Search Results (213)

At 47° S in Argentine Patagonia, the interaction between the Southern Westerly Winds (SWW) and the Andean barrier generates a steep climatic gradient, providing an ideal setting to evaluate Holocene vegetation responses. This study focuses on the extra-Andean sector, where new pollen records from La Tapera (LTap) and Cisne 7 provide insights into steppe vegetation dynamics under dry conditions. These sequences are contrasted with previously studied records further west (LF, ZB, CMN1, CMN2, and COCU) to assess west–east gradients in vegetation change and moisture availability throughout the Holocene. Western records indicate that the Early Holocene was dominated by grass–dwarf-shrub steppe under arid conditions, followed by increased humidity around 7600 cal yr BP that promoted the development of forest–steppe ecotonal environments. The Middle Holocene was characterised by aridity, reflected in shrub dominance and reduced forest signals, whereas the Late Holocene included a humid pulse between ~1750 and 1000 cal yr BP, followed by renewed aridity over the last millennium. In contrast, eastern records show persistent shrub–dwarf-shrub steppes since ~4700 cal yr BP, with vegetation changes expressed mainly as shifts in the relative dominance of shrubs and dwarf–shrubs rather than floristic replacements. Archaeological sites corroborated and complemented the continuous records, strengthening the reconstruction of environmental variability across different temporal windows. Overall, this west–east comparison highlights the differential sensitivity of ecosystems to SWW fluctuations, reinforcing their role as an important forcing of hydrological balance and vegetation dynamics in mid-latitude Patagonia. Full article

Background/Objectives: Quercetin is a bitter compound with demonstrated anticancer effects in preclinical models of head and neck squamous cell carcinoma (HNSCC). In taste transduction, bitter compounds activate bitter taste receptors (T2Rs), a group of G protein-coupled receptors with downstream signaling that includes cytosolic calcium (Ca²⁺) release. T2Rs are expressed in HNSCC cells, where their activation induces apoptosis in vitro. Increased T2R expression in HNSCC also correlates with improved patient survival. The objective of this study was to investigate the role of quercetin as an anticancer T2R agonist in HNSCC cells in vitro and ex vivo. Methods: Quercetin-mediated Ca²⁺ responses were assessed using live cell Ca²⁺ imaging in the presence of the T2R14 antagonist LF1 and G-protein inhibitor YM-254980 (YM) in UM-SCC-47 and FaDu HNSCC cell lines. Cell viability was evaluated using crystal violet assays in cell lines and MTS assays in patient-derived tumor slices. Mitochondrial depolarization was measured with TMRE in the presence and absence of T2R pathway inhibitors. Results: Quercetin induced a Ca²⁺ response in HNSCC cells, which was significantly reduced by LF1 and YM. Quercetin also decreased cell viability in vitro. Ex vivo experiments showed a decrease in viability that was not statistically significant. Finally, quercetin caused mitochondrial depolarization, which was reduced in the presence of LF1 but not by YM. Conclusions: In HNSCC cells, quercetin causes a Ca²⁺ response that is likely mediated by T2R14, although genetic knockdown or knockout models are needed to more definitively support this hypothesis. Additionally, quercetin decreases viability in vitro and causes mitochondrial depolarization. Full article

Background/Objectives: Early life is crucial for infant gut development and intestinal homeostasis. Lactoferrin (LF) and osteopontin (OPN) are bioactive breast milk proteins that are supplemented into infant formula to promote gut development. However, the combined effect of LF and OPN (LOP) on in vivo gut maturation has not been fully elucidated. This study investigated the effects of LF, OPN, and LOP on intestinal epithelium maturation in C57BL/6N mice from postnatal days 7 to 21. Methods: 3-day-old pups were assigned to four groups: Control group, LF group: 300 mg/kg LF; OPN group: 300 mg/kg OPN, LOP group: 300 mg/kg of a 1:5 (w/w) mixture of LF and OPN. Results: Compared to controls, LOP reduced plasma Diamine Oxidase (DAO) activity by 1.54-fold and D-lactate levels by 1.41-fold, demonstrating greater efficacy than LF or OPN alone in reducing intestinal permeability. LOP also significantly increased intestinal absorptive cells versus controls or single proteins. Mechanistically, LOP promoted directional intestinal stem cell differentiation, increasing jejunal transit-amplifying cells by 1.40-fold in 21-day-old mice. LOP upregulated expression of the Notch pathway target Hes1 by 1.70-fold. Further investigations revealed LOP activated Notch signaling via the transcription factor Brg1. Validation using intestinal organoids and IEC-6 cells confirmed intact OPN within LOP mediates increased Brg1 expression, activating the Notch pathway to direct intestinal stem cell differentiation into absorptive cells. Conclusions: Collectively, these findings in neonatal mice suggest that LOP cooperatively enhances intestinal barrier maturation and directs stem cell differentiation via Brg1-Notch signaling, offering potential insights for future research on bioactive protein supplementation in infant nutrition. Full article

