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Unlike ballistic movements such as reaching, little is known about how aging affects tracking performance. The current study initially aimed at confirming the differences in tracking performance between younger and older people. The experiment included 14 young and 10 older subjects who were instructed to perform a circular tracking task on the fronto-parallel and sagittal planes with a visible target in the first half interval (called the feedback (FB) interval) and an invisible target disappearing and reappearing in the remaining interval (called the feedforward (FF) interval). The results demonstrated that the aging effect was sufficient to deteriorate tracking performance, regardless of the environment. However, the older group’s performance was not significantly influenced by the depth movement, whereas the younger group’s performance significantly deteriorated during the FB interval. Finally, the differences in errors between the FF and FB intervals for each movement plane were investigated, demonstrating the vulnerability of the internal model used for each plane to the visibility of the target. The younger group demonstrated a significant difference between the fronto-parallel and sagittal planes; however, the older group exhibited no significant difference, indicating that the younger group acquired an internal model sensitive to visual information. We confirmed aging-induced tracking performance degradation and highlighted the impact of aging on tracking performance, resulting in an increased understanding of cerebellar motor control rather than conventional ballistic movements.

As the realization of carbon neutrality has been a main assignment for coping with the global climate change, it became necessary to analyze upcoming changes in electricity mix with economic and technical viewpoints. This paper presents a newly-developed simulation model that reflects the daily intermittency of renewable energy by applying daily average power supply-demand patterns for each season. Also, the paper provides an economic analysis in the viewpoint of investment cost, annual cost and power generation cost by utilizing the calculations from the simulation model. Four scenarios are selected for the analyses, one based on the Korean Government's 2050 Carbon Neutrality Scenario and three Nuclear Power Build-up scenarios, which are newly suggested by the authors. The simulation results show that the increase of nuclear energy from 5.7% of Government's Scenario to 37.7% of the proposed Nuclear Power Build-up Scenario leads to the decrease of about 704 billion US$ in investment cost and about 181 billion US$ in annual cost; with reduction in the increase of 2050 expected generation cost from 3.1 to 1.6 times compared with the referenced 2021 average cost. Further, this study has significance in performing the economic analysis with the expected daily power supply-demand patterns in 2050.

Abstract Stretchable electronics are of huge interest as they can be useful in various irregular non-planar or deformable surfaces including human bodies. High density multi-functional stretchable electronics are beneficial as they can be reliably used in more compact regions. However, simply stacking multiple layers may increase induced strain, reducing degree of stretchability. Here, we present the design approach for the stretchable multilayer electronics that provide a similar degree of stretchability compare to a single layer electronics although the multilayer electronics are in much more compact form. We provide experimental and computational analyses for the benefits of the approach along with demonstrations with compact form of the multi-functional stretchable implantable bio-electronics and of the stretchable multilayer passive matrix LEDs array. The results presented here should be useful for a wide range of applications that require stretchable high-density electronics.

Transformers have demonstrated notable efficacy in computer vision, extending beyond their initial success in natural language processing. The application of vision transformers (ViTs) to resource-constrained mobile and edge devices is hampered by their extensive computational demands and large parameter sets. To address this, research has explored pruning redundant components of ViTs. Given that the computational burden of ViTs scales quadratically with token count, previous efforts have aimed to decrease the number of tokens or to linearize the computational cost of self-attention. However, such methods often incur significant accuracy losses due to the disruption of critical information pathways within the ViT, which primarily focuses on shape rather than texture, potentially aligning its image interpretation more closely with human perception than convolutional neural network (CNN) models. This observation parallels the effectiveness of JPEG, a predominant image compression standard, which maintains high compression efficacy with minimal quality degradation by discarding high-frequency details that have less impact on human object recognition. In this work, we harness the discrete cosine transform (DCT), an integral component of JPEG, to enhance ViT performance. We considerably reduced computational demands by selectively eliminating high-frequency tokens via DCT while maintaining model accuracy. For instance, our DCT-enhanced ViT model exhibited a 25% reduction in computational costs relative to the DeiT-Small model on ImageNet, with an accuracy increase of 0.18% and only a 0.72% accuracy decrease at a 44% computational reduction. Compared to the DeiT-Tiny model, our approach improved accuracy by 0.17% despite a 47% decrease in computational costs. Furthermore, the proposed DCT-ViT model necessitates significantly fewer parameters than existing approaches, offering a more efficient alternative for deploying ViTs on edge devices.

Abstract Unlike ballistic arm movements such as reaching, the contribution of depth information to the performance of manual tracking movements is unclear. Thus, to understand how the brain handles information, we investigated how a required movement along the depth axis would affect behavioral tracking performance, postulating that it would be affected by the amount of depth movement. We designed a visually guided planar tracking task that requires movement on three planes with different depths: a fronto-parallel plane called ROT (0), a sagittal plane called ROT (90), and a plane rotated by 45° with respect to the sagittal plane called ROT (45). Fifteen participants performed a circular manual tracking task under binocular and monocular visions in a three-dimensional (3D) virtual reality space. As a result, under binocular vision, ROT (90), which required the largest depth movement among the tasks, showed the greatest error in 3D. Similarly, the errors (deviation from the target path) on the depth axis revealed significant differences among the tasks. Under monocular vision, significant differences in errors were observed only on the lateral axis. Moreover, we observed that the errors in the lateral and depth axes were proportional to the required movement on these axes under binocular vision and confirmed that the required depth movement under binocular vision determined depth error independent of the other axes. This finding implies that the brain may independently process binocular vision information on each axis. Meanwhile, the required depth movement under monocular vision was independent of performance along the depth axis, indicating an intractable behavior. Our findings highlight the importance of handling depth movement, especially when a virtual reality situation, involving tracking tasks, is generated.

Abstract Metal oxides derived from metal–organic frameworks have received considerable attention for water treatment. Herein, MIL-68 (In)–derived rod-like carbon (C) and nitrogen (N) codoped In2O3–modified GdFeO3 (CN–InO/GdF) Z-scheme heterostructures were rationally constructed through simple pyrolysis and a wet-chemical route. The synthesized CN–InO/GdF catalysts were characterized using several physicochemical techniques. Moreover, the obtained CN–InO/GdF exhibited excellent photocatalytic performance toward tetracycline (TC) degradation. Impressively, the optimized CN–InO/GdF-3 heterostructure catalyst displayed a boosted TC removal efficiency of 99.06% within 60 min of irradiation, which was 7.1 and 9.7 times higher than those of bare CN–InO and GdFeO3, respectively. The remarkably enhanced photoactivity of CN–InO/GdF was principally attributed to the synergistically combined interfaces hindering the recombination of photoinduced carriers, and the maintained redox capacity via Z-scheme spatial charge transfer. Finally, the tentative degradation mechanism was determined on the basis of density functional theory calculations and intermediates analysis. This work elucidated the design of highly efficient catalysts for various applications.

