55 research outputs found
Remote Inference of Cognitive Scores in ALS Patients Using a Picture Description
Amyotrophic lateral sclerosis is a fatal disease that not only affects
movement, speech, and breath but also cognition. Recent studies have focused on
the use of language analysis techniques to detect ALS and infer scales for
monitoring functional progression. In this paper, we focused on another
important aspect, cognitive impairment, which affects 35-50% of the ALS
population. In an effort to reach the ALS population, which frequently exhibits
mobility limitations, we implemented the digital version of the Edinburgh
Cognitive and Behavioral ALS Screen (ECAS) test for the first time. This test
which is designed to measure cognitive impairment was remotely performed by 56
participants from the EverythingALS Speech Study. As part of the study,
participants (ALS and non-ALS) were asked to describe weekly one picture from a
pool of many pictures with complex scenes displayed on their computer at home.
We analyze the descriptions performed within +/- 60 days from the day the ECAS
test was administered and extract different types of linguistic and acoustic
features. We input those features into linear regression models to infer 5 ECAS
sub-scores and the total score. Speech samples from the picture description are
reliable enough to predict the ECAS subs-scores, achieving statistically
significant Spearman correlation values between 0.32 and 0.51 for the model's
performance using 10-fold cross-validation.Comment: conference pape
PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization
Chronic pain is a pervasive disorder which is often very disabling and is
associated with comorbidities such as depression and anxiety. Neuropathic Pain
(NP) is a common sub-type which is often caused due to nerve damage and has a
known pathophysiology. Another common sub-type is Fibromyalgia (FM) which is
described as musculoskeletal, diffuse pain that is widespread through the body.
The pathophysiology of FM is poorly understood, making it very hard to
diagnose. Standard medications and treatments for FM and NP differ from one
another and if misdiagnosed it can cause an increase in symptom severity. To
overcome this difficulty, we propose a novel framework, PainPoints, which
accurately detects the sub-type of pain and generates clinical notes via
summarizing the patient interviews. Specifically, PainPoints makes use of large
language models to perform sentence-level classification of the text obtained
from interviews of FM and NP patients with a reliable AUC of 0.83. Using a
sufficiency-based interpretability approach, we explain how the fine-tuned
model accurately picks up on the nuances that patients use to describe their
pain. Finally, we generate summaries of these interviews via expert
interventions by introducing a novel facet-based approach. PainPoints thus
enables practitioners to add/drop facets and generate a custom summary based on
the notion of "facet-coverage" which is also introduced in this work
Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge
Motivation: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and ‘translating' those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin
A crowd-sourcing approach for the construction of species-specific cell signaling networks
Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs. Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics onlin
Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge
Motivation: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein. Results: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams. Availability: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin
A crowd-sourcing approach for the construction of species-specific cell signaling networks
Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs. Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online
Norma DIN 476, su uso para desarrollar algunos temas de matemática de un programa de segundo año
Se presenta una propuesta de enseñanza, utilizando un material concreto que nos permite desarrollar algunos temas del programa de Matemática correspondiente al 2do año de la Escuela Industrial Superior de la ciudad de Santa Fe. La selección del material tiene que ver con una búsqueda de relaciones con otras asignaturas del mismo nivel (u otros) porque creemos que la enseñanza y aprendizaje de los contenidos de nuestra área tienen mejor recepción en los alumnos cuando se la contextualiza, cuando se evidencia su necesidad, valor o colaboración en otras áreas de estudio. El abordaje transdisciplinario requiere de mentes creativas, abiertas y capaces de resolver situaciones problemáticas especÃficas desde muchas perspectivas. Esto indica que el docente debe diseñar estrategias de enseñanza basadas en una concepción cognitiva del aprendizaje, favoreciendo el tratamiento de los contenidos disciplinares desde una perspectiva crÃtica y reflexiva; en la cual el joven pueda poner en juego sus propias capacidades y posibilidades para participar activamente del proceso y construir el conocimiento.Facultad de Humanidades y Ciencias de la Educació
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