The vascular endothelial growth factor (VEGF) family members, VEGF-A, placenta growth factor (PlGF), and to a lesser extent VEGF-B, play an essential role in tumor-associated angiogenesis, tissue infiltration, and metastasis formation. Although VEGF-A can activate both VEGFR-1 and VEGFR-2 membrane receptors, PlGF and VEGF-B exclusively interact with VEGFR-1. Differently from VEGFR-2, which is involved both in physiological and pathological angiogenesis, in the adult VEGFR-1 is required only for pathological angiogenesis. Besides this role in tumor endothelium, ligand-mediated stimulation of VEGFR-1 expressed in tumor cells may directly induce cell chemotaxis and extracellular matrix invasion. Furthermore, VEGFR-1 activation in myeloid progenitors and tumor-associated macrophages favors cancer immune escape through the release of immunosuppressive cytokines. These properties have prompted a number of preclinical and clinical studies to analyze VEGFR-1 involvement in the metastatic process. The aim of the present review is to highlight the contribution of VEGFs/VEGFR-1 signaling in the progression of different tumor types and to provide an overview of the therapeutic approaches targeting VEGFR-1 currently under investigation.Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) "skeleton" joints on the human body at 30 frames per second, and the skeleton data often have acceptable accuracy. However, the skeleton data obtained from the sensor sometimes exhibit a high level of jitter due to noise and estimation error. This jitter is worse when there is occlusion or a subject moves slightly out of the field of view of the sensor for a short period of time. Therefore, this paper proposed a novel approach to simultaneously handle the noise and error in the skeleton data derived from Kinect. Initially, we adopted classification processing to divide the skeleton data into noise data and erroneous data. Furthermore, we used a Kalman filter to smooth the noise data and correct erroneous data. We performed an occlusion experiment to prove the effectiveness of our algorithm. The proposed method outperforms existing techniques, such as the moving mean filter and traditional Kalman filter. The experimental results show an improvement of accuracy of at least 58.7%, 47.5% and 22.5% compared to the original Kinect data, moving mean filter and traditional Kalman filter, respectively. Our method provides a new perspective for Kinect data processing and a solid data foundation for subsequent research that utilizes Kinect.An improved method of physical activity accelerometer data processing, involving a wider frequency filter than the most commonly used ActiGraph filter, has been shown to better capture variations in physical activity intensity in a lab setting. The aim of the study was to investigate how this improved measure of physical activity affected the relationship with markers of cardiometabolic health. Accelerometer data and markers of cardiometabolic health from 725 adults from two samples, LIV 2013 and SCAPIS pilot, were analyzed. The accelerometer data was processed using both the original ActiGraph method with a low-pass cut-off at 1.6 Hz and the improved method with a low-pass cut-off at 10 Hz. The relationship between the physical activity intensity spectrum and a cardiometabolic health composite score was investigated using partial least squares regression. The strongest association between physical activity and cardiometabolic health was shifted towards higher intensities with the 10 Hz output compared to the ActiGraph method. In addition, the total explained variance was higher with the improved method. The 10 Hz output enables correctly measuring and interpreting high intensity physical activity and shows that physical activity at this intensity is stronger related to cardiometabolic health compared to the most commonly used ActiGraph method.A high-precision acceleration measurement system based on an ultra-sensitive tunnel magneto-resistance (TMR) sensor is presented in this paper. A "force-magnetic-electric" coupling structure that converts an input acceleration into a change in magnetic field around the TMR sensor is designed. In such a structure, a micro-cantilever is integrated with a magnetic field source on its tip. Under an acceleration, the mechanical displacement of the cantilever causes a change in the spatial magnetic field sensed by the TMR sensor. The TMR sensor is constructed with a Wheatstone bridge structure to achieve an enhanced sensitivity. Meanwhile, a low-noise differential circuit is developed for the proposed system to further improve the precision of the measured acceleration. The experimental results show that the micro-system achieves a measurement resolution of 19 μg/√Hz at 1 Hz, a scale factor of 191 mV/g within a range of ± 2 g, and a bias instability of 38 μg (Allan variance). The noise sources of the proposed system are thoroughly investigated, which shows that low-frequency 1/f noise is the dominant noise source. We propose to use a high-frequency modulation technique to suppress the 1/f noise effectively. Measurement results show that the 1/f noise is suppressed about 8.6-fold at 1 Hz and the proposed system resolution can be improved to 2.2 μg/√Hz theoretically with this high-frequency modulation technique.In the absence of a vaccine, current antibiotic-dependent efforts to reduce the prevalence of Neisseria gonorrhoeae in high prevalence populations have been shown to result in extremely high levels of antibiotic consumption.  https://www.selleckchem.com/products/methylene-blue-trihydrate.html  No randomized controlled trials have been conducted to validate this strategy and an important concern of this approach is that it may induce antimicrobial resistance. To contribute to this debate, we assessed if mass treatment in the related species, Neisseria meningitidis, was associated with the emergence of antimicrobial resistance. To this end, we conducted a historical review of the effect of mass meningococcal treatment programmes on the prevalence of N. meningitidis and the emergence of antimicrobial resistance. We found evidence that mass treatment programmes were associated with the emergence of antimicrobial resistance.