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"Becker, Tim"
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Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools.The 2018 Data Science Bowl challenged competitors to develop an accurate tool for segmenting stained nuclei from diverse light microscopy images. The winners deployed innovative deep-learning strategies to realize configuration-free segmentation.
Journal Article
CellProfiler 3.0: Next-generation image processing for biology
CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler's infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.
Journal Article
Predicting compound activity from phenotypic profiles and chemical structures
Predicting assay results for compounds virtually using chemical structures and phenotypic profiles has the potential to reduce the time and resources of screens for drug discovery. Here, we evaluate the relative strength of three high-throughput data sources—chemical structures, imaging (Cell Painting), and gene-expression profiles (L1000)—to predict compound bioactivity using a historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity for 6–10% of assays, and in combination they predict 21% of assays with high accuracy, which is a 2 to 3 times higher success rate than using a single modality alone. In practice, the accuracy of predictors could be lower and still be useful, increasing the assays that can be predicted from 37% with chemical structures alone up to 64% when combined with phenotypic data. Our study shows that unbiased phenotypic profiling can be leveraged to enhance compound bioactivity prediction to accelerate the early stages of the drug-discovery process.
Experimental assays are used to determine if compounds cause a desired activity in cells. Here the authors demonstrate that computational methods can predict compound bioactivity given their chemical structure, imaging and gene expression data from historic screening libraries.
Journal Article
Genome-wide meta-analysis in alopecia areata resolves HLA associations and reveals two new susceptibility loci
Alopecia areata (AA) is a prevalent autoimmune disease with 10 known susceptibility loci. Here we perform the first meta-analysis of research on AA by combining data from two genome-wide association studies (GWAS), and replication with supplemented ImmunoChip data for a total of 3,253 cases and 7,543 controls. The strongest region of association is the major histocompatibility complex, where we fine-map four independent effects, all implicating human leukocyte antigen-DR as a key aetiologic driver. Outside the major histocompatibility complex, we identify two novel loci that exceed the threshold of statistical significance, containing ACOXL/BCL2L11(BIM) (2q13); GARP (LRRC32) (11q13.5), as well as a third nominally significant region SH2B3(LNK)/ATXN2 (12q24.12). Candidate susceptibility gene expression analysis in these regions demonstrates expression in relevant immune cells and the hair follicle. We integrate our results with data from seven other autoimmune diseases and provide insight into the alignment of AA within these disorders. Our findings uncover new molecular pathways disrupted in AA, including autophagy/apoptosis, transforming growth factor beta/Tregs and JAK kinase signalling, and support the causal role of aberrant immune processes in AA.
Alopecia areata (AA) is a common autoimmune disease with a known genetic component. Here, the authors analyse 3,253 AA patients and 7,543 healthy controls, and identify two new risk loci and disrupted immune response pathways associated with the disease.
Journal Article
Use of digital teaching resources and predictors of medical student performance during the pandemic: A prospective study
The coronavirus pandemic has led to increased use of digital teaching formats in medical education. A number of studies have assessed student satisfaction with these resources. However, there is a lack of studies investigating changes in student performance following the switch from contact to virtual teaching. Specifically, there are no studies linking student use of digital resources to learning outcome and examining predictors of failure.
Student performance before (winter term 2019/20: contact teaching) and during (summer term 2020: no contact teaching) the pandemic was compared prospectively in a cohort of 162 medical students enrolled in the clinical phase of a five-year undergraduate curriculum. Use of and performance in various digital resources (case-based teaching in a modified flipped classroom approach; formative key feature examinations of clinical reasoning; daily multiple choice quizzes) was recorded in summer 2020. Student scores in summative examinations were compared to examination scores in the previous term. Associations between student characteristics, resource use and summative examination results were used to identify predictors of performance.
Not all students made complete use of the digital learning resources provided. Timely completion of tasks was associated with superior performance compared to delayed completion. Female students scored significantly fewer points in formative key feature examinations and digital quizzes. Overall, higher rankings within the student cohort (according to summative exams) in winter term 2019/20 as well as male gender predicted summative exam performance in summer 2020. Scores achieved in the first formative key feature examination predicted summative end-of-module exam scores.
The association between timely completion of tasks as well as early performance in a module and summative exams might help to identify students at risk and offering help early on. The unexpected gender difference requires further study to determine whether the shift to a digital-only curriculum disadvantages female students.
