bluuebunny/crossref_metadata_2025 huggingface dataset:
Microfluidic Organ-on-a-Chip Devices for Liver Disease Modeling In Vitro
Authors: Kanabekova Perizat , Kadyrova Adina , Kulsharova Gulsim
Published: March 2022

Mortality from liver disease conditions continues to be very high. As liver diseases manifest and progress silently, prompt measures after diagnosis are essential in the treatment of these conditions. Microfluidic organs-on-chip platforms have significant potential for the study of the pathophysiology of liver diseases in vitro. Different liver-on-a-chip microphysiological platforms have been reported to study cell-signaling pathways such as those activating stellate cells within liver diseases. Moreover, the drug efficacy for liver conditions might be evaluated on a cellular metabolic level. Here, we present a comprehensive review of microphysiological platforms used for modelling liver diseases. First, we briefly introduce the concept and importance of organs-on-a-chip in studying liver diseases in vitro, reflecting on existing reviews of healthy liver-on-a-chip platforms. Second, the techniques of cell cultures used in the microfluidic devices, including 2D, 3D, and spheroid cells, are explained. Next, the types of liver diseases (NAFLD, ALD, hepatitis infections, and drug injury) on-chip are explained for a further comprehensive overview of the design and methods of developing liver diseases in vitro. Finally, some challenges in design and existing solutions to them are reviewed
Experimental Polymorphism Survey in Intergenic Regions of the icaADBCR Locus in Staphylococcus aureus Isolates from Periprosthetic Joint Infections
Authors: Morales-Laverde Liliana , Echeverz Maite , Trobos Margarita , Solano Cristina , Lasa Iñigo
Published: March 2022

Staphylococcus aureus is a leading cause of prosthetic joint infections (PJI) characterized by bacterial biofilm formation and recalcitrance to immune-mediated clearance and antibiotics. The molecular events behind PJI infection are yet to be unraveled. In this sense, identification of polymorphisms in bacterial genomes may help to establish associations between sequence variants and the ability of S. aureus to cause PJI. Here, we report an experimental nucleotide-level survey specifically aimed at the intergenic regions (IGRs) of the icaADBCR locus, which is responsible for the synthesis of the biofilm exopolysaccharide PIA/PNAG, in a collection of strains sampled from PJI and wounds. IGRs of the icaADBCR locus were highly conserved and no PJI-specific SNPs were found. Moreover, polymorphisms in these IGRs did not significantly affect transcription of the icaADBC operon under in vitro laboratory conditions. In contrast, an SNP within the icaR coding region, resulting in a V176E change in the transcriptional repressor IcaR, led to a significant increase in icaADBC operon transcription and PIA/PNAG production and a reduction in S. aureus virulence in a Galleria mellonella infection model. In conclusion, SNPs in icaADBCR IGRs of S. aureus isolates from PJI are not associated with icaADBC expression, PIA/PNAG production and adaptation to PJI.
Simulation of Grinding Media Motion and Collisions in Wet-Operated Accelerator-Mill Using DEM Coupled with CFD
Authors: He Kang , Jia Minping , Sun Fei , Yang Quan , Wu Bo , Li Xiaobiao , Meng Chao
Published: March 2022

The relative motion and collision between grinding media in a wet-stirred accelerator mill are modelled using the discrete element method (DEM) coupled with computational fluid dynamics (CFD). The local average normalized grinding media velocity profile is employed to divide the grinding chamber into four volumes, which facilitates the analysis of the stress energy distribution. The local grinding media filling levels, velocity distributions, stress energy distributions and energy efficiencies are compared at various total grinding media filling levels and stirrer speeds. The rationality of the division of stress energy volume was verified by the stress energy distribution. The stress intensity, stress number and collision stress energy from the grinding media’s motion and particle collisions exhibited different distributions, with the stress number playing a leading role in the collision stress energy distribution. Moreover, energy efficiency is quantitatively characterized under different process conditions. This simulation-based research provides a theoretical reference for the industrial application and product development of accelerator mills used for grinding applications.
Associations of Maternal rs1801131 Genotype in MTHFR and Serum Folate and Vitamin B12 with Gestational Diabetes Mellitus in Chinese Pregnant Women
Authors: Li Shuying , Tian Xiubiao , Wang Yiyun , Zhang Xumei , Zhang Liwen , Li Chen , Li Jing , Wang Chunhua , Liu Huihuan , Liu Juan , Liu Hongjuan , Yang Xueli , Li Weiqin , Leng Junhong , Yang Xilin , Tang Naijun , Zhang Qiang
Published: March 2022

