Automated Scoring of Alzheimer's Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation.

Background: Application of visual scoring scales for regional atrophy in Alzheimer's disease (AD) in clinical settings is limited by their high time cost and low intra/inter-rater agreement. Objective: To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI). Methods: We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impairment [MCI], and 633 with AD). The occupancy indices for each regional volume were calculated by dividing each volume by the size of the lateral and inferior ventricular volumes. MR images from 355 participants (119 CN, 119 MCI, and 117 AD) from three different centers were used for validation. Two neuroradiologists performed visual assessments of the medial temporal, posterior, and global cortical atrophy scores in the frontal lobe using T1-weighted MR images. Images were also analyzed using the deep learning-based segmentation software, Neurophet AQUA. Cutoff values for the three scores were determined using the data distribution according to age. The scoring results were compared for consistency and reliability. Results: Four volumetric-driven scoring results showed a high correlation with the visual scoring results for AD, MCI, and CN. The overall agreement with human raters was weak-to-moderate for atrophy scoring in CN participants, and good-to-almost perfect in AD and MCI participants. AD subtyping by automated scores also showed usefulness as a research tool. Conclusions: Determining AD subtypes using automated atrophy scoring for late-MCI and AD could be useful in clinical settings or multicenter studies with large datasets.

Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review.

In this review, recent advances regarding the integration of machine learning into electrochemical analysis are overviewed, focusing on the strategies to increase the analytical context of electrochemical data for enhanced machine learning applications. While information-rich electrochemical data offer great potential for machine learning applications, limitations arise when sensors struggle to identify or quantitatively detect target substances in a complex matrix of non-target substances. Advanced machine learning techniques are crucial, but equally important is the development of methods to ensure that electrochemical systems can generate data with reasonable variations across different targets or the different concentrations of a single target. We discuss five strategies developed for building such electrochemical systems, employed in the steps of preparing sensing electrodes, recording signals, and analyzing data. In addition, we explore approaches for acquiring and augmenting the datasets used to train and validate machine learning models. Through these insights, we aim to inspire researchers to fully leverage the potential of machine learning in electroanalytical science.

Development of a prediction model for cognitive impairment of sarcopenia using multimodal neuroimaging in non-demented older adults.

INTRODUCTION: Despite prior research on the association between sarcopenia and cognitive impairment in the elderly, a comprehensive model that integrates various brain pathologies is still lacking. METHODS: We used data from 528 non-demented older adults with or without sarcopenia in the Catholic Aging Brain Imaging (CABI) database, containing magnetic resonance imaging scans, positron emission tomography scans, and clinical data. We also measured three key components of sarcopenia: skeletal muscle index (SMI), hand grip strength (HGS), and the five times sit-to-stand test (5STS). RESULTS: All components of sarcopenia were significantly correlated with global cognitive function, but cortical thickness and amyloid-beta (Aß) retention had distinctive relationships with each measure. In the path model, brain atrophy resulting in cognitive impairment was mediated by Aß retention for SMI and periventricular white matter hyperintensity for HGS, but directly affected by the 5STS. DISCUSSION: Treatments targeting each sub-domain of sarcopenia should be considered to prevent cognitive decline. HIGHLIGHTS: We identified distinct impacts of three sarcopenia measures on brain structure and Aß. Muscle mass is mainly associated with Aß and has an influence on the brain atrophy. Muscle strength linked with periventricular WMH and brain atrophy. Muscle function associated with cortical thinning in specific brain regions. Interventions on sarcopenia may be important to ease cognitive decline in the elderly.

A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.

Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer's Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician's early diagnosis and treatment plan design.

The Effects of Microbial Additive Supplementation on Growth Performance, Blood Metabolites, Fecal Microflora, and Carcass Characteristics of Growing-Finishing Pigs.

