Search

Abstract Background The presence of nucleated red blood cells (NRBCs) in the peripheral blood of critically ill patients is associated with poor outcome. Evidence regarding the predictive value of NRBCs in patients with SARS-CoV-2-induced acute respiratory distress syndrome (ARDS) remains elusive. The aim of this study was to evaluate the predictive validity of NRBCs in these patients. Methods Daily NRBC values of adult patients with SARS-CoV-2-induced ARDS were assessed and their predictive validity for mortality was statistically evaluated. A cut-off level based on the patient’s maximum NRBC value during ICU stay was calculated and further specified according to Youden’s method. Based on this cut-off value, further analyses such as logistic regression models and survival were performed. Results 413 critically ill patients with SARS-CoV-2-induced ARDS were analyzed. Patients who did not survive had significantly higher NRBC values during their ICU stay compared to patients who survived (1090/µl [310; 3883] vs. 140/µl [20; 500]; p

Forest diebacks pose a major threat to global ecosystems. Identifying and mapping both living and dead trees is crucial for understanding the causes and implementing effective management strategies. This study explores the efficacy of Mask R–CNN for automated forest dieback monitoring. The method detects individual trees, delineates their crowns, and classifies them as alive or dead. We evaluated the approach using aerial imagery and canopy height models in the Harz Mountains, Germany, a region severely affected by forest dieback. To assess the model's ability to track changes over time, we applied it to images from three separate flight campaigns (2009, 2016, and 2022). This evaluation considered variations in acquisition dates, cameras, post-processing techniques, and image tilting. Forest changes were analyzed based on the detected trees' number, spatial distribution, and height. A comprehensive accuracy assessment demonstrated the Mask R–CNN's robust performance, with precision scores ranging from 0.80 to 0.88 and F1-scores from 0.88 to 0.91. These results confirm the model's ability to generalize across diverse image acquisition conditions. While minor changes were observed between 2009 and 2016, the period between 2016 and 2022 witnessed substantial dieback, with a 64.57% loss of living trees. Notably, taller trees appeared to be particularly affected. This study highlights Mask R–CNN's potential as a valuable tool for automated forest dieback monitoring. It enables efficient detection, delineation, and classification of both living and dead trees, providing crucial data for informed forest management practices.

Earth observation missions such as Sentinel and Landsat support the large-scale identification of agricultural crops by providing free radar and multispectral satellite images. The extraction of representative image information as well as the combination of different image sources for improved feature selection still represent a major challenge in the field of remote sensing. In this paper, we propose a novel three-dimensional (3D) deep learning U-Net model to fuse multi-level image features from multispectral and synthetic aperture radar (SAR) time series data for seasonal crop-type mapping at a regional scale. For this purpose, we used a dual-stream U-Net with a 3D squeeze-and-excitation fusion module applied at multiple stages in the network to progressively extract and combine multispectral and SAR image features. Additionally, we introduced a distinctive method for generating patch-based multitemporal multispectral composites by selective image sampling within a 14-day window, prioritizing those with minimal cloud cover. The classification results showed that the proposed network provided the best overall accuracy (94.5%) compared to conventional two-dimensional (2D) and three-dimensional U-Net models (2D: 92.6% and 3D: 94.2%). Our network successfully learned multi-modal dependencies between the multispectral and SAR satellite images, leading to improved field mapping of spectrally similar and heterogeneous classes while mitigating the limitations imposed by persistent cloud coverage. Additionally, the feature representations extracted by the proposed network demonstrated their transferability to a new cropping season, providing a reliable mapping of spatio-temporal crop type patterns.

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures.

The documentation of historical remains and cultural heritage is of great importance to preserve historical knowledge. Many studies use low-resolution airplane-based laser scanning and manual interpretation for this purpose. In this study, a concept to automatically detect terrain anomalies in a historical conflict landscape using high-resolution UAV-LiDAR data was developed. We applied different ground filter algorithms and included a spline-based approximation step in order to improve the removal of low vegetation. Due to the absence of comprehensive labeled training data, a one-class support vector machine algorithm was used in an unsupervised manner in order to automatically detect the terrain anomalies. We applied our approach in a study site with different densities of low vegetation. The morphological ground filter was the most suitable when dense near-ground vegetation is present. However, with the use of the spline-based processing step, all filters used could be significantly improved in terms of the F1-score of the classification results. It increased by up to 42% points in the area with dense low vegetation and by up to 14% points in the area with sparse low vegetation. The completeness (recall) reached maximum values of 0.8 and 1.0, respectively, when taking into account the results leading to the highest F1-score for each filter. Therefore, our concept can support on-site field prospection.

