Search Results (1,725)

Over the past decade, antifungal vaccinology has produced genuine proof of concept: adhesin-based candidates such as NDV-3A have completed Phase II human trials, and reverse-vaccinology pipelines now routinely generate multi-epitope constructs with high predicted population coverage against Candida albicans and the multidrug-resistant Candida auris. However, no antifungal vaccine has reached the market, and the reasons for this gap are rarely examined together in a single, critical account. This review argues that the bottleneck is not primarily antigen discovery, but a layered set of translational barriers: biological constraints inherent to a commensal, morphologically plastic pathogen; manufacturing and purity requirements that complicate subunit vaccine production; a historical bias in preclinical animal models toward chemically immunosuppressed, rather than genetically susceptible, hosts; an underutilization of key insights from veterinary medicine; and a fragmented regulatory and commercial landscape that structurally disincentivizes investment in niche, high-risk patient populations. We further examine emerging immunotherapeutic strategies—passive antibody engineering, cytokine-based adjuvant approaches, phytocompound-derived antifungals, and dual-action nanovaccines—as partial, rather than complete, solutions to the immunocompromised-host paradox that limits active immunization in the patients who need protection most. Finally, we situate Candida vaccine development within a One Health and global-equity framework, arguing that diagnostic infrastructure, HLA population diversity, and cold-chain economics must be addressed as part of vaccine design rather than as an afterthought. Full article

The 7-year update of the phase 3 CROWN study is the most impressive milestone in anaplastic lymphoma kinase (ALK)+ non-small-cell lung cancer (NSCLC) since the discovery of the oncogene 20 years ago and inaugurates an entire new paradigm. The progression-free survival (PFS) plateau at a high level > 50%, with an estimated very low annual progression rate < 2% after the third year, suggests that lorlatinib can inactivate the disease in most patients. The complete abrogation of on-target resistance abolishes ALK tyrosine kinase inhibitor (TKI) sequencing and will seriously impact future drug development. In the new era of extraordinary drug efficacy, the design of clinical trials, pathways of marketing authorization, and dosing optimization policies will also need to be reconsidered. Lorlatinib will dominate the therapeutic landscape of ALK+ NSCLC until the advent of combination therapies, which are the most reasonable next step. Further strengthening of the ALK blockade with additional ALK-directed drugs could likely eliminate the small residual “leak” of late off-target resistance after the third year, while combination with emerging neoantigen-directed immunotherapies may also help control the highly aggressive and more immunogenic early-progressing tumors and facilitate cure of the disease. Universal state-of-the-art DNA/RNA NGS for newly diagnosed NSCLC is now imperative to identify and treat all ALK+ patients according to the latest therapeutic advances, which will hopefully soon also spread to earlier stages and other oncogenic drivers. Full article

The domestic pigeon (Columba livia domestica), recognized as one of the earliest species to be domesticated by humans, has sustained a profound and enduring association with human society. These birds fulfill significant social and economic roles, particularly in the context of pigeon racing competitions. To ensure fairness and authenticity in such events, as well as to address concerns related to meat fraud and adulteration, we have developed a six-color fluorescent multiplex PCR amplification system incorporating 24 short tandem repeat (STR) loci and the chromobox helicase DNA-binding gene (CHD) specific to domestic pigeons. The multiplex assay underwent comprehensive validation in accordance with the guidelines established by the Scientific Working Group on DNA Analysis Methods (SWGDAM) and the Committee on Wildlife Forensic Science Guidelines, encompassing evaluations of PCR conditions, precision, species specificity, sensitivity, stability, repeatability, and reproducibility. Furthermore, population genetic analyses and evaluations using simulated forensic case samples were conducted. The assay’s applicability to closely related species was also evaluated. Our results show that the developed pigeon STR genotyping system exhibits high levels of accuracy, specificity, repeatability, stability, and robustness. Complete genotyping profiles were obtained with only 125 pg of domestic pigeon genomic DNA. Analysis of 248 unrelated domestic pigeons obtained from a local market in Shanghai, China, yielded the cumulative probability of exclusion (CPE) and the combined power of discrimination (CPD) values of 0.999999659383865638452 and 0.99999999999999999999918448912103896, respectively. Additionally, the majority of primers effectively amplified loci in congeneric species, indicating the assay’s broad applicability. These results suggest that the multiplex system is highly polymorphic and well-suited for individual identification and kinship analysis in domestic pigeons, with potential applicability extending to related species. Full article

