LESS(sc)-VL is a safe and effective minimally invasive surgical alternative for varicocelectomy. Compared with CTL-VL, LESS(sc)-VL may decrease postoperative pain and hide the surgical incision better within the umbilicus.

Due to the complex physiological structure, microenvironment and multiple physiological barriers, traditional anti‐cancer drugs are severely restricted from reaching the tumour site. Cell‐penetrating peptides (CPPs) are typically made up of 5–30 amino acids, and can be utilised as molecular transporters to facilitate the passage of therapeutic drugs across physiological barriers. Up to now, CPPs have widely been used in many anti‐cancer treatment strategies, serving as an excellent potential choice for oncology treatment. However, their drawbacks, such as the lack of cell specificity, short duration of action, poor stability in vivo, compatibility problems (i.e. immunogenicity), poor therapeutic efficacy and formation of unwanted metabolites, have limited their further application in cancer treatment. The cellular uptake mechanisms of CPPs involve mainly endocytosis and direct penetration, but still remain highly controversial in academia. The CPPs‐based drug delivery strategy could be improved by clever design or chemical modifications to develop the next‐generation CPPs with enhanced cell penetration capability, stability and selectivity. In addition, some recent advances in targeted cell penetration that involve CPPs provide some new ideas to optimise CPPs.

Trajectories, representing the movements of objects in the real world, carry significant stop/move semantics. The detection of trajectory stops poses a critical problem in the study of moving objects and becomes even more challenging due to the inevitable noise recorded along with true data. To extract stops with a variety of shapes and sizes from single trajectories with noise, this paper presents a sequence oriented clustering approach, in which noise points within the sequence of a stop can be identified and classified as a part of the stop. In our method, two key concepts are first introduced: (1) a core sequence that defines sequence density based not only on proximity in space but also continuity in time as well as the duration over time; and (2) an Eps-reachability sequence that aggregates core sequences that overlap or meet over time. Then, three criteria are presented to merge Eps-reachability sequences interrupted by noise. Further, an algorithm, called SOC (Sequence Oriented Clustering), is developed to automatically extract stops from a single trajectory. In addition, a reachability graph is designed that visually illustrates the spatio-temporal clustering structure and levels of a trajectory. Finally, the proposed algorithm is evaluated against two baseline methods through extensive experiments based on real world trajectories, some with serious noise, and the results show that our approach is fairly effective in recognizing trajectory stops.Keywords: trajectory stop; core sequence; reachability graph; sequence oriented clustering BackgroundA trajectory represents the evolving locations of a moving object in geographical space over a given time interval. From the viewpoint of the computer world, a trajectory is a discrete record structure containing information about the evolving positions of a moving object in geographical space during a given time interval. Such a structure is composed of spatio-temporal points, each of which contains at least two components: an x-y position and a timestamp. The formal definition for a trajectory is given below.Definition 1 (Trajectory). A trajectory T = (tid,

) is a two-tuple structure, where tid is a unique trajectory identifier. We have: (1) p i = (x i , y i , t i ), i = 0, . . . , N, x i , y i , t i P R, as a spatio-temporal point; and (2) @0 ď i < j ď N, t i < t j .Here, we present a trajectory point as p = (x, y, t), instead of p = (x, y, z, t), because: (1) the z-part, i.e., elevation, is not always available in a trajectory dataset; (2) in our study and similar works, only latitude (the y-part), longitude (the x-part) and timestamp (the t-part) are required to compute space (using x and y) closeness and time proximity (using t); and (3) the changes of the z-part are very small, especially for trajectories recorded within cities, and therefore it is not necessary to apply the z-part on the computation of geographical distances.

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Abstract:The long production cycle and huge cost of collecting road network data often leave the data lagging behind the latest real conditions. However, this situation is rapidly changing as the positioning techniques ubiquitously used in mobile devices are gradually being implemented in road network research and applications. Currently, the predominant approaches infer road networks from mobile location information (e.g., GPS trajectory data) directly using various extracting algorithms, which leads to expensive consumption of computational resources in the case of large-scale areas. For this reason, we propose an alternative that renews road networks with a novel spiral strategy, including a hidden Markov model (HMM) for detecting potential problems in existing road network data and a method to update the data, on the local scale, by generating new road segments from trajectory data. The proposed approach reduces computation costs on roads with completed or updated information by updating problem road segments in the minimum range of the road network. We evaluated the performance of our proposals using GPS traces collected from taxies and OpenStreetMap (OSM) road networks covering urban areas of Wuhan City.