Investments to prevent tropical deforestation and to limit wildlife trade will protect against future zoonosis outbreaks For a century, two new viruses per year have spilled from their natural hosts into humans (1). The MERS, SARS, and 2009 H1N1 epidemics, and the HIV and coronavirus disease 2019 (COVID-19) pandemics, testify to their damage. Zoonotic viruses infect people directly most often when they handle live primates, bats, and other wildlife (or their meat) or indirectly from farm animals such as chickens and pigs. The risks are higher than ever (2, 3) as increasingly intimate associations between humans and wildlife disease reservoirs accelerate the potential for viruses to spread globally. Here, we assess the cost of monitoring and preventing disease spillover driven by the unprecedented loss and fragmentation of tropical forests and by the burgeoning wildlife trade. Currently, we invest relatively little toward preventing deforestation and regulating wildlife trade, despite well-researched plans that demonstrate a high return on their investment in limiting zoonoses and conferring many other benefits. As public funding in response to COVID-19 continues to rise, our analysis suggests that the associated costs of these preventive efforts would be substantially less than the economic and mortality costs of responding to these pathogens once they have emerged.
Blanca De-la-Cruz-Torres, Anselmo Ruiz-de-Alarcón-Quintero, Miguel Navarro-Castro
Introduction: Ball velocity is a critical determinant of shot effectiveness in football, yet its influence on advanced post-shot metrics, such as expected shot impact timing (xSIT) and expected goals on target (xGOT), remains poorly understood, particularly in the context of sex-specific differences. This study examined the relationship between ball velocity and these metrics in men’s and women’s elite European tournaments. Methods: A total of 2174 shots were analyzed from all matches of the 2024 UEFA Men’s EURO (<i>n</i> = 1305) and 2025 UEFA Women’s EURO (<i>n</i> = 869), classified as goal shots on target, non-goal shots on target, and shots off target. Ball velocity was measured for each shot, and its associations with xSIT, our own xGOT model and the StatsBomb xGOT model were quantified using correlation coefficients. Results: Ball velocity differed significantly between sexes (<i>p</i> < 0.001), with higher values in men, and goal shots on target exhibited lower velocities than non-goal or off-target shots, indicating a speed–accuracy trade-off. Only xSIT and our own xGOT model were sensitive to ball velocity, reflecting sex-specific differences (<i>p</i> < 0.001). When comparing shot types across advanced metrics, a consistent trend was observed in both tournaments: xSIT showed no significant differences between goal and non-goal shots, whereas both xGOT models were higher for goal shots on target. Correlations indicated a moderate positive relationship between xSIT and ball velocity, and moderate negative correlations for both xGOT models, slightly stronger in men. Conclusions: Ball velocity is a critical factor influencing shot performance and advanced post-shot metrics, with notable sex-specific differences.
Bibliography. Library science. Information resources
Saren H. Seeley, Rachel Fremont, Zoe Schreiber
et al.
Background: Despite their exposure to potentially traumatic stressors, the majority of World Trade Center (WTC) responders—those who worked on rescue, recovery, and cleanup efforts on or following September 11, 2001—have shown psychological resilience, never developing long-term psychopathology. Psychological resilience may be protective against the earlier age-related cognitive changes associated with posttraumatic stress disorder (PTSD) in this cohort. In the current study, we calculated the difference between estimated brain age from structural magnetic resonance imaging (MRI) data and chronological age in WTC responders who participated in a parent functional MRI study of resilience (N = 97). We hypothesized that highly resilient responders would show the least brain aging and explored associations between brain aging and psychological and cognitive measures. Method: WTC responders screened for the absence of cognitive impairment were classified into 3 groups: a WTC-related PTSD group (n = 32), a Highly Resilient group without lifetime psychopathology despite high WTC-related exposure (n = 34), and a Lower WTC-Exposed control group also without lifetime psychopathology (n = 31). We used BrainStructureAges, a deep learning algorithm that estimates voxelwise age from T1-weighted MRI data to calculate decelerated (or accelerated) brain aging relative to chronological age. Results: Globally, brain aging was decelerated in the Highly Resilient group and accelerated in the PTSD group, with a significant group difference (p = .021, Cohen’s d = 0.58); the Lower WTC-Exposed control group exhibited no significant brain age gap or group difference. Lesser brain aging was associated with resilience-linked factors including lower emotional suppression, greater optimism, and better verbal learning. Conclusions: Cognitively healthy WTC responders show differences in brain aging related to resilience and PTSD.
