Lo que sigue después de las palabras son las imágenes y la construcción minuciosa y sistemática de algo que por su naturaleza percibimos fragmentado, espontáneo y sin forma, de manera que parece más un desestructurar que un construir, que aparenta ser un conjunto de reiteraciones; indeterminado y sin orden reconocible, como las libretas con la que permanentemente dialoga un dibujante.
Laurensius Laka, Alfonsus Krismiyanto, Marieta Jona
Education was regarded as a long-term investment to develop superior human resources. Unfortunately, the education system in Indonesia remained overshadowed by inequality and systemic obsolescence, making the reinforcement of self-regulated learning (SRL) as the foundation of students’ learning autonomy urgently necessary. The objective of this study was to investigate the impact of Technology Compatibility and Social Support on the SRL of students at Catholic Senior High School Bhakti Luhur Malang, Indonesia. Employing a quantitative approach, data were collected from 144 students selected through a stratified random sampling technique. The research instrument consisted of questionnaires for the three measured constructs, all of which had been validated in advance. Multiple linear regression analysis was conducted using SPSS software. The results revealed that the correlation coefficient between the independent and dependent variables was r = 0.786, indicating a strong relationship. In terms of causality, the simultaneous regression analysis yielded p = 0.001 < 0.05, suggesting that the regression model was appropriate for predicting students' SRL. Furthermore, the variables of Technology Compatibility and Social Support were found to significantly affect SRL individually, with each showing a p = 0.001. Therefore, both simultaneously and individually, the two independent variables significantly influenced students’ SRL, with Social Support contributing more than Technology Compatibility.
Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by the accumulation of amyloid-beta (A$β$) and phosphorylated tau (p-tau) proteins, leading to cognitive decline measured by the Alzheimer's Disease Assessment Scale (ADAS) score. In this study, we develop and analyze a system of ordinary differential equation models to describe the interactions between A$β$, p-tau, and ADAS score, providing a mechanistic understanding of disease progression. To ensure accurate model calibration, we employ Bayesian inference and Physics-Informed Neural Networks (PINNs) for parameter estimation based on Alzheimer's Disease Neuroimaging Initiative data. The data-driven Bayesian approach enables uncertainty quantification, improving confidence in model predictions, while the PINN framework leverages neural networks to capture complex dynamics directly from data. Furthermore, we implement an optimal control strategy to assess the efficacy of an anti-tau therapeutic intervention aimed at reducing p-tau levels and mitigating cognitive decline. Our data-driven solutions indicate that while optimal drug administration effectively decreases p-tau concentration, its impact on cognitive decline, as reflected in the ADAS score, remains limited. These findings suggest that targeting p-tau alone may not be sufficient for significant cognitive improvement, highlighting the need for multi-target therapeutic strategies. The integration of mechanistic modelling, advanced parameter estimation, and control-based therapeutic optimization provides a comprehensive framework for improving treatment strategies for AD.
ShahZeb Khan, Chiara Pallara, Barbara Monti
et al.
Alzheimers disease (AD), a progressive neuro-degenerative disorder, currently lacks effective therapeutic strategies that can modify disease progression. Recent studies have highlighted the circadian rhythm critical role in AD pathophysiology, implicating circadian clock kinases, such as the Salt-Inducible Kinase 3 (SIK3), as promising therapeutic target. Generative AI models have surpassed traditional methods of drug discovery, untapping the vast unexplored chemical space of drug-like molecules. We present a sequence-to-sequence Variational Autoencoder (Seq2Seq-VAE) model guided by an Active Learning (AL) approach to optimize molecular generation. Our pipeline iteratively guided a pre-trained Seq2Seq-VAE model towards the pharmacological landscape relevant to SIK3 using a two-step framework, an inner loop that iteratively improves physiochemical properties profile, drug likeliness and synthesizability, followed by an outer loop that steer the latent space towards high-affinity ligands for SIK3. Our approach introduces feedback-driven optimization without requiring large labeled datasets, making it particularly suited for early-stage drug discovery in under-explored therapeutic targets. Our results demonstrate the models convergence toward SIK3-specific small molecules with desired properties and high binding affinity. This work highlights the use of generative AI combined with AL for rational drug discovery that can be extended to other protein targets with minimal modifications, offering a scalable solution to the molecular design bottleneck in drug design.
