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A binocular vision-based approach for the restoration of images captured in a scattering medium is presented. The scene depth is computed by triangulation using stereo matching. Next, the atmospheric parameters of the medium are determined with an introduced estimator based on the Monte Carlo method. Finally, image restoration is performed using an atmospheric optics model. The proposed approach effectively suppresses optical scattering effects without introducing noticeable artifacts in processed images. The accuracy of the proposed approach in the estimation of atmospheric parameters and image restoration is evaluated using synthetic hazy images constructed from a well-known database. The practical viability of our approach is also confirmed through a real experiment for depth estimation, atmospheric parameter estimation, and image restoration in a scattering medium. The results highlight the applicability of our approach in computer vision applications in challenging atmospheric conditions.

IMPORTANCE:. Vascular dysfunction and capillary leak are common in critically ill COVID-19 patients, but identification of endothelial pathways involved in COVID-19 pathogenesis has been limited. Angiopoietin-like 4 (ANGPTL4) is a protein secreted in response to hypoxic and nutrient-poor conditions that has a variety of biological effects including vascular injury and capillary leak. OBJECTIVES:. To assess the role of ANGPTL4 in COVID-19–related outcomes. DESIGN, SETTING, AND PARTICIPANTS:. Two hundred twenty-five COVID-19 ICU patients were enrolled from April 2020 to May 2021 in a prospective, multicenter cohort study from three different medical centers, University of Washington, University of Southern California and New York University. MAIN OUTCOMES AND MEASURES:. Plasma ANGPTL4 was measured on days 1, 7, and 14 after ICU admission. We used previously published tissue proteomic data and lung single nucleus RNA (snRNA) sequencing data from specimens collected from COVID-19 patients to determine the tissues and cells that produce ANGPTL4. RESULTS:. Higher plasma ANGPTL4 concentrations were significantly associated with worse hospital mortality (adjusted odds ratio per log2 increase, 1.53; 95% CI, 1.17–2.00; p = 0.002). Higher ANGPTL4 concentrations were also associated with higher proportions of venous thromboembolism and acute respiratory distress syndrome. Longitudinal ANGPTL4 concentrations were significantly different during the first 2 weeks of hospitalization in patients who subsequently died compared with survivors (p for interaction = 8.1 × 10–5). Proteomics analysis demonstrated abundance of ANGPTL4 in lung tissue compared with other organs in COVID-19. ANGPTL4 single-nuclear RNA gene expression was significantly increased in pulmonary alveolar type 2 epithelial cells and fibroblasts in COVID-19 lung tissue compared with controls. CONCLUSIONS AND RELEVANCE:. ANGPTL4 is expressed in pulmonary epithelial cells and fibroblasts and is associated with clinical prognosis in critically ill COVID-19 patients.

This study tries to find evidence that points towards the best typeface to use in e-commerce websites to maximize usability, trust, loyalty, appearance and overall user satisfaction. We tested the difference between serif and sans serif inside the same font family. A total of 246 volunteers participating in the experiment were asked to complete a set of tasks and a questionnaire on an e-commerce website prototype. We measured task completion time, reading speed and reading comprehension. From the results, using multiple linear regression, we deduced that only gender determines user preferences. Females tend to prefer the serif version of the typeface under study. Although most e-commerce websites use sans serif typefaces, we could not find evidence supporting this decision. The serif and sans serif characteristic inside the same font family does not affect usability on a website, as it was found that it has no impact on reading speed and user preference.

Purpose: Repeated-sprint training in hypoxia (RSH) leads to great improvements in anaerobic performance. However, there is no consensus about the optimal level of hypoxia that should be used during training to maximize subsequent performances. This study aimed to establish whether such an optimal altitude can be determined and whether pulse oxygen saturation during RSH is correlated with training-induced improvement in performance.Methods: Peak and mean power outputs of healthy young males [age (mean ± SD) 21.7 ± 1.4 years] were measured during a Wingate (30 s) and a repeated-sprint ability (RSA; 10 x 6-s sprint with 24-s recovery) test before and after RSH. Participants performed six cycling sessions comprising three sets of 8 x 6-s sprint with 24-s recovery in normobaric hypoxia at a simulated altitude of either 1,500 m, 2,100 m, or 3,200 m (n = 7 per group). Heart rate variability was assessed at rest and during recovery from Wingate test before and after RSH.Results: The subjective rating of perceived exertion and the relative exercise intensity during training sessions did not differ between the three groups, contrary to pulse oxygen saturation (p < 0.001 between each group). Mean and peak power outputs were significantly increased in all groups after training, except for the mean power in the RSA test for the 3200 m group. Change in mean power on RSA test (+8.1 ± 6.6%) was the only performance parameter significantly correlated with pulse oxygen saturation during hypoxic training (p < 0.05, r = 0.44). The increase in LnRMSSD during recovery from the Wingate test was enhanced after training in the 1,500 m (+22%) but not in the two other groups (≈– 6%). Moreover, the increase in resting heart rate with standing after training was negatively correlated with SpO2 (p < 0.01, r =–0.63) suggesting that hypoxemia level during training differentially altered autonomic nervous system activity.Conclusion: These data indicate that RSH performed as early as 1,500 m of altitude is effective in improving anaerobic performance in moderately trained subjects without strong association with pulse oxygen saturation monitoring during training.

