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Despite the gradual development of students' sedentary habits and associated health problems, only a few studies have extensively and systematically measured campus built environments (CBE) and their impact on street walking activity. This study explores the association between CBEs and pedestrian volume (PV). Comprehensive questionnaires, field audits, and GIS were used to measure the CBE variables and PV of 892 street segments on eight Chinese campuses in Tianjin. We used negative binomial regression models without spatial autocorrelations to investigate the relationship between the CBEs and PV. The findings indicated that campus Walk Score, facility and residential land ratio, campus design qualities, sidewalk conditions, street amenities, and other streetscape features were positively associated with PV. This study presents implications for campus research and planning practices in designing a pedestrian-friendly, sustainable, and healthy campus.

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Metastasis is responsible for the majority of cancer-related fatalities. We previously identified specific cancer cell populations responsible for metastatic events which are cytokeratin-14 (CK14) and E-cadherin positive in luminal tumors, and E-cadherin and vimentin positive in triple-negative tumors. Since cancer cells evolve within a complex ecosystem comprised of immune cells and stromal cells, we sought to decipher the spatial interactions of these aggressive cancer cell populations within the tumor microenvironment (TME). We used imaging mass cytometry to detect 36 proteins in tumor microarrays containing paired primary and metastatic lesions from luminal or triple-negative breast cancers (TNBC), resulting in a dataset of 1,477,337 annotated cells. Focusing on metastasis-initiating cell populations, we observed close proximity to specific fibroblast and macrophage subtypes, a relationship maintained between primary and metastatic tumors. Notably, high CK14 in luminal cancer cells and high vimentin in TNBC cells correlated with close proximity to specific macrophage subtypes (CD163intCD206intPDL1intHLA-DR+ or PDL1highARG1high). Our in-depth spatial analysis demonstrates that metastasis-initiating cancer cells consistently colocalizes with distinct cell populations within the TME, suggesting a role for these cell-cell interactions in promoting metastasis.

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Pancreatic ductal adenocarcinoma (PDAC) is highly lethal and resistant to immunotherapy. Although immune recognition can be enhanced with immunomodulatory agents including checkpoint inhibitors and vaccines, few patients experience clinical efficacy because the tumor immune microenvironment (TiME) is dominated by immunosuppressive myeloid cells that impose T cell inhibition. Inhibition of phosphodiesterase-5 (PDE5) was reported to downregulate metabolic regulators arginase and iNOS in immunosuppressive myeloid cells and enhance immunity against immune-sensitive tumors including head and neck cancers. We show for the first time that combining a PDE5 inhibitor, tadalafil, with a mesothelin-specific vaccine, anti-PD1, and anti-CTLA4 yields antitumor efficacy even against immune-resistant PDAC. To determine immunologic advantages conferred by tadalafil, we profiled the TiME using mass cytometry and single-cell RNA analysis with Domino to infer intercellular signaling. Our analyses demonstrated that tadalafil reprograms myeloid cells to be less immunosuppressive. Moreover, tadalafil synergized with the vaccine, enhancing T cell activation including mesothelin-specific T cells. Tadalafil treatment was also associated with myeloid-T cell signaling axes important for antitumor responses (e.g., Cxcr3, Il12). Our study shows that PDE5 inhibition combined with vaccine-based immunotherapy promotes pro-inflammatory states of myeloid cells, activation of T cells, and enhanced myeloid-T cell crosstalk to yield antitumor efficacy against immune-resistant PDAC.

