bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Genistein Drug Delivery Systems Based on Bacterial Nanocellulose for Potential Colorectal Cancer Chemoprevention: Effect of Nanocellulose Surface Modification on Genistein Adsorption
Authors: Castaño Melissa , Martínez Estefanía , Osorio Marlon , Castro Cristina
Published: October 2022

Genistein is an isoflavone with antioxidant, anti-inflammatory, and anticancer properties. That said, its use in the industry is limited by its low solubility in aqueous systems. In this work, bacterial nanocellulose (BNC) and BNC modified with cetyltrimethylammonium (BNC-CTAB) were evaluated as genistein-encapsulating materials for their controlled release in cancer chemoprevention. Thin films were obtained and characterized by contact angle, AFM, TEM, UV–Vis spectroscopy FTIR, and TGA techniques to verify surface modification and genistein encapsulation. The results show a decrease in hydrophilization degree and an increase in diameter after BNC modification. Furthermore, the affinity of genistein with the encapsulating materials was determined in the context of monolayer and multilayer isotherms, thermodynamic parameters and adsorption kinetics. Spontaneous, endothermic and reversible adsorption processes were found for BNC-GEN and BNC-CTAB-GEN. After two hours, the maximum adsorption capacity corresponded to 4.59 mg GEN∙g−1 BNC and 6.10 mg GEN∙g−1 BNC-CTAB; the latter was a more stable system. Additionally, in vitro release assays performed with simulated gastrointestinal fluids indicated controlled and continuous desorption in gastric and colon fluids, with a release of around 5% and 85%, respectively, for either system. Finally, the IC50 tests made it possible to determine the amounts of films required to achieve therapeutic concentrations for SW480 and SW620 cell lines.
Using a Very Low Energy Diet to Achieve Substantial Preconception Weight Loss in Women with Obesity: A Review of the Safety and Efficacy
Authors: Price Sarah A , Sumithran Priya
Published: October 2022

Obesity in women of reproductive age is common. Emerging evidence suggests that maternal obesity not only increases the risk of adverse pregnancy outcomes but also has an enduring impact on the metabolic health of the offspring. Given this, management of obesity prior to pregnancy is critically important. Almost all international guidelines suggest that women with obesity should aim to achieve weight loss prior to pregnancy. However, current pre-conception weight loss therapies are sub-optimal. Lifestyle modification typically results in modest weight loss. This may assist fertility but does not alter pregnancy outcomes. Bariatric surgery results in substantial weight loss, which improves pregnancy outcomes for the mother but may be harmful to the offspring. Alternative approaches to the management of obesity in women planning pregnancy are needed. Very low energy diets (VLEDs) have been proposed as a possible tool to assist women with obesity achieve weight loss prior to conception. While VLEDs can induce substantial and rapid weight loss, there are concerns about the impact of rapid weight loss on maternal nutrition prior to pregnancy and about inadvertent exposure of the early fetus to ketosis. The purpose of this review is to examine the existing literature regarding the safety and efficacy of a preconception VLED program as a tool to achieve substantial weight loss in women with obesity.
Dietary Pattern Influences Gestational Weight Gain: Results from the ProcriAr Cohort Study—São Paulo, Brazil
Authors: Saldiva Silvia Regina Dias Medici , De Arruda Neta Adélia da Costa Pereira , Teixeira Juliana Araujo , Peres Stela Verzinhasse , Marchioni Dirce Maria Lobo , Carvalho Mariana Azevedo , Vieira Sandra Elisabete , Francisco Rossana Pulcineli Vieira
Published: October 2022

The maternal pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) influence maternal and infant outcomes. This study identified patterns of habitual dietary intake in 385 pregnant women in São Paulo and explored their associations with excessive weight gain (EGWG). Weight at the first visit (<14 weeks) was used as a proxy for pre-pregnancy weight. Food consumption was assessed using the 24HR method, administered twice at each gestational trimester, and dietary patterns were identified by principal component analysis. Three dietary patterns were identified: “Vegetables and Fruits,” “Western,” and “Brazilian Traditional.” Descriptive data analysis was performed using absolute and relative frequencies for each independent variable and multilevel mixed-effects logistic regression was used to analyze excessive gestational gain weight (EGWG) and dietary patterns (DP). The Brazilian Traditional dietary pattern showed a protective effect on EGWG (p = 0.04) and age > 35 years (p = 0.03), while subjects overweight at baseline had a higher probability of EGWG (p = 0.02), suggesting that the identification of dietary and weight inadequacies should be observed from the beginning of pregnancy, accompanied by nutritional intervention and weight monitoring throughout the gestational period to reduce risks to the mother and child’s health.
Breastfeeding Practices and Associated Factors in Shanghai: A Cross-Sectional Study
Authors: Hao Yanhui , Wang Lulu , Wang Caifeng , Peng Aiping , Gao Wei , Marc Isabelle , Semenic Sonia , Fraser William D. , Narayan Anuradha , Wu Yanting , Chang Suying , Huang Hefeng
Published: October 2022

