bluuebunny/crossref_metadata_2025 huggingface dataset:
Selenium Yeast and Fish Oil Combination Diminishes Cancer Stem Cell Traits and Reverses Cisplatin Resistance in A549 Sphere Cells
Authors: Lai I-Chun , Liao Chien-Huang , Hu Ming-Hung , Chang Chia-Lun , Lai Gi-Ming , Chiou Tzeon-Jye , Hsia Simon , Tsai Wei-Lun , Lin Yu-Yin , Chuang Shuang-En , Whang-Peng Jacqueline , Chen Hsuan-Yu , Yao Chih-Jung
Published: August 2022

Cisplatin is a prevalent chemotherapeutic agent used for non-small cell lung cancer (NSCLC) that is difficult to treat by targeted therapy, but the emergence of resistance severely limits its efficacy. Thus, an effective strategy to combat cisplatin resistance is required. This study demonstrated that, at clinically achievable concentrations, the combination of selenium yeast (Se-Y) and fish oil (FO) could synergistically induce the apoptosis of cancer stem cell (CSC)-like A549 NSCLC sphere cells, accompanied by a reversal of their resistance to cisplatin. Compared to parental A549 cells, sphere cells have higher cisplatin resistance and possess elevated CSC markers (CD133 and ABCG2), epithelial–mesenchymal transition markers (anexelekto (AXL), vimentin, and N-cadherin), and cytoprotective endoplasmic reticulum (ER) stress marker (glucose-regulated protein 78) and increased oncogenic drivers, such as yes-associated protein, transcriptional coactivator with PDZ-binding motif, β-catenin, and cyclooxygenase-2. In contrast, the proapoptotic ER stress marker CCAAT/enhancer-binding protein homologous protein and AMP-activated protein kinase (AMPK) activity were reduced in sphere cells. The Se-Y and FO combination synergistically counteracted the above molecular features of A549 sphere cells and diminished their elevated CSC-like side population. AMPK inhibition by compound C restored the side population proportion diminished by this nutrient combination. The results suggest that the Se-Y and FO combination can potentially improve the outcome of cisplatin-treated NSCLC with phenotypes such as A549 cells.
Divergent Gene Expression Profiles in Alaskan Sea Otters: An Indicator of Chronic Domoic Acid Exposure?
Authors: Bowen Lizabeth , Knowles Susan , Lefebvre Kathi , St. Martin Michelle , Murray Michael , Kloecker Kim , Monson Daniel , Weitzman Benjamin , Ballachey Brenda , Coletti Heather , Waters Shannon , Cummings Caroline
Published: August 2022

An opportunistic investigation into ecosystem instability in Kachemak Bay (KBay), Alaska, has led us to investigate exposure to toxic algae in sea otters. We used gene expression to explore the physiological health of sea otters sampled in KBay in May 2019. We found altered levels of gene transcripts in comparison with reference sea otters from clinically normal, oil-exposed, and nutritionally challenged populations sampled over the past decade. KBay sea otters were markedly divergent from the other groups for five genes, which indicated the involvement of neurological, cardiac, immune, and detoxification systems. Further, analyses of urine and fecal samples detected domoic acid in the KBay sea otters. In combination, these results may point to chronic, low-level exposure to an algal toxin, such as domoic acid. With a warming climate, the frequency and severity of harmful algal blooms in marine environments is anticipated to increase, and novel molecular technologies to detect sublethal or chronic exposure to algal toxins will help provide an early warning of threats to the stability of populations and ecosystems.
Dihydrooxazine Byproduct of a McMurry–Melton Reaction en Route to a Synthetic Bacteriochlorin
Authors: Tran Vy-Phuong , Matsumoto Nobuyuki , Nalaoh Phattananawee , Jing Haoyu , Chen Chih-Yuan , Lindsey Jonathan S.
Published: August 2022

A synthetic route to gem-dimethyl-substituted bacteriochlorins—models of native bacteriochlorophylls—relies on the formation of a dihydrodipyrrin precursor via a series of established reactions: van Leusen pyrrole formation, Vilsmeier formylation, Henry reaction, borohydride reduction, Michael addition, and McMurry–Melton pyrroline formation. The latter is the least known of the series. Here, the McMurry–Melton reaction of a 2-(6-oxo-2-nitrohexyl)pyrrole in the presence of TiCl3 and an ammonium acetate buffer formed the expected Δ1-pyrroline, as well as an unexpected polar, cyclic byproduct (a 5,6-dihydro-4H-1,2-oxazin-6-ol), each attached to the 2-methylpyrrole unit. Both species were characterized by single-crystal X-ray diffraction. The McMurry–Melton reaction is a type of intercepted Nef reaction (the transformation of a nitroalkyl motif into a carbonyl group), where both the Δ1-pyrroline and the dihydrooxazine derive from the reaction of the nitrogen derived from the nitro group upon complete or partial reductive deoxygenation, respectively, with the γ-keto group. The report also considers competing Nef and McMurry–Melton reactions, the nature of available TiCl3 reagents, and the use of ammonium acetate for buffering the TiCl3/HCl reagent.
Intestinal Absorption Study: Challenges and Absorption Enhancement Strategies in Improving Oral Drug Delivery
Authors: Azman Maisarah , Sabri Akmal H. , Anjani Qonita Kurnia , Mustaffa Mohd Faiz , Hamid Khuriah Abdul
Published: August 2022