Circumstantial evidence links one-carbon metabolism (OCM) related nutrients, such as folate and vitamin B12, with gestational diabetes mellitus (GDM). However, few studies have evaluated the combined effects of these nutrients with OCM related gene polymorphisms on GDM. This study investigated whether OCM related genetic variants modified the associations of folate and B12 with GDM. Logistic regression was used to estimate odds ratios (ORs) for OCM related nutrients and single nucleotide polymorphisms (SNPs) in genes encoding main OCM related enzymes (MTHFR, MTR, and MTRR) on GDM. Higher folate concentrations were associated with increased GDM risk (OR: 1.59; 95% CI: 1.22, 2.13). However, higher B12 concentrations were associated with reduced GDM risk (OR: 0.76; 95% CI: 0.65, 0.92). Pregnancies with MTHFR rs1801131 G alleles had a significantly lower risk of GDM than pregnancies with T alleles (OR: 0.65; 95% CI: 0.47, 0.91) under the dominant model. The genotype-stratified analysis revealed the association between folate and GDM (OR: 1.66, 95% CI: 1.20, 2.30) or B12 and GDM (OR: 0.80, 95% CI: 0.65, 0.98) was more evident in pregnancies with TT genotype. Higher folate and lower B12 are associated with GDM. Pregnancies with MTHFR rs1801131 TT genotype are more susceptible to OCM nutrient-related GDM.
Comparison of Plate Waste between Vegetarian and Meat-Containing Meals in a Hospital Setting: Environmental and Nutritional Considerations
Authors: Berardy Andrew , Egan Brianna , Birchfield Natasha , Sabaté Joan , Lynch Heidi
Published: March 2022

Vegetarian diets can satisfy nutritional requirements and have lower environmental impacts than those containing meat. However, fruits and vegetables are wasted at higher rates than meat. Reducing both food waste (FW) and the environmental impacts associated with food production is an important sustainability goal. Therefore, the aim of this study was to examine potential tradeoffs between vegetarian meals’ lower impacts but potentially higher FW compared to meat-containing meals. To examine this, seven consecutive days of plate FW data from Loma Linda University Medical Center (LLUMC) patients were collected and recorded from 471 meals. Mean total FW and associated greenhouse gas emissions (GHGE) were higher among meat-containing meals (293 g/plate, 604 g CO2-eq/plate) than vegetarian meals (259 g/plate, 357 g CO2-eq/plate) by 34 g (p = 0.05) and 240 g CO2-eq (p < 0.001), respectively. Statistically significant differences were observed in both FW and associated GHGE across major food categories, except fruit, when comparing vegetarian and meat-containing meals. Overall, vegetarian meals were preferable to meat-containing meals served at LLUMC both in terms of minimizing FW and lowering environmental impacts. Other institutions serving vegetarian meal options could expect similar advantages, especially in reduced GHGE due to the high CO2 embodied in meat.
Oral Cavity and Candida albicans: Colonisation to the Development of Infection
Authors: Patel Mrudula
Published: March 2022

Candida colonisation of the oral cavity increases in immunocompromised individuals which leads to the development of oral candidiasis. In addition, host factors such as xerostomia, smoking, oral prostheses, dental caries, diabetes and cancer treatment accelerate the disease process. Candida albicans is the primary causative agent of this infection, owing to its ability to form biofilm and hyphae and to produce hydrolytic enzymes and candialysin. Although mucosal immunity is activated, from the time hyphae-associated toxin is formed by the colonising C. albicans cells, an increased number and virulence of this pathogenic organism collectively leads to infection. Prevention of the development of infection can be achieved by addressing the host physiological factors and habits. For maintenance of oral health, conventional oral hygiene products containing antimicrobial compounds, essential oils and phytochemicals can be considered, these products can maintain the low number of Candida in the oral cavity and reduce their virulence. Vulnerable patients should be educated in order to increase compliance.
Impact of HCV Infection on Hepatocyte Polarity and Plasticity
Authors: Agnetti Jean , Desterke Christophe , Gassama-Diagne Ama
Published: March 2022

The hepatitis C virus (HCV) is an oncogenic virus that alters the cell polarization machinery in order to enter the hepatocyte and replicate. While these alterations are relatively well defined, their consequences in the evolution of the disease remain poorly documented. Since 2012, HCV infection can be effectively cured with the advent of direct acting antivirals (DAA). Nevertheless, patients cured of their HCV infection still have a high risk of developing hepatocellular carcinoma (HCC). Importantly, it has been shown that some of the deregulations induced by HCV are maintained despite a sustained virologic response (SVR), including the down-regulation of some hepatocyte functions such as bile acid metabolism, exemplifying cell dedifferentiation, and the up-regulation of the epithelial–mesenchymal transition (EMT). EMT is a process by which epithelial cells lose their differentiation and their specific polarity to acquire mesenchymal cell properties, including migration and extracellular matrix remodeling capabilities. Of note, epithelial cell polarity acts as a gatekeeper against EMT. Thus, it remains important to elucidate the mechanisms by which HCV alters polarity and promotes EMT that could participate in viral-induced hepatic carcinogenesis. In this review, we define the main steps involved in the polarization process of epithelial cells and recall the essential cellular actors involved. We also highlight the particularities of hepatocyte polarity, responsible for their unique morphology. We then focus on the alterations by HCV of epithelial cell polarity and the consequences of the transformation of hepatocytes involved in the carcinogenesis process.
The Poly-Histidine Tag H6 Mediates Structural and Functional Properties of Disintegrating, Protein-Releasing Inclusion Bodies
Authors: Sánchez Julieta María , Carratalá José Vicente , Serna Naroa , Unzueta Ugutz , Nolan Verónica , Sánchez-Chardi Alejandro , Voltà-Durán Eric , López-Laguna Hèctor , Ferrer-Miralles Neus , Villaverde Antonio , Vazquez Esther
Published: March 2022