The status of breastfeeding practices remains unsatisfactory across China, but regional differences persist. However, disaggregated data for specific provinces are limited. This representative survey determined the status of breastfeeding and factors associated with breastfeeding practices in Shanghai. The questionnaire was designed in compliance with indicators for assessing infant and young child-feeding practices defined by the World Health Organization and the United Nations Children’s Fund (UNICEF). A total of 2665 children aged two years and younger (0–730 days) were investigated, among whom 1677 were aged under six months. The early initiation of breastfeeding (EIBF) rate was 60.3%. Among children aged under six months, 43.4% were exclusively breastfed (EBF). The univariate regression analysis showed that the EBF rate was influenced by multiple factors, including individual, socioeconomic, workplace and employment, and health system. The subsequent multivariate analysis suggested that mothers with a higher rate of EBF shared the following characteristics: intention to breastfeed during pregnancy, breastfeeding knowledge, and higher satisfaction with support through the healthcare system after delivery. The rate of EBF in Shanghai is over 40%, and supporting breastfeeding requires measures at multiple levels, including individual attributes, women’s work and employment conditions, breastfeeding knowledge, and health services.
PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets
Authors: Stefanidis Kiriakos , Tsatsou Dorothea , Konstantinidis Dimitrios , Gymnopoulos Lazaros , Daras Petros , Wilson-Barnes Saskia , Hart Kathryn , Cornelissen Véronique , Decorte Elise , Lalama Elena , Pfeiffer Andreas , Hassapidou Maria , Pagkalos Ioannis , Argiriou Anagnostis , Rouskas Konstantinos , Hadjidimitriou Stelios , Charisis Vasileios , Dias Sofia Balula , Diniz José Alves , Telo Gonçalo , Silva Hugo , Bensenousi Alex , Dimitropoulos Kosmas
Published: October 2022

AI-based software applications for personalized nutrition have recently gained increasing attention to help users follow a healthy lifestyle. In this paper, we present a knowledge-based recommendation framework that exploits an explicit dataset of expert-validated meals to offer highly accurate diet plans spanning across ten user groups of both healthy subjects and participants with health conditions. The proposed advisor is built on a novel architecture that includes (a) a qualitative layer for verifying ingredient appropriateness, and (b) a quantitative layer for synthesizing meal plans. The first layer is implemented as an expert system for fuzzy inference relying on an ontology of rules acquired by experts in Nutrition, while the second layer as an optimization method for generating daily meal plans based on target nutrient values and ranges. The system’s effectiveness is evaluated through extensive experiments for establishing meal and meal plan appropriateness, meal variety, as well as system capacity for recommending meal plans. Evaluations involved synthetic data, including the generation of 3000 virtual user profiles and their weekly meal plans. Results reveal a high precision and recall for recommending appropriate ingredients in most user categories, while the meal plan generator achieved a total recommendation accuracy of 92% for all nutrient recommendations.
Higher Potassium Intake and Lower Sodium Intake May Help in Reducing CVD Risk by Lowering Salt Sensitivity of Blood Pressure in the Han Chinese Population
Authors: Xie Yunyi , Qi Han , Peng Wenjuan , Li Bingxiao , Wen Fuyuan , Zhang Fengxu , Zhang Ling
Published: October 2022

Sodium (Na) reduction with a parallel supplemental potassium (K) intake can prevent cardiovascular diseases (CVDs). The relationship of the urinary Na/K ratio and salt sensitivity of blood pressure (SSBP) with CVDs is not clearly explained. We assumed that the SSBP mediates the relationship between the Na/K ratio and CVDs. In total, 2055 subjects who had 24 h urine collected and SSBP determined were included in this study. CVD risk was estimated using the China-PAR equation. MediationMultivariate logistic regression was used to explore the associations between the Na/K ratio or SSBP with CVD risk. Mediation analysis using a logistic regression model was performed. Both the urinary Na/K ratio and SSBP were related to the estimated CVD risk (p < 0.05). The mediation analysis found that SSBP mediated approximately 12% of the association between Na/K ratio and CVD risk. Our findings indicate that higher K intake and lower Na intake may help in preventing CVD risk by reducing SSBP risk in individuals with normotension or stage-one hypertension.
Association of Low Handgrip Strength with Chemotherapy Toxicity in Digestive Cancer Patients: A Comprehensive Observational Cohort Study (FIGHTDIGOTOX)
Authors: Martin Pierre , Botsen Damien , Brugel Mathias , Bertin Eric , Carlier Claire , Mahmoudi Rachid , Slimano Florian , Perrier Marine , Bouché Olivier
Published: October 2022

In the FIGHTDIGO study, digestive cancer patients with dynapenia experienced more chemotherapy-induced neurotoxicities. FIGHTDIGOTOX aimed to evaluate the relationship between pre-therapeutic handgrip strength (HGS) and chemotherapy-induced dose-limiting toxicity (DLT) or all-grade toxicity in digestive cancer patients. HGS measurement was performed with a Jamar dynamometer. Dynapenia was defined according to EWGSOP2 criteria (<27 kg (men); <16 kg (women)). DLT was defined as any toxicity leading to dose reduction, treatment delay, or permanent discontinuation. We also performed an exploratory analysis in patients below the included population’s median HGS. A total of 244 patients were included. According to EWGSOP2 criteria, 23 patients had pre-therapeutic dynapenia (9.4%). With our exploratory median-based threshold (34 kg for men; 22 kg for women), 107 patients were dynapenic (43.8%). For each threshold, dynapenia was not an independent predictive factor of overall DLT and neurotoxicity. Dynapenic patients according to EWGSOP2 definition experienced more hand-foot syndrome (p = 0.007). Low HGS according to our exploratory threshold was associated with more all-grade asthenia (p = 0.014), anemia (p = 0.006), and asthenia with DLT (p = 0.029). Pre-therapeutic dynapenia was not a predictive factor for overall DLT and neurotoxicity in digestive cancer patients but could be a predictive factor of chemotherapy-induced anemia and asthenia. There is a need to better define the threshold of dynapenia in cancer patients.
Fatty Liver Is an Independent Risk Factor for Elevated Intraocular Pressure
Authors: Lee Jun-Hyuk , Kwon Yu-Jin , Lee Hye Sun , Han Jee Hye , Joung Boyoung , Kim Sung Jin
Published: October 2022

