bluuebunny/crossref_metadata_2025 huggingface dataset:
Pro-Inflammatory Diet Is Correlated with High Veillonella rogosae, Gut Inflammation and Clinical Relapse of Inflammatory Bowel Disease
Authors: Rocha Ilanna Marques Gomes da , Torrinhas Raquel , Fonseca Danielle , Lyra Clelia de Oliveira , de Sousa Alves Neri Julianna Lys , Balmant Bianca Depieri , Callado Letícia , Charlton Karen , Queiroz Natalia , Waitzberg Dan L.
Published: September 2023

Inflammatory bowel diseases (IBD) are chronic conditions arising from an intricate interplay of genetics and environmental factors, and are associated with gut dysbiosis, inflammation, and gut permeability. In this study, we investigated whether the inflammatory potential of the diet is associated with the gut microbiota profile, inflammation, and permeability in forty patients with IBD in clinical remission. The dietary inflammatory index (DII) score was used to assess the inflammatory potential of the diet. The fecal microbiota profile was analyzed using 16SrRNA (V3–V4) gene sequencing, while fecal zonulin and calprotectin levels were measured with enzyme-linked immunosorbent assays. We found a positive correlation between the DII score and elevated calprotectin levels (Rho = 0.498; p = 0.001), but not with zonulin levels. Although α- and β-diversity did not significantly differ across DII quartiles, the most pro-inflammatory diet group exhibited a higher fecal abundance of Veillonella rogosae (p = 0.026). In addition, the abundance of some specific bacteria sequences showed an exponential behavior across DII quartiles and a correlation with calprotectin or zonulin levels (p ≤ 0.050). This included a positive correlation between sq702. Veillonella rogosae and fecal calprotectin levels (Rho = 0.419, p = 0.007). DII, calprotectin, and zonulin levels were identified as significant predictors of 6-month disease relapse (p ≤ 0.050). Our findings suggest a potential relationship of a pro-inflammatory diet intake with Veillonella rogosae and calprotectin levels in IBD patients in clinical remission, which may contribute to disease relapse.
Understanding the Knowledge, Attitudes, and Practices Concerning Sugar-Sweetened Beverages and Beverage Taxation among Saudi University Students
Authors: Alothmani Noor M. , Almoraie Noha M.
Published: September 2023

University students should be informed about sugar-sweetened beverages (SSBs). Given the high consumption level of SSBs in Saudi Arabia, this study aims to examine the knowledge, attitudes, and practices related to SSBs and taxes. This cross-sectional study was conducted on 380 students at King Abdulaziz University in Jeddah between September 2022 and March 2023. The data were collected using an online self-administered questionnaire. The results reveal that 93% of the students had high knowledge scores, 99% have positive attitudes, and 37% present good practices toward SSBs. Moreover, 73% of students have high knowledge scores, 83% have positive attitudes, and 32% present good practices regarding SSB taxes. Each of these variables, college education, smoking, family income, body mass index, activities, and sports, showed statistically significant differences between gender with regard to the scores for knowledge, attitudes, and practice towards SSBs and their taxes. Thus, a high percentage of university students present sufficient knowledge of and good attitudes towards SSBs and taxation, unlike the practical results. Policymakers should develop strategic approaches and education campaigns to improve practices towards SSBs.
Enhancing Solubility and Dissolution Rate of Antifungal Drug Ketoconazole through Crystal Engineering
Authors: Yu Hongmei , Zhang Li , Liu Meiju , Yang Dezhi , He Guorong , Zhang Baoxi , Gong Ningbo , Lu Yang , Du Guanhua
Published: September 2023

To improve the solubility and dissolution rate of the BCS class II drug ketoconazole, five novel solid forms in 1:1 stoichiometry were obtained upon liquid-assisted grinding, slurry, and slow evaporation methods in the presence of coformers, namely, glutaric, vanillic, 2,6-dihydroxybenzoic, protocatechuic, and 3,5-dinitrobenzoic acids. Single-crystal X-ray diffraction analysis revealed that the hydroxyl/carboxylic acid. . .N-imidazole motif acts as the dominant supramolecular interaction in the obtained solid forms. The solubility of ketoconazole in distilled water significantly increased from 1.2 to 2165.6, 321.6, 139.1, 386.3, and 191.7 μg mL−1 in the synthesized multi-component forms with glutaric, vanillic, 2,6-dihydroxybenzoic, protocatechuic, and 3,5-dinitrobenzoic acid, respectively. In particular, the cocrystal form with glutaric acid showed an 1800-fold solubility increase in water concerning ketoconazole. Our study provides an alternative approach to improve the solubility and modify the release profile of poorly water-soluble drugs such as ketoconazole.
Isolation, Identification, Anti-Inflammatory, and In Silico Analysis of New Lignans from the Resin of Ferula sinkiangensis
Authors: Wang Junchi , Zheng Qi , Shi Minghui , Wang Huaxiang , Fan Congzhao , Wang Guoping , Zhao Yaqin , Si Jianyong
Published: September 2023

