Mining Relational Paths in Integrated Biomedical Data

Much life science and biology research requires an understanding of complex relationships between biological entities (genes, compounds, pathways, diseases, and so on). There is a wealth of data on such relationships in publicly available datasets and publications, but these sources are overlapped and distributed so that finding pertinent relational data is increasingly difficult. Whilst most public datasets have associated tools for searching, there is a lack of searching methods that can cross data sources and that in particular search not only based on the biological entities themselves but also on the relationships between them. In this paper, we demonstrate how graph-theoretic algorithms for mining relational paths can be used together with a previous integrative data resource we developed called Chem2Bio2RDF to extract new biological insights about the relationships between such entities. In particular, we use these methods to investigate the genetic basis of side-effects of thiazolinedione drugs, and in particular make a hypothesis for the recently discovered cardiac side-effects of Rosiglitazone (Avandia) and a prediction for Pioglitazone which is backed up by recent clinical studies

Correlation analysis between 18F-fluorodeoxyglucose positron emission tomography and cognitive function in first diagnosed Parkinson’s disease patients

ObjectiveEvaluation of the correlation between 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG PET) and cognitive function in first-diagnosed and untreated Parkinson’s disease (PD) patients.Materials and methodThis cross-sectional study included 84 first diagnosed and untreated PD patients. The individuals were diagnosed by movement disorder experts based on the 2015 MDS Parkinson’s disease diagnostic criteria. The patients also underwent 18F-FDG PET scans and clinical feature assessments including the Montreal Cognitive Assessment (MoCA) scale. Glucose metabolism rates were measured in 26 brain regions using region of interest (ROI) and pixel-wise analyses with displayed Z scores. The cognitive function was assessed by professionals using the MoCA scale, which covers five cognitive domains. Spearman’s linear correlation and linear regression models were used to compare the correlations between 18F-FDG metabolism in each brain region and cognitive domain, using SPSS 25.0 software.ResultThe results indicated a positive correlation between executive function and glucose metabolism in the lateral prefrontal cortex of the left hemisphere (p = 0.041). Additionally, a positive correlation between memory function and glucose metabolism in the right precuneus (p = 0.014), right lateral occipital cortex (p = 0.017), left lateral occipital cortex (p = 0.031), left primary visual cortex (p = 0.008), and right medial temporal cortex (p = 0.046). Further regression analysis showed that for every one-point decrease in the memory score, the glucose metabolism in the right precuneus would decrease by 0.3 (B = 0.30, p = 0.005), the glucose metabolism in the left primary visual cortex would decrease by 0.25 (B = 0.25, p = 0.040), the glucose metabolism in the right lateral occipital cortex would decrease by 0.38 (B = 0.38, p = 0.012), and the glucose metabolism in the left lateral occipital cortex would decrease by 0.32 (B = 0.32, p = 0.045).ConclusionThis study indicated that cognitive impairment in PD patients mainly manifests as changes in executive function, visual-spatial function and memory functions, while glucose metabolism mainly decreases in the frontal and posterior cortex. Further analysis shows that executive function is related to glucose metabolism in the left lateral prefrontal cortex. On the other hand, memory ability involves changes in glucose metabolism in a more extensive brain region. This suggests that cognitive function assessment can indirectly reflect the level of glucose metabolism in the relevant brain regions

Characteristics and influencing factors of 11C-CFT PET imaging in patients with early and late onset Parkinson’s disease

