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Los impactos de la violencia en la salud mental global : contribuciones de la Terapia de Exposición Narrativa en diferentes sistemas de salud
No full textPost-traumatic stress disorder represents a substantial global mental health burden, particularly in the face of cumulative violence, forced migration, and structural inequities. Narrative Exposure Therapy (NET) configures a brief trauma-focused intervention that supports the reconstruction of autobiographical memory by the chronological narration of life events. By integrating fragmented traumatic experiences into a coherent narrative, NET facilitates emotional processing and restores continuity to disrupted life stories. This regional case series examines the integration of NET into the mental health systems in Brazil, the Democratic Republic of Congo, Germany, Switzerland, Japan, Mexico, the United Kingdom, and Scandinavia. Drawing on diverse implementation experiences, the study identifies both enabling conditions and persistent challenges. Results highlight that NET is feasible and adaptable across different sociocultural and resource settings, especially when supported by sustained supervision, task-shifting strategies, and intersectoral collaboration. Embedding NET into existing service structures expanded access to evidence-based trauma care for populations often excluded from specialized treatment. These findings underscore the critical role of trauma-informed public policies in responding to the mental health consequences of violence on a global scale.publishe
Probing the differential stress granule proteome by mass spectrometry-based proteomics
No full textStress granules (SG), as part of the biomolecular organization of the cell, are membraneless, predominantly formed by liquid-liquid phase separation (LLPS), and are essential for cellular homeostasis. Dysregulation of their assembly has been implicated in severe diseases such as cancer, amyotrophic lateral sclerosis (ALS), and frontotemporal dementia. Thus a comprehensive understanding of SG assembly, composition, and maintenance is of great interest. Their properties, such as small size, dynamic exchange with surrounding cytoplasm, and multivalent interactions, make their enrichment and analysis technically challenging.
To overcome these obstacles, this study adapted an SG enrichment workflow based on green- fluorescent protein (GFP)-tagged G3BP1, the SG core protein. The approach combines chemical cross-linking for structural stabilization, enrichment via fluorescence-activated particle sorting (FAPS), and downstream quantitative mass spectrometry (MS) analysis.
A screen for a suitable cross-linker on arsenate-stressed HeLa cells, considering the compatibility with FAPS, identified disuccinimidyl glutarate (DSG), with its short spacer arm, as the most effective. Alongside a gentle lysis procedure, involving syringe and cannula, and a short centrifugation protocol, yielded a clear and reproducible size-pre-enriched sample suitable for FAPS. FAPS discriminated the input according to ‘high’ or ‘medium’ intense particles, referring to the GFP-intensity.
Subsequent label-free quantification (LFQ) in data-independent acquisition (DIA) MS analysis identified 362 statistically significant proteins in arsenate-stressed samples. Among these, 187 proteins were enriched in the stress high compared to the stress medium sorted fractions, indicating a subset of proteins whose association with SGs is highly stress dependent. Ratio- based filtering further refined the list, yielding 42 proteins confidently assigned as SG core components.
Expanding the dataset to include samples subjected to heat stress and osmotic shock revealed stressor-specific protein sets alongside a shared cluster of proteins consistently present across all stress conditions.
Using the chemical cross-linking and the fluorescent features of GFP tagged to G3BP1 for enrichment enables robust downstream data acquisition, yielding a highly reliable list of SG- associated and SG core proteins. This workflow is also suitable for comparative analysis across different stress conditions. Overall, it offers a specific approach for capturing in-depth quantitative proteomic information on SGs, and will help to unravel their composition, dynamics, and regulation.publishe
How English orthographic proficiency modulates visual attention span in Italian learners with and without dyslexia
No full textVisual attention span (VAS) refers to the number of visual elements processed simultaneously in a multielement array. It is causally related to reading skills and may be impaired in readers with dyslexia. VAS is influenced by orthographic depth with opaque orthographies boosting it. Such orthography-specific VAS modulations are subject to crosslinguistic interactions in early biliterates, leading to advantages associated with learning to read in an opaque orthography. However, little is known about potential VAS bootstrapping effects in late biliterates. This study investigates potential VAS modulation in late biliterates with and without dyslexia. Participants were first language (L1) Italian native speakers (transparent orthography) learning English as a second language (L2). Our results show that the VAS capacity of typical readers is modulated by English orthographic knowledge, providing the first evidence that experience with a nonnative orthography boosts VAS skills also in late biliterates. This effect was reduced in dyslexic learners, possibly due to a VAS deficit.publishe
Relationship between narrative ability and executive functions : A longitudinal study in kindergarten classrooms
No full textNarrative ability and executive functions develop rapidly in children during the preschool years. The aim of this study was to investigate the nature of the longitudinal relationship between these two abilities by examining two competing theoretical accounts: direct reciprocal influence and the role of shared underlying factors. The sample consisted of 280 kindergarten children who were assessed in three waves over 18 months. A dual-model approach was used, employing both a Cross-Lagged Panel Model with lag-2 effects (CL2PM) and a Random Intercept Cross-Lagged Panel Model (RI-CLPM). The CL2PM revealed a directional relationship, where higher executive functions predicted subsequent growth in narrative ability, but not the inverse. This cumulative, directional influence helps explain the robust, stable connection between the abilities observed at the between-person level in the RI-CLPM (r = .58, p < .001) - a finding consistent with the hypothesis of shared underlying factors. This robust predictive relationship was observed despite evidence from descriptive data that the two skills were otherwise differentiating. No evidence was found for a more immediate, dynamic interplay at the within-person level. The findings suggest a complex relationship characterized by a robust, stable connection, likely stemming from both shared underlying factors and a cumulative, directional influence. Further research is warranted to identify these shared factors and experimentally test this directional influence in order to inform effective interventions.publishe
Ethnicity and Elections : Electoral Context Affects Parties' Use of Ethnic References
