A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features.

One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is critical for the patient's health. Many automated artificial intelligence (AI) methods have recently been developed to diagnose tumors.

Vernier effect assisted sucrose sensor based on a cascaded Sagnac interferometer with no-core fiber.

We propose a sucrose concentration sensor by utilizing a fiber Sagnac interferometer with no-core fiber (SI-NCF) based on the Vernier effect. The Vernier effect is realized by introducing a single Sagnac interferometer (SI) with a similar free spectral range of SI-NCF. When the NCF is exposed to the external sucrose solution, the cladding state of NCF is changed, which induces the wavelength shift of the SI-NCF.

Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images.

This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke.

Anxiety and depression severity in neuropsychiatric SLE are associated with perfusion and functional connectivity changes of the frontolimbic neural circuit: a resting-state f(unctional) MRI study.

Objective: To examine the hypothesis that perfusion and functional connectivity disturbances in brain areas implicated in emotional processing are linked to emotion-related symptoms in neuropsychiatric SLE (NPSLE).

Methods: Resting-state fMRI (rs-fMRI) was performed and anxiety and/or depression symptoms were assessed in 32 patients with NPSLE and 18 healthy controls (HC). Whole-brain time-shift analysis (TSA) maps, voxel-wise global connectivity (assessed through intrinsic connectivity contrast (ICC)) and within-network connectivity were estimated and submitted to one-sample t-tests.

Analysis of the Effects of Nutrient Intake and Dietary Habits on Depression in Korean Adults.

While several studies have explored nutrient intake and dietary habits associated with depression, few studies have reflected recent trends and demographic factors. Therefore, we examined how nutrient intake and eating habits are associated with depression, according to gender and age. We performed simple and multiple regressions using nationally representative samples of 10,106 subjects from the Korea National Health and Nutrition Examination Survey.

A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds.

Medicinal plants and their extracts have been used as important sources for drug discovery. In particular, plant-derived natural compounds, including phytochemicals, antioxidants, vitamins, and minerals, are gaining attention as they promote health and prevent disease. Although several methods have been developed to confirm the biological activities of natural compounds, there is still considerable room to reduce time and cost.

Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems.

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time-frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs.

An interactive retrieval system for clinical trial studies with context-dependent protocol elements.

A well-defined protocol for a clinical trial guarantees a successful outcome report. When designing the protocol, most researchers refer to electronic databases and extract protocol elements using a keyword search. However, state-of-the-art database systems only offer text-based searches for user-entered keywords.

High-Precision Continuous-Wave Terahertz Spectroscopy Based on a Photomixing Technique for Identifying Pearls.

We present the accurate terahertz spectra of between imitation and cultured pearls using continuous-wave terahertz (CW-THz) spectroscopy. Using Fourier transform infrared (FT-IR) spectroscopy and optical coherence tomography (OCT) measurements, cultured pearls can be distinguished from imitation pearls by observing distinct absorption peaks and discriminative boundaries. The THz absorption spectra up to 0.

Investigating the transferring capability of capsule networks for text classification.

Text classification has been attracting increasing attention with the growth of textual data created on the Internet. Great progress has been made by deep neural networks for domains where a large amount of labeled training data is available. However, providing sufficient data is time-consuming and labor-intensive, establishing substantial obstacles for expanding the learned models to new domains or new tasks.

Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier.

Background: Ontology has attracted substantial attention from both academia and industry. Handling uncertainty reasoning is important in researching ontology. For example, when a patient is suffering from cirrhosis, the appearance of abdominal vein varices is four times more likely than the presence of bitter taste.

KMR: knowledge-oriented medicine representation learning for drug-drug interaction and similarity computation.

Efficient representations of drugs provide important support for healthcare analytics, such as drug-drug interaction (DDI) prediction and drug-drug similarity (DDS) computation. However, incomplete annotated data and drug feature sparseness create substantial barriers for drug representation learning, making it difficult to accurately identify new drug properties prior to public release. To alleviate these deficiencies, we propose KMR, a knowledge-oriented feature-driven method which can learn drug related knowledge with an accurate representation.

