Optimization of Mg-Al Layered Double Hydroxide Film Preparation and Corrosion Resistance Study on AZ91D Mg Alloy by Multivariate Polynomial Regression Fitting.

Layered double hydroxide (LDH) films have received extensive attention for their unique physical barrier function and ion exchange properties, which make them promising candidates for corrosion protection of magnesium alloys. In this paper, we used the multiple polynomial regression fitting method to establish a regression equation for the electrochemical corrosion resistance with the reaction temperature (T), pH, and reaction time (t) of the Mg-Al LDH film on the AZ91D magnesium alloy. The goodness of fit, confidence, and residual analyses confirmed the high accuracy of the model equation. According to the calculation using the fmincon function, the best corrosion resistance of the prepared samples could be achieved when the parameters are T = 135 °C, pH = 12.0, and t = 15 h. Then, the experimental results showed that the corrosion current density (Icorr) of the obtained LDH film under the above conditions could be 1.07 × 10-7 A/cm2, approximately 3 orders of magnitude lower than the magnesium alloy substrate, after immersion in a 3.5 wt % NaCl solution for 180 h, the surface structure of the LDH film did not change significantly, and the Icorr was still 2 orders of magnitude higher than that of the magnesium alloy substrate. Hence, a synergistic effect equation for the reaction temperature, pH, and reaction time on the corrosion resistance of the LDH film on a magnesium alloy surface prepared by the hydrothermal method was obtained. Moreover, using this equation, we obtained an LDH film with good corrosion resistance and durability, providing theoretical guidance for optimizing the process of preparing the LDH film by the hydrothermal method in practical applications.

CIPL: Counterfactual Interactive Policy Learning to Eliminate Popularity Bias for Online Recommendation.

Popularity bias, as a long-standing problem in recommender systems (RSs), has been fully considered and explored for offline recommendation systems in most existing relevant researches, but very few studies have paid attention to eliminate such bias in online interactive recommendation scenarios. Bias amplification will become increasingly serious over time due to the existence of feedback loop between the user and the interactive system. However, existing methods have only investigated the causal relations among different factors statically without considering temporal dependencies inherent in the online interactive recommendation system, making them difficult to be adapted to online settings. To address these problems, we propose a novel counterfactual interactive policy learning (CIPL) method to eliminate popularity bias for online recommendation. It first scrutinizes the causal relations in the interactive recommender models and formulates a novel temporal causal graph (TCG) to guide the training and counterfactual inference of the causal interactive recommendation system. Concretely, TCG is used to estimate the causal relations of item popularity on prediction score when the user interacts with the system at each time during model training. Besides, it is also used to remove the negative effect of popularity bias in the test stage. To train the causal interactive recommendation system, we formulated our CIPL by the actor-critic framework with an online interactive environment simulator. We conduct extensive experiments on three public benchmarks and the experimental results demonstrate that our proposed method can achieve the new state-of-the-art performance.

Ultra-Early Screening of Cognitive Decline Due to Alzheimer's Pathology.

Alzheimer's pathology can be assessed and defined via Aß and tau biomarkers. The preclinical period of Alzheimer's disease is long and lasts several decades. Although effective therapies to block pathological processes of Alzheimer's disease are still lacking, downward trends in the incidence and prevalence of dementia have occurred in developed countries. Accumulating findings support that education, cognitive training, physical exercise/activities, and a healthy lifestyle can protect cognitive function and promote healthy aging. Many studies focus on detecting mild cognitive impairment (MCI) and take a variety of interventions in this stage to protect cognitive function. However, when Alzheimer's pathology advances to the stage of MCI, interventions may not be successful in blocking the development of the pathological process. MCI individuals reverting to normal cognitive function exhibited a high probability to progress to dementia. Therefore, it is necessary to take effective measures before the MCI stage. Compared with MCI, an earlier stage, transitional cognitive decline, may be a better time window in which effective interventions are adopted for at-risk individuals. Detecting this stage in large populations relies on rapid screening of cognitive function; given that many cognitive tests focus on MCI detection, new tools need to be developed.

Routing User-Interest Markov Tree for Scalable Personalized Knowledge-Aware Recommendation.