ObjectiveThis study aims to explore the difference between 11C-methyl-N-2β-carbomethoxy-3β-(4-fluorophenyl)-tropanel (11C-CFT) positron emission tomography (PET) imaging in the early-onset Parkinson’s disease (EOPD) and late-onset Parkinson’s disease (LOPD), and to analyze the correlation between 11C-CFT PET imaging and disease duration, Hoehn & Yahr (H&Y) stage, motor symptoms, and non-motor symptoms in patients with idiopathic Parkinson’s disease (PD), so as to explore its application value in assessing the severity of Parkinson’s disease.Materials and methodsA total of 113 patients with idiopathic PD were included in this study. The patients were divided into EOPD and LOPD groups according to the age of 60 years, of which 58 were early-onset and 55 were late-onset. All patients underwent 11C-CFT PET imaging and manually sketched regions of interest (ROI) to delineate the caudate nucleus, anterior putamen, and posterior putamen ROI layer-by-layer, and the corresponding values were recorded. Clinical data [age of onset, disease duration, H&Y stage, total Unified Parkinson’s Disease Rating Scale (UPDRS) score, UPDRS III score, tremor score, postural instability/gait difficulty (PIGD) score, rigidity score, bradykinesia score, and Montreal Cognitive Assessment (MoCA) score] were collected from all patients. The differences in striatal 11C-CFT uptake between patients with EOPD and LOPD were compared, and the correlation between striatal 11C-CFT uptake and the clinical data of patients with idiopathic PD was evaluated.ResultsThe caudate nucleus 11C-CFT uptake was higher in EOPD than in the LOPD group (t = 3.002, p = 0.003). 11C-CFT uptake in the caudate nucleus in patients with PD was negatively correlated with the age of onset, H&Y stage, disease duration, total UPDRS score, UPDRS III score, rigidity score, and bradykinesia score (p < 0.05). The anterior and posterior putamen 11C-CFT uptake was negatively correlated with H&Y stage, disease duration, total UPDRS score, UPDRS III score, PIGD score, rigidity score, and bradykinesia score (p < 0.05).Conclusion11C-CFT PET provides an objective molecular imaging basis for the difference in disease progression rates between patients with EOPD and LOPD. Secondly, 11C-CFT PET can be used as an important objective indicator to assess disease severity and monitor disease progression

Stability of SARS-CoV-2 in cold-chain transportation environments and the efficacy of disinfection measures

BackgroundLow temperature is conducive to the survival of COVID-19. Some studies suggest that cold-chain environment may prolong the survival of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and increase the risk of transmission. However, the effect of cold-chain environmental factors and packaging materials on SARS-CoV-2 stability remains unclear.MethodsThis study aimed to reveal cold-chain environmental factors that preserve the stability of SARS-CoV-2 and further explore effective disinfection measures for SARS-CoV-2 in the cold-chain environment. The decay rate of SARS-CoV-2 pseudovirus in the cold-chain environment, on various types of packaging material surfaces, i.e., polyethylene plastic, stainless steel, Teflon and cardboard, and in frozen seawater was investigated. The influence of visible light (wavelength 450 nm-780 nm) and airflow on the stability of SARS-CoV-2 pseudovirus at -18°C was subsequently assessed.ResultsExperimental data show that SARS-CoV-2 pseudovirus decayed more rapidly on porous cardboard surfaces than on nonporous surfaces, including polyethylene (PE) plastic, stainless steel, and Teflon. Compared with that at 25°C, the decay rate of SARS-CoV-2 pseudovirus was significantly lower at low temperatures. Seawater preserved viral stability both at -18°C and with repeated freeze−thaw cycles compared with that in deionized water. Visible light from light-emitting diode (LED) illumination and airflow at -18°C reduced SARS-CoV-2 pseudovirus stability.ConclusionOur studies indicate that temperature and seawater in the cold chain are risk factors for SARS-CoV-2 transmission, and LED visible light irradiation and increased airflow may be used as disinfection measures for SARS-CoV-2 in the cold-chain environment