The shortage of mental health professionals has driven the web to become a primary avenue for accessible psychological support. While Large Language Models (LLMs) offer promise for scalable web-based counseling, existing approaches often lack emotional understanding, adaptive strategies, and long-term memory. These limitations pose risks to digital well-being, as disjointed interactions can fail to support vulnerable users effectively. To address these gaps, we introduce TheraMind, a strategic and adaptive agent designed for trustworthy online longitudinal counseling. The cornerstone of TheraMind is a novel dual-loop architecture that decouples the complex counseling process into an Intra-Session Loop for tactical dialogue management and a Cross-Session Loop for strategic therapeutic planning. The Intra-Session Loop perceives the patient's emotional state to dynamically select response strategies while leveraging cross-session memory to ensure continuity. Crucially, the Cross-Session Loop empowers the agent with long-term adaptability by evaluating the efficacy of the applied therapy after each session and adjusting the method for subsequent interactions. We validate our approach in a high-fidelity simulation environment grounded in real clinical cases. Extensive evaluations show that TheraMind outperforms other methods, especially on multi-session metrics like Coherence, Flexibility, and Therapeutic Attunement, validating the effectiveness of its dual-loop design in emulating strategic, adaptive, and longitudinal therapeutic behavior. The code is publicly available at https://github.com/Emo-gml/TheraMind.
Current mental-health conversational systems are usually based on fixed, generic dialogue patterns. This paper proposes an adaptive framework based on large language models that aims to personalize therapeutic interaction according to a user's psychological state, quantified with the Acceptance of Illness Scale (AIS). The framework defines three specialized agents, L, M, and H, each linked to a different level of illness acceptance, and adjusts conversational behavior over time using continuous feedback signals. The AIS-stratified architecture is treated as a diegetic prototype placed in a plausible near-future setting and examined through the method of design fiction. By embedding the architecture in narrative scenarios, the study explores how such agents might influence access to care and therapeutic relationship. The goal is to show how clinically informed personalization, technical feasibility, and speculative scenario analysis can together inform the responsible design of LLM-based companions for mental-health support.
Vaibhaw Kumar, Dimitris Alevras, Mihir Metkar
et al.
Accurate prediction of mRNA secondary structure is critical for understanding gene expression, translation efficiency, and advancing mRNA-based therapeutics. However, the combinatorial complexity of possible foldings, especially in long sequences, poses significant computational challenges for classical algorithms. In this work, we propose a scalable, quantum-centric optimization framework that integrates quantum sampling with classical post-processing to tackle this problem. Building on a Quadratic Unconstrained Binary Optimization (QUBO) formulation of the mRNA folding task, we develop two complementary workflows: a Conditional Value at Risk (CVaR)-based variational quantum algorithm enhanced with gauge transformations and local search, and an Instantaneous Quantum Polynomial (IQP) circuit-based scheme where training is done classically and sampling is delegated to quantum hardware. We demonstrate the effectiveness of these approaches using IBM quantum processors, solving problem instances with up to 156 qubits and circuits containing up to 950 nonlocal gates, corresponding to mRNA sequences of up to 60 nucleotides. Additionally, we validate scalability of the CVaR algorithm on a tensor network simulator, reaching up to 354 qubits in noiseless settings. These results demonstrate the growing practical capabilities of hybrid quantum-classical methods for tackling large-scale biological optimization problems.
