Search Results (189)

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

With the development of various new types of instrumental aroma sensing technologies, there is a need for methodologies that help developers and users evaluate the performance of the different devices. This study introduces a simple method that uses standard coffee beverages, reproducible worldwide, thus allowing users to compare aroma sensing devices and technologies globally. Eight different variations of commercial coffee capsules were used to brew espresso coffees (40 mL), consisting of either Arabica coffee or a blend of Robusta and Arabica coffee, covering a wide range of sensory attributes. The AlphaMOS Astree electronic tongue (equipped with sensors based on chemically modified field-effect transistor technology) and the AlphaMOS Heracles NEO and the Volatile Scout3 electronic noses (both using separation technology based on gas chromatography) were used to describe the taste and odor profiles of the freshly brewed coffee samples and also to compare them to the various sensory characteristics declared on the original packaging, such as intensity, roasting, acidity, bitterness, and body. Linear discriminant analysis (LDA) results showed that these technologies were able to classify the samples similarly to the pattern of the coffees based on the human sensory characteristics. In general, the arrangement of the different coffee types in the LDA results—i.e., the similarities and dissimilarities in the types based on their taste or smell—was the same in the case of the Astree electronic tongue and the Heracles electronic nose, while slightly different arrangements were found for the Scout3 electronic nose. The results of the Astree electronic tongue and those of the Heracles electronic nose showed the taste and smell profiles of the decaffeinated coffees to be different from their caffeinated counterparts. The Heracles and Scout3 electronic noses provided high accuracies in classifying the samples based on their odor into the sensory classes presented on the coffee capsules’ packaging. Despite the technological differences in the investigated devices, the introduced coffee test could assess the similarities in the taste and odor profiling capacities of the aroma fingerprinting technologies. Since the coffee capsules used for the test can be purchased all over the world in the same quality, these coffees can be used as global standard samples during the comparison of different devices applying different measurement technologies. The test can be used to evaluate instrumentational and data analytical developments worldwide and to assess the potential of novel, cost-effective, accurate, and rapid solutions for quality assessments in the food and beverage industry. Full article

Clouds are important for Earth’s energy budget and water cycles, and precisely characterizing their vertical structure is essential for understanding their impact. Although passive remote sensing offers broad coverage and high temporal resolution, sensor and algorithmic limitations impede the accurate depiction of cloud vertical profiles. To improve estimates of their key structural parameters, e.g., cloud top height (CTH) and cloud vertical extent (CVE), we propose a multi-source collaborative optimization algorithm. The algorithm synergizes the wide-coverage FY-4A (FengYun-4A) and DSCOVR (Deep Space Climate Observatory) cloud products with high-precision CloudSat vertical profile data and establishes LightGBM-based CTH/CVE optimization models. The models effectively reduce systematic errors in the FY-4A and DSCOVR cloud products, lowering the CTH Mean Absolute Error (MAE) to 1.8 km for multi-layer clouds, an improvement of 4–8 km over the original. The CVE MAEs for single- and multi-layer clouds are ~2.5 km. Some bias remains in complex cases, e.g., multi-layer thin clouds at low altitudes, and error tracing analysis suggests this may be related to cloud layer number misclassification. The proposed algorithm facilitates daytime near-hourly cloud retrievals over China and neighboring regions. Full article

This paper introduces SmartBarrel, an innovative IoT-based sensory system that monitors and forecasts wine fermentation processes. At the core of SmartBarrel are two compact, attachable devices—the probing nose (E-nose) and the probing tongue (E-tongue), which mount directly onto stainless steel wine tanks. These devices periodically measure key fermentation parameters: the nose monitors gas emissions, while the tongue captures acidity, residual sugar, and color changes. Both utilize low-cost, low-power sensors validated through small-scale fermentation experiments. Beyond the sensory hardware, SmartBarrel includes a robust cloud infrastructure built on open-source Industry 4.0 tools. The system leverages the ThingsBoard platform, supported by a NoSQL Cassandra database, to provide real-time data storage, visualization, and mobile application access. The system also supports adaptive breakpoint alerts and real-time adjustment to the nonlinear dynamics of wine fermentation. The authors developed a novel deep learning model called V-LSTM (Variable-length Long Short-Term Memory) to introduce intelligence to enable predictive analytics. This auto-calibrating architecture supports variable layer depths and cell configurations, enabling accurate forecasting of fermentation metrics. Moreover, the system includes two fuzzy logic modules: a device-level fuzzy controller to estimate alcohol content based on sensor data and a fuzzy encoder that synthetically generates fermentation profiles using a limited set of experimental curves. SmartBarrel experimental results validate the SmartBarrel’s ability to monitor fermentation parameters. Additionally, the implemented models show that the V-LSTM model outperforms existing neural network classifiers and regression models, reducing RMSE loss by at least 45%. Furthermore, the fuzzy alcohol predictor achieved a coefficient of determination ($R^2$) of 0.87, enabling reliable alcohol content estimation without direct alcohol sensing. Full article

