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Account activation associated with FcRn Mediates an immediate Weight Reply to Sorafenib throughout Hepatocellular Carcinoma simply by Single-Cell RNA Sequencing.
The proposed method is evaluated on four different simulated motorway traffic scenarios (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms.Understanding neck pain is an important societal issue. Kinematic data from sensors may help to gain insight into the pathophysiological mechanisms associated with neck pain through a quantitative sensorimotor assessment of one patient. The objective of this study was to evaluate the potential usefulness of artificial intelligence with several machine learning (ML) algorithms in assessing neck sensorimotor performance. Angular velocity and acceleration measured by an inertial sensor placed on the forehead during the DidRen laser test in thirty-eight acute and subacute non-specific neck pain (ANSP) patients were compared to forty-two healthy control participants (HCP). Seven supervised ML algorithms were chosen for the predictions. The most informative kinematic features were computed using Sequential Feature Selection methods. The best performing algorithm is the Linear Support Vector Machine with an accuracy of 82% and Area Under Curve of 84%. The best discriminative kinematic feature between ANSP patients and HCP is the first quartile of head pitch angular velocity. This study has shown that supervised ML algorithms could be used to classify ANSP patients and identify discriminatory kinematic features potentially useful for clinicians in the assessment and monitoring of the neck sensorimotor performance in ANSP patients.Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC.Traditionally, an elevation-angle-dependent weighting method is usually used for Global Navigation Satellite System (GNSS) positioning with a geodetic receiver. As smartphones adopt linearly polarized antenna and low-cost GNSS chips, different GNSS observation properties are exhibited. As a result, a carrier-to-noise ratio (C/N0)-dependent weighting method is mostly used for smartphone-based GNSS positioning. However, the C/N0 is subject to the effects of the observation environment, resulting in an unstable observation weight. In this study, we propose a combined elevation angle and C/N0 weighting method for smartphone-based GNSS precise point positioning (PPP) by normalizing the C/N0-derived variances to the scale of the elevation-angle-derived variances. The proposed weighting method is validated in two kinematic PPP tests with different satellite visibility conditions. Compared with the elevation-angle-only and C/N0-only weighting methods, the combined weighting method can effectively enhance the smartphone-based PPP accuracy in a three-dimensional position by 22.7% and 24.2% in an open-sky area, and by 52.0% and 26.0% in a constrained visibility area, respectively.In this work, a framework is proposed for decision fusion utilizing features extracted from vehicle images and their detected wheels. Siamese networks are exploited to extract key signatures from pairs of vehicle images. Our approach then examines the extent of reliance between signatures generated from vehicle images to robustly integrate different similarity scores and provide a more informed decision for vehicle matching. To that end, a dataset was collected that contains hundreds of thousands of side-view vehicle images under different illumination conditions and elevation angles. Experiments show that our approach could achieve better matching accuracy by taking into account the decisions made by a whole-vehicle or wheels-only matching network.Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them via a late fusion based approach. To tackle this challenge, we develop and investigate the usefulness of a novel deep learning method called tower networks. selleckchem This method is able to learn from multiple input data sources at once. We apply the tower network to the problem of short-term temperature forecasting. First, we compare our method to a number of meteorological baselines and simple statistical approaches. Further, we compare the tower network with two core network architectures that are often used, namely the convolutional neural network (CNN) and convolutional long short-term memory (convLSTM). The methods are compared for the task of weather forecasting performance, and the deep learning methods are also compared in terms of memory usage and training time. The tower network performs well in comparison both with the meteorological baselines, and with the other core architectures. Compared with the state-of-the-art operational Norwegian weather forecasting service, yr.no, the tower network has an overall 11% smaller root mean squared forecasting error. For the core architectures, the tower network documents competitive performance and proofs to be more robust compared to CNN and convLSTM models.Cancer is the deadliest disease among all the diseases and the main cause of human mortality. Several types of cancer sicken the human body and affect organs. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. The early diagnosis of stomach cancer is essential to reduce the mortality rate. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. Many computerized techniques have been introduced in the literature but due to a few challenges (i.e., high similarity among the healthy and infected regions, irrelevant features extraction, and so on), there is much room to improve the accuracy and reduce the computational time. In this paper, a deep-learning-based stomach disease classification method employing deep feature extraction, fusion, and optimization using WCE images is proposed. The proposed method comprises several phases data augmentation performed to increase the dataset images, deep transfer learning adopted for deep features extraction, feature fusion performed on deep extracted features, fused feature matrix optimized with a modified dragonfly optimization method, and final classification of the stomach disease was performed. The features extraction phase employed two pre-trained deep CNN models (Inception v3 and DenseNet-201) performing activation on feature derivation layers. Later, the parallel concatenation was performed on deep-derived features and optimized using the meta-heuristic method named the dragonfly algorithm. The optimized feature matrix was classified by employing machine-learning algorithms and achieved an accuracy of 99.8% on the combined stomach disease dataset. A comparison has been conducted with state-of-the-art techniques and shows improved accuracy.Unsafe food is estimated to cause 600 million cases of foodborne disease, annually. Thus, the development of methods that could assist in the prevention of foodborne diseases is of high interest. This review summarizes the recent progress toward rapid microbial assessment through (i) spectroscopic techniques, (ii) spectral imaging techniques, (iii) biosensors and (iv) sensors designed to mimic human senses. These methods often produce complex and high-dimensional data that cannot be analyzed with conventional statistical methods. Multivariate statistics and machine learning approaches seemed to be valuable for these methods so as to "translate" measurements to microbial estimations. However, a great proportion of the models reported in the literature misuse these approaches, which may lead to models with low predictive power under generic conditions. Overall, all the methods showed great potential for rapid microbial assessment. Biosensors are closer to wide-scale implementation followed by spectroscopic techniques and then by spectral imaging techniques and sensors designed to mimic human senses.Software products from all vendors have vulnerabilities that can cause a security concern. Malware is used as a prime exploitation tool to exploit these vulnerabilities. Machine learning (ML) methods are efficient in detecting malware and are state-of-art. The effectiveness of ML models can be augmented by reducing false negatives and false positives. In this paper, the performance of bagging and boosting machine learning models is enhanced by reducing misclassification. Shapley values of features are a true representation of the amount of contribution of features and help detect top features for any prediction by the ML model. Shapley values are transformed to probability scale to correlate with a prediction value of ML model and to detect top features for any prediction by a trained ML model. The trend of top features derived from false negative and false positive predictions by a trained ML model can be used for making inductive rules. In this work, the best performing ML model in bagging and boosting is determined by the accuracy and confusion matrix on three malware datasets from three different periods. The best performing ML model is used to make effective inductive rules using waterfall plots based on the probability scale of features. This work helps improve cyber security scenarios by effective detection of false-negative zero-day malware.Ultrasonic inspection techniques and non-destructive tests are widely applied in evaluating products and equipment in the oil, petrochemical, steel, naval, and energy industries. These methods are well established and efficient for inspection procedures at room temperature. However, errors can be observed in the positioning and sizing of the flaws when such techniques are used during inspection procedures under high working temperatures. In such situations, the temperature gradients generate acoustic anisotropy and consequently distortion of the ultrasonic beams. Failure to consider such distortions in ultrasonic signals can result, in extreme situations, in mistaken decision making by inspectors and professionals responsible for guaranteeing product quality or the integrity of the evaluated equipment. In this scenario, this work presents a mathematical tool capable of mitigating positioning errors through the correction of focal laws. For the development of the tool, ray tracing concepts are used, as well as a model of heat propagation in solids and an experimentally defined linear approximation of dependence between sound speed and temperature.
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