Browse Articles

Epigenetic instability and hypofunctionality of fetal Tregs allow a permissive regulatory environment for T effector memory maturation

The human gestational environment is commonly perceived to be predominantly suppressive and incompatible for T effector maturation. However, evidence of a competent effector fetal environment is mounting in the field. Here, we employed a high parametric, mass cytometry–based approach to study the fetal circulatory and microenvironmental immunomes, with the aim to understand the inception and extent of fetal effector T cell priming and its relation with regulatory mechanisms. We found evidence of fetal thymic immune imprinting, coupled with both circulatory and tissue effector memory development. Correspondingly, in the regulatory compartment, we detected the presence of Tbet + Treg in fetal tissues at elevated levels compared to adult tissues. Fetal Tregs, though capable of suppression, were hyposuppressive as compared with adult counterparts. We found that a proportion of fetal Tregs lost FoxP3 commitment during proliferation and exhibited higher TCR clonotype sharing with effector T cells, indicating higher plasticity in fetal Tregs than adult. Epigenetic profiling of the FoxP3 promoter locus reveals that fetal Tregs were only partially demethylated, possibly explaining the observed instability. In summary, our data provide evidence of a regulatory environment in the 2nd trimester permissive for T effector maturation, in part contributed by the relative instability and hypofunctionality in fetal Tregs.

The diagnostic value of LncRNA NEAT1 targeting miR-129-5p in pancreatic cancer patients

Abstract Early pancreatic cancer (PC) identification and management has gained much clinical and research attention. Recent reports have demonstrated the important function of circulating noncoding RNAs (ncRNAs) in the diagnosis and prognosis of malignancies. However, the clinical value of serum ncRNAs in PC has not been fully clarified. Hence, we investigated serum levels of long-ncRNA NEAT1 and miR-129-5p in PC cases, exploring their relationship with related targets BCL2 and TGF-β1. Serum NEAT1 and miR-129-5p levels were evaluated in 60 treatment-naïve PC cases and 30 apparent healthy individuals by RT-PCR. Besides, serum TGF-β1 and BCL2 levels were measured by ELISA, whereas CA19-9 and CEA were measured utilizing the chemiluminescence technique. We demonstrated that serum NEAT1, BCL2, and TGF-β1 levels were significantly upregulated, whereas serum levels of miR-129-5p were markedly reduced in PC cases compared to controls. miR-129-5p had a higher diagnostic value for PC than NEAT1, with specificity, sensitivity, and AUC of 100%, 95%, and 0.96 versus 93.3%, 83.3%, and 0.89, respectively. Moreover, miR-129-5p had a higher AUC than CA19-9 and CEA. Additionally, serum miR-129-5p was significantly downregulated in PC cases with T3 or T4 stages compared to those with T2 stage & negatively correlated with NEAT1, BCL2, and TGF-β1, whereas NEAT1 was notably positively correlated with BCL2 and TGF-β1. Collectively, serum NEAT1 and miR-129-5p could be beneficial biomarkers for the detection of PC. Also, our findings accentuate the correlation between NEAT1, miR-129-5p, BCL2, and TGF-β1 and provide PC therapeutic targets.

Cell type–specific purifying selection of synonymous mitochondrial DNA variation

While somatic variants are well-characterized drivers of tumor evolution, their influence on cellular fitness in nonmalignant contexts remains understudied. We identified a mosaic synonymous variant (m.7076A > G) in the mitochondrial DNA (mtDNA)-encoded cytochrome c-oxidase subunit 1 (MT-CO1, p.Gly391=), present at homoplasmy in 47% of immune cells from a healthy donor. Single-cell multiomics revealed strong, lineage-specific selection against the m.7076G allele in CD8 + effector memory T cells, but not other T cell subsets, mirroring patterns of purifying selection of pathogenic mtDNA alleles. The limited anticodon diversity of mitochondrial tRNAs forces m.7076G translation to rely on wobble pairing, unlike the Watson–Crick–Franklin pairing used for m.7076A. Mitochondrial ribosome profiling confirmed stalled translation of the m.7076G allele. Functional analyses demonstrated that the elevated translational and metabolic demands of short-lived effector T cells (SLECs) amplify dependence on MT-CO1, driving this selective pressure. These findings suggest that synonymous variants can alter codon syntax, impacting mitochondrial physiology in a cell type–specific manner.

