Neural models typically emphasize dominant trends in data while treating the remaining variability as random noise. In biological neural systems, however, variability plays more nuanced and multifaceted roles. Although variability arising from noise sources can hinder functionality, biological systems have evolved to incorporate it in ways that enhance robustness, while flexibly enabling diversity in structure and function, both within individuals and across populations. This reduces the genetic instructions needed to construct and operate neural systems, compared to a uniquely prescribed building plan. These benefits are often overlooked in computational modeling. Here, we advocate for modeling approaches that replace unmodeled variability with mechanisms supporting efficiency, robustness, and flexibility. Such strategies have successfully replaced the notion of noisy and unreliable sensory neurons with a principled account of their activity. Similar advances are ongoing in the study of brain development. We discuss studies on neural growth, plasticity, and self-organization indicating that variability should not be treated as a nuisance, but as a means of discovering key operating principles of neural systems. Generative models, which are focused on the principles underlying variability, could play a key role in advancing theoretical neuroscience.

Nanoparticles have been a focus of drug delivery research for nearly three decades, yet their extremely low clinical translation rates and mediocre clinical performance fail to justify the substantial investment. The unclear in vivo fate of nanoparticles remains a significant obstacle to further enhancing their application potential. Therefore, it is crucial to evaluate the in vivo fate of nanoparticles with high spatial resolution, high temporal resolution, and high sensitivity through advanced tracking technologies. Meanwhile, as various omics technologies have been employed to characterize molecular features in both normal and pathological states of organisms, they are also increasingly applied to analyze interactions between nanoparticles and biological systems. However, technical limitations and interdisciplinary challenges have confined much research to examining the distribution characteristics of nanoparticles and the secondary responses they mediate in organisms, while leaving the bio-disposition of nanoparticles within organisms poorly understood-a scenario regarded as an incomplete picture of nanoparticle-organism interactions. This review will summarize recent advances in nanoparticle imaging technologies and omics approaches. It then synthesizes and discusses research paradigms investigating interactions between nanocarriers and biological systems through multi-omics studies. In particular, the application of multi-omics in nanoparticle optimization, target discovery, the characterization of the nanoparticle bio-disposition will also be examined. STATEMENT OF SIGNIFICANCE: (1) The novelty and significance of the work with respect to the existing literature. In this review, we conclude the applicability of omics technologies in three aspects: the mechanism of action, pharmacodynamic performance, and toxicological performance of nanoparticles. Meanwhile, this review outlines how omics technologies can be leveraged to investigate the in vivo processing of nanoparticles. In short, this review describes how to study the in vivo fate of nanoparticles through the fresh perspective of omics. (2) The scientific impact. Omics technologies have been widely applied in pathology and physiology. We believe omics approaches can also complement traditional imaging techniques, thereby advancing the development of more controllable and effective nanoparticles and biomaterials.

Shift of nitrogen metabolism drives the evolution of multidrug resistance in wastewater microbiomes under long-term fluoride stress.

Physiologically based toxicokinetic-toxicodynamic (PBTK-TD) modeling of oral exposure to bongkrekic acid in rats.

Teleonomical intelligence across scales: A categorical and partial differential equation framework for biology and artificial systems.

Time-resolved neutron computed tomography of hydration kinetics in nanoprimed seeds using chitosan-stabilized iron oxide nanoparticles.

AIE-based dual-mode chemosensor for hypochlorite detection in water: Environmental and biological applications in diverse water samples, zebrafish, artemia, and plants.

The Alvra experimental station at the Swiss X-ray free-electron laser, SwissFEL, investigates ultrafast dynamics in chemical and biological systems using X-ray scattering and spectroscopy techniques. A key feature of Alvra is its unique capability to perform time-resolved X-ray emission and resonant inelastic X-ray scattering in the tender X-ray regime, currently not available at other XFEL endstations, combined with simultaneous access to X-ray absorption spectroscopy and X-ray solution scattering. Together with sub-35 fs time resolution enabled by advanced timing diagnostics, this positions Alvra as a versatile instrument for ultrafast chemical dynamics in the liquid phase. Here we cover the various technical aspects of the beamline and experimental station, and present some examples of experimental results measured during the first years of SwissFEL commissioning and user operation.

