Geosocial networking smartphone applications (apps) are popular tools for seeking sexual partners among men who have sex with men (MSM). We evaluated app use and risk of sexually transmitted ...infections (STIs) in app-using MSM (app-users) by a systematic review and meta-analysis.
A literature search for relevant studies was performed. We extracted date of STIs (ever being diagnosed with human immunodeficiency virus HIV, syphilis, gonorrhea and chlamydia) and sexual behavior (e.g., number of app-met partners, unprotected anal/oral sex, HIV testing) from the eligible studies. Pooled proportions and odds ratios (ORs) with 95% confidence intervals (95% CIs) were estimated.
Twenty-five studies were included. The self-reported prevalence of prior diagnosis of HIV among app-users ranged from 2.2 to 37.7%, and the pooled prevalence of HIV infection was 6% (95% CI, 4-11%). Compared with non-users, app-users were more likely to have gonorrhea (OR = 2.36; 95% CI, 2.07-2.70) and chlamydia (OR = 2.22; 95% CI, 1.92-2.56). The two groups were similar in terms of diagnoses of HIV (OR = 0.89, 95% CI, 0.68-1.16) and syphilis (OR = 1.92; 95% CI, 0.91-4.03). However, when one study that caused substantial heterogeneity was omitted, the pooled OR for app-users to contract syphilis became 3.00 (95% CI, 1.84-4.91) .
MSM who seek sexual partners using apps may be more likely to have STIs as than are non-users.
Radar-based human activity recognition (HAR) finds various applications like assisted living and driver behavior monitoring. As radar data are heavily environment-dependent, it is becoming ...increasingly important to develop a transfer learning mechanism that enables a radar-based HAR system with desirable cross-environment adaptation feasibility. This paper concerns the issue of how radar-based HAR system can adapt to a new environment without source data. To this end, we devote to using the source hypothesis transfer learning architecture to build such an environment adaptation mechanism towards cross-environment radar-based HAR. In doing this, it is a challenging task to develop a reliable self-supervised labeling strategy for generating pseudo labels associated with the unlabeled target data, which is crucial to facilitate the learning of a target-specific feature extractor being responsible for environment adaptation. This paper presents the neighbor-aggregating-based labeling method and incorporates it with the existing clustering-based labeling method to perform the self-supervised labeling task. The logic behind our approach is that the above two labeling methods are complementary to each other in terms of making use of both local and global structures of adaptation data to supervise the labeling task. The coordination of both labeling methods is motivated to be implemented in the weighted combination form, which contributes to improving the reliability of generated labels. Experimental results on a public HAR dataset based on the frequency modulated continuous wave (FMCW) radar demonstrate the effectiveness of our approach.
Organic-mineral interactions are universal in natural environments. They cause the majority of the total organic carbon (TOC) in sediments and sedimentary rocks to combine with clay minerals to form ...organo-clay composites. However, the role of organo-clay composites in hydrocarbon generation is not clear. In this study, we select a suite of successively deposited shales to examine the association between organic matter (OM) and minerals, and to analyze the correlations of TOC with different mineral surface areas (MSAs) and the Rock-Eval pyrolysis in both bulk shales and their organo-clay composites. We find that OM in shale is dominantly incorporated with clay minerals by the main way of adsorbing on internal mineral surfaces rather than external mineral surfaces, which forms organo-clay composites. Further analyses on TOC-MSA correlations demonstrate the great heterogeneity of OM occurrence within shale. Also, the OM quality of organo-clay composites is poorer for hydrocarbon generation than that of bulk shales. We conclude that the variations in organic geochemistry between bulk shales and organo-clay composites are caused by hydrocarbon generation, which reduces the OM quality for further generation. Taken together, organo-clay composites dominate OM occurrence and have been generating hydrocarbons, which highlight the main control of organo-clay composites on hydrocarbon generation in natural samples. In comparison with the long-standing theory of hydrocarbon generated from kerogen, we propose the organo-clay composites to be the in situ and main source for hydrocarbon generation. This new hypothesis improves the existing knowledge of the organic origin of hydrocarbons in natural systems.
