Reconstruction of the Chemical Gas Concentration Distribution Using Partial Convolution-Based Image Inpainting.

An interpolation method, which estimates unknown values with constrained information, is based on mathematical calculations. In this study, we addressed interpolation from an image-based perspective and expanded the use of image inpainting to estimate values at unknown points. When chemical gas is dispersed through a chemical attack or terrorism, it is possible to determine the concentration of the gas at each location by utilizing the deployed sensors. By interpolating the concentrations, we can obtain the contours of gas concentration. Accurately distinguishing the contours of a contaminated region from a map enables the optimal response to minimize damage. However, areas with an insufficient number of sensors have less accurate contours than other areas. In order to achieve more accurate contour data, an image inpainting-based method is proposed to enhance reliability by erasing and reconstructing low-accuracy areas in the contour. Partial convolution is used as the machine learning approach for image-inpainting, with the modified loss function for optimization. In order to train the model, we developed a gas diffusion simulation model and generated a gas concentration contour dataset comprising 100,000 contour images. The results of the model were compared to those of Kriging interpolation, one of the conventional spatial interpolation methods, finally demonstrating 13.21% higher accuracy. This suggests that interpolation from an image-based perspective can achieve higher accuracy than numerical interpolation on well-trained data. The proposed method was validated using gas concentration contour data from the verified gas dispersion modeling software Nuclear Biological Chemical Reporting And Modeling System (NBC_RAMS), which was developed by the Agency for Defense Development, South Korea.

Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum.

Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance.

Worsening of health disparities across COVID-19 pandemic stages in Korea.

OBJECTIVES: With the end of the coronavirus disease 2019 (COVID-19) pandemic, the health outcomes of this disease in Korea must be examined. We aimed to investigate health outcomes and disparities linked to socioeconomic status during the COVID-19 pandemic in Korea and to identify risk factors for hospitalization and mortality. METHODS: This nationwide retrospective study incorporated an analysis of individuals with and without COVID-19 in Korea between January 1, 2020 and December 31, 2022. The study period was divided into 4 stages. Prevalence, hospitalization, mortality, and case-fatality rates were calculated per 100,000 population. Multivariate logistic regression was performed to identify risk factors for COVID-19 hospitalization and mortality. RESULTS: Overall, the incidence rate was 40,601 per 100,000 population, the mortality rate was 105 per 100,000 population, and the case-fatality rate was 259 per 100,000 cases. A total of 12,577,367 new cases (24.5%) were recorded in stage 3 and 8,979,635 cases (17.5%) in stage 4. Medical Aid recipients displayed the lowest 3-year cumulative incidence rate (32,737 per 100,000) but the highest hospitalization (5,663 cases per 100,000), mortality (498 per 100,000), and case-fatality (1,521 per 100,000) rates. Male sex, older age, lower economic status, non-metropolitan area of residence, high Charlson comorbidity index, and disability were associated with higher risk of hospitalization and death. Vaccination was found to reduce mortality risk. CONCLUSIONS: As the pandemic progressed, surges were observed in incidence, hospitalization, and mortality, exacerbating disparities associated with economic status and disability. Nevertheless, Korea has maintained a low case-fatality rate across all economic groups.

Developing a deep learning model for sleep stage prediction in obstructive sleep apnea cohort using 60 GHz frequency-modulated continuous-wave radar.

Given the significant impact of sleep on overall health, radar technology offers a promising, non-invasive, and cost-effective avenue for the early detection of sleep disorders, even prior to relying on polysomnography (PSG)-based classification. In this study, we employed an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model to accurately predict sleep stages using 60 GHz frequency-modulated continuous-wave (FMCW) radar. Our dataset comprised 78 participants from an ongoing obstructive sleep apnea (OSA) cohort, recruited between July 2021 and November 2022, who underwent overnight polysomnography alongside radar sensor monitoring. The dataset encompasses comprehensive polysomnography recordings, spanning both sleep and wakefulness states. The predictions achieved a Cohen's kappa coefficient of 0.746 and an overall accuracy of 85.2% in classifying wakefulness, rapid-eye-movement (REM) sleep, and non-REM (NREM) sleep (N1 + N2 + N3). The results demonstrated that the models incorporating both Radar 1 and Radar 2 data consistently outperformed those using only Radar 1 data, indicating the potential benefits of utilising multiple radars for sleep stage classification. Although the performance of the models tended to decline with increasing OSA severity, the addition of Radar 2 data notably improved the classification accuracy. These findings demonstrate the potential of radar technology as a valuable screening tool for sleep stage classification.

