wEscore: quality assessment method of multichannel image visualization with regard to angular resolution

This work considers the problem of quality assessment of multichannel image visualization methods. One approach to such an assessment, the Escore quality measure, is studied. This measure, initially proposed for decolorization methods evaluation, can be generalized for the assessment of hyperspectral image visualization methods. It is shown that Escore does not account for the loss of local contrast at the supra-pixel scale. The sensitivity to the latter in humans depends on the observation conditions, so we propose a modified wEscore measure which includes the parameters allowing for the adjustment of the local contrast scale based on the angular resolution of the images. We also describe the adjustment of wEscore parameters for the evaluation of known decolorization algorithms applied to the images from the COLOR250 and the Cadik datasets with given observational conditions. When ranking the results of these algorithms and comparing it to the ranking based on human perception, wEscore turned out to be more accurate than Escore.This work was supported by Russian Science Foundation (Project No. 20-61-47089)

Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images.

Although extreme learning machines (ELM) have been successfully applied for the classification of hyperspectral images (HSIs), they still suffer from three main drawbacks. These include: 1) ineffective feature extraction (FE) in HSIs due to a single hidden layer neuron network used; 2) ill-posed problems caused by the random input weights and biases; and 3) lack of spatial information for HSIs classification. To tackle the first problem, we construct a multilayer ELM for effective FE from HSIs. The sparse representation is adopted with the multilayer ELM to tackle the ill-posed problem of ELM, which can be solved by the alternative direction method of multipliers. This has resulted in the proposed multilayer sparse ELM (MSELM) model. Considering that the neighboring pixels are more likely from the same class, a local block extension is introduced for MSELM to extract the local spatial information, leading to the local block MSELM (LBMSELM). The loopy belief propagation is also applied to the proposed MSELM and LBMSELM approaches to further utilize the rich spectral and spatial information for improving the classification. Experimental results show that the proposed methods have outperformed the ELM and other state-of-the-art approaches

An Object-Oriented Color Visualization Method with Controllable Separation for Hyperspectral Imagery

Publisher's version (útgefin grein)Most of the available hyperspectral image (HSI) visualization methods can be considered as data-oriented approaches. These approaches are based on global data, so it is difficult to optimize display of a specific object. Compared to data-oriented approaches, object-oriented visualization approaches show more pertinence and would be more practical. In this paper, an object-oriented hyperspectral color visualization approach with controllable separation is proposed. Using supervised information, the proposed method based on manifold dimensionality reduction methods can simultaneously display global data information, interclass information, and in-class information, and the balance between the above information can be adjusted by the separation factor. Output images are visualized after considering the results of dimensionality reduction and separability. Five kinds of manifold algorithms and four HSI data were used to verify the feasibility of the proposed approach. Experiments showed that the visualization results by this approach could make full use of supervised information. In subjective evaluations, t-distributed stochastic neighbor embedding (T-SNE), Laplacian eigenmaps (LE), and isometric feature mapping (ISOMAP) demonstrated a sharper detailed pixel display effect within individual classes in the output images. In addition, T-SNE and LE showed clarity of information (optimum index factor, OIF), good correlation (ρ), and improved pixel separability () in objective evaluation results. For Indian Pines data, T-SNE achieved the best results in regard to both OIF and, which were 0.4608 and 23.83, respectively. However, compared with other methods, the average computing time of this method was also the longest (1521.48 s).This research was funded by the National Natural Science Foundation of China, grant numbers 61275010 and 61675051. The authors would like to thank D. Landgrebe from Purdue University for providing the AVIRIS Indian Pines data set and Prof. P. Gamba from the University of Pavia for providing the ROSIS-3 University of Pavia data set. The authors would like to express their appreciation to Jon Qiaosen Chen from the University of Iceland and Di Chen for helping improve the language of the paper.Peer Reviewe

Potassium deficiency diagnosis method of apple leaves based on MLR-LDA-SVM

IntroductionAt present, machine learning and image processing technology are widely used in plant disease diagnosis. In order to address the challenges of subjectivity, cost, and timeliness associated with traditional methods of diagnosing potassium deficiency in apple tree leaves. MethodsThe study proposes a model that utilizes image processing technology and machine learning techniques to enhance the accuracy of detection during each growth period. Leaf images were collected at different growth stages and processed through denoising and segmentation. Color and shape features of the leaves were extracted and a multiple regression analysis model was used to screen for key features. Linear discriminant analysis was then employed to optimize the data and obtain the optimal shape and color feature factors of apple tree leaves during each growth period. Various machine-learning methods, including SVM, DT, and KNN, were used for the diagnosis of potassium deficiency. ResultsThe MLR-LDA-SVM model was found to be the optimal model based on comprehensive evaluation indicators. Field experiments were conducted to verify the accuracy of the diagnostic model, achieving high diagnostic accuracy during different growth periods. DiscussionThe model can accurately diagnose whether potassium deficiency exists in apple tree leaves during each growth period. This provides theoretical guidance for intelligent and precise water and fertilizer management in orchards