Abstract Background Chest x-ray is commonly used for pulmonary abnormality screening. However, since the image characteristics of x-rays highly depend on the machine specifications, an artificial intelligence (AI) model developed for specific equipment usually fails when clinically applied to various machines. To overcome this problem, we propose an image manipulation pipeline. Methods A total of 15,010 chest x-rays from systems with different generators/detectors were retrospectively collected from five institutions from May 2020 to February 2021. We developed an AI model to classify pulmonary abnormalities using x-rays from a single system. Then, we externally tested its performance on chest x-rays from various machine specifications. We compared the area under the receiver operating characteristics curve (AUC) of AI models developed using conventional image processing pipelines (histogram equalization [HE], contrast-limited histogram equalization [CLAHE], and unsharp masking [UM] with common data augmentations) with that of the proposed manipulation pipeline (XM-pipeline). Results The XM-pipeline model showed the highest performance for all the datasets of different machine specifications, such as chest x-rays acquired from a computed radiography system (n = 356, AUC 0.944 for XM-pipeline versus 0.917 for HE, 0.705 for CLAHE, 0.544 for UM, p $$\le$$ ≤ 0.001, for all) and from a mobile x-ray generator (n = 204, AUC 0.949 for XM-pipeline versus 0.933 for HE, p = 0.042, 0.932 for CLAHE (p = 0.009), 0.925 for UM (p = 0.001). Conclusions Applying the XM-pipeline to AI training increased the diagnostic performance of the AI model on the chest x-rays of different machine configurations. Relevance statement The proposed training pipeline would successfully promote a wide application of the AI model for abnormality screening when chest x-rays are acquired using various x-ray machines. Key points • AI models developed using x-rays of a specific machine suffer from generalization. • We proposed a new image processing pipeline to address the generalization problem. • AI models were tested using multicenter external x-ray datasets of various machines. • AI with our pipeline achieved the highest diagnostic performance than conventional methods. Graphical Abstract

Abstract Changes in dopamine and fronto-striato-thalamic (FST) circuit functional connectivity are prominent in schizophrenia. Dopamine is thought to underlie connectivity changes, but experimental evidence for this hypothesis is lacking. Previous studies examined the association in some of the connections using positron emission tomography (PET) and functional MRI (fMRI); however, PET has disadvantages in scanning patients, such as invasiveness. Excessive dopamine induces neuromelanin (NM) accumulation, and NM-MRI is suggested as a noninvasive proxy measure of dopamine function. We aimed to investigate the association between NM and FST circuit connectivity at the network level in patients with schizophrenia. We analysed substantia nigra NM-MRI and resting-state fMRI data from 29 schizophrenia patients and 63 age- and sex-matched healthy controls (HCs). We identified the FST subnetwork with abnormal connectivity found in schizophrenia patients compared to that of HCs and investigated the relationship between constituting connectivity and NM-MRI signal. We found a higher NM signal (t = −2.12, p = 0.037) and a hypoconnected FST subnetwork (FWER-corrected p = 0.014) in schizophrenia patients than in HCs. In the hypoconnected subnetwork of schizophrenia patients, lower left supplementary motor area-left caudate connectivity was associated with a higher NM signal (β = −0.38, p = 0.042). We demonstrated the association between NM and FST circuit connectivity. Considering that the NM-MRI signal reflects dopamine function, our results suggest that dopamine underlies changes in FST circuit connectivity, which supports the dopamine hypothesis. In addition, this study reveals implications for the future use of NM-MRI in investigations of the dopamine system.

As the emerging treatments that target grey matter pathology in Alzheimer's Disease have limited effectiveness, there is a critical need to identify new neural targets for treatments. White matter's (WM) metabolic vulnerability makes it a promising candidate for new interventions. This study examined the age and sex differences in estimates of axonal content, as well the associations of with highly prevalent modifiable health risk factors such as metabolic syndrome and adiposity. We estimated intra-axonal volume fraction (ICVF) using the Neurite Orientation Dispersion and Density Imaging (NODDI) in a sample of 89 cognitively and neurologically healthy adults (20–79 years). We showed that ICVF correlated positively with age and estimates of myelin content. The ICVF was also lower in women than men, across all ages, which difference was accounted for by intracranial volume. Finally, we found no association of metabolic risk or adiposity scores with the current estimates of ICVF. In addition, the previously observed adiposity-myelin associations (Burzynska et al., 2023) were independent of ICVF. Although our findings confirm the vulnerability of axons to aging, they suggest that metabolic dysfunction may selectively affect myelin content, at least in cognitively and neurologically healthy adults with low metabolic risk, and when using the specific MRI techniques. Future studies need to revisit our findings using larger samples and different MRI approaches, and identify modifiable factors that accelerate axonal deterioration as well as mechanisms linking peripheral metabolism with the health of myelin.

Abstract Image sensors capable of capturing individual photons have made tremendous progress in recent years. However, this technology faces a major limitation. Because they capture scene information at the individual photon level, the raw data is sparse and noisy. Here we propose CASPI: Collaborative Photon Processing for Active Single-Photon Imaging, a technology-agnostic, application-agnostic, and training-free photon processing pipeline for emerging high-resolution single-photon cameras. By collaboratively exploiting both local and non-local correlations in the spatio-temporal photon data cubes, CASPI estimates scene properties reliably even under very challenging lighting conditions. We demonstrate the versatility of CASPI with two applications: LiDAR imaging over a wide range of photon flux levels, from a sub-photon to high ambient regimes, and live-cell autofluorescence FLIM in low photon count regimes. We envision CASPI as a basic building block of general-purpose photon processing units that will be implemented on-chip in future single-photon cameras.

The in-vivo profiling of iron and myelin across cortical depths and underlying white matter has important implications for advancing knowledge about their roles in brain development and degeneration. Here, we utilize χ-separation, a recently-proposed advanced susceptibility mapping that creates positive (χpos) and negative (χneg) susceptibility maps, to generate the depth-wise profiles of χpos and χneg as surrogate biomarkers for iron and myelin, respectively. Two regional sulcal fundi of precentral and middle frontal areas are profiled and compared with findings from previous studies. The results show that the χpos profiles peak at superificial white matter (SWM), which is an area beneath cortical gray matter known to have the highest accumulation of iron within the cortex and white matter. On the other hand, the χneg profiles increase in SWM toward deeper white matter. These characteristics in the two profiles are in agreement with histological findings of iron and myelin. Furthermore, the χneg profiles report regional differences that agree with well-known distributions of myelin concentration. When the two profiles are compared with those of QSM and R2*, different shapes and peak locations are observed. This preliminary study offers an insight into one of the possible applications of χ-separation for exploring microstructural information of the human brain, as well as clinical applications in monitoring changes of iron and myelin in related diseases.

