Search Results (2,334)

Background: Quantitative genetic analysis of image- or video-derived phenotypes is increasingly being performed for a wide range of traits. Pig body weight values estimated by a conventional approach or a computer vision system can be considered two different measurements of the same trait but with different sources of phenotyping error. Previous studies have shown that trait measurement error, defined as the difference between manually collected phenotypes and image-derived phenotypes, can be influenced by genetics, suggesting that the error is systematic rather than random and is more likely to lead to misleading quantitative genetic analysis results. Therefore, we investigated the effect of trait measurement error on the genetic analysis of pig body weight (BW). Results: Calibrated scale-based and image-based BW showed high coefficients of determination and goodness of fit. Genomic heritability estimates for scale-based and image-based BW were mostly identical across growth periods. Genomic heritability estimates for trait measurement error were consistently negligible, regardless of the choice of computer vision algorithm. In addition, genome-wide association analysis revealed no overlap between the top markers identified for scale-based BW and those associated with trait measurement error. Overall, the deep learning-based regressions outperformed the adaptive thresholding segmentation methods. Conclusion: This study showed that manually measured scale-based and image-based BW phenotypes yielded the same quantitative genetic results. We found no evidence that BW trait measurement error could be influenced, at least in part, by genetic factors. This suggests that trait measurement error in pig BW does not contain systematic errors that could bias downstream genetic analysis. Full article

Purpose: Neuroinflammation and oxidative stress are increasingly recognized as central, interconnected drivers of neurodegeneration in the visual system. This review examines the pathogenic mechanisms shared across glaucoma, age-related macular degeneration (AMD), diabetic retinopathy (DR), and Alzheimer’s disease (AD), and evaluates the therapeutic rationale for targeting both pathways simultaneously. Methods: A narrative review of peer-reviewed literature was conducted using PubMed. Searches included the following MeSH terms: neuroinflammation, oxidative stress, retinal neurodegeneration, microglia, Müller glia, mitochondrial dysfunction, glaucoma, age-related macular degeneration, diabetic retinopathy, and Alzheimer’s disease. Priority was given to original research, systematic reviews, and high-impact publications from 2000 through 2025. However, seminal foundational works were included regardless of publication date. Studies were selected based on relevance to glial activation, mitochondrial dysfunction, reactive oxygen and nitrogen species, and disease-specific neuronal outcomes. Results: Across all four diseases, persistent microglial and Müller glial activation, mitochondrial electron transport chain dysfunction, and excess reactive oxygen species (ROS) and reactive nitrogen species (RNS) production form a self-amplifying feed-forward loop that accelerates neuronal injury. In glaucoma, these mechanisms drive intraocular pressure-independent retinal ganglion cell loss. In AMD and DR, lipid dysregulation, complement activation, and chronic hyperglycemia sustain oxidative-inflammatory injury to the retinal pigment epithelium, photoreceptors, and neurovasculature. In AD, retinal amyloid deposition and oxidative burden mirror cortical pathology, positioning the retina as a noninvasive biomarker site. Conclusions: Neuroinflammation and oxidative stress constitute unifying upstream mechanisms across major vision-threatening neurodegenerative diseases. Combination therapeutic strategies that simultaneously modulate glial activation and restore redox homeostasis may offer superior neuroprotective efficacy compared to approaches targeting isolated downstream mediators. Full article

