Migration to gliomas of bone mesenchymal stem cells transfected with cytosine deaminase gene following transplantation in vivo

This experiment sought to observe the migration and distribution of bone mesenchymal stem cells transfected with the cytosine deaminase gone （BMSCs-CD/eGFP） after transplantation in vivo through three pathways. In addition, we examined the tropism of these cells to glioma. Intracranial C6 glioma models were established in Sprague-Dawley rats using an intracranial stereotactic inoculation method. When tumors were 7 days old, rats were inoculated with lx106 BMSCs-CD/eGFP cells via the tumor-bearing internal carotid artery, the contralateral hemisphere and the tumor-bearing glioma. Fluorescence microscopy revealed that BMSCs-CD/eGFP exhibited a strong capacity for migration to tumors. BMSCs-CD/eGFP transplanted via the tumor-bearing intemal carotid artery were observed to distribute in glioma tissues. BMSCs-CD/eGFP inoculated via the ipsilateral glioma mainly located within and at the edge of glioma tissues. BMSCs-CD/eGFP inoculated via the contralateral hemisphere mainly distributed at the proximal end of the tumor at the incubation site.

Prediction of urban human mobility using large-scale taxi traces and its applications

This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting human mobility from discovering patterns of in the number of passenger pick-ups quantity （PUQ） from urban hotspots. This paper proposes an improved ARIMA based prediction method to forecast the spatial-temporal variation of passengers in a hotspot. Evaluation with a large-scale real- world data set of 4 000 taxis＇ GPS traces over one year shows a prediction error of only 5.8%. We also explore the applica- tion of the pl^di^fioti approach to help drivers find their next passetlgerS, The sinatllation results using historical real-world data demonstrate that, with our guidance, drivers can reduce the time taken and distance travelled, to find their next pas- senger＋ by 37.1% and 6.4% respectively,

ScenicPlanner： planning scenic travel routes leveraging heterogeneous user-generated digital footprints

To facilitate the travel preparation process to a city, a lot of work has been done to recommend a POI or a sequence of POIs automatically to satisfy users＇ needs. How- ever, most of the existing work ignores the issue of planning the detailed travel routes between POIs, leaving the task to online map services or commercial GPS navigators. Such a service or navigator in terms of suggesting the shortest travel distance or time, which cannot meet the diverse requirements of users. For instance, in the case of traveling by driving for leisure purpose, the scenic view along the travel routes would be of great importance to users, and a good planning ser- vice should put the sceneries of the route in higher priority rather than the distance or time taken. To this end, in this paper, we propose a novel framework called ScenicPlanner for route recommendation, leveraging a combination of get- tagged image and check-in digital footprints from location- based social networks （LBSNs）. First, we enrich the road net- work and assign a proper scenic view score to each road seg- ment to model the scenic road network, by extracting relevant information from get-tagged images and check-ins. Then, we apply heuristic algorithms to iteratively add road segment and determine the travelling order of added road segments with the objective of maximizing the total scenic view score while satisfying the user-specified constraints （i.e., origin, desti- nation and the total travel distance）. Finally, to validate the efficiency and effectiveness of the proposed framework, we conduct extensive experiments on three real-world data sets from the Bay Area in the city of San Francisco, which con- tain a road network crawled from OpenStreetMap, more than 31 000 geo-tagged images generated by 1 571 Flickr users in one year, and 110 214 check-ins left by 15 680 Foursquare users in six months.

Non-intrusive sleep pattern recognition with ubiquitous sensing in elderly assistive environment

The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novel multi-parametric sensing system called sleep pattern recognition system （SPRS）. This system, equipped with a combination of various non-invasive sensors, can mon- itor an elderly user＇s sleep behavior. It accumulates the de- tecting data from a pressure sensor matrix and ultra wide band （UWB） tags. Based on these two types of complemen- tary sensing data, SPRS can assess the user＇s sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users＇ sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.

Real-time and generic queue time estimation based on mobile crowdsensing

People often have to queue for a busy service in many places around a city, and knowing the queue time can be helpful for making better activity plans to avoid long queues. Traditional solutions to the queue time monitoring are based on pre-deployed infrastructures, such as cameras and infrared sensors, which are costly and fail to deliver the queue time information to scattered citizens. This paper presents CrowdQTE, a mobile crowdsensing system, which utilizes the sensor-enhanced mobile devices and crowd hu- man intelligence to monitor and provide real-time queue time information for various queuing scenarios. When people are waiting in a line, we utilize the accelerometer sensor data and ambient contexts to automatically detect the queueing behav- ior and calculate the queue time. When people are not waiting in a line, it estimates the queue time based on the information reported manually by participants. We evaluate the perfor- mance of the system with a two-week and 12-person deploy- ment using commercially-available smartphones. The results demonstrate that CrowdQTE is effective in estimating queu- ing status.

For the health of the people—commemorating the 135th anniversary of the founding of the Chinese Medical Journal

The year (2022) marks the 135th anniversary of the founding of the Chinese Medical Journal. Over the past 135 years, the journal has witnessed tremendous changes in the health conditions of the Chinese people, the important role of modern medical technology in improving health, reducing diseases, and extending the life span, and the continuous improvement in medical and healthcare services and social security systems.