The tomato variety affects the survival of Shigella flexneri 2a in fruit pericarp

The presence of enteric pathogens in produce can serve as a significant means of transmitting infections to consumers. Notably, tomatoes, as a type of produce, have been implicated in outbreaks caused by various human pathogens, such as Salmonella enterica and pathogenic Escherichia coli. However, the survival characteristics of Shigella spp. in tomatoes have not been thoroughly investigated. In this study, we assess the survival of S. flexneri 2a in two distinct varieties of post-harvested tomatoes. S. flexneri 2a was used to inoculate both regular-sized Vine tomatoes and cherry-type Mini Plum tomatoes. Our findings reveal no significant difference in Shigella survival in the pericarp of both varieties on day 2 post-inoculation. However, a significant disparity emerges on day 6, where all recovered Shigella colonies exclusively belong to the Mini Plum variety, with none associated with the Vine type. When Shigella was inoculated into the locular cavity (deep inoculation), no significant difference between varieties was observed. Additionally, we investigate the potential role of the SRL pathogenicity island (SRL PAI) in the survival and fitness of S. flexneri 2a in post-harvested tomatoes. Our results indicate that while the SRL PAI is not linked to the survival of the strains in tomatoes, it does impact their fitness. These findings underscore the variability in Shigella strains’ survival capabilities depending on the tomato variety, highlighting the importance of understanding Shigella ecology beyond the human host and identifying molecular determinants influencing bacterial survival to mitigate the risk of future outbreaks. The significance of this data on Shigella persistence in fresh vegetables should not be underestimated, as even a small number of Shigella cells can pose a threat to the health of individuals

The developmental effects of media-ideal internalization and self-objectification processes on adolescents’ negative body-feelings, dietary restraint, and binge eating

Despite accumulated experimental evidence of the negative effects of exposure to media-idealized images, the degree to which body image, and eating related disturbances are caused by media portrayals of gendered beauty ideals remains controversial. On the basis of the most up-to-date meta-analysis of experimental studies indicating that media-idealized images have the most harmful and substantial impact on vulnerable individuals regardless of gender (i.e., “internalizers” and “self-objectifiers”), the current longitudinal study examined the direct and mediated links posited in objectification theory among media-ideal internalization, self-objectification, shame and anxiety surrounding the body and appearance, dietary restraint, and binge eating. Data collected from 685 adolescents aged between 14 and 15 at baseline (47 % males), who were interviewed and completed standardized measures annually over a 3-year period, were analyzed using a structural equation modeling approach. Results indicated that media-ideal internalization predicted later thinking and scrutinizing of one’s body from an external observer’s standpoint (or self-objectification), which then predicted later negative emotional experiences related to one’s body and appearance. In turn, these negative emotional experiences predicted subsequent dietary restraint and binge eating, and each of these core features of eating disorders influenced each other. Differences in the strength of these associations across gender were not observed, and all indirect effects were significant. The study provides valuable information about how the cultural values embodied by gendered beauty ideals negatively influence adolescents’ feelings, thoughts and behaviors regarding their own body, and on the complex processes involved in disordered eating. Practical implications are discussed

Financial crises and the attainment of the SDGs: an adjusted multidimensional poverty approach

This paper analyses the impact of financial crises on the Sustainable Development Goal of eradicating poverty. To do so, we develop an adjusted Multidimensional Poverty Framework (MPF) that includes 15 indicators that span across key poverty aspects related to income, basic needs, health, education and the environment. We then use an econometric model that allows us to examine the impact of financial crises on these indicators in 150 countries over the period 1980–2015. Our analysis produces new estimates on the impact of financial crises on poverty’s multiple social, economic and environmental aspects and equally important captures dynamic linkages between these aspects. Thus, we offer a better understanding of the potential impact of current debt dynamics on Multidimensional Poverty and demonstrate the need to move beyond the boundaries of SDG1, if we are to meet the target of eradicating poverty. Our results indicate that the current financial distress experienced by many low-income countries may reverse the progress that has been made hitherto in reducing poverty. We find that financial crises are associated with an approximately 10% increase of extreme poor in low-income countries. The impact is even stronger in some other poverty aspects. For instance, crises are associated with an average decrease of government spending in education by 17.72% in low-income countries. The dynamic linkages between most of the Multidimensional Poverty indicators, warn of a negative domino effect on a number of SDGs related to poverty, if there is a financial crisis shock. To pre-empt such a domino effect, the specific SDG target 17.4 on attaining long-term debt sustainability through coordinated policies plays a key role and requires urgent attention by the international community

pysteps - a community-driven open-source library for precipitation nowcasting

Póster presentado en: 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Assessing whether artificial intelligence is an enabler or an inhibitor of sustainability at indicator level

