Search Results (6)

Aeolian sediments accumulated along the desert-loess transition zone of the Tengger Desert include heterogeneous textures and complex component structures in their grain-size distributions (GSD). However, the sources of these aeolian sediments have not been resolved due to the lack of large reference GSD sample datasets from adjacent regions that contain various types of sediments; such datasets could be used for fingerprinting based on grain-size properties. This lack of knowledge hinders our understanding of the mechanism of aeolian dust releases in these regions and the effects of forcing of atmospheric circulations on the transportation and accumulation of sediments in this region. In this study, we employed a multi-scale grain-size analysis method, i.e., a combination of the single-sample unmixing (SSU) and the parametric end-member modelling (PEMM) techniques, to resolve the component structures of sediments that had accumulated along the desert-loess transition zone of the Tengger Desert. We have also analyzed the component structures of GSDs of various types of sediments, including mobile and fixed sand dunes, lake sediments, and loess sediments from surrounding regions. Our results demonstrate that the patterns observed in coarser fractions of sediments (i.e., sediments with a mode grain size of >100 μm) from the transition zone match well with the patterns of component structures of several types of sediments from the interior of the Tengger Desert, and the patterns seen in the finer fractions (i.e., fine, medium, and coarse silts with a modal size of <63 μm) were broadly consistent with those of loess sediments from the Qilian Mountains. The deflation/erosion of loess from the Qilian Mountains by wind was the most important mechanism underlying the production of these finer grain-size fractions. The East Asia winter monsoon (EAWM) played a key role in transportation of the aeolian dust from these source regions to the desert-loess transition zone of the desert. Full article

Multispectral, multiplex immunofluorescence (mIF) microscopy has been used to great effect in research to identify cellular co-expression profiles and spatial relationships within tissue, providing a myriad of diagnostic advantages. As these technologies mature, it is essential that image data from mIF microscopes is reproducible and standardizable across devices. We sought to characterize and correct differences in illumination intensity and spectral sensitivity between three multispectral microscopes. We scanned eight melanoma tissue samples twice on each microscope and calculated their average tissue region flux intensities. We found a baseline average standard deviation of 29.9% across all microscopes, scans, and samples, which was reduced to 13.9% after applying sample-specific corrections accounting for differences in the tissue shown on each slide. We used a basic calibration model to correct sample- and microscope-specific effects on overall brightness and relative brightness as a function of the image layer. We tested the generalizability of the calibration procedure and found that applying corrections to independent validation subsets of the samples reduced the variation to 2.9 ± 0.03%. Variations in the unmixed marker expressions were reduced from 15.8% to 4.4% by correcting the raw images to a single reference microscope. Our findings show that mIF microscopes can be standardized for use in clinical pathology laboratories using a relatively simple correction model. Full article

Natural vegetation provides various benefits to human society, but also acts as fuel for wildfires. Therefore, mapping fuel types is necessary to prevent wildfires, and hyperspectral imagery has applications in multiple fields, including the mapping of wildfire fuel types. This paper presents an automatic semisupervised machine learning approach for discriminating between wildfire fuel types and a procedure for fuel mapping using hyperspectral imagery (HSI) from PRISMA, a recently launched satellite of the Italian Space Agency. The approach includes sample generation and pseudolabelling using a single spectral signature as input data for each class, unmixing mixed pixels by a fully constrained linear mixing model, and differentiating sparse and mountainous vegetation from typical vegetation using biomass and DEM maps, respectively. Then the procedure of conversion from a classified map to a fuel map according to the JRC Anderson Codes is presented. PRISMA images of the southern part of Sardinia, an island off Italy, were considered to implement this procedure. As a result, the classified map obtained an overall accuracy of 87% upon validation. Furthermore, the stability of the proposed approach was tested by repeating the procedure on another HSI acquired for part of Bulgaria and we obtained an overall stability of around 84%. In terms of repeatability and reproducibility analysis, a degree of confidence greater than 95% was obtained. This study suggests that PRISMA imagery has good potential for wildfire fuel mapping, and the proposed semisupervised learning approach can generate samples for training the machine learning model when there is no single go-to dataset available, whereas this procedure can be implemented to develop a wildfire fuel map for any part of Europe using LUCAS land cover points as input. Full article