To facilitate more accurate and explainable recommendation, it is crucial to incorporate side information into user-item interactions. Recently, knowledge graph (KG) has attracted much attention in a variety of domains due to its fruitful facts and abundant relations. However, the expanding scale of real-world data graphs poses severe challenges. In general, most existing KG-based algorithms adopt exhaustively hop-by-hop enumeration strategy to search all the possible relational paths, this manner involves extremely high-cost computations and is not scalable with the increase of hop numbers. To overcome these difficulties, in this article, we propose an end-to-end framework Knowledge-tree-routed UseR-Interest Trajectories Network (KURIT-Net). KURIT-Net employs the user-interest Markov trees (UIMTs) to reconfigure a recommendation-based KG, striking a good balance for routing knowledge between short-distance and long-distance relations between entities. Each tree starts from the preferred items for a user and routes the association reasoning paths along the entities in the KG to provide a human-readable explanation for model prediction. KURIT-Net receives entity and relation trajectory embedding (RTE) and fully reflects potential interests of each user by summarizing all reasoning paths in a KG. Besides, we conduct extensive experiments on six public datasets, our KURIT-Net significantly outperforms state-of-the-art approaches and shows its interpretability in recommendation.

Taylor Neural Network for Real-World Image Super-Resolution.

Due to the difficulty of collecting paired Low-Resolution (LR) and High-Resolution (HR) images, the recent research on single image Super-Resolution (SR) has often been criticized for the data bottleneck of the synthetic image degradation between LRs and HRs. Recently, the emergence of real-world SR datasets, e.g., RealSR and DRealSR, promotes the exploration of Real-World image Super-Resolution (RWSR). RWSR exposes a more practical image degradation, which greatly challenges the learning capacity of deep neural networks to reconstruct high-quality images from low-quality images collected in realistic scenarios. In this paper, we explore Taylor series approximation in prevalent deep neural networks for image reconstruction, and propose a very general Taylor architecture to derive Taylor Neural Networks (TNNs) in a principled manner. Our TNN builds Taylor Modules with Taylor Skip Connections (TSCs) to approximate the feature projection functions, following the spirit of Taylor Series. TSCs introduce the input connected directly with each layer at different layers, to sequentially produces different high-order Taylor maps to attend more image details, and then aggregate the different high-order information from different layers. Only via simple skip connections, TNN is compatible with various existing neural networks to effectively learn high-order components of the input image with little increase of parameters. Furthermore, we have conducted extensive experiments to evaluate our TNNs in different backbones on two RWSR benchmarks, which achieve a superior performance in comparison with existing baseline methods.

Comparing the reliability of different ICA algorithms for fMRI analysis.

Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets. ICA can extract independent spatial maps and their corresponding time courses from fMRI data without a priori specification of time courses. Some popular ICA algorithms such as Infomax or FastICA generate different results after repeated analysis from the same data volume, which is generally acknowledged as a drawback for ICA approaches. The reliability of some ICA algorithms has been explored by methods such as ICASSO and RAICAR (ranking and averaging independent component analysis by reproducibility). However, the exact algorithmic reliability of different ICA algorithms has not been examined and compared with each other. Here, the quality index generated with ICASSO and spatial correlation coefficients were used to examine the reliability of different ICA algorithms. The results demonstrated that Infomax running 10 times with ICASSO could generate consistent independent components from fMRI data sets.

Botulinum Toxin Injection for the Treatment of Upper Esophageal Sphincter Dysfunction.

Dysphagia associated with upper esophageal sphincter (UES) dysfunction remarkably affects the quality of life of patients. UES injection of botulinum toxin is an effective treatment for dysphagia. In comparison with skeletal muscles of the limb and trunk, the UES is a special therapeutic target of botulinum toxin injection, owing to its several anatomical, physiological, and pathophysiological features. This review focuses on (1) the anatomy and function of the UES and the pathophysiology of UES dysfunction in dysphagia and why the entire UES rather than the cricopharyngeal muscle before/during botulinum toxin injection should be examined and targeted; (2) the therapeutic mechanisms of botulinum toxin for UES dysfunction, including the choice of injection sites, dose, and volume; (3) the strengths and weaknesses of guiding techniques, including electromyography, ultrasound, computed tomography, and balloon catheter dilation for botulinum toxin injection of the UES.

Intra-Articular Mesenchymal Stem Cell Injection for Knee Osteoarthritis: Mechanisms and Clinical Evidence.

Knee osteoarthritis presents higher incidences than other joints, with increased prevalence during aging. It is a progressive process and may eventually lead to disability. Mesenchymal stem cells (MSCs) are expected to repair damaged issues due to trilineage potential, trophic effects, and immunomodulatory properties of MSCs. Intra-articular MSC injection was reported to treat knee osteoarthritis in many studies. This review focuses on several issues of intra-articular MSC injection for knee osteoarthritis, including doses of MSCs applied for injection and the possibility of cartilage regeneration following MSC injection. Intra-articular MSC injection induced hyaline-like cartilage regeneration, which could be seen by arthroscopy in several studies. Additionally, anatomical, biomechanical, and biochemical changes during aging and other causes participate in the development of knee osteoarthritis. Conversely, appropriate intervention based on these anatomical, biomechanical, biochemical, and functional properties and their interactions may postpone the progress of knee OA and facilitate cartilage repair induced by MSC injection. Hence, post-injection rehabilitation programs and related mechanisms are discussed.

