Browse Articles

Information bounds on the accuracy of cell polarization

Here we characterized an information measure for cell polarity that applies to non-motile cells responding to a chemical gradient. The central idea is that polarization represents information about the direction of the gradient. We applied a theory of optimal gradient sensing and response in the presence of external noise based on the information capacity of a Gaussian channel. First, we formulated an information framework that describes spatial gradient sensing and polarization response. As part of this section, we modeled ligand diffusion and receptor-binding dynamics as a mixed Poisson distribution, confirming the single receptor accuracy limits derived by ten Wolde and colleagues. Second, we performed numerical calculations of stochastic ligand levels at the cell surface to estimate the information provided about the directional component of the gradient vector, which was close to the Gaussian channel bound for low signal-to-noise ratios. Third, we used the information framework to evaluate the noise-robustness of three generic models of cell polarity, demonstrating that a filter-amplifier architecture and time integration can attenuate the detrimental impact of noise on polarity so that the model can approach the theoretical limits. Fourth, we compared the theory to published experimental data on yeast mating projection growth in a pheromone gradient, identifying the ligand association rate and integration time as two key parameters affecting directional accuracy. By varying these parameters, we showed that for certain ranges the theory is roughly in agreement with the data, and that the slow binding rate constant is a key limiting factor. We concluded that temporal averaging can help overcome the slow binding rate to achieve greater accuracy, but with the drawback of a slow mating response.

<i>Inseparable/IER3IP1</i> are essential for cytokinesis in <i>Drosophila</i> neuroblasts and human cells

To unveil the molecular players that maintain neural stem cell (NSC) homeostasis, we conducted a genetic screen in Drosophila and isolated an uncharacterized gene that we named Inseparable ( Insep ). Insep is the Drosophila homologue of human IER3IP1 , a gene associated with Microcephaly, Epilepsy, and Neonatal Diabetes Syndrome-1 (MEDS-1). We show that Insep loss leads to early larval lethality with small brains and these phenotypes can be rescued by expressing IER3IP1 indicating that their biological function is conserved through evolution. The Insep deficient neuroblasts fail to complete cytokinesis and show excessive accumulation of Rab11 vesicles in the cytoplasm. Similarly, IER3IP1 depletion in human cells leads to cytokinesis failure and accumulation of Rab11 vesicles. Insep and IER3IP1 localize to Rab11 vesicles and interact with Rab11. The pathogenic mutations in IER3IP1 perturb its localization to Rab11 vesicles. These results suggest that Insep and IER3IP1 work along with Rab11 and may regulate fusion of Rab11 vesicles to the advancing furrow during cytokinesis.

Metastability and teleconnection of atmospheric circulation via hidden Markov models and network modularity

Abstract The low-frequency variability of the mid-latitude atmosphere involves complex nonlinear and chaotic dynamical processes posing predictability challenges. It is characterized by sporadically recurring, often long-lived patterns of atmospheric circulation of hemispheric scale known as weather regimes. The evolution of these circulation regimes in addition to their link to large-scale teleconnections can help to extend the limits of atmospheric predictability. They also play a key role in sub- and inter-seasonal weather forecasting. Their identification and modeling remains an issue, however, due to their intricacy, including a clear conceptual picture. In recent years, the concept of metastability has been developed to explain regimes formation. This suggests an interpretation of circulation regimes as communities of states in the neighborhood of which the atmospheric system remains abnormally longer than typical baroclinic timescales. Here we develop a new and effective method to identify such communities by constructing and analyzing an operator of the system’s evolution via hidden Markov model (HMM). The method makes use of graph theory and is based on probabilistic approach to partition the HMM transition matrix into weakly interacting blocks – communities of hidden states – associated with regimes. The approach involves nonlinear kernel principal component mapping to consistently embed the system state space for HMM building. Application to northern winter hemisphere using geopotential heights from reanalysis yields four persistent and recurrent circulation regimes. Statistical and dynamical characteristics of these circulation regimes and surface impacts are discussed. In particular, unexpected high correlations are obtained with EL-Niño Southern Oscillation and Pacific decadal oscillation with lead times of up to one year.

Predictive modeling of tax compliance risks: A comparative study of machine learning approaches

Modern enterprises grapple with complex financial data and multidimensional risk interdependencies in their operations. Machine learning offers transformative potential for tax risk assessment and smart auditing solutions. This research analyzes 3,232 tax records from regional manufacturing and service sectors (2021–2023) to evaluate three predictive models: SVM, XGBoost, and Random Forest. Results demonstrate Random Forest’s superior performance, achieving 92.00% (manufacturing) and 93.39% (service) accuracy – substantially outperforming XGBoost and SVM (85–90%). Key manufacturing risk indicators follow a “high tax-high volatility-high scrutiny” pattern, with tax burden rate (0.129 weight), profit fluctuation (0.100), and audit frequency (0.091) being most predictive. Service sector risks manifest as “volatility-declaration-tax burden” dynamics, where profit volatility (0.142) emerges as the strongest predictor. These findings both validate machine learning’s efficacy in tax analysis and equip regulators with intelligent risk management tools.

