Search Results (17)

To mitigate CO₂ emissions from raw material decomposition and reduce the consumption of natural resources, this study investigated the use of mussel and oyster shell waste as secondary raw materials in earthenware production. Mineralogical, chemical and thermal analyses confirmed their suitability as sources of bio-calcite. Specimens incorporating various replacement levels (0–100%) showed no significant differences in key properties. Plates produced with mussel-derived bio-calcite in a pilot plant exhibited comparable properties to standard ceramics, demonstrating their industrial viability. CO₂ emissions were reduced by 14% and 10% in mussel and oyster shell-based ceramics, respectively, potentially saving up to 53 kgCO₂eq/t under the European Emissions Trading System, if the shells are classified as by-products. These findings demonstrated that bivalve shell waste can effectively replace mineral calcite in earthenware products, reducing CO₂ emissions and virgin raw material consumption, diverting waste from landfills and promoting sustainability in the ceramic industry. Full article

Metal exposure is a potential risk factor for amyotrophic lateral sclerosis (ALS). Increasing evidence suggests that elevated levels of DNA damage are present in both familial (fALS) and sporadic (sALS) forms of ALS, characterized by the selective loss of motor neurons in the brain, brainstem, and spinal cord. However, identifying and differentiating initial biomarkers of DNA damage response (DDR) in both forms of ALS remains unclear. The toxicological profiles from the Agency for Toxic Substances and Disease Registry (ATSDR) and our previous studies have demonstrated the influence of metal exposure-induced genotoxicity and neurodegeneration. A comprehensive overview of the ATSDR’s toxicological profiles and the available literature identified 15 metals (aluminum (Al), arsenic (As), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), mercury (Hg), nickel (Ni), selenium (Se), uranium (U), vanadium (V), and zinc (Zn)) showing exposure-induced genotoxicity indicators associated with ALS pathogenesis. Genetic factors including mutations seen in ALS types and with concomitant metal exposure were distinguished, showing that heavy metal exposure can exacerbate the downstream effect of existing genetic mutations in fALS and may contribute to motor neuron degeneration in sALS. Substantial evidence associates heavy metal exposure to genotoxic endpoints in both forms of ALS; however, a data gap has been observed for several of these endpoints. This review aims to (1) provide a comprehensive overview of metal exposure-induced genotoxicity in ALS patients and experimental models, and its potential role in disease risk, (2) summarize the evidence for DNA damage and associated biomarkers in ALS pathogenesis, (3) discuss possible mechanisms for metal exposure-induced genotoxic contributions to ALS pathogenesis, and (4) explore the potential distinction of genotoxic biomarkers in both forms of ALS. Our findings support the association between metal exposure and ALS, highlighting under or unexplored genotoxic endpoints, signaling key data gaps. Given the high prevalence of sALS and studies showing associations with environmental exposures, understanding the mechanisms and identifying early biomarkers is vital for developing preventative therapies and early interventions. Limitations include variability in exposure assessment and the complexity of gene–environment interactions. Studies focusing on longitudinal exposure assessments, mechanistic studies, and biomarker identification to inform preventative and therapeutic strategies for ALS is warranted. Full article

Chlorophyll content is a critical indicator of the physiological status and fruit quality of pear trees, with Soil Plant Analysis Development (SPAD) values serving as an effective proxy due to their advantages in rapid and non-destructive acquisition. However, current remote sensing-based SPAD retrieval studies are primarily limited to single phenological stages or rely on a narrow set of input features, lacking systematic exploration of multi-temporal feature fusion and comparative model analysis. In this study, pear leaves were selected as the research object, and Sentinel-2 remote sensing data combined with in situ SPAD measurements were used to conduct a comprehensive retrieval study across multiple growth stages, including flowering, fruit-setting, fruit enlargement, and maturity. First, spectral reflectance and representative vegetation indices were extracted and subjected to Pearson correlation analysis to construct three input feature schemes. Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. The results demonstrated that (1) both spectral reflectance and vegetation indices exhibited significant correlations with SPAD values, indicating strong retrieval potential; (2) the OIA model consistently outperformed individual algorithms, achieving the highest accuracy when using the combined feature scheme; (3) among the phenological stages, the fruit-enlargement stage yielded the best retrieval performance, with R² values of 0.740 and 0.724 for the training and validation sets, respectively. This study establishes a robust SPAD retrieval framework that integrates multi-source features and multiple models, enhancing prediction accuracy across different growth stages and providing technical support for intelligent orchard monitoring and precision management. Full article

The olive fruit fly Bactrocera oleae (Rossi) (Diptera: Tephritidae) is regarded as the most harmful pest insect for olive trees worldwide. In order to control olive fruit fly populations and mitigate the damage and economic losses they produce, the development of novel strategies to control the olive fruit fly within an integrated pest management scope has become a major concern. Here we show that a 24-h pre-exposure to the male-specific γ-hexalactone significantly reduces the oviposition on an artificial substrate. The number of eggs per female laid by those females pre-exposed to 1 mg of γ-hexalactone was significantly reduced (6.8 ± 6.1 eggs/female) in comparison to naïve (i.e., non-exposed) females (22.4 ± 10.9 eggs/female), yielding a mean oviposition activity index (OAI) of −0.56 ± 0.22. Contrarily, no significant effect was observed when females were pre-exposed to 0.5 mg of compound, even though the number of eggs per female (14.2 ± 6.3) was lower than that of naïve females, resulting in a mean OIA of −0.24 ± 0.17. Overall, this research represents a preliminary basis for delving into the potential of γ-hexalactone for being used as an oviposition disruptant, albeit further research is still required to address this issue. Full article

Chlorophyll is crucial for pear tree growth and fruit quality. In order to integrate the unmanned aerial vehicle (UAV) multispectral vegetation indices and textural features to realize the estimation of the SPAD value of pear leaves, this study used the UAV multispectral remote sensing images and ground measurements to extract the vegetation indices and textural features, and analyze their correlation with the SPAD value of leaves during the fruit expansion period of the pear tree. Finally, four machine learning methods, namely XGBoost, random forest (RF), back-propagation neural network (BPNN), and optimized integration algorithm (OIA), were used to construct inversion models of the SPAD value of pear trees, with different feature inputs based on vegetation indices, textural features, and their combinations, respectively. Moreover, the differences among these models were compared. The results showed the following: (1) both vegetation indices and textural features were significantly correlated with SPAD values, which were important indicators for estimating the SPAD values of pear leaves; (2) combining vegetation indices and textural features significantly improved the accuracy of SPAD value estimation compared with a single feature type; (3) the four machine learning algorithms demonstrated good predictive ability, and the OIA model outperformed the single model, with the model based on the OIA inversion model combining vegetation indices and textural features having the best accuracy, with $R^2$ values of 0.931 and 0.877 for the training and validation sets, respectively. This study demonstrated the efficacy of integrating multiple models and features to accurately invert SPAD values, which, in turn, supported the refined management of pear orchards. Full article

The difficulty of classifying retinal fundus images with one or more illnesses present or missing is known as fundus multi-lesion classification. The challenges faced by current approaches include the inability to extract comparable morphological features from images of different lesions and the inability to resolve the issue of the same lesion, which presents significant feature variances due to grading disparities. This paper proposes a multi-disease recognition network model, Fundus-DANet, based on the dilated convolution. It has two sub-modules to address the aforementioned issues: the interclass learning module (ILM) and the dilated-convolution convolutional block attention module (DA-CBAM). The DA-CBAM uses a convolutional block attention module (CBAM) and dilated convolution to extract and merge multiscale information from images. The ILM uses the channel attention mechanism to map the features to lower dimensions, facilitating exploring latent relationships between various categories. The results demonstrate that this model outperforms previous models in classifying fundus multilocular lesions in the OIA-ODIR dataset with 93% accuracy. Full article

Euiin-tang reduces obesity and hypertension. Patients with obesity may develop obesity-induced asthma (OIA) owing to phlegm dampness. This study aimed to determine whether euiin-tang alleviates high-fat diet (HFD)-induced OIA in C57BL/6 mice. OIA was developed by HFD feeding for 15 weeks in C57BL/6 mice, and euiin-tang (5 mg/10 g/day) was orally administered for the last five weeks. Oral administration of euiin-tang suppressed HFD-induced changes in body weight, liver weight, airway hypersensitivity (AHR), and immune cell infiltration in bronchoalveolar lavage fluid. Histological analysis revealed that euiin-tang treatment suppressed HFD-induced mucosal inflammation, hypersecretion, and fibrosis. The lungs and gonadal white adipose tissue showed increased expression of inflammatory cytokines (IL-1β, IL-17A, TNF-α, IL-6, IL-13, IFN-γ, MPO, and CCL2) following HFD, whereas euiin-tang inhibited this increase. HFD also increased the number of pro-inflammatory CD86⁺ M1 macrophages and decreased the number of anti-inflammatory CD206⁺ M2 macrophages in the lungs, whereas euiin-tang treatment reversed these effects. HFD induced a decrease in adiponectin and an increase in leptin, which was reversed by euiin-tang. Therefore, euiin-tang may be a potential therapeutic agent for OIA because it suppresses metaflammation as demonstrated in the present study. Full article