Ramkesh Meena, M. K. Chaudhary, P. S. Gurjar
et al.
Abstract Background Date palm (Phoenix dactylifera L.) is a vital fruit crop cultivated in hot, arid regions due to its economic, nutritional, and ecological significance. Understanding the morphological diversity among different genotypes is crucial for breeding, conservation, and improving yield potential. The current study aimed to assess the phenotypic diversity of 37 date palm genotypes from 27 accessions grown in India's arid climate, focusing on 26 morphological traits. Results The study employed statistical analyses such as ANOVA, correlation analysis, and principal component analysis (PCA) to evaluate trait variability and their contributions to yield improvement. Key traits like leaf length (CH1), leaflet length (CH2), bunch length (CH9), bunch weight (CH15), fruit weight (CH16), and pulp weight (CH20) exhibited significant genetic variation. Correlation analysis revealed strong positive associations between bunch weight (CH15) and key yield components, including yield per plant (CH26), number of bunches per plant (CH7), and fruit traits like weight (CH16), width (CH18), and length (CH17). Additionally, negative correlations were observed, such as stone weight (CH19) with the pulp-to-stone ratio (CH21) and total soluble solids (CH25), highlighting trade-offs in fruit composition. The impact of both genetic and environmental factors on trait expression was demonstrated by PCA, which further separated genotypes such as Medjool (MDL) and Tayer (TYR) based on fruit weight and yield, respectively. Principal component analysis (PCA) demonstrated that PC1, PC2, and PC3 accounted for a significant portion of the total variation, with leaf length, bunch length, fruit weight, and pulp weight being key contributors. Additionally, cluster analysis grouped genotypes into two major clusters, identifying genetically similar accessions such as Gizej and Sakaloti, as well as Binet-A-Isha and Tayer, which could serve as promising breeding material. Conclusion These findings emphasize the rich morphological diversity within date palm accessions and highlight the potential of key morphological traits in breeding programs aimed at enhancing fruit quality and yield stability under hot-arid conditions.
With the advancement of computer vision and image processing technologies, scene recognition has gradually become a research hotspot. However, in practical applications, it is necessary to detect the categories and locations of objects in images while recognizing scenes. To address these issues, this paper proposes an indoor object detection and scene recognition algorithm based on the Apriori algorithm and the Mobile-EFSSD model, which can simultaneously obtain object category and location information while recognizing scenes. The specific research contents are as follows: (1) To address complex indoor scenes and occlusion, this paper proposes an improved Mobile-EFSSD object detection algorithm. An optimized MobileNetV3 with ECA attention is used as the backbone. Multi-scale feature maps are fused via FPN. The localization loss includes a hyperparameter, and focal loss replaces confidence loss. Experiments show that the method achieves stable performance, effectively detects occluded objects, and accurately extracts category and location information. (2) To improve classification stability in indoor scene recognition, this paper proposes a naive Bayes-based method. Object detection results are converted into text features, and the Apriori algorithm extracts object associations. Prior probabilities are calculated and fed into a naive Bayes classifier for scene recognition. Evaluated using the ADE20K dataset, the method outperforms existing approaches by achieving a better accuracy–speed trade-off and enhanced classification stability. The proposed algorithm is applied to indoor scene images, enabling the simultaneous acquisition of object categories and location information while recognizing scenes. Moreover, the algorithm has a simple structure, with an object detection average precision of 82.7% and a scene recognition average accuracy of 95.23%, making it suitable for practical detection requirements.
Hanna Fjellström, Emelie Sandberg, Johanna Blomgren
et al.
