High Fidelity Computational Modeling and Analysis of Voice Production

This research aims to improve the fundamental understanding of the multiphysics nature of voice production, particularly, the dynamic couplings among glottal flow, vocal fold vibration and airway acoustics through high-fidelity computational modeling and simulations. Built upon in-house numerical solvers, including an immersed-boundary-method based incompressible flow solver, a finite element method based solid mechanics solver and a hydrodynamic/aerodynamic splitting method based acoustics solver, a fully coupled, continuum mechanics based fluid-structure-acoustics interaction model was developed to simulate the flow-induced vocal fold vibrations and sound production in birds and mammals. Extensive validations of the model were conducted by comparing to excised syringeal and laryngeal experiments. The results showed that, driven by realistic representations of physiology and experimental conditions, including the geometries, material properties and boundary conditions, the model had an excellent agreement with the experiments on the vocal fold vibration patterns, acoustics and intraglottal flow dynamics, demonstrating that the model is able to reproduce realistic phonatory dynamics during voice production. The model was then utilized to investigate the effect of vocal fold inner structures on voice production. Assuming the human vocal fold to be a three-layer structure, this research focused on the effect of longitudinal variation of layer thickness as well as the cover-body thickness ratio on vocal fold vibrations. The results showed that the longitudinal variation of the cover and ligament layers thicknesses had little effect on the flow rate, vocal fold vibration amplitude and pattern but affected the glottal angle in different coronal planes, which also influenced the energy transfer between glottal flow and the vocal fold. The cover-body thickness ratio had a complex nonlinear effect on the vocal fold vibration and voice production. Increasing the cover-body thickness ratio promoted the excitation of the wave-type modes of the vocal fold, which were also higher-eigenfrequency modes, driving the vibrations to higher frequencies. This has created complex nonlinear bifurcations. The results from the research has important clinical implications on voice disorder diagnosis and treatment as voice disorders are often associated with mechanical status changes of the vocal fold tissues and their treatment often focus on restoring the mechanical status of the vocal folds

Relationship Among Children’s Social-emotional Competence, Social Support, Academic Achievement and Aggressive Behavior in the Primary School in China

With the development of humanistic education, scholars believe that children’s social-emotional competence mostly will take charge of their future family, school and life success in the future. Because of too much time focusing on social-skills training and few about children’ s social-emotional competence and relationship between social-emotional competence and aggressive behavior in China, and this article firstly shows concepts of social-emotional competence, social support, academic achievement and aggressive behavior. Secondly, social-emotional competence and social support were hypothesized to have strong influences on academic achievement and aggressive behavior in the study. Participants were 301 pupils (151 boys and 150 girls) from 2 elementary schools in Nanjing, China. The findings suggest that the students with stronger social-emotional competence performed fewer aggressive behaviors than the other peers. Keywords: children; social-emotions competence; social support; aggressio

Pengaruh Konsentrasi Ekstrak Daun Kepel (Stelechocarpus Burahol (Bl) Hook F. & Th.) Terhadap Aktivitas Antioksidan Dan Sifat Fisik Sediaan Krim

This research was aimed to determine the effect of concentrations of Kepel leaves\u27 (Stelechocarpus burahol (BL) Hook f. & Th.)extract to antioxidant activity and physical properties of cream. Kepel leaves\u27 extract were made by infundation method. The antioxidant activity was tested by DPPH (1,1-diphenyl-2-picrylhydrazyl) radical scavenging method. Cream was made in three formulas with variation concentrations of Kepel leaves\u27 extract (2,5; 5,0; 7,5%b/b) using w/o basis. Physical stability parameters tested in this research were homogenity, dispersive power, adhesion, and viscosity. Data were then analyzed statistically by ANOVA One Way and Turkey Test at 95% level of significance. The results showed that concentration of Kepel leaves\u27 extract as an active ingredient cause different color, odor, and viscosity of the cream. The concentrationdifference of Kepel Leaves\u27 extract as an active ingredient was not affected the homogenity, adhesion, and the separation ratio of the cream. The difference concentration was not cause affected daya sebar cream unless the formula II (5.0% w/w) and formula III (7.5% w/w). Increasing concentration of Kepel leaves\u27 extract caused a different antioxidant activity unless the formula II (5.0% w/w) and formula III (7.5% w/w)

Aerodynamics and motor control of ultrasonic vocalizations for social communication in mice and rats.

