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Background/Objective: Repair of long bone defects remains a major challenge in clinical practice, necessitating the use of bone grafts, growth factors, and mechanical stability. Hence, a combination therapy involving a 3D-printed polycaprolactone (PCL)/β-tricalcium phosphate (β-TCP) scaffold coated with polydopamine (PDA) and alginate microbeads (AM) for sustained delivery of bone morphogenetic protein-2 (BMP-2) was investigated to treat long bone segmental defects. Methods: Several in vitro analyses were performed to evaluate the scaffold osteogenic effects in vitro such as PDA surface modification, namely, hydrophilicity and cell adhesion; cytotoxicity and BMP-2 release kinetics using CCK-8 assay and ELISA, respectively; osteogenic differentiation in canine adipose-derived mesenchymal stem cells (Ad-MSCs); formation of mineralized nodules using ALP staining and ARS staining; and mRNA expression of osteogenic differentiation markers using RT-qPCR. Bone regeneration in femoral bone defects was evaluated in vivo using a rabbit femoral segmental bone defect model by performing radiography, micro-computed tomography, and histological observation (hematoxylin and eosin and Masson's trichrome staining). Results: The PDA-coated 3D-printed scaffold demonstrated increased hydrophilicity, cell adhesion, and cell proliferation compared with that of the control. BMP-2 release kinetics assessment showed that BMP-2 AM showed a reduced initial burst and continuous release for 28 days. In vitro co-culture with canine Ad-MSCs showed an increase in mineralization and mRNA expression of osteogenic markers in the BMP-2 AM group compared with that of the BMP-2-adsorbed scaffold group. In vivo bone regeneration evaluation 12 weeks after surgery showed that the BMP-2 AM/PDA group exhibited the highest bone volume in the scaffold, followed by the BMP-2/PDA group. High cortical bone connectivity was observed in the PDA-coated scaffold groups. Conclusion: These findings suggest that the combined use of PDA-coated 3D-printed bone scaffolds and BMP-2 AM can successfully induce bone regeneration even in load-bearing bone segmental defects. The translational potential of this article: A 3D-printed PCL/β-TCP scaffold was fabricated to mimic the cortical bone of the femur. Along with the application of PDA surface modification and sustained BMP-2 release via AM, the developed scaffold could provide suitable osteoconduction, osteoinduction, and osteogenesis in both in vitro settings and in vivo rabbit femoral segmental bone defect models. Therefore, our findings suggest a promising therapeutic option for treating challenging long bone segmental defects, with potential for future clinical application.

This article presents an efficient method for isolating cellulose nanocrystals (CNcs) from seaweed waste using a combination of electron beam (E-beam) irradiation and acid hydrolysis. This approach not only reduces the chemical consumption and processing time, but also improves the crystallinity and yield of the CNcs. The isolated CNcs were then thermally annealed at 800 and 1000 °C to produce porous nanocarbon materials, which were characterized using scanning electron microscopy, X-ray diffraction, Raman spectroscopy, and X-ray photoelectron spectroscopy to assess their structural and chemical properties. Electrochemical testing of electrical double-layer capacitors demonstrated that nanocarbon materials derived from seaweed waste-derived CNcs annealed at 1000 exhibited superior capacitance and stability. This performance is attributed to the formation of a highly ordered graphitic structure with a mesoporous architecture, which facilitates efficient ion transport and enhanced electrolyte accessibility. These findings underscore the potential of seaweed waste-derived nanocarbon as a sustainable and high-performance material for energy storage applications, offering a promising alternative to conventional carbon sources.

Abstract Recent advancements in deep learning have facilitated significant progress in medical image analysis. However, there is lack of studies specifically addressing the needs of surgeons in terms of practicality and precision for surgical planning. Accurate understanding of anatomical structures, such as the liver and its intrahepatic structures, is crucial for preoperative planning from a surgeon’s standpoint. This study proposes a deep learning model for automatic segmentation of liver parenchyma, vascular and biliary structures, and tumor mass in hepatobiliary phase liver MRI to improve preoperative planning and enhance patient outcomes. A total of 120 adult patients who underwent liver resection due to hepatic mass and had preoperative gadoxetic acid-enhanced MRI were included in the study. A 3D residual U-Net model was developed for automatic segmentation of liver parenchyma, tumor mass, hepatic vein (HV), portal vein (PV), and bile duct (BD). The model’s performance was assessed using Dice similarity coefficient (DSC) by comparing the results with manually delineated structures. The model achieved high accuracy in segmenting liver parenchyma (DSC 0.92 ± 0.03), tumor mass (DSC 0.77 ± 0.21), hepatic vein (DSC 0.70 ± 0.05), portal vein (DSC 0.61 ± 0.03), and bile duct (DSC 0.58 ± 0.15). The study demonstrated the potential of the 3D residual U-Net model to provide a comprehensive understanding of liver anatomy and tumors for preoperative planning, potentially leading to improved surgical outcomes and increased patient safety.

In this paper, we present a cascaded deep convolution neural network (CNN) for assessing enlarged perivascular space (ePVS) within the basal ganglia region using T2-weighted MRI. Enlarged perivascular spaces (ePVSs) are potential biomarkers for various neurodegenerative disorders, including dementia and Parkinson’s disease. Accurate assessment of ePVS is crucial for early diagnosis and monitoring disease progression. Our approach first utilizes an ePVS enhancement CNN to improve ePVS visibility and then employs a quantification CNN to predict the number of ePVSs. The ePVS enhancement CNN selectively enhances the ePVS areas without the need for additional heuristic parameters, achieving a higher contrast-to-noise ratio (CNR) of 113.77 compared to Tophat, Clahe, and Laplacian-based enhancement algorithms. The subsequent ePVS quantification CNN was trained and validated using fourfold cross-validation on a dataset of 76 participants. The quantification CNN attained 88% accuracy at the image level and 94% accuracy at the subject level. These results demonstrate significant improvements over traditional algorithm-based methods, highlighting the robustness and reliability of our deep learning approach. The proposed cascaded deep CNN model not only enhances the visibility of ePVS but also provides accurate quantification, making it a promising tool for evaluating neurodegenerative disorders. This method offers a novel and significant advancement in the non-invasive assessment of ePVS, potentially aiding in early diagnosis and targeted treatment strategies.

