Search Results (579)

Miscanthus lutarioriparius exhibits strong potential for cadmium (Cd) accumulation, making it a promising candidate for the phytoremediation of Cd-contaminated soils. However, its full remediation potential remains underexploited, highlighting the need for targeted genetic improvement This study presents a comprehensive genome-wide identification and systematic characterization of 20 Ml4CL (4-coumarate: CoA ligase genes) in the M. lutarioriparius. Results indicate that the Ml4CL gene family has undergone substantial evolutionary divergence and expansion. Phylogenetic classification is highly consistent with gene structures ad conserved motifs suggesting potential functional diversification. Promoter analysis revealed a complex cis-regulatory landscape enriched in n ABA- and light-responsive elements, frequently co-occuring with hormone-responsive elements associated with jasmonic acid (JA), gibberellins (GAs), salicylic acid (SA), and strigolactones (SLs) signaling. This pattern suggests that the Ml4CL family may function as an integrative regulatory node linking multiple stress and hormonal signaling pathways. Importantly, under Cd stress, Ml4CL genes exhibited diverse expression dynamics, including gene-specific repression and dose-dependent biphasic responses. Notably, Ml4CL4 showed strong repression, while other members displayed “induction-then-repression” or “repression-then-induction” patterns, suggesting a staged or hierarichical transcriptional response. These findings further suggest that Cd-responsive signaling networks may involve non-linear or threshold-dependent mechanismsthat activate distinct transcriptional programs depending on stress levels. Collectively, this study highlights the regulatory role of the Ml4CL family in plant adaptation to complex environments and identifies candidate dose-resonsive regulatory elements and key allelic variations. These findings provide valuable targets for molecular breeding and synthetic biology aimed at improving crop stress resilience. Full article

Many cities adopt greening strategies to reduce contamination from combined sewer overflows (CSOs). Nonetheless, quantifying the impact of urban greening on CSO-affected water quality at the city scale remains challenging. To address this challenge, this work leveraged supervised learning to link water swimmability with the greening of a CSO shed (the drainage area of a CSO outfall), using New York City (NYC) as a case study. Random forest classification models were built to predict water swimmability after rainfall at 46 sites in NYC water bodies impacted by CSOs. A 14-feature model (AUROC =0.81, accuracy = 0.78) revealed that greening improved local water quality. However, water flow speed, antecedent rain depth, and CSO shed area were also influential. A simplified four-feature model (AUROC = 0.8, accuracy = 0.75) explored links between levels of greening and the probability of non-swimmable waters ($P_{ns}$) following different 18 h rainfall depths. Increased greening was found to be most impactful in reducing $P_{ns}$ for CSO sheds discharging to water bodies with flow speeds < 6 cm/s. For CSO sheds discharging to water bodies with flow speeds $\ge$ 14.7 cm/s, urban greening had no impact on $P_{ns}$. The work illustrates the utility of supervised learning in supporting citywide decisions regarding urban greening investments. Full article

Insect farming generates frass as a co-product alongside insect biomass, creating interest in its valorization within circular bioeconomy strategies and in its use as a fertilizer, soil improver, or plant biostimulant. This review adopts a claim-led framework linking product classification, composition, post-treatment, microbiological safety, environmental risks, and the evidence required to support specific agronomic claims, with particular emphasis on the EU regulatory context. Evidence from incubation, pot, greenhouse, and field studies, together with regulatory and technical sources, show that frass is a heterogeneous material whose performance depends on insect species, rearing substrate, product fraction, soil conditions, application rate, and processing history. Its relevance is increasing, particularly in regions where insect farming is expanding under established regulatory and industrial frameworks, including the European Union, North America, and parts of Asia. Across the reviewed evidence, the most scientifically and regulatorily defensible current positioning of frass is as a product-specific fertilizer or soil improver, whereas broader biostimulant or plant-protection claims require stronger product-level evidence. The review further concludes that safe and credible deployment depends on transparent characterization, appropriate hygienization and storage, contaminant screening where relevant, and claim-specific alignment with the applicable regulatory route. Full article

