bluuebunny/crossref_metadata_2025 huggingface dataset:
Toll-like Receptor-9 (TLR-9) Signaling Is Crucial for Inducing Protective Immunity following Immunization with Genetically Modified Live Attenuated Leishmania Parasites
Authors: Bhattacharya Parna , Gannavaram Sreenivas , Ismail Nevien , Saxena Ankit , Dagur Pradeep K. , Akue Adovi , KuKuruga Mark , Nakhasi Hira L.
Published: March 2023

No human vaccine is available for visceral leishmaniasis (VL). Live attenuated centrin gene-deleted L. donovani (LdCen−/−) parasite vaccine has been shown to induce robust innate immunity and provide protection in animal models. Toll-like receptors (TLRs) are expressed in innate immune cells and are essential for the early stages of Leishmania infection. Among TLRs, TLR-9 signaling has been reported to induce host protection during Leishmania infection. Importantly, TLR-9 ligands have been used as immune enhancers for non-live vaccination strategies against leishmaniasis. However, the function of TLR-9 in the generation of a protective immune response in live attenuated Leishmania vaccines remains unknown. In this study, we investigated the function of TLR-9 during LdCen−/− infection and found that it increased the expression of TLR-9 on DCs and macrophages from ear-draining lymph nodes and spleen. The increase in TLR-9 expression resulted in changes in downstream signaling in DCs mediated through signaling protein myeloid differentiation primary response 88 (MyD88), resulting in activation and nuclear translocation of nuclear factor-κB (NF-κB). This process resulted in an increase in the DC’s proinflammatory response, activation, and DC-mediated CD4+T cell proliferation. Further, LdCen−/− immunization in TLR-9−/− mice resulted in a significant loss of protective immunity. Thus, LdCen−/− vaccine naturally activates the TLR-9 signaling pathway to elicit protective immunity against virulent L. donovani challenge.
Novel Identification of the Collection of Pathogenic Fungal Species Verticillium with the Development of Species-Specific SSR Markers
Authors: Jeseničnik Taja , Kaurin Anela , Grgič Zarja , Radišek Sebastjan , Jakše Jernej , Štajner Nataša
Published: March 2023

The genus Verticillium is a group of ascomycete fungi that includes several pathogenic plant species. In 2011, a new taxonomic classification, proposed by Inderbitzin and coworkers (2011), re-defined the genus as Verticillium sensu stricto. The objective of our study was the re-classification of the fungal species held in the culture collection in the Slovenian Institute of Hop Research and Brewing in accordance with the newly established taxonomy. With the PCR marker system proposed by Inderbitzin and coworkers in 2011, we re-classified 88 Verticillium isolates out of the 105 samples that are held in the institute’s bank, which were obtained from different geographic locations in Europe, North America, and Japan, and from different host plants, including alfalfa, cotton, hop, olive, potato, and tomato. However, the PCR marker for the V. dahliae identification proved to be less specific, and it resulted in the positive amplification of Gibellulopsis nigrescens, V. isaacii, and V. longisporum. To enable the accurate distinction of the fungi, the SSR and LAMP markers were added to the analyses. The 12 newly identified SSR markers, which were used in simplex PCR reactions or in combination, enabled the accurate identification of all included Verticillium isolates and could potentially be used as biomarkers for rapid and easy species identification.
Development of In Vitro Assays with the Canine Hookworm Uncinaria stenocephala and Assessment of Natural Plant Products for Anti-Parasitic Activity
Authors: Geisshirt Heidi A. , Bonde Charlotte S. , Marcussen Caroline , Mejer Helena , Williams Andrew R.
Published: March 2023

Enteric helminth infection is an increasing concern in companion animals due to reports of resistance to commonly used anthelmintic drugs. Thus, the assessment of new therapeutic options such as bioactive dietary additives is of high importance. Here, we adapted egg hatch, larval migration, and larval motility assays to screen extracts of several natural ingredients against the canine hookworm Uncinaria stenocephala, a prevalent parasite of dogs in northern Europe. Egg hatch and larval migration assays were established showing that the anthelmintic drugs levamisole and albendazole had strong anti-parasitic activity against U. stenocephala, validating the use of these assays for the assessment of novel anti-parasitic substances. Subsequently, we identified that extracts from the seaweed Saccharina latissima, but not extracts from grape seed or chicory, significantly inhibited both hatching and larval migration. Finally, we showed that α-linolenic acid, a putative anti-parasitic compound from S. latissima, also exhibited anti-parasitic activity. Collectively, our results established a platform for the screening for anthelmintic resistance or novel drug candidates against U. stenocephala and highlighted the potential use of seaweed extracts as a functional food component to help control hookworm infection in dogs.
Tubeimoside-2 Triggers Methuosis in Hepatocarcinoma Cells through the MKK4–p38α Axis
Authors: Gan Yichao , Wang Chen , Chen Yunyun , Hua Linxin , Fang Hui , Li Shu , Chai Shoujie , Xu Yang , Zhang Jiawei , Gu Ying
Published: March 2023