BACKGROUND: Rodent ultrasonic vocalizations (USVs) are crucial to their social communication and a widely used translational tool for linking gene mutations to behavior. To maximize the causal interpretation of experimental treatments, we need to understand how neural control affects USV production. However, both the aerodynamics of USV production and its neural control remain poorly understood. RESULTS: Here, we test three intralaryngeal whistle mechanisms-the wall and alar edge impingement, and shallow cavity tone-by combining in vitro larynx physiology and individual-based 3D airway reconstructions with fluid dynamics simulations. Our results show that in the mouse and rat larynx, USVs are produced by a glottal jet impinging on the thyroid inner wall. Furthermore, we implemented an empirically based motor control model that predicts motor gesture trajectories of USV call types. CONCLUSIONS: Our results identify wall impingement as the aerodynamic mechanism of USV production in rats and mice. Furthermore, our empirically based motor control model shows that both neural and anatomical components contribute to USV production, which suggests that changes in strain specific USVs or USV changes in disease models can result from both altered motor programs and laryngeal geometry. Our work provides a quantitative neuromechanical framework to evaluate the contributions of brain and body in shaping USVs and a first step in linking descending motor control to USV production

The development of non-coding RNA ontology

Identification of non-coding RNAs (ncRNAs) has been significantly improved over the past decade. On the other hand, semantic annotation of ncRNA data is facing critical challenges due to the lack of a comprehensive ontology to serve as common data elements and data exchange standards in the field. We developed the Non-Coding RNA Ontology (NCRO) to handle this situation. By providing a formally defined ncRNA controlled vocabulary, the NCRO aims to fill a specific and highly needed niche in semantic annotation of large amounts of ncRNA biological and clinical data

COVID-19 disease identification network based on weakly supervised feature selection

The coronavirus disease 2019 (COVID-19) outbreak has resulted in countless infections and deaths worldwide, posing increasing challenges for the health care system. The use of artificial intelligence to assist in diagnosis not only had a high accuracy rate but also saved time and effort in the sudden outbreak phase with the lack of doctors and medical equipment. This study aimed to propose a weakly supervised COVID-19 classification network (W-COVNet). This network was divided into three main modules: weakly supervised feature selection module (W-FS), deep learning bilinear feature fusion module (DBFF) and Grad-CAM++ based network visualization module (Grad-Ⅴ). The first module, W-FS, mainly removed redundant background features from computed tomography (CT) images, performed feature selection and retained core feature regions. The second module, DBFF, mainly used two symmetric networks to extract different features and thus obtain rich complementary features. The third module, Grad-Ⅴ, allowed the visualization of lesions in unlabeled images. A fivefold cross-validation experiment showed an average classification accuracy of 85.3%, and a comparison with seven advanced classification models showed that our proposed network had a better performance

Non-invasive and accurate risk evaluation of cerebrovascular disease using retinal fundus photo based on deep learning

BackgroundCerebrovascular disease (CeVD) is a prominent contributor to global mortality and profound disability. Extensive research has unveiled a connection between CeVD and retinal microvascular abnormalities. Nonetheless, manual analysis of fundus images remains a laborious and time-consuming task. Consequently, our objective is to develop a risk prediction model that utilizes retinal fundus photo to noninvasively and accurately assess cerebrovascular risks.Materials and methodsTo leverage retinal fundus photo for CeVD risk evaluation, we proposed a novel model called Efficient Attention which combines the convolutional neural network with attention mechanism. This combination aims to reinforce the salient features present in fundus photos, consequently improving the accuracy and effectiveness of cerebrovascular risk assessment.ResultOur proposed model demonstrates notable advancements compared to the conventional ResNet and Efficient-Net architectures. The accuracy (ACC) of our model is 0.834 ± 0.03, surpassing Efficient-Net by a margin of 3.6%. Additionally, our model exhibits an improved area under the receiver operating characteristic curve (AUC) of 0.904 ± 0.02, surpassing other methods by a margin of 2.2%.ConclusionThis paper provides compelling evidence that Efficient-Attention methods can serve as effective and accurate tool for cerebrovascular risk. The results of the study strongly support the notion that retinal fundus photo holds great potential as a reliable predictor of CeVD, which offers a noninvasive, convenient and low-cost solution for large scale screening of CeVD