Background This study describes the evaluation of a twinning initiative between the Ghana Register Midwives Association and the Swedish Association of Midwives. Recognising the importance of midwives being supported by a national midwife association, the initiative was to strengthen the professional association in Ghana as a labour union and to inspire Swedish midwives to involve themselves in international work. Objective The study aimed to evaluate a twinning initiative between the Ghana Registered Midwives Association and the Swedish Association of Midwives. Method Two focus group discussions and four individual interviews were held with nine midwives from the Ghana Registered Midwives Association (n = 6) and the Swedish Association of Midwives (n = 3). The interviews and analysis were guided by a process evaluation framework using content analysis. Results The twinning initiative was successfully implemented regarding fidelity, dose, and reach, despite adaptations to the original project plan. Both associations gained visibility, with the Ghana Registered Midwives Association growing its paid membership by 97%, from 631 to 1,245 members during the twinning initiative. The results suggest that the Swedish Association of Midwives enhanced its understanding of international midwifery, promoted knowledge exchange, and raised awareness of midwives’ global roles in improving care. Conclusion The Ghana Registered Midwives Association and the Swedish Association of Midwives had a positive experience with the twinning initiative, despite deviations from the original plan. Midwives from both associations benefitted from sharing best practices and mutual support in their roles as newly formed labour trade unions. These findings could benefit other midwife associations in future twinning initiatives.
Peter Sseruwagi, Peter Sseruwagi, Edouard Lehmann
et al.
Hass avocado production and trade are rapidly expanding globally, with increasing consumer demands on quality, safety and sustainability. Last decade, the contribution of East Africa has increased tremendously following several comparative advantages. However, despite substantial recent public and private investments, Uganda’s Hass production and export lags behind neighboring countries. This is mainly due to the sector’s limited organization, resulting in a fragmented market with varying socio-economic, environmental, and agronomic conditions. Consequently, the limited data and insights on these variable production systems negatively impact the effectiveness of interventions and investments in the sector. In this study, Hass avocado producers were randomly selected across Uganda. Field visits included farm and field surveys, GPS mapping of production areas, and soil sampling for wet-chemistry analysis. Descriptive statistics, multivariate logistic regression, and ANOVA were used to assess the impact of farm and field characteristics on production practices and access to advisory services and certification. Farming systems and dynamics were characterized by assessing demographics, economic data, marketing, farmer organization, and farming practices including soil and nutrient management, irrigation, pest and disease control, and post-harvest management. Results show a fragmented and immature but expanding Hass sector in Uganda. Production mostly occurs in small- to medium-sized fields with no or limited inputs (i.e., fertilisers, pesticides, irrigation), using manual labor (family or hired) under mixed cropping systems, but lacking critical infrastructure, agronomic knowledge, extension services, and access to markets. In contrast to farmer’s belief that soils are suitable and fertile for Hass avocado, soil analyses indicate the urgent need for site specific soil management interventions. Implementation of good agronomic practices and access to inputs and advisory services seem mostly related to farm and field size, and to a lesser extent influenced by farmer age, orchard age, and agroecology, while membership of farmer organizations/associations currently seem to bring limited benefits. This study highlights several comparative advantages and opportunities for the Hass sector in Uganda and identifies the priority challenges to be tackled in future investments and interventions targeting a sustainable avocado industry.
Nutrition. Foods and food supply, Food processing and manufacture
To understand how fluctuations arise and are distributed in international trade, a question crucial for economic risk assessment and policymaking, we analyze strong adverse fluctuations-collapsed trades-defined as individual trades with sharp annual volume declines. Adopting a hypergraph framework for a fine-scale trade-centric representation of international trade, we find that collapsed trades (hyperedges) are clustered and their occurrence decays algebraically with trade volume (weight), which suggests inhomogeneous, epidemic-like spreading of collapse in the international trade hypergraph. Modeling collapse propagation as a contagion process and analyzing its dynamics, we show that a positive degree-weight correlation and a volume-decaying collapse rate synergistically suppress the onset of global collective collapse. Notably, the degree-weight correlation persisted but the volume-decay of the collapse rate weakened during the 2008-2009 global economic recession, resulting in a broader collapse spread. Our study shows how the interplay between structure and dynamics stabilizes complex systems.
