Wavelet and modified VGG-16 deep learning to identify COVID-19 based on X-ray images

COVID-19 is caused by the SARS coronavirus 2 family (SARS-CoV-2). A quick antibody or antigen test can detect the presence of COVID-19, but further testing is needed to confirm a positive result. Radiologists use chest X-rays to diagnose chest diseases early. The proposed system integrates discrete wavelet transformation and deep learning to help radiologists categorise conditions. Wavelets break down images into multiple spatial resolutions depending on a high pass and low pass frequency components and efficiently extract characteristics from lung X-rays. Here, we use a hybrid wavelet-CNN model to diagnose lung X-rays. The proposed CNN model is trained and verified on different source COVID 19 chest X-ray images for binary and three classes. The proposed studies suggest significant improvement in outcomes, with the best parameters achieving 99.42% accuracy and 96.43% accuracy for binary and three classes. The depiction of feature maps shows that our suggested network collected features from the corona virus-affected lung properly. Results suggest that the proposed model is successful enough for COVID 19 diagnosis. © 2022 IEEE.

Spatio-temporal dynamics of aerosol distribution in an urban environment recorded in situ by means of a bike based monitoring system

Aerosol pollution in urban areas is highly variable due to numerous single emission sources such as automobiles, industrial and commercial activities as well as domestic heating, but also due to complex building structures redirecting air mass flows, producing leeward and windward turbulences and resuspension effects. In this publication, it is shown that one or even few aerosol monitoring sites are not able to reflect these complex patterns. In summer 2019, aerosol pollution was recorded in high spatial resolution during six night and daytime tours with a mobile sensor platform on a trailer pulled by a bicycle. Particle mass loadings showed a high variability with PM10 values ranging from 1.3 to 221 µg m-3 and PM2.5 values from 0.7 to 69.0 µg m-3. Geostatistics were used to calculate respective models of the spatial distributions of PM2.5 and PM10. The resulting maps depict the variability of aerosol concentrations within the urban space. These spatial distribution models delineate the distributions without cutting out the built-up structures. Elsewise, the overall spatial patterns do not become visible because of being sharply interrupted by those outcuts in the resulting maps. Thus, the spatial maps allow to identify most affected urban areas and are not restricted to the street space. Furthermore, this method provides an insight to potentially affected areas, and thus can be used to develop counter measures. It is evident that the spatial aerosol patterns cannot be directly derived from the main wind direction, but result far more from an interplay between main wind direction, built-up patterns and distribution of pollution sources. Not all pollution sources are directly obvious and more research has to be carried out to explain the micro-scale variations of spatial aerosol distribution patterns. In addition, since aerosol load in the atmosphere is a severe issue for health and well-being of city residents more attention has to be paid to these local inhomogeneities.

Image Scanning Microscopy to Investigate Polycomb Protein Colocalization onto Chromatin

Super-resolution microscopy has been recently applied to understand the 3D topology of chromatin at an intermediated genomic scale (kilobases to a few megabases), as this corresponds to a sub-diffraction spatial scale crucial for the regulation of gene transcription. In this context, polycomb proteins are very renowned gene repressors that organize into the multiprotein complexes Polycomb Repressor Complex 1 (PRC1) and 2 (PRC2). PRC1 and PRC2 operate onto the chromatin according to a complex mechanism, which was recently recapitulated into a working model. Here, we present a functional colocalization study at 100–140 nm spatial resolution targeting PRC1 and PRC2 as well as the histone mark H3K27me3 by Image Scanning Microscopy (ISM). ISM offers a more flexible alternative to diffraction-unlimited SRMs such as STORM and STED, and it is perfectly suited to investigate the mesoscale of PRC assembly. Our data suggest a partially simultaneous effort of PRC1 and PRC2 in locally shaping the chromatin topology.

