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Leg pain can be caused by both lumbar spinal disease and chronic venous disorder (CVD) of leg veins, but their clinical differences have not been thoroughly investigated. This study aimed to determine the incidence of CVD among patients visiting a spine center for leg pain. A total of 196 cases underwent ultrasound examination with a diagnosis rate were 85.7% (168 cases). CVD-diagnosed cases were divided into two groups based on the severity of lumbar spinal disease. The Clinical grades, symptom areas, and symptom types were compared. The differences in symptom improvements with vasoactive medication were also assessed. The most common symptom area was calf then the foot in CVD, while calf then thigh in lumbar spinal disease. Tingling-paresthesia was the most common symptom type for both, with pain and cramping similarly common in CVD and pain more common than cramping in lumbar spinal disease. Considering that the majority of CVD cases (78.6%) had minor cutaneous changes and almost half of cases (41.7%) had refluxes only in tributaries, significant differences in symptom improvement in CVD-dominant group suggested that early-stage venous reflux is a symptomatic disease and a possible cause of leg pain and other symptoms. [ABSTRACT FROM AUTHOR]

Downsizing a catalyst nanoparticle (NP) to a single atom (SA) has proven to be highly effective in increasing catalytic activity and decreasing the amount of catalyst required for various electrochemical reactions. However, insufficient stability of the single‐atom site catalysts (SACs) is still a significant challenge for their practical application. Here, SACs firmly bound to stable metal oxide NPs are proposed to dramatically increase the electrochemical activity and stability of SA‐based catalysts for hydrogen evolution reaction (HER). Starting from a Ru‐infiltrated, Zr‐based metal‐organic framework (MOF), the tetragonal zirconium oxide (ZrO2‐x) NPs‐embedded carbon matrix is fabricated as support through facile pyrolysis. Simultaneously, Ru SAs as active sites are well dispersed on the surface of ZrO2‐x NPs due to the generation of oxygen vacancies in the tetragonal ZrO2‐x. The Ru‐ZrO2‐x SAC exhibits a 4–5 times higher mass activity than commercial Pt and Ru catalysts and superior durability due to strong metal‐support interaction (SMSI) between Ru atoms and ZrO2‐x substrate. [ABSTRACT FROM AUTHOR]

Background: In Alzheimer's Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. Results: DGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. Conclusion: Overall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a complex neurodegenerative disorder and the most common type of dementia. AD is characterized by a decline of cognitive function and brain atrophy, and is highly heritable with estimated heritability ranging from 60 to 80 % . The most straightforward and widely used strategy to identify AD genetic basis is to perform genome-wide association study (GWAS) of the case-control diagnostic status. These GWAS studies have identified over 50 AD related susceptibility loci. Recently, imaging genetics has emerged as a new field where brain imaging measures are studied as quantitative traits to detect genetic factors. Given that many imaging genetics studies did not involve the diagnostic outcome in the analysis, the identified imaging or genetic markers may not be related or specific to the disease outcome. Results: We propose a novel method to identify disease-related genetic variants enriched by imaging endophenotypes, which are the imaging traits associated with both genetic factors and disease status. Our analysis consists of three steps: (1) map the effects of a genetic variant (e.g., single nucleotide polymorphism or SNP) onto imaging traits across the brain using a linear regression model, (2) map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model, and (3) detect SNP-diagnosis association via correlating the SNP effects with the diagnostic effects on the brain-wide imaging traits. We demonstrate the promise of our approach by applying it to the Alzheimer's Disease Neuroimaging Initiative database. Among 54 AD related susceptibility loci reported in prior large-scale AD GWAS, our approach identifies 41 of those from a much smaller study cohort while the standard association approaches identify only two of those. Clearly, the proposed imaging endophenotype enriched approach can reveal promising AD genetic variants undetectable using the traditional method. Conclusion: We have proposed a novel method to identify AD genetic variants enriched by brain-wide imaging endophenotypes. This approach can not only boost detection power, but also reveal interesting biological pathways from genetic determinants to intermediate brain traits and to phenotypic AD outcomes. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is one of the most common neurodegenerative disorders characterized by progressive decline in cognitive function. Targeted genetic analyses, genome-wide association studies, and imaging genetic analyses have been performed to detect AD risk and protective genes and have successfully identified dozens of AD susceptibility loci. Recently, brain imaging transcriptomics analyses have also been conducted to investigate the relationship between neuroimaging traits and gene expression measures to identify interesting gene-traits associations. These imaging transcriptomic studies typically do not involve the disease outcome in the analysis, and thus the identified brain or transcriptomic markers may not be related or specific to the disease outcome. Results: We propose an innovative two-stage approach to identify genes whose expression profiles are related to diagnosis phenotype via brain transcriptome mapping. Specifically, we first map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model. Then, the gene-diagnosis association is assessed by spatially correlating the brain transcriptome map with the diagnostic effect map on the brain-wide imaging traits. To demonstrate the promise of our approach, we apply it to the integrative analysis of the brain transcriptome data from the Allen Human Brain Atlas (AHBA) and the amyloid imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our method identifies 12 genes whose brain-wide transcriptome patterns are highly correlated with six different diagnostic effect maps on the amyloid imaging traits. These 12 genes include four confirmatory findings (i.e., AD genes reported in DisGeNET) and eight novel genes that have not be associated with AD in DisGeNET. Conclusion: We have proposed a novel disease-related brain transcriptomic mapping method to identify genes whose expression profiles spatially correlated with regional diagnostic effects on a studied brain trait. Our empirical study on the AHBA and ADNI data shows the promise of the approach, and the resulting AD gene discoveries provide valuable information for better understanding biological pathways from transcriptomic signatures to intermediate brain traits and to phenotypic disease outcomes. [ABSTRACT FROM AUTHOR]

