ABSTRACT To check the occurrence of new natural β-diketones in Helichrysum italicum L. a series of seven β-diketones was synthesized and used in co-injection experiments to detect their presence in several essential oil and hydrolate samples of wild Helichrysum italicum L. from Corsica, Bosnia and Croatia. Two diketones were identified for the first time in this species: 4-methylheptan-3,5-dione (already known as an insect pheromone), as well as 3-methylhexan-2,4-dione, which has never been reported so far as a natural product.

We introduce entropic strict minimum message length (SMML), a risk-sensitive generalization of strict minimum message length coding. The proposed criterion replaces expected two-part codelength under the prior predictive distribution with an exponential certainty equivalent, thereby defining a one-parameter family of coding rules that interpolates between Bayesian average-case coding and worst-case minimax coding. We show that ordinary SMML is recovered in the risk-neutral limit, while the extreme risk-sensitive limit yields a minimax codelength criterion. Applying the same entropic soft maximum to regret relative to the oracle maximum likelihood codelength recovers the normalized maximum likelihood (NML) minimax-regret principle. We further prove that entropic SMML admits a variational characterization as a Kullback--Leibler-regularized worst-case expected codelength, giving it a PAC--Bayes-type interpretation. We establish joint \(n\)--\(\tau\) asymptotics that identify how the risk parameter must scale with sample size in order to recover Bayesian average-case, intermediate robust, and worst-case minimax coding behavior. For regular exponential families, the fixed-codebook partition remains affine in sufficient-statistic space, while the codepoints satisfy a tilted moment-matching condition and admit an interpretation as tilted Bregman centroids. These results position entropic SMML as an information theoretic bridge between MML, PAC--Bayes, and MDL.

Metal additive manufacturing (AM) relies on alloy feedstock powders that may come into contact with the workers’ skin during handling, yet skin-relevant data on metal release and biological reactivity remain limited. Here, we assessed the cutaneous bioactivity of the fine particle fraction of four gas-atomized Fe-based AM powders (316L stainless steel, Fe-powder A, and tooling steels B and C). Powders were sieved to <10 μm and characterized by scanning electron microscopy and X-ray photoelectron spectroscopy before and after incubation in artificial sweat (ASW). Metal biodissolution was quantified in ASW and keratinocyte culture medium using atomic absorption spectrophotometry. Cellular responses were evaluated in HaCaT keratinocytes using Cell Painting-based phenomics and multiplex cytokine/chemokine profiling and in an ex vivo full-thickness human skin explant model, including superficial barrier disruption, IL-8/CXCL8 quantification, and histological assessment. ASW exposure induced marked shifts in the outermost surface composition across powders, indicating sweat-driven surface transformation. Biodissolution was low and medium-dependent, with Fe dominating the release in ASW, and with an overall metal release remaining limited in cell culture medium. In HaCaT cells, MCP-1/CCL2, IL-6, and IL-8/CXCL8 were quantifiable but showed no significant changes following powder exposure. Cell Painting revealed subtle, shared phenotypic signatures, primarily involving mitochondrial-associated features, without evidence of broad cellular stress. In the ex vivo skin model, AM powders did not increase IL-8/CXCL8 secretion, the particles remained localized to the skin surface without detectable penetration, and coexposure with Staphylococcus epidermidis did not enhance bacterial colonization or induce inflammation. To the best of our knowledge, this is the first study that applies a human skin explant model to evaluate dermal responses to metal AM powders. Overall, the tested AM powders showed low short-term cutaneous reactivity under skin-relevant conditions, providing human-relevant evidence to inform occupational risk assessment in AM environments.

Deploying post-quantum cryptography on highly constrained devices remains challenging due to the large key sizes and substantial storage and memory-traffic demands of leading lattice-based schemes. Although constructions such as Kyber, Dilithium, and NTRU offer strong resistance against quantum adversaries, their multi-kilobyte public keys and intensive memory access patterns limit practical adoption in microcontrollers, smart cards, and low-power edge environments. This work proposes a hybrid key-encapsulation mechanism that integrates a compact, seed-generated Module-LWE structure with a quantum-secure hash-based authentication layer. The design employs a small public seed to instantiate lattice matrices on demand via a lightweight pseudorandom generator and incorporates a Merkle-tree commitment to represent compressed auxiliary error information. Additional design considerations—including sparsity-aware secret keys, SIMD-friendly polynomial operations, and cache-efficient decryption paths—are intended to reduce runtime memory usage and computational overhead. The security of the proposed construction is analysed under both Module-LWE and hash-based one-way assumptions, with further consideration of constant-time execution and cache-line alignment to mitigate side-channel risks. This hybrid approach outlines a design pathway toward post-quantum key-encapsulation mechanisms suitable for deployment on memory-limited and energy-constrained platforms.