Liver cancer, consisting mainly of hepatocellular carcinoma, is the third leading cause of cancer-related mortality worldwide. Despite advances in targeted therapies, these approaches remain insufficient in meeting the pressing clinical demands. Here, we present a novel alternative that calls for a non-apoptotic program to solve the current dilemma. Specifically, we identified that tubeimoside 2 (TBM-2) could induce methuosis in hepatocellular carcinoma cells, a recently recognized mode of cell death characterized by pronounced vacuolization, necrosis-like membrane disruption, and no response to caspase inhibitors. Further proteomic analysis revealed that TBM-2-driven methuosis is facilitated by the hyperactivation of the MKK4–p38α axis and the boosted lipid metabolism, especially cholesterol biosynthesis. Pharmacological interventions targeting either the MKK4–p38α axis or cholesterol biosynthesis effectively suppress TBM-2-induced methuosis, highlighting the pivotal role of these mechanisms in TBM-2-mediated cell death. Moreover, TBM-2 treatment effectively suppressed tumor growth by inducing methuosis in a xenograft mouse model of hepatocellular carcinoma. Taken together, our findings provide compelling evidence of TBM-2’s remarkable tumor-killing effects by inducing methuosis, both in vitro and in vivo. TBM-2 represents a promising avenue for the development of innovative and effective therapies for hepatocellular carcinoma, one that may ultimately offer significant clinical benefits for patients with this devastating disease.
Overcoming Treatment Challenges in Posterior Segment Diseases with Biodegradable Nano-Based Drug Delivery Systems
Authors: Wu Kevin Y. , Joly-Chevrier Maxine , Akbar Dania , Tran Simon D.
Published: March 2023

Posterior segment eye diseases present a challenge in treatment due to the complex structures in the eye that serve as robust static and dynamic barriers, limiting the penetration, residence time, and bioavailability of topical and intraocular medications. This hinders effective treatment and requires frequent dosing, such as the regular use of eye drops or visits to the ophthalmologist for intravitreal injections, to manage the disease. Moreover, the drugs must be biodegradable to minimize toxicity and adverse reactions, as well as small enough to not affect the visual axis. The development of biodegradable nano-based drug delivery systems (DDSs) can be the solution to these challenges. First, they can stay in ocular tissues for longer periods of time, reducing the frequency of drug administration. Second, they can pass through ocular barriers, offering higher bioavailability to targeted tissues that are otherwise inaccessible. Third, they can be made up of polymers that are biodegradable and nanosized. Hence, therapeutic innovations in biodegradable nanosized DDS have been widely explored for ophthalmic drug delivery applications. In this review, we will present a concise overview of DDSs utilized in the treatment of ocular diseases. We will then examine the current therapeutic challenges faced in the management of posterior segment diseases and explore how various types of biodegradable nanocarriers can enhance our therapeutic arsenal. A literature review of the pre-clinical and clinical studies published between 2017 and 2023 was conducted. Through the advances in biodegradable materials, combined with a better understanding of ocular pharmacology, the nano-based DDSs have rapidly evolved, showing great promise to overcome challenges currently encountered by clinicians.
Effect of Ionic Liquid on Silver-Nanoparticle-Complexed Ganoderma applanatum and Its Topical Film Formulation
Authors: Maneewattanapinyo Pattwat , Pichayakorn Wiwat , Monton Chaowalit , Dangmanee Nattakan , Wunnakup Thaniya , Suksaeree Jirapornchai
Published: March 2023

Imidazolium-based ionic liquids have been widely utilized as versatile solvents for metal nanoparticle preparation. Silver nanoparticles and Ganoderma applanatum have displayed potent antimicrobial activities. This work aimed to study the effect of 1-butyl-3-methylimidazolium bromide-based ionic liquid on the silver-nanoparticle-complexed G. applanatum and its topical film. The ratio and conditions for preparation were optimized by the design of the experiments. The optimal ratio was silver nanoparticles: G. applanatum extract: ionic liquid at 97:1:2, and the conditions were 80 °C for 1 h. The prediction was corrected with a low percentage error. The optimized formula was loaded into a topical film made of polyvinyl alcohol and Eudragit®, and its properties were evaluated. The topical film was uniform, smooth, and compact and had other desired characteristics. The topical film was able to control the release of silver-nanoparticle-complexed G. applanatum from the matrix layer. Higuchi’s model was used to fit the kinetic of the release. The skin permeability of the silver-nanoparticle-complexed G. applanatum was improved by about 1.7 times by the ionic liquid, which might increase solubility. The produced film is suitable for topical applications and may be utilized in the development of potential future therapeutic agents for the treatment of diseases.
Terminal Residues and Risk Assessment of Spiromesifen and Spirodiclofen in Tomato Fruits
Authors: Abdallah Osama I. , Abd El-Hamid Rania M. , Ahmed Nevein S. , Saleh Sayed M. , Alminderej Fahad M.
Published: March 2023

