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Even today, the automatic digitisation of scanned documents in general, but especially the automatic optical music recognition (OMR) of historical manuscripts, still remains an enormous challenge, since both handwritten musical symbols and text have to be identified. This paper focuses on the Medieval so-called square notation developed in the 11th–12th century, which is already composed of staff lines, staves, clefs, accidentals, and neumes that are roughly spoken connected single notes. The aim is to develop an algorithm that captures both the neumes, and in particular its melody, which can be used to reconstruct the original writing. Our pipeline is similar to the standard OMR approach and comprises a novel staff line and symbol detection algorithm based on deep Fully Convolutional Networks (FCN), which perform pixel-based predictions for either staff lines or symbols and their respective types. Then, the staff line detection combines the extracted lines to staves and yields an F\(_1\) -score of over 99% for both detecting lines and complete staves. For the music symbol detection, we choose a novel approach that skips the step to identify neumes and instead directly predicts note components (NCs) and their respective affiliation to a neume. Furthermore, the algorithm detects clefs and accidentals. Our algorithm predicts the symbol sequence of a staff with a diplomatic symbol accuracy rate (dSAR) of about 87%, which includes symbol type and location. If only the NCs without their respective connection to a neume, all clefs and accidentals are of interest, the algorithm reaches an harmonic symbol accuracy rate (hSAR) of approximately 90%. In general, the algorithm recognises a symbol in the manuscript with an F\(_1\) -score of over 96%.
Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.
Fungi of the order Mucorales colonize all kinds of wet, organic materials and represent a permanent part of the human environment. They are economically important as fermenting agents of soybean products and producers of enzymes, but also as plant parasites and spoilage organisms. Several taxa cause life-threatening infections, predominantly in patients with impaired immunity. The order Mucorales has now been assigned to the phylum Mucoromycota and is comprised of 261 species in 55 genera. Of these accepted species, 38 have been reported to cause infections in humans, as a clinical entity known as mucormycosis. Due to molecular phylogenetic studies, the taxonomy of the order has changed widely during the last years. Characteristics such as homothallism, the shape of the suspensors, or the formation of sporangiola are shown to be not taxonomically relevant. Several genera including Absidia, Backusella, Circinella, Mucor, and Rhizomucor have been amended and their revisions are summarized in this review. Medically important species that have been affected by recent changes include Lichtheimia corymbifera, Mucor circinelloides, and Rhizopus microsporus. The species concept of Rhizopus arrhizus (syn. R. oryzae) is still a matter of debate. Currently, species identification of the Mucorales is best performed by sequencing of the internal transcribed spacer (ITS) region. Ecologically, the Mucorales represent a diverse group but for the majority of taxa, the ecological role and the geographic distribution remain unknown. Understanding the biology of these opportunistic fungal pathogens is a prerequisite for the prevention of infections, and, consequently, studies on the ecology of the Mucorales are urgently needed.
Synergy of chemo- and photodynamic therapies with C\(_{60}\) Fullerene-Doxorubicin nanocomplex
(2019)
A nanosized drug complex was explored to improve the efficiency of cancer chemotherapy, complementing it with nanodelivery and photodynamic therapy. For this, nanomolar amounts of a non-covalent nanocomplex of Doxorubicin (Dox) with carbon nanoparticle C\(_{60}\) fullerene (C\(_{60}\)) were applied in 1:1 and 2:1 molar ratio, exploiting C\(_{60}\) both as a drug-carrier and as a photosensitizer. The fluorescence microscopy analysis of human leukemic CCRF-CEM cells, in vitro cancer model, treated with nanocomplexes showed Dox’s nuclear and C\(_{60}\)'s extranuclear localization. It gave an opportunity to realize a double hit strategy against cancer cells based on Dox's antiproliferative activity and C\(_{60}\)'s photoinduced pro-oxidant activity. When cells were treated with 2:1 C\(_{60}\)-Dox and irradiated at 405 nm the high cytotoxicity of photo-irradiated C\(_{60}\)-Dox enabled a nanomolar concentration of Dox and C\(_{60}\) to efficiently kill cancer cells in vitro. The high pro-oxidant and pro-apoptotic efficiency decreased IC\(_{50}\) 16, 9 and 7 × 10\(^3\)-fold, if compared with the action of Dox, non-irradiated nanocomplex, and C\(_{60}\)'s photodynamic effect, correspondingly. Hereafter, a strong synergy of therapy arising from the combination of C\(_{60}\)-mediated Dox delivery and C\(_{60}\) photoexcitation was revealed. Our data indicate that a combination of chemo- and photodynamic therapies with C\(_{60}\)-Dox nanoformulation provides a promising synergetic approach for cancer treatment.
Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue “Remote Sensing-Based Forest Inventories from Landscape to Global Scale”, which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide.
Dead wood comprises a vast amount of biological legacies that set the scene for ecological regeneration after wildfires, yet its removal is the most frequent management strategy worldwide. Soil-dwelling organisms are conspicuous, and they provide essential ecosystem functions, but their possible affection by different post-fire management strategies has so far been neglected. We analyzed the abundance, richness, and composition of belowground macroarthropod communities under two contrasting dead-wood management regimes after a large wildfire in the Sierra Nevada Natural and National Park (Southeast Spain). Two plots at different elevation were established, each containing three replicates of two experimental treatments: partial cut, where trees were cut and their branches lopped off and left over the ground, and salvage logging, where all the trees were cut, logs were piled, branches were mechanically masticated, and slash was spread on the ground. Ten years after the application of the treatments, soil cores were extracted from two types of microhabitat created by these treatments: bare-soil (in both treatments) and under-logs (in the partial cut treatment only). Soil macroarthropod assemblages were dominated by Hemiptera and Hymenoptera (mostly ants) and were more abundant and richer in the lowest plot. The differences between dead-wood treatments were most evident at the scale of management interventions: abundance and richness were lowest after salvage logging, even under similar microhabitats (bare-soil). However, there were no significant differences between microhabitat types on abundance and richness within the partial cut treatment. Higher abundance and richness in the partial cut treatment likely resulted from higher resource availability and higher plant diversity after natural regeneration. Our results suggest that belowground macroarthropod communities are sensitive to the manipulation of dead-wood legacies and that management through salvage logging could reduce soil macroarthropod recuperation compared to other treatments with less intense management even a decade after application.
Here, we present a small Iranian family, where the index patient received a diagnosis of restrictive cardiomyopathy (RCM) in combination with atrioventricular (AV) block. Genetic analysis revealed a novel homozygous missense mutation in the DES gene (c.364T > C; p.Y122H), which is absent in human population databases. The mutation is localized in the highly conserved coil-1 desmin subdomain. In silico, prediction tools indicate a deleterious effect of the desmin (DES) mutation p.Y122H. Consequently, we generated an expression plasmid encoding the mutant and wildtype desmin formed, and analyzed the filament formation in vitro in cardiomyocytes derived from induced pluripotent stem cells and HT-1080 cells. Confocal microscopy revealed a severe filament assembly defect of mutant desmin supporting the pathogenicity of the DES mutation, p.Y122H, whereas the wildtype desmin formed regular intermediate filaments. According to the guidelines of the American College of Medical Genetics and Genomics, we classified this mutation, therefore, as a novel pathogenic mutation. Our report could point to a recessive inheritance of the DES mutation, p.Y122H, which is important for the genetic counseling of similar families with restrictive cardiomyopathy caused by DES mutations.
The alarming increase in the magnitude and spatiotemporal patterns of changes in composition, structure and function of forest ecosystems during recent years calls for enhanced cross-border mitigation and adaption measures, which strongly entail intensified research to understand the underlying processes in the ecosystems as well as their dynamics. Remote sensing data and methods are nowadays the main complementary sources of synoptic, up-to-date and objective information to support field observations in forest ecology. In particular, analysis of three-dimensional (3D) remote sensing data is regarded as an appropriate complement, since they are hypothesized to resemble the 3D character of most forest attributes. Following their use in various small-scale forest structural analyses over the past two decades, these sources of data are now on their way to be integrated in novel applications in fields like citizen science, environmental impact assessment, forest fire analysis, and biodiversity assessment in remote areas. These and a number of other novel applications provide valuable material for the Forests special issue “3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function”, which shows the promising future of these technologies and improves our understanding of the potentials and challenges of 3D remote sensing in practical forest ecology worldwide.
Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people's physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses.
Estimating penetration-related X-band InSAR elevation bias: a study over the Greenland ice sheet
(2019)
Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R\(^2\) = 68% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection.