The age at onset (AAO) is an important determinant in Parkinson's disease (PD). Neuroimaging genetics is suitable for studying AAO in PD as it jointly analyzes imaging and genetics. We aimed to identify features associated with AAO in PD by applying the objective-specific neuroimaging genetics approach and constructing an AAO prediction model. Our objective-specific neuroimaging genetics extended the sparse canonical correlation analysis by an additional data type related to the target task to investigate possible associations of the imaging–genetic, genetic–target, and imaging–target pairs simultaneously. The identified imaging, genetic, and combined features were used to construct analytical models to predict the AAO in a nested five-fold cross-validation. We compared our approach with those from two feature selection approaches where only associations of imaging–target and genetic–target were explored. Using only imaging features, AAO prediction was accurate in all methods. Using only genetic features, the results from other methods were worse or unstable compared to our model. Using both imaging and genetic features, our proposed model predicted the AAO well (r = 0.5486). Our findings could have significant impacts on the characterization of prodromal PD and contribute to diagnosing PD early because genetic features could be measured accurately from birth. [ABSTRACT FROM AUTHOR]

Imaging genetics is a method used to detect associations between imaging and genetic variables. Some researchers have used sparse canonical correlation analysis (SCCA) for imaging genetics. This study was conducted to improve the efficiency and interpretability of SCCA. We propose a connectivity-based penalty for incorporating biological prior information. Our proposed approach, named joint connectivity-based SCCA (JCB-SCCA), includes the proposed penalty and can handle multi-modal neuroimaging datasets. Different neuroimaging techniques provide distinct information on the brain and have been used to investigate various neurological disorders, including Parkinson’s disease (PD). We applied our algorithm to simulated and real imaging genetics datasets for performance evaluation. Our algorithm was able to select important features in a more robust manner compared with other multivariate methods. The algorithm revealed promising features of single-nucleotide polymorphisms and brain regions related to PD by using a real imaging genetic dataset. The proposed imaging genetics model can be used to improve clinical diagnosis in the form of novel potential biomarkers. We hope to apply our algorithm to cohorts such as Alzheimer’s patients or healthy subjects to determine the generalizability of our algorithm. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlight • Practical growth for 2D MoS 2 -graphene heterostructure (MGH) was introduced. • Low-temperature sulfurization of Mo thin film was realized by H 2 S plasma. • As-grown MoS 2 film on graphene naturally contains large number of active sites. • The MGH was shown enhanced electrocatalytic performance. Abstract In this study, we successfully demonstrate the fabrication of a MoS 2 -graphene heterostructure (MGH) on a 4 inch wafer at 300 °C by depositing a thin Mo film seed layer on graphene followed by sulfurization using H 2 S plasma. By utilizing Raman spectroscopy and high-resolution transmission electron microscopy, we have confirmed that 5–6 MoS 2 layers with a large density of sulfur vacancies are grown uniformly on the entire substrate. The chemical composition of MoS 2 on graphene was evaluated by X-ray photoelectron spectroscopy, which confirmed the atomic ratio of Mo to S to be 1:1.78, which is much lower than the stoichiometric value of 2 from standard MoS 2. To exploit the properties of the nanocrystalline and defective MGH film obtained in our process, we have utilized it as a catalyst for hydrodesulfurization and as an electrocatalyst for the hydrogen evolution reaction. Compared to MoS 2 grown on an amorphous SiO 2 substrate, the MGH has smaller onset potential and Tafel slope, indicating its enhanced catalytic performance. Our practical growth approach can be applied to other two-dimensional crystals, which are potentially used in a wide range of applications such as electronic devices and catalysis. [ABSTRACT FROM AUTHOR]