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BACKGROUND: Patients with craniomaxillofacial trauma (CMT) are at increased risk of airway compromise and may necessitate airway stabilization via a tracheostomy (TO) or cricothyroidotomy. The submental airway (SMA) is an alternative airway and can avoid complications related to TO. PURPOSE: The purpose of this study was to compare the SMA to the TO with respect to hospital length of stay (LOS) and number of days with an advanced airway, termed airway days (ADs). STUDY DESIGN, SETTING, SAMPLE: This single-center retrospective cohort study included subjects with isolated CMT who required operative treatment with either a TO or SMA at Harborview Medical Center. Subjects were excluded if they required a TO for reasons other than treating their CMT. PREDICTOR VARIABLE: The predictor variable was airway type. MAIN OUTCOME VARIABLE: Primary outcome variables were LOS and ADs measured as number of days between admission and discharge dates, and days between establishment of airway and extubation or decannulation dates, respectively. COVARIATES: Covariates included age, sex, American Society of Anesthesiology classification, weight, body mass index, substance use, hospital status, facial injury severity scale scores, and airway placement location. ANALYSES: Bivariate analysis using independent t test, Mann-Whitney U test, and multiple linear regression analyses were used. P value of <.05 was considered significant. RESULTS: Of the 14 subjects identified, 6 underwent a TO and 8 a SMA. The mean LOS in the TO group was 20.3 ± 15.8 days versus 3.9 ± 4.7 days in the SMA group (P = .02). Mean AD in the TO group was 13.2 ± 8.4 versus 0.6 ± 0.7 in the SMA group (P = .01). The TO group had a higher number of operations at 2.0 ± 0.6 versus 1.1 ± 0.4 with a SMA (P = .02). After linear regression analysis adjusting for sex, age, weight, and facial injury severity scale, there were no statistically significant differences between the groups. CONCLUSIONS AND RELEVANCE: Compared to, SMA has a 5-fold decrease in LOS and 22-fold decrease in ADs on bivariate analysis. An SMA is a viable airway management option for operative treatment of isolated CMT. Further studies are warranted with a larger sample size.

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Improving walkability in the campus environment and socio-psychological environments can promote students' mental health and subjective well-being. This study aimed to propose a theoretical model to investigate the link of perceived campus walkability (PCW) with mental health and life satisfaction (LS), and to disentangle the mediating impact of socio-psychological environments and academic performance on this relationship, while simultaneously considering the effect of the COVID-19 pandemic. We applied structural equation modeling to analyze the data collected through a questionnaire survey conducted at six universities and colleges in Yantai, China. PCW had both direct and indirect positive effects on mental health and LS. However, indirect effects are greater than direct effects. Walking attitudes, social capital, and academic performance were critical to the relationship between PCW, mental health, and LS. Academic performance had the strongest indirect effect on mental health, while social capital had the strongest indirect effect on LS. We also found that during the COVID-19 pandemic, body mass index and family income were significantly correlated with mental health and LS. The findings indicate that campus planners and policymakers should improve PCW and support the socio-psychological environment to promote students' mental and social health during situations like the COVID-19 pandemic.

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BACKGROUND: The prevalence of maxillofacial and head injuries associated with electric scooters (e-scooter, ES) has risen in concordance with its popularity. PURPOSE: The purpose of this study was to compare maxillofacial and head injury location, type, and severity related to ES and bicycle accidents and to identify factors contributing to injury severity. STUDY DESIGN, SETTING, SAMPLE: The authors implemented a multicenter retrospective cohort study in Seattle, Washington, and enrolled a sample of ES riders and bicyclists who sustained maxillofacial injuries between September 2020 and September 2022. The exclusion criteria included nonmotorized scooters, motorized bicycles, injuries with other operators, or vehicles, and pre-evaluation deaths. PREDICTOR VARIABLE: The predictor variable was vehicle type, bicycle or ES. OUTCOME VARIABLES: The outcome variables included maxillofacial injury location, distinguished by horizontal facial thirds and injury type, defined as hard or soft tissue. Associated head injury types were also reported as hard (calvaria) or soft (scalp) tissue injuries. The severity of these injuries was quantified using both the injury severity score and the face and head abbreviated injury scale. COVARIATES: Demographic, injury, and treatment-related variables were collected. ANALYSES: Bivariate, multivariate, and regression statistics were computed. Statistical significance was P < .05. RESULTS: The final sample was composed of 205 total subjects, of which 52 (25.4%) were in the ES group and 153 (74.6%) in the bicycle group. Isolated midface injuries were the most common hard tissue location in the ES (15.4%) and bicycle (29.4%) groups. The most common soft tissue injury location included the upper face and midface in the ES group (19.2%) and the midface in the bicycle group (22.9%). Both hard and soft tissue head injuries were more prevalent in the ES group (P < .0002 and P < .0001). Moreover, intracranial injuries were seen in 36.5% of ES subjects compared to 9.8% bicycle subjects (P < .0001). Between the two groups there was no difference in maxillofacial injury severity, but head injuries were more severe in the ES group (P < .0002). Using regression analysis, drug use was found to have a significant impact on the mean injury severity score (P < .002) and helmet use did not have significant impact on face or head injury severity. CONCLUSION: Maxillofacial injury location, type, and severity are comparable among ES and bicycles. However, ES riders are at greater risk of severe head injuries compared to bicycles, and riding while intoxicated has the greatest effect on injury severity.

