CyberSec Research

This work presents a comprehensive theory of consciousness grounded in mathematical formalism and supported by clinical data analysis. The framework developed herein demonstrates that consciousness exists as a continuous, non-monotonic function across a high-dimensional neurochemical space, with dopamine serving as the primary intensity regulator and serotonin (5-HT2A) as the complexity modulator. This work offers mechanistic explanations for the full spectrum of conscious states, from deep sleep and psychosis to the ultimate collapse in neural death. The theory explains paradoxical phenomena such as prefrontal cortex hypoactivity during seizures, the evolutionary persistence of psychosis-prone individuals, and why controlled administration of classical 5-HT2A agonists shows a comparatively low incidence of serious medical events (< 0.01 % in modern clinical trials), while dopaminergic excess proves rapidly lethal. The framework is tested using 70,290 sleep nights from 242 Parkinson's disease patients, using disease severity (UPDRS) as a proxy for system integrity and medication (LEDD) as a proxy for dopaminergic input. The analysis reveals a significant LEDD x UPDRS interaction (beta=-1.7, p<.0001), confirming the model's prediction of state-dependent, non-linear dynamics.

This study presents a rigorous assessment of the growth performance of Gmelina arborea (melina) in a 67-hectare plantation located in Chontaduro, Tabiazo Parish, Esmeraldas, Ecuador. The plantation was established in 2017 under a high-density planting system (650 trees/ha). Permanent monitoring techniques were applied in 16 one-hectare plots to analyze structural growth variables, including survival rate, diameter at breast height (DBH), total height, commercial height, basal area, volume, and mean annual increment (MAI). The results show an average survival rate of 80.2%, with a mean DBH of 25.3 cm at five years, indicating sustained growth under favorable edaphoclimatic conditions. Volume was calculated using the equation V = G HT Ff, yielding average values of 183.262 m3 for total volume and 166.19 m3 for commercial volume. The estimated MAI for diameter and height was 5.06 cm/year and 3.61 m/year, respectively, with values comparable to studies conducted in other Ecuadorian sites, although lower productivity was observed in Esmeraldas, attributed to edaphic and climatic differences identified through soil type and environmental condition analyses. The research highlights the significant influence of edaphic conditions, silvicultural management, and environmental variables on the performance of Gmelina arborea in tropical Ecuador. The findings provide a foundation for optimizing forest management strategies and improving growth indicators in commercial plantations, contributing to the sustainable development of forest resources in the region and strengthening silvicultural planning based on predictive models tailored to local conditions. This study represents a step forward in the scientific assessment of melina growth under Ecuadorian conditions, promoting more precise and sustainable silvicultural practices

The present study aimed to investigate the genetic diversity in 42 German chamomile populations using morphological, biochemical, and ssr molecular markers, in both field and laboratory sections. Different populations of German chamomile collected from different regions were as statistical populations. Phenological, physiological and phytochemical traits, were measured. DNA extraction was done by CTAB method and DNA quantity and quality were checked by spectrophotometer and gel electrophoresis. 5 target populations were genetically evaluated with the help of 6 SSR markers. According to the obtained results, Jam 1 and Shahijan populations had the most effective substances (essential oil and chamazulene). The correlation analysis revealed a positive and significant correlation of 59% between the percentage of chamazulene and the percentage of essential oil. Chamazulene percentage and fresh weight as the most important traits were entered into the regression model step by step. These traits were found to explain 0.43% of the changes in the data. These findings have significant implications for future research aimed at identifying the most effective populations for essential oil and chamazulene production. The cluster analysis divided the genotypes into five groups. The second group was the most important, and Jam1 and Shahijan genotypes were in this group. The results of the molecular analysis showed that the Seho Sermak-Dashti population had the most effective allele, and this population was superior to other populations in terms of Shannon's index and nei diversity coefficient. According to the diagram, analysis into main coordinates showed that the genotypes are scattered on the surface of the diagram and this indicates the appropriate diversity of the studied genotypes. As a result, it can be stated that the grouping of phenotypic and molecular data was very consistent with each other.

