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Molecular dynamics (MD) simulations based on detailed all-atom models offer a powerful approach to study the structure and dynamics of biological membranes. However, the complexity of biological membranes in terms of chemical diversity presents an outstanding challenge. Particularly, difficulties are encountered when a given lipid type is present at very low abundance. While considering a very large simulation system with a small number of the low abundance lipid may offer a practical solution in some cases, resorting to increasingly large system rapidly becomes computationally costly and impractical. More fundamentally, an additional issue may be encountered if the low abundance lipid displays a high affinity for some protein in the simulation system. What is needed is to treat the simulation box as an open system in which the number of lipids can naturally fluctuate, as in the Grand Canonical Monte Carlo (MC) algorithm. However, this approach, in which a whole lipid molecule needs to be inserted or annihilated, is essentially impractical in the context of an all-atom simulation. To enforce equilibrium between a simulated system and an infinite surrounding bath, we propose a hybrid non-equilibrium (neMD)–MC algorithm, in which a randomly chosen lipid molecule in the simulated system is swapped with a lipid picked in a separate system standing as a thermodynamic "reservoir" with the desired mole fraction for all lipid components. The neMD/MC algorithm consists in driving the system via short non-equilibrium trajectories to generate a new state of the system that are subsequently accepted or rejected via a Metropolis MC step. The probability of exchanges in the context of an infinite reservoir with the desired mole fraction for all lipid components is derived and tested with a few illustrative systems for phosphatidylcholine and phosphatidylglycerol lipid mixtures. [ABSTRACT FROM AUTHOR]

Households are a significant source of SARS-CoV-2 transmission, even during periods of low community-level spread. Comparing household transmission rates by SARS-CoV-2 variant may provide relevant information about current risks and prevention strategies. This investigation aimed to estimate differences in household transmission risk comparing the SARS-CoV-2 Delta and Omicron variants using data from contact tracing and interviews conducted from November 2021 through February 2022 in five U.S. public health jurisdictions (City of Chicago, Illinois; State of Connecticut; City of Milwaukee, Wisconsin; State of Maryland; and State of Utah). Generalized estimating equations were used to estimate attack rates and relative risks for index case and household contact characteristics. Data from 848 households, including 2,622 individuals (median household size = 3), were analyzed. Overall transmission risk was similar in households with Omicron (attack rate = 47.0%) compared to Delta variant (attack rate = 48.0%) circulation. In the multivariable model, a pattern of increased transmission risk was observed with increased time since a household contact's last COVID-19 vaccine dose in Delta households, although confidence intervals overlapped (0–3 months relative risk = 0.8, confidence interval: 0.5–1.2; 4–7 months relative risk = 1.3, 0.9–1.8; ≥8 months relative risk = 1.2, 0.7–1.8); no pattern was observed in Omicron households. Risk for household contacts of symptomatic index cases was twice that of household contacts of asymptomatic index cases (relative risk = 2.0, 95% confidence interval: 1.4–2.9), emphasizing the importance of symptom status, regardless of variant. Uniquely, this study adjusted risk estimates for several index case and household contact characteristics and demonstrates that few characteristics strongly dictate risk, likely reflecting the complexity of the biological and social factors which combine to impact SARS-CoV-2 transmission. [ABSTRACT FROM AUTHOR]

The urbanisation of coastal areas increases both Artificial Light At Night (ALAN) and man-made structures. ALAN poses significant challenges to coastal ecosystems by altering species physiology and behaviour. Its effects might differ considerably between man-made and natural habitats due to varying habitat complexity and biological assemblages. This systematic review assesses the current knowledge and gaps regarding the ecological effects of the interaction between ALAN and different coastal hard-bottom habitats on intertidal and shallow-subtidal reefs. Of the 57 retrieved studies, most are laboratory experiments (40) on the physiology and behaviour of rocky shore (24) or coral reef (16) species. Field studies were conducted in artificial (6) and natural habitats (9), with only 2 comparing the two habitat types. These studies illustrate ALAN impacts on various species, with potential cascading effects on entire communities through the alteration of competitive and consumer-resource interactions. Different habitat structures may interact with ALAN by generating highly heterogeneous lightscapes (e.g., natural rocky shores), regular light-shadow mosaics (e.g., breakwaters), vast homogeneously lit areas (e.g., seawalls), or constantly shaded areas (e.g., pontoons). This creates a variable mismatch in natural light conditions, which may be further influenced by the introduction of additional light sources (e.g., moored boats with underwater coloured lights). As coastal development and light pollution continue to grow, research should prioritise understanding their interactive effects in shaping species relationships and ecosystem dynamics. Although the available evidence suggests that ALAN effects may vary between natural and man-made habitats, further research is needed to draw any general conclusion. [ABSTRACT FROM AUTHOR]

Pregnancy establishment and progression in cattle are pivotal research areas with significant implications for the industry. Despite high fertilization rates, ~50% of bovine pregnancies are lost, pinpointing the need to keep studying the biological principles leading to a successful pregnancy. The increasing access to and generation of omics data have aided in defining the molecular characteristics of pregnancy, i.e. embryo and fetal development and communication with the maternal environment. Large datasets generated through omics technologies are usually analyzed through pipelines that could lack the power to deeply explore the complexity of biological data. Machine learning (ML), a branch of artificial intelligence, offers a promising approach to address this challenge by effectively handling large-scale, heterogeneous and high-dimensional data. This review explores the role of ML in unraveling the intricacies of bovine embryo–maternal communication, including the identification of biomarkers associated with pregnancy outcome prediction and uncovering key genes and pathways involved in embryo development and survival. Through discussing recent studies, we define the contributions of ML towards advancing our understanding of bovine pregnancy, with the final goal of reducing pregnancy losses and enhancing reproductive efficiency while also addressing current limitations and future perspectives of ML in this field. High embryo losses in cattle negatively impact the livestock industry. Modern techniques can measure the biological molecules involved in embryo–maternal communication at a large scale. These datasets are usually analysed through traditional pipelines. However, machine learning (ML) tools can learn from data to make predictions, helping decipher the complexity of biological data. Here, we review the contribution of ML in understanding pregnancy establishment in cattle, as well as the current challenges and future potential of ML in this field. Diagram created with BioRender. This article belongs to the Collection: Proceedings of the Annual Conference of the International Embryo Technology Society, Fort Worth, TX, USA, 18–22 January 2025. [ABSTRACT FROM AUTHOR]