KGDDS: A System for Drug-Drug Similarity Measure in Therapeutic Substitution based on Knowledge Graph Curation.

Measuring drug-drug similarity is important but challenging. Significant progresses have been made in drugs whose labeled training data is sufficient and available. However, handling data skewness and incompleteness with domain-specific knowledge graph, is still a relatively new territory and an under-explored prospect.

CBN: Constructing a clinical Bayesian network based on data from the electronic medical record.

The process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on knowledge bases such as ontology that are manually compiled through a labour-intensive process or automatically derived using simple pairwise statistics. We explore CBN, a Clinical Bayesian Network construction for medical ontology probabilistic inference, to learn high-quality Bayesian topology and complete ontology directly from EMRs.

New Binary Locally Repairable Codes with Locality 2 and Uneven Availabilities for Hot Data.

In this paper, a new family of binary LRCs (BLRCs) with locality 2 and uneven availabilities for hot data is proposed, which has a high information symbol availability and low parity symbol availabilities for the local repair of distributed storage systems. The local repair of each information symbol for the proposed codes can be done not by accessing other information symbols but only by accessing parity symbols. The proposed BLRCs with k = 4 achieve the optimality on the information length for their given code length, minimum Hamming distance, locality, and availability in terms of the well-known theoretical upper bound.

EAPB: entropy-aware path-based metric for ontology quality.

Background: Entropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path.

Results: We propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes.

An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription.

Background: The available antibiotic decision-making systems were developed from a physician's perspective. However, because infectious diseases are common, many patients desire access to knowledge via a search engine. Although the use of antibiotics should, in principle, be subject to a doctor's advice, many patients take them without authorization, and some people cannot easily or rapidly consult a doctor.

Room-Temperature-Processed Flexible Amorphous InGaZnO Thin Film Transistor.

A room-temperature flexible amorphous indium-gallium-zinc oxide thin film transistor (a-IGZO TFT) technology is developed on plastic substrates, in which both the gate dielectric and passivation layers of the TFTs are formed by an anodic oxidation (anodization) technique. While the gate dielectric AlO is grown with a conventional anodization on an Al:Nd gate electrode, the channel passivation layer AlO is formed using a localized anodization technique. The anodized AlO passivation layer shows a superior passivation effect to that of PECVD SiO.

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis.

Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques.

Texture-based block partitioning method for motion compensated frame interpolation.

This paper presents a novel motion compensated frame interpolation (MCFI) algorithm that includes texture-based wedgelet partitioning (TWP) and multiple prediction based search (MPS). TWP partitions a rectangular block into two wedge-shaped sub-blocks using the texture information, which makes a better approximation for an actual object region. Thus, detailed motions around the object boundaries can be more precisely represented than by existing MCFI algorithms.

Detection of Organic Compounds in Water by an Optical Absorbance Method.

This paper proposes an optical method which allows determination of the organic compound concentration in water by measurement of the UV (ultraviolet) absorption at a wavelength of 250 nm~300 nm. The UV absorbance was analyzed by means of a multiple linear regression model for estimation of the total organic carbon contents in water, which showed a close correlation with the UV absorbance, demonstrating a high adjusted coefficient of determination, 0.997.

Effects of custom-made insoles on idiopathic pes cavus foot during walking.

From a subject group of pes cavus, the purpose of this study was to evaluate the biomechanical characteristics of lower limbs, based on plantar foot pressure and electromyography (EMG) activities, by the effects on two kind of custom-made insoles. Ten individuals among thirty females with a clinical diagnosis of idiopathic pes cavus (mean age (SD): 22.3 (0.

Sleep snoring detection using multi-layer neural networks.

Snoring detection is important for diagnosing obstructive sleep apnea syndrome (OSAS) and other respiratory sleep disorders. In general, audio signal processing such as snoring sound analysis uses the frequency characteristics of the signal. Recently, a correlational filter Multilayer Perceptron neural network (f-MLP) has been proposed, which has the first hidden layer of correlational filter operations in frequency domain.

Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system.

This research aims to depict the methodological steps and tools about the combined operation of case-based reasoning (CBR) and multi-agent system (MAS) to expose the ontological application in the field of clinical decision support. The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians.