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Background This retrospective study was performed to estimate the clinical role of whole-body positron emission tomography/computed tomography (PET/CT) using 2-[18F] fluoro-2-deoxy-D-glucose (FDG) in the differential diagnosis of primary benign and malignant unilateral adrenal tumors. Methods A total of 64 patients (31 male, 33 female; age range: 3-76 years, mean: 48.5) with a confirmed unilateral adrenal tumor underwent 18F-FDG PET/CT examination for diagnosis and staging. The whole-body 18F-FDG PET/CT examination excluded metastasis, and all patients were confirmed by operation and biopsy pathology. Their clinical data and pathological results were collected. On visual analysis of PET/CT imaging, adrenal uptake was based on a three-scale grading system. The region of interest (ROI) was delineated in the liver and the renal lesion site. Standardized uptake value (SUV) measurements were determined on a standardized reconstruction, and the maximum values (SUVmax) of the lesion and liver were measured. The ratio of tumor to the liver was defined as T/L. Visual interpretation, SUVmax-receiver operating characteristics (ROC) method, and T/L-ROC method were used to analyze the diagnostic accuracy. Results A total of 64 lesions (48 benign, 16 malignant lesions) were detected. The visual analysis found that 100% of Grade I cases were benign, 90.9% of Grade II cases were benign, and 65.1% of Grade III cases were benign. The SUVmax of malignant lesions (10.0±5.8) was higher than that of benign lesions (5.4±5.3, P

Biliary atresia (BA) in twins is extremely rare reported in the literature, but twin studies are useful methods of examining the associated factors of a complex disease. The objective of this study was to analyze the characteristics and patterns of biliary atresia in twins from reviewing available articles. PubMed and EMBASE databases were reviewed for related articles using the keywords ‘‘biliary atresia’’, ‘‘twins’’, ‘‘monozygotic (MZ)’’, and ‘‘dizygotic (DZ)’’, including relevant papers in the reference lists. This analysis was extracted from 12 articles, with a total of 35 twin pairs included. BA was found in 36 out of 70 twin subjects (51.4%), of which had an even gender split. 97.1% twins were discordant, among 55.9% of which were monozygotic twin sets, indicating that BA may be related to genetic phenotype or penetrance. Isolated BA was the largest group with 27 (75%) affected twins. Only one pair of dizygotic twins (2.9%) demonstrate concordance for BA, and have one affected family member. BA was found in nearly half of twin subjects with an even gender split. Isolated BA was the largest group, in which the number of monozygotic twins was similar with dizygotic twins, so the onset of the disease may not associate with the zygosity of twins. Most of twin sets had discordant disease presentation, especially monozygotic twins therein, emphasizing the role of epigenetic factor in the pathogenesis of BA. Future studies should take genetic testing among any twin sets in BA, especially the disease-associated mutations, thus be useful to investigate the etiology of disease.

The nonanesthetic noble gas helium (He) can protect many organs against ischemia and reperfusion injury, such as liver and heart. However, the role of He on cardiac dysfunction during sepsis is not clear. In this study, we established a lipopolysaccharide (LPS)-induced cardiac dysfunction mouse model to examine the influence of He on the impaired cardiac function, and further investigated the possible innate immune mechanisms that may be involved. LPS induced left ventricular dysfunction and cavity enlargement, as indicated by decreased percent ejection fraction, percent fractional shortening, left ventricular anterior wall thickness in systole, and left ventricular posterior wall thickness in systole, while increased left ventricular end-systolic diameter and left ventricular end-systolic volume. He improved the impaired left ventricular function and cavity enlargement in a dose-dependent manner, and it was beneficial at 1.0 mL/100 g. Mechanistically, He inhibited toll-like receptor 4 (TLR4) expression, reduced the phosphorylation of nuclear factor κB (NF-κB), and subsequently alleviated tumor necrosis factor-alpha (TNF-α) and interleukin-18 (IL-18) expression in heart. Therefore, He protects against LPS-induced cardiac dysfunction in mice partially via inhibiting myocardial TLR4-NF-κB-TNF-α/IL-18 signaling.

The MOVES model was developed by the U.S. Environmental Protection Agency (U.S. EPA) to estimate emissions from on-road mobile sources and nonroad sources in the United States. Coupling high-resolution on-road vehicle activity data with appropriate MOVES emission rates further advances research efforts designed to assess the environmental impacts of transportation design and operation strategies. However, the complicated MOVES interface and slow performance makes it difficult to assess large, regional scale transportation networks and to undertake analyses of large-scale systems that are dynamic in nature. The MOVES-Matrix system develops an initial Large Matrix of MOVES outputs by running MOVES 146,853 times on the PACE high performance computing cluster to generate more than 90 billion emission rates to populate the matrix for a single area with one fuel regime and one inspection and maintenance program. A total of 117 such Large Matrices would be needed for the entire United States. The MOVES-Matrix system developed can be used to conduct the emissions modeling 200-times faster than using MOVES. The hypothetical case study shows that MOVES-Matrix is able to generate the exact same emission results as the MOVES model to ensure the validity for regulatory analysis. The resulting matrix allows users to link emission rates to big data projects and to evaluate changes in emissions for dynamic transportation systems in near-real-time. MOVES-Matrix does not currently estimate emissions from starts, hoteling or evaporative emissions, and the research team is working on MOVES-Matrix version 2 that supports incorporating off-network modeling.Implications: MOVES-Matrix should be of interest to a broad readership including those interested in vehicle emission modeling, near-road air quality modeling, transportation conformity analysis. The paper should also interest engineers who are involved in transportation regulatory and conformity analysis, state implementation plan, and who are seeking an efficient way of conducting regulatory emission modeling and air quality analysis in the United States.

