Noise-related inaccuracies in the quantitative evaluation of CT artifacts.

Accuracies of measuring the artifact index (AI), a quantitative artifact evaluation index in X-ray CT images, were investigated. The AI is calculated based not only on the standard deviation (SD) of the artifact area in the image, but also on the SD of noise components for considering the noise influence. However, conventional measurement methods may not follow this consideration, for example the non-uniformity of the noise distribution is not taken into account, resulting in reducing the accuracy of AI.

Clear zone formation in microdroplets for high-throughput screening for lactic acid bacteria.

Droplet microfluidic-based technology is a powerful tool for biotechnology, and it is also expected that it will be applied to culturing and screening methods. Using this technology, a new high-throughput screening method for lactic acid bacteria was developed. In this study, the conventional culture of lactic acid bacteria that form clear zones on an agar medium was reproduced in water-in-oil droplets, and only the droplets in which lactic acid bacteria grew were collected one by one.

Receptive-field nonlinearities in primary auditory cortex: a comparative perspective.

Cortical processing of auditory information can be affected by interspecies differences as well as brain states. Here we compare multifeature spectro-temporal receptive fields (STRFs) and associated input/output functions or nonlinearities (NLs) of neurons in primary auditory cortex (AC) of four mammalian species. Single-unit recordings were performed in awake animals (female squirrel monkeys, female, and male mice) and anesthetized animals (female squirrel monkeys, rats, and cats).

Inconsistency between Human Observation and Deep Learning Models: Assessing Validity of Postmortem Computed Tomography Diagnosis of Drowning.

Drowning diagnosis is a complicated process in the autopsy, even with the assistance of autopsy imaging and the on-site information from where the body was found. Previous studies have developed well-performed deep learning (DL) models for drowning diagnosis. However, the validity of the DL models was not assessed, raising doubts about whether the learned features accurately represented the medical findings observed by human experts.

How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images.

Over the past decade, the use of deep learning has been widely increasing in the medical image diagnosis field. Deep learning-based methods' (DLMs) performance strongly relies on training data. Therefore, researchers often focus on collecting as much data as possible from different medical facilities or developing approaches to avoid the impact of inter-category imbalance (ICI), which means a difference in data quantity among categories.

Risk Analysis of Breast Cancer by Using Bilateral Mammographic Density Differences: A Case-Control Study.

The identification of risk factors helps radiologists assess the risk of breast cancer. Quantitative factors such as age and mammographic density are established risk factors for breast cancer. Asymmetric breast findings are frequently encountered during diagnostic mammography.

Deep Learning-Based Diagnosis of Fatal Hypothermia Using Post-Mortem Computed Tomography.

In forensic medicine, fatal hypothermia diagnosis is not always easy because findings are not specific, especially if traumatized. Post-mortem computed tomography (PMCT) is a useful adjunct to the cause-of-death diagnosis and some qualitative image character analysis, such as diffuse hyperaeration with decreased vascularity or pulmonary emphysema, have also been utilized for fatal hypothermia. However, it is challenging for inexperienced forensic pathologists to recognize the subtle differences of fatal hypothermia in PMCT images.

A 2.5D Deep Learning-Based Method for Drowning Diagnosis Using Post-Mortem Computed Tomography.

It is challenging to diagnose drowning in autopsy even with the help of post-mortem multi-slice computed tomography (MSCT) due to the complex pathophysiology and the shortage of forensic specialists equipped with radiology knowledge. Therefore, a computer-aided diagnosis (CAD) system was developed to help with diagnosis. Most deep learning-based CAD systems only utilize 2D information, which is proper for 2D data such as chest X-ray images.

Diagnosing Drowning in Postmortem CT Images Using Artificial Intelligence.

Imaging features of the lung in postmortem computed tomography (CT) scans have been reported in drowning cases. However, it is difficult for forensic pathologists with limited experience to distinguish subtle differences in CT images. In this study, artificial intelligence (AI) with deep learning capability was used to diagnose drowning in postmortem CT images, and its performance was evaluated.

Effect of heat-moisture treated brown rice crackers on postprandial flow-mediated dilation in adults with mild endothelial dysfunction.

Background: Endothelial dysfunction is an early pathophysiological feature and independent predictor of a poor prognosis in most forms of cardiovascular disease. We evaluated the effect of brown rice crackers (BR-C) on endothelial function.

Methods: Effect of heat-moisture treated (HMT) -BR-C on postprandial flow-mediated dilation (FMD) in adults with mild endothelial dysfunction was compared with that of BR-C and white rice crackers (WR-C) in 12 adults with mild endothelial dysfunction (less than 7.

[A Review of Current Knowledge for X-ray Energy in CT: Practical Guide for CT Technologist].