Recent developments in the Internet of Bio-Nano Things (IoBNT) are laying the groundwork for innovative applications across the healthcare sector. Nanodevices designed to operate within the body, managed remotely via the internet, are envisioned to promptly detect and actuate on potential diseases. In this vision, an inherent challenge arises due to the limited capabilities of individual nanosensors; specifically, nanosensors must communicate with one another to collaborate as a cluster. Aiming to research the boundaries of the clustering capabilities, this survey emphasizes data-driven communication strategies in molecular communication (MC) channels as a means of linking nanosensors. Relying on the flexibility and robustness of machine learning (ML) methods to tackle the dynamic nature of MC channels, the MC research community frequently refers to neural network (NN) architectures. This interdisciplinary research field encompasses various aspects, including the use of NNs to facilitate communication in MC environments, their implementation at the nanoscale, explainable approaches for NNs, and dataset generation for training. Within this survey, we provide a comprehensive analysis of fundamental perspectives on recent trends in NN architectures for MC, the feasibility of their implementation at the nanoscale, applied explainable artificial intelligence (XAI) techniques, and the accessibility of datasets along with best practices for their generation. Additionally, we offer open-source code repositories that illustrate NN-based methods to support reproducible research for key MC scenarios. Finally, we identify emerging research challenges, such as robust NN architectures, biologically integrated NN modules, and scalable training strategies.

First discovered by L. R. Taylor (1961, Nature), Taylor's Power Law (TPL) correlates the mean (M) population abundances and the corresponding variances (V) across a set of insect populations using a power function (V=aM^b). TPL has demonstrated its 'universality' across numerous fields of sciences, social sciences, and humanities. This universality has inspired two main prongs of exploration: one from mathematicians and statisticians, who might instinctively respond with a convergence theorem similar to the central limit theorem of the Gaussian distribution, and another from biologists, ecologists, physicists, etc., who are more interested in potential underlying ecological or organizational mechanisms. Over the past six decades, TPL studies have produced a punctuated landscape with three relatively distinct periods (1960s-1980s; 1990s-2000s, and 2010s-2020s) across the two prongs of abstract and physical worlds. Eight themes have been identified and reviewed on this landscape, including population spatial aggregation and ecological mechanisms, TPL and skewed statistical distributions, mathematical/statistical mechanisms of TPL, sample vs. population TPL, population stability, synchrony, and early warning signals for tipping points, TPL on complex networks, TPL in macrobiomes, and in microbiomes. Three future research directions including fostering reciprocal interactions between the two prongs, heterogeneity measuring, and exploration in the context of evolution. The significance of TPL research includes practically, population fluctuations captured by TPL are relevant for agriculture, forestry, fishery, wildlife-conservation, epidemiology, tumor heterogeneity, earthquakes, social inequality, stock illiquidity, financial stability, tipping point events, etc.; theoretically, TPL is one form of power laws, which are related to phase transitions, universality, scale-invariance, etc.

Merely by existing, all physical systems contain information, and physical dynamics transforms and processes that information. This note investigates the information processing power of living systems. Living systems harvest free energy from the sun, from geothermal sources, and from each other. They then use that free energy to drive the complex set of chemical interactions that underlie life. All molecules -- be they simple molecules such as water, or complex molecules such as DNA -- register information via their chemical composition. When these molecules undergo chemical reactions, that information is transformed and processed. These chemical transformations can be thought of as elementary logical operations: such bio-ops include the absorption of a photon in a chromophore during photosynthesis, the formation or breaking of covalent, hydrogen, and van der Waals bonds in the process of metabolism and reproduction, or the release of a neurotransmitter molecule when a synapse fires in the brain. This paper estimates the total number of bio-ops that have been, and are being performed, by life on earth. We find that the current number of bio-ops performed by all life on earth is around $10^{33}-10^{35}$ bio-ops per second. The cells in an individual human being perform around $10^{20}-10^{22}$ bio-ops per second, comparable to the information processing power of all the computers, cell phones, and server farms on earth. Depending on how one defines a neural operation, at most a few percent of human bio-ops take place in the firing of neurons and synapses in the brain. Over the course of life on earth, about $10^{50}-10^{52}$ bio-ops have taken place.