Depression is one of the most common and important neuropsychiatric symptoms in Parkinson’s disease and often becomes worse as Parkinson’s disease progresses. However, the underlying mechanisms of depression in Parkinson’s disease are not clear. The aim of our study was to find genetic features related to depression in Parkinson’s disease using an imaging genetics approach and to construct an analytical model for predicting the degree of depression in Parkinson’s disease. The neuroimaging and genotyping data were obtained from an openly accessible database. We computed imaging features through connectivity analysis derived from tractography of diffusion tensor imaging. The imaging features were used as intermediate phenotypes to identify genetic variants according to the imaging genetics approach. We then constructed a linear regression model using the genetic features from imaging genetics approach to describe clinical scores indicating the degree of depression. As a comparison, we constructed other models using imaging features and genetic features based on references to demonstrate the effectiveness of our imaging genetics model. The models were trained and tested in a five-fold cross-validation. The imaging genetics approach identified several brain regions and genes known to be involved in depression, with the potential to be used as meaningful biomarkers. Our proposed model using imaging genetic features predicted and explained the degree of depression in Parkinson’s disease appropriately (adjusted R2 larger than 0.6 over five training folds) and with a lower error and higher correlation than with other models over five test folds. [ABSTRACT FROM AUTHOR]

The cover art depicts the strong interaction between single-atom site Ru catalysts and chemically stable ZrO SB 2-x sb nanoparticles, enabling superior stability and improved activity of the catalysts for hydrogen evolution reactions. Hydrogen evolution reaction, metal-organic frameworks, metal-support interactions, oxygen-deficient zirconia, reconstruction engineering, Ru catalysts, single-atom sites catalysts Keywords: hydrogen evolution reaction; metal-organic frameworks; metal-support interactions; oxygen-deficient zirconia; reconstruction engineering; Ru catalysts; single-atom sites catalysts EN hydrogen evolution reaction metal-organic frameworks metal-support interactions oxygen-deficient zirconia reconstruction engineering Ru catalysts single-atom sites catalysts 1 1 1 07/21/23 20230718 NES 230718 B Single-Atom Sites Catalysts b In article number 2300673, Dongmok Whang, Sung Jong Yoo, Namgee Jung, Joseph T. Hupp, and co-workers present a strategy to enhance the efficiency and durability of sub-nanometer catalysts for electrochemical reactions. [Extracted from the article]

Background. It is critical to distinguish between Parkinson’s disease (PD) and scans without evidence of dopaminergic deficit (SWEDD), because the two groups are different and require different therapeutic approaches. Objective. The aim of this study was to distinguish SWEDD patients from PD patients using connectivity information derived from diffusion tensor imaging tractography. Methods. Diffusion magnetic resonance images of SWEDD (n=37) and PD (n=40) were obtained from a research database. Tractography, the process of obtaining neural fiber information, was performed using custom software. Group-wise differences between PD and SWEDD patients were quantified using the number of connected fibers between two regions, and correlation analyses were performed based on clinical scores. A support vector machine classifier (SVM) was applied to distinguish PD and SWEDD based on group-wise differences. Results. Four connections showed significant group-wise differences and correlated with the Unified Parkinson’s Disease Rating Scale sponsored by the Movement Disorder Society. The SVM classifier attained 77.92% accuracy in distinguishing between SWEDD and PD using these identified connections. Conclusions. The connections and regions identified represent candidates for future research investigations. [ABSTRACT FROM AUTHOR]

We prepared a flexible transparent electrode comprising a single wall carbon nanotube/poly(3,4-ethylenedioxythiophene) (SWNT/PEDOT) nanocomposite in which the PEDOT matrix was in situ polymerized on the preformed SWNT conducting network. With the aid of the SWNT network, the SWNT/PEDOT nanocomposite film gave a much lower surface resistance (∼160 Ω/□) with a high transmittance (86% at 550 nm), compared with the neat PEDOT film of the same thickness. Current mapping of the nanocomposite film surface by conductive-atomic force microscopy (C-AFM) exhibited that most of SWNTs participate in the conducting network while the PEDOT matrix provides electrical pathways across the separated SWNT chains and enables a smoother, pore-free film with a uniform current flow on the overall surface of the film. When the SWNT/PEDOT nanocomposite film was employed as an anode of Alq3-based flexible organic light-emitting diode (FOLED), the device demonstrated ∼3 times enhanced brightness and ∼1.9 times enhanced current efficiency than the device with the neat PEDOT anode. [ABSTRACT FROM AUTHOR]

