Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time

Expression of a barley cystatin gene in maize enhances resistance against phytophagous mites by altering their cysteine-proteases

Phytocystatins are inhibitors of cysteine-proteases from plants putatively involved in plant defence based on their capability of inhibit heterologous enzymes. We have previously characterised the whole cystatin gene family members from barley (HvCPI-1 to HvCPI-13). The aim of this study was to assess the effects of barley cystatins on two phytophagous spider mites, Tetranychus urticae and Brevipalpus chilensis. The determination of proteolytic activity profile in both mite species showed the presence of the cysteine-proteases, putative targets of cystatins, among other enzymatic activities. All barley cystatins, except HvCPI-1 and HvCPI-7, inhibited in vitro mite cathepsin L- and/or cathepsin B-like activities, HvCPI-6 being the strongest inhibitor for both mite species. Transgenic maize plants expressing HvCPI-6 protein were generated and the functional integrity of the cystatin transgene was confirmed by in vitro inhibitory effect observed against T. urticae and B. chilensis protein extracts. Feeding experiments impaired on transgenic lines performed with T. urticae impaired mite development and reproductive performance. Besides, a significant reduction of cathepsin L-like and/or cathepsin B-like activities was observed when the spider mite fed on maize plants expressing HvCPI-6 cystatin. These findings reveal the potential of barley cystatins as acaricide proteins to protect plants against two important mite pests

Intelligent biofilm detection with ensemble of deep learning networks

Dental biofilm is traditionally identified visually, which can be challenging and time-consuming due to its color similarity with the tooth. The aim of this study was to evaluate the performance of U-Net neural networks for the automatic detection of dental biofilm without disclosing agents on intraoral photographs of deciduous and permanent teeth using an ensemble strategy. This retrospective exploratory study was conducted on two datasets of intraoral images obtained from deciduous and permanent dentitions. The first dataset was used to validate dental biofilm annotations by an expert with disclosing agents. The second dataset, without disclosing agents, was employed to train and evaluate the U-Net neural network in the identification of dental biofilms using an ensemble strategy. The performance of the ensemble method was assessed using a cross-validation procedure, with six groups dedicated to training, one group for validation, and one group exclusively taken as a test set for the final evaluation of the ensemble. The performance of the neural network was evaluated using accuracy, F1 score, sensitivity, and specificity. The U-Net neural network achieved an accuracy of 93.1%, sensitivity of 65.1%, specificity of 95.9%, and an F1 score of 63.0%. The U-Net neural network using the ensemble strategy was able to automatically identify visually detecTable dental biofilms on intraoral photographs. The application of this new knowledge will soon be available in clinical practice

Unveiling relationships between crime and property in England and Wales via density scale-adjusted metrics and network tools

Scale-adjusted metrics (SAMs) are a significant achievement of the urban scaling hypothesis. SAMs remove the inherent biases of per capita measures computed in the absence of isometric allometries. However, this approach is limited to urban areas, while a large portion of the world’s population still lives outside cities and rural areas dominate land use worldwide. Here, we extend the concept of SAMs to population density scale-adjusted metrics (DSAMs) to reveal relationships among different types of crime and property metrics. Our approach allows all human environments to be considered, avoids problems in the definition of urban areas, and accounts for the heterogeneity of population distributions within urban regions. By combining DSAMs, cross-correlation, and complex network analysis, we find that crime and property types have intricate and hierarchically organized relationships leading to some striking conclusions. Drugs and burglary had uncorrelated DSAMs and, to the extent property transaction values are indicators of affluence, twelve out of fourteen crime metrics showed no evidence of specifically targeting affluence. Burglary and robbery were the most connected in our network analysis and the modular structures suggest an alternative to "zero-tolerance" policies by unveiling the crime and/or property types most likely to affect each other

OPTN recruitment to a Golgi-proximal compartment regulates immune signalling and cytokine secretion.