Graphene-based aerogels (GAs) exhibit outstanding performance in the adsorption of organic contaminants. Consequently, numerous studies have investigated the use of GAs for this purpose. In this work, the synthesis methods commonly used to produce GAs are first briefly described, and their key characteristics are summarized. Subsequently, GAs are classified according to the modifications applied to improve their adsorption properties toward organic pollutants. Furthermore, the quantitative relationships between surface area, density, surface chemistry, and adsorption performance for organic contaminants are systematically reviewed. The analysis revealed that the adsorption of two representative organic contaminants, toluene and methylene blue, is not dependent on the surface area of GAs. In contrast, GAs with lower density exhibit an improved adsorption capacity for toluene. Additionally, the relationship between the surface chemistry of GAs and their adsorption capacity toward methylene blue was analyzed considering the concentration of carboxylic sites. The available data suggests a potential correlation between the concentration of carboxylic groups on the surface of GAs and their adsorption capacity for methylene blue. This observation is supported by the analysis of methylene blue species in aqueous solution and the pH at the point of zero charge of GAs, which indicate that the interaction occurs mainly through electrostatic attractions resulting from the deprotonation of acidic surface sites. Finally, several opportunity areas and future research directions regarding the use of GAs for pollutant adsorption are discussed. Full article

This study proposes and evaluates a specialized Recreational Water Quality Index (IR-WQI) designed to prioritize the bathers’ safety and comfort. Focusing on the Năvodari–Mamaia sector (2022–2024), the research investigates how different weighting configurations—prioritizing either microbiological safety or physicochemical stability—affect the accuracy of bathing water assessments. The IR-WQI was tested across four scenarios, comparing the sensitivity of a specialized pH-based “bather-comfort” penalty function against models that include salinity as a weighted constant. Results demonstrate high categorical stability, with 93.3% of monitoring sites maintaining their qualitative classification regardless of the weighting scheme. However, the inclusion of salinity was found to inflate quality scores, potentially masking fecal contamination at vulnerable sites. Scenario 1, which prioritizes microbiological indicators (60% weight) and incorporates a pH filter, provides a transparent and conservative diagnostic tool for coastal managers, thereby supporting sustainable tourism and informed decision-making for beach safety. Full article

Diffuse pollution from agricultural runoff, characterized by intermittent discharges of complex contaminant mixtures, including nutrients, pesticides, and heavy metals (HMs), poses a persistent threat to global water quality. Conventional “end-of-pipe” strategies often fail to address these decentralized, nonpoint sources. This review examines the evolution of Permeable Reactive Barriers (PRBs) from static, abiotic filters into modern Permeable Reactive Bio-Barriers (PRBBs), engineered as dynamic, fixed-bed biofilm reactors. A key advancement in PRBB efficacy is the exploitation of biofilm plasticity, particularly in response to coexistence with organic and inorganic pollutants. While heavy metals are traditionally viewed as inhibitors, this review synthesizes evidence showing that subinhibitory HM levels can act as structural and functional drivers. These metals induce the upregulation of Extracellular Polymeric Substances (EPSs), creating a “protective shield” that sequesters metals and confers functional resilience on the microbial consortia responsible for nutrient removal and pesticide biodegradation. The review analyzes contaminant removal mechanisms, highlighting the bio-chemo synergy between reactive media and biofilms, and proposes a classification framework based on target contaminants, media, and technological integration. Significant focus is placed on emerging hybrid multi-media systems designed to protect the microbial community from toxic metal shocks, alongside the integration of artificial intelligence for predictive control. While challenges in hydraulic sustainability and field validation remain, PRBBs represent a compact, low-energy, and scalable ecotechnology. PRBBs offer a strategically targeted solution within the Nature-Based Solutions toolkit for building resilient protection of aquatic ecosystems at the critical land-water interface. Full article

