dataesr/scientific-paragraphs-categorization huggingface dataset:
C. Ontology-based semantical validation Semantic Web Technologies, such as ontologies, are often proposed in the healthcare domain to represent the clinical knowledge contained in CPGs in a standard and semantically interoperable way  [12]. The use of annotation properties is promoted to add labels to the ontology classes and to link each concept with its definition in validated and available standard terminologies, such as SNOMED CT  1  or NCI Thesaurus 2. This guaranties the interoperability of the implemented knowledge with stable and unique codes for each biomedical concept. In our approach, we have developed a Jena API  3  based tool for interacting with any ontology formalized in the Resource Description Framework (RDF) language to be used along with the CIG formalization module. Focusing on the classes and the defined properties relating them within the model, we restricted the knowledge to be used in the CIG formalization module to the one defined in the ontology.
1) Rule Engine A rule engine can be defined as any system that implements rules, which when applied to data result into an outcome. In our case, rules come from the formalized CIGs and inputted patient data are evaluated to return the treatment that best fits the clinical case. Following Drools  4  , we have built up a java-based rule engine with two main components (i) an automatic rule file generator in Drools Rule Language (. drl extension) leaning on our java-based Rule object (contained within the DE formalization) and (ii) a standalone runtime execution environment. The first module assures that our system is domain independent and will work for any CIG formalized following our java structure. To achieve these objectives, Rule Templates 5  were used. These templates write the pattern of the rules, and populate them after with concrete rules content, generating a.
drl file from java objects specifications. In our approach, four attributes are specified in the Rule java object to be mapped with the template: name (i. e. the name of the rule), object (i. e. the patient to be analyzed), condition (i. e. the conditional part of the rule to be accomplish by the patient clinical data) and action (i. e. the recommendations defined by the rule). First, formalized CIGs are retrieved from a source (e. g. xml file, database) and stored into the Rule java object. This Rule object is transformed into a Java Map object that simplifies its complexity into four key attributes and their values to be mapped to the template, where each attribute will be substituted by its value in the Java Map object. Once all rules are mapped, the final. drl file is generated and is ready to be triggered by the standalone runtime execution environment.
1) Decision tree model generation DTs are classification methods widely used for mining large datasets  [20]. It relies on a hierarchical structure or tree that groups homogeneously data according to the variable to be predicted (e. g. predict if the patient will survive or not). The tree structure is made of nodes and depending on their position they will represent a recommendation (i. e. termination node) or a conditional part to be accomplished by the evaluated variables (i. e. non-termination nodes). The termination nodes define the stop criterion to abort the recursive partitioning into branches during the learning process of the model. Hence the model applies recursive partitioning until a stop criterion is reached. Our approach implements the most frequently used DTs (e. g. C4. 5/J48, C5.
2) Authoring tool for DT modification An authoring tool is meant to provide a tool allowing a user to edit or update content in an easy-to-use way. There are several software proposals for the creation and visualization of DTs, such as Orange 6  , RapidMiner 7  , Weka 8  or KNIME  9. These tools can generate different machine learning models, visualize them and test them. Nevertheless, they do not allow any modification once the model is generated, restricting the interaction with the end user. Other approaches give some tools for interacting with the user while creating the model  [26] ,  [27]. They visualize the dataset to be modelled to give the clinician a way to define more accurately the nodes of the tree. The main pitfall is that these visualizations are difficult to understand and hence, difficult to interact with for the end user.
V. USE CASE: BREAST CANCER MANAGEMENT We present a simplified use case in primary breast cancer at the diagnostic stage. We implemented the local protocol of Onkologikoa hospital as the knowledge base of the GL-CDSS. A simplified dataset of about 300 patients with 15 attributes was used for the DT generation implemented in the data mining-based CDSS, having as outcome the appearance of toxicities. First, we generated the DT considering the different treatment groups available for treating primary Breast Cancer in diagnostic stage (i. e. surgery, oncology). The first node of the tree split the dataset by treatment group, predicting 0. 99 of probability of having toxicities when treated with oncology versus 0. 01 of probability for surgical interventions. Hence, we decided to regenerate the model ignoring this variable and we observed that patients with a tumor size larger than 7.
ACKNOWLEDGMENT This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  690238. Additionally, the authors acknowledge support from the four clinical partners involved in the project:  Onkologikoa (San Sebastian, Spain ),  Eresa Grupo Médico (Valencia, Spain), Hôpital Européen Georges Pompidou (Paris, France) and Hôpital Saint-Louis (Paris, France ).
Robot Operating System (ROS) ROS is an open-source, C++ based, a general software framework for robot software development which gives operating system functionalities  [14]. Those functionalities are hardware abstraction, low-level device control, implementation of commonly-used features, message-passing between processes, and package management. It is based on a graph architecture where each node receives and process several messages from/to sensor, actuators about their state. This operating system works on top of Linux (Ubuntu) and can be used in Windows with some reduced features. ROS creates an ecosystem where different components called nodes are interconnected using a system of message communication between them. ROS is one of the key driving components, that power the commercial indoor robots.
