UNASSIGNED: Gait analysis, an expanding research area, employs non-invasive sensors and machine learning techniques for a range of applications. In this study, we investigate the impact of cognitive decline conditions on gait performance, drawing connections between gait deterioration in Parkinson\'s Disease (PD) and healthy individuals dual tasking.
UNASSIGNED: We employ Explainable Artificial Intelligence (XAI) specifically Layer-Wise Relevance Propagation (LRP), in conjunction with Convolutional Neural Networks (CNN) to interpret the intricate patterns in gait dynamics influenced by cognitive loads.
UNASSIGNED: We achieved classification accuracies of 98% F1 scores for PD dataset and 95.5% F1 scores for the combined PD dataset. Furthermore, we explore the significance of cognitive load in healthy gait analysis, resulting in robust classification accuracies of 90% ± 10% F1 scores for subject cognitive load verification. Our findings reveal significant alterations in gait parameters under cognitive decline conditions, highlighting the distinctive patterns associated with PD-related gait impairment and those induced by multitasking in healthy subjects. Through advanced XAI techniques (LRP), we decipher the underlying features contributing to gait changes, providing insights into specific aspects affected by cognitive decline.
UNASSIGNED: Our study establishes a novel perspective on gait analysis, demonstrating the applicability of XAI in elucidating the shared characteristics of gait disturbances in PD and dual-task scenarios in healthy individuals. The interpretability offered by XAI enhances our ability to discern subtle variations in gait patterns, contributing to a more nuanced comprehension of the factors influencing gait dynamics in PD and dual-task conditions, emphasizing the role of XAI in unraveling the intricacies of gait control.

We studied simultaneous EMG and midline EEG responses, the latter including over the cerebellum, in 10 standing subjects (35 ± 15 yrs; 5 females) following repeated taps to the sternum. We confirmed previous reports that this evokes short latency EMG responses in leg muscles, consistent with postural reflexes. EEG power had relatively more high frequency components (> 30 Hz) when recorded from electrodes over the cerebellum (Iz, SIz) compared to other midline electrodes. We showed clear midline evoked EEG potentials occurring at short latency over the cerebellum (P23, N31, N42, P54) and frontally (N28, N57) prior to the previously-described perturbation evoked potential (P1/N1/P2). The P23 response correlated with the subsequent EMG response in the tibialis anterior muscles. We conclude that early activity occurs from electrodes over the cerebellum in response to a brief tap to the sternum. This is likely to represent cerebellar activity and it appears to modulate short latency postural EMG responses.

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.

OBJECTIVE: This study aimed to examine the effects of cognitive tasks during walking with perturbation on the cerebral blood flow.
METHODS: The subjects were a total of 20 persons, consisting of 12 healthy adults aged 21-47 years (adult group) and 8 retirement home residents aged 67-85 years who led an independent daily life and could walk independently (elderly group). Oxyhemoglobin was measured using wireless functional near-infrared spectroscopy (fNIRS). An analysis was conducted using the Wilcoxon rank sum test to compare the variation of oxyhemoglobin between walking with perturbation (WP) and walking with perturbation and cognitive tasks (WPC) in each group. In addition, we compared the variation of oxyhemoglobin between groups by analysis of covariance adjusting for the value of WP.
RESULTS: In the adult group, the left and right oxyhemoglobin significantly increased under WPC (p=0.0122, 0.0015, respectively). On the other hand, in the elderly group, the right and left oxyhemoglobin did not significantly change under WPC.
CONCLUSIONS: These results suggest that the effect of a cognitive task during unstable walking conditions differs between healthy adults and elderly persons, and that this may be important when considering postural control strategies, especially in the elderly.

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

UNASSIGNED: Gingivitis in response to biofilm formation may exhibit different trajectories. The purposes of the present study were to characterize the composition of the supragingival microbiota and salivary cytokine and protein levels in healthy individuals with different gingivitis patterns, to test the hypothesis that manifestations of gingivitis associate with specific profiles in terms of supragingival microbiota, salivary cytokines, and proteins.
UNASSIGNED: Forty orally and systemically healthy individuals refrained from all oral hygiene procedures for a period of 14 days, followed by a resolution period of 14 days with regular oral care. Supragingival plaque level and bleeding on probing (BOP) were recorded, and supragingival plaque as well as saliva samples were collected at baseline, day 14, and day 28. Based on change in BOP% from baseline to day 14, rapid (n = 15), moderate (n = 10), and slow (n = 15) responders were identified. Supragingival microbiota composition, salivary cytokine, and protein levels were compared between groups at baseline, day 14, and day 28.
UNASSIGNED: A significantly higher baseline abundance of Capnocytophaga, Eikenella, and Campylobacter species were recorded in rapid responders, whereas a significantly higher baseline abundance of Streptococcus species were detected in slow responders. Slow responders expressed a high degree of resilience, with minimal difference in microbial composition at baseline and after 14 days of resolution (day 28). On the contrary, significant differences in relative abundance of members of the core microbiota, Streptococcus, Actinomyces, and Rothia species, was noted in baseline samples versus day 28 samples in rapid responders. Comparable baseline cytokine and protein levels were recorded in all groups.
UNASSIGNED: Supragingival microbiota composition, but not saliva cytokine and protein profiles, seems to influence the extent of the inflammatory response during development of gingivitis in systemically healthy individuals.
Baseline composition of the supragingival microbiota might predict different gingivitis trajectories.Microbial resilience after gingivitis might augment oral homeostasis in individuals with a slow gingivitis trajectory.

