Characterizing microglia activation: a spatial statistics approach to maximize information extraction.
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ABSTRACT:
SUBMITTER: Davis BM
PROVIDER: S-EPMC5431479 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Maxwell's daemon is a popular personification of a principle connecting information gain and extractable work in thermodynamics. A Szilard Engine is a particular hypothetical realization of Maxwell's daemon, which is able to extract work from a single thermal reservoir by measuring the position of particle(s) within the system. Here we investigate the role of particle statistics in the whole process; namely, how the extractable work changes if instead of classical particles fermions or bosons are used as the working medium. We give a unifying argument for the optimal work in the different cases: the extractable work is determined solely by the information gain of the initial measurement, as measured by the mutual information, regardless of the number and type of particles which constitute the working substance.
| S-EPMC4227042 | biostudies-other
Project description:BACKGROUND:Gender information frequently exists in the eligibility criteria of clinical trial text as essential information for participant population recruitment. Particularly, current eligibility criteria text contains the incompleteness and ambiguity issues in expressing transgender population, leading to difficulties or even failure of transgender population recruitment in clinical trial studies. METHODS:A new gender model is proposed for providing comprehensive transgender requirement specification. In addition, an automated approach is developed to extract and summarize gender requirements from unstructured text in accordance with the gender model. This approach consists of: 1) the feature extraction module, and 2) the feature summarization module. The first module identifies and extracts gender features using heuristic rules and automatically-generated patterns. The second module summarizes gender requirements by relation inference. RESULTS:Based on 100,134 clinical trials from ClinicalTrials.gov , our approach was compared with 20 commonly applied machine learning methods. It achieved a macro-averaged precision of 0.885, a macro-averaged recall of 0.871 and a macro-averaged F1-measure of 0.878. The results illustrated that our approach outperformed all baseline methods in terms of both commonly used metrics and macro-averaged metrics. CONCLUSIONS:This study presented a new gender model aiming for specifying the transgender requirement more precisely. We also proposed an approach for gender information extraction and summarization from unstructured clinical text to enhance transgender-related clinical trial population recruitment. The experiment results demonstrated that the approach was effective in transgender criteria extraction and summarization.
| S-EPMC6454593 | biostudies-literature
Project description:BackgroundStructural cerebral asymmetries are hypothesized to provide an architectural foundation for functional asymmetries and behavioral lateralities. Studies of structural asymmetries typically focus on gray matter measures that are influenced by gross deformation fields used for normalization, and thus characterize a combination of different morphologic influences on structural asymmetries.New methodA deformation-based morphometry approach was developed to characterize structural asymmetries at different spatial scales of resolution, which can provide relatively more specific inference about the morphologic reason(s) for structural asymmetries, using a dataset of 347 typically developing children (7.00-12.92 years).ResultsSignificant structural asymmetries were observed for a larger lobar spatial scale (e.g., frontal petalia) and for a smaller gyral/sulcal spatial scale of resolution (e.g., marginal sulcus). Total intracranial volume was significantly associated with asymmetries at the larger spatial scale of normalization, while age was significantly associated with asymmetries at the smaller scale of normalization. There were no significant anti- or fluctuating asymmetry effects based on Hartigan Dip Tests and Bonnett Tests, respectively.Comparison with existing method(s)While spatially similar asymmetries were observed in both gray matter and deformation field data (e.g., medial planum temporale/Heschl's gyrus), the deformation approach characterizes asymmetries based on three iterations of successively smaller scales of normalization.ConclusionsStructural asymmetries can be identified in normalization deformations with a procedure that is tailored for sensitivity to structures at different spatial scales of resolution where there may be different mechanisms for the expression of asymmetry.