Sleep is commonly studied through neurochemical, evolutionary, and behavioral frameworks, typically emphasizing circadian rhythms and energy conservation. However, these approaches do not fully explain a deeper biophysical question: why does sleep universally involve physical stillness, a lying posture, and disconnection from conscious control? This paper introduces a new hypothesis that sleep is not merely a biological function, but a state of vibrational synchronization between the human body and natural frequencies generated by the Earth. In this state, the body reduces its autonomous activity and aligns with external environmental rhythms, allowing for energy restoration, internal recalibration, and systemic reorganization. This perspective reframes life as a continuous process of internally driven vibration influenced by external physical fields. The proposed model offers new avenues for understanding aging, health, death, and consciousness.

Synthetic data has emerged as a powerful resource in life sciences, offering solutions for data scarcity, privacy protection and accessibility constraints. By creating artificial datasets that mirror the characteristics of real data, allows researchers to develop and validate computational methods in controlled environments. Despite its promise, the adoption of synthetic data in Life Sciences hinges on rigorous evaluation metrics designed to assess their fidelity and reliability. To explore the current landscape of synthetic data evaluation metrics in several Life Sciences domains, the ELIXIR Machine Learning Focus Group performed a systematic review of the scientific literature following the PRISMA guidelines. Six critical domains were examined to identify current practices for assessing synthetic data. Findings reveal that, while generation methods are rapidly evolving, systematic evaluation is often overlooked, limiting researchers ability to compare, validate, and trust synthetic datasets across different domains. This systematic review underscores the urgent need for robust, standardized evaluation approaches that not only bolster confidence in synthetic data but also guide its effective and responsible implementation. By laying the groundwork for establishing domain-specific yet interoperable standards, this scoping review paves the way for future initiatives aimed at enhancing the role of synthetic data in scientific discovery, clinical practice and beyond.

Single-cell omics technologies have transformed our understanding of cellular diversity by enabling high-resolution profiling of individual cells. However, the unprecedented scale and heterogeneity of these datasets demand robust frameworks for data integration and annotation. The Cell Ontology (CL) has emerged as a pivotal resource for achieving FAIR (Findable, Accessible, Interoperable, and Reusable) data principles by providing standardized, species-agnostic terms for canonical cell types - forming a core component of a wide range of platforms and tools. In this paper, we describe the wide variety of uses of CL in these platforms and tools and detail ongoing work to improve and extend CL content including the addition of transcriptomically defined types, working closely with major atlasing efforts including the Human Cell Atlas and the Brain Initiative Cell Atlas Network to support their needs. We cover the challenges and future plans for harmonising classical and transcriptomic cell type definitions, integrating markers and using Large Language Models (LLMs) to improve content and efficiency of CL workflows.

We present a comprehensive investigation into plant bioelectric responses to human presence and emotional states, building on five years of systematic research. Using custom-built plant sensors and machine learning classification, we demonstrate that plants generate distinct bioelectric signals correlating with human proximity, emotional states, and physiological conditions. A deep learning model based on ResNet50 architecture achieved 97% accuracy in classifying human emotional states through plant voltage spectrograms, while control models with shuffled labels achieved only 30% accuracy. This study synthesizes findings from multiple experiments spanning 2020-2025, including individual recognition (66% accuracy), eurythmic gesture detection, stress prediction, and responses to human voice and movement. We propose that these phenomena represent evolved anti-herbivory early warning systems, where plants detect approaching animals through bioelectric field changes before physical contact. Our results challenge conventional understanding of plant sensory capabilities and suggest practical applications in agriculture, healthcare, and human-plant interaction research.