A highly potent antitoxin for botulinum neurotoxin (BoNT) serotypes A and B has been developed that comprises three monoclonal antibodies (mAbs) targeting BoNT/A and three targeting BoNT/B. These oligoclonal antibody combinations neutralize toxin by simultaneously binding non-overlapping epitopes, thereby promoting rapid toxin clearance. All six mAbs use the same human Fc and framework and have been individually manufactured using the same expression platform and purification process. To minimize the time and labor required to produce the divalent antitoxin, we tested a co-expression and co-purification strategy for the three mAbs per serotype. The mAbs were expressed in CHO-K1 cells, and the media were optimized for co-expression in 10 L bioreactors. Chromatographic co-purification consisted of Protein A capture, followed by strong anion exchange chromatography in flow-through mode and cation-exchange chromatography in bind-elute mode. Co-expression experiments demonstrated that expression of the three anti-BoNT/A antibodies remained within approximately ±30% of the optimal equimolar ratio, whereas the anti-BoNT/B antibodies showed greater variability. Downstream purification steps achieved recoveries greater than 95% per chromatographic step, resulting in overall process yields of approximately 63–75%. This strategy provided sufficient purity of all six mAbs while largely preserving their relative ratios. These results demonstrate the feasibility of producing oligoclonal antitoxin antibodies using co-expression and shared purification strategies. Such approaches may simplify the manufacturing of antibody cocktails while maintaining product quality and biological activity. Full article

The power system is the backbone of the energy network, and overhead lines are its vital structures. Weather threats may jeopardise the reliability of lines and make them a weak link. In particular, heavy rainfall episodes can cause failures, especially in mountain areas. Current climate changes may exacerbate the effects on the ground, intensifying rainfall episodes and increasing the frequency of extreme events. In this context, debris flows triggered by rather intense precipitation and characterised by fast kinematics can destroy pylons and electric connections, affecting the infrastructures not only in the upper ridges but also downstream across the fan apex, where powerlines are much more distributed. This study presents an in-depth back-analysis of two debris flow events triggered in concomitance with a heavy cloudburst that occurred in Talamona (Sondrio Province, Italy) in July 2008 and in Campo Tartano (Sondrio Province, Italy) in April 2024. These events hit onsite powerlines, causing blackouts and showing the potential vulnerabilities of the local electricity system. An analysis of rainfall-induced landslide failure is carried out using the numerical model CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment) and MIST-DF (Modelling Impulsive Sediment Transport—Debris Flow) with the aim of reconstructing the dynamics of the first (i.e., Talamona) geo-hydrological event. Powerline vulnerability is also investigated against debris flow dynamics, discussing possible strategies to reduce pylon exposure and to increase the resilience of the local electro-energetic network. Since, under climate change scenarios, heavy rainfall episodes are projected to intensify, an alternative approach based on rainfall-threshold curves is presented and applied to both cases of study. The latter, already implemented for civil protection purposes, could be useful in early-warning procedures against potential debris flow hazards. For both methodologies, the findings from the study confirm the strength of the approaches and foster their application in different situations (back-analysis and early warning) to reduce powerlines’ geo-hydrological risks. Full article

Targeted delivery systems offer a promising approach for selectively modulating cellular processes; yet the intracellular consequences of targeted nutrient delivery to trophoblast cells remain poorly defined. Here, we investigated a previously validated placenta-targeting peptide conjugated to liposomes encapsulating stable isotope-labelled L-arginine and L-lysine to examine cellular uptake and downstream molecular responses in a trophoblast-like cell model. Peptide-dependent uptake of fluorescently labelled liposomes was confirmed in BeWo cells, demonstrating selective internalisation compared with non-targeted controls. Encapsulation of isotope-labelled amino acids enabled direct quantification of intracellular delivery and incorporation into the cellular proteome using stable isotope labelling by amino acids in cell culture (SILAC). Quantitative proteomic analysis revealed coordinated changes in proteins associated with translation, metabolism, and nitric oxide synthase regulation following targeted liposomal uptake. Notably, V-type proton ATPase subunit G1 (ATP6V1G1) and large neutral amino acid transporter small subunit 1 (SLC7A5) showed increased incorporation of labelled amino acids and were independently validated by Western blotting. Together, these findings establish a proof-of-concept platform for targeted intracellular amino acid delivery to trophoblast-like cells and define the resulting proteomic responses. This work provides mechanistic insight into intracellular amino acid utilisation and a framework for future studies in placental cell biology. Full article