Oil sand mining in northern Alberta/Canada in the Athabasca region is a major intrusion into the otherwise pristine natural environment. The various types of oil sands mining, transport, and processing are causing large-scale discharge of pollutants. Accordingly, this study examined the gradual changes in the physically undisturbed vegetation, that occurred from 1984 to 2021 in the Athabasca oil sands monitoring region. First, the abrupt changes were masked out with the help of auxiliary and Landsat data. Subsequently, a normalized burn ratio Landsat time-series was applied to the LandTrendr algorithm on the Google Earth Engine. In order to interpret gradual changes, measurement criteria were used to describe vegetation development, vulnerability, and variability. In addition, the spatial and temporal relationship of these to oil sand opencast mines, processing facilities, and steam assisted gravity drainage (SAGD) mines was examined. The results showed that a major part of the vegetation in the Athabasca oil sand monitoring region underwent a positive development (65.9%). However, around the opencast mines a negative vegetation development and stability within a radius of 10 km could be observed. In the surroundings of processing facilities, the development and stability of vegetation was disturbed within a radius of 2 km. Thereby the analysis of land cover classes showed that deciduous, coniferous, and mixed forest are disproportionately affected. Conversely, no negative influences on neighboring vegetation could be detected around SAGD mines. The temporal analysis showed that vegetation disturbance was most pronounced between 1990 and 2000, but recovered in recent years.

Introduction: Bacterial Infection of various body fluids can leads to serious invasive infection, high morbidity and mortality. It is a clinical urgency; so early detection and identification of pathogen is essential for appropriate management of patient. Aim: To determine the bacterial profile and their susceptibility to antimicrobial agents of isolates from various body fluids. Materials and Methods: This retrospective cross-sectional study was conducted from January, 2021 to December 2021 in the Microbiology Department, CRG Hospital, RD Gardi Medical College, Ujjain, Madhya Pradesh, India. Body fluids were collected and transported aseptically from the patients visited in this hospital. All received samples were processed as per standard microbiology methods. Antimicrobial susceptibility testing was performed using the Kirby-Bauer disk diffusion method and interpreted as per Clinical and Laboratory Standards Institute (CLSI) guidelines. Results: A total of 298 body fluids samples were received for culture and sensitivity, of these, 63 (21.1%) fluid samples showed growth. Out of them, maximum pathogens were isolated from Broncho Alveolar Lavage (BAL) fluid 28 (44.4%), followed by peritoneal fluid 17 (27%), pleural fluid 12 (19%), CSF 1 (1.6%), pericardial fluid 1 (1.6%) and synovial 4 (6.3%). Most of the isolates were Gram-Negative Bacteria (GNB) 58 (92%), predominantly Pseudomonas aeruginosa 20(32%) followed by Klebsiella species 18 (29%), E.coli 14 (22%), All gram negative isolates were sensitive to Tigecycline and Colistin, However, susceptibility to the carbapenem group (Imipenem, Miropenem and Ertapenem) were (80-100%). Among gram positive bacteria, Staphylococcus aureus were 5 (8%) isolated. Of these, Methicillin Resistant Staphylococcus aureus (MRSA) was 4 (6.3%) and Methicillin Sensitive Staphylococcus Aureus (MSSA) was 1 (1.6%). All isolate of Staphylococcus aureus were susceptible to Vancomycin, Linezolide, Teicoplanin and Levofloxacin. Conclusion: Knowledge of bacteriological and antimicrobial profile of body fluids is necessary, so that the life threatening infections can be effectively treated. In this study, BAL fluid is the commonest received sample. GNB were main isolates among them Pseudomonas aeruginosa is frequently isolated pathogen. All isolated pathogens were susceptible for higher group of antimicrobial agents (tigecycline, colistin) and showed reduced sensitivity towards other antibiotics. So, regular monitoring of the pattern of Antimicrobial Resistance (AMR) of bacteriological isolates in the patients is critical to develop antibiotic policy to combat these infections and reduce morbidity and mortality.