Insecticides are important to increase crop yields, but their overuse has damaged the environment and endangered human health. In this study, residues of spiromesifen and spirodiclofen were determined in tomato fruit using a simple and efficient analytical procedure based on acetonitrile extraction, extract dilution, and UPLC-MS/MS. The linearity range was 1–100 µg/kg and 0.5–100 µg/kg, and the correlation coefficient (R2) and residuals were ≥0.9991 and ≤16.4%, respectively. The limit of determination (LOD) was 0.26 and 0.08 µg/kg, while the limit of quantification (LOQ) was verified at 5 µg/kg. The relative standard deviation of spiked replicates at 5 µg/kg analyzed in one day (RSDr, n = 6) was ≤8.35%, and within three different days (RSDR, n = 18) it was ≤15.85%, with recoveries exceeding 91.34%. The method recovery test showed a satisfactory value of 89.23–97.22% with an RSD of less than 12.88%. The matrix effect was determined after a 4-fold dilution of the raw extract and was −9.8% and −7.2%, respectively. The validated method was used to study the dissipation behavior of the tested analytes in tomato fruit under field conditions. First-order kinetics best described the dissipation rates. The calculated half-lives were 1.49–1.83 and 1.91–2.38 days for spiromesifen and spirodiclofen, respectively, after application of the authorized and doubled authorized doses, indicating that spiromesifen dissipated more rapidly than spirodiclofen. The final residue concentrations of spiromesifen and spirodiclofen were 0.307–0.751 mg/kg and 0.101–0.398 mg/kg, respectively, after two or three applications, and were below the European Union (EU) maximum residue limits. The chronic risk assessment indicates that both insecticides are safe for adult consumers.
Meta-Analysis of the Effect of Nitric Oxide Application on Heavy Metal Stress Tolerance in Plants
Authors: Liu Xiaoxiao , Gong Di , Ke Qingbo , Yin Lina , Wang Shiwen , Gao Tianpeng
Published: March 2023

Substantial single-species studies have reported the facility of nitric oxide (NO) in alleviating heavy metal-induced stress in plants. Understanding the mechanisms of NO-involved stress alleviation is progressing; however, a quantitative description of the alleviative capacity of NO against heavy metal stress is still lacking. We combined the results of 86 studies using meta-analysis to statistically assess the responses of heavy metal-stressed plants to NO supply across several metal stresses and plant families. The results showed that plant biomass was consistently improved following NO supply to metal-stressed plants. NO played an important role in mitigating oxidative damage caused by heavy metal stress by significantly stimulating the activities of antioxidant enzymes. Moreover, NO supply consistently increased the Ca, Fe, and Mg contents in both leaves and roots. Plant tissues accumulated less heavy metals when exposed to heavy metal stress after NO addition. Additionally, the best concentration of SNP (an NO donor) for hydroponic culture is in the range of 75–150 μM. We further confirmed that NO application can generally alleviate plant heavy metal stress and its action pathway. The results presented here can help guide future applications of NO as a plant growth regulator in agriculture and breeding plants for heavy metal stress tolerance.
Chromium Toxicity in Plants: Signaling, Mitigation, and Future Perspectives
Authors: Ali Sajad , Mir Rakeeb A. , Tyagi Anshika , Manzar Nazia , Kashyap Abhijeet Shankar , Mushtaq Muntazir , Raina Aamir , Park Suvin , Sharma Sandhya , Mir Zahoor A. , Lone Showkat A. , Bhat Ajaz A. , Baba Uqab , Mahmoudi Henda , Bae Hanhong
Published: March 2023

Plants are very often confronted by different heavy metal (HM) stressors that adversely impair their growth and productivity. Among HMs, chromium (Cr) is one of the most prevalent toxic trace metals found in agricultural soils because of anthropogenic activities, lack of efficient treatment, and unregulated disposal. It has a huge detrimental impact on the physiological, biochemical, and molecular traits of crops, in addition to being carcinogenic to humans. In soil, Cr exists in different forms, including Cr (III) “trivalent” and Cr (VI) “hexavalent”, but the most pervasive and severely hazardous form to the biota is Cr (VI). Despite extensive research on the effects of Cr stress, the exact molecular mechanisms of Cr sensing, uptake, translocation, phytotoxicity, transcript processing, translation, post-translational protein modifications, as well as plant defensive responses are still largely unknown. Even though plants lack a Cr transporter system, it is efficiently accumulated and transported by other essential ion transporters, hence posing a serious challenge to the development of Cr-tolerant cultivars. In this review, we discuss Cr toxicity in plants, signaling perception, and transduction. Further, we highlight various mitigation processes for Cr toxicity in plants, such as microbial, chemical, and nano-based priming. We also discuss the biotechnological advancements in mitigating Cr toxicity in plants using plant and microbiome engineering approaches. Additionally, we also highlight the role of molecular breeding in mitigating Cr toxicity in sustainable agriculture. Finally, some conclusions are drawn along with potential directions for future research in order to better comprehend Cr signaling pathways and its mitigation in sustainable agriculture.
On the Donor: Acceptor Features for Poly(3-hexylthiophene): TiO2 Quantum Dots Hybrid Materials Obtained via Water Vapor Flow Assisted Sol-Gel Growth
Authors: Mombrú Dominique , Romero Mariano , Faccio Ricardo , Mombrú Alvaro W.
Published: March 2023