The increasing deployment of dense offshore wind turbine monopile foundations pose significant challenges for accurately simulating tidal-flow modification and energy transport at the array scale. Balancing physical realism with computational efficiency remains a key challenge in hydrodynamic modelling of offshore wind farms. In this study, an established drag-based source-term approach is implemented through a dedicated module developed within the TELEMAC-3D framework to represent the momentum-blocking effects of offshore wind-farm arrays. A representative dense 8 × 10 wind turbine monopile array configuration is constructed in a typical tidal channel to systematically examine array-induced tidal-flow responses. The results indicate that the drag-based source-term approach preserves the regional-scale tidal flow structure while effectively capturing array-induced local velocity adjustments and pronounced downstream wake attenuation and recovery. Detailed analyses further reveal distinct spatial and temporal characteristics of the velocity response, including the decay and recovery of velocity deviations downstream of the array. In addition, the monopile array induces a clear modulation of flow kinetic energy, characterized by enhanced energy dissipation and a finite array-scale redistribution of kinetic energy. These findings demonstrate that this approach efficiently simulates the array-scale hydrodynamic and energetic impacts of large offshore wind farms and contribute to a better understanding of array-induced tidal flow modification and energy redistribution. Full article

Reliable short-term prediction of bus travel time on signalized urban arterials is essential for improving service reliability and may provide a useful forecasting basis for prediction-informed transit signal priority (TSP) and arterial coordination applications. However, bus operations on urban arterials are highly variable due to stop dwell times, signal delays, and interactions with mixed traffic, leading to nonlinear and nonstationary travel time patterns with strong spatiotemporal dependence. This study proposes a hybrid KNN–ConvLSTM framework for short-term arterial bus travel time prediction using real-world field data. A K-nearest neighbors (KNNs) module is first employed to retrieve historical operation sequences that are most similar to the current corridor state, thereby reducing interference from mismatched traffic regimes and improving robustness. Smart-card (IC card) transaction data are incorporated as demand-related features to represent passenger activity and its impact on dwell time and travel time variability. The selected sequences are then organized into a corridor-ordered spatiotemporal representation and further refined by lightweight temporal enhancement operations, including relevance gating, multi-scale aggregation, adaptive feature fusion, and residual enhancement, before being fed into the convolutional long short-term memory (ConvLSTM) predictor. The proposed approach is evaluated using weekday service-hour data extracted from 30 days of real-world bus operation records collected from a typical urban arterial corridor in Changchun, China, and is compared with several benchmark models, including ARIMA, KNN, LSTM, CNN, ConvLSTM, Transformer, and DCRNN. The results indicate that the proposed KNN–ConvLSTM framework achieves an MAE of 40.1 s, an RMSE of 55.8 s, a SMAPE of 10.7%, and an R² of 0.878, outperforming all benchmark models. Specifically, compared with the Transformer baseline, the proposed framework reduces MAE by 1.5%, RMSE by 5.1%, and SMAPE by 7.0%, while increasing R² by 0.014. Compared with the DCRNN baseline, it reduces MAE by 10.7%, RMSE by 1.9%, and SMAPE by 2.7%, while increasing R² by 0.008. These findings demonstrate that similarity-aware retrieval combined with spatiotemporal deep learning can substantially enhance short-term bus travel time prediction on signalized urban arterials. More accurate short-term forecasts may support prediction-informed transit signal priority and arterial coordination by providing more reliable downstream arrival-time estimates. However, the generalizability of the reported results is still constrained by the relatively short 30-day observation period and the single-corridor case setting, and the operational and environmental effects of downstream applications remain to be validated through dedicated closed-loop control evaluation in future work. Full article

