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TARGETING HUMAN MITOCHONDRIAL ClpP AS A NOVEL THERAPEUTIC APPROACH TO H3K27-ALTERED DIFFUSE MIDLINE GLIOMA
No full textH3K27-altered diffuse midline glioma (DMG) is one of the most aggressive pediatric brain tumors, with a median overall survival of less than 12 months. Radiotherapy treatment (RT) provides a transient clinical response, with an increasing average survival of approximately 3 months. Given that the tumor often recurs within few months after RT, there is a strong need for targeted therapeutic strategies that directly address the H3K27-alterations.1 One promising drug under study is Dordaviprone, which API is ONC201, a brain penetrant compound belonging to imipridone family (NCT02525692).2 Nonetheless, despite initial clinical promise, ONC201 has shown limited efficacy in patients, underscoring the need to identify alternative molecules.2 More recent studies have identified the target of ONC201, being a potent activator of the human mitochondrial serin-protease ClpP. Together with ClpX, ClpP is involved in the degradation of mitochondrial respiratory chain
proteins, thereby disrupting energy homeostasis. When abnormally activated by small molecules, ClpP triggers uncontrolled degradation of essential mitochondrial proteins, leading to mitochondrial failure, metabolic collapse, and ultimately cell death. This mechanism is particularly effective in tumor cells that are highly dependent on mitochondrial function, making ClpP hyperactivation an attractive anti-tumor strategy (Fig. 1).3 To enhance the efficacy of ONC201 and ClpP activation, we developed a series of its structurally simplified derivatives, representing the molecular evolution of ClpP activators. These novel molecules retain high target selectivity for ClpP, exhibit stronger mitochondrial protease activation, resulting in potent degradation of mitochondrial matrix and consequent collapse of oxidative phosphorylation. This ClpP-dependent disruption of mitochondrial function represents a potent anti-cancer mechanism, particularly relevant for tumors with high mitochondrial reliance
Giant red shrimp and blue and red shrimp in the central-eastern Mediterranean
No full textGiant red shrimp (Aristaeomorpha foliacea) and blue and red shrimp (Aristeus antennatus) are two of the most valuable fishery resources in the Mediterranean and comprise most of the landed value from deep-water capture fisheries in the central Mediterranean. Stock assessments conducted in the central Mediterranean have mostly found the species in a status of overexploitation over the past years, while sparse data from the eastern Mediterranean are generally insufficient for the provision of quantitative advice on stock status, although significant efforts to improve stock assessments are ongoing. To support the sustainability of these fisheries, the GFCM adopted three multiannual management plans in 2022 for giant red shrimp and blue and red shrimp. This publication presents the results of a collaborative effort to compile comprehensive data, information and research concerning the biology, ecology and fishery of these species in the central-eastern Mediterranean. The analyses and data contained within this study highlight the significant progress that has been made towards increasing the understanding of giant red shrimp and blue and red shrimp and emphasize the additional work needed to address gaps and inform the development of effective long-term adaptive management measures within the existing management plans. In particular, standardized targeted biological surveys should be expanded to strengthen knowledge on the biology and ecology of the species as well as tailored data collection supporting the multiple methods approach to increase understanding of stock units, towards improved coverage and quality of stock assessments. Finally, deep-water red shrimp fishing grounds and their overlap with essential fish habitats and interactions with vulnerable marine ecosystem indicator species should be identified to support the design of effective spatio-temporal management measures. Addressing these gaps will contribute to significantly bolstering the efficacy of future management measures
Zerstörte Dorfidylle. Tiefbohrungen in Wulf Kirstens Landschaftslyrik
No full text„das zaundürre waschbrettland . . . schmeckt nach braunkohle und vergammeltem sozialismus“ (märchenhafte geschichte): Durch den Hinweis auf die durch intensive Bergbauarbeit versehrte Landschaft des sächsisch-thüringischen Braunkohlengebiets bringt Wulf Kirsten hier das für seine Lyrik charakteristische Motiv der zerstörten Natur zur Sprache. Die Gedichte des „Landschafters“ Kirsten erkunden die Topographie der Heimatwelt („die erde bei Meißen“) und entwerfen dabei Sprachlandschaften, die von einem sperrigen („kornigen“), regional gefärbten Deutsch gekennzeichnet sind. In seinen literarischen Erkundungen stellt Kirsten die Veränderungen fest, die sich als Folgen komplexer geschichtlicher und sozialer Prozesse (der Agrarrevolution und der Industrialisierung) in die Natur tief eingeprägt und sie zerstört haben (s. den programmatischen Titel der von Kirsten herausgegebenen Anthologie ostdeutscher Lyrik Veränderte Landschaft, 1979). In meinem Vortrag werde ich – ausgehend von Gedichten wie der bleibaum oder das dorf und den poetologischen Schriften – auf Kirstens Erkundungen seines „Erdstrichs“ durch eine poetische „Gegensprache“ eingehen („seine rauhe, rissige erde / nehm ich ins wort“, satzanfang 1970). Sie bringt die komplexe geschichtliche Verschichtung der Landschaft, die Umbrüche und die Risse der unwiederbringlich verlorenen Naturschönheit an den Tag