Correction for Pederson, Étienne-Émile Baulieu (1929–2025): Scientist of steroids and champion of women’s reproductive rights

Automating prostate volume acquisition using abdominal ultrasound scans for prostate-specific antigen density calculations

Abstract Proposed methods for prostate cancer screening are currently prohibitively expensive (due to the high costs of imaging equipment such as magnetic resonance imaging and traditional ultrasound systems), inadequate in their detection rates, require highly trained specialists, and/or are invasive, resulting in patient discomfort. These limitations make population-wide screening for prostate cancer challenging. Machine learning techniques applied to abdominal ultrasound scanning may help alleviate some of these disadvantages. Abdominal ultrasound scans are comparatively low cost and exhibit minimal patient discomfort, and machine learning can be applied to mitigate against the high operator-dependent variability of ultrasound scanning. In this study, a state-of-the-art machine learning model was compared to an expert radiologist and trainee radiologist registrars of varying experience when estimating prostate volume from abdominal ultrasound images, a crucial step in detecting prostate cancer using prostate-specific antigen density. The machine learning model calculated prostatic volume by marking out dimensions of the prolate ellipsoid formula from two orthogonal images of the prostate acquired with abdominal ultrasound scans (which could be conducted by operators with minimal experience in a primary care setting). While both the algorithm and the registrars showed high correlation with the expert ( $$r\ge 0.8$$ ) it was found that the model outperformed the trainees in both accuracy (lowest average volume error of $$15\%$$ ) and consistency (lowest IQR of $$19\%$$ and lowest average volume standard deviation of $$13\%$$ ). The results are promising for the future development of an automated prostate cancer screening workflow using machine learning and abdominal ultrasound scans.

Correction: Determining engineering properties of ultra-high-performance fiber-reinforced geopolymer concrete modified with different waste materials

Anion–π interaction–induced phase separation as a prebiotic pathway to oxygenation

Compartmentalization and chemical reactivity serve as the key elements driving prebiotic chemistry for evolution and selection. However, the potential coupling of compartmentalization chemistry with the intrinsic chemical activity of prebiotic compartments remains largely unexplored. Here, we demonstrate that anion–π interactions, which are largely overlooked in the chemistry of phase transition, can drive the formation of micron-sized assemblies. These structures further recruit cations to form anion–π–cation triads. Such assemblies mediate spontaneous oxygenation reactions through their electrochemical environments. This process provides a plausible prebiotic pathway for bioenergetics and molecular oxygen generation on early Earth, leading to the formation of primitive pigments via the oxidation of small molecules and the nontemplated selection of protocells through oxidation-dependent lipid degradation. Our findings highlight a simple yet functionally significant noncovalent interaction that introduces chemical functions into self-assembly and phase transition chemistry, delivering generalizable principles for engineering electrochemically active supramolecular assemblies and a conceptual framework in understanding abiotic evolution and selection.

Optimizing breast cancer classification based on cat swarm-enhanced ensemble neural network approach for improved diagnosis and treatment decisions

Correction: Climate: The dominant factor influencing the spatial distribution pattern of the leaf trait network of Populus euphratica along the main stream of the Tarim River

Intranasal hemagglutinin protein boosters induce protective mucosal immunity against influenza A viruses in mice

Licensed parenteral influenza vaccines induce systemic antibody responses and alleviate disease severity but do not efficiently induce local mucosal immune responses. Here, we describe an intranasal booster strategy with unadjuvanted recombinant hemagglutinin (HA) following initial messenger RNA-lipid nanoparticle (mRNA-LNP) vaccination, Prime and HA. This regimen establishes highly protective HA-specific mucosal immune memory responses in the respiratory tract. Intranasal HA boosters resulted in significantly reduced viral replication compared to parenteral mRNA-LNP boosters in both young and old mice. Correlation analysis revealed that slightly increased levels of nasal Immunoglobulin A (IgA) are significantly associated with a reduced viral burden in the upper respiratory tract. Intranasal boosting with bivalent H1 HA induced mucosal immunity against vaccine-matched and mismatched heterologous influenza viruses. Additionally, a heterosubtypic intranasal H5 HA booster elicited H5-reactive mucosal humoral responses in H1-surviving mice. Our work illustrates the potential of a nasal HA protein booster as an adjuvant-free strategy to prevent infection and disease from influenza A viruses.