BDI Frameworks Russel et al. [19]  developed the agent factory framework as an open-source collection of various tools, platforms and languages that ultimately facilitate the development and development of multi-agent systems. Winikoff  [20]  built a highly portable, robust and cross-platform environment called JACK for building, running and integrating commercial-grade multi-agent systems. In the BDI framework called JADE  [21] , the agent platform can be distributed among different independent machines and controlled remotely. The configuration can even be changed at run-time by the moving agents from one machine to another one during the implementation. Braubach and Pokahr  [22]  developed a framework called JADEX, based on XML and Java, that follows BDI model and facilitates easier intelligent agent system construction with an engineering perspective.
Use Case Scenario For this study, we consider a visual image-based activity for adding a proactivity behavior in the social robot. The indoor robot Waldo has cameras installed in its eyes which gather the image feeds from the environment. This image-feeds help to set up a belief about the situation. The camera feeds are processed using a module in OpenCV for person detection. This module establishes a belief for the robot concerning the presence of a person in the environment. In case a person detected by the OpenCV module, Waldo sets up a goal of greeting the person without any explicit commands from the person. In this experiment, the robot can perform two precise actions. Upon the detection of the person, using the BDI framework, the robot greets the detected people with a sentence. Upon continuous detection of the person for a fixed time, the robot changes its belief and offers additional help to the person by speaking a different sentence.
Social Indoor Robot-Waldo The robot under consideration for this study is Waldo, which is a multi-service robot manufactured by Immersive Robotics  [35]. Waldo is a telepresence service robot with advanced vision capabilities. The robot has an Arduino card for basic control functions and a Linux card with ROS installed as an operating system for more advanced and elaborate functionalities. Waldo has an adjustable height of 130 cm to 170 cm. The robot is autonomous with LIDARs, sonars, microphones, and cameras. Waldo is built in a humanoid shape as an indoor social robot for a welcoming, talking, understanding and communicating with people. The movement of Waldo can be controlled remotely by using a mouse, keyboard, joysticks, pads, smartphones, tablets or any other desired peripherals. Waldo is shown in Figure  1.
The logical models are responsible for deriving various knowledge from the data about the environment collected by sensors of the robot. Based on this knowledge base, the BDI framework establishes the beliefs and set up goals for any given instant of time. The framework also chooses a set of actions from the predefined plan list to accomplish the goal. The actions are relayed to Waldo PC through a wireless connection that directs different actuators in Waldo to perform a wide range of action. The internal mechanisms in ROS manage the communication between the nodes and Waldo PC. The Waldo PC, which is equipped with Linux and ROS, can have several logical modules installed on it or monitoring PC to establish one or more beliefs about the environment. The modules in the logical layer can be BDI framework, OpenCV for vision processing and other intelligent bricks for establishing important beliefs about the environment.
The set of beliefs, desires, and intentions as per BDI model for the case study are defined as follows: Belief: personDetected("Yes"), personDetected("No") and personDetected("Next") Desire: DoNothing(), GreetPeople() and OfferHelp() Intentions: stayIdle(), speak() The PROFETA framework can be implemented for behavior modeling using the following steps: Algorithm 1 Implementation of PROFETA framework. 1: Import necessary PROFETA libraries 2: Define beliefs and goals as classes in the script 3: Define user actions by creating classes and overriding the method execute() 4: Start PROFETA engine 5: Define rules by using declarative syntax 6: Run the engine Moreover, PROFETA framework also facilitates the definition of sensor class which can itself add or remove a set of beliefs depending upon the environment.
This can be done in PROFETA by declaring a subclass Sensor, overriding the sense() method and informing the PROFETA engine about the addition of a new sensor in the program. PROFETA uses declarative language so as to express the behaviors of agents. The declarative syntax for the behavior of an agent is described below: "Event"/"Condition" » "setofActions" In this declarative syntax, an event can be any one of belief assert or retract, goal accomplishment or request or even goal failures. The condition in the syntax refers to a particular set of knowledge base while actions can be goal accomplishment request, user-defined set of actions, or adding or removing beliefs. This syntax can be exemplified as: +objectAt("A", "B")/objectGot("no") » [moveTo("A","B"), pickObject()].
Work Distribution in Proposed System Architecture One of the key features expected from the proposed framework is the rational distribution of works required in the development of proactive behaviors in the robot. The study implements the test use case in ROS with an overlaid layer of the BDI framework of PROFETA. A module in the OpenCV library does the image processing. The proposed system design distributed the operations over two PCs, Waldo PC, installed in the robot and Workstation PC. The test experiments utilized the distributed working architecture of ROS as the design offloaded the Waldo PC from heavy processing of image feeds collected from the cameras. The bulky and more compute-intensive modules of person detection and practical reasoning (BDI framework) are installed in a comparatively powerful Workstation PC.