Ferula sinkiangensis K. M. Shen (Apiaceae) is distributed in arid desert areas of Xinjiang, and its resin is a traditional Chinese medicine to treat gastrointestinal digestive diseases. To explore bioactive components from F. sinkiangensis, three new lignans and thirteen known components were isolated. The structural elucidation of the components was established utilizing spectroscopic analyses together with ECD calculations. Griess reaction results indicated new compounds 1 and 2 significantly decreased NO production in LPS-stimulated RAW 264.7 macrophages, and ELISA results indicated that they effectively attenuated LPS-induced inflammation by inhibiting TNF-α, IL-1β, and IL-6 expressions. The in silico approach confirmed that compound 1 docked into the receptors with strong binding energies of −5.84~−10.79 kcal/mol. In addition, compound 6 inhibited the proliferation of AGS gastric cancer cells with IC50 values of 15.2 μM by suppressing the cell migration and invasion. This study disclosed that F. sinkiangensis might be a promising potential resource for bioactive components.
Inhibition of SARS-CoV-2 Infection in Vero Cells by Bovine Lactoferrin under Different Iron-Saturation States
Authors: Alves Nathalia S. , Azevedo Adriana S. , Dias Brenda M. , Horbach Ingrid S. , Setatino Bruno P. , Denani Caio B. , Schwarcz Waleska D. , Lima Sheila Maria B. , Missailidis Sotiris , Ano Bom Ana Paula D. , Silva Andréa M. V. , Barreto Vieira Débora F. , Silva Marcos Alexandre N. , Barros Caroline A. , Carvalho Carlos Alberto M. , Gonçalves Rafael B.
Published: September 2023

Despite the rapid mass vaccination against COVID-19, the emergence of new SARS-CoV-2 variants of concern, such as omicron, is still a great distress, and new therapeutic options are needed. Bovine lactoferrin (bLf), a multifunctional iron-binding glycoprotein available in unsaturated (apo-bLf) and saturated (holo-bLf) forms, has been shown to exert broad-spectrum antiviral activity against many viruses. In this study, we evaluated the efficacy of both forms of bLf at 1 mg/mL against infection of Vero cells by SARS-CoV-2. As assessed with antiviral assays, an equivalent significant reduction in virus infection by about 70% was observed when either form of bLf was present throughout the infection procedure with the SARS-CoV-2 ancestral or omicron strain. This inhibitory effect seemed to be concentrated during the early steps of virus infection, since a significant reduction in its efficiency by about 60% was observed when apo- or holo-bLf were incubated with the cells before or during virus addition, with no significant difference between the antiviral effects of the distinct iron-saturation states of the protein. However, an ultrastructural analysis of bLf treatment during the early steps of virus infection revealed that holo-bLf was somewhat more effective than apo-bLf in inhibiting virus entry. Together, these data suggest that bLf mainly acts in the early events of SARS-CoV-2 infection and is effective against the ancestral virus as well as its omicron variant. Considering that there are no effective treatments to COVID-19 with tolerable toxicity yet, bLf shows up as a promising candidate.
Special Issue “Hybrid Drugs: Design and Applications”
Authors: Singh Parvesh , Kumar Vipan
Published: September 2023

The widely held belief in the potential superiority of agents capable of modulating multiple biological targets has led to the adoption of molecular hybridization as an effective technique in the realm of drug discovery and development [...]
Folate-Targeted Nanoliposomal Chemophototherapy
Authors: Chitgupi Upendra , Qin Yiru , Ghosh Sanjana , Quinn Breandan , Carter Kevin , He Xuedan , Sunar Ulas , Lovell Jonathan F.
Published: September 2023

Light-responsive liposomes have been developed for the on-demand release of drugs. However, efficient delivery of chemotherapeutic drugs to tumor for cancer theranostics remains a challenge. Herein, folic acid (FA), an established ligand for targeted drug delivery, was used to decorate light-sensitive porphyrin-phospholipid (PoP) liposomes, which were assessed for FA-targeted chemophototherapy (CPT). PoP liposomes and FA-conjugated PoP liposomes were loaded with Doxorubicin (Dox), and physical properties were characterized. In vitro, FA-PoP liposomes that were incubated with FA receptor-overexpressing human KB cancer cells showed increased uptake compared to non-targeted PoP liposomes. Dox and PoP contributed towards chemophototherapy (CPT) in vitro, and PoP and FA-PoP liposomes induced cell killing. In vivo, mice bearing subcutaneous KB tumors treated with PoP or FA-PoP liposomes loaded with Dox, followed by 665 nm laser treatment, had delayed tumor growth and improved survival. Dox delivery to tumors increased following laser irradiation for both PoP and FA-PoP liposomes. Thus, while Dox-FA-PoP liposomes were effective following systemic administration and local light irradiation in this tumor model, the FA targeting moiety did not appear essential for anti-tumor responses.
Compatibility of Commonly Used Active Pharmaceutical Ingredients in a Ready-to-Use Oral Suspending Vehicle
Authors: Mansourian Mercedeh , Dijkers Eli , Silva Carolina C. V. , Polonini Hudson C.
Published: September 2023