•Organic matter within shale is predominantly adsorbed on internal mineral surface.•Organo-clay composites are the main form of organic matter occurrence.•Quality of organic matter in organo-clay composites is poorer than in bulk shales.•Organo-clay composites play a major role in hydrocarbon generation.
Home-based movement neuro-rehabilitation is quite necessary when the patient goes back home from hospital. Due to lack of supervision from doctors, rehabilitation at home is often forgotten. As an ...alternate to doctor-supervision, in this research, we explore the wireless device-free localization technique to assist the rehabilitation procedure. The localization technique can judge whether the patient is near the rehabilitation equipment and even obtain the movement trajectory. The most challenging problem in the wireless device-free localization system is that the received-signal-strength (RSS) of the electromagnetic-wave is unpredictable, which increases the localization error. How to select the informative RSS is pretty important. This research proposes a new criterion (i.e., fluctuation-level) to select the informative RSS. Experimental results show the effectiveness of the proposed fluctuation-level in reducing the localization error.
Activity recognition is fundamental to many applications envisaged in pervasive computing, especially in smart environments where the resident's data collected from sensors will be mapped to human ...activities. Previous research usually focuses on scripted or pre-segmented sequences related to activities, whereas many real-world deployments require information about the ongoing activities in real time. In this paper, we propose an online activity recognition model on streaming sensor data that incorporates the spatio-temporal correlation-based dynamic segmentation method and the stigmergy-based emergent modeling method to recognize activities when new sensor events are recorded. The dynamic segmentation approach integrating sensor correlation and time correlation judges whether two consecutive sensor events belong to the same window or not, avoiding events from very different functional areas or with a long time interval in the same window, thus obtaining the segmented window for every single event. Then, the emergent paradigm with marker-based stigmergy is adopted to build activity features that are explicitly represented as a directed weighted network to define the context for the last sensor event in this window, which does not need sophisticated domain knowledge. We validate the proposed method utilizing the real-world dataset Aruba from the CASAS project and the results show the effectiveness.
This letter proposes a novel rotation-invariant feature for object detection in optical remote sensing images. Different from previous rotation-invariant features, the proposed rotation-invariant ...matrix (RIM) can incorporate partial angular spatial information in addition to radial spatial information. Moreover, it can be further calculated between different rings for a redundant representation of the spatial layout. Based on the RIM, we further propose an RIM_FV_RPP feature for object detection. For an image region, we first densely extract RIM features from overlapping blocks; then, these RIM features are encoded into Fisher vectors; finally, a pyramid pooling strategy that hierarchically accumulates Fisher vectors in ring subregions is used to encode richer spatial information while maintaining rotation invariance. Both of the RIM and RIM_FV_RPP are rotation invariant. Experiments on airplane and car detection in optical remote sensing images demonstrate the superiority of our feature to the state of the art.
The crucial function of the internal transcribed spacer 2 (ITS2) region in ribosome biogenesis depends on its secondary and tertiary structures. Despite rapidly evolving, ITS2 is under evolutionary ...constraints to maintain the specific secondary structures that provide functionality. A link between function, structure and evolution could contribute an understanding to each other and recently has created a growing point of sequence-structure phylogeny of ITS2. Here we briefly review the current knowledge of ITS2 processing in ribosome biogenesis, focusing on the conservative characteristics of ITS2 secondary structure, including structure form, structural motifs, cleavage sites, and base-pair interactions. We then review the phylogenetic implications and applications of this structure information, including structure-guiding sequence alignment, base-pair mutation model, and species distinguishing. We give the rationale for why incorporating structure information into tree construction could improve reliability and accuracy, and some perspectives of bioinformatics coding that allow for a meaningful evolutionary character to be extracted. In sum, this review of the integration of function, structure and evolution of ITS2 will expand the traditional sequence-based ITS2 phylogeny and thus contributes to the tree of life. The generality of ITS2 characteristics may also inspire phylogenetic use of other similar structural regions.
