Browse Articles
Discover research articles across all indexed journals
Minimal implicit-solvent coarse-grained simulation of Pluronic block copolymers with ionic liquids
Pluronic block copolymers, composed of poly(ethylene oxide) (PEO) and poly(propylene oxide) (PPO) in a triblock structure (PEO–PPO–PEO), are well known for their amphiphilic character and ability to self-assemble into micelles in aqueous solution. The addition of ionic liquids (ILs) can further modulate the core–shell structures of these copolymers, influencing their stability, critical micellization temperature, and size. However, fully atomistic simulations often become prohibitively expensive due to the size and complexity of these systems. In this work, coarse-grained simulations using a minimal implicit-solvent model were performed to examine how two classes of ILs, namely, 1-alkyl-3-methylimidazolium ([CnC1im]) and 1-alkyl-3-methylpyrrolidinium ([CnC1pyrr]), change the micellization of Pluronic block copolymers in aqueous solution. The effects of IL concentration and alkyl group length were investigated, and the model greatly improved the efficiency of simulating large-scale micelle systems. The numerical simulations are qualitatively compared with experimental investigations. Our results show that adding ILs expands the micelle core by embedding IL tails among the PPO blocks, thereby increasing overall micelle size. Less polar ILs generally induce more pronounced micellar growth. However, the effect of IL tail length on conformation and micellar packing is non-monotonic. Up to moderate chain lengths (around C8–C10), the IL tails can extend sufficiently to increase local separation within the micelle; at longer tail lengths, enhanced hydrophobic clustering and steric hindrance cause the tails to bend or fold, capping further expansion. In addition, although block copolymer chains tend to pack more closely in the presence of longer-tailed ILs, the random coil size of an individual polymer chain does not necessarily shrink. Meanwhile, these insights provide a deeper understanding of how Pluronic/IL systems interact, informing applications in drug delivery, cosmetics, food, and environmental engineering. Finally, our minimal implicit-solvent model can be applied to larger systems and longer timescales, substantially reducing computational cost while reproducing key structural trends observed experimentally.
Eye tracking demonstrates the influence of autistic traits on social attention in a community sample from India
Abstract The ability to attend to social stimuli is fundamental for processing social cues and shaping social behavior, yet cultural variability in this capacity remains relatively unexplored. Social attention is typically tested using preferential-looking paradigms in labs, which have demonstrated that autistic individuals attend less to social stimuli. Such studies are limited, by the fact that they have almost all been conducted in Western Europe and the USA. To address this gap, our objective was to test the cultural generalizability of these results by investigating whether autistic symptoms are negatively associated with social attention in a traditionally understudied sample: Indian adults. Additionally, we tested the specificity of this relation by investigating whether a similar association exists with the traits of attention-deficit/hyperactivity disorder (ADHD). Our study involved 121 young adults from Kerala, India. Autistic and ADHD traits were evaluated using the Autism Spectrum Quotient (AQ) and Adult ADHD Self-Report Scale (ASRS), respectively. The participants’ gaze behavior was recorded during a preferential-looking task, where pairs of social and non-social images were presented simultaneously. Individuals with higher autistic traits exhibited a reduced preference for social stimuli. No such association of social attention was noted with ADHD traits. Follow-up analysis of AQ subscales indicated that the association between gaze duration and autistic traits was driven by the social, and not the attention to detail factor of autistic traits. Our results provide new evidence for the cultural generalizability of the social attention task and offer the potential for culture-agnostic phenotypic assessments for adults with autism.
A denaturation-free protocol for in situ visualization of short nuclear DNA sequences using padlock probes with rolling-circle amplification
We report an approach for in situ detection of genomic DNA sequences, where transiently opening DNA duplexes are captured by circularizing DNA strands – padlock probes – that lock in place in a sequence-specific manner through the action of a DNA ligase. Reacted probes, wound around their target strands, are then replicated by rolling-circle amplification for localized fluorescence detection. The technique serves to shorten assay time and enables detection of shorter specific DNA sequences compared to standard fluorescence in situ hybridization, FISH. Genomic sequences with thousands of locally repeated copies were detected in human leukocytes with greater than 99% efficiency and less than 0.15% false positives in just a few hours. Using a longer variant of the protocol targets of as little as 36 or 112 nt were visualized, albeit at lower efficiency and with a higher false positive rate. The technique of targeting sequences in duplex DNA using padlock probes is promising for both research and clinical diagnostics.