Executive function is an essential cognitive domain for typical human behavior which is disrupted in neurodevelopmental and neurodegenerative disorders, but little is known about its underlying molecular basis. To address this, we perform genome-wide association studies (GWAS) using three different measures of executive function in UK Biobank (N = 84,238) and NIHR BioResource’s Genes and Cognition (N = 9932) study participants, followed by a meta-analysis. The trail-making alphanumeric (TMA) measure is the most heritable phenotype (h²=7-26%), associated with 18 independent loci that exhibit a similar direction of effect in both cohorts. Across these loci, in-silico follow-up implicates 178 genes, of which NT5DC2 and RP11-579E24.2 are independently replicated prior to meta-analysis. TMA is linked to pan-cerebral differences in brain structure, with brain-enriched genes showing a biphasic expression profile from early development through to later life. Our data implicate specific cell types, histone modifications and butyrophilin immunoglobulin family proteins as potential targets for promoting cognitive resilience. A genome-wide association meta analysis of Trail Making enriches our understanding of the genetic landscape of executive functioning, identifies cognitive and neural correlates, and reveals a cell-type specific developmental origin.

BACKGROUND AND AIMS The timing of aortic valve replacement (AVR) in severe asymptomatic aortic stenosis (AS) remains debated. Preserved ejection fraction (EF) may mask subclinical dysfunction, while global longitudinal strain (GLS), brain natriuretic peptide (BNP), and diastolic indices (E/E') provide complementary prognostic information. A predictive model for adverse outcomes after AVR integrating GLS, BNP, and E/E' has not been previously investigated. METHODS Ninety-six patients with severe asymptomatic AS and preserved EF (>50%) undergoing AVR were assessed at baseline and 1, 3, and 6 months. Echocardiography (GLS, EF, LVMI, IVSd, LVIDd, E/E'), BNP, and clinical outcomes were analyzed. Primary endpoint was LV remodeling; secondary endpoint was major adverse cardiovascular events (MACE). RESULTS Despite preserved EF, 76% had impaired GLS (<15%), and 64% remained in negative remodeling at 6 months. Baseline GLS ≤15% was the only independent predictor of adverse remodeling in multivariable logistic regression (OR 4.7 at 3 months; OR 3.5 at 6 months). For MACE, baseline E/E' >13 was the strongest independent predictor (OR 3.15, 95% CI 1.58-7.57, p = 0.004). The integrated GLS-BNP-E/E' model demonstrated superior predictive strength compared with individual parameters, with Nagelkerke R2 values of 0.41 for remodeling and 0.31 for MACE. CONCLUSION A multimodal risk model integrating GLS, BNP, and E/E' predicts adverse remodeling and MACE in severe asymptomatic AS. These findings highlight the complementary role of imaging and biomarkers in risk stratification before AVR-a concept that warrants confirmation in future multicenter studies.

Selecting a machine learning model for higher-education quality assurance is a multi-criteria decision problem that cannot be reduced to a single leaderboard metric. This paper presents a two-step disclosure protocol in which users first make an unaided model choice and then revise it after structured multi-criteria disclosure including criterion weights, normalized values, and a ranked recommendation. The overall study used a three-step experimental protocol, with the disclosure manipulation itself implemented as a two-stage intervention within Step 2. The protocol was evaluated with 38 participants under a stable Dean-oriented advisory framing across three institutional prediction tasks, yielding 228 confirmatory scenarios after predefined quality filters. Decision quality was operationalized as regret reduction relative to a frozen governance-oriented multi-criteria scoring policy. Results show that structured disclosure significantly improved policy-aligned decision quality (Wilcoxon p = 2.30 × 10⁻11, rank-biserial r = 0.864) and increased self-reported decision confidence (p = 7.62 × 10⁻12, r = 0.646). Importantly, significant improvement was already observed in the information-only stage before any explicit recommendation was shown (p = 5.18 × 10⁻5, r = 0.629). Post-decision trust changes were small and did not reach significance in the confirmatory analysis, and are therefore treated as exploratory. The findings provide protocol-level evidence that structured multi-criteria disclosure can improve alignment with a predefined governance-oriented model selection policy in educational QA settings.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is an emerging global threat. This study aimed to determine the prevalence of CRKP, the genetic basis of antimicrobial resistance, including beta-lactamase production, efficacy of novel beta-lactam/beta-lactamase inhibitor (BLBLI) combinations, hypervirulence, and genetic diversity of circulating clones in a Serbian hospital setting. From 2022-2023, 2001 K. pneumoniae isolates were collected from 16 hospitals across Serbia. The prevalence of CRKP was 53.4% (N = 1069). Among these, 191 randomly selected CRKP isolates were subjected to expanded antimicrobial susceptibility testing, string test, and carbapenemase production, with 150 further randomly chosen for whole-genome sequencing. Resistance rates to ceftazidime-avibactam, imipenem-relebactam, and meropenem-vaborbactam were 50.8% (N = 97), 84.8% (N = 162), and 90.6% (N = 173), respectively. The majority of CRKP isolates (N = 146; 97.3%) harboured carbapenemase-encoding genes: blaNDM-1 (N = 65; 44.5%), blaOXA-48 (N = 64; 43.8%), and blaKPC-2 (N = 11; 7.5%). Additionally, six CRKP isolates co-harbored blaNDM-1 and blaOXA-48 (4.1%). This study revealed ten sequence types (STs) and six clonal complexes (CCs), with ST147/CC147/blaNDM-1 being the most prevalent (N = 44; 29.3%) followed by ST101/CC101/blaOXA-48 (N = 40; 26.7%). One CRKP isolate, ST101/blaNDM-1/blaSHV-1/blaCTX-M15 was resistant to cefiderocol. The predominant hypervirulence-associated genes were ybt (N = 138; 92%) and iuc (N = 80; 53.3%). According to the genotypic analysis, 75 out of 150 (50.0%) CRKP isolates had iuc and rmpA2/rmpADC genes, whereas 26 (13.6%) strains exhibited the hypermucoviscous phenotype. The emergence of hypervirulent, K. pneumoniae clone ST147/blaNDM-1, suggests a high potential for regional spread of a high-risk clone resistant to last-line antibiotics.