Nanocellulose (NC) has emerged as a promising biodegradable material with applications in various industrial fields owing to its high mechanical strength, thermal stability, and eco-friendly properties. Traditional methods for isolating NC from wood-based biomass (WB) involve high energy consumption and extensive chemical usage, leading to environmental and sustainability concerns. This study explored an alternative approach to isolate NC from seaweed-based biomass (SB) (SNC), which contains fewer non-cellulosic components and a higher cellulose content than WB, thereby yielding a more efficient e-isolation process. We employed a combination of modified-acid solution and electron beam (E-beam) technology to isolate NC from SB. The E-beam process enhanced the crystallinity while reducing the particle size, thus facilitating NC isolation with reduced environmental impact and processing time. Moreover, our method significantly reduced the need for harsh chemical reagents and energy-intensive processes, which are typically associated with traditional NC isolation methods. We fabricated biodegradable films with improved mechanical properties using NC as a reinforcing agent in polymer composites, thereby demonstrating the potential of NC-based materials for various applications. Therefore, our proposed approach offers a sustainable and efficient method for NC isolation and serves as a guide for the development of eco-friendly industrial processes. Our findings contribute to ongoing efforts to create sustainable materials and reduce the environmental footprint of the manufacturing industry.

The development of low-cost and low-power gas sensors for reliable NO2 gas detection is important due to the highly toxic nature of NO2 gas. Herein, initially, SnO2 nanowires (NWs) were synthesized through a simple vapor–liquid–solid growth mechanism. Subsequently, different amounts of SnO2 NWs were composited with MoS2 nanosheets (NSs) to fabricate SnO2 NWs/MoS2 NS nanocomposite gas sensors for NO2 gas sensing. The operation of the sensors in self-heating mode at 1–3.5 V showed that the sensor with 20 wt.% SnO2 (SM-20 nanocomposite) had the highest response of 13 to 1000 ppb NO2 under 3.2 V applied voltage. Furthermore, the SM-20 nanocomposite gas sensor exhibited high selectivity and excellent long-term stability. The enhanced NO2 gas response was ascribed to the formation of n-n heterojunctions between SnO2 NWs and MoS2, high surface area, and the presence of some voids in the SM-20 composite gas sensor due to having different morphologies of SnO2 NWs and MoS2 NSs. It is believed that the present strategy combining MoS2 and SnO2 with different morphologies and different sensing properties is a good approach to realize high-performance NO2 gas sensors with merits such as simple synthesis and fabrication procedures, low cost, and low power consumption, which are currently in demand in the gas sensor market.

Magnesium (Mg) alloys have a low density among the structural metals, but it is hard to be plastically deformed due to their crystallographic nature. Wire arc additive manufacturing (WAAM) could be one of the alternative processes to fabricate the Mg products for various industrial applications. Nonetheless, it is used in limited industrial fields due to challenging issues such as low commercial availability of wire, high oxygen affinity, explosive and flammable hazard, and typical defects of WAAM like distortion and side collapse, grain coarsening, etc. In this research, it was tried to find out the better way to fabricate the WAAM of AZ31 alloy. An additional Ar gas side shielding was given during the WAAM process, and compared with the conventional shielding method. It was observed that the global shielding affects the microstructural evolution of the WAAM product, and influences the mechanical property in a positive way.

We aimed to develop efficient data labeling strategies for ground truth segmentation in lower-leg magnetic resonance imaging (MRI) of patients with Charcot-Marie-Tooth disease (CMT) and to develop an automated muscle segmentation model using different labeling approaches. The impact of using unlabeled data on model performance was further examined. Using axial T1-weighted MRIs of 120 patients with CMT (60 each with mild and severe intramuscular fat infiltration), we compared the performance of segmentation models obtained using several different labeling strategies. The effect of leveraging unlabeled data on segmentation performance was evaluated by comparing the performances of few-supervised, semi-supervised (mean teacher model), and fully-supervised learning models. We employed a 2D U-Net architecture and assessed its performance by comparing the average Dice coefficients (ADC) using paired t-tests with Bonferroni correction. Among few-supervised models utilizing 10% labeled data, labeling three slices (the uppermost, central, and lowermost slices) per subject exhibited a significantly higher ADC (90.84±3.46%) compared with other strategies using a single image slice per subject (uppermost, 87.79±4.41%; central, 89.42±4.07%; lowermost, 89.29±4.71%, p < 0.0001) or all slices per subject (85.97±9.82%, p < 0.0001). Moreover, semi-supervised learning significantly enhanced the segmentation performance. The semi-supervised model using the three-slices strategy showed the highest segmentation performance (91.03±3.67%) among 10% labeled set models. Fully-supervised model showed an ADC of 91.39±3.76. A three-slice-based labeling strategy for ground truth segmentation is the most efficient method for developing automated muscle segmentation models of CMT lower leg MRI. Additionally, semi-supervised learning with unlabeled data significantly enhances segmentation performance.

Abstract We report on the layer-dependent stability of muscovite-type two-dimensional (2D) mica nanosheets (KAl3Si3O10(OH)2). First-principles calculations on mica nanosheets with different layer thicknesses (n = 1, 2, and 3) reveal their layer-dependent stability; odd-numbered 2D mica nanosheets are more stable than even-numbered ones, and the preferable stability of odd-numbered layers originates from electronic effects. A core-shielding model is proposed with a reasonable assumption, successfully proving the instability of the even-numbered mica nanosheets. Raman imaging supports that the population of odd-numbered mica nanosheets is predominant in exfoliated mica products. The alternating charge states with odd/even layers were evidenced by Kelvin probe force microscopy. We also demonstrate a unique photocatalytic degradation, opening new doors for environmental applications of mica nanosheets.