The oral route is the most common and practical means of drug administration, particularly from a patient’s perspective. However, the pharmacokinetic profile of oral drugs depends on the rate of drug absorption through the intestinal wall before entering the systemic circulation. However, the enteric epithelium represents one of the major limiting steps for drug absorption, due to the presence of efflux transporters on the intestinal membrane, mucous layer, enzymatic degradation, and the existence of tight junctions along the intestinal linings. These challenges are more noticeable for hydrophilic drugs, high molecular weight drugs, and drugs that are substrates of the efflux transporters. Another challenge faced by oral drug delivery is the presence of first-pass hepatic metabolism that can result in reduced drug bioavailability. Over the years, a wide range of compounds have been investigated for their permeation-enhancing effect in order to circumvent these challenges. There is also a growing interest in developing nanocarrier-based formulation strategies to enhance the drug absorption. Therefore, this review aims to provide an overview of the challenges faced by oral drug delivery and selected strategies to enhance the oral drug absorption, including the application of absorption enhancers and nanocarrier-based formulations based on in vitro, in vivo, and in situ studies.
Stability, Permeability and Cytotoxicity of Buccal Films in Allergy Treatment
Authors: Pamlényi Krisztián , Regdon Géza , Nemes Dániel , Fenyvesi Ferenc , Bácskay Ildikó , Kristó Katalin
Published: August 2022

Oral mucoadhesive systems, such as polymer films, are among innovative pharmaceutical products. These systems can be applied in swallowing problems and can also be used in geriatrics and paediatrics. In our earlier work, we successfully formulated buccal mucoadhesive polymer films, which contained cetirizine-hydrochloride (CTZ) as the API. The present study focused on investigating the stability and permeability of the prepared films. The stability of the films was studied with an accelerated stability test. During the stability test, thickness, breaking hardness and in vitro mucoadhesivity were analysed. Furthermore, the interactions were studied with FT-IR spectroscopy, and the changes in the amount of the API were also monitored. Cytotoxicity and cell line permeability studies were carried out on TR 146 buccal cells. Compositions that can preserve more than 85% of the API after 6 months were found. Most of the compositions had a high cell viability of more than 50%. Citric acid (CA) decreased the stability and reduced every physical parameter of the films. However, cell line studies showed that the permeability of the films was enhanced. In our work, we successfully formulated CTZ-containing buccal films with adequate stability, high cell viability and appropriate absorption properties.
Glasdegib Dimaleate: Synthesis, Characterization and Comparison of Its Properties with Monomaleate Analogue
Authors: Peklar Boris , Perdih Franc , Makuc Damjan , Plavec Janez , Cluzeau Jérôme , Kitanovski Zoran , Časar Zdenko
Published: August 2022

Glasdegib is a recently approved drug for the treatment of acute myeloid leukemia. It is formulated and marketed in monomaleate salt form. In our investigation, we were able to prepare a glasdegib dimaleate form, which could, in theory, exist in double-salt form or as a mixture of salt and co-crystal species. Therefore, the obtained crystals of glasdegib dimaleate were characterized via 15N ssNMR and single-crystal X-ray diffraction, which revealed that the obtained glasdegib dimaleate exists in double-salt form. This is a surprising finding based on the pKa values for glasdegib and maleic acid. Furthermore, we fully characterized the new dimaleate form using thermal analyses (DSC and TGA) and spectroscopy (IR and Raman). Finally, the physicochemical properties, such as solubility and chemical stability, of both forms were determined and compared.
Ultrasonic Microbubble Cavitation Enhanced Tissue Permeability and Drug Diffusion in Solid Tumor Therapy
Authors: He Jide , Liu Zenan , Zhu Xuehua , Xia Haizhui , Gao Huile , Lu Jian
Published: August 2022

Chemotherapy has an essential role not only in advanced solid tumor therapy intervention but also in society’s health at large. Chemoresistance, however, seriously restricts the efficiency and sensitivity of chemotherapeutic agents, representing a significant threat to patients’ quality of life and life expectancy. How to reverse chemoresistance, improve efficacy sensitization response, and reduce adverse side effects need to be tackled urgently. Recently, studies on the effect of ultrasonic microbubble cavitation on enhanced tissue permeability and retention (EPR) have attracted the attention of researchers. Compared with the traditional targeted drug delivery regimen, the microbubble cavitation effect, which can be used to enhance the EPR effect, has the advantages of less trauma, low cost, and good sensitization effect, and has significant application prospects. This article reviews the research progress of ultrasound-mediated microbubble cavitation in the treatment of solid tumors and discusses its mechanism of action to provide new ideas for better treatment strategies.
Amphotericin B-PEG Conjugates of ZnO Nanoparticles: Enhancement Antifungal Activity with Minimal Toxicity
Authors: Alshahrani Saad M. , Khafagy El-Sayed , Riadi Yassine , Al Saqr Ahmed , Alfadhel Munerah M. , Hegazy Wael A. H.
Published: August 2022

