bluuebunny/crossref_metadata_2025 huggingface dataset:
Amurensin G Sensitized Cholangiocarcinoma to the Anti-Cancer Effect of Gemcitabine via the Downregulation of Cancer Stem-like Properties
Authors: Na Yun-Jung , Lee Hong Kyu , Choi Kyung-Chul
Published: December 2023

Cholangiocarcinoma (CCA) is a malignant biliary tract tumor with a high mortality rate and refractoriness to chemotherapy. Gemcitabine is an anti-cancer chemotherapeutic agent used for CCA, but the efficacy of gemcitabine in CCA treatment is limited, due to the acquisition of chemoresistance. The present study evaluated the chemosensitizing effects of Amurensin G (AMG), a natural sirtuin-1 inhibitor derived from Vitis amurensis, in the SNU-478 CCA cells. Treatment with AMG decreased the SNU-478 cell viability and the colony formation ability. Annexin V/ Propidium iodide staining showed that the AMG increased apoptotic death. In addition, AMG downregulated anti-apoptotic Bcl-2 expression, while upregulating pro-apoptotic cleaved caspase-3 expression. Treatment with AMG decreased the migratory ability of the cells in a wound healing assay and transwell migration assay. It was observed that AMG decreased the gemcitabine-induced increase in CD44highCD24highCD133high cell populations, and the expression of the Sox-2 protein was decreased by AMG treatment. Co-treatment of AMG with gemcitabine significantly enhanced the production of reactive oxygen species, as observed through mitochondrial superoxide staining, which might be associated with the downregulation of the Sirt1/Nrf2 pathway by AMG. These results indicate that AMG enhances the chemotherapeutic ability of gemcitabine by downregulating cancer stem-like properties in CCA cells. Hence, a combination therapy of AMG with gemcitabine may be an attractive therapeutic strategy for cholangiocarcinoma.
The Effects of Six-Gram D-Aspartic Acid Supplementation on the Testosterone, Cortisol, and Hematological Responses of Male Boxers Subjected to 11 Days of Nocturnal Exposure to Normobaric Hypoxia
Authors: Płoszczyca Kamila , Czuba Miłosz , Zakrzeska Agnieszka , Gajda Robert
Published: December 2023

The aim of this study was to evaluate the effects of D-aspartic acid (DAA) supplementation during a simulated altitude protocol on the hormonal and hematological responses in athletes. We hypothesized that DAA supplementation would contribute to an increase in the luteinizing hormone (LH), free, and testosterone and a greater increase in hematological variables. Sixteen male boxers participated; they were randomly assigned to an experimental group (DAA) or a control group (C) and underwent 14 days of supplementation, 6 g/day of DAA. Both DAA and C participants were exposed to normobaric hypoxia (FiO2 = 15.5%; 2500 m) for 10–12 h a day over a period of 11 days. The results showed that DAA had no significant effect on resting, LH, or the testosterone/cortisol ratio during the training camp. Hypoxic exposure significantly (p < 0.05) increased red blood cell and reticulocyte counts as well as hemoglobin and hematocrit concentrations in both groups, but DAA had no significant effect on these changes. In conclusion, we found that DAA supplementation at a dose of 6 g/day for 14 days does not affect the testosterone, cortisol, or hematological responses of athletes during.
A Genome-Wide Association Study of Metabolic Syndrome in the Taiwanese Population
Authors: Ho Chih-Yi , Lee Jia-In , Huang Shu-Pin , Chen Szu-Chia , Geng Jiun-Hung
Published: December 2023

The purpose of this study was to investigate genetic factors associated with metabolic syndrome (MetS) by conducting a large-scale genome-wide association study (GWAS) in Taiwan, addressing the limited data on Asian populations compared to Western populations. Using data from the Taiwan Biobank, comprehensive clinical and genetic information from 107,230 Taiwanese individuals was analyzed. Genotyping data from the TWB1.0 and TWB2.0 chips, including over 650,000 single nucleotide polymorphisms (SNPs), were utilized. Genotype imputation using the 1000 Genomes Project was performed, resulting in more than 9 million SNPs. MetS was defined based on a modified version of the Adult Treatment Panel III criteria. Among all participants (mean age: 50 years), 23% met the MetS definition. GWAS analysis identified 549 SNPs significantly associated with MetS, collectively mapping to 10 genomic risk loci. Notable risk loci included rs1004558, rs3812316, rs326, rs4486200, rs2954038, rs10830963, rs662799, rs62033400, rs183130, and rs34342646. Gene-set analysis revealed 22 associated genes: CETP, LPL, APOA5, SIK3, ZPR1, APOC1, BUD13, MLXIPL, TOMM40, GCK, YKT6, RPS6KB1, FTO, VMP1, TUBD1, BCL7B, C19orf80 (ANGPTL8), SIDT2, SENP7, PAFAH1B2, DOCK6, and FOXA2. This study identified genomic risk loci for MetS in a large Taiwanese population through a comprehensive GWAS approach. These associations provide novel insights into the genetic basis of MetS and hold promise for the potential discovery of clinical biomarkers.
Medical Cannabis Increases Appetite but Not Body Weight in Patients with Inflammatory Bowel Diseases
Authors: Fliss Isakov Naomi , Seidenberg Chen , Meiri David , Yackobovitch-Gavan Michal , Maharshak Nitsan , Hirsch Ayal
Published: December 2023