The binary anomaly detection (classification) of ionospheric data related to Very Low Frequency (VLF) signal amplitude in prior research demonstrated the potential for development and further advancement. Further data quality improvement is integral for advancing the development of machine learning (ML)-based ionospheric data (VLF signal amplitude) anomaly detection. This paper presents the transition from binary to multi-class classification of ionospheric signal amplitude datasets. The dataset comprises 19 transmitter–receiver pairs and 383,041 manually labeled amplitude instances. The target variable was reclassified from a binary classification (normal and anomalous data points) to a six-class classification that distinguishes between daytime undisturbed signals, nighttime signals, solar flare effects, instrument errors, instrumental noise, and outlier data points. Furthermore, in addition to the dataset, we developed a freely accessible web-based tool designed to facilitate the conversion of MATLAB data files to TRAINSET-compatible formats, thereby establishing a completely free and open data pipeline from the WALDO world data repository to data labeling software. This novel dataset facilitates further research in ionospheric signal amplitude anomaly detection, concentrating on effective and efficient anomaly detection in ionospheric signal amplitude data. The potential outcomes of employing anomaly detection techniques on ionospheric signal amplitude data may be extended to other space weather parameters in the future, such as ELF/LF datasets and other relevant datasets. Full article

Accurate estimation of the State of Charge (SOC) of lithium-ion batteries under complex operating conditions remains challenging, as the SOC signal combines a global linear (quasi-linear) trend with localized dynamic fluctuations driven by polarization, ion diffusion, temperature gradients, and load transients. In practice, open-circuit-voltage (OCV) approaches are affected by hysteresis and parameter drift, while high-fidelity electrochemical models require extensive parameterization and significant computational resources that hinder their real-time deployment in battery management systems (BMS). Purely data-driven methods capture temporal patterns but may under-represent abrupt local fluctuations and blur the distinction between trend and fluctuation, leading to biased SOC tracking when operating conditions change. To address these issues, LF-Net is proposed. The architecture decomposes battery time series into long-term trend and local fluctuation components. A linear branch models the quasi-linear SOC evolution. Multi-scale convolutional and differential branches enhance sensitivity to transient dynamics. An adaptive Fusion Module aggregates the representations, improving interpretability and stability, and keeps the parameter budget small for embedded hardware. Our experimental results demonstrate that the proposed model achieves a mean absolute error (MAE) of 0.0085 and a root-mean-square error (RMSE) of 0.0099 at 40 °C, surpassing mainstream models and confirming the method’s efficacy. Full article

Herein we report the very first experiments which were conducted in an attempt to demonstrate the ability of low-field (LF), compact benchtop NMR spectrometers to provide spectral profiles of whole human biofluids, which took place in September–November 2014, and this paper represents a 10-year (decennial) anniversary of this work. LF ¹H NMR analysis was performed on ²H₂O-reconstituted lyophilizates of urine samples (pH 7.00) collected from untreated Niemann-Pick type C1 (NPC1) disease patients and their heterozygous carrier controls (n = 3 in each case). ¹H NMR spectra were acquired on a 60 MHz Oxford Instruments Pulsar compact benchtop spectrometer with spectral filter widths of 5000 Hz, using 1000–1600 scans, and relaxation delays of 15 or 30 s. Further, 400 MHz spectra were also obtained on these samples. Following parameter optimisation, the benchtop system generated reasonable quality urinary ¹H NMR profiles containing ca. 30 signals. Benchtop ¹H NMR analysis confirmed the abnormal urinary metabolic signature of NPC1 disease, and also revealed a gastric permeability disorder in one patient (detection of upregulated urinary sucrose, verified by 400 MHz NMR analysis). Early LF NMR experiments also demonstrated that glucose was trackable in control urine samples pre-spiked with this metabolite. This paper continues with further developments made on LF NMR-based metabolomics technologies, which are systematically discussed for related investigations conducted since 2014. In conclusion, such ‘first-time’ bioanalytical information regarding spectral quality served to pave the way forward for benchtop NMR-based metabolomics investigations of biofluids, which could provide invaluable disease-engendered ‘snapshots’ of disturbances to metabolic pathways and activities, along with those of any co-linked or unlinked comorbidities. Full article