Abstract: Polymer electrolyte membrane fuel cell (PEMFC) stacks in a fuel cell vehicle can be inevitably exposed to harsh environments such as cold weather in winter, causing water flooding by the direct flow of condensed water to the electrodes. In this study, anode flooding was experimentally investigated with condensed water generated by cooling the anode gas line during a long-term operation (∼1600 h). The results showed that the performance of the PEMFC was considerably degraded. After the long-term experiment, the thickness of the anode decreased, and the ratio of Pt to carbon in the anode increased. Moreover, repeated fuel starvation of the half-cell severely oxidized the carbon surface due to the high induced potential (>1.5 VRHE). The cyclic voltammogram of the anode in the half-cell experiments indicated that the characteristic feature of the oxidized carbon surface was similar to that of the anode in the single cell under anode flooding conditions during the long-term experiment. Therefore, repeated fuel starvation by anode flooding caused severe carbon corrosion in the anode because the electrode potential locally increased to >1.0 VRHE. Consequently, the density of the tri-phase boundary decreased due to the corrosion of carbons supporting the Pt nanoparticles in the anode. [Copyright &y& Elsevier]

• A new canonical correlation analysis model, which incorporates the biologically meaningful structures with the multi-task learning framework, is proposed to discover sparse, discriminative, and interpretable shared representation across multi-modal imaging genetics data. • An efficient iteratively reweighted algorithm via alternating optimization is proposed to solve the proposed problem with proven convergence. • Extensive experiments on both simulation and real data demonstrate the promise and clinical benefits of the proposed model over multiple competing algorithms. [Display omitted] The advances in technologies for acquiring brain imaging and high-throughput genetic data allow the researcher to access a large amount of multi-modal data. Although the sparse canonical correlation analysis is a powerful bi-multivariate association analysis technique for feature selection, we are still facing major challenges in integrating multi-modal imaging genetic data and yielding biologically meaningful interpretation of imaging genetic findings. In this study, we propose a novel multi-task learning based structured sparse canonical correlation analysis (MTS2CCA) to deliver interpretable results and improve integration in imaging genetics studies. We perform comparative studies with state-of-the-art competing methods on both simulation and real imaging genetic data. On the simulation data, our proposed model has achieved the best performance in terms of canonical correlation coefficients, estimation accuracy, and feature selection accuracy. On the real imaging genetic data, our proposed model has revealed promising features of single-nucleotide polymorphisms and brain regions related to sleep. The identified features can be used to improve clinical score prediction using promising imaging genetic biomarkers. An interesting future direction is to apply our model to additional neurological or psychiatric cohorts such as patients with Alzheimer's or Parkinson's disease to demonstrate the generalizability of our method. [ABSTRACT FROM AUTHOR]

Background: The "motor reserve" is an emerging concept based on the discrepancy between the severity of parkinsonism and dopaminergic degeneration; however, the related brain structures have not yet been elucidated. Objective: We investigated brain structures relevant to the motor reserve in Parkinson's disease (PD) in this study. Methods: Patients with drug-naïve, early PD were enrolled, who then underwent dopamine transporter (DAT) scan and diffusion tensor imaging (DTI). The severity of motor symptoms was evaluated with the Unified Parkinson's Disease Rating Scale score of bradykinesia and rigidity on the more affected side and dopaminergic degeneration of DAT uptake of the more affected putamen. Individual motor reserve estimate (MRE) was evaluated based on the discrepancy between the severity of motor symptoms and dopaminergic degeneration. Using DTI and the Brainnetome atlas, brain structures correlated with MRE were identified. Results: We enrolled 193 patients with drug-naïve PD (mean disease duration of 15.6±13.2 months), and the MRE successfully predicted the increase of levodopa equivalent dose after two years. In the DTI analysis, fractional anisotropy values of medial, inferior frontal, and temporal lobes, limbic structures, nucleus accumbens, and thalamus were positively correlated with the MRE, while no brain structures were correlated with mean diffusivity. Additionally, degree centrality derived from the structural connectivity of the frontal and temporal lobes and limbic structures was positively correlated with the MRE. Conclusion: Our results show empirical evidence for MR in PD and brain structures relevant to MR, particularly, the extra-basal ganglia system including the limbic and frontal structures. [ABSTRACT FROM AUTHOR]

