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Prevalence of hearing loss in children with autism spectrum disorder (ASD) is uncertain, as it is more challenging to assess hearing function in children with developmental difficulties (DD). We aimed to determine the prevalence and profiles of hearing loss in preschool children with ASD in a Southeast-Asian population who passed newborn hearing screening. A retrospective study of preschool children with DD (ASD, Global Developmental Delay (GDD), and Speech and Language Delay (SLD)) attending the Child Development Unit (CDU) at our hospital was performed. Three hundred and thirty-three children (ASD: n = 129; GDD: n = 110; and SLD: n = 94) underwent hearing assessments. Of these, 10.8% of children (n = 36, comprising 15 with ASD, 12 with GDD and 9 with SLD) had confirmed hearing loss. Hearing loss was predominantly bilateral in children with ASD and GDD; in those with SLD, unilateral and bilateral hearing loss were equally common. Conductive hearing loss occurred as frequently as sensorineural hearing loss in children with ASD and SLD, but was the dominant subtype in those with GDD. Moderate to severe hearing loss (n = 2) was noted only in children with ASD. Children with ASD and GDD required significantly more audiology visits and procedures to obtain conclusive hearing test results, compared to those with SLD. The need to identify hearing loss and monitor for resolution is particularly important in vulnerable populations with communication deficits, such as in those with ASD.

Background: CD276 is a promising immune checkpoint molecule with significant therapeutic potential. Several clinical trials are currently investigating CD276-targeted therapies. Purpose: This study aims to assess the prognostic significance of CD276 expression levels and to predict its expression using a radiomic approach in breast cancer (BC). Methods: A cohort of 840 patients diagnosed with BC from The Cancer Genome Atlas was included in this study. The Cancer Imaging Archive provided 98 magnetic resonance imaging (MRI) scans, which were randomly allocated to training and validation datasets in a 7:3 ratio. The association between CD276 expression and patient survival was assessed using Cox regression analysis. Feature selection was performed using the maximum relevance minimum redundancy algorithm and recursive feature elimination. Subsequently, support vector machine (SVM) and logistic regression (LR) models were constructed to predict CD276 expression. Results: The expression of CD276 was found to be elevated in BC. It was an independent risk factor for overall survival (hazard ratio = 1.579, 95 % CI: 1.054–2.366). There were eight radiomic features selected in total. In both the training and validation subsets, the SVM and LR models demonstrated favorable predictive abilities with AUC values of 0.744 and 0.740 for the SVM model and 0.742 and 0.735 for the LR model. These results indicate that the radiomic models efficiently differentiate the CD276 expression status. Conclusions: CD276 expression levels can have an impact on cancer prognosis. The MRI-based radiomic signature described in this study can discriminate the CD276 expression status.

The corrosion of steel stirrups is recognized as a significant contributing factor to shear deterioration in reinforced concrete (RC) structures, leading to detrimental effects on structural performance and potential early failures during service. To investigate the effect of corrosion levels on the shear behavior of RC beams, a series of three-point bending tests were conducted in this study. Furthermore, finite element analyses were employed to duplicate the shear behavior and failure modes observed in the experiments. The numerical outcomes are aligned with experimental results, confirming the validity of the developed constitutive models. The findings reveal that a low corrosion level (5%) in steel stirrups exhibits a limited impact on the overall structural shear capacity (-4%-0.9%). However, a higher corrosion level (12%) in stirrups induces a more pronounced reduction in structural shear capacity. This reduction is particularly prominent in scenarios involving a high shear span-to-depth (av/d) ratio. The developed finite element model, incorporating comprehensive constitutive models for individual components, offers engineers a prospective approach for forecasting the long-term shear performance of corroded RC beams.

The quantitative evaluation of restocking and stock replenishment is essential for providing operational feedback and implementing adaptive management for future restoration projects. Since 2010, approximately 700 million juvenile shrimp (Penaeus penicillatus) have been released into Xiamen Bay, Fujian Province, China, each year, through stock replenishment programs. The recruited shrimp were sampled through three-year bottom trawl surveys from 2014 to 2017. The biological characteristics and catch equation were used to evaluate the effect of restocking and stock replenishment. The analysis uses the FAO-ICLARM Stock Assessment Tool (FISAT II) program. In general, there are two sources of recruitments—one from spawning brood stock and the other from released juvenile shrimp. We constructed an evaluation model for an effect evaluation based on Baranov’s catch equation to separate the initial recruitment volume using survey data. The relationship between body weight and total length was W = 1.638 TL2.9307. There is no statistically significant difference between males and females. The von Bertalanffy growth parameters derived for prawns, using FiSAT II, were L∞ = 209.6 mm and K = 0.51 per year. In spring 2014, the initial resource amount was 49,200, while the ratio of effective recruitment and parent amount was 3.92. The survival rate of the released shrimp larvae, 1.88‱, seems to be very unsatisfactory. The resource amount in summer and autumn is higher than in winter and spring. Obviously, the restocking effect is lower and the programs need to be improved. To improve the restocking effect, the replenishment performance should be adjusted to reduce the mortality rate and increase its release effectiveness. Therefore, corresponding implementations are recommended, including standard extensive culture, reduction in stress during transportation, and temporary culture.