Amphotericin B (AMB) is commonly used to treat life-threatening systemic fungal infections. AMB formulations that are more efficient and less nephrotoxic are currently unmet needs. In the current study, new ZnO-PEGylated AMB (ZnO-AMB-PEG) nanoparticles (NPs) were synthesized and their antifungal effects on the Candida spp. were investigated. The size and zeta potential values of AMB-PEG and ZnO-AMB-PEG NPs were 216.2 ± 26.9 to 662.3 ± 24.7 nm and −11.8 ± 2.02 to −14.2 ± 0.94 mV, respectively. The FTIR, XRD, and EDX spectra indicated that the PEG-enclosed AMB was capped by ZnO, and SEM images revealed the ZnO distribution on the surface NPs. In comparison to ZnO-AMB NPs and free AMB against C.albicans and C.neoformans, ZnO-AMB-PEG NPs significantly reduced the MIC and MFC. After a week of single and multiple dosage, the toxicity was investigated utilizing in vitro blood hemolysis, in vivo nephrotoxicity, and hepatic functions. ZnO-AMB-PEG significantly lowered WBC count and hematocrit concentrations when compared to AMB and ZnO-AMB. RBC count and hemoglobulin content, on the other hand, were unaltered. ZnO-AMB-PEG considerably lowered creatinine and blood urea nitrogen (BUN) levels when compared to AMB and ZnO-AMB. The difference in liver function indicators was determined to be minor by all formulae. These findings imply that ZnO-AMB-PEG could be utilized in the clinic with little nephrotoxicity, although more research is needed to determine the formulation’s in vivo efficacy.
The Current State of the Art in PARP Inhibitor-Based Delivery Nanosystems
Authors: Cai Lisha , Xu Xiaoling , Chen Wei
Published: August 2022

Poly (adenosine diphosphate [ADP]–ribose) polymerases inhibitors (PARPi), the first clinically approved drug that exhibits synthetic lethality, are moving to the forefront of cancer treatments. Currently, the oral bioavailability of PARPi is quite low; thus, it is a major challenge to effectively and safely deliver PARPi during clinical cancer therapy. Nanotechnology has greatly advanced the development of drug delivery. Based on the basic characteristics and various forms of nanoparticles, drug delivery systems can prolong the time that drugs circulate, realize the controlled release of drugs, provide drugs with an active targeting ability, and spatiotemporally present combination treatment. Furthermore, nanosystems may not only enhance drug efficiency but also reduce adverse side effects. This review focuses on strategies involving nanoparticle-based delivery for PARPi, including single administration and codelivery with other agents. We believe that nanosystems have great potential in advancing PARPi efficacy for cancer therapy.
A Review on the Modification of Cellulose and Its Applications
Authors: Aziz Tariq , Farid Arshad , Haq Fazal , Kiran Mehwish , Ullah Asmat , Zhang Kechun , Li Cheng , Ghazanfar Shakira , Sun Hongyue , Ullah Roh , Ali Amjad , Muzammal Muhammad , Shah Muddaser , Akhtar Nosheen , Selim Samy , Hagagy Nashwa , Samy Mennatalla , Al Jaouni Soad K.
Published: August 2022

The latest advancements in cellulose and its derivatives are the subject of this study. We summarize the characteristics, modifications, applications, and properties of cellulose. Here, we discuss new breakthroughs in modified cellulose that allow for enhanced control. In addition to standard approaches, improvements in different techniques employed for cellulose and its derivatives are the subject of this review. The various strategies for synthetic polymers are also discussed. The recent advancements in polymer production allow for more precise control, and make it possible to make functional celluloses with better physical qualities. For sustainability and environmental preservation, the development of cellulose green processing is the most abundant renewable substance in nature. The discovery of cellulose disintegration opens up new possibilities for sustainable techniques. Based on the review of recent scientific literature, we believe that additional chemical units of cellulose solubility should be used. This evaluation will evaluate the sustainability of biomass and processing the greenness for the long term. It appears not only crucial to dissolution, but also to the greenness of any process.
Durability and Long-Term Performance Prediction of Carbon Fiber Reinforced Polymer Laminates
Authors: Alsuhaibani Eyad , Yazdani Nur , Beneberu Eyosias
Published: August 2022