Immune system development-related signature predicts prognosis and sorafenib-treatment resistance of hepatocellular carcinoma by intergrating machine learning and single-cell analyses

Surrogate modeling for time-dependent reliability analysis of robotic manipulator trajectories

The kinematic reliability analysis of robotic manipulators is crucial due to uncertainties such as joint variations, manufacturing tolerances, and external disturbances. Traditional methods often rely on analytical techniques that struggle with nonlinear performance functions and fail to account for trajectory-based reliability. To overcome these limitations, this paper proposes a novel surrogate model-based approach using Kriging to estimate the reliability of robotic manipulator kinematics while considering end-effector trajectories. The methodology begins with building an initial Kriging surrogate model to analyze reliability, effectively capturing how input uncertainties influence trajectory accuracy. This model is then refined through statistical sampling techniques, ensuring an efficient evaluation of manipulator performance against specified tolerances. The approach reduces computational complexity while maintaining prediction accuracy. Compared to Monte Carlo Simulation (MCS), the proposed Kriging-based method reduces the number of function evaluations by over 98%, achieving comparable reliability predictions with significantly fewer function calls, and enhancing efficiency in kinematic reliability analysis. The proposed method is validated on two 6-DOF industrial robots, including the UR5, demonstrating improved computational efficiency and accuracy. This work has practical applications in manufacturing and healthcare, where enhanced kinematic reliability leads to greater operational efficiency and safety.

Glycosylated cannabinoids in <i>Cannabis sativa</i> and enzyme design to modulate their synthesis

Despite extensive study of its chemical composition and long history of medicinal use, the occurrence of glycosylated cannabinoid derivatives in Cannabis sativa has not been documented to date. Here, we identified glycosylated cannabinoids and their common intermediate olivetolic acid (OA) in various C. sativa tissues and cultivars. We moreover identified four UDP-glycosyltransferases (UGTs) from C. sativa with OA glycosylating activity. Enhancing the water solubility of cannabinoids through glycosylation holds potential for pharmaceutical development and cosmetic applications. However, glycosylation of pathway intermediates such as OA may divert metabolic flux away from cannabinoid production, complicating efforts to engineer glycosylated forms. To resolve this, we applied FuncLib design to an AlphaFold-predicted structure of one of the identified enzymes CsUGT14, generating active-site variants. Through functional screening, we identified mutants that display increased specificity toward cannabinoid end products over the OA intermediate. Moreover, we recognized a single point mutation that dictates OA positioning within the active site, thereby altering isomer formation. These findings expand the known repertoire of natural cannabinoids and provide a rare example of crystallography-free enzyme design to improve stability, substrate selectivity, and isomer specificity. Furthermore, this work lays the foundation for the tailored biosynthesis of soluble glycosylated cannabinoids in heterologous systems.

Computational repurposing of drugs against dengue virus targeting NS5 and methyltransferase proteins

Navigating the dementia caregiving journey: A scoping review protocol of interventions and their responsiveness to caregivers’ evolving needs across the illness trajectory

Introduction Family caregivers of persons with dementia (PWD) provide critical and often sustained support across the dementia trajectory. Despite growing recognition of their evolving needs, many interventions remain episodic and not tailored to different caregiving phases. Frameworks like the Timing It Right (TIR) and the Caregiver-Identified Phases of Alzheimer’s Disease (CIP-AD) offer structured approaches to understanding how caregiver needs change over time. However, little is known about whether current interventions align with these phase-specific needs. Objectives This scoping review aims to (1) explore the extent to which existing dementia caregiving interventions address different stages of the caregiving journey or dementia progression; (2) summarize and characterize these interventions using the Template for Intervention Description and Replication (TIDieR) checklist; and (3) identify characteristics of the populations targeted by these interventions. Methods Following JBI methodology and the PRISMA-ScR checklist, we will conduct a scoping review of randomized controlled trials, non-randomized controlled trials, and quasi-experimental studies focused on interventions for unpaid family caregivers of community-dwelling PWD. Studies will be included if they address at least one phase of the dementia trajectory and are delivered in community-based settings. A comprehensive search of six databases from 1995 to 2025 will be developed and peer-reviewed using the PRESS framework. Data will be extracted using a standardized form and analyzed thematically. Significance This review will map how dementia caregiving interventions address the evolving needs of caregivers over time and inform future development and implementation of phase-responsive support strategies. The findings will guide research, policy, and practice in creating caregiver-centered interventions that reflect the realities of caregiving across the dementia continuum.

