Semi-automated classification an uncertainty estimation of cell type-specific protein expression patterns in human testis
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens (human)
SUBMITTER: Cecilia Lindskog
PROVIDER: S-BSST554 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:BackgroundAir pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity.MethodsWe developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants.ResultsOverall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs.ConclusionsAutomated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies.
| S-EPMC9733291 | biostudies-literature
Project description:Background and purposeProton therapy is sensitive to range uncertainties, which typically are accounted for by margins or robust optimization, based on tissue-independent uncertainties. However, range uncertainties have been shown to depend on the specific tissues traversed. The aim of this study was to investigate the differences between range margins based on stopping power ratio (SPR) uncertainties which were tissue-specific (applied voxel-wise) or fixed (tissue-independent or composite).Materials and methodsUncertainties originating from imaging, computed tomography (CT) number estimation, and SPR estimation were calculated for low-, medium-, and high-density tissues to quantify the tissue-specific SPR uncertainties. Four clinical treatment plans (four different tumor sites) were created and recomputed after applying either tissue-specific or fixed SPR uncertainties. Plans with tissue-specific and fixed uncertainties were compared, based on dose-volume-histogram parameters for both targets and organs-at-risk.ResultsThe total SPR uncertainties were 7.0% for low-, 1.0% for medium-, and 1.3% for high-density tissues. Differences between the proton plans with tissue-specific and fixed uncertainties were mainly found in the vicinity of the target. Composite uncertainties were found to capture the tissue-specific uncertainties more accurately than the tissue-independent uncertainties.ConclusionDifferent SPR uncertainties were found for low-, medium-, and high-density tissues indicating that range margins based on tissue-specific uncertainties may be more exact than the standard approach of using tissue-independent uncertainties. Differences between applying tissue-specific and fixed uncertainties were found, however, a fixed uncertainty might still be sufficient, but with a magnitude that depends on the body region.
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Project description:MotivationRNA-Seq is a promising new technology for accurately measuring gene expression levels. Expression estimation with RNA-Seq requires the mapping of relatively short sequencing reads to a reference genome or transcript set. Because reads are generally shorter than transcripts from which they are derived, a single read may map to multiple genes and isoforms, complicating expression analyses. Previous computational methods either discard reads that map to multiple locations or allocate them to genes heuristically.ResultsWe present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNA-Seq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels. Unlike previous methods, our method is capable of modeling non-uniform read distributions. Simulations with our method indicate that a read length of 20-25 bases is optimal for gene-level expression estimation from mouse and maize RNA-Seq data when sequencing throughput is fixed.
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Project description:Cell-type specific genes were recognized by interrogating microarrays carrying Dictyostelium gene fragments with probes prepared from fractions enriched in prestalk and prespore cells. Cell-type specific accumulation of mRNA from 17 newly identified genes was confirmed by Northern analyses. DNA microarrays carrying 690 targets were used to determine expression profiles during development. The profiles were fit to a biologically based kinetic equation to extract the times of transcription onset and cessation. Although the majority of the genes that were cell-type enriched at the slug stage were first expressed as the prespore and prestalk cells sorted out in aggregates, some were found to be expressed earlier before the cells had even aggregated. These early genes may have been initially expressed in all cells and then preferentially turned over in one or the other cell type. Alternatively, cell type divergence may start soon after the initiation of development.
| S-EPMC59697 | biostudies-literature
Project description:BackgroundAs a result of decades of effort by many investigators we now have an advanced level of understanding about several molecular systems involved in the control of gene expression. Examples include CpG islands, promoters, mRNA splicing and epigenetic signals. It is less clear, however, how such systems work together to integrate the functions of a living organism. Here I describe the results of a study to test the idea that a contribution might be made by focusing on genes specifically expressed in a particular tissue, the human testis.Experimental designA database of 239 testis-specific genes was accumulated and each was examined for the presence of features relevant to control of gene expression. These include: (1) the presence of a promoter, (2) the presence of a CpG island (CGI) within the promoter, (3) the presence in the promoter of a transcription factor binding site near the transcription start site, (4) the level of gene expression, and (5) the above features in genes of testis-specific cell types such as spermatocyte and spermatid that differ in their extent of differentiation.ResultsOf the 107 database genes with an annotated promoter, 56 were found to have one or more transcription factor binding sites near the transcription start site. Three of the binding sites observed, Pax-5, AP-2αA and GRα, stand out in abundance suggesting they may be involved in testis-specific gene expression. Compared to less differentiated testis-specific cells, genes of more differentiated cells were found to be (1) more likely to lack a CGI, (2) more likely to lack introns and (3) higher in expression level. The results suggest genes of more differentiated cells have a reduced need for CGI-based regulatory repression, reduced usage of gene splicing and a smaller set of expressed proteins.
