Gaze Patterns in Children With Autism Spectrum Disorder to Emotional Faces: Scanpath and Similarity.

Autism spectrum disorder (ASD) one of the fastest-growing diseases in the world is a group of neurodevelopmental disorders. Eye movement as a biomarker and clinical manifestation represents unconscious brain processes that can objectively disclose abnormal eye fixation of ASD. With the aid of eye-tracking technology, plentiful methods that identify ASD based on eye movements have been developed, but there are rarely works specifically for scanpaths. Scanpaths as visual representations describe eye movement dynamics on stimuli. In this paper, we propose a scanpath-based ASD detection method, which aims to learn the atypical visual pattern of ASD through continuous dynamic changes in gaze distribution. We extract four sequence features from scanpaths that represent changes and the differences in feature space and gaze behavior patterns between ASD and typical development (TD) are explored based on two similarity measures, multimatch and dynamic time warping (DTW). It indicates that ASD children show more individual specificity, while normal children tend to develop similar visual patterns. The most noticeable contrasts lie in the duration of attention and the spatial distribution of visual attention along the vertical direction. Classification is performed using Long Short-Term Memory (LSTM) network with different structures and variants. The experimental results show that LSTM network outperforms traditional machine learning methods.

Three-Stream Convolutional Neural Network for Depression Detection With Ocular Imaging.

Depression is a prevalent and severe mental disorder that significantly affects both mind and body, leading to persistent feelings of sadness, despair, and impaired functionality. Diagnosis of depression primarily relies on clinical assessment and observation of symptoms. However, due to the lack of objective indicators, the experience and skills of doctor may lead to misdiagnosis. Current researches indicate that eye movement patterns and pupil dilation can serve as potential biomarkers for emotional and cognitive dysregulation in individuals with depression. However, most studies are based on manually extracted eye movement features, overlooking a significant portion of information available in ocular imaging. This paper proposes Three-Stream Convolutional Neural Network (TSCNN) for detecting depression, leveraging both spatio-temporal information of raw ocular imaging and paradigmatic semantic features. We suggest using optical flow with different sampling intervals to capture temporal features. In the third stream, we employ an encoder to learn semantic information from paradigm images and use it as prior knowledge. Finally, we utilize a fully connected network for classification, achieving an accuracy of 79.3% on our self-collected dataset. The proposed method may demonstrate significant clinical utility in the future.

Trial Selection Tensor Canonical Correlation Analysis (TSTCCA) for Depression Recognition with Facial Expression and Pupil Diameter.

Facial expressions have been widely used for depression recognition because it is intuitive and convenient to access. Pupil diameter contains rich emotional information that is already reflected in facial video streams. However, the spatiotemporal correlation between pupillary changes and facial behavior changes induced by emotional stimuli has not been explored in existing studies. This paper presents a novel multimodal fusion algorithm - Trial Selection Tensor Canonical Correlation Analysis (TSTCCA) to optimize the feature space and build a more robust depression recognition model, which innovatively combines the spatiotemporal relevance and complementarity between facial expression and pupil diameter features. TSTCCA explores the interaction between trials and obtains an effective fusion representation of two modalities from a trial subset related to depression. The experimental results show that TSTCCA achieves the highest accuracy of 78.81% with the subset of 25 trials.

Detecting depression based on facial cues elicited by emotional stimuli in video.

Recently, depression research has received considerable attention and there is an urgent need for objective and validated methods to detect depression. Depression detection based on facial expressions may be a promising adjunct to depression detection due to its non-contact nature. Stimulated facial expressions may contain more information that is useful in detecting depression than natural facial expressions. To explore facial cues in healthy controls and depressed patients in response to different emotional stimuli, facial expressions of 62 subjects were collected while watching video stimuli, and a local face reorganization method for depression detection is proposed. The method extracts the local phase pattern features, facial action unit (AU) features and head motion features of a local face reconstructed according to facial proportions, and then fed into the classifier for classification. The classification accuracy was 76.25%, with a recall of 80.44% and a specificity of 83.21%. The results demonstrated that the negative video stimuli in the single-attribute stimulus analysis were more effective in eliciting changes in facial expressions in both healthy controls and depressed patients. Fusion of facial features under both neutral and negative stimuli was found to be useful in discriminating between healthy controls and depressed individuals. The Pearson correlation coefficient (PCC) showed that changes in the emotional stimulus paradigm were more strongly correlated with changes in subjects' facial AU when exposed to negative stimuli compared to stimuli of other attributes. These results demonstrate the feasibility of our proposed method and provide a framework for future work in assisting diagnosis.