The feasibility of strengthening deteriorated or under-capacity concrete structures with external carbon-fiber-reinforced polymer (CFRP) laminates has been widely validated in the literature. However, there is a lack of knowledge on the in situ long-term performance and age-related environmental degradation of the mechanical properties of the laminates. The current study involved the immersion of coupons from a common new CFRP laminate in heated water at 23, 45, and 60 °C for 224 days. The coupons were then tested for residual tensile properties, such as tensile capacity and elastic modulus, using ASTM D3039 (2017) specifications. The CFRP tensile capacity and elastic modulus decreased by a maximum of 33% and 26%, respectively, for 224 days of exposure. Based on the test data, an age-based long-term prediction model with excellent reliability for CFRP laminate tensile capacity was developed. The model was then calibrated with test results from old CFRP coupons collected from an existing CFRP laminate retrofitted concrete bridge. The calibrated model output was then compared with the environmental reduction factor from ACI 440.2R-17 and a few other common sources. It was found that the ACI specified a reduction factor of 0.75, which does not consider the CFRP age and overestimates the design tensile strength of the CFRP laminate by approximately 13%, which may compromise the structural safety of retrofitted bridges. The reduction factors from the other guidelines varied between 0.51 and 0.85.
Mitigation and Remediation Technologies of Waxy Crude Oils’ Deposition within Transportation Pipelines: A Review
Authors: Elkatory Marwa R. , Soliman Emad A. , El Nemr Ahmed , Hassaan Mohamed A. , Ragab Safaa , El-Nemr Mohamed A. , Pantaleo Antonio
Published: August 2022

Deposition of wax is considered one of the most significant culprits in transporting petroleum crude oils, particularly at low temperatures. When lowering pressure and temperature during the flow of crude oil, the micelle structure of the crude oil is destabilized, allowing oil viscosity to increase and precipitating paraffin (wax) in the well tubulars and pipeline, which increase the complexity of this culprit. These deposited substances can lead to the plugging of production and flow lines, causing a decline in oil production and, subsequently, bulk economic risks for the oil companies. Hence, various approaches have been commercially employed to prevent or remediate wax deposition. However, further research is still going on to develop more efficient techniques. These techniques can be categorized into chemical, physical, and biological ones and hybridized or combined techniques that apply one or more of these techniques. This review focused on all these technologies and the advantages and disadvantages of these technologies.
Late Pleistocene Geomorphic Evolution of Cephalonia Island, Western Greece, Inferred from Uplifted Marine Terraces
Authors: Tsanakas Konstantinos , Saitis Giannis , Evelpidou Niki , Karymbalis Efthimios , Karkani Anna
Published: August 2022

Combined with eustatic sea-level changes, uplifted Quaternary marine terraces provide insight into the tectonics of coastal areas. Cephalonia Island lies 35 km off the western coast of mainland Greece and 15 km northeast of the Hellenic subduction zone. Late Pleistocene eustatic sea-level oscillations and the long-term tectonic movements are imprinted on the landscape of the southern part of the island, in the form of seven uplifted marine terraces. In the present study we aim to identify and map in detail these terraces, applying Digital Elevation Model analysis, utilizing Geographic Information Systems techniques and extensive fieldwork. The GIS-based analysis combined with field geomorphological observations revealed a sequence of seven marine terraces at the southern part of the main island ranging in elevation between 4 m and 176 m asl. Microscope, petrological and microgeomorphological analyses on two caprock samples suggest strong marine influence during the deposition of the sediments covering the marine terraces. The age of the formation of the 32 m marine terrace was assigned to the MIS 3e, based on OSL dating of a caprock sample, and an average uplift rate of 1.4 ± 0.35 mm a−1 was calculated for the last 61 ± 5.5 ka. Assuming a uniform uplift rate for the Late Pleistocene allowed us to correlate the marine terrace with the sea-level highstands and constrain their ages.
Fusion of CMONOC and ERA5 PWV Products Based on Backpropagation Neural Network
Authors: Ren Dong , Wang Yong , Wang Guocheng , Liu Lintao
Published: August 2022

Data fusion is an effective method to obtain high-precision and high-spatiotemporal-resolution precipitable water vapor (PWV) products, which play an important role in understanding climate change and meteorological monitoring. However, existing fusion methods have some shortcomings, such as ignoring the applicability of the model space or the high complexity of model operation. In this study, the high-precision and high-temporal-resolution Global Navigation Satellite System (GNSS) PWV was used to calibrate and optimize the ERA5 PWV product of the European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) with high spatial resolution to improve its accuracy, and its applicability was verified at the spatiotemporal scale. First, this study obtained accurate GNSS PWV from meteorological data from stations and used it as the true value to analyze the distribution of the ERA5 PWV in mainland China. The results showed that the ERA5 PWV showed significant spatial and temporal differences. Then, a backpropagation neural network (BPNN) fusion correction model with additional constraints was established. The correction results showed that the bias of the ERA5 PWV mainly fluctuated near 0, the correlation between the ERA5 PWV and GNSS PWV was increased to 0.99, and the positive improvement rate of the root-mean-square error (RMSE) was 95%. In the temporal scale validation, the RMSE of the ERA5 PWV decreased from 2.05 mm to 1.67 mm, an improvement of 18.54%. In the spatial scale validation, the RMSE of the four seasons decreased by 0.26–80% (spring), 0.28–70.71% (summer), 0.28–45.23% (autumn), and 0.30–40.75% (winter). Especially in the summer and plateau mountainous areas where the ERA5 PWV performance was poor, the model showed suitable stability. Finally, the fusion model was used to generate a new PWV product, which improved the accuracy of ERA5 PWV on the basis of ensuring the spatial resolution.
Deriving Urban Boundaries of Henan Province, China, Based on Sentinel-2 and Deep Learning Methods
Authors: Li Xiaojia , Zheng Kang , Qin Fen , Wang Haiying , Zhao Chunhong
Published: August 2022