Electric vehicles (EVs) will play a central role in future energy-efficient and sustainable transportation systems. Compared to the operation of conventional vehicles, EVs provide significantly reduced energy consumption and lower operating costs. With conventional vehicles, fuel use ties directly to the instantaneous energy consumption required to provide motive power to the wheels. Predicting the energy use can be much more complex for EVs with hybridized powertrains because the onboard vehicle systems are trying to balance the provision of power to the wheels as well as manage the state of charge (SOC) of the battery pack. Traditional modeling methodologies for estimating real-world vehicle energy consumption either depend on numerical analysis of laboratory or on-road vehicle test data or the use of full-system simulation tools. Unfortunately, full-system simulation tools suffer from scaling problems in the context of large transportation network, necessitating the development of approaches that support large transportation network projections of modal EV operations and applicable modal energy use rates (energy use for various on-road modes of operation) to predict EV energy consumption. In this study, a new modal-based approach for estimating EV energy consumption is proposed. The modal-based approach considers the variance of vehicle operating conditions and supports energy estimation for large-scale transportation networks. The Department of Energy’s full-system vehicle simulation tool known as Autonomie® is used to generate energy consumption rates for specific simulations of on-road operating conditions. A sample of EV models was first developed in Autonomie® to simulate a wide range of operating conditions and generate energy use rates for selected EVs. Classification and regression tree (CART) analysis was then applied to the simulation output data to generate energy consumption rates under distinct on-road operating conditions, as represented by combinations of vehicle speed, acceleration rate, and battery state of charge (SOC). A large-scale regional travel demand analysis was performed for the Atlanta, GA metropolitan area, integrating a variety of EV market share scenarios. The CART-derived energy consumption rates are then applied to the model-predicted link-by-link traffic attributes to estimate fleet energy consumption. The modeling framework employs MOVES-embedded driving cycles to represent on-road operations for average speed operating conditions and random initial SOC start levels as model inputs. The model results suggest a 50% PHEV market share can achieve a 30% energy savings without significantly adding to electricity load. This modeling approach can be used to assess network-level energy use for a wide-variety of modeled transportation studies, such as evaluating transportation improvement plans, assessing the net impact on the electric grid, and forecasting the potential benefits of electrifying shared-autonomous vehicles under future scenarios.

Major depressive disorder is a heterogeneous disease involving widespread disruptions in functional brain networks, the neurobiological mechanisms of which are poorly understood. Amassing evidence supports innate immune activation as one pathophysiologic mechanism contributing to depression in a subgroup of patients with elevated inflammatory markers. Although inflammation is known to alter monoamine and glutamate neurotransmitters, little work has been done to understand its role in network dysfunction in patients with depression. Here we conducted a large-scale network-based analyses of resting-state functional magnetic resonance imaging (rfMRI) data acquired from depressed patients with varying levels of inflammation to develop a comprehensive characterization of network alterations as an effect of inflammation. Complementary approaches of global brain connectivity and parcellation-based network analysis applied to the whole brain revealed that increased plasma C-reactive protein (CRP) was associated with reduced functional connectivity in a widely-distributed network including ventral striatum, parahippocampal gyrus/amygdala, orbitofrontal and insular cortices, and posterior cingulate cortex. These broad alterations were centralized in the ventral medial prefrontal cortex (vmPFC), representing a hub for the effects of inflammation on network function in the whole brain. When feeding the identified multivariate network features into a machine learning algorithm of support vector regression, we achieved high prediction accuracies for depressive symptoms that have been associated with inflammation in previous studies including anhedonia and motor slowing. These findings extend and broaden previous observations from hypothesis-driven studies, providing further support for inflammation as a distinct contributing factor to network dysfunction and symptom severity in depression.

Numerous resting-state functional magnetic resonance imaging studies have revealed that major depressive disorder (MDD) is associated with abnormal functional connectivity (FC) within and between large-scale functional networks such as the default mode network (DMN), cognitive control network (CCN) and affective network (AN). Compared with healthy controls, individuals with MDD usually show (i) increased FC within the anterior DMN and decreased FC within the posterior DMN, (ii) decreased FC within the CCN and (iii) increased FC within limbic system and decreased FC in the reward system in the AN. Depression related interactive changes between networks have also been reported: (i) decreased FC between DMN and CCN, (ii) increased FC between DMN and AN, and (iii) decreased FC between CCN and AN. These findings on network interaction may represent impaired resource allocation and information integration in MDD. Major weakness in the present rfMRI studies of depression resides in small sample and lack of multidimensional features. Meanwhile, as several brain disorders may show commonly disrupted functional architectures, depression-related specific alterations are typically lacking. We suggest that future studies may advance by combining multidimensional big data and individualized characterization, as well as examining shared and distinct functional network mechanisms of MDD in the spectrum of psychiatric disorders.