This study aimed to assess the effects of microbial additives that produce antimicrobial and digestive enzymes on the growth performance, blood metabolites, fecal microflora, and carcass characteristics of growing-finishing pigs. A total of 180 growing-finishing pigs (Landrace × Yorkshire × Duroc; mixed sex; 14 weeks of age; 58.0 ± 1.00 kg) were then assigned to one of three groups with three repetitions (20 pigs) per treatment for 60 days of adaptation and 7 days of collection. Dietary treatments included 0, 0.5, and 1.0% microbial additives in the basal diet. For growth performance, no significant differences in the initial and final weights were observed among the dietary microbial additive treatments, except for the average daily feed intake, average daily gain, and feed efficiency. In terms of blood metabolites and fecal microflora, immunoglobulin G (IgG), blood urea nitrogen, blood glucose, and fecal lactic acid bacteria count increased linearly, and fecal E. coli counts decreased linearly with increasing levels of microbial additives but not growth hormones and Salmonella. Carcass quality grade was improved by the microbial additive. In addition, carcass characteristics were not influenced by dietary microbial additives. In conclusion, dietary supplementation with 1.0% microbial additive improved average daily gain, feed efficiency, IgG content, and fecal microflora in growing-finishing pigs.

Impact of Apolipoprotein E4 on the Locus Coeruleus Functional Connectivity in Preclinical Alzheimer's Disease.

Background: Recent interest has surged in the locus coeruleus (LC) for its early involvement in Alzheimer's disease (AD), notably concerning the apolipoprotein ɛ4 allele (APOE4). Objective: This study aimed to discern LC functional connectivity (FC) variations in preclinical AD subjects, dissecting the roles of APOE4 carrier status and amyloid-ß (Aß) deposition. Methods: A cohort of 112 cognitively intact individuals, all Aß-positive, split into 70 APOE4 noncarriers and 42 carriers, underwent functional MRI scans, neuropsychological assessments, and APOE genotyping. The research utilized seed to voxel analysis for illustrating LC rsFC discrepancies between APOE4 statuses and employed a general linear model to examine the interactive influence of APOE4 carrier status and Aß deposition on LC FC values. Results: The investigation revealed no significant differences in sex, age, or SUVR between APOE4 carriers and noncarriers. It found diminished LC FC with the occipital cortex in APOE4 carriers and identified a significant interaction between APOE4 carrier status and temporal lobe SUVR in LC FC with the occipital cortex. This interaction suggested a proportional increase in LC FC for APOE4 carriers. Additional notable interactions were observed affecting LC FC with various brain regions, indicating a proportional decrease in LC FC for APOE4 carriers. Conclusions: These findings confirm that APOE4 carrier status significantly influences LC FC in preclinical AD, showcasing an intricate relationship with regional Aß deposition. This underscores the critical role of genetic and pathological factors in early AD pathophysiology, offering insights into potential biomarkers for early detection and intervention strategies.

Safety and feasibility of optimized transcranial direct current stimulation in patients with mild cognitive impairment due to Alzheimer's disease: a multicenter study protocol for a randomized controlled trial.

Introduction: Transcranial direct current stimulation (tDCS) may effectively preserve and improve cognitive function in patients with mild cognitive impairment (MCI). Research has shown that Individual brain characteristics can influence the effects of tDCS. Computer three-dimensional brain modeling based on magnetic resonance imaging (MRI) has been suggested as an alternative for determining the most accurate tDCS electrode position based on the patients' individual brain characteristics to enhance tDCS effects. Therefore, this study aims to determine the feasibility and safety of applying tDCS treatment using optimized and personalized tDCS electrode positions in patients with Alzheimer's disease (AD)-induced MCI using computer modeling and compare the results with those of a sham group to improve cognitive function. Method: A prospective active-sham group feasibility study was set to recruit 40 participants, who will be randomized into Optimized-tDCS and Sham-tDCS groups. The parameters for tDCS will be 2 mA (disk electrodes R = 1.5 cm) for 30 min during two sets of 15 sessions (2 weeks of resting period in between), using two electrodes in pairs. Using computer modeling, the tDCS electrode positions of each participant will be personalized. Outcome measurements are going to be obtained at three points: baseline, first post-test, and second post-test. The AD assessment scale-cognitive subscale (ADAS-Cog) and the Korean version of Mini-Mental State Examination (K-MMSE), together with other secondary outcomes and safety tests will be used. Discussion: For the present study, we hypothesize that compared to a sham group, the optimized personalized tDCS application would be effective in improving the cognitive function of patients with AD-induced MCI and the participants would tolerate the tDCS intervention without any significant adverse effects.Clinical trial registration: https://cris.nih.go.kr, identifier [KCT0008918].