Intelligent routing for human activity recognition in wireless body area networks

Abstract Human activity recognition (HAR), driven by machine learning techniques, offer the detection of diverse activities such as walking, running, and more. Considering the dynamic nature, limited energy and mobility of wireless body area networks (WBANs), HAR can play a significant role in enhancing WBANs performance. This paper genuinely bridges HAR’s activity recognition capability using machine learning to develop a novel WBAN routing decisions adoptively. Being optimum in power consumption, we employed Random Forest classification algorithm for activity recognition. The resulted system holds great promise for optimizing routing decisions, improving energy efficiency, and enhancing the overall performance of WBANs in healthcare and related domains. To evaluate the performance of the proposed protocol, we have measured various performance metrics, including energy consumption, throughput, and the number of dead nodes. The results have been compared with mobTHE protocol to demonstrate the effectiveness of our HAR based Routing protocol.

Exploring the social life of urban spaces through AI

We analyze changes in pedestrian behavior over a 30-y period in four urban public spaces located in New York, Boston, and Philadelphia. Building on William Whyte’s observational work, which involved manual video analysis of pedestrian behaviors, we employ computer vision and deep learning techniques to examine video footage from 1979–80 and 2008–10. Our analysis measures changes in walking speed, lingering behavior, group sizes, and group formation. We find that the average walking speed has increased by 15%, while the time spent lingering in these spaces has halved across all locations. Although the percentage of pedestrians walking alone remained relatively stable (from 67% to 68%), the frequency of group encounters declined, indicating fewer interactions in public spaces. This shift suggests that urban residents are using streets as thoroughfares rather than as social spaces, which has important implications for the role of public spaces in fostering social engagement.

Interpretable graph Kolmogorov–Arnold networks for multi-cancer classification and biomarker identification using multi-omics data

Soil nitrogen drives inverse acclimation of xylem growth cessation to rising temperature in Northern Hemisphere conifers

Controlled experiments suggest that the seasonal build-up of nitrogen (N) limitation constrains the responses of forest autumn phenology to elevated temperatures. Therefore, rising soil N is expected to increase the delaying effects of elevated temperature on the end of the season, i.e., leaf senescence. However, the interactive effects of temperature, soil N, and aridity on xylem autumn phenology remain unknown. We conducted a wide spatial analysis from 75 conifer sites in the Northern Hemisphere and found that rising soil N increases the delaying effects of elevated temperature on the end of xylem cell wall thickening but reduced the delaying effects on the cessation of cell enlargement, especially in humid regions. The contrasting effects of elevated soil N on cell enlargement versus cell wall thickening could affect xylem cell anatomy, thereby induce changes in wood density, and induce a decoupling of stem size growth from photosynthate production. These analyses extend previous findings on forest autumn phenology by systematically investigating the spatial variation in the interactive effects of temperature and soil N on xylem autumn phenology at the cellular scale.

Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning

Abstract This study investigates the effectiveness of inclined double cutoff walls installed beneath hydraulic structures by employing five machine learning models: Random Forest (RF), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). A comprehensive dataset of 630 samples was gathered from previous studies, including key input variables such as the relative distance between the cutoff wall and the structure’s apron width (L/B), the inclination angle ratio between downstream and upstream cutoffs (θ 2/θ 1), the depth ratio of downstream to upstream cutoff walls (d 2/d 1), and the relative downstream cutoff depth to the permeable layer depth (d 2/D). Outputs considered were the relative uplift force (U/U o ), the relative exit hydraulic gradient (i R /i Ro ), and the relative seepage discharge per unit structure length (q/q o ). The dataset was split with a 70:30 ratio for training and testing. Hyperparameter optimization was conducted using Bayesian Optimization (BO) coupled with five-fold cross-validation to enhance model performance. Results showed that the CatBoost model demonstrated superior performance over other models, consistently yielding high R2 values, specifically surpassing 0.95, 0.93, and 0.97 for U/U o , i R /i Ro , and q/q o , respectively, along with low RMSE scores below 0.022, 0.089, and 0.019 for the same variables. A feature importance analysis is conducted using SHapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP). The analysis revealed that L/B was the most influential predictor for U/U o and i R /i Ro , while d 2/D played a crucial role in determining q/q o . Moreover, PDPs illustrated a positive linear relationship between L/B and U/U o , a V-shaped impact of d 2/d 1 on i R /i Ro and q/q o , and complex nonlinear interactions for θ 2/θ 1 across all target variables. Furthermore, an interactive Graphical User Interface (GUI) was developed, enabling engineers to efficiently predict output variables and apply model insights in practical scenarios.

