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.

This paper presents a concise, application-focused description of using EEZ Studio (Envox Experimental Zone) as a practical environment for system identification, data acquisition, and implementation of controllers on the programmable power supply EEZ BB3. The work demonstrates how EEZ Studio integrates SCPI control, MicroPython/JS scripting, and direct interaction with instruments (oscilloscopes and BB3) to perform identification and closed-loop control. The experimental implementation (water-level and DC-motor speed control tasks) used the EEZ BB3 as the actuator with an STM32 Nucleo micro-controller for signal acquisition and UART communication. The results show reliable acquisition, effective controller deployment in the BB3 and stable closed-loop behavior, indicating that EEZ Studio is a feasible tool for rapid prototyping and educational control experiments.

Simple Summary Disease recurrence after allogeneic stem cell transplantation remains the principal cause of treatment failure in patients with myeloid malignancies. One possible explanation is the acquisition of additional genetic alterations by malignant cells over time. In this study, we investigated the emergence of cytogenetic changes at relapse and their association with prior treatment exposure and clinical outcomes. Nearly half of the patients developed cytogenetic changes at relapse. These alterations occurred more frequently in individuals with complex cytogenetic abnormalities at diagnosis, suggesting that an unstable genomic background may predispose further clonal diversification. In contrast, prior chemotherapy, conditioning regimen, and donor type were not associated with the emergence of new abnormalities. Although patients with cytogenetic changes showed a lower early response rate, long-term survival outcomes were not significantly affected. Overall, our findings suggest that cytogenetic alterations at relapse may primarily be driven by disease-intrinsic biological features rather than treatment-related genomic damage.

Abstract Objectives Clinical trial data are lacking for treatment of patients with juvenile systemic sclerosis (jSSc). Three published recommendations exist for jSSc but real-world data on treatment patterns are lacking. The aim of this study was to analyse treatments used in the jSSc inception cohort (jSSci) and compare to published recommendations on the treatment of jSSc. Methods Data was extracted for patients with 24 months follow-up visits in the jSSci up until June 2023. Medications used and their association with clinical characteristics were analysed. Logistic regression analyses were performed to compare treatments between limited and diffuse cutaneous jSSc subtypes, organ involvement and time of initiation of treatment. Multilevel mixed effects logistic regression analyses were used to evaluate the change in medication use in follow-up. Treatment patterns were compared against published recommendations. Results 93 patients had 24 months follow-up data. 77% of patients were receiving disease-modifying treatment (DMARD) at enrolment, which increased to 91% at 24 months (p<0.001). Patients with diffuse cutaneous jSSc subtype had significantly more frequently active ulcerations, skin involvement and received any kind of treatment more often compared with limited (97% vs 91%, p=0.047). Methotrexate was used in 52% of patients at enrolment which decreased to 37% at 24 months (p=0.001). Mycophenolate mofetil use increased from 24% to 46% (p<0.001). Biological DMARDs increased from 5% to 22% (p<0.001). The treatment pattern strongly overlapped with the published paediatric guidance. Conclusion This is the first report regarding the pattern of medication use in real life in the currently largest patient cohort of patients with jSSc. The observed pattern overlaps with the published recommendations.