Hydrophilic/Hydrophobic Janus Nanofibers Containing Compound K for Cartilage Regeneration.

Introduction: Cartilage regeneration is a challenging issue due to poor regenerative properties of tissues. Electrospun nanofibers hold enormous potentials for treatments of cartilage defects. However, nanofibrous materials used for the treatment of cartilage defects often require physical and/or chemical modifications to promote the adhesion, proliferation, and differentiation of cells. Thus, it is highly desirable to improve their surface properties with functionality. We aim to design hydrophilic, adhesive, and compound K-loaded nanofibers for treatments of cartilage defects. Methods: Hydrophilic and adhesive compound K-containing polycaprolactone nanofibers (CK/PCL NFs) were prepared by coatings of gallic acid-conjugated chitosan (CHI-GA). Therapeutic effects of CHI-GA/CK/PCL NFs were assessed by the expression level of genes involved in the cartilage matrix degradation, inflammatory response, and lipid accumulations in the chondrocytes. In addition, Cartilage damage was evaluated by safranin O staining and immunohistochemistry of interleukin-1ß (IL-1ß) using OA animal models. To explore the pathway associated with therapeutic effects of CHI-GA/CK/PCL NFs, cell adhesion, phalloidin staining, and the expression level of integrins and peroxisome proliferator-activated receptor (PPARs) were evaluated. Results: CHI-GA-coated side of the PCL NFs showed hydrophilic and adhesive properties, whereas the unmodified opposite side remained hydrophobic. The expression levels of genes involved in the degradation of the cartilage matrix, inflammation, and lipogenesis were decreased in CHI-GA/CK/PCL NFs owing to the release of CK. In vivo implantation of CHI-GA/CK/PCL NFs into the cartilage reduced cartilage degradation induced by destabilization of the medial meniscus (DMM) surgery. Furthermore, the accumulation of lipid deposition and expression levels of IL-1ß was reduced through the upregulation of PPAR. Conclusion: CHI-GA/CK/PCL NFs were effective in the treatments of cartilage defects by inhibiting the expression levels of genes involved in cartilage degradation, inflammation, and lipogenesis as well as reducing lipid accumulation and the expression level of IL-1ß via increasing PPAR.

Comparative efficacy and safety of subcutaneous infliximab and vedolizumab in patients with Crohn's disease and ulcerative colitis included in randomised controlled trials.

BACKGROUND: While indirect comparison of infliximab (IFX) and vedolizumab (VDZ) in adults with Crohn's disease (CD) or ulcerative colitis (UC) shows that IFX has better effectiveness during induction, and comparable efficacy during maintenance treatment, comparative data specific to subcutaneous (SC) IFX (i.e., CT-P13 SC) versus VDZ are limited. AIM: Pooled analysis of randomised studies to compare efficacy and safety with IFX SC and VDZ in moderate-to-severe inflammatory bowel disease. METHODS: Parallel-group, randomised studies evaluating IFX SC and VDZ in patients with moderate-to-severe CD or UC were identified. Eligible studies reported ≥ 1 prespecified outcome of interest at Week 6 (reflecting treatment during the induction phase) and/or at 1 year (Weeks 50-54; reflecting treatment during the maintenance phase). Prespecified efficacy and safety outcomes considered in this pooled analysis included the proportions of patients achieving disease-specific clinical responses, clinical remission, or discontinuing due to lack of efficacy, and the proportions of patients experiencing adverse events (AEs), serious AEs, infections, serious infections, or discontinuing due to AEs. Data from multiple studies or study arms were extracted and pooled using a random-effect model; comparative analyses were performed separately for patients with CD and UC. RESULTS: We identified three eligible CD trials and four eligible UC trials that assigned over 1200 participants per disease cohort to either IFX SC or VDZ. In patients with CD, intravenous induction therapy with IFX demonstrated better efficacy (non-overlapping 95% confidence intervals [CIs]) compared with VDZ; during the maintenance phase, IFX SC showed numerically better efficacy (overlapping 95% CIs) than VDZ. A lower proportion of IFX SC-treated patients discontinued therapy due to lack of efficacy over 1 year. In patients with UC, efficacy profiles were similar with IFX SC and VDZ during the induction and maintenance phases, and a lower proportion of IFX SC-treated patients discontinued therapy due to lack of efficacy over 1 year. In both cohorts, safety profiles for IFX SC and VDZ were generally comparable during 1 year. CONCLUSION: IFX SC demonstrated better efficacy than VDZ in patients with CD, and similar efficacy to VDZ in patients with UC; 1-year safety was comparable with IFX SC and VDZ.