Optineurin (OPTN) is a multifunctional protein involved in autophagy and secretion, as well as nuclear factor κB (NF-κB) and IRF3 signalling, and OPTN mutations are associated with several human diseases. Here, we show that, in response to viral RNA, OPTN translocates to foci in the perinuclear region, where it negatively regulates NF-κB and IRF3 signalling pathways and downstream pro-inflammatory cytokine secretion. These OPTN foci consist of a tight cluster of small membrane vesicles, which are positive for ATG9A. Disease mutations in OPTN linked to primary open-angle glaucoma (POAG) cause aberrant foci formation in the absence of stimuli, which correlates with the ability of OPTN to inhibit signalling. By using proximity labelling proteomics, we identify the linear ubiquitin assembly complex (LUBAC), CYLD and TBK1 as part of the OPTN interactome and show that these proteins are recruited to this OPTN-positive perinuclear compartment. Our work uncovers a crucial role for OPTN in dampening NF-κB and IRF3 signalling through the sequestration of LUBAC and other positive regulators in this viral RNA-induced compartment, leading to altered pro-inflammatory cytokine secretion

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Mechanisms of T cell organotropism

F.M.M.-B. is supported by the British Heart Foundation, the Medical Research Council of the UK and the Gates Foundation

Health-related factors correlate with behavior trends in physical activity level in old age: longitudinal results from a population in São Paulo, Brazil

<p>Abstract</p> <p>Background</p> <p>Physical inactivity in leisure time is common among elderly in Brazil and this finding is particularly alarming considering that this population is greatly affected by chronic diseases. The identification of health factors that influence physical activity level (PAL) will help in the development of strategies for increasing PAL older adults. The current research aimed to identify variables that independently affect behavior trends in PAL over the course of two years among elderly.</p> <p>Methods</p> <p>A survey entitled the Epidoso Project ("Epidemiology of aging") studied 1,667 community-based older individuals in São Paulo city, Brazil over the course of two years. Physical activity level was determined through questions about frequency and duration of physical activities. Body Mass Index was calculated; functional capacity was assessed through the ADL (activities of daily living) scale; cognition was assessed by Mini-Mental State Examination; and mental health was assessed through the Dysthymia Screening. Experiences of falls and fractures were also assessed. Subjects were divided into three groups according to their self-report of Physical Activity Level: a - Regularly Active; b - Insufficiently Active and c - Physically Inactive. Behavior trends in PAL were also measured after two years. Multivariate regression model methodology was used to test associations longitudinally.</p> <p>Results</p> <p>Results from the final model demonstrated that the risk of a not favorable behavior trend in PAL, which included the group who remained physically inactive and the group that displayed decreased PAL, in this cohort of older adults was significantly increased if the individual was female (OR = 2.50; 95% CI = 1.60-3.89; <it>P < 0.01</it>), older (80 y vs. 65 y, OR = 6.29, 95% CI = 2.69-14.67; <it>P < 0.01</it>), dependent on help from others for activities in the ADL scale (moderate-severe = 4-7+ vs. 0 ADLs) (OR = 2.25, 95% CI = 1.20-4.21; <it>P < 0.011</it>) or had experienced a history of falls with consequences (OR = 6.88, 95% CI = 0.91-52.01; <it>P < 0.062</it>).</p> <p>Conclusions</p> <p>Age, gender, ADL scores and falls were associated with a not favorable behavior trend in PAL. Promotion programs should target these factors, reducing barriers to achieve desired changes in PAL.</p

Therapeutic DNA Vaccine Encoding Peptide P10 against Experimental Paracoccidioidomycosis

Paracoccidioidomycosis (PCM), caused by Paracoccidioides brasiliensis, is the most prevalent invasive fungal disease in South America. Systemic mycoses are the 10th most common cause of death among infectious diseases in Brazil and PCM is responsible for more than 50% of deaths due to fungal infections. PCM is typically treated with sulfonamides, amphotericin B or azoles, although complete eradication of the fungus may not occur and relapsing disease is frequently reported. A 15-mer peptide from the major diagnostic antigen gp43, named P10, can induce a strong T-CD4+ helper-1 immune response in mice. The TEPITOPE algorithm and experimental data have confirmed that most HLA-DR molecules can present P10, which suggests that P10 is a candidate antigen for a PCM vaccine. In the current work, the therapeutic efficacy of plasmid immunization with P10 and/or IL-12 inserts was tested in murine models of PCM. When given prior to or after infection with P. brasiliensis virulent Pb 18 isolate, plasmid-vaccination with P10 and/or IL-12 inserts successfully reduced the fungal burden in lungs of infected mice. In fact, intramuscular administration of a combination of plasmids expressing P10 and IL-12 given weekly for one month, followed by single injections every month for 3 months restored normal lung architecture and eradicated the fungus in mice that were infected one month prior to treatment. The data indicate that immunization with these plasmids is a powerful procedure for prevention and treatment of experimental PCM, with the perspective of being also effective in human patients