Objective: The etiology of biliary atresia is unclear, but viral infection has been implicated. The aim of the current meta-analysis was to investigate relationships between cytomegalovirus (CMV) and the prognosis of biliary atresia.Methods: PubMed, Embase, the Cochrane Library, the China National Knowledge Infrastructure database, and Wanfang Data electronic databases were searched for eligible studies. Each relevant text was thoroughly reviewed and examined, including related papers in their reference lists.Results: A total of nine studies including 784 patients were included in the analysis. Biliary atresia patients with CMV exhibited significantly lower jaundice clearance (odds ratio: 0.46, p < 0.0001; I2 = 15%, p = 0.31). There were no significant differences in the rates of cholangitis or native liver survival. CMV-pp65-positive biliary atresia patients had a significantly lower rate of jaundice clearance (odds ratio: 5.87, p = 0.003; I2 = 0%, p = 0.71) and a significantly higher rate of cholangitis (odds ratio: 0.21, p = 0.01; I2 = 0%, p = 0.43) than CMV antibody-positive biliary atresia patients.Conclusion: Biliary atresia patients who were also infected with CMV had a poorer prognosis, particularly with respect to jaundice clearance. CMV status may influence the prognosis of biliary atresia. Clinicians should be able to routinely identify the subset of biliary atresia patients who are also CMV-positive, in order to improve native liver survival.

This study was aimed to determine the diagnostic performance of perfusion-related parameters derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) by comparing them with quantitative parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on differentiation grades of rectal cancer. We retrospectively analyzed 98 patients with rectal cancer. Perfusion-related IVIM parameters ( D ∗ , f , and f · D ∗ ) and quantitative DCE parameters ( K trans , K ep , V e , and V p ) were obtained by plotting the volume-of-interest on in-house software. Furthermore, we compared the difference and diagnostic performance of all well-moderately and poorly differentiated rectal cancer parameters. Finally, we analyzed the correlation between those DCE and IVIM parameters and pathological differentiation grade. The values of f , K trans , and K ep significantly differentiated poor and well-moderate rectal cancers. K trans achieved the highest area under the curve (AUC) value compared to perfusion-related IVIM and DCE parameters. Furthermore, K trans showed a better correlation with pathological differentiation grade than f . The diagnostic efficiency of DCE-MRI was greater than perfusion-related IVIM parameters. The f value derived from perfusion-related IVIM offered a diagnostic performance similar to DCE-MRI for patients with renal insufficiency.

BackgroundAlthough complete resolution and recovery occurs in most children with an initial attack of acute pancreatitis (AP), a subset of children may progress to recurrent AP (RAP). RAP has serious effects to the individual and the socioeconomic burden. The aim of this project was to identify the independent risk factors for pediatric RAP so as to provide evidence for its prevention, early diagnosis and treatment.MethodsA retrospective cohort study of children discharged from Tianjin Children’s Hospital from June 2017 to January 2020 was performed. Demographic and clinical variables, treatment strategies, clinical course and outcomes were collected. Independent risk factors of RAP were identified using the logistic regression model.ResultsOf the total 96 enrolled children, 30 (31.3%) developed RAP during the follow-up period. The majority (27/30, 90%) of the children with AP developed RAP within 6 months of their first AP attack. The presence of systemic inflammatory response syndrome (SIRS) [odds ratio (OR)=6.652, 95% confidence interval (CI) 1.989 to 22.247], fasting time (OR=1.267, 95% CI 1.104 to 1.583), whether meet all three AP diagnostic criteria (OR=7.438, 95% CI 1.346 to 41.103) and abnormal amylase/lipase value on the seventh day of hospitalization (OR=3.601, 95% CI 0.972 to 13.342) were independent risk factors of RAP in children.ConclusionsMost children who developed RAP had progressed within 6 months after their first episode of AP. RAP was more common in children who met all three AP diagnostic criteria at initial attack and in children with SIRS, long fasting time and abnormal amylase/lipase value on the seventh day of hospitalization.

Texture analysis (TA) techniques derived from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps of rectal cancer can both achieve good diagnosis performance. This study was to compare TA from T2WI and ADC maps between different pathological T and N stages to confirm which TA analysis is better in diagnosis performance. 146 patients were enrolled in this study. Tumor TA was performed on every patient’s T2WI and ADC maps, respectively; then, skewness, kurtosis, uniformity, entropy, energy, inertia, and correlation were calculated. Our results demonstrated that those significant different parameters derived from T2WI had better diagnostic performance than those from ADC maps in differentiating pT3b-4 and pN1-2 stage tumors. In particular, the energy derived from T2WI was an optimal parameter for diagnostic efficiency. High-resolution T2WI plays a key point in the local stage of rectal cancer; thus, TA derived from T2WI may be a more useful tool to aid radiologists and surgeons in selecting treatment.

This paper presents a framework for coupled in - frastructure studies between the electric power transmission system and transportation networks. The proposed methodology evaluates the impact of electric vehicle (EV) charging demand on grid. operation and power genaration, Key modeling and coupling considerations are presented for each network, Case studies for various EV charging schemes are presented on networks situated in Travis County, TX for illustration. System loading and generation dispatch provide examples of two of the many analyses enabled by this coupled infrastructure simulation framework.