Human locomotion-control brain networks detected with independent component analysis.

Walking is a fundamental movement skill in humans. However, how the brain controls walking is not fully understood. In this functional magnetic resonance imaging study, the rhythmic, bilaterally alternating ankle movements were used as paradigm to simulate walking. In addition to the resting state, several motor tasks with different speeds were tested. Independent component analysis was performed to detect four components shared by all task conditions and the resting state. According to the distributed brain regions, these independent components were the cerebellum, primary auditory cortex-secondary somatosensory cortex-inferior parietal cortex-presupplementary motor area, medial primary sensorimotor cortex-supplementary area-premotor cortex-superior parietal lobule, and lateral primary somatosensory cortex-superior parietal lobule-dorsal premotor cortex networks, which coordinated limb movements, controlled the rhythm, differentiated speed, and performed a function as a basic actor network, respectively. These brain networks may be used as biomarkers of the neural control of normal human walking and as targets for neural modulation to improve different aspects of walking, such as rhythm and speed.

Deductive Reinforcement Learning for Visual Autonomous Urban Driving Navigation.

Existing deep reinforcement learning (RL) are devoted to research applications on video games, e.g., The Open Racing Car Simulator (TORCS) and Atari games. However, it remains under-explored for vision-based autonomous urban driving navigation (VB-AUDN). VB-AUDN requires a sophisticated agent working safely in structured, changing, and unpredictable environments; otherwise, inappropriate operations may lead to irreversible or catastrophic damages. In this work, we propose a deductive RL (DeRL) to address this challenge. A deduction reasoner (DR) is introduced to endow the agent with ability to foresee the future and to promote policy learning. Specifically, DR first predicts future transitions through a parameterized environment model. Then, DR conducts self-assessment at the predicted trajectory to perceive the consequences of current policy resulting in a more reliable decision-making process. Additionally, a semantic encoder module (SEM) is designed to extract compact driving representation from the raw images, which is robust to the changes of the environment. Extensive experimental results demonstrate that DeRL outperforms the state-of-the-art model-free RL approaches on the public CAR Learning to Act (CARLA) benchmark and presents a superior performance on success rate and driving safety for goal-directed navigation.

Multi-Task Classification and Segmentation for Explicable Capsule Endoscopy Diagnostics.

Capsule endoscopy is a leading diagnostic tool for small bowel lesions which faces certain challenges such as time-consuming interpretation and harsh optical environment inside the small intestine. Specialists unavoidably waste lots of time on searching for a high clearness degree image for accurate diagnostics. However, current clearness degree classification methods are based on either traditional attributes or an unexplainable deep neural network. In this paper, we propose a multi-task framework, called the multi-task classification and segmentation network (MTCSN), to achieve joint learning of clearness degree (CD) and tissue semantic segmentation (TSS) for the first time. In the MTCSN, the CD helps to generate better refined TSS, while TSS provides an explicable semantic map to better classify the CD. In addition, we present a new benchmark, named the Capsule-Endoscopy Crohn's Disease dataset, which introduces the challenges faced in the real world including motion blur, excreta occlusion, reflection, and various complex alimentary scenes that are widely acknowledged in endoscopy examination. Extensive experiments and ablation studies report the significant performance gains of the MTCSN over state-of-the-art methods.

Functional MRI Reveals Locomotion-Control Neural Circuits in Human Brainstem.

The cuneiform nucleus (CN) and the pedunculopontine nucleus (PPN) in the midbrain control coordinated locomotion in vertebrates, but whether similar mechanisms exist in humans remain to be elucidated. Using functional magnetic resonance imaging, we found that simulated gait evoked activations in the CN, PPN, and other brainstem regions in humans. Brain networks were constructed for each condition using functional connectivity. Bilateral CN-PPN and the four pons-medulla regions constituted two separate modules under all motor conditions, presenting two brainstem functional units for locomotion control. Outside- and inside-brainstem nodes were connected more densely although the links between the two groups were sparse. Functional connectivity and network analysis revealed the role of brainstem circuits in dual-task walking and walking automaticity. Together, our findings indicate that the CN, PPN, and other brainstem regions participate in locomotion control in humans.