Semantic inference using chemogenomics data for drug discovery

<p>Abstract</p> <p>Background</p> <p>Semantic Web Technology (SWT) makes it possible to integrate and search the large volume of life science datasets in the public domain, as demonstrated by well-known linked data projects such as LODD, Bio2RDF, and Chem2Bio2RDF. Integration of these sets creates large networks of information. We have previously described a tool called WENDI for aggregating information pertaining to new chemical compounds, effectively creating evidence paths relating the compounds to genes, diseases and so on. In this paper we examine the utility of automatically inferring new compound-disease associations (and thus new links in the network) based on semantically marked-up versions of these evidence paths, rule-sets and inference engines.</p> <p>Results</p> <p>Through the implementation of a semantic inference algorithm, rule set, Semantic Web methods (RDF, OWL and SPARQL) and new interfaces, we have created a new tool called Chemogenomic Explorer that uses networks of ontologically annotated RDF statements along with deductive reasoning tools to infer new associations between the query structure and genes and diseases from WENDI results. The tool then permits interactive clustering and filtering of these evidence paths.</p> <p>Conclusions</p> <p>We present a new aggregate approach to inferring links between chemical compounds and diseases using semantic inference. This approach allows multiple evidence paths between compounds and diseases to be identified using a rule-set and semantically annotated data, and for these evidence paths to be clustered to show overall evidence linking the compound to a disease. We believe this is a powerful approach, because it allows compound-disease relationships to be ranked by the amount of evidence supporting them.</p

Toward Never-Ending Object Learning for Robots

Thesis (Ph.D.)--University of Washington, 2016-06A household robot usually works in a complex working environment, where it will continuously see new objects and encounter new concepts in its lifetime. Therefore, being able to learn more objects is crucial for the robot to be continuously useful over its lifespan. Moving beyond previous object learning research problem, of which mostly focuses on learning with given training objects and concepts, this research addresses the problem of enabling a robot to learn new objects and concepts continuously. Specifically, our contributions are as follows: First, we study how to accurately identify target objects in scenes based on human users' language descriptions. We propose a novel identification system using an object's visual attributes and names to recognize objects. We also propose a method to enable the system to recognize objects based on new names without seeing any training instances of the names. The \textit{attribute-based identification system} improves both usability and accuracy over the previous ID-based object identification methods. Next, we consider the problem of organizing a large number of concepts into a semantic hierarchy. We propose a principle approach for creating semantic hierarchies of concepts via crowdsourcing. The approach can build hierarchies for various tasks and capture the uncertainty that naturally exists in these hierarchies. Experiments demonstrate that our method is more efficient, scalable, and accurate than previous methods. We also design a crowdsourcing evaluation to compare the hierarchies built by our method to expertly built ones. Results of the evaluation demonstrate that our approach outputs task-dependent hierarchies that can significantly improve user's performance of desired tasks. Finally, we build the first never-ending object learning framework, NEOL, that lets robots learn objects continuously. \neol\ automatically learns to organize object names into a semantic hierarchy using the crowdsourcing method we propose. It then uses the hierarchy to improve the consistency and efficiency of annotating objects. Further, it adapts information from additional image datasets to learn object classifiers from a very small number of training examples. Experiments show that NEOL significantly improves robots' accuracy and efficiency in learning objects over previous methods

Attribute Based Object Identification

Abstract — Over the last years, the robotics community has made substantial progress in detection and 3D pose estimation of known and unknown objects. However, the question of how to identify objects based on language descriptions has not been investigated in detail. While the computer vision community recently started to investigate the use of attributes for object recognition, these approaches do not consider the task settings typically observed in robotics, where a combination of appearance attributes and object names might be used in referral language to identify specific objects in a scene. In this paper, we introduce an approach for identifying objects based on natural language containing appearance and name attributes. To learn rich RGB-D features needed for attribute classification, we extend recently introduced sparse coding techniques so as to automatically learn attribute-dependent features. We introduce a large data set of attribute descriptions of objects in the RGB-D object dataset. Experiments on this data set demonstrate the strong performance of our approach to language based object identification. We also show that our attribute-dependent features provide significantly better generalization to previously unseen attribute values, thereby enabling more rapid learning of new attribute values. I