The present study aimed to evaluate the stability of active pharmaceutical ingredients (APIs) from different pharmacological classes in a compounded oral suspending vehicle. Oral suspensions of amoxicillin trihydrate (50 mg/mL), clozapine (25 mg/mL), indomethacin (5.0 mg/mL), levodopa/carbidopa (10.0/2.5 mg/mL), levothyroxine sodium (T4, 25 µg/mL), lomustine (4.0 and 10.0 mg/mL), methyldopa (25 mg/mL) and procarbazine (10.0 mg/mL) were formulated in SyrSpend® SF PH4 and the stability was monitored for up to 90 days, except for amoxicillin trihydrate, which was evaluated for 30 days only. The APIs’ stability was determined by measuring percent recovery using stability-indicating high-performance liquid chromatography (HPLC or UHPLC) or titration (amoxicillin trihydrate only). The stability of amoxicillin trihydrate, clozapine, indomethacin and levodopa/carbidopa were studied at both refrigerated (2–8 °C) and room temperature (20–25 °C). Lomustine, procarbazine, and methyldopa were studied at refrigerated temperature only. Our data demonstrated promising stability for the compounded suspensions containing various APIs, investigated in SyrSpend® SF PH4, as all APIs exhibited stability throughout the study duration and met content uniformity criteria. These findings lead to the conclusion that the tested compounded oral suspensions present a viable approach for creating personalized, age-appropriate formulations. The capacity to ensure dose consistency and stability using APIs from diverse pharmacological classes renders them suitable choices for both pediatric and geriatric patients.
Advancing Plastic Recycling: Challenges and Opportunities in the Integration of 3D Printing and Distributed Recycling for a Circular Economy
Authors: Kassab Ali , Al Nabhani Dawood , Mohanty Pravansu , Pannier Christopher , Ayoub Georges Y.
Published: September 2023

The concept of the circular economy has emerged as a promising solution to address the mounting concerns surrounding plastic waste and the urgent need for sustainable resource management. While conventional centralized recycling remains a common practice for plastic waste, centralized facilities may prove inadequate in handling the ever-increasing volumes of plastic waste generated globally. Consequently, exploring alternative recycling methods, such as distributed recycling by additive manufacturing, becomes paramount. This innovative approach encompasses actively involving communities in recycling practices and promotes a circular economy. This comprehensive review paper aims to explore the critical aspects necessary to realize the potential of distributed recycling by additive manufacturing. In this paper, our focus lies on proposing schemes that leverage existing literature to harness the potential of distributed recycling by additive manufacturing as an effective approach to plastic waste management. We explore the intricacies of the recycling process, optimize 3D printing parameters, address potential challenges, and evaluate the mechanical properties of recycled materials. Our investigation draws heavily from the literature of the last five years, as we conduct a thorough critical assessment of DRAM implementation and its influence on the properties of 3D printing structures. Through comprehensive analysis, we reveal the potential of recycled materials in delivering functional components, with insights into their performance, strengths, and weaknesses. This review serves as a comprehensive guide for those interested in embracing distributed recycling by additive manufacturing as a transformative approach to plastic recycling. By fostering community engagement, optimizing 3D printing processes, and incorporating suitable additives, it is possible to collectively contribute to a more sustainable future while combatting the plastic waste crisis. As progress is made, it becomes essential to further delve into the complexities of material behavior, recycling techniques, and the long-term durability of recycled 3D printed components. By addressing these challenges head-on, it is feasible to refine and advance distributed recycling by additive manufacturing as a viable pathway to minimize plastic waste, fostering a circular economy and cultivating a cleaner planet for generations to come.
Preparation and Characterization of TiO2-Coated Hollow Glass Beads for Functionalization of Deproteinized Natural Rubber Latex via UVA-Activated Photocatalytic Degradation
Authors: Nijpanich Supinya , Nimpaiboon Adun , Rojruthai Porntip , Park Jae-Hyeok , Hagio Takeshi , Ichino Ryoichi , Sakdapipanich Jitladda
Published: September 2023

The photochemical degradation of natural rubber (NR) is a prevalent method used to modify its inherent properties. Natural rubber, predominantly derived from the Hevea Brasiliensis tree, exhibits an exceptionally high molecular weight (MW), often reaching a million daltons (Da). This high MW restricts its solubility in various solvents and its reactivity with polar compounds, thereby constraining its versatile applications. In our previous work, we employed TiO2 in its powdered form as a photocatalyst for the functionalization of NR latex. However, the post-process separation and reuse of this powder present substantial challenges. In this present study, we aimed to functionalize deproteinized NR (DPNR) latex. We systematically reduced its MW via photochemical degradation under UVA irradiation facilitated by H2O2. To enhance the efficiency of the degradation process, we introduced TiO2-coated hollow glass beads (TiO2-HGBs) as photocatalysts. This approach offers the advantage of easy collection and repeated reuse. The modified DPNR showed a reduction in its number-average MW from 9.48 × 105 to 0.28 × 105 Da and incorporated functional groups, including hydroxyl, carbonyl, and epoxide. Remarkably, the TiO2-HGBs maintained their performance over seven cycles of reuse. Due to their superior efficacy, TiO2-HGBs stand out as promising photocatalysts for the advanced functionalization of NR across various practical applications.
Freeze–Thaw Damage Mechanism Analysis of SBS Asphalt Mixture Containing Basalt Fiber and Lignocellulosic Fiber Based on Microscopic Void Characteristics
Authors: Wang Wensheng , Yang Liansheng , Cui Honghai , Wu Fei , Cheng Yongchun , Liang Chunyu
Published: September 2023