Here, we present a novel methodology for the preparation of P3HT:TiO2 quantum dots hybrid materials via water vapor flow-assisted sol-gel growth focusing on the structural, optical and electrical property characterization complemented with first-principles calculations as a promising donor–acceptor system for polymer and hybrid solar cells. X-ray diffraction and UV-Vis spectroscopy analyses suggest that the increasing concentration of TiO2 quantum dots leads to the formation of higher amounts of amorphous regions while the crystalline regions exhibited interesting aspect ratio modifications for the P3HT polymer. Raman spectra evidenced the formation of charge carriers in the P3HT with increasing TiO2 quantum dots content and the P3HT:TiO2 50:50 weight ratio resulted in the best composition for optimizing the bulk electronic conductivity, as evidenced by impedance spectroscopy studies. Our DFT calculations performed for a simplified model of the P3HT:TiO2 interface revealed that there is an important contribution of the thiophene carbon atoms states in the conduction band at the Fermi level. Finally, our DFT calculations also reveal an evident gain of electron density at the TiO2 (101) surface while the thiophene rings showed a loss of the electron density, thus confirming that the P3HT:TiO2 junction acts as a good donor–acceptor system. In our opinion, these results not only present a novel methodology for the preparation of P3HT:TiO2 quantum dots hybrid materials but also reveal some key aspects to guide the more rational design of polymer and hybrid solar cells.
Preparation and Antimicrobial Characterization of Poly(butylene adipate-co-terephthalate)/Kaolin Clay Biocomposites
Authors: Venkatesan Raja , Alagumalai Krishnapandi , Kim Seong-Cheol
Published: March 2023

The biodegradable polymer poly(butylene adipate-co-terephthalate) (PBAT) starts decomposing at room temperature. Kaolin clay (KO) was dispersed and blended into PBAT composites using a solution-casting method. Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) were used to evaluate the structure and morphology of the composite materials. PBAT/kaolin clay composites were studied by thermogravimetric analysis (TGA). The PBAT composite loaded with 5.0 wt% kaolin clay shows the best characteristics. The biocomposites of PBAT/kaolin [PBC-5.0 (37.6MPa)] have a good tensile strength when compared to virgin PBAT (18.3MPa). The oxygen transmission rate (OTR), with ranges from 1080.2 to 311.7 (cc/m2/day), leads the KO content. By including 5.0 wt% kaolin 43.5 (g/m2/day), the water vapor transmission rate (WVTR) of the PBAT/kaolin composites was decreased. The pure PBAT must have a WVTR of 152.4 (g/m2/day). Gram-positive (S. aureus) and Gram-negative (E. coli) food-borne bacteria are significantly more resistant to the antimicrobial property of composites. The results show that PBAT/kaolin composites have great potential as food packaging materials due to their ability to decrease the growth of bacteria and improve the shelf life of packaged foods.
Highly Stretchable PPy/PDMS Strain Sensors Fabricated with Multi-Step Oxygen Plasma Treatment
Authors: Muhammad Waqar , Kim Sam-Dong
Published: March 2023

We present highly stretchable polypyrrole (PPy)/polydimethylsiloxane strain sensors of highly improved sensitivity and durability fabricated by a chemical oxidative polymerization with oxygen plasma treatment (O2 PT). In this study, O2 PT was performed for 30, 60, and 90 s at each growth stage of the PPy film in three steps to investigate the effects on the sensor performance as well as the microstructural properties of the PPy films. Bonding characteristics with underlying layers and resistance to microcrack generation of the multi-layer PPy films under our given strained state were significantly enhanced by the O2 PT. The best sensor performance in terms of sensitivity and stability were achieved by PT for 30 s with a maximum gauge factor of ~438 at a uniaxial strain of 50%, excellent durability over 500 stretching/release cycles, and a fast response time of ~50 ms.
Experimental Study on the Vibration Reduction Performance of the Spindle Rotor of a Rubbing Machine Based on Aluminium Foam Material
Authors: Yue Yao , Tian Haiqing , Li Dapeng , Liu Fei , Wang Xin , Ren Xianguo , Zhao Kai
Published: March 2023

Larger vibration and noise often exist in agricultural machinery due to the harsh working environment and high power. The rubbing machine is one of the indispensable pieces of equipment in the agriculture and livestock industry, and it is affected by the vibration of large constraints on its promotion and use. To reduce the vibration of the rubbing machine, the vibration characteristics of the spindle rotor were first analysed by modal simulation, thus determining the larger contributions to the spindle rotor vibration. Second, aluminium foam material was installed in the large deformation part of the spindle rotor. Its vibration reduction and energy absorption characteristics were used to optimise the vibration reduction design by increasing the damping. Third, a steel ball impact test was conducted to analyse the vibration characteristics of the optimised spindle rotor. The results show that the maximum impact accelerations were reduced by 28.4% and 64.75% in the axial and radial directions, respectively, and the impact energies were reduced by 67.3% and 90.65% in the axial and radial directions within 2 s of impact collision, respectively, indicating that the optimised spindle rotor damping increased significantly. In addition, the vibration reduction effect of the optimised rubbing machine was verified by a bench test. By measuring the change degree of the static component of the spindle rotor vibration, the axial, radial, and vertical vibrations of the spindle rotor were improved by 5.78%, 10.32%, and 23.96%, respectively. Therefore, optimising the spindle rotor with aluminium foam material can reduce the vibration generated during the impact of the material on the spindle rotor. The rubbing machine’s vibration, damping, and energy absorption were also realised in real working conditions.
Modeling Linkages among Urban Agglomeration, Construction Industry, Non-Renewable Energy, and Zero-Carbon Future
Authors: Guo Weishang , Atchike Desire Wade , Ahmad Munir , Chen Yaxiao , Gu Shili
Published: March 2023