Density dependent embedding potentials for piecewise exact densities
In Frozen Density Embedding Theory (FDET) [T. A. Wesolowski, Phys. Rev. A 77, 012504 (2008)], the total N-electron density is represented as a sum of two components ρ1 and ρ2, where ρ1 is obtained from a Schrödinger-like N′-electron eigenvalue equation with N′ < N and ρ2 is an arbitrary non-negative real function integrating with respect to N − N′. It is shown that the exact total ground-state electron density ρvo cannot be obtained from FDET even if ρ2 is piecewise equal to ρvo (i.e., equal to ρvo on some measurable volume element). The result is discussed in the context of subsystem approach in density functional theory, pseudopotential theory, and embedding potentials derived from inverted Kohn–Sham equation.
Risk factors for postoperative recurrence of deep infiltrating endometriosis during a 6- to 12-year follow-up
An extended Hegselmann-Krause model incorporating agent heterogeneity and influence propagation
Traditional models of opinion dynamics provide a simplified framework for understanding human behavior in basic social scenarios. However, with the rise of complex communication patterns and heterogeneous social interactions in modern networks, more comprehensive and nuanced models are required. This paper proposes an extended opinion dynamics model that integrates individual heterogeneity, homophily-based influence weights, and multi-layer influence propagation mechanisms. First, we modify the classical Hegselmann-Krause (HK) model by introducing a selective influence neighborhood based on individuals’ social network connections, thereby capturing the structure-dependent nature of interpersonal interactions. Second, drawing on the theory of homophily, we model the influence weights between individuals according to their opinion similarity and domain-specific attributes. Third, we incorporate a k-layer influence propagation mechanism to simulate indirect social influence through extended paths in the network. Finally, simulation experiments and validation using real-world data demonstrate that the proposed model effectively captures the dynamics of opinion evolution and enhances predictive accuracy in complex social systems.
All you need is water: Converging ligand binding simulations with hydration collective variables
Selecting appropriate collective variables (CVs) is a crucial bottleneck in enhanced sampling molecular dynamics simulations. Although progress has been made with data-driven and intuition-based approaches, optimal CVs remain system-specific. Meanwhile, simple geometric descriptors are still widely used due to their transferability. A promising, yet under-explored, candidate for a more efficient CV is solvation. Indeed, despite its central role in ligand binding and folding, the complexity of solvent behavior has hindered its widespread use. Here, we introduce a data-driven and automatic strategy to construct robust solvation-based CVs. Our method identifies critical hydration sites by analyzing the radial distribution function of water around a ligand. Remarkably, using only these hydration CVs within on-the-fly probability enhanced sampling simulations, we successfully converge the binding free energy landscapes for a series of host–guest systems. These landscapes show excellent agreement with those from more computationally expensive benchmark methods. We further demonstrate that the choice of where to bias water is key to efficient convergence, providing clear guidelines for implementation. This work not only underscores the central role of water in molecular recognition but also offers a powerful and generalizable framework for enhancing the sampling of complex biomolecular events.
SARS-CoV-2 spike mutations alter structure and energetics to modulate ACE2 binding immune evasion and viral adaptation
A surface defect detection method for electronic products based on improved YOLOv11
Traditional manual inspection approaches face challenges due to the reliance on the experience and alertness of operators, which limits their ability to meet the growing demands for efficiency and precision in modern manufacturing processes. Deep learning techniques, particularly in object detection, have shown significant promise for various applications. This paper proposes an improved YOLOv11-based method for surface defect detection in electronic products, aiming to address the limitations of existing YOLO models in handling complex backgrounds and small target defects. By introducing the MD-C2F module, DualConv module, and Inner_MPDIoU loss function, the improved YOLOv11 model has achieved significant improvements in precision, recall rate, detection speed, and other aspects. The improved YOLOv11 model demonstrates notable improvements in performance, with a precision increase from 90.9% to 93.1%, and a recall rate improvement from 77.0% to 84.6%. Furthermore, it shows a 4.6% rise in mAP50, from 84.0% to 88.6%. When compared to earlier YOLO versions such as YOLOv7, YOLOv8, and YOLOv9, the improved YOLOv11 achieves a significantly higher precision of 89.3% in resistor detection, surpassing YOLOv7’s 54.3% and YOLOv9’s 88.0%. In detecting defects like LED lights and capacitors, the improved YOLOv11 reaches mAP50 values of 77.8% and 85.3%, respectively, both outperforming the other models. Additionally, in the generalization tests conducted on the PKU-Market-PCB dataset, the model’s detection accuracy improved from 91.4% to 94.6%, recall from 82.2% to 91.2%, and mAP50 from 91.8% to 95.4%.These findings emphasize that the proposed YOLOv11 model successfully tackles the challenges of detecting small defects in complex backgrounds and across varying scales. It significantly enhances detection accuracy, recall, and generalization ability, offering a dependable automated solution for defect detection in electronic product manufacturing.