Postharvest fruit quality differs among various fruits and alters during sorting, packaging, transportation and storage. Therefore, it is important to have an efficient and objective grading method that causes little mechanical damage to ensure the uniformity of products and decrease market price and losses in the supply chain. This paper describes the grading norms, detection techniques, processing algorithms, structural designs and practical uses in different types of fruits. It examines the external features, internal quality and hidden faults by using machine vision, visible-near-infrared spectroscopy, hyperspectral and X-ray imaging, acoustic and mechanical detection, electronic noses and the integration of multiple sensors. In addition, it also investigates conventional machine learning, deep learning, transfer learning and lightweight implementations. The research has developed from grading according to size, weight and color to a complete evaluation of ripeness, juice volume, hardness, internal defects and shelf life. Moreover, single detection devices are joined together to construct integrated systems including feeding, separation, inspection, classification, redirection, packaging and data management. However, the application is restricted by the discrepancy between the grading criteria and measurable results, the lack of cross-species and batch generalization ability, and the difficulty in coordinating multiple sensors in real time. The systems should maintain a balance between production speed, mechanical damage and costs. Some matters needing attention are to standardize the quality description, choose multi-source fusion, develop adaptive lightweight models, design modular structures and guarantee end-to-end traceability. Solving these problems will facilitate the transition from accurate laboratory identification to reliable, economic and extensive commercial grading. Full article

We study a three-firm market following the breakdown of an initially complete cartel, taking both the breakdown and the identity of the departing firm as exogenously given. In the baseline model, the former cartel members no longer coordinate: all three firms choose quantities simultaneously and non-cooperatively. Thus, the term failed cartel describes the institutional origin of the post-cartel market rather than continued partial coordination or an endogenously profitable deviation. Because firms’ optimal quantities need not be unique, we represent their strategic behavior by set-valued best-response correspondences. We establish equilibrium existence for this simultaneous post-cartel Cournot model as a three-player specialization of the classical Debreu–Glicksberg–Fan framework, combining Berge’s maximum theorem with the Kakutani–Fan–Glicksberg fixed-point theorem. The generalized concavity of inverse demand and convexity of costs provide sufficient conditions for existence without requiring unique optimal responses. We also examine the single-valued case, distinguishing the uniqueness of individual best responses from the uniqueness of equilibrium. To enrich the benchmark analysis following the cartel breakdown, we introduce a separate anticipatory extension belonging to the class of hierarchical multi-leader–follower games. Our extension is narrowly focused on separating strategic from technological sources of production asymmetry. In our specialization, Firm 3 has a uniquely determined continuous response, while Firms 1 and 2 choose their quantities simultaneously and anticipate that response, yielding a two-leader–one-follower game. Equilibrium existence is established separately under explicit continuity and quasi-concavity assumptions on the induced payoffs. The extension shows that, even under identical technologies, the ordering of equilibrium quantities depends on the sign of Firm 3’s response to the leaders’ production decisions: depending on this response, either Firms 1 and 2 or Firm 3 may individually produce the larger equilibrium quantity. Thus, firms’ incentives to leave the cartel may differ according to the post-cartel market structure and their individual strategic positions within that structure. Full article

This paper develops a quantitative partial-equilibrium overlapping-generations model to study how private tutoring regulation affects human capital formation, household welfare, educational inequality, and intergenerational mobility. The analysis is motivated by a broader development-economics concern: how unequal access to complementary educational investment interacts with competitive education systems to shape the intergenerational distribution of economic opportunities. The model features heterogeneous households, endogenous ranking-based college admission, and intergenerational human capital transmission. Private tutoring can affect exam performance and productive human capital differently, which allows the analysis to distinguish its competitive and productive roles. Calibrated to Chinese microdata and reduced-form empirical evidence, the model is used as a benchmark quantitative exercise to compare a complete ban, a ban with imperfect enforcement and black-market tutoring, and a welfare-selected tutoring tax that partly finances public education. In the benchmark calibration, a complete ban generates large model-implied welfare and human-capital losses, especially when tutoring contributes to productive skills. Imperfect enforcement preserves part of tutoring activity but can widen group differences in access when informal tutoring costs vary across household types. Among the specific policy experiments considered, the tutoring tax performs better than prohibition-based policies while keeping mean child human capital close to the baseline. This ranking is conditional on the assumed enforcement and public-financing arrangements. The results highlight how education regulation, household heterogeneity, and public-finance design interact in shaping human-capital accumulation and inequality. Full article