On the one hand, the twin perspectives of the construction industry and urban agglomeration proliferate economic prosperity. However, on the other hand, construction activities and increased population density give rise to environmental challenges. This study is an initial attempt to explore links between the construction industry, urban agglomeration, non-renewable energy utilization, carbon dioxide emissions (CO2e), and economic output within a system of simultaneous equation modeling. This study develops modeling specifications to include the construction industry as a shifting factor and CO2e as a determinant of technical efficiency. A heterogeneous fully modified ordinary least squares (FMOLS) technique, capable of providing concrete empirical outcomes even in the presence of modern panel-data econometric issues, is utilized for the data of China’s 30 provincial/ city divisions during the 2003–2019 period. It has been revealed that: (i) the influence of urban agglomeration on the economic output was heterogeneous, with an adverse link for China’s western part, neutrality connection for the central part, and favorable for China’s eastern part; and (ii) shifting from China’s western to eastern parts, the expansion of the construction industry has a significant impact on economic output, and, hence, has been described as “the Economic Effects of Urban agglomeration” for the Chinese economy. The policies of this study have crucial lessons for global economies.
Is the Black Church Dead?: Religious Resilience and the Contemporary Functions of Black Christianity
Authors: Allen Shaonta’ E.
Published: March 2023

Recent sociological literature draws attention to the changes in religious affiliation and expression among younger generations. More specifically, extant research suggests that the increase in religious “nones” (those who no longer identify with a religion) and “dones” (those who no longer affiliate with a religious congregation) substantiates the secularization thesis which contends that religion is becoming increasingly insignificant in modern society. However, when these trends are disaggregated across racial lines, religious affiliation remains high among Black Millennials. The present study explores this anomaly further, drawing on data from in-depth interviews with 65 Black Christian Millennials to assess how, if at all, the historic functions of the Black Church are still prevalent among Black Christians today. Findings suggest that while some functions have remained constant across generations (i.e., social–cultural), others have evolved (i.e., socio-political and socio-educational) or become defunct (i.e., socio-economic) and new functions have emerged (i.e., socio-emotional). As such, I introduce the concept religious resilience as a framework for understanding how and why a religion’s “afterlife” is sustained despite macro-level processes that might undermine its prominence. Altogether, this study has implications for how we conceptualize racialized religion and how we measure, operationalize, and understand religious expression across social locations.
Segmentation of Sandplain Lupin Weeds from Morphologically Similar Narrow-Leafed Lupins in the Field
Authors: Danilevicz Monica F. , Rocha Roberto Lujan , Batley Jacqueline , Bayer Philipp E. , Bennamoun Mohammed , Edwards David , Ashworth Michael B.
Published: March 2023

Narrow-leafed lupin (Lupinus angustifolius) is an important dryland crop, providing a protein source in global grain markets. While agronomic practices have successfully controlled many dicot weeds among narrow-leafed lupins, the closely related sandplain lupin (Lupinus cosentinii) has proven difficult to control, reducing yield and harvest quality. Here, we successfully trained a segmentation model to detect sandplain lupins and differentiate them from narrow-leafed lupins under field conditions. The deep learning model was trained using 9171 images collected from a field site in the Western Australian grain belt. Images were collected using an unoccupied aerial vehicle at heights of 4, 10, and 20 m. The dataset was supplemented with images sourced from the WeedAI database, which were collected at 1.5 m. The resultant model had an average precision of 0.86, intersection over union of 0.60, and F1 score of 0.70 for segmenting the narrow-leafed and sandplain lupins across the multiple datasets. Images collected at a closer range and showing plants at an early developmental stage had significantly higher precision and recall scores (p-value < 0.05), indicating image collection methods and plant developmental stages play a substantial role in the model performance. Nonetheless, the model identified 80.3% of the sandplain lupins on average, with a low variation (±6.13%) in performance across the 5 datasets. The results presented in this study contribute to the development of precision weed management systems within morphologically similar crops, particularly for sandplain lupin detection, supporting future narrow-leafed lupin grain yield and quality.
Shadow Enhancement Using 2D Dynamic Stochastic Resonance for Hyperspectral Image Classification
Authors: Liu Qiuyue , Fu Min , Liu Xuefeng
Published: March 2023

With the development of remote sensing technology, classification has become a meaningful way to explore the rich information in hyperspectral images (HSIs). However, various environmental factors may cause noise and shadow areas in HSIs, resulting in weak signals and difficulties in fully utilizing information. In addition, classification methods based on deep learning have made considerable progress, but features extracted from most networks have much redundancy. Therefore, a method based on two-dimensional dynamic stochastic resonance (2D DSR) shadow enhancement and convolutional neural network (CNN) classification combined with an attention mechanism (AM) for HSIs is proposed in this paper. Firstly, to protect the spatial correlation of HSIs, an iterative equation of 2D DSR based on the pixel neighborhood relationship was derived, which made it possible to perform matrix SR in the spatial dimension of the image, instead of one-dimensional vector resonance. Secondly, by using the noise in the shadow area to generate resonance, 2D DSR can help increase the signals in the shadow regions by preserving the spatial characteristics, and enhanced HSIs can be obtained. Then, a 3DCNN embedded with two efficient channel attention (ECA) modules and one convolutional block attention module (CBAM) was designed to make the most of critical features that significantly affect the classification accuracy by giving different weights. Finally, the performance of the proposed method was evaluated on a real-world HSI, and comparative studies were carried out. The experimental results showed that the proposed approach has promising prospects in HSIs’ shadow enhancement and information mining.
LDANet: A Lightweight Dynamic Addition Network for Rural Road Extraction from Remote Sensing Images
Authors: Liu Bohua , Ding Jianli , Zou Jie , Wang Jinjie , Huang Shuai
Published: March 2023