Switching from intravenous to subcutaneous infliximab maintenance therapy in inflammatory bowel disease: Post hoc longitudinal analysis of a randomized trial.

BACKGROUND: Pharmacokinetic non-inferiority of subcutaneous (SC) to intravenous (IV) CT-P13 maintenance therapy was demonstrated in a randomized trial (NCT02883452). This post hoc analysis evaluated longitudinal clinical outcomes with the two infliximab treatment strategies. METHODS: Patients with Crohn's disease or ulcerative colitis received CT­P13 IV loading doses (5 mg/kg; Week [W] 0 and W2) before randomization (1:1) to receive CT-P13 SC (body weight-based dosing every 2 weeks [Q2W]; W6-54; 'SC maintenance group') or CT­P13 IV (5 mg/kg Q8W; W6-22) then CT-P13 SC (Q2W; W30-54; 'IV-to-SC switch group'). Paired W30/W54 patient-level data were analyzed. RESULTS: Fifty-three (IV-to-SC switch) and fifty-nine (SC maintenance) patients were analyzed. Median trough serum CT-P13 concentrations were significantly higher at W54 versus W30 in the IV-to-SC switch group (20.4 versus 2.3 µg/mL; p < 0.00001), while remaining consistent in the SC maintenance group. Statistically significant improvements in pharmacokinetics, efficacy, fecal calprotectin levels, and quality of life were seen following switch to SC administration at W30 in the IV-to-SC switch group; safety findings were similar pre- and post-switch. CONCLUSION: Formulation switching from IV to SC infliximab maintenance therapy was well tolerated and may provide additional clinical improvements. Findings require confirmation in larger prospective studies.

Determining optimal bead central angle by applying machine learning to wire arc additive manufacturing (WAAM).

Wire arc additive manufacturing (WAAM) is being extensively used in various industrial fields. In WAAM, if a bead is deposited without considering the central angle, its shape may collapse with increasing number of layers. To address this problem, a new method for optimizing the bead geometry using a support vector machine (SVM) classifier was established in this study. The ranges of the optimal deposition conditions were determined using the SVM classifier and verified by experiments. Geometric data of deposited beads were extracted using a laser profiler, and an SVM binary classifier was used to predict suitable ranges of the deposition conditions. Data were extracted through 20 single-layer basic experiments, classification was performed based on 4°, and the appropriateness of SVM classification was found through 8 single-layer and 3 multi-layer verification experiments. The results showed that the SVM classifier successfully selected the ranges of the optimal deposition conditions. Verification experiments revealed that the results in all cases were appropriately classified based on the boundary of the classification line. Moreover, the SVM classifier was efficient even when a small amount of input data was available. The contribution of this study is that the developed method can help build desired bead geometries in scenarios where deposition is required in the WAAM process, such as re-manufacturing. Thus, this method can be used in real-world industrial applications through further research on the bead shape with multi-layer deposition.

Associations between Education Years and Resting-state Functional Connectivity Modulated by APOE ε4 Carrier Status in Cognitively Normal Older Adults.