We aimed to elucidate the effect of Medical Cannabis (MC) on appetite and nutritional status among patients with inflammatory bowel disease (IBD). A case series of patients with IBD were initiating treatment with MC for disease-related symptoms, at the IBD clinic of a tertiary referral medical center. Patients’ demographics, anthropometrics, medical history and treatment and MC use were systematically recorded. An appetite and food frequency questionnaire (SNAQ and FFQ) were filled before, and at 3 and 6 months of treatment. Patients with IBD initiating MC were enrolled (n = 149, age 39.0 ± 14.1 years, 42.3% female), and 33.6% (n = 50) were treated for improvement of nutritional status. A modest increase in appetite after 3 months was detected among all patients enrolled (Pv = 0.08), but there were no significant differences in energy or macronutrient intake, and in patients’ body mass index (BMI). A significant appetite improvement after 3 months was detected among 34.0% (n = 17) of patients, but this was not associated with increased caloric intake or BMI at 3 or 6 months. Among patients without increased appetite after 3 months of MC therapy, BMI decreased at 6 months (24.1 ± 3.7 vs. 23.4 ± 3.6, Pv = 0.010). MC may be a potential strategy to improve appetite among some patients with IBD, but not caloric intake or BMI.
A Positive Causal Effect of Shrimp Allergy on Major Depressive Disorder Mediated by Allergy- and Immune-Related Pathways in the East Asian Population
Authors: Rao Shitao , Chen Xiaotong , Ou Olivia Yanlai , Chair Sek Ying , Chien Wai Tong , Liu Guangming , Waye Mary Miu Yee
Published: December 2023

Background: Observational studies have implied a potential correlation between allergic diseases and major depressive disorder (MDD). However, the relationship is still inconclusive as it is likely to be interfered with by substantial confounding factors and potential reverse causality. The present study aimed to investigate causal correlation of the two diseases by a Mendelian randomization (MR) study and further elucidate the underlying molecular mechanisms. Methods: With the biggest summary datasets of a genome-wide association study (GWAS) in the East Asian population, we conducted a two-sample, bidirectional MR study to assess the causal correlation between shrimp allergy (SA) and MDD. Subsequently, we identified the pleiotropic genes’ susceptibility to the two diseases at whole-genome and tissue-specific levels, respectively. Enriched GO sets and KEGG pathways were also discovered to elucidate the potential underlying mechanisms. Results: With the most suitable MR method, SA was identified as a causal risk factor for MDD based on three different groups of independent genetic instruments, respectively (p < 2.81 × 10−2). In contrast, we did not observe a significant causal effect of MDD on SA. The GWAS-pairwise program successfully identified seven pleiotropic genetic variants (PPA3 > 0.8), indicating that the two diseases indeed have a shared genetic basis. At a whole-genome level, the MAGMA program identified 44 pleiotropic genes, which were enriched in allergy-related pathways, such as antigen processing and presentation pathway (p = 1.46 × 10−2). In brain-specific tissue, the S-MultiXcan program found 17 pleiotropic genes that were significantly enriched in immune-related pathways and GO sets, including asthma-related pathway, T-cell activation-related, and major histocompatibility complex protein-related GO sets. Regarding whole-blood tissue, the program identified six pleiotropic genes that are significantly enriched in tolerance induction-related GO sets. Conclusions: The present study for the first time indicated a significant causal effect of SA on the occurrence of MDD, but the reverse was not true. Enrichment analyses of pleiotropic genes at whole-genome and tissue-specific levels implied the involvement of allergy and immune-related pathways in the shared genetic mechanism of the two diseases. Elucidating the causal effect and the acting direction may be beneficial in reducing the incidence rate of MDD for the massive group of SA patients in the East Asian region.
Cutaneous Disorders Masking Celiac Disease: Case Report and Mini Review with Proposal for a Practical Clinical Approach
Authors: Ancona Silvana , Bianchin Silvia , Zampatti Noemi , Nosratian Valentina , Bigatti Carolina , Ferro Jacopo , Trambaiolo Antonelli Chiara , Viglizzo Gianmaria , Gandullia Paolo , Malerba Federica , Crocco Marco
Published: December 2023

Celiac disease (CD) is an immune-mediated systemic gluten-related disorder characterized by a wide spectrum of intestinal and extra-intestinal manifestations, including damage to cutaneous and connective tissue. We report a rare case of chronic severe dermatitis involving connective tissue and cutaneous vascular vessels as the main clinical presentation of undiagnosed seronegative gluten disorder. A gluten-free diet dramatically improved the intestinal and cutaneous clinical damage in the patient. Pitfalls and the steps of differential diagnosis are described. We also review the literature regarding studies of CD and connective tissue diseases to extend the knowledge of these rare associations. We propose a practical diagnostic approach in suspected CD in autoimmune cutaneous disorders.
New 2,4-bis[(substituted-aminomethyl)phenyl]phenylquinazoline and 2,4-bis[(substituted-aminomethyl)phenyl]phenylquinoline Derivatives: Synthesis and Biological Evaluation as Novel Anticancer Agents by Targeting G-Quadruplex
Authors: Guillon Jean , Le Borgne Marc , Milano Vittoria , Guédin-Beaurepaire Aurore , Moreau Stéphane , Pinaud Noël , Ronga Luisa , Savrimoutou Solène , Albenque-Rubio Sandra , Marchivie Mathieu , Kalout Haouraa , Walker Charley , Chevallier Louise , Buré Corinne , Largy Eric , Gabelica Valérie , Mergny Jean-Louis , Baylot Virginie , Ferrer Jacky , Idrissi Yamina , Chevret Edith , Cappellen David , Desplat Vanessa , Schelz Zsuzsanna , Zupkó István
Published: December 2023