Designing sequences that satisfy multiple, often conflicting, objectives is a central challenge in therapeutic and biomolecular engineering. Existing generative frameworks largely operate in continuous spaces with single-objective guidance, while discrete approaches lack guarantees for multi-objective Pareto optimality. We introduce AReUReDi (Annealed Rectified Updates for Refining Discrete Flows), a discrete optimization algorithm with theoretical guarantees of convergence to the Pareto front. Building on Rectified Discrete Flows (ReDi), AReUReDi combines Tchebycheff scalarization, locally balanced proposals, and annealed Metropolis-Hastings updates to bias sampling toward Pareto-optimal states while preserving distributional invariance. Applied to peptide and SMILES sequence design, AReUReDi simultaneously optimizes up to five therapeutic properties (including affinity, solubility, hemolysis, half-life, and non-fouling) and outperforms both evolutionary and diffusion-based baselines. These results establish AReUReDi as a powerful, sequence-based framework for multi-property biomolecule generation.
Prediction for ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) plays a crucial role in drug discovery and development, accelerating the screening and optimization of new drugs. Existing methods primarily rely on single-task learning (STL), which often fails to fully exploit the complementarities between tasks. Besides, it requires more computational resources while training and inference of each task independently. To address these issues, we propose a new unified Quantum-enhanced and task-Weighted Multi-Task Learning (QW-MTL) framework, specifically designed for ADMET classification tasks. Built upon the Chemprop-RDKit backbone, QW-MTL adopts quantum chemical descriptors to enrich molecular representations with additional information about the electronic structure and interactions. Meanwhile, it introduces a novel exponential task weighting scheme that combines dataset-scale priors with learnable parameters to achieve dynamic loss balancing across tasks. To the best of our knowledge, this is the first work to systematically conduct joint multi-task training across all 13 Therapeutics Data Commons (TDC) classification benchmarks, using leaderboard-style data splits to ensure a standardized and realistic evaluation setting. Extensive experimental results show that QW-MTL significantly outperforms single-task baselines on 12 out of 13 tasks, achieving high predictive performance with minimal model complexity and fast inference, demonstrating the effectiveness and efficiency of multi-task molecular learning enhanced by quantum-informed features and adaptive task weighting.
Personalized decision systems in healthcare and behavioral support often rely on static rule-based or engagement-maximizing heuristics that overlook users' emotional context and ethical constraints. Such approaches risk recommending insensitive or unsafe interventions, especially in domains involving serious mental illness, substance use disorders, or depression. To address this limitation, we propose a Responsible Reinforcement Learning (RRL) framework that integrates emotional and contextual understanding with ethical considerations into the sequential decision-making process. RRL formulates personalization as a Constrained Markov Decision Process (CMDP), where the agent optimizes engagement and adherence while ensuring emotional alignment and ethical safety. We introduce a multi-objective reward function that explicitly balances short-term behavioral engagement with long-term user well-being, and define an emotion-informed state representation that captures fluctuations in emotional readiness, affect, and risk. The proposed architecture can be instantiated with any RL algorithm (e.g., DQN, PPO) augmented with safety constraints or Lagrangian regularization. Conceptually, this framework operationalizes empathy and responsibility within machine learning policy optimization, bridging safe RL, affective computing and responsible AI. We discuss the implications of this approach for human-centric domains such as behavioral health, education, and digital therapeutics, and outline simulation-based validation paths for future empirical work. This paper aims to initiate a methodological conversation about ethically aligned reinforcement learning for emotionally aware and trustworthy personalization systems.
Mahdi Pourmirzaei, Farzaneh Esmaili, Salhuldin Alqarghuli
et al.