Constipation is linked to accelerated cognitive and motor decline in isolated REM sleep behavior disorder.

INTRODUCTION: Constipation is associated with higher clinical severity and predicts cognitive decline in Parkinson's disease (PD). Whether the non-motor marker is associated with unfavorable motor and cognitive trajectories from the prodromal stage remains unclear. METHODS: In a longitudinal prospective cohort of subjects with isolated REM sleep behavior disorders (iRBD), subjects underwent repeated MDS-UPDRS and Mini-Mental Status Examination (MMSE) assessments. Generalized-estimating-equations (GEE) regression model was used to compare the time-dependent trajectories of MDS-UPDRS-III and MMSE scores between subjects with and without constipation at baseline. RESULTS: Twenty-nine subjects with constipation at baseline (iRBD+constipation) and 24 without (iRBD-constipation) were followed over 4.085 ± 2.645 years. The iRBD+constipation group presented faster decline of both MDS-UPDRS-III and MMSE scores, with additional estimated annual progression of +1.242 and -0.713 points, respectively, compared to the iRBD-constipation group (time*group p < 0.05). CONCLUSION: Constipation in isolated RBD is associated with accelerated progression of cognitive impairment and motor symptoms.

Brain olfactory-related atrophy in isolated rapid eye movement sleep behavior disorder.

OBJECTIVE: To investigate structural and functional connectivity changes in brain olfactory-related structures in a longitudinal prospective cohort of isolated REM sleep behavior disorder (iRBD) and their clinical correlations, longitudinal evolution, and predictive values for phenoconversion to overt synucleinopathies, especially Lewy body diseases. METHODS: The cohort included polysomnography-confirmed iRBD patients and controls. Participants underwent baseline assessments including olfactory tests, neuropsychological evaluations, the Movement Disorders Society-Unified Parkinson's Disease Rating Scale, 3T brain MRI, and 18 F-FP-CIT PET scans. Voxel-based morphometry (VBM) was performed to identify regions of atrophy in iRBD, and volumes of relevant olfactory-related regions of interest (ROI) were estimated. Subgroups of patients underwent repeated volumetric MRI and resting-state functional MRI (fMRI) scans after four years. RESULTS: A total of 51 iRBD patients were included, with 20 of them converting to synucleinopathy (mean time to conversion 3.08 years). Baseline VBM analysis revealed atrophy in the right olfactory cortex and gyrus rectus in iRBD. Subsequent ROI comparisons with controls showed atrophy in the amygdala. These olfactory-related atrophies tended to be associated with worse depression, anxiety, and urinary problems in iRBD. Amygdala 18 F-FP-CIT uptake tended to be reduced in iRBD patients with hyposmia (nonsignificant after multiple comparison correction) and correlated with urinary problems. Resting-state fMRI of 23 patients and 32 controls revealed multiple clusters with aberrant olfactory-related functional connectivity. Hypoconnectivity between the putamen and olfactory cortex was associated with mild parkinsonian signs in iRBD. Longitudinal analysis of volumetric volumetric MRI in 22 iRBD patients demonstrated four-year progression of olfactory-related atrophy. Cox regression analysis revealed that this atrophy significantly predicted phenoconversion. INTERPRETATION: Progressive atrophy of central olfactory structures may be a potential indicator of Lewy body disease progression in iRBD.

Development of a Rapid and Accurate Vapor Generation System for Real-Time Monitoring of a Chemical Warfare Agent (CWA) by Coupling Fourier Transform Infrared (FT-IR) Spectroscopy.

Developing an accurate chemical warfare agent (CWA) vapor generator is critical for homeland security because it enables real-time monitoring of target agent concentration for testing and evaluation. We designed and built an elaborate CWA vapor generator that offers reliable long-term stability and real-time monitoring capabilities by coupling it with Fourier transform infrared (FT-IR) spectroscopy. We evaluated the reliability and stability of the vapor generator using a gas chromatography-flame ion detector (GC-FID) and conducted a comparison between the experimental and theoretical results of sulfur mustard (HD, bis-2-chloroethylsulfide), a real CWA, at concentrations ranging from 1 to 5 ppm. Our FT-IR-coupled vapor generation system showed real-time monitoring ability, which enables rapid and accurate evaluation of chemical detectors. The vapor generation system was able to generate CWA vapor continuously for over 8 h, demonstrating its long-term vapor generation capability. In addition, we vaporized another representative CWA, viz., GB (Sarin, propan-2-yl ethylphosphonofluoridate), and conducted real-time monitoring of GB vapor concentration with high accuracy. This versatile vapor generator approach can enable the rapid and accurate evaluation of CWAs for homeland security against chemical threats and can be used in constructing a versatile real-time monitoring vapor generation system for CWAs.

Longitudinal evolution of cortical thickness signature reflecting Lewy body dementia in isolated REM sleep behavior disorder: a prospective cohort study.

BACKGROUND: The isolated rapid-eye-movement sleep behavior disorder (iRBD) is a prodromal condition of Lewy body disease including Parkinson's disease and dementia with Lewy bodies (DLB). We aim to investigate the longitudinal evolution of DLB-related cortical thickness signature in a prospective iRBD cohort and evaluate the possible predictive value of the cortical signature index in predicting dementia-first phenoconversion in individuals with iRBD. METHODS: We enrolled 22 DLB patients, 44 healthy controls, and 50 video polysomnography-proven iRBD patients. Participants underwent 3-T magnetic resonance imaging (MRI) and clinical/neuropsychological evaluations. We characterized DLB-related whole-brain cortical thickness spatial covariance pattern (DLB-pattern) using scaled subprofile model of principal components analysis that best differentiated DLB patients from age-matched controls. We analyzed clinical and neuropsychological correlates of the DLB-pattern expression scores and the mean values of the whole-brain cortical thickness in DLB and iRBD patients. With repeated MRI data during the follow-up in our prospective iRBD cohort, we investigated the longitudinal evolution of the cortical thickness signature toward Lewy body dementia. Finally, we analyzed the potential predictive value of cortical thickness signature as a biomarker of phenoconversion in iRBD cohort. RESULTS: The DLB-pattern was characterized by thinning of the temporal, orbitofrontal, and insular cortices and relative preservation of the precentral and inferior parietal cortices. The DLB-pattern expression scores correlated with attentional and frontal executive dysfunction (Trail Making Test-A and B: R = - 0.55, P = 0.024 and R = - 0.56, P = 0.036, respectively) as well as visuospatial impairment (Rey-figure copy test: R = - 0.54, P = 0.0047). The longitudinal trajectory of DLB-pattern revealed an increasing pattern above the cut-off in the dementia-first phenoconverters (Pearson's correlation, R = 0.74, P = 6.8 × 10-4) but no significant change in parkinsonism-first phenoconverters (R = 0.0063, P = 0.98). The mean value of the whole-brain cortical thickness predicted phenoconversion in iRBD patients with hazard ratio of 9.33 [1.16-74.12]. The increase in DLB-pattern expression score discriminated dementia-first from parkinsonism-first phenoconversions with 88.2% accuracy. CONCLUSION: Cortical thickness signature can effectively reflect the longitudinal evolution of Lewy body dementia in the iRBD population. Replication studies would further validate the utility of this imaging marker in iRBD.

Effects of music therapy as an alternative treatment on depression in children and adolescents with ADHD by activating serotonin and improving stress coping ability.