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For the next generation of lithium-ion batteries (LIBs), it is primary to seek high capacity and long-lifetime electrode materials. Li-excess disordered rock-salt structure (DRS) cathodes have gained much attention due to their high specific capacity. However, Li-excess can lead to a decrease in the structural stability of an electrode material. A new Li-rich DRS oxyfluorides, Li1.23Ni0.3Nb0.3Fe0.16O0.85F0.15 (F0.15) with a series amounts of LiNbOx (LN) coating (0, 5, 10, and 15 wt % denoted as F0.15-LN0, F0.15-LN5, F0.15-LN10, and F0.15-LN15, respectively), are successfully synthesized and evaluated as cathode materials in LIBs. Among them, F0.15-LN10 exhibits the highest initial discharge specific capacity of 296.1 mAh g-1 (at a current density of 20 mA g-1) with the capacity retention rate of 14% higher than that of the uncoated F0.15 after 80 cycles. Even at 300 mA g-1, F0.15-LN10 still delivers the highest discharge specific capacity of 130 mAh g-1. After 20 cycles, the charge-transfer impedance of F0.15-LN10 remained the smallest. The characterizations indicate that LN coating reduces the surface polarization of the cathode materials, slows the interfacial side reactions between the electrolyte and the electrode, and speeds up the Li+ diffusion. These results demonstrate that LN coating is an effective strategy to improve the electrochemical performance.

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BACKGROUND AND OBJECTIVES: The prognosis of patients with spontaneous intracerebral hemorrhage (ICH) is often influenced by hematoma volume, a well-established predictor of poor outcome. However, the optimal intraventricular hemorrhage (IVH) volume cutoff for predicting poor outcome remains unknown. METHODS: We analyzed 313 patients with spontaneous ICH not undergoing evacuation, including 7 cases with external ventricular drainage (EVD). These patients underwent a baseline CT scan, followed by a 24-hour CT scan for measurement of both hematoma and IVH volume. We defined hematoma growth as hematoma growth > 33 % or 6 mL at follow-up CT, and poor outcome as modified Rankin Scale score≥3 at three months. Cutoffs with optimal sensitivity and specificity for predicting poor outcome were identified using receiver operating curves. RESULTS: The receiver operating characteristic analysis identified 6 mL as the optimal cutoff for predicting poor outcome. IVH volume> 6 mL was observed in 53 (16.9 %) of 313 patients. Patients with IVH volume>6 mL were more likely to be older and had higher NIHSS score and lower GCS score than those without. IVH volume>6 mL (adjusted OR 2.43, 95 % CI 1.13-5.30; P = 0.026) was found to be an independent predictor of poor clinical outcome at three months in multivariable regression analysis. CONCLUSIONS: Optimal IVH volume cutoff represents a powerful tool for improving the prediction of poor outcome in patients with ICH, particularly in the absence of clot evacuation or common use of EVD. Small amounts of intraventricular blood are not independently associated with poor outcome in patients with intracerebral hemorrhage. The utilization of optimal IVH volume cutoffs may improve the clinical trial design by targeting ICH patients that will obtain maximal benefit from therapies.

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Mitochondria, the versatile organelles crucial for cellular and organismal viability, play a pivotal role in meeting the energy requirements of cells through the respiratory chain located in the inner mitochondrial membrane, concomitant with the generation of reactive oxygen species (ROS). A wealth of evidence derived from contemporary investigations on reproductive longevity strongly indicates that the aberrant elevation of ROS level constitutes a fundamental factor in hastening the aging process of reproductive systems which are responsible for transmission of DNA to future generations. Constant changes in redox status, with a pro-oxidant shift mainly through the mitochondrial generation of ROS, are linked to the modulation of physiological and pathological pathways in gametes and reproductive tissues. Furthermore, the quantity and quality of mitochondria essential to capacitation and fertilization are increasingly associated with reproductive aging. The article aims to provide current understanding of the contributions of ROS derived from mitochondrial respiration to the process of reproductive aging. Moreover, understanding the impact of mitochondrial dysfunction on both female and male fertility is conducive to finding therapeutic strategies to slow, prevent or reverse the process of gamete aging, and thereby increase reproductive longevity.

