varunr14/newsummary huggingface dataset:
Most of today s embedded systems are very complex . These systems controlled by computer programs continuously interact with their physical environments through network of sensory input and output devices . Consequently the operations of such embedded systems are highly reactive and concurrent . Since embedded systems are deployed in many safety critical applications where failures can lead to catastrophic events an approach that combines mathematical logic and formal verification is employed in order to ensure correct behavior of the control algorithm . This paper presents What You Prove Is What You Execute compilation strategy for a Globally Asynchronous Locally Synchronous programming language called Safey Critical SystemJ . SC SystemJ is a safety critical subset of the SystemJ language . A formal big step transition semantics of SC SystemJ is developed for compiling SC SystemJ programs into propositional Linear Temporal Logic formulas . These LTL formulas are then converted into a network of Mealy automata using a novel and efficient compilation algorithm . The resultant Mealy automata have a straightforward syntactic translation into Promela code . The resultant Promela models can be used for verifying correctness properties via the SPIN model checker . Finally there is a single translation procedure to compile both Promela and C Java code for execution which satisfies the De Bruijn index i.e . this final translation step is simple enough that is can be manually verified . 


Summary: Introduction of safety critical subset of the SystemJ language called Safety Critical SC SystemJ. Automata based compilation approach for the SC SystemJ language. A tool chain for verifying correctness properties e.g. liveness and safety of the SC SystemJ programs and generating executable from the verified code for deployment. The new compiler generates both faster and smaller executable compared to the original SystemJ compiler.
This paper presents the design implementation and applications of a software testing tool TAO which allows users to specify and generate test cases and oracles in a declarative way . Extended from its previous grammar based test generation tool TAO provides a declarative notation for defining denotational semantics on each productive grammar rule such that when a test case is generated its expected semantics will be evaluated automatically as well serving as its test oracle . TAO further provides a simple tagging mechanism to embed oracles into test cases for bridging the automation between test case generation and software testing . Two practical case studies are used to illustrate how automated oracle generation can be effectively integrated with grammar based test generation in different testing scenarios locating fault inducing input patterns on Java applications and Selenium based automated web testing . 


Summary: TAO provides a declarative framework for automated test and oracle generation. TAO integrates test and oracle generation as a whole to promote better automated software testing. TAO is the first attempt to apply the methodology of denotational semantics in test and oracle generation.
The chaetognaths constitute a small and enigmatic phylum of little marine invertebrates . Both nuclear and mitochondrial genomes have numerous originalities some phylum specific . Until recently their mitogenomes seemed containing only one tRNA gene but a recent study found in two chaetognath mitogenomes two and four tRNA genes . Moreover apparently two conspecific mitogenomes have different tRNA gene numbers . Reanalyses by tRNAscan SE and ARWEN softwares of the five available complete chaetognath mitogenomes suggest numerous additional tRNA genes from different types . Their total number never reaches the 22 found in most other invertebrates using that genetic code . Predicted error compensation between codon anticodon mismatch and tRNA misacylation suggests translational activity by tRNAs predicted solely according to secondary structure for tRNAs predicted by tRNAscan SE not ARWEN . Numbers of predicted stop suppressor tRNAs coevolve with predicted overlapping frameshifted protein coding genes including stop codons . Sequence alignments in secondary structure prediction with non chaetognath tRNAs suggest that the most likely functional tRNAs are in intergenic regions as regular mt tRNAs . Due to usually short intergenic regions generally tRNA sequences partially overlap with flanking genes . Some tRNA pairs seem templated by sense antisense strands . Moreover 16S rRNA genes but not 12S rRNAs appear as tRNA nurseries as previously suggested for multifunctional ribosomal like protogenomes . 