Background and Purpose: Gliomas have been found to alter iron metabolism and transport in ways that result in an expansion of their intracellular iron compartments to support aggressive tumor growth. This study used deep neural network trained quantitative susceptibility mapping to assess basal ganglia iron concentrations in glioma patients. Materials and Methods: Ninety-two patients with brain lesions were initially enrolled in this study and fifty-nine met the inclusion criteria. Susceptibility-weighted images were collected at 3.0 T and used to construct quantitative susceptibility maps via a deep neural network-based method. The regions of interest were manually drawn within basal ganglia structures and the mean voxel intensities were extracted and averaged across multiple slices. One-way ANCOVA tests were conducted to compare the susceptibility values of groups of patients based on tumor grade while controlling for age, sex, and tumor type. Results: The mean basal ganglia susceptibility for patients with grade IV tumors was higher than that for patients with grade II tumors (p = 0.00153) and was also higher for patients with grade III tumors compared to patients with grade II tumors (p = 0.020), after controlling for age, sex, and tumor type. Patient age influenced susceptibility values (p = 0.00356), while sex (p = 0.69) and tumor type (p = 0.11) did not. Conclusions: The basal ganglia iron content increased with glioma severity. Basal ganglia iron levels may thus be a useful biomarker in glioma prognosis and treatment, especially with regard to iron-based cancer therapies.

Smart design and construction have emerged as pivotal forces in the construction industry. Numerous studies have been conducted in the realm of design optimization, using artificial intelligence data-driven approaches and optimization theories. This increase in research has sparked interest in generative design, a process that automatically generates algorithm-based design alternatives, thereby reducing human effort and time by a significant margin. The objective of this study was to explore the potential of generative design to boost productivity within architectural practices and reduce redundant and unnecessary tasks for an aging construction workforce. Specifically, it illustrates the process of selecting superior alternatives by generating various three-dimensional layouts, using a generative design methodology. This occurs during the creation of a building layout concept with subsequent partial evaluations. The methodology of this study was mainly divided into four stages: objective setting, design algorithm development, the establishment of evaluation methodology, and the comparison of the results’ values. The findings of this study confirmed that the design algorithm and evaluation methodology form a single loop, generating a multitude of design alternatives that satisfy the algorithm designer’s evaluation criteria.

Although several previous studies on laterality of upper limb motor control have reported functional differences, this conclusion has not been agreed upon. It may be conjectured that the inconsistent results were caused because upper limb motor control was observed in multi-joint tasks that could generate different inter-joint motor coordination for each arm. Resolving this, we employed a single wrist joint tracking task to reduce the effect of multi-joint dynamics and examined the differences between the dominant and non-dominant hands in terms of motor control. Specifically, we defined two sections to induce feedback (FB) and feedforward (FF) controls: the first section involved a visible target for FB control, and the other section involved an invisible target for FF control. We examined the differences in the position errors of the tracer and the target. Fourteen healthy participants performed the task. As a result, we found that during FB control, the dominant hand performed better than the non-dominant hand, while we did not observe significant differences in FF control. In other words, in a single-joint movement that is not under the influence of the multi-joint coordination, only FB control showed laterality and not FF control. Furthermore, we confirmed that the dominant hand outperformed the non-dominant hand in terms of responding to situations that required a change in control strategy.

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage grade prediction by fire based on simplified building information. This paper’s primary objective is to propose and verify a framework for predicting the scale of property damage caused by fire using machine learning (ML). Korean public datasets are collected and preprocessed, and ML algorithms are trained with only 15 input data using building register and fire scenario information. Four models (artificial neural network (ANN), decision tree (DT), k-nearest neighbor (KNN), and random forest (RF)) are used for ML. The RF model is the most suitable for this study, with recall and precision of 74.2% and 73.8%, respectively. Structure, floor, causes, and total floor area are the critical factors that govern the fire size. This study proposes a novel approach by utilizing ML models to accurately and rapidly predict the size of fire damage based on basic building information. By analyzing domestic fire incident data and creating fire scenarios, a similar ML model can be developed.

Metabolic syndrome (MetS) is a cluster of conditions that affects ∼25% of the global population, including excess adiposity, hyperglycemia, dyslipidemia, and elevated blood pressure. MetS is one of major risk factors not only for chronic diseases, but also for dementia and cognitive dysfunction, although the underlying mechanisms remain poorly understood. White matter is of particular interest in the context of MetS due to the metabolic vulnerability of myelin maintenance, and the accumulating evidence for the importance of the white matter in the pathophysiology of dementia. Therefore, we investigated the associations of MetS risk score and adiposity (combined body mass index and waist circumference) with myelin water fraction measured with myelin water imaging. In 90 cognitively and neurologically healthy adults (20–79 years), we found that both high MetS risk score and adiposity were correlated with lower myelin water fraction in late-myelinating prefrontal and associative fibers, controlling for age, sex, race, ethnicity, education and income. Our findings call for randomized clinical trials to establish causality between MetS, adiposity, and myelin content, and to explore the potential of weight loss and visceral adiposity reduction as means to support maintenance of myelin integrity throughout adulthood, which could open new avenues for prevention or treatment of cognitive decline and dementia.

Neuromelanin (NM)-sensitive MRI using a magnetization transfer (MT)-prepared T1-weighted sequence has been suggested as a tool to visualize NM contents in the brain. In this study, a new NM-sensitive imaging method, sandwichNM, is proposed by utilizing the incidental MT effects of spatial saturation RF pulses in order to generate consistent high-quality NM images using product sequences. The spatial saturation pulses are located both superior and inferior to the imaging volume, increasing MT weighting while avoiding asymmetric MT effects. When the parameters of the spatial saturation were optimized, sandwichNM reported a higher NM contrast ratio than those of conventional NM-sensitive imaging methods with matched parameters for comparability with sandwichNM (SandwichNM: 23.6 ± 5.4%; MT-prepared TSE: 20.6 ± 7.4%; MT-prepared GRE: 17.4 ± 6.0%). In a multi-vendor experiment, the sandwichNM images displayed higher means and lower standard deviations of the NM contrast ratio across subjects in all three vendors (SandwichNM vs. MT-prepared GRE; Vendor A: 28.4 ± 1.5% vs. 24.4 ± 2.8%; Vendor B: 27.2 ± 1.0% vs. 13.3 ± 1.3%; Vendor C: 27.3 ± 0.7% vs. 20.1 ± 0.9%). For each subject, the standard deviations of the NM contrast ratio across the vendors were substantially lower in SandwichNM (SandwichNM vs. MT-prepared GRE; subject 1: 1.5% vs. 8.1%, subject 2: 1.1 % vs. 5.1%, subject 3: 0.9% vs. 4.0%, subject 4: 1.1% vs. 5.3%), demonstrating consistent contrasts across the vendors. The proposed method utilizes product sequences, requiring no alteration of a sequence and, therefore, may have a wide practical utility in exploring the NM imaging.