Background To explore the relevant factors for early liver transplantation (LT) after Kasai portoenterostomy (KP). Methods Retrospective analysis was performed for 200 children with biliary atresia, who underwent LT with hepatic failure after KP. According to the interval between KP and LT, they were divided into three groups: G1 (≤6-month), G2 (6-month~ 2-year) and G3 (> 2-year). Gender, age of Kasai portoenterostomy, jaundice-clearance, cholangitis after KP and liver function indexes before LT were compared among the three groups. Results The proportion of patients with age of KP (≤90-day) in G1 was lower than that in G3 (P = 0.003). Jaundice-clearance occurred in 6 (7.6%), 26(28.6%) and 26 (86.7%) patients after KP in G1, G2 and G3 respectively (P P = 0.035, 90-day was lower than that of children at operation age ≤ 90-day (P = 0.002). The NLS rate of the children with jaundice-clearance after KP was significantly better than that of the children without jaundice-clearance (P P = 0.026). The NLS rate of patients of G2 and G3 with cholangitis after KP was lower than that in children without cholangitis (P = 0.017). Multiple logistic regression analysis showed uncleared jaundice after KP was a risk factor for the NLS time in patients. Conclusion The age of KP (> 90-day), jaundice-unclear and early cholangitis could reduce the NLS time after KP, which were related to early liver transplantation. Jaundice-unclear was a risk factor for early liver transplantation.

The selfishness and randomness of users in the mobile crowd sensing network could cause them unwilling to participate in sensing activities and lead to lower completion rates of sensing tasks. In order to deal with these problems, this paper proposes a novel incentive mechanism based on a new auction model for mobile crowd sensing, which consists of two consecutive stages. In the first stage, a novel Incentive Method based on Reverse Auction for Location-aware sensing (IMRAL) is proposed to maximize user utility. By introducing a task-centric method to determine the winning bids, it can provide higher user utility and higher task coverage ratio. To ensure the truthfulness of IMRAL, we design a unique payment determination algorithm based on critical payment for the incentive platform. In the second stage, we propose a user-interaction incentive model (UIBIM) to cover the situation that a user may drop out of the sensing activity. This new incentive model includes a dynamic double auction framework prompting users' interaction and a user matching algorithm based on a bipartite graph. The proposed new mechanism achieves the goal of improving task completion rates without increasing the cost of the incentive platform. The simulation results show that comparing with other solutions, such as a truthful auction for location-aware collaborative sensing in mobile crowdsourcing and incentive mechanism for crowdsourcing in the single-requester single-bid-model, IMRAL can achieve better performance in terms of average user utility and tasks coverage ratio, and the UIBIM can significantly improve task completion rates.

Graphene has great application prospects in the field of optoelectronics. We investigate a field effect transistor with a graphene channel. Carrier density and chemical potential of the channel can be spatially modified by topping the channel with dielectric structures consisting of pure and lithium enriched SU-8 layers. As an example, we demonstrate that application of a single-gate voltage can induce a P–N junction to a channel with an appropriate dielectric architecture. Electronic and photoelectric properties of the junction are studied. The photocurrent mapping is investigated, which clearly shows the origin of the photocurrent from the P–N junction. The proposed technology makes fabrication of graphene-based photodetectors simple and flexible, and may also be interesting for the development of future optoelectronic components using other two-dimensional materials.

Oxidative stress and excessive lipid accumulation in liver tissues are considered as important factors in development of non-alcoholic fatty liver disease (NAFLD), which is the most common chronic liver disease worldwide and strongly associated with insulin resistance, obesity and dyslipidemia. In this study, the effect of PMFs-enriched extract of Daoxianyeju (Citrus daoxianensis S.W. He) (EDX) on NAFLD were examined using high fat diet-induced obesity (DIO) C57BL/6 mice. Daily supplementation of 0.2% and 0.5% EDX with high fat diet (w/w) over ten weeks markedly improved glucose tolerance and insulin resistance as well as lipid profiles, alleviated hepatic steatosis, inflammatory and oxidative stress. The benefit of EDX on improving pathology of NAFLD induced by high fat diet may associate with the activation of nuclear factor erythroid-2 related factor 2 (Nrf2) pathways in liver tissues. Our study showed that citrus PMFs may use as a dietary therapy for NAFLD and related metabolic disorders.

Conventional photothermal therapy (PTT) is insufficient to induce a strong and potent anti-tumor immune response. Herein, we present a vanadyl nanocomplex, which simultaneously serves as a photothermal agent (PTA) and an immunogenic cell death (ICD) inducer to enhance the anti-tumor immunity of PTT. The vanadyl nanocomplex (STVN) is constructed via facile one-step coordination assembly under ambient conditions. STVN not only has a strong and stable photothermal effect under near-infrared (NIR) irradiation, but also can cause severe endoplasmic reticulum (ER) stress by itself, leading to ICD and activating the systemic immune responses. In the absence of any adjuvants, NIR-irradiated STVN almost completely ablates primary tumors and simultaneously inhibits distant tumors in mice bearing bilateral melanoma. Meanwhile, the intratumorally injected STVN combined with NIR effectively suppressed melanoma lung metastasis as well as tumor recurrence, displaying that local STVN-mediated PTT could trigger a systemic anti-tumor immunity. Therefore, STVN, as a novel immunogenicity-enhanced PTA, affords a "one stone two birds" strategy for improved photothermia-induced cancer immunotherapy.

