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An opsin 5–dopamine pathway mediates light-dependent vascular development in the eye
摘要： During mouse postnatal eye development, the embryonic hyaloid vascular network regresses from the vitreous as an adaption for high-acuity vision. This process occurs with precisely controlled timing. Here, we show that opsin 5 (OPN5; also known as neuropsin)-dependent retinal light responses regulate vascular development in the postnatal eye. In Opn5-null mice, hyaloid vessels regress precociously. We demonstrate that 380-nm light stimulation via OPN5 and VGAT (the vesicular GABA/glycine transporter) in retinal ganglion cells enhances the activity of inner retinal DAT (also known as SLC6A3; a dopamine reuptake transporter) and thus suppresses vitreal dopamine. In turn, dopamine acts directly on hyaloid vascular endothelial cells to suppress the activity of vascular endothelial growth factor receptor 2 (VEGFR2) and promote hyaloid vessel regression. With OPN5 loss of function, the vitreous dopamine level is elevated and results in premature hyaloid regression. These investigations identify violet light as a developmental timing cue that, via an OPN5–dopamine pathway, regulates optic axis clearance in preparation for visual function.
关键词： Hyaloid regression,Vascular development,Dopamine,VEGFR2,Light-dependent,Eye,Opsin 5,Retinal ganglion cells
更新于2025-11-21 11:20:42
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The Effects of Global Signal Regression on Estimates of Resting-State Blood Oxygen-Level-Dependent Functional Magnetic Resonance Imaging and Electroencephalogram Vigilance Correlations
摘要： Global signal regression (GSR) is a commonly used although controversial preprocessing approach in the analysis of resting-state blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data. Although the effects of GSR on resting-state functional connectivity measures have received much attention, there has been relatively little attention devoted to its effects on studies looking at the relationship between resting-state BOLD measures and independent measures of brain activity. In this study, we used simultaneously acquired electroencephalogram (EEG)–fMRI data in humans to examine the effects of GSR on the correlation between resting-state BOLD fluctuations and EEG vigilance measures. We show that GSR leads to a positive shift in the correlation between the BOLD and vigilance measures. This shift leads to a reduction in the spatial extent of negative correlations in widespread brain areas, including the visual cortex, but leads to the appearance of positive correlations in other areas, such as the cingulate gyrus. The results obtained using GSR are consistent with those of a temporal censoring process in which the correlation is computed using a temporal subset of the data. Since the data from these retained time points are unaffected by the censoring process, this finding suggests that the positive correlations in cingulate gyrus are not simply an artifact of GSR.
关键词： resting-state fMRI,vigilance,global signal regression,simultaneous EEG–fMRI
更新于2025-09-23 15:23:52
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[SPIE Image Processing - Houston, United States (2018.2.10-2018.2.15)] Medical Imaging 2018: Image Processing - Deep learning for biomarker regression: application to osteoporosis and emphysema on chest CT scans
摘要： Introduction: Biomarker computation using deep-learning often relies on a two-step process, where the deep learning algorithm segments the region of interest and then the biomarker is measured. We propose an alternative paradigm, where the biomarker is estimated directly using a regression network. We showcase this image-to-biomarker paradigm using two biomarkers: the estimation of bone mineral density (BMD) and the estimation of lung percentage of emphysema from CT scans. Materials and methods: We use a large database of 9,925 CT scans to train, validate and test the network for which reference standard BMD and percentage emphysema have been already computed. First, the 3D dataset is reduced to a set of canonical 2D slices where the organ of interest is visible (either spine for BMD or lungs for emphysema). This data reduction is performed using an automatic object detector. Second, The regression neural network is composed of three convolutional layers, followed by a fully connected and an output layer. The network is optimized using a momentum optimizer with an exponential decay rate, using the root mean squared error as cost function. Results: The Pearson correlation coefficients obtained against the reference standards are r = 0.940 (p < 0.00001) and r = 0.976 (p < 0.00001) for BMD and percentage emphysema respectively. Conclusions: The deep-learning regression architecture can learn biomarkers from images directly, without indicating the structures of interest. This approach simplifies the development of biomarker extraction algorithms. The proposed data reduction based on object detectors conveys enough information to compute the biomarkers of interest.
