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Machine learning enhanced immunologic risk assessments for solid organ transplantation
Development of a leaf metabolite-based intact sample distinguishing algorithm for the three varieties of Panax Vietnamensis
Nonreciprocal bipartite and tripartite entanglement in cavity-magnon optomechanics via the Barnett effect
Neural similarity and interaction success in autistic and non-autistic adolescents
Abstract High-quality social interactions promote well-being for typically developing and autistic youth. One factor that may contribute to the quality of social interactions is neural similarity, a metric which may capture shared perspectives and experiences of the world. The current research investigates relations between neural similarity to peers and day-to-day interaction success as measured through ecological momentary assessment in a sample of autistic and non-autistic youth aged 11–14 years old. Neural similarity was operationalized as the between-participant correlation of participants’ neural response to naturalistic video stimuli in areas of the brain implicated in mental state understanding and reward processing. Neural similarity did not have a main effect on interaction success. However, across the full sample, neural similarity significantly interacted with reported closeness, such that there were more positive relations between neural similarity and interaction success for closer interactions. Neural similarity also marginally interacted with social partner (i.e., interactions featuring peers versus others) to predict interaction success, suggesting more positive relations between neural similarity and interaction success in peer interactions. In addition, non-autistic youth reported significantly better peer interactions than autistic youth. These findings suggest that similarity to one’s peers in neural processing in mentalizing and reward regions is important for understanding interaction success. They also highlight the challenge peer interactions may pose for autistic youth and propose novel links between peer interaction success and the brain’s mentalizing processes.
Defining the concept of physical resilience and quantifying recovery during standing balance in middle-aged and older adults
Author Correction: Assessing the role of dryness and burning sensation in diagnosing laryngopharyngeal reflux
Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning
Spatial pattern of attractiveness and road network accessibility of scenic spots in Guangdong Province based on network information
The pattern change of hepatitis B virus genetic diversity in Northwestern Tanzania
The suberin transporter StABCG1 is required for barrier formation in potato leaves
Abstract Suberin is a hydrophobic biopolymer that acts as an internal and external diffusion and transpiration barrier in plants. It is involved in two phases of wound healing, i.e. initial closing layer formation and subsequent wound periderm development. Transcriptomic and metabolomic analyses of wounded potato leaf tissue revealed preferential induction of cell wall modifying processes during closing layer formation, accompanied by a highly active defense response. To address the importance of suberin in this process, we generated loss of function mutants by CRISPR-Cas9 editing the suberin transporter gene StABCG1. Both wound-induced StABCG1 transcript levels and suberin formation around wounded leaf tissue were reduced in CRISPR-lines. Moreover, wound-induced tissue damage was characterized by browning of wound-adjacent areas. Transcriptome analyses of these areas revealed up-regulation of genes encoding defense proteins and enzymes of the phenylpropanoid pathway. Levels of hydroxycinnamic acid amides, acting in defense and in cell wall reinforcement, were drastically enhanced in CRISPR compared to control plants. These results suggest that the reduction in suberin formation around wounded tissue leads to a loss of barrier function, resulting in tissue browning due to enhanced exposure to oxygen.
Attachment is in the eye of the beholder: a pupillometry study on emotion processing
Research on fuzzy evaluation of ecological safety of land resources in Pearl river Delta area based on DPSIR framework
Leveraging YOLO deep learning models to enhance plant disease identification
Integrated analysis of single-cell and bulk RNA-sequencing to predict prognosis and therapeutic response for colorectal cancer
Predicting spatial familiarity by exploiting head and eye movements during pedestrian navigation in the real world
Abstract Spatial familiarity has seen a long history of interest in wayfinding research. To date, however, no studies have been done which systematically assess the behavioral correlates of spatial familiarity, including eye and body movements. In this study, we take a step towards filling this gap by reporting on the results of an in-situ, within-subject study with $$N=52$$ pedestrian wayfinders that combines eye-tracking and body movement sensors. In our study, participants were required to walk both a familiar route and an unfamiliar route by following auditory, landmark-based route instructions. We monitored participants’ behavior using a mobile eye tracker, a high-precision Global Navigation Satellite System receiver, and a high-precision, head-mounted Inertial Measurement Unit. We conducted machine learning experiments using Gradient-Boosted Trees to perform binary classification, testing out different feature sets, i.e., gaze only, Inertial Measurement Unit data only, and a combination of the two, to classify a person as familiar or unfamiliar with a particular route. We achieve the highest accuracy of $$89.9\%$$ using exclusively Inertial Measurement Unit data, exceeding gaze alone at $$67.6\%$$ , and gaze and Inertial Measurement Unit data together at $$85.9\%$$ . For the highest accuracy achieved, yaw and acceleration values are most important. This finding indicates that head movements (“looking around to orient oneself”) are a particularly valuable indicator to distinguish familiar and unfamiliar environments for pedestrian wayfinders.