Accurate urban boundary data can directly reflect the expansion of urban space, help us accurately grasp the scale and form of urban space, and play a vital role in urban land development and policy-making. However, the lack of reliable multiscale and high-precision urban boundary data products and relevant training datasets has become one of the major factors hindering their application. The purpose of this study is to combine Sentinel-2 remote-sensing images and supplementary geographic data to generate a reliable high-precision urban boundary dataset for Henan Province (called HNUB2018). First, this study puts forward a clear definition of “urban boundary”. Using this concept as its basis, it proposes a set of operable urban boundary delimitation rules and technical processes. Then, based on Sentinel-2 remote-sensing images and supplementary geographic data, the urban boundaries of Henan Province are delimited by a visual interpretation method. Finally, the applicability of the dataset is verified by using a classical semantic segmentation deep learning model. The results show that (1) HNUB2018 has clear and rich detailed features as well as a detailed spatial structure of urban boundaries. The overall accuracy of HNUB2018 is 92.82% and the kappa coefficient reaches 0.8553, which is better than GUB (Henan) in overall accuracy. (2) HNUB2018 is well suited for deep learning, with excellent reliability and scientific validity. The research results of this paper can provide data support for studies of urban sprawl monitoring and territorial spatial planning, and will support the development of reliable datasets for fields such as intelligent mapping of urban boundaries, showing prospects and possibilities for wide application in urban research.
Multidecade Trends of Sea Surface Temperature, Chlorophyll-a Concentration, and Ocean Eddies in the Gulf of Mexico
Authors: Li Geng , Wang Zhankun , Wang Binbin
Published: August 2022

This study characterizes the spatial patterns of the overall and monthly trends in sea surface temperature (SST) and chlorophyll-a (Chl-a) of the Gulf of Mexico (GoM) to investigate the seasonal variations in oceanic climate trends. We also investigate the trends in mesoscale eddies using three parameters to identify ocean-eddy-related energetic features in their area, strength, and intensity. Multidecadal remote-sensing-based observations of monthly SST, Chl-a, and sea surface height are used to detect trends at both basin and grid scales. Prominent warming trends are found in most regions of the GoM in all months, with the largest trends in the northern GoM. Winter cooling trends are also detected along the Texas and Florida coast. The overall summer warming trend (~0.22 °C/decade) is larger than the winter trend (~0.05 °C/decade), suggesting seasonal variations of increase in SST with warming. Chl-a trends and variations are confined on the continental shelf and slope in the northern GoM. The largest increase trends are found near the Mississippi River Delta. No obvious Chl-a trend is detected in the deepwater of the GoM, consistent with previous studies. Small but significant changes are found in eddy characteristics, indicating the eddy activities might be slowly affected by climate change in the GoM. The detailed monthly trends at per-grid scale are valuable for regional resource management, environmental protection, and policy making in the GoM.
End-to-End Prediction of Lightning Events from Geostationary Satellite Images
Authors: Brodehl Sebastian , Müller Richard , Schömer Elmar , Spichtinger Peter , Wand Michael
Published: August 2022

While thunderstorms can pose severe risks to property and life, forecasting remains challenging, even at short lead times, as these often arise in meta-stable atmospheric conditions. In this paper, we examine the question of how well we could perform short-term (up to 180 min) forecasts using exclusively multi-spectral satellite images and past lighting events as data. We employ representation learning based on deep convolutional neural networks in an “end-to-end” fashion. Here, a crucial problem is handling the imbalance of the positive and negative classes appropriately in order to be able to obtain predictive results (which is not addressed by many previous machine-learning-based approaches). The resulting network outperforms previous methods based on physically based features and optical flow methods (similar to operational prediction models) and generalizes across different years. A closer examination of the classifier performance over time and under masking of input data indicates that the learned model actually draws most information from structures in the visible spectrum, with infrared imaging sustaining some classification performance during the night.
Learned Design of a Compressive Hyperspectral Imager for Remote Sensing by a Physics-Constrained Autoencoder
Authors: Heiser Yaron , Stern Adrian
Published: August 2022

Designing and optimizing systems by end-to-end deep learning is a recently emerging field. We present a novel physics-constrained autoencoder (PyCAE) for the design and optimization of a physically realizable sensing model. As a case study, we design a compressive hyperspectral imaging system for remote sensing based on this approach, which allows capturing hundreds of spectral bands with as few as four compressed measurements. We demonstrate our deep learning approach to design spectral compression with a spectral light modulator (SpLM) encoder and a reconstruction neural network decoder. The SpLM consists of a set of modified Fabry–Pérot resonator (mFPR) etalons that are designed to have a staircase-shaped geometry. Each stair occupies a few pixel columns of a push-broom-like spectral imager. The mFPR’s stairs can sample the earth terrain in along-track scanning from an airborne or spaceborne moving platform. The SpLM is jointly designed with an autoencoder by a data-driven approach, while spectra from remote sensing databases are used to train the system. The SpLM’s parameters are optimized by integrating its physically realizable sensing model in the encoder part of the PyCAE. The decoder part of the PyCAE implements the spectral reconstruction.
Efficient Supervised Image Clustering Based on Density Division and Graph Neural Networks
Authors: Zhao Qingchao , Li Long , Chu Yan , Yang Zhen , Wang Zhengkui , Shan Wen
Published: August 2022

