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Modulation of test anxiety-induced salivary protein secretion by ovarian steroid hormones: a preliminary study
No full textIn women the menstrual cycle influences mood and anxiety. Aim of this study was to preliminarily investigate whether different ovarian steroid hormone levels may modulate the psychophysiological responses elicited by test anxiety. Specifically, we compared the secretion of anxiety-induced salivary proteins of healthy women in the early follicular (Pre-Ov group) (low ovarian steroid hormones levels) and mid-luteal (Post-Ov group) (medium/high ovarian steroid hormones levels) phase of the menstrual cycle, during the simulation of an oral examination. Saliva samples were collected before and after a relaxation period and at two post-simulation times and analyzed by two-dimensional electrophoresis and western blot. Proteins corresponding to spots differentially expressed in the two groups across the session were identified through mass spectrometry and most of them corresponded to acute stress and/or oral mucosa immunity biomarkers. The task induced an increase in alpha-amylase, carbonic anhydrase and cystatin S, and a decrease in immunoglobulin light/J chains in both groups. Analogous changes in these proteins have previously been linked to psychological or physical stress. However, specific spots corresponding, for example, to cystatins and 14-3-3 protein, changed exclusively in the Pre-Ov group, while prolactin-inducible protein, polymeric immunoglobulin receptor, fragments of alpha-amylase and immunoglobulins only in the Post-Ov group, indicating a potential modulation of their secretion by ovarian steroid hormones. Overall, the results provide preliminary evidence that ovarian steroid hormones may be a driving factor for differences in physiological responses induced by test anxiety. The results are promising, but further validation in a larger sample is needed
Political question
No full textSintetica esposizione della nozione e degli sviluppi nelle giurisdizioni ordinarie e costituzional
Robustness and limitations of maximum entropy in plant community assembly
Get PDFAn in-depth understanding of local plant community assembly is critical to direct conservation efforts to promising areas and increase the efficiency of management strategies. This, however, remains elusive due to the sheer complexity of ecological processes. The maximum entropy-based Community Assembly via Trait Selection (CATS) model was designed to quantify the relative contributions of trait-based filtering, dispersal mass effects, and stochastic processes on community assembly. As a maximum entropy model, it does so without introducing additional bias or assumptions. Despite its increasing use, questions regarding its robustness and potential limitations remain. Here, we compared model predictions using either local or database-derived trait values, across different levels of species richness and between different taxonomic levels. A total of 19 datasets and 790 plots were analysed, spanning multiple habitat types (n = 18) and biomes (n = 7). Results indicate trait value origin does indeed influence model outcomes, warranting caution in selecting the method for obtaining trait data. We hypothesise that, for example, intraspecific trait variation combined with trait-based filtering or stochastic processes causes local and database trait values to deviate, potentially even further exacerbated by imputing missing trait data. Furthermore, trait-related information obtained from the model decreased with increasing species richness. We further hypothesise this could signal that stochastic processes are more dominant within species-rich systems, for example, due to functional redundancy or the existence of multiple fitness strategies. This general pattern was conserved across biomes, although with varying strength, showing CATS’ robustness despite these challenges
Long-term monitoring of plant diversity data in the “Montagna di Torricchio” Strict Nature Reserve, Italy
Get PDFLong-term monitoring is pivotal for the collection of data capable of unravelling spatio-temporal changes in plant diversity. Here we present a dataset including plant presence and abundance data collected using a probabilistic sampling design in the “Montagna di Torricchio” Strict Nature Reserve, central Apennines, Italy. Five surveys were conducted in 35 plots during a period spanning 22 years (2002–2024). This dataset allows for the study of plant diversity changes over space and time across different habitat types by using statistical inference based on solid sampling theory
Long-term Dynamics of Understory Plant Diversity in Italian Forest Ecosystems: Trends and Drivers
No full textForest ecosystems are widely distributed across Europe and are under threat from global changes. Understory vascular plants represent the predominant component of plant biodiversity and are influenced by multiple factors, including climate, soil characteristics, and
canopy structure. Given the variability of environmental factors and the successional dynamics of vegetation, long-term monitoring is crucial for studying changes in plant communities.