Understanding behavior requires datasets that capture humans while carrying out complex tasks. The kitchen is an excellent environment for assessing human motor and cognitive function, as many complex actions are naturally exhibited in kitchens from chopping to cleaning. Here, we introduce the EPFL-Smart-Kitchen-30 dataset, collected in a noninvasive motion capture platform inside a kitchen environment. Nine static RGB-D cameras, inertial measurement units (IMUs) and one head-mounted HoloLens~2 headset were used to capture 3D hand, body, and eye movements. The EPFL-Smart-Kitchen-30 dataset is a multi-view action dataset with synchronized exocentric, egocentric, depth, IMUs, eye gaze, body and hand kinematics spanning 29.7 hours of 16 subjects cooking four different recipes. Action sequences were densely annotated with 33.78 action segments per minute. Leveraging this multi-modal dataset, we propose four benchmarks to advance behavior understanding and modeling through 1) a vision-language benchmark, 2) a semantic text-to-motion generation benchmark, 3) a multi-modal action recognition benchmark, 4) a pose-based action segmentation benchmark. We expect the EPFL-Smart-Kitchen-30 dataset to pave the way for better methods as well as insights to understand the nature of ecologically-valid human behavior. Code and data are available at https://github.com/amathislab/EPFL-Smart-Kitchen

Many people suffer from mental health problems but not everyone seeks professional help or has access to mental health care. AI chatbots have increasingly become a go-to for individuals who either have mental disorders or simply want someone to talk to. This paper presents a study on participants who have previously used chatbots and a scenario-based testing of large language model (LLM) chatbots. Our findings indicate that AI chatbots were primarily utilized as a "Five minute therapist" or as a non-judgmental companion. Participants appreciated the anonymity and lack of judgment from chatbots. However, there were concerns about privacy and the security of sensitive information. The scenario-based testing of LLM chatbots highlighted additional issues. Some chatbots were consistently reassuring, used emojis and names to add a personal touch, and were quick to suggest seeking professional help. However, there were limitations such as inconsistent tone, occasional inappropriate responses (e.g., casual or romantic), and a lack of crisis sensitivity, particularly in recognizing red flag language and escalating responses appropriately. These findings can inform both the technology and mental health care industries on how to better utilize AI chatbots to support individuals during challenging emotional periods.

This study evaluated the impact of Zinc (Zn) supplementation on the growth, yield, and seed quality of chickpea (Pisum sativum L.) under the semi-arid conditions of Kerman, Iran, across two growing seasons (2021-2022). A randomized complete block design was used with six treatments, including varying concentrations of zinc sulphate applied via foliar spraying (0.1%, 0.25%, 0.5%) or irrigation (4, 8, 16 kg/ha), each applied twice-one and two months after greening. Results from the first year revealed significant differences in yield and yield components across treatments. The highest seed yield and protein content were achieved with foliar application of 0.5% and irrigation application of 8 kg/ . Zinc application enhanced reproductive processes, including pollen viability and stigma receptivity, leading to improved pod and seed attributes. However, excessive Zn application (e.g., 0.4% Zn) resulted in reduced plant performance, likely due to phytotoxicity. Leaf Zn concentration was significantly higher with 16 kg/ha applied via irrigation, while foliar applications at 0.1% also increased Zn uptake efficiently. The second growing season, however, showed no significant differences in traits across treatments, which was attributed to favorable climatic conditions mitigating Zn deficiency. Zn deficiency remains a critical challenge globally, particularly in calcareous and nutrient depleted soils, adversely affecting plant metabolism, root development, and nitrogen pathways. This study underscores the importance of optimizing Zn supplementation strategies to enhance yield and quality while avoiding toxicity. Findings provide practical recommendations for addressing Zn deficiencies in semi-arid cropping systems, offering valuable insights for sustainable chickpea production

Bi-directional brain computer interfaces (BD-BCIs) may restore brain-controlled walking and artificial leg sensation after spinal cord injury. Current BD-BCIs provide only simplistic "tingling" feedback, which lacks proprioceptive information to perceive critical gait events (leg swing, double support). This information must also be perceived adequately fast to facilitate timely motor responses. Here, we investigated utilizing primary sensory cortex (S1) direct cortical electrical stimulation (DCES) to deliver leg proprioceptive information and measured response times to artificial leg sensations. Subjects with subdural electrocorticogram electrodes over S1 leg areas participated in two tasks: (1) Proprioceptive acuity: subjects identified the difference between DCES-induced percepts emulating various leg swing speeds; (2) Sensory response: measuring subjects' reaction time to DCES-induced leg sensations, with DCES-hand, visual and auditory control conditions. Three subjects were recruited. Only one completed the proprioceptive assessment, achieving 80%, 70%, 60%, and 53% accuracy in discriminating between fast/slow, fast/medium, medium/slow, and same speeds, respectively (p-value=1.9x10$^{-5}$). Response times for leg/hand percepts were 1007$\pm$413/599$\pm$171 ms, visual leg/hand responses were 528$\pm$137/384$\pm$84 ms, and auditory leg/hand responses were 393$\pm$106/352$\pm$93 ms, respectively. These results suggest proprioceptive information can be delivered artificially, but perception may be significantly delayed. Future work should address improving acuity, reducing response times, and expanding sensory modalities.