The coordination between histidine-rich peptides and divalent cations supports the formation of nano- and micro-scale protein biomaterials, including toxic and non-toxic functional amyloids, which can be adapted as drug delivery systems. Among them, inclusion bodies (IBs) formed in recombinant bacteria have shown promise as protein depots for time-sustained protein release. We have demonstrated here that the hexahistidine (H6) tag, fused to recombinant proteins, impacts both on the formation of bacterial IBs and on the conformation of the IB-forming protein, which shows a higher content of cross-beta intermolecular interactions in H6-tagged versions. Additionally, the addition of EDTA during the spontaneous disintegration of isolated IBs largely affects the protein leakage rate, again protein release being stimulated in His-tagged materials. This event depends on the number of His residues but irrespective of the location of the tag in the protein, as it occurs in either C-tagged or N-tagged proteins. The architectonic role of H6 in the formation of bacterial IBs, probably through coordination with divalent cations, offers an easy approach to manipulate protein leakage and to tailor the applicability of this material as a secretory amyloidal depot in different biomedical interfaces. In addition, the findings also offer a model to finely investigate, in a simple set-up, the mechanics of protein release from functional secretory amyloids.
Nano-Based Drug Delivery Systems of Potent MmpL3 Inhibitors for Tuberculosis Treatment
Authors: Hanieh Patrizia Nadia , Consalvi Sara , Forte Jacopo , Cabiddu Gianluigi , De Logu Alessandro , Poce Giovanna , Rinaldi Federica , Biava Mariangela , Carafa Maria , Marianecci Carlotta
Published: March 2022

Tuberculosis remains one of the world’s deadliest infectious diseases, accounting for nearly 1.3 million deaths every year. Tuberculosis treatment is challenging because of the toxicity, decreased bioavailability at the target site of the conventional drugs and, most importantly, low adherence of patients; this leads to drug resistance. Here, we describe the development of suitable nanocarriers with specific physicochemical properties to efficiently deliver two potent antimycobacterial compounds. We prepared nanoemulsions and niosomes formulations and loaded them with two different MmpL3 inhibitors previously identified (NEs + BM635 and NIs + BM859). NEs + BM635 and NIs + BM859 were deeply characterized for their physicochemical properties and anti-mycobacterial activity. NEs + BM635 and NIs + BM859 showed good hydrodynamic diameter, ζ-Potential, PDI, drug-entrapment efficiency, polarity, and microviscosity and stability. Even though both formulations proved to perform well, only NIs + BM859 showed potent antimycobacterial activity against M. tuberculosis (MIC = 0.6 µM) compared to that of the free compound. This is most probably caused by the fact that BM635, being highly hydrophobic, encounters maximum hindrance in diffusion, whereas BM859, characterized by high solubility in aqueous medium (152 µM), diffuses more easily. The niosomal formulation described in this work may be a useful therapeutic tool for tuberculosis treatment, and further studies will follow to characterize the in vivo behavior of the formulation.
Engaging Patient Advisory Boards of African American Community Members with Type 2 Diabetes in Implementing and Refining a Peer-Led Medication Adherence Intervention
Authors: Maurer Martha A. , Shiyanbola Olayinka O. , Mott Mattigan L. , Means Julia
Published: March 2022

African Americans are more likely than non-Hispanic whites to be diagnosed with and die from diabetes. A contributing factor to these health disparities is African Americans’ poor diabetes medication adherence that is due in part to sociocultural barriers (e.g., medicine and illness misperceptions), which negatively affect diabetes management. In our prior work, we engaged with community stakeholders to develop and test a brief version of a culturally adapted intervention to address these barriers to medication adherence. The objective of this study was to elicit feedback to inform the refinement of the full 8-week intervention. We utilized a community-engaged study design to conduct a series of meetings with two cohorts of patient advisory boards of African Americans with type 2 diabetes who were adherent to their diabetes medicines (i.e., peer ambassadors). In total, 15 peer ambassadors were paired with 21 African American participants (i.e., peer buddies) to provide specific intervention support as peers and serve in an advisory role as a board member. Data were collected during nine board meetings with the patient stakeholders. A qualitative thematic analysis of the data was conducted to synthesize the findings. Feedback from the patient advisory board contributed to refining the intervention in the immediate-term, short-term, and long-term. The inclusion of African American community members living with type 2 diabetes on the advisory board contributed to further tailoring the intervention to the specific needs of African Americans with type 2 diabetes in the community.
Microwave Photonic Signal Generation in an Optically Injected Discrete Mode Semiconductor Laser
Authors: Chang Da , Zhong Zhuqiang , Valle Angel , Jin Wei , Jiang Shan , Tang Jianming , Hong Yanhua
Published: March 2022