The paramount value of image fusion is manifested in effectively enhancing downstream tasks. However, compatibility with subsequent tasks is compromised due to the semantic deficiency of fusion representations generated by current approaches. To mitigate this limitation, a semantic-guided multi-level collaborative fusion network is proposed, termed DSIFuse. By leveraging semantic priors and global context extracted from auxiliary segmentation branches, a multi-level interaction space is constructed to explicitly refine cross-modal features. Specifically, a cross-modal feature correction mechanism is designed to enhance semantic alignment by injecting complementary visible–infrared information at each layer, while a three-level interaction strategy gradually integrates unimodal features and semantic maps to generate semantically enriched representations. To mitigate semantic information loss during image reconstruction, a semantic compensation block is employed, incorporating interactive representations from prior layers and global semantic maps into the multi-scale decoder. Finally, the overall loss integrates semantic supervision, gradient, and intensity loss. Experiments conducted on public datasets indicate that clear fusion images are generated by DSIFuse, with improved structural consistency and reduced artifacts. Under a unified benchmark, the fused representations subsequently yield improved performance in downstream object detection tasks. Full article

Accurately characterizing the statistical fluctuations of vehicle radar cross sections (RCSs) across polarization states and azimuthal sectors is essential for evaluating detection performance, conducting probabilistic simulations, and analyzing target features in millimeter-wave radar systems. Existing one-dimensional RCS statistical models, including Weibull, Chi-square, Lognormal, Rice, and Gaussian distributions, are often limited by their restricted functional expressiveness, making it difficult to simultaneously capture skewness, tail thickness, and azimuthal dependence under narrow angular-domain conditions. In addition, purely nonparametric approaches tend to produce spurious modes under finite-sample conditions and lack interpretable structural priors. To address these limitations, this paper proposes a Unimodal RCS Semiparametric Density Estimator (URCS-SDE) tailored for ground-vehicle targets. The proposed approach adopts kernel density estimation (KDE) as a data-driven baseline representation and incorporates physically plausible structural constraints through unimodal shape projection. Then a beta-type tail template is further introduced in the normalized amplitude domain to regulate boundary decay behavior. Finally, weighted least-squares calibration is performed on the histogram grid of the empirical probability density function (PDF), achieving a balanced trade-off between fitting accuracy and stability in both the peak and tail regions. Using multi-azimuth RCS measurements of two representative ground vehicles, the URCS-SDE is systematically compared with five classical parametric distributions and a representative regularized mixture density network (MDN) baseline. Performance is evaluated under both full-azimuth and directional-window conditions using the sum of squared errors (SSE), root mean squared error (RMSE), coefficient of determination (R-square) and held-out negative log-likelihood (NLL). The results show that the URCS-SDE consistently provides the most accurate and stable density estimates, especially in narrow angular windows. In addition, a threshold-based detection-support example derived from the fitted PDFs demonstrates that the advantage of the URCS-SDE transfers from density reconstruction to a directly engineering-relevant downstream quantity. Full article

Limonitic laterites typically contain low Ni and Co contents and significant impurities, making the development of technical and economically feasible processes challenging. To address this challenge, this study investigates and evaluates an integrated hydrometallurgical process comprising sulfation roasting, water leaching, and solvent extraction (SX) for the selective recovery of Ni and Co from limonite-type laterite. Response Surface Methodology coupled with a Central Composite Design (RSM-CCD) was employed as a statistical experimental design tool to efficiently optimize the sulfation roasting conditions. Under the optimal sulfation roasting conditions (temperature 703 °C), selective leaching efficiencies of 87.2% for Ni and 96.6% for Co were achieved, with only 3.8% Fe co-leaching. A multi-stage SX scheme was subsequently applied to purify the pregnant leach solution (PLS) of water leaching. In the first SX step, D2EHPA at pH 2.8 selectively removed more than 95% of the impurities, including Mn, Zn, Al, Ca, and Fe. In the second SX step, Cyanex 272 at pH 5.8 enabled the extraction of more than 99.9% of Co and 86.0% of Mg into the organic phase, and Ni remained in the raffinate. Subsequent stripping with H₂SO₄ enabled the recovery of 99.9% of both Co and Mg from the loaded organic phase. Finally, selective carbonate precipitation is proposed as a potential downstream recovery method for Ni after enrichment. This approach is considered relatively less energy-intensive than sulfate crystallization. The process developed in this study was benchmarked against similar processes reported in the literature, and a conceptual flowsheet for the selective extraction and separation of Ni and Co from limonitic laterite was proposed. Findings demonstrated the feasibility of the integrated sulfation roasting-water leaching, solvent extraction process for treating impurity-rich laterite leach solutions. Full article