Journal Article
Optical Constants of Martian Dust Analogs at UV–Visible–Near-infrared Wavelengths
We present an advanced light-scattering model to retrieve the optical constants of three Martian dust analogs: Johnson Space Center regolith simulant, Enhanced Mojave Mars Simulant, and Mars Global Simulant. The samples are prepared to have narrow particle-size distributions within the geometric-optics domain. We carry out laboratory measurements to obtain the particle-size distributions, shapes, and diffuse reflectance spectra of the Martian analogs deposited on a surface. Our model framework includes a ray-optics code to compute scattering properties for individual particles, and a radiative-transfer treatment to simulate the surface. The irregular shapes of the dust particles are taken into account in the model. We compare our derived imaginary parts of the refractive indices with those in the literature and find that they are much smaller than the ones that are commonly used for Martian dust. A sensitivity study shows that the retrieved optical constants are sensitive to the particle shape, which needs to be accounted for in applications that use different shapes. Finally, the derived values are validated by using them to reproduce the reflectance spectrum of the Martian surface regolith as observed by the Nadir and Occultation for Mars Discovery instrument on board the ExoMars mission.
Journal Article
Use of digital teaching resources and predictors of medical student performance during the pandemic: A prospective study
BackgroundThe coronavirus pandemic has led to increased use of digital teaching formats in medical education. A number of studies have assessed student satisfaction with these resources. However, there is a lack of studies investigating changes in student performance following the switch from contact to virtual teaching. Specifically, there are no studies linking student use of digital resources to learning outcome and examining predictors of failure.MethodsStudent performance before (winter term 2019/20: contact teaching) and during (summer term 2020: no contact teaching) the pandemic was compared prospectively in a cohort of 162 medical students enrolled in the clinical phase of a five-year undergraduate curriculum. Use of and performance in various digital resources (case-based teaching in a modified flipped classroom approach; formative key feature examinations of clinical reasoning; daily multiple choice quizzes) was recorded in summer 2020. Student scores in summative examinations were compared to examination scores in the previous term. Associations between student characteristics, resource use and summative examination results were used to identify predictors of performance.ResultsNot all students made complete use of the digital learning resources provided. Timely completion of tasks was associated with superior performance compared to delayed completion. Female students scored significantly fewer points in formative key feature examinations and digital quizzes. Overall, higher rankings within the student cohort (according to summative exams) in winter term 2019/20 as well as male gender predicted summative exam performance in summer 2020. Scores achieved in the first formative key feature examination predicted summative end-of-module exam scores.ConclusionsThe association between timely completion of tasks as well as early performance in a module and summative exams might help to identify students at risk and offering help early on. The unexpected gender difference requires further study to determine whether the shift to a digital-only curriculum disadvantages female students.
Journal Article
Meta-analysis identifies novel risk loci and yields systematic insights into the biology of male-pattern baldness
Male-pattern baldness (MPB) is a common and highly heritable trait characterized by androgen-dependent, progressive hair loss from the scalp. Here, we carry out the largest GWAS meta-analysis of MPB to date, comprising 10,846 early-onset cases and 11,672 controls from eight independent cohorts. We identify 63 MPB-associated loci (
P
<5 × 10
−8
, METAL) of which 23 have not been reported previously. The 63 loci explain ∼39% of the phenotypic variance in MPB and highlight several plausible candidate genes (
FGF5
,
IRF4
,
DKK2
) and pathways (melatonin signalling, adipogenesis) that are likely to be implicated in the key-pathophysiological features of MPB and may represent promising targets for the development of novel therapeutic options. The data provide molecular evidence that rather than being an isolated trait, MPB shares a substantial biological basis with numerous other human phenotypes and may deserve evaluation as an early prognostic marker, for example, for prostate cancer, sudden cardiac arrest and neurodegenerative disorders.
Male-pattern baldness is a common condition in which hair is progressively lost from the scalp. Here, the authors find 23 new genetic variants associated with this condition and suggest that it is not an isolated trait but may share an underlying biological basis with various diseases.
Journal Article
Experimental Scattering Matrices of Martian Dust Aerosols with Narrow Particle-size Distributions
We present experimental scattering matrices of the JSC Mars-1, MMS-2, and MGS-1 simulants at 488 and 640 nm. The analogs were processed so that narrow size distributions representative of Martian dust aerosols during different dust cycles were obtained. We find that the forward peak of the phase function depends on particle size as it becomes narrower with increasing size, whereas the side- and backscattering directions depend on both composition and size so that increasing size and decreasing absorption produce a flatter curve. The position and maximum of the degree of linear polarization varies based on particle size and composition, and the negative polarization branch is more prominent for wavelength-scale particles diminishing with increasing size. The linear depolarization is strongly affected by size and composition. Finally, we compare sky-brightness curves measured by the Navcam and Hazcam engineering cameras on board the Mars Science Laboratory rover to the measured phase functions. The observations show a narrower peak at the forward direction and a flatter curve toward the side- and backscattering directions with an increasing dust load in the atmosphere, similar to what can be seen for the measured phase functions of the analogs with increasing particle size. In the case of the analogs, the flattening of the curve can be caused by an increase in multiple scattering within a particle by wavelength-scale surface roughness and/or internal inclusions. For the observed sky brightnesses, particle aggregation and multiple scattering among particles in denser dust conditions play a major role.
Journal Article