OBJECTIVE: The objective of this study was to determine the effect of music therapy as an alternative treatment on depression in children and adolescents with attention-deficit hyperactivity disorder (ADHD) by activating serotonin (5-HT) and improving stress coping ability. METHODS: This study is designed based on randomization method. A total of 36 subjects participated in the experiment, consisting of an ADHD control group (n = 18) and ADHD music therapy group (n = 18). The ADHD control group received standard care, while the ADHD music therapy group received music therapy and standard care. The ADHD music therapy group received both active music therapy (improvisation) and receptive music therapy (music listening) for 50 minutes, twice a week, for 3 months: a total of 24 times. From a neurophysiological perspective, changes in depression and stress were tracked by measuring 5-HT secretion, cortisol expression, blood pressure (BP), heart rate (HR), and CDI and DHQ psychological scales. RESULTS: The ADHD music therapy group's 5-HT secretion increased (p < 0.001), whereas cortisol expression (p < 0.001), BP (p < 0.001) and HR (p < 0.001) decreased. The CDI and DHQ psychological scales also showed positive changes (p < 0.01 and p < 0.001, respectively). However, the ADHD Con G's (who did not receive music therapy) 5-HT secretion did not increase, whereas cortisol expression, BP, and HR did not decrease. In addition, the CDI and DHQ psychological scales did not display positive changes. CONCLUSIONS: In conclusion, the application of music therapy as an alternative treatment for ADHD children and adolescents showed positive neurophysiological and psychological effects. Therefore, this study would like to propose a new alternative to medicine for preventing and treating depression through various uses of music therapy.

Detection of hazardous chemical using dual-wavelength Raman spectroscopy in the ultraviolet region.

This study proposes a stand-off Raman spectroscopy system using dual-wavelength in the ultraviolet (UV) region to detect hazardous chemicals. The Raman spectrum generated by the UV excitation source avoids solar background noise during daytime for chemical detection as the spectrum is in the solar blind range. Wavelengths of 213 and 266 nm by 5th and 4th harmonics are generated from Nd:YAG laser. However, Raman spectra of chemicals exhibit different signal-to-noise ratios for both the excitation wavelengths; therefore, to detect such chemicals, Raman spectra by two sources are required. Raman spectra were acquired using a dual-wavelength laser and spectrometer with a single grating and detector at the wavelengths of 213 and 266 nm simultaneously. The Raman spectra of sulfuric acid, 2-chloroethyl ethyl sulfide, and dimethyl methylphosphonate were acquired and analyzed, thus highlighting the application of dual-wavelength Raman spectroscopy. For efficient chemical detection in the field, we have ensured that the system developed in this study is robust.

Sleep quality of patients with papillary thyroid carcinoma: a prospective longitudinal study with 5-year follow-up.

We evaluated the pre- and postoperative sleep quality of patients with newly diagnosed papillary thyroid carcinoma (PTC) who underwent thyroid surgery, and investigated the factors associated with persistent poor sleep quality. The Pittsburgh sleep quality index (PSQI), Epworth sleepiness scale, and Stanford sleepiness scale were used to estimate sleep quality and daytime sleepiness. Face-to-face surveys were conducted preoperatively, and 1, 4, and 10 months after thyroid surgery. The PSQI was administered during a telephone interview about after 5 years after surgery. Forty-six patients (mean age 47.3 ± 10.1 years) with PTC (11 males, 35 females) were included in this study. Twenty-one participants underwent lobectomy and 25 underwent total thyroidectomy. Preoperatively, 35 (76.1%) patients showed poor sleep quality. PSQI scores at postoperative 1, 4, and 10 months were significantly lower than preoperative scores (p < 0.001). Postoperative 5-year PSQI scores decreased significantly compared to the preoperative scores (p < 0.001). Patients newly diagnosed with PTC suffered from sleep disturbance before and after surgery for at least 10 months, recovering to a comparable rate of sleep disturbance with the general population by 5 years after surgery. Higher preoperative PSQI score was at risk for prolonged poor sleep quality in patients with PTC.

Automated Detection of Sleep Apnea-Hypopnea Events Based on 60 GHz Frequency-Modulated Continuous-Wave Radar Using Convolutional Recurrent Neural Networks: A Preliminary Report of a Prospective Cohort Study.