Elevated intraocular pressure (EIOP) is a major risk factor for glaucoma. Both EIOP and fatty liver share metabolic risk factors, which implies a possible link between EIOP and fatty liver. We aimed to determine the association of fatty liver with EIOP and estimate the effect of fatty liver on EIOP directly and indirectly through insulin resistance. Data from 16,240 adults who underwent health examinations at a single center were analyzed. Multiple logistic regression analyses revealed that fully adjusted odds ratio (OR) and 95% confidence interval (CI) for EIOP in the fatty liver group compared to the non-fatty liver group were 1.36 and 1.08–1.71. Alcoholic liver disease was associated with EIOP in subgroup analysis (OR = 1.80, 95% CI: 1.27–2.56). There was a linear dose–response relationship between EIOP and the severity of fatty liver. Mediation analysis revealed that the total effect of fatty liver on intraocular pressure was 0.90 (0.81–0.99), with a direct effect of 0.81 (0.71–0.90) and an indirect effect of 0.09 (0.06–0.11) through insulin resistance. Fatty liver is independently associated with EIOP. It primarily has a direct effect on intraocular pressure. This suggests that evaluation of EIOP should be considered in patients with fatty liver.
Nutritional Composition and Environmental Impact of Meals Selected in Workplace Canteens before and after an Intervention Promoting the Adherence to the Mediterranean Diet
Authors: Rosi Alice , Biasini Beatrice , Monica Elisa , Rapetti Valeria , Deon Valeria , Scazzina Francesca
Published: October 2022

Enhancing healthy and sustainable food systems is one of the key goals of the current European Commission policy. In this light, the creation of a food environment where people are properly informed about the healthiness and sustainability of food choices is essential. This study aimed to evaluate the nutritional profile and the environmental impact of meals consumed in a workplace canteen in Italy in the presence of a nudge (i.e., the Double Pyramid logo) combined with a web-based application promoting the Mediterranean Diet. Energy and nutrient contents and the carbon, water, and ecological footprints of 29,776 meals were compared across three subsequent periods (from June to April) through one-way ANOVA. Although the choice of dishes labelled with the Double Pyramid logo was comparable across periods, the selection of fish- and plant-based dishes increased from +2% (fish, vegetables) up to +17% (whole-grain cereals), with a concurrent reduction of meat-based options (−2%). Although the consumption of healthy items increased (p < 0.001), they were not added as a replacement for alternative options, leading to a higher content in energy (p < 0.001) and nutrients (p < 0.001) and worse environmental footprints, contrarily to what was observed when data were adjusted for energy. The intervention significantly improved food choices; however, as the higher selection of desired dishes was not adequately compensated for, it was not fully effective.
Exclusive Enteral Nutrition Exerts Anti-Inflammatory Effects through Modulating Microbiota, Bile Acid Metabolism, and Immune Activities
Authors: Xiao Fangfei , Gao Xuefeng , Hu Hui , Le Jun , Chen Yongheng , Shu Xingsheng , Liang Ziwei , Xu Yang , Wang Yizhong , Zhang Ting
Published: October 2022

Exclusive enteral nutrition (EEN) can induce remission in patients with pediatric Crohn’s disease (CD). This study aims to depict EEN’s modification of bile acid (BA) metabolism in pediatric CD and explores the effect of the EEN-enriched BA in inhibiting the inflammatory response. The twelve enrolled pediatric CD patients showed BA dysmetabolism, represented by decreased levels of fecal secondary and unconjugated BAs as determined by UPLC–TQMS, which were accompanied by gut microbiota dysbiosis and reduced BA-metabolizing bacteria including Eubacterium and Ruminococcus genera, assessed by shotgun metagenomic sequencing. EEN treatment induced remission in these patients at eight weeks, and nine patients remained in stable remission for longer than 48 weeks. EEN improved BA dysmetabolism, with some enriched BAs, including hyocholic acid (HCA), α-muricholic acid (αMCA), strongly associated with decreased severity of CD symptoms. These BAs were significantly correlated with the increased abundance of certain bacteria, including Clostridium innocuum and Hungatella hathewayi, which express 3β-hydroxysteroid dehydrogenase and 5β-reductase. HCA could suppress TNF-α production by CD4+ T cells in the peripheral blood mononuclear cells (PBMCs) of CD patients. Moreover, intraperitoneal injection of HCA could attenuate dextran sulfate sodium (DSS)-induced mouse colitis. Our data suggests that BA modification may contribute to the EEN-induced remission of pediatric CD.
An Algae-Made RBD from SARS-CoV-2 Is Immunogenic in Mice
Authors: Govea-Alonso Dania O. , Malla Ashwini , Bolaños-Martínez Omayra C. , Vimolmangkang Sornkanok , Rosales-Mendoza Sergio
Published: October 2022