| S-EPMC6546427 | biostudies-literature
Project description:BackgroundLung cancer is the leading cause of cancer deaths worldwide. Clinical staging of lung cancer plays a crucial role in making treatment decisions and evaluating prognosis. However, in clinical practice, approximately one-half of the clinical stages of lung cancer patients are inconsistent with their pathological stages. As one of the most important diagnostic modalities for staging, chest computed tomography (CT) provides a wealth of information about cancer staging, but the free-text nature of the CT reports obstructs their computerization.ObjectiveWe aimed to automatically extract the staging-related information from CT reports to support accurate clinical staging of lung cancer.MethodsIn this study, we developed an information extraction (IE) system to extract the staging-related information from CT reports. The system consisted of the following three parts: named entity recognition (NER), relation classification (RC), and postprocessing (PP). We first summarized 22 questions about lung cancer staging based on the TNM staging guideline. Next, three state-of-the-art NER algorithms were implemented to recognize the entities of interest. Next, we designed a novel RC method using the relation sign constraint (RSC) to classify the relations between entities. Finally, a rule-based PP module was established to obtain the formatted answers using the results of NER and RC.ResultsWe evaluated the developed IE system on a clinical data set containing 392 chest CT reports collected from the Department of Thoracic Surgery II in the Peking University Cancer Hospital. The experimental results showed that the bidirectional encoder representation from transformers (BERT) model outperformed the iterated dilated convolutional neural networks-conditional random field (ID-CNN-CRF) and bidirectional long short-term memory networks-conditional random field (Bi-LSTM-CRF) for NER tasks with macro-F1 scores of 80.97% and 90.06% under the exact and inexact matching schemes, respectively. For the RC task, the proposed RSC showed better performance than the baseline methods. Further, the BERT-RSC model achieved the best performance with a macro-F1 score of 97.13% and a micro-F1 score of 98.37%. Moreover, the rule-based PP module could correctly obtain the formatted results using the extractions of NER and RC, achieving a macro-F1 score of 94.57% and a micro-F1 score of 96.74% for all the 22 questions.ConclusionsWe conclude that the developed IE system can effectively and accurately extract information about lung cancer staging from CT reports. Experimental results show that the extracted results have significant potential for further use in stage verification and prediction to facilitate accurate clinical staging.
| S-EPMC8339987 | biostudies-literature
Project description:A lot of high quality data on the biological activity of chemical compounds are required throughout the whole drug discovery process: from development of computational models of the structure-activity relationship to experimental testing of lead compounds and their validation in clinics. Currently, a large amount of such data is available from databases, scientific publications, and patents. Biological data are characterized by incompleteness, uncertainty, and low reproducibility. Despite the existence of free and commercially available databases of biological activities of compounds, they usually lack unambiguous information about peculiarities of biological assays. On the other hand, scientific papers are the primary source of new data disclosed to the scientific community for the first time. In this study, we have developed and validated a data-mining approach for extraction of text fragments containing description of bioassays. We have used this approach to evaluate compounds and their biological activity reported in scientific publications. We have found that categorization of papers into relevant and irrelevant may be performed based on the machine-learning analysis of the abstracts. Text fragments extracted from the full texts of publications allow their further partitioning into several classes according to the peculiarities of bioassays. We demonstrate the applicability of our approach to the comparison of the endpoint values of biological activity and cytotoxicity of reference compounds.
| S-EPMC8194363 | biostudies-literature
Project description:BackgroundThe rate of community-acquired Clostridium difficile infection (CA-CDI) is increasing. While receipt of antibiotics remains an important risk factor for CDI, studies related to acquisition of C. difficile outside of hospitals are lacking. As a result, risk factors for exposure to C. difficile in community settings have been inadequately studied.Main objectiveTo identify novel environmental risk factors for CA-CDI.MethodsWe performed a population-based retrospective cohort study of patients with CA-CDI from 1/1/2007 through 12/31/2014 in a 10-county area in central North Carolina. 360 Census Tracts in these 10 counties were used as the demographic Geographic Information System (GIS) base-map. Longitude and latitude (X, Y) coordinates were generated from patient home addresses and overlaid to Census Tracts polygons using ArcGIS; ArcView was used to assess "hot-spots" or clusters of CA-CDI. We then constructed a mixed hierarchical model to identify environmental variables independently associated with increased rates of CA-CDI.ResultsA total of 1,895 unique patients met our criteria for CA-CDI. The mean patient age was 54.5 years; 62% were female and 70% were Caucasian. 402 (21%) patient addresses were located in "hot spots" or clusters of CA-CDI (p<0.001). "Hot spot" census tracts were scattered throughout the 10 counties. After adjusting for clustering and population density, age ? 60 years (p = 0.03), race (<0.001), proximity to a livestock farm (0.01), proximity to farming raw materials services (0.02), and proximity to a nursing home (0.04) were independently associated with increased rates of CA-CDI.ConclusionsOur study is the first to use spatial statistics and mixed models to identify important environmental risk factors for acquisition of C. difficile and adds to the growing evidence that farm practices may put patients at risk for important drug-resistant infections.