Radar is a promising non-contact sensor for overnight polysomnography (PSG), the gold standard for diagnosing obstructive sleep apnea (OSA). This preliminary study aimed to demonstrate the feasibility of the automated detection of apnea-hypopnea events for OSA diagnosis based on 60 GHz frequency-modulated continuous-wave radar using convolutional recurrent neural networks. The dataset comprised 44 participants from an ongoing OSA cohort, recruited from July 2021 to April 2022, who underwent overnight PSG with a radar sensor. All PSG recordings, including sleep and wakefulness, were included in the dataset. Model development and evaluation were based on a five-fold cross-validation. The area under the receiver operating characteristic curve for the classification of 1-min segments ranged from 0.796 to 0.859. Depending on OSA severity, the sensitivities for apnea-hypopnea events were 49.0-67.6%, and the number of false-positive detections per participant was 23.4-52.8. The estimated apnea-hypopnea index showed strong correlations (Pearson correlation coefficient = 0.805-0.949) and good to excellent agreement (intraclass correlation coefficient = 0.776-0.929) with the ground truth. There was substantial agreement between the estimated and ground truth OSA severity (kappa statistics = 0.648-0.736). The results demonstrate the potential of radar as a standalone screening tool for OSA.

Cerebral small vessel disease as imaging biomarker predicting ocular cranial nerve palsy of presumed ischemic origin at admission.

Ocular cranial nerve palsy of presumed ischemic origin (OCNPi) is the most common type of ocular cranial nerve palsy (OCNP) in patients aged ≥ 50 years; however, no definite diagnostic test exists. As diagnostic criteria include clinical improvement, diagnoses are often delayed. Diagnostic biomarkers for OCNPi are required; we hypothesized that cerebral small vessel disease is associated with OCNPi. We analyzed 646 consecutive patients aged ≥ 50 years with isolated unilateral OCNP who underwent work-ups at two referral hospitals. White matter hyperintensities (WMHs), silent infarctions, and cerebral microbleeds (CMBs) were assessed. In multivariate analyses, mild (grades 1-3) and moderate to severe (grades 4-6) WMHs were significantly associated with OCNPi compared to OCNP of other origins (odds ratio [OR] 3.51, 95% confidence interval [CI] 1.91-6.43, P < 0.001; OR 3.47, 95% CI 1.42-8.48, P = 0.006, respectively). Silent infarction and CMBs did not remain associated (OR 0.96, 95% CI 0.54-1.70, P = 0.870; OR 0.55, 95% CI 0.28-1.08, P = 0.080, respectively). Associations between WMH and OCNPi remained after excluding patients with vascular risk factors. In conclusion, the presence of WMH could independently predict ischemic origin in patients with isolated unilateral OCNP at early stage of admission.

5-Bromo-3,4-dihydroxybenzaldehyde Promotes Hair Growth through Activation of Wnt/ß-Catenin and Autophagy Pathways and Inhibition of TGF-ß Pathways in Dermal Papilla Cells.

Various studies addressing the increasing problem of hair loss, using natural products with few side effects, have been conducted. 5-bromo-3,4-dihydroxybenzaldehyde (BDB) exhibited anti-inflammatory effects in mouse models of atopic dermatitis and inhibited UVB-induced oxidative stress in keratinocytes. Here, we investigated its stimulating effect and the underlying mechanism of action on hair growth using rat vibrissa follicles and dermal papilla cells (DPCs), required for the regulation of hair cycle and length. BDB increased the length of hair fibers in rat vibrissa follicles and the proliferation of DPCs, along with causing changes in the levels of cell cycle-related proteins. We investigated whether BDB could trigger anagen-activating signaling pathways, such as the Wnt/ß-catenin pathway and autophagy in DPCs. BDB induces activation of the Wnt/ß-catenin pathway through the phosphorylation of GSG3ß and ß-catenin. BDB increased the levels of autophagic vacuoles and autophagy regulatory proteins Atg7, Atg5, Atg16L, and LC3B. We also investigated whether BDB inhibits the TGF-ß pathway, which promotes transition to the catagen phase. BDB inhibited the phosphorylation of Smad2 induced by TGF-ß1. Thus, BDB can promote hair growth by modulating anagen signaling by activating Wnt/ß-catenin and autophagy pathways and inhibiting the TGF-ß pathway in DPCs.

Brain Metabolism Related to Mild Cognitive Impairment and Phenoconversion in Patients With Isolated REM Sleep Behavior Disorder.

