Multiclass classification of Alzheimer's disease prodromal stages using sequential feature embeddings and regularized multikernel support vector machine.

The detection of patients in the cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) stages of neurodegeneration is crucial for early treatment interventions. However, the heterogeneity of MCI data samples poses a challenge for CN vs. MCI vs.

Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma.

Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of recurrent glioblastoma. This study addresses the need for non-invasive approaches to map heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We developed BioNet, a biologically-informed neural network, to predict regional distributions of two primary tissue-specific gene modules: proliferating tumor (Pro) and reactive/inflammatory cells (Inf).

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma.

Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient.

Correlation enhanced distribution adaptation for prediction of fall risk.

With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults.

Usability of the novel ankle training equipment with spring resistance-based plantar press exercises in the standing position: A focus on chronic stroke patients with hemiplegic gait.

Background: To improve gait disability in patients with chronic stroke, ankle muscle strengthening and calf muscle stretching exercises are required. However, currently available ankle training equipment limit ankle exercises based on the position. Recently developed ankle training equipment enables spring resistance-based plantar press exercises to be performed in the standing position with weight support.

Harmonizing Healthy Cohorts to Support Multicenter Studies on Migraine Classification using Brain MRI Data.

Multicenter and multi-scanner imaging studies might be needed to provide sample sizes large enough for developing accurate predictive models. However, multicenter studies, which likely include confounding factors due to subtle differences in research participant characteristics, MRI scanners, and imaging acquisition protocols, might not yield generalizable machine learning models, that is, models developed using one dataset may not be applicable to a different dataset. The generalizability of classification models is key for multi-scanner and multicenter studies, and for providing reproducible results.

Prediction of fall risk among community-dwelling older adults using a wearable system.

Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test.

AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction.

The prediction of Mild Cognitive Impairment (MCI) patients who are at higher risk converting to Alzheimer's Disease (AD) is critical for effective intervention and patient selection in clinical trials. Different biomarkers including neuroimaging have been developed to serve the purpose. With extensive methodology development efforts on neuroimaging, an emerging field is deep learning research.

Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images.

Introduction: The aim of this study was to use a Deep Learning (DL) algorithm for the automated segmentation of cone-beam computed tomographic (CBCT) images and the detection of periapical lesions.

Methods: Limited field of view CBCT volumes (n = 20) containing 61 roots with and without lesions were segmented clinician dependent versus using the DL approach based on a U-Net architecture. Segmentation labeled each voxel as 1 of 5 categories: "lesion" (periapical lesion), "tooth structure," "bone," "restorative materials," and "background.

CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks.

Mobile devices such as sensors are used to connect to the Internet and provide services to users. Web services are vulnerable to automated attacks, which can restrict mobile devices from accessing websites. To prevent such automated attacks, CAPTCHAs are widely used as a security solution.

Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI.

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions.

Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis.

Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. The convolutional neural network (CNN) is an ideal model for this task because of its powerful learning capabilities through the large number of layers and trainable parameters. In this research, we propose a new architecture of residual inception encoder-decoder neural network (RIED-Net) to learn the nonlinear mapping between the input images and targeting output images.

A secure and efficient audit mechanism for dynamic shared data in cloud storage.

With popularization of cloud services, multiple users easily share and update their data through cloud storage. For data integrity and consistency in the cloud storage, the audit mechanisms were proposed. However, existing approaches have some security vulnerabilities and require a lot of computational overheads.

Immunomodulatory effects of Aureobasidium pullulans SM-2001 exopolymers on the cyclophosphamide-treated mice.

The immunomodulatory effects of exopolymers of Aureobasidium pullulans SM-2001 containing beta-1,3/1,6-glucan were evaluated on the cyclophosphamide (CPA)-treated mice. To induce immunosuppress, 150 and 110 mg/kg of CPA were intraperitoneally injected at 1 and 3 days before start of test material administrations, respectively. Exopolymers were subcutaneously or orally administered in a volume of 10 ml/kg, 4 times; 12-hr intervals from 24 hrs after second treatment of CPA.

High efficient white organic light-emitting diodes using BCzVBi as blue fluorescent dopant.

Efficiency improvement and color optimization of white organic light-emitting diodes (WOLEDs) were achieved via employing blue host DPVBi doped with blue fluorescent, BCzVBi. The structure of high efficient WOLED device was composed of ITO/NPB/DPVBi:BCzVBi-6%/MADN:DCM2-0.5%/Bphen/Liq/Al.

Transcriptional profiling of the developmentally important signalling pathways in human embryonic stem cells.

Background: Embryonic stem cells (ESC) maintain their 'stemness' by self-renewal. However, the molecular mechanisms underlying self-renewal of human embryonic stem cells (hESC) remain to be elucidated. In this study, expression profiles of the molecules of developmentally important signalling pathways were investigated to better understand the relationships of the signalling pathways for self-renewal in hESC.