Soot and black carbon (BC) are typically regarded as troublesome products of incomplete combustion; however, growing interest in circular economy strategies and sustainable manufacturing highlights their potential as secondary functional carbon materials, including additive manufacturing (AM). This review synthesises the recovery, upgrading, and valorization pathways for soot/BC and recovered carbon black (rCB), with a particular focus on streams captured by mandatory emission-control systems (e.g., diesel/gasoline particulate filters, electrostatic precipitators, baghouse filters, and chimney soot) and the requirements for transforming these heterogeneous residues into reproducible AM feedstocks. A two-stage approach was applied, combining (i) an analysis of the European Union regulatory context (waste classification, end-of-waste routes, and chemical safety obligations, including REACH) with (ii) a structured literature review of studies published in 2017–2026 indexed in the Web of Science and Scopus, culminating in a qualitative synthesis of 152 papers. Evidence indicates that scale-up is primarily constrained by strong compositional variability and contaminant burdens (ash, metals, and PAHs), which affect dispersion, rheology, and property reproducibility, necessitating robust standardisation and risk assessment. This review maps key preparation and upgrading strategies (e.g., classification, ash/metal reduction, and control of organic fractions) and discusses their relevance across AM routes such as FDM/FFF, SLS, DLP, and DIW. Overall, realising credible waste-to-value pathways requires aligning technical performance targets with regulatory compliance and developing consistent characterisation protocols to enable the safe and predictable use of soot/rCB-derived fillers in AM. Full article

Background: Sensors and mass spectrometry (MS) are frequently used in combination for food safety and quality assessment, yet their functional integration lacks a formal methodological framework. This review categorizes the synergies between these technologies into distinct Relational Connections. Methodology: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 155 original research articles published between 2015 and 2025 were systematically analyzed. Records were identified via the Scopus database within the food science domain. Experimental meta-data, including extraction protocols, instrumental configurations (ionization source, mass analyzer, cost tier), and chemometric strategies, were extracted to identify core methodological patterns. Statistical associations were quantified using chi-squared tests with Cramer’s V effect sizes. Results: Five Relational Connections were identified: (1) MS as reference for sensor validation (25.2%); (2) MS-sensor correlative analysis (10.3%); (3) MS quantifying data to train predictive sensor models (6.5%); (4) MS identifying targets for sensor detection (7.1%); and (5) MS enabling sensor classification models (51.0%). Technology pairing is governed by a three-level hierarchy: analyte polarity determines the ionization source (V = 0.69), required precision determines the mass analyzer (V = 0.64), and cost/availability constraints shape the practical integration strategy. Gas Chromatography (GC)-MS is predominantly coupled with Electronic Noses for volatile profiling (86% of classification studies), while Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) pairs with biosensors for contaminant analysis (74% of reference validation studies). Systematic analysis of the full pairing matrix reveals that 75% of theoretically possible MS-sensor combinations remain unexplored or underrepresented, identifying both technical boundaries and innovation frontiers. Discussion: The findings clarify the strategic logic behind technology pairings, demonstrating that MS provides the quantitative molecular data required for sensor training. The hierarchical decision framework and identification of underexplored pairings provide an evidence-based guide for designing future integrated food analysis systems. Full article

With rising incidence in recent years, Listeriosis, a severe foodborne disease in humans primarily transmitted through ready-to-eat (RTE) foods contaminated with Listeria monocytogenes, became the most severe zoonotic disease in the European Union (EU) in 2024 with the highest hospitalization and mortality rates, prompting stricter regulatory requirements under Regulation (EC) No 2073/2005 and its recent amendments. This systematic literature review aimed to evaluate the availability, validity and quality of published challenge test data on the growth potential and maximum growth rate of Listeria monocytogenes in RTE foods to identify data gaps and, if possible, to support the derivation of a classification of RTE foods into the two existing regulatory categories, a and b (not able and able to support the growth of Listeria monocytogenes). Conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook, a comprehensive database search was done to identify eligible challenge test studies on Listeria monocytogenes growth in RTE foods, followed by structured screening and quality assessment based on the EURL Lm Technical Guidance Documents. A limited and heterogeneous body of published challenge test data on the growth potential and maximum growth rate of Listeria monocytogenes in RTE foods was identified, with substantial data gaps across multiple food groups, precluding meta-analysis and limiting regulatory applicability under the current regulations. Overall, the available literature is insufficient to reliably support regulatory classification or to enable direct extrapolation by food business operators (FBO), underscoring the need for product-specific investigations and food group-specific guidance for food safety. Full article