Despite the current advances in global vaccination against SARS-CoV-2, boosting is still required to sustain immunity in the population, and the induction of sterilizing immunity remains as a pending goal. Low-cost oral immunogens could be used as the basis for the design of affordable and easy-to-administer booster vaccines. Algae stand as promising platforms to produce immunogens at low cost, and it is possible to use them as oral delivery carriers since they are edible (not requiring complex purification and formulation processes). Herein, a Chlamydomonas-made SARS-CoV-2 RBD was evaluated as an oral immunogen in mice to explore the feasibility of developing an oral algae-based vaccine. The test immunogen was stable in freeze-dried algae biomass and able to induce, by the oral route, systemic and mucosal humoral responses against the spike protein at a similar magnitude to those induced by injected antigen plus alum adjuvant. IgG subclass analysis revealed a Th2-bias response which lasted over 4 months after the last immunization. The induced antibodies showed a similar reactivity against either Delta or Omicron variants. This study represents a step forward in the development of oral vaccines that could accelerate massive immunization.
Valorisation of the Inhibitory Potential of Fresh and Dried Fruit Extracts of Prunus spinosa L. towards Carbohydrate Hydrolysing Enzymes, Protein Glycation, Multiple Oxidants and Oxidative Stress-Induced Changes in Human Plasma Constituents
Authors: Magiera Anna , Kołodziejczyk-Czepas Joanna , Skrobacz Karolina , Czerwińska Monika Ewa , Rutkowska Magdalena , Prokop Aleksandra , Michel Piotr , Olszewska Monika Anna
Published: October 2022

Prunus spinosa fruits (sloes), both fresh and dried, are underexplored dietary components and ethno-phytotherapeutic remedies applied to treat chronic oxidative-stress-related diseases, including diabetes. The present study aimed to evaluate drying-related changes in the antidiabetic potential of sloe extracts and some bioactivity mechanisms, which might be connected with their traditional application. The polyphenol-enriched extracts, prepared by fractionated extraction and phytochemically standardised, i.a., by LC-MS/MS, were tested in vitro using a set of biological and chemical models. The experiments revealed the significant extracts’ ability to counteract the generation of advanced glycation end products (AGEs) and inhibit the activity of key glycolytic enzymes, i.e., α-glucosidase and α-amylase. Moreover, they were proved to effectively scavenge multiple oxidants of physiological importance (O2•−, HO•, H2O2, NO•, HOCl), increase the non-enzymatic antioxidant capacity of human plasma (NEAC) under oxidative stress conditions induced by peroxynitrite, and protect plasma proteins and lipids against peroxidation and nitration at in vivo-relevant levels (1–50 µg/mL, equivalent to 0.03–6.32 µg polyphenols/mL). In most cases, the activity of fresh fruit extracts surpassed that of dried-based products. The correlation studies and tests on model compounds proved polyphenols as dominant contributors to the observed effects. Furthermore, the co-occurring representatives of various polyphenolic classes were found to contribute to the biological activity of sloes through additive and synergistic effects. Considering the extraction yield and activity parameters, especially the superior outcomes compared to anti-diabetic drugs aminoguanidine and acarbose in the anti-glycation and α-glucosidase inhibition tests, the methanol–water (75:25, v/v) extract of fresh fruits and its phenolic-enriched fractions revealed the most advantageous potential for functional application.
Isoniazid Linked to Sulfonate Esters via Hydrazone Functionality: Design, Synthesis, and Evaluation of Antitubercular Activity
Authors: Koçak Aslan Ebru , Han Muhammed İhsan , Krishna Vagolu Siva , Tamhaev Rasoul , Dengiz Cagatay , Doğan Şengül Dilem , Lherbet Christian , Mourey Lionel , Tønjum Tone , Gündüz Miyase Gözde
Published: October 2022

Isoniazid (INH) is one of the key molecules employed in the treatment of tuberculosis (TB), the most deadly infectious disease worldwide. However, the efficacy of this cornerstone drug has seriously decreased due to emerging INH-resistant strains of Mycobacterium tuberculosis (Mtb). In the present study, we aimed to chemically tailor INH to overcome this resistance. We obtained thirteen novel compounds by linking INH to in-house synthesized sulfonate esters via a hydrazone bridge (SIH1–SIH13). Following structural characterization by FTIR, 1H NMR, 13C NMR, and HRMS, all compounds were screened for their antitubercular activity against Mtb H37Rv strain and INH-resistant clinical isolates carrying katG and inhA mutations. Additionally, the cytotoxic effects of SIH1–SIH13 were assessed on three different healthy host cell lines; HEK293, IMR-90, and BEAS-2B. Based on the obtained data, the synthesized compounds appeared as attractive antimycobacterial drug candidates with low cytotoxicity. Moreover, the stability of the hydrazone moiety in the chemical structure of the final compounds was confirmed by using UV/Vis spectroscopy in both aqueous medium and DMSO. Subsequently, the compounds were tested for their inhibitory activities against enoyl acyl carrier protein reductase (InhA), the primary target enzyme of INH. Although most of the synthesized compounds are hosted by the InhA binding pocket, SIH1–SIH13 do not primarily show their antitubercular activities by direct InhA inhibition. Finally, in silico determination of important physicochemical parameters of the molecules showed that SIH1–SIH13 adhered to Lipinski’s rule of five. Overall, our study revealed a new strategy for modifying INH to cope with the emerging drug-resistant strains of Mtb.
Overview on Strategies and Assays for Antibiotic Discovery
Authors: Rütten Anika , Kirchner Teresa , Musiol-Kroll Ewa Maria
Published: October 2022