Quantifying nanoplastic cellular interactions and uptake pathways by label-free plasmonic imaging.

Access denied: Plato's cave and the epistemic limits of cellular life.

In light of the centennial of the Schrödinger equation, this article addresses the popular idea that quantum mechanical effects play an important role in biological systems. We start by defining what is qualified as a quantum effect. We then clarify the idea that quantum mechanical tunneling is a crucial factor in enzyme catalysis. Here we show that quantum tunneling is in fact anticatalytic and that there is no consistent theoretical and experimental evidence that the tunneling effects are enhanced significantly by enzymes relative to the corresponding reference solution reactions. We next turn to electron transfer reactions, arguing that in most cases the efficiency of the reaction is controlled by classical effects. We also consider the low barrier hydrogen bond idea and clarify its anticatalytic nature. In addition, we present a specific case study of electron transfer in a protein system potentially responsible for bird navigation, providing a concrete example for evaluating the role of quantum and classical effects under biologically relevant conditions. We argue that for the electron transfer step in this system, the most important control is associated with the classical control of the relevant potential surfaces and the fluctuations of the key energy gaps.

Nanoplastic-driven white pollution: Biochemical insights into ecosystem and human health impacts - a critical-interpretative narrative review.

A rapid and selective phenothiazine-derived fluorescent probe for hypochlorous acid monitoring in living systems: from cellular imaging to in vivo systemic inflammatory response syndrome visualization.

Lgr6 has attracted significant attention in biomedical research in recent years. As a member of the G protein-coupled receptor family, Lgr6 plays a crucial role in the occurrence and development of various diseases, including diabetic cardiomyopathy, bone regeneration defects, and skin injury repair, where it is vitally involved in cellular signal transduction. This study endeavors to investigate the distribution and functions of Lgr6+ cells across organisms, particularly during homeostasis and damage scenarios. Lgr6+ expression occurs across skin, mammary glands, kidneys, and intestines, crucial for development and tissue repair. Abnormal expression of Lgr6 is also observed in the onset and progression of major systemic diseases, especially in tumors. Thus, Lgr6 has been identified as a promising therapeutic target for cancer and other diseases, influencing their onset, progression, and treatment.

A water-stable La-MOF as a dual-target sensor for selective and rapid detection of guanosine and arginine in biological and food system.

Multilayer microbial framework of aerobic granule microecosystems: Integrating community ecology, genetic networks, and enhancement strategies.

Human cognitive mechanism depends on a sophisticated information processing framework, including perception, attention, memory, language, reasoning, problem solving and decision-making. However, current research only focuses on isolated process rather than systematically simulating human cognitive mechanism. Meanwhile, with the rapid development of large language models, related works have predominantly centered on language-level exploration, while in-depth mining of visual information remains insufficient. Here, to deeply activate the multi-modal understanding ability, a Systematic Human-like Cognitive (SHC) method is proposed for visual question answering, where the above mentioned sophisticated seven processes are systematically modeled as three core modules: hierarchical perception, semantic refinement and dynamic reasoning. The Hierarchical Perception Module (HPM) extracts hierarchical features from different levels to simulate the incremental integration mode of biological neural system. Based on the selective attention theory, one Semantic Refinement Module (SRM) is designed as a key-value accumulation optimization mechanism that enhances high-level semantics from low-level features via a multi-level cascaded attention structure. Finally, the Dynamic Reasoning Module (DRM), following the utility maximization decision theory, employs a dual weighting mechanism to dynamically fuse high-level semantic features and low-level fine-grained features, forming a unified high-quality visual representation that is then fed into the large language model for reasoning together with the text input. Experimental results demonstrate that SHC achieves competitive performance on multiple visual question answering benchmarks, including VQA-v2, Text-VQA, GQA, and ScienceQA, as well as multimodal evaluation benchmarks such as POPE, MMB, MME, and MM-Vet. Comparative experiments with multiple models of the same-scale validate the latent capacity of SHC to prompt the performance of multi-modal understanding tasks and its superiority in fine-grained visual information perception, and even surpasses multimodal models with larger-scale on certain tasks.