Freeze–thaw effects pose the significant challenge to asphalt pavement durability, leading to various types of distress and deterioration. This study investigates the freeze–thaw damage mechanism of Styrene–Butadiene–Styrene (SBS) asphalt mixtures containing reinforcement fibers, specifically basalt fiber as well as lignocellulosic fiber, through a microscopic void characteristics analysis. This investigation aims to understand how the presence of basalt fiber as well as lignocellulosic fiber influences void characteristics for SBS asphalt mixtures during freeze–thaw cycles. A comprehensive experimental program was conducted for the void and mechanical characteristics, which involved the preparation of SBS asphalt mixtures containing basalt fiber as well as lignocellulosic fiber. The mechanical performances of the two types of asphalt mixtures decrease with more freeze–thaw cycles. The decline is faster initially and gradually slows down. Basalt-fiber-modified SMA-13 has higher air void content and mechanical properties compared to lignocellulosic-fiber-modified SMA-13, indicating that adding basalt fibers improves the mechanical performances of SMA-13 asphalt mixture. Both types of asphalt mixtures experience increasing damage with more freeze–thaw cycles, indicating irreversible damage. The stability damage levels are similar, but basalt-fiber-modified SMA-13 has lower splitting strength damage and stiffness modulus damage compared to lignocellulosic-fiber-modified SMA-13. This suggests that adding basalt fibers enhances the resistance to freeze–thaw damage. Surface wear of asphalt mixtures under repeated freeze–thaw cycles is a complex and dynamic process. Fractal theory can uncover the mechanism of surface wear, while describing surface wear behavior and void deformation characteristics using fractal dimension, angularity, roundness, and aspect ratio is a logical and effective approach. The findings provide insights into freeze–thaw damage mechanisms at the microscopic level, highlighting the effects of reinforcement fibers. They provide valuable insights that can be used to optimize the design and maintenance of asphalt pavements.
Gentamicin Release Study in Uniaxial and Coaxial Polyhydroxybutyrate–Polyethylene Glycol–Gentamicin Microfibers Treated with Atmospheric Plasma
Authors: Transito-Medina Josselyne , Vázquez-Vélez Edna , Castillo Marilú Chávez , Martínez Horacio , Campillo Bernardo
Published: September 2023

The skin is the largest organ and one of the most important in the human body, and is constantly exposed to pathogenic microorganisms that cause infections; then, pharmacological administration is required. One of the basic medical methods for treating chronic wounds is to use topical dressings with characteristics that promote wound healing. Fiber-based dressings mimic the local dermal extracellular matrix (ECM), maintaining an ideal wound-healing climate. This work proposes electrospun PHB/PEG polymeric microfibers as dressings for administering the antibiotic gentamicin directed at skin infections. PHB-PEG/gentamicin fibers were characterized before and after plasma treatment by Raman spectroscopy, FTIR, and XRD. SEM was used to evaluate fiber morphology and yarn size. The plasma treatment improved the hydrophilicity of the PHB/PEG/gentamicin fibers. The release of gentamicin in the plasma-treated fibers was more sustained over time than in the untreated ones.
Optimization of Bromocriptine-Mesylate-Loaded Polycaprolactone Nanoparticles Coated with Chitosan for Nose-to-Brain Delivery: In Vitro and In Vivo Studies
Authors: Badran Mohamed M. , Alanazi Abdulrahman E. , Ibrahim Mohamed Abbas , Alshora Doaa Hasan , Taha Ehab , H. Alomrani Abdullah
Published: September 2023

Bromocriptine mesylate (BM), primarily ergocryptine, is a dopamine agonist derived from ergot alkaloids. This study aimed to formulate chitosan (CS)-coated poly ε-caprolactone nanoparticles (PCL NPs) loaded with BM for direct targeting to the brain via the nasal route. PCL NPs were optimized using response surface methodology and a Box–Behnken factorial design. Independent formulation parameters for nanoparticle attributes, including PCL payload (A), D-α-tocopherol polyethylene glycol 1000 succinate (TPGS) concentration (B), and sonication time (C), were investigated. The dependent variables were nanoparticle size (Y1), zeta potential (Y2), entrapment efficiency (EE; Y3), and drug release rate (Y4). The optimal formulation for BM-PCL NPs was determined to be 50 mg PCL load, 0.0865% TPGS concentration, and 8 min sonication time, resulting in nanoparticles with a size of 296 ± 2.9 nm having a zeta potential of −16.2 ± 3.8 mV, an EE of 90.7 ± 1.9%, and a zero-order release rate of 2.6 ± 1.3%/min. The optimized BM-PCL NPs were then coated with CS at varying concentrations (0.25, 0.5, and 1%) to enhance their effect. The CS-PCL NPs exhibited different particle sizes and zeta potentials depending on the CS concentration used. The highest EE (88%) and drug load (DL; 5.5%) were observed for the optimized BM-CS-PCL NPs coated with 0.25% CS. The BM-CS-PCL NPs displayed a biphasic release pattern, with an initial rapid drug release lasting for 2 h, followed by a sustained release for up to 48 h. The 0.25% CS-coated BM-CS-PCL NPs showed a high level of permeation across the goat nasal mucosa, with reasonable mucoadhesive strength. These findings suggested that the optimized 0.25% CS-coated BM-CS-PCL NPs hold promise for successful nasal delivery, thereby improving the therapeutic efficacy of BM.
Auditing Congregational Health: Exploring Members’ Well-Being in the Church and Commitment to the Congregation
Authors: Zwijze-Koning Karen , De Roest Hendrik Pieter
Published: September 2023