In this study, a novel flexible ethanol gas sensor was created by the deposition of a CoFe2O4 (CFO) thin film on a thin mica substrate using the pulsed laser deposition technique. Transition electron microscopy (TEM) investigations clearly demonstrated the successful growth of CFO on the mica, where a well-defined interface was observed. Ethanol gas-sensing studies showed optimal performance at 200 °C, with the highest response of 19.2 to 100 ppm ethanol. Operating the sensor in self-heating mode under 7 V applied voltage, which corresponds to a temperature of approximately 200 °C, produced a maximal response of 19.2 to 100 ppm ethanol. This aligned with the highest responses observed during testing at 200 °C, confirming the sensor’s accuracy and sensitivity to ethanol under self-heating conditions. In addition, the sensor exhibited good selectivity to ethanol and excellent flexibility, maintaining its high performance after bending and tilting up to 5000 times. As this is the first report on flexible self-heated CFO gas sensors, we believe that this research holds great promise for the future development of high-quality sensors based on this approach.

We introduce a novel method for fabricating perovskite solar modules using selective spin-coating on various Au/ITO patterned substrates. These patterns were engineered for two purposes: (1) to enhance selectivity of monolayers primarily self-assembling on the Au electrode, and (2) to enable seamless interconnection between cells through direct contact of the top electrode and the hydrophobic Au connection electrode. Utilizing SAMs-treated Au/ITO, we achieved sequential selective deposition of the electron transport layer (ETL) and the perovskite layer on the hydrophilic amino-terminated ITO, while the hole transport layer (HTL) was deposited on the hydrophobic CH3-terminated Au connection electrodes. Importantly, our approach had a negligible impact on the series resistance of the solar cells, as evidenced by the measured specific contact resistivity of the multilayers. A significant outcome was the production of a six-cell series-connected solar module with a notable average PCE of 8.32%, providing a viable alternative to the conventional laser scribing technique.

Abstract Organic light-emitting diode (OLED) fibers with favorable electroluminescence properties and interconnectable pixel configurations have represented the potential for wearable electronic textile displays. Nevertheless, the current technology of OLED fiber-based textile displays still leaves to be desired due to several challenges, including limited emission area and lack of encapsulation systems. Here we present a fibrous OLED textile display that can attain a large emission area and long-term stability by implementing addressable networks comprised of integrated phosphorescence OLED fibers and by designing multilayer encapsulations. The integrated fiber configuration offers decoupled functional fiber surfaces for an interconnectable 1-dimensional OLED pixel array and a data-addressing conductor. Tailored triadic metal/ultrathin oxide/polymer multilayer enables not only the oxygen/water permeation inhibition but also the controllable conductive channels of dielectric antifuses. Together with reliable bending stability, the long-term operation of OLED textiles in water manifests the feasibility of the present device concept toward water-resistant full-emitting-area fibrous textile displays.

Formation of galvanic cells between constituent phases is largely responsible for corrosion in Mg-based alloys. We develop a methodology to calculate the electrochemical potentials of intermetallic compounds and alloys using a simple model based on the Born-Haber cycle. Calculated electrochemical potentials are used to predict and control the formation of galvanic cells and minimize corrosion. We demonstrate the applicability of our model by minimizing galvanic corrosion in Mg-3wt%Sr-xZn alloy by tailoring the Zn composition. The methodology proposed in this work is applicable for any general alloy system and will facilitate efficient design of corrosion resistant alloys.

Abstract Background Although the obesity paradox is a topic of immense interest for oncologists and epidemiologists, the mechanism underlying this unexpected benefit of obesity is poorly understood. We explored the prognostic value of obesity and its association with skeletal muscle mass. Methods This retrospective study evaluated the data of patients who underwent surgical excision for lung adenocarcinoma between January 2011 and December 2015. Body mass index was categorized according to the criteria of the Asia‐Pacific classification. Cross‐sectional areas of the skeletal muscle, subcutaneous fat, and visceral fat were measured. Skeletal muscle mass status was defined based on the cut‐offs of skeletal muscle index (cm2/m2), calculated as the area of skeletal muscle divided by height squared. Overall survival was estimated using the Kaplan–Meier method, and differences in survival probabilities were compared using the log‐rank test. Cox proportional hazards regression analysis was conducted to determine the association with overall survival. Results A total of 636 patients with a median age of 61 years (interquartile range, 54.0–68.5 years; 321 men and 315 women) were included. Obese patients (body mass index ≥ 25 kg/m2) had longer overall survival than non‐obese patients (mean, 110.2 months vs. 98.7 months; log‐rank P = 0.015). Under multivariable Cox proportional hazard regression analysis, obesity was associated with longer overall survival after adjusting for covariates (hazard ratio, 0.59; 95% confidence interval, 0.40–0.86; P = 0.007). The prognostic value of obesity remained and predicted favourable overall survival after additional adjusting for skeletal muscle mass status (hazard ratio, 0.57; 95% confidence interval, 0.36–0.89; P = 0.014), skeletal muscle index (hazard ratio, 0.53; 95% confidence interval, 0.33–0.84; P = 0.008), or skeletal muscle area (hazard ratio, 0.61; 95% confidence interval, 0.38–0.98; P = 0.041). No association was observed between skeletal muscle mass status and the impact of body mass index on overall survival (P for interaction = 0.512). Conclusions Obesity was associated with favourable overall survival, independent of skeletal muscle mass, after surgical excision of lung adenocarcinoma.

Background: the objective of this study is to evaluate the predictive power of the survival model using deep learning of diffusion-weighted images (DWI) in patients with non-small-cell lung cancer (NSCLC). Methods: DWI at b-values of 0, 100, and 700 sec/mm2 (DWI0, DWI100, DWI700) were preoperatively obtained for 100 NSCLC patients who underwent curative surgery (57 men, 43 women; mean age, 62 years). The ADC0-100 (perfusion-sensitive ADC), ADC100-700 (perfusion-insensitive ADC), ADC0-100-700, and demographic features were collected as input data and 5-year survival was collected as output data. Our survival model adopted transfer learning from a pre-trained VGG-16 network, whereby the softmax layer was replaced with the binary classification layer for the prediction of 5-year survival. Three channels of input data were selected in combination out of DWIs and ADC images and their accuracies and AUCs were compared for the best performance during 10-fold cross validation. Results: 66 patients survived, and 34 patients died. The predictive performance was the best in the following combination: DWI0-ADC0-100-ADC0-100-700 (accuracy: 92%; AUC: 0.904). This was followed by DWI0-DWI700-ADC0-100-700, DWI0-DWI100-DWI700, and DWI0-DWI0-DWI0 (accuracy: 91%, 81%, 76%; AUC: 0.889, 0.763, 0.711, respectively). Survival prediction models trained with ADC performed significantly better than the one trained with DWI only (p-values < 0.05). The survival prediction was improved when demographic features were added to the model with only DWIs, but the benefit of clinical information was not prominent when added to the best performing model using both DWI and ADC. Conclusions: Deep learning may play a role in the survival prediction of lung cancer. The performance of learning can be enhanced by inputting precedented, proven functional parameters of the ADC instead of the original data of DWIs only.