Abstract: We report on a flexible organic light-emitting diode (FOLED) prepared by using a poly(3,4-ethylenedioxythiophene) (PEDOT) anode formed via in-situ polymerization of ethylenedioxythiophene (EDOT) on a polyethersulfone (PES) substrate. Ferric p-toluene sulfonate (FTS) was used as a dopant, and N,N-dimethylacetamide (DMAc) was added to enhance the anode properties. The PEDOT:FTS film polymerized in the presence of DMAc (DMAc-PEDOT:FTS) exhibited better performance than those polymerized without DMAc (Normal-PEDOT:FTS) in terms of both optical transmittance and surface resistance, and the DMAc-PEDOT:FTS film gave a work function of 4.67eV which is 0.3eV higher than that of the Normal-PEDOT:FTS film. Optical transmittance and surface resistance of the DMAc-PEDOT:FTS films could be controlled to be 52–87% and 130–390Ω/sq, respectively. When an optimized DMAc-PEDOT:FTS film was employed as an anode, the Alq3 [(8-hydroxy-quinolinato)-aluminium] based FOLED device exhibited 30% higher luminous efficiency and more than 20 times higher max luminance than the device with the Normal-PEDOT:FTS anode. [Copyright &y& Elsevier]

Abstract: The etching mechanism of ZrO2 thin films in BCl3/Ar plasma was investigated using a combination of experimental and modeling methods. It was found that an increase in the Ar mixing ratio causes the non-monotonic behavior of the ZrO2 etch rate which reaches a maximum of 41.4nm/min at about 30–35% Ar. Langmuir probe measurements and plasma modeling indicated the noticeable influence of a BCl3/Ar mixture composition on plasma parameters and active species kinetics that results in non-linear changes of both densities and fluxes for Cl, BCl2 and . From the model-based analysis of surface kinetics, it was shown that the non-monotonic behavior of the ZrO2 etch rate can be associated with the concurrence of chemical and physical pathways in ion-assisted chemical reaction. [Copyright &y& Elsevier]

• A new method incorporating GraphNet and simplex constraints is proposed to estimate interpretable and structural enriched functional brain networks. • An efficient optimization algorithm using the projected gradient descent method is proposed for the construction of structural enriched functional brain networks. • Extensive experiments demonstrate the promise of the proposed structural enriched functional brain networks on predicting interesting behavioral outcomes. The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models. [Display omitted] [ABSTRACT FROM AUTHOR]

Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer's disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP–SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts. [ABSTRACT FROM AUTHOR]

• Levodopa-induced dyskinesia (LID) in Parkinson's disease needs imaging biomarkers. • A GraphNet regularization was applied to identify surface-based imaging biomarkers. • Our biomarkers performed better than existing models. Levodopa-induced dyskinesia (LID) is a disabling complication of Parkinson's disease (PD). Imaging-based measurements, especially those related to the surface shape of the basal ganglia, have shown potential for explaining the severity of LID in PD. Here, we aimed to explore a novel application of the methodology to find biomarkers of LID severity in PD using regularization. We proposed an application of graph-constrained elastic net (GraphNet) regularization to detect surface-based shape biomarkers explaining the severity of LID and compared the approach with other conventional regularization methods. To examine the methods, we used two independent datasets, one as a training dataset to build the model, and the other dataset was used to validate the constructed model. We found that the left striatum (putamen was the greatest and the caudate was second) was the most significant surface-based biomarker related to the severity of LID. Our results improved the interpretability of identified surface-based biomarkers compared to competing methods. We also found that GraphNet regularization improved prediction of the severity of LID better than the conventional regularization methods. Our model performed better in terms of root-mean-squared error and correlation coefficient between predicted and actual clinical scores. The proposed algorithm offers an advantage of interpretable anatomical variations related to the deformation of the cortical surface. The experimental results showed that GraphNet regularization was robust to identify surface-based shape biomarkers related to both hypokinetic and hyperkinetic movement disorders. [ABSTRACT FROM AUTHOR]

