Search Results (8,119)

Spreading depolarizations (SDs) are major pathophysiological events in several brain diseases, including migraine, brain ischemia, trauma, and epilepsy. However, the electrophysiological detection of SDs remains challenging. In this study, we examined changes in spikes (action potentials (APs) and action currents (ACs)) in layer 5 neurons of the somatosensory cortex of anesthetized rats during transient excitation at the onset of high-potassium-induced SDs. During whole-cell recordings, spike amplitude progressively decreased while spike duration increased during gradual neuronal depolarization at SD onset, culminating in depolarization block. A similar decrease in spike amplitude and increase in spike duration were observed during the pre-SD excitation phase in loose cell-attached recordings from single neurons and in cluster analysis of extracellular spikes. Multiple (non-clustered) unit activity also showed decrease in spike amplitude and spike broadening during pre-SD excitation. These findings suggest that dynamic changes in spike amplitude and duration at SD onset could serve as markers for SD detection. Full article

Fully maintenance-free smart collars for range cattle, sheep and deer must survive years of uncontrolled grazing under highly variable shade and motion conditions. This paper presents an ultra-low-power buck converter governed by a fast integral terminal sliding mode controller (FITSMC) with a fixed-time observer. A new reaching law retains the initial sliding manifold and a negative-power term maintains the constant switching gain to preserve robustness near the surface while attenuating chattering without widening the bandwidth. The fixed-time observer estimates the irradiance and load changes and provides a feed-forward correction, tightening the output regulation regardless of initial conditions. Load step tests with moderate resistance swings showed the proposed method recovers noticeably faster and exhibits slightly lower overshoot than a recent method based on a two-phase power reaching law, while visible inductor current spikes are also suppressed. Simulations under daily grazing profiles confirmed tight output regulation adequate for microwatt data logging and periodic long-range (LoRa) bursts. The sleep mode quiescent current remained in the 9 microamps range, eliminating the need for manual recharge across multi-season field deployments. By integrating robust power electronics with collar-grade solar harvesting, the circuit offers a truly maintenance-free energy path for untethered livestock wearables and supports sustainable precision agriculture. Full article

Bothriochloa ischaemum is a key forage species with strong grazing tolerance and high nutritional value, making precise quantification of spike and seed traits essential for germplasm evaluation and yield prediction. However, the compact architecture and minute seed size in natural field conditions render manual counting inefficient and labor-intensive. To address this limitation, this study presents a non-destructive and automated quantification framework integrating advanced object detection and regression analysis for accurate in situ estimation of spikes and seed numbers. To further address the challenges of dense spike detection caused by occlusion and small object sizes, this study developed a modified model named YOLOv12-DAN by integrating DySample dynamic upsampling, ASFF feature fusion, and NWD loss, which achieved a mean average precision (mAP) of 91.6%. Meanwhile, for the detection of dense kernels on compact spikes, an improved YOLOv12 architecture incorporating an Explicit Visual Center (EVC) module was proposed to enhance multi-scale feature representation. The optimized model attained a bounding box precision of 96.5%, a recall rate of 86.4%, an mAP50 of 94.3%, and an mAP50-95 of 73.9%. Furthermore, a univariate linear regression model based on 132 spike samples verified the reliable consistency between the predicted and actual seed counts, with a mean absolute error (MAE) of 6.30, a mean absolute percentage error (MAPE) of 9.35, and an R-squared (R²) value of 0.808. Finally, the model was deployed through a lightweight end-to-end web application, enabling real-time field operation and promoting its applicability in breeding programs and agronomic decision-making. This study provides a robust technical pathway for automated phenotyping and precision forage improvement. Full article