Immunotherapy has revolutionized cancer treatment, yet only a minority of individuals respond clinically, necessitating alternative strategies that can benefit these patients. Novel immuno-oncology targets may achieve this through bypassing resistance mechanisms to standard therapies. We introduce Mining Immunotherapy Drug tArgetS (MIDAS), a multimodal graph neural network system for immuno-oncology target discovery. MIDAS leverages gene interactions, multi-omic patient profiles, immune cell biology, antigen processing, disease associations and phenotypic consequences of genetic perturbations. It generalizes to time-sliced data, outcompetes state-of-the-art baselines (including OpenTargets) and ranks approved targets above those in clinical development. Moreover, MIDAS recovers immunotherapy-response-associated genes in unseen patients, thereby capturing immunotherapy response determinants. Interpretability analyses reveal a reliance on autoimmunity, regulatory networks and immuno-oncology pathways. Functionally perturbing oncostatin M–oncostatin M receptor signalling, a proposed MIDAS target, in TRACERx melanoma-patient-derived explants yielded reduced dysfunctional CD8+ T cells, which associate with immunotherapy response, and reduced CCL4 levels. Furthermore, oncostatin M and oncostatin M receptor expression is associated with altered T cell and macrophage profiles in bulk transcriptomic data from patient samples. These data are consistent with a role for oncostatin M–oncostatin M in modulating the tumour microenvironment towards immunosuppressive, tumour-promoting phenotypes. Our results present a machine learning framework for analysing multimodal data for immuno-oncology target discovery. Augustine et al. present a multimodal graph neural network that identifies cancer immunotherapy targets. It distinguishes approved and prospective targets, and promising candidates are validated using a clinically relevant patient-derived platform.

Immune cells mediate acute and chronic renal failure in native and transplanted kidneys, initiating auto- and allo-immunity, and acting as effectors in other diseases such as diabetes, hypertension, and cancer. Many drugs already in use or in development for these diseases target the putative immune mechanisms at play, based on in vitro cell experiments and animal models. Here, we review how recent and upcoming advanced tissue imaging techniques-many of them applicable to human kidney samples as well as animal models-could further improve drug development by providing insights into immune cell types, activation states, and behaviours in kidney disease. We illustrate an innovative cross-scale multimodal imaging pipeline and its application to the investigation of immune cells in human kidney samples.

This paper presents a finite-time super-twisting sliding mode control (STWSMC) framework for robust three-dimensional (3D) trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) operating under exogenous disturbances. The proposed approach ensures continuous control action while preserving finite-time convergence properties. A complete non-linear dynamic model of the quadrotor is considered, including translational-rotational couplings and gravitational effects. Separate STWSMC structures are developed for the altitude and attitude subsystems, guaranteeing robustness against bounded disturbances and model uncertainties without requiring explicit disturbance estimation. A Lyapunov-based stability analysis is carried out, proving finite-time convergence of the sliding variables and finite-time stability of the closed-loop tracking errors. Simulations demonstrate improved transient performance, reduced chattering amplitude, and enhanced robustness—particularly in yaw dynamics—when compared to a conventional second-order sliding mode control (SOSMC) scheme. The obtained results indicate that the proposed STWSMC strategy provides a theoretically sound and practically viable solution for high-performance quadrotor control.