Background: Although diabetes is considered a major risk factor for carpal tunnel syndrome (CTS), the characteristics of diabetic CTS have not been fully understood. Objective: This study is aimed at evaluation of the clinical, electrophysiological, and ultrasonographic findings of non-diabetic and diabetic CTS. Methods: This retrospective, cross-sectional study included patients diagnosed with CTS. Patient age, sex, involved side, body mass index, clinical and electrophysiological findings, and median nerve cross-sectional area (CSA) were identified. Diabetes was identified through patient or guardian interviews, medical records, and medication history. Linear and binary logistic regression models were established to confirm the associations between the electrophysiological findings, median nerve CSA, and clinical outcomes. Covariates, such as age, sex, body mass index, diabetes, symptom duration, and thenar muscle weakness were adjusted. Results: Out of the 920 hands, 126 and 794 belonged to the diabetic and non-diabetic CTS groups, respectively. The patients were significantly older in the diabetic CTS group (P < 0.001). The rate of thenar weakness in the diabetic CTS group was also significantly higher than that in the non-diabetic CTS group (P = 0.009). The diabetic CTS group had a more severe electrodiagnostic grade (P = 0.001). The prolonged onset latency of the compound motor nerve action potential (CMAP) and median nerve CSA were well associated with the degree of clinical symptoms. Increased median nerve CSA was significantly associated with prolonged CMAP onset latency (β = 0.64; P = 0.012), prolonged transcarpal latency (β = 0.95; P = 0.044), and decreased CMAP amplitude (β = -0.17; P = 0.002) in the non-diabetic CTS group. Conclusion: Diabetic CTS had more profound electrophysiological abnormalities. Distal motor latency and median nerve CSA were not only associated with each other, but also with clinical symptoms. Further studies are needed to investigate the pathophysiological mechanisms underlying diabetic CTS. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with both underlying genetic factors and neuroimaging findings. Existing neuroimaging studies related to the genome in PD have mostly focused on certain candidate genes. The aim of our study was to construct a linear regression model using both genetic and neuroimaging features to better predict clinical scores compared to conventional approaches. We obtained neuroimaging and DNA genotyping data from a research database. Connectivity analysis was applied to identify neuroimaging features that could differentiate between healthy control (HC) and PD groups. A joint analysis of genetic and imaging information known as imaging genetics was applied to investigate genetic variants. We then compared the utility of combining different genetic variants and neuroimaging features for predicting the Movement Disorder Society-sponsored unified Parkinson's disease rating scale (MDS-UPDRS) in a regression framework. The associative cortex, motor cortex, thalamus, and pallidum showed significantly different connectivity between the HC and PD groups. Imaging genetics analysis identified PARK2, PARK7, HtrA2, GIGYRF2, and SNCA as genetic variants that are significantly associated with imaging phenotypes. A linear regression model combining genetic and neuroimaging features predicted the MDS-UPDRS with lower error and higher correlation with the actual MDS-UPDRS compared to other models using only genetic or neuroimaging information alone. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a degenerative disorder that affects the central nervous system. PD-related alterations in structural and functional neuroimaging have not been fully explored. This study explored multi-modal PD neuroimaging and its application for predicting clinical scores on the Movement Disorder Society-sponsored Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Multi-modal imaging that combined 123I-Ioflupane single-photon emission computed tomography (SPECT) and diffusion tensor imaging (DTI) were adopted to incorporate complementary brain imaging information. SPECT and DTI images of normal controls (NC; n = 45) and PD patients (n = 45) were obtained from a database. The specific binding ratio (SBR) was calculated from SPECT. Tractography was performed using DTI. Group-wise differences between NC and PD patients were quantified using SBR of SPECT and structural connectivity of DTI for regions of interest (ROIs) related to PD. MDS-UPDRS scores were predicted using multi-modal imaging features in a partial least-squares regression framework. Three regions and four connections within the cortico-basal ganglia thalamocortical circuit were identified using SBR and DTI, respectively. Predicted MDS-UPDRS scores using identified regions and connections and actual MDS-UPDRS scores showed a meaningful correlation (r = 0.6854, p < 0.001). Our study provided insight on regions and connections that are instrumental in PD. [ABSTRACT FROM AUTHOR]

Epoxy-ZrP (α-zirconium phosphate) nanocomposites were wet-coated on various inorganic layers to improve their barrier properties. Inorganic layers were deposited on a plastic substrate by sputtering or atomic layer deposition (ALD). The water vapor transmission rate (WVTR) of the inorganic layers varied from 10 −1 g/m 2 /day to 10 −4 g/m 2 /day. ZrP nanoplatelets were synthesized using a reflux method and were well dispersed to form an epoxy-ZrP solution for wet coating. TEM images confirmed that the ZrP nanoplatelets were well dispersed in the epoxy matrix. The coating solution was bar-coated on the inorganic layer and thermally cured. The WVTR dramatically decreased up to 92% after epoxy-ZrP nanocomposite coating on all choices of inorganic layer. The optical properties remained constant or in some cases decreased by about 4%. The optimal structure composed of inorganic and epoxy-ZrP nanocomposite layers was used in OLED encapsulation, resulting in an extended lifetime compared to OLEDs encapsulated with bare inorganic layers without a nanocomposite coating. [ABSTRACT FROM AUTHOR]

