Prospects of induced pluripotent stem cells in treating advancing Alzheimer's disease: A review.

The World Health Organization has identified Alzheimer's disease (AD), the leading cause of dementia globally, as a public health priority. However, the complex multifactorial pathology of AD means that its etiology remains incompletely understood. Despite being recognized a century ago, incomplete knowledge has hindered the development of effective treatments for AD. Recent scientific advancements, particularly in induced pluripotent stem cell (iPSC) technology, show great promise in elucidating the fundamental mechanisms of AD. iPSCs play a dual role in regenerating damaged cells for therapeutic purposes and creating disease models to understand AD pathology and aid in drug screening. Nevertheless, as an emerging field, iPSC technology requires further technological advancement to develop effective AD treatments in the future. Thus, this review summarizes recent advances in stem cell therapies, specifically iPSCs, aimed at understanding AD pathology and developing treatments.

Trainable Quaternion Extended Kalman Filter with Multi-Head Attention for Dead Reckoning in Autonomous Ground Vehicles.

Extended Kalman filter (EKF) is one of the most widely used Bayesian estimation methods in the optimal control area. Recent works on mobile robot control and transportation systems have applied various EKF methods, especially for localization. However, it is difficult to obtain adequate and reliable process-noise and measurement-noise models due to the complex and dynamic surrounding environments and sensor uncertainty. Generally, the default noise values of the sensors are provided by the manufacturer, but the values may frequently change depending on the environment. Thus, this paper mainly focuses on designing a highly accurate trainable EKF-based localization framework using inertial measurement units (IMUs) for the autonomous ground vehicle (AGV) with dead reckoning, with the goal of fusing it with a laser imaging, detection, and ranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM) estimation for enhancing the performance. Convolution neural networks (CNNs), backward propagation algorithms, and gradient descent methods are implemented in the system to optimize the parameters in our framework. Furthermore, we develop a unique cost function for training the models to improve EKF accuracy. The proposed work is general and applicable to diverse IMU-aided robot localization models.

Online Incremental Classification Resonance Network and Its Application to Human-Robot Interaction.

In human-robot interaction (HRI), classification is one of the most important problems, and it is essential particularly when the robot recognizes the surroundings and chooses a reaction based on a certain situation. Each interaction is different since new people appear or the environment changes, and the robot should be able to adapt to different situations during a brief interaction. Thus, it is imperative that the classification is performed incrementally in real time. In this sense, we propose an online incremental classification resonance network (OICRN) that enables incremental class learning in multi-class classification with high performance online. In OICRN, a scale-preserving projection process is introduced to use the raw input vectors online without a normalization process in advance. The integrated network of the convolutional neural network (CNN) for feature extraction and the OICRN for classification is applied to a robotic system that learns human identities through HRIs. To demonstrate the effectiveness of our network, experiments are carried out on benchmark data sets and on a humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory, KAIST.

Incremental Class Learning for Hierarchical Classification.

Objects can be described in hierarchical semantics, and people also perceive them this way. It leads to the need for hierarchical classification in machine learning. On the other hand, when a new data that belongs to a new class is given, the existing classification methods should be retrained for all data including the new data. To deal with these issues, we propose an adaptive resonance theory-supervised predictive mapping for hierarchical classification (ARTMAP-HC) network that allows incremental class learning for raw data without normalization in advance. Our proposed ARTMAP-HC is composed of hierarchically stacked modules, and each module incorporates two fuzzy ARTMAP networks. Regardless of the level of the class hierarchy and the number of classes for each level, ARTMAP-HC is able to incrementally learn sequentially added input data belonging to new classes. By using a novel online normalization process, ARTMAP-HC can classify the new data without prior knowledge of the maximum value of the dataset. By adopting the prior labels appending process, the class dependency between class hierarchy levels is reflected in ARTMAP-HC. The effectiveness of the proposed ARTMAP-HC is validated through experiments on hierarchical classification datasets. To demonstrate the applicability, ARTMAP-HC is applied to a multimedia recommendation system for digital storytelling.

Dependence on p38 MAPK signalling in the up-regulation of TLR2, TLR4 and TLR9 gene expression in Trichomonas vaginalis-treated HeLa cells.

Toll-like receptors (TLRs) are pattern recognition receptors (PRRs) that recognize conserved pathogen-associated molecular patterns (PAMPs) synthesized by micro-organisms. Despite the essential requirement for TLRs in prokaryotic infection, the pattern and regulation of TLR gene expression by Trichomonas vaginalis in the mucocutaneous barrier are still unknown. Our hypothesis is that T. vaginalis-infected epithelial cells are major effector cells in the skin barrier. These cells function as a central regulator of TLR gene expression, thus accelerating the process of barrier dysfunction via increased release of chemokines and proinflammatory cytokines. To test this hypothesis, RT-PCR was performed on TLRs, interleukin (IL)-8 and tumour necrosis factor (TNF)-alpha. Stimulation of HeLa cells by T. vaginalis was observed to up-regulate TLR2, 4 and 9 mRNA expression as well as that of IL-8 and TNF-alpha. To further clarify the molecular mechanism of barrier devastation triggered by these up-regulatory stimuli, we examined the profiles of extracellular signal-regulated kinase (ERK), p38 mitogen-activated protein kinase (MAPK) and nuclear factor (NF)-kappaB activation in HeLa cells using specific inhibitors. Interestingly, pretreatment of HeLa cells with the p38 MAPK inhibitor SB203580 demonstrated inhibition of T. vaginalis-induced up-regulation of TLR2, 4, and 9 mRNA expression. By contrast, inhibition of ERK or NF-kappaB activation failed to block T. vaginalis-induced up-regulation of TLR9 mRNA expression or TLR2 and TLR4 mRNA expression, respectively. In addition, pretreatment with SB203580 reduced epithelium-derived IL-8 and TNF-alpha release evoked by T. vaginalis. Our results show that T. vaginalis infection of the mucocutaneous barrier could up-regulate TLR2, 4 and 9 gene expression via the p38 MAPK signalling pathway in epithelial cells; this process then leads to modulation of p38 MAPK-dependent IL-8 and TNF-alpha release from the epithelium.

Involvement of MAP kinases in apoptosis of macrophage treated with Trichomonas vaginalis.

A primitive protozoan parasite Trichomonas vaginalis selectively activates the signal transduction pathways in macrophages (RAW264.7). This study evaluated the correlation of these signaling pathways and T. vaginalis-induced cell apoptosis. In macrophages infected with T. vaginalis, apoptosis was assessed on the basis of DNA fragmentation on agarose gel electrophoresis. Infection of macrophages with T. vaginalis induced tyrosine phosphorylation of several proteins. Infected cells with T. vaginalis were shown to associate with phosphorylation of the extracellular signal-regulated (ERK)1/2 kinase, p38, c-Jun N-terminal kinase (JNK) mitogen-activated protein (MAP) kinases on Western blot analysis. The present finding also demonstrated a link between the ERK1/2, JNK and p38 apoptotic pathways that was modulated by T. vaginalis infection.