The field of congregational health research is missing a relational approach to the member–congregation connection. We developed and tested a questionnaire that evaluates the perception of church health factors in statements that stay close to the feelings and attitudes of the members. We applied the instrument to map feelings of well-being and commitment with 513 members of a large, Dutch, Protestant congregation. This study thus explores relational concepts, such as members feeling at home in the congregation, their turnover intention, the atmosphere within the community, and whether members feel seen and heard within the community. Seven factors emerge from the analysis, and all of them show significant empirical correlation with members’ overall satisfaction and commitment levels. The instrument also makes it possible to discern between the evaluations of sleeping members, involved members, and insiders (frequent attenders), which is a discernment that existing instruments lack.
An Ecumenical Spirituality
Authors: Binns John
Published: September 2023

The modern ecumenical movement is a part of a wider ecumenism which expresses the universal character of the Christian faith. It is an approach to faith which is aware of the world-wide context of church life and the variety of the cultures and communities where it is practiced. The Orthodox Church of Ethiopia shows the importance of ecumenism because here we find a style of worship and theology which has taken a very different character from other parts of the church, especially in its relations with other faiths. Ecumenical faith recognises and welcomes difference and always seeks fresh ways of witness and proclamation. In a changing society, this ecumenical character of faith is an essential part of an effective mission and church life.
Supervised Geomorphic Mapping of Himalayan Rivers Based on Sentinel-2 Data
Authors: Mukhtar Zarka , Bizzi Simone , Comiti Francesco
Published: September 2023

The Himalayan region is a hotspot in terms of expected future hydrological and geomorphological variations induced by climate change on proglacial areas and the related implications for human societies established along the downstream rivers. Due to the remoteness of the proglacial zones in the Himalayas and the associated logistical problems in carrying out traditional field and UAV-based morphological monitoring activities, remote sensing here plays a crucial role to monitor past and current fluvial dynamics, which could be used to anticipate future changes; however, there has been, so far, limited research on morphological changes in Himalayan proglacial rivers. To address this gap, a morphological classification model was designed to classify recent changes in Himalayan proglacial rivers using the Google Earth Engine platform. The model is the first of its kind developed for the Himalayan region and uses multispectral S-2 satellite data to delineate submerged water channels, vegetated surfaces, and emerged, unvegetated sediment bars, and then to track their variations over time. The study focused on three training sites: Langtang-Khola (Nepal), Saltoro (Pakistan), and Nubra (Jammu and Kashmir) rivers, and one testing site, the Ganga-Bhagirathi River (India). A total of 900 polygons were used as training samples for the random forest classifier, which were further divided into 70% calibration and 30% validation datasets for the training sites, and a separate validation dataset was acquired from the testing site to assess the model performance. The model achieved high accuracy, with an average overall accuracy of 96% and a kappa index of 0.94, indicating the reliability of the S2 data for modeling proglacial geomorphic features in the Himalayan region. Therefore, this study provides a reliable tool to detect past and current morphological changes occurring in the Himalayan proglacial rivers, which will be of great value for both research and river management purposes.
EL-NAS: Efficient Lightweight Attention Cross-Domain Architecture Search for Hyperspectral Image Classification
Authors: Wang Jianing , Hu Jinyu , Liu Yichen , Hua Zheng , Hao Shengjia , Yao Yuqiong
Published: September 2023

Deep learning (DL) algorithms have demonstrated important breakthroughs for hyperspectral image (HSI) classification. Despite the remarkable success of DL, the burden of a manually designed DL structure with increased depth and size aroused the difficulty for the application in the mobile and embedded devices in a real application. To tackle this issue, in this paper, we proposed an efficient lightweight attention network architecture search algorithm (EL-NAS) for realizing an efficient automatic design of a lightweight DL structure as well as improving the classification performance of HSI. First, aimed at realizing an efficient search procedure, we construct EL-NAS based on a differentiable network architecture search (NAS), which can greatly accelerate the convergence of the over-parameter supernet in a gradient descent manner. Second, in order to realize lightweight search results with high accuracy, a lightweight attention module search space is designed for EL-NAS. Finally, further for alleviating the problem of higher validation accuracy and worse classification performance, the edge decision strategy is exploited to perform edge decisions through the entropy of distribution estimated over non-skip operations to avoid further performance collapse caused by numerous skip operations. To verify the effectiveness of EL-NAS, we conducted experiments on several real-world hyperspectral images. The results demonstrate that the proposed EL-NAS indicates a more efficient search procedure with smaller parameter sizes and high accuracy performance for HSI classification, even under data-independent and sensor-independent scenarios.
Retrieval of Tropospheric NO2 Vertical Column Densities from Ground-Based MAX-DOAS Measurements in Lhasa, a City on the Tibetan Plateau
Authors: Cheng Siyang , Pu Guijuan , Ma Jianzhong , Hong Hyunkee , Du Jun , Yudron Tseten , Wagner Thomas
Published: September 2023