As urbanization progresses rapidly, cities face growing challenges of land resource scarcity and the pressure on green ecological spaces. This not only affects the sustainable development of cities but also presents a major challenge to the resilience of urban ecosystems (UER). As an emerging land use model, land financialization (LF), which involves the circulation and financing of land as a financial asset, has become an important means to promote UER. Therefore, this paper examines 221 prefecture-level cities across mainland China to explore the impact of land financialization on urban ecological resilience and aims to reveal the specific pathways through which land financialization improves urban ecological resilience through mechanisms like resource reallocation, industrial structure rationalization, green innovation, green signals, and environmental regulation. This paper employs a two-way fixed effects model, robustness tests, and endogeneity tests, supplemented by mechanism and heterogeneity analysis, to explore the impact of LF on UER. The findings show that LF plays a significant role in improving UER. Mechanism analysis reveals that LF significantly boosts UER by optimizing the distribution of land and financial resources, as well as enhancing the rationalization of the industrial structure. Additionally, enterprise green technology innovation, green value, and the intensity of environmental regulation play a positive moderating role in this process. In addition, the heterogeneity analysis reveals the inclusive characteristics of LF on urban ecological transformation. In cities with higher levels of land price distortion, as well as in old industrial cities, small cities, and peripheral cities with poorer resource characteristics and administrative resources, LF has a more significant impact on promoting the improvement of UER. Based on the findings, this paper proposes policy recommendations to promote the improvement of urban green ecology and support the innovation of land financialization. These insights contribute to the theoretical discourse on greenization and provide essential, practical guidance for optimizing the allocation of land and financial resources, as well as establishing a framework for green and high-quality development. Full article

Light-field (LF) imaging transforms occlusion removal by using multiview data to reconstruct hidden regions, overcoming the limitations of single-view methods. However, this advanced capability often comes at the cost of increased computational complexity. To overcome this, we propose the U²-LFOR network, an end-to-end neural network designed to remove occlusions in LF images without compromising performance, addressing the inherent complexity of LF imaging while ensuring practical applicability. The architecture employs Residual Atrous Spatial Pyramid Pooling (ResASPP) at the feature extractor to expand the receptive field, capture localized multiscale features, and enable deep feature learning with efficient aggregation. A two-stage U²-Net structure enhances hierarchical feature learning while maintaining a compact design, ensuring accurate context recovery. A dedicated refinement module, using two cascaded residual blocks (ResBlock), restores fine details to the occluded regions. Experimental results demonstrate its competitive performance, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 29.27 dB and Structural Similarity Index Measure (SSIM) of 0.875, which are two widely used metrics for evaluating reconstruction fidelity and perceptual quality, on both synthetic and real-world LF datasets, confirming its effectiveness in accurate occlusion removal. Full article

Low-frequency (LF) time code timing technology holds significant importance in civilian applications such as radio-controlled clocks. This study focuses on the design and implementation of a high-precision Binary Phase Code (BPC) LF time code signal generator. A generator system was constructed, demonstrating good stability, superior resolution, and flexible adjustment capabilities for both amplitude and phase. The system employs an ARM + FPGA cooperative architecture: the ARM processor is responsible for parsing and scheduling the time code data, while the FPGA implements carrier wave generation and high-precision digital modulation. This digital processing is combined with analog circuitry to achieve digital-to-analog (D/A) signal conversion. Compared to traditional methods, carrier generation is achieved using Direct Digital Synthesis (DDS) technology. Digital modulation techniques enable the precise control of the modulation depth (adjustable between 70% and 90%) and phase (with a resolution of 1 ns). A sliding window algorithm was utilized for time difference calculation and compensation. Testing confirmed the stability of key signal parameters, including integrity, carrier frequency and modulation depth. These results validate the feasibility and superiority of the digital LF time code generation technology proposed in this study, providing a valuable reference for the development of next-generation timing equipment. Full article