Live-stake systems used in red pitahaya (Hylocereus undatus) cultivation in the Ecuadorian Amazon represent a sustainable alternative for crop production; however, their effects on phenological development and fruit quality under humid tropical conditions remain poorly understood. This study evaluated phenological timing and fruit quality of red pitahaya cultivated under two live-stake systems (Spondias mombin and Erythrina sp.) and inert concrete stakes was evaluated over two consecutive production years. Phenological development was characterised using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, while physical and chemical fruit quality attributes were assessed across different ripening stages. The duration of the principal phenological phases was highly consistent among stake systems, with only minor differences detected during reproductive bud development (BBCH 510–519), whereas flowering, fruit development, fruit ripening and the complete reproductive cycle did not differ significantly among systems. Analysis of covariance further showed that neither canopy shade (%) nor soil organic matter (OM), used as an indicator of soil fertility, significantly explained variation in the duration of the principal BBCH phenological phases. The complete reproductive cycle lasted approximately 56–57 days. Fruit quality was predominantly determined by ripening stage. Fruits harvested at BBCH 817 exhibited the highest fruit weight, pulp percentage and soluble solids content, while firmness declined progressively with ripening. Differences among stake systems were comparatively limited and trait-specific, with variation detected mainly in fruit weight, diameter and soluble solids content. Based on ripening-related changes in fruit quality, BBCH sub-stages 811–815 were more suitable for markets requiring greater firmness and postharvest potential. Contrastingly, sub-stage 817 was more appropriate for markets prioritising higher pulp percentage and soluble solids content. Reproductive phenology remained stable across the contrasting stake systems, while fruit quality responded primarily to ripening stage and, to a lesser extent, to the conditions associated with the different support systems. These findings provide practical criteria for harvest planning and support the use of live stakes as a viable agroforestry-based alternative for red pitahaya production under humid tropical conditions. Full article

Agro-food processing by-products represent valuable sources of bioactive compounds with potential applications in sustainable crop production. This study evaluated the agronomic and postharvest responses of greenhouse-grown tomato to two commercial formulations enriched in hydroxytyrosol (Formula A and Formula B) and derived from olive oil processing by-products. Plants were grown in a soilless coco-peat system, and the formulations were applied manually to the root system according to their respective manufacturer-recommended regimes. The substrate bag was considered the experimental unit (n = 6 per treatment), with the greenhouse block retained in the statistical model. Formula A and Formula B increased total yield by 9.6% and 15.7%, respectively, and marketable yield by 14.5% and 21.0%. Both formulations increased fruit firmness, whereas total soluble solids remained unaffected. Shelf life increased from 23 days in control fruit to 32 and 38 days following Formula A and Formula B application, respectively. Antioxidant-related responses were treatment-dependent: Formula B increased DPPH radical-scavenging capacity and total and reduced vitamin C, whereas lycopene increased significantly with Formula A and showed an intermediate response with Formula B; total polyphenols and anthocyanins were unaffected. The treatments also modified fruit primary-metabolite profiles, including sugars, organic acids, and amino acids, and both formulations increased fruit Ca, K, and N concentrations at the final sampling. Overall, these findings demonstrate the potential of hydroxytyrosol-enriched commercial formulations to improve tomato productivity and selected postharvest-quality traits while providing a valorization route for olive oil processing by-products. However, because the formulations differed in composition, hydroxytyrosol concentration, dose, and application frequency, the specific contribution of hydroxytyrosol cannot be isolated from that of the complete formulations. Full article

Recycled aluminum has emerged as an excellent alternative for producing new alloys. The development of multicomponent alloys has led to products with enhanced properties, and the aluminum alloy market has increased. This study utilized squeeze casting to produce Al alloy samples. The effects of 5 wt.% of both the Si content and the Al-25.5 Cu−12 Fe quasicrystal (QC) content on the microstructural formation and microhardness analyzed. A cellular microstructural formation was found for the recycled aluminum. A dendritic structure was characterized in both the Alrec−5% Si and Alrec−5% QC alloys. After adding 5% wt.% QCs, the resulting hardness became higher (varying between 110 and 130 HV) than the alloy with 5 wt.% Si (90 and 110 HV) and the recycled aluminum alloy (between 70 and 80 HV). The portion of the QC alloy completely dissolved into the bulk of the recycled alloy during squeeze casting. This was associated with a more complex phase arrangement characterized by the QC alloy. This was characterized by a dendritic microstructural array with binary eutectic mixtures (α-Al + Mg₂Si) and refined ternary eutectic mixtures (α-Al + Mg₂Si + AlFe(Si)). In contrast, the sample with Si content and the recycled alloy sample without additions depicted resulting microstructural arrays with reduced secondary phase formations. These contributed to improved mechanical behavior through traditional strengthening mechanisms. Full article