In recent research, supervised image clustering based on Graph Neural Networks (GNN) connectivity prediction has demonstrated considerable improvements over traditional clustering algorithms. However, existing supervised image clustering algorithms are usually time-consuming and limit their applications. In order to infer the connectivity between image instances, they usually created a subgraph for each image instance. Due to the creation and process of a large number of subgraphs as the input of GNN, the computation overheads are enormous. To address the high computation overhead problem in the GNN connectivity prediction, we present a time-efficient and effective GNN-based supervised clustering framework based on density division namely DDC-GNN. DDC-GNN divides all image instances into high-density parts and low-density parts, and only performs GNN subgraph connectivity prediction on the low-density parts, resulting in a significant reduction in redundant calculations. We test two typical models in the GNN connectivity prediction module in the DDC-GNN framework, which are the graph convolutional networks (GCN)-based model and the graph auto-encoder (GAE)-based model. Meanwhile, adaptive subgraphs are generated to ensure sufficient contextual information extraction for low-density parts instead of the fixed-size subgraphs. According to the experiments on different datasets, DDC-GNN achieves higher accuracy and is almost five times quicker than those without the density division strategy.
Clustering of Handheld Thermal Camera Images in Volcanic Areas and Temperature Statistics
Authors: Cirillo Francesca , Avvisati Gala , Belviso Pasquale , Marotta Enrica , Peluso Rosario , Pescione Romano Antonio
Published: August 2022

Thermal camera use is becoming ever more widespread in volcanic and environmental research and monitoring activities. Depending on the scope of an investigation and on the type of thermal camera used, different software for thermal infrared (IR) images analysis is employed. The Osservatorio Vesuviano Sezione in Napoli of the Istituto Nazionale di Geofisica e Vulcanologia (INGV-OV) processes the images acquired during thermal monitoring activities acquired in the Neapolitan areas (Vesuvio, Ischia and Campi Flegrei) with different FLIR software that returns for each image, or for each selected area within the image, a series of parameters (maximum temperature, average temperature, standard deviation, etc.). An operator selects the area of interest and later “manually” inserts the relevant parameters in Excel sheets to generate graphs. Such a tedious, time- and resource-consuming procedure gave reason to implement a software able to automatically analyze sets of thermal images taken with a handheld thermal camera without any manual action. This paper describes the method and the software implemented to “automate” and refine the extrapolation process and the analysis of the relevant information. The employed method clusters thermal images by applying K-MEANS and DBSCAN techniques. After clustering a series of images, the software displays the necessary statistics to highlight possible fluctuations in temperature values. The software, “StaTistical Analysis clusteRed ThErmal Data” (STARTED), is already available. Although it has been developed mostly to support monitoring of the volcanoes in Campania, it is quite versatile and can be used for any activity that implies thermal data analysis. In this paper, we describe the workflow and the dataset used to develop the software, as well as the first result obtained from it.
Direction of Arrival Estimation of Acoustic Sources with Unmanned Underwater Vehicle Swarm via Matrix Completion
Authors: Xu Liya , Huang Jianjun , Zhang Hao , Liao Bin
Published: August 2022

With the rapid development of vibration and noise reduction technologies, underwater target detection is facing great challenges. Particularly, the task of high-resolution direction of arrival (DOA) estimation with sonar array is becoming more and more tough. In recent years, unmanned underwater vehicles (UUVs) have been developed considerably, with the improvements of target localization performance in terms of adaptability, detection range, operation efficiency, and anti-interference ability. Nevertheless, in general, the size of UUV is small such that current passive sonar systems usually have relatively limited localization accuracy, detection distance, and environmental robustness in complex ocean noise. This motivates us to present a new approach to construct a large-aperture virtual array with multiple small-aperture arrays of unmanned underwater vehicle swarm (UUVS) which consists of multiple UUVs in this paper. However, for the UUVS array, the received data could suffer from unobserved and corrupted samples. This makes it challenging to analyze and process large-aperture array data. Towards this end, the matrix completion technique is employed to recover the unobserved and corrupted data for virtual array construction based on the low rank property of array data matrix. The recovered matrix is then exploited for underwater target bearing estimation using the traditional DOA estimation approach. Numerical results verify that the proposed method is capable of detecting underwater targets with high precision and resolution.
CAPS: A New Method for the Identification of Different Surface Displacements in Landslide and Subsidence Environments through Correlation Analysis on Persistent Scatterers Time-Series from PSI
Authors: Balbi Evandro , Ferretti Gabriele , Ferrando Andrea , Faccini Francesco , Crispini Laura , Cianfarra Paola , Scafidi Davide , Barani Simone , Tosi Silvano , Terrone Martino
Published: August 2022