Background: Root motor conduction time (RMCT) can noninvasively evaluate the status of the proximal root segment. However, its clinical application remains limited, and wider studies regarding its use are scarce. We aimed to investigate the association between C8/T1 level radiculopathy and RMCT.Methods: This was a retrospective cross-sectional study. Subjects were extracted from a general hospital's spine clinic database. A total of 48 C8/T1 root lesions from 37 patients were included, and 48 C8/T1 root levels from control subjects were matched for age, sex, and height. RMCT was measured in the abductor pollicis brevis muscle and the assessment of any delays owing to C8/T1 radiculopathy.Results: The RMCT of the C8/T1 radiculopathy group was 1.7 ± 0.6 ms, which was significantly longer than that in the control group (1.2 ± 0.8 ms; p = 0.001). The delayed RMCT was independently associated with radiculopathy (adjusted odds ratio, 1.15; 95% confidence interval, 1.06-1.27; p = 0.011) after adjusting for the peripheral motor conduction time, amplitude of median compound motor nerve action potential, and shortest F-wave latency. The area under the Receiver Operating Characteristic curve for diagnosing C8/T1 radiculopathy using RMCT was 0.72 (0.61-0.82). The RMCT was significantly correlated with symptom duration (coefficient = 0.58; p < 0.001) but was not associated with the degree of arm pain.Conclusion: Our findings illustrate the clinical applicability of the RMCT by demonstrating its utility in diagnosing radiculopathy at certain spinal levels. [ABSTRACT FROM AUTHOR]

We synthesized a new donor–acceptor type (D–A type) green fluorescent emitter, 3,7-bis(1-(biphenyl-4-yl)-1 H -benzo[ d ]imidazole-2-yl)-10-phenyl-10 H -phenothiazine (PhBBPP), which consists of phenylphenothiazine as an electron donor and biphenylbenzimidazole as an acceptor. Its thermal and photophysical properties, and energy band structure were studied, and also organic light-emitting diodes (OLEDs) with various device structures were fabricated using PhBBPP as an emitting layer. The optimized OLED device with a structure of [ITO/NPB (40 nm)/PhBBPP (30 nm)/TPBi (30 nm)/Alq3 (10 nm)/LiF (1 nm)/Al (100 nm)] showed a turn-on voltage of 3.4 V, a luminous efficiency of 12.1 cd/A, and an external quantum efficiency of 4.0% at 100 cd/m 2 with a maximum luminance of 8200 cd/m 2 . [ABSTRACT FROM AUTHOR]

We investigated the thermographic findings of carpal tunnel syndrome (CTS). We enrolled 304 hands with electrodiagnostically identified CTS and 88 control hands. CTS hands were assigned to duration groups (D1, < 3 months; D2, 3‒6 months; D3, 6‒12 months; D4, ≥ 12 months) and severity groups (S1, very mild; S2, mild; S3, moderate; S4, severe). The temperature difference between the median and ulnar nerve territories (ΔM-U territories) decreased as CTS duration and severity increased. Significant differences in ΔM-U territories between the D1 and D3, D1 and D4, D2 and D4, and S1 and S4 groups (P = 0.003, 0.001, 0.001, and < 0.001, respectively) were observed. Thermal anisometry increased as CTS duration and severity increased. Significant differences in thermal anisometry between the D1 and D4 as well as the D2 and D4 groups (P = 0.005 and 0.04, respectively) were noted. Thermal anisometry was higher in the S4 group than in the S1, S2, and S3 groups (P = 0.009, < 0.001, and 0.003, respectively). As CTS progresses, skin temperature tends to decrease and thermal variation tends to increase in the median nerve-innervated area. Thermographic findings reflect the physiological changes of the entrapped median nerve. [ABSTRACT FROM AUTHOR]

Abstract: The investigation of Al2O3 etch characteristics in the BCl3/Ar inductively coupled plasma was carried out in terms of effects of input process parameters (gas pressure, input power, bias power) on etch rate and etch selectivity over poly-Si and photoresist. It was found that, with the changes in gas pressure and input power, the Al2O3 etch rate follows the behavior of ion current density while the process rate is noticeably contributed by the chemical etch pathway. The influence of input power on the etch threshold may be connected with the concurrence of chemical and physical etch pathways in ion-assisted chemical reaction. [Copyright &y& Elsevier]

Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2–81.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0–80.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations. [ABSTRACT FROM AUTHOR]

Variations in body mass index (BMI) have been suggested to relate to atypical brain organization, yet connectome-level substrates of BMI and their neurobiological underpinnings remain unclear. Studying 325 healthy young adults, we examined associations between functional connectivity and inter-individual BMI variations. We utilized non-linear connectome manifold learning techniques to represent macroscale functional organization along continuous hierarchical axes that dissociate low level and higher order brain systems. We observed an increased differentiation between unimodal and heteromodal association networks in individuals with higher BMI, indicative of a disrupted modular architecture and hierarchy of the brain. Transcriptomic decoding and gene enrichment analyses identified genes previously implicated in genome-wide associations to BMI and specific cortical, striatal, and cerebellar cell types. These findings illustrate functional connectome substrates of BMI variations in healthy young adults and point to potential molecular associations. Bo-yong Park et al. use non-linear connectome manifold learning to examine the association between brain connectivity and inter-individual body mass index (BMI) in 325 young adults. They supplement these analyses with existing transcriptomic data, altogether suggesting several neural and molecular associations that may underlie BMI variations in healthy young adults. [ABSTRACT FROM AUTHOR]