With the acceleration of aging process of population structure, the single resident lifestyle is increasing on account of the high cost of care services and the privacy invasion concern. It is ...essential to monitor the activities of solitary elderly to find the emergency and lifestyle deviation, as independent life cannot be maintained due to physical or mental problems. The unobtrusive systems are the most preferred choice for the real-life long-term monitoring, while the camera and wearable devices based systems are not suitable due to the privacy and uncomfortableness, respectively. We propose a novel sensor-based activity recognition model based on the two-layer multi-granularity framework and the emergent paradigm with marker-based stigmergy. The stigmergy based marking subsystem builds features by aggregating the context-aware information and generating the two-dimensional activity pheromone trail. The two-layer framework consists of coarse-grained and fine-grained classification subsystems. The coarse-grained subsystem identifies whether the input completed activity segmented by the traditional method is easily-confused, and utilizes our generalized segmentation method to increase the inter-cluster distance. The fine-grained subsystem employs machine learning or deep learning classifiers to realize the activity recognition task. The proposed model is a data-driven model based on the information self-organization. It does not need sophisticated domain knowledge, and can fully mine the hidden feature structure containing semantically related information and spatio-temporal characteristics. The experimental results demonstrate the effectiveness of the proposed method.
On the basis of the characterization of microscopic pore-throats in shale oil reservoirs by high-pressure mercury intrusion technique, a grading evaluation standard of shale oil reservoirs and a ...lower limit for reservoir formation were established. Simultaneously, a new method for the classification of shale oil flow units based on logging data was established. A new classification scheme for shale oil reservoirs was proposed according to the inflection points and fractal features of mercury injection curves: microscopic pore-throats (less than 25 nm), small pore-throats (25−100 nm), medium pore-throats (100−1 000 nm) and big pore-throats (greater than 1 000 nm). Correspondingly, the shale reservoirs are divided into four classes, I, II, III and IV according to the number of microscopic pores they contain, and the average pore-throat radii corresponding to the dividing points are 150 nm, 70 nm and 10 nm respectively. By using the correlation between permeability and pore-throat radius, the permeability thresholds for the reservoir classification are determined at 1.00× 10−3 μm2, 0.40×10−3 μm2 and 0.05×10−3 μm2 respectively. By using the exponential relationship between porosity and permeability of the same hydrodynamic flow unit, a new method was set up to evaluate the reservoir flow belt index and to identify shale oil flow units with logging data. The application in the Dongying sag shows that the standard proposed is suitable for grading evaluation of shale oil reservoirs.
Non-invasive fetal electrocardiography (NI-FECG) plays an important role in fetal heart rate (FHR) measurement during the pregnancy. However, despite the large number of methods that have been ...proposed for adult ECG signal processing, the analysis of NI-FECG remains challenging and largely unexplored. In this study, we propose a prefix tree-based framework, called QRStree, for FHR measurement directly from the abdominal ECG (AECG). The procedure is composed of three stages: Firstly, a preprocessing stage is employed for noise elimination. Secondly, the proposed prefix tree-based method is used for fetal QRS complexes (FQRS) detection. Finally, a correction stage is applied for false positive and false negative correction. The novelty of the framework relies on using the range of FHR to establish the connections between the FQRS. The consecutive FQRS can be considered as strings composed of alphabet items, thus we can use the prefix tree to store them. A vertex of the tree contains an alphabet, thus a path of the tree gives a string. Such that, by storing the connections of the FQRS into the prefix tree structure, the problem of FQRS detection converts to a problem of optimal path selection. Specifically, after selecting the optimal path of the tree, the nodes in the optimal path are collected as detected FQRS. Since the prefix tree can cover every possible combination of the FQRS candidates, it has the potential to reduce the occurrence of miss detections. Results on two different databases show that the proposed method is effective in FHR measurement from single-channel AECG. The focus on single-channel FHR measurement facilitates the long-term monitoring for healthcare at home.
PDF