In order to investigate the abundance of and temporal variation in nitrogen dioxide (NO2) in the troposphere and validate the corresponding satellite products during a normal year and the lockdown period of coronavirus disease 2019 (COVID-19) in Lhasa, a city on the Tibetan Plateau (TP), ground-based remote-sensing measurements captured by applying multi-axis differential optical absorption spectroscopy (MAX-DOAS) were recorded from August 2021 to March 2023 at the Lhasa site (91.14°E, 29.66°N; 3552.5 m altitude). The NO2 differential slant column densities (dSCDs) were retrieved from the spectra of scattered solar light at different elevation angles. Then, the tropospheric NO2 vertical column densities (VCDs) were calculated with the geometric approximation method. Based on the retrieved tropospheric NO2 VCDs, we found that the pattern of monthly variation in tropospheric NO2 VCDs in Lhasa presented two peaks, one in winter and one around May. According to the monthly means of tropospheric NO2 VCDs during the COVID-19 lockdown, the NO2 background level in Lhasa was estimated to be 0.53 × 1015 molecules·cm−2. For diurnal variations in tropospheric NO2 VCDs, the morning and evening peaks disappeared during the COVID-19 lockdown period. The east–west direction (i.e., along the river valley) was the main path of NO2 transport and dispersion in Lhasa, but the tropospheric NO2 VCDs were little dependent on the wind direction or wind speed during the COVID-19 lockdown. The correlation coefficient of tropospheric NO2 VCDs was R = 0.33 (R = 0.43), with the averaged relative deviation of −28% (99%) for the TROPOMI (GEMS) relative to ground-based MAX-DOAS. The monthly deviations of tropospheric NO2 VCDs between ground-based MAX-DOAS and the satellite showed a dependence on NO2 abundance, with the maxima of the monthly positive deviations during the COVID-19 lockdown period. The GEMS could not capture the strong and systematic diurnal variation in tropospheric NO2 VCDs in the “normal” year well. During the COVID-19 lockdown, the GEMS (>2 × 1015 molecules·cm−2) overestimated the hourly levels measured by ground-based MAX-DOAS (<1.6 × 1015 molecules·cm−2). As a whole, these results are beneficial to understanding the influences of anthropogenic activities on NO2 background levels in Lhasa and to learning the accuracy of satellite products over the TP, with its high altitude and complex terrain.
AOGC: Anchor-Free Oriented Object Detection Based on Gaussian Centerness
Authors: Wang Zechen , Bao Chun , Cao Jie , Hao Qun
Published: September 2023

Oriented object detection is a challenging task in scene text detection and remote sensing image analysis, and it has attracted extensive attention due to the development of deep learning in recent years. Currently, mainstream oriented object detectors are anchor-based methods. These methods increase the computational load of the network and cause a large amount of anchor box redundancy. In order to address this issue, we proposed an anchor-free oriented object detection method based on Gaussian centerness (AOGC), which is a single-stage anchor-free detection method. Our method uses contextual attention FPN (CAFPN) to obtain the contextual information of the target. Then, we designed a label assignment method for the oriented objects, which can select positive samples with higher quality and is suitable for large aspect ratio targets. Finally, we developed a Gaussian kernel-based centerness branch that can effectively determine the significance of different anchors. AOGC achieved a mAP of 74.30% on the DOTA-1.0 datasets and 89.80% on the HRSC2016 datasets, respectively. Our experimental results show that AOGC exhibits superior performance to other methods in single-stage oriented object detection and achieves similar performance to the two-stage methods.
Non-Ignorable Differences in NIRv-Based Estimations of Gross Primary Productivity Considering Land Cover Change and Discrepancies in Multisource Products
Authors: Jin Jiaxin , Hou Weiye , Wang Longhao , Wang Songhan , Wang Ying , Zhu Qiuan , Fang Xiuqin , Ren Liliang
Published: September 2023

The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm−2yr−1 across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP.
Bibliometric Analysis of Spatial Technology for World Heritage: Application, Trend and Potential Paths
Authors: Chen Guolong , Yang Ruixia , Zhao Xiangli , Li Lanyi , Luo Lei , Liu Honghao
Published: September 2023