Tensor-based quantum phase difference estimation for large-scale demonstration

We develop an energy calculation algorithm leveraging quantum phase difference estimation (QPDE) scheme and a tensor-network-based unitary compression method in the preparation of superposition states and time-evolution gates. Alongside its efficient implementation, this algorithm reduces depolarization noise affections exponentially. We demonstrated energy gap calculations for one-dimensional Hubbard models on IBM superconducting devices using circuits up to 32-system (plus one-ancilla) qubits, a five-fold increase over previous Quantum phase estimation (QPE) demonstrations, at the 7242 controlled-Z gate level of standard transpilation, utilizing a Q-CTRL error suppression module. Additionally, we propose a technique toward molecular executions using spatial orbital localization and index sorting, verified linear polyene simulations up to 21 qubits. Since QPDE can handle the same objectives as QPE, our algorithm represents a leap forward in quantum computing on real devices.

Tumor extracellular matrix enhances invasive gene expression of breast cancer cells in 3D patient-derived scaffolds

Mechanistic insights into the iron–sulfur cluster-dependent interaction of the autophagy receptor NCOA4 with the E3 ligase HERC2

NCOA4, a dedicated autophagy receptor for mediating selective autophagy of ferritin (ferritinophagy), plays a vital role in maintaining cellular iron homeostasis. The cellular abundance of NCOA4 is regulated by the E3 ligase HERC2 that can specifically target NCOA4 for proteasomal degradation under iron-replete conditions. However, the detailed molecular mechanism governing the iron-dependent recognition of NCOA4 by HERC2 remains elusive. Here, using multidisciplinary approaches, we systematically characterize the HERC2-binding domain (HBD) of NCOA4 and its interaction with HERC2. We uncover that NCOA4 HBD harbors a [2Fe-2S] cluster and can exist in two different states, the apo -form state and the [2Fe-2S] cluster–bound state. Moreover, we unravel that HERC2 can effectively recognize the [2Fe-2S] cluster–bound NCOA4 HBD through its Cullin-7-PARC-HERC2 (CPH) domain and iron–sulfur cluster–dependent NCOA4-binding domain (INBD) with a synergistic binding mode. The determined crystal structures of HERC2(2540-2700) and its complex with the [2Fe-2S] cluster–bound NCOA4 HBD together with relevant biochemical and cellular results not only elucidate how NCOA4 HBD specifically senses cellular iron level by binding a [2Fe-2S] cluster but also reveal the molecular basis underlying the specific interaction of HERC2 with the [2Fe-2S] cluster–bound NCOA4 HBD. In summary, our findings provide mechanistic insights into the iron-dependent turnover of NCOA4 by HERC2 and expand our understanding of the regulatory mechanism of NCOA4-mediated ferritinophagy.

Optimizing net-zero energy strategies in airports through a hybrid multi-method framework

Optimal kinetics for catalytic cycles from a single path-sampling simulation

A catalyst’s efficiency for accelerating a reaction rate is determined by its molecular structure and interactions with the substrate. While one can predict kinetics for a particular molecular model, tuning the (potentially many) model parameters to reach a desired or optimal kinetics for a catalytic cycle is usually considered computationally prohibitively expensive, especially in solvated systems. Here, we show for a simple model representing a minimal catalytic cycle that such optimization is possible using only one single (path-sampling) simulation, by applying a maximum caliber based path reweighting method. We compute the path ensemble for a single parameter setting of the molecular interactions and then expand the kinetic landscape around these parameters. We find that optimal catalytic turnover or efficiency is orders of magnitude improved and is achieved by relevant parameters that induce strain in the system. Thus, path-reweighting based optimization is not only capable of finding important ingredients that lead to desired kinetic rates but can also identify the mechanistic origins of the rate optimization at a fraction of the costs of a direct evaluation. We demonstrate the versatility of the methodology on a minimal model for kinase signaling. The approach promises efficient computational design of (complex) catalysts using realistic models.