SARS-CoV-2 mutant spectrum complexity is an epidemiologically evolvable trait

RNA virus populations consist of complex and dynamic mutant spectra in which most individual genomes differ in one or more positions from the other genomes of the same population. This behavior, known as quasispecies dynamics, applies to SARS-CoV-2 which exhibits intrahost genetic and functional heterogeneity while evolving at a high rate in the human population. In the present study, we describe a remarkable reduction in mutant spectrum complexity (intrahost viral genome heterogeneity) in SARS-CoV-2 isolates of late relative to early COVID-19 waves, as they reached Madrid (Spain) from 2020 until 2022. In contrast, the consensus (average) sequence of the corresponding isolates displayed a continuing divergence from the initial Wuhan-Hu-1 virus as the pandemic advanced. The mutant spectrum complexity developed upon replication in Vero E6 cells of the isolates from the first and sixth COVID-19 waves, as well as of biological clones retrieved from them, was similar. Therefore, the mutant spectrum complexity reduction observed in vivo was not due to an increased accuracy of the viral replicative machinery, but rather to other factors related to viral epidemiology or pathogenesis. Such possible factors and their implications for viral trait modifications in the course of a viral pandemic are discussed. The results establish that mutant spectrum complexity of genetically variable viruses can be an epidemiologically evolvable trait.

Predicting system dynamics of pervasive growth patterns in complex systems

The efficacy of DPP IV inhibitors as adjunct therapy for patients with auto-immune Diabetes: A systematic review and meta-analysis

Rationale Type 1 diabetes mellitus (T1DM) is characterized by autoimmune destruction of pancreatic β-cells, leading to insulin deficiency and hyperglycemia. Although insulin therapy remains the cornerstone of T1DM management, achieving optimal glycemic control remains challenging. Dipeptidyl peptidase-4 (DPP-4) inhibitors, approved for type 2 diabetes, enhance endogenous incretin action and may enhance β-cell function. Some clinical trials have explored their adjunctive use in T1DM. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of DPP-4 inhibitors as an adjunct to insulin in patients with T1DM. Methods We systematically searched PubMed, Cochrane Library, Medline (OVID), Scopus, and ClinicalTrials.gov up to January 2025 for eligible studies. Randomized controlled trials (RCTs) investigating DPP-4 inhibitors versus placebo, both on top of insulin therapy for at least 12 weeks in T1DM patients, were included. The primary outcome was the change in HbA1c. Secondary outcomes included blood glucose, C-peptide, insulin dosage, BMI, weight, adverse events, and HOMA2-β scores. Risk of bias was assessed using the Cochrane RoB 2.0 tool. Data were pooled using a random-effects model, with effect sizes expressed as mean differences (MD) and 95% confidence intervals (CI). Results Out of 1,117 identified studies, seven RCTs comprising 333 participants (176 in the experimental group, 157 in the control group) were included. The addition of DPP-4 inhibitors did not result in a significant or sustained reduction in HbA1c overall, except for a transient improvement between 3 and 6 months (MD −0.10%, 95% CI −0.16 to −0.05, p = 0.0003). DPP-4 inhibitors significantly reduced daily insulin requirements, particularly bolus doses, and postprandial blood glucose (by −34.40 mg/dL), especially in patients with a BMI &lt; 25 kg/m² and diabetes duration &lt;3 years. No significant effects were observed on weight, BMI, fasting blood glucose, fasting or postprandial C-peptide beyond three months. HOMA2-β scores were significantly higher with DPP-4 inhibitors. Safety outcomes were comparable between groups. Conclusions DPP-4 inhibitors appear safe as adjunct therapy to insulin in patients with T1DM. Although they do not offer sustained HbA1c reduction, they may reduce daily insulin requirements, improve postprandial glucose, and transiently enhance β-cell function. Further large-scale studies are needed to better define the subgroups that might benefit from this strategy. Registration This study was registered in PROSPERO (CRD42024610965)

Neuron-reactive KIR+CD8+ T cells display an encephalitogenic transcriptional program in autoimmune encephalitis

Abstract Autoreactive CD8+ T cells targeting neurons are the principal suspects in autoimmune encephalitis (AIE), but supporting data is still lacking. Here we identify neuron-reactive CD8+ T cells in a cohort of six healthy donors and one patient with anti-Ri encephalitis (Ri-AIE) by querying natural antigen presentation of neurons that are derived from human induced pluripotent stem cells. Single-cell RNA sequencing of ex vivo CD8+ T cells in an extended cohort of seven Ri-AIE patients and three aged-matched controls further reveal that these neuron-reactive CD8+ T cells correspond to cytotoxic KIR+CD8+ regulatory T cells. Intriguingly, KIR+CD8+ T cells from most Ri-AIE patients have reduced expression of KIR and the key regulatory transcription factor, Helios, encoded by the IKZF2 gene; by contrast, these cells show activated TCR signaling and increased TNF and IFNG gene expression. Importantly, Ri-AIE-derived KIR+CD8+ T cells from blood also express higher levels of TOX, a gene associated with encephalitogenic potential, and is expressed in cytotoxic CD8+ T cells in the brain lesions of one Ri-AIE patient. Altogether, our data hints that dysregulated activity of neuron-reactive cytotoxic KIR+CD8+ T cells may contribute to Ri-AIE pathogenesis.