The increase in antibiotic resistance poses a major threat to global health. Actinomycetes, the Gram-positive bacteria of the order Actinomycetales, are fertile producers of bioactive secondary metabolites, including antibiotics. Nearly two-thirds of antibiotics that are used for the treatment of bacterial infections were originally isolated from actinomycetes strains belonging to the genus Streptomyces. This emphasizes the importance of actinomycetes in antibiotic discovery. However, the identification of a new antimicrobial compound and the exploration of its mode of action are very challenging tasks. Therefore, different approaches that enable the “detection” of an antibiotic and the characterization of the mechanisms leading to the biological activity are indispensable. Beyond bioinformatics tools facilitating the identification of biosynthetic gene clusters (BGCs), whole cell-screenings—in which cells are exposed to actinomycete-derived compounds—are a common strategy applied at the very early stage in antibiotic drug development. More recently, target-based approaches have been established. In this case, the drug candidates were tested for interactions with usually validated targets. This review focuses on the bioactivity-based screening methods and provides the readers with an overview on the most relevant assays for the identification of antibiotic activity and investigation of mechanisms of action. Moreover, the article includes examples of the successful application of these methods and suggestions for improvement.
Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence
Authors: Noonan Theresa , Denzinger Katrin , Talagayev Valerij , Chen Yu , Puls Kristina , Wolf Clemens Alexander , Liu Sijie , Nguyen Trung Ngoc , Wolber Gerhard
Published: October 2022

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.
Vascular Effects of the Fetal Hemoglobin Inducer Agent 3-(1,3-Dioxoisoindolin-2-yl) Benzyl Nitrate
Authors: Terroni Barbara , de Moraes Luis Henrique Oliveira , Pavan Aline Renata , Rodrigues Gerson Jhonatan , Dos Santos Jean Leandro
Published: October 2022

Vascular endothelium is a protective layer of cells lining the lumen of blood vessels that plays important roles by releasing factors responsible for controlling the vascular tone, regulating the expression of pro-inflammatory cytokines, and expressing adhesion molecules involved in vascular hemostasis. Imbalance of vascular properties leads to endothelial dysfunction (ED) and cardiovascular damage. Some diseases, such as sickle cell anemia, are characterized by ED with reduction in the levels of nitric oxide (NO). Previously, we have shown that the fetal hemoglobin inducer agent 3-(1,3-dioxoisoindolin-2-yl) benzyl nitrate (Lapdesf-4c) could act as NO donor, inhibiting platelet aggregation and reducing the inflammation associated with SCA. However, the vascular effect of this compound was not yet studied. Herein, we evaluated the effects of Lapdesf-4c in vascular reactivity experiments using aortic rings from male Wistar rats (300 g/90 days). We have found that Lapdesf-4c induced vasodilation in the presence (E+) or absence of endothelium (E−) with an average of EMax values of 101.8 ± 3.33% and 111.8 ± 3.21%. The mechanism of action was studied using 1H-[1,2,4]oxadiazolo[4,3-alpha]quinoxalin-1-one (ODQ), L-NG-nitroarginine methyl ester (L-NAME), and hydroxocobalamin. The EMax values for those pathways were hydroxocobalamin (30.6 ± 2.21%), ODQ (4.75 ± 0.51%), and L-NAME (109 ± 3.65), suggesting that Lapdesf-4c exhibits NO-dependent mechanisms. Lapdesf-4c was able to prevent angiotensin-induced ED after incubation of aorta rings for 1 h. We found based on the concentration–effect curve using acetylcholine (ACh) that pEC50 values for the control, Ang II, and combination of (Ang II + Lapdesf-4c) were 6.73, 6.46, and 7.15, respectively. In conclusion, Lapdesf-4c has emerged as a new drug candidate that can promote vasodilation and act as a protective agent against ED, being useful to prevent vascular damage.
A Deep Learning Based Method for Railway Overhead Wire Reconstruction from Airborne LiDAR Data
Authors: Zhang Lele , Wang Jinhu , Shen Yueqian , Liang Jian , Chen Yuyu , Chen Linsheng , Zhou Mei
Published: October 2022