Mycetoma is a chronic, slow progressive granulomatous infection of skin and subcutaneous tissue; is classified into Eumycetoma (caused by fungi) and Actinomycetoma (caused by Actinomycetes). Fusarium, is a known cause of mycetoma. This case report describes the isolation and identification of Fusarium sp. from a lesion on the right foot of a female patient. This case report is about a 23-year-old female with complaints of gradual enlarging nodular swelling with multiple discharging sinuses along with granules on the right foot, on and off for last seven years. The granules obtained from the lesions are the best specimen for culture as they contain microcolonies of causative agents and it’s a hallmark of mycetoma. Granules were collected aseptically, crushed and examined microscopically on 20% KOH preparation. Plenty of thin hyaline, septate hyphae were seen. Granules were washed with sterile saline and inoculated on Sabouraud’s Dextrose Agar (SDA) and potato dextrose agar and incubated aerobically at 25°C and 37°C in two sets of each. Colonies grew at inoculated site, pinkish in the centre and whitish in the periphery. The isolate was identified as Fusarium sp. on the basis of morphology. The patient was started on Itraconazole 200 mg and is currently on the same treatment, improving and under follow-up. Mycetoma is a rare disease caused by both fungal and bacterial microorganisms. Definitive diagnosis should be made on the basis of culture or histopathological examination. Once the diagnosis is made, use of appropriate anti-fungal therapy with or without surgical interventions can result in complete cure without recurrence.

Earth observation missions such as Sentinel and Landsat support the large-scale identification of agricultural crops by providing free radar and multispectral satellite images. The extraction of representative image information as well as the combination of different image sources for improved feature selection still represent a major challenge in the field of remote sensing. In this paper, we propose a novel three-dimensional (3D) deep learning U-Net model to fuse multi-level image features from multispectral and synthetic aperture radar (SAR) time series data for seasonal crop-type mapping at a regional scale. For this purpose, we used a dual-stream U-Net with a 3D squeeze-and-excitation fusion module applied at multiple stages in the network to progressively extract and combine multispectral and SAR image features. Additionally, we introduced a distinctive method for generating patch-based multitemporal multispectral composites by selective image sampling within a 14-day window, prioritizing those with minimal cloud cover. The classification results showed that the proposed network provided the best overall accuracy (94.5%) compared to conventional two-dimensional (2D) and three-dimensional U-Net models (2D: 92.6% and 3D: 94.2%). Our network successfully learned multi-modal dependencies between the multispectral and SAR satellite images, leading to improved field mapping of spectrally similar and heterogeneous classes while mitigating the limitations imposed by persistent cloud coverage. Additionally, the feature representations extracted by the proposed network demonstrated their transferability to a new cropping season, providing a reliable mapping of spatio-temporal crop type patterns. [ABSTRACT FROM AUTHOR]

Salt marshes act as an important natural buffer in terms of coastal protection in the light of the rising sea level. Due to weather events like extreme storms the extent of salt marshes changes. Hence, it is of great importance to regularly monitor these changes, especially for managing interventions and reporting their ecological status in the frame of environmental policies, like Natura 2000. In this study, the potential of freely available sallite imagery is investigated and a methodological approach suggested to map superior salt marsh types (pioneer zone, lower and upper salt marshes) for supporting regular monitoring compliances. Therefore, (spectral-)temporal metrics of optical Sentinel-2 (S2) and Landsat 8 as well as SAR Sentinel-1 were calculated and used in different classification setups. The classifications were performed using a basic Random Forest classifier. A detailed accuracy assessment shows the impact of different datasets on the overall accuracy. The best result was achieved using S2 data, which led to an overall accuracy of 90.3 %. The combination of optical and SAR data, on the other hand, did not increase the classification accuracy. Overall, the freely available datasets and the proposed method proof useful and are considered well suited for monitoring salt marshes.