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INTRODUCTION: A recently developed deep-learning-based automatic evaluation model provides reliable and efficient Cobb angle measurements for scoliosis diagnosis. However, few studies have explored its clinical application, and external validation is lacking. Therefore, this study aimed to explore the value of automated assessment models in clinical practice by comparing deep-learning models with manual measurement methods. METHODS: The 481 spine radiographs from an open-source dataset were divided into training and validation sets, and 119 spine radiographs from a private dataset were used as the test set. The mean Cobb angle values assessed by three physicians in the hospital's PACS system served as the reference standard. The results of Seg4Reg, VFLDN, and manual measurement were statistically analyzed. The intra-class correlation coefficients (ICC) and the Pearson correlation coefficient (PCC) were used to compare their reliability and correlation. The Bland-Altman method was used to compare their agreement. The Kappa statistic was used to compare the consistency of Cobb angles at different severity levels. RESULTS: The mean Cobb angle values measured were 35.89° ± 9.33° with Seg4Reg, 31.54° ± 9.78° with VFLDN, and 32.23° ± 9.28° with manual measurement. The ICCs for the reliability of Seg4Reg and VFLDN were 0.809 and 0.974, respectively. The PCC and MAD between Seg4Reg and manual measurements were 0.731 (p<0.001) and 6.51°, while those between VFLDN and manual measurements were 0.952 (p<0.001) and 2.36°. The Kappa statistic indicated VFLDN (k= 0.686, p< 0.001) was superior to Seg4Reg and manual measurements for Cobb angle severity classification. CONCLUSION: The deep-learning-based automatic scoliosis Cobb angle assessment model is feasible in clinical practice. Specifically, the keypoint-based VFLDN is more valuable in actual clinical work with higher accuracy, transparency, and interpretability.

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BACKGROUND: Quercetin is widely distributed in nature and abundant in the human diet, which exhibits diverse biological activities and potential medical benefits. However, there remains a lack of comprehensive understanding about its cellular targets, impeding its in-depth mechanistic studies and clinical applications. PURPOSE: This study aimed to profile protein targets of quercetin at the proteome level. METHODS: A label-free CETSA-MS proteomics technique was employed for target enrichment and identification. The R package Inflect was used for melting curve fitting and target selection. D3Pocket and LiBiSco tools were used for binding pocket prediction and binding pocket analysis. Western blotting, molecular docking, site-directed mutagenesis and pull-down assays were used for target verification and validation. RESULTS: We curated a library of direct binding targets of quercetin in cells. This library comprises 37 proteins that show increased thermal stability upon quercetin binding and 33 proteins that display decreased thermal stability. Through Western blotting, molecular docking, site-directed mutagenesis and pull-down assays, we validated CBR1 and GSK3A from the stabilized protein group and MAPK1 from the destabilized group as direct binding targets of quercetin. Moreover, we characterized the shared chemical properties of the binding pockets of quercetin with targets. CONCLUSION: Our findings deepen our understanding of the proteins pivotal to the bioactivity of quercetin and lay the groundwork for further exploration into its mechanisms of action and potential clinical applications.