Objective: : Cognitive reserve has emerged as a concept to explain the variable expression of clinical symptoms in the pathology of Alzheimer's disease (AD). The association between years of education, a proxy of cognitive reserve, and resting-state functional connectivity (rFC), a representative intermediate phenotype, has not been explored in the preclinical phase, considering risk factors for AD. We aimed to evaluate whether the relationship between years of education and rFC in cognitively preserved older adults differs depending on amyloid-beta deposition and APOE ε4 carrier status as effect modifiers. Methods: : A total of 121 participants underwent functional magnetic resonance imaging, [18F] flutemetamol positron emission tomography-computed tomography, APOE genotyping, and a neuropsychological battery. Potential interactions between years of education and AD risk factors for rFC of AD-vulnerable neural networks were assessed with whole-brain voxel-wise analysis. Results: : We found a significant education years-by-APOE ε4 carrier status interaction for the rFC from the seed region of the central executive (CEN) and dorsal attention networks. Moreover, there was a significant interaction of rFC between right superior occipital gyrus and the CEN seed region by APOE ε4 carrier status for memory performances and overall cognitive function. Conclusion: : In preclinical APOE ε4 carriers, higher years of education were associated with higher rFC of the AD vulnerable network, but this contributed to lower cognitive function. These results contribute to a deeper understanding of the impact of cognitive reserve on sensitive functional intermediate phenotypic markers in the preclinical phase of AD.

Development of Efficient Brain Age Estimation Method Based on Regional Brain Volume From Structural Magnetic Resonance Imaging.

OBJECTIVE: We aimed to create an efficient and valid predicting model which can estimate individuals' brain age by quantifying their regional brain volumes. METHODS: A total of 2,560 structural brain magnetic resonance imaging (MRI) scans, along with demographic and clinical data, were obtained. Pretrained deep-learning models were employed to automatically segment the MRI data, which enabled fast calculation of regional brain volumes. Brain age gaps for each subject were estimated using volumetric values from predefined 12 regions of interest (ROIs): bilateral frontal, parietal, occipital, and temporal lobes, as well as bilateral hippocampus and lateral ventricles. A larger weight was given to the ROIs having a larger mean volumetric difference between the cognitively unimpaired (CU) and cognitively impaired group including mild cognitive impairment (MCI), and dementia groups. The brain age was predicted by adding or subtracting the brain age gap to the chronological age according to the presence or absence of the atrophy region. RESULTS: The study showed significant differences in brain age gaps among CU, MCI, and dementia groups. Furthermore, the brain age gaps exhibited significant correlations with education level and measures of cognitive function, including the clinical dementia rating sum-of-boxes and the Korean version of the Mini-Mental State Examination. CONCLUSION: The brain age that we developed enabled fast and efficient brain age calculations, and it also reflected individual's cognitive function and cognitive reserve. Thus, our study suggested that the brain age might be an important marker of brain health that can be used effectively in real clinical settings.

Increased Likelihood of Dementia with Coexisting Atrophy of Multiple Regions of Interest.

BACKGROUND: Brain volume is associated with cognitive decline in later life, and cortical brain atrophy exceeding the normal range is related to inferior cognitive and behavioral outcomes in later life. OBJECTIVE: To investigate the likelihood of cognitive decline, mild cognitive impairment (MCI), or dementia, when regional atrophy is present in participants' magnetic resonance imaging (MRI). METHODS: Multi-center MRI data of 2,545 adults were utilized to measure regional volumes using NEUROPHET AQUA. Four lobes (frontal, parietal, temporal, and occipital), four Alzheimer's disease-related regions (entorhinal, fusiform, inferior temporal, and middle temporal area), and the hippocampus in the left and right hemispheres were measured and analyzed. The presence of regional atrophy from brain MRI was defined as ≤1.5 standard deviation (SD) compared to the age- and sex-matched cognitively normal population. The risk ratio for cognitive decline was investigated for participants with regional atrophy in contrast to those without regional atrophy. RESULTS: The risk ratio for cognitive decline was significantly higher when hippocampal atrophy was present (MCI, 1.84, p < 0.001; dementia, 4.17, p < 0.001). Additionally, participants with joint atrophy in multiple regions showed a higher risk ratio for dementia, e.g., 9.6 risk ratio (95% confidence interval, 8.0-11.5), with atrophy identified in the frontal, temporal, and hippocampal gray matter, than those without atrophy. CONCLUSIONS: Our study showed that individuals with multiple regional atrophy (either lobar or AD-specific regions) have a higher likelihood of developing dementia compared to the age- and sex-matched population without atrophy. Thus, further consideration is needed when assessing MRI findings.