What Can Facial Movements Reveal? Depression Recognition and Analysis Based on Optical Flow Using Bayesian Networks.

Recent evidence have demonstrated that facial expressions could be a valid and important aspect for depression recognition. Although various works have been achieved in automatic depression recognition, it is a challenge to explore the inherent nuances of facial expressions that might reveal the underlying differences between depressed patients and healthy subjects under different stimuli. There is a lack of an undisturbed system that monitors depressive patients' mental states in various free-living scenarios, so this paper steps towards building a classification model where data collection, feature extraction, depression recognition and facial actions analysis are conducted to infer the differences of facial movements between depressive patients and healthy subjects. In this study, we firstly present a plan of dividing facial regions of interest to extract optical flow features of facial expressions for depression recognition. We then propose facial movements coefficients utilising discrete wavelet transformation. Specifically, Bayesian Networks equipped with construction of Pearson Correlation Coefficients based on discrete wavelet transformation is learnt, which allows for analysing movements of different facial regions. We evaluate our method on a clinically validated dataset of 30 depressed patients and 30 healthy control subjects, and experiments results obtained the accuracy and recall of 81.7%, 96.7%, respectively, outperforming other features for comparison. Most importantly, the Bayesian Networks we built on the coefficients under different stimuli may reveal some facial action patterns of depressed subjects, which have a potential to assist the automatic diagnosis of depression.

Predicting Recurrence in Pancreatic Ductal Adenocarcinoma after Radical Surgery Using an AX-Unet Pancreas Segmentation Model and Dynamic Nomogram.

This study aims to investigate the reliability of radiomic features extracted from contrast-enhanced computer tomography (CT) by AX-Unet, a pancreas segmentation model, to analyse the recurrence of pancreatic ductal adenocarcinoma (PDAC) after radical surgery. In this study, we trained an AX-Unet model to extract the radiomic features from preoperative contrast-enhanced CT images on a training set of 205 PDAC patients. Then we evaluated the segmentation ability of AX-Unet and the relationship between radiomic features and clinical characteristics on an independent testing set of 64 patients with clear prognoses. The lasso regression analysis was used to screen for variables of interest affecting patients' post-operative recurrence, and the Cox proportional risk model regression analysis was used to screen for risk factors and create a nomogram prediction model. The proposed model achieved an accuracy of 85.9% for pancreas segmentation, meeting the requirements of most clinical applications. Radiomic features were found to be significantly correlated with clinical characteristics such as lymph node metastasis, resectability status, and abnormally elevated serum carbohydrate antigen 19-9 (CA 19-9) levels. Specifically, variance and entropy were associated with the recurrence rate (p < 0.05). The AUC for the nomogram predicting whether the patient recurred after surgery was 0.92 (95% CI: 0.78-0.99) and the C index was 0.62 (95% CI: 0.48-0.78). The AX-Unet pancreas segmentation model shows promise in analysing recurrence risk factors after radical surgery for PDAC. Additionally, our findings suggest that a dynamic nomogram model based on AX-Unet can provide pancreatic oncologists with more accurate prognostic assessments for their patients.

Simulation Study on the Structure Design of p-GaN/AlGaN/GaN HEMT-Based Ultraviolet Phototransistors.

