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514). Transoral endoscopic thyroidectomy using a self-retaining retractor as an alternative to CO2 gas insufflation is feasible and safe. The superiority of transoral endoscopic thyroidectomy would be emphasized by avoiding CO2 gas insufflation, thus eliminating the risk of CO2 gas-related complications.For the context-dependent Text-to-SQL task, the generation of SQL query is placed in a multi-turn interaction scenario. Each turn of Text-to-SQL must take historical interactive information and database schema into account. Accordingly, how to encode and integrate these different types of texts (the question sentence, the corresponding SQL query, and database schema) is a tough problem. In previous work, these series of texts are usually concatenated into sequences and encoded by various variants of recurrent neural networks (RNN). However, the RNNs cannot model the intrinsic relationship of the text directly. To this end, we propose an interaction-modeling mechanism to represent and aggregate these texts. Firstly, different types of texts are represented as individual graphs. Then, heterogeneous graph aggregation is used to capture the interactions and aggregate graphs into a holistic representation. Finally, the corresponding SQL query is generated based on the current question and the aggregated information. We evaluate our model on the SparC and CoSQL dataset to demonstrate the benefits of interaction-modeling. Experimentally, our model has a competitive performance and space-time cost.A novel convolutional neural network is proposed for local prior feature embedding and imbalanced dataset modeling for multi-channel time-varying signal classification. This model consists of a single-channel signal feature parallel extraction unit, a multi-channel signal feature integration unit, a local feature embedding and feature similarity measurement unit, a full connection layer, and a Softmax classifier. An algorithm combining dynamic clustering and sliding window was used to select segments signals with typical local features in each pattern class, forming a typical local feature set. The one-dimensional CNNs were used to extract features from the single-channel signal in parallel, a comprehensive feature matrix of the multi-channel signal and the local feature matrix templates were produced. Using the method of external embedding, based on the sliding window and dynamic time warping (DTW) algorithm, the local feature similarities between the local feature template of each pattern class and the comprehensive feature sub-matrix of the input signal were measured, and the maximum values were selected to construct a local feature similarity vector in order. The information fusion was realized through a full connection layer. The proposed methodology can extract and represent both global and local signals features, strengthen the role of prior local feature in classification and improve the modeling properties of imbalanced datasets. A comprehensive learning algorithm is presented in this paper. The classification diagnosis of cardiovascular disease based on 12-lead ECG signals was used as a verification experiment. Results showed that the accuracy and generalization for the proposed technique were significantly improved.Previous studies have shown that women perceive male faces with a more reactive immune system as more attractive, but whether body odor might likewise provide cues to immune function has not been investigated yet. These two studies tested a possible relationship between body odor quality and immunoreactivity (Study 1) and immune system function (Study 2). In Study 1, we collected body odor samples from 21 men just before and two weeks after vaccination against hepatitis A/B and meningococcus. We determined the levels of specific antibodies (selected as markers of immune system's reactivity), testosterone, and cortisol. Subsequently, 88 female raters assessed the odor samples for their attractiveness, intensity, and healthiness. In Study 2, we collected body odor and blood samples from 35 men and women. We assessed key parameters of their innate and adaptive immunity, such as complement activity or total lymphocyte T and B counts and asked 95 raters to assess the odor samples for their attractiveness, intensity, and healthiness. In Study 1, we found no significant association between antibody levels induced by vaccination and perceived body odor attractiveness, intensity, and healthiness. We also found no significant relationship between antibody levels and steroid hormones (testosterone and cortisol). In Study 2, we likewise found no association between basal key parameters (innate and adaptive) of the immune system and body odor quality. Our results indicate that body odor may not serve as a cue to the reactivity of the immune system.Herein we report a novel electrochemical sensing chip and a point-of-care device (PoC) for enzyme-free electrochemical detection of urea in human blood. The electrochemical sensing chip was developed by 3-D printing of conductive Ag ink and subsequent electrodeposition of AuNP-rGO nanocomposite. Linsitinib solubility dmso Material characterization of the sensing chip was conducted to find a plausible mechanism for the electrochemical reaction with urea. Subsequently, the response with varying concentrations of urea in solution and human blood samples was tested. High peak response current (~5 times than that of the highest reported value), low impedance, rapid sensor fabrication procedure, high selectivity towards urea, excellent linear response (R2 = 0.99), high sensitivity of 183 μA mM-1 cm-2, the fast response indicated by high diffusion coefficient, the limit of detection of 0.1 µM, tested shelf life of more than 6 months and recovery rate of >99% ensured the application of the developed sensor chip towards PoC urea detection test kit. A PoC device housing an electronic circuitry following the principles of linear sweep voltammetry and compatible with a sensing chip was developed. A maximum percentage error of 4.86% and maximum RSD of 3.63% confirmed the use of the PoC device for rapid urea measurements in human blood.In this work, we build upon the Active Inference (AIF) and Predictive Coding (PC) frameworks to propose a neural architecture comprising a generative model for sensory prediction, and a distinct generative model for motor trajectories. We highlight how sequences of sensory predictions can act as rails guiding learning, control and online adaptation of motor trajectories. We furthermore inquire the effects of bidirectional interactions between the motor and the visual modules. The architecture is tested on the control of a simulated robotic arm learning to reproduce handwritten letters.We present a neural network model for familiarity recognition of different types of images in the perirhinal cortex (the FaRe model). The model is designed as a two-stage system. At the first stage, the parameters of an image are extracted by a pretrained deep learning convolutional neural network. At the second stage, a two-layer feed forward neural network with anti-Hebbian learning is used to make the decision about the familiarity of the image. FaRe model simulations demonstrate high capacity of familiarity recognition memory for natural pictures and low capacity for both abstract images and random patterns. These findings are in agreement with psychological experiments.Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of applications where incoming data is non stationary, like natural language processing and robotics. However, the existing body of work on the topic is still fragmented, with approaches which are application-specific and whose assessment is based on heterogeneous learning protocols and datasets. In this paper, we organize the literature on CL for sequential data processing by providing a categorization of the contributions and a review of the benchmarks. We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications. We also provide a broad empirical evaluation of CL and Recurrent Neural Networks in class-incremental scenario, by testing their ability to mitigate forgetting with a number of different strategies which are not specific to sequential data processing. Our results highlight the key role played by the sequence length and the importance of a clear specification of the CL scenario.The essential problem of multi-view spectral clustering is to learn a good common representation by effectively utilizing multi-view information. A popular strategy for improving the quality of the common representation is utilizing global and local information jointly. Most existing methods capture local manifold information by graph regularization. However, once local graphs are constructed, they do not change during the whole optimization process. This may lead to a degenerated common representation in the case of existing unreliable graphs. To address this problem, rather than directly using fixed local representations, we propose a dynamic strategy to construct a common local representation. Then, we impose a fusion term to maximize the common structure of the local and global representations so that they can boost each other in a mutually reinforcing manner. With this fusion term, we integrate local and global representation learning in a unified framework and design an alternative iteration based optimization procedure to solve it. Extensive experiments conducted on a number of benchmark datasets support the superiority of our algorithm over several state-of-the-art methods.