Background We retrospectively compared the prognostic value between the AJCC 8th edition anatomic (AS) and prognostic staging (PS) system for triple negative breast cancer (TNBC) in a cohort from two involved institutions and a large population database. Methods Clinicopathological data of TNBCs were identified in two involved institutions (SYSUCC-PWH cohort). Data from SEER database during 2010–2015 was also accessed. We restaged all cases into AS and PS group according to the AJCC 8th staging system. Results A total of 611 and 31,941 TNBCs were identified in two cohorts, with a median follow-up of 53.5 and 27 months respectively. PS upstaged 46.1% of patients in SYSUCC-PWH cohort, and 62.4% in SEER cohort. No significant difference was observed in C index between AS and PS models for disease-specific survival (DSS), progression-free survival (PFS) or overall survival (OS) in either cohort. χ2 statistic and Hazard Ratio for PFS, DSS and OS showed better discrimination between IA and IB, IIB and IIIA, IIIA and IIIB in AS model than PS model. Besides, patients with IIIC unchanged stage showed worse PFS compared to those with AS IIIA or IIIB upstaged to PS IIIC in both cohorts(p = 0.049, p Conclusions Our findings demonstrated that prognostic staging system did not provide better discriminatory ability in predicting TNBCs prognosis than anatomic staging system.

In order to cope with the large-scale electric vehicles (EVs) access to the grid, a hierarchical optimization model of distribution network-electric vehicle centralized control center is established by using the way of EVs zonal cluster. The charging and discharging plan of each centralized control center is formulated by the distribution network layer. Considering the safety of the distribution network and the power demand constraints during the control cycle of EVs, the objective is to minimize the load variance and network loss of the distribution network after the access of distributed wind power. According to the charge and discharge plan, the centralized control center carries out rolling optimization control for EVs in their respective areas. The NSGA-II algorithm is used to solve the multi-objective optimization problem of the distribution network layer. The centralized control center performs the orderly charge and discharge control by calculating the priority of charging and discharging. Finally, the improved IEEE-33 node system with 3 distributed wind power and 3 centralized control centers is used as an example for simulation, and the results verify the effectiveness of the proposed model and method.

Hyperglycemia-induced cardiac fibrosis is a prominent characteristic of diabetic cardiomyopathy. Changes in proinflammatory cytokines have been shown to lead to cardiac fibrosis in patients with diabetes mellitus. This study aimed to investigate the role of miR-150-5p in mediating cardiac inflammation and fibrosis in cardiac fibroblasts (CFs). Herein, we found that high-glucose (HG) treatment significantly induced cardiac inflammation, as manifested by increased proinflammatory cytokine production (IL-1β) and NF-κB activity in CFs. Moreover, HG markedly aggravated cardiac fibrosis and increased levels of fibrotic markers (CTGF, FN, α-SMA) and extracellular matrix proteins (Col-I, Col-III) in CFs. At the same time, HG disturbed the TGF-β1/Smad signaling pathway, as evidenced by increases in TGF-β1 and p-Smad2/3 levels and decreases in Smad7 levels in CFs. Furthermore, we found that miR-150-5p was upregulated by HG, which negatively regulated Smad7 expression at the posttranscription level. Further study demonstrated that cardiac inflammation and fibrosis in CFs were corrected following miR-150-5p knockdown, but exacerbated by miR-150-5p overexpression. These data indicated that miR-150-5p inhibition could ameliorate NF-κB-related inflammation and TGF-β1/Smad-induced cardiac fibrosis through targeting Smad7. Thus, miR-150-5p may be a novel promising target for treating diabetic cardiomyopathy.

Novel horizontal tubes and triangular prism (HTTP) baffles that generate flow vortices were developed to increase solution velocity between dark and light areas and thus improve microalgal growth in a flat-panel photo-bioreactor. Solution velocity, mass-transfer coefficient, and mixing time were measured with a particle-imaging velocimeter, dissolved oxygen probes, and pH probes. The solution mass-transfer coefficient increased by 30% and mixing time decreased by 21% when the HTTP baffles were used. The solution velocity between dark and light areas increased from ∼0.9cm/s to ∼3.5cm/s, resulting in a decreased dark-light cycle period to one-fourth. This enhanced flashing light effect with the HTTP baffles dramatically increased microalgae biomass yield by 70% in the flat-panel photo-bioreactor.

Many plasmon-induced transparency (PIT) metamaterials previously reported had limited functions. Their tunabilities were realized by complex discrete structures, which greatly increased the difficulty and cost of device fabrication and adversely affected their resonance characteristics. It is an open question to adjust the Fermi levels of many graphene patterns with only a few in-plane electrodes. We propose and numerically study a novel electrically tunable and multifunctional trigate graphene metamaterial (TGGM) based on the concept of “Lakes of Wada”. Benefiting from the trigate regulation, our proposed TGGM turns out to exhibit excellent characteristics, that can not only be used for terahertz band-stop filter, terahertz refractive index sensor, near-field optical switch, slow-light device, but also for double PIT window metamaterial with broad transparency windows and large tunable frequency range.