The transcription factors belonging to the Forkhead box (FOX) family are essential for the coordination of multiple cellular processes that are vital for the development of embryos, maintenance of tissue homeostasis, and the genesis of diseases. Of these, FOXC1 stands out as a crucial regulator that has a significant impact both during and after organogenesis. One of the several transcription factors that make up the FOX family, FOXC1, is known for its ability to control cellular motility, proliferation, differentiation, and cell fate determination. It is important for many organ systems, such as the ocular, central neurological, and cardiovascular systems, where it controls important developmental processes. Furthermore, new data highlights the role FOXC1 plays in a variety of clinical situations, from prenatal abnormalities to the advancement of cancer. FOXC1 also modulates gene expression networks critical for skeletal morphogenesis and cartilage integrity. This review attempts at exploring the role of FOXC1, elucidating its dynamics in organogenesis, developmental complexities, and numerous functions and providing insight into its critical role in determining biological complexity. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by limited effective treatments, underscoring the critical need for early detection and diagnosis to improve intervention outcomes. This study integrates various bioinformatics methodologies with interpretable machine learning to identify reliable biomarkers for AD diagnosis and treatment. By leveraging differentially expressed genes (DEGs) analysis, weighted gene co-expression network analysis (WGCNA), and construction of Protein-Protein Interaction (PPI) Networks, we meticulously analyzed the AD dataset from the GEO database to pinpoint Hub genes. Subsequently, various machine learning algorithms were employed to construct diagnostic models, which were then elucidated using SHapley Additive exPlanations (SHAP). To visualize our findings, we generated an insightful bioinformatics map of 10 Hub genes. We then conducted experimental validation on less-studied Hub genes, revealing significant differential mRNA expression of MYH9 and RHOQ in an AD cell model. Finally, we explored the biological significance of these two genes at the single-cell transcriptome level. This study not only introduces interactive SHAP panels for precise decision-making in AD but also offers novel insights into the identification of AD biomarkers through interpretable machine learning diagnostic models. Particularly, MYH9 has emerged as a promising new potential biomarker, pointing the way towards enhanced diagnostic accuracy and personalized therapeutic strategies for AD. Although the mRNA expression patterns of RHOQ are opposite in AD cell models and human brain tissue samples, the role of RHOQ in AD remains worthy of further exploration due to the diversity and complexity of biological molecular regulation. [ABSTRACT FROM AUTHOR]

Multi-omics data integration has become increasingly crucial for a deeper understanding of the complexity of biological systems. However, effectively integrating and analyzing multi-omics data remains challenging due to their heterogeneity and high dimensionality. Existing methods often struggle with noise, redundant features, and the complex interactions between different omics layers, leading to suboptimal performance. Additionally, they face difficulties in adequately capturing intra-omics interactions due to simplistic concatenation techiniques, and they risk losing critical inter-omics interaction information when using hierarchical attention layers. To address these challenges, we propose a novel Denoised Multi-Omics Integration approach that leverages the Transformer multi-head self-attention mechanism (DMOIT). DMOIT consists of three key modules: a generative adversarial imputation network for handling missing values, a sampling-based robust feature selection module to reduce noise and redundant features, and a multi-head self-attention (MHSA) based feature extractor with a noval architecture that enchance the intra-omics interaction capture. We validated model porformance using cancer datasets from the Cancer Genome Atlas (TCGA), conducting two tasks: survival time classification across different cancer types and estrogen receptor status classification for breast cancer. Our results show that DMOIT outperforms traditional machine learning methods and the state-of-the-art integration method MoGCN in terms of accuracy and weighted F1 score. Furthermore, we compared DMOIT with various alternative MHSA-based architectures to further validate our approach. Our results show that DMOIT consistently outperforms these models across various cancer types and different omics combinations. The strong performance and robustness of DMOIT demonstrate its potential as a valuable tool for integrating multi-omics data across various applications. [ABSTRACT FROM AUTHOR]

Biological organisms are composed of billions of molecules organized across various length scales. Direct visualization of these biomolecules in situ enables the retrieval of vast molecular information, including their location, species, and quantities, which is essential for understanding biological processes. The programmability of DNA interactions has made DNA‐based reactions a major driving force in extending the limits of fluorescence microscopy, allowing for the study of biological complexity at different scales. This review article provides an overview of recent technological advancements in DNA‐based fluorescence microscopy, highlighting how these innovations have expanded the technique's capabilities in terms of target multiplexity, signal amplification, super‐resolution, and mechanical properties. These advanced DNA‐based fluorescence microscopy techniques have been widely used to uncover new biological insights at the molecular level. [ABSTRACT FROM AUTHOR]

Reverse micelles (RMs) have emerged as useful tools for the study of membrane associated proteins. With a nanoscale water core surrounded by surfactant and solubilized in a non-polar solvent, RMs stand apart as a unique membrane model. While RMs have been utilized as tools to investigate the physical properties of membranes and their associated water, RMs also effectively house membrane associated proteins for a variety of studies. High-resolution protein NMR revealed a need for development of improved RM formulations, which greatly enhanced the use of RMs for aqueous proteins. Protein-optimized RM formulations enabled encapsulation of challenging membrane associated protein types, including lipidated proteins, transmembrane proteins, and peripheral membrane proteins. Improvements in biological accuracy of RMs using phospholipid-based surfactants has advanced their utility as a membrane mimetic even further, better matching the chemistry of the most common cellular membrane lipids. Natural lipid extracts may also be used to construct RMs and house proteins, resulting in a membrane model that better represents the complexity of biological membranes. Recent applications in high-resolution investigations of protein-membrane interactions and inhibitor design of membrane associated proteins have demonstrated the usefulness of these systems in addressing this difficult category of protein. Further developments of RMs as membrane models will enhance the breadth of investigations facilitated by these systems and will enhance their use in biophysical, structural, and drug discovery pursuits of membrane associated proteins. In this review, we present the development of RMs as membrane models and their application to structural and biophysical study of membrane proteins. [ABSTRACT FROM AUTHOR]

Cellular membrane interfacial processes are highly complex phenomena that are challenging to explore in vivo, often limiting their mechanistic understanding. The plethora of participating biological entities and their unprecedented complex interactions might also mask their core guiding principles. In vitro reconstitution experiments with a reductionist bottom-up approach attempt to dissect the biological complexity by recreating biological reactions in their simplest form. This review focuses on the cellular membrane interfacial processes crucial for cellular transport, neurodegeneration, and host–pathogen interactions. We discuss how reconstitution methodologies have played a crucial role in addressing many of the fundamental questions dealing with the mechanisms of clathrin-dependent or independent endocytosis and the Endosomal Sorting Complex Required for Transport (ESCRT) pathway. In addition, we have discussed how reconstitution-based approaches helped explore membrane-mediated alterations in the onset of neurodegenerative diseases and host–pathogen interactions. Importantly, the reconstitution approach, though predominantly applied but not limited to addressing questions on cellular membrane shaping, is gradually gaining attention across multiple areas of cell biology. [ABSTRACT FROM AUTHOR]

This paper critically revises the organisational account of teleology, which argues that living systems are first and foremost oriented towards a goal: maintaining their own conditions of existence. It points out some limitations of this account, mainly in the capability to account for the richness and complexity of biological systems and their purposeful behaviours. It identifies the reason of these limitations in the theoretical grounding of this account, specifically in the too narrow notion of closure of constraints, focused on self‐production. It proposes to ground an organisational account of biological teleology in the capability of living system not just to produce and replace their parts, but to control their own internal dynamics and behaviours in such a way as to maintain themselves. This theoretical framework has two advantages. It better captures the distinctive features of biological organisations and consequently the richness and active nature of their purposeful behaviours. By doing so, it makes it possible to apply this framework beyond minimal theoretical models to real biological cases. [ABSTRACT FROM AUTHOR]