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures. [ABSTRACT FROM AUTHOR]

This article discusses a randomized controlled trial that aimed to improve compliance with daily sunscreen application through the use of a simple, daily, goal-setting reminder. The study utilized a two-arm design, with one group receiving sunscreen instruction only and the other group receiving sunscreen instruction along with a calendar to track sunscreen application. The results showed that participants in the calendar group used an additional 25 mL of sunscreen compared to the control group, indicating a notable increase in compliance. The study suggests that incorporating practical tools like calendars may be effective in promoting sustained sunscreen adherence and reducing the incidence of sun-related skin damage and diseases. Further research is needed to validate these findings in larger and more diverse populations. [Extracted from the article]

Frass (fine powdery refuse or fragile perforated wood produced by the activity of boring insects) of larvae of the European house borer (EHB) and of drywood termites was tested as a natural and novel feedstock for 3D-printing of wood-based materials. Small particles produced by the drywood termite Incisitermes marginipennis and the EHB Hylotrupes bajulus during feeding in construction timber, were used. Frass is a powdery material of particularly consistent quality that is essentially biologically processed wood mixed with debris of wood and faeces. The filigree-like particles flow easily permitting the build-up of wood-based structures in a layer wise fashion using the Binder Jetting printing process. The quality of powders produced by different insect species was compared along with the processing steps and properties of the printed parts. Drywood termite frass with a Hausner Ratio HR = 1.1 with ρBulk = 0.67 g/cm3 and ρTap = 0.74 g/cm3 was perfectly suited to deposition of uniformly packed layers in 3D printing. We suggest that a variety of naturally available feedstocks could be used in environmentally responsible approaches to scientific material sciences/additive manufacturing.

Wetlands are highly dynamic, with many natural and anthropogenic drivers causing seasonal, periodic or permanent changes in their structure and composition. Thus, it is necessary to use time series of images for accurate classifications and monitoring. We used all available Sentinel-1 and Sentinel-2 images to produce a national wetlands map for Albania. We derived different indices and temporal metrics and investigated their impacts and synergies in terms of mapping accuracy. Best results were achieved when combining Sentinel-1 with Sentinel-2 and its derived indices. We reduced systematic errors and increased the thematic resolution using morphometric characteristics and knowledge-based rules, achieving an overall accuracy of 82%. Results were also validated against field inventories. This methodology can be reproducible to other countries and can be made operational for an integrated planning that considers the food, water, and energy nexus.

Magnetocaloric composite wires have been studied by pulsed-field measurements up to μ0ΔH = 10 T with a typical rise time of 13 ms in order to evaluate the evolution of the adiabatic temperature change of the core, ΔTad, and to determine the effective temperature change at the surrounding steel jacket, ΔTeff, during the field pulse. An inverse thermal hysteresis is observed for ΔTad due to the delayed thermal transfer. By numerical simulations of application-relevant sinusoidal magnetic field profiles, it can be stated that for field-frequencies of up to two field cycles per second heat can be efficiently transferred from the core to the outside of the jacket. In addition, intense numerical simulations of the temperature change of the core and jacket were performed by varying different parameters, such as frequency, heat capacity, thermal conductivity and interface resistance in order to shed light on their impact on ΔTeff at the outside of the jacket in comparison to ΔTad provided by the core.

Mapping heathland habitats is generally challenging due to fine-scale habitats as well as spectral ambiguities between different classes. A multi-seasonal time-series of multispectral RapidEye data from several phenological stages was analysed towards the classification of different vegetation communities.A 3-level hierarchical dependent classification using Import Vector Machines was tested, based on the assumption that a probabilistic output per class would help the mapping. The first level of the hierarchical classification was related to the moisture gradient, which was derived from Ellenberg’s moisture indicative value. The second level aimed to separate plant alliances; the third level differentiated individual plant associations.For the final integration of the three classification levels, two approaches were implemented: (i) the F1-score and (ii) the maximum classification probability. The overall classification accuracies of both methods were found to be similar, around 0.7.Nevertheless, based on our expert knowledge we found the probabilistic approach to provide a more realistic picture and to be more practical compared to the result using the F1-score from the management point of view. In addition, the overall performance of the maximum probabilistic approach is better in the sense that the same accuracy of 0.7 was achieved with a differentiation of 33 classes instead of only 13 classes for the F1-score, meaning that the method is able to separate more spectral classes at a more detailed level providing the same accuracy.