In this paper, microwave photonic signal generation based on the period-one dynamic of optically injected discrete mode (DM) semiconductor lasers has been experimentally demonstrated and numerically simulated. The results show that the frequency of the generated microwave increases linearly with the frequency detuning or optical injection ratio. In addition, a single optical feedback loop is sufficient to reduce the microwave linewidth without significantly deteriorating side mode suppression. The simulation results using a model considering the nonlinear dependencies of the carrier recombination agree well with the experimental results, which indicates that the nonlinear carrier recombination effect is important in determining the nonlinear dynamics of optically injected DM lasers.
sCMOS Noise-Corrected Superresolution Reconstruction Algorithm for Structured Illumination Microscopy
Authors: Zhou Bo , Huang Xiaoshuai , Fan Junchao , Chen Liangyi
Published: March 2022

Structured illumination microscopy (SIM) is widely applied due to its high temporal and spatial resolution imaging ability. sCMOS cameras are often used in SIM due to their superior sensitivity, resolution, field of view, and frame rates. However, the unique single-pixel-dependent readout noise of sCMOS cameras may lead to SIM reconstruction artefacts and affect the accuracy of subsequent statistical analysis. We first established a nonuniform sCMOS noise model to address this issue, which incorporates the single-pixel-dependent offset, gain, and variance based on the SIM imaging process. The simulation indicates that the sCMOS pixel-dependent readout noise causes artefacts in the reconstructed SIM superresolution (SR) image. Thus, we propose a novel sCMOS noise-corrected SIM reconstruction algorithm derived from the imaging model, which can effectively suppress the sCMOS noise-related reconstruction artefacts and improve the signal-to-noise ratio (SNR).
Intensity-Averaged Double Three-Step Phase-Shifting Algorithm with Color-Encoded Fringe Projection
Authors: Wang Yuwei , Zhu Haojie , Cai Jiaxu , Wang Yajun
Published: March 2022

Fringe projection profilometry (FPP) has been broadly employed for three-dimensional shape measurements. However, the measurement accuracy suffers from gamma nonlinearity. This paper proposes an intensity-averaged double three-step phase-shifting (IDTP) algorithm making use of color-encoded fringe projection, which does not require complex calibration processes or extra fringe patterns. Specifically, two phase maps with π/2 phase shift are encoded into the red and blue channels of color fringe patterns. The average fringe patterns of the red and blue channels are approximately in sinusoidal waveform with little harmonics, thus can be directly used for accurate phase recovery. Additionally, an adaptive weight is also estimated for average operation to suppress the effect of color crosstalk. Both simulations and experiments demonstrate that the proposed IDTP algorithm can effectively eliminate nonlinear phase errors.
A Comparison on Physical and Rheological Properties of Three Different Waste Plastic-Modified Bitumen
Authors: Mashaan Nuha , Chegenizadeh Amin , Nikraz Hamid
Published: March 2022

This study aims to investigate the effect and the possibility of using waste plastic as a sustainable cost-effective polymer to modify bitumen binders. Different types of waste plastic have been used in this modification, including polyethylene terephthalate (PET), high-density polyethylene (HDPE), and low-density polyethylene (LDPE). The modification targets the physical characteristics, rheological properties, and binders’ resistance to ageing. Both long- and short-term ageing are investigated to determine the durability and ageing resistance of the modified binder using rolling thin film oven tests (RTFOT) and pressure ageing vessels (PAVs). Penetration tests and dynamic shear rheometer (DSR) tests were conducted to investigate and evaluate the complex shear modulus, stiffness, elasticity, and viscous properties. The results show that 2% and 4% of HDPE and LDPE are recommended as ideal contents for good performance, as reflected by the penetration tests before and after ageing. However, higher contents, such as 6% and 8% HDPE and LDPE, are not significant in improving the stiffness, elasticity, and ageing resistance. Therefore, samples of 6–8% HDPE and LDPE are more vulnerable to permanent deformation. Furthermore, using waste PET exhibits obvious improvements in terms of the physical characteristics, rheological properties, stiffness, elasticity, and ageing resistance with up to 8% PET-modified bitumen. Based on the results, the ideal type and content is 6–8% PET waste plastic.
The Introductory Part of Udayana’s Critique of the Buddhist Doctrine of Momentariness
Authors: Chakrabarti Kisor
Published: March 2022