Trichosanthes kirilowii Maxim. is one of the original plants for traditional Chinese medicines Trichosanthis Fructus, Trichosanthis Semen, Trichosanthis Pericarpium and Trichosanthis Radix. Amino acids, nucleosides and carbohydrates are usually considered to have nutritional value and health-care efficacy. In this study, methods involving high-performance liquid chromatography coupled with evaporative light scattering detector (HPLC-ELSD), UV-visible spectrophotometry and ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) were established for quantifying carbohydrates (fructose, glucose, stachyose, raffinose and polysaccharide), fourteen nucleosides and twenty one amino acids. Moreover, sixty-three samples from nine different parts, including pericarp, seed, fruit pulp, stem, leaf, main root, main root bark, lateral root and lateral root bark of T. kirilowii from different cultivated varieties were examined. The established methods were validated with good linearity, precision, repeatability, stability, and recovery. The results showed that the average content of total amino acids in roots (15.39 mg/g) and root barks (16.38 mg/g) were relatively higher than for others. Contents of nucleosides in all parts of T. kirilowii were below 1.5 mg/g. For carbohydrates, fruit pulp has a higher content than others for glucose (22.91%), fructose (20.63%) and polysaccharides (27.29%). By using partial least-squared discriminate analysis (PLS-DA), Variable importance in the projection (VIP) plots and analysis of variance (ANOVA) analysis, the characteristic components of the different organs (fruit, stems and leaves, roots) were found. This analysis suggested there were potential medicinal and nutritive health care values in various parts of the T. kirilowii, which provided valuable information for the development and utilization of T. kirilowii.

Varying types of shots is a fundamental element in the language of film, commonly used by a visual storytelling director. The technique is often used in creating professional recordings of a live concert, but meanwhile may not be appropriately applied in audience recordings of the same event. Such variations could cause the task of classifying shots in concert videos, professional or amateur, very challenging. To achieve more reliable shot classification, we propose a novel probabilistic-based approach, named as coherent classification net (CC-Net), by addressing three crucial issues. First, we focus on learning more effective features by fusing the layer-wise outputs extracted from a deep convolutional neural network (CNN), pretrained on a large-scale data set for object recognition. Second, we introduce a frame-wise classification scheme, the error weighted deep cross-correlation model (EW-Deep-CCM), to boost the classification accuracy. Specifically, the deep neural network-based cross-correlation model (deep-CCM) is constructed to not only model the extracted feature hierarchies of CNN independently, but also relate the statistical dependencies of paired features from different layers. Then, a Bayesian error weighting scheme for a classifier combination is adopted to explore the contributions from individual Deep-CCM classifiers to enhance the accuracy of shot classification in each image frame. Third, we feed the frame-wise classification results to a linear-chain conditional random field module to refine the shot predictions by taking into account the global and temporal regularities. We provide extensive experimental results on a data set of live concert videos to demonstrate the advantage of the proposed CC-Net over existing popular fusion approaches for shot classification. [ABSTRACT FROM AUTHOR]

Interaction Style (IS) refers to patterns of interaction containing highly contextual and innate information. Awareness of our IS can help us discover interpersonal conflicts and guide us how to interact with others. Recently, automatic IS recognition is becoming increasingly important in the design of a dialogue system for harmonious interaction. With the goal to select appropriate responses, four IS types proposed by Berens are selected as the basis for our study. In this study, multiple views (multi-views) of the utterances during interaction, including emotions and dialogue topics, are recognized first. Inspired by the emotion profile theory, the IS profiles are then extracted using the multi-view features to better characterize the IS of the interactional utterances. Similar to the multilayer architectures in deep neural networks, a multi-layer multi-view IS profile representation method, structured layer by layer through embedding the multi-views, is proposed to better interpret intermediate representations in the feature space of the interactional utterances based on a probabilistic fusion model. The IS is finally recognized by using the Support Vector Machine (SVM) based on the obtained IS profiles. Experimental results demonstrate that the proposed method achieved an encouraging IS recognition accuracy and outperformed the previous method. [ABSTRACT FROM PUBLISHER]

IL11 contributes to the development of non-alcoholic steatohepatitis (NASH) through incompletely understood mechanisms. Here, the authors report that lipotoxicity-driven autocrine IL11 activity underlies hepatocyte metabolic dysfunction and death via a NOX4/ERK-mediated mechanism while paracrine IL11 activity stimulates hepatic stellate cells contributing to fibrosis and inflammation in the context of NASH.