Zinc silicate (Zn2SiO4) is a promising anode material for Li-ion batteries, although its reversible capacity is relatively low. In this study, we designed a Si-Zn2SiO4 composite using a simple mechanical milling and heat treatment. The synthetic phase transformation from the starting materials to the final composite was confirmed via X-ray diffraction (XRD) analysis, and the morphology and microstructure of the composite were analyzed using electron microscopy techniques. The electrochemical test results demonstrated that the composite electrode exhibited a high reversible capacity of 715 mAh g–1 after 400 cycles without carbon incorporation. Further, the electrochemical reaction mechanism of the Si–Zn2SiO4 composite electrode with Li was investigated via ex situ XRD analyses. The stable cycling stability of the electrode was attributed to the role of Zn2SiO4 in buffering the volume change of Si.

The purpose of this study was to determine the prognostic impact of fat loss after immune checkpoint inhibitor (ICI) treatment in patients with metastatic clear cell renal cell carcinoma (ccRCC). Data from 60 patients treated with ICI therapy for metastatic ccRCC were retrospectively analyzed. Changes in cross-sectional areas of subcutaneous fat (SF) between the pre-treatment and post-treatment abdominal computed tomography (CT) images were expressed as percentages and were divided by the interval between the CT scans to calculate ΔSF (%/month). SF loss was defined as ΔSF < −5%/month. Survival analyses for overall survival (OS) and progression-free survival (PFS) were performed. Patients with SF loss had shorter OS (median, 9.5 months vs. not reached; p < 0.001) and PFS (median, 2.6 months vs. 33.5 months; p < 0.001) than patients without SF loss. ΔSF was independently associated with OS (adjusted hazard ratio (HR), 1.49; 95% confidence interval (CI), 1.07–2.07; p = 0.020) and PFS (adjusted HR, 1.57; 95% CI, 1.17–2.12; p = 0.003), with a 5%/month decrease in SF increasing the risk of death and progression by 49% and 57%, respectively. In conclusion, Loss of SF after treatment initiation is a significant and independent poor prognostic factor for OS and PFS in patients with metastatic ccRCC who receive ICI therapy.

With the global effort to reduce fossil fuels and to use eco-friendly energy, interest in Li-ion batteries (LIBs) is rapidly increasing. In the LIB system, the separator is an important component for determining the rate performance and safety of cells. Although polyolefin separators are commercially used in LIBs, they still suffer from inferior electrolyte wettability and low thermal stability issues. Here, we introduce a chemical surface modification for polyethylene (PE) separators using a poly(ether ether ketone) (PEEK) coating. The separators were pretreated in a tannic acid solution to enforce the adhesion of the coated layers. Then, PEEK was coated onto the PE surface by a doctor blading method. The separators were examined by infrared spectroscopy, and the surface properties were characterized by electrolyte uptake and contact angle measurements. The treated surface was hydrophilic, and the ionic conductivity of the cell with the modified separator was significantly improved. As a result, the corresponding rate performance was significantly improved. The surface modification strategy proposed here can be applied to polyolefin-based separators as well.

Since flexible devices are being used in various states of charge (SoCs), it is important to investigate SoCs that are durable against external mechanical deformations. In this study, the effects of a mechanical fatigue test under various initial SoCs of batteries were investigated. More specifically, ultrathin pouch-type Li-ion polymer batteries with different initial SoCs were subjected to repeated torsional stress and then galvanostatically cycled 200 times. The cycle performance of the cells after the mechanical test was compared to investigate the effect of the initial SoCs. Electrochemical impedance spectroscopy was employed to analyze the interfacial resistance changes of the anode and cathode in the cycled cells. When the initial SoC was at 70% before mechanical deformation, both electrodes well maintained their initial state during the mechanical fatigue test and the cell capacity was well retained during the cycling test. This indicates that the cells could well endure mechanical fatigue stress when both electrodes had moderate lithiation states. With initial SoCs at 0% and 100%, the batteries subjected to the mechanical test exhibited relatively drastic capacity fading. This indicates that the cells are vulnerable to mechanical fatigue stress when both electrodes have high lithiation states. Furthermore, it is noted that the stress accumulated inside the batteries caused by mechanical fatigue can act as an accelerated degradation factor during cycling.

We have designed a new ternary structure to enhance the sensing properties of WS2 nanosheet (NS)-based gas sensors at room temperature (RT) in self-heating mode. SnO2 nanowires (NWs, 10–30 wt%) were added to WS2 NSs and then Au nanoparticles (NPs) were deposited on the surface of the resulting composites by UV irradiation. The Au-decorated 10 wt% SnO2–WS2 composition showed the highest gas sensing properties. The presence of SnO2 NWs on the WS2 NSs effectively enhanced the diffusion and adsorption of gas species into deeper parts of the gas sensor. Furthermore, the chemical sensitization of Au (increase in oxygen ionosorption; spillover effect and catalytic effect towards CO) contributed to an enhanced response to CO gas. Gas sensing tests performed in the self-heating mode demonstrated the possibility of realizing a low-voltage, low-power-consumption CO gas sensor based on the Au-decorated 10 wt% SnO2–WS2. The sensor response under 60% relative humidity (RH) conditions was 84% of that under dry conditions, which shows that CO sensing is possible in wet environments at room temperature operation.