Open Set Bioacoustic Signal Classification based on Class Anchor Clustering with Closed Set Unknown Bioacoustic Signals.

Recently, deep learning-driven studies have been introduced for bioacoustic signal classification. Most of them, however, have the limitation that the input of the classifier needs to match with a trained label which is known as closed set recognition (CSR). To this end, the classifier trained by CSR would not cover a real stream task since the input of the classifier has so many variations. To combat real-world tasks, open set recognition (OSR) has been developed. In OSR, randomly collected inputs are fed to the classifier and the classifier predicts target classes and Unknown class. However, this OSR has been spotlighted in the studies of computer vision and speech domains while the domain of bioacoustic signal is less developed. Especially, to our best knowledge, OSR for animal sound classification has not been studied. This paper proposes a novel method for open set bioacoustic signal classification based on Class Anchored Clustering (CAC) loss with closed set unknown bioacoustic signals. To use the closed set unknown signals for training, a total of n +1 classes are used by adding one additional Unknown class to n target classes, and n +1 cross-entropy loss is added to the CAC loss. To evaluate the proposed method, we build an animal sound dataset that includes 101 species of sounds and compare its performance with baseline methods. In the experiments, our proposed method shows higher performance than other baseline methods in the area under the receiver operating curve for detecting target class and unknown class, the classification accuracy of open set signals, and classification accuracy for target classes. As a result, the closed set class samples are well classified while the open set unknown class can be also recognized with high accuracy at the same time.

The effect of deep brain structure modeling on transcranial direct current stimulation-induced electric fields: An in-silico study.

To study transcranial direct current stimulation (tDCS) and its effect on the brain, it could be useful to predict the distribution of the electric field induced in the brain with given tDCS parameters. As a solution, simulation with realistic computational models using magnetic resonance images (MRIs) have been widely used in the fields. With the recent advance of deep learning-based segmentation techniques of the brain, questions have been raised about if tDCS-induced electric field is affected by the deep brain structures. This study aimed to investigate the effect of the deep brain structure modeling on the induced electric field. To this end, we generated models with and without the deep brain structures by using an open MRI dataset comprising tDCS parameters, electric field simulation results and in-vivo intracranial recordings in the deep brain structures. We investigated the difference between the simulation results of the two models with a statistical analysis. Our results indicated that tDCS-induced electric fields and current flow in the brain are significantly different when the deep brain structures are considered.

Effect of Non-invasive Spinal Stimulation on Self-sustained Firing Motoneuron Model: In-Silico Study Using Human Body Model.

Transcutaneous spinal electrical stimulation (tSCS) is a non-invasive neuromodulation approach using a low intensity direct current. Recent developments in the technique have opened the possibility that tSCS can help restore motor function after spinal cord injury (SCI). However, the exact mechanism of action tSCS has on the spinal circuits is still unknown. Due to the complexity of experimental synthesis in a human model to delineate the mechanisms, models that link the stimulation paradigm and circuit behaviors are advantageous. Thus, this study aims to simulate the underlying changes in motor circuit firing rates in response to external stimuli induced by tSCS. Serial stimulations combining a high-fidelity finite element model with the human torso and spinal cord with a lumped motor neuron model is constructed. The parameters for both components of the model were derived from previous studies. We focused our analysis on a lumped motor neuron model that describes sustained firing behavior of the motor neuron driven primarily by persistent inward current (PIC), a signature behavior of the motor neuron after SCI. Modulation of the PIC behaviors was achieved by stimulating voltage-dependent calcium and sodium channels in the dendrite using a tSCS-induced electric field (E-field) expressed at different a spatial locations of the motor neuron in the gray matter. The PIC behaviors of spinal motor neurons in the left ventral horn were suppressed, while for the most part invariant in the right ventral horn. These initial simulations will provide a steppingstone for future examinations that incorporate additional neuronal models of inhibitory and excitatory interneurons to access the circuit-level effect of spinal stimulation.

A Study on the Characteristics of 304 Stainless Steel According to the Water Temperature Changes in Underwater Laser Beam Machining.