This paper aimed to explore the mechanical properties of a cement-based material with carbon nanotube (CNT) under drying and freeze-thaw environments. Mercury Intrusion Porosimetry and Scanning Electron Microscopy were used to analyze the pore structure and microstructure of CNT/cement composite, respectively. The experimental results showed that multi-walled CNT (MWCNT) could improve to different degrees the mechanical properties (compressive and flexural strengths) and physical performances (shrinkage and water loss) of cement-based materials under drying and freeze-thaw conditions. This paper also demonstrated that MWCNT could interconnect hydration products to enhance the performance of anti-microcracks for cement-based materials, as well as the density of materials due to CNT's filling action. [ABSTRACT FROM AUTHOR]

Speaking effect is a crucial issue that may dramatically degrade performance in emotion recognition from facial expressions. To manage this problem, an eigenface conversion-based approach is proposed to remove speaking effect on facial expressions for improving accuracy of emotion recognition. In the proposed approach, a context-dependent linear conversion function modeled by a statistical Gaussian Mixture Model (GMM) is constructed with parallel data from speaking and non-speaking facial expressions with emotions. To model the speaking effect in more detail, the conversion functions are categorized using a decision tree considering the visual temporal context of the Articulatory Attribute (AA) classes of the corresponding input speech segments. For verification of the identified quadrant of emotional expression on the Arousal-Valence (A-V) emotion plane, which is commonly used to dimensionally define the emotion classes, from the reconstructed facial feature points, an expression template is constructed to represent the feature points of the non-speaking facial expressions for each quadrant. With the verified quadrant, a regression scheme is further employed to estimate the A-V values of the facial expression as a precise point in the A-V emotion plane. Experimental results show that the proposed method outperforms current approaches and demonstrates that removing the speaking effect on facial expression is useful for improving the performance of emotion recognition. [ABSTRACT FROM PUBLISHER]

A complete emotional expression typically contains a complex temporal course in face-to-face natural conversation. To address this problem, a bimodal hidden Markov model (HMM)-based emotion recognition scheme, constructed in terms of sub-emotional states, which are defined to represent temporal phases of onset, apex, and offset, is adopted to model the temporal course of an emotional expression for audio and visual signal streams. A two-level hierarchical alignment mechanism is proposed to align the relationship within and between the temporal phases in the audio and visual HMM sequences at the model and state levels in a proposed semi-coupled hidden Markov model (SC-HMM). Furthermore, by integrating a sub-emotion language model, which considers the temporal transition between sub-emotional states, the proposed two-level hierarchical alignment-based SC-HMM (2H-SC-HMM) can provide a constraint on allowable temporal structures to determine an optimal emotional state. Experimental results show that the proposed approach can yield satisfactory results in both the posed MHMC and the naturalistic SEMAINE databases, and shows that modeling the complex temporal structure is useful to improve the emotion recognition performance, especially for the naturalistic database (i.e., natural conversation). The experimental results also confirm that the proposed 2H-SC-HMM can achieve an acceptable performance for the systems with sparse training data or noisy conditions. [ABSTRACT FROM PUBLISHER]

This paper presents an approach to the automatic recognition of human emotions from audio-visual bimodal signals using an error weighted semi-coupled hidden Markov model (EWSC-HMM). The proposed approach combines an SC-HMM with a state-based bimodal alignment strategy and a Bayesian classifier weighting scheme to obtain the optimal emotion recognition result based on audio-visual bimodal fusion. The state-based bimodal alignment strategy in SC-HMM is proposed to align the temporal relation between audio and visual streams. The Bayesian classifier weighting scheme is then adopted to explore the contributions of the SC-HMM-based classifiers for different audio-visual feature pairs in order to obtain the emotion recognition output. For performance evaluation, two databases are considered: the MHMC posed database and the SEMAINE naturalistic database. Experimental results show that the proposed approach not only outperforms other fusion-based bimodal emotion recognition methods for posed expressions but also provides satisfactory results for naturalistic expressions. [ABSTRACT FROM PUBLISHER]

ObjectiveTo assess the impact of the COVID-19 pandemic on the presentation, treatment, and survival of patients with early breast cancer (Stage I–III).MethodsThis study utilized data from the Surveillance, Epidemiology, and End Results database from January 1, 2018, to December 31, 2020. Patients diagnosed with primary breast cancer in 2020 were compared to those diagnosed in 2018 or 2019. The primary outcomes were stage distribution and changes in the treatment modalities for early breast cancer. The secondary outcomes were overall survival (OS) and breast cancer-specific survival (BCSS).ResultsWe analyzed 142,038 patients. There has been a decrease in breast cancer diagnoses in 2020, as well as the smaller number of surgeries. The distribution of breast cancer stages among patients exhibited a notable shift in 2020, with a decrease in the proportion of Stage 0-I and an increase in advanced-stage. Additionally, there was a significant decrease in the proportion of breast-conserving surgery (BCS) performed in 2020. The proportion of patients undergoing radiation decreased, while that of chemotherapy cases increased significantly in 2020. Patients showed a shorter treatment delay in 2020 than in 2018 or 2019 (2018: hazard ratio [HR] = 0.969, 95% confidence interval [CI] = 0.956–0.982, p < 0.001; 2019: HR=0.959, 95% CI = 0.946–0.972, p < 0.001). Diagnosis in 2020 showed a significant correlation with worse OS than diagnosis in 2018 (HR = 0.861, 95% CI = 0.743-0.996, p = 0.045).ConclusionWe observed a shift to advanced-stage and a change of treatment modalities of early breast cancer in 2020. The OS of patients with breast cancer was worse during the pandemic than before the pandemic. The findings could provide empirical basis for optimizing cancer prevention and control strategies in future public health emergencies.