Brainstem Encephalitis Caused by Listeria monocytogenes.

International outbreaks of listerial infections have become more frequent in recent years. Listeria monocytogenes, which usually contaminates food, can cause potentially fatal infections. Listerial cerebritis is a rare disease that is encountered mostly in immunocompromised or elderly patients. However, listerial brainstem encephalitis (mesenrhombencephalitis or rhombencephalitis) is found in persons who were formerly in good health, and recognizing this disease, particularly at its early stages, is challenging. Listerial brainstem encephalitis has high mortality, and serious sequelae are frequently reported in survivors. Early recognition and correct diagnosis, as well as the timely use of appropriate antibiotics, can reduce the severity of listerial infections. The trigeminal nerve is proposed as a pathway through which L. monocytogenes reaches the brainstem after entering damaged oropharyngeal mucosa or periodontal tissues. This review introduces the clinical manifestations, pathology, magnetic resonance imaging (MRI) findings, diagnosis, and treatment of listerial brainstem encephalitis. Moreover, it proposes that L. monocytogenes may also invade the brainstem along the vagus nerve after it infects enteric neurons in the walls of the gastrointestinal tract.

3D Human Pose Machines with Self-Supervised Learning.

Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests. In fact, completing this task is quite challenging due to the diverse appearances, viewpoints, occlusions and inherently geometric ambiguities inside monocular images. Most of the existing methods focus on designing some elaborate priors /constraints to directly regress 3D human poses based on the corresponding 2D human pose-aware features or 2D pose predictions. However, due to the insufficient 3D pose data for training and the domain gap between 2D space and 3D space, these methods have limited scalabilities for all practical scenarios (e.g., outdoor scene). Attempt to address this issue, this paper proposes a simple yet effective self-supervised correction mechanism to learn all intrinsic structures of human poses from abundant images. Specifically, the proposed mechanism involves two dual learning tasks, i.e., the 2D-to-3D pose transformation and 3D-to-2D pose projection, to serve as a bridge between 3D and 2D human poses in a type of "free" self-supervision for accurate 3D human pose estimation. The 2D-to-3D pose implies to sequentially regress intermediate 3D poses by transforming the pose representation from the 2D domain to the 3D domain under the sequence-dependent temporal context, while the 3D-to-2D pose projection contributes to refining the intermediate 3D poses by maintaining geometric consistency between the 2D projections of 3D poses and the estimated 2D poses. Therefore, these two dual learning tasks enable our model to adaptively learn from 3D human pose data and external large-scale 2D human pose data. We further apply our self-supervised correction mechanism to develop a 3D human pose machine, which jointly integrates the 2D spatial relationship, temporal smoothness of predictions and 3D geometric knowledge. Extensive evaluations on the Human3.6M and HumanEva-I benchmarks demonstrate the superior performance and efficiency of our framework over all the compared competing methods.

Design of Virtual Guiding Tasks With Haptic Feedback for Assessing the Wrist Motor Function of Patients With Upper Motor Neuron Lesions.

Impaired motor function is a common consequence of upper motor neuron lesions (UMNLs). Fine motor skills involved in small movements occurring in the fingers, hand, and wrist are usually regained by patient self-training at home. Most studies focus on the rehabilitation of the fingers but ignore the recovery of wrist motor function. In this paper, three virtual guiding tasks were designed to assess wrist motor functions, including the basic motor flexibility, motion stability, and a range of active motion. A haptic device was used to provide haptic feedback to users who performed virtual tasks in a virtual reality (VR) environment. In total, 46 healthy subjects and 10 UMNL patients were included to test the effectiveness of the designed tasks on improving wrist motor assessments. Quantitative performances, including the completion time, contact force, and motion trajectory, were automatically acquired during the tasks. Measurements for 95% of control subjects were used to establish normative references. Patient deficiencies in the wrist motor function were identified when their quantitative performances were outside the normative control ranges. The results suggest that the designed virtual tasks are sensitive for patients in the later period of rehabilitation, making the assessment suitable for using at home.

Treatment for upper esophageal sphincter dysfunction in a patient with poststroke dysphagia: A case report.