We investigate the effects of the molar ratio (x) of PbBr2 on the phases, microstructure, surface morphology, optical properties, and structural defects of mixed lead halides PbI2(1−x)Br2x for use in solar cell devices. Results indicate that as x increased to 0.3, the surface morphology continued to improve, accompanied by the growth of PbI2 grains. This resulted in lead halide films with a very smooth and continuous morphology, including large grains when the film was formed at x = 0.3. In addition, the microstructure changed from (001)-oriented pure PbI2 to a highly (001)-oriented β (PbI2-rich) phase. The plausible mechanism for the enhanced morphology of the lead halide films by the addition of PbBr2 is proposed based on the growth of a Br-saturated lead iodide solid solution. Furthermore, iodine vacancies, identified by X-ray photoelectron spectroscopy, decreased as the ratio of PbBr2 increased. Finally, an electrical analysis of the solar cells was performed by using a PN heterojunction model, revealing that structural defects, such as iodine vacancies and grain boundaries, are the main contributors to the degradation of the performance of pure PbI2-based solar cells (including high leakage, low stability, and high hysteresis), which was significantly improved by the addition of PbBr2. The solar cell fabricated at x = 0.3 in air showed excellent stability and performance. The device lost merely 20% of the initial efficiency of 4.11% after 1500 h without encapsulation. This may be due to the dense microstructure and the reduced structural defects of lead halides formed at x = 0.3.

Halo coronal mass ejections (CMEs) originating from solar activities give rise to geomagnetic storms when they reach the Earth. Variations in the geomagnetic field during a geomagnetic storm can damage satellites, communication systems, electrical power grids, and power systems, and induce currents. Therefore, automated techniques for detecting and analyzing halo CMEs have been eliciting increasing attention for the monitoring and prediction of the space weather environment. In this study, we developed an algorithm to sense and detect halo CMEs using large angle and spectrometric coronagraph (LASCO) C3 coronagraph images from the solar and heliospheric observatory (SOHO) satellite. In addition, we developed an image processing technique to derive the morphological and dynamical characteristics of halo CMEs, namely, the source location, width, actual CME speed, and arrival time at a 21.5 solar radius. The proposed halo CME automatic analysis model was validated using a model of the past three halo CME events. As a result, a solar event that occurred at 03:38 UT on Mar. 23, 2014 was predicted to arrive at Earth at 23:00 UT on Mar. 25, whereas the actual arrival time was at 04:30 UT on Mar. 26, which is a difference of 5 hr and 30 min. In addition, a solar event that occurred at 12:55 UT on Apr. 18, 2014 was estimated to arrive at Earth at 16:00 UT on Apr. 20, which is 4 hr ahead of the actual arrival time of 20:00 UT on the same day. However, the estimation error was reduced significantly compared to the ENLIL model. As a further study, the model will be applied to many more events for validation and testing, and after such tests are completed, on-line service will be provided at the Korean Space Weather Center to detect halo CMEs and derive the model parameters.

In this article, we propose a deep-learning-based estimation model for rating enlarged perivascular spaces (EPVS) in the brain’s basal ganglia region using T2-weighted magnetic resonance imaging (MRI) images. The proposed method estimates the EPVS rating directly from the T2-weighted MRI without using either the detection or the segmentation of EVPS. The model uses the cropped basal ganglia region on the T2-weighted MRI. We formulated the rating of EPVS as a multi-class classification problem. Model performance was evaluated using 96 subjects’ T2-weighted MRI data that were collected from two hospitals. The results show that the proposed method can automatically rate EPVS—demonstrating great potential to be used as a risk indicator of dementia to aid early diagnosis.

Si-based anodes for Li-ion batteries (LIBs) are considered to be an attractive alternative to graphite due to their higher capacity, but they have low electrical conductivity and degrade mechanically during cycling. In the current study, we report on a mass-producible porous Si-CoSi2-C composite as a high-capacity anode material for LIBs. The composite was synthesized with two-step milling followed by a simple chemical etching process. The material conversion and porous structure were characterized by X-ray diffraction, X-ray photoelectron spectroscopy, and electron microscopy. The electrochemical test results demonstrated that the Si-CoSi2-C composite electrode exhibits greatly improved cycle and rate performance compared with conventional Si-C composite electrodes. These results can be ascribed to the role of CoSi2 and inside pores. The CoSi2 synthesized in situ during high-energy mechanical milling can be well attached to the Si; its conductive phase can increase electrical connection with the carbon matrix and the Cu current collectors; and it can accommodate Si volume changes during cycling. The proposed synthesis strategy can provide a facile and cost-effective method to produce Si-based materials for commercial LIB anodes.

Abstract One-dimensional, hollow nanostructured materials are among the most promising materials for sensing applications owing to their high surface area that facilitates the adsorption of target gases. Accordingly, for gas sensing studies, hollow ZnO nanowires (NWs) with different surface areas were successfully synthesized herein by using polycarbonate membranes with different pore sizes as templates, and deposition of ZnO via the atomic layer deposition technique. The sensing properties of the synthesized hollow ZnO NWs were examined for CO and NO2, revealing their comparative sensing performances with ZnO nanomaterials-based sensors reported in literature. This study highlights a novel way of synthesizing hollow ZnO NWs by using membrane template and their promising sensing properties as well.

Cu nanofoams are promising materials for a variety of applications, including anodes in high-performance lithium-ion batteries. The high specific surface area of these materials supports a high capacity and porous structure that helps accommodate volume expansion which occurs as batteries are charged. One of the most efficient methods to produce Cu nanofoams is the dealloying of Cu alloy precursors. This process often yields nanofoams that have low strength, thus requiring additional heat treatment to improve the mechanical properties of Cu foams. This paper provides the effects of heat treatment on the microstructures, mechanical properties, and electrochemical performance of Cu nanofoams. Annealing was conducted under both inert and oxidizing atmospheres. These studies ultimately reveal the underlying mechanisms of ligament coarsening during heat treatment.

Coronal Mass Ejections (CME), which originate from active regions of the Sun’s surface, e.g., sunspots, result in geomagnetic storms on Earth. The variation of the Earth’s geomagnetic field during such storms induces surface currents that could cause breakdowns in electricity power grids. Hence, it is essential to both monitor Geomagnetically Induced Currents (GICs) in real time and analyze previous GIC data. In 2012, in order to monitor the variation of GICs, the Korean Space Weather Center (KSWC) installed an induced current measurement system at SINGAPYEONG Substation, which is equipped with 765 kV extra-high-voltage transformers. Furthermore, in 2014, two induced current measurement systems were installed on the 345 kV high-voltage transformers at the MIGEUM and SINPOCHEON substations. This paper reports the installation process of the induced current measurement systems at these three substations. Furthermore, it presents the results of both an analysis performed using GIC data measured at the SINGAPYEONG Substation during periods of geomagnetic storms from July 2013 through April 2015 and the comparison between the obtained GIC data and magnetic field variation (dH/dt) data measured at the Icheon geomagnetic observatory.