Summary: Chaetognath mitogenomes exhibit more than one tRNA like sequence as previously suggested. 16S rRNA genes appear as chaetognath tRNA nurseries. TRNA pairs seem templated by sense antisense strands as previously suggested for multifunctional ribosomal like protogenomes.
Background and purpose Poor device design that fails to adequately account for user needs cognition and behavior is often responsible for use errors resulting in adverse events . This poor device design is also often latent and could be responsible for No Fault Found reporting in which medical devices sent for repair by clinical users are found to be operating as intended . Unresolved NFF reports may contribute to incident under reporting clinical user frustration and biomedical engineering technologist inefficacy . This study uses human factors engineering methods to investigate the relationship between NFF reporting frequency and device usability . Material and methods An analysis of medical equipment maintenance data was conducted to identify devices with a high NFF reporting frequency . Subsequently semi structured interviews and heuristic evaluations were performed in order to identify potential usability issues . Finally usability testing was conducted in order to validate that latent usability related design faults result in a higher frequency of NFF reporting . Results The analysis of medical equipment maintenance data identified six devices with a high NFF reporting frequency . Semi structured interviews heuristic evaluations and usability testing revealed that usability issues caused a significant portion of the NFF reports . Other factors suspected to contribute to increased NFF reporting include accessory issues intermittent faults and environmental issues . Usability testing conducted on three of the devices revealed 23 latent usability related design faults . Conclusions These findings demonstrate that latent usability related design faults manifest themselves as an increase in NFF reporting and that devices containing usability related design faults can be identified through an analysis of medical equipment maintenance data . 


Summary: Human factors methods were used to investigate No Fault Found NFF incidents. Medical equipment maintenance data was used to identify devices with high NFF rates. Interviews and heuristic analyses revealed usability issues cause NFF incidents. Usability testing validated the results. Our methodology can be used to identify devices with usability related design flaws.
Objective Publications are a key data source for investigator profiles and research networking systems . We developed ReCiter an algorithm that automatically extracts bibliographies from PubMed using institutional information about the target investigators . Methods ReCiter executes a broad query against PubMed groups the results into clusters that appear to constitute distinct author identities and selects the cluster that best matches the target investigator . Using information about investigators from one of our institutions we compared ReCiter results to queries based on author name and institution and to citations extracted manually from the Scopus database . Five judges created a gold standard using citations of a random sample of 200 investigators . Results About half of the 10 471 potential investigators had no matching citations in PubMed and about 45 had fewer than 70 citations . Interrater agreement for the gold standard was 0.81 . Scopus achieved the best recall of 0.81 while name based queries had 0.78 and ReCiter had 0.69 . ReCiter attained the best precision of 0.93 while Scopus had 0.85 and name based queries had 0.31 . Discussion ReCiter accesses the most current citation data uses limited computational resources and minimizes manual entry by investigators . Generation of bibliographies using named based queries will not yield high accuracy . Proprietary databases can perform well but requite manual effort . Automated generation with higher recall is possible but requires additional knowledge about investigators . 


Summary: Publications are a key data source for research networking systems. ReCiter extracts bibliographies from PubMed given information about investigators. ReCiter has better precision but worse recall than Scopus a proprietary database. It is challenging to link publications to investigators at a given institution. It is difficult to identify research staff at an institution using publication data.
Objective Electronic medical records data is increasingly incorporated into genome phenome association studies . Investigators hope to share data but there are concerns it may be re identified through the exploitation of various features such as combinations of standardized clinical codes . Formal anonymization algorithms can prevent such violations but prior studies suggest that the size of the population available for anonymization may influence the utility of the resulting data . We systematically investigate this issue using a large scale biorepository and EMR system through which we evaluate the ability of researchers to learn from anonymized data for genome phenome association studies under various conditions . Methods We use a k anonymization strategy to simulate a data protection process for resources of similar size to those found at nine academic medical institutions within the United States . Following the protection process we replicate an existing genome phenome association study and compare the discoveries using the protected data and the original data through the correlation of the p values of association significance . Results Our investigation shows that anonymizing an entire dataset with respect to the population from which it is derived yields significantly more utility than small study specific datasets anonymized unto themselves . When evaluated using the correlation of genome phenome association strengths on anonymized data versus original data all nine simulated sites results from largest scale anonymizations retained better utility to those on smaller sizes . We observed a general trend of increasing for larger data set sizes 0.9481 for small sized datasets 0.9493 for moderately sized datasets 0.9934 for large sized datasets . Conclusions This research implies that regardless of the overall size of an institution s data there may be significant benefits to anonymization of the entire EMR even if the institution is planning on releasing only data about a specific cohort of patients . 