Although various measures are being implemented to address antimicrobial resistance (AMR) at its sources, wastewater treatment plants (WWTPs), major point sources, continue to contribute to the spread of AMR in ambient environments. Research on the efficacy of existing treatment systems in removing antibiotic resistance genes (ARGs) remains limited. The current study investigates the removal of six ARGs, namely, sul1, czcA, tetA, acrA, qnrS, and blaTEM, along with one integron integrase gene (intI1), the yccT gene (a bacterial marker for Escherichia coli), and the 16S rRNA gene (a general bacterial DNA marker) using a 10-L lab-scale modified sequencing batch reactor (SBR). The reactor treated a mixture of hospital and domestic wastewater, with equal cycle durations for aerobic and anoxic phases. The influence of ciprofloxacin, triclosan, chromium, zinc, and arsenic on ARG proliferation was also assessed. The SBR demonstrated efficient removal of most ARGs, with sul1 reduced by three orders of magnitude and tetA by over five orders of magnitude between the inlet and outlet. Notably, blaTEM levels increased in the effluent, likely due to hospital wastewater inputs and selection for β-lactam resistance during settling phases. The yccT and intI1 gene markers showed strong positive correlations with mixed liquor suspended solids, indicating that biomass influences ARG persistence. Ciprofloxacin and triclosan were removed with efficiencies exceeding 86%, while chromium and zinc were removed at more than 83% (arsenic removal was limited to 26%). The significant Spearman correlation analysis revealed a negative correlation between the targeted genes and the coselectors, contradicting classical coselection theory paradigms and highlighting the complex interactions between the ARGs, coselectors, and environmental behavior in actual WWTPs. These findings suggest that variations in design parameters significantly improve ARG removal without structural overhauls or chemical disinfectants. The study offers valuable insights into optimizing existing SBR-based WWTPs, contributing to the reduction of emerging contaminants and minimizing public health risks associated with AMR dissemination into the ambient environment. This research also emphasizes the potential of modifying biological treatment systems to mitigate the environmental impact of AMR, especially in low- and middle-income countries like India, which face infrastructural and economic challenges.

Hydrogen peroxide (H2O2) plays a significant role in biological and environmental systems. Therefore, it is crucial to develop an efficient biosensor for H2O2 determination, which is important in environmental monitoring and biomedical diagnostics. In this work, we have synthesized an amine functionalized, single-layered Ti3C2Tx MXene (NH2-S-Ti3C2Tx), which was modified over a glassy carbon electrode (GCE) to construct NH2-S-Ti3C2Tx/GCE. Thus obtained NH2-S-Ti3C2Tx/GCE was utilized as a platform for the covalent cross-linking of water-soluble horseradish peroxidase (HRP) enzyme through a glutaraldehyde coupling reaction to form HRP/NH2-S-Ti3C2Tx/GCE. The fabricated HRP/NH2-S-Ti3C2Tx/GCE biosensor demonstrated a well-defined redox peak with a formal potential of -0.45 V, corresponding to the FeIII/FeII redox center of HRP. Additionally, the designed biosensor has been employed towards the electrocatalytic detection of H2O2 under both static and dynamic conditions. The HRP/NH2-S-Ti3C2Tx/GCE sensor displayed a broad linear range of 20 µM to 1160 µM with a low limit of detection of 5.3 µM and a high sensitivity of 0.078 µA mM-1 cm-2. The reported sensor demonstrated excellent analytical performance due to direct electron transfer through the covalent immobilization of HRP over NH2-S-Ti3C2Tx. Furthermore, the sensor was employed for the real-time detection of H2O2, and it portrayed good recovery results.

Stage-dependent susceptibility of the ring-legged earwig Euborellia annulipes to entomopathogenic nematodes and diatomaceous earth.