Automatic road extraction from remote sensing images has an important impact on road maintenance and land management. While significant deep-learning-based approaches have been developed in recent years, achieving a suitable trade-off between extraction accuracy, inference speed and model size remains a fundamental and challenging issue for real-time road extraction applications, especially for rural roads. For this purpose, we developed a lightweight dynamic addition network (LDANet) to exploit rural road extraction. Specifically, considering the narrow, complex and diverse nature of rural roads, we introduce an improved Asymmetric Convolution Block (ACB)-based Inception structure to extend the low-level features in the feature extraction layer. In the deep feature association module, the depth-wise separable convolution (DSC) is introduced to reduce the computational complexity of the model, and an adaptation-weighted overlay is designed to capture the salient features. Moreover, we utilize a dynamic weighted combined loss, which can better solve the sample imbalance and boosts segmentation accuracy. In addition, we constructed a typical remote sensing dataset of rural roads based on the Deep Globe Land Cover Classification Challenge dataset. Our experiments demonstrate that LDANet performs well in road extraction with fewer model parameters (<1 MB) and that the accuracy and the mean Intersection over Union reach 98.74% and 76.21% on the test dataset, respectively. Therefore, LDANet has potential to rapidly extract and monitor rural roads from remote sensing images.
Centimeter-Level Orbit Determination of GRACE-C Using IGS-RTS Data
Authors: Li Duoduo , Zhou Xuhua , Li Kai
Published: March 2023

GNSS real-time applications greatly benefit from the International GNSS Service’s (IGS) real-time service (RTS). This service does more than provide for terrestrial precise point positioning (PPP); it also brings more possibilities for space-borne technology. With this service, the State-Space Representation (SSR) product, which includes orbit corrections and clock corrections, is finally available to users. In this paper, the GPS real-time orbit and clock corrections provided by 11 analysis centers (ACs) from the day of the year (DOY) 144 to 153 of 2022 are discussed from 3 perspectives: integrity, continuity, and accuracy. Moreover, actual observation data from the GRACE-C satellite are processed, along with SSR corrections from different ACs. The following can be concluded: (1) In terms of integrity and continuity, the products provided by CNE, ESA, and GMV perform better. (2) CNE, ESA, and WHU are the most accurate, with values of about 5 cm for the satellite orbit and 20 ps for the satellite clock. Additionally, the clock accuracy is related to the Block. Block IIR and Block IIR-M are slightly worse than Block IIF and Block IIIA. (3) The accuracy of post-processing reduced-dynamic precise orbit determination (POD) and kinematic POD are at the centimeter level in radius, and the reduced-dynamic POD is more accurate and robust than the kinematic POD.
FusionRCNN: LiDAR-Camera Fusion for Two-Stage 3D Object Detection
Authors: Xu Xinli , Dong Shaocong , Xu Tingfa , Ding Lihe , Wang Jie , Jiang Peng , Song Liqiang , Li Jianan
Published: March 2023

Accurate and reliable perception systems are essential for autonomous driving and robotics. To achieve this, 3D object detection with multi-sensors is necessary. Existing 3D detectors have significantly improved accuracy by adopting a two-stage paradigm that relies solely on LiDAR point clouds for 3D proposal refinement. However, the sparsity of point clouds, particularly for faraway points, makes it difficult for the LiDAR-only refinement module to recognize and locate objects accurately. To address this issue, we propose a novel multi-modality two-stage approach called FusionRCNN. This approach effectively and efficiently fuses point clouds and camera images in the Regions of Interest (RoI). The FusionRCNN adaptively integrates both sparse geometry information from LiDAR and dense texture information from the camera in a unified attention mechanism. Specifically, FusionRCNN first utilizes RoIPooling to obtain an image set with a unified size and gets the point set by sampling raw points within proposals in the RoI extraction step. Then, it leverages an intra-modality self-attention to enhance the domain-specific features, followed by a well-designed cross-attention to fuse the information from two modalities. FusionRCNN is fundamentally plug-and-play and supports different one-stage methods with almost no architectural changes. Extensive experiments on KITTI and Waymo benchmarks demonstrate that our method significantly boosts the performances of popular detectors. Remarkably, FusionRCNN improves the strong SECOND baseline by 6.14% mAP on Waymo and outperforms competing two-stage approaches.
Remote Sensing Analysis of Typhoon-Induced Storm Surges and Sea Surface Cooling in Chinese Coastal Waters
Authors: Li Xiaohui , Han Guoqi , Yang Jingsong , Wang Caixia
Published: March 2023