This work investigates the impacts of structural parameters on the performances of p-GaN/AlGaN/GaN HEMT-based ultraviolet (UV) phototransistors (PTs) using Silvaco Atlas. The simulation results show that a larger Al content or greater thickness for the AlGaN barrier layer can induce a higher two-dimensional electron gas (2DEG) density and produce a larger photocurrent. However, they may also lead to a larger dark current due to the incomplete depletion of the GaN channel layer. The depletion conditions with various Al contents and thicknesses of the AlGaN layer are investigated in detail, and a borderline between full depletion and incomplete depletion was drawn. An optimized structure with an Al content of 0.23 and a thickness of 14 nm is achieved for UV-PT, which exhibits a high photocurrent density of 92.11 mA/mm, a low dark current density of 7.68 × 10-10 mA/mm, and a large photo-to-dark-current ratio of over 1011 at a drain voltage of 5 V. In addition, the effects of other structural parameters, such as the thickness and hole concentration of the p-GaN layer as well as the thickness of the GaN channel layer, on the performances of the UV-PTs are also studied in this work.

ALKBH5-Mediated m6A Demethylation of GLUT4 mRNA Promotes Glycolysis and Resistance to HER2-Targeted Therapy in Breast Cancer.

Resistance to HER2-targeted therapy represents a significant challenge for the successful treatment of patients with breast cancer with HER2-positive tumors. Through a global mass spectrometry-based proteomics approach, we discovered that the expression of the N6-methyladenosine (m6A) demethylase ALKBH5 was significantly upregulated in HER2-targeted therapy-resistant breast cancer cells. Elevated expression of ALKBH5 was sufficient to confer resistance to HER2-targeted therapy, and specific knockdown of ALKBH5 rescued the efficacy of trastuzumab and lapatinib in resistant breast cancer cells. Mechanistically, ALKBH5 promoted m6A demethylation of GLUT4 mRNA and increased GLUT4 mRNA stability in a YTHDF2-dependent manner, resulting in enhanced glycolysis in resistant breast cancer cells. In breast cancer tissues obtained from patients with poor response to HER2-targeted therapy, increased expression of ALKBH5 or GLUT4 was observed and was significantly associated with poor prognosis in the patients. Moreover, suppression of GLUT4 via genetic knockdown or pharmacologic targeting with a specific inhibitor profoundly restored the response of resistant breast cancer cells to trastuzumab and lapatinib, both in vitro and in vivo. In conclusion, ALKBH5-mediated m6A demethylation of GLUT4 mRNA promotes resistance to HER2-targeted therapy, and targeting the ALKBH5/GLUT4 axis has therapeutic potential for treating patients with breast cancer refractory to HER2-targeted therapies. SIGNIFICANCE: GLUT4 upregulation by ALKBH5-mediated m6A demethylation induces glycolysis and resistance to HER2-targeted therapy and represents a potential therapeutic target for treating HER2-positive breast cancer.

P21-activated kinase 2-mediated ß-catenin signaling promotes cancer stemness and osimertinib resistance in EGFR-mutant non-small-cell lung cancer.

Osimertinib (AZD9291) is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI), used for treating patients with advanced non-small-cell lung cancer (NSCLC) harboring EGFR-activating mutations or the resistant T790M mutation. However, acquired resistance to osimertinib is inevitable in EGFR-mutant NSCLC. By employing a global mass spectrometry-based phosphoproteomics approach, we identified that the activated p21-activated kinase 2 (PAK2)/ß-catenin axis acts as a driver of osimertinib resistance. We found that PAK2 directly phosphorylates ß-catenin and increases the nuclear localization of ß-catenin, leading to the increased expression and transcriptional activity of ß-catenin, which in turn enhances cancer stem-like properties and osimertinib resistance. Moreover, we revealed that HER3 as an upstream regulator of PAK2, drives the activation of PAK2/ß-catenin pathways in osimertinib-resistant cells. The clinical relevance of these findings was further confirmed by examining tissue specimens from patients with EGFR-mutant NSCLC. The results demonstrated that the levels of HER3, phospho-PAK2 (p-PAK2) and ß-catenin in the tissues from patients with EGFR-mutant NSCLC, that had relapsed after treatment with osimertinib, were elevated compared to those of the corresponding untreated tissues. Additionally, the high levels of HER3, p-PAK2 and ß-catenin correlated with shorter progression-free survival (PFS) in patients with EGFR-TKI-treated NSCLC. We additionally observed that the suppression of PAK2 via knockdown or pharmacological targeting with PAK inhibitors markedly restored the response of osimertinib-resistant NSCLC cells to osimertinib both in vitro and in vivo. In conclusion, these results indicated that the PAK2-mediated activation of ß-catenin is important for osimertinib resistance and targeting the HER3/PAK2/ß-catenin pathway has potential therapeutic value in NSCLCs with acquired resistance to osimertinib.

