scholarly journals The Nucleome of Developing Murine Rod Photoreceptors

2018 ◽  
Author(s):  
Issam Al Diri ◽  
Marc Valentine ◽  
Beisi Xu ◽  
Daniel Putnam ◽  
Lyra Griffiths ◽  
...  
Keyword(s):  
Retinal Development ◽  
Markov Modeling ◽  
Rod Photoreceptors ◽  
Bipolar Neuron ◽  
Regulatory Circuit ◽  
Super Enhancer ◽  
Hidden Markov Modeling ◽  
Cell Type Specific ◽  
Heterochromatin Domain

AbstractThe nuclei of rod photoreceptors in mice and other nocturnal species have an unusual inverted chromatin structure: the heterochromatin is centrally located to help focus light and improve photosensitivity. To better understand this unique nuclear organization, we performed ultra-deep Hi-C analysis on murine retina at 3 stages of development and on purified rod photoreceptors. Predicted looping interactions from the Hi-C data were validated with fluorescence in situ hybridization (FISH). We discovered that a subset of retinal genes that are important for retinal development, cancer, and stress response are localized to the facultative heterochromatin domain. We also used machine learning to develop an algorithm based on our chromatin Hidden Markov Modeling (chromHMM) of retinal development to predict heterochromatin domains and study their dynamics during retinogenesis. FISH data for 264 genomic loci were used to train and validate the algorithm. The integrated data were then used to identify a developmental stage– and cell type-specific core regulatory circuit super-enhancer (CRC-SE) upstream of the Vsx2 gene, which is required for bipolar neuron expression. Deletion of the Vsx2 CRC-SE in mice led to the loss of bipolar neurons in the retina.

Deriving Effective Decision-Making Strategies of Prosthetists: Using Hidden Markov Modeling and Qualitative Analysis to Compare Experts and Novices

2021 ◽  
pp. 001872082110328
Author(s):  
Pratima Saravanan ◽  
Jessica Menold
Keyword(s):  
Decision Making ◽  
Qualitative Analysis ◽  
Clinical Decision Making ◽  
Hidden Markov ◽  
Activity Level ◽  
Markov Modeling ◽  
Support Tool ◽  
Case Complexity ◽  
Hidden Markov Modeling ◽  
Expert And Novice

Objective This research focuses on studying the clinical decision-making strategies of expert and novice prosthetists for different case complexities. Background With an increasing global amputee population, there is an urgent need for improved amputee care. However, current prosthetic prescription standards are based on subjective expertise, making the process challenging for novices, specifically during complex patient cases. Hence, there is a need for studying the decision-making strategies of prosthetists. Method An interactive web-based survey was developed with two case studies of varying complexities. Navigation between survey pages and time spent were recorded for 28 participants including experts ( n = 20) and novices ( n = 8). Using these data, decision-making strategies, or patterns of decisions, during prosthetic prescription were derived using hidden Markov modeling. A qualitative analysis of participants’ rationale regarding decisions was used to add a deep contextualized understanding of decision-making strategies derived from the quantitative analysis. Results Unique decision-making strategies were observed across expert and novice participants. Experts tended to focus on the personal details, activity level, and state of the residual limb prior to prescription, and this strategy was independent of case complexity. Novices tended to change strategies dependent upon case complexity, fixating on certain factors when case complexity was high. Conclusion The decision-making strategies of experts stayed the same across the two cases, whereas the novices exhibited mixed strategies. Application By modeling the decision-making strategies of experts and novices, this study builds a foundation for development of an automated decision-support tool for prosthetic prescription, advancing novice training, and amputee care.

Decision-Feedback Stages Revealed by Hidden Markov Modeling of EEG

2021 ◽  
pp. 2150031
Author(s):  
Qin Tao ◽  
Yajing Si ◽  
Fali Li ◽  
Peiyang Li ◽  
Yuqin Li ◽  
...  
Keyword(s):  
Frontal Lobe ◽  
Visual Information ◽  
Hidden Markov ◽  
Decision Feedback ◽  
Markov Modeling ◽  
Feedback Process ◽  
Feedback Systems ◽  
Left Frontal Lobe ◽  
Hidden Markov Modeling ◽  
Independent Decision

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

scholarly journals Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jieming Li ◽  
Leyou Zhang ◽  
Alexander Johnson-Buck ◽  
Nils G. Walter
Keyword(s):  
Deep Learning ◽  
Single Molecule ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Markov Modeling ◽  
Single Molecule Fluorescence ◽  
Hidden Markov Modeling ◽  
Improved Performance ◽  
Automated Screening ◽  
Expectation Bias

AbstractTraces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we use deep learning to develop a rapid, automatic SMFM trace selector, termed AutoSiM, that improves the sensitivity and specificity of an assay for a DNA point mutation based on single-molecule recognition through equilibrium Poisson sampling (SiMREPS). The improved performance of AutoSiM is based on accepting both more true positives and fewer false positives than the conventional approach of hidden Markov modeling (HMM) followed by hard thresholding. As a second application, the selector is used for automated screening of single-molecule Förster resonance energy transfer (smFRET) data to identify high-quality traces for further analysis, and achieves ~90% concordance with manual selection while requiring less processing time. Finally, we show that AutoSiM can be adapted readily to novel datasets, requiring only modest Transfer Learning.

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