Automatically and accurately reconstructing the overhead wires of railway from airborne laser scanning (ALS) data are an efficient way of railway monitoring to ensure stable and safety transportation services. However, due to the complex structure of the overhead wires, it is challenging to extract these wires using the existing methods. This work proposes a workflow for railway overhead wire reconstruction using deep learning for wire identification collaborating with the RANdom SAmple Consensus (RANSAC) algorithm for wire reconstruction. First, data augmentation and ground points down-sampling are performed to facilitate the issues caused by insufficient and non-uniformity of LiDAR points. Then, a network incorporating with PointNet model is proposed to segment wires, pylons and ground points. The proposed network is composed of a Geometry Feature Extraction (GFE) module and a Neighborhood Information Aggregation (NIA) module. These two modules are introduced to encode and describe the local geometric features. Therefore, the capability of the model to discriminate geometric details is enhanced. Finally, a wire individualization and multi-wire fitting algorithm is proposed to reconstruct the overhead wires. A number of experiments are conducted using ALS point cloud data of railway scenarios. The results show that the accuracy and MIoU for wire identification are 96.89% and 82.56%, respectively, which demonstrates a better performance compared to the existing methods. The overall reconstruction accuracy is 96% over the study area. Furthermore, the presented strategy also demonstrated its applicability to high-voltage powerline scenarios.
A Group-Wise Feature Enhancement-and-Fusion Network with Dual-Polarization Feature Enrichment for SAR Ship Detection
Authors: Xu Xiaowo , Zhang Xiaoling , Shao Zikang , Shi Jun , Wei Shunjun , Zhang Tianwen , Zeng Tianjiao
Published: October 2022

Ship detection in synthetic aperture radar (SAR) images is a significant and challenging task. However, most existing deep learning-based SAR ship detection approaches are confined to single-polarization SAR images and fail to leverage dual-polarization characteristics, which increases the difficulty of further improving the detection performance. One problem that requires a solution is how to make full use of the dual-polarization characteristics and how to excavate polarization features using the ship detection network. To tackle the problem, we propose a group-wise feature enhancement-and-fusion network with dual-polarization feature enrichment (GWFEF-Net) for better dual-polarization SAR ship detection. GWFEF-Net offers four contributions: (1) dual-polarization feature enrichment (DFE) for enriching the feature library and suppressing clutter interferences to facilitate feature extraction; (2) group-wise feature enhancement (GFE) for enhancing each polarization semantic feature to highlight each polarization feature region; (3) group-wise feature fusion (GFF) for fusing multi-scale polarization features to realize polarization features’ group-wise information interaction; (4) hybrid pooling channel attention (HPCA) for channel modeling to balance each polarization feature’s contribution. We conduct sufficient ablation studies to verify the effectiveness of each contribution. Extensive experiments on the Sentinel-1 dual-polarization SAR ship dataset demonstrate the superior performance of GWFEF-Net, with 94.18% in average precision (AP), compared with the other ten competitive methods. Specifically, GWFEF-Net can yield a 2.51% AP improvement compared with the second-best method.
Spatiotemporal Patterns and Driving Factors of Ecological Vulnerability on the Qinghai-Tibet Plateau Based on the Google Earth Engine
Authors: Zhao Zhengyuan , Li Ting , Zhang Yunlong , Lü Da , Wang Cong , Lü Yihe , Wu Xing
Published: October 2022

With the background of climate change and intensified human activities, environmental problems are becoming increasingly prominent on the Qinghai-Tibet Plateau (QTP). For the development of efficient environmental policies and protection measures, quick and accurate assessments of the spatiotemporal patterns in ecological vulnerability are crucial. Based on the Google Earth Engine (GEE) platform, we used Moderate Resolution Imaging Spectroradiometer (MODIS), Shuttle Radar Topography Mission (SRTM), and human footprint (HFP) datasets to analyze the spatiotemporal distributions and main driving factors of the remote sensing ecological vulnerability index (RSEVI) for the QTP. Moreover, spatial autocorrelation analysis and the standard deviational ellipse (SDE) were used to analyze the spatiotemporal characteristics. Our results showed that the RSEVI gradually increased from the southeast to the northwest of the QTP. From 2000 to 2018, the potential vulnerability area increased by 6.59 × 104 km2, while the extreme vulnerability area decreased by 1.84 × 104 km2. Moran’s I value of the RSEVI was greater than 0 and increased, indicating that the aggregation degree was increasing. The gravity center was located in Nagqu, Tibet, and shifted to the northwest from 2000 to 2015 and to the southeast from 2015 to 2018. The SDE rotated in a counterclockwise direction. The three most important driving factors of ecological vulnerability were wetness, land surface temperature (LST), and the normalized difference vegetation index (NDVI), indicating that climate and vegetation were the dominant factors. Moreover, this study developed a promising method for the ecological vulnerability assessment of large-scale and long time series datasets, and it provides theoretical support for the ecological conservation and sustainable development of the QTP under global change.
A Novel Waveform Decomposition and Spectral Extraction Method for 101-Channel Hyperspectral LiDAR
Authors: Xia Yuhao , Xu Shilong , Fang Jiajie , Hou Ahui , Chen Youlong , Zhang Xinyuan , Hu Yihua
Published: October 2022