In the Buddhist view, all real things are subject to constant change, and nothing real endures for more than one moment. The Buddhist holds that only causally productive things are real and offers arguments to prove that anything that produces an effect must undergo immediate change and cannot be permanent. In his Ātmatattvaviveka (ATV), Udayana (a Nyaya philosopher of the 11th century) raises objections to Buddhist arguments and tries to show that a causal condition can endure through time. Moreover, although a causal condition may have the ability to produce an effect, the production of an effect may be delayed until all other causal conditions are available; during this time, a causal condition may continue to exist and remain unchanged. I explain Udayana’s critique of the Buddhist position with the help of selections from the Sanskrit text. I translate the selected texts into English with notes and provide expository comments. (Diacritical marks are omitted from names).
Environmental Factors Influencing the Sustenance of the Camel Milk Value Chain in Isiolo County, Northern Kenya
Authors: Machan Steve N. , Agwata Jones F. , Oguge Nicholas O.
Published: March 2022

Dryland areas in northern Kenya experience challenges due to various factors, including environmental degradation associated with unstable weather conditions and climate change. These and related risks and stressors are threats to the sustenance of camel milk production for many of Kenya’s northern communities. We conducted a study among the pastoral communities in Isiolo County whose principal source of livelihood is dependent on camel production. In this paper, we discuss the drivers of the environmental factors influencing the resilience and sustainability of the camel milk value chain in Isiolo County, northern Kenya. We analyzed (i) the internal factors that influence the system, which includes: the categories and economic contributions of livestock-based value chains, the occupation of the camel milk value chain micro players, and land-use practices in Isiolo County, and (ii) the external factors influencing the system: the climatic variability (2014–2017) and its effect on the camel milk value chain, and the vegetation condition index (VCI) indicating drought trends for Isiolo County (2002–2020). The study adopted a field survey through interviews with randomly selected households involved in the camel milk value chain by using a survey questionnaire and a survey guide for focus group discussions (FGD) and key informant interviews (KII). The results from the study showed camel milk as a dominant value chain among the livestock-based value chain products in Isiolo County, the variations in quantities of camel milk supplied due to changes in seasonality, and we show that the current system is not sustainable. We conclude with recommendations to establish a modernized camel milk value chain based on improved natural resources management for a resilient and sustainable system.
Financial Performance and Working Capital Management Practices in the Retail Sector: Empirical Evidence from South Africa
Authors: Mandipa Garikai , Sibindi Athenia Bongani
Published: March 2022

This study examines the relationship between the financial performance and working capital management practices of South African retail firms listed on the Johannesburg Stock Exchange. The study sample comprised a panel of 16 South African retail firms for the period 2010–2019. A fixed-effects estimator was employed in the analysis. The working capital management was proxied by average age of inventory (AAI), average collection period (ACP), average payment period (APP), and cash conversion cycle (CCC), while the financial performance was proxied by net operating profit margin (NOPM), return on assets (ROA), and return on equity (ROE). The key findings of the study documented the following: (1) There is a negative relationship between average collection period and financial performance. (2) A negative relationship between average age of inventory and financial performance measures (NOPM and ROA) was found. (3) The average payment period was found to be negatively related to return on equity. (4) The cash conversion cycle and net operating profit margin variables were found to be negatively related. The study concludes that working capital management practices influenced the financial performance of the South African retail firms. It is recommended that South African retail firms observe prudent optimal working capital management practices, as these influence their financial performance.
A Hybrid Deep Learning Model for the Bias Correction of SST Numerical Forecast Products Using Satellite Data
Authors: Fei Tonghan , Huang Binghu , Wang Xiang , Zhu Junxing , Chen Yan , Wang Huizan , Zhang Weimin
Published: March 2022

Sea surface temperature (SST) has important practical value in ocean related fields. Numerical prediction is a common method for forecasting SST at present. However, the forecast results produced by the numerical forecast models often deviate from the actual observation data, so it is necessary to correct the bias of the numerical forecast products. In this paper, an SST correction approach based on the Convolutional Long Short-Term Memory (ConvLSTM) network with multiple attention mechanisms is proposed, which considers the spatio-temporal relations in SST data. The proposed model is appropriate for correcting SST numerical forecast products by using satellite remote sensing data. The approach is tested in the region of the South China Sea and reduces the root mean squared error (RMSE) to 0.35 °C. Experimental results reveal that the proposed approach is significantly better than existing models, including traditional statistical methods, machine learning based methods, and deep learning methods.
Deep Internal Learning for Inpainting of Cloud-Affected Regions in Satellite Imagery
Authors: Czerkawski Mikolaj , Upadhyay Priti , Davison Christopher , Werkmeister Astrid , Cardona Javier , Atkinson Robert , Michie Craig , Andonovic Ivan , Macdonald Malcolm , Tachtatzis Christos
Published: March 2022