Early detection of plant water status is crucial for efficient crop management. In this research, proximal sensing tools (i.e., hyperspectral imaging HSI and thermal IR camera) were used to monitor changes in spectral and thermal profiles of a citrus orchard in Sicily (Italy), managed under five irrigation systems. The irrigation systems differ in the amount of water distribution and allow four different strategies of deficit irrigation to be obtained. The physiological traits, stem water potential, net photosynthetic rate, stomatal conductance and the amount of leaf chlorophyll were measured over the crop’s growing season for each treatment. The proximal sensing data consisted of thermal and hyperspectral imagery acquired in June–September during the irrigation seasons 2023–2024 and 2024–2025. Significant variation in physiological traits was observed in relation to the different irrigation strategies, highlighting the highest plant water stress in July, in particular for the partial root-zone drying irrigation system. The water-use efficiency (WUE) values in subsurface drip irrigation were similar to the moderate deficit irrigation treatment and more efficient (up to 50%) as compared to control. Proximal sensing measures confirmed a different plant water status in relation to the five different irrigations strategies. Moreover, four spectral indices (Normalized Difference Vegetation Index NDVI; Water Index WI; Photochemical Reflectance Index PRI; Transformed Chlorophyll Absorption Ratio Index TCARI), calculated from HSI spectra, highlighted strong correlations with physiological traits, especially with stem water potential and the amount of leaf chlorophyll (coefficient of correlation ranged between −0.4 and −0.5). This study demonstrated the effectiveness of using proximal sensing tools in precision agriculture and ecosystem monitoring, helping to ensure optimal plant health and water use efficiency. Full article

The surface profile and structural alignment of fibers and yarns in fabrics are critical factors affecting the electrical properties of conductive textile surfaces. This study aimed to investigate the impact of fabric surface roughness and the geometrical parameters of woven fabrics on their electrical resistance properties. Surface roughness was assessed using the MicroSpy^® Profile profilometer FRT (Fries Research & Technology) Metrology™, while electrical resistance was evaluated using the Van der Pauw method. The findings indicate that rougher fabric surfaces exhibit higher electrical resistance due to surface irregularities and lower yarn compactness. In contrast, smoother fabrics improve conductivity by enhancing surface uniformity and yarn contact. Fabric density, particularly weft density, governs the structural alignment of yarns. A 35% increase in weft density (W19–W27) resulted in a 13–15% reduction in resistance, confirming that denser fabrics facilitate current flow. Higher weft density also increases directional resistance differences, enhancing anisotropic behavior. Angular distribution analysis showed lower resistance and greater anisotropy at perpendicular orientations (0° and 180°, the weft direction; 90° and 270°, the warp direction), while diagonal directions (45°, 135°, 225°, and 315°) exhibited higher resistance. Surface roughness further hindered current flow, whereas increased weft density and surface mass reduced resistance and improved the directional dependencies of the electrical resistances. This analysis was conducted based on research using woven fabrics produced from silver-plated polyamide yarns (Shieldex^® 117/17 HCB). These insights support the optimization of these conductive fabrics for smart textiles, wearable sensors, and e-textiles. Fabric variants W19 and W21, with lower resistance variability and better isotropic behavior under the S electrode arrangement, could be proposed as suitable materials for integration into compact sensing systems like heart rate or bio-signal monitors. Full article