The syntheses of novel 2,4-bis[(substituted-aminomethyl)phenyl]phenylquinazolines 12 and 2,4-bis[(substituted-aminomethyl)phenyl]phenylquinolines 13 are reported here in six steps starting from various halogeno-quinazoline-2,4-(1H,3H)-diones or substituted anilines. The antiproliferative activities of the products were determined in vitro against a panel of breast (MCF-7 and MDA-MB-231), human adherent cervical (HeLa and SiHa), and ovarian (A2780) cell lines. Disubstituted 6- and 7-phenyl-bis(3-dimethylaminopropyl)aminomethylphenyl-quinazolines 12b, 12f, and 12i displayed the most interesting antiproliferative activities against six human cancer cell lines. In the series of quinoline derivatives, 6-phenyl-bis(3-dimethylaminopropyl)aminomethylphenylquinoline 13a proved to be the most active. G-quadruplexes (G4) stacked non-canonical nucleic acid structures found in specific G-rich DNA, or RNA sequences in the human genome are considered as potential targets for the development of anticancer agents. Then, as small aza-organic heterocyclic derivatives are well known to target and stabilize G4 structures, their ability to bind G4 structures have been determined through FRET melting, circular dichroism, and native mass spectrometry assays. Finally, telomerase inhibition ability has been also assessed using the MCF-7 cell line.
HIV-1 Transcription Inhibition Using Small RNA-Binding Molecules
Authors: Khatkar Pooja , Mensah Gifty , Ning Shangbo , Cowen Maria , Kim Yuriy , Williams Anastasia , Abulwerdi Fardokht A. , Zhao Yunjie , Zeng Chen , Le Grice Stuart F. J. , Kashanchi Fatah
Published: December 2023

The HIV-1 transactivator protein Tat interacts with the transactivation response element (TAR) at the three-nucleotide UCU bulge to facilitate the recruitment of transcription elongation factor-b (P-TEFb) and induce the transcription of the integrated proviral genome. Therefore, the Tat–TAR interaction, unique to the virus, is a promising target for developing antiviral therapeutics. Currently, there are no FDA-approved drugs against HIV-1 transcription, suggesting the need to develop novel inhibitors that specifically target HIV-1 transcription. We have identified potential candidates that effectively inhibit viral transcription in myeloid and T cells without apparent toxicity. Among these candidates, two molecules showed inhibition of viral protein expression. A molecular docking and simulation approach was used to determine the binding dynamics of these small molecules on TAR RNA in the presence of the P-TEFb complex, which was further validated by a biotinylated RNA pulldown assay. Furthermore, we examined the effect of these molecules on transcription factors, including the SWI/SNF complex (BAF or PBAF), which plays an important role in chromatin remodeling near the transcription start site and hence regulates virus transcription. The top candidates showed significant viral transcription inhibition in primary cells infected with HIV-1 (98.6). Collectively, our study identified potential transcription inhibitors that can potentially complement existing cART drugs to address the current therapeutic gap in current regimens. Additionally, shifting of the TAR RNA loop towards Cyclin T1 upon molecule binding during molecular simulation studies suggested that targeting the TAR loop and Tat-binding UCU bulge together should be an essential feature of TAR-binding molecules/inhibitors to achieve complete viral transcription inhibition.
Synthesis and Antiviral Activity of Novel β-D-N4-Hydroxycytidine Ester Prodrugs as Potential Compounds for the Treatment of SARS-CoV-2 and Other Human Coronaviruses
Authors: Darnotuk Elizaveta S. , Siniavin Andrei E. , Shastina Natal’ya S. , Luyksaar Sergey I. , Inshakova Anna M. , Bondareva Natalia E. , Zolotov Sergey A. , Lubenec Nadezhda L. , Sheremet Anna B. , Logunov Denis Y. , Zigangirova Nailya A. , Gushchin Vladimir A. , Gintsburg Alexander L.
Published: December 2023

The spread of COVID-19 infection continues due to the emergence of multiple transmissible and immune-evasive variants of the SARS-CoV-2 virus. Although various vaccines have been developed and several drugs have been approved for the treatment of COVID-19, the development of new drugs to combat COVID-19 is still necessary. In this work, new 5′-O-ester derivatives of N4-hydroxycytidine based on carboxylic acids were developed and synthesized by Steglich esterification. The antiviral activity of the compounds was assessed in vitro—inhibiting the cytopathic effect of HCoV-229E, and three variants of SARS-CoV-2, on huh-7 and Vero E6 cells. Data have shown that most synthesized derivatives exhibit high activity against coronaviruses. In addition, the relationship between the chemical structure of the compounds and their antiviral effect has been established. The obtained results show that the most active compound was conjugate SN_22 based on 3-methyl phenoxyacetic acid. The results of this study indicate the potential advantage of the chemical strategies used to modify NHC as a promising avenue to be explored in vivo, which could lead to the development of drugs with improved pharmacological properties that potently inhibit SARS-CoV-2.
Advancements and Perspectives in Polysaccharide-Based Nanoparticles for Theranostic Nanomedicine
Authors: Chen Jingdi
Published: December 2023