OBJECTIVES:. Respiratory virus infections cause significant morbidity and mortality ranging from mild uncomplicated acute respiratory illness to severe complications, such as acute respiratory distress syndrome, multiple organ failure, and death during epidemics and pandemics. We present a protocol to systematically study patients with severe acute respiratory infection (SARI), including severe acute respiratory syndrome coronavirus 2, due to respiratory viral pathogens to evaluate the natural history, prognostic biomarkers, and characteristics, including hospital stress, associated with clinical outcomes and severity. DESIGN:. Prospective cohort study. SETTING:. Multicenter cohort of patients admitted to an acute care ward or ICU from at least 15 hospitals representing diverse geographic regions across the United States. PATIENTS:. Patients with SARI caused by infection with respiratory viruses that can cause outbreaks, epidemics, and pandemics. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Measurements include patient demographics, signs, symptoms, and medications; microbiology, imaging, and associated tests; mechanical ventilation, hospital procedures, and other interventions; and clinical outcomes and hospital stress, with specimens collected on days 0, 3, and 7–14 after enrollment and at discharge. The primary outcome measure is the number of consecutive days alive and free of mechanical ventilation (VFD) in the first 30 days after hospital admission. Important secondary outcomes include organ failure-free days before acute kidney injury, shock, hepatic failure, disseminated intravascular coagulation, 28-day mortality, adaptive immunity, as well as immunologic and microbiologic outcomes. CONCLUSIONS:. SARI—Preparedness is a multicenter study under the collaboration of the Society of Critical Care Medicine Discovery, Resilience Intelligence Network, and National Emerging Special Pathogen Training and Education Center, which seeks to improve understanding of prognostic factors associated with worse outcomes and increased resource utilization. This can lead to interventions to mitigate the clinical impact of respiratory virus infections associated with SARI.

Mixed Integer Linear Programs (MILPs) are usually NP-hard mathematical programming problems, which present difficulties to obtain optimal solutions in a reasonable time for large scale models. Nowadays, metaheuristics are one of the potential tools for solving this type of problems in any context. In this paper, we focus our attention on MILPs in the specific framework of Data Envelopment Analysis (DEA), where the determination of a score of technical efficiency of a set of Decision Making Units (DMUs) is one of the main objectives. In particular, we propose a new hyper-matheuristic grounded on a MILP-based decomposition in which the optimization problem is divided into two hierarchical subproblems. The new approach decomposes the model into discrete and continuous variables, treating each subproblem through different optimization methods. In particular, metaheuristics are used for dealing with the discrete variables, whereas exact methods are used for the set of continuous variables. The metaheuristics use an indirect representation that encodes an incomplete solution for the problem, whereas the exact method is applied to decode the solution and generate a complete solution. The experimental results, based on simulated data in the context of Data Envelopment Analysis, show that the solutions obtained through the new approach outperform those found by solving the problem globally using a metaheuristic method. Finally, regarding the new hyper-matheuristic scheme, the best algorithm selection is found for a set of cooperative metaheuristics ans exact optimization algorithms.

A stereo matching method based on adaptive morphological correlation is presented. The point correspondences of an input pair of stereo images are determined by matching locally adaptive image windows using the suggested morphological correlation that is optimal with respect to an introduced binary dissimilarity-to-matching ratio criterion. The proposed method is capable of determining the point correspondences in homogeneous image regions and at the edges of scene objects of input stereo images with high accuracy. Furthermore, unknown correspondences of occluded and not matched points in the scene can be successfully recovered using a simple proposed post-processing. The performance of the proposed method is exhaustively tested for stereo matching in terms of objective measures using known database images. In addition, the obtained results are discussed and compared with those of two similar state-of-the-art methods.

Mobile devices now rival desktop computers as the most popular devices for web surfing and E-commerce. As screen sizes of mobile devices continue to get larger, operating smartphones with a single-hand becomes increasingly difficult. Automatic operating hand detection would enable E-commerce applications to adapt their interfaces to better suit their user’s handedness interaction requirements. This paper addresses the problem of identifying the operative hand by avoiding the use of mobile sensors that may pose a problem in terms of battery consumption or distortion due to different calibrations, improving the accuracy of user categorization through an evaluation of different classification strategies. A supervised classifier based on machine learning was constructed to label the operating hand as left or right. The classifier uses features extracted from touch traces such as scrolls and button clicks on a data-set of 174 users. The approach proposed by this paper is not platform-specific and does not rely on access to gyroscopes or accelerometers, widening its applicability to any device with a touchscreen.

