RESUMEN

The lateral parabrachial nucleus (LPBN) is known to play a key role in relaying noxious information from the spinal cord to the brain. Different LPBN efferent mediate different aspects of the nocifensive response. However, the function of the LPBN â†’ lateral hypothalamus (LH) circuit in response to noxious stimuli has remained unknown. Here, we show that LPBN â†’ LH circuit is activated by noxious stimuli. Interestingly, either activation or inhibition of this circuit induced analgesia. Optogenetic activation of LPBN afferents in the LH elicited spontaneous jumping and induced place aversion. Optogenetic inhibition inhibited jumping behavior to noxious heat. Ablation of LH glutamatergic neurons could abolish light-evoked analgesia and jumping behavior. Our study revealed a role for the LPBN â†’ LH pathway in nocifensive behaviors.

Asunto(s)

Área Hipotalámica Lateral , Núcleos Parabraquiales , Humanos , Núcleos Parabraquiales/fisiología , Dolor/metabolismo , Encéfalo , Neuronas/metabolismo

RESUMEN

Introduction: The successful use of machine learning (ML) for medical diagnostic purposes has prompted myriad applications in cancer image analysis. Particularly for hepatocellular carcinoma (HCC) grading, there has been a surge of interest in ML-based selection of the discriminative features from high-dimensional magnetic resonance imaging (MRI) radiomics data. As one of the most commonly used ML-based selection methods, the least absolute shrinkage and selection operator (LASSO) has high discriminative power of the essential feature based on linear representation between input features and output labels. However, most LASSO methods directly explore the original training data rather than effectively exploiting the most informative features of radiomics data for HCC grading. To overcome this limitation, this study marks the first attempt to propose a feature selection method based on LASSO with dictionary learning, where a dictionary is learned from the training features, using the Fisher ratio to maximize the discriminative information in the feature. Methods: This study proposes a LASSO method with dictionary learning to ensure the accuracy and discrimination of feature selection. Specifically, based on the Fisher ratio score, each radiomic feature is classified into two groups: the high-information and the low-information group. Then, a dictionary is learned through an optimal mapping matrix to enhance the high-information part and suppress the low discriminative information for the task of HCC grading. Finally, we select the most discrimination features according to the LASSO coefficients based on the learned dictionary. Results and discussion: The experimental results based on two classifiers (KNN and SVM) showed that the proposed method yielded accuracy gains, compared favorably with another 5 state-of-the-practice feature selection methods.

RESUMEN

In order to explore the forming mechanism of the fiber whipping motion in slot-die melt blowing, the turbulent airflow in slot-die melt blowing was measured online with the approach of the Particle Image Velocimetry (PIV) technique. The PIV results visualized the structure of the turbulent airflow and provided the distributions of air velocity components (vx, vy, and vz). Moreover, the PIV results also demonstrated the evolutive process of turbulent airflow at successive time instants. By comparing the characteristics of the turbulent airflow with the fiber whipping path, the PIV results provide a preliminary explanation for the specific fiber whipping motion in slot-die melt blowing.