Persistent Scatterer Interferometry (PSI) is one of the most powerful tools for identifying and monitoring areas exposed to surface deformations such as landslides or subsidence. In this work, we propose a new method that we named CAPS (Correlation Analysis on Persistent Scatterers), to extend the capability of PSI in recognizing and characterising areas influenced by complex ground deformations and differential motions. CAPS must be applied to both ascending and descending orbits separately and comprises three major steps: (i) calculating the cross-correlation matrix on detrended PS time-series; (ii) extracting PS pairs with similarity greater than a given threshold; (iii) grouping PS in families by sorting and classification. Thus, in both orbits, PS Families identify groups of PS with similar movements. This allows distinguishing sectors characterised by different displacements over time even in areas with similar LOS (Line of Sight) velocities. As test sites, we considered four different known geological scenarios: two representing landslide environments (Santo Stefano d’Aveto and Arzeno, both in Liguria, NW Italy) and two subsidence environments (Rome and Venice, urban and surrounding areas). This method proved to be versatile, applicable to different geological situations and at different scales of observation, for recognizing both regional and local differential deformations.
Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification
Authors: Tang Haojin , Li Yanshan , Huang Zhiquan , Zhang Li , Xie Weixin
Published: August 2022

Hyperspectral image (HSI) classification has attracted widespread concern in recent years. However, due to the complexity of the HSI gathering environment, it is difficult to obtain a great number of HSI labeled samples. Therefore, how to effectively extract the spatial–spectral feature with small-scale training samples is the crucial point of HSI classification. In this paper, a novel fusion framework for small-sample HSI classification is proposed to fully combine the advantages of multidimensional CNN and handcrafted features. Firstly, a 3D fuzzy histogram of oriented gradients (3D-FHOG) descriptor is proposed to fully extract the handcrafted spatial–spectral feature of HSI pixels, which is suggested to be more robust by overcoming the local spatial–spectral feature uncertainty. Secondly, a multidimensional Siamese network (MDSN), which is updated by minimizing both contrastive loss and classification loss, is designed to effectively exploit the CNN-based spatial–spectral features from multiple dimensions. Finally, the proposed MDSN combined with 3D-FHOG is utilized for small-sample HSI classification to verify the effectiveness of our proposed fusion framework. The experimental results on three public data sets indicate that the proposed MDSN combined with 3D-FHOG is significantly better than the representative handcrafted feature-based and CNN-based methods, which in turn demonstrates the superiority of the proposed fusion framework.
An Improved QAA-Based Method for Monitoring Water Clarity of Honghu Lake Using Landsat TM, ETM+ and OLI Data
Authors: Chen Miaomiao , Xiao Fei , Wang Zhou , Feng Qi , Ban Xuan , Zhou Yadong , Hu Zhengzheng
Published: August 2022

Secchi disk depth (ZSD) is used to quantify water clarity as an important water-quality parameter, and one of the most used mechanistic models for ZSD is the quasi-analytical algorithm (QAA), of which the latest version is QAA_v6. There are two models in QAA for clear and turbid waters (referred to as QAA_clear and QAA_turbid). QAA_v6 switches between the two models by setting a threshold value for the remote sensing reflectance (Rrs, sr−1) at the selected reference band of 656 nm. However, some researchers found that this reference band or the threshold value does not apply to many turbid inland lakes. In Honghu Lake, the Rrs (656) (Rrs at 656 nm) in the whole lake is less than 0.0015 sr−1; therefore, only QAA_turbid can be applied. Moreover, we found that QAA_clear resulted in overestimation while QAA_turbid resulted in significant underestimations. The waters of inland lakes usually continuously vary between clear and turbid water. We proposed a hypothesis that QAA_turbid and QAA_clear transition evenly, rather than being distinguished by one threshold value, and we developed a model that combined QAA_clear and QAA_turbid according to our assumption. This model simulated the process of continuous change in water clarity. The results showed that our model had a better performance with an RMSE that reduced from 0.5 to 0.28, an MAE that reduced from 0.43 to 0.21, and bias that reduced from −0.4 to −0.05 m compared with QAA_v6. We applied QAA_Honghu to Landsat TM, ETM+, and OLI data and obtained 205 ZSD maps with high spatial resolution in Honghu Lake. The results were consistent with the existing in situ measurements. From 1987–2020, the ZSD results of Honghu Lake showed an overall downward trend and a distinct seasonal pattern.
Introducing Improved Transformer to Land Cover Classification Using Multispectral LiDAR Point Clouds
Authors: Zhang Zhiwen , Li Teng , Tang Xuebin , Lei Xiangda , Peng Yuanxi
Published: August 2022