关键词： regression,deep learning,bone mineral density,computed tomography,emphysema
更新于2025-09-23 15:23:52
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Analysis of NIR spectroscopic data using decision trees and their ensembles
摘要： Decision trees and their ensembles became quite popular for data analysis during the past decade. One of the main reasons for that is current boom in big data, where traditional statistical methods (such as, e.g., multiple linear regression) are not very efficient. However, in chemometrics these methods are still not very widespread, first of all because of several limitations related to the ratio between number of variables and observations. This paper presents several examples on how decision trees and their ensembles can be used in analysis of NIR spectroscopic data both for regression and classification. We will try to consider all important aspects including optimization and validation of models, evaluation of results, treating missing data and selection of most important variables. The performance and outcome of the decision tree-based methods are compared with more traditional approach based on partial least squares.
关键词： Decision trees,Classification and regression trees,Random forests,NIR spectroscopy
更新于2025-09-23 15:23:52
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A novel dynamical approach in continuous cuffless blood pressure estimation based on ECG and PPG signals
摘要： Continuous cu?ess blood pressure (BP) monitoring has attracted much interest in ?nding the ideal treatment of diseases and the prevention of premature death. This paper presents a novel dynamical method, based on pulse transit time (PTT) and photoplethysmogram intensity ratio (PIR), for the continuous cu?ess BP estimation. By taking the advantages of both the modeling and the prediction approaches, the proposed framework e?ectively estimates diastolic BP (DBP), mean BP (BP), and systolic BP (SBP). Adding past states of the cardiopulmonary system as well as present states of the cardiac system to our model caused two main improvements. First, high accuracy of the method in the beat to beat BP estimation. Second, notwithstanding noticeable BP changes, the performance of the model is preserved over time. The experimental setup includes comparative studies on a large, standard dataset. Moreover, the proposed method outperformed the most recent and cited algorithms with improved accuracy.
关键词： Cu?ess blood pressure estimation,Taken’s theorem,Multivariate adaptive regression spline,Pulse transit time,Photoplethysmogram intensity ratio
更新于2025-09-23 15:23:52
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Assessment of different combinations of meteorological parameters for predicting daily global solar radiation using artificial neural networks
摘要： In this study, for determining the best-input scenarios of the used parameters in predicting the Daily Global Solar Radiation (DGSR), a new approach based on Artificial Neural Networks (ANNs) was presented. The proposed approach is based on comparisons between all possible input combinations for determining the best scenarios that can give perfect correlations and approximations with DGSR. Recorded data from 35 stations belonging to different climatic zones (27 in Morocco and 8 in neighboring countries) were reported for training and testing the obtained results. The used input parameters include geographical coordinates, sun declination, day length, day number, clearness index (KI), Top Of Atmosphere (TOA), average ambient temperature (Ta), maximum temperature (Tmax), minimum temperature (Tmin), difference temperature (ΔT), temperature ratio (TR), relative humidity (Rh) and wind speed (Ws). The results revealed 128 best-input scenarios, where the first relevant input combination was found for KI, Ta, ΔT, TR and TOA. This result indicated that the best-input scenario for predicting DGSR is based only on three climatological parameters: KI, function of Ta f(Ta) and TOA. In addition, based on these found best-input scenarios and on the least square regression (LSR) technique, 128 new linear relationships between DGSR and the found best-input combinations were developed. The statistical analysis expressed through statistical criteria indicated perfect correlations and approximations between the predicted and measured values of DGSR.
关键词： Best scenarios,ANNs,Least square regression,Statistical analysis,Solar radiation modelling,Forecasting
更新于2025-09-23 15:23:52
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Damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression
摘要： The assessment of structural damage is of great significance for ensuring the service safety of carbon fiber reinforced plastics (CFRP) structures. In this paper, the damage degree prediction method of CFRP structure based on fiber Bragg grating and epsilon-support vector regression was studied. The structural dynamic response signals were detected by fiber Bragg grating sensors. Then, the Fourier transform was used to extract the dynamic characteristics of the structure as the damage feature, and the damage feature dimensionality was reduced by using the RReliefF algorithm. On this basis, the damage degree prediction model of CFRP structure based on epsilon-support vector regression was established. Finally, the method proposed in this paper was experimentally verified. The results showed that the epsilon-support vector regression model can accurately predict the damage degree of unknown samples, and the absolute relative error of 27 experiments was less than 10% for 30 testing experiments. This paper provided a feasible method for predicting the damage degree of CFRP structures.