BACKGROUND AND OBJECTIVES: Mild cognitive impairment (MCI) in isolated REM sleep behavior disorder (iRBD) is a risk factor for subsequent neurodegeneration. We aimed to identify brain metabolism and functional connectivity changes related to MCI in patients with iRBD and the neuroimaging markers' predictive value for phenoconversion. METHODS: This is a prospective cohort study of patients with iRBD with a mean follow-up of 4.2 ± 2.6 years. At baseline, patients with iRBD and age- and sex-matched healthy controls (HCs) underwent 18F-fluorodeoxyglucose (FDG)-PET and resting-state fMRI scans and a comprehensive neuropsychological test battery. Voxel-wise group comparisons for FDG-PET data were performed using a general linear model. Seed-based connectivity maps were computed using brain regions showing significant hypometabolism associated with MCI in patients with iRBD and compared between groups. A Cox regression analysis was applied to investigate the association between brain metabolism and risk of phenoconversion. RESULTS: Forty patients with iRBD, including 21 with MCI (iRBD-MCI) and 19 with normal cognition (iRBD-NC), and 24 HCs were included in the study. The iRBD-MCI group revealed relative hypometabolism in the inferior parietal lobule, lateral and medial occipital, and middle and inferior temporal cortex bilaterally compared with HC and the iRBD-NC group. In seed-based connectivity analyses, the iRBD-MCI group exhibited decreased functional connectivity of the left angular gyrus with the occipital cortex. Of 40 patients with iRBD, 12 patients converted to Parkinson disease (PD) or dementia with Lewy bodies (DLB). Hypometabolism of the occipital pole (hazard ratio [95% CI] 6.652 [1.387-31.987]), medial occipital (4.450 [1.143-17.327]), and precuneus (3.635 [1.009-13.093]) was associated with higher phenoconversion rate to PD/DLB. DISCUSSION: MCI in iRBD is related to functional and metabolic changes in broad brain areas, particularly the occipital and parietal areas. Moreover, hypometabolism in these brain regions was a predictor of phenoconversion to PD or DLB. Evaluation of cognitive function and neuroimaging characteristics could be useful for risk stratification in patients with iRBD.

A Case of Perampanel Overdose Presenting with Respiratory Failure.

Perampanel is a novel antiepileptic drug that has been used as an adjunctive treatment for focal-onset seizures. No reports to date have documented respiratory suppression as a side effect of perampanel in adults. Herein, we report a 51-year-old man with focal epilepsy presented with type 2 respiratory failure after accidently consuming of 66 mg of perampanel. Clinicians should consider the possibility of respiratory compromise whenever a high dose of perampanel needs to be administered to patients.

BALLU2: A Safe and Affordable Buoyancy Assisted Biped.

This work presents the first full disclosure of BALLU, Buoyancy Assisted Lightweight Legged Unit, and describes the advantages and challenges of its concept, the hardware design of a new implementation (BALLU2), a motion analysis, and a data-driven walking controller. BALLU is a robot that never falls down due to the buoyancy provided by a set of helium balloons attached to the lightweight body, which solves many issues that hinder current robots from operating close to humans. The advantages gained also lead to the platform's distinct difficulties caused by severe nonlinearities and external forces such as buoyancy and drag. The paper describes the nonconventional characteristics of BALLU as a legged robot and then gives an analysis of its unique behavior. Based on the analysis, a data-driven approach is proposed to achieve non-teleoperated walking: a statistical process using Spearman Correlation Coefficient is proposed to form low-dimensional state vectors from the simulation data, and an artificial neural network-based controller is trained on the same data. The controller is tested both on simulation and on real-world hardware. Its performance is assessed by observing the robot's limit cycles and trajectories in the Cartesian coordinate. The controller generates periodic walking sequences in simulation as well as on the real-world robot even without additional transfer learning. It is also shown that the controller can deal with unseen conditions during the training phase. The resulting behavior not only shows the robustness of the controller but also implies that the proposed statistical process effectively extracts a state vector that is low-dimensional yet contains the essential information of the high-dimensional dynamics of BALLU's walking.