The 101-channel full-waveform hyperspectral LiDAR (FWHSL) is able to simultaneously obtain geometric and spectral information of the target, and it is widely applied in 3D point cloud terrain generation and classification, vegetation detection, automatic driving, and other fields. Currently, most waveform data processing methods are mainly aimed at single or several wavelengths. Hidden components are revealed mainly through optimization algorithms and comparisons of neighbor distance in different wavelengths. The same target may be misjudged as different ones when dealing with 101 channels. However, using the gain decomposition method with dozens of wavelengths will change the spectral intensity and affect the classification. In this paper, for hundred-channel FWHSL data, we propose a method that can detect and re-decompose the channels with outliers by checking neighbor distances and selecting specific wavelengths to compose a characteristic spectrum by performing PCA and clustering on the decomposition results for object identification. The experimental results show that compared with the conventional single channel waveform decomposition method, the average accuracy is increased by 20.1%, the average relative error of adjacent target distance is reduced from 0.1253 to 0.0037, and the degree of distance dispersion is reduced by 95.36%. The extracted spectrum can effectively characterize and distinguish the target and contains commonly used wavelengths that make up the vegetation index (e.g., 670 nm, 784 nm, etc.).
Research on Blunder Detection Methods of Pseudorange Observation in GNSS Observation Domain
Authors: Ma Xiaping , Wang Qing , Yu Kegen , He Xiaoxing , Zhao Lidu
Published: October 2022

Global Navigation Satellite System (GNSS) signal quality, type of receiver equipment, and external environment can cause GNSS observations to be anomalous, and these anomalies are sometimes reflected in GNSS pseudorange observations rather than phase observations. To better detect blunders in pseudorange observations, this paper proposes three pseudorange blunder detection methods under the same frequency and different code types (case1), and the same code type and different frequencies (case2), of pseudorange observations, which are the Code Observation Difference Method (CODM), the Inter-satellite Code Observation Difference Method (ICODM), and the Inter-epoch and Inter-satellite Code Observation Difference Method (IICODM). The corresponding thresholds for the constructed test statistics of the three detection methods were derived based on the Bessel formula. Performance analysis of the three detection methods was performed under case1 based on C2 and P2 code observation data of Global Positioning System (GPS) at 137 Multi-GNSS Experiment (MGEX) stations, and case2 based on BDS B1I and B3I frequency observation data of BeiDou Navigation Satellite System (BDS) at 232 MGEX stations, on 29 July 2022. The results show that the statistical information value of the three methods in case1 was significantly smaller than that in case2. In the first case, the maximum values of test statistics, RMSE and threshold mean values were 0.526, 0.752 and 2.243 m, respectively, while the corresponding values in case2 were 7.066, 4.490 and 13.480 m respectively. The reason for this is that the data quality of global GPS is higher than that of BDS and the differential observation equation eliminates or weakens more errors with the same frequency and different types of code pseudorange observations. Under the same conditions, compared with ICODM and IICODM, CODM has high computational efficiency and a simple mathematical model. It is recommended to use CODM first for pseudorange blunder detection in the GNSS observation domain. According to the RMSE of 3 times as the limit, it is recommended that the threshold be set to 5 m under case1 for GPS and 15 m under case2 for BDS, which is half the existing reference value. Finally, the blunder detection methods proposed can improve positioning performance through actual data verification.
A Demonstration of Three-Satellite Stereo Winds
Authors: Carr James L. , Daniels Jaime , Wu Dong L. , Bresky Wayne , Tan Bin
Published: October 2022

Stereo tracking of clouds from multiple satellites permits the simultaneous retrieval of an atmospheric motion vector (“wind”) and its height in the atmosphere. The direct measurement of height is a major advantage of stereo methods over observations made from a single satellite where the height must be inferred from infrared brightness temperatures. A pair of operational geostationary satellites over the Americas provides stereo coverage where their two fields of view intersect. Stereo coverage can be extended to nearly a full hemisphere with a third satellite. We demonstrate this configuration with the operational GOES-R constellation of GOES-16 (east) and GOES-17 (west) augmented by GOES-18 in its central test slot and use the 500-m resolution Advanced Baseline Imager Band 2. We examine the consistency of the pairwise products created from GOES-18 and -16 versus GOES-18 and -17 and create a fused triple-GOES product that spans nearly the full hemisphere seen from GOES-18. We also examine the retrieval of ground points observed under clear skies and compare their retrievals to zero speed and known terrain heights. The results are compatible with a wind accuracy about 0.1 m/s with height assignment uncertainty of 175 m.
Cognitive Sparse Imaging Method for MIMO Radar under Wideband Interference
Authors: Feng Weike , Wan Pengcheng , Hu Xiaowei , Guo Yiduo , Zhu Hangui
Published: October 2022

Target three-dimensional (3D) high-resolution imaging via multiple-input multiple-output (MIMO) radar may suffer from a heavy sampling burden and complicated radio frequency interferences. Considering a collocated two-dimensional wideband MIMO radar under dynamic wideband interference (WBI), this paper proposes a cognitive method to achieve a 3D high-resolution target image with a reduced sampling cost. Firstly, based on the known knowledge of the target and WBI, provided by previous measurements, optimal sparse sampling in the 3D signal domain is conducted to reduce the number of sub-pulses and transceiving antennas by solving an optimization problem. Then, the detection and removal of the interfered signal components are conducted to provide the WBI information for following measurements and the interference-free signal cube for the target imaging process. Finally, by using the tensor-based smoothed L0 algorithm, the 3D high-resolution image of the target is obtained, providing the target information for the next measurement. Based on these three steps, a cognitive sparse imaging loop is formed for MIMO radar under WBI situations. The simulation and experiment results demonstrate the effectiveness and advantage of the proposed methods.
TISD: A Three Bands Thermal Infrared Dataset for All Day Ship Detection in Spaceborne Imagery
Authors: Li Liyuan , Yu Jianing , Chen Fansheng
Published: October 2022