Large language models (LLMs) have demonstrated strong potential in clinical question answering, with recent multi-agent frameworks further improving diagnostic accuracy via collaborative reasoning. However, we identify a recurring issue of Silent Agreement, where agents prematurely converge on diagnoses without sufficient critical analysis, particularly in complex or ambiguous cases. We present a new concept called Catfish Agent, a role-specialized LLM designed to inject structured dissent and counter silent agreement. Inspired by the ``catfish effect'' in organizational psychology, the Catfish Agent is designed to challenge emerging consensus to stimulate deeper reasoning. We formulate two mechanisms to encourage effective and context-aware interventions: (i) a complexity-aware intervention that modulates agent engagement based on case difficulty, and (ii) a tone-calibrated intervention articulated to balance critique and collaboration. Evaluations on nine medical Q&A and three medical VQA benchmarks show that our approach consistently outperforms both single- and multi-agent LLMs frameworks, including leading commercial models such as GPT-4o and DeepSeek-R1.

Interspecific communication plays a critical role in mediating human-animal interactions, particularly in contexts involving access to anthropogenic resources. This study investigates the influence of human gazing on the begging strategies of free-ranging dogs in urban and peri-urban environments. Begging behaviour, commonly observed in dogs seeking food from humans, offers insights into their behavioural flexibility and cognitive attunement to human social cues. We observed 650 adult dogs in both solitary and group settings to assess how social context shapes the expression of begging behaviour in free-ranging dogs. Our findings indicate that solitary dogs beg more frequently than those in groups, and that female dogs exhibit higher rates of begging, predominantly through passive strategies. Moreover, dogs modulate their active begging in response to subtle variations in human gazing and food availability. These results suggest that passive begging is influenced by stable situational factors such as sex and group composition, while active begging is more responsive to immediate environmental cues, including human attentional state. Collectively, our findings highlight the social competence and behavioural plasticity of free-ranging dogs in navigating interspecies interactions, and contribute to a broader understanding of how communicative strategies evolve in response to social and ecological pressures.

As humanity prepares for sustained off-world habitation, the development of regolith-based agriculture (RBA) is essential for achieving self-sufficiency in space crop production. However, lunar regolith's alkaline pH, poor water retention, and high metal content pose severe physiological and biochemical challenges to plant growth. This study evaluates the performance of Solanum lycopersicum 'Inkspot', a stress-adaptive, anthocyanin-rich tomato variant, in comparison to its progenitor 'Tiny Tim', under control and simulated lunar regolith (LHS-2) conditions. A randomized complete block design was used to assess germination dynamics, morphology, fruit quality, antioxidant activity, and root architecture across 80 replicates over 65 days in controlled chambers. Inkspot maintained high germination rates (85% in regolith) with low variation (CV = 14%) and showed only moderate reductions in height and biomass, while Tiny Tim suffered a 45% biomass reduction and 60% fruit yield loss. Inkspot fruits increased anthocyanin content 2.5-fold in regolith, functioning as a stress-response mechanism and potential bioindicator. Physiological assessments revealed greater retention of chlorophyll, Fv/Fm efficiency, and stomatal conductance in Inkspot, correlated with higher SOD and CAT enzyme activity and lower lipid peroxidation. Root imaging showed Inkspot developed a significantly larger, more complex root system, while Tiny Tim's roots contracted under stress. These findings highlight Inkspot's abiotic stress tolerance and potential as a candidate for closed-loop life support and in-situ resource utilization strategies in RBA systems.

Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI implementation.