Correction: Emerin deficiency drives MCF7 cells to an invasive phenotype

ATP synthesis driven by atmospheric hydrogen concentrations

All cells require a continuous supply of the universal energy currency, adenosine triphosphate (ATP), to drive countless cellular reactions. The universally conserved F 1 F o -ATP synthase regenerates ATP from ADP and P i by harnessing a transmembrane electrochemical proton gradient ( pmf ). Bacteria have evolved diverse pmf -forming strategies using light, organic, and inorganic energy sources. Recently, we proposed that many bacteria survive using atmospheric trace gases to produce ATP when limited for other energy sources. However, direct evidence that atmospheric energy sources are sufficient to generate pmf or drive ATP synthesis is still lacking. Here, we show that the membrane-associated hydrogen:quinone oxidoreductase Huc from Mycobacterium smegmatis can enable ATP synthesis from air. Purified Huc couples H 2 oxidation to the reduction of various ubiquinone and menaquinone analogues. We designed a minimal respiratory chain in which Huc interacts with liposomes containing the nonpumping, but pmf -generating, bd -I oxidase and F 1 F o -ATP synthase from Escherichia coli . Our experiments show that passive hydrogen exchange from air to solution is sufficient for the electron transfer and pmf generation required to accumulate ATP. By combining continuous culture bioenergetics measurements with theoretical calculations, we show this process is sufficient for mycobacteria to sustain pmf and ATP synthesis (two ATP molecules per H 2 oxidized) for maintenance energy requirements during nutrient starvation. These findings confirm that atmospheric energy sources can be dependable ‘lifeline’ substrates that enable continuous energy conservation during nutrient starvation. In addition, this work provides a unique tool for ATP production in synthetic applications, which unlike other approaches is traceless without by-product accumulation.

Neurodevelopmental status of children aged 12 to 60 months conceived with artificial oocyte activation in a Cross-Sectional study

The kinesin KIF3AC recycles endocytosed integrin to polarize new adhesion formation toward the leading edge

The recycling of integrin endocytosed during focal adhesion (FA) disassembly is critical for cell migration and contributes to the polarized formation of new FAs toward the leading edge. How this occurs is unclear. Here, we sought to identify the kinesin motor protein(s) that is involved in recycling endocytosed integrin back to the plasma membrane. We show that the kinesin-2 heterodimer, KIF3AC, and the Rab11 adaptor protein Rab coupling protein (RCP) are required for FA reformation after the disassembly of FAs in mouse and human fibroblasts. In the absence of KIF3AC, integrin does not return to the cell surface after FA disassembly and is found in the Rab11 endocytic recycling compartment. Biochemical pulldowns revealed that KIF3C associated with β1 integrin in an RCP-dependent fashion, but only after FA disassembly. KIF3AC knockdown inhibited cell migration, trafficking of RCP toward the leading edge, and polarized formation of FAs at the leading edge. These results show that KIF3AC promotes cell migration by recycling integrin so that it generates new FAs in a polarized fashion.

Studying the performance of YOLOv11 incorporating DHSA BRA and PPA modules in railway track fasteners defect detection

Kinetic pathways of solid–solid phase transitions dictated by short-range interactions

Structural phase transformations allow us to design materials from the ground up. Predicting the structural transformation of crystals during solid–solid phase transitions, however, is challenging, as the transition can proceed through multiple pathways that are difficult to probe experimentally. Using minimal computational models, we show that distinct kinetic pathways between body-centered cubic (bcc) and face-centered cubic (fcc) crystal structures can be encoded into a system by specific particle interactions. By investigating the dynamics of these transitions, we resolve three different pathways at a particle-by-particle level: a direct bcc-to-fcc transition, a transition involving an intermediate, long-lived body-centered tetragonal (bct) phase, and a microstructure-dependent transition pathway with a competing hexagonal close-packed (hcp) phase. These kinetic pathways are intrinsically linked to the shape of the underlying particle–particle interactions, suggesting routes for controlling the transformation pathways of soft matter systems. Furthermore, our investigations provide fundamental insights into solid–solid phase transition mechanisms generalizable across length scales.

Impact of adverse environmental conditions on the fatigue behavior of carbon epoxy adhesive joints under mode II fracture