Motional narrowing of spin relaxation in 2D perovskites by correlated exciton fluctuations
Two-dimensional organic–inorganic hybrid perovskite (2D-OIHP) quantum wells exhibit a triplet of bright exciton fine structure states near the band edge, enabling the generation of transient macroscopic spin alignments with circularly polarized light. Here, we investigate the microscopic origin of photoinduced spin relaxation in 2D-OIHPs using multidimensional coherent spectroscopy together with a theoretical framework that combines time-dependent perturbation theory with the Fokker–Planck equation. Analysis of the spectral line shapes reveals highly correlated exciton fluctuations within the fine structure manifolds of a pair of 2D-OIHPs featuring different organic layer thicknesses and polaron binding energies. In particular, the Gaussian correlation coefficients determined for the two lead-iodide-based systems range from 0.67 to 0.80, while their polaron binding energies span 11.8–18.9 meV. Incorporating time-coincident solvation dynamics into a stochastic model shows that these energy level correlations reduce the exciton–bath couplings and extend dephasing times for spin-flip transitions, even in spectral broadening regimes governed by Marcus-like kinetics (which are typically considered incompatible with motional narrowing). Since photoexcitation occurs on the seam of intersection between the excited-state free energy surfaces, spin relaxation can proceed without an activation barrier, provided it outpaces energy dissipation into the environment. Overall, these results demonstrate that correlated exciton fluctuations play a central role in accelerating spin depolarization in 2D-OIHPs through motional narrowing of coherences between exciton states.
Attenuation tomography using large-scale seafloor and land network data in northeast Japan
Correction: “My skills are going to be exposed” – Anxiety, meaning and professional identity during simulation-based learning in medical students: A mixed method study
Representing spherical tensors with scalar-based machine-learning models
Rotational symmetry plays a central role in physics, providing an elegant framework to describe how the properties of 3D objects—from atoms to the macroscopic scale—transform under the action of rigid rotations. Equivariant models of 3D point clouds are able to approximate structure–property relations in a way that is fully consistent with the structure of the rotation group by combining intermediate representations that are themselves spherical tensors. The symmetry constraints, however, make this approach computationally demanding and cumbersome to implement, which motivates increasingly popular unconstrained architectures that learn approximate symmetries as part of the training process. In this work, we explore a third route to tackle this learning problem, where equivariant functions are expressed as the product of a scalar function of the point cloud coordinates and a small basis of tensors with the appropriate symmetry. In particular, we show that it is always possible to separate the learning of an equivariant property into learnable scalars and fixed geometric terms built as the maximal coupling of interatomic vectors. We also propose approximations of the general expressions that, while lacking universal approximation properties, are fast, simple to implement, and accurate in practical settings.
Enhanced insecticidal activity of isoparaffin by ozone as an adjuvant
Prevalence and phenotypic findings of pathogenic or likely pathogenic copy number variants in 10,537 pregnancies
Background Pathogenic and likely pathogenic copy number variations (p/lpCNVs) detected through chromosomal microarray analysis (CMA) are crucial for understanding the etiology of birth defects. However, due to incomplete penetrance and variable phenotypic expression, the intrauterine phenotypic characteristics and genotype-phenotype correlations of these variations remain unclear. Therefore, this study aims to explore the prevalence and clinical implications of p/lpCNVs in a large cohort of pregnant women. Methods and findings This study retrospectively analyzed 10,537 prenatal diagnostic cases from 2013 to 2022 at the Affiliated Hospital of Jining Medical University. All pregnant women underwent amniocentesis and chromosomal microarray analysis (CMA). Cases were divided into two groups: the CMA group (194 cases) and the karyotype analysis group (259 cases), based on whether CNVs could be detected by traditional karyotype analysis. The primary study outcomes included the incidence of pathogenic or likely pathogenic CNVs, the distribution of variations in specific chromosomal regions, and the correlation between these variations and clinical phenotypes (e.g., cardiovascular abnormalities, developmental delays). Statistical analyses were performed using the chi-square test and the Mann-Whitney U test, with p < 0.05 considered statistically significant. Among 7,663 amniocentesis CMA cases, 453 cases of pathogenic or likely pathogenic CNVs were identified, with 194 cases in the CMA group and 259 cases in the karyotype analysis group. Specific chromosomal regions, such as 22q11.21 and 16p13.11, were associated with clinical phenotypes such as cardiovascular abnormalities and developmental delays. The incidence of pathogenic CNVs was higher in pregnant women with polyhydramnios and those conceived via assisted reproductive technology (ART). The main limitation of this study is the lack of long-term follow-up data on the clinical outcomes of pathogenic CNVs. Conclusions This study demonstrates for the first time that chromosomal microarray analysis (CMA) is superior to traditional karyotype analysis in high-risk pregnancies, especially in those with a single clinical indication, by more effectively detecting small copy number variations. Pathogenic CNVs are more likely to cause structural abnormalities, highlighting the stronger association between pathogenic variations and significant phenotypic consequences. Our data also suggest that factors such as assisted reproductive technology (ART) and polyhydramnios may be associated with the occurrence of p/lpCNVs. Future research should focus on clarifying the genotype-phenotype correlations of p/lpCNVs and exploring the potential impact of ART on genetic variations. Long-term longitudinal studies will help deepen the understanding of these variations’ long-term effects on maternal and fetal health, ultimately improving prenatal diagnostics and genetic counseling.