Foreign plastic objects (FPOs) in poultry products present significant food safety risks and cause economic losses for the industry. Conventional detection methods, including X-rays and color imaging, often struggle to identify small or low-density plastics. Hyperspectral imaging (HSI) offers both spatial and spectral information but suffers from high computational cost when applied for FPO identification in industrial environments. This study introduces a parameter-efficient and computationally efficient spatial–spectral transformer framework for pixel-level classification of FPOs on broiler meat using NIR-HSI (1000–1700 nm). The framework integrates three innovations: (1) center-focused linear attention (CFLA) to reduce computational complexity from $O\left(n^2\right)$ to $O\left(n\right)$; (2) patch-local mixed-axis 2D rotary position embedding to preserve geometric relationships within hyperspectral patches; and (3) low-rank factorized projection (LRP) matrices to reduce parameters by approximately 50% within projection weight matrices. The framework was trained and evaluated on a dataset of 52 chicken fillets, comprising 295,340 labeled target hyperspectral pixels from 12 common polymer types and 1 fillet class. The model achieved 99.39% overall accuracy, 99.57% average accuracy, and a 99.31 Kappa coefficient across 248,540 test pixels. Per-class precision, recall, and F1-score exceeded 98.05%, 98.59%, and 98.76%, respectively, across all classes. Efficiency analyses showed an 83% reduction in multiply–accumulate operations (MACs), a 22% reduction in trainable parameters, and a model size reduction from 1.72 MB to 1.35 MB relative to the baseline configuration. These gains also translated into practical inference benefits, with the final model achieving a throughput of 212,971.5 hyperspectral patch cubes/s and a 4.19× speedup over the baseline. These results demonstrate that the proposed framework combines strong classification performance with high efficiency, supporting high-throughput inference for real-time monitoring and enabling contamination source traceability and preventive quality control in industrial poultry processing. The approach provides a benchmark for applying transformer-based models to food safety inspection tasks. Full article

In agriculture, the use of per- and polyfluoroalkyl substances (PFASs) as active substances in pesticides has increased over recent decades due to their chemical stability, their ability to alter cell membrane permeability, and their capacity to bind to target proteins. However, their intentional application to agricultural soils has led to progressive environmental accumulation. Their high persistence, mobility, and bioaccumulation potential, combined with documented toxicological effects, raise concerns for aquatic organisms and ecosystems. Monitoring surface and groundwater is essential to assess PFAS contamination. Data from the Italian monitoring plan show widespread contamination, despite the existing European regulatory framework designed to safeguard ecosystems and public health. The contamination is likely underestimated because monitoring programs currently target only a limited number of substances and PFAS metabolites and co-formulants are not included. Approximately 46 PFASs have been identified as active ingredients in pesticides, 29 of which are still authorized within the European Union, posing challenges for drinking water production and ecosystem protection. Existing regulatory regimes also differ in their evaluation procedures, which may lead to inconsistent conclusions regarding PFAS applications. Within the framework of the European “One Substance One Assessment” (OSOA) approach aimed at to ensuring the protection of human health and natural resources, this paper examines the properties of PFASs used as active substances in pesticides, their regulatory status, and their monitoring in Italy, highlighting the regulatory inconsistencies that result in the differential treatment of these substances compared with PFASs used in other sectors. Full article

Background/Objectives: Spirituality is a key component of coping and well-being in chronic and advanced illness, yet its assessment remains inconsistent across clinical settings. To identify, classify, and critically analyze the most commonly used and validated instruments for measuring spirituality in clinical contexts, focusing on their ability to assess the current spiritual state from a multidimensional perspective (cognitive, behavioral, and affective expressions). Methods: A systematic literature review was conducted using PubMed, Scopus, and Web of Science (2011–2024). Inclusion criteria targeted validation studies of instruments assessing spirituality in adults with chronic or advanced illnesses or in palliative care. A dual conceptual–functional classification was applied, and a custom scoring system was developed to evaluate psychometric quality. Contamination and tautological aspects were also examined. Results: Forty-three instruments were identified across 42 studies. Of these, 93.02% included cognitive, affective, and behavioral dimensions. Most were validated in oncology or chronic disease populations. Content validity and internal consistency were the most reported psychometric properties; responsiveness was rarely evaluated. Conclusions: The available instruments reflect several conceptual and functional approaches. The classification proposed in this review provides practical guidance for selecting scales according to specific clinical goals and settings, supporting the evaluation of the current spiritual state and the integration of spirituality into healthcare practice. Further research is recommended to develop culturally sensitive and responsive instruments suitable for diverse clinical contexts. Full article