Objectives:. To describe the impact of coronavirus disease 2019 on family engagement among ICUs participating in a multicenter collaborative promoting implementation of family-centered care projects and to report sites’ experiences with the collaborative itself prior to its cancelation due to the pandemic in March 2020. Design:. Cross-sectional survey. Setting:. Twenty-seven academic and community ICUs in the United States and South Korea. Subjects:. Site leaders. Interventions:. Prior to March 2020, all sites had participated in 6 months of webinars, monthly calls, and listserv communication to facilitate projects and to collect preimplementation family satisfaction and clinician perception data. Measurements and Main Results:. Planned projects included ICU orientation initiatives (12, 44.4%), structured family care conferences (6, 22.2%), and ICU diaries (5, 18.5%). After cancelation of the collaborative, 22 site leaders (81.5%) were surveyed by phone from June 2020 to July 2020. Twenty (90.1%) reported having stopped their site project; projects that continued were 1) a standardized palliative extubation protocol and 2) daily written clinical summaries for families. Sites described significant variability in visitor restriction policies and uncertainty regarding future policy changes. Four sites (18.2%) reported that their hospital did not provide personal protective equipment to visitors. Regarding video conferencing with families, 11 sites (52.4%) reported clinicians’ using their own personal devices. Two-hundred twelve family surveys and 346 clinician surveys collected prior to cancelation highlighted a broad need for family support. When leaders were asked on a scale from 0 to 10 how helpful collaborative activities had been prior to cancelation, mean response was 8.0 (sd 2.5). Conclusions:. While the collaborative model can help promote ICU family engagement initiatives, coronavirus disease 2019 has impeded implementation of these initiatives even among motivated units. ICUs need adequate personal protective equipment for visitors and video conferencing capabilities on hospital devices while strict visitor restrictions continue to evolve.

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.

BackgroundThe upper-airway microbiome is involved in asthma exacerbations despite inhaled corticosteroid (ICS) treatment. Although human genetics regulates microbiome composition, its influence on asthma-related airway bacteria remains unknown.ObjectiveWe sought to identify genes and biological pathways regulating airway-microbiome traits involved in asthma exacerbations and ICS response.MethodsSaliva, nasal, and pharyngeal samples from 257 European patients with asthma were analyzed. The association of 6,296,951 genetic variants with exacerbation-related microbiome traits despite ICS treatment was tested through microbiome genome-wide association studies. Variants with 1 × 10-4 -6 were examined in gene-set enrichment analyses. Significant results were sought for replication in 114 African American and 158 Latino children with and without asthma. ICS-response-associated single nucleotide polymorphisms reported in the literature were evaluated as microbiome quantitative trait loci. Multiple comparisons were adjusted by the false discovery rate.ResultsGenes associated with exacerbation-related airway-microbiome traits were enriched in asthma comorbidities development (ie, reflux esophagitis, obesity, and smoking), and were likely regulated by trichostatin A and the nuclear factor-κB, the glucocorticosteroid receptor, and CCAAT/enhancer-binding protein transcription factors (7.8 × 10-13 ≤ false discovery rate ≤ 0.022). Enrichment in smoking, trichostatin A, nuclear factor-κB, and glucocorticosteroid receptor were replicated in the saliva samples from diverse populations (4.42 × 10-9 ≤ P ≤ .008). The ICS-response-associated single nucleotide polymorphisms rs5995653 (APOBEC3B-APOBEC3C), rs6467778 (TRIM24), and rs5752429 (TPST2) were identified as microbiome quantitative trait loci of Streptococcus, Tannerella, and Campylobacter in the upper airway (0.027 ≤ false discovery rate ≤ 0.050).ConclusionsGenes associated with asthma exacerbation-related microbiome traits might influence asthma comorbidities. We reinforced the therapeutic interest of trichostatin A, nuclear factor-κB, the glucocorticosteroid receptor, and CCAAT/enhancer-binding protein in asthma exacerbations.

Smartphones can provide coverage in large areas all around the world and with the availability of powerful operating systems they can become solid sensing infrastructures. In fact, static sensors are hard to deploy and maintain while modern mobile devices include many sensors that can be used to sense and benefit from collaborative communities. This project tries to improve urban computing by developing a framework able to create monitoring applications for mobile devices, focusing on obtaining the highest degree of interoperability between sensors. A prototype application has been developed to demonstrate the feasibility of creating multidisciplinary applications with several different approaches. The application developed consists of a Road Roughness Information System that measures smoothness and detects irregularities on the roads