The use of Transformer-based networks has been proposed for the processing of general point clouds. However, there has been little research related to multispectral LiDAR point clouds that contain both spatial coordinate information and multi-wavelength intensity information. In this paper, we propose networks for multispectral LiDAR point cloud point-by-point classification based on an improved Transformer. Specifically, considering the sparseness of different regions of multispectral LiDAR point clouds, we add a bias to the Transformer to improve its ability to capture local information and construct an easy-to-implement multispectral LiDAR point cloud Transformer (MPT) classification network. The MPT network achieves 78.49% mIoU, 94.55% OA, 84.46% F1, and 0.92 Kappa on the multispectral LiDAR point cloud testing dataset. To further extract the topological relationships between points, we present a standardization set abstraction (SSA) module, which includes the global point information while considering the relationships among the local points. Based on the SSA module, we propose an advanced version called MPT+ for the point-by-point classification of multispectral LiDAR point clouds. The MPT+ network achieves 82.94% mIoU, 95.62% OA, 88.42% F1, and 0.94 Kappa on the same testing dataset. Compared with seven point-based deep learning algorithms, our proposed MPT+ achieves state-of-the-art results for several evaluation metrics.
An Ornithologist’s Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio
Authors: Liu Ming , Sun Qiyu , Brewer Dustin E. , Gehring Thomas M. , Eickholt Jesse
Published: August 2022

Reliable and efficient avian monitoring tools are required to identify population change and then guide conservation initiatives. Autonomous recording units (ARUs) could increase both the amount and quality of monitoring data, though manual analysis of recordings is time consuming. Machine learning could help to analyze these audio data and identify focal species, though few ornithologists know how to cater this tool for their own projects. We present a workflow that exemplifies how machine learning can reduce the amount of expert review time required for analyzing audio recordings to detect a secretive focal species (Sora; Porzana carolina). The deep convolutional neural network that we trained achieved a precision of 97% and reduced the amount of audio for expert review by ~66% while still retaining 60% of Sora calls. Our study could be particularly useful, as an example, for those who wish to utilize machine learning to analyze audio recordings of a focal species that has not often been recorded. Such applications could help to facilitate the effective conservation of avian populations.
Supervised Machine Learning Algorithms for Ground Motion Time Series Classification from InSAR Data
Authors: Mirmazloumi S. Mohammad , Gambin Angel Fernandez , Palamà Riccardo , Crosetto Michele , Wassie Yismaw , Navarro José A. , Barra Anna , Monserrat Oriol
Published: August 2022

The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deformation identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS.
Quantitative Evaluation of Reclamation Intensity Based on Regional Planning Theory and Human–Marine Coordination Since 1974: A Case Study of Shandong, China
Authors: Liu Baijing , Gong Meng , Wu Xiaoqing , Wang Ziyang
Published: August 2022

Increased reclamation activity has adversely affected the conservation of coastal environments. The interactions between reclamation activities and their interference with the natural and functional properties of coastal zones increase the difficulty of marine spatial planning and eco-environmental management. In this study, an evaluation method for describing the intensity of the reclamation activity (RAI) based on regional planning theory and human–marine coordination theory was proposed, and a quantitative evaluation index system was constructed. The method was applied to Shandong Province in China via geographic information system (GIS) spatial analysis. The results reveal that there was an obvious increase in the RAI from 1974 to 2021, with the total reclamation scale index and coordination of reclamation activities index being the most prominent. In addition, it was found that 2007–2017 was the peak period of infilling reclamation in Shandong Province. The natural coastlines are mainly occupied by enclosed mariculture and saltern, which should be strictly managed. The proposed index system can effectively identify the spatiotemporal characteristics of the reclamation intensity and can be used to efficiently determine management priorities. It provides a theoretical basis for regional reclamation management and can be conveniently adopted by management departments for coastal environmental protection.
Role of the Nyainrong Microcontinent in Seismogenic Mechanism and Stress Partitioning: Insights from the 2021 Nagqu Mw 5.7 Earthquake
Authors: Liu Xiaoge , Xie Lei , Li Yujiang , Han Bingquan , Chen Zhidan , Xu Wenbin
Published: August 2022

The Nyainrong microcontinent carries key information about the ongoing evolution of the central Tibetan Plateau. The 2021 Mw 5.7 Nagqu earthquake is the largest instrumentally recorded event inside this microcontinent, which provides an ideal opportunity to elucidate the influence of this ancient microcontinent on the seismogenic mechanisms, stress heterogeneity and strain partitioning across the Tibetan Plateau. Here, we constrain the seismogenic fault geometry and distributed fault slip using Interferometric Synthetic Aperture Radar (InSAR) observations. By using the regional focal mechanism solutions, we invert the stress regimes surrounding the Nyainrong microcontinent. Our analysis demonstrates that the mainshock was caused by a normal fault with a comparable sinistral strike-slip component on a North-West dipping fault plane. The Nyainrong microcontinent is surrounded by a dominant normal faulting stress regime to the northeast and a dominant strike-slip stress regime to the southwest. Moreover, the clockwise rotation of the maximum horizontal stress (SHmax) from the southwest to the northeast is ~20°. This indicates that the Nyainrong microcontinent is involved in the mainshock occurrence as well as regional stress heterogeneity, and strain partitioning. Our results highlight the significance of the ancient microcontinent in the tectonic evolution of the Tibetan Plateau.