Initially, the camera node publishes the image feed collected by the eyes of Waldo. The converter node, with CvBridge interface, subscribes to the topic messages of camera node and converts the ROS images to OpenCV images. The node then publishes the converted images. There is a node called person detector running in Workstation PC, that subscribes to the converted image messages. This node executes the person detection module of OpenCV library. There is an additional node called BDI engine in Workstation PC that subscribes to the messages about person detection published by the person detector. BDI engine executes all the necessary behavior modeling to publish the action to be done by the robot finally. There is another node, listener, that subscribes to BDI engine node and publishes messages for actuators in the robot to undertake the actions.
Performance Analysis For the performance analysis and evaluation, Waldo is kept at a fixed position in the corridor. The eyes of the robot, which have cameras, are at the height of about 162 cm. The tests are carried repeatedly where both natural and artificial lights influence the lighting condition. Figures  5  and 6  represent the execution of BDI actions and image feeds collected by the camera installed in the robot, for no person detected and person detected cases, respectively. When a person is not detected in the image feed, the BDI engine establishes the belief about the environment and create a goal of not greeting the people. Accordingly, the engine selects the action of NoTalk to achieve the goal, as shown in Figure  5a. Similarly, when a person is detected, the BDI engine asserts a belief of encountering a person, enabled by the person detection module running on the Workstation PC.
Conclusions The recent rise of humanoid and animaloid indoor social robots in commercial spaces, including home has necessitated the "act-like-human" behavior in those robots for a more friendly human-robot interaction. The proactivity in such social robots adds more utility to the robots. As such, in this paper, we presented a validated use-case of such proactive behavior in an indoor commercial social robot, Waldo enabled by the behavior model framework, PROFETA. We clearly defined a fundamental system architecture with the features of flexibility, modularity, and rational work distribution to integrate the BDI framework into the distributed ecosystem of ROS. In the architecture, multiple ROS nodes can be independently created over multiple machines, connected by wireless communication. We demonstrated how an external module, such as OpenCV library, can be used to enhance the capability of an indoor robot in a plug-and-play fashion.
PURPOSE. Age-related macular degeneration (AMD) is the worldwide leading cause of blindness among the elderly. Although genome-wide association studies (GWAS) have identified AMD risk variants, their roles in disease etiology are not well-characterized, and they only explain a portion of AMD heritability. METHODS. We performed pathway analyses using summary statistics from the International AMD Genomics Consortium's 2016 GWAS and multiple pathway databases to identify biological pathways wherein genetic association signals for AMD may be aggregating. We determined which genes contributed most to significant pathway signals across the databases. We characterized these genes by constructing protein-protein interaction networks and performing motif analysis. RESULTS. We determined that eight genes (C2, C3, LIPC, MICA, NOTCH4, PLCG2, PPARA, and RAD51B) ''drive'' the statistical signals observed across pathways curated in the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and Gene Ontology (GO) databases.
We further refined our definition of statistical driver gene to identify PLCG2 as a candidate gene for AMD due to its significant gene-level signals (P < 0. 0001) across KEGG, Reactome, GO, and NetPath pathways. CONCLUSIONS. We performed pathway analyses on the largest available collection of advanced AMD cases and controls in the world. Eight genes strongly contributed to significant pathways from the three larger databases, and one gene (PLCG2) was central to significant pathways from all four databases. This is, to our knowledge, the first study to identify PLCG2 as a candidate gene for AMD based solely on genetic burden. Our findings reinforce the utility of integrating in silico genetic and biological pathway data to investigate the genetic architecture of AMD.
Rather than investigating associations between single genetic variants and a phenotype, pathway analysis of GWAS data interrogates alterations in biological pathways for a trait of interest. Generally, this is done by aggregating summary statistics for these variants into genes, which are then grouped into pathways based on data in curated pathway databases. 8 e hypothesize that applying this more comprehensive approach may help elucidate the genetic etiology of advanced AMD that has been indiscernible from GWAS. In this study, we performed in silico pathway analysis using the Pathway Analysis by Randomization Incorporating Structure (PARIS) software to identify biological pathways and processes enriched in genetic variation potentially associated with AMD in individuals of European descent.
METHODS Study Subjects and GWAS Summary Statistics The participants for this study were previously ascertained by cohorts in the IAMDGC as described. 5 This included 16,144 individuals with advanced AMD and 17,832 unaffected individuals. Of the advanced AMD cases, 3235 individuals have GA only and 10,749 have CNV only. The remaining cases have both GA and CNV. All of the cases and controls used for our analyses were of European ancestry. All participants provided informed consent, and the study protocol was approved by institutional review boards as previously described. 5 Data were previously collected in accordance with the tenets of the Declaration of Helsinki. The summary statistics we analyzed in this study were obtained in the 2016 GWAS performed by the IAMDGC. 5 Specifically, these data include P values for 445,115 directly genotyped common and rare variants from the advanced AMD case-control results.