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BACKGROUND: For autosegmentation models, the data used to train the model (e.g., public datasets and/or vendor-collected data) and the data on which the model is deployed in the clinic are typically not the same, potentially impacting the performance of these models by a process called domain shift. Tools to routinely monitor and predict segmentation performance are needed for quality assurance. Here, we develop an approach to perform such monitoring and performance prediction for cardiac substructure segmentation. PURPOSE: To develop a quality assurance (QA) framework for routine or continuous monitoring of domain shift and the performance of cardiac substructure autosegmentation algorithms. METHODS: A benchmark dataset consisting of computed tomography (CT) images along with manual cardiac substructure delineations of 241 breast cancer radiotherapy patients were collected, including one "normal" image domain of clean images and five "abnormal" domains containing images with artifact (metal, contrast), pathology, or quality variations due to scanner protocol differences (field of view, noise, reconstruction kernel, and slice thickness). The QA framework consisted of an image domain shift detector which operated on the input CT images and a shape quality detector on the output of an autosegmentation model, and a regression model for predicting autosegmentation model performance. The image domain shift detector was composed of a trained denoising autoencoder (DAE) and two hand-engineered image quality features to detect normal versus abnormal domains in the input CT images. The shape quality detector was a variational autoencoder (VAE) trained to estimate the shape quality of the auto-segmentation results. The output from the image domain shift and shape quality detectors was used to train a regression model to predict the per-patient segmentation accuracy, measured by Dice coefficient similarity (DSC) to physician contours. Different regression techniques were investigated including linear regression, Bagging, Gaussian process regression, random forest, and gradient boost regression. Of the 241 patients, 60 were used to train the autosegmentation models, 120 for training the QA framework, and the remaining 61 for testing the QA framework. A total of 19 autosegmentation models were used to evaluate QA framework performance, including 18 convolutional neural network (CNN)-based and one transformer-based model. RESULTS: When tested on the benchmark dataset, all abnormal domains resulted in a significant DSC decrease relative to the normal domain for CNN models ( p < 0.001 $p < 0.001$ ), but only for some domains for the transformer model. No significant relationship was found between the performance of an autosegmentation model and scanner protocol parameters ( p = 0.42 $p = 0.42$ ) except noise ( p = 0.01 $p = 0.01$ ). CNN-based autosegmentation models demonstrated a decreased DSC ranging from 0.07 to 0.41 with added noise, while the transformer-based model was not significantly affected (ANOVA, p = 0.99 $p=0.99$ ). For the QA framework, linear regression models with bootstrap aggregation resulted in the highest mean absolute error (MAE) of 0.041 ± 0.002 $0.041 \pm 0.002$ , in predicted DSC (relative to true DSC between autosegmentation and physician). MAE was lowest when combining both input (image) detectors and output (shape) detectors compared to output detectors alone. CONCLUSIONS: A QA framework was able to predict cardiac substructure autosegmentation model performance for clinically anticipated "abnormal" domain shifts.

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Background and purpose: Intracerebral hemorrhage (ICH) is a severe form of stroke that remains understudied in the young adults. We aimed to investigate the clinical presentation, and risk factors associated with ICH in this age group and compare them to older patients. Methods: Our study included ICH patients admitted between March 2016 and December 2021 in the First Affiliated Hospital of Chongqing Medical University from our ongoing prospective cohort database. Demographic characteristics, etiology, risk factors, and clinical outcomes were compared between elderly and young patients. Furthermore, logistic regression analysis was employed to explore risk factors associated with the functional outcome at 3-months. Results: We selected 1,003 patients (mean age, 59.9 ±13.8 years old), 746 (74.4%) patients were aged >50 years. The logistic regression analysis showed young patients have a higher proportion of secondary ICH, higher white blood cell count and higher body mass index (BMI), but less diabetes mellitus. Of all patients, predictors of 3-month functional independence was first-ever ICH and age ≤50 years. The history of nephropathy and stroke, higher baseline NIHSS score, larger hematoma volume, and the presence of hydrocephalus were associated with poor outcomes. And the white blood cell count could significantly influence the prognosis among young ICH patients. Three-month functional outcome based on modified Rankin scale score was better in young patients than the elderly (OR, 1.232; 95% CI, 1.095-1.388; p < 0.001). Conclusions: The highest incidence of ICH occurs in the age groups of 50-59 and 60-69. ICH in young adults had higher white blood cell and BMI compared to the elderly, and differs in etiological distribution. The young patients also had similar short-term mortality but more favorable functional outcomes than the elderly. Furthermore, NIHSS score and larger hematoma volumes were associated with poor outcome in all patients.

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Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) plays a significant role in early Alzheimer's disease (AD) diagnosis, which can effectively boost the life quality of patients. Recently, convolutional neural network (CNN)- based methods using structural magnetic resonance imaging (sMRI) images have shown effective for AD identification. However, these CNN-based methods fail to effectively explore the feature extraction of disease-related multi-scale tissues, such as ventricles, hippocampi and cerebral cortex. To address this issue, we propose an end-to-end disease-related attentional UNet framework (DAUF) for identifying pMCI and sMCI, by embedding a devised dual disease-related attention module (D2AM) and a novel tree-structured feature fusion classifier (TFFC). Specifically, D2AM leverages the complementarity between feature maps and attention maps and the complementary features from the encoder and decoder, so as to highlight discriminative semantic and detailed features. Additionally, TFFC is a powerfully joint multi-scale feature fusion and classification head, by employing the homogeneity among multi-scale features, so that the discriminative features of the multi-scale tissues are adequately fused for enhancing classification performance. Finally, extensive experiments demonstrate the superior performance of DAUF, with the effectiveness of D2AM and TFFC on identifying pMCI and sMCI subjects.