FEA for Optimizing Design and Fabrication of Frame Structure of Elevating Work Platforms
No full textThis study investigated the application of Finite Element Analysis (FEA) to optimize the design and material selection for the construction of the telescopic arm of an elevating work platform (EWP) used in agricultural environments. By comparing the structural performance of four materials—Aluminum Alloy (EN-AW 1200), Aluminum Alloy (EN-AW 2014), High-Strength Low-Alloy (HSLA) Steel Fe275JR, and HSLA Steel S700—under simulated operational conditions, this research identified the most suitable material for robust yet lightweight platforms. The results revealed that HSLA Steel S700 provides superior performance in terms of strength, low deformation, and high safety factors, making it ideal for scenarios requiring maximum durability and load-bearing capacity. Conversely, Aluminum Alloy (EN-AW 2014), while exhibiting lower strength compared with HSLA Steel S700, significantly reduces platform weight by approximately 60% and lowers the center of gravity, enhancing maneuverability and compatibility with smaller, less powerful tractors. These findings highlight the potential of FEA in optimizing EWP design by enabling precise adjustments to material selection and structural geometry. The outcomes of this research contribute to the development of safer, more efficient, and cost-effective EWPs, with a specific focus on improving productivity and safety in agricultural operations such as pruning and harvesting. Future work will explore advanced geometries and hybrid materials to further enhance the performance and versatility of these platforms
Credence in code: Consumer engagement and responses to blockchain-enabled sustainability in tomato Purée
No full textUnderstanding how digital technologies can support more transparent food choices is essential for driving sustainability in agri-food systems. In this context, this study explores the role of blockchain technology (BCT) in shaping consumer preferences for tomato puree featuring credence attributes. An integrated approach that combines the Technology Acceptance Model (TAM) with a choice modelling framework has been used for this purpose. The originality of this research lies in extending the TAM framework by examining how consumer sensitivity to environmental, social, and economic sustainability attributes influences behavioral intention to adopt blockchain-traced food products. Based on a representative sample of 1549 Italian respondents collected through an online survey, a Discrete Choice Experiment (DCE) and a Latent Class Model (LCM) were additionally applied to identify three distinct consumer segments according to their attitudes toward blockchain and sensitivity to credence attributes. The results reveal that a small group of respondents (6.6 %) presents low interest in both aspects, while 32.7 % prioritize sustainability-related attributes. The largest segment (60.7 %) values blockchain for ensuring authenticity and transparency. With respect to a bottle of baseline tomato puree (700 mL), differences in willingness to pay among respondents reveal that sustainability-engaged consumers value information on water footprint reduction (+5.78 EUR) and product origin (+0.84 EUR) conveyed through BCT, while those more inclined toward technology prioritize information related to organic production methods (+3.40 EUR) and labor-related sustainability (+2.74 EUR). The results provide valuable insights for producers and policymakers aiming to promote more responsible consumption and enhance sustainability communication through technological innovation
A RAG-Enhanced AI Feedback for UML Education
No full textSoftware modeling education often lacks immediate and personalized feedback, making it challenging for novice learners to comprehend modeling principles. This paper continues our ongoing work on developing an AI-driven feedback mechanism to support UML diagram construction. Building on previous efforts, we improved the system by enhancing the RAG-LLM component within the existing UML Miner plugin. The system analyzes students’ modeling behavior and provides real-time, personalized guidance to support learning and improve modeling skills
MASS-CSP: mining with answer set solving for contrast sequential pattern mining
No full textIn this paper, we present MASS-CSP (Mining with Answer Set Solving - Contrast Sequential Patterns), a declarative approach to the Contrast Sequential Pattern Mining (CSPM) task, which is based on the logic-based framework of Answer Set Programming (ASP). The CSPM task focuses on identifying significant differences in frequent sequences relative to specific classes, leading to the concept of a contrast sequential pattern. The article describes how MASS-CSP addresses the CSPM task and related extensions-mining closed, maximal and constrained patterns. Evaluation aims at comparing the basic version of MASS-CSP against the extended versions as regards the size of output and time-memory requirements