Mass spectrometry-based omics technologies are increasingly used in perturbation studies to map drug effects to biological pathways by identifying significant molecular events. Significance is influenced by fold change and variation of each molecular parameter, but also by multiple testing corrections. While the fold change is largely determined by the biological system, the variation is determined by experimental workflows. Here, it is shown that memory effects of prior subculture can influence the variation of perturbation profiles using the two colon carcinoma cell lines SW480 and HCT116. These memory effects are largely driven by differences in growth states that persist into the perturbation experiment. In SW480 cells, memory effects combined with moderate treatment effects amplify the variation in multiple omics levels, including eicosadomics, proteomics, and phosphoproteomics. With stronger treatment effects, the memory effect was less pronounced, as demonstrated in HCT116 cells. Subculture homogeneity was controlled by real-time monitoring of cell growth. Controlled homogeneous subculture resulted in a perturbation network of 321 causal conjectures based on combined proteomic and phosphoproteomic data, compared to only 58 causal conjectures without controlling subculture homogeneity in SW480 cells. Some cellular responses and regulatory events were identified that extend the mode of action of arsenic trioxide (ATO) only when accounting for these memory effects. Controlled prior subculture led to the finding of a synergistic combination treatment of ATO with the thioredoxin reductase 1 inhibitor auranofin, which may prove useful in the management of NRF2-mediated resistance mechanisms.

Balance control is an important indicator of mobility and independence in activities of daily living. How the functional coupling between the cortex and the muscle for balance control is affected following stroke remains to be known. We investigated the changes in coupling between the cortex and leg muscles during a challenging balance task over multiple frequency bands in chronic stroke survivors. Fourteen participants with stroke and ten healthy controls performed a challenging balance task. They stood on a computerized support surface that was either fixed (low difficulty condition) or sway-referenced with varying gain (medium and high difficulty conditions). We computed corticomuscular coherence between electrodes placed over the sensorimotor area (electroencephalography) and leg muscles (electromyography) and assessed balance performance using clinical and laboratory-based tests. We found significantly lower delta frequency band coherence in stroke participants when compared with healthy controls under medium difficulty condition, but not during low and high difficulty conditions. These differences were found for most of the distal but not for proximal leg muscle groups. No differences were found at other frequency bands. Participants with stroke showed poor balance clinical scores when compared with healthy controls, but no differences were found for laboratory-based tests. The observation of effects at distal but not at proximal muscle groups suggests differences in the (re)organization of the descending connections across two muscle groups for balance control. We argue that the observed group difference in delta band coherence indicates balance context-dependent alteration in mechanisms for the detection of somatosensory modulation resulting from sway-referencing of the support surface for balance maintenance following stroke.

Image classification, a fundamental task in computer vision, faces challenges concerning limited data handling, interpretability, improved feature representation, efficiency across diverse image types, and processing noisy data. Conventional architectural approaches have made insufficient progress in addressing these challenges, necessitating architectures capable of fine-grained classification, enhanced accuracy, and superior generalization. Among these, the vision transformer emerges as a noteworthy computer vision architecture. However, its reliance on substantial data for training poses a drawback due to its complexity and high data requirements. To surmount these challenges, this paper proposes an innovative approach, MetaV, integrating meta-learning into a vision transformer for medical image classification. N-way K-shot learning is employed to train the model, drawing inspiration from human learning mechanisms utilizing past knowledge. Additionally, deformational convolution and patch merging techniques are incorporated into the vision transformer model to mitigate complexity and overfitting while enhancing feature representation. Augmentation methods such as perturbation and Grid Mask are introduced to address the scarcity and noise in medical images, particularly for rare diseases. The proposed model is evaluated using diverse datasets including Break His, ISIC 2019, SIPaKMed, and STARE. The achieved performance accuracies of 89.89%, 87.33%, 94.55%, and 80.22% for Break His, ISIC 2019, SIPaKMed, and STARE, respectively, present evidence validating the superior performance of the proposed model in comparison to conventional models, setting a new benchmark for meta-vision image classification models.

The roll coating method is of considerable significance in several industries, as it is applied practically in the production of paint, the manufacturing of PVC-coated cloth, and the plastic industry. The current study theoretically and computationally analyses the Powell-Eyring fluids with variable viscosity during the non-isothermal roll-over web phenomenon. Based on the lubrication approximation theory (LAT), the problem was formulated. The system of partial differential equations (PDEs) obtained from the mathematical modeling was further simplified to a set of ordinary differential equations (ODEs) using suitable transformations. A regular perturbation method was implemented to obtain the solution in terms of velocity, pressure gradient, pressure, and flow rate per unit width. This study also captures important engineering characteristics such as coating thickness, Nusselt number, shear stress, roll/sheet separating force, and roll-transmitted power to the fluid. Along with a comparison between the present work and published work, both graphical and tabular representations wer made to study the effects of various factors. It was observed that the velocity profile is the decreasing function of non-Newtonian and Reynold viscosity parameters. In addition, the response surface methodology (RSM) was employed to investigate the sensitivity of the shear stress and the Nusselt number.