Integer Aperture Bootstrapping (IAB) is a crucial method for testing ambiguity acceptance in carrier-phase differential global navigation satellite system (CDGNSS) positioning. It has the advantage that integrity parameters, such as the failure rate, can be analytically calculated, which is essential in safety-of-life applications. Although the IAB methods have been extensively studied, their threshold-determining method is still not well explained, theoretically. In this study, a new method, named Analytical Integer Aperture Bootstrapping (AIAB), is theoretically derived to determine the optimal IAB threshold. AIAB is novel in that: (1) a precise and easy-to-compute expression has been developed to describe the relationship between the IAB threshold and the failure rate, (2) an analytical function model has been derived from the expression to determine the IAB threshold; moreover, the function model is simplified, and (3) a data-constraint approach has been proposed to reduce the complexity of IAB. In the global CDGNSS simulations, AIAB is shown to outperform the existing IAB methods under both strong and weak models, particularly at low fix rates, which are 23% to 40% higher than the basic IAB method. The Monte Carlo simulation results show that AIAB can obtain almost theoretically the same performance as Optimal Integer Aperture (OIA). Full article

To understand and optimise downstream processing of ores, reliable information about mineral abundance, association, liberation and textural characteristics is needed. Such information can be obtained by using Optical Image Analysis (OIA) in reflected light, which can achieve good discrimination for the majority of minerals. However, reliable automated segmentation of non-opaque minerals, such as quartz, which have reflectivity close to that of the epoxy they are embedded in, has always been problematic. Application of standard thresholding techniques for that purpose typically results in significant misidentifications. This paper presents a sophisticated segmentation mechanism, based on enhanced thresholding of non-opaque minerals developed for Commonwealth Scientific and Industrial Research Organisation’s (CSIRO) Mineral5/Recognition5 OIA software, which significantly improves segmentation in many applications. The method utilises an enhanced image view using an adjusted reflectivity scale for more precise initial thresholding, and comprehensive clean-up procedures for further segmentation improvement. For more complex cases, the method also employs specific particle border thresholding with subsequent selective erosion-based “reduction to borders”, while “particle restoration” prevents the detachment of non-opaque grains from larger particles. This method can be combined with “relief-based discrimination of non-opaque minerals” to achieve improved overall segmentation of non-opaque minerals. Full article

Balneotherapy can cause adverse reactions to the usual doses of application of treatments, and consists of a nonspecific clinical picture, the so-called “thermal crisis” or “balneointoxication”. Despite its clinical similarity (gastric discomfort, hepatic congestive outbreaks, cutaneous reactions, etc.) with that observed in acute cyanotoxin poisonings, a thermal crisis has never been associated with the abundant growth of potentially toxic cyanobacteria in mineral water sources. The aim of this work was to verify the hypothetical involvement of cyanotoxins in this clinical picture. Several samples (21) of 18 sources, representative of the different mineral–medicinal waters present in Galicia (northwest Spain) and mostly used for balneotherapy, were collected during September 2018. Samples were filtered and the algae retained were extracted with sonication using acidified methanol and analysed with LC-ESI-HRMS. The target analysis of the cellular matrix samples (limit of detection = 0.01–0.05 µg L⁻¹) did not show nodularin or any of the microcystins (MCs) for which standards are available (MC-dmRR, RR, dmLR, YR, LR, WR, LA, LY, LW and LF). The presence of other MCs, nodularins and related cyanobacterial peptides (CPs) was observed with a suspect screening analysis of cyanotoxins, based on an HRMS home-made database of 157 MCs, 10 nodularins, cylindrospermopsin and 29 CPs previously described in the literature. Signals not referenced in the literature were identified as CPs. Based on HRMS and restrictive criteria (accuracy, isotopic pattern, diagnostic fragments, elements considered, charge, ring plus double bond equivalents and nitrogen rule), the signals were confirmed. In summary, 12 MCs, 2 nodularins and 2 CPs were qualitatively detected. A list of all tentatively identified cyanopeptides in each sample was reported, including the retention times, ion signal type, proposed molecular formula, theoretical m/z, samples where each signal was detected, mass accuracy of measures and their isotopic pattern scores. CP-2 was detected in 14 samples, and it was confirmed as a signal from a cyanobacterial peptide, but with more unsaturations than analogous MCs. The presence of MC-OiaA and MC-OiaAba in three samples was also noteworthy. In addition, [seco-2/3]NOD-R was detected in five samples. Full article

