University of Aosta Valley

p-values are ubiquitous in scientific research, yet they fundamentally fail to quantify the strength of evidence for or against competing hypotheses. This limitation is particularly problematic in neuroimaging meta-analyses, where researchers need to assess how strongly the available data support specific and spatially consistent patterns of brain activation across studies. In this work, we present a practical approach that transforms p-values into their corresponding upper bounds on the Bayes factor, which quantify the maximum plausible evidence in favor of the alternative hypothesis given the observed data. The method is illustrated within the framework of Activation Likelihood Estimation, the most widely used coordinate-based meta-analytic technique in neuroimaging and applied to a reference dataset comprising 73 finger-tapping experiments. The results show that effects traditionally classified as statistically significant using the canonical Activation Likelihood Estimation framework actually span a wide range of evidential strengths, with Bayes factor bounds varying approximately from 46 to 410. This finding reveals substantial heterogeneity in weight of evidence that is concealed by conventional threshold-based inference. By enabling the construction of voxel-wise maps of evidential strength while remaining fully compatible with existing analysis pipelines, the proposed approach helps to avoid common misinterpretations of p-values and improves the interpretability and reliability of neuroimaging meta-analytic conclusions. It therefore provides a conservative, Bayesian-inspired complement to standard significance maps.

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The human-Nature relationship is a valuable resource through which children can mature, evolve, investigate their subjectivity, and get in touch with their own emotions and subsequently with those of other living beings, ultimately leading them to pay attention to the preservation and protection of ecosystems. With this experimental study, we aimed to investigate the biophilia hypothesis in preschool children. We proceeded with a protocol where children (0-2 years and 3-5 years) played freely for 5-10 minutes. Then, without warning, a single novel stimulus was introduced: a pet, a remote-controlled car, a plant, or a painting. We recorded their reaction time to become aware of (to shift their attention to) the presence of the new stimulus. The objective of the experimental observations was to verify differences in the latency to fascination (bottom-up attention) when the children encountered living (pets and plants) and non-living (moving and stationary objects) entities. We hypothesize that pets will shift children's attention more quickly and more frequently than non-living objects. Furthermore, among non-living objects, moving ones will shift attention more than stationary ones. We are therefore operating within E.O. Wilson's prediction that biophilia is “our innate tendency to focus on life and life-like forms and, in some cases, to associate emotionally with them”. Considering the age of the young participants, this is most likely an innate behaviour that reinforces the idea that biophilia is an evolutionary adaptation.

Drawing on contributions to the conference Natures and Human Nature. Ethics, Aesthetics, Ecologies. A Comparative Approach, held on April 24, 2024, this article proposes a reading based on Douglas, Vildavsky and Thompson’s Cultural Theory of different “visions” of the natural world. Cultural theory posits that there are four cultural configurations or worldviews that influence how human beings represent their relationships with the environment and how they think about themselves. Individualists believe that nature is robust and benign and that human beings are autonomous and driven by self-interest. Hierarchical groupings perceive nature as a stable system governed by authoritative positions. In such a society, individuals are regarded as passive and require governance by robust and reliable institutions. Egalitarianism views nature as delicate and interconnected, highlighting reciprocal care and equality as fundamental principles. Fatalism embraces a perspective characterized by chaos, wherein neither nature nor humanity adheres to a discernible or rational order. Ethnographic experiences and theoretical reflections demonstrate how these configurations can be useful when evaluating both field research results and disciplinary epistemological frameworks.

Cyber Dating Violence (CDV) is a prevalent and concerning phenomenon among young people, characterized by abusive behaviors toward a romantic partner through digital platforms. Fear of Missing Out (FoMO), defined as the anxiety of being excluded from rewarding experiences, is closely linked to excessive digital engagement and the need for constant connectivity. While previous studies have associated FoMO with bullying and cyberstalking, its relationship with dating violence remains unexplored. This study’s aim is to bridge this gap by analyzing the association between FoMO and both perpetration and victimization in CDV. The sample consisted of 911 young adults aged 18 to 30 (Mage = 22.0; SDage = 2.57; 74% women), recruited via an online survey. Findings indicate that FoMO significantly predicts both the perpetration and victimization of CDV. Additionally, for perpetration, significant interactions emerged with gender and sexual orientation, suggesting that these factors may moderate the relationship between FoMO and abusive behaviors in digital dating contexts. These results highlight the need for further research to better understand CDV and inform prevention and intervention strategies focused on digital platform use and healthy relationship dynamics.

ABSTRACT Several scholars have examined the impact of critical events in international politics, such as the Syrian civil war, on transnational violent extremism and terrorism. The same complex question arose following the war that broke out between Hamas and Israel on 7 October 2023. Building on original data on attacks, this article aims to examine the effects of this war in the Middle East on terrorist violence inspired by global jihadism in Europe, in the interplay among individual, organizational/group, and societal factors. The original attack data show that the Israel-Hamas war contributed to increasing the frequency of attacks carried out in Europe and led to a growth in the number of attacks against Jewish and Israeli targets; however, it did not have profound effects on the level of organization and the severity of these acts of violence. Taking this empirical evidence into account, the article argues that this armed conflict was generally able to indirectly affect, from a distance, the intentions of European-based sympathizers and militants to mobilize but, unlike other wars in the past, it did not provide them with direct opportunities to join major organizations and to increase their capabilities in the use of violence.

Motivation:Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms.Results:Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: i) infer the presence/absence of each protein isoform (via a posterior probability), ii) estimate its abundance (and credible interval), and iii) target isoforms where transcript and protein relative abundances significantly differ.We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances.Availability and implementation:IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

In economics, individuals’ decisions are not always guided by pure rationality; emotional factors often influence choices related to consumption, investment, and saving. Emotions can thus determine market confidence, influence economic cycles, and even affect monetary and fiscal policies. In this regard, the development of social media has increased the formation and dissemination of fake news, whose impact requires particular attention. To detect and contain the spread of fake news in real time, some Artificial Intelligence tools are used. This contributes to promoting more reliable information and protecting society from media manipulation. A focus was placed on the impact of fake news on political elections. Building on this, the capabilities of the Bidirectional Encoder Representations from Transformers (BERT) classifier were tested to distinguish real news from fake news in order to mitigate the aforementioned problem and provide useful tools for the real-time identification of news that could alter not only political impact but also other areas.

In a novel contribution to the literature on airline flight delays, this paper explores the effects of airline schedule buffers on arrival delays, showing that buffers reduce delays and are thus beneficial. The exercise relies on the approach followed in our previous work, which allows us to measure buffer sizes and flight delays by using government data on the flight sequences of individual aircraft. Our empirical results roughly match the predictions of an analytical model regarding the expected sizes of the buffer effects. • This paper empirically estimates the effect of airline flight and ground buffers on flight delays. • A model relates the arrival delay to the buffers the incoming flight’s arrival delay. • The relationship is estimated using instrumental variables to account for buffer endogeneity. • The results show coefficient magnitudes mostly consistent with the model.