The diverse nature of protein prediction tasks has traditionally necessitated specialized models, hindering the development of broadly applicable and computationally efficient Protein Language Models (PLMs). In this work, we introduce Prot2Token, a unified framework that overcomes these challenges by converting a wide spectrum of protein-related predictions-from sequence-level properties and residue-specific attributes to complex inter-protein interactions-into a standardized next-token prediction format. At its core, Prot2Token employs an autoregressive decoder, conditioned on embeddings from pre-trained protein encoders and guided by learnable task tokens, to perform diverse predictions. This architecture uniquely facilitates multi-task learning, enabling general-purpose decoders to generalize across five distinct categories. We present extensive experimental validation across a variety of benchmarks, demonstrating Prot2Token's predictive power in different types of protein-prediction tasks. In 3D structure prediction, Prot2Token delivers substantial speedups (up to 1000x faster than AlphaFold2 with MSA on the same hardware) while, across other numerous tasks, matching or surpassing specialized methods. Beyond that, we introduce an auxiliary self-supervised decoder pre-training approach to improve spatially sensitive task performance. Prot2Token thus offers a step towards standardizing biological prediction into a generative interface, promising to accelerate biological discovery and the development of novel therapeutics. The code is available at https://github.com/mahdip72/prot2token .
Jamini Bhagu, Lissa C. Anderson, Samuel C. Grant
et al.
Monoclonal antibodies (mAb) represent an important class of biologic therapeutics that can treat a variety of diseases including cancer, autoimmune disorders or respiratory conditions (e.g. COVID-19). However, throughout their development, mAb are exposed to air-water or oil-water interfaces that may trigger mAb partial unfolding that can lead to the formation of proteinaceous aggregates. Using a combination of dynamic surface tensiometry and spatially resolved 1D 1H NMR spectroscopy, this study investigates if adsorption of a model IgG2a-\k{appa} mAb to the oil-water interface affects its structure. Localized NMR spectroscopy was performed using voxels of 375 um, incrementally approaching the oil-water interface. Dynamic interfacial tension progressively decreases at the oil-water interface over time, confirming mAb adsorption to the interface. Localized NMR spectroscopy results indicate that, while the number of mAb-related chemical resonances and chemical shift frequencies remain unaffected, spectral line broadening is observed as voxels incrementally approach the oil-water interface. Moreover, the spin-spin (T2) relaxation of the mAb molecule was measured for a voxel centered at the interface and shown to be affected differentially across the mAb resonances, indicating a rotational restriction for mAb molecules due to presence of the interface. Finally, the apparent diffusion coefficient of the mAb for the voxel centered at the interface is lower than the bulk mAb. These results suggest that this specific mAb interacts with and may be in exchange with bulk mAb phase in the vicinity of the interface. As such, these localized NMR techniques offer the potential to probe and quantify alterations of mAb properties near interfacial layers.
This paper reports on an art-based autoethnography study aimed at helping the author, an international student, reflect on her personal distress during tertiary study in Australia. Over an 8-week period, the author engaged in meditation and art-making activities to articulate and reflect upon her experiences and emotions. Distress levels were recorded using the Visual Analogue Scale before and after each session of art making and reflection. The findings indicate a reduction in the author’s distress levels following engagement in art making and reflection. Furthermore, the process revealed the importance of maintaining a positive mindset and cultivating self-love, which contributed to the author’s reduced distress levels. In conclusion, this study reveals that engaging in art making effectively alleviated distress for the author during her time as an international student in Australia.
Addressing Alzheimer's disease (AD) requires innovative strategies beyond current single-target drugs. This Letter to the Editor suggests that multitarget molecules, especially those targeting neuronal membrane protection, could offer a comprehensive approach to AD therapy, advocating for further research into their mechanisms and therapeutic potential.
Este artículo tiene dos objetivos: el primero es dar a conocer la psicoterapia con niños desde el psicodrama. El segundo, situar en la vida de su creador Jacobo Leví Moreno los sucesos que contribuyeron a crear lo que él mismo denominó la tercera revolución psiquiátrica, la psicoterapia de grupo. Para cubrir estos dos objetivos, hemos pensado su vida, sus sentimientos, sus ideas, y el contexto cultural de su época. Lo hemos hecho siguiendo la idea que guía el hacer psicodramático, la creatividad acompañada del rigor con los hechos. Por último, mostramos la aplicación del psicodrama en la clínica infantil de la mano de algunos ejemplos de intervención.