Sinter quality is a key element for stable blast furnace operation. Sinter strength and reducibility depend considerably on the mineral composition and associated textural features. During sinter optical image analysis (OIA), it is important to distinguish different morphologies of the same mineral such as primary/secondary hematite, and types of silico-ferrite of calcium and aluminum (SFCA). Standard red, green and blue (RGB) thresholding cannot effectively segment such morphologies one from another. The Commonwealth Scientific Industrial Research Organization’s (CSIRO) OIA software Mineral4/Recognition4 incorporates a unique textural identification module allowing various textures/morphologies of the same mineral to be discriminated. Together with other capabilities of the software, this feature was used for the examination of iron ore sinters where the ability to segment different types of hematite (primary versus secondary), different morphological sub-types of SFCA (platy and prismatic), and other common sinter phases such as magnetite, larnite, glass and remnant aluminosilicates is crucial for quantifying sinter petrology. Three different sinter samples were examined. Visual comparison showed very high correlation between manual and automated phase identification. The OIA results also gave high correlations with manual point counting, X-ray Diffraction (XRD) and X-ray Fluorescence (XRF) analysis results. Sinter textural classification performed by Recognition4 showed a high potential for deep understanding of sinter properties and the changes of such properties under different sintering conditions. Full article

In this paper, we propose a novel opportunistic interference alignment technique for spectrum-shared radar and uplink cellular communication systems where both systems are equipped with multiple antennas. In the proposed OIA technique, the radar system sends its signal so that the radar signal is received into interference space at base stations (BSs) of the cellular system, while each uplink user (UE) generates its transmit beamforming vector so that communication signals are received within interference space at the radar receiver. Moreover, to achieve better sum-rate performance of the cellular communication system, the BS selects the UEs which results in sufficiently small interference to other cells for the uplink communication. With the proposed OIA technique, detection performance of the radar system is protected, while the communication system achieves satisfactory sum-rate performance. Through extensive computer simulations, we show that the performances of both radar and communication systems with the proposed technique significantly outperform a conventional null-space projection based spectrum sharing scheme. Full article

Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model. Full article

Okadaic acid (OA) and other toxins of the diarrheic shellfish poisoning (DSP) group are accumulated and transformed mainly in many bivalves, inside the digestive gland cells. In this work the absorption of okadaic acid by those cells has been studied by supplying the toxin dissolved in water and including it in oil droplets given to primary cell cultures, and by checking if the uptake is saturable and/or energy-dependent. Okadaic acid was found to be absorbed preferentially from the dissolved phase, and the uptake from oil droplets was substantially lower. The process did not require energy and was non-saturable, indicating that it involved a simple diffusion across the cellular membrane. Some apparent saturation was found due to the quick biotransformation of OA to 7-O-acyl esters. Full article

In Global navigation satellite system (GNSS) data processing, integer ambiguity acceptance test is considered as a challenging problem. A number of ambiguity acceptance tests have been proposed from different perspective and then unified into the integer aperture estimation (IA) framework. Among all the IA estimators, the optimal integer aperture (OIA) achieves the highest success rate with the fixed failure rate tolerance. However, the OIA is of less practical appealing due to its high computation complexity. On the other hand, the popular discrimination tests employ only two integer candidates, which are the essential reason for their sub-optimality. In this study, a generalized difference test (GDT) is proposed to exploit the benefit of including three or more integer candidates to improve their performance from theoretical perspective. The simulation results indicate that the third best integer candidates contribute to more than 70% success rate improvement for integer bootstrapping success rate higher than 0.8 case. Therefore, the GDT with three integer candidates (GDT3) achieves a good trade-off between the performance and computation burden. The threshold function is also applied for rapid determination of the fixed failure rate (FF)-threshold for GDT3. The performance improvement of GDT3 is validated with real GNSS data set. The numerical results indicate that GDT3 achieves higher empirical success rate while the empirical failure rate remains comparable. In a 20 km baseline test, the success rate GDT3 increase 7% with almost the same empirical failure rate. Full article