Cloud cover remains a significant limitation to a broad range of applications relying on optical remote sensing imagery, including crop identification/yield prediction, climate monitoring, and land cover classification. A common approach to cloud removal treats the problem as an inpainting task and imputes optical data in the cloud-affected regions employing either mosaicing historical data or making use of sensing modalities not impacted by cloud obstructions, such as SAR. Recently, deep learning approaches have been explored in these applications; however, the majority of reported solutions rely on external learning practices, i.e., models trained on fixed datasets. Although these models perform well within the context of a particular dataset, a significant risk of spatial and temporal overfitting exists when applied in different locations or at different times. Here, cloud removal was implemented within an internal learning regime through an inpainting technique based on the deep image prior. The approach was evaluated on both a synthetic dataset with an exact ground truth, as well as real samples. The ability to inpaint the cloud-affected regions for varying weather conditions across a whole year with no prior training was demonstrated, and the performance of the approach was characterised.
Exploration of Multi-Mission Spaceborne GNSS-R Raw IF Data Sets: Processing, Data Products and Potential Applications
Authors: Li Weiqiang , Cardellach Estel , Ribó Serni , Oliveras Santi , Rius Antonio
Published: March 2022

Earth reflected Global Navigation Satellite System (GNSS) signals can be received by dedicated orbital receivers for remote sensing and Earth observation (EO) purposes. Different spaceborne missions have been launched during the past years, most of which can only provide the delay-Doppler map (DDM) of the power of the reflected GNSS signals as their main data products. In addition to the power DDM products, some of these missions have collected a large amount of raw intermediate frequency (IF) data, which are the bit streams of raw signal samples recorded after the analog-to-digital converters (ADCs) and prior to any onboard digital processing. The unprocessed nature of these raw IF data provides an unique opportunity to explore the potential of GNSS Reflectometry (GNSS-R) technique for advanced geophysical applications and future spaceborne missions. To facilitate such explorations, the raw IF data sets from different missions have been processed by Institute of Space Sciences (ICE-CSIC, IEEC), and the corresponding data products, i.e., the complex waveform of the reflected signal, have been generated and released through our public open-data server. These complex waveform data products provide the measurements from different GNSS constellations (e.g., GPS, Galileo and BeiDou), and include both the amplitude and carrier phase information of the reflected GNSS signal at higher sampling rate (e.g., 1000 Hz). To demonstrate these advanced features of the data products, different applications, e.g., inland water detection and surface altimetry, are introduced in this paper. By making these complex waveform data products publicly available, new EO capability of the GNSS-R technique can be further explored by the community. Such early explorations are also relevant to ESA’s next GNSS-R mission, HydroGNSS, which will provide similar complex observations operationally and continuously in the future.
Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa
Authors: Liu Zac Yung-Chun , Chamberlin Andrew J. , Tallam Krti , Jones Isabel J. , Lamore Lance L. , Bauer John , Bresciani Mariano , Wolfe Caitlin M. , Casagrandi Renato , Mari Lorenzo , Gatto Marino , Diongue Abdou Ka , Toure Lamine , Rohr Jason R. , Riveau Gilles , Jouanard Nicolas , Wood Chelsea L. , Sokolow Susanne H. , Mandle Lisa , Daily Gretchen , Lambin Eric F. , De Leo Giulio A.
Published: March 2022

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in tropical and subtropical areas. Changes to hydrology and salinity linked to water infrastructure development may create conditions favorable to the aquatic vegetation that is suitable habitat for the intermediate snail hosts of schistosome parasites. With thousands of small and large water reservoirs, irrigation canals, and dams developed or under construction in Africa, it is crucial to accurately assess the spatial distribution of high-risk environments that are habitat for freshwater snail intermediate hosts of schistosomiasis in rapidly changing ecosystems. Yet, standard techniques for monitoring snails are labor-intensive, time-consuming, and provide information limited to the small areas that can be manually sampled. Consequently, in low-income countries where schistosomiasis control is most needed, there are formidable challenges to identifying potential transmission hotspots for targeted medical and environmental interventions. In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computer vision technique called semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination.
Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine
Authors: Reyes-Muñoz Pablo , Pipia Luca , Salinero-Delgado Matías , Belda Santiago , Berger Katja , Estévez José , Morata Miguel , Rivera-Caicedo Juan Pablo , Verrelst Jochem
Published: March 2022

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016–2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.
Do We Need a Higher Resolution? Case Study: Sentinel-1-Based Change Detection of the 2018 Hokkaido Landslides, Japan
Authors: Kovács István Péter , Tessari Giulia , Ogushi Fumitaka , Riccardi Paolo , Ronczyk Levente , Kovács Dániel Márton , Lóczy Dénes , Pasquali Paolo
Published: March 2022