Alzheimer’s disease (AD) is characterized by progressive cognitive decline and the accumulation of amyloid-β (Aβ) plaques and tau neurofibrillary tangles. Beyond genetic and proteostatic mechanisms, infection- and dysbiosis-based models of AD have gained renewed attention, including the antimicrobial protection hypothesis, in which Aβ may participate in innate immune defense. Here, we reanalyzed ribosomal depleted (Ribo-Zero) RNA-seq data from dorsolateral prefrontal cortex (DLPFC) samples from the Mount Sinai Brain Bank cohort (GSE53697) to screen for non-human transcripts. Reads underwent quality control and adapter trimming, taxonomic classification with Kraken2, abundance re-estimation with Bracken, and differential abundance testing with edgeR. Across 17 samples (9 advanced AD and 8 controls), we detected low-biomass microbial signals, with Acinetobacter radioresistens showing enrichment in the AD group (FDR = 0.018). Several additional taxa showed suggestive group differences but did not remain significant after multiple testing correction, including Lactobacillus iners (FDR = 0.051). We also performed an exploratory in silico analysis of an A. radioresistens biofilm-associated protein homolog, identifying predicted amyloidogenic motifs and surface-exposed regions that may be relevant to cross-seeding hypotheses, although no mechanistic inference can be drawn without experimental validation. Given the technical challenges of inferring microbial signals from post-mortem brain RNA-seq data, including contamination risk, low microbial biomass, and overwhelming host background, these findings should be interpreted as hypothesis-generating and warrant orthogonal validation in larger, microbiome-aware cohorts. Full article

Feature weighting plays a central role in medical classification by enhancing predictive accuracy, interpretability, and clinical trust through the explicit quantification of variable relevance. Despite their widespread use, existing filter-, wrapper-, and embedded-based feature weighting methods are predominantly deterministic and exhibit pronounced sensitivity to label noise and outliers, which are pervasive in real-world medical data. This often results in unstable importance estimates and unreliable clinical interpretations. In this work, we introduce a novel Bayesian feature weighting model that fundamentally departs from existing approaches by jointly integrating simplex-constrained Dirichlet priors for global feature weights, hierarchical shrinkage priors for coefficient regularization, and contamination-aware priors for explicit modeling of label noise within a single coherent probabilistic framework. Unlike conventional Bayesian feature selection or robust classification models, the proposed formulation yields globally interpretable feature weights defined on the probability simplex, while simultaneously providing full posterior uncertainty quantification and robustness to both mislabeled observations and aberrant feature values through principled influence control. Comprehensive simulation studies across diverse contamination scenarios, together with applications to multiple real-world medical datasets, demonstrate that the proposed model consistently outperforms classical and state-of-the-art baselines in terms of discrimination, probabilistic calibration, and stability of feature-importance estimates. These results highlight the practical and methodological significance of the proposed framework as a robust, uncertainty-aware, and interpretable solution for medical decision making under noisy data conditions. Full article

As part of a NASA University Leadership Initiative (ULI) program, this work supports the continued development and evaluation of a fatigue-based process window for stress-relieved Ti-6Al-4V specimens produced via laser powder bed fusion (PBF-LB/M). Four-point bend and axial fatigue specimens were fabricated by NASA ULI collaborators across a range of scan velocities (800–2000 mm/s) at a constant power of 370 W using an EOS M290 system. All fatigue specimens were low-stress-ground by a commercial vendor and tested at Case Western Reserve University (CWRU) under load-controlled cyclic loading at a stress ratio of R = 0.1. This paper presents a curated dataset linking PBF-LB/M process parameters to fatigue outcomes across 175 specimens. Of these, 136 fractured and this study includes fatigue crack initiation site identification and defect morphology metrics derived from post mortem SEM analysis. Specimens that reached runout (10⁷ cycles) and did not fracture under subsequent fatigue testing are retained in the dataset, with fractographic fields marked as ‘NA’ to indicate non-applicability. The dataset includes specimen metadata, processing parameters, fatigue life data, fatigue initiation site classification (e.g., keyhole, gas-entrapped pore (GeP), lack-of-fusion (LoF), contamination), defect size and shape descriptors, and spatial location relative to the free surface. These data are intended to support defect-based fatigue life prediction, probabilistic modeling, process–structure–property studies, and machine learning frameworks linking process parameters to fatigue performance in PBF-LB/M Ti-6Al-4V. Full article