The development of infrared remote sensing technology improves the ability of night target observation, and thermal imaging systems (TIS) play a key role in the military field. Ship detection using thermal infrared (TI) remote sensing images (RSIs) has aroused great interest for fishery supervision, port management, and maritime safety. However, due to the high secrecy level of infrared data, thermal infrared ship datasets are lacking. In this paper, a new three-bands thermal infrared ship dataset (TISD) is proposed to evaluate all-day ship target detection algorithms. All images are from SDGSAT-1 satellite TIS three bands RSIs of the real world. Based on the TISD, we use the state-of-the-art algorithm as a baseline to do the following. (1) Common ship detection methods and existing ship datasets from synthetic aperture radar, visible, and infrared images are elementarily summarized. (2) The proposed standard deviation of single band, correlation coefficient of combined bands, and optimum index factor features of three-bands datasets are analyzed, respectively. Combined with the above theoretical analysis, the influence of the bands’ information input on the detection accuracy of a neural network model is explored. (3) We construct a lightweight network based on Yolov5 to reduce the number of floating-point operations, which is beneficial to reduce the inference time. (4) By utilizing up-sampling and registration pre-processing methods, TI images are fused with glimmer RSIs to verify the detection accuracy at night. In practice, the proposed datasets are expected to promote the research and application of all-day ship detection.
Adversarial Patch Attack on Multi-Scale Object Detection for UAV Remote Sensing Images
Authors: Zhang Yichuang , Zhang Yu , Qi Jiahao , Bin Kangcheng , Wen Hao , Tong Xunqian , Zhong Ping
Published: October 2022

Although deep learning has received extensive attention and achieved excellent performance in various scenarios, it suffers from adversarial examples to some extent. In particular, physical attack poses a greater threat than digital attack. However, existing research has paid less attention to the physical attack of object detection in UAV remote sensing images (RSIs). In this work, we carefully analyze the universal adversarial patch attack for multi-scale objects in the field of remote sensing. There are two challenges faced by an adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for an adversarial patch to show an adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide height range of the photography platform causes the size of objects to vary a great deal, which presents challenges for the generation of universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method of object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of the adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Furthermore, we raise the scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures that the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.
Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections
Authors: Cohen Sagy , Peter Brad G. , Haag Arjen , Munasinghe Dinuke , Moragoda Nishani , Narayanan Anuska , May Sera
Published: October 2022

The Floodwater Depth Estimation Tool (FwDET) calculates water depth from a remote sensing-based inundation extent layer and a Digital Elevation Model (DEM). FwDET’s low data requirement and high computational efficiency allow rapid and large-scale calculation of floodwater depth. Local biases in FwDET predictions, often manifested as sharp transitions or stripes in the water depth raster, can be attributed to spatial or resolution mismatches between the inundation map and the DEM. To alleviate these artifacts, we are introducing a boundary cell smoothing and slope filtering procedure in version 2.1 of FwDET (FwDET2.1). We present an optimization analysis that quantifies the effect of differing parameterization on the resulting water depth map. We then present an extensive intercomparison analysis in which 16 DEMs are used as input for FwDET Google Earth Engine (FwDET-GEE) implementation. We compare FwDET2.1 to FwDET2.0 using a simulated flood and a large remote sensing derived flood map (Irrawaddy River in Myanmar). The results show that FwDET2.1 results are sensitive to the smoothing and filtering values for medium and coarse resolution DEMs, but much less sensitive when using a finer resolution DEM (e.g., 10 m NED). A combination of ten smoothing iterations and a slope threshold of 0.5% was found to be optimal for most DEMs. The accuracy of FwDET2.1 improved when using finer resolution DEMs except for the MERIT DEM (90 m), which was found to be superior to all the 30 m global DEMs used.
A Novel 0.1 mm 3D Laser Imaging Technology for Pavement Safety Measurement
Authors: Yang Guangwei , Wang Kelvin C. P. , Li Joshua Q. , Wang Guolong
Published: October 2022

Traditionally, pavement safety performance in terms of texture, friction, and hydroplaning speed are measured separately via different devices with various limitations. This study explores the feasibility of using a novel 0.1 mm 3D Safety Sensor for pavement safety evaluation in a non-contact and continuous manner with a single hardware sensor. The 0.1 mm 3D images were collected for pavement safety measurement from 12 asphalt concrete (AC) and Portland cement concrete (PCC) field sites with various texture characteristics. The results indicate that the Safety Sensor was able to measure pavement texture data as traditional devices do with better repeatability. Moreover, pavement friction numbers can be estimated using 0.1 mm 3D data via the proposed 3D texture parameters with good accuracy using an artificial neural network, especially for asphalt pavement. Lastly, a case study of pavement hydroplaning speed prediction was performed using the Safety Sensor. The results demonstrate the potential of using ultra high-resolution 3D imaging to measure pavement safety, including texture, friction, and hydroplaning, in a non-contact, continuous, and accurate manner.