It is with great enthusiasm [...]
Intestinal Membrane Function in Inflammatory Bowel Disease
Authors: Nakai Daisuke , Miyake Masateru
Published: December 2023

Inflammatory bowel disease is a set of chronic inflammatory diseases that mainly develop in the gastrointestinal mucosa, including ulcerative colitis and Crohn’s disease. Gastrointestinal membrane permeability is an important factor influencing the pharmacological effects of pharmaceuticals administered orally for treating inflammatory bowel disease and other diseases. Understanding the presence or absence of changes in pharmacokinetic properties under a disease state facilitates effective pharmacotherapy. In this paper, we reviewed the gastrointestinal membrane function in ulcerative colitis and Crohn’s disease from the perspective of in vitro membrane permeability and electrophysiological parameters. Information on in vivo permeability in humans is summarized. We also overviewed the inflammatory bowel disease research using gut-on-a-chip, in which some advances have recently been achieved. It is expected that these findings will be exploited for the development of therapeutic drugs for inflammatory bowel disease and the optimization of treatment options and regimens.
Cancer Immunotherapy with Lipid Nanoparticles Loaded with a Stimulator of Interferon Genes Agonist against Renal Tumor Lung Metastasis
Authors: Nakamura Takashi , Sasaki Shun , Sato Yusuke , Harashima Hideyoshi
Published: December 2023

Metastatic renal cell carcinoma (RCC) has a poor prognosis, and the major organ of metastasis is the lung. Immunotherapy with immune checkpoint inhibitors (ICIs) is the first-line therapy, but the response rates are low. Thus, the development of a more effective immunotherapy against metastatic RCC would be highly desirable. We previously demonstrated how a stimulator of an interferon gene (STING) agonist-loaded lipid nanoparticles (STING-LNPs) significantly activates natural killer (NK) cells and induces an antitumor effect against cases of melanoma lung metastasis that have shown ICI resistance. In this study, we evaluated the potential of using STING-LNPs in the treatment of lung metastatic RCC (Renca). An intravenous injection of STING-LNPs drastically decreased the amount of Renca tumor colonies. In contrast, monotherapies using ICIs showed no antitumor effect, and even a combination of ICI and STING-LNP therapies failed to enhance the antitumor effects. The main effector cells would be NK cells, and the activation of NK cells by the STING-LNPs may avoid the increased expression of immune checkpoint molecules. These findings provide useful insights into the development of an effective immunotherapy against metastatic RCC.
Synergistic Phototherapy-Molecular Targeted Therapy Combined with Tumor Exosome Nanoparticles for Oral Squamous Cell Carcinoma Treatment
Authors: Li Ming , Yin Shiyao , Xu Anan , Kang Liyuan , Ma Ziqian , Liu Fan , Yang Tao , Sun Peng , Tang Yongan
Published: December 2023

Oral squamous cell carcinoma (OSCC) contributes to more than 90% of all oral malignancies, yet the performance of traditional treatments is impeded by limited therapeutic effects and substantial side effects. In this work, we report a combinational treatment strategy based on tumor exosome-based nanoparticles co-formulating a photosensitizer (Indocyanine green) and a tyrosine kinase inhibitor (Gefitinib) (IG@EXOs) for boosting antitumor efficiency against OSCC through synergistic phototherapy-molecular targeted therapy. The IG@EXOs generate distinct photothermal/photodynamic effects through enhanced photothermal conversion efficiency and ROS generation, respectively. In vivo, the IG@EXOs efficiently accumulate in the tumor and penetrate deeply to the center of the tumor due to passive and homologous targeting. The phototherapy effects of IG@EXOs not only directly induce potent cancer cell damage but also promote the release and cytoplasmic translocation of Gefitinib for achieving significant inhibition of cell proliferation and tumor angiogenesis, eventually resulting in efficient tumor ablation and lymphatic metastasis inhibition through the synergistic phototherapy-molecular targeted therapy. We envision that the encouraging performances of IG@EXOs against cancer pave a new avenue for their future application in clinical OSCC treatment.
Evolution of the Microstructure of PP-LDHs Nanocomposites during Melt Compounding: A Simulation Approach
Authors: Bernagozzi Giulia , Arrigo Rossella , Frache Alberto
Published: December 2023