Vehicle electrification plays a central role in reducing global energy use and greenhouse gas emissions. Predicting electric vehicle (EV) energy use for future transportation networks is critical for the planning, design, and operations of sustainable transportation systems. However, there is currently a lack of EV energy modeling approaches that are fully-scalable to large transportation network applications and consider actual on-road vehicle operating conditions. Such an approach is required for the accurate assessment of EV energy impact under various transportation scenarios. Here we present a simulation-based quasi-statistical approach to estimate EV energy consumption under various on-road vehicle operating conditions. In this approach, a Bayesian Network method is used to integrate outputs from full-system vehicle simulation tools for specific makes and models of EVs under a wide-variety of on-road operating conditions. These outputs are used to develop inference models that greatly improve computational efficiency, while maintaining most of the prediction accuracy of the complete system models. This approach is both highly scalable and transferable for analyzing the energy impact of EV fleet deployment in different regions, can facilitate the estimation of network-level EV energy consumption, and can be incorporated into a wide-variety of transportation planning models. In our case study of Atlanta, GA, the results indicate that if 6.2% of urban travel distances and 4.9% of rural travel distances were to be driven by EVs, regional fuel savings would be around 4.0% for a typical travel day in 2024.

Background The American Joint Committee on Cancer (AJCC) eighth edition cancer staging manual has updated with a prognostic stage group for breast cancer by incorporating traditional TNM anatomic parameters and biomarkers (ER, PR, Her-2 status and tumor grade, etc.). In this study, we retrospectively compared the prognostic value between the AJCC 8th Edition anatomic and prognostic staging system for triple negative breast cancer(TNBC) in a cohort from two involved institutions and a large population database. Design Clinicopathological data of patients who underwent breast surgery as the initial intervention and with TNBC were identified in Sun Yat-sen University Cancer Center(SYSUCC) during 2005-2013 and Prince of Wales Hospital (PWH) during 2002-2008. Data from surveillance, epidemiology, and end results (SEER) database during 2010-2015 was also accessed. We restaged all cases into anatomic stage (AS) and prognostic stage (PS) group according to the AJCC 8th Edition staging system. The Kaplan-Meier analysis with log-rank test was used to compare differences in disease-specific survival (DSS), overall survival (OS) and progression-free survival (PFS) between groups. The Harrell concordance index (C index) was calculated to measure predictive performance for the AS and PS models. The relationshipof AS and PS with DSS, PFS or OS was examined using a Cox proportional hazards regression model. Results A total of 611 patients with stage I to IIIC TNBC were identified in the SYSUCC-PWH cohort with a median follow-up of 53.5 months. Compared with the AS, PS upstaged 46.1% of patients. No patient was downstaged by PS in this cohort. No significant difference was observed in C index between AS and PS models for either DSS (0.84 vs. 0.83, p=0.943) or PFS (0.82 vs. 0.80, p=0.887), demonstrating that PS do not reflect a more accurate predictive model than AS. A total of 31941 patients with stage I to IIIC TNBC were identified in the SEER cohort with a median follow-up of 27 months. PS upstaged 62.4% of patients and downstaged eight patients (from IIIC to IIIB) in this cohort. Similarly, PS model do not performed better than the AS in either DSS (C index, 0.85 vs. 0.86, p=0.95) or OS (C index, 0.90 vs. 0.90, p=0.98). Compared those with stage changed to other without, in SYSUCC-PWH cohort, patients with IIIC unchanged stage showed worse PFS compared to patients with AS IIIA or IIIB upstaged to PS IIIC (p=0.049). Similarly, in SEER cohort, patients with IIIC unchanged stage showed worse OS and DSS compared to patients with AS IIIA or IIIB upstaged to PS IIIC (p Table 1. 5-year PFS and DSS by AS and PS of TNBCs included in SYSUCC-PWH cohort (N=611)StageNo. at risk5-year PFS (95% CI)p-value5-year DSS (95% CI)p-valueASI(A*)10086.5(76.2,92.5) Table 2. 5-year OS and DSS by AS and PS of TNBCs included in SEER cohort (N=31941)StageNo. at risk5-year OS (95% CI)p-value5-year DSS (95% CI)p-valueASI1270087.2(86.2,88.0) Citation Format: Peng Sun, JYS Tsang, Xiaodan Xu, Jibin Li, Mei Li, Xue Chao, Rongzhen Luo, Gary Tse, Jiehua He. AJCC 8th edition prognostic staging system provide no better discriminatory ability in prognosis than anatomical staging system in triple negative breast cancer: A cohort from two institutions and a large population based study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-06-19.