Gulf War illness (GWI) refers to the multitude of chronic health symptoms, spanning from fatigue, musculoskeletal pain, and neurological complaints to respiratory, gastrointestinal, and dermatologic symptoms experienced by about 250,000 GW veterans who served in the 1991 Gulf War (GW). Longitudinal studies showed that the severity of these symptoms often remain unchanged even years after the GW, and these veterans with GWI continue to have poorer general health and increased chronic medical conditions than their non-deployed counterparts. For better management and treatment of this condition, there is an urgent need for developing objective biomarkers that can help with simple and accurate diagnosis of GWI. In this study, we applied multiple neuroimaging techniques, including T1-weighted magnetic resonance imaging (T1W-MRI), diffusion tensor imaging (DTI), and novel neurite density imaging (NDI) to perform both a group-level statistical comparison and a single-subject level machine learning (ML) analysis to identify diagnostic imaging features of GWI. Our results supported NDI as the most sensitive in defining GWI characteristics. In particular, our classifier trained with white matter NDI features achieved an accuracy of 90% and F-score of 0.941 for classifying GWI cases from controls after the cross-validation. These results are consistent with our previous study which suggests that NDI measures are sensitive to the microstructural and macrostructural changes in the brain of veterans with GWI, which can be valuable for designing better diagnosis method and treatment efficacy studies.

Antimony (Sb)-based materials are considered to be attractive for use in Li secondary battery anodes because of their high capacity. However, their huge volume change during Li insertion-extraction cycling limits their cycle performance. The Sb-active material can be combined with intercalation-based active materials to address these issues. In this study, spherical Sb core/Nb2O5 shell structured composite materials were synthesized through a simple solvothermal process and a carbon coating was simultaneously added during heat treatment using a naphthalene precursor. The resulting double-shelled materials were characterized with X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, and electron microscopy. The electrochemical test results showed that a reversible capacity of more than 450 mAh g−1 was retained after 100 cycles. This improved performance is ascribed to the double-shelled structure. The large volume change of the nano-sized Sb core material was alleviated by the double-shelled structure, which consisted of crystalline orthorhombic Nb2O5 and amorphous carbon. The shell materials also aided rapid charge transport.

Given that the insula plays a contributory role in the perception of chronic pain, we examined the resting-state functional connectivity between the insular cortex and other brain regions to investigate neural underpinnings of persisting perception of background pain in patients with complex regional pain syndrome (CRPS). A total of 25 patients with CRPS and 25 matched healthy controls underwent functional magnetic resonance imaging at rest. With the anterior and posterior insular cortices as seed regions, we compared the strength of the resting-state functional connectivity between the two groups. Functional connectivity between the anterior and posterior insular cortices and the postcentral and inferior frontal gyri, cingulate cortices was reduced in patients with CRPS compared with controls. Additionally, greater reductions in functional connectivity between the anterior insula and right postcentral gyrus were associated with more severe sensory pain in patients with CRPS (short-form McGill Pain Questionnaire sensory subscores, r = -.517, P = .023). The present results imply a possible role of the insula in aberrant processing of pain information in patients with CRPS. The findings suggest that a functional derangement of the connection between one of the somatosensory cortical functions of perception and one of the insular functions of awareness can play a significant role in the persistent experience of regional pain that is not confined to a specific nerve territory.

The objective of this study was to examine the tumor spatial heterogeneity in myxoid-containing soft-tissue tumors (STTs) using texture analysis of diffusion-weighted imaging (DWI). A total of 40 patients with myxoid-containing STTs (23 benign and 17 malignant) were included in this study. The region of interest (ROI) was manually drawn on the apparent diffusion coefficient (ADC) map. For texture analysis, the global (mean, standard deviation, skewness, and kurtosis), regional (intensity variability and size-zone variability), and local features (energy, entropy, correlation, contrast, homogeneity, variance, and maximum probability) were extracted from the ADC map. Student's t-test was used to test the difference between group means. Analysis of covariance (ANCOVA) was performed with adjustments for age, sex, and tumor volume. The receiver operating characteristic (ROC) analysis was performed to compare diagnostic performances. Malignant myxoid-containing STTs had significantly higher kurtosis (P = 0.040), energy (P = 0.034), correlation (P

Pd nanoparticle-functionalized, xIn2O3 (x = 0.05, 0.1, and 0.15)-loaded ZnO nanofibers were synthesized by an electrospinning and ultraviolet (UV) irradiation method and assessed for their hydrogen gas sensing properties. Morphological and chemical analyses revealed the desired morphology and chemical composition of the synthesized nanofibers. The optimal gas sensor namely Pd-functionalized, 0.1In2O3-loaded ZnO nanofibers showed a very strong response to 172−50 ppb hydrogen gas at 350 °C, which is regarded as the optimal sensing temperature. Furthermore, the gas sensors showed excellent selectivity to hydrogen gas due to the much lower response to CO and NO2 gases. The enhanced gas response was attributed to the excellent catalytic activity of Pd to hydrogen gas, and the formation of Pd/ZnO and In2O3/ZnO heterojunctions, ZnO−ZnO homojunction, as well as the formation of PdHx. Overall, highly sensitive and selective hydrogen gas sensors can be produced based on a simple methodology using a synergistic effect from Pd functionalization and In2O3 loading in ZnO nanofibers.

The gas sensing properties of two novel series of Mg-incorporated metal−organic frameworks (MOFs), termed Mg-MOFs-I and -II, were assessed. The synthesized iso-reticular type Mg-MOFs exhibited good crystallinity, high thermal stability, needle-shape morphology and high surface area (up to 2900 m2·g−1), which are promising for gas sensing applications. Gas-sensing studies of gas sensors fabricated from Mg-MOFs-II revealed better sensing performance, in terms of the sensor dynamics and sensor response, at an optimal operating temperature of 200 °C. The MOF gas sensor with a larger pore size and volume showed shorter response and recovery times, demonstrating the importance of the pore size and volume on the kinetic properties of MOF-based gas sensors. The gas-sensing results obtained in this study highlight the potential of Mg-MOFs gas sensors for the practical monitoring of toxic gases in a range of environments.