PARIS: Knowledge-Driven Pathway Analysis of GWAS Data To identify biological pathways enriched in genetic variants possibly contributing to advanced AMD risk, we performed in silico pathway analysis using the PARIS v2. 4 software. 10, 11 ARIS uses variant summary statistics from GWAS, clusters them into features defined by the linkage disequilibrium (LD) structure of the genome based on a reference catalog of common genetic variants, and assigns significance to pathways based on permutation of the genome. 10, 11 In our analyses, we performed 100,000 permutations. PARIS also assigns empirical P values to the genes composing a pathway based on permutation testing of features within each of the genes. 10, 11 e performed PARIS using multiple pathway databases, including Kyoto Encyclopedia of Genes and Genomes (KEGG),  12  Reactome,  13  Gene Ontology (GO),  14  and NetPath.
15 EGG, Reactome, and GO databases are extensive, curated biological pathway data repositories. NetPath is a specialized database that covers signaling pathways. Pathways with a P value less than 0. 0001 were prioritized for further investigation. This permutation P value was calculated using the following equation: P ¼ (1 þ b)/(1 þ M) , where M ¼ the number of permutations and b is the number of randomly sampled permutation scores that are greater than the observed score. To determine if the pathway associations we observed were driven by known AMD loci, we reperformed our pathway analyses excluding variants from the 34 susceptibility loci identified by the IAMDGC (defined by the 52 genomic variants) and their proxies (r 2 ‡ 0. 5) within 500 kb. 5.
Identification of Statistical Pathway Driver Genes Due to disparate nomenclature and composition of pathways in the databases, we identified genes that overlapped across significant pathways within a database and across databases (regardless of pathway). This served to internally validate and complement our results. To interrogate the significant signals obtained from the pathways identified by PARIS, we queried which significant (P < 0. 0001) genes overlapped among the significant (P < 0. 0001) pathways within a pathway database. These genes were compared across the analyses done with each of the pathway databases (KEGG, Reactome, GO, and NetPath) to find statistical driver genes that had significant signals across three or more databases for the advanced AMD results.
Protein-Protein Interaction (PPI) Network for Statistical Pathway Driver Genes We searched the Search Tool for Recurring Instances of Neighbouring Genes (STRING) database 16 version 10. 5 for PPIs involving the proteins encoded by the genes identified as statistical driver genes. The STRING database is composed of known and predicted PPIs based on data from curated interactions databases, high-throughput lab experiments, coexpression, and text mining in the literature. We used the high confidence (0. 700) minimum required interaction score to construct the protein-protein networks of interactions based on experimental data, database entries, and coexpression.
Motif Analysis for Statistical Pathway Driver Genes We extracted reference genome sequences for the statistical driver genes using the UCSC Genome Table  Browser. 17 We included 600 nucleotides upstream from the first exon and the 5 0 untranslated region (UTR) in the sequences for each gene. To identify potential sequence motifs for each of these gene sets, we utilized the Multiple Expectation Maximization (EM) for Motif Elucidation (MEME) software suite. 18 Sequences were considered motifs if their lengths were between 6 and 50 nucleotides. MEME was not required to find a motif in every sequence, but motifs were required to have an E-value of 0. 0001. Each motif from the gene sets was then investigated in Tomtom, which looks for transcription factors (TFs) that are associated with the motif.
TF binding motifs were evaluated based on the known human TF database from JASPAR  19  using HOCOMOCO. 20 To validate the motifs found and to test the null hypothesis of random motifs found unrelated to the statistical driver genes, 10 permutations were run on a random gene set generator for eight genes and performed the same analyses via MEME and Tomtom. We removed motifs and TFs that appeared in both the random and actual gene sets from further analysis.
In Silico Pathway Analysis We identified several biological pathways and processes from KEGG, Reactome, GO, and NetPath databases (Table  1 ; Supplementary Tables  S1-S4 ) to be significantly associated with advanced AMD using PARIS. A pathway was considered significant if it had a pathway-level P value less than 0. 0001. The vast majority of pathways in the four databases were not significant (Table  1 ). When we reperformed our pathway analyses excluding the 34 known AMD loci, 5 ~40% of the previously significant KEGG (n ¼ 10) and GO (n ¼ 53) pathways and over 60% of the Reactome (n ¼ 32) pathways remained significant (Supplementary Tables  S1-S3 ). The single NetPath pathway that was significant in our initial analysis (Wnt; Supplementary Table  S4 ) was no longer significant in this sensitivity analysis (P ¼ 0.