Since 2014, Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data have become an important source in the field of displacement detection thanks to regular acquisitions and 7.5 years of temporal coverage at global level. Despite the increasing number of publications on the role of S1 in landslide detection, there is still a need for research to further clarify the capabilities of the sensor and the applicable image analysis techniques. Previous studies have successfully exploited high-resolution ALOS-PALSAR image-based intensity and coherence analysis at the 2018 Hokkaido landslides. Nevertheless, they expressed a clear need to analyse the capabilities of other sensors (such as S1). This raises the question: Do we need SAR imagery with higher spatial resolution (such as ALOS-PALSAR) or are freely available S1 imagery also suitable for rapid landslide detection? The S1 images could provide suitable material for a comparative analysis and could answer the aforementioned question. Therefore, 17 ascending and 19 descending S1 images were analysed to test S1 accuracy on landslide detection. Multitemporal analyses of both intensity and coherence were performed along with coherence differences, multitemporal features (MTF) and MTF differences of coherence images. In addition, the spatial analysis of the classification results was also evaluated to highlight the potential of S1 coherence analysis. S1 was found to have limitations at the site, as single coherence differences provided low-quality results. However, the results were significantly improved by calculating the MTF on coherence and almost reached the success rate of the ALOS-PALSAR-based coherence analysis, even though the improvement of the results with intensity was not possible. Half of the false positives were identified in the 30–45-m buffer zone of the agreement, underlining that the spatial resolution of the S1 is not appropriate for accurate landslide detection. Only an approximation of the landslide-affected area can be given with considerable overestimation. Due to the inclusion of post-event images, the sensor is not perfectly applicable for rapid detection purposes here.
Design of a Grating Lobes-Free Architecture for Distributed Sensor System with Arbitrary Element Spacing
Authors: Huang Jiyan , Liu Chaoyang , Hu Wei , Liao Kuo
Published: March 2022

Two main difficulties for the accurate direction of arrival (DOA) estimation in a distributed sensor system are grating lobes and system synchronization. Different from the traditional phased array system based on single carrier frequency, this paper designs a novel architecture for distributed sensor system using dual carrier frequency measurements. Both two carrier frequencies are combined and transmitted simultaneously in the proposed architecture. Instead of single carrier frequency f1 in the traditional method, the frequency separation f2−f1 is used in the proposed algorithm to determine a maximum unambiguous DOA estimate. Since f2−f1 is much less than f1 and the choice of f2−f1 is more flexible, the proposed method can provide an unambiguous DOA estimate in the whole field of view (FOV) for arbitrary element spacing and a looser requirement on system synchronization than the traditional method based on single carrier frequency. For a case with f1=3 Ghz, f2−f1=150 Mhz and 10 times element spacing, there is no grating lobe for the proposed method in the whole FOV [−π/2,π/2], whereas the traditional method has many grating lobes and the FOV of the traditional method without grating lobes is only 5.73∘. To obtain the same combining efficiency in this case, the requirement on the positioning error of the proposed method is 0.4 m and the location error of the traditional method must be smaller than 0.02 m. Hence, the proposed method can greatly reduce hardware requirements. The performances of the proposed architecture for the cases with perfect and imperfect system synchronizations are evaluated in terms of the average beampattern and combining efficiency, respectively. Some characteristics of the derived beampattern and combining efficiency are also presented in the paper.
Rapid Assessment of Building Damage Using Multi-Source Data: A Case Study of April 2015 Nepal Earthquake
Authors: Chen Jin , Tang Hong , Ge Jiayi , Pan Yaozhong
Published: March 2022

It is of great significance for emergency rescue to rapidly assess damage of buildings after an earthquake. Some previous methods are time-consuming, data are difficult to obtain, or there is lack of regional damage assessment. We proposed a novel way to rapidly assess building damage by comprehensively utilizing earth observation-derived data and field investigation to alleviate the above problems. These data are related to hazard-causing factors, hazard-formative environment, and hazard-affected body. Specifically, predicted ground motion parameters are used to reflect hazard-causing factors, e.g., peak ground velocity (PGV), peak ground acceleration (PGA), and pseudo-spectral acceleration (PSA). The hazard-formative environment is denoted by the underground 30 m shear wave velocity. Vulnerability of buildings is reflected by their structure type, age, and height. We take the April 2015 Nepal earthquake as a case study, and building damage data interpreted from satellite images are used to validate the effectiveness of the proposed method. Based on the gradient boosting machine, this paper rapidly assesses building damage from two different spatial levels, i.e., pixel and microzone, and obtains the potentially affected position and regional damage rate. Compared with the method of fragility function, the machine learning method provide a better estimation of the building damage rate. Compared with the assessment method based on remote sensing image, the method in this paper is very efficient since spatial distribution of hazard-causing factors, e.g., PGA, can be quickly predicted shortly after an earthquake. The comparison of experiment with and without vulnerability data of buildings shows that data on the vulnerability of buildings are very important to improve the assessment accuracy of building damage.
Combining Crop Modeling with Remote Sensing Data Using a Particle Filtering Technique to Produce Real-Time Forecasts of Winter Wheat Yields under Uncertain Boundary Conditions
Authors: Zare Hossein , Weber Tobias K. D. , Ingwersen Joachim , Nowak Wolfgang , Gayler Sebastian , Streck Thilo
Published: March 2022