Formaldehyde (HCHO) is an important indicator of indoor air quality and one of the markers for detecting lung cancer. Both medical and air quality applications require the detection of formaldehyde in the sub-ppm range. Nanocomposites SnO2/TiO2 are promising candidates for HCHO detection, both in dark conditions and under UV illumination. Nanocomposites TiO2@SnO2 were synthesized by ALD method using nanocrystalline SnO2 powder as a substrate for TiO2 layer growth. The microstructure and composition of the samples were characterized by ICP-MS, TEM, XRD and Raman spectroscopy methods. The active surface sites were investigated using FTIR and TPR-H2 methods. The mechanism of formaldehyde oxidation on the surface of semiconductor oxides was studied by in situ DRIFTS method. The sensor properties of nanocrystalline SnO2 and TiO2@SnO2 nanocomposites toward formaldehyde (0.06−0.6 ppm) were studied by in situ electrical conductivity measurements in dark conditions and under periodic UV illumination at 50−300 °C. Nanocomposites TiO2@SnO2 exhibit a higher sensor signal than SnO2 and a decrease in the optimal measurement temperature by 50 °C. This result is explained based on the model considering the formation of n-n heterocontact at the SnO2/TiO2 interface. UV illumination leads to a decrease in sensor response compared with that obtained in dark conditions because of the photodesorption of oxygen involved in the oxidation of formaldehyde.

Hydrogen is one of the most important gases that can potentially replace fossil fuels in the future. Nevertheless, it is highly explosive, and its leakage should be detected by reliable gas sensors for safe operation during storage and usage. Most hydrogen gas sensors operate at high temperatures, which introduces the risk of hydrogen explosion. Gasochromic WO3 sensors work based on changes in their optical properties and color variation when exposed to hydrogen gas. They can work at low or room temperatures and, therefore, are good candidates for the detection of hydrogen leakage with low risk of explosion. Once their morphology and chemical composition are carefully designed, they can be used for the realization of sensitive, selective, low-cost, and flexible hydrogen sensors. In this review, for the first time, we discuss different aspects of gasochromic WO3 gas sensor-based hydrogen detection. Pristine, heterojunction, and noble metal-decorated WO3 nanostructures are discussed for the detection of hydrogen gas in terms of changes in their optical properties or visible color. This review is expected to provide a good background for research work in the field of gas sensors.

High-performance hydrogen sensors are important in many industries to effectively address safety concerns related to the production, delivering, storage and use of H2 gas. Herein, we present a highly sensitive hydrogen gas sensor based on SnO2-loaded ZnO nanofibers (NFs). The xSnO2-loaded (x = 0.05, 0.1 and 0.15) ZnO NFs were fabricated using an electrospinning technique followed by calcination at high temperature. Microscopic analyses demonstrated the formation of NFs with expected morphology and chemical composition. Hydrogen sensing studies were performed at various temperatures and the optimal working temperature was selected as 300 °C. The optimal gas sensor (0.1 SnO2 loaded ZnO NFs) not only showed a high response to 50 ppb hydrogen gas, but also showed an excellent selectivity to hydrogen gas. The excellent performance of the gas sensor to hydrogen gas was mainly related to the formation of SnO2-ZnO heterojunctions and the metallization effect of ZnO.

Occipital nerve stimulation (ONS) is a form of peripheral nerve stimulation used to treat refractory headaches. The trial of ONS was carried with the midline incision C1-2 level, inserted electrical lead subcutaneously to oblique and cephalad direction followed by trajectory of blunt dissection. We used 8 pole electrical lead to cover lesser occipital nerve, greater occipital nerve, third occipital nerve and great auricular nerve. We anchored the lead at the midline insertion site after confirming the stimulation of the patient. And then we looped and tightened the lead loosely, connected the lead and the extension under right supraspinatus muscle region. After 1 week trial period, we performed the permanent implantation of occipital nerve stimulator. We inserted internal pulse generator under a pocket located at right infraclavicular region. The VAS score dropped from 8/10 to 1-2/10. No serious complications were detected during 1 month follow-up.

The differential diagnosis of headache is often difficult because the symptom of headache is overlapping. Superficial cervical plexus block is useful in diagnosis and treatment of headache. Headache arising from the neck and radiating to the frontotemporal regions and possibly to the supraorbital region has been defined as cervicogenic headache. A positive response to anesthetic blocks is one of the diagnostic criteria of cervicogenic headache. We experienced a case of headache arising from direct lymph node metastasis of hepatocellular carcinoma adjacent to the superficial cervical plexus during treatment of cervicogenic headache under ultrasonographic guidance. Especially in patients with medical history of cancer, practitioners should consider the possibility of metastasis to cervical lymph nodes and using ultrasonography to evaluate the cervical area prior to the practice.

We have investigated a statistical relationship between sawtooth oscillations and geomagnetic storms during 2000-2004. First of all we selected a total of 154 geomagnetic storms based on the Dst index, and distinguished between different drivers such as Coronal Mass Ejection (CME) and Co-rotating Interaction Region (CIR). Also, we identified a total of 48 sawtooth oscillation events based on geosynchronous energetic particle data for the same 2000-2004 period. We found that out of the 154 storms identified, 47 storms indicated the presence of sawtooth oscillations. Also, all but one sawtooth event identified occurred during a geomagnetic storm interval. It was also found that sawtooth oscillation events occur more frequently for storms driven by CME (˜62%) than for storms driven by CIR (˜30%). In addition, sawtooth oscillations occurred mainly (˜82%) in the main phase of storms for CME-driven storms while they occurred mostly (˜78%) during the storm recovery phase for CIR-driven storms. Next we have examined the average characteristics of the Bz component of IMF, and solar wind speed, which were the main components for driving geomagnetic storm. We found that for most of the sawtooth events, the IMF Bz corresponds to --15 to 0 nT and the solar wind speed was in the range of 400˜700 km/s. We found that there was a weak tendency that the number of teeth for a given sawtooth event interval was proportional to the southward IMF Bz magnitude.