In underwater laser beam machining (ULBM), water provides a cooling effect by reducing the influence of the laser heat source, which makes ULBM more suitable for marking, cutting, and postprocessing than laser beam machining (LBM). Because the laser heat source not only affects the substrate temperature, but also heats the water, this study analyzes how the cooling effect occurs when water is heated. In this study, the heat-transformed zones in ULBM and heated underwater laser beam machining (HULBM) were improved by approximately 33% and 24%, respectively, compared to LBM at 400 W. In addition, the heat-affected zones in ULBM and HULBM improved by approximately 15% and 9%, respectively, compared to LBM. The hardness of ULBM and HULBM was higher than that of LBM. Based on these results, it was confirmed that water can reduce the effect of the laser heat source and improve the mechanical properties. Experiments will be conducted on the underwater laser beam machining of various substrates, such as Inconel718 and Ti-6Al-4V, in a future study. In addition, experiments will be conducted on the underwater laser beam machining of various substrates using a cooling system that can lower the temperature of water.

Manufacturing of stretchable substrate with biaxial strain control for highly-efficient stretchable solar cells and displays.

There has been significant research focused on the development of stretchable materials that can provide a large area with minimal material usage for use in solar cells and displays. However, most materials exhibit perpendicular shrinkage when stretched, which is particularly problematic for polymer-based substrates commonly used in stretchable devices. To address this issue, biaxial strain-controlled substrates have been proposed as a solution to increase device efficiency and conserve material resources. In this study, we present the design and fabrication of a biaxial strain-controlled substrate with a re-entrant honeycomb structure and a negative Poisson's ratio. Using a precisely machined mold with a shape error of less than 0.15%, we successfully fabricated polydimethylsiloxane substrates with a 500 µm thick re-entrant honeycomb structure, resulting in a 19.1% reduction in perpendicular shrinkage. This improvement translates to a potential increase in device efficiency by 9.44% and an 8.60% reduction in material usage for substrate fabrication. We demonstrate that this design and manufacturing method can be applied to the fabrication of efficient stretchable devices, such as solar cells and displays.

A regional brain volume-based age prediction model for neonates and the derived brain maturation index.

OBJECTIVE: To develop a postmenstrual age (PMA) prediction model based on segmentation volume and to evaluate the brain maturation index using the proposed model. METHODS: Neonatal brain MRIs without clinical illness or structural abnormalities were collected from four datasets from the Developing Human Connectome Project, the Catholic University of Korea, Hammersmith Hospital (HS), and Dankook University Hospital (DU). T1- and T2-weighted images were used to train a brain segmentation model. Another model to predict the PMA of neonates based on segmentation data was developed. Accuracy was assessed using mean absolute error (MAE), root mean square error (RMSE), and mean error (ME). The brain maturation index was calculated as the difference between the PMA predicted by the model and the true PMA, and its correlation with postnatal age was analyzed. RESULTS: A total of 247 neonates (mean gestation age 37 ± 4 weeks; range 24-42 weeks) were included. Thirty-one features were extracted from each neonate and the three most contributing features for PMA prediction were the right lateral ventricle, left caudate, and corpus callosum. The predicted and true PMA were positively correlated (coefficient = 0.88, p < .001). MAE, RMSE, and ME of the external dataset of HS and DU were 1.57 and 1.33, 1.79 and 1.37, and 0.37 and 0.06 weeks, respectively. The brain maturation index negatively correlated with postnatal age (coefficient = - 0.24, p < .001). CONCLUSION: A model that calculates the regional brain volume can predict the PMA of neonates, which can then be utilized to show the brain maturation degree. CLINICAL RELEVANCE STATEMENT: A brain maturity index based on regional volume of neonate's brain can be used to measure brain maturation degree, which can help identify the status of early brain development. KEY POINTS: • Neonatal brain MRI segmentation model could be used to assess neonatal brain maturation status. • A postmenstrual age (PMA) prediction model was developed based on a neonatal brain MRI segmentation model. • The brain maturation index, derived from the PMA prediction model, enabled the estimation of the neonatal brain maturation status.