The purpose of the present study was to evaluate the histological and histomorphometric characteristics of post-extraction sites grafted with decellularized bovine compact bone from bovine femur, mixed and unmixed with leukocyte- and platelet-rich fibrin after four months of healing. This study was designed as a randomized controlled trial of parallel groups. Patients in need of a single, implant-supported restoration to replace a hopeless tooth were recruited for tooth extraction and implant placement four months after socket preservation procedure. After tooth extraction, patients were randomly allocated to receive decellularized bovine compact bone from bovine femur, mixed and unmixed with leukocyte- and platelet-rich fibrin. After four months of healing, tapered implants were inserted with an insertion torque between 35 and 45 Ncm. Two months later, implants were loaded with screw-retained definitive crowns. Outcome measures were implant (ISR) and prosthesis (PSR) survival rates, complications, histological and histomorphometric analyses, radiographic marginal bone-level changes, and patients’ satisfaction. Clinical data were collected up to one year after tooth extraction and socket preservation procedures. Thirty patients were consecutively enrolled in the trial (15 in each group). Unfortunately, due to the COVID-19 pandemic, bone samples were collected only in 19 patients. Two implants failed before definitive prosthesis delivery (ISR 93.3%). No prosthesis failed (PSR 100%). Three complications were experienced in the control group. The mean bone percentage was 40.64 ± 18.76 in the test group and 33.40 ± 22.38 in the control group. The difference was not statistically significant (p = 0.4846). The mean soft tissue percentage was 32.55 ± 19.45 in the test group and 55.23 ± 17.64 in the control group. The difference was statistically significant (p = 0.0235). The mean residual graft was 24.59 ± 18.39 in the test group and 11.37 ± 12.12 in the control group. The difference was not statistically significant (p = 0.0992). Mean marginal bone loss, as well as patient satisfaction, showed no differences between groups. With the limitations of the present study, socket preservation with L-PRF mixed with decellularized bovine compact bone demonstrated favorable results, comparing with decellularized bovine compact bone from bovine femur alone. Further studies with larger sample size and longer follow-up are needed to confirm these preliminary results. Full article

To solve the challenge of single-channel blind image separation (BIS) caused by unknown prior knowledge during the separation process, we propose a BIS method based on cascaded generative adversarial networks (GANs). To ensure that the proposed method can perform well in different scenarios and to address the problem of an insufficient number of training samples, a synthetic network is added to the separation network. This method is composed of two GANs: a U-shaped GAN (UGAN), which is used to learn image synthesis, and a pixel-to-attention GAN (PAGAN), which is used to learn image separation. The two networks jointly complete the task of image separation. UGAN uses the unpaired mixed image and the unmixed image to learn the mixing style, thereby generating an image with the “true” mixing characteristics which addresses the problem of an insufficient number of training samples for the PAGAN. A self-attention mechanism is added to the PAGAN to quickly extract important features from the image data. The experimental results show that the proposed method achieves good results on both synthetic image datasets and real remote sensing image datasets. Moreover, it can be used for image separation in different scenarios which lack prior knowledge and training samples. Full article

In this work, biomolecules, such as membrane proteins, lipids, and DNA, were identified and their spatial distribution was mapped within a single Escherichia coli cell by Raman hyperspectral imaging. Raman spectroscopy allows direct, nondestructive, rapid, and cost-effective analysis of biological samples, minimizing the sample preparation and without the need of chemical label or immunological staining. Firstly, a comparison between an air-dried and a freeze-dried cell was made, and the principal vibrational modes associated to the membrane and nucleic acids were identified by the bacterium’s Raman chemical fingerprint. Then, analyzing the Raman hyperspectral images by multivariate statistical analysis, the bacterium biological status was investigated at a subcellular level. Principal components analysis (PCA) was applied for dimensionality reduction of the spectral data, then spectral unmixing was performed by multivariate curve resolution–alternating least squares (MCR-ALS). Thanks to multivariate data analysis, the DNA segregation and the Z-ring formation of a replicating bacterial cell were detected at a sub-micrometer level, opening the way to real-time molecular analysis that could be easily applied on in vivo or ex vivo biological samples, avoiding long preparation and analysis process. Full article