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Project description:We examined whether a tool for determining Johnsen scores automatically using artificial intelligence (AI) could be used in place of traditional Johnsen scoring to support pathologists' evaluations. Average precision, precision, and recall were assessed by the Google Cloud AutoML Vision platform. We obtained testicular tissues for 275 patients and were able to use haematoxylin and eosin (H&E)-stained glass microscope slides from 264 patients. In addition, we cut out of parts of the histopathology images (5.0 × 5.0 cm) for expansion of Johnsen's characteristic areas with seminiferous tubules. We defined four labels: Johnsen score 1-3, 4-5, 6-7, and 8-10 to distinguish Johnsen scores in clinical practice. All images were uploaded to the Google Cloud AutoML Vision platform. We obtained a dataset of 7155 images at magnification 400× and a dataset of 9822 expansion images for the 5.0 × 5.0 cm cutouts. For the 400× magnification image dataset, the average precision (positive predictive value) of the algorithm was 82.6%, precision was 80.31%, and recall was 60.96%. For the expansion image dataset (5.0 × 5.0 cm), the average precision was 99.5%, precision was 96.29%, and recall was 96.23%. This is the first report of an AI-based algorithm for predicting Johnsen scores.
| S-EPMC8107178 | biostudies-literature
Project description:Gastroesophageal adenocarcinoma (GEAC) poses a significant challenge due to its poor prognosis and limited treatment options. Recently, Cancer/testis antigens (CTAs) have emerged as potential therapy targets due to their high expression in tumor cells and their immunogenic nature. We aimed to explore the expression and co-expression of CTAs in GEAC. We analyzed 63 GEAC patients initially and validated our findings in 329 patients from The Cancer Genome Atlas (TCGA) database. CTA expression was measured after RNA sequencing, while clinical information, including survival outcomes and treatment details, was collected from an institutional database. Co-expression patterns among CTAs were determined using Spearman correlation analysis. The majority of the study cohort were male (87%), Caucasian (94%), and had stage IV disease (64%). CTAs were highly prevalent, ranging from 58 to 19%. The MAGE gene family showed the highest expression, consistent across both cohorts. The correlation matrix revealed a distinct cluster of significantly co-expressed genes, including MAGEA3, NY-ESO-1, and others (0.27 ≤ r ≤ 0.73). Survival analysis revealed that individual CTAs were associated with poorer survival outcomes in patients not receiving immunotherapy while showing potential for improved survival in those undergoing immunotherapy, although these findings lacked robust reliability. Our study provides a comprehensive characterization of CTA expression and co-expression in GEAC. The strong correlation among CTAs like MAGE, NY-ESO-1, and GAGE suggests a potential for therapies targeting multiple CTAs simultaneously. Further research, including prospective trials, is warranted to assess the prognostic value of CTAs and their suitability as therapeutic targets.
| S-EPMC11324668 | biostudies-literature
Project description:MotivationRecent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis.ResultsWe present a new algorithm called utomated ell-type iscovery and lassification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data.Availability and implementationA Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc .Contactbrian.kidd@mssm.edu or joel.dudley@mssm.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Whole body plethysmography (WBP) monitors respiratory rate and depth but conventional analysis fails to capture the diversity of waveforms. Our first purpose was to develop a waveform cluster analysis method for quantifying dynamic changes in respiratory waveforms. WBP data, from adult Sprague-Dawley rats, were sorted into time domains and principle component analysis was used for hierarchical clustering. The clustering method effectively sorted waveforms into categories including sniffing, tidal breaths of varying duration, and augmented breaths (sighs). We next used this clustering method to quantify breathing after opioid (fentanyl) overdose and treatment with ampakine CX1942, an allosteric modulator of AMPA receptors. Fentanyl caused the expected decrease in breathing, but our cluster analysis revealed changes in the temporal appearance of inspiratory efforts. Ampakine CX1942 treatment shifted respiratory waveforms toward baseline values. We conclude that this method allows for rapid assessment of breathing patterns across extended data recordings. Expanding analyses to include larger portions of recorded WBP data may provide insight on how breathing is affected by disease or therapy.
| S-EPMC8417563 | biostudies-literature
Project description:ObjectivesNew chest compression detection technology allows for the recording and graphical depiction of clinical cardiopulmonary resuscitation (CPR) chest compressions. The authors sought to determine the inter-rater reliability of chest compression pattern classifications by human raters. Agreement with automated chest compression classification was also evaluated by computer analysis.MethodsThis was an analysis of chest compression patterns from cardiac arrest patients enrolled in the ongoing Resuscitation Outcomes Consortium (ROC) Continuous Chest Compressions Trial. Thirty CPR process files from patients in the trial were selected. Using written guidelines, research coordinators from each of eight participating ROC sites classified each chest compression pattern as 30:2 chest compressions, continuous chest compressions (CCC), or indeterminate. A computer algorithm for automated chest compression classification was also developed for each case. Inter-rater agreement between manual classifications was tested using Fleiss's kappa. The criterion standard was defined as the classification assigned by the majority of manual raters. Agreement between the automated classification and the criterion standard manual classifications was also tested.ResultsThe majority of the eight raters classified 12 chest compression patterns as 30:2, 12 as CCC, and six as indeterminate. Inter-rater agreement between manual classifications of chest compression patterns was κ = 0.62 (95% confidence interval [CI] = 0.49 to 0.74). The automated computer algorithm classified chest compression patterns as 30:2 (n = 15), CCC (n = 12), and indeterminate (n = 3). Agreement between automated and criterion standard manual classifications was κ = 0.84 (95% CI = 0.59 to 0.95).ConclusionsIn this study, good inter-rater agreement in the manual classification of CPR chest compression patterns was observed. Automated classification showed strong agreement with human ratings. These observations support the consistency of manual CPR pattern classification as well as the use of automated approaches to chest compression pattern analysis.
| S-EPMC4329029 | biostudies-literature