World heritage sites are monuments and natural landscapes recognised by all humanity as being of outstanding significance and universal value. Spatial technology provides new ideas for the conservation and sustainable development of world heritage sites. Using a bibliometric analysis, this study extracted 401 relevant documents from the Web of Science database from 1990–2022. Meta information, such as abstracts, keywords of the papers were extracted and cleaned using bibliometric package and analysed the applications, partnerships and development trends of existing spatial technologies for world heritage sites. The results of the study show the “4D” characteristics of space technology in world heritage sites: (1) Development: Spatial applications in world heritage sites have gradually developed with an annual growth rate of 10.22% during the period 1990–2022. (2) Discrepancy: More than 70 per cent of countries have not been able to fully apply space technology on the ground at world heritage sites. (3) Desirability: Shared exchanges between research institutions are rare, and more cooperation and exchanges are expected, especially between transnationals. (4) Diversity: The future outlook for technology will be multidisciplinary, multi-method integrated research.
Integrated Spatial Analysis of Forest Fire Susceptibility in the Indian Western Himalayas (IWH) Using Remote Sensing and GIS-Based Fuzzy AHP Approach
Authors: Pragya  , Kumar Manish , Tiwari Akash , Majid Syed Irtiza , Bhadwal Sourav , Sahu Netrananda , Verma Naresh Kumar , Tripathi Dinesh Kumar , Avtar Ram
Published: September 2023

Forest fires have significant impacts on economies, cultures, and ecologies worldwide. Developing predictive models for forest fire probability is crucial for preventing and managing these fires. Such models contribute to reducing losses and the frequency of forest fires by informing prevention efforts effectively. The objective of this study was to assess and map the forest fire susceptibility (FFS) in the Indian Western Himalayas (IWH) region by employing a GIS-based fuzzy analytic hierarchy process (Fuzzy-AHP) technique, and to evaluate the FFS based on forest type and at district level in the states of Jammu and Kashmir, Himachal Pradesh, and Uttarakhand. Seventeen potential indicators were chosen for the vulnerability assessment of the IWH region to forest fires. These indicators encompassed physiographic factors, meteorological factors, and anthropogenic factors that significantly affect the susceptibility to fire in the region. The significant factors in FFS mapping included FCR, temperature, and distance to settlement. An FFS zone map of the IWH region was generated and classified into five categories of very low, low, medium, high, and very high FFS. The analysis of FFS based on the forest type revealed that tropical moist deciduous forests have a significant vulnerability to forest fire, with 86.85% of its total area having very high FFS. At the district level, FFS was found to be high in sixteen districts and very high in seventeen districts, constituting 25.7% and 22.6% of the area of the IWH region. Particularly, Lahul and Spiti had 63.9% of their total area designated as having very low FSS, making it the district least vulnerable to forest fires, while Udham Singh Nagar had a high vulnerability with approximately 86% of its area classified as having very high FFS. ROC-AUC analysis, which provided an appreciable accuracy of 79.9%, was used to assess the validity of the FFS map produced in the present study. Incorporating the FFS map into sustainable development planning will assist in devising a holistic strategy that harmonizes environmental conservation, community safety, and economic advancement. This approach can empower decision makers and relevant stakeholders to take more proactive and informed actions, promoting resilience and enhancing long-term well-being.
Deep Learning and Machine Learning Models for Landslide Susceptibility Mapping with Remote Sensing Data
Authors: Hussain Muhammad Afaq , Chen Zhanlong , Zheng Ying , Zhou Yulong , Daud Hamza
Published: September 2023

Karakoram Highway (KKH) is an international route connecting South Asia with Central Asia and China that holds socio-economic and strategic significance. However, KKH has extreme geological conditions that make it prone and vulnerable to natural disasters, primarily landslides, posing a threat to its routine activities. In this context, the study provides an updated inventory of landslides in the area with precisely measured slope deformation (Vslope), utilizing the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) and PS-InSAR (persistent scatterer interferometric synthetic aperture radar) technology. By processing Sentinel-1 data from June 2021 to June 2023, utilizing the InSAR technique, a total of 571 landslides were identified and classified based on government reports and field investigations. A total of 24 new prospective landslides were identified, and some existing landslides were redefined. This updated landslide inventory was then utilized to create a landslide susceptibility model, which investigated the link between landslide occurrences and the causal variables. Deep learning (DL) and machine learning (ML) models, including convolutional neural networks (CNN 2D), recurrent neural networks (RNNs), random forest (RF), and extreme gradient boosting (XGBoost), are employed. The inventory was split into 70% for training and 30% for testing the models, and fifteen landslide causative factors were used for the susceptibility mapping. To compare the accuracy of the models, the area under the curve (AUC) of the receiver operating characteristic (ROC) was used. The CNN 2D technique demonstrated superior performance in creating the landslide susceptibility map (LSM) for KKH. The enhanced LSM provides a prospective modeling approach for hazard prevention and serves as a conceptual reference for routine management of the KKH for risk assessment and mitigation.
Assessing the Spatiotemporal Evolution and Drivers of Ecological Environment Quality Using an Enhanced Remote Sensing Ecological Index in Lanzhou City, China
Authors: Duo Linghua , Wang Junqi , Zhang Fuqing , Xia Yuanping , Xiao Sheng , He Bao-Jie
Published: September 2023