Low‐Altitude UAV Imaging Accurately Quantifies Eelgrass Wasting Disease From Alaska to California

Declines in eelgrass, an important and widespread coastal habitat, are associated with wasting disease in recent outbreaks on the Pacific coast of North America. This study presents a novel method for mapping and predicting wasting disease using Unoccupied Aerial Vehicle (UAV) with low‐altitude autonomous imaging of visible bands. We conducted UAV mapping and sampling in intertidal eelgrass beds across multiple sites in Alaska, British Columbia, and California. We designed and implemented a UAV low‐altitude mapping protocol to detect disease prevalence and validated against in situ results. Our analysis revealed that green leaf area index derived from UAV imagery was a strong and significant (inverse) predictor of spatial distribution and severity of wasting disease measured on the ground, especially for regions with extensive disease infection. This study highlights a novel, efficient, and portable method to investigate seagrass disease at landscape scales across geographic regions and conditions.Alternate abstract:Plain Language SummaryDiseases of marine organisms are increasing in many regions worldwide, therefore, efficient time‐series monitoring is critical for understanding the dynamics of disease and examining its progression in time to implement management interventions. In the first study of its kind, we use high‐resolution Unoccupied Aerial Vehicle (UAV) imagery collected to detect disease at 12 sites across the North‐East Pacific coast of North America spanning 18 degrees of latitude. The low altitude UAV visible‐bands imagery achieved 1.5 cm spatial resolution, and analysis was performed at the seagrass leaf scale based on object‐oriented image analysis. Our findings suggest that drone mapping of coastal plants may substantially increase the scale of disease risk assessments in nearshore habitats and further our understanding of seagrass meadow spatial‐temporal dynamics. These can be scaled up by searching for environmental signals of the pathogen, for example, with surveillance of wastewater for signs of Covid in human populations. This application could easily apply to other areas to construct a high‐resolution monitoring network for seagrass conservation.

Influence of geospatial resolution on sociodemographic predictors of COVID-19 in Massachusetts.

PURPOSE: When studying health risks across a large geographic region such as a state or province, researchers often assume that finer-resolution data on health outcomes and risk factors will improve inferences by avoiding ecological bias and other issues associated with geographic aggregation. However, coarser-resolution data (e.g., at the town or county-level) are more commonly publicly available and packaged for easier access, allowing for rapid analyses. The advantages and limitations of using finer-resolution data, which may improve precision at the cost of time spent gaining access and processing data, have not been considered in detail to date. METHODS: We systematically examine the implications of conducting town-level mixed-effect regression analyses versus census-tract-level analyses to study sociodemographic predictors of COVID-19 in Massachusetts. In a series of negative binomial regressions, we vary the spatial resolution of the outcome, the resolution of variable selection, and the resolution of the random effect to allow for more direct comparison across models. RESULTS: We find stability in some estimates across scenarios, changes in magnitude, direction, and significance in others, and tighter confidence intervals on the census-tract level. Conclusions regarding sociodemographic predictors are robust when regions of high concentration remain consistent across town and census-tract resolutions. CONCLUSIONS: Inferences about high-risk populations may be misleading if derived from town- or county-resolution data, especially for covariates that capture small subgroups (e.g., small racial minority populations) or are geographically concentrated or skewed (e.g., % college students). Our analysis can help inform more rapid and efficient use of public health data by identifying when finer-resolution data are truly most informative, or when coarser-resolution data may be misleading.

14th International Conference on Synchrotron Radiation Instrumentation (SRI 2021), 28 March to 1 April 2022

