE from other multiple-profile-based approaches  [18] [19] [20]  (see section "Analysis of a benchmark dataset from SCOP" in Results for a comparison to the strategy previously developed by SUPERFAMILY). CLADE's third step: Protein architectures filter predictions. Domain co-occurrence is expected to enhance the level of confidence in a prediction  [34, 35]. This is because: 1. the majority of proteins are multi-domain, and 2. we observe fewer domain combinations than statistically expected  [34, 36, 37]. Intuitively, co-occurrence suggests functional cooperation, that is, two or more domains can interact to determine the protein function  [38] [39] [40]. Once domains are selected, CLADE calls DAMA, a tool that considers domain co-occurrence and domain overlapping, and that combines several domains into most probable architectures (Fig.
Multi-source versus mono-source approach to annotation To demonstrate that "multi-source" domain modeling is more appropriate than "monosource" domain modeling for capturing remote homology, we consider several datasets: 1. three datasets of single-domain sequences constructed from the SCOP database  [21]  as gold standard, 2. four datasets of randomly generated single-domain sequences satisfying different statistical hypotheses, 3. two datasets of randomly generated multi-domain sequences satisfying different statistical hypotheses. We evaluate the improvement in using an annotation that exploits multi-source domain modeling by comparing the performance of CLADE, that favors the multi-source strategy, to the performance of HMMScan and HHblits, that favor the mono-source one. We recall that HMMScan and HHblits are widely used search methods based on sequence-profile and profile-profile comparison, respectively.
We estimate the false discovery rate (FDR) for the three methods. Also, with the SCOP datasets, we show that agreement among models is an important feature present in CLADE, and missing in existing multisource domain approaches, like SUPERFAMILY. Note that CLADE skips its third step (DAMA) on datasets 1 and 2 because they comprise single-domain sequences. Analysis of a benchmark dataset from SCOP. We considered three datasets of sequences coming from the database SCOP: ASTRAL95, ASTRAL30 and ASTRAL10. They are constructed from domain families sharing the same structure and they contain sequences with at most 95%, 30%, 10% of sequence identity (see section "Databases" in Methods). On these reference datasets, we tested mono-source and multi-source annotation strategies with HMMScan and HHblits supporting the mono-source strategy, and with CLADE, CLADE BEv , CLA-DE_HHblits and CLADE BEv HHblits supporting the multi-source strategy.