The increased incidence of invasive species introductions is a hallmark of global change, but their associated environmental and economic impacts are vastly underestimated. Assessing and managing the impact of invasive species requires understanding their weather driven dynamics as a basis for predicting their potential geographic distribution and relative abundance. Current de-facto standards for invasive species assessment are correlative approaches lacking mechanistic underpinnings, and hence fail to capture the weather driven biology limiting their explanatory and predictive capacity to forewarn policy makers of species invasiveness (i.e., its potential geographic distribution and relative abundance under extant and/or climate change weather). The idiosyncratic time-place nature of biological invasions and the inability of correlative approaches to incorporate biological information call for development of a unifying prospective approach across species. Physiologically based demographic models (PBDMs) provide a holistic basis for assessment of invasive species addressing many limitations of correlative approaches while accommodating higher level of biological complexity using a similar number of parameters. We use the South American tomato pinworm Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) as a case study in the Palearctic and compare the predictions of our PBDM model to those of three analyses based on the correlative CLIMEX model. The PBDM outperformed CLIMEX with comparable CLIMEX predictions only after the pest had reached its potential geographic distribution (i.e., post hoc), using 6–10 vs. 13 parameters, respectively. We suggest creating dedicated laboratories to gather appropriate biological data and developing generalized software to build mechanistic models for assessing invasive species of any taxa. [ABSTRACT FROM AUTHOR]

The mathematical modeling of multicellular systems is an important branch of biophysics, which focuses on how the system properties emerge from the elementary interaction between the constituent elements. Recently, mathematical structures have been proposed within the thermostatted kinetic theory for the modeling of complex living systems and have been profitably employed for the modeling of various complex biological systems at the cellular scale. This paper deals with a class of generalized thermostatted kinetic theory frameworks that can stand in as background paradigms for the derivation of specific models in biophysics. Specifically, the fundamental homogeneous thermostatted kinetic theory structures of the recent literature are recovered and generalized in order to take into consideration further phenomena in biology. The generalizations concern the conservative, the nonconservative, and the mutative interactions between the inner system and the outer environment. In order to sustain the strength of the new structures, some specific models of the literature are reset into the style of the new frameworks of the thermostatted kinetic theory. The selected models deal with breast cancer, genetic mutations, immune system response, and skin fibrosis. Future research directions from the theoretical and modeling viewpoints are discussed in the whole paper and are mainly devoted to the well-posedness in the Hadamard sense of the related initial boundary value problems, to the spatial–velocity dynamics and to the derivation of macroscopic-scale dynamics. [ABSTRACT FROM AUTHOR]

Genetic diversity is the fundamental building block of biodiversity and the necessary ingredient for adaptation. Specifically, the intraspecific diversity (biocomplexity) comprising phenotypic and genetic variation partitioned within and among populations can determine the ability of a species to respond to changing environmental conditions. Here, we explore the biocomplexity of California's Central Valley Chinook Salmon (Oncorhynchus tshawytscha) population complex at the genomic level by quantifying population genomic diversity among and within migration life-history phenotypes. Notably, despite apparent gene flow among populations with the same migration (life-history) phenotypes that inhabit different tributaries, each group is characterized by a distinct component of unique genomic diversity. While enumerating biodiversity contained within individual hierarchical levels is informative, it is important to consider inter- and intraspecific diversity simultaneously, because there may be emergent properties at higher levels as a result of the presence of diversity at lower ones. Our results emphasize the importance of formulating conservation goals focused on maintaining biocomplexity at both the phenotypic and genotypic level. Doing so will preserve the species' adaptive potential and increase the probability of persistence of the population complex despite changing environmental pressures. [ABSTRACT FROM AUTHOR]

The philosophy of herbal medicines is that they contain multiple active components that target many aspects of a given disease. This is in line with the recent multiple-target strategy adopted due to shortcomings with the previous "magic bullet", single-target strategy. The complexity of biological systems means it is often difficult to elucidate the mechanisms of synergistic, additive, and/or multi-target (SAM) effects. However, the use of methodologies employing molecular docking offers some insight into these. The aim of this work was to review the uses of molecular-docking simulations in the detection and/or elucidation of SAM effects with herbal medicines. Molecular docking has revealed the potential for SAM effects with many different, individual herbal medicines. Docking can also improve the fundamental understanding of SAM effects as part of systems biology approaches, such as providing quantitative weightings for the connections within static networks or supplying estimates of kinetic parameters for dynamic metabolic networks. Molecular docking can also be combined with pharmacophore modeling in a hybrid method that greatly improves the efficiency of screening. Overall, molecular docking has been shown to be a highly useful tool to provide evidence for the efficacy of herbal medicines, previously only supported by traditional usage. [ABSTRACT FROM AUTHOR]

AbstractQuantitative analysis of peptides in biological fluids offers a high diagnostic and prognostic tool to reflect the pathophysiological condition of the patient. Recently, methods based on liquid chromatography coupled with mass spectrometry (LC-MS) for the quantitative determination of intact peptides have been replacing traditionally used ligand-binding assays, which suffer from cross-reactivity issues. The use of “top-down” analysis of peptides is rapidly increasing since it does not undergo incomplete or non-reproducible digestion like “bottom-up” approaches. However, the low abundance of peptides and their peculiar characteristics, as well as the complexity of biological fluids, make their quantification challenging. Herein, the analytical pitfalls that may be encountered during the development of an LC-MS method for the analysis of intact peptides in biological fluids are discussed. Challenges in the pre-analytical phase, stability after sampling and sample processing, significantly impact the accuracy of peptide quantification. Emerging techniques, such as microextractions, are becoming crucial for improved sample cleanup and enrichment of target analytes. A comparison between the roles of high-resolution and low-resolution mass spectrometry in the quantification of intact peptides, as well as the introduction of supercharging reagents to enhance ionization, will be discussed. [ABSTRACT FROM AUTHOR]

Structural biology is a field of research that investigates the structure and organization of macromolecules to decipher their functional mechanisms. Traditionally, it has adopted a reductionist approach out of necessity, focusing on the high-resolution analysis of macromolecules outside their natural cellular contexts. This contrasts sharply with clinical research, which examines human biology holistically through patient studies and cellular disease models, albeit with less detailed structural insights. Recent advancements are bridging this gap, enabling the study of macromolecular structures within more complex biological systems, from single cells to multicellular organisms and primary human tissues. These developments signal a pivotal shift within structural biology from traditional methodologies towards a more integrated understanding of biological complexity. Here, we highlight recent technological advancements that facilitate structural studies in tissue environments, showcasing the discoveries made possible with these approaches and the prospect of their applications in human clinical research. This commentary of the Sparks of Science series from the Catalysts program reflects on the contribution of technological advances in cryo-EM to medically relevant studies. [ABSTRACT FROM AUTHOR]