Background: Rapidly progressive glomerulonephritis (RPGN) is a severe nephrological emergency, frequently secondary to anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis. In older adults, the coexistence of comorbidities and monoclonal gammopathy of undetermined significance (MGUS) makes it difficult to distinguish between ANCA vasculitis and monoclonal gammopathy of renal significance (MGRS), which differ in prognosis and treatment. The coexistence of PR3-ANCA-associated vasculitis and MGUS is uncommon and sparsely documented. Case Presentation: A 72-year-old woman with hypertension and type 2 diabetes presented with acute deterioration and rapidly progressive renal failure, requiring hemodialysis. She had subnephrotic proteinuria, hematuria, and an active urinary sediment. The autoimmune workup showed ANCA negativity using immunofluorescence, but PR3-ANCA positivity using ELISA. Hematologic characterization documented an IgG kappa monoclonal spike; no bone lesions, amyloidosis, or criteria for multiple myeloma were found; and the patient was classified as MGUS. Renal biopsy revealed necrotizing extracapillary pauci-immune glomerulonephritis with cellular and fibrocellular crescents and no monoclonal deposits, consistent with PR3-ANCA vasculitis. Induction therapy with methylprednisolone pulses and oral prednisone was initiated; cyclophosphamide was not administered because of catheter-associated Staphylococcus aureus bacteremia and upper gastrointestinal bleeding complicated by disseminated intravascular coagulation. The patient died on day 25 due to infectious and hemorrhagic complications. Conclusions: This case provides additional documentation of an uncommon overlap between PR3-ANCA-associated vasculitis and MGUS in a Latin American patient and highlights the role of renal biopsy in distinguishing MGRS from pauci-immune vasculitis in the presence of paraproteinemia. It also underscores the need to tailor immunosuppression in frail older adults, balancing disease control against the risk of severe infection. Full article

The brain’s primary sensory processing areas often present a topographical organization and are distributed following hierarchical architecture, permitting the integration of the information in higher levels of its hierarchy: a process referred to as multisensory integration. A system with such characteristics naturally computes in a parallel and distributed manner and is based in associations between the different symbols built from our perceptions of the environment. In this work, we take inspiration from the sensory processing areas of the brain and propose proof-of-concept of a multi-layered neuromorphic system with parallel and distributed computing capabilities by means of simulation. The proposed neuromorphic architecture is constituted by identical self-organizing modules which are trained with on-line unsupervised-friendly learning rules, such as the spike-timing-dependent plasticity (STDP). These self-organizing modules are constituted by oxide-based resistive random access memory (OxRAM) devices, which play the analog synaptic role. The different modules display a topographical organization according to the input dataset features they have been trained with and are organized following a hierarchical system. The system exhibits conceptual associative behavior between inputs with clustering capabilities, able to classify inputs which have never been seen before by the system, according to their similarity with the ones it has been trained with. Full article

The push for higher energy density in electric vehicles has resulted in large-sized lithium-ion batteries, but their geometric upscaling exacts a heavy thermal price. Under high-rate discharge, these massive cells become heat traps, risking thermal runaway. To tame this instability, this paper engineered a hybrid management strategy fusing liquid cooling, Phase Change Materials (PCMs), and flow deflectors. With a primary focus on the structural optimization of the cooling channel, a three-dimensional numerical model, calibrated using experimentally determined thermophysical properties, was developed to overcome the thermal bottlenecks of conventional cooling architectures. Results indicated that the initial channel optimization effectively reduced the maximum temperature to 327.7 K, but it still remained near the safety threshold. Integrating PCM radically altered the thermal landscape, slashing the outlet temperature differential by 41.67% (from 2.76 K to 1.61 K) compared to pure liquid cooling and blunting peak thermal spikes. Furthermore, to overcome laminar stagnation, strategic deflector baffles were introduced to agitate the coolant, enhancing heat dissipation. Specifically, the optimal half-coverage (L = 1/2) baffle configuration successfully lowered the maximum temperature to 322.42 K while substantially reducing the system pressure drop from 948.16 Pa to 627.57 Pa, achieving a 33.33% reduction compared to the full-coverage scheme. Finally, a multi-variable sensitivity analysis confirmed the extraordinary engineering robustness of the optimized configuration, demonstrating a negligible maximum temperature fluctuation of less than 0.5% despite ±10% operational and material uncertainties. This synergistic system actively stabilizes the thermal envelope, offering a robust engineering blueprint for next-generation high-power battery packs. Full article