Parent-child relationship is developed and changed through reciprocal interactions between a child and his/her parent, and these interactions can strongly influence the child's development across domains (e.g., emotional, physical, and intellectual). However, little is known about the parental perception of the child's contribution to the dyadic parent-child relationship in terms of positive and negative behaviors. We therefore aimed to develop and validate an economical parent-report instrument to assess these important aspects. The validation study included 1642 mothers (M\(_{age}\) = 37.1) and 1068 fathers (M\(_{age}\) = 40.4) of 1712 children aged 2–10 years (M\(_{age}\) = 6.6) who completed the new instrument, the Child Relationship Behavior Inventory (CRBI). Statistical results indicated that the CRBI is a reliable and valid measure. Mothers reported more positive child behaviors towards them, whereas fathers perceived fewer problems with problematic relationship behavior than mothers. In their parents' perception, girls showed more positive and less problematic relationship behaviors than boys. The frequency of problematic child relationship behavior significantly decreased with increasing child age while positive relationship behavior did not show any correlation with the child's age. To assess both positive and negative child relationship behaviors could be helpful to better understand the relevance of these different aspects for the development of the parent-child relationship.
Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38% (for SPOT-6), and 85.27% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8% (for RapidEye) and 7.22% (for SPOT-6), and R\(^2\) values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37%, whereas it decreased by up to 37.96% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions.
Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1's polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R\(^2\) and RMSE in a suggested GA fitness function. We calculated R\(^2\), RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model's stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.
The proliferative darkening syndrome (PDS) is a lethal disease of brown trout (Salmo trutta fario) which occurs in several alpine Bavarian limestone rivers. Because mortality can reach 100%, PDS is a serious threat for affected fish populations. Recently, Kuehn and colleagues reported that a high throughput RNA sequencing approach identified a piscine orthoreovirus (PRV) as a causative agent of PDS. We investigated samples from PDS-affected fish obtained from two exposure experiments performed at the river Iller in 2008 and 2009. Using a RT-qPCR and a well-established next-generation RNA sequencing pipeline for pathogen detection, PRV-specific RNA was not detectable in PDS fish from 2009. In contrast, PRV RNA was readily detectable in several organs from diseased fish in 2008. However, similar virus loads were detectable in the control fish which were not exposed to Iller water and did not show any signs of the disease. Therefore, we conclude that PRV is not the causative agent of PDS of brown trout in the rhithral region of alpine Bavarian limestone rivers. The abovementioned study by Kuehn used only samples from the exposure experiment from 2008 and detected a subclinical PRV bystander infection. Work is ongoing to identify the causative agent of PDS.
The pharmacokinetics in patients with cystic fibrosis (CF) has long been thought to differ considerably from that in healthy volunteers. For highly protein bound β-lactams, profound pharmacokinetic differences were observed between comparatively morbid patients with CF and healthy volunteers. These differences could be explained by body weight and body composition for β-lactams with low protein binding. This study aimed to develop a novel population modeling approach to describe the pharmacokinetic differences between both subject groups by estimating protein binding. Eight patients with CF (lean body mass [LBM]: 39.8 ± 5.4kg) and six healthy volunteers (LBM: 53.1 ± 9.5kg) received 1027.5 mg cefotiam intravenously. Plasma concentrations and amounts in urine were simultaneously modelled. Unscaled total clearance and volume of distribution were 3% smaller in patients with CF compared to those in healthy volunteers. After allometric scaling by LBM to account for body size and composition, the remaining pharmacokinetic differences were explained by estimating the unbound fraction of cefotiam in plasma. The latter was fixed to 50% in male and estimated as 54.5% in female healthy volunteers as well as 56.3% in male and 74.4% in female patients with CF. This novel approach holds promise for characterizing the pharmacokinetics in special patient populations with altered protein binding.
In forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is analyzed. The considered data-generating processes include different autoregressive schemes with varying model orders, count models with overdispersion or zero inflation, counts with a bounded range, and counts exhibiting trend or seasonality. We conclude that Gaussian forecast approximations should be avoided.