Inthis study, remote sensing measurements were utilized to examine the characteristics of storm surges and sea surface cooling in Chinese coastal waters caused by typhoons. Altimetric data from satellite altimeters were used to determine the magnitude, cross-shelf decaying scale, and propagating speed of storm surges from typhoons. The results were in agreement with estimates obtained from a theoretical model and tide gauge data, showing that the two storm surges propagated as continental shelf waves along the southeastern coast of China. The sea surface cooling, driven by Typhoons 1319Usagi and 1323Fitow, was analyzed using the remote sensing sea surface temperature product, named the global 1 km sea surface temperature (G1SST) dataset, revealing a considerable decrease in the temperature, with the largest decrease reaching 4.5 °C after the passage of 1319Usagi, in line with buoy estimates of 4.6 °C. It was found that 1323Fitow and 1324Danas jointly impacted the southeastern coast of China, resulting in a significant temperature drop of 4.0 °C. Our study shows that incorporating remotely sensed measurements into the study of oceanic responses to typhoons has significant benefits and complements the traditional tide gauge network and buoy data.
Deep Learning-Based Speech Enhancement of an Extrinsic Fabry–Perot Interferometric Fiber Acoustic Sensor System
Authors: Chai Shiyi , Guo Can , Guan Chenggang , Fang Li
Published: March 2023

To achieve high-quality voice communication technology without noise interference in flammable, explosive and strong electromagnetic environments, the speech enhancement technology of a fiber-optic external Fabry–Perot interferometric (EFPI) acoustic sensor based on deep learning is studied in this paper. The combination of a complex-valued convolutional neural network and a long short-term memory (CV-CNN-LSTM) model is proposed for speech enhancement in the EFPI acoustic sensing system. Moreover, the 3 × 3 coupler algorithm is used to demodulate voice signals. Then, the short-time Fourier transform (STFT) spectrogram features of voice signals are divided into a training set and a test set. The training set is input into the established CV-CNN-LSTM model for model training, and the test set is input into the trained model for testing. The experimental findings reveal that the proposed CV-CNN-LSTM model demonstrates exceptional speech enhancement performance, boasting an average Perceptual Evaluation of Speech Quality (PESQ) score of 3.148. In comparison to the CV-CNN and CV-LSTM models, this innovative model achieves a remarkable PESQ score improvement of 9.7% and 11.4%, respectively. Furthermore, the average Short-Time Objective Intelligibility (STOI) score witnesses significant enhancements of 4.04 and 2.83 when contrasted with the CV-CNN and CV-LSTM models, respectively.
Data Acquisition Control for UAV-Enabled Wireless Rechargeable Sensor Networks
Authors: Yoon Ikjune
Published: March 2023

In the realm of Internet of Things (IoT), wireless sensor networks (WSNs) have been the subject of ongoing research into the use of energy harvesting to capture ambient energy, and wireless power transfer (WPT) via a mobile charger to overcome the energy limitations of sensors. Moreover, to mitigate energy imbalance and reduce the number of hops, strategies have been developed to leverage cars or unmanned aerial vehicles (UAVs) as mobile sinks. The primary objective of this work is to increase network lifetime by reducing energy consumption of hotspot nodes and increasing the amount of data acquired from all sensors in an environment that combines the methods mentioned above.To achieve this objective, the proposed method involves developing multiple minimum depth trees (MDTs) for all nodes, considering the energy of the UAV and sensor nodes. Parent nodes prevent their own energy depletion and ensure data transmission without imbalance by adaptively controlling the data sensed at the nodes and their child nodes. Consequently, the energy depletion of nodes in hotspots is prevented, more sensory data is acquired, and balanced data collection from all nodes is achieved. Simulation results demonstrate that the proposed scheme outperforms other state-of-the-art methods in terms of reduced energy depletion, increased network connectivity, and the amount of data collected at the sink node. This scheme will be applied to applications that collect environmental data outdoors, such as climate measurement, to collect data uniformly and increase the lifespan of the network, thereby reducing network maintenance costs while collecting data effectively.
Statistical Characterization and Modeling of Indoor RF-EMF Down-Link Exposure
Authors: Mulugeta Biruk Ashenafi , Wang Shanshan , Ben Chikha Wassim , Liu Jiang , Roblin Christophe , Wiart Joe
Published: March 2023