Background: MSCs possess strong immunoregulatory properties and play a central role in maintaining immune homeostasis by limiting inflammatory responses. Their function is highly plastic and influenced by environmental cues, including viral signals. How SARS-CoV-2-derived antigens affect MSC immunoregulation remains incompletely understood. This study aimed to investigate the impact of SARS-CoV-2 peptides on MSC-mediated immune modulation of T-cells. Methods: MSCs were stimulated directly with SARS-CoV-2 spike protein S peptides or cocultured with SARS-CoV-2 peptide-activated T-cells. TLR4 surface expression and receptor downstream signaling were assessed to evaluate pathway activation. MSC immunoregulatory function was analyzed by measuring suppression of TNF-α and IFN-γ expression and induction of CD4⁺FOXP3⁺ regulatory T-cells. TLR4 inhibition and lipopolysaccharide (LPS) stimulation were used to examine pathway specificity and interaction. Results: SARS-CoV-2 peptides activated TLR4-associated signaling in MSCs, increasing TLR4 expression and NF-κB phosphorylation. Peptide-treated MSCs showed impaired suppression of pro-inflammatory cytokines and reduced induction of regulatory T-cells. TLR4 inhibition prevented these effects. LPS induced similar effects, while combining LPS and peptide stimulation partially restored physiological T-cell cytokine suppression. Conclusions: SARS-CoV-2 peptides modulate MSC immunoregulatory function on T-cells via TLR4-dependent mechanisms. Full article

Vibration-assisted water flooding (VA-WF) can improve sweep efficiency. However, unclear macro-scale mechanisms limit its wider adoption in heavy oil reservoirs. This study combines previous sandpack experiments with two-dimensional Volume-of-Fluid (VOF) simulations to show how vibrations reshape permeability fields and, in turn, pressure and production behaviour. Heavy oil sandpacks were water-flooded under conditions of no vibration and 2 Hz and 5 Hz axial excitation. Measured injection pressure histories and oil production were used to calibrate a VOF model in which absolute permeability follows a log-normal distribution with directional anisotropy. Only when axial and radial permeabilities were assigned a negative local correlation did the model reproduce key observations: secondary pressure spikes, irregular viscous-fingering morphologies, delayed production drops, and variability in cumulative recovery. Parameter sweeps quantify the sensitivity of VA-WF performance to the variance and correlation of the permeability field, and multiple runs estimate the variability in outcomes introduced by stochastic heterogeneity. This study proposes a transferable workflow—comprising sample testing, parameter inference, and probabilistic simulation—to screen excitation conditions and forecast VA-WF performance prior to field implementation, enabling operators to optimize vibration frequency based on reservoir-specific permeability characteristics and to anticipate production variability under uncertainty. These results highlight the dominant factors affecting swept volume and oil recovery, supporting data-driven decision making in VA-WF projects. Full article

Trace quantifications of arsenic (As) in foods by energy-dispersive X-ray fluorescence (ED-XRF) spectrometry are hindered by spectral overlap from lead (Pb) at characteristic emission lines. This study employed artificial neural networks (ANN) to statistically model and correct for As/Pb spectral overlap, enabling accurate As quantifications in rice-based foods. Calibration standards were prepared by pelletizing milled rice spiked with As and Pb, and validation was performed using a certified reference material, commercial rice-based foods, and Pb-spiked commercial foods. As calibration metrics were great (R² = 0.92, standard error in calibration = 41.20 µg kg⁻¹). The validation assessment achieved acceptable error using the As reference material (−19.43% error) and in commercial rice-based foods containing low Pb content (6 of 11 As determinations in agreement with the reference method). Additionally, accurate predictions of As were found in the presence of significant Pb interference (absolute mean error = 14.11% in Pb-spiked commercial foods). Overall, ANN modeling for Pb exhibited poor performance during both calibration and validation. This work demonstrates the usability of an ANN to address the As/Pb overlapping issue while offering insights into the strengths and weaknesses of ANNs when coupled with ED-XRF for trace elemental quantifications in foods. Full article

The porcine epidemic diarrhea virus (PEDV) causes a gastrointestinal disease generating mortality rates approaching 100% in piglets worldwide. The S glycoprotein of PEDV is the main target for the development of vaccines. Two vaccines approved by the Ministry of Agriculture and Rural Development are used in Mexico: the first vaccine is based on an inactivated virus isolated more than a decade ago, whereas the second vaccine is based on mRNA technology. The most important tool for controlling PEDV outbreaks is vaccination; however, coronaviruses are characterized by the accumulation of multiple mutations, which compromise the immune response elicited by outdated vaccines. In this work, we classified the Mexican strains of PEDV reported so far in GenBank, according to their genotypes. Subsequently, we searched for B and T cell epitopes conserved in Mexican PEDV strains using bioinformatic tools. In addition, we explored whether these epitopes can induce allergies, autoimmunity, and/or toxic effects. Next, we determined the localization of B cell epitopes in the S glycoprotein using the protein crystal and protein modeling of several S glycoproteins. Finally, we carried out molecular docking analysis to assess whether these T cell epitopes could interact with the peptide-binding groove of the Swine Leukocyte Antigens (SLAs). Five conserved B cell epitopes were found to be exposed on the surface of the S glycoprotein, whereas several promiscuous CTL and HTL epitopes were bound, with low free energy, to the peptide-binding grooves of SLA-I and SLA-II, respectively. The best epitopes were used to generate a plasmid carrying the sequence to produce a recombinant protein. This plasmid was used for transfection experiments in PK-15 cell culture. The B cell epitopes reported here were recognized by the sera from pigs infected with PEDV but not by the sera from uninfected animals. These results justify future evaluations of the ability of these epitopes to stimulate cytokine production by T cells, antibody generation, and their neutralizing activity. Full article

