Role of Phosphatidylinositol-3-Kinase Pathway in Head and Neck Squamous Cell Carcinoma

Activation of the phosphatidylinositol-3-kinase (PI3K) pathway is one of the most frequently observed molecular alterations in many human malignancies, including head and neck squamous cell carcinoma (HNSCC). A growing body of evidence demonstrates the prime importance of the PI3K pathway at each stage of tumorigenesis, that is, tumor initiation, progression, recurrence, and metastasis. Expectedly, targeting the PI3K pathway yields some promising results in both preclinical studies and clinical trials for certain cancer patients. However, there are still many questions that need to be answered, given the complexity of this pathway and the existence of its multiple feedback loops and interactions with other signaling pathways. In this paper, we will summarize recent advances in the understanding of the PI3K pathway role in human malignancies, with an emphasis on HNSCC, and discuss the clinical applications and future direction of this field

Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.Comment: Accepted by AAAI202

Interictal Abnormalities of Neuromagnetic Gamma Oscillations in Migraine Following Negative Emotional Stimulation

Here, we aimed to investigate brain activity in migraineurs in response to emotional stimulation. Magnetoencephalography (MEG) was used to examine 20 patients with episodic migraine (EM group), 15 patients with chronic migraine (CM group), and 35 healthy participants (control group). Neuromagnetic brain activity was elicited by emotional stimulation using photographs of facial expressions. We analyzed the latency and amplitude of M100 and M170 components and used Morlet wavelet and beamformers to analyze the spectral and spatial signatures of MEG signals in gamma band (30–100 Hz). We found that the timing and frequency of MEG activity differed across the three groups in response negative emotional stimuli. First, peak M170 amplitude was significantly lower in the CM group than in the control group. Second, compared with the control group, the average spectral power was significantly lower in the EM group and CM group at M100 and M170. Third, the average spectral powers of the M100 and M170 in the CM group were negatively correlated with either HAM-D scores or migraine attack frequency. No significant differences across groups was found for positive or neutral emotional stimuli. Furthermore, after negative emotional stimuli, the MEG source analysis demonstrated that the CM group showed a significantly higher percentage of amygdala activation than the control group for M100 and M170. Thus, during headache free phases, migraineurs have abnormal brain activity in the gamma band in response to negative emotional stimuli.Trial Registration:ChiCTR-RNC-17012599. Registered 7 September, 2017

Central Angiotensin II Stimulation Promotes β Amyloid Production in Sprague Dawley Rats

BACKGROUND: Stress and various stress hormones, including catecholamines and glucocorticoids, have recently been implicated in the pathogenesis of Alzheimer's disease (AD), which represents the greatest unresolved medical challenge in neurology. Angiotensin receptor blockers have shown benefits in AD and prone-to-AD animals. However, the mechanisms responsible for their efficacy remain unknown, and no studies have directly addressed the role of central angiotensin II (Ang II), a fundamental stress hormone, in the pathogenesis of AD. The present study focused on the role of central Ang II in amyloidogenesis, the critical process in AD neuropathology, and aimed to provide direct evidence for the role of this stress hormone in the pathogenesis of AD. METHODOLOGY/PRINCIPAL FINDINGS: Increased central Ang II levels during stress response were modeled by intracerebroventricular (ICV) administration of graded doses of Ang II (6 ng/hr low dose, 60 ng/hr medium dose, and 600 ng/hr high dose, all delivered at a rate of 0.25 µl/hr) to male Sprague Dawley rats (280-310 g) via osmotic pumps. After 1 week of continuous Ang II infusion, the stimulation of Ang II type 1 receptors was accompanied by the modulation of amyloid precursor protein, α-, β-and γ-secretase, and increased β amyloid production. These effects could be completely abolished by concomitant ICV infusion of losartan, indicating that central Ang II played a causative role in these alterations. CONCLUSIONS/SIGNIFICANCE: Central Ang II is essential to the stress response, and the results of this study suggest that increased central Ang II levels play an important role in amyloidogenesis during stress, and that central Ang II-directed stress prevention and treatment might represent a novel anti-AD strategy

Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller

Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft

This paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed via dynamic inversion method using neural network. To deal with input constraints, the additional compensation system is employed to help engine recover from input saturation rapidly. The highlight is that high order integral chained differentiator is used to estimate the newly defined variables and an adaptive neural controller is designed for the altitude subsystem where only one neural network is employed to approximate the lumped uncertain nonlinearity. The altitude subsystem controller is considerably simpler than the ones based on backstepping. It is proved using Lyapunov stability theory that the proposed control law can ensure that all the tracking error converges to an arbitrarily small neighborhood around zero. Numerical simulation study demonstrates the effectiveness of the proposed strategy, during the morphing process, in spite of some uncertain system nonlinearity

Altered Functional and Causal Connectivity of Cerebello-Cortical Circuits between Multiple System Atrophy (Parkinsonian Type) and Parkinson’s Disease

Lesions of the cerebellum lead to motor and non-motor deficits by influencing cerebral cortex activity via cerebello-cortical circuits. It remains unknown whether the cerebello-cortical “disconnection” underlies motor and non-motor impairments both in the parkinsonian variant of multiple system atrophy (MSA-P) and Parkinson’s disease (PD). In this study, we investigated both the functional and effective connectivity of the cerebello-cortical circuits from resting-state functional magnetic resonance imaging (rs-fMRI) data of three groups (26 MSA-P patients, 31 PD patients, and 30 controls). Correlation analysis was performed between the causal connectivity and clinical scores. PD patients showed a weakened cerebellar dentate nucleus (DN) functional coupling in the posterior cingulate cortex (PCC) and inferior parietal lobe compared with MSA-P or controls. MSA-P patients exhibited significantly enhanced effective connectivity from the DN to PCC compared with PD patients or controls, as well as declined causal connectivity from the left precentral gyrus to right DN compared with the controls, and this value is significantly correlated with the motor symptom scores. Our findings demonstrated a crucial role for the cerebello-cortical networks in both MSA-P and PD patients in addition to striatal-thalamo-cortical (STC) networks and indicated that different patterns of cerebello-cortical loop degeneration are involved in the development of the diseases