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The present study aimed to predict the envelope surfaces from facial morphology. Condylar envelope surfaces for 34 healthy adults were formed and simplified as sagittal section curves. Cephalometric and maximum mandibular moving distances measurement were performed on the participants. There was no statistically significant difference (p = 0.763) between the left and right maximum lateral movements. There was a statistically significant difference in the mandibular body length between the sexes. The envelope surfaces were divided into type 1 with Hp2 ≥ 1/3 Hp1 and type 2 with Hp2 < 1/3 × Hp1. SNA and SNB for type 2 were significantly greater than those for type 1 (p < 0.001). Therefore, the participants were divided into four groups based on gender and envelope surface morphology. The curves could be fitted using the second-order Fourier function (R-square ≥0.95). Six facial parameters were selected and a matrix was used to map facial morphology to the envelope surface. Individual sagittal curves were predicted using the matrix and facial parameters, and the envelope surface was predicted using the curve and the condyle model. Deviation analysis for the predicted envelope surface using the actual envelope as a reference was carried out (root mean square = 0.9970 mm ± 0.2918 mm). This method may lay a foundation for the geometric design of artificial fossa components of temporomandibular joint replacement systems. It may improve prosthesis design without flexible tissue repair and guide the movement of the artificial joint head.

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Engineering nanocontainers with encapsulated inhibitors onto graphene has been an emerging technology for developing self-healing anticorrosion coatings. However, the loading contents of inhibitors are commonly limited by inhomogeneous nanostructures of graphene platforms. Here, we propose an activation-induced ultrathin graphene platform (UG-BP) with the homogeneous growth of polydopamine (PDA) nanocontainers encapsulated with benzotriazole (BTA). Ultrathin graphene prepared by catalytic exfoliation and etching activation provides an ideal platform with an ultrahigh specific surface area (1646.8 m2/g) and homogeneous active sites for the growth of PDA nanocontainers, which achieves a high loading content of inhibitors (40 wt %). The obtained UG-BP platform exhibits pH-sensitive corrosion inhibition effects due to its charged groups. The epoxy/UG-BP coating possesses integrated properties of enhanced mechanical properties (>94%), efficient pH-sensitive self-healing behaviors (98.5% healing efficiency over 7 days), and excellent anticorrosion performance (4.21 × 109 Ω·cm2 over 60 days), which stands out from previous related works. Moreover, the interfacial anticorrosion mechanism of UG-BP is revealed in detail, which can inhibit the oxidation of Fe2+ and promote the passivation of corrosion products by a dehydration process. This work provides a universal activation-induced strategy for developing loading-enhanced and tailor-made graphene platforms in extended smart systems and demonstrates a promising smart self-healing coating for advanced anticorrosion applications.

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Ti6Al4V titanium alloys, with high specific strength and good biological compatibility with the human body, are ideal materials for medical surgical implants. However, Ti6Al4V titanium alloys are prone to corrosion in the human environment, which affects the service life of implants and harms human health. In this work, hollow cathode plasm source nitriding (HCPSN) was used to generate nitrided layers on the surfaces of Ti6Al4V titanium alloys to improve their corrosion resistance. Ti6Al4V titanium alloys were nitrided in NH3 at 510 °C for 0, 1, 2, and 4 h. The microstructure and phase composition of the Ti-N nitriding layer was characterized by high-resolution transmission electron microscopy, atomic force microscopy, scanning electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. This modified layer was identified to be composed of TiN, Ti2N, and α-Ti (N) phase. To study the corrosion properties of different phases, the nitriding 4 h samples were mechanically ground and polished to obtain the various surfaces of Ti2N and α-Ti (N) phases. The potentiodynamic polarization and electrochemical impedance measurements were conducted in Hank's solution to characterize the corrosion resistance of Ti-N nitriding layers in the human environment. The relationship between corrosion resistance and the microstructure of the Ti-N nitriding layer was discussed. The new Ti-N nitriding layer that can improve corrosion resistance provides a broader prospect for applying Ti6Al4V titanium alloy in the medical field.