In the context of polymer-based nanocomposites containing layered nanofillers, the achievement of good extents of dispersion and distribution of the embedded nanoparticles and, even more, the obtainment of intercalated and/or exfoliated structures through melt compounding still represents a persistent challenge, especially in the case of anionic layered double hydroxides (LDHs)-containing systems and non-polar polymeric matrices. In this work, a simulation approach is proposed to evaluate the influence of the processing conditions on the morphology of polypropylene (PP)-based nanocomposites containing organomodified LDHs. In particular, the effect of the screw rotation speed and the feed rate on the final microstructure of the materials formulated through melt compounding in a twin-screw extruder was assessed. The rheological and morphological characterizations demonstrated that a more homogeneous morphology was achieved when high levels of both exploited processing parameters are selected. The results coming from the simulation of the processing were used to establish some relationships between the flow parameters and the microstructure of the nanocomposites, demonstrating that low residence times coupled with high local shear rates are required to ensure the achievement of homogenous morphologies, likely involving the occurrence of intercalation phenomena.
Identification of Plastics in Mixtures and Blends through Pyrolysis-Gas Chromatography/Mass Spectrometry
Authors: Gnoffo Chiara , Frache Alberto
Published: December 2023

In this paper, the possibility of detecting polymers in plastic mixtures and extruded blends has been investigated. Pyrolysis–gas chromatography/mass spectrometry (py-GC/MS) allows researchers to identify multicomponent mixtures and low amounts of polymers without high spatial resolution, background noise and constituents mix interfering, as with molecular spectrometry techniques normally used for this purpose, such as Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy and differential scanning calorimetry (DSC). In total, 15 solid mixtures of low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), polyamide (PA) and polycarbonate (PC) in various combinations have been qualitatively analyzed after choosing their characteristic pyrolysis products and each polymer has been detected in every mix; thus, in extruded blends of high-density polyethylene (HDPE), PP and PS had varying weight percentages of the individual constituents ranging from 10 up to 90. Moreover, quantitative analysis of these polymers has been achieved in every blend with a trend that can be considered linear with coefficients of determination higher than 0.9, even though the limits of quantification are lower with respect to the ones reported in the literature, probably due to the extrusion process.
Fast and Accurate Hyperspectral Image Classification with Window Shape Adaptive Singular Spectrum Analysis
Authors: Bai Xiaotian , Qi Biao , Jin Longxu , Li Guoning , Li Jin
Published: December 2023

Hyperspectral classification is a task of significant importance in the field of remote sensing image processing, with attaining high precision and rapid classification increasingly becoming a research focus. The classification accuracy depends on the degree of raw HSI feature extraction, and the use of endless classification methods has led to an increase in computational complexity. To achieve high accuracy and fast classification, this study analyzes the inherent features of HSI and proposes a novel spectral–spatial feature extraction method called window shape adaptive singular spectrum analysis (WSA-SSA) to reduce the computational complexity of feature extraction. This method combines similar pixels in the neighborhood to reconstruct every pixel in the window, and the main steps are as follows: rearranging the spectral vectors in the irregularly shaped region, constructing an extended trajectory matrix, and extracting the local spatial and spectral information while removing the noise. The results indicate that, given the small sample sizes in the Indian Pines dataset, the Pavia University dataset, and the Salinas dataset, the proposed algorithm achieves classification accuracies of 97.56%, 98.34%, and 99.77%, respectively. The classification speed is more than ten times better than that of other methods, and a classification time of only about 1–2 s is needed.
Regional Accuracy Assessment of 30-Meter GLC_FCS30, GlobeLand30, and CLCD Products: A Case Study in Xinjiang Area
Authors: Liu Jingpeng , Ren Yu , Chen Xidong
Published: December 2023

With the development of remote sensing technology, a number of fine-resolution (30-m) global/national land cover (LC) products have been developed. However, accuracy assessments for the developed LC products are commonly conducted at global and national scales. Due to the limited availability of representative validation observations and reference data, knowledge relating to the accuracy and applicability of existing LC products on a regional scale is limited. Since Xinjiang, China, exhibits diverse surface cover and fragmented urban landscapes, existing LC products generally have high classification uncertainty in this region. This makes Xinjiang suitable for assessing the accuracy and consistency of exiting fine-resolution land cover products. In order to improve knowledge of the accuracy of existing fine-resolution LC products at the regional scale, Xinjiang province was selected as the case area. First, we employed an equal-area stratified random sampling approach with climate, population density, and landscape heterogeneity information as constraints, along with the hexagonal discrete global grid system (HDGGS) as basic sampling grids to develop a high-density land cover validation dataset for Xinjiang (HDLV-XJ) in 2020. This is the first publicly available regionally high-density validation dataset that can support analysis at a regional scale, comprising a total of 20,932 validation samples. Then, based on the generated HDLV-XJ dataset, the accuracies and consistency among three widely used 30-m LC products, GLC_FCS30, GlobeLand30, and CLCD, were quantitatively evaluated. The results indicated that the CLC_FCS30 exhibited the highest overall accuracy (88.10%) in Xinjiang, followed by GlobeLand30 (with an overall accuracy of 83.58%) and CLCD (81.57%). Moreover, through a comprehensive analysis of the relationship between different environmental conditions and land cover product performance, we found that GlobeLand30 performed best in regions with high landscape fragmentation, while GLC_FCS30 stood out as the most outstanding product in areas with uneven proportions of land cover types. Our study provides a novel insight into the suitability of these three widely-used LC products under various environmental conditions. The findings and dataset can provide valuable insights for the application of existing LC products in different environment conditions, offering insights into their accuracies and limitations.
Refraction Correction Based on ATL03 Photon Parameter Tracking for Improving ICESat-2 Bathymetry Accuracy
Authors: Chen Li , Xing Shuai , Zhang Guoping , Guo Songtao , Gao Ming
Published: December 2023