As machine learning models are increasingly embedded into society through high-stakes decision-making, selecting the right algorithm for a given task, audience, and sector presents a critical challenge, particularly in the context of fairness. Traditional assessments of model fairness have often framed fairness as an objective mathematical property, treating model selection as an optimization problem under idealized informational conditions. This overlooks model multiplicity as a consideration--that multiple models can deliver similar performance while exhibiting different fairness characteristics. Legal scholars have engaged this challenge through the concept of Less Discriminatory Algorithms (LDAs), which frames model selection as a civil rights obligation. In real-world deployment, this normative challenge is bounded by constraints on fairness experimentation, e.g., regulatory standards, institutional priorities, and resource capacity. Against these considerations, the paper revisits Lee and Floridi (2021)'s relational fairness approach using updated 2021 Home Mortgage Disclosure Act (HMDA) data, and proposes an expansion of the scope of the LDA search process. We argue that extending the LDA search horizontally, considering fairness across model families themselves, provides a lightweight complement, or alternative, to within-model hyperparameter optimization, when operationalizing fairness in non-experimental, resource constrained settings. Fairness metrics alone offer useful, but insufficient signals to accurately evaluate candidate LDAs. Rather, by using a horizontal LDA search approach with the relational trade-off framework, we demonstrate a responsible minimum viable LDA search on real-world lending outcomes. Organizations can modify this approach to systematically compare, evaluate, and select LDAs that optimize fairness and accuracy in a sector-based contextualized manner.
Nicolas Apfel, Holger Breinlich, Nick Green
et al.
Gravity equations are often used to evaluate counterfactual trade policy scenarios, such as the effect of regional trade agreements on trade flows. In this paper, we argue that the suitability of gravity equations for this purpose crucially depends on their out-of-sample predictive power. We propose a methodology that compares different versions of the gravity equation, both among themselves and with machine learning-based forecast methods such as random forests and neural networks. We find that the 3-way gravity model is difficult to beat in terms of out-of-sample average predictive performance, especially if a flexible specification is used. This result further justifies its place as the predominant tool for applied trade policy analysis. However, when the goal is to predict individual bilateral trade flows, the 3-way model can be outperformed by an ensemble machine learning method.
We introduce a novel equilibrium concept that incorporates Knightian uncertainty into the cursed equilibrium (Eyster and Rabin, 2005). This concept is then applied to a two-player game in which agents can engage in trade or refuse to do so. While the Bayesian Nash equilibrium predicts that trade never happens, players do trade in a cursed equilibrium. The inclusion of uncertainty enhances this effect for cursed and uncertainty averse players. This contrasts general findings that uncertainty reduces trade but is consistent with behavior that has been observed in experiments.
Directed Trade Associations (DTAs) are highly focused meta-organizations with few member companies as their funding members. They take direct aim at making substantial contributions and impacts on the medium- and long-term viability of the industries they represent. This paper discusses what makes a trade association a DTA, what the advantages of membership in a DTA are, utilizing innovation as a case in point, and outlining how the structural and management of a DTA are nuanced and specialized. The historical background and relevant context of trade associations is reviewed, a stakeholder mapping and influence analysis is undertaken, and a comparative diffusion of innovation analysis is conducted. DTAs excel in their focused structure, allowing them to efficiently manage complex stakeholder dynamics and effectively drive industry-wide innovation. Unlike traditional, broad-based associations, DTAs facilitate quicker consensus-building and targeted strategic initiatives due to their specialized, strategically aligned membership. Effective leadership within DTAs requires nuanced consensus-driven communication and relationship-building skills to navigate roles that often overlap among stakeholders. As industries continue facing challenges from consolidation, digital transformation, shifting political landscapes, and evolving regulatory pressures, DTAs offer critical mechanisms for maintaining competitiveness, fostering collective innovation, and achieving sustained growth and industry resilience.