Optimization and evaluation of facial recognition models for Williams-Beuren syndrome.

Williams-Beuren syndrome (WBS) is a rare genetic disorder characterized by special facial gestalt, delayed development, and supravalvular aortic stenosis or/and stenosis of the branches of the pulmonary artery. We aim to develop and optimize accurate models of facial recognition to assist in the diagnosis of WBS, and to evaluate their effectiveness by using both five-fold cross-validation and an external test set. We used a total of 954 images from 135 patients with WBS, 124 patients suffering from other genetic disorders, and 183 healthy children. The training set comprised 852 images of 104 WBS cases, 91 cases of other genetic disorders, and 145 healthy children from September 2017 to December 2021 at the Guangdong Provincial People's Hospital. We constructed six binary classification models of facial recognition for WBS by using EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN. Transfer learning was used to pre-train the models, and each model was modified with a variable cosine learning rate. Each model was first evaluated by using five-fold cross-validation and then assessed on the external test set. The latter contained 102 images of 31 children suffering from WBS, 33 children with other genetic disorders, and 38 healthy children. To compare the capabilities of these models of recognition with those of human experts in terms of identifying cases of WBS, we recruited two pediatricians, a pediatric cardiologist, and a pediatric geneticist to identify the WBS patients based solely on their facial images. We constructed six models of facial recognition for diagnosing WBS using EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN. The model based on VGG-19BN achieved the best performance in terms of five-fold cross-validation, with an accuracy of 93.74% ± 3.18%, precision of 94.93% ± 4.53%, specificity of 96.10% ± 4.30%, and F1 score of 91.65% ± 4.28%, while the VGG-16BN model achieved the highest recall value of 91.63% ± 5.96%. The VGG-19BN model also achieved the best performance on the external test set, with an accuracy of 95.10%, precision of 100%, recall of 83.87%, specificity of 93.42%, and F1 score of 91.23%. The best performance by human experts on the external test set yielded values of accuracy, precision, recall, specificity, and F1 scores of 77.45%, 60.53%, 77.42%, 83.10%, and 66.67%, respectively. The F1 score of each human expert was lower than those of the EfficientNet-b3 (84.21%), ResNet-50 (74.51%), VGG-16 (85.71%), VGG-16BN (85.71%), VGG-19 (83.02%), and VGG-19BN (91.23%) models. CONCLUSION: The results showed that facial recognition technology can be used to accurately diagnose patients with WBS. Facial recognition models based on VGG-19BN can play a crucial role in its clinical diagnosis. Their performance can be improved by expanding the size of the training dataset, optimizing the CNN architectures applied, and modifying them with a variable cosine learning rate. WHAT IS KNOWN: • The facial gestalt of WBS, often described as "elfin," includes a broad forehead, periorbital puffiness, a flat nasal bridge, full cheeks, and a small chin. • Recent studies have demonstrated the potential of deep convolutional neural networks for facial recognition as a diagnostic tool for WBS. WHAT IS NEW: • This study develops six models of facial recognition, EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN, to improve WBS diagnosis. • The VGG-19BN model achieved the best performance, with an accuracy of 95.10% and specificity of 93.42%. The facial recognition model based on VGG-19BN can play a crucial role in the clinical diagnosis of WBS.

SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data.

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.

Multiview behavior and neurotransmitter analysis of zebrafish dyskinesia induced by 6PPD and its metabolites.

The typical tire manufacturing additive 6PPD, its metabolites 6PPDQ and 4-Hydroxy should be monitored because of their ubiquitous presence in the environment and the high toxicity of 6PPDQ to coho salmon. The toxic effect of 6PPD and its metabolites have been revealed superficially, especially on behavioral characteristics. However, the behavioral indicators explored so far are relatively simple and the toxic causes are poorly understood. With this in mind, our work investigated the toxic effects of 6PPD, 6PPDQ and 4-Hydroxy on adult zebrafish penetratingly through machine vision, and the meandering, body angle, top time and 3D trajectory are used for the first time to show the abnormal behaviors induced by 6PPD and its metabolites. Moreover, neurotransmitter changes in the zebrafish brain were measured to explore the causes of abnormal behavior. The results showed that high-dose treatment of 6PPD reduced the velocity by 42.4% and decreased the time at the top of the tank by 91.0%, suggesting significant activity inhibition and anxiety. In addition, γ-aminobutyric acid and acetylcholine were significantly impacted by 6PPD, while dopamine exhibited a slight variation, which can explain the bradykinesia, unbalance and anxiety of zebrafish and presented similar symptoms as Huntingdon's disease. Our study explored new abnormal behaviors of zebrafish induced by 6PPD and its metabolites in detail, and the toxic causes were revealed for the first time by studying the changes of neurotransmitters, thus providing an important reference for further studies of the biological toxicity of 6PPD and its metabolites.

Luminescence and energy transfer of Tb3+-doped BaO-Gd2O3-Al2O3-B2O3-SiO2 glasses.

Transparent Tb3+-doped BaO-Gd2O3-Al2O3-B2O3-SiO2 glasses with the greater than 4g/cm3 were prepared by high temperature melting method and its luminescent properties have been investigated by measured UV-vis transmission, excitation, emission and luminescence decay spectra. The transmission spectrum shows there are three weak absorption bands locate at about 312, 378 and 484nm in the glasses and it has good transmittance in the visible spectrum region. Intense green emission can be observed under UV excitation. The effective energy transfer from Gd3+ ion to Tb3+ ion could occur and sensitize the luminescence of Tb3+ ion. The green emission intensity of Tb3+ ion could change with the increasing SiO2/B2O3 ratio in the borosilicate glass matrix. With the increasing concentration of Tb3+ ion, 5D4→7FJ transitions could be enhanced through the cross relaxation between the two nearby Tb3+ ions. Luminescence decay time of 2.12ms from 546nm emission is obtained. The results indicate that Tb3+-doped BaO-Gd2O3-Al2O3-B2O3-SiO2 glasses would be potential scintillating material for applications in X-ray imaging.