Studying how cambial age and axial height affects wood anatomical traits may improve our understanding of xylem hydraulics, heartwood formation and axial growth. Radial strips were collected from six different heights (0–11.3 m) along the main trunk of three Manchurian catalpa (Catalpa bungei) trees, yielding 88 samples. In total, thirteen wood anatomical vessel and fiber traits were observed usinglight microscopy (LM) and scanning electron microscopy (SEM), and linear models were used to analyse the combined effect of axial height, cambial age and their interaction. Vessel diameter differed by about one order of magnitude between early- and latewood, and increased significantly with both cambial age and axial height in latewood, while it was positively affected by cambial age and independent of height in earlywood. Vertical position further had a positive effect on earlywood vessel density, and negative effects on fibre wall thickness, wall thickness to diameter ratio and length. Cambial age had positive effects on the pit membrane diameter and vessel element length, while the annual diameter growth decreased with both cambial age and axial position. In contrast, early- and latewood fiber diameter were unaffected by both cambial age and axial height. We further observed an increasing amount of tyloses from sapwood to heartwood, accompanied by an increase of warty layers and amorphous deposits on cell walls, bordered pit membranes and pit apertures. This study highlights the significant effects of cambial age and vertical position on xylem anatomical traits, and confirms earlier work that cautions to take into account xylem spatial position when interpreting wood anatomical structures, and thus, xylem hydraulic functioning.
With the technological advances of the last decade, it is now feasible to analyze microbiome samples, such as human stool specimens, using multi-omic techniques. Given the inherent sample complexity, there exists a need for sample methods which preserve as much information as possible about the biological system at the time of sampling. Here, we analyzed human stool samples preserved and stored using different methods, applying metagenomics as well as metaproteomics. Our results demonstrate that sample preservation and storage have a significant effect on the taxonomic composition of identified proteins. The overall identification rates, as well as the proportion of proteins from Actinobacteria were much higher when samples were flash frozen. Preservation in RNAlater overall led to fewer protein identifications and a considerable increase in the share of Bacteroidetes, as well as Proteobacteria. Additionally, a decrease in the share of metabolism-related proteins and an increase of the relative amount of proteins involved in the processing of genetic information was observed for RNAlater-stored samples. This suggests that great care should be taken in choosing methods for the preservation and storage of microbiome samples, as well as in comparing the results of analyses using different sampling and storage methods. Flash freezing and subsequent storage at −80 °C should be chosen wherever possible.
Background: Accurate assessment of hepatic fibrosis in patients with chronic HBeAg-negative Hepatitis B is of crucial importance not only to predict the long-term clinical course, but also to evaluate antiviral therapy indication. The aim of this study was to prospectively assess the utility of point shear wave elastography (pSWE) for longitudinal non-invasive fibrosis assessment in a large cohort of untreated patients with chronic HBeAg-negative hepatitis B virus (HBV) infection. Methods: 407 consecutive patients with HBeAg-negative HBV infection who underwent pSWE, transient elastography (TE) as well as laboratory fibrosis markers, including fibrosis index based on four factors (FIB-4), aspartate to platelet ratio index (APRI) and FibroTest, on the same day were prospectively followed up for six years. Patients were classified into one of the three groups: inactive carriers (IC; HBV-DNA <2000 IU/mL and ALT <40 U/L); grey zone group 1 (GZ-1; HBV DNA <2000 IU/mL and ALT >40 U/L); grey zone group 2 (GZ-2; HBV-DNA >2000 IU/mL and ALT <40 U/L). Results: pSWE results were significantly correlated with TE (r = 0.29, p < 0.001) and APRI (r = 0.17; p = 0.005). Median pSWE values did not differ between IC, GZ-1 and GZ-2 patients (p = 0.82, p = 0.17, p = 0.34). During six years of follow-up, median pSWE and TE values did not differ significantly over time (TE: p = 0.27; pSWE: p = 0.05). Conclusion: Our data indicate that pSWE could be useful for non-invasive fibrosis assessment and follow-up in patients with HBeAg-negative chronic HBV infection.
The helical distribution of the electronic density in chiral molecules, such as DNA and bacteriorhodopsin, has been suggested to induce a spin–orbit coupling interaction that may lead to the so-called chirality-induced spin selectivity (CISS) effect. Key ingredients for the theoretical modelling are, in this context, the helically shaped potential of the molecule and, concomitantly, a Rashba-like spin–orbit coupling due to the appearance of a magnetic field in the electron reference frame. Symmetries of these models clearly play a crucial role in explaining the observed effect, but a thorough analysis has been largely ignored in the literature. In this work, we present a study of these symmetries and how they can be exploited to enhance chiral-induced spin selectivity in helical molecular systems.