Biomarkers of inflammation, including inflammatory cytokines and the acute-phase reactant C-reactive protein (CRP), are reliably increased in a subset of patients with depression, anxiety disorders and post-traumatic stress disorder (PTSD). Administration of innate immune stimuli to laboratory subjects and the associated release of inflammatory cytokines has been shown to affect brain regions involved in fear, anxiety and emotional processing such as the amygdala. However, the role of inflammation in altered circuitry involving amygdala and other brain regions and its subsequent contribution to symptom severity in depression, anxiety disorders and PTSD is only beginning to be explored. Herein, medically-stable, currently unmedicated outpatients with a primary diagnosis of major depressive disorder (MDD; n = 48) underwent resting-state functional MRI (rfMRI) to determine whether altered connectivity between the amygdala and whole brain was observed in a subset of patients with high inflammation and symptoms of anxiety. Whole-brain, voxel-wise functional connectivity analysis of the right and left amygdala as a function of inflammation (plasma CRP concentrations) revealed that increased CRP predicted decreased functional connectivity between right amygdala and left ventromedial prefrontal cortex (vmPFC) (corrected p 0.05). Amygdala-vmPFC connectivity was, in turn, negatively correlated with symptoms of anxiety (r = -0.33, df = 46, p = 0.022). In exploratory analyses, relationships between low amygdala-vmPFC connectivity and high anxiety were only observed in patients with a secondary diagnosis of an anxiety disorder or PTSD (r = -0.54 to -0.87, p 0.05). More work is needed to understand the role of inflammation and its effects on amygdala-vmPFC circuitry and symptoms of anxiety in MDD patients with comorbid anxiety disorders or PTSD.

Estimating transportation network emissions requires multiplying estimates of on-road vehicle activity (by source type and operating mode) by applicable emission rates for the observed source type and operating conditions. Coupling microsimulation model runs with emissions modeling can make fast assessments possible in transportation air quality planning. This research developed a tool with automated linkage between the Vissim microsimulation model and the Motor Vehicle Emission Simulator (MOVES) model. To link the two models, the research team used MOVES-Matrix, which was prepared by iteratively running MOVES across all possible iterations of vehicle source type, fuel, environmental and operating conditions, and other parameters (hundreds of millions of model runs) to create a multidimensional emission rate lookup matrix. A Vissim simulation of the major arterial roads and freeways at I-85 and Jimmy Carter Boulevard in Gwinnett County, Georgia, provided the case study for this MOVES-matrix application. The researchers present predicted emissions and the results of a sensitivity analysis to identify the potential impacts of various internal Vissim modeling parameters (such as minimum headway, maximum deceleration rate for cooperative braking, and emergency stop distance) on a case study’s emissions outputs. The sensitivity analysis found that internal Vissim parameters impacted emissions and that proper care should be taken in using Vissim for emissions analysis at the corridor and link level. The case study demonstrates that Vissim coupled with MOVES-Matrix can be an effective tool for emissions analysis.

Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly. Recently, a significant number of outlier detection methods have been witnessed and successfully applied in a wide range of fields, including medical health, credit card fraud and intrusion detection. They can be used for conventional data analysis. However, it is not a trivial work to identify rare behaviors or patterns out from complicated data. In this paper, we provide a brief overview of the outlier detection methods for high-dimensional data, and offer comprehensive understanding of the-state-of-the-art techniques of outlier detection for practitioners. Specifically, we firstly summarize the recent advances on outlier detection for high-dimensional data, and then make an extensive experimental comparison to the popular detection methods on public datasets. Finally, several challenging issues and future research directions are discussed.

Recently, graphene has attracted wide attentions for its unique properties in optics and electronics. Many excellent designs were proposed base on the structured graphene such as modulators, optical polarizers and limiters [1]. Thus, the fabrication ability of graphene structures can be thought as the cornerstone of the development of the graphene optical and electrical devices. The common used methods include laser direct writing (LDW), focus ion beam etching, electron beam lithography and etc. Among them, LDW was considered as one of the most valuable methods for its non-mask and no secondary deposition. Many efforts were made to improve the resolution and the precision of the LDW fabrication. However, the results are not very ideal. As far as we know, the best achievement was presented by R. Sanin. et al [2]. They generate the third harmonic of the 1030 nm femtosecond laser (343 nm) and convert the Gaussian laser beam to a Bessel beam. 480 nm average width ablation lines with precision ±80 nm were achieved by adjusting the laser power and the scanning speed. However, this width doesn't break the diffraction limit of the laser.

MOVES and AERMOD are the U.S. Environmental Protection Agency's recommended models for use in project-level transportation conformity and hot-spot analysis. However, the structure and algorithms involved in running MOVES make analyses cumbersome and time-consuming. Likewise, the modeling setup process, including extensive data requirements and required input formats, in AERMOD lead to a high potential for analysis error in dispersion modeling. This study presents a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix, a high-performance emission modeling tool, with the microscale dispersion models CALINE4 and AERMOD. MOVES-Matrix was prepared by iteratively running MOVES across all possible iterations of vehicle source-type, fuel, operating conditions, and environmental parameters to create a huge multi-dimensional emission rate lookup matrix. AERMOD and CALINE4 are connected with MOVES-Matrix in a distributed computing cluster using a series of Python scripts. This streamlined system built on MOVES-Matrix generates exactly the same emission rates and concentration results as using MOVES with AERMOD and CALINE4, but the approach is more than 200 times faster than using the MOVES graphical user interface. Because AERMOD requires detailed meteorological input, which is difficult to obtain, this study also recommends using CALINE4 as a screening tool for identifying the potential area that may exceed air quality standards before using AERMOD (and identifying areas that are exceedingly unlikely to exceed air quality standards). CALINE4 worst case method yields consistently higher concentration results than AERMOD for all comparisons in this paper, as expected given the nature of the meteorological data employed.The paper demonstrates a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix with the CALINE4 and AERMOD. This streamlined system generates exactly the same emission rates and concentration results as traditional way to use MOVES with AERMOD and CALINE4, which are regulatory models approved by the U.S. EPA for conformity analysis, but the approach is more than 200 times faster than implementing the MOVES model. We highlighted the potentially significant benefit of using CALINE4 as screening tool for identifying potential area that may exceeds air quality standards before using AERMOD, which requires much more meteorology input than CALINE4.