RATIONALE: Botulinum toxin injection is a widely used procedure for the treatment of the dysfunction of the upper esophageal sphincter (UES). Although the injection can be guided by ultrasound, electromyography, or computed tomography, such techniques cannot determine the exact extent of narrowed UES and ensure that the narrowed extent is fully covered by the treatment. This report describes a dual guiding technique with ultrasound and the balloon catheter in a patient with poststroke dysphagia to improve these weaknesses. PATIENT CONCERNS: The patient was admitted to a rehabilitation hospital 2 weeks postcerebral infarction. DIAGNOSES: Clinical presentation of the patient included severe hemiplegia and dysphagia. The fiberoptic endoscopic evaluation of swallowing (FEES) revealed penetration/aspiration when swallowing 1 ml water and 1 ml yogurt and pooling in the postcricoid region. INTERVENTIONS: Balloon catheter dilatation procedures and Botulinum toxin injection were performed. We used a dual guiding technique with ultrasound and the balloon catheter to determine the whole segment of UES dysfunction by locating the lowest level of the impaired UES opening and to reduce difficulty in differentiating UES from adjacent tissues during Botulinum toxin injection. OUTCOMES: No persistent progress was observed on the symptoms and volume of the balloon during dilatation. The patient showed quick responses after Botulinum toxin injection. The postinjection balloon catheter dilatation showed an increased maximum volume (preinjection, 5.5 ml vs. postinjection, 14 ml), and the patient was able to eat yogurt, congee, or semi-solid food 100-150 ml 4 weeks after the injection. LESSONS: The dual guiding method holds several advantages, suggesting that it may be considered as a promising choice in dealing with UES dysfunction.

Min-Entropy Latent Model for Weakly Supervised Object Detection.

Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors. The inconsistency between the weak supervision and learning objectives introduces significant randomness to object locations and ambiguity to detectors. In this paper, a min-entropy latent model (MELM) is proposed for weakly supervised object detection. Min-entropy serves as a model to learn object locations and a metric to measure the randomness of object localization during learning. It aims to principally reduce the variance of learned instances and alleviate the ambiguity of detectors. MELM is decomposed into three components including proposal clique partition, object clique discovery, and object localization. MELM is optimized with a recurrent learning algorithm, which leverages continuation optimization to solve the challenging non-convexity problem. Experiments demonstrate that MELM significantly improves the performance of weakly supervised object detection, weakly supervised object localization, and image classification, against the state-of-the-art approaches.

Weak but Critical Links between Primary Somatosensory Centers and Motor Cortex during Movement.

Motor performance is improved by stimulation of the agonist muscle during movement. However, related brain mechanisms remain unknown. In this work, we perform a functional magnetic resonance imaging (fMRI) study in 21 healthy subjects under three different conditions: (1) movement of right ankle alone; (2) movement and simultaneous stimulation of the agonist muscle; or (3) movement and simultaneous stimulation of a control area. We constructed weighted brain networks for each condition by using functional connectivity. Network features were analyzed using graph theoretical approaches. We found that: (1) the second condition evokes the strongest and most widespread brain activations (5147 vs. 4419 and 2320 activated voxels); and (2) this condition also induces a unique network layout and changes hubs and the modular structure of the brain motor network by activating the most "silent" links between primary somatosensory centers and the motor cortex, particularly weak links from the thalamus to the left primary motor cortex (M1). Significant statistical differences were found when the strength values of the right cerebellum (P < 0.001) or the left thalamus (P = 0.006) were compared among the three conditions. Over the years, studies reported a small number of projections from the thalamus to the motor cortex. This is the first work to present functions of these pathways. These findings reveal mechanisms for enhancing motor function with somatosensory stimulation, and suggest that network function cannot be thoroughly understood when weak ties are disregarded.

Strong Functional Connectivity among Homotopic Brain Areas Is Vital for Motor Control in Unilateral Limb Movement.

The mechanism underlying brain region organization for motor control in humans remains poorly understood. In this functional magnetic resonance imaging (fMRI) study, right-handed volunteers were tasked to maintain unilateral foot movements on the right and left sides as consistently as possible. We aimed to identify the similarities and differences between brain motor networks of the two conditions. We recruited 18 right-handed healthy volunteers aged 25 ± 2.3 years and used a whole-body 3T system for magnetic resonance (MR) scanning. Image analysis was performed using SPM8, Conn toolbox and Brain Connectivity Toolbox. We determined a craniocaudally distributed, mirror-symmetrical modular structure. The functional connectivity between homotopic brain areas was generally stronger than the intrahemispheric connections, and such strong connectivity led to the abovementioned modular structure. Our findings indicated that the interhemispheric functional interaction between homotopic brain areas is more intensive than the interaction along the conventional top-down and bottom-up pathways within the brain during unilateral limb movement. The detected strong interhemispheric horizontal functional interaction is an important aspect of motor control but often neglected or underestimated. The strong interhemispheric connectivity may explain the physiological phenomena and effects of promising therapeutic approaches. Further accurate and effective therapeutic methods may be developed on the basis of our findings.