The International Conference on Synchrotron Radiation Instrumentation (SRI) is a unique and significant international forum held every three years in the community of synchrotron radiation (SR) and free electron lasers (FEL). It is the prime forum for fostering connections between cutting-edge synchrotron radiation instrumentation, science, and the requirements of the user community. The SRI 2021 had originally been scheduled to take place in Hamburg in summer 2021. Due to the COVID-19 pandemic, it was postponed to 2022 and held as an online event.More than 1160 international participants from 25 countries met virtually at the SRI 2021. In nearly 290 talks and 450 posters, latest results were presented. Although it was an online-only conference, lively discussions took place in the nearly 40 parallel sessions, and the eight poster sessions were also very well attended.The main topics of the SRI conference were: new SR and FEL facilities, update plans of these facilities, and recent developments in various instrumentation areas like beamline design, X-ray optics, sample environments, detectors and spectrometers, data acquisition, and data analysis techniques or automation. These innovations contributed to new results for a wide range of experimental techniques and scientific applications such as X-ray scattering and spectroscopy, bio- and scanning imaging, structural biology crystallography, coherent techniques, or in-situ/operando methods. A dedicated session concerned industrial applications of synchrotron radiation.The field of synchrotron radiation instrumentation is currently seeing very active development due to various factors. Firstly, the number of SR sources world-wide is increasing significantly, with new sources in particular in Europe and in Asia. Secondly, a new generation of storage rings with new multi-bend achromat lattices are being implemented at a growing number of existing facilities. These facilities offer a significant increase of brilliance and coherence and thereby lead to new and improved applications of synchrotron radiation, in particular in the areas of imaging and high spatial resolution. Thirdly, the increase of soft and hard X-ray FEL sources worldwide and the maturation of their experimental techniques and scientific applications is the background for a strongly increasing number of developments for ultrafast time-resolved investigations of dynamic behaviour of materials and reactions. Most of the keynote speakers and many invited and contributed talks or posters at the conference showed new results directly related to these three major developments.Lists of International Advisory Committee (listed by facility), Scientific Programme Committee (listed by facility), Local Organising Committee (listed by facility) are available in this PDF.

Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI

The aim of this paper is to highlight how TROPOspheric Monitoring Instrument (TROPOMI) trace gas data can best be used and interpreted to understand event-based impacts on air quality from regional to city scales around the globe. For this study, we present the observed changes in the atmospheric column amounts of five trace gases (NO2, SO2, CO, HCHO, and CHOCHO) detected by the Sentinel-5P TROPOMI instrument and driven by reductions in anthropogenic emissions due to COVID-19 lockdown measures in 2020. We report clear COVID-19-related decreases in TROPOMI NO2 column amounts on all continents. For megacities, reductions in column amounts of tropospheric NO2 range between 14 % and 63 %. For China and India, supported by NO2 observations, where the primary source of anthropogenic SO2 is coal-fired power generation, we were able to detect sector-specific emission changes using the SO2 data. For HCHO and CHOCHO, we consistently observe anthropogenic changes in 2-week-averaged column amounts over China and India during the early phases of the lockdown periods. That these variations over such a short timescale are detectable from space is due to the high resolution and improved sensitivity of the TROPOMI instrument. For CO, we observe a small reduction over China, which is in concert with the other trace gas reductions observed during lockdown;however, large interannual differences prevent firm conclusions from being drawn. The joint analysis of COVID-19-lockdown-driven reductions in satellite-observed trace gas column amounts using the latest operational and scientific retrieval techniques for five species concomitantly is unprecedented. However, the meteorologically and seasonally driven variability of the five trace gases does not allow for drawing fully quantitative conclusions on the reduction in anthropogenic emissions based on TROPOMI observations alone. We anticipate that in future the combined use of inverse modeling techniques with the high spatial resolution data from S5P/TROPOMI for all observed trace gases presented here will yield a significantly improved sector-specific, space-based analysis of the impact of COVID-19 lockdown measures as compared to other existing satellite observations. Such analyses will further enhance the scientific impact and societal relevance of the TROPOMI mission.

Basic principles of ultrasound and the use of lung ultrasound in the COVID-19 pandemic

Given that ultrasound use is increasing in healthcare, operators must be familiar with its physics in order to optimise the image and interpret potential artifacts. Ultrasound are sound waves at frequencies above the range of human hearing, that are transmitted from and received by an ultrasound transducer with piezoelectric properties. As it propagates through tissues, some of the ultrasound waves are reflected at tissue boundaries, leading to its detection by the ultrasound transducer. These are processed by the ultrasound machine and result in the generation of an image. Various settings can be adjusted to optimise the image, such as the frequency of the transmitted ultrasound wave, depth of the focal zone and the gain. Artifacts are presentations on the monitor of the ultrasound machine which are added, omitted, or are of improper brightness, location, shape, and size compared with true anatomical features. It can result in falsely perceived objects, missing structures or degraded images. The presence or absence of such artifacts in lung ultrasound can be valuable in the interpretation of the resulting image. In the setting of COVID-19, lung ultrasound has become increasingly useful in evaluating disease progression and providing a point-of-care radiological adjunct in clinical decision making. © World Federation of Societies of Anaesthesiologists 2022.