Evaluations of financial early-warning models can appear stronger when temporal separation is incomplete or when the event definition uses information unavailable at the forecast origin. This study examines one-day-ahead risk ranking of large absolute Bitcoin returns using 334 daily observations from 31 August 2024 to 30 July 2025, five expanding-window folds, lagged predictors, training-fold preprocessing, and train-only classification-threshold selection. The original q85 and q90 cutoffs, estimated from the full study-period return distribution, are retained as retrospective fixed-label benchmarks and are complemented by fold-specific train-only cutoffs. Under fixed q85 labels, class-weighted Logistic Regression with technical and market-activity variables achieved pooled ROC-AUC = 0.723 (moving-block 95% CI [0.556, 0.822]) and Average Precision = 0.338 ([0.103, 0.565]); Random Forest yielded 0.739 and 0.352. Pooled MCC was 0.207, but the primary model issued no positive warnings in three of the five fixed-q85 test folds. Under train-only q85 labels, ROC-AUC was 0.674 and Average Precision was 0.174, but the retained MCC threshold-selection rule produced no true positives. Conventional GARCH(1,1), EWMA, and 14-day historical-volatility scores did not exceed the primary model on both ranking metrics. Adding the complete six-variable sentiment and attention block lowered point estimates, although one-variable ablations and a matched-dimension control do not isolate sentiment content from dimensionality and small-sample overfitting. The results provide limited, sample-specific evidence for risk ranking, not evidence of deployment readiness, economic profitability, or cross-asset generalizability. Full article

We propose an end-to-end differentiable framework for hedonic agricultural land pricing that integrates deep learning-based land cover classification with spatial econometric modeling into a single neural architecture. Traditional hedonic pricing approaches typically separate land cover extraction from price regression, leading to suboptimal feature representations that fail to capture the spatial spillover effects inherent to agricultural markets. In our system, a Swin Transformer-based semantic segmentation network extracts pixel-level land cover features from high-resolution multispectral imagery, which are then aggregated within parcel boundaries to produce composition vectors. These features are combined with static parcel attributes and fed into a graph isomorphism network that models spatial dependencies among neighboring parcels through message passing. The central methodological innovation is a differentiable Moran’s I operator that computes spatial autocorrelation from predicted parcel prices and incorporates this statistic into the training objective as a regularizing loss term. This constraint explicitly penalizes deviations from empirically observed target levels of positive spatial autocorrelation in agricultural land markets, thereby ensuring that the learned land cover features are optimized to explain spatial price clustering rather than generic class categories. The complete pipeline, including the segmentation backbone, graph neural network, and spatial autocorrelation computation, is fully differentiable, allowing gradients from the spatial loss to flow backwards and update pixel-level features. This design transforms land cover classification from a mere preprocessing step into an economically informed feature-learning process. The unified framework thereby produces parcel valuations that are both pixel-accurate and spatially coherent, capturing complex nonlinear dependencies such as irrigation network effects or soil-type continuity that conventional spatial econometric models cannot represent. By jointly optimizing segmentation features and their spatial spillover effects on market prices, our approach represents a significant departure from the two-stage hedonic pricing methodology. Full article

Firm location in manufacturing supply chain networks is shaped by regional tax policies and network equilibrium responses. This study examines the spatial price equilibrium associated with alternative locations for an entrant manufacturer and evaluates how regional tax incentives affect firm profit and location choice. A bi-level programming model is developed. The upper-level model selects the location that maximizes after-tax profit, while the lower-level model solves the corresponding spatial price equilibrium to obtain the equilibrium transaction flows, market prices, and composite unit costs. Each composite unit cost comprises a flow-dependent transaction cost and a shortest-path transportation cost determined by the selected location. The upper-level problem is solved by complete enumeration, while the lower-level spatial price equilibrium is computed using a projection method. Numerical results show that firm profit is jointly determined by transaction volumes, equilibrium prices, transportation costs, production costs, fixed investment costs, and regional tax rates. Uniform tax-rate reductions increase profit without changing the location ranking, whereas sufficiently large location-specific reductions can reverse the ranking and alter the optimal location. The effectiveness of these incentives depends on location-specific equilibrium revenues and initial profit differences. By integrating firm location, regional tax incentives, and spatial price equilibrium within a unified bi-level framework, this study provides analytical support for manufacturing location decisions and regional investment-incentive design. Full article