关键词： Frequency response,Carbon fiber reinforced plastics,Epsilon-support vector regression,RReliefF,Damage degree prediction
更新于2025-09-23 15:23:52
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Reweighted Local Collaborative Sparse Regression for Hyperspectral Unmixing
摘要： Sparse unmixing is based on the assumption that each mixed pixel in the hyperspectral image can be expressed in the form of linear combinations of known pure signatures in the spectral library. Collaborative sparse regression improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. However, hyperspectral images exhibit rich spatial correlation that can be exploited to better estimate endmember abundances. The work, based on the iterative reweighted algorithm and local collaborative sparse unmixing, utilized a reweighted local collaborative sparse unmixing (RLCSU). The simultaneous utilization of iterative reweighted minimization and local collaborative sparse unmixing (including spectral information and spatial information in the formulation, respectively) significantly improved the sparse unmixing performance. The optimization problem was simply solved by the variable splitting and augmented Lagrangian algorithm. Our experimental results were obtained by using both simulated and real hyperspectral data sets. The proposed RLCSU algorithm obtain better signal-to-reconstruction error (SRE, measured in dB) results than LCSU and CLSUnSAL algorithms in all considered signal-to-noise ratio (SNR) levels, especially in the case of low noise values. The RLCSU algorithm obtains a better SRE(dB) result (30.01) than LCSU (20.08) and CLSUnSAL (17.28) algorithms for the simulated data 1 with SNR=50dB. It demonstrated that the proposed method is an effective and accurate spectral unmixing algorithm.
关键词： Hyperspectral unmixing,spectral unmixing,reweighted local collaborative,spatial information,sparse regression
更新于2025-09-23 15:23:52
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Prediction model optimization using full model selection with regression trees demonstrated with FTIR data from bovine milk
摘要： Predictive modeling is the development of a model that is best able to predict an outcome based on given input variables. Model algorithms are different processes that are used to define functions that transform the data within models. Common algorithms include logistic regression (LR), linear discriminant analysis (LDA), classification and regression trees (CART), na?ve Bayes (NB), and k-nearest neighbor (KNN). Data preprocessing option, such as feature extraction and reduction, and model algorithms are commonly selected empirically in epidemiological studies even though these decisions can significantly affect model performance. Accordingly, full model selection (FMS) methods were developed to provide a systematic approach to select predictive modeling methods; however, current limitations of FMS, such as its dependency on user-selected hyperparameters, have prevented their routine incorporation into analyses for model performance optimization. Here we present the use of regression trees as an innovative method to apply FMS. Regression tree FMS (rtFMS) requires the development of a model for every combination of predictive modeling method options under consideration. The iterated, cross-validation performances of these models are then passed through a regression tree for selection of a final model. We demonstrate the benefits of rtFMS using a milk Fourier transform infrared spectroscopy dataset, wherein we build prediction models for two blood metabolic health parameters in dairy cows, nonesterified fatty acids (NEFA) and β-hydroxybutyrate acid (BHBA). The goal for building NEFA and BHBA prediction models is to provide a milk-based screening tool for metabolic health in dairy cattle that can be incorporated automatically in milk analysis routines. These models could be used in conjunction with physical exams, cow side tests, and other indications to initiate medical intervention. In contrast to previously reported FMS methods, rtFMS is not a black box, is simple to implement and interpret, it does not have hyperparameters, and it illustrates the relative importance of modeling options. Additionally, rtFMS allows for indirect comparisons among models developed using different datasets. Finally, rtFMS eliminates user bias due to personal preference for certain methods and rtFMS removes the dependency on published comparisons of methods. Thus, rtFMS provides clear benefits over the empirical selection of data preprocessing options and model algorithms.
关键词： Prediction model,Fourier-transform infrared spectra,Regression tree,Preprocessing,Full model selection
更新于2025-09-23 15:23:52
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MultOpt++: a fast regression-based model for the development of compositions with high robustness against scatter of element concentrations
摘要： Alloys-by-design is a term used to describe new alloy development techniques based on numerical simulation. These approaches are extensively used for nickel-base superalloys to increase the chance of success in alloy development. During alloy production of numerically optimized compositions, unavoidable scattering of the element concentrations occurs. In the present paper, we investigate the effect of this scatter on the alloy properties. In particular, we describe routes to identify alloy compositions by numerical simulations that are more robust than other compositions. In our previously developed alloy development program package MultOpt, we introduced a sensitivity parameter that represents the influence of alloying variations on the final alloy properties in the post-optimization process, because the established sensitivity calculations require high computational effort. In this work, we derive a regression-based model for calculating the sensitivity that only requires one-time calculation of the regression coefficients. The model can be applied to any function with nearly linear behavior within the uncertainty range. The model is then successfully applied to the computational alloys-by-design work flow to facilitate alloy selection using the sensitivity of a composition owing to the inaccuracies in the manufacturing process as an additional minimization goal.
关键词： sensitivity,CALPHAD,regression analysis,alloys-by-design,superalloys
更新于2025-09-23 15:23:52