This study investigates the antioxidant properties of several synthetic peptides derived from milk proteins previously identified in milk permeate, a by-product of the dairy industry. The aim of the research is to identify which peptides present in milk permeate are responsible for its antioxidant activity. A comprehensive experimental strategy was employed to evaluate their antioxidant potential, including in silico selection, in vitro free radical scavenging assays and cellular models using Caco-2 and HCT116 cell lines. The peptides were screened using a molecular docking approach for their potential interaction with the Kelch-like ECH-associated protein 1/nuclear factor erythroid 2-related factor 2 (Keap1/Nrf2) pathway, and eight out of twenty-eight were selected and synthesized for further analyses. In vitro, six of the selected peptides demonstrated significant direct antioxidant activity in the DPPH scavenging assay, and two in the ABTS scavenging test. In cellular environments, three peptides (LPAPELGPRQA, LPIIQKLEPQI and NGQVWEESLKRL) effectively protect cells from oxidative stress induced by tert-butyl hydroperoxide, reducing reactive oxygen species production and partially mitigating lipid peroxidation. Further investigation showed that two of them (LPAPELGPRQA and LPIIQKLEPQI) effectively induce the Keap1/Nrf2 pathway, as evidenced by a ∼1.5-fold increase in Nrf2 levels and overexpression of downstream proteins. Permeability studies revealed that these peptides can cross the intestinal monolayer (2–3% in 2 h), suggesting potential systemic effects. Overall, these findings highlight the multifunctional antioxidant properties of the investigated peptides and support their potential application as nutraceuticals or therapeutic agents for oxidative stress-related conditions. Full article

Understanding the unsteady aerodynamic behavior of quadrotor unmanned aerial vehicle (UAV) is critical for improving flight stability, control, and performance, particularly in complex operational environments. In closely spaced multirotor configurations, coherent tip vortices shed from each blade convect downstream and form helical vortex streets that interact with subsequent blades and neighboring rotors. These interactions induce rapid fluctuations in local inflow velocity and effective angle of attack, resulting in transient lift variations, increased vibratory loads, and elevated acoustic emissions. This study presents a comprehensive computational investigation of quadrotor rotor interactions and wake dynamics using a large-eddy simulation (LES). Detailed analyses reveal that the formation and evolution of tip vortices and blade–vortex interaction phenomena significantly influence lift fluctuations and aerodynamic loading. The simulations capture transient wake structures and their effects on neighboring rotors, highlighting unsteady aerodynamic mechanisms that are not adequately predicted by conventional RANS or URANS approaches. Parametric studies examining vortex-street offset distance demonstrate the sensitivity of wake-induced instabilities to design and operational parameters. The results provide new physical insights into multirotor wake dynamics and establish the LES as a predictive framework for quantifying unsteady aerodynamic loading in quadrotor drones. The findings provide insights into the complex flow physics of multirotor systems, offering guidance for more accurate modeling, rotorcraft design optimization, and the development of control strategies that mitigate adverse unsteady aerodynamic effects. This study provides new insights into rotor–vortex-street interactions, with applications to multirotor UAVs, by isolating multi-vortex coupling effects and quantifying the influence of horizontal vortex spacing on unsteady aerodynamic loading, complementing existing high-fidelity LES research. Full article

