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Travel time estimation has been recognized as an important research topic that can find broad applications. Existing approaches aim to explore mobility patterns via trajectory embedding for travel time estimation. Though state-of-the-art methods utilize estimated traffic condition (by explicit features such as average traffic speed) for auxiliary supervision of travel time estimation, they fail to model their mutual influence and result in inaccuracy accordingly. To this end, in this article, we propose an improved traffic-aware model, called TAML, which adopts a multi-task learning network to integrate a travel time estimator and a traffic estimator in a shared space and improves the accuracy of estimation by enhanced representation of traffic condition, such that more meaningful implicit features are fully captured. In TAML, multi-task learning is further applied for travel time estimation in multi-granularities (including road segment, sub-path, and entire path). The multiple loss functions are combined by considering the homoscedastic uncertainty of each task. Extensive experiments on two real trajectory datasets demonstrate the effectiveness of our proposed methods.

Travel time estimation (TTE) is an important research topic in many geographic applications for smart city research. However, existing approaches either ignore the impact of transportation modes, or assume the mode information is known for each training trajectory and the query input. In this paper, we propose a multi-task learning model for travel time estimation called MTLM, which recommends the appropriate transportation mode for users, and then estimates the related travel time of the path. It integrates transportation-mode recommendation task and travel time estimation task to capture the mutual influence between them for more accurate TTE results. Furthermore, it captures spatio-temporal dependencies and transportation mode effect by learning effective representations for TTE. It combines the transportation-mode recommendation loss and TTE loss for training. Extensive experiments on real datasets demonstrate the effectiveness of our proposed methods.

Obesity, a global public health issue, is characterized by excessive adiposity and is strongly related to some chronic diseases including cardiovascular diseases and diabetes. Extra energy intake-induced adipogenesis involves various transcription factors and long noncoding RNAs (lncRNAs) that control lipogenic mRNA expression. Currently, lncRNAs draw much attention for their contribution to adipogenesis and adipose tissue function. Increasing evidence also manifests the pivotal role of lncRNAs in modulating white, brown, and beige adipose tissue development and affecting the progression of the diseases induced by adipose dysfunction. The aim of this review is to summarize the roles of lncRNAs in adipose tissue development and obesity-caused diseases to provide novel drug targets for the treatment of obesity and metabolic diseases.

Radiofrequency thermocoagulation through the supraorbital foramen with a different puncture method is a new approach for the treatment of ophthalmic division trigeminal neuralgia.To compare the efficacy of the vertical puncture method and the transverse puncture method in the treatment of ophthalmic division trigeminal neuralgia during radiofrequency thermocoagulation through the supraorbital foramen.Randomized, longitudinal prospective, clinical research study.Department of Anesthesiology and Pain Medicine, Jiaxing, China.A total of 57 patients with ophthalmic division trigeminal neuralgia were enrolled in the study between October 2011 and April 2018, and prospectively randomized into the vertical puncture group (n = 29) or transverse puncture group (n = 28). All these patients received computed tomography guided radiofrequency thermocoagulation through the supraorbital foramen. Patients in the vertical puncture group were treated with a vertical puncture method; patients in the transverse puncture group received a transverse puncture method. Facial pain was evaluated using the Numeric Rating Scale preoperatively and at 1 day, 6 months, 1 year, and 2 years after treatment; facial numbness degree was analyzed at 1 day and 2 years after the treatments were recorded. The short-term and long-term complications during the period of postoperative follow-up were recorded.All surgical procedures were successfully completed. The Numeric Rating Scale scores at 6 months, 1 year, and 2 years after the treatment were significantly lower in the transverse puncture group compared to the vertical puncture group (P0.05), while no difference was observed on day one after the treatment (P0.05). The numbness degree at 2 years was significantly decreased compared to day one in both groups (P0.05). The radiofrequency thermocoagulation for ophthalmic division trigeminal neuralgia in the transverse puncture group showed better long-term outcomes than those in the vertical puncture group (P0.05). No short-term or long-term postoperative complications were observed in any of the groups.Additional clinical data should be collected to preserve the results in future work.The transverse puncture method during radiofrequency thermocoagulation through the supraorbital foramen had better efficacy and fewer complications in comparison with the vertical puncture method when treating ophthalmic division trigeminal neuralgia.