Cooperative effects in atom-photon interactions
Get PDFWe investigate cooperative photon emission by a random cold atomic cloud described by a Gaussian distribution in three dimensions. We formulate the problem in terms of Euclidean random matrices (ERMs), whose entries are a function of the atomic distances. After formulating and physically motivating the model, we present a detailed analysis of the Euclidean random matrix S, related to the dissipative dynamics of the atomic cloud, induced by photon-mediated interaction. We first focus on the bulk spectral properties of this matrix, characterizing the microscopic spectral statistics, the level spacing and the eigenvectors corresponding to the central part of the spectrum. We then analyze the extremely subradiant part of the spectrum of S, finding evidence of a phase transition, controlled by the cooperativeness parameter b, related to the number of atoms that coherently cooperate in photon emission. Finally, we present ongoing research activity on a different, non-Hermitian Euclidean random matrix, describing both the Hamiltonian and the dissipative photon-mediated dynamics of the cloud, and shed light on the connections between these two ERMs and a more general open system approach
The insulin-releasing agent quercetin-3-oleate stimulates CaV1.2 channels similarly to quercetin, though with a reduced vasorelaxant activity
No full textThe synthetic derivative quercetin-3-oleate (AV2), a partial agonist of the G-protein-coupled receptor 40
(GPR40), and its parent compound quercetin, a stimulator of CaV1.2 channels, promote insulin secretion from
INS-1 pancreatic β cells. An in vitro and in silico approach was pursued to assess whether the incorporation of oleic
acid at the C3 position maintains quercetin stimulation of CaV1.2 channels (key to triggering insulin release)
while reducing its vasorelaxant properties. In rat tail main artery myocytes, AV2, like quercetin, stimulated Ba2+
currents via CaV1.2 channels (IBa1.2), demonstrating favourable interaction in molecular docking analyses and
molecular dynamics simulations. Although AV2 also stimulated K+ currents via KCa1.1 channels (IKCa1.1), its
vasorelaxant effect in vascular rings was significantly lower than that of quercetin. AV2 exhibited a positive
inotropic effect and increased the frequency of Langendorff-perfused isolated rat hearts, albeit at concentrations
one order of magnitude higher than those required for significant IBa1.2 stimulation. In in vitro settings, AV2
maintained a significant H2O2-induced radical scavenging activity. In conclusion, the conjugation of oleic acid
with the dietary flavonoid quercetin in AV2 attenuated its spasmolytic effects on vascular smooth muscle,
without affecting its IBa1.2 stimulatory activity. This, along with its GPR40 activation, highlights AV2 as a
bifunctional agent with the potential to improve selective insulin secretion with minimal vascular effect
Personalized colorectal cancer risk assessment through explainable AI and Gut microbiome profiling
No full textThe clinical adenoma–carcinoma progression represents a well-established framework for understanding colorectal cancer (CRC) development, although the molecular mechanisms underlying this transition remain only partially understood. Increasing evidence suggests the gut microbiome (GM) as a key modulator of colorectal carcinogenesis, positioning microbial profiling as a promising avenue for noninvasive risk stratification and early detection. In this study, Machine Learning (ML) classifiers integrated with eXplainable Artificial Intelligence (XAI) techniques were employed to identify microbiome-derived biomarkers predictive of CRC and adenomatous lesions. The models were trained on 16S rRNA sequencing data from 453 patients and evaluated through cross-validation, achieving AU-ROC and AU-PRC scores of 0.71 and 0.67, respectively. External validation on an independent Italian cohort ((Formula presented.)) yielded AU-ROC and AU-PRC scores of 0.70 and 0.89, respectively. XAI-based interpretation revealed consistent microbial signatures across datasets. In detail, taxa belonging to the Fusobacterium and Peptostreptococcus genera were associated with increased CRC risk, whereas the Eubacterium eligens group was identified as a robust negative predictor. Beyond classification, patient-level explanations enabled by XAI facilitated the identification of adenoma subgroups exhibiting microbiome profiles converging toward those of CRC, suggesting the presence of transitional microbial states. Moreover, SHAP-based interaction networks uncovered microbial hubs and inter-species dependencies characterizing high-risk configurations, providing insights into the ecological dynamics of colorectal tumorigenesis. These findings demonstrate the added XAI value in elucidating microbiome interactions, enhancing model interpretability, and supporting biologically informed hypotheses. This integrative, explainable framework highlights the potential of AI-driven microbiome analysis in precision oncology and advances the development of interpretable, noninvasive tools for CRC risk prediction and management