Flood Risk and Exposure: The Case of the Tajuña Valley (Madrid, Spain)

This paper aims to update the exposure to flood risk in a catchment area of the Community of Madrid (Spain) linked to primary sector activities, albeit affected by the urban expansion of the capital. This research starts with the updating of the flood inventory, encompassing episodes documented between 1629 and 2020. The inadequate occupation of the territory means that floods continue to cause significant damage nowadays. It is worth highlighting the two recent floods (2019) that occurred just 15 days apart and caused serious damage to several towns in the basin. The areas at risk of flooding are obtained from the National Floodplain Mapping System, and the maximum and minimum floodable volume in the sector of the Tajuña River basin with the highest exposure to flooding has been calculated. The Sentinel 2 image in false colour (RGB bands 11-2-3, 11-8-3 and 12-11-8) and its transformation to colour properties (Intensity, Hue and Saturation) has made it possible to determine the extension of the riparian vegetation and the irrigated crops located in the alluvial plain. The SPOT 6 image with higher spatial resolution has allowed us to update the mapping of buildings located in areas at risk of flooding. Finally, based on cadastral data, a detailed cartography of built-up areas in areas at risk of flooding is provided. They affect buildings built mainly between the 1960s and 1990s, although the most recent buildings are built on agricultural land in the alluvial plain, even though current regulations prevent the occupation of these lands.

All-Fabric-Based Flexible Capacitive Sensors with Pressure Detection and Non-Contact Instruction Capability

The flexible and wearable capacitive sensors have captured tremendous interest due to their enormous potential for healthcare monitoring, soft robotics, and human−computer interface. However, despite recent progress, there are still pressing challenges to develop a fully integrated textile sensor array with good comfort, high sensitivity, multisensing capabilities, and ultra-light detection. Here, we demonstrate a pressure and non-contact bimodal fabric-only capacitive sensor with highly sensitive and ultralight detection. The graphene nanoplatelets-decorated multidimensional honeycomb fabric and nickel-plated woven fabric serve as the dielectric layer and electrode, respectively. Our textile-only capacitive bimodal sensor exhibits an excellent pressure-sensing sensitivity of 0.38 kPa−1, an ultralow detection limit (1.23 Pa), and cycling stability. Moreover, the sensor exhibits superior non-contact detection performance with a detection distance of 15 cm and a maximum relative capacitance change of 10%. The sensor can successfully detect human motion, such as finger bending, saliva swallowing, etc. Furthermore, a 4 × 4 (16 units) textile-only capacitive bimodal sensor array was prepared and has excellent spatial resolution and response performance, showing great potential for the wearable applications.

A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images

Visual acuity (VA) is a measure of the ability to distinguish shapes and details of objects at a given distance and is a measure of the spatial resolution of the visual system. Vision is one of the basic health indicators closely related to a person’s quality of life. It is one of the first basic tests done when an eye disease develops. VA is usually measured by using a Snellen chart or E-chart from a specific distance. However, in some cases, such as the unconsciousness of patients or diseases, i.e., dementia, it can be impossible to measure the VA using such traditional chart-based methodologies. This paper provides a machine learning-based VA measurement methodology that determines VA only based on fundus images. In particular, the levels of VA, conventionally divided into 11 levels, are grouped into four classes and three machine learning algorithms, one SVM model and two CNN models, are combined into an ensemble method in order to predict the corresponding VA level from a fundus image. Based on a performance evaluation conducted using randomly selected 4000 fundus images, we confirm that our ensemble method can estimate with 82.4% of the average accuracy for four classes of VA levels, in which each class of Class 1 to Class 4 identifies the level of VA with 88.5%, 58.8%, 88%, and 94.3%, respectively. To the best of our knowledge, this is the first paper on VA measurements based on fundus images using deep machine learning.