Summary: Anonymization of large scale clinical codes allows for reliable genome phenome analysis. Across various repository sizes full EMR most reliable. Preserves utility for finding genome phenome associations.
Background Gene name recognition and normalization is together with detection of other named entities a crucial step in biomedical text mining and the underlying basis for development of more advanced techniques like extraction of complex events . While the current state of the art solutions achieve highly promising results on average performance can drop significantly for specific genes with highly ambiguous synonyms . Depending on the topic of interest this can cause the need for extensive manual curation of such text mining results . Our goal was to enhance this curation step based on tools widely used in pharmaceutical industry utilizing the text processing and classification capabilities of the Konstanz Information Miner along with publicly available sources . Results F score achieved on gene specific test corpora for highly ambiguous genes could be improved from values close to zero due to very low precision to values 0.9 for several cases . Interestingly the presented approach even resulted in an increased F score for genes showing already good results in initial gene name normalization . For most test cases we could significantly improve precision while retaining a high recall . Conclusions We could show that KNIME can be used to assist in manual curation of text mining results containing high numbers of false positive hits . Our results also indicate that it could be beneficial for future development in the field of gene name normalization to create gene specific training corpora based on incorrectly identified genes common to current state of the art algorithms . 


Summary: False positive removal after gene recognition and normalization. Integration of existing annotations acronym resolution and classification. Based on well established technologies in pharmaceutical industry. Improvement in precision while keeping recall high. We propose to use existing gene taggers to create corpora for common problematic cases.
The ubiquity of Online Social Networks is creating new sources for healthcare information particularly in the context of pharmaceutical drugs . We aimed to examine the impact of a given OSN s characteristics on the content of pharmaceutical drug discussions from that OSN . We compared the effect of four distinguishing characteristics from ten different OSNs on the content of their pharmaceutical drug discussions General versus Health OSN OSN moderation OSN registration requirements and OSNs with a question and answer format . The effects of these characteristics were measured both quantitatively and qualitatively . Our results show that an OSN s characteristics indeed affect the content of its discussions . Based on their information needs healthcare providers may use our findings to pick the right OSNs or to advise patients regarding their needs . Our results may also guide the creation of new and more effective domain specific health OSNs . Further future researchers of online healthcare content in OSNs may find our results informative while choosing OSNs as data sources . We reported several findings about the impact of OSN characteristics on the content of pharmaceutical drug discussion and synthesized these findings into actionable items for both healthcare providers and future researchers of healthcare discussions on OSNs . Future research on the impact of OSN characteristics could include user demographics quality and safety of information and efficacy of OSN usage . 


Summary: Psychotherapeutic drugs dominate health OSNs especially non moderated health OSNs. Genitourinary tract agents and nutritional drugs chatter dominates general OSNs. Respiratory and hormonal drugs appear more often in OSNs that require registration. OSNs with a question and answer format are less subjective. Users ask more questions about gastrointestinal and metabolic drugs.
The workflow models of the patient journey in a Pediatric Emergency Department seems to be an effective approach to develop an accurate and complete representation of the PED processes . This model can drive the collection of comprehensive quantitative and qualitative service delivery and patient treatment data as an evidence base for the PED service planning . Our objective in this study is to identify crowded situation indicators and bottlenecks that contribute to over crowding . The greatest source of delay in patient flow is the waiting time from the health care request and especially the bed request to exit from the PED for hospital admission . It represented 70 of the time that these patients occupied in the PED waiting rooms . The use of real data to construct the workflow model of the patient path is effective in identifying sources of delay in patient flow and aspects of the PED activity that could be improved . The development of this model was based on accurate visits made in the PED of the Regional University Hospital Center of Lille . This modeling which has to represent most faithfully possible the reality of the PED of CHRU of Lille is necessary . It must be detailed enough to produce an analysis allowing to identify the dysfunctions of the PED and also to propose and to estimate prevention indicators of crowded situations . Our survey is integrated into the French National Research Agency project titled Hospital Optimization Simulation and avoidance of strain . H pital Optimisation Simulation et vitement des Tensions . 