Eating disorder is highly associated with obesity and it is related to brain dysfunction as well. Still, the functional substrates of the brain associated with behavioral traits of eating disorder are underexplored. Existing neuroimaging studies have explored the association between eating disorder and brain function without using all the information provided by the eating disorder related questionnaire but by adopting summary factors. Here, we aimed to investigate the multivariate association between brain function and eating disorder at fine-grained question-level information. Our study is a retrospective secondary analysis that re-analyzed resting-state functional magnetic resonance imaging of 284 participants from the enhanced Nathan Kline Institute-Rockland Sample database. Leveraging sparse canonical correlation analysis, we associated the functional connectivity of all brain regions and all questions in the eating disorder questionnaires. We found that executive- and inhibitory control-related frontoparietal networks showed positive associations with questions of restraint eating, while brain regions involved in the reward system showed negative associations. Notably, inhibitory control-related brain regions showed a positive association with the degree of obesity. Findings were well replicated in the independent validation dataset (n = 34). The results of this study might contribute to a better understanding of brain function with respect to eating disorder. [ABSTRACT FROM AUTHOR]

The effects of anisotropic bending stiffness of a gas diffusion layer (GDL) on membrane electrode assembly (MEA) degradation were investigated. We prepared GDLs with a fiber direction perpendicular to the major flow (i.e., “90° GDL”) and with a fiber direction parallel to the major flow (i.e., “0° GDL”). To analyze the mechanical durability as a function of GDL anisotropy, we examined cell performances such as the I–V characteristics and impedances and the hydrogen crossover characteristics during wet/dry cycles. The results showed that the 90° GDL fuel cell is superior to the 0° GDL fuel cell in terms of higher I–V performance, lower resistance at high frequency, and lower hydrogen crossover through the MEA. Mechanical degradation of the 0° GDL was investigated using scanning electron microscopy (SEM). [ABSTRACT FROM AUTHOR]

[Display omitted] • A new fast Li-ion conductor with an organic framework is demonstrated. • Li-SPAF1 shows superior ion-conductivity of 0.102 mS cm−1 at room temperature. • DFT calculation proves the vacancy-mediated diffusion for Li-ion conduction. Solid polymer electrolytes (SPEs) have been received significant attention due to their inherent advantages of high safety and high electrochemical stability as fast Li-ion conductors. However, conventional SPEs of dual-ion conductors (i.e. , conducting both anions and cations) are prone to induce concentration polarization and thus decrease in ionic conductivities, which remains a significant hurdle for their practical applications. Here, we propose a new host of porous aromatic frameworks (PAFs) as a promising single Li-ion conducting SPEs. PAF1 is one of the promising types for crystalline polymers with the integration of aromatic organic building units into a 3-dimensional framework, having plenty of voids space inside. By covalently tethering the sulfonate on aromatic groups, anions are immobilized onto the frameworks, and the exchanging process of protons with Li-ions makes it perform as a fast Li-ionic conductor. Consequently, Li sulfonated PAF1 (denoted as Li-SPAF1) shows superior ion-conducting properties of 0.102 mS cm−1 at room temperature with the activation energy of 0.21 eV. It is demonstrated that the aromatic groups have high chemical and thermal stabilities, even stable with Li metal, which is advantageous for delivering stable electrochemical performances. Also, the structure and conduction mechanism are theoretically investigated by using density functional theory calculations. [ABSTRACT FROM AUTHOR]

Abstract: An investigation of the Ba2Ti9O20 (BTO) and Pt thin films etch mechanism in the Cl2/Ar inductively coupled plasma was carried out. It was found that an increase in Ar mixing ratio at fixed gas pressure and input power causes a fast decrease in the BTO etch rate (26.9–1.2nm/min for 0–100% Ar) while the Pt etch rate increases slightly from 17.4–23.0nm/min. Langmuir probe diagnostics and zero-dimensional plasma modeling provided the data on plasma parameters, steady-state composition and fluxes of active species on the etched surface. From the model-based analysis of etch kinetics, it was shown that the behavior of the BTO etch rate corresponds to the reaction-rate-limited etch regime, where the etch rate is limited neither by physical sputtering of the main material nor by the ion-stimulated desorption of low-volatile reaction products. The etch process of Pt appears in the transitional regime and is controlled by the neutral and ion fluxes together. [Copyright &y& Elsevier]