We report the use of four-layer graphene (4LG) as a highly reliable transparent conductive electrode (TCE) for polymer-dispersed liquid crystal (PDLC)-based smart window devices. The adhesion between 4LG and the substrate was successfully improved through a water-induced interface-cleaning (WIIC) process. We compared the performance of a device with a WIIC-processed 4LG electrode with that of devices with a conventional indium tin oxide (ITO) electrode and a 4LG electrode without a WIIC. With the application of the WIIC process, the PDLC smart window with a 4LG electrode exhibited reduced turn-on voltage and haze compared to 4LG without the WIIC process and characteristics comparable to those of the ITO electrode. The WIIC-processed 4LG electrode demonstrated enhanced electrical properties and better optical performance, leading to improved device efficiency and reliability. Furthermore, our study revealed that the WIIC process not only improved the adhesion between 4LG and the substrate but also enhanced the compatibility and interfacial interactions, resulting in the superior performance of the smart window device. These findings suggest that 4LG with WIIC holds great promise as a transparent conductive electrode for flexible smart windows, offering a cost-effective and efficient alternative to conventional ITO electrodes.

Background/Aims Connective tissue growth factor (CTGF) is a profibrotic factor implicated in pressure overload-mediated myocardial fibrosis. In this study, we determined the role of predicted CTGF-targeting microRNAs (miRNAs) in rat models of aortic stenosis and reverse cardiac remodeling. Methods Minimally invasive ascending aortic banding was performed in 24 7-week-old male Sprague-Dawley rats, which were divided into three groups. The banding group consisted of eight rats that were sacrificed immediately after 6 weeks of aortic constriction. The debanding group underwent aortic constriction for 4 weeks and was sacrificed 2 weeks after band removal. The third group underwent sham surgery. We investigated the expression of CTGF, transforming growth factor-β1 (TGFβ1), and matrix metalloproteinase-2 using ELISA and examined miRNA-26b, miRNA-133a, and miRNA-19b as predicted CTGF-targeting miRNAs based on miRNA databases in 24-hour TGFβ-stimulated and TGFβ- washed fibroblasts and myocardial tissues from all subjects. Results CTGF was elevated in 24-hour TGFβ-stimulated fibroblasts and decreased in 24-hour TGFβ-washed fibroblasts. miRNA-26b was significantly increased in TGFβ-washed fibroblasts compared with control and TGFβ-stimulated fibroblasts (p < 0.05). CTGF expression was significantly higher in the banding group than that in the sham and debanding groups. The relative expression levels of miRNA-26b were higher in the debanding group than in the banding group. Conclusions The results of our study using models of aortic banding and debanding suggested that miRNA-26b was significantly increased after aortic debanding. The in vitro model yielded the same results: miRNA-26b was upregulated after removal of TGFβ from fibroblasts.

Abstract Recent progresses on nanocapillary-driven water transport under metastable conditions have substantiated the potential of artificial trees for dewatering applications in a wide pressure range. This paper presents a comprehensive performance analysis of artificial trees encompassing the principle for negative capillary pressure generation; impacts of structural, compositional, and environmental conditions on dewatering performance; and design considerations. It begins by delineating functionalities of artificial trees for evaporation (leaves), conduction (xylem), and filtration (root) of water, in the analogy to natural trees. The analysis revealed that the magnitude of (negative) capillary pressure in the artificial leaves and xylem must be sufficiently large to overcome the osmotic pressure of feed at the root. The required magnitude can be reduced by increasing the osmotic pressure in the artificial xylem conduits, which reduces the risk of cavitation and subsequent blockage of water transport. However, a severe concentration polarization that can occur in long xylem conduits would negate such compensation effect of xylem osmotic pressure, leading to vapor pressure depression at the artificial leaves and therefore reduced dewatering rates. Enhanced Taylor dispersions by increasing xylem conduit diameters are found to alleviate the concentration polarization, allowing for water flux enhancement directly by increasing leaf-to-root membrane area ratio.

Abstract This document presents the physics case and ancillary studies for the proposed CODEX-b long-lived particle (LLP) detector, as well as for a smaller proof-of-concept demonstrator detector, CODEX- $$\beta $$ β , to be operated during Run 3 of the LHC. Our development of the CODEX-b physics case synthesizes ‘top-down’ and ‘bottom-up’ theoretical approaches, providing a detailed survey of both minimal and complete models featuring LLPs. Several of these models have not been studied previously, and for some others we amend studies from previous literature: In particular, for gluon and fermion-coupled axion-like particles. We moreover present updated simulations of expected backgrounds in CODEX-b’s actively shielded environment, including the effects of shielding propagation uncertainties, high-energy tails and variation in the shielding design. Initial results are also included from a background measurement and calibration campaign. A design overview is presented for the CODEX- $$\beta $$ β demonstrator detector, which will enable background calibration and detector design studies. Finally, we lay out brief studies of various design drivers of the CODEX-b experiment and potential extensions of the baseline design, including the physics case for a calorimeter element, precision timing, event tagging within LHCb, and precision low-momentum tracking.

The stripe fixed pattern noise (FPN) of infrared images significantly corrupts image quality, so that most infrared imaging systems suffer from the degradation of visibility and detectability during operation. Therefore, the FPN de-striping method, which eliminates stripe patterns without substantial loss of image information, remains a core technology in the field of infrared image processing. In this article, we propose the dual-branch structure based FPN de-striping deep convolutional neural network (DBS-DCN) to effectively extract structural features of FPN and preserve the image details in a single infrared image. In addition, we have established the parametric FPN model through the diagnostic experiments of infrared images based on the physical principle of an infrared detector and its signal response. We have optimized each parameter of the FPN model using measured data, which acquired on a wide range of detector temperatures. Further, we generate the training data using our FPN model to ensure stable learning performance against various stripe patterns. We performed comparative experiments with state-of-the-art methods using artificially corrupted infrared images and real corrupted infrared data, and our proposed method achieved outstanding de-striping results in both qualitative and quantitative evaluation compared to existing methods.

Purpose Head rotation is thought to have an effect on obstructive sleep apnea (OSA) severity. However, keeping the head rotated fully during sleep is difficult to maintain, and the effect of head rotation is not the same in all OSA patients. Thus, this study aimed to identify whether less head rotation has an effect on airway patency and determine the responder characteristics to the head rotation maneuver (HRM). Methods We recruited 221 patients who underwent overnight polysomnography and drug-induced sleep endoscopy (DISE) in a tertiary hospital from June 2019 to July 2020. Airway patency and the site of airway collapse were determined in the supine position with the head at 0, 30, and 60 degrees of rotation (HRM0°, HRM30°, and HRM60°, respectively) during DISE. The site of collapse was determined using the VOTE classification system: the velum (palate), oropharyngeal lateral walls, tongue base, and epiglottis. Each structure was labeled as 0, 1, or 2 (patent, partially obstructed, and completely obstructed, respectively). Airway response to the HRM30° and 60° and the clinical characteristics associated with airway opening were analyzed. Results The study population had a median age of 52 (25–61) years, a body mass index of 26.7(24.6–29.4) kg/m2, and the apnea-hypopnea index (AHI) of 28.2(13.7–71.9) events/h. HRM influenced airway patency positively not only with HRM60° (pConclusion Head rotation improved airway obstruction in OSA patients, even with a small degree of rotation, and should be further explored as a potential form of therapy in appropriately selected patients.