Background/Objectives: Influenza, RSV, and SARS-CoV-2 co-circulate and evolve under immune and therapeutic pressures, complicating decision-making for both vaccine formulation and antiviral use. Fragmented, pathogen-specific sequencing approaches limit cross-virus comparability. Methods: We applied a standardized, multiplexed, amplicon-based next-generation sequencing (NGS) workflow to 34 diagnostic specimens (Ct < 35) positive for influenza A/B, RSV-A/B, or SARS-CoV-2. Sequencing libraries were generated and run on an Illumina MiSeq platform (2 × 250 bp). Although the wet-lab workflow is standardized across pathogens, consensus generation and annotation utilized two different analysis environments: Geneious Prime for influenza and MicrobioChek for RSV and SARS-CoV-2. Quality metrics included genome breadth and depth of coverage. Results: Near-complete genomes (mean coverage ≥98%) were recovered for all samples. Influenza A(H1N1)pdm09 sequences clustered in clade 6B.1A; A(H3N2) clustered in subclade 3C.2a1b.2a.2; and influenza B belonged to the Victoria lineage V1A.3a.2. RSV sequences were assigned to Nextclade clades A.D.5.1, A.D.1.10, A.D.2.1, and A.D.3 (RSV-A) and to B.D.4.1.3 and B.D.E.1 (RSV-B), consistent with the ON1 (RSV-A) and BA (RSV-B) genotypes prevalent in recent seasons. Clinically relevant mutations included changes in the influenza HA site and neuraminidase substitutions, RSV F-protein polymorphisms, and spike protein substitutions associated with recent Omicron sublineages (L455F/S, F456L) in SARS-CoV-2. Conclusions: A unified amplicon–NGS approach yields harmonized genomic data across respiratory viruses, enabling timely detection of antigenic drift and resistance markers while supporting integrated, cross-pathogen surveillance. Full article

A novel sensitive and selective electrochemiluminescence (ECL) sensor using nitrogen-doped graphyne as the platform was proposed for kanamycin (KAN) detection. First, nitrogen-doped graphyne nanomaterial (1N-GY) with high conductivity was synthesized using a high-energy ball milling method. Compared with ordinary graphyne, the addition of nitrogen atoms can improve the conductivity of the material and reduce the electronic migration energy barrier. Then it was used as a substrate material of the ECL sensor, not only increasing the conductivity of the biosensor but also improving the sensitivity of the ECL sensor by providing more immobilization space for the luminescent probe of Nafion-coated mesoporous silica adsorbed Ru(bpy)₃²⁺ (mSiO₂@Nafion@Ru(bpy)₃²⁺). On this basis, mSiO₂@Nafion@Ru(bpy)₃²⁺ functionalized DNA probes were used as luminescent and capture probes to specifically recognize different concentrations of KAN to produce ECL signals. Under optimal conditions, the proposed ECL sensor exhibited good linearity (10⁻¹²–10⁻⁶ M KAN) and a low detection limit of 1.08 pM. The prepared biosensor with good stability and selectivity successfully detected KAN in honey and milk samples, with spiked recovery rates ranging from 98% to 111.79%. This method not only expands the application of 1N-GY as a novel graphitic material in ECL biosensors but also provides an effective way to check antibiotics in dairy products. Full article