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West Dongting Lake is a protected wetland with the potential for high levels of mercury release via wastewater and deposition from industry and agriculture during the last decade. To find out the ability of various plant species to accumulate mercury pollutants from soil and water, nine sites were studied in the downstream direction of the flow of the Yuan and Li Rivers, which are tributaries of the Yellow River flowing into West Dongting Lake, where mercury levels arere high in soil and plant tissues. The total mercury (THg) concentration in wetland soil was 0.078-1.659 mg/kg, which varied along the gradient of water flow along the river. According to canonical correspondence analysis and correlation analysis, there was a positive correlation between the soil THg concentration and the soil moisture in West Dongting Lake. There is high heterogeneity in the spatial distribution of soil THg concentration in West Dongting Lake, which may be related to the spatial heterogeneity of the soil moisture. Some plant species had higher THg concentrations in aboveground tissues (translocation factor >1), but none of these plant species fit the criteria as hyperaccumulators of mercury. And some species of the same ecological type (e.g., emergent, submergent, floating-leaved) exhibited very different strategies for mercury uptake. The concentrations of mercury in these species were lower than in other studies but these had relatively higher translocation factors. To phytoremediate soil mercury in West Dongting Lake, the regular harvest of plants could help remove mercury from soil and plant tissue.

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OBJECTIVE: Dysphagia is one of the most common complications of acute ischemic stroke, and prediction of dysphagia is crucial for post-stroke treatment. We aimed to identify predictors of dysphagia and swallowing function recovery following ischemic stroke and to investigate dysphagia-associated lesion location. METHODS: We prospectively enrolled patients with acute ischemic stroke confirmed on diffusion-weighted imaging. All patients received swallowing evaluation within 48 h after admission. Follow-up oral intake ability was measured on 7 and 30 days after stroke onset. Voxel-based lesion-symptom mapping was performed to determine locations associated with dysphagia. RESULTS: Of 126 patients included in the final analysis, 23 patients (18.3%) were classified as initial dysphagia. The presence of facial palsy (P = 0.008) and larger white matter hyperintensity (WMH) volume (P = 0.003) was associated with initial dysphagia. Initial risk of aspiration assessed by Any2 score (P = 0.001) at baseline was identified as independent predictor for dysphagia at day 7. Patients with higher Any2 score (P < 0.001), aphasia (P = 0.013), and larger WMH volume (P = 0.010) were less likely to have a full swallowing function recovery at 1 month. Acute infarcts in right corona radiata and right superior longitudinal fasciculus were correlated with impaired recovery of swallowing ability at 1 month. CONCLUSIONS: Initial risk of aspiration was identified as risk factor for short-term and long-term dysphagia. Aphasia and larger WMH volume were revealed to be significant predictors for swallowing function recovery at 1 month. Right corona radiata was identified as an essential brain area for dysphagia.

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BACKGROUND: Motion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling. PURPOSE: This study aims to develop a deep-learning method to generate high-quality motion models for MoCo reconstruction to improve the quality of final 4D-CBCT images. METHODS: A 3D artifact-reduction convolutional neural network (CNN) was proposed to improve conventional phase-correlated Feldkamp-Davis-Kress (PCF) reconstructions by reducing undersampling-induced streaking artifacts while maintaining motion information. The CNN-generated artifact-mitigated 4D-CBCT images (CNN enhanced) were then used to build a motion model which was used by MoCo reconstruction (CNN+MoCo). The proposed procedure was evaluated using in-vivo patient datasets, an extended cardiac-torso (XCAT) phantom, and the public SPARE challenge datasets. The quality of reconstructed images for XCAT phantom and SPARE datasets was quantitatively assessed using root-mean-square-error (RMSE) and normalized cross-correlation (NCC). RESULTS: The trained CNN effectively reduced the streaking artifacts of PCF CBCT images for all datasets. More detailed structures can be recovered using the proposed CNN+MoCo reconstruction procedure. XCAT phantom experiments showed that the accuracy of estimated motion model using CNN enhanced images was greatly improved over PCF. CNN+MoCo showed lower RMSE and higher NCC compared to PCF, CNN enhanced and conventional MoCo. For the SPARE datasets, the average (± standard deviation) RMSE in mm-1 for body region of PCF, CNN enhanced, conventional MoCo and CNN+MoCo were 0.0040 ± 0.0009, 0.0029 ± 0.0002, 0.0024 ± 0.0003 and 0.0021 ± 0.0003. Corresponding NCC were 0.84 ± 0.05, 0.91 ± 0.05, 0.91 ± 0.05 and 0.93 ± 0.04. CONCLUSIONS: CNN-based artifact reduction can substantially reduce the artifacts in the initial 4D-CBCT images. The improved images could be used to enhance the motion modeling and ultimately improve the quality of the final 4D-CBCT images reconstructed using MoCo.

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