Obtaining a histological fingerprint from the in-vivo brain has been a long-standing target of magnetic resonance imaging (MRI). In particular, non-invasive imaging of iron and myelin, which are involved in normal brain functions and are histopathological hallmarks in neurodegenerative diseases, has practical utilities in neuroscience and medicine. Here, we propose a biophysical model that describes the individual contribution of paramagnetic (e.g., iron) and diamagnetic (e.g., myelin) susceptibility sources to the frequency shift and transverse relaxation of MRI signals. Using this model, we develop a method, χ-separation, that generates the voxel-wise distributions of the two sources. The method is validated using computer simulation and phantom experiments, and applied to ex-vivo and in-vivo brains. The results delineate the well-known histological features of iron and myelin in the specimen, healthy volunteers, and multiple sclerosis patients. This new technology may serve as a practical tool for exploring the microstructural information of the brain.

Many patients with upper limb defects desire myoelectric prosthetic hands, but they are still not used for some reasons. One of the most significant reasons is its external appearance, which has the discomfort caused by the structural difference between a human hand and a robotic link. The structure must be based on human anatomy to create a more natural-looking prosthesis. This study designed a biomimetic prosthetic hand with bones, ligaments, tendons, and multiple muscles based on the human musculoskeletal system. We verified the proposed prosthetic hand using the viscoelastic angle sensor to determine whether it works like a human hand. We also compared the finger force of the prosthetic hand with that of a human finger. It could be capable of controlling the angle and the stiffness of the joint by multiple extensor and flexor muscles, like humans.

Abstract Background Demineralized dentin matrix (DDM) is commonly used as a bone-graft substitute. This study measured and compared human hepatitis B viruses (HBV) DNA in fresh dentin to that of dentin processed into DDM extracted during dental treatment from HBV-infected patients. The hypothesis was that the processing procedure for DDM would inactivate or eliminate HBV in the dentin matrix obtained from infected patients. Methods Dentin from eighteen HBV-infected patients was collected and each dentin specimen was divided into two fragments. One fragment was used before processing as fresh dentin (control group) and the other was processed into DDM (experimental group). DNA was extracted and purified from each fresh and processed dentin specimen and the HBV DNA copy number quantitated by real time polymerase chain reaction. The HBV DNA copy number in the fresh dentin specimens were compared relative to serologic test results. The second parameter was to evaluate the effectiveness of the processing procedure (defatting, demineralization, freeze-drying, and sterilization) to inactivate or eliminate HBV by comparing the DNA copy number in the processed DDM with that in the matched fresh dentin specimens. All results were analyzed using Mann–Whitney U test to compare numerical measurements between groups and differences were considered statistically significant at P-values less than 0.05. Results The presence of HBV DNA was detected in 55.56% (10/18) of the fresh dentin specimens. For the ten HBV DNA-positive fresh dentin specimens, HBV DNA was detected in two (20%) of the matched processed dentin specimens. The copy number of HBV DNA in the two positive processed dentin specimens was 1.79 and 4.03, which were statistically lower than that of the fresh dentin specimens (P = 0.0167). Conclusions The results from this study suggested that fresh dentin may be a carrier of HBV and that the procedure used to generate DDM extensively reduced the levels of HBV DNA. Further studies are needed to evaluate the infectivity of HBV in processed dentin.

Respiration-induced B0 fluctuation corrupts MRI images by inducing phase errors in k-space. A few approaches such as navigator have been proposed to correct for the artifacts at the expense of sequence modification. In this study, a new deep learning method, which is referred to as DeepResp, is proposed for reducing the respiration-artifacts in multi-slice gradient echo (GRE) images. DeepResp is designed to extract the respiration-induced phase errors from a complex image using deep neural networks. Then, the network-generated phase errors are applied to the k-space data, creating an artifact-corrected image. For network training, the computer-simulated images were generated using artifact-free images and respiration data. When evaluated, both simulated images and in-vivo images of two different breathing conditions (deep breathing and natural breathing) show improvements (simulation: normalized root-mean-square error (NRMSE) from 7.8 ± 5.2% to 1.3 ± 0.6%; structural similarity (SSIM) from 0.88 ± 0.08 to 0.99 ± 0.01; ghost-to-signal-ratio (GSR) from 7.9 ± 7.2% to 0.6 ± 0.6%; deep breathing: NRMSE from 13.9 ± 4.6% to 5.8 ± 1.4%; SSIM from 0.86 ± 0.03 to 0.95 ± 0.01; GSR 20.2 ± 10.2% to 5.7 ± 2.3%; natural breathing: NRMSE from 5.2 ± 3.3% to 4.0 ± 2.5%; SSIM from 0.94 ± 0.04 to 0.97 ± 0.02; GSR 5.7 ± 5.0% to 2.8 ± 1.1%). Our approach does not require any modification of the sequence or additional hardware, and may therefore find useful applications. Furthermore, the deep neural networks extract respiration-induced phase errors, which is more interpretable and reliable than results of end-to-end trained networks.

The analysis of visually guided tracking movements is important to the understanding of imitation exercises and movements carried out using the human visuomotor control system. In this study, we analyzed the characteristics of visuomotor control in the intermittent performance of circular tracking movements by applying a system that can differentiate between the conditions of invisible and visible orbits and visible and invisible target phases implemented in a 3D VR space. By applying visuomotor control based on velocity control, our study participants were able to track objects with visible orbits with a precision of approximately 1.25 times greater than they could track objects with invisible orbits. We confirmed that position information is an important parameter related to intermittent motion at low speeds (below 0.5 Hz) and that tracked target velocity information could be obtained more precisely than position information at speeds above 0.5 Hz. Our results revealed that the feedforward (FF) control corresponding to velocity was delayed under the visible-orbit condition at speeds over 0.5 Hz, suggesting that, in carrying out imitation exercises and movements, the use of visually presented 3D guides can interfere with exercise learning and, therefore, that the effects of their use should be carefully considered.