In computed tomography (CT) systems, the optimal X-ray energy in imaging depends on the material composition and the subject size. Among the parameters related to the X-ray energy, we can arbitrarily change only the tube voltage. For years, the tube voltage has often been set at 120 kVp.

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women.

In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences of successive NN intervals (RMSSD), were measured using a heart rate monitor (MyBeat) and four different emotions including "happy," as a positive emotion and "anxiety," "sad," "frustrated," as negative emotions were self-recorded on a smartphone application, during 1 week starting from 23rd to 32nd weeks of pregnancy from 85 pregnant women. The k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR), random forest (RF), naïve bayes (NB), decision tree (DT), gradient boosting trees (GBT), stochastic gradient descent (SGD), extreme gradient boosting (XGBoost), and artificial neural network (ANN) machine learning methods were applied to predict the four different emotions based on the heart rate-relevant information.

Manganese Dynamics in Mouse Brain After Systemic MnCl Administration for Activation-Induced Manganese-Enhanced MRI.

Activation-induced manganese-enhanced MRI (AIM-MRI) is an attractive tool for non-invasively mapping whole brain activities. Manganese ions (Mn) enter and accumulate in active neurons via calcium channels. Mn shortens the longitudinal relaxation time (T1) of H, and the longitudinal relaxation rate R1 (1/T1) is proportional to Mn concentration.

Letter on Convergence of In-Parameter-Linear Nonlinear Neural Architectures With Gradient Learnings.

This letter summarizes and proves the concept of bounded-input bounded-state (BIBS) stability for weight convergence of a broad family of in-parameter-linear nonlinear neural architectures (IPLNAs) as it generally applies to a broad family of incremental gradient learning algorithms. A practical BIBS convergence condition results from the derived proofs for every individual learning point or batches for real-time applications.

Comments on "Convergence Analysis of Adaptive Exponential Functional Link Network".

This article is to comment on the derivation of the weight-update stability of in-parameter-linear nonlinear learning system with the gradient descent learning rule in the above article. Our comments are not to disqualify the commented article's whole contribution; however, the issues should be pointed out to avoid their proliferation.

Lemniscal Corticothalamic Feedback in Auditory Scene Analysis.

Sound information is transmitted from the ear to central auditory stations of the brain via several nuclei. In addition to these ascending pathways there exist descending projections that can influence the information processing at each of these nuclei. A major descending pathway in the auditory system is the feedback projection from layer VI of the primary auditory cortex (A1) to the ventral division of medial geniculate body (MGBv) in the thalamus.

Plasticity of Multidimensional Receptive Fields in Core Rat Auditory Cortex Directed by Sound Statistics.

Sensory cortical neurons can nonlinearly integrate a wide range of inputs. The outcome of this nonlinear process can be approximated by more than one receptive field component or filter to characterize the ensuing stimulus preference. The functional properties of multidimensional filters are, however, not well understood.

Information diversity in individual auditory cortical neurons is associated with functionally distinct coordinated neuronal ensembles.

Neuronal activity in auditory cortex is often highly synchronous between neighboring neurons. Such coordinated activity is thought to be crucial for information processing. We determined the functional properties of coordinated neuronal ensembles (cNEs) within primary auditory cortical (AI) columns relative to the contributing neurons.

Comparison of Visible and Infrared Video Plethysmography Captured from Different Regions of the Human Face.

Recently, video plethysmography (VPG) - a heart rate estimation technique using a video camera - has gained significant attention. Most studies of VPG have used a visible RGB camera; only a limited number of studies investigating near-infrared light (wavelength 750-2500 nm), which can be used even in a dark environment, have been performed. The purpose of this study was to investigate the differences between VPG data collected using visible light (VPG) or near-infrared light (VPG) from four facial areas (forehead, right cheek, left cheek, and nose).

A Deep Learning Aided Drowning Diagnosis for Forensic Investigations using Post-Mortem Lung CT Images.

Feasibility of computer-aided diagnosis (CAD) systems has been demonstrated in the field of medical image diagnosis. Especially, deep learning based CAD systems showed high performance thanks to its capability of image recognition. However, there is no CAD system developed for post-mortem imaging diagnosis and thus it is still unclear if the CAD system is effective for this purpose.

Auditory Cortical Plasticity Dependent on Environmental Noise Statistics.

During critical periods, neural circuits develop to form receptive fields that adapt to the sensory environment and enable optimal performance of relevant tasks. We hypothesized that early exposure to background noise can improve signal-in-noise processing, and the resulting receptive field plasticity in the primary auditory cortex can reveal functional principles guiding that important task. We raised rat pups in different spectro-temporal noise statistics during their auditory critical period.