High-dimensional gene expression data cause challenges for traditional statistical tools, particularly when dealing with non-linear relationships and outliers. The present study addresses these challenges by employing a generalized correlation coefficient (GCC) that incorporates a flexibility parameter, allowing it to adapt to varying levels of symmetry and asymmetry in the data distribution. This adaptability is crucial for analyzing gene association networks, where the GCC demonstrates advantages over traditional measures such as Kendall, Pearson, and Spearman coefficients. We introduce two novel adaptations of this metric, enhancing its precision and broadening its applicability in the context of complex gene interactions. By applying the GCC to relevance networks, we show how different levels of the flexibility parameter reveal distinct patterns in gene interactions, capturing both linear and non-linear relationships. The maximum likelihood and Spearman-based estimators of the GCC offer a refined approach for disentangling the complexity of biological networks, with potential implications for precision medicine. Our methodology provides a powerful tool for constructing and interpreting relevance networks in biomedicine, supporting advancements in the understanding of biological interactions and healthcare research. [ABSTRACT FROM AUTHOR]

The evolution of complex multicellularity in land plants represents a pivotal event in the history of life on Earth, characterized by significant increases in biological complexity. This transition, classified as a Major Evolutionary Transition (MET), is best understood through the framework of Evolutionary Transitions in Individuality (ETIs), which focuses on formerly independent entities forming higher-level units that lose their reproductive autonomy. While much of the ETI literature has concentrated on the early stages of multicellularity, such as the formation and maintenance stages, this paper seeks to address the less explored transformation stage. To do so, we apply an approach that we call Transitions in Structural Complexity (TSCs), which focuses on the emergence of new units of organization via the three key evolutionary processes of modularization, subfunctionalization, and integration to the evolution of land plants. To lay the groundwork, we first explore the relationships between sex, individuality, and units of selection to highlight a sexual life cycle-based perspective on ETIs by examining the early stages of the transition to multicellularity (formation) in the sexual life cycle of the unicellular common ancestor of land plants, emphasizing the differences between the transition to multicellularity in eumetazoans and land plants. We then directly apply the TSC approach in this group, identifying key evolutionary events such as the distinct evolutionary innovations like shoot, root, vascular systems, and specialized reproductive structures, arguing that bringing these under the broader rubric of TSCs affords a degree of explanatory unification. By examining these evolutionary processes, this paper provides a new perspective on the evolution of multicellularity in land plants, highlighting both parallels and distinctions with the animal kingdom. [ABSTRACT FROM AUTHOR]

Deciphering the underlying gene regulatory networks (GRNs) that govern early human embryogenesis is critical for understanding developmental mechanisms yet remains challenging due to limited sample availability and the inherent complexity of the biological processes involved. To address this, we developed InPheRNo-ChIP, a computational framework that integrates multimodal data, including RNA-seq, transcription factor (TF)–specific ChIP-seq, and phenotypic labels, to reconstruct phenotype-relevant GRNs associated with endoderm development. The core of this method is a probabilistic graphical model that models the simultaneous effect of TFs on their putative target genes to influence a particular phenotypic outcome. Unlike the majority of existing GRN inference methods that are agnostic to the phenotypic outcomes, InPheRNo-ChIP directly incorporates phenotypic information during GRN inference, enabling the distinction between lineage-specific and general regulatory interactions. We integrated data from three experimental studies and applied InPheRNo-ChIP to infer the GRN governing the differentiation of human embryonic stem cells into definitive endoderm. Benchmarking against a scRNA-seq CRISPRi study demonstrated InPheRNo-ChIP's ability to identify regulatory interactions involving endoderm markers FOXA2 , SMAD2 , and SOX17 , outperforming other methods. This highlights the importance of incorporating the phenotypic context during network inference. Furthermore, an ablation study confirms the synergistic contribution of ChIP-seq, RNA-seq, and phenotypic data, highlighting the value of multimodal integration for accurate phenotype-relevant GRN reconstruction. [ABSTRACT FROM AUTHOR]

Spatial transcriptomics offers deep insights into cellular functional localization and communication by mapping gene expression to spatial locations. Traditional approaches that focus on selecting spatially variable genes often overlook the complexity of biological pathways and the interactions among genes. Here, we introduce a novel framework that shifts the focus towards directly identifying functional pathways associated with spatial variability by adapting the Brownian distance covariance test in an innovative manner to explore the heterogeneity of biological functions over space. Unlike most other methods, this statistical testing approach is free of gene selection and parameter selection and allows nonlinear and complex dependencies. It allows for a deeper understanding of how cells coordinate their activities across different spatial domains through biological pathways. By analyzing real human and mouse datasets, the method found significant pathways that were associated with spatial variation, as well as different pathway patterns among inner- and edge-cancer regions. This innovative framework offers a new perspective on analyzing spatial transcriptomic data, contributing to our understanding of tissue architecture and disease pathology. The implementation is publicly available at https://github.com/tianlq-prog/STpathway. [ABSTRACT FROM AUTHOR]

Various systemic metabolic diseases arise from prolonged crosstalk across multiple organs, triggering serious impairments in various physiological systems. These diseases are intricate systemic pathologies characterized by complex mechanisms and an unclear etiology, making the treatment challenging. Efforts have been made to develop in vitro models to understand these diseases and devise new treatments. However, there are limitations in reconstructing the causal relationships between diseases and interorgan crosstalk, including the tissue‐specific microenvironment. Alternatively, multi‐organ microphysiological systems (MOMPS) present new possibilities for capturing the complexity of systemic metabolic diseases by replicating human microphysiology and simulating diverse interorgan crosstalk. Controlled interactions and scalable representations of biological complexity in MOMPS offer a more accurate portrayal of organ interactions, enabling the identification of novel relationships between organ crosstalk, metabolism, and immunity. This, in turn, can yield valuable insights into disease mechanisms and drug development research and enhance the efficiency of preclinical studies. In this review, the examples and technical capabilities of MOMPS pathological modeling for various diseases are discussed, leveraging state‐of‐the‐art biofabrication technology of MOMPS. It evaluates the current opportunities and challenges in this field. [ABSTRACT FROM AUTHOR]

Inborn errors of monoamine neurotransmitter metabolism are rare diseases characterized by nonspecific neurological symptoms. These symptoms appear in early childhood and correspond to movement disorders, epilepsy, sleep disorders and/or mental disability. Cerebrospinal fluid biomarkers have been identified and validated to allow specific diagnosis of these diseases. Biomarkers of inborn errors of monoamine neurotransmitter metabolites are divided in two groups: monoamine neurotransmitter metabolites and pterins. Biomarkers quantification in cerebrospinal fluid is based on high-performance liquid chromatography separation coupled to electrochemical detection, fluorescence detection, or mass spectrometry. The following article reviews the advances in the proposed routine methods for the measurement of these analytes in cerebrospinal fluid. The purpose of this review is to compare the various proposed methods in terms of sample preparation, chromatographic conditions and detection modes. Despite the broad range of proposed methods, quantification of inborn errors of monoamine neurotransmitter biomarkers remains a great challenge, given the complexity of biological fluids and the low amounts of analytes that are present in cerebrospinal fluid. [ABSTRACT FROM AUTHOR]

Background/Objectives: Profiling of metabolites and lipids in biological samples can provide invaluable insights into life-sustaining chemical processes. The ability to detect both metabolites and lipids in the same sample can enhance these understandings and connect cellular dynamics. However, simultaneous detection of metabolites and lipids is generally hampered by chromatographic systems tailored to one molecular type. This void can be filled by direct infusion mass spectrometry (MS), where all ionizable molecules can be detected simultaneously. However, in direct infusion MS, the high chemical complexity of biological samples can introduce limitations in detectability due to matrix effects causing ionization suppression. Methods: Decreased sample complexity and increased detectability and molecular coverage was provided by combining our direct infusion probe (DIP) with liquid–liquid extraction (LLE) and directly sampling the different phases for direct infusion. Three commonly used LLE methods for separating lipids and metabolites were evaluated. Results: The butanol–methanol (BUME) method was found to be preferred since it provides high molecular coverage and have low solvent toxicity. The established BUME DIP-MS method was used as a fast and sensitive analysis tool to study chemical changes in insulin-secreting cells upon glucose stimulation. By analyzing the metabolome at distinct time points, down to 1-min apart, we found high dynamics of the intracellular metabolome. Conclusions: The rapid workflow with LLE DIP-MS enables higher sensitivity of phase separated metabolites and lipids. The application of BUME DIP-MS provides novel information on the dynamics of the intracellular metabolome of INS-1 during the two phases of insulin release for both metabolite and lipid classes. [ABSTRACT FROM AUTHOR]

This review highlights the critical role of software sensors in advancing biosystem monitoring and control by addressing the unique challenges biological systems pose. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors offer a transformative approach by integrating hardware sensor data with advanced computational models, enabling the indirect estimation of hard-to-measure variables, such as stress indicators, health metrics in animals and humans, and key soil properties. This article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of a Kalman filter in greenhouse control, the remote detection of soil organic matter, and sound recognition algorithms for the early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This review emphasizes the potential of software sensors to enhance decision-making and promote sustainability in agriculture, healthcare, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Bioassembly encompasses various techniques such as bioprinting, microfluidics, organoids, and self-assembly, enabling advances in tissue engineering and regenerative medicine. Advancements in bioassembly technologies have enabled the precise arrangement and integration of various cell types to more closely mimic the complexity functionality of the neurovascular unit (NVU) and that of other biodiverse multicellular tissue structures. In this context, bioprinting offers the ability to deposit cells in a spatially controlled manner, facilitating the construction of interconnected networks. Scaffold-based assembly strategies provide structural support and guidance cues for cell growth, enabling the formation of complex bio-constructs. Self-assembly approaches utilize the inherent properties of cells to drive the spontaneous organization and interaction of neuronal and vascular components. However, recreating the intricate microarchitecture and functional characteristics of a tissue/organ poses additional challenges. Advancements in bioassembly techniques and materials hold great promise for addressing these challenges. The further refinement of bioprinting technologies, such as improved resolution and the incorporation of multiple cell types, can enhance the accuracy and complexity of the biological constructs; however, developing bioinks that support the growth of cells, viability, and functionality while maintaining compatibility with the bioassembly process remains an unmet need in the field, and further advancements in the design of bioactive and biodegradable scaffolds will aid in controlling cell adhesion, differentiation, and vascularization within the engineered tissue. Additionally, integrating advanced imaging and analytical techniques can provide real-time monitoring and characterization of bioassembly, aiding in quality control and optimization. While challenges remain, ongoing research and technological advancements propel the field forward, paving the way for transformative developments in neurovascular research and tissue engineering. This work provides an overview of the advancements, challenges, and future perspectives in bioassembly for fabricating neurovascular constructs with an add-on focus on bioprinting technologies. [ABSTRACT FROM AUTHOR]

Methodological improvement to single‐cell manipulation is critical for exploring the fundamentals of cellular life and unraveling biological complexity. Although micro‐manipulation technologies capable of precise cell localization have been widely established, scaling existing platforms for highly efficient single‐cell immobilization without sacrificing cell viability and sample quantity has proven challenging. Here, a highly efficient single‐cell trapping and arraying approach is introduced by advancing the performance of a microfluidic mechanical trapping chip. The chip can achieve representative single‐cell capture with over 99% efficiency and at least a 75% success rate of perfect capture, a precisely controlled single‐cell array, absolute sequential cell captures without cell loss, and the maintenance of high cell viability during the whole manipulation process. This approach enables diverse single‐cell trapping, large‐scale arraying manipulations, and dynamic cellular and molecular analysis, and offers a path toward the development of high‐performance single‐cell systems. [ABSTRACT FROM AUTHOR]

Continuous and long-term therapeutic monitoring of medicine molecules in biological systems will revolutionize healthcare by offering personalized pharmacokinetic reports. However, the extremely complex biological environment brings great challenges for in vivo molecule detection in living organisms. Here we introduce an in vivo photoelectrochemical biosensor following a reverse design strategy with single atoms as molecular recognition units. Atomic dispersion of Cu single atoms on TiO2-x substrate create synergistic anchoring triple-site for efficiently and selectively capturing of dual-carbonyl group and neighboring dual-hydroxyl group of tetracycline molecules. The photoelectrode is encapsulated with antibiofouling layer and implanted into the vein of living mouse to enable long-term in vivo monitoring of tetracycline in real biological environments. It is important to note that our approach was exclusively tested in male mice, and therefore, the findings may not be generalizable to female mice or other species without further research. The rationally designed biological-components-free in vivo biosensor with excellent selectivity, robustness, and stability endows possibility for enabling personalized medicine guidance through real-time feedbacking information and providing direct and authentic medicine molecular analysis. Continuous and long-term monitoring of medicines in biological systems could offer personalized pharmacokinetic reports but is challenging due to the complexity of the biological environment in living organisms. Here, the authors present a photoelectrochemical biosensor with single atom Cu on TiO2-x as recognition unit for selective, precise, long-term tetracycline monitoring in living organisms. [ABSTRACT FROM AUTHOR]

Regeneration varies largely among metazoans. Aside molecular processes, this epiphenomenon depends on the biological complexity and evolutive history of each species during the adaptation to their specific environment. While most species adapted to marine or freshwater conditions can extensively regenerate, those adapted to terrestrial conditions and parasitism lost the ability to regenerate. They are mainly represented from ascelmintes evolving eutely and numerous arthropods and amniotes. High regeneration can only occur in water‐adapted species and requires high tissue hydration, indirect development through metamorphosis and often also presence of asexual propagation. Metamorphosis allows the anatomical‐physiological transformation of a larva in an adult through an initial destructive phase followed by a constructive (regenerative) phase. Invertebrates and vertebrates that possess genomes including metamorphic genes can re‐utilize in part or largely similar genes for the regeneration of lost organs. I submit that during land adaptation in both invertebrates and vertebrates the initial larval stages and metamorphosis were lost or altered as some key genes, including those for telomerases, could no longer be expressed in the dry environment. Consequently, also the initial regenerative ability was lost while other epiphenomena were gained, including complex immunity and behaviour but also an evident process of ageing. [ABSTRACT FROM AUTHOR]

Tissues are formed and shaped by cells of many different types and are orchestrated through countless interactions. Deciphering a tissue's biological complexity thus requires studying it at cell-level resolution, where molecular and biochemical features of different cell types can be explored and thoroughly dissected. Unfortunately, the lack of comprehensive methods to identify, isolate, and culture each cell type from many tissues has impeded progress. Here, we present a method for the breadth of cell types composing the human breast. Our goal has long been to understand the essence of each of these different breast cell types, to reveal the underlying biology explaining their intrinsic features, the consequences of interactions, and their contributions to the tissue. This biological exploration has required cell purification, deep-RNA sequencing, and a thorough dissection of the genes and pathways defining each cell type. While the molecular analysis is presented in an adjoining article, we present here an exhaustive cellular dissection of the human breast and explore its cellular composition and histological organization. Moreover, we introduce a novel FACS antibody panel and rigorous gating strategy capable of isolating each of the 12 major breast cell types to purity. Finally, we describe the creation of primary cell models from nearly every breast cell type--some the first of their kind--and submit these as critical tools for studying the dynamic cellular interactions within breast tissues and tumors. Together, this body of work delivers a unique perspective of the breast, revealing insights into its cellular, molecular, and biochemical composition. [ABSTRACT FROM AUTHOR]

In pursuit of early therapeutic interventions for Parkinson's disease, the proposed SynNeurGe classification system integrates α-synuclein pathology (S), neurodegeneration evidence (N), and pathogenic gene variants (G). This approach aims to address the disease's complexity and biological diversity. It suggests categorizing patients based on the presence or absence of α-synuclein pathology in tissues or cerebrospinal fluid, neurodegeneration indicators from specific imaging techniques, and identification of pathogenic gene variants associated with Parkinson's disease. The proposed system emphasizes the future need for precision medicine and aims to facilitate both basic and clinical research toward disease-modifying therapies. However, the authors stress that initial implementation should be confined to research settings, considering ethical implications and current limitations. Prospective validation of these criteria is deemed necessary to ensure their efficacy and ethical application in clinical practice. [ABSTRACT FROM AUTHOR]

Simple Summary: Developmental plasticity allows organisms to adapt quickly by altering their behaviour or physiology, often at a high energy or time cost. This flexibility can lead to more permanent genetic changes, simplifying the organism's response to similar future challenges. Our research shows that plasticity not only speeds up the evolution of complex behaviours in organisms but also plays a crucial role in the development of increasingly complex biological systems. As organisms face more difficult environmental tasks, plasticity becomes a more powerful tool in facilitating rapid evolutionary advancements and the diversification of species. Developmental plasticity enables organisms to cope with new environmental challenges. If deploying such plasticity is costly in terms of time or energy, the same adaptive behaviour could subsequently evolve through piecemeal genomic reorganisation that replaces the requirement to acquire that adaptation by individual plasticity. Here, we report a new dimension to the way in which plasticity can drive evolutionary change, leading to an ever-greater complexity in biological organisation. Plasticity dramatically accelerates the evolutionary accumulation of adaptive systems in model organisms with relatively low rates of mutation. The effect of plasticity on the evolutionary growth of complexity is even greater when the number of elements needed to construct a functional system is increased. These results suggest that, as the difficulty of challenges from the environment becomes greater, plasticity exerts an ever more powerful role in meeting those challenges and in opening up new avenues for the subsequent evolution of complex adaptations. [ABSTRACT FROM AUTHOR]

Widespread interest in nondestructive biomarkers of aging has led to a multitude of biological ages that each proffers a "true" health-adjusted individual age. Although each measure provides salient information on the aging process, they are each univariate, in contrast to the "hallmark" and "pillar" theories of aging, which are explicitly multidimensional, multicausal, and multiscale. Fortunately, multiple biological ages can be systematically combined into a multidimensional network representation. The interaction network between these biological ages permits analysis of the multidimensional effects of aging, as well as quantification of causal influences during both natural aging and, potentially, after anti-aging intervention. The behavior of the system as a whole can then be explored using dynamical network stability analysis, which identifies new, efficient biomarkers that quantify long-term resilience scores on the timescale between measurements (years). We demonstrate this approach using a set of 8 biological ages from the longitudinal Swedish Adoption/Twin Study of Aging (SATSA). After extracting an interaction network between these biological ages, we observed that physiological age, a proxy for cardiometabolic health, serves as a central node in the network, implicating it as a key vulnerability for slow, age-related decline. We furthermore show that while the system as a whole is stable, there is a weakly stable direction along which recovery is slow—on the timescale of a human lifespan. This slow direction provides an aging biomarker, which correlates strongly with chronological age and predicts longitudinal decline in health—suggesting that it estimates an important driver of age-related changes. [ABSTRACT FROM AUTHOR]

Both pathology and cytopathology still rely on recognizing microscopical morphologic features, and image analysis plays a crucial role, enabling the identification, categorization, and characterization of different tissue types, cell populations, and disease states within microscopic images. Historically, manual methods have been the primary approach, relying on expert knowledge and experience of pathologists to interpret microscopic tissue samples. Early image analysis methods were often constrained by computational power and the complexity of biological samples. The advent of computers and digital imaging technologies challenged the exclusivity of human eye vision and brain computational skills, transforming the diagnostic process in these fields. The increasing digitization of pathological images has led to the application of more objective and efficient computer-aided analysis techniques. Significant advancements were brought about by the integration of digital pathology, machine learning, and advanced imaging technologies. The continuous progress in machine learning and the increasing availability of digital pathology data offer exciting opportunities for the future. Furthermore, artificial intelligence has revolutionized this field, enabling predictive models that assist in diagnostic decision making. The future of pathology and cytopathology is predicted to be marked by advancements in computer-aided image analysis. The future of image analysis is promising, and the increasing availability of digital pathology data will invariably lead to enhanced diagnostic accuracy and improved prognostic predictions that shape personalized treatment strategies, ultimately leading to better patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Biological invasions are a major threat to global biodiversity, with freshwater ecosystems being among the most susceptible to the successful establishment of non-native species and their respective potential impacts. In Poland, the introduction and spreading of non-native fish has led to biodiversity loss and ecosystem homogenisation. Methods: Our study applies the Dispersal-Origin-Status-Impact (DOSI) assessment scheme, which is a population-level specific assessment that integrates multiple factors, including dispersal mechanisms, origin, status, and impacts, providing a nuanced framework for assessing invasion risks at local and regional levels. We used this tool to evaluate the risks associated with non-native fish species across three major Polish rivers (Pilica, Bzura, and Skrwa Prawa) and to prioritise them for management actions. Results: Using DOSI, we assessed eight non-native species identified in the three studied rivers: seven in both Pilica and Bzura and four in Skrwa Prawa. The DOSI assessment scheme identified high variability in the ecological impacts and management priorities among the identified non-native species. Notably, species such as the Ponto-Caspian gobies exhibited higher risk levels due to their rapid spread and considerable ecological effects, contrasting with other species that demonstrated lower impact levels and, hence, received a lower priority for intervention. Conclusion: The adoption of the DOSI scheme in three major rivers in Poland has provided valuable insights into the complexities of managing biological invasions, suggesting that localised, detailed assessments are crucial for effective conservation strategies and highlighting the importance of managing non-native populations locally. [ABSTRACT FROM AUTHOR]