No full textLinks to specific ethnic groups constitute a defining feature of ethnic parties. Yet, whether and how references to ethnic identities appear in ethnic parties' political communication often remains unstudied despite the promise it carries. This paper investigates to what extent the electoral cycle and ethnic competition influence ethnic parties' usage of ethnic references on social media. Analyzing 1.3 million social media posts by 112 ethnic parties in 38 countries, we find that the number of ethnic references by a party increases as election day approaches. Moreover, a higher number of competing ethnic parties in elections is also associated with higher usage of ethnic references. This research contributes to our understanding of when and why parties address ethnicity on social media.publishe
Broadening the applicability of local completeness analysis with intensional and extensional guarantees
No full textLocal Completeness Logic (LCL) is a proof system for program analysis rooted in abstract interpretation. The program semantics is under-approximated by any provable postcondition, like incorrectness logic does, but it is also over-approximated by a (locally) complete abstraction of such a postcondition, like Hoare logic does. Therefore, any derivable triple will either prove the program to be correct or unveil true bugs. While the completeness of a program's function with respect to an abstract domain is inherently extensional, LCL's rules demand the preservation of local completeness throughout the abstract interpreter's computations. This characteristic renders LCL analysis intensional, meaning it depends on the way the program is written. Consequently, LCL proof system may not derive all the valid triples. This paper addresses this discrepancy by: 1) designing new rules that allow one to perform part of the intensional analysis in different (complete) abstract domains whenever necessary; and 2) to compare their expressiveness. Notably, some of these new rules enable the derivation of all extensionally valid triples, thereby decoupling the set of provable properties from the way the program is writtenpublishe
Advancing Sustainable Development : Sustainability Science Summit 2025
No full textAgainst the backdrop of populist attacks on science and sustainable development, the Sustainability Science Summit 2025 focused on advancing sustainability transformations. With over 30 sessions structured around three high-level panel discussions, it addressed global challenges in sustainability science, the transformation of research practices, and the science-policy interface.publishe
The interplay between satiation and temptation drives cleaner fish Labroides dimidiatus foraging behavior and service quality toward client reef fish
No full textSupply and demand affect the values of goods exchanged in cooperative trades where high demand typically leads to a higher cost. An exception has been described in the marine cleaning mutualism involving the cleaner fish Labroides dimidiatus and its variety of “client” coral reef fishes. Cleaner fish feed on clients' ectoparasites (ie gnathiid isopods) but prefer eating clients' mucus instead, which constitutes cheating. Here, we provide field observations, followed by a set of laboratory experiments with real client fish and Plexiglas feeding plates as surrogates for clients. In the field and in three experiments with real clients, we found that satiated cleaner fish were more cooperative, even though low hunger levels should make them less dependent on cleaning interactions. Similarly, the more abstract version of the cleaner–client experiments using Plexiglas plates offering two food types as stand-ins for client ectoparasites and mucus showed that satiation led cleaner fish to feed more against their preferences—an indicator of cooperative behaviour. However, this outcome occurred only if the temptation to eat the preferred food was low. When temptation to cheat was high, cleaner fish did so. We provide a further general support to these findings with a game-theoretic model. Many mutualisms involve food as a commodity. Thus, identifying foraging decision rules will enhance our understanding of how individuals adjust to variations in market conditions in real-time rather than playing a fixed strategy based on average market conditions.publishe
Computer Vision for Protest Analysis
No full textHow can computer vision help us to understand protests better? Every day, people take to the streets to protest, and images of these events are shared thousands of times on social media. While qualitative studies have effectively demonstrated that protests are diverse and highly dynamic, quantitative research faces the challenge of capturing this nuanced information. However, protest images offer a unique opportunity to do so, as each image provides detailed documentation of what is happening at a particular time and place. Since these images are shared thousands of times on protest days, they can be used to reconstruct the events as they unfold. Researchers have rarely analyzed these images due to the difficulty of extracting protest-related information from them. Fortunately, recent advances in computer vision are changing this landscape. Computers are now capable of performing many visual tasks, including extracting high-level insights from images and videos. Dedicated models have already been trained to recognize protest images and assess the level of violence depicted in them. Additionally, many generic models can be adapted from computer science to applications in social sciences. For instance, segmentation models can identify a wide range of objects in images, such as people and faces. Although these tasks could theoretically be performed manually, the large scale of images on social media renders this infeasible. Therefore, researchers increasingly rely on computer vision methods to efficiently extract information from these images. This dissertation explores different applications in which computer vision enhances our understanding of protests. To achieve this, readily available computer vision methods are adopted, trained, and optimized specifically for analyzing protest images. These methods facilitate the extraction of various characteristics from these images, enabling a deeper analysis of the protests themselves. A distinct image dataset complements each method. The first dataset comprises more than 140,000 images collected from social media, with annotations indicating whether each image depicts a protest or not. This dataset aims to provide a comprehensive overview across ten different countries. The second dataset focuses on capturing protest periods in specific cities, covering 13 protest episodes and incorporating approximately 22,000 images. The findings reveal that persons, flags, and signboards are important objects in protest images. But particular features of protests vary across different countries and protest episodes. The results also indicate that the escalation of protest events can be tracked through images shared on social media, allowing for predictions of protest dynamics on the same day. However, predictions for the following day show only marginal improvements. Experimental results highlight how individuals perceive protests through sequences of images. If generative computer vision models manipulate crowds in these protest images, it threatens public perception, as estimates of crowd sizes become distorted. Overall, these findings expand our understanding of protests in a world saturated with visual information, opening exciting avenues for future research in protest studies and other fields of social science.publishe