| S-EPMC5433765 | biostudies-literature
Project description:BackgroundThe emergence and spread of antimicrobial resistance and infectious agents have challenged hospitals in recent decades. Our aim was to investigate the circulation of target infectious agents using Geographic Information System (GIS) and spatial-temporal statistics to improve surveillance and control of healthcare-associated infection and of antimicrobial resistance (AMR), using Klebsiella pneumoniae complex as a model.MethodsA retrospective study carried out in a 450-bed federal, tertiary hospital, located in Rio de Janeiro. All isolates of K. pneumoniae complex from clinical and surveillance cultures of hospitalized patients between 2014 and 2016, identified by the use of Vitek-2 system (BioMérieux), were extracted from the hospital's microbiology laboratory database. A basic scaled map of the hospital's physical structure was created in AutoCAD and converted to QGis software (version 2.18). Thereafter, bacteria according to resistance profiles and patients with carbapenem-resistant K. pneumoniae (CRKp) complex were georeferenced by intensive and nonintensive care wards. Space-time permutation probability scan tests were used for cluster signals detection.ResultsOf the total 759 studied isolates, a significant increase in the resistance profile of K. pneumoniae complex was detected during the studied years. We also identified two space-time clusters affecting adult and paediatric patients harbouring CRKp complex on different floors, unnoticed by regular antimicrobial resistance surveillance.ConclusionsIn-hospital GIS with space-time statistical analysis can be applied in hospitals. This spatial methodology has the potential to expand and facilitate early detection of hospital outbreaks and may become a new tool in combating AMR or hospital-acquired infection.
| S-EPMC8207788 | biostudies-literature
Project description:Given the intensity and frequency of environmental change, the linked and cross-scale nature of social-ecological systems, and the proliferation of big data, methods that can help synthesize complex system behavior over a geographical area are of great value. Fisher information evaluates order in data and has been established as a robust and effective tool for capturing changes in system dynamics, including the detection of regimes and regime shifts. Methods developed to compute Fisher information can accommodate multivariate data of various types and requires no a priori decisions about system drivers, making it a unique and powerful tool. However, the approach has primarily been used to evaluate temporal patterns. In its sole application to spatial data, Fisher information successfully detected regimes in terrestrial and aquatic systems over transects. Although the selection of adjacently positioned sampling stations provided a natural means of ordering the data, such an approach limits the types of questions that can be answered in a spatial context. Here, we expand the approach to develop a method for more fully capturing spatial dynamics. Results reflect changes in the index that correspond with geographical patterns and demonstrate the utility of the method in uncovering hidden spatial trends in complex systems.
| S-EPMC6688651 | biostudies-literature
Project description:The vast compositional space of metallic materials provides ample opportunity to design stronger, more ductile and cheaper alloys. However, the substantial complexity of deformation micro-mechanisms makes simulation-based prediction of microstructural performance exceedingly difficult. In absence of predictive tools, tedious experiments have to be conducted to screen properties. Here, we develop a purely empirical model to forecast microstructural performance in advance, bypassing these challenges. This is achieved by combining in situ deformation experiments with a novel methodology that utilizes n-point statistics and principle component analysis to extract key microstructural features. We demonstrate this approach by predicting crack nucleation in a complex dual-phase steel, achieving substantial predictive ability (84.8% of microstructures predicted to crack, actually crack), a substantial improvement upon the alternate simulation-based approaches. This significant accuracy illustrates the utility of this alternate approach and opens the door to a wide range of alloy design tools.
| S-EPMC6391476 | biostudies-literature
Project description:Human perception, cognition, and action are laced with seemingly arbitrary mappings. In particular, sound has a strong spatial connotation: Sounds are high and low, melodies rise and fall, and pitch systematically biases perceived sound elevation. The origins of such mappings are unknown. Are they the result of physiological constraints, do they reflect natural environmental statistics, or are they truly arbitrary? We recorded natural sounds from the environment, analyzed the elevation-dependent filtering of the outer ear, and measured frequency-dependent biases in human sound localization. We find that auditory scene statistics reveals a clear mapping between frequency and elevation. Perhaps more interestingly, this natural statistical mapping is tightly mirrored in both ear-filtering properties and in perceived sound location. This suggests that both sound localization behavior and ear anatomy are fine-tuned to the statistics of natural auditory scenes, likely providing the basis for the spatial connotation of human hearing.
| S-EPMC4000839 | biostudies-other