Environmental monitoring systems often operate continuously, measuring various parameters, including carbon dioxide levels (CO₂), relative humidity (RH), temperature (T), and other factors that affect environmental conditions. Such systems are often referred to as smart systems because they can autonomously monitor and respond to environmental conditions and can be integrated both indoors and outdoors to detect, for example, structural anomalies. However, these systems typically have high energy consumption, data overload, and large equipment sizes, which makes them difficult to install in constrained spaces. Therefore, three challenges remain unresolved: efficient energy use, accurate data measurement, and compact installation. To address these limitations, this study proposes a two-to-one threshold sampling approach, where the CO₂ measurement is activated when the specified T and $RH$ change thresholds are exceeded. This event-driven method avoids redundant data collection, minimizes power consumption, and is suitable for resource-constrained embedded systems. The proposed approach was implemented on a low-power, small-form and self-made multivariate sensor based on the PIC16LF19156 microcontroller. In contrast, a commercial monitoring system and sensor modules based on the Arduino Uno were used for comparison. As a result, by activating only key points in the T and $RH$ signals, the number of CO₂ measurements was significantly reduced without loss of essential signal characteristics. Signal reconstruction from the reduced points demonstrated high accuracy, with a mean absolute error (MAE) of 0.0089 and root mean squared error (RMSE) of 0.0117. Despite reducing the number of CO₂ measurements by approximately 41.9%, the essential characteristics of the signal were saved, highlighting the efficiency of the proposed approach. Despite its effectiveness in controlled conditions (in buildings, indoors), environmental factors such as the presence of people, ventilation systems, and room layout can significantly alter the dynamics of CO₂ concentrations, which may limit the implementation of this approach. Future studies will focus on the study of adaptive threshold mechanisms and context-dependent models that can adjust to changing conditions. This approach will expand the scope of application of the proposed two-to-one sampling technique in various practical situations. Full article

Alzheimer’s disease (AD) early screening requires non-invasive, high-sensitivity detection of low-abundance biomarkers in complex biofluids like saliva. In this study, we present a miniaturized, silicon-based electrochemical sensor for sequential detection of two AD salivary biomarkers, lactoferrin (Lf) and amyloid β-protein 1-42 (Aβ_1-42), on a single reusable electrode. The sensor features a three-electrode system fabricated by sputter-coating a quartz substrate with gold (Au) sensing electrodes, which are further modified with gold nanoparticles (AuNPs) to form 3D dendritic structures that enhance surface area and electron transfer. To improve specificity, immunomagnetic beads (MBs) are employed to selectively capture and isolate target biomarkers from saliva samples. These MB–biomarker complexes are introduced into a polydimethylsiloxane chamber aligned with Au sensing electrodes, where a detachable magnet localizes the complexes onto the electrode surface to amplify redox signals. The AuNPs/MBs sensor achieves detection limits of 2 μg/mL for Lf and 0.1 pg/mL for Aβ_1-42, outperforming commercial ELISA kits (37.5 pg/mL for Aβ_1-42) and covering physiological salivary concentrations. After the MBs capture the biomarkers, the sensor can output the result within one minute. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) measurements confirm enhanced electron transfer kinetics on AuNP-decorated surfaces, while linear correlations (R² > 0.95) validate quantitative accuracy across biomarker ranges. The compact and integrated design eliminates reliance on bulky instrumentation and enables user-friendly operation, establishing a promising platform for portable, cost-effective AD screening and monitoring. Full article