The refraction phenomenon causes ICESat-2 nearshore bathymetry errors by deviating seafloor photons’ coordinates. A refraction correction method based on ATL03 photon parameter tracking was proposed to improve the ICESat-2 bathymetry accuracy. The method begins by searching for sea–air intersections using photon parameters. Instead of relying on mathematical operations, it uses logical relations to establish a relationship between the seafloor and the surface, which improves efficiency. Then, a refraction correction model is designed based on Snell’s law for different sea surface fluctuations. This model is clear and suitable for scholars new to refraction correction. The results show the effectiveness of the proposed method since the RMSE is reduced by 1.8842 m~5.2319 m compared with the raw data. Our method has better tolerance than other methods at different water depth ranges.
Incorporating Bioclimatic Zones into Informing Ecological Networks for Better Biodiversity Conservation
Authors: Duan Jiaquan , Cao Yue’e , Yu Shulin , Fang Xuening , Li Renqiang , Xu Zhen , Long Cheng , Wang Jichun , Wang Pan
Published: December 2023

Building ecological networks can effectively enhance the quality and stability of ecosystems and better conserve biodiversity. Previous studies mainly determined ecological corridors based on selecting ecological sources at a regional scale (e.g., an administrative area), without considering the bioclimatic heterogeneity within the study area. Here, we propose a novel integrating approach involving bioclimatic zoning and selecting ecological sources from various bioclimatic zones to design ecological corridors. Taking Xi’an City, China, as an example, key bioclimatic variables were first chosen, and we partitioned the study area based on its bioclimatic characteristics through a combination of K-means clustering and variance inflation factor (VIF). Ecological sources were then identified from the combination of ecosystem services and habitats of 36 endangered species. Subsequently, the minimum cumulative resistance (MCR) model was used to build ecological networks within different bioclimatic zones and across the entire region. We found the following: (1) In Xi’an city, a total of 49 source areas and 117 corridors were identified. The identified network can protect 97.77% of species, facilitating connectivity between 30.50% of ecosystems and 35.5% of species-rich areas. (2) The integrating approach protects 12.26% more species richness and 10.95% more ecosystem services than the average value of the regional and bioregional approaches. Compared to regional and bioregional methods, integrating approaches demonstrate greater advantages in preserving species richness and ecosystem services. This study introduces a novel approach to constructing regional ecological networks, which integrates the impact of bioclimatic zoning into the process of network construction to improve ecosystem services and protect species habitats.
High-Accuracy Mapping of Soil Parent Material Types in Hilly Areas at the County Scale Using Machine Learning Algorithms
Authors: Zeng Xueliang , Guo Xi , Jiang Yefeng , Li Weifeng , Guo Jiaxin , Zhou Qiqing , Zou Hengyu
Published: December 2023

Conventional maps of soil parent material (SPM) types obtained by field survey and manual mapping or predictions from other map data have limited accuracy. Digital soil mapping of SPM types necessitates accurate acquisition of SPM distribution information, which is still a challenge in hilly areas. This study developed a high-accuracy method for SPM identification in hilly areas at the county scale. Based on geographic information system technology, seven feature variables were extracted from the geological map, geomorphic map, digital elevation model, and remote sensing image data of Shanggao County, Jiangxi Province, China. Different feature combination schemes were designed to develop SPM identification models based on random forest (RF), support vector machine (SVM), and maximum likelihood classification (MLC) algorithms. The best SPM identification results were obtained from the RF algorithm using the combination of geological type, geomorphic type, elevation, and slope. Confusion matrices were constructed based on a field survey of 586 validation samples, and the results were evaluated in terms of overall accuracy, precision, recall, F1 score, and Kappa coefficient. The overall accuracy and Kappa coefficient of the results from the optimal RF model were 83.11% and 0.79, respectively, which were 26.11% and 0.31 higher than those of the conventional map, respectively. Its precision and recall for various SPM types were greater than 75%. A comprehensive comparison of the accuracy, uncertainty, and plotting performance of the SPM recognition results reveals that the RF algorithm outperforms the SVM algorithm and the MLC algorithm. Geological type was the largest contributor to SPM identification, followed by geomorphic type, elevation, and slope. The importance of different feature variables varied for distinct SPM types. The accuracy of SPM identification was not improved by selecting more feature variables, such as land use type, normalised difference vegetation index, and topographic wetness index. This study demonstrates the feasibility of high-accuracy county-level SPM mapping in hilly areas based on the RF algorithm using geological type, geomorphic type, elevation, and slope as feature variables. As hilly areas have typical topographic features and SPM types, the proposed method of SPM mapping can be useful for application in other similar areas. There are a few limitations in this study with regard to data quality and resolution, feature variable selection, classification algorithm generalisation, and study area representativeness, which may affect the outcomes and need to be solved.
Attention-Guided Fusion and Classification for Hyperspectral and LiDAR Data
Authors: Huang Jing , Zhang Yinghao , Yang Fang , Chai Li
Published: December 2023