Humans learn motor skills (MSs) through practice and experience and may then retain them for recruitment, which is effective as a rapid response for novel contexts. For an MS to be recruited for novel contexts, its recruitment range must be extended. In addressing this issue, we hypothesized that an MS is dynamically modulated according to the feedback context to expand its recruitment range into novel contexts, which do not involve the learning of an MS. The following two sub-issues are considered. We previously demonstrated that the learned MS could be recruited in novel contexts through its modulation, which is driven by dynamically regulating the synergistic redundancy between muscles according to the feedback context. However, this modulation is trained in the dynamics under the MS learning context. Learning an MS in a specific condition naturally causes movement deviation from the desired state when the MS is executed in a novel context. We hypothesized that this deviation can be reduced with the additional modulation of an MS, which tunes the MS-produced muscle activities by using the feedback gain signals driven by the deviation from the desired state. Based on this hypothesis, we propose a feedback gain signal-driven tuning model of a learned MS for its robust recruitment. This model is based on the neurophysiological architecture in the cortico-basal ganglia circuit, in which an MS is plausibly retained as it was learned and is then recruited by tuning its muscle control signals according to the feedback context. In this study, through computational simulation, we show that the proposed model may be used to neurophysiologically describe the recruitment of a learned MS in novel contexts.

Recently, deep neural network-powered quantitative susceptibility mapping (QSM), QSMnet, successfully performed ill-conditioned dipole inversion in QSM and generated high-quality susceptibility maps. In this paper, the network, which was trained by healthy volunteer data, is evaluated for hemorrhagic lesions that have substantially higher susceptibility than healthy tissues in order to test “linearity” of QSMnet for susceptibility. The results show that QSMnet underestimates susceptibility in hemorrhagic lesions, revealing degraded linearity of the network for the untrained susceptibility range. To overcome this limitation, a data augmentation method is proposed to generalize the network for a wider range of susceptibility. The newly trained network, which is referred to as QSMnet+, is assessed in computer-simulated lesions with an extended susceptibility range (−1.4 ​ppm to +1.4 ​ppm) and also in twelve hemorrhagic patients. The simulation results demonstrate improved linearity of QSMnet+ over QSMnet (root mean square error of QSMnet+: 0.04 ​ppm vs. QSMnet: 0.36 ​ppm). When applied to patient data, QSMnet+ maps show less noticeable artifacts to those of conventional QSM maps. Moreover, the susceptibility values of QSMnet+ in hemorrhagic lesions are better matched to those of the conventional QSM method than those of QSMnet when analyzed using linear regression (QSMnet+: slope ​= ​1.05, intercept ​= ​−0.03, R2 ​= ​0.93; QSMnet: slope ​= ​0.68, intercept ​= ​0.06, R2 ​= ​0.86), consolidating improved linearity in QSMnet+. This study demonstrates the importance of the trained data range in deep neural network-powered parametric mapping and suggests the data augmentation approach for generalization of network. The new network can be applicable for a wide range of susceptibility quantification.

This review surveys physiological, behavioral, and morphological evidence converging to the view of the cerebro-cerebellum as loci of internal forward models. The cerebro-cerebellum, the phylogenetically newest expansion in the cerebellum, receives convergent inputs from cortical, subcortical, and spinal sources, and is thought to perform the predictive computation for both motor control, motor learning, and cognitive functions. This predictive computation is known as an internal forward model. First, we elucidate the theoretical foundations of an internal forward model and its role in motor control and motor learning within the framework of the optimal feedback control model. Then, we discuss a neural mechanism that generates various patterns of outputs from the cerebro-cerebellum. Three lines of supporting evidence for the internal-forward-model hypothesis are presented in detail. First, we provide physiological evidence that the cerebellar outputs (activities of dentate nucleus cells) are predictive for the cerebellar inputs [activities of mossy fibers (MFs)]. Second, we provide behavioral evidence that a component of movement kinematics is predictive for target motion in control subjects but lags behind a target motion in patients with cerebellar ataxia. Third, we provide morphological evidence that the cerebellar cortex and the dentate nucleus receive separate MF projections, a prerequisite for optimal estimation. Finally, we speculate that the predictive computation in the cerebro-cerebellum could be deployed to not only motor control but also to non-motor, cognitive functions. This review concludes that the predictive computation of the internal forward model is the unifying algorithmic principle for understanding diverse functions played by the cerebro-cerebellum.

Abstract We have demonstrated metal-on-silicon thermocouples with a noticeably high Seebeck coefficient and an excellent temperature-sensing resolution. Fabrication of the thermocouples involved only simple photolithography and metal-liftoff procedures on a silicon substrate. The experimentally measured Seebeck coefficient of our thermocouple was 9.17 × 10−4 V/°K, which is 30 times larger than those reported for standard metal thin-film thermocouples and comparable to the values of alloy-based thin-film thermocouples that require sophisticated and costly fabrication processes. The temperature-voltage measurements between 20 to 80 °C were highly linear with a linearity coefficient of 1, and the experimentally demonstrated temperature-sensing resolution was 0.01 °K which could be further improved up to a theoretical limit of 0.00055 °K. Finally, we applied this approach to demonstrate a flexible metal-on-silicon thermocouple with enhanced thermal sensitivity. The outstanding performance of our thermocouple combined with an extremely thin profile, bending flexibility, and simple, highly-compatible fabrication will proliferate its use in diverse applications such as micro-/nanoscale biometrics, energy management, and nanoscale thermography.

Many emerging location- or proximity-based applications use Bluetooth low energy (BLE) beacons thanks to the increasing popularity of the technology in mobile systems. An outstanding example is the BLE beacon-based electronic attendance system (BEAS) used in many universities today to increase the efficiency of lectures. Despite its popularity and usefulness, however, BEAS has not been thoroughly analyzed for its potential vulnerabilities. In this paper, we neutralize a university's BEAS by maliciously cheating attendance (i.e., faking attendance while the subject is not physically present at the location) in various scenarios using signal imitation attack, and investigate its possible vulnerabilities. The BEAS exploited in this paper is a commercial system actually used in a well-known university. After the exploitation experiment, we analyze the system's weaknesses and present possible counter-measures. Furthermore, additional attack methods are shown to re-counteract those possible counter-measures and to discuss the fundamental challenges, deficiencies, and suggestions in electronic attendance systems using BLE beacons.

Goal-directed movements are predictive and multimodal in nature, especially for moving targets. For instance, during a reaching movement for a moving target, humans need to predict both motion of the target and movement of the limb. Recent computational studies show that the cerebellum predicts current and future states of the body and its environment using internal forward models. Sensory feedback signals from the periphery have delays in reaching the central nervous system, ranging between tens to hundreds of milliseconds. It is well known in engineering that feedback control based on time-delayed inputs can result in oscillatory and often unstable movements. In contrast, the brain predicts a current state from a previous state using forward models. This predictive mechanism most likely underpins stable and dexterous control of reaching movements. Although the cerebro-cerebellum has long been suggested as loci of various forward models, few methods are available to evaluate accuracy of the forward models in patients with cerebellar ataxia. Recently, we developed a non-invasive method to analyze receipt of motor commands in terms of movement kinematics for the wrist joint (Br/Kr ratio). In the present study, we have identified two components (F1 and F2) of the smooth pursuit movement. We found that the two components were in different control modes with different Br/Kr ratios. The major F1 component in a lower frequency range encodes both velocity and position of the moving target (higher Br/Kr ratio) to synchronize movement of the wrist joint with motion of the target in a predictive manner. The minor F2 component in a higher frequency range is biased to position control in order to generate intermittent small step-wise movements. In cerebellar patients, the F1 component shows a selective decrease in the Br/Kr ratio, which is correlated with decrease in accuracy of the pursuit movement. We conclude that the Br/Kr ratio of the F1 component provides a unique parameter to evaluate accuracy of the predictive control. We also discuss the pathophysiological and clinical implications for clinical ataxiology.