Improved monitoring of shipping NO2 with TROPOMI: decreasing NOx emissions in European seas during the COVID-19 pandemic

TROPOMI (TROPOspheric Monitoring Instrument) measurements of tropospheric NO2 columns provide powerful information on emissions of air pollution by ships on open sea. This information is potentially useful for authorities to help determine the (non-)compliance of ships with increasingly stringent NOx emission regulations. We find that the information quality is improved further by recent upgrades in the TROPOMI cloud retrieval and an optimal data selection. We show that the superior spatial resolution of TROPOMI allows for the detection of several lanes of NO2 pollution ranging from the Aegean Sea near Greece to the Skagerrak in Scandinavia, which have not been detected with other satellite instruments before. Additionally, we demonstrate that under conditions of sun glint TROPOMI's vertical sensitivity to NO2 in the marine boundary layer increases by up to 60 %. The benefits of sun glint are most prominent under clear-sky situations when sea surface winds are low but slightly above zero (±2 m s-1). Beyond spatial resolution and sun glint, we examine for the first time the impact of the recently improved cloud algorithm on the TROPOMI NO2 retrieval quality, both over sea and over land. We find that the new FRESCO+ (Fast Retrieval Scheme for Clouds from the Oxygen A band) wide algorithm leads to 50 hPa lower cloud pressures, correcting a known high bias, and produces 1–4×1015 molec. cm-2 higher retrieved NO2 columns, thereby at least partially correcting for the previously reported low bias in the TROPOMI NO2 product. By training an artificial neural network on the four available periods with standard and FRESCO+ wide test retrievals, we develop a historic, consistent TROPOMI NO2 data set spanning the years 2019 and 2020. This improved data set shows stronger (35 %–75 %) and sharper (10 %–35 %) shipping NO2 signals compared to co-sampled measurements from OMI. We apply our improved data set to investigate the impact of the COVID-19 pandemic on ship NO2 pollution over European seas and find indications that NOx emissions from ships reduced by 10 %–20 % during the beginning of the COVID-19 pandemic in 2020. The reductions in ship NO2 pollution start in March–April 2020, in line with changes in shipping activity inferred from automatic identification system (AIS) data on ship location, speed, and engine.

Super-resolution using Deep Networks for Chest X-Ray Images

Super-resolution imaging is extensively deliberated in medical imaging modalities nowadays, there being a wide panic on the effect of COVID-19 virus impression. Generally, spatial resolutions of CXR are insufficient due to the constraints such as image acquisition time, hardware limits and physical limits. It is a clinically challenging task to recover the high resolution CXR images. A significant concern in CXR imaging is X-Ray contrast disparity and the demand to attain high quality images with adequate structural and imaging details. To address these problems, we propose an effective deep network for the super-resolution reconstruction method to recover high-resolution CXR images while retaining diagnostic capabilities. Specifically, the reinforcement subnetwork is hosted to generate sharp and informative qualitative features. The quantitative and qualitative assessments found that the proposed model based on the evaluation index improves the CXR super-resolution. In addition, the PSNR index of the proposed model has 0.30 higher than that of the SRCNN network. © 2021 IEEE.

Variability of nitrogen oxide emission fluxes and lifetimes estimated from Sentinel-5P TROPOMI observations