Average IPO returns hide what most investors earn. This study examines 197 Indian main-board IPOs listed between 2016 and 2022, with daily prices to 2026, giving every issue a complete 780-trading-day record. Buy-and-hold abnormal returns are measured against the NIFTY 50 and the NIFTY 500 over seven horizons from 20 to 780 trading days. Mean abnormal return reaches 26.39 per cent at 780 days, and the median over the same window is −31.24 per cent. Only 41.1 per cent of IPOs beat the market. Returns are concentrated, with a Gini coefficient of 0.557, and the top decile of positive performers earn 42.9 per cent of all positive abnormal returns. Removing ten firms from 197 turns the mean negative. Calendar-time portfolios produce no significant alpha at any window, and a placebo test using random start dates yields higher abnormal returns than the actual post-listing windows. Search attention is associated with returns over the first sixty days. IPO volume becomes negatively associated with returns from 240 days onward, and hot-market timing is significant at 20 days and again from 240 days onward. Mean-based evidence overstates what a typical IPO investor earns, and the distribution matters more than the average. Full article

The growing penetration of variable renewable energy (VRE) is increasing the frequency of very low and negative prices, although these events also depend on demand, transmission capacity, price-regime persistence and flexibility resources. This study examines which pre-auction and diagnostic variables are associated with negative day-ahead prices across European bidding zones, and whether these relationships remain stable over time and transferable across markets. More than 736,000 observations from 12 bidding zones in 2019–2025 were analysed, with sample coverage varying by data completeness. A gate-closure-audited Extreme Gradient Boosting (XGBoost) model achieved moderate risk-ranking performance and positive probabilistic skill in 2024–2025. The strongest pre-auction signals were completed-auction price history, calendar features and structural load relationships. Leave-one-market-out validation showed partial and heterogeneous transferability, supporting local calibration. A separate diagnostic layer revealed market-specific, non-linear associations involving VRE forecasts, residual load and cross-border exchange. Negative prices are therefore interpreted as screening signals for market configurations potentially associated with limited surplus absorption, rather than direct evidence of a flexibility shortfall. The framework separates operational pre-auction prediction from later market diagnosis and provides a reproducible basis for local early-warning applications. All SHAP, ALE and scenario results are interpreted as predictive associations and model diagnostics, not as causal effects or direct measures of physical flexibility. Full article

Generative AI (genAI) is increasingly being integrated into critical appraisal and knowledge synthesis of research, with growing discourse on its value for qualitative research. Moreover, ChatGPT covers a major portion of the market share and is widely used by researchers and academics. However, a major gap remains in genAI’s ability to assess the reporting quality of qualitative research studies. This pilot study aimed to determine whether GPT-5 could reliably assess the reporting quality of health-related qualitative research studies. The Standards for Reporting Qualitative Research (SRQR) checklist was used as a framework for completing both human and AI chatbot assessments. The SRQR is a widely used validated checklist for enhancing the reporting of qualitative research. Overall, 68 peer-reviewed studies were selected for testing, comparing human SRQR assessments with GPT-5 assessments. Findings revealed a wide variability in agreement between human and GPT-5 assessments, with crude agreement ranging between 60 and 100% and interrater reliability ranging between 0 and 0.85 across checklist items. Simpler, objective checklist items such as study purpose were often correctly identified and had stronger agreement compared to nuanced items. The model struggled with the following SRQR items leading to an “Unclear” response: research design/paradigm (41%), researcher reflexivity (41%), data processing (27%), and trustworthiness (46%). These preliminary results suggest that GPT-5 may struggle with tasks requiring nuance and interpretation of qualitative research, which is rich in complex methods and differing worldviews. Currently, GPT-5 and other AI chatbots should be used with caution and alongside human oversight when assessing the reporting quality of qualitative research within public health and healthcare. Full article