An Effective Baseline Correction Algorithm Using Broad Gaussian Vectors for Chemical Agent Detection with Known Raman Signature Spectra.

Raman spectroscopy, which analyzes a Raman scattering spectrum of a target, has emerged as a key technology for non-contact chemical agent (CA) detection. Many CA detection algorithms based on Raman spectroscopy have been studied. However, the baseline, which is caused by fluorescence generated when measuring the Raman scattering spectrum, degrades the performance of CA detection algorithms. Therefore, we propose a baseline correction algorithm that removes the baseline, while minimizing the distortion of the Raman scattering spectrum. Assuming that the baseline is a linear combination of broad Gaussian vectors, we model the measured spectrum as a linear combination of broad Gaussian vectors, bases of background materials and the reference spectra of target CAs. Then, we estimate the baseline and Raman scattering spectrum together using the least squares method. Design parameters of the broad Gaussian vectors are discussed. The proposed algorithm requires reference spectra of target CAs and the background basis matrix. Such prior information can be provided when applying the CA detection algorithm. Via the experiment with real CA spectra measured by the Raman spectrometer, we show that the proposed baseline correction algorithm is more effective for removing the baseline and improving the detection performance, than conventional baseline correction algorithms.

Effect of obstructive sleep apnea on cerebrovascular compliance and cerebral small vessel disease.

Reduced cerebrovascular compliance is the major mechanism of cerebral small vessel disease (SVD). Obstructive sleep apnea (OSA) also promotes SVD development, but the underlying mechanism was not elucidated. We investigated the association among OSA, cerebrovascular compliance, and SVD parameters. This study retrospectively included individuals ≥ 50 years of age, underwent overnight polysomnographic (PSG) for the evaluation of OSA, and performed MRI and transcranial Doppler (TCD) within 12 months of interval without a neurological event between the evaluations. TCD parameters for the cerebrovascular compliance included middle cerebral artery pulsatility index (MCA PI) and mean MCA resistance index ratio (MRIR). SVD parameters included white matter hyperintensity (WMH) volume, number of lacunes, enlarged perivascular space (ePVS) score, and the presence of microbleeds or lacunes. Ninety-seven individuals (60.8% male, mean age 70.0±10.5 years) were included. MRIR was associated with higher respiratory distress index (B = 0.003; 95% confidence interval [CI] 0.001-0.005; P = 0.021), while MCA PI was not associated with any of the PSG markers for OSA severity. Apnea-hypopnea index was associated with the log-transformed total WMH volume (B = 0.008; 95% confidence interval [CI] 0.001-0.016; P = 0.020), subcortical WMH volume (B = 0.015; 95% CI 0.007-0.022; P<0.001), total ePVS score (B = 0.024; 95% CI 0.003-0.045; P = 0.026), and centrum semiovale ePVS score (B = 0.026; 95% CI 0.004-0.048; P = 0.019), and oxygen-desaturation index with periventricular WMH volume, independently from age, MCA PI, and MRIR. This study concluded that OSA is associated with reduced cerebrovascular compliance and also with SVD independently from cerebrovascular compliance. Underlying pathomechanistic link might be region specific.

Dynamic one-shot target detection and classification using a pseudo-Siamese network and its application to Raman spectroscopy.

Target detection and classification by Raman spectroscopy are important techniques for biological and chemical defense in military operations. Conventionally, these techniques preprocess the observed spectra using smoothing or baseline correction and apply detection algorithms like the generalized likelihood ratio test, independent component analysis, nonnegative matrix factorization, etc. These conventional detection algorithms need preprocessing and multiple shots of Raman spectra to get a reasonable accuracy. Recently, techniques based on deep learning are being used for target detection and classification due to its great adaptability and high accuracy over other methods and due to no requirement for preprocessing. Deep learning may give a good performance, but need retraining when untrained class targets are introduced which is time-consuming and bothersome. We devise a novel algorithm using a variant of the pseudo-Siamese network, one of the deep learning algorithms, that does not need retraining to detect and classify untrained class targets. Our algorithm detects and classifies targets with only one shot. In addition, our algorithm does not need preprocessing. We verify our algorithm with Raman spectra measured using a Raman spectrometer.