Predicting Panel Ratings for Semantic Characteristics of Lung Nodules

In reading CT scans with potentially malignant lung nodules, radiologists make use of high level information (semantic characteristics) in their analysis. CAD systems can assist radiologists by offering a “second opinion” - predicting these semantic characteristics for lung nodules. In our previous work, we developed such a CAD system, training and testing it on the publicly available Lung Image Database Consortium (LIDC) dataset, which includes semantic annotations by up to four human radiologists for every nodule. However, due to the lack of ground truth and the uncertainty in the dataset, each nodule was viewed as four distinct instances when training the classifier. In this work, we propose a way of predicting the distribution of opinions of the four radiologists using a multiple-label classification algorithm based on belief decision trees. We evaluate our results using a distance-threshold curve and, measuring the area under this curve, obtain 69% accuracy on the testing subset. We conclude that multiple-label classification algorithms are an appropriate method of representing the diagnoses of multiple radiologists on lung CT scans when a single ground truth is not available

Transforming the invisible into the visible: disparities in the access to health in LGBT+ older people

Objectives: To compare variables of access to healthcare between the LGBT+ population aged 50 and over and those non-LGBT+. Methods: A cross-sectional study was carried out in Brazil through a confidential online questionnaire. The use of the health system was characterized by the number of preventive tests performed and measured by the PCATool-Brasil scale (a 10-point scale in which higher scores were associated with better assistance in healthcare). The association between being LGBT+ and access to health was analyzed in Poisson regression models. Results: 6693 participants (1332 LGBT+ and 5361 non-LGBT+) with a median age of 60 years were included. In the univariate analysis, it was observed not only lower scores on the PCATool scale (5.13 against 5.82, p < 0.001), but a greater proportion of individuals among those classified with the worst quintile of access to healthcare (< 4 points), 31% against 18% (p < 0.001). Being LGBT+ was an independent factor associated with worse access to health (PR = 2.5, 95% CI 2.04‒3.06). The rate of screening cancer, for breast, colon, and cervical cancer was also found to be lower in the LGBT+ population. Conclusion: Healthcare access and health service experiences were worse in the LGBT+ group than in their non-LGBT peers. Inclusive and effective healthcare public policies are essential to promote healthy aging for all

A Run-Length Encoding Approach for Path Analysis of C. elegans

The nematode Caenorhabditis elegans explores the environment using a combination of different movement patterns, which include straight movement, reversal, and turns. We propose to quantify C. elegans movement behavior using a computer vision approach based on run-length encoding of step-length data. In this approach, the path of C. elegans is encoded as a string of characters, where each character represents a path segment of a specific type of movement. With these encoded string data, we perform k-means cluster analysis to distinguish movement behaviors resulting from different genotypes and food availability. We found that shallow and sharp turns are the most critical factors in distinguishing the differences among the movement behaviors. To validate our approach, we examined the movement behavior of tph-1 mutants that lack an enzyme responsible for serotonin biosynthesis. A k-means cluster analysis with the path string-encoded data showed that tph-1 movement behavior on food is similar to that of wild-type animals off food. We suggest that this run-length encoding approach is applicable to trajectory data in animal or human mobility data

Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG)

We present a comprehensive benchmark dataset for Knowledge Graph Question Answering in Materials Science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs). A knowledge graph for metal-organic frameworks (MOF-KG) has been constructed by integrating structured databases and knowledge extracted from the literature. To enhance MOF-KG accessibility for domain experts, we aim to develop a natural language interface for querying the knowledge graph. We have developed a benchmark comprised of 161 complex questions involving comparison, aggregation, and complicated graph structures. Each question is rephrased in three additional variations, resulting in 644 questions and 161 KG queries. To evaluate the benchmark, we have developed a systematic approach for utilizing ChatGPT to translate natural language questions into formal KG queries. We also apply the approach to the well-known QALD-9 dataset, demonstrating ChatGPT's potential in addressing KGQA issues for different platforms and query languages. The benchmark and the proposed approach aim to stimulate further research and development of user-friendly and efficient interfaces for querying domain-specific materials science knowledge graphs, thereby accelerating the discovery of novel materials.Comment: In 17th International Conference on Metadata and Semantics Research, October 202

Presynaptic BK channel localization is dependent on the hierarchical organization of alpha-catulin and dystrobrevin and fine-tuned by CaV2 calcium channels

BACKGROUND: Large conductance, calcium-activated BK channels regulate many important physiological processes, including smooth muscle excitation, hormone release and synaptic transmission. The biological roles of these channels hinge on their unique ability to respond synergistically to both voltage and cytosolic calcium elevations. Because calcium influx is meticulously regulated both spatially and temporally, the localization of BK channels near calcium channels is critical for their proper function. However, the mechanism underlying BK channel localization near calcium channels is not fully understood. RESULTS: We show here that in C. elegans the localization of SLO-1/BK channels to presynaptic terminals, where UNC-2/CaV2 calcium channels regulate neurotransmitter release, is controlled by the hierarchical organization of CTN-1/alpha-catulin and DYB-1/dystrobrevin, two proteins that interact with cortical cytoskeletal proteins. CTN-1 organizes a macromolecular SLO-1 channel complex at presynaptic terminals by direct physical interaction. DYB-1 contributes to the maintenance or stabilization of the complex at presynaptic terminals by interacting with CTN-1. We also show that SLO-1 channels are functionally coupled with UNC-2 calcium channels, and that normal localization of SLO-1 to presynaptic terminals requires UNC-2. In the absence of UNC-2, SLO-1 clusters lose the localization specificity, thus accumulating inside and outside of presynaptic terminals. Moreover, CTN-1 is also similarly localized in unc-2 mutants, consistent with the direct interaction between CTN-1 and SLO-1. However, localization of UNC-2 at the presynaptic terminals is not dependent on either CTN-1 or SLO-1. Taken together, our data strongly suggest that the absence of UNC-2 indirectly influences SLO-1 localization via the reorganization of cytoskeletal proteins. CONCLUSION: CTN-1 and DYB-1, which interact with cortical cytoskeletal proteins, are required for the presynaptic punctate localization of SLO-1 in a hierarchical manner. In addition, UNC-2 calcium channels indirectly control the fidelity of SLO-1 puncta localization at presynaptic terminals. We suggest that the absence of UNC-2 leads to the reorganization of the cytoskeletal structure that includes CTN-1, which in turn influences SLO-1 puncta localization

Metadata for Scientific Experiment Reporting: A Case Study in Metal-Organic Frameworks

Research methods and procedures are core aspects of the research process. Metadata focused on these components is critical to supporting the FAIR principles, particularly reproducibility. The research reported on in this paper presents a methodological framework for metadata documentation supporting the reproducibility of research producing Metal Organic Frameworks (MOFs). The MOF case study involved natural language processing to extract key synthesis experiment information from a corpus of research literature. Following, a classification activity was performed by domain experts to identify entity-relation pairs. Results include: 1) a research framework for metadata design, 2) a metadata schema that includes nine entities and two relationships for reporting MOF synthesis experiments, and 3) a growing database of MOF synthesis reports structured by our metadata scheme. The metadata schema is intended to support discovery and reproducibility of metal-organic framework research and the FAIR principles. The paper provides background information, identifies the research goals and objectives, research design, results, a discussion, and the conclusion.Comment: Accepted by the 17th International Conference on Metadata and Semantics Researc