In the prospective multicenter Genesis study, we developed a prediction model for Cesarean delivery (CD) in term nulliparous women. The objective of this secondary analysis was to determine whether the Genesis model has the potential to predict maternal and neonatal morbidity associated with vaginal delivery.

The national prospective Genesis trial recruited 2,336 nulliparous women with a vertex presentation between 39+0- and 40+6-weeks' gestation from seven tertiary centers. The prediction model used five parameters to assess the risk of CD maternal age, maternal height, body mass index, fetal head circumference and fetal abdominal circumference. Simple and multiple logistic regression analyses were used to develop the Genesis model. The risk score calculated using this model were correlated with maternal and neonatal morbidity in women who delivered vaginally postpartum hemorrhage (PPH), obstetric anal sphincter injury (OASI), shoulder dystocia, one- and five-minute Apgar score≤7, neonatal intensive careasing risk score from 1.005 at risk score of 5% to 2.507 for risk score of>50%.

In women who ultimately achieved a vaginal birth, we have shown more maternal and neonatal morbidity in the setting of a Genesis nomogram-determined high-risk score for intrapartum CD. Therefore, the Genesis prediction tool also has the potential to predict a more morbid vaginal delivery.

In women who ultimately achieved a vaginal birth, we have shown more maternal and neonatal morbidity in the setting of a Genesis nomogram-determined high-risk score for intrapartum CD. Therefore, the Genesis prediction tool also has the potential to predict a more morbid vaginal delivery.