AX-Unet: A Deep Learning Framework for Image Segmentation to Assist Pancreatic Tumor Diagnosis.

Image segmentation plays an essential role in medical imaging analysis such as tumor boundary extraction. Recently, deep learning techniques have dramatically improved performance for image segmentation. However, an important factor preventing deep neural networks from going further is the information loss during the information propagation process. In this article, we present AX-Unet, a deep learning framework incorporating a modified atrous spatial pyramid pooling module to learn the location information and to extract multi-level contextual information to reduce information loss during downsampling. We also introduce a special group convolution operation on the feature map at each level to achieve information decoupling between channels. In addition, we propose an explicit boundary-aware loss function to tackle the blurry boundary problem. We evaluate our model on two public Pancreas-CT datasets, NIH Pancreas-CT dataset, and the pancreas part in medical segmentation decathlon (MSD) medical dataset. The experimental results validate that our model can outperform the state-of-the-art methods in pancreas CT image segmentation. By comparing the extracted feature output of our model, we find that the pancreatic region of normal people and patients with pancreatic tumors shows significant differences. This could provide a promising and reliable way to assist physicians for the screening of pancreatic tumors.

A multi-modal open dataset for mental-disorder analysis.

According to the WHO, the number of mental disorder patients, especially depression patients, has overgrown and become a leading contributor to the global burden of disease. With the rising of tools such as artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and recordings of spoken language data from clinically depressed patients and matching normal controls, who were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes data collected using a traditional 128-electrodes mounted elastic cap and a wearable 3-electrode EEG collector for pervasive computing applications. The 128-electrodes EEG signals of 53 participants were recorded as both in resting state and while doing the Dot probe tasks; the 3-electrode EEG signals of 55 participants were recorded in resting-state; the audio data of 52 participants were recorded during interviewing, reading, and picture description.

Targeting nicotinamide N-methyltransferase overcomes resistance to EGFR-TKI in non-small cell lung cancer cells.

Activating mutations of epidermal growth factor receptor (EGFR) contributes to the progression of non-small cell lung cancer (NSCLC). EGFR tyrosine kinase inhibitor (TKI)-targeted therapy has become the standard treatment for NSCLC patients with EGFR-mutations. However, acquired resistance to these agents remains a major obstacle for managing NSCLC. Here, we investigated a novel strategy to overcome EGFR TKI resistance by targeting the nicotinamide N-methyltransferase (NNMT). Using iTRAQ-based quantitative proteomics analysis, we identified that NNMT was significantly increased in EGFR-TKI-resistant NSCLC cells. Moreover, we found that NNMT expression was increased in EGFR-TKI-resistant NSCLC tissue samples, and higher levels were correlated with shorter progression-free survival in EGFR-TKI-treated NSCLC patients. Knockdown of NNMT rendered EGFR-TKI-resistant cells more sensitive to EGFR-TKI, whereas overexpression of NNMT in EGFR-TKI-sensitive cells resulted in EGFR-TKI resistance. Mechanically, upregulation of NNMT increased c-myc expression via SIRT1-mediated c-myc deacetylation, which in turn promoted glycolysis and EGFR-TKI resistance. Furthermore, we demonstrated that the combination of NNMT inhibitor and EGFR-TKI strikingly suppressed the growth of EGFR-TKI-resistant NSCLC cells both in vitro and in vivo. In conclusion, our research indicated that NNMT overexpression is important for acquired resistance to EGFR-TKI and that targeting NNMT might be a potential therapeutic strategy to overcome resistance to EGFR TKI.