The joint use of hyperspectral image (HSI) and Light Detection And Ranging (LiDAR) data has been widely applied for land cover classification because it can comprehensively represent the urban structures and land material properties. However, existing methods fail to combine the different image information effectively, which limits the semantic relevance of different data sources. To solve this problem, in this paper, an Attention-guided Fusion and Classification framework based on Convolutional Neural Network (AFC-CNN) is proposed to classify the land cover based on the joint use of HSI and LiDAR data. In the feature extraction module, AFC-CNN employs the three dimensional convolutional neural network (3D-CNN) combined with a multi-scale structure to extract the spatial-spectral features of HSI, and uses a 2D-CNN to extract the spatial features from LiDAR data. Simultaneously, the spectral attention mechanism is adopted to assign weights to the spectral channels, and the cross attention mechanism is introduced to impart significant spatial weights from LiDAR to HSI, which enhance the interaction between HSI and LiDAR data and leverage the fusion information. Then two feature branches are concatenated and transferred to the feature fusion module for higher-level feature extraction and fusion. In the fusion module, AFC-CNN adopts the depth separable convolution connected through the residual structures to obtain the advanced features, which can help reduce computational complexity and improve the fitting ability of the model. Finally, the fused features are sent into the linear classification module for final classification. Experimental results on three datasets, i.e., Houston, MUUFL and Trento datasets show that the proposed AFC-CNN framework achieves better classification accuracy compared with the state-of-the-art algorithms. The overall accuracy of AFC-CNN on Houston, MUUFL and Trento datasets are 94.2%, 95.3% and 99.5%, respectively.
Remote Sensing Image Retrieval Algorithm for Dense Data
Authors: Li Xin , Liu Shibin , Liu Wei
Published: December 2023

With the rapid development of remote sensing technology, remote sensing products have found increasingly widespread applications across various fields. Nevertheless, as the volume of remote sensing image data continues to grow, traditional data retrieval techniques have encountered several challenges such as substantial query results, data overlap, and variations in data quality. Users need to manually browse and filter a large number of remote sensing datasets, which is a cumbersome and inefficient process. In order to cope with these problems of traditional remote sensing image retrieval methods, this paper proposes a remote sensing image retrieval algorithm for dense data (DD-RSIRA). The algorithm establishes evaluation metrics based on factors like imaging time, cloud coverage, and image coverage. The algorithm utilizes the global grids to create an ensemble coverage relation between images and grids. A locally optimal initial solution is obtained by a greedy algorithm, and then a local search is performed to search for the optimal solution by combining the strategies of weighted gain-loss scheme and novel mechanism. Ultimately, it achieves an optimal coverage of remote sensing images within the region of interest. In this paper, it is shown that the method obtains a smaller number of datasets with lower redundancy and higher data utilization and ensures the data quality to a certain extent in order to accurately meet the requirements of the regional coverage of remote sensing images.
Landslide Hazard Assessment Combined with InSAR Deformation: A Case Study in the Zagunao River Basin, Sichuan Province, Southwestern China
Authors: Shan Yunfeng , Xu Zhou , Zhou Shengsen , Lu Huiyan , Yu Wenlong , Li Zhigang , Cao Xiong , Li Pengfei , Li Weile
Published: December 2023

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas.
Analysis of the Spatial Distribution and Deformation Types of Active Landslides in the Upper Jinsha River, China, Using Integrated Remote Sensing Technologies
Authors: Zhou Shengsen , Chen Baolin , Lu Huiyan , Shan Yunfeng , Li Zhigang , Li Pengfei , Cao Xiong , Li Weile
Published: December 2023

The Upper Jinsha River (UJSR) has great water resource potential, but large-scale active landslides hinder water resource development and utilization. It is necessary to understand the spatial distribution and deformation trend of active landslides in the UJSR. In areas of high elevations, steep terrain or otherwise inaccessible to humans, extensive landslide studies remain challenging using traditional geological surveys and monitoring equipment. Stacking interferometry synthetic aperture radar (stacking-InSAR) technology, optical satellite images and unmanned aerial vehicle (UAV) photography are applied to landslide identification. Small baseline subset interferometry synthetic aperture radar (SBAS-InSAR) was used to obtain time-series deformation curves of samples to reveal the deformation types of active landslides. A total of 246 active landslides were identified within the study area, of which 207 were concentrated in three zones (zones I, II and III). Among the 31 landslides chosen as research samples, six were linear-type landslides, three were upward concave-type landslides, 10 were downward concave-type landslides, and 12 were step-type landslides based on the curve morphology. The results can aid in monitoring and early-warning systems for active landslides within the UJSR and provide insights for future studies on active landslides within the basin.
Generating Edge Cases for Testing Autonomous Vehicles Using Real-World Data
Authors: Karunakaran Dhanoop , Berrio Perez Julie Stephany , Worrall Stewart
Published: December 2023