This study evaluated the potential of Sentinel-2-derived NDVI (Normalized Difference Vegetation Index) for predicting within-field variability of winter wheat grain yield in central Lithuania during the 2024 growing season. Reliable within-field yield prediction remains challenging in regions with heterogeneous soils and limited region-specific models, particularly in northeastern Europe. Grain yield data were obtained from combine harvesters equipped with GPS yield monitoring across 13 fields with a total area of 283.6 ha. NDVI values were calculated for four half-monthly periods from March to May, corresponding to key phenological stages (from tillering to spike emergence). Spatial and temporal variability in NDVI–yield relationships was observed, with early May consistently showing the strongest correlations (r up to 0.49), particularly in lower-fertility fields, indicating its critical role in yield prediction. Machine learning models (Random Forest, XGBoost, and Deep Neural Networks), along with linear regression, were applied to predict yields based on NDVI from four growth stages. Random Forest achieved the highest predictive accuracy (MAE = 0.951 t/ha), outperforming the other models. The model also showed the highest correlation with observed yields (Pearson r = 0.717), indicating strong agreement between predicted and measured values. Feature importance analysis confirmed NDVI from 1 to 15 May as the most influential predictor across all models. Predicted yield maps closely matched observed patterns, with the largest discrepancies near field edges due to combine harvester effects. These findings highlight the utility of mid-season NDVI for precise estimation of within-field grain yield variability and demonstrate that Random Forest models can effectively capture the NDVI–yield relationship, particularly under heterogeneous field conditions. Full article

Consistency checking across independently developed implementations of the same industrial control protocol provides an effective signal for defect discovery because an implementation whose response deviates from the majority under identical inputs is more likely to contain faults or robustness issues. However, existing consistency checking methods remain difficult to apply to complex stateful protocols in practice, since sequence dependencies can cause error propagation, large test suites incur high execution cost across multiple implementations, and inconsistent outputs are costly to triage. This paper proposes ConDiffFuzz, a dependency-aware and dynamically adjusted hierarchical consistency checking method for industrial control protocol implementations. ConDiffFuzz analyzes dependencies among check sequences to optimize execution order and dynamically prunes and regenerates dependent sequences after failures to mitigate inconsistency error propagation. The checking process derives implementation-specific finite state machines and inconsistency records, which further support focused differential fuzzing, parallel execution across multiple implementations, and log-based anomaly triage. Experiments on five Modbus over Modbus/TCP implementations show that ConDiffFuzz achieves a test case acceptance rate of 86.00%, increases average path coverage to 74.46%, improves the average number of triggered anomalies by 12.28%, and reduces the false-positive rate by 20.94% compared with four representative baseline fuzzers (SPIKE, BooFuzz, PeachFuzzer, and Kitty). Full article

Managing tillage intensity and diversifying crop rotation are important sustainability levers for conservation agriculture (CA) with the potential to enhance crop resilience, resource efficiency, and yield stability. Accordingly, this study aimed to determine the effect of reduced tillage intensities and cereal–legume rotation systems on the agronomic and physiological performance of rainfed durum wheat grown under Mediterranean semi-arid conditions. To this end, a two cropping seasons field experiment was conducted in northeast Tunisia where the combined effects of two reduced tillage intensities (minimum and no-tillage; MT and NT) and two legume-based crop rotation systems (biennial and triennial; B and T) were compared to the more traditional conventionally tilled monocropping system (CT and M). Crop rotation, particularly when integrated with no-tillage (NT), significantly improved wheat development and grain yield, along with key yield attributes such as thousand-kernel weight and spike density. The interaction between tillage and crop sequence was highly influential; for instance, the NT × T (no-tillage × triennial rotation) combination achieved the highest grain yields (240 and 236 g m⁻² in 2020–2021 and 2021–2022, respectively), while the CT × M (conventional tillage × monoculture) interaction resulted in the lowest productivity (143 and 135 g m⁻²). Physiologically, the integration of reduced tillage and legume–cereal rotations optimized the photosynthetic apparatus, as evidenced by significantly improved chlorophyll fluorescence parameters. However, a prominent trade-off was identified: while NT × T maximized productivity, conventional tillage (CT) maintained superior grain protein (18.6%) and gluten concentrations, indicating a nitrogen dilution effect in high-yielding conservation systems. These results demonstrate that while no-tillage and triennial rotations (faba bean–wheat–barley) are robust strategies for climate-resilient yields in semi-arid environments, they must be coupled with optimized nitrogen management to offset quality declines. Consequently, this study establishes the NT × T interaction as a superior model for sustainable rainfed farming, provided that nutrient synchronization is addressed to ensure nutritional security under increasingly unpredictable Mediterranean climates. Full article