Complexity is inherent in most biological phenomena, yet there is little effort to teach biological complexity per se in the classroom. Levels of organization and hierarchical complexity are familiar features of living systems and taken for granted in most instructional materials including the Next Generation Science Standards (NGSS) of the United States. However, the evolution of the hierarchical organization of life is not being taught because there has been no instructional framework for doing so until now. We seek to address this gap in instruction by translating recent research on evolutionary transitions in individuality (ETI theory) into an integrative, instructional framework for teaching the hierarchy of life that aligns with the three dimensions of the NGSS. ETI theory presents an evolutionary framework for teaching hierarchical complexity using the social principles of cooperation and conflict. These principles are intuitive for students because they are analogous to many of the social situations in their lives. By making use of the ETI framework, instructors can explicitly teach the evolution of the hierarchical organization of life, the organizing framework for all of biology. [ABSTRACT FROM AUTHOR]

Background Objective Materials and Methods Results Discussion and Conclusions Primary spermatogenic disorders represent a severe form of male infertility whereby sperm production is impaired due to testicular dysfunction, leading to reduced quality or quantity of spermatozoa. Gene‐centered research has certainly demonstrated the importance of the genetic factor in the etiology of both poor sperm morphology or motility and reduced sperm count. In the last decade, next‐generation sequencing has expanded the research to whole exome which has transformed our understanding of male infertility genetics, but uncertainty persists in its diagnostic yield, especially in large unrelated populations.To evaluate the utility of exome sequencing in detecting genetic factors contributing to various traits of primary spermatogenic disorders, which is a crucial step before interpreting the diagnostic yield of the platform.We manually curated 415 manuscripts and included 19 research studies that predominantly performed whole exome sequencing in cohorts of unrelated cases with primary spermatogenic defects.The detection rate, defined as the fraction of cases with an identifiable genetic cause, typically remained below 25% for quantitative defects of spermatozoa, whereas improved rates were observed for traits of abnormal sperm morphology/motility and in populations enriched with consanguineous families. Unlike the quantitative defects, the genetic architecture of the qualitative issues of spermatozoa featured a small number of recurrent genes describing a large fraction of studied cases. These observations were also in line with the lower biological complexity of the pathways affected by the reported genes.This review demonstrates the variability in detection rates of exome sequencing across semen phenotypes, which may have an impact on the expectations of the diagnostic yield in the clinical setting. [ABSTRACT FROM AUTHOR]

A major next step in hematopoietic stem cell (HSC) biology is to enhance our quantitative understanding of cellular and evolutionary dynamics involved in undisturbed hematopoiesis. Mathematical models have been and continue to be key in this respect, and are most powerful when parameterized experimentally and containing sufficient biological complexity. In this paper, we use data from label propagation experiments in mice to parameterize a mathematical model of hematopoiesis that includes homeostatic control mechanisms as well as clonal evolution. We find that nonlinear feedback control can drastically change the interpretation of kinetic estimates at homeostasis. This suggests that short-term HSC and multipotent progenitors can dynamically adjust to sustain themselves temporarily in the absence of long-term HSCs, even if they differentiate more often than they self-renew in undisturbed homeostasis. Additionally, the presence of feedback control in the model renders the system resilient against mutant invasion. Invasion barriers, however, can be overcome by a combination of age-related changes in stem cell differentiation and evolutionary niche construction dynamics based on a mutant-associated inflammatory environment. This helps us understand the evolution of e.g., TET2 or DNMT3A mutants, and how to potentially reduce mutant burden. [ABSTRACT FROM AUTHOR]

Assessing changes in protein–protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the complexity of the biological mechanisms involved. In the present work, a geometric deep learning framework called MuToN is introduced for quantifying protein binding affinity change upon residue mutations. The method, designed with geometric attention networks, is mechanism‐aware. It captures changes in the protein binding interfaces of mutated complexes and assesses the allosteric effects of amino acids. Experimental results highlight MuToN's superiority compared to existing methods. Additionally, MuToN's flexibility and effectiveness are illustrated by its precise predictions of binding affinity changes between SARS‐CoV‐2 variants and the ACE2 complex. [ABSTRACT FROM AUTHOR]

Recent advancements in omics and single-cell analysis highlight the necessity of numerical methods for managing the complexity of biological data. This paper introduces a simulation program for biochemical reaction systems based on the natural number simulation (NNS) method. This novel approach ensures the equitable treatment of all molecular entities, such as DNA, proteins, H2O, and hydrogen ions (H+), in biological systems. Central to NNS is its use of stoichiometric formulas, simplifying the modeling process and facilitating efficient and accurate simulations of diverse biochemical reactions. The advantage of this method is its ability to manage all molecules uniformly, ensuring a balanced representation in simulations. Detailed in Python, NNS is adept at simulating various reactions, ranging from water ionization to Michaelis-Menten kinetics and complex gene-based systems, making it an effective tool for scientific and engineering research. [ABSTRACT FROM AUTHOR]

This brief commentary discusses the problem of unpredictable ("emergent") properties of biological complex systems that result from the interactions between their constituents. The emphasis is put on principal impossibility of determining the status of a cell within an organism on the basis of data obtained after its isolation from the multicellular system. The current status of research focused on the origin of multicellularity and the challenges that its emergence introduced into the discussion of the evolution of life on Earth, is briefly reviewed. Integration of the available data in the form of an axiom stating that in a multicellular organisms, at each level of organization, properties arise that are unpredictable based on the properties of individual modules and cells, is suggested. The properties of cells in an organism cannot be deduced from the properties of its constituent cells. [ABSTRACT FROM AUTHOR]

The effects of climate change on coastal biodiversity are a major concern because altered community compositions may change associated rates of ecosystem functioning and services. Whilst responses of single species or taxa have been studied extensively, it remains challenging to estimate responses to climate change across different levels of biological organisation. Studies that consider the effects of moderate realistic near‐future levels of ocean warming and acidification are needed to identify and quantify the gradual responses of species to change. Also, studies including different levels of biological complexity may reveal opportunities for amelioration or facilitation under changing environmental conditions. To test experimentally for independent and combined effects of predicted near‐future warming and acidification on key benthic species, we manipulated three levels of temperature (winter ambient, +0.8 and +2°C) and two levels of pCO2 (ambient at 450 ppm and elevated at 645 ppm) and quantified their effects on mussels and algae growing separately and together (to also test for inter‐specific interactions). Warming increased mussel clearance and mortality rates simultaneously, which meant that total biomass peaked at +0.8°C. Surprisingly, however, no effects of elevated pCO2 were identified on mussels or algae. Moreover, when kept together, mussels and algae had mutually positive effects on each other's performance (i.e. mussel survival and condition index, mussel and algal biomass and proxies for algal productivity including relative maximum electron transport rate [rETRmax], saturating light intensity [Ik] and maximum quantum yield [Fv/Fm]), independent of warming and acidification. Our results show that even moderate warming affected the functioning of key benthic species, and we identified a level of resistance to predicted ocean acidification. Importantly, we show that the presence of a second functional group enhanced the functioning of both groups (mussels and algae), independent of changing environmental conditions, which highlights the ecological and potential economic benefits of conserving biodiversity in marine ecosystems. [ABSTRACT FROM AUTHOR]