Lanzhou City is located in the semi-arid region of northwest China, which experiences serious desertification. Moreover, the high intensity of land development, with the accelerated industrialization and urbanization, causes increasingly aggravated conflict between humans and the environment. Exploring the response of the ecological environment quality to the natural environment and anthropogenic activities is important to protect the sustainable development of urban economic construction and the environment. Based on the Google Earth Engine (GEE) platform, this paper constructed a modified Remote Sensing Ecological Index (MRSEI) model which could reflect the ecological environment quality by integrating the desertification index (DI) into the Remote Sensing Ecological index (RSEI) model. This paper explores the spatiotemporal variation in the environmental quality from 2000 to 2020 in Lanzhou, China, and analyzes the natural and anthropogenic factors affecting the environment quality in terms of temperature, precipitation, gross domestic product (GDP), land use, night lighting, and population. The results showed that the mean value of MRSEI ranged from 0.254 to 0.400. The area undergoing fast growth in ecological quality was in the northwestern part of Lanzhou, and the area of decrease was in the central part. Various factors have different degrees of influence on the ecosystem, with temperature, precipitation, and land use having a greater impact, and GDP and population having a limited impact. Precipitation and temperature showed a strong impact when interacting with other factors, demonstrating that precipitation and temperature were also key factors affecting MRSEI. Overall, climate change and the implementation of ecological restoration projects have led to an improvement in the quality of the ecological environment in Lanzhou. This study provides a reference for understanding the spatiotemporal changes in the ecological environment in semi-arid Lanzhou and is conducive to formulating proper protection strategies.
Deep Seasonal Network for Remote Sensing Imagery Classification of Multi-Temporal Sentinel-2 Data
Authors: Cheng Keli , Scott Grant J.
Published: September 2023

As a medium-resolution multi-temporal data source, Sentinel-2 data has the potential to match the performance of using very-high-resolution (VHR) images in deep learning applications. To fully leverage the multi-temporal nature of Sentinel-2 data, we introduce the Deep Seasonal Network (DeepSN). This composite architecture combines a pre-trained deep convolutional neural network (DCNN) for visual feature extraction with a long short-term memory (LSTM) model to capture temporal information and make classification predictions. We evaluate the effectiveness of DeepSN on a Maasai Boma classification task in the Tanzania region. The DeepSN takes a sequence of four seasonal data, each spanning three months, for Boma prediction. Through cross-season validation experiments, we compare various advanced DCNNs and select EfficientNet as the backbone for DeepSN, as it performs the best. DeepSN with an EfficientNet backbone achieves a significant 19% improvement in the F1 score compared to plain EfficientNet for the Boma classification task. This work introduces a versatile composite architecture capable of handling multi-temporal data efficiently, providing flexibility in choosing the most suitable feature extraction backbone. The performance of DeepSN demonstrates the viability of utilizing medium-resolution multi-temporal data instead of high-resolution images for diverse tasks.
Investigating the ERA5-Based PWV Products and Identifying the Monsoon Active and Break Spells with Dense GNSS Sites in Guangxi, China
Authors: Liu Wen , Zhang Lulu , Xiong Si , Huang Liangke , Xie Shaofeng , Liu Lilong
Published: September 2023

Precipitable water vapor (PWV) with high precision and high temporal resolution estimated by Global Navigation Satellite System (GNSS) is widely used in atmospheric research and weather forecasting. However, most previous works are not consensual concerning the characteristics of the PWV at different time scales and the identification of active and break spells during summ er monsoon climate in Guangxi, China. Taking radiosonde (RS) observations as reference, a strong correlation (R > 0.97) exists between GNSS PWV and RS PWV with a mean root mean square error (RMSE) of 2.68 mm. The annual, seasonal, monthly, and diurnal PWV variations of three years (2017, 2018 and 2020) over Guangxi in were comprehensively investigated using 104 GNSS stations and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5). The mean annual bias and RMSE between GNSS PWV and ERA5 PWV are −1.04 mm and 2.63 mm, respectively. The monthly bias and RMSE range are −0.77 to 3.87 mm, 1.32 to 4.45 mm, and the daily range is −1.41 to 1.07 mm and 1.11 to 5.02 mm, respectively. Additionally, the adopted average standardized rainfall anomaly criteria also identified 7/7/3 active spells and 5/3/7 break spells during the summer monsoon (June–September) from 2017 to 2020, respectively. During the three-year period, the daily amplitude ranges for active spells varied from 1.41 to 2.49 mm, 0.69 to 5.4 mm, and 0.88 to 1.41 mm, while the ranges for break spells were 2.45 to 6.76 mm, 1.66 to 8.17 mm, and 1.48 to 2.99 mm, respectively. The results show a superior performance of GNSS PWV compared to ERA5 PWV in Guangxi, and the maximum, minimum and occurrence time of PWV anomaly vary slightly with the season and the topography of stations. Despite temperature primarily exhibiting a negative correlation with rainfall, acting as a dampener, a positive correlation remains evident between PWV and rainfall. Therefore, densely distributed GNSS stations exhibit excellent capabilities in quantifying atmospheric water vapor and facilitating real-time monitoring of small and medium-scale weather phenomena.