Semi-analytical dynamic modeling and impact mechanism analysis of a hard-coating cylindrical shell with arbitrary circular perforations
Physics-informed machine learning for automatic model reduction in chemical reaction networks
Abstract Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computational costs of detailed mechanistic models while leveraging the predictive power of machine learning. This study applies this fusion to the biomedical challenge of A $$\beta$$ fibril aggregation, a key factor in Alzheimer’s disease. Central to the research is the introduction of an automatic reaction order model reduction framework, designed to optimize reduced-order kinetic models. This framework represents a shift in model construction, automatically determining the appropriate level of detail for reaction network modeling. The proposed approach significantly improves simulation efficiency and accuracy, particularly in systems like A $$\beta$$ aggregation, where precise modeling of nucleation and growth kinetics can reveal potential therapeutic targets. Additionally, the automatic model reduction technique has the potential to generalize to other network models. The methodology offers a scalable and adaptable tool for applications beyond biomedical research. Its ability to dynamically adjust model complexity based on system-specific needs ensures that models remain both computationally feasible and scientifically relevant, accommodating new data and evolving understandings of complex phenomena.
Structural analysis of $$\hbox{Sn}$$ on $${\hbox{Au}(111)}$$ at low coverages: Towards the $${\hbox {Au}_{2}\hbox {Sn}}$$ surface alloy with alternating fcc and hcp domains
Abstract We report on the structural and chemical evolution of submonolayer $$\hbox{Sn}$$ on $${\hbox{Au}(111)}$$ up to the formation of the striped $${\hbox {Au}_{2}\hbox {Sn}}$$ surface alloy. Using Low-Energy Electron Diffraction (LEED) and Scanning Tunneling Microscopy (STM), we identify a previously unobserved hexagonal $$(2\times 2)$$ -reconstruction at a $$\hbox{Sn}$$ film thickness of $$\approx 0.28$$ monolayers (ML). X-ray Photoelectron Spectroscopy (XPS) analysis reveals that the $$(2\times 2)$$ -structure is not chemically bonded to the $${\hbox{Au}(111)}$$ substrate. With increasing $$\hbox{Sn}$$ coverage, the $$(2\times 2)$$ -reconstruction performs a structural transition into a mixed phase before forming a local $$(\sqrt{3} \times \sqrt{3})\text {R}{30}^{\circ }$$ -reconstruction at a $$\hbox{Sn}$$ film thickness of $$0.33\,\textrm{ML}$$ . This reconstruction is superimposed by a larger periodicity resembling the herringbone reconstruction of clean $${\hbox{Au}(111)}$$ . Our XPS analysis identifies this phase as an $${\hbox {Au}_{2}\hbox {Sn}}$$ -alloy. By combining high-resolution x-ray photoelectron diffraction (XPD) measurements of $$\hbox{Au}\,\hbox{4f}$$ and $$\hbox{Sn}\,\hbox{4d}$$ 4d core levels with simulations based on a genetic algorithm, we propose a structural model for the $${\hbox {Au}_{2}\hbox {Sn}}$$ -supercell, revealing an unusually large unit cell with $$\text {Rec}(26\times \sqrt{3})$$ -periodicity. This study advances the understanding of the structural evolution of $$\hbox{Sn}$$ surface reconstructions on $${\hbox{Au}(111)}$$ up to the formation of the $${\hbox {Au}_{2}\hbox {Sn}}$$ surface alloy. Furthermore, it provides insights into the structural arrangements emerging at higher submonolayer $$\hbox{Sn}$$ coverages on $${\hbox{Au}(111)}$$ , offering potential pathways towards realizing freestanding stanene.
An efficient cell micronucleus classification network based on multi-layer perception attention mechanism
Abstract Cellular micronucleus detection plays an important role in pathological toxicology detection and early cancer diagnosis. To address the challenges of tiny targets, high inter-class similarity, limited sample data and class imbalance in the field of cellular micronucleus image detection, this paper proposes a lightweight network called MobileViT-MN (Micronucleus), which integrates a multilayer perceptual attention mechanism. Considering that limited data and class imbalance may lead to overfitting of the model, we employ data augmentation to mitigate this problem. Additionally, based on domain adaptation, we innovatively introduce transfer learning. Furthermore, a novel Deep Separation-Decentralization module is designed to implement the reconstruction of the network, which employs attention mechanisms and an alternative strategy of deep separable convolution. Numerous ablation experiments are performed to validate the effectiveness of our method. The experimental results show that MobileViT-MN obtains outstanding performance on the augmented cellular micronucleus dataset. Avg_Acc reaches 0.933, F1 scores 0.971, and ROC scores 0.965. Compared with other classical algorithms, MobileViT-MN is more superior in classification performance.