Species responses often exhibit temporal delays relative to environmental changes. Despite the growing number of studies employing the “resurvey” approach, this method often provides a static representation of communities at two distinct points in time, limiting the ability to comprehensively capture community dynamics. Our study is based on frequent resampling of 31 permanent plots 50m X 50m within the ICP Forests LII network (ConEcoFor), classified into four biomes present in Italy, over a 24-year period (1999–2023). The study aims to assess: (i) temporal trends in plant diversity (alpha and beta) and (ii) the climatic, edaphic, and forest structural drivers of plant diversity. Climate data on daily mean temperature and
total precipitation were extracted from the E-OBS dataset provided by the Copernicus Climate Change Service and used to calculate climate indices for the reference period. Soil variables
(pH, NH4, SO4, K, NO3) and forest structure parameters (canopy and shrub cover, and defoliation degree) were directly measured in the field. Linear mixed models were employed, with years considered as a “fixed factor” for the first objective, and environmental variables
for the second objective. Plots were included as a “random factor”. The analyses revealed a reduction in species richness in nemoral biomes (beech and deciduous oak forests) and
the borealbiome(spruceforests), in contrast to the Mediterranean biome (holmoakforests), which showed no significant temporal trends. However, the lack of trends in species richness
in the Mediterranean biome conceal a significant species turnover. The parameters influencing species richness include structural variables, soil pH, aridity indices, and precipitation variability, with different roles and importance depending on the biome considered. After many years of data collection, the ICP Forests monitoring sites reveal significant changes in plant diversity across Italian forests. These changes require further exploration from a functional perspective and using a multi-taxon approac
All’ombra del Chiostro: Analisi Microclimatiche delle Strutture Claustrali e Strategie per il Progetto fra Conservazione e Innovazione
No full textThis study explored the potential of cloisters in historical buildings to act as passive microclimate regulators and investigated design strategies for their adaptation to climate change. Focusing on the Saint Agostino monastery in Ascoli Piceno, Italy, the research uses an integrated approach based on microclimate simulations to analyse the cloister’s thermal performance. This study considers the building’s historical context and current use, as well as the local climate, which is characterised by increasing heat stress. Initial analysis revealed significant differences in perceived temperature (UTCI) across the cloister loggia during the hottest week, with higher values on the south and west sides indicating thermal discomfort. A design intervention involving the partial closure of the cloister with transparent and opaque elements was proposed. Simulations showed a drastic improvement in thermal comfort after the intervention, with UTCI values becoming uniform and significantly lower across all sides of the loggia. The results confirmed the effectiveness of the targeted design strategies in enhancing the microclimatic performance of historical cloisters. The integrated simulation approach provides valuable insights for the sustainable conservation and adaptive reuse of these spaces, balancing heritage preservation with climate change adaptation and user comfort. Further studies can extend this methodology to other historical buildings
TARNAS: A Software Tool for Abstracting and Translating RNA Secondary Structures
No full textRibonucleic acids (RNAs) fold into complex structures that are strongly associated with their biological functions. These can be abstracted into secondary structures, represented as nucleotide sequences annotated with base-pairing information. This abstraction is both biologically relevant and computationally manageable. Comparing and classifying RNA molecules typically relies on these secondary structure representations, which exist in multiple formats. In this work, we introduce TARNAS 1.0, a software tool designed to convert RNA secondary structure representations across multiple formats, including Base Pair Sequence (BPSEQ), Connect Table (CT), dot-bracket, Arc-Annotated Sequence (AAS), Fast-All (FASTA), and RNA Markup Language (RNAML). The tool offers options for retaining or removing comments, blank lines, and headers during the conversion process. These format translation and preprocessing capabilities are specifically designed to support the batch handling of large collections of RNA molecules, making TARNAS well suited for large dataset construction and database curation. Beyond format translation, TARNAS computes three levels of abstraction for RNA secondary structures, namely core, core plus, and shape, as well as a set of statistical descriptors for both primary and secondary structure. These abstraction and analysis features are intended to facilitate the comparison of molecules and the identification of recurring structural patterns, which are essential steps for associating structural motifs with molecular function. TARNAS is available as both a standalone desktop application and a web-based tool. The desktop version supports batch processing of large datasets, while the web version is optimized for the analysis of single molecules
Lightweight Vision Transformer for Frame-Level Ergonomic Posture Classification in Industrial Workflows
No full textWork-related musculoskeletal disorders (WMSDs) are a leading concern in industrial ergonomics, often stemming from sustained non-neutral postures and repetitive tasks. This paper presents a vision-based framework for real-time, frame-level ergonomic risk classification using a lightweight Vision Transformer (ViT). The proposed system operates directly on raw RGB images without requiring skeleton reconstruction, joint angle estimation, or image segmentation. A single ViT model simultaneously classifies eight anatomical regions, enabling efficient multi-label posture assessment. Training is supervised using a multimodal dataset acquired from synchronized RGB video and full-body inertial motion capture, with ergonomic risk labels derived from RULA scores computed on joint kinematics. The system is validated on realistic, simulated industrial tasks that include common challenges such as occlusion and posture variability. Experimental results show that the ViT model achieves state-of-the-art performance, with F1-scores exceeding 0.99 and AUC values above 0.996 across all regions. Compared to previous CNN-based system, the proposed model improves classification accuracy and generalizability while reducing complexity and enabling real-time inference on edge devices. These findings demonstrate the model’s potential for unobtrusive, scalable ergonomic risk monitoring in real-world manufacturing environments
Learning time-varying Gaussian quantum lossy channels
No full textTime-varying quantum channels are essential for modeling realistic quantum systems with evolving noise properties. Here, we consider Gaussian lossy channels varying from one use to another and we employ neural networks to classify, regress, and forecast the behavior of these channels from their Choi-Jamiołkowski states. The networks achieve at least 87% of accuracy in distinguishing between non-Markovian, Markovian, memoryless, compound, and deterministic channels. In regression tasks, the model accurately reconstructs the loss parameter sequences, and in forecasting, it predicts future values, with improved performance as the memory parameter approaches 1 for Markovian channels. These results demonstrate the potential of neural networks in characterizing and predicting the dynamics of quantum channels
HORIZON-EIC-2025-PATHFINDEROPEN Briefing and evaluation proposal no. 1012580**
No full textContract number: CT-EX2002B071944-11