In the past decade, automotive companies have invested significantly in autonomous vehicles (AV), but achieving widespread deployment remains a challenge in part due to the complexities of safety evaluation. Traditional distance-based testing has been shown to be expensive and time-consuming. To address this, experts have proposed scenario-based testing (SBT), which simulates detailed real-world driving scenarios to assess vehicle responses efficiently. This paper introduces a method that builds a parametric representation of a driving scenario using collected driving data. By adopting a data-driven approach, we are then able to generate realistic, concrete scenarios that correspond to high-risk situations. A reinforcement learning technique is used to identify the combination of parameter values that result in the failure of a system under test (SUT). The proposed method generates novel, simulated high-risk scenarios, thereby offering a meaningful and focused assessment of AV systems.
Personal and Clinical Determinants of Brace-Wearing Time in Adolescents with Idiopathic Scoliosis
Authors: Fregna Giulia , Rossi Raccagni Sara , Negrini Alessandra , Zaina Fabio , Negrini Stefano
Published: December 2023

Adolescent idiopathic scoliosis (AIS) is a three-dimensional spine and trunk deformity. Bracing is an effective treatment for medium-degree curves. Thermal sensors help monitor patients’ adherence (compliance), a critical issue in bracing treatment. Some studies investigated adherence determinants but rarely through sensors or in highly adherent cohorts. We aimed to verify the influence of personal and clinical variables routinely registered by physicians on adherence to brace treatment in a large cohort of consecutive AIS patients from a highly adherent cohort. We performed a cross-sectional study of patients consecutively recruited in the last three years at a tertiary referral institute and treated with braces for one year. To ensure high adherence, for years, we have provided specific support to brace treatment through a series of cognitive-behavioural interventions for patients and parents. We used iButton thermal sensor systematic data collection to precisely analyse the real brace-wearing time. We included 514 adolescents, age 13.8 ± 1.6, with the worst scoliosis curve of 34.5 ± 10.3° Cobb. We found a 95% (95CI 60–101%) adherence to the brace prescription of 21.9 ± 1.7 h per day. Determinants included gender (91% vs. 84%; females vs. males) and age < 14 years (92% vs. 88%). Brace hours prescription, BMI, and all clinical variables (worst curve Cobb degrees, angle of trunk rotation, and TRACE index for aesthetics) did not influence adherence.
Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysis
Authors: Calderón-Díaz Mailyn , Silvestre Aguirre Rony , Vásconez Juan P. , Yáñez Roberto , Roby Matías , Querales Marvin , Salas Rodrigo
Published: December 2023

There is a significant risk of injury in sports and intense competition due to the demanding physical and psychological requirements. Hamstring strain injuries (HSIs) are the most prevalent type of injury among professional soccer players and are the leading cause of missed days in the sport. These injuries stem from a combination of factors, making it challenging to pinpoint the most crucial risk factors and their interactions, let alone find effective prevention strategies. Recently, there has been growing recognition of the potential of tools provided by artificial intelligence (AI). However, current studies primarily concentrate on enhancing the performance of complex machine learning models, often overlooking their explanatory capabilities. Consequently, medical teams have difficulty interpreting these models and are hesitant to trust them fully. In light of this, there is an increasing need for advanced injury detection and prediction models that can aid doctors in diagnosing or detecting injuries earlier and with greater accuracy. Accordingly, this study aims to identify the biomarkers of muscle injuries in professional soccer players through biomechanical analysis, employing several ML algorithms such as decision tree (DT) methods, discriminant methods, logistic regression, naive Bayes, support vector machine (SVM), K-nearest neighbor (KNN), ensemble methods, boosted and bagged trees, artificial neural networks (ANNs), and XGBoost. In particular, XGBoost is also used to obtain the most important features. The findings highlight that the variables that most effectively differentiate the groups and could serve as reliable predictors for injury prevention are the maximum muscle strength of the hamstrings and the stiffness of the same muscle. With regard to the 35 techniques employed, a precision of up to 78% was achieved with XGBoost, indicating that by considering scientific evidence, suggestions based on various data sources, and expert opinions, it is possible to attain good precision, thus enhancing the reliability of the results for doctors and trainers. Furthermore, the obtained results strongly align with the existing literature, although further specific studies about this sport are necessary to draw a definitive conclusion.
Joint Beamforming and Phase Shifts Design for RIS-Aided Multi-User Full-Duplex Systems in Smart Cities
Authors: Pan Kunbei , Zhou Bin , Zhang Wei , Ju Cheng
Published: December 2023

Full-duplex (FD) and reconfigurable intelligent surface (RIS) are potential technologies for achieving wireless communication effectively. Therefore, in theory, the RIS-aided FD system is supposed to enhance spectral efficiency significantly for the ubiquitous Internet of Things devices in smart cities. However, this technology additionally induces the loop-interference (LI) of RIS on the residual self-interference (SI) of the FD base station, especially in complicated urban outdoor environments, which will somewhat counterbalance the performance benefit. Inspired by this, we first establish an objective and constraints considering the residual SI and LI in two typical urban outdoor scenarios. Then, we decompose the original problem into two subproblems according to the variable types and jointly design the beamforming matrices and phase shifts vector methods. Specifically, we propose a successive convex approximation algorithm and a soft actor–critic deep reinforcement learning-related scheme to solve the subproblems alternately. To prove the effectiveness of our proposal, we introduce benchmarks of RIS phase shifts design for comparison. The simulation results show that the performance of the low-complexity proposed algorithm is only slightly lower than the exhaustive search method and outperforms the fixed-point iteration scheme. Moreover, the proposal in scenario two is more outstanding, demonstrating the application predominance in urban outdoor environments.