The increasing severity of water scarcity in southern Europe, caused by climate change, requires advanced and more efficient approaches to agricultural water management. In particular, in this paper, we address this problem for olive groves—a cornerstone of the region’s economy. We propose a novel framework for generating high-resolution maps of irrigated olive groves that integrates remote sensing imagery and machine learning. Our approach leverages multi-temporal Sentinel-2 data, specifically the Normalized Difference Vegetation Index (NDVI), to capture seasonal vegetation dynamics. For classification, we explore two distinct models: (1) A Dynamic Time Warping (DTW)-based approach (with and without the Sakoe–Chiba Band constraints), where DTW aligns temporal NDVI sequences to enable robust comparisons of irrigation regimes, followed by a K-Nearest Neighbor classifier (KNN) that classifies plots as irrigated or rainfed. (2) An eXtreme Gradient Boosting (XGBoost) model that directly uses temporal NDVI profiles. Additionally, we compare the dependence of model performance on the length of the NDVI time series (ranging from one to seven seasons), finding that XGBoost requires a shorter time series to achieve optimal results, while KNN with DTW can benefit from longer historical records. Indeed, XGBoost nearly reaches its maximum accuracy using only data based on three seasons, achieving 0.79 compared to its peak performance of 0.80. Hence, our results indicate that this approach can accurately differentiate between irrigated and rainfed plots, enabling the generation of high-resolution irrigation maps for southern Spain. Finally, we argue that the results of this paper go beyond mere mapping: they lay the foundation for a comprehensive management guide that can optimize water use, with broad implications. Such implications range from empowering precision agriculture to providing a roadmap for land management, ensuring both the sustainability and productivity of olive groves in drought-affected regions. Full article

Food quality assessment is a critical aspect of food production and safety, ensuring that products meet both regulatory and consumer standards. Traditional methods such as sensory evaluation, chromatography, and spectrophotometry are widely used but often suffer from limitations, including subjectivity, high costs, and time-consuming procedures. In recent years, the development of electronic nose (e-nose) and electronic tongue (e-tongue) technologies has provided rapid, objective, and reliable alternatives for food quality monitoring. These bio-inspired sensing systems mimic human olfactory and gustatory functions through sensor arrays and advanced data processing techniques, including artificial intelligence and pattern recognition algorithms. The e-nose is primarily used for detecting volatile organic compounds (VOCs) in food, making it effective for freshness evaluation, spoilage detection, aroma profiling, and adulteration identification. Meanwhile, the e-tongue analyzes liquid-phase components and is widely applied in taste assessment, beverage authentication, fermentation monitoring, and contaminant detection. Both technologies are extensively used in the quality control of dairy products, meat, seafood, fruits, beverages, and processed foods. Their ability to provide real-time, non-destructive, and high-throughput analysis makes them valuable tools in the food industry. This review explores the principles, advantages, and applications of e-nose and e-tongue systems in food quality assessment. Additionally, it discusses emerging trends, including IoT-based smart sensing, advances in nanotechnology, and AI-driven data analysis, which are expected to further enhance their efficiency and accuracy. With continuous innovation, these technologies are poised to revolutionize food safety and quality control, ensuring consumer satisfaction and compliance with global standards. Full article

Grazing shapes rangelands globally, but it is difficult to study. Acoustic monitoring enables grazing to be described in terms of jaw movements, which are fundamental to how herbivores interact with their foraging environment. In an observational study on Mediterranean herbaceous rangeland, 10 beef cattle cows were monitored continuously over multiple days in two seasons. The algorithm used to analyze the acoustic signal furnished (without classification) a data sample of ≈5 M ingestive and ruminatory jaw movements. These were analyzed as between-event intervals and as minutely rates. The rumination displayed a consistent, strong rhythm and pattern of jaw movements. In contrast, there was no single “signature” jaw movement pattern for grazing (i.e., non-rumination). Although the underlying natural rhythm of rumination dominated non-rumination, it was intermittently and irregularly interrupted by longer intervals, whose size scaled logarithmically. There was evidence of further substructure, with a degree of separation between “grazing” and “resting” in the conventional sense. Three broad grazing styles emerged. In the “intense” style, animals sustained long runs of jaw movements in the natural rhythm, with relatively few interruptions. In the “regular” style, comprising the majority of non-rumination jaw activity, the natural rhythm still dominated, but was punctuated at irregular intervals by eruptions of somewhat longer intervals. The “diffuse” style comprised shorter runs in the natural rhythm, punctuated by highly erratic intervals spanning orders of magnitude. When the jaw movement events were viewed as minutely rates, the non-rumination population showed strong bimodality in the distribution of non-zero rates, with peaks at ≈60 and ≈15 jaw movements min⁻¹, suggesting two modes of grazing. The results strongly support the notion of behavioral grazing intensity and call into question the approach of viewing grazing as a binary state or expecting measures of grazing time to be strongly indicative of intake rate. Rate- and interval-based analyses of information at the jaw movement level can yield a penetrating profile of how an animal interacts with its foraging environment, epitomized in a graphical formulation termed the time accumulation curve. These results strengthen the case for the further development of this sensor technology. Full article