The spatiotemporal heterogeneity of moisture distribution causes the coal pillar dams in underground water reservoirs to undergo long-term dry–wet cycles (DWCs) under varying moisture content increments (MCIs). Accurately measuring the pore damage and fractal dimensions (D_f) of coal rock by different MCIs under DWCs is a prerequisite for in-depth disclosure of the strength deterioration mechanism of underground reservoir coal pillar dams. This study employed low-field nuclear magnetic resonance (LF-NMR) to quantitatively characterize the pore structural evolution and fractal dimension with different MCI variations (Δw = 4%, 6%, 8%) after one to five DWCs. The results indicate that increasing MCIs at constant DWC numbers (N_DWC) induces significant increases in pore spectrum area, adsorption pore area, and seepage pore area. MRI visualization demonstrates a progressive migration of NMR signals from sample peripheries to internal regions, reflecting enhanced moisture infiltration with higher MCIs. Total porosity increases monotonically with MCIs across all tested cycles. Permeability, T₂ cutoff (T_2C), and D_f of free pores exhibit distinct response patterns. A porosity-based damage model further reveals that the promoting effect of cycle numbers on pore development and expansion outweighs that of MCIs at N_DWC = 5. This pore-scale analysis provides essential insights into the strength degradation mechanisms of coal pillar dams under hydro-mechanical coupling conditions. Full article

The probiotic bacteria Lactobacillus fermentum ZC529 (L.f ZC529) has been identified from the colon of the Diannan small-ear (DSE) pig, but its intestinal protective function still lacks investigation. Here, we established a dextran sodium sulfate (DSS)-induced intestinal oxidative stress model in both Drosophila and porcine small intestinal epithelial (IPEC-J2) cell lines to explore the anti-oxidative and anti-inflammatory effects of L.f ZC529. The data showed that the intestinal colonization of L.f ZC529 counteracted DSS-induced intestinal oxidative stress and excessive reactive oxygen species (ROS) generation by activation of the CncC pathway, a homology of the nuclear factor erythroid 2-related factor 2 (Nrf2) in mammalian systems. Moreover, L.f ZC529 supplementation prevented flies from DSS-induced intestinal barrier damage, inflammation, abnormal excretory function, and shortened lifespan. Finally, L.f ZC529 also attenuated DSS-induced intestinal injury in the IPEC-J2 cell line by activating the Keap1-Nrf2 signaling and inhibiting the NF-κB signaling pathways. Together, this study unraveled the profound intestinal protective function of L.f ZC529 and provides its potential application as a new antioxidant in improving animal intestinal health as well as in developing a new probiotic in the food industry. Full article

This study investigates the impact on the Earth’s ionosphere of a severe geomagnetic storm (Dst $\sim -212$ nT) that began on 23 April 2023 at around 17:37 UT according to very low-frequency (VLF, 3–30 kHz) or low-frequency (LF, 30–300 kHz) radio signals and ionosonde data. We analyze VLF/LF signals received by SuperSID monitors located in mid-latitude (Europe) and low-latitude (South America, Colombia) areas across nine different propagation paths in the Northern Hemisphere. Mid-latitude regions exhibited a daytime amplitude perturbation, mostly an increase, by ∼3–5 dB during the storm period, with a subsequent recovery after 7–8 days post April 23. In contrast, signals received in low-latitude regions (UTP, Colombia) did not show significant variation during the storm-disturbed days. We also observe that the 3-hour average of foF2 data declined by up to 3 MHz on April 23 and April 24 at the European Digisonde stations. However, no significant variation in foF2 was observed at the low-latitude Digisonde stations in Brazil. Both the VLF and ionosonde data exhibited anomalies during the storm period in the European regions, confirming that both D- and F-region ionospheric perturbation was caused by the severe geomagnetic storm. Full article

This paper presents a new high-speed RGB image transmission system over Free-Space Optics (FSO) channel employing Optical Code Division Multiple Access (OCDMA) with Permutation Vector (PV) codes. Four RGB images are transmitted simultaneously at 10 Gbps per image, achieving a total capacity of 40 Gbps. The system’s performance is evaluated under various atmospheric conditions, including three fog levels and real-world visibility data from Alexandria city, Egypt. Image Quality Assessment (IQA) metrics, including Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), correlation coefficients, and Structural Similarity Index Measure (SSIM), are evaluated for both unfiltered and median-filtered images. The results show significant degradation in image quality due to transmission distance and atmospheric attenuation. In Alexandria’s clear atmospheric conditions, the system achieves a maximum transmission range of 15 km with acceptable visual quality, while the range is reduced to 2.6 km, 1.6 km, and 1 km for Low Fog (LF), Medium Fog (MF), and Heavy Fog (HF), respectively. At these distances, the RGB images achieve minimum SNR, RMSE, and SSIM values of 7.27 dB, 47.66, and 0.20, respectively, with further improvements when applying median filtering. Full article