Traumatic axonal injury (TAI), a signature injury of traumatic brain injury (TBI), is increasingly known to involve myelin damage. The objective of this study was to demonstrate the clinical relevance of myelin water imaging (MWI) by first quantifying changes in myelin water after TAI and then correlating those changes with measures of injury severity and neurocognitive performance. Scanning was performed at 3 months post-injury in 22 adults with moderate to severe diffuse TBI and 30 demographically matched healthy controls using direct visualization of short transverse component (ViSTa) MWI. Fractional anisotropy (FA) and radial diffusivity (RD) were also obtained using diffusion tensor imaging. Duration of post-traumatic amnesia (PTA) and cognitive processing speed measured by the Processing Speed Index (PSI) from Wechsler Adult Intelligence Scale-IV, were assessed. A between-group comparison using Tract-Based Spatial Statistics revealed that there was a widespread reduction of apparent myelin water fraction (aMWF) in TBI, consistent with neuropathology involving TAI. The group difference map of aMWF yielded topography that was different from FA and RD. Importantly, aMWF demonstrated significant associations with PTA (r = −0.564, p = .006) and PSI (r = 0.452, p = .035). In conclusion, reduced myelin water, quantified by ViSTa MWI, is prevalent in moderate-to-severe diffuse TBI and could serve as a potential biomarker for injury severity and prediction of clinical outcomes. Keywords: Traumatic brain injury, Myelin water imaging, Diffusion tensor imaging, Processing speed index, Post traumatic amnesia

The demand for high-performance two-dimensional gas barrier materials is increasing owing to their potential for application in optoelectronic devices. These materials can help the devices maintain their properties over a long period. Therefore, in this study, we investigated the gas barrier performance of hexagonal boron nitride (h-BN) monolayers grown on copper foils via electrochemical polishing (ECP). The ECP treatment helped reduce the surface roughness of the copper foils. As a result, the nucleation density was reduced and highly crystalline h-BN monolayers were produced. The gas barrier performance of h-BN monolayers on copper foils with ECP was comparable to that of graphene. Our finding demonstrates the potential of monolayer h-BN as a high-performance and economical gas barrier material for organic-based optoelectronic devices.

Motor control characteristics of the human visuomotor control system need to be analyzed in the three-dimensional (3D) space to study and imitate human movements. In this paper, we examined circular tracking movements on two planes in 3D space from a motor control perspective based on three temporospatial parameters in polar coordinates. Sixteen healthy human subjects participated in this study and performed circular target tracking movements rotating at 0.125, 0.25, 0.5, and 0.75 Hz in the frontal or sagittal planes in three-dimensional space. The results showed that two temporal parameter errors on each plane were proportional to the change in the target velocity. Furthermore, frontal plane circular tracking errors without depth for a spatial parameter were lower than those for sagittal plane circular tracking with depth. The experimental protocol and data analysis allowed us to analyze the motor control characteristics temporospatially for circular tracking movement with various depths and speeds in the 3D VR space.

Myelin water imaging can be achieved using multicomponent T2 relaxation analysis to quantify in vivo measurement of myelin content, termed the myelin water fraction (MWF). Therefore, myelin water imaging can be a valuable tool to better understand the underlying white matter pathology in demyelinating diseases, such as multiple sclerosis. To apply myelin water imaging in multisite studies and clinical applications, it must be acquired in a clinically feasible scan time (less than 15 min) and be reproducible across sites and scanner vendors. Here, we assessed the reproducibility of MWF measurements in regional and global white matter in 10 healthy human brains across two sites with two different 3 T magnetic resonance imaging scanner vendors (Philips and Siemens), using a 32-echo gradient and spin echo (GRASE) sequence. A strong correlation was found between the MWF measurements in the global white matter (Pearson’s r = 0.91; p < 0.001) for all participants across the two sites. The mean intersite MWF coefficient of variation across participants was 2.77% in the global white matter and ranged from 4.47% (splenium of the corpus callosum) to 17.89% (genu of the corpus callosum) in white matter regions of interest. Bland-Altman analysis showed a good agreement in MWF measurements between the two sites with small bias of 0.002. Overall, MWF estimates were in good agreement across the two sites and scanner vendors. Our findings support the use of quantitative multi-echo T2 relaxation metrics, such as the MWF, in multicenter studies and clinical trials to gain deeper understanding about the pathological processes resulting from the underlying disease progression in neurodegenerative diseases.

In this paper, we examined the age-related changes in control of preprogramed movement, with emphasis on its accuracy. Forty-nine healthy subjects participated in this study, and were divided into three groups depending on their ages: the young group (20-39 years) (n = 16), the middle-age group (40-59 years) (n = 16), and the elderly group (60-79 years) (n = 17). We asked the subjects to perform step-tracking movements of the wrist joint with a manipulandum, and recorded the movements. We evaluated the accuracy of control of preprogramed movement in the three groups in terms of the primary submovement, which was identified as the first segment of the step-tracking movement based on the bell-shaped velocity profile, and calculated the distance between the end position of the primary submovement and the target (i.e. error). The error in the young group was found to be significantly smaller than that in the middle-age and elderly groups, i.e., the error was larger for the higher age groups. These results suggest that young subjects have better control of preprogramed movement than middle-age or elderly subjects. Finally, we examined the temporal property of the primary submovement and its age-related changes. The duration of the primary submovement tended to be longer for the aged groups, although significance was reached only for the elderly group. In particular, the ratio of the duration of the primary submovement to total movement time tended to be lower for the aged groups, suggesting that the proportion of additional movements that are required to compensate for the incomplete control in the preprogramed movement, which are under feedback control, was higher for the aged groups. Consequently, our results indicate that the distance between the end point of the primary submovement and the target center (i.e. error) in the step-tracking movement is a useful parameter to evaluate the age-related changes in control of preprogramed movement.