The process of manufacturing drug delivery systems (DDSs) by fused deposition modeling (FDM) with 3D printing requires the availability of a polymeric filament containing the drug of interest. This filament is fused in the printer heating system and used to print polymer/drug volumetric parts. Polymers with pH-dependent solubility are widely known in the literature for their controlled release and drug dissolution-enhancing properties, biocompatibility, and variety of release profiles. Given these characteristics, the study of pH-responsive 3D printing filaments appears as a potential alternative for the development of new 3D printing functional materials for healthcare area applications. In this sense, this work aimed at the preparation and characterization of pH-dependent filaments of the Eudragit E 100 copolymer (E100) containing the model drug Amlodipine (Aml) for potential application in the manufacturing of DDSs by 3D printing. The E100/Aml filaments with two distinct drug concentrations were produced by hot-melt extrusion at 105 °C. The posterior chemical protonation treatment of the filaments for 60 min provided a significant improvement in their flexibility. Microstructural analysis (SEM, XRD, FTIR, and DLS) and thermal studies by DSC proved the feasibility of producing the filaments by hot-melt extrusion without the degradation of their constituent materials. The in vitro dissolution profiles of the E100/Aml samples were evaluated in simulated gastric and intestinal fluids. The facilitated solubility of the polymer in an acidic medium (pH = 1.2) was preserved in the filament form, with rapid and reproducible drug release from the polymer matrix. The saturation of the drug concentration in the medium occurred after 30 min of testing for E100/Aml models. A customized 3D part with geometry and fill control was also printed from E100/Aml filaments as proof of concept. Full article

The tick-borne encephalitis virus is a pathogen endemic to northern Europe and Asia, transmitted through bites from infected ticks. It is a member of the Flaviviridae family and possesses a positive-sense, single-stranded RNA genome encoding a polypeptide that is processed into seven non-structural and three structural proteins, including the envelope (E) protein. The glycosylation of the E protein, involving a single N-linked glycan at position N154, plays a critical role in viral infectivity and pathogenesis. Here, we dissected the entire glycosylation profile of the E protein using liquid chromatography-tandem mass spectrometry and identified three novel O-linked glycans, which were found at relatively low frequency. One of the O-linked glycans was positioned close to the highly conserved N-linked glycan site, and structural analysis suggested that it may be relevant for the function of the E 150-loop. The N154 site was found to be glycosylated with a high frequency, containing oligomannose or complex-type structures, some of which were fucosylated. An unusually high portion of oligomannose N-linked glycan structures exhibited compositions that are normally observed on proteins when they are translocated from the endoplasmic reticulum to the trans-Golgi network, suggesting disruption of the glycan processing pathway in the infected cells from which the E protein was obtained. Full article

Oceanic mesoscale eddies are a kind of typical geostrophic dynamic process which can cause vertical movement in water bodies, thereby changing the temperature, salinity, density, and chlorophyll concentration of the surface water in the eddy. Based on multisource remote sensing data and Argo profiles, this study analyzes and compares the mesoscale eddy properties in four major western boundary current regions (WBCs), i.e., the Kuroshio Extension (KE), the Gulf Stream (GS), the Agulhas Current (AC), and the Brazil Current (BC). The 30-year sea surface height anomaly (SSHA) data are used to identify mesoscale eddies in the four WBCs. Among the four WBCs, the GS eddies have the largest amplitude and the BC eddies have the smallest amplitude. Combining the altimeter-detected eddy results with the simultaneous observations of sea surface temperature, sea surface salinity, sea surface density, and chlorophyll concentration, the local impacts of eddy activities in each WBCs are analyzed. The eddy surface temperature and salinity signals are positively correlated with the eddy SSHA signals, while the eddy surface density and chlorophyll concentrations are negatively correlated with eddy SSHA signals. The correlation analysis of eddy surface signals in the WBCs reveals that eddies have regional differences in the surface signal changes of eddy activities. Based on the subsurface temperature and salinity information provided by Argo profiles, the analysis of the vertical thermohaline characteristics of mesoscale eddies in the four WBCs is carried out. Eddies in the four WBCs have deep influence on the vertical thermohaline characteristics of water masses, which is not only related to the strong eddy activities but also to the thick thermocline and halocline of water masses in the WBCs. Full article