Study of Biodegradation and Bioremediation

Despite many years of efforts to reduce the emission of toxic pollutants into the environment, the contamination of air, soils and water by heavy metals and organic xenobiotics is still a serious problem. This has urged many scientists around the world to undertake research that aims to find effective methods of removing pollutants from the environment. Special attention is paid to biological methods, which, thanks to their numerous advantages, meet the expectations of the whole society. As part of the Special Issue “Study of Biodegradation and Bioremediation”, in the MDPI journal Processes, several valuable articles have been published, which together form a picture of the current state of advanced research on the effective fight against environmental pollution. These include papers on the biodegradation of petroleum compounds or synthetic dyes by microorganisms or the enzymes they produce. In addition, the Special Issue includes papers on the bioremediation of dangerous heavy metals such as mercury and copper, and the results make a valuable contribution to our current state of knowledge on this topic. A separate and valuable part of this collection of publications are review articles devoted to the remediation of antineoplastic drugs, as well as the hopes and challenges connected with the application of nanotechnology in bioremediation. We are pleased that so many researchers from different parts of the world have submitted their articles on this topic. We are very grateful to them. We hope that readers of this collection will find many interesting ideas and relevant information that will lead to new solutions in the bioremediation and biodegradation of emerging environmental contaminants. Prof. Ewa Kaczorek Dr. Wojciech Smułe

Hydrogel-Tissue Chemistry: Principles and Applications

Over the past five years, a rapidly developing experimental approach has enabled high-resolution and high-content information retrieval from intact multicellular animal (metazoan) systems. New chemical and physical forms are created in the hydrogel-tissue chemistry process, and the retention and retrieval of crucial phenotypic information regarding constituent cells and molecules (and their joint interrelationships) are thereby enabled. For example, rich data sets defining both single-cell-resolution gene expression and single-cell-resolution activity during behavior can now be collected while still preserving information on three-dimensional positioning and/or brain-wide wiring of those very same neurons—even within vertebrate brains. This new approach and its variants, as applied to neuroscience, are beginning to illuminate the fundamental cellular and chemical representations of sensation, cognition, and action. More generally, reimagining metazoans as metareactants—or positionally defined three-dimensional graphs of constituent chemicals made available for ongoing functionalization, transformation, and readout—is stimulating innovation across biology and medicine

Fluorescence Multiplexing with Combination Probes for Biological and Diagnostic Applications

Cancer refers to a group of diseases containing more than 200 different subtypes. Cancer is a heterogeneous disease by nature, meaning that there are differences among tumors of the same type in different patients, and there are differences among cancer cells within a single tumor of one patient. Since cancer is not a single disease, nor does it have a single cause, it proves to be incredibly hard to diagnose and treat. The ability to study cellular markers, cell and tissue spatial arrangement, and gene function are all integral parts of cancer diagnostic and treatment efforts. Here, I first present a review of current techniques for quantitative tissue imaging at cellular resolution. I broadly divide current imaging techniques into three categories: fluorescence-based, mass spectrometry-based, and sequencing-based. In this work, I primarily concentrate on fluorescence-based methods, with the focus being on our recently developed theory Multiplexing using Spectral Imaging and Combinatorics (MuSIC). The basis for MuSIC is to create combinations of fluorescent molecules (whether it be small molecule fluorophores or fluorescent proteins) to create unique spectral signatures. I then present a protocol for labeling antibodies with combinations of small molecule fluorophores, which I refer to as MuSIC probes. I use fluorescent oligonucleotides (oligos) to arrange the fluorophores at specified distances and orientations from one another in order to produce complex fluorescence spectra when the probe is excited. This labeling protocol is demonstrated using a 3-probe experimental setup, bound to Protein A beads, and analyzed via spectral flow cytometry. When translating this method to staining human cells, our staining intensity was not comparable to that of a conventional antibody labeling kit. Therefore, next I present an improved method to label antibodies with MuSIC probes with increased signal intensity. I re-arrange the oligo-fluorophore arrangement of the MuSIC probe to emit an increased fluorescent signal. Then I validate this approach by comparing the staining intensity of MuSIC probe-labeled antibodies to a conventional antibody labeling kit using human peripheral blood mononuclear cells. Lastly, I present simulation theories for the multiplexing capabilities of MuSIC probes for various biological and diagnostic applications. First, I present a theory for high-throughput genetic interaction screening using MuSIC probes generated from 18 currently available fluorescent proteins. Simulation studies based on constraints of current spectral flow cytometry equipment suggest our ability to perform genetic interaction screens at the human genome-scale. Finally, I adapt this simulation protocol to generate MuSIC probes from 30 currently available small-molecule fluorophores. Using the same constraints as before, I predict that I can perform cell-type profiling of 200+ analytes. I hope that the work presented here provides a foundation for the use of combination probes for various biological and disease applications and ultimately help to better diagnose and treat different types of cancer

Bioactive extracts from persimmon waste: influence of extraction conditions and ripeness.

In this work, a bioactive persimmon extract was produced from discarded fruits. A central composite design was used to evaluate the effect of different extraction parameters and ripeness stages of persimmon fruits on the total phenolic content and antioxidant activity of the resulting extracts. Significantly greater phenolic contents were obtained from immature persimmon (IP) fruits. The optimum IP extract with the conditions set by the experimental design was industrially up-scaled and its composition and functional properties were evaluated and compared with those obtained under lab-scale conditions. Both extracts contained significant protein (>20%) and phenolic contents (∼11-27 mg GA/g dry extract) and displayed significant antiviral activity against murine norovirus and hepatitis A virus. Moreover, the extract showed no toxicity and significantly reduced the fat content and the cellular ageing of Caenorhabditis elegans (C. elegans) without affecting the worm development. These effects were mediated by down-regulation of fat-7, suggesting an anti-lipogenic activity of this extract