Land plants are astounding processors of information; due to their sessile nature, they adjust the molecular programs that define their development and physiology in accordance with the environment in which they dwell. Transduction of the external input to the respective internal programs hinges to a large degree on molecular signaling cascades, many of which have deep evolutionary origins in the ancestors of land plants and its closest relatives, streptophyte algae. In this Review, we discuss the evolutionary history of the defining factors of streptophyte signaling cascades, circuitries that not only operate in extant land plants and streptophyte algae, but that also likely operated in their extinct algal ancestors hundreds of millions of years ago. We hope this Review offers a starting point for future studies on the evolutionary mechanisms contributing to the current diversity and complexity of plant signaling pathways, with an emphasis on recognizing potential biases. [ABSTRACT FROM AUTHOR]

The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features' connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering's user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose the Multi-layer Bundling (MLB) method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters "bundles". This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating the bundle co-cluster matrix with the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed. [ABSTRACT FROM AUTHOR]

In vitro models are invaluable tools for deconstructing the biological complexity of the periodontal ligament (PDL). Model systems that closely reproduce the 3-dimensional (3D) configuration of cell–cell and cell–matrix interactions in native tissue can deliver physiologically relevant insights. However, 3D models of the PDL that incorporate mechanical loading are currently lacking. Hence, we developed a model where periodontal tissue constructs (PTCs) are made by casting PDL cells in a collagen gel suspended between a pair of slender, silicone posts for magnetic tensile loading. Specifically, one of the posts was rigid and the other was flexible with a magnet embedded in its tip so that PTCs could be subjected to tensile loading with an external magnet. Additionally, the deflection of the flexible post could be used to measure the contractile force of PDL cells in the PTCs. Prior to tensile loading, second harmonics generation analysis of collagen fibers in PTCs revealed that incorporation of PDL cells resulted in collagen remodeling. Biomechanical testing of PTCs by tensile loading revealed an elastic response at 4 h, permanent deformation by 1 d, and creep elongation by 1 wk. Subsequently, contractile forces of PDL cells were substantially lower for PTCs under tensile loading. Immunofluorescence analysis revealed that tensile loading caused PDL cells to increase in number, express higher levels of F-actin and α–smooth muscle actin, and become aligned to the tensile axis. Second harmonics generation analysis indicated that collagen fibers in PTCs progressively remodeled over time with tensile loading. Gene expression analysis also confirmed tension-mediated upregulation of the F-actin/Rho pathway and osteogenic genes. Our model is novel in demonstrating the mechanobiological behavior that results in cell-mediated remodeling of the PDL tissue in a 3D context. Hence, it can be a valuable tool to develop therapeutics for periodontitis, periodontal regeneration, and orthodontics. [ABSTRACT FROM AUTHOR]

Studying the behavior of electroactive cells, such as firing dynamics and chemical secretion, is crucial for developing human disease models and therapeutics. Following the recent advances in cell culture technology, traditional monolayers are optimized to resemble more 3D, organ‐like structures. The biological and electrochemical complexity of these structures requires devices with adaptive shapes and novel features, such as precise electrophysiological mapping and stimulation in the case of brain‐ and heart‐derived tissues. However, conventional organ‐on‐chip platforms often fall short, as they do not recreate the native environment of the cells and lack the functional interfaces necessary for long‐term monitoring. Origami‐on‐a‐chip platforms offer a solution for this problem, as they can flexibly adapt to the structure of the desired biological sample and can be integrated with functional components enabled by chosen materials. In this review, the evolution of origami‐on‐a‐chip biointerfaces is discussed, emphasizing folding stimuli, materials, and critical findings. In the prospects, microfluidic integration, functional tissue engineering scaffolds, and multi‐organoid networks are included, allowing patient‐specific diagnoses and therapies through computational and in vitro disease modeling. [ABSTRACT FROM AUTHOR]

Neuromorphic electronics, inspired by the complexity of the biological brain, hold promise for revolutionizing information technology by enabling more efficient computing paradigms. Here, we report the potential of oxide heterostructure materials as building blocks for synapses in neuromorphic computer networks. Our approach involves utilizing two-dimensional electron gas (2DEG) within the oxide heterostructure EuO–KTaO3 (KTO), which exhibits optoelectronic properties essential for emulating key cognitive functions such as sensory perception, learning, and memory. The 2DEG at the EuO–KTO interface demonstrates the ability to switch from volatile to nonvolatile resistive states as the gate voltage sweeps using an optical signal as stimuli, unlike memristors using compliance current. Our single device not only accurately replicates the short- and long-term plasticity seen in biological synapses but also performs logic gate operations, enhancing its versatility and functionality. [ABSTRACT FROM AUTHOR]

Several medical techniques are based on the application of electromagnetic fields (EMFs) on the human body with therapeutic and/or diagnostic aims. The response of human tissues to the applied EMF is mediated by the tissues' dielectric properties, which must therefore be characterized at the frequencies of the considered technique. Due to the heterogeneity and complexity of biological tissues, it is necessary to know their properties in vivo for the specific condition of interest. Traditional techniques for the dielectric characterization of biological tissues are invasive and, as such, not adoptable for this aim. Accordingly, alternative sensors and/or sensing methods are needed. Recently, a new wideband spectroscopy technique was proposed, based on quantities derived from the Magnetic Resonance (MRI) signal. Among these quantities, the water content was proposed to evaluate the dielectric properties at frequencies around a few GHz. This work verifies the possibility of deriving tissues' dielectric properties in the frequency range of 1–20 GHz based on knowledge of the water content. The water content was retrieved through a dehydration procedure for five different ex vivo tissues. The achieved results were compared with references from the literature. [ABSTRACT FROM AUTHOR]

Epidemic models study the spread of undesired agents through populations, be it infectious diseases through a country, misinformation in social media or pests infesting a region. In combating these epidemics, we rely neither on global top-down interventions, nor solely on individual adaptations. Instead, interventions commonly come from local institutions such as public health departments, moderation teams on social media platforms or other forms of group governance. Classic models, which are often individual or agent-based, are ill-suited to capture local adaptations. We leverage developments of institutional dynamics based on cultural group selection to study how groups attempt local control of an epidemic by taking inspiration from the successes and failures of other groups. Incorporating institutional changes into epidemic dynamics reveals paradoxes: a higher transmission rate can result in smaller outbreaks as does decreasing the speed of institutional adaptation. When groups perceive a contagion as more worrisome, they can invest in improved policies and, if they maintain these policies long enough to have impact, lead to a reduction in endemicity. By looking at the interplay between the speed of institutions and the transmission rate of the contagions, we find rich coevolutionary dynamics that reflect the complexity of known biological and social contagions. [ABSTRACT FROM AUTHOR]

Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC = 0.62, AUC = 0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell 'avatars' capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability. [ABSTRACT FROM AUTHOR]