The article examines the multifaceted phenomenon of adolescents’s value orientations, the mechanisms of its formation, and emphasizes the significant influence of gender-role conformity features on this hierarchy of the individual’s motivational structure. The influence of the level of adolescent conformity on the formation of value orientations of the individual was considered. Since the social situation actively begins to change during the transitional age, adolescents often enter into confrontation with adults, the process of separation begins, and at the same time, adolescents begin to unite in reference groups to strengthen their opinion, receive support and acceptance. Often, with a high level of conformity, they begin to focus on the norms, behavior patterns, sex-role expectations, goals of their peers in order to be accepted by their reference group, which can influence the formation of adolescent value orientations. Were invastigated that the contribution of the phenomenon of conformity to the formation of adolescents’s value orientations is 82.3%. Makes a positive contribution to the formation of such a value orientation as help and mercy to others; does not make such a contribution to the formation of a high social status and people’s management. Separately, it was found that the contribution of gender-role characteristics to the formation of adolescents’ value orientations, is 32.1%. The scale of femininity contributes to the formation of such a value orientation as: love. It does not have such an impact on the formation of such a value orientation as pleasant pastime and recreation. The scale of masculinity contributes to the formation of such value orientations as material well-being and high social status and people management. Does not have such an impact on health as value orientation.
Some context
I brought these words together in 2022 following a weekend I spent with my younger sister, Annemarie, and my younger brother, James, who came to visit me together, something that hadn’t happened before and I was left humbled by their visit.
The conversations we created as the remaining elder generation of our family were moving and it was the first time such conversations took place. The context for this coming together was triggered by a chemotherapy treatment phase following my recent diagnosis of cancer.
This weekend became an important event in our lives thus far, and as I was exploring poetry as an antidote to patient-hood, as well as making my voice find its relationship to myself inhabited by cancer, I try to capture our conversations and share them with you here.
My sister and brother have given me permission to publish this. I honour them as “us” and our experiences we have shared.
DNA origami is a widely used method to construct nanostructures by self-assembling designed DNA strands. These structures are often used as "pegboards" for templated assembly of proteins, gold nanoparticles, aptamers, and other molecules, with applications ranging from therapeutics and diagnostics to plasmonics and photonics. Imaging these structures using AFM or TEM does not capture their full conformation ensemble as they only show their shape flattened on a surface. However, certain conformations of the nanostructure can position guest molecules into distances unaccounted for in their intended design, thus leading to spurious interactions between guest molecules that are designed to be separated. Here, we use molecular dynamics simulations to capture conformational ensemble of 2D DNA origami tiles and show that introducing single-stranded overhangs, which are typically used for functionalization of the origami with guest molecules, induces a curvature of the tile structure in the bulk. We show that the shape deformation is of entropic origin, with implications for design of robust DNA origami breadboards as well as potential approach to modulate structure shape by introducing overhangs. We then verify experimentally that the DNA overhangs introduce curvature into the DNA origami tiles in divalent as well as monovalent salt buffer conditions. We further experimentally verify that DNA origami functionalized with attached proteins also experience such induced curvature. We provide the developed simulation code implementing the enhanced sampling to characterize conformational space of DNA origami as open source software.
The aim of this study was to compare help-seeking intentions among youth living in areas with different Covid-19 infection rates during the pandemic. Data was collected using demographical questions and the General Help-seeking Questionnaire from a sample of 1,340 adolescents (971 females, 369 males) between August 11th and 21st, 2020. Of these participants, 423 (122 females, 301 males) were eligible for analysis. The data were analyzed using descriptive analysis and ANOVA. The results showed that youth living in areas with low Covid-19 infection rates had higher help-seeking intentions than those living in areas with high infection rates or no Covid-19 cases. These findings suggest that the rate of Covid-19 infection in an individual's living area may impact their help-seeking intentions during the pandemic. Further research is needed to fully understand the factors that contribute to help-seeking intentions during times of crisis and to develop interventions to support individuals in need of help. It is also important to consider the potential impact of other factors, such as access to resources and support systems, on help-seeking intentions among youth.