With the increasing use of wireless communication systems, assessment of exposure to radio-frequency electromagnetic field (RF-EMF) has now become very important due to the rise of public risk perception. Since people spend more than 70% of their daily time in indoor environments, including home, office, and car, the efforts devoted to indoor RF-EMF exposure assessment has also increased. However, assessment of indoor exposure to RF-EMF using a deterministic approach is challenging and time consuming task as it is affected by uncertainties due to the complexity of the indoor environment and furniture structure, existence of multiple reflection, refraction, diffraction and scattering, temporal variability of exposure, and existence of many obstructions with unknown dielectric properties. Moreover, it is also affected by the existence of uncontrolled factors that can influence the indoor RF-EMF exposure such as the constant movement of people and random movement of furniture and doors as people are working in the building. In this study, a statistical approach is utilized to characterize and model the total indoor RF-EMF down-link (DL) exposure from all cellular bands on each floor over the length of a wing since the significance of distance is very low between any two points on each floor in a wing and the variation of RF-EMF DL exposure is mainly influenced by the local indoor environment. Measurements were conducted in three buildings that are located within a few hundred meters vicinity of two base station sites supporting several cellular technologies (2G, 3G, 4G, and 5G). We apply the one-sample Kolmogorov–Smirnov test on the measurement data, and we prove that the indoor RF-EMF DL exposure on each floor over the length of a wing is a random process governed by a Gaussian distribution. We validate this proposition using leave-one-out cross validation technique. Consequently, we conclude that the indoor RF-EMF DL exposure on each floor over the length of a wing can be modeled by a Gaussian distribution and, therefore, can be characterized by the mean and the standard deviation parameters.
A Novel Master–Slave Interventional Surgery Robot with Force Feedback and Collaborative Operation
Authors: Song Yu , Li Liutao , Tian Yu , Li Zhiwei , Yin Xuanchun
Published: March 2023

In recent years, master–slave vascular robots have been developed to address the problem of radiation exposure during vascular interventions for surgeons. However, the single visual feedback reduces surgeon immersion and transparency of the system. In this work, we have developed a haptic interface based on the magnetorheological fluid (MRF) on the master side. The haptic interface can provide passive feedback force with high force fidelity and low inertia. Additionally, the manipulation of the master device does not change the operating posture of traditional surgery, which allows the surgeon to better adapt to the robotic system. For the slave robot, the catheter and guidewire can be navigated simultaneously which allows the two degrees of action on the catheter and axial action of a guidewire. The resistance force of the catheter navigation is measured and reflected to the user through the master haptic interface. To verify the proposed master–slave robotic system, the evaluation experiments are carried out in vitro, and the effectiveness of the system was demonstrated experimentally.
A Monitoring System for Carbon Dioxide in Honeybee Hives: An Indicator of Colony Health
Authors: Bencsik Martin , McVeigh Adam , Tsakonas Costas , Kumar Tarun , Chamberlain Luke , Newton Michael I.
Published: March 2023

Non-dispersive infra-red (NDIR) detectors have become the dominant method for measuring atmospheric CO2, which is thought to be an important gas for honeybee colony health. In this work we describe a microcontroller-based system used to collect data from Senserion SCD41 NDIR sensors placed in the crown boards and queen excluders of honeybee colonies. The same sensors also provide relative humidity and temperature data. Several months of data have been recorded from four different hives. The mass change measurements, from hive scales, when foragers leave the hive were compared with the data from the gas sensors. Our data suggest that it is possible to estimate the colony size from the change in measured CO2, however no such link with the humidity is observed. Data are presented showing the CO2 decreasing over many weeks as a colony dies.
Pilot Study: Magnetic Motion Analysis for Swallowing Detection Using MEMS Cantilever Actuators
Authors: Hoffmann Johannes , Roldan-Vasco Sebastian , Krüger Karolin , Niekiel Florian , Hansen Clint , Maetzler Walter , Orozco-Arroyave Juan Rafael , Schmidt Gerhard
Published: March 2023

The swallowing process involves complex muscle coordination mechanisms. When alterations in such mechanisms are produced by neurological conditions or diseases, a swallowing disorder known as dysphagia occurs. The instrumental evaluation of dysphagia is currently performed by invasive and experience-dependent techniques. Otherwise, non-invasive magnetic methods have proven to be suitable for various biomedical applications and might also be applicable for an objective swallowing assessment. In this pilot study, we performed a novel approach for deglutition evaluation based on active magnetic motion sensing with permanent magnet cantilever actuators. During the intake of liquids with different consistency, we recorded magnetic signals of relative movements between a stationary sensor and a body-worn actuator on the cricoid cartilage. Our results indicate the detection capability of swallowing-related movements in terms of a characteristic pattern. Consequently, the proposed technique offers the potential for dysphagia screening and biofeedback-based therapies.
An Explainable Student Fatigue Monitoring Module with Joint Facial Representation
Authors: Li Xiaomian , Lin Jiaqin , Tian Zhiqiang , Lin Yuping
Published: March 2023

Online fatigue estimation is, inevitably, in demand as fatigue can impair the health of college students and lower the quality of higher education. Therefore, it is essential to monitor college students’ fatigue to diminish its adverse effects on the health and academic performance of college students. However, former studies on student fatigue monitoring are mainly survey-based with offline analysis, instead of using constant fatigue monitoring. Hence, we proposed an explainable student fatigue estimation model based on joint facial representation. This model includes two modules: a spacial–temporal symptom classification module and a data-experience joint status inferring module. The first module tracks a student’s face and generates spatial–temporal features using a deep convolutional neural network (CNN) for the relevant drivers of abnormal symptom classification; the second module infers a student’s status with symptom classification results with maximum a posteriori (MAP) under the data-experience joint constraints. The model was trained on the benchmark NTHU Driver Drowsiness Detection (NTHU-DDD) dataset and tested on an Online Student Fatigue Monitoring (OSFM) dataset. Our method outperformed the other methods with an accuracy rate of 94.47% under the same training–testing setting. The results were significant for real-time monitoring of students’ fatigue states during online classes and could also provide practical strategies for in-person education.