Summary: Proposal a model workflow to identify crowding indicators and bottlenecks. Approach based on the optimization of the patient path in the PED. The use of real data to construct the workflow model of the patient path. Build BPMN models to represent the patient journey through the PED.
Risk stratification is instrumental to modern clinical decision support systems . Comprehensive risk stratification should be able to provide the clinicians with not only the accurate assessment of a patient s risk but also the clinical context to be acted upon . However existing risk stratification techniques mainly focus on predicting the risk score for individual patients at the cohort level they offer little insight beyond a flat score based segmentation . This essentially reduces a patient to a score and thus removes him her from his her clinical context . To address this limitation in this paper we propose a bilinear model for risk stratification that simultaneously captures the three key aspects of risk stratification it predicts the risk of each individual patient it stratifies the patient cohort based on not only the risk score but also the clinical characteristics and it embeds all patients into clinical contexts with clear interpretation . We apply our model to a cohort of 4977 patients 1127 among which were diagnosed with Congestive Heart Failure . We demonstrate that our model can not only accurately predict the onset risk of CHF but also provide rich and actionable clinical insights into the patient cohort . 


Summary: Bilinear risk prediction model for EHR data. Low dimensional embedding of patients in a learned risk space. Identification of meaningful clinical contexts. Significant improvement in accuracy over existing risk prediction model.
The focus of this paper is on the challenges and opportunities presented by developing scenarios of use for interactive medical devices . Scenarios are integral to the international standard for usability engineering of medical devices and are also applied to the development of health software . The 62366 standard lays out a process for mitigating risk during normal use . However this begs the question of whether real use matches normal use . In this paper we present an overview of the product lifecycle and how it impacts on the type of scenario that can be practically applied . We report on the development and testing of a set of scenarios intended to inform the design of infusion pumps based on real use . The scenarios were validated by researchers and practitioners experienced in clinical practice and their utility was assessed by developers and practitioners representing different stages of the product lifecycle . These evaluations highlighted previously unreported challenges and opportunities for the use of scenarios in this context . Challenges include integrating scenario based design with usability engineering practice covering the breadth of uses of infusion devices and managing contradictory evidence . Opportunities included scenario use beyond design to guide marketing to inform purchasing and as resources for training staff . This study exemplifies one empirically grounded approach to communicating and negotiating the realities of practice . 


Summary: Scenarios are day in the life descriptions designed to represent user needs. Their use for medical device certification differs from user centred design practice. We investigated this discrepancy and provide recommendations for their application. Scenarios elicit variations in use and difference between actual and intended practice.
The task of recognizing and normalizing protein name mentions in biomedical literature is a challenging task and important for text mining applications such as protein protein interactions pathway reconstruction and many more . In this paper we present ProNormz an integrated approach for human proteins tagging and normalization . In Homo sapiens a greater number of biological processes are regulated by a large human gene family called protein kinases by post translational phosphorylation . Recognition and normalization of human protein kinases is considered to be important for the extraction of the underlying information on its regulatory mechanism from biomedical literature . ProNormz distinguishes HPKs from other HPs besides tagging and normalization . To our knowledge ProNormz is the first normalization system available to distinguish HPKs from other HPs in addition to gene normalization task . ProNormz incorporates a specialized synonyms dictionary for human proteins and protein kinases a set of 15 string matching rules and a disambiguation module to achieve the normalization . Experimental results on benchmark BioCreative II training and test datasets show that our integrated approach achieve a fairly good performance and outperforms more sophisticated semantic similarity and disambiguation systems presented in BioCreative II GN task . As a freely available web tool ProNormz is useful to developers as extensible gene normalization implementation to researchers as a standard for comparing their innovative techniques and to biologists for normalization and categorization of HPs and HPKs mentions in biomedical literature . URL http www.biominingbu.org pronormz . 


Summary: We present ProNormz as a freely available web based tool for human protein normalization. Besides normalization ProNormz distinguishes human protein kinases from other proteins. ProNormz is the first system to distinguish human protein kinases from other proteins. The methodology can be useful to developers for extensible gene normalization implementation.