Cooking-related emissions represent a major contributor to indoor air pollution in residential kitchens, producing complex mixtures of volatile organic compounds (VOCs), odor-causing gases, oil vapors, particulate matter (PM_2.5), and combustion-related pollutants (CO and NO_x). In this study, a controlled ozone-assisted oxidation approach was integrated into a recirculating (ductless) domestic kitchen hood equipped with a confined reaction chamber and experimentally evaluated under closed-loop operating conditions where treated air was returned to the indoor environment after post-treatment. A multivariate Response Surface Methodology (RSM) framework based on the Box–Behnken design was employed to quantify and optimize the coupled effects of temperature (20–30 °C), relative humidity (40–60%), ozone dosage (1–3 ppm within the confined reaction zone), and airflow rate (150–250 m³/h) on multi-pollutant removal performance. The results demonstrate that ozone assistance substantially improves the abatement of oxidation-sensitive pollutants, particularly VOCs and odor, while airflow rate strongly governs transport-dominated pollutants such as PM_2.5 and oil vapors. In contrast, CO and NO_x exhibited limited improvement, indicating that ozone-assisted oxidation alone is insufficient for comprehensive control of combustion-related gases under short-residence-time recirculating hood conditions. The main contribution of this work is the implementation of a demand-driven ozone management strategy, supported by dual ozone sensing for reaction-zone control and outlet safety verification, where ozone generation is activated only in the presence of reactive gaseous pollutants and automatically reduced or terminated once pollutant concentrations fall below predefined thresholds, minimizing unnecessary oxidant release. Residual ozone downstream of the reaction stage was continuously monitored to prevent excess ozone return to the occupied zone. Overall, the proposed closed-loop, feedback-controlled ozone-assisted recirculating range hood concept demonstrated device-level reductions in measured VOC/odor signals under controlled conditions, while also highlighting the need for complementary post-treatment components for particle- and combustion-related pollutants. However, the potential formation of secondary oxidation byproducts was not characterized in this study, and therefore the results should be interpreted with respect to device-level pollutant removal rather than comprehensive indoor air quality improvement. Full article

Neurodegenerative diseases, including Alzheimer’s Disease (AD), Parkinson’s Disease (PD), Lewy Body Disease (LBD), and related dementias, represent a global health challenge, particularly in aging populations. The simultaneous occurrence of neurodegenerative diseases in an aging population suggests a potential link between causative proteins. Such neurodegenerative proteins, including amyloid-β (Aβ), τ-protein (tau), α-synuclein, TAR DNA-binding protein 43 (TDP-43), and Fused in Sarcoma (FUS), share key characteristics of intrinsically disordered proteins (IDPs), which can explain promiscuous physical interactions, cross-seeding, co-occurrence, pathological synergy, and shared upstream and downstream mechanisms. This review synthesizes current evidence on (1) shared biophysical features of neurodegeneration-associated proteins, (2) mechanisms driving mixed neuropathology, (3) therapeutic implications of disorder-driven interactions, and (4) key unresolved questions shaping future research. By framing neurodegeneration as a network of interacting, disorder-driven proteinopathies rather than isolated entities, this perspective highlights the need for integrative, systems-level approaches to better understand disease heterogeneity and to identify novel targets for intervention. Full article

The MADS-box family is a multifunctional family of transcription factors characterized by the presence of a unique MADS domain, which plays an important part in regulating essential biological processes, including metabolic synthesis and the stress response. In this review, we analyze the structural features and classification of MADS-box proteins, then summarize the functions of the MADS-box family in the stress response. The MADS-box family can directly regulate downstream functional genes by binding to the CArG-box in the promoters of target genes, thereby influencing growth, development, and stress responses. Also, MADS-box transcription factors can form protein complexes with both MADS-box proteins and other types of transcription factors and chromatin regulatory proteins to modulate the chromatin state or transcriptional activation. Furthermore, they can regulate plant physiological responses by facilitating the synthesis of essential signaling molecules, including hormones and non-coding RNA. Finally, we discuss the potential of the MADS-box family in crop molecular breeding, offering a novel approach for developing high-yield and stress-resistant cultivars for solving global food security and climate change challenges. Full article