Within-season crop yield forecasting at national and regional levels is crucial to ensure food security. Yet, forecasting is a challenge because of incomplete knowledge about the heterogeneity of factors determining crop growth, above all management and cultivars. This motivates us to propose a method for early forecasting of winter wheat yields in low-information systems regarding crop management and cultivars, and uncertain weather condition. The study was performed in two contrasting regions in southwest Germany, Kraichgau and Swabian Jura. We used in-season green leaf area index (LAI) as a proxy for end-of-season grain yield. We applied PILOTE, a simple and computationally inexpensive semi-empirical radiative transfer model to produce yield forecasts and assimilated LAI data measured in-situ and sensed by satellites (Landsat and Sentinel-2). To assimilate the LAI data into the PILOTE model, we used the particle filtering method. Both weather and sowing data were treated as random variables, acknowledging principal sources of uncertainties to yield forecasting. As such, we used the stochastic weather generator MarkSim® GCM to produce an ensemble of uncertain meteorological boundary conditions until the end of the season. Sowing dates were assumed normally distributed. To evaluate the performance of the data assimilation scheme, we set up the PILOTE model without data assimilation, treating weather data and sowing dates as random variables (baseline Monte Carlo simulation). Data assimilation increased the accuracy and precision of LAI simulation. Increasing the number of assimilation times decreased the mean absolute error (MAE) of LAI prediction from satellite data by ~1 to 0.2 m2/m2. Yield prediction was improved by data assimilation as compared to the baseline Monte Carlo simulation in both regions. Yield prediction by assimilating satellite-derived LAI showed similar statistics as assimilating the LAI data measured in-situ. The error in yield prediction by assimilating satellite-derived LAI was 7% in Kraichgau and 4% in Swabian Jura, whereas the yield prediction error by Monte Carlo simulation was 10 percent in both regions. Overall, we conclude that assimilating even noisy LAI data before anthesis substantially improves forecasting of winter wheat grain yield by reducing prediction errors caused by uncertainties in weather data, incomplete knowledge about management, and model calibration uncertainty.
Relevance of the Spectral Analysis Method of Tilted Fiber Bragg Grating-Based Biosensors: A Case-Study for Heart Failure Monitoring
Authors: Vidal Miguel , Soares Maria Simone , Loyez Médéric , Costa Florinda M. , Caucheteur Christophe , Marques Carlos , Pereira Sónia O. , Leitão Cátia
Published: March 2022

Optical fiber technology has rapidly progressed over the years, providing valuable benefits for biosensing purposes such as sensor miniaturization and the possibility for remote and real-time monitoring. In particular, tilted fiber Bragg gratings (TFBGs) are extremely sensitive to refractive index variations taking place on their surface. The present work comprises a case-study on the impact of different methods of analysis applied to decode spectral variations of bare and plasmonic TFBGs during the detection of N-terminal B-type natriuretic peptide (NT-proBNP), a heart failure biomarker, namely by following the most sensitive mode, peaks of the spectral envelopes, and the envelopes’ crossing point and area. Tracking the lower envelope resulted in the lowest limits of detection (LOD) for bare and plasmonic TFBGs, namely, 0.75 ng/mL and 0.19 ng/mL, respectively. This work demonstrates the importance of the analysis method on the outcome results, which is crucial to attain the most reliable and sensitive method with lower LOD sensors. Furthermore, it makes the scientific community aware to take careful attention when comparing the performance of different biosensors in which different analysis methods were used.
Spoofing Attacks on FMCW Radars with Low-Cost Backscatter Tags
Authors: Lazaro Antonio , Porcel Arnau , Lazaro Marc , Villarino Ramon , Girbau David
Published: March 2022

This work studies the feasibility of using backscatter-modulated tags to introduce false information into a signal received by a frequency-modulated continuous-wave (FMCW) radar. A proof-of-concept spoofing device was designed in the 24 GHz ISM band. The spoofing device was based on an amplifier connected between two antennas, and modulation was carried out by switching the amplifier bias. The use of an amplifier allowed us to increase the level of spoofing signal compared with other modulated backscattering methods. The simulated and experimental results show that our method has the ability to generate a pair of false targets at different ranges and velocities depending on the modulation frequency of the chosen tag, since sidebands appear due to this modulation. Countermeasures to detect the spoofing attack based on changes in the slope of the frequency sweep between frames are also proposed.