Wongnedergaard4223

Further signal pathway inhibitors and the Cignal Finder 45-pathway reporter array illustrated that the up- and downstream pathways were TGFβ1-smad2/3 and p38MAPK, and Krüppel-like factor 4 (KLF4), respectively. In conclusion, AOBEE promoted KLF4 degradation leading to the attenuation of pulmonary fibrosis by inhibiting TGFβ1-smad/p38MAPK-lnc865/lnc556-miR-29b-2-5p-STAT3 signal pathway. We hope this work will provide valuable information to design new drugs and therapeutic targets of lncRNAs for pulmonary fibrosis treatment.[This corrects the article DOI 10.2196/25807.].Depression is the result of a complex interaction of social, psychological and physiological elements. It is now considered to be a major threat to people's physical health, and even as a threat to their lives. Research into the brain disorders of patients suffering from depression can help doctors to understand the pathogenesis of depression and facilitate its diagnosis and treatment. Functional near-infrared spectroscopy (fNIRS) is a non-invasive approach to the detection of brain functions and activities based on changes to the hemoglobin's oxygenation. In this paper, a comprehensive fNIRS-based depression-processing architecture, including the layers of source, feature and model, is first established to guide the deep modeling for fNIRS. In view of the complexity of depression, we propose a methodology in the time and frequency domains for feature extraction and deep neural networks for depression recognition and combining with current research. It is found that compared to non-depressed people, patients with depression have a weaker encephalic area connectivity and lower level of activation in the prefrontal lobe during brain activity. Finally, based on raw data, manual features and channel correlations, to recognize depression, the AlexNet model shows the best performance, especially in terms of the correlation features and presents an accuracy rate of 0.90 and a precision rate of 0.91, which is higher than ResNet18 and machine-learning algorithms on other data. Therefore, the correlation of brain regions can effectively recognize depression (from cases of non-depression), making it significant for the recognition of brain functions in the clinical diagnosis and treatment of depression.Enhancing the quality of low-light (LOL) images plays a very important role in many image processing and multimedia applications. In recent years, a variety of deep learning techniques have been developed to address this challenging task. A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces, causing many unfavorable outcomes, e.g., details loss, color unsaturation, and artifacts. To address these issues, we develop a new context-sensitive decomposition network (CSDNet) architecture to exploit the scene-level contextual dependencies on spatial scales. More concretely, we build a two-stream estimation mechanism including reflectance and illumination estimation network. We design a novel context-sensitive decomposition connection to bridge the two-stream mechanism by incorporating the physical principle. The spatially varying illumination guidance is further constructed for achieving the edge-aLiteCSDNet for short). SLiteCSDNet just contains 0.0301M parameters but achieves the almost same performance as CSDNet. Code is available at https//github.com/KarelZhang/CSDNet-CSDGAN.One of the pillar generative machine learning approaches in time series data study and analysis is the hidden Markov model (HMM). https://www.selleckchem.com/products/n6022.html Early research focused on the speech recognition application of the model with later expansion into numerous fields, including video classification, action recognition, and text translation. The recently developed generalized Dirichlet HMMs have proven efficient in proportional sequential data modeling. As such, we focus on investigating a maximum a posteriori (MAP) framework for the inference of its parameters. The proposed approach differs from the widely deployed Baum-Welch through the placement of priors that regularizes the estimation process. A feature selection paradigm is also integrated simultaneously in the algorithm. For validation, we apply our proposed approach in the classification of dynamic textures and the recognition of infrared actions.Haptic search is a common everyday task, usually consisting of two processes target search and target analysis. During target search we need to know where our fingers are in space, remember the already completed path and the outline of the remaining space. During target analysis we need to understand whether the detected potential target is the desired one. Here we characterized dynamics of exploratory movements in these two processes. In our experiments participants searched for a particular configuration of symbols on a rectangular tactile display. We observed that participants preferentially moved the hand parallel to the edges of the tactile display during target search, which possibly eased orientation within the search space. After a potential target was detected by any of the fingers, there was higher probability that subsequent exploration was performed by the index or the middle finger. At the same time, these fingers ramatically slowed down. Being in contact with the potential target, the index and the middle finger moved within a smaller area than the other fingers, which rather seemed to move away to leave them space. These results suggest that the middle and the index finger are specialized for fine analysis in haptic search.Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other. While protein-protein interaction (PPI) network-based methods for this purpose account for interactions between genes' protein products, current PPI network data are context-unspecific, spanning different biological conditions. Instead, here, we focus on an aging-specific subnetwork of the entire PPI network, obtained by integrating aging-specific gene expression data and PPI network data. The potential of such data integration \emphhas been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting aging-related genes from an aging-specific PPI subnetwork. In a systematic and comprehensive evaluation, we find that in many of the evaluation tests (i) using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network, and (ii) predictive methods from our framework that have not previously been used for supervised prediction of aging-related genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.Dysarthria is a disorder that affects an individual's speech intelligibility due to the paralysis of muscles and organs involved in the articulation process. As the condition is often associated with physically debilitating disabilities, not only do such individuals face communication problems, but also interactions with digital devices can become a burden. For these individuals, automatic speech recognition (ASR) technologies can make a significant difference in their lives as computing and portable digital devices can become an interaction medium, enabling them to communicate with others and computers. However, ASR technologies have performed poorly in recognizing dysarthric speech, especially for severe dysarthria, due to multiple challenges facing dysarthric ASR systems. We identified these challenges are due to the alternation and inaccuracy of dysarthric phonemes, the scarcity of dysarthric speech data, and the phoneme labeling imprecision. This paper reports on our second dysarthric-specific ASR system, called Speech Vision (SV) that tackles these challenges by adopting a novel approach towards dysarthric ASR in which speech features are extracted visually, then SV learns to see the shape of the words pronounced by dysarthric individuals. This visual acoustic modeling feature of SV eliminates phoneme-related challenges. To address the data scarcity problem, SV adopts visual data augmentation techniques, generates synthetic dysarthric acoustic visuals, and leverages transfer learning. Benchmarking with other state-of-the-art dysarthric ASR considered in this study, SV outperformed them by improving recognition accuracies for 67% of UA-Speech speakers, where the biggest improvements were achieved for severe dysarthria.Contemporary scientific data sets require fast and scalable topological analysis to enable visualization, simplification and interaction. Within this field, parallel merge tree construction has seen abundant recent contributions, with a trend of decentralized, task-parallel or SMP-oriented algorithms dominating in terms of total runtime. However, none of these recent approaches computed complete merge trees on distributed systems, leaving this field to traditional divide & conquer approaches. This article introduces a scalable, parallel and distributed algorithm for merge tree construction outperforming the previously fastest distributed solution by a factor of around three. This is achieved by a task-parallel identification of individual merge tree arcs by growing regions around critical points in the data, without any need for ordered progression or global data structures, based on a novel insight introducing a sufficient local boundary for region growth.Deep reinforcement learning (DRL) targets to train an autonomous agent to interact with a pre-defined environment and strives to achieve specific goals through deep neural networks (DNN). Recurrent neural network (RNN) based DRL has demonstrated superior performance, as RNNs can effectively capture the temporal evolution of the environment and respond with proper agent actions. However, apart from the outstanding performance, little is known about how RNNs understand the environment internally and what has been memorized over time. Revealing these details is extremely important for deep learning experts to understand and improve DRLs, which in contrast, is also challenging due to the complicated data transformations inside these models. In this paper, we propose Deep Reinforcement Learning Interactive Visual Explorer (DRLIVE), a visual analytics system to effectively explore, interpret, and diagnose RNN-based DRLs. Focused on DRL agents trained for different Atari games, DRLIVE targets to accomplish three tasks game episode exploration, RNN hidden/cell state examination, and interactive model perturbation. Using the system, one can flexibly explore a DRL agent through interactive visualizations, discover interpretable RNN cells by prioritizing RNN hidden/cell states with a set of metrics, and further diagnose the DRL model by interactively perturbing its inputs. Through concrete studies with multiple deep learning experts, we validated the efficacy of DRLIVE.