Quantitative evaluation of motor functions of patients with cerebellar ataxia is vital for evidence-based treatments and has been a focus in previous investigations of movement kinematics. Due to redundancy of the musculoskeletal system, muscle activities contain more information than the movement kinematics. Therefore, it is preferable to analyze causal anomalies of muscle activities to evaluate motor functions in patients. Here we propose a new method to evaluate the motor functions at the level of muscle activities and movement kinematics. Nineteen patients and 10 control subjects performed two movement tasks of the wrist joint, a step-tracking task and a pursuit task, with a manipulandum. The movements of the wrist joint and activities of the four wrist prime movers were recorded. We developed a linear model for the wrist joint to approximate the causal relationship between muscle activities and movement kinematics in terms of the wrist joint torque. We used a canonical correlation analysis to verify the causality between the muscle activities and the movement kinematics in the model. We found that the activities of the four muscles were related almost entirely to the position and velocity, with negligible correlation with the acceleration of the wrist joint. Moreover, the ratio of the weights for position- and velocity-related torque components characterized the contents of the muscle activities in terms of the movement kinematics. Next, we compared the ratios for the two movement tasks between the controls and patients. In control subjects, the ratios indicated clear task-specific changes that conformed to the functional requirements of the tasks. In contrast, in patients, the task-specific changes diminished highly significantly. The present results indicate that this ability to accommodate motor commands to the task requirements provides a novel quantitative parameter to characterize motor functions in patients with cerebellar ataxia.

The cerebellum generates its vast amount of output to the cerebral cortex through the dentate nucleus (DN) that is essential for precise limb movements in primates. Nuclear cells in DN generate burst activity prior to limb movement, and inactivation of DN results in cerebellar ataxia. The question is how DN cells become active under intensive inhibitory drive from Purkinje cells (PCs). There are two excitatory inputs to DN, mossy fiber and climbing fiber collaterals, but neither of them appears to have sufficient strength for generation of burst activity in DN. Therefore, we can assume two possible mechanisms: post-inhibitory rebound excitation and disinhibition. If rebound excitation works, phasic excitation of PCs and a concomitant inhibition of DN cells should precede the excitation of DN cells. On the other hand, if disinhibition plays a primary role, phasic suppression of PCs and activation of DN cells should be observed at the same timing. To examine these two hypotheses, we compared the activity patterns of PCs in the cerebrocerebellum and DN cells during step-tracking wrist movements in three Japanese monkeys. As a result, we found that the majority of wrist-movement-related PCs were suppressed prior to movement onset and the majority of wrist-movement-related DN cells showed concurrent burst activity without prior suppression. In a minority of PCs and DN cells, movement-related increases and decreases in activity, respectively, developed later. These activity patterns suggest that the initial burst activity in DN cells is generated by reduced inhibition from PCs, i.e., by disinhibition. Our results indicate that suppression of PCs, which has been considered secondary to facilitation, plays the primary role in generating outputs from DN. Our findings provide a new perspective on the mechanisms used by PCs to influence limb motor control and on the plastic changes that underlie motor learning in the cerebrocerebellum.

Effective point-of-use devices for providing safe drinking water are urgently needed to reduce the global burden of waterborne disease. Here we show that plant xylem from the sapwood of coniferous trees--a readily available, inexpensive, biodegradable, and disposable material--can remove bacteria from water by simple pressure-driven filtration. Approximately 3 cm(3) of sapwood can filter water at the rate of several liters per day, sufficient to meet the clean drinking water needs of one person. The results demonstrate the potential of plant xylem to address the need for pathogen-free drinking water in developing countries and resource-limited settings.

Substantial changes have occurred in the Information Technology (IT) sectors and with these changes, the demand for remote access to field sensor information has increased. This allows visualization, monitoring, and control through various electronic devices, such as laptops, tablets, i-Pads, PCs, and cellular phones. The smart phone is considered as a more reliable, faster and efficient device to access and monitor industrial systems and their corresponding information interfaces anywhere and anytime. This study describes the deployment of a protocol whereby industrial system information can be securely accessed by cellular phones via a Supervisory Control And Data Acquisition (SCADA) server. To achieve the study goals, proprietary protocol interconnectivity with non-proprietary protocols and the usage of interconnectivity services are considered in detail. They support the visualization of the SCADA system information, and the related operations through smart phones. The intelligent sensors are configured and designated to process real information via cellular phones by employing information exchange services between the proprietary protocol and non-proprietary protocols. SCADA cellular access raises the issue of security flaws. For these challenges, a cryptography-based security method is considered and deployed, and it could be considered as a part of a proprietary protocol. Subsequently, transmission flows from the smart phones through a cellular network.

There is a long history and a growing interest in the canine as a subject of study in neuroscience research and in translational neurology. In the last few years, anatomical and functional magnetic resonance imaging (MRI) studies of awake and anesthetized dogs have been reported. Such efforts can be enhanced by a population atlas of canine brain anatomy to implement group analyses. Here we present a canine brain atlas derived as the diffeomorphic average of a population of fifteen mesaticephalic dogs. The atlas includes: 1) A brain template derived from in-vivo, T1-weighted imaging at 1 mm isotropic resolution at 3 Tesla (with and without the soft tissues of the head); 2) A co-registered, high-resolution (0.33 mm isotropic) template created from imaging of ex-vivo brains at 7 Tesla; 3) A surface representation of the gray matter/white matter boundary of the high-resolution atlas (including labeling of gyral and sulcal features). The properties of the atlas are considered in relation to historical nomenclature and the evolutionary taxonomy of the Canini tribe. The atlas is available for download (https://cfn.upenn.edu/aguirre/wiki/public:data_plosone_2012_datta).

Procedural motor learning includes a period when no substantial gain in performance improvement is obtained even with repeated, daily practice. Prompted by the potential benefit of high-frequency transcutaneous electrical stimulation, we examined if the stimulation to the hand reduces redundant motor activity that likely exists in an acquired hand motor skill, so as to further upgrade stable motor performance. Healthy participants were trained until their motor performance of continuously rotating two balls in the palm of their right hand became stable. In the series of experiments, they repeated a trial performing this cyclic rotation as many times as possible in 15 s. In trials where we applied the stimulation to the relaxed thumb before they initiated the task, most reported that their movements became smoother and they could perform the movements at a higher cycle compared to the control trials. This was not possible when the dorsal side of the wrist was stimulated. The performance improvement was associated with reduction of amplitude of finger displacement, which was consistently observed irrespective of the task demands. Importantly, this kinematic change occurred without being noticed by the participants, and their intentional changes of motor strategies (reducing amplitude of finger displacement) never improved the performance. Moreover, the performance never spontaneously improved during one-week training without stimulation, whereas the improvement in association with stimulation was consistently observed across days during training on another week combined with the stimulation. The improved effect obtained in stimulation trials on one day partially carried over to the next day, thereby promoting daily improvement of plateaued performance, which could not be unlocked by the first-week intensive training. This study demonstrated the possibility of effectively improving a plateaued motor skill, and pre-movement somatic stimulation driving this behavioral change.