Biochar has gained interest as a feed ingredient in livestock nutrition due to its functional properties, circularity, potential to reduce environmental impact, and alignment with sustainable agro-zootechnical practices. The in vivo effects of biochar are closely tied to its physical characteristics, which vary depending on the biomass used as feedstock and the production process. This variability can result in heterogeneity among biochar types used in animal nutrition, leading to inconsistent outcomes. The aim of this study was to characterize the metabolomic and functional properties of an aqueous biochar extract from vine pruning waste, in order to predict its potential in vivo effects as a functional feed ingredient. A metabolomic analysis of the biochar extracts was conducted using quadrupole time-f-light (QQTOF) high-performance liquid chromatography tandem mass spectrometry (HPLC MS/MS). Antimicrobial activity against E. coli F18+ and E. coli F4+ was assessed using standard growth inhibition assays, while quorum sensing in E. coli exposed to biochar extracts was evaluated using real-time PCR. Prebiotic activity was assessed by exposing selected Lactobacillus strains to the biochar extract, monitoring growth patterns to determine species-specific responses. The metabolomic profile revealed several distinct molecular classes, including multiple peaks for phenolic compounds. The extract significantly inhibited the growth of both E. coli pathotypes, reducing growth by 29% and 16% for the F4+ and F18+, respectively (p < 0.001). The relative expression of the genes involved in quorum sensing (MotA, FliA for biofilm formation, and FtsE, HflX for cell division) indicated that the observed inhibitory effects likely resulted from interference with flagellar synthesis, motility, and reduced cell division. The biochar extract also showed species-specific prebiotic potential. In conclusion, biochar derived from vine pruning waste represents a valuable feed ingredient with functional properties that may help to reduce antibiotic use in livestock production. Full article

The efficient, economical, and sustainable production of biodiesel from waste cooking oils (WCOs) depends on the availability of simple, rapid, and low-cost methods to test the quality of potential feedstocks. The aim of this study was to establish the applicability of stochastic modeling of e-nose profiles in the evaluation of recycled WCO characteristics. Olfactory profiles of 10 WCOs were determined using a Sensigent Cyranose^® 320 chemical vapor-sensing device with a 32 sensor-array, and a stepwise multiple linear regression (MLR) analysis was performed to select stochastic parameters (explanatory variables) for inclusion in the final predictive models of the physicochemical properties of the WCOs. The most important model parameters for the characterization of WCOs were those relating to the time of inception of the e-nose signal “plateau” and to the concentration of volatile organic compounds (VOCs) in the sensor region. A comparison of acid values, peroxide values, water contents, and kinematic viscosities predicted by the MLR models with those determined by conventional laboratory methods revealed that goodness of fit and predictor accuracy varied from good to excellent, with all metric values >90%. Combining e-nose profiling with stochastic modeling was successful in predicting the physicochemical characteristics of WCOs and could be used to select suitable raw materials for efficient and sustainable biodiesel production. Full article

The functions of bacterial plasmid-encoded toxin–antitoxin (TA) systems are unambiguous in the sense of controlling cells that fail to inherit a plasmid copy. However, its role in chromosomal copies is contradictory, including stress-response-promoting fitness and antibiotic treatment survival. A hybrid pathogenic Escherichia coli strain may have the ability to colonize distinct host niches, facing contrasting stress environments. Herein, we determined the influence of multiple environmental stress factors on the bacterial growth dynamic and expression profile of previously described TA systems present in the chromosome of a hybrid atypical enteropathogenic and extraintestinal E. coli strain. Genomic analysis revealed 26 TA loci and the presence of five type II TA systems in the chromosome. Among the tested stress conditions, osmotic and acid stress significantly altered the growth dynamics of the hybrid strain, enhancing the necessary time to reach the stationary phase. Using qPCR analyses, 80% of the studied TA systems were differentially expressed in at least one of the tested conditions, either in the log or in the stationary phase. These data indicate that type II TA systems may contribute to the physiology of pathogenic hybrid strains, enabling their adaptation to different milieus. Full article