Satellite observations of the high-resolution TROPOspheric Monitoring Instrument (TROPOMI) on Sentinel-5 Precursor can be used to observe nitrogen dioxide (NO2) at city scales to quantify short time variability of nitrogen oxide (NOx) emissions and lifetimes on a daily and seasonal basis. In this study, 2 years of TROPOMI tropospheric NO2 columns, having a spatial resolution of up to 3.5 km × 5.5 km, have been analyzed together with wind and ozone data. NOx lifetimes and emission fluxes are estimated for 50 different NOx sources comprising cities, isolated power plants, industrial regions, oil fields, and regions with a mix of sources distributed around the world. The retrieved NOx emissions are in agreement with other TROPOMI-based estimates and reproduce the variability seen in power plant stack measurements but are in general lower than the analyzed stack measurements and emission inventory results. Separation into seasons shows a clear seasonal dependence of NOx emissions with in general the highest emissions during winter, except for isolated power plants and especially sources in hot desert climates, where the opposite is found. The NOx lifetime shows a systematic latitudinal dependence with an increase in lifetime from 2 to 8 h with latitude but only a weak seasonal dependence. For most of the 50 sources including the city of Wuhan in China, a clear weekly pattern of NOx emissions is found, with weekend-to-weekday ratios of up to 0.5 but with a high variability for the different locations. During the Covid-19 lockdown period in 2020, strong reductions in the NOx emissions were observed for New Delhi, Buenos Aires, and Madrid.

Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods

The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, they are unsuitable for vineyards of different types and structures. By calibrating the light extinction coefficient of a digital photography algorithm for proximal LAI measurements, this study aimed to develop VI-LAI models for pergola-trained vineyards based on high-resolution RGB and multispectral images captured by an unmanned aerial vehicle (UAV). The models were developed by comparing five machine learning (ML) methods, and a robust ensemble model was proposed using the five models as base learners. The results showed that the ensemble model outperformed the base models. The highest R2 and lowest RMSE values that were obtained using the best combination of VIs with multispectral data were 0.899 and 0.434, respectively;those obtained using the RGB data were 0.825 and 0.547, respectively. By improving the results by feature selection, ML methods performed better with multispectral data than with RGB images, and better with higher spatial resolution data than with lower resolution data. LAI variations can be monitored efficiently and accurately for large areas of pergola-trained vineyards using this framework.

UAV-BASED 3D VIRTUAL TOUR CREATION

Lately, improvements in game engines have increased the interest in virtual reality (VR) technologies, that engages users with an artificial environment, and have led to the adoption of VR systems to display geospatial data. Because of the ongoing COVID-19 pandemic, and thus the necessity to stay at home, VR tours became very popular. In this paper, we tried to create a three-dimensional (3D) virtual tour for Gebze Technical University (GTU) Southern Campus by transferring high-resolution unmanned air vehicle (UAV) data into a virtual domain. UAV data is preferred in various applications because of its high spatial resolution, low cost and fast processing time. In this application, the study area was captured from different modes and altitudes of UAV flights with a minimum ground sampling distance (GSD) of 2.18 cm using a 20 MP digital camera. The UAV data was processed in Structure from Motion (SfM) based photogrammetric evaluation software Agisoft Metashape and high-quality 3D textured mesh models were generated. Image orientation was completed using an optimal number of ground control points (GCPs), and the geometric accuracy was calculated as ±8 mm (~0.4 pixels). To create the VR tour, UAV-based mesh models were transferred into the Unity game engine and optimization processes were carried out by applying occlusion culling and space subdivision algorithms. To improve the visualization, 3D object models such as trees, lighting poles and arbours were positioned on VR. Finally, textual metadata about buildings and a player with a first-person camera were added for an informative VR experience.

Territorial impact of the COVID-19 pandemic in Galicia (Spain): A geographical approach

The coronavirus pandemic is causing a huge impact around the world. Its real magnitude presents very important regional differences, which are appreciable in the number of infected and victims in the different countries. The outbreak of the pandemic and the ignorance of the virus mean that, even today, there are many unknowns about essential aspects related to it. In this sense, geographic knowledge can help answer many questions from the territorial analysis of the data. The objective of this article will be to analyze the behavior of the coronavirus pandemic within the Spanish region of Galicia. The authors of this study propose a multiscale analysis that allows deciphering the most common propagation patterns. For this, we have high spatial resolution data that has been provided by the competent authority under confidentiality. The results of this work allow us to represent and interpret the territorial impact of the pandemic, understanding its behavior as far as possible, allowing future dynamics to be predicted. © 2021 Asociacion de Geografos Espanoles. All rights reserved.