Exploring potential reactivity by optical polarization dependent coherent vibrational spectroscopy
Identifying reaction coordinates is a central challenge across all fields of chemistry. While the advancement of femtosecond spectroscopic techniques has enabled direct identification of reaction coordinates in ultrafast processes, new experimental or analytical approaches are required to access such information in photoinitiated processes of broader interest. In this study, we theoretically demonstrate a spectroscopic method capable of identifying vibrational modes with potential reactivities in non-reactive photochemical and photophysical systems. The method is based on femtosecond transient absorption spectroscopy and leverages the polarization dependence in wavepacket propagation arising from the position-dependent electronic character within the Born–Oppenheimer approximation. The vibrational wavepacket associated with the reactive mode exhibited clear dependencies on both polarization and detection frequency, whereas the non-reactive mode showed only weak polarization dependence. This contrast highlights a fundamental distinction between reactive and non-reactive modes in terms of their polarization-sensitive spectral responses. Leveraging this property, it may be possible to extract reaction coordinate information even for general photochemical and photophysical processes.
Interaction effects of outdoor thermal comfort and air pollution
Enhanced local feature extraction of lite network with scale-invariant CNN for precise segmentation of small brain tumors in MRI
Deep learning has emerged as the preeminent technique for semantic segmentation of brain MRI tumors. However, existing methods often rely on hierarchical downsampling to generate multi-scale feature maps, effectively capturing fine-grained global features but struggling with large-scale local features due to insufficient network depth. This limitation is particularly detrimental for segmenting diminutive targets such as brain tumors, where local feature extraction is crucial. Augmenting network depth to address this issue leads to excessive parameter counts, incompatible with resource-constrained devices. To tackle this challenge, we propose that object recognition should exhibit scale invariance, so we introduce a shared CNN network architecture for image encoding. The input MRI image is directly downsampled into three scales, with a shared 10-layer convolutional network employed across all scales to extract features. This approach enhances the network’s ability to capture large-scale local features without increasing the total parameter count. Further, we utilize a Transformer on the smallest scale to extract global features. The decoding stage follows the UNet structure, incorporating incremental upsampling and feature fusion from previous scales. Comparative experiments on the LGG Segmentation Dataset and BraTS21 dataset demonstrate that our proposed LiteMRINet achieves higher segmentation accuracy while significantly reducing parameter count. This makes our approach particularly advantageous for devices with limited memory resources. Our code is available at https://github.com/chinaericy/MRINet .
Composition-dependent atomic transport properties of liquid Ti–Al alloys: Correlation between local structure and dynamic behavior
This study investigates the composition-dependent atomic transport properties and structural characteristics of liquid Ti–Al alloys across the entire composition range using ab initio molecular dynamics simulations. We systematically analyze self-diffusion coefficients, inter-diffusion coefficients, and shear viscosity alongside structural features characterized through Honeycutt–Andersen bond-pair analysis and fivefold symmetry parameters. Our results reveal composition-dependent atomic mobility, with Al atoms exhibiting enhanced diffusivity in Al-rich compositions, while Ti atoms show complex non-monotonic behavior influenced by local chemical environments. Inter-diffusion coefficients display a pronounced maximum at the equiatomic composition, coinciding with the peak in the thermodynamic factor, indicating thermodynamically driven diffusion enhancement. Structural analysis demonstrates that chemical short-range order reaches its maximum at the equiatomic composition, where Ti–Al heterogeneous coordination is the strongest. The total fivefold symmetry parameter exhibits nonlinear behavior with a maximum value near the equiatomic composition. We identify two key structure–dynamics correlations: a negative correlation between packing efficiency and self-diffusion coefficients, and a positive correlation between fivefold symmetry and inter-diffusion coefficients.