"Since the early phase of the artificial-intelligence (AI) era expectations towards AI are high, with experts believing that AI paves the way for managing and handling various global challenges. However, the significant enabling and inhibiting influence of AI for sustainable development needs to be assessed carefully, given that the technology diffuses rapidly and affects millions of people worldwide on a day-to-day basis. To address this challenge, a panel discussion was organized by the KTH Royal Institute of Technology, the AI Sustainability Center and MIT Massachusetts Institute of Technology, gathering a wide range of AI experts. This paper summarizes the insights from the panel discussion around the following themes: The role of AI in achieving the Sustainable Development Goals (SDGs) AI for a prosperous 21st century Transparency, automated decision-making processes, and personal profiling and Measuring the relevance of Digitalization and Artificial Intelligence (D&AI) at the indicator level of SDGs. The research-backed panel discussion was dedicated to recognize and prioritize the agenda for addressing the pressing research gaps for academic research, funding bodies, professionals, as well as industry with an emphasis on the transportation sector. A common conclusion across these themes was the need to go beyond the development of AI in sectorial silos, so as to understand the impacts AI might have across societal, environmental, and economic outcomes. The recordings of the panel discussion can be found at: https://www.kth.se/en/2.18487/evenemang/the-role-of-ai-in-achieving-the-sdgs-enabler-or-inhibitor-1.1001364?date=2020â 08â 20&length=1&orglength=185&orgdate=2020â 06â 30 Short link: https://bit.ly/2Kap1tE © 2021"The authors acknowledge the KTH Sustainability Office and the KTH Digitalization Platform for their provided funding, which enabled the organization of this panel discussion. SG acknowledges the funding provided by the German Federal Ministry for Education and Research (BMBF) for the project “digitainable”. SDL acknowledges support through the Spanish Governmen

Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall

The rapid temporal evolution of convective rainfall poses a challenge for quantitative rainfall nowcasting models that forecast rainfall on timescales ranging from 5 min to 6 h. With the growing potential of machine learning models for precipitation nowcasting to produce realistic-looking nowcasts for long lead times, it is important to investigate whether the nowcasts also produce realistic development for convective rainfall. Common verification metrics traditionally used to validate nowcasting models are often dominated by large-scale stratiform rainfall, and averaging the metrics across entire precipitation fields obscures how accurately the models replicate individual convective cells, which makes it difficult to distinguish the model skill for the growth and decay of convective rainfall. In this study, we present a framework based on the tracking of convective cells to investigate how accurately nowcasting models reproduce the development of convective rainfall. In the framework, a cell identification and tracking algorithm is applied first to the input observation rainfall fields and then separately to the target observation and nowcast rainfall fields where the tracks identified in the input observations are continued. Features describing the cells and cell tracks, such as the cell volume rain rate and area, are then extracted. In addition to the errors in these feature values, the models' skill in reproducing the existence of convective cells is estimated by calculating several contingency table metrics, such as the critical success index. The results allow the analysis of how accurately the models reproduce the growth and decay of convective rainfall and quantify the differences between the models, for example, due to differences in how the models smooth the nowcasts (i.e. blurring). The framework also allows differentiation of the results based on the initial conditions of the cell tracks, demonstrated here by separating the tracks into decaying or growing cell tracks based on the cell status when the nowcast is created. We demonstrate the framework with four open-source nowcasting models: the advection nowcast, the S-PROG (Spectral Prognosis; Seed, 2003) and LINDA (Lagrangian Integro-Difference equation model with Autoregression; Pulkkinen et al., 2021) models from the pysteps library, and the L-CNN (Lagrangian Convolutional Neural Network; Ritvanen et al., 2023) model, with data from the Swiss radar network. The results indicate that the L-CNN model reproduced the existence of convective cells best among the models and had smaller errors in the cell volume rain rate than LINDA and S-PROG. LINDA had the smallest underestimation in the cell mean rain rate, whereas S-PROG significantly overestimated the cell volume rain rate and area because of blurring.</p

Nowcasting of thunderstorm severity with Machine Learning in the Alpine Region

Presentación realizada en la 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Assessing compassionate abilities: Translation and psychometric properties of the Italian version of the compassionate engagement and action scales (CEAS)

This study aimed to develop the Italian version of the Compassionate Engagement and Action Scales (CEAS) and examine its validity and reliability among Italian-speaking adults. A total of 374 (mean age = 23.11) Italian speaking participants took part in the study. All of them completed a questionnaire comprising the CEAS, together with measures of self-compassion, self-criticism, social support, empathy, well-being and general distress, used to estimate the scale’s convergent and criterion-related validity. Confirmatory Factor Analysis (CFA) revealed a satisfactory fit for a model in which three second-order factors (Self-compassion, Compassion for others and Compassion from others) were further articulated in two first-order factors (Engagement and Action). All the scales presented good reliability in terms of internal consistency. Correlations with measures of social support, empathy, self-compassion, self-criticism, well-being, and general distress indicated good convergent and criterion-related validity of the Italian version of the CEAS. Taken together, these results suggest that the CEAS can be properly used with Italian-speaking individuals in order to assess the three compassion flows in terms of both engagement and action

Connecting climate action with other sustainable development goals

The international community has committed to combat climate change and achieve 17 Sustainable Development Goals (SDGs). Here we explore (dis)connections in evidence and governance between these commitments. Our structured evidence review suggests that climate change can undermine 16 SDGs, while combatting climate change can reinforce all 17 SDGs but undermine efforts to achieve 12. Understanding these relationships requires wider and deeper interdisciplinary collaboration. Climate change and sustainable development governance should be better connected to maximize the effectiveness of action in both domains. The emergence around the world of new coordinating institutions and sustainable development planning represents promising progress