We report three sawtooth oscillation events observed at geosynchronous orbit where we find quasi-periodic (every 2-3 hours) sudden flux increases followed by slow flux decreases at the energy levels of ˜50-400 keV. For these three sawtooth events, we have examined variations of the outer boundary of the outer radiation belt. In order to determine L values of the outer boundary, we have used data of relativistic electron flux observed by the SAMPEX satellite. We find that the outer boundary of the outer radiation belt oscillates periodically being consistent with sawtooth oscillation phases. Specifically, the outer boundary of the outer radiation belt expands (namely, the boundary L value increases) following the sawtooth particle flux enhancement of each tooth, and then contracts (namely, the boundary L value decreases) while the sawtooth flux decreases gradually until the next flux enhancement. On the other hand, it is repeatedly seen that the asymmetry of the magnetic field intensity between dayside and nightside decreases (increases) due to the dipolarization (the stretching) on the nightside as the sawtooth flux increases (decreases). This implies that the periodic magnetic field variations during the sawtooth oscillations are likely responsible for the expansion-contraction oscillations of the outer boundary of the outer radiation belt.

OBJECTIVES: We aimed to analyze the CT findings of ground-glass opacity nodules diagnosed pathologically as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma in order to investigate whether quantitative CT parameters enable distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma. METHODS: We reviewed CT images and pathologic specimens from 191 resected ground-glass opacity nodules with little or no solid component at CT. Nodule size, volume, density, mass, skewness/kurtosis, and CT attenuation values at the 2.5th-97.5th percentiles on histogram, and texture parameters (uniformity and entropy) were assessed from CT datasets. RESULTS: Of 191 tumors, 38 were AISs (20%), 61 were MIAs (32%), and 92 (48%) were invasive adenocarcinomas. Multivariate logistic regression analysis helped identify the 75th percentile CT attenuation value (P = 0.04) and entropy (P

BACKGROUND AND PURPOSE: To correlate changes of various CT parameters after the neoadjuvant treatment in patients with lung adenocarcinoma with pathologic responses, focused on their relationship with different therapeutic options, particularly of EGFR-TKI and concurrent chemoradiation therapy (CCRT) settings. MATERIALS AND METHODS: We reviewed pre-operative CT images of primary tumors and surgical specimens obtained after neoadjuvant therapy (TKI, n = 23; CCRT, n = 28) from 51 patients with lung adenocarcinoma. Serial changes in tumor volume, density, mass, skewness/kurtosis, and size-zone variability/intensity variability) were assessed from CT datasets. The changes in CT parameters were correlated with histopathologic responses, and the relationship between CT variables and histopathologic responses was compared between TKI and CCRT groups. RESULTS: Tumor volume, mass, kurtosis, and skewness were significant predictors of pathologic response in CCRT group in univariate analysis. Using multivariate analysis, kurtosis was found to be independent predictor. In TKI group, intensity variability and size-zone variability were significantly decreased in pathologic responder group. Intensity variability was found to be an independent predictor for pathologic response on multivariate analysis. CONCLUSIONS: Quantitative CT variables including histogram or texture analysis have potential as a predictive tool for response evaluation, and it may better reflect treatment response than standard response criteria based on size changes.

Functionalization of metal nanoparticles (NPs) on oxide materials is a commonly employed technique for enhancing the sensitivity and selectivity of materials for gas sensing applications. In this study, we functionalized electrospinning-synthesized SnO2 nanofibers (NFs) with various amounts of Pt NPs to enhance the toluene-sensing properties. In particular, Pt NPs were prepared by deposition of Pt films by sputtering and subsequent heat treatment. Electronic and chemical sensitizations by the Pt NPs were responsible for the improved toluene sensitivity. The best sensing properties were achieved at an optimized amount of Pt NPs, showing a volcano shape in relation to the amount of Pt NPs. The method used in this study is useful for the development of toluene-sensitive and -selective chemiresistive NF-based gas sensors.

Pulmonary fibrosis is a deadly disease that involves the dysregulation of fibroblasts and myofibroblasts, which are mechanosensitive. Previous computational models have succeeded in modeling stiffness-mediated fibroblasts behaviors; however, these models have neglected to consider stretch-mediated behaviors, especially stretch-sensitive channels and the stretch-mediated release of latent TGF-β. Here, we develop and explore an agent-based model and spring network model hybrid that is capable of recapitulating both stiffness and stretch. Using the model, we evaluate the role of mechanical signaling in homeostasis and disease progression during self-healing and fibrosis, respectively. We develop the model such that there is a fibrotic threshold near which the network tends towards instability and fibrosis or below which the network tends to heal. The healing response is due to the stretch signal, whereas the fibrotic response occurs when the stiffness signal overpowers the stretch signal, creating a positive feedback loop. We also find that by changing the proportional weights of the stretch and stiffness signals, we observe heterogeneity in pathological network structure similar to that seen in human IPF tissue. The system also shows emergent behavior and bifurcations: whether the network will heal or turn fibrotic depends on the initial network organization of the damage, clearly demonstrating structure's pivotal role in healing or fibrosis of the overall network. In summary, these results strongly suggest that the mechanical signaling present in the lungs combined with network effects contribute to both homeostasis and disease progression. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop an automated segmentation system for biliary structures using a deep learning model, based on data from magnetic resonance cholangiopancreatography (MRCP). Materials and methods: Living liver donors who underwent MRCP using the gradient and spin echo technique followed by threedimensional modeling were eligible for this study. A three-dimensional residual U-Net model was implemented for the deep learning process. Data were divided into training and test sets at a 9:1 ratio. Performance was assessed using the dice similarity coefficient to compare the model's segmentation with the manually labeled ground truth. Results: The study incorporated 250 cases. There was no difference in the baseline characteristics between the train set (n =225) and test set (n=25). The overall mean Dice Similarity Coefficient was 0.80 ± 0.20 between the ground truth and inference result. The qualitative assessment of the model showed relatively high accuracy especially for the common bile duct (88%), common hepatic duct (92%), hilum (96%), right hepatic duct (100%), and left hepatic duct (96%), while the third-order branch of the right hepatic duct (18.2%) showed low accuracy. Conclusion: The developed automated segmentation model for biliary structures, utilizing MRCP data and deep learning techniques, demonstrated robust performance and holds potential for further advancements in automation. [ABSTRACT FROM AUTHOR]