The fabrication ability of graphene nanostructures is the cornerstone of graphene-based devices, which are of particular interest because of their broad optical response and gate-tunable properties. Here, via laser-induced redox reaction of graphene and silica, we fabricate nano-scale graphene structures by femtosecond laser direct writing. The resolution of destructed graphene lines is far beyond the diffraction limit up to 100 nm with a precision as small as ± 7 nm. Consequently, graphene nanostructures are fabricated precisely and excellent plasmon responses are detected. This novel fabrication method of graphene nanostructures has the advantages of low costs, high efficiency, maskless and especially high precision, which would pave the way for practical application of graphene-based optical and electronic devices.

Background: Hepatocellular carcinoma (HCC) is one of the most common cancers and the second leading cause of cancer-related deaths in men worldwide. Surgical resection of HCC remains the mainstay treatment procedure. As a result of hepatitis viral infection, the postoperative survival outcome in patients with HCC is not satisfactory. Recently, studies have reported that due to its treatment effect on hepatitis infection, pegylated interferon (Peg-IFN)-based therapy could improve the survival outcome after the treatment of hepatitis-related HCC. However, the postoperative effect of this regimen on the survival outcomes in patients with hepatitis-related HCC remains debatable. The present study conducted a meta-analysis to evaluate the effects of adjuvant Peg-IFN-based therapy on the survival outcomes in patients with hepatitis-related HCC after the curative treatment. Methods: A systematic search was conducted to identify studies on the survival outcomes in patients with hepatitis-related HCC after a curative treatment with adjuvant Peg-IFN. PubMed, EmBase, and Cochrane Library databases were searched until September 20, 2017. The retrieved studies were independently assessed by 2 reviewers, to identify the potentially eligible studies and extract data of interest. STATA software (Version 10.0, STATA Corporation, College Station, Texas) software was used for all statistical analyses. Results: The pooled results showed that adjuvant Peg-IFN-based therapy improved the 3- and 5-year recurrence-free survival (RFS) rates of patients with hepatitis-related HCC (3-year RFS, HR = 0.80; 95% CI: 0.64–0.99, P = .04; P = .81 for heterogeneity; 5-year RFS, HR = 0.82; 95% CI: 0.67–0.99, P = .04; P = .84 for heterogeneity). For the 5-year overall survival (OS) outcomes of Peg-IFN therapy for hepatitis-related HCC after the curative treatment, the pooled results showed a significant difference between the 2 groups (HR = 0.67; 95% CI: 0.47–0.97, P = .03; P = .99 for heterogeneity). Conclusions: Adjuvant Peg-IFN-based therapy could improve the RFS and OS outcomes in patients after curative treatment of hepatitis-related HCC, with no severe adverse effects.

To study the effects of keratinocyte growth factor (KGF) modified umbilical cord mesenchymal stem cells (MSC) on chronic liver injury in rats, adenovirus carrying human KGF gene (Ad-KGF) was used. Rat liver injury model was established by subcutaneous injection of CCl4 - olive oil solution; 56 male Wistar rats were randomly divided into normal control, model, MSC, KGF, and KGF/MSC groups. Of all three treatments, KGF/MSC had the most obvious therapeutic effects on liver injury, and cell injections did not cause adverse reaction. The experiment provides new data for clinical research of KGF/MSC and possible methods for liver injury treatment.

A simple and effective universal serial bus (USB) flash disk type microfluidic chip electrophoresis (MCE) was developed by using poly(dimethylsiloxane) based soft lithography and dry film based printed circuit board etching techniques in this paper. The MCE had a microchannel diameter of 375 μm and an effective length of 25 mm. Equipped with a conventional online electrochemical detector, the device enabled effectively separation of bovine serum albumin, lysozyme, and cytochrome c in 80 s under the ultra low voltage from a computer USB interface. Compared with traditional capillary electrophoresis, the USB flash disk type MCE is not only portable and inexpensive but also fast with high separation efficiency.

Sub-topic clustering is a crucial step in multi-document summarization. The traditional k-means clustering method is not effective for topic clustering because the number of clusters k must be given in advance. This paper describes a new method for sub-topic clustering based on semi-supervised learning: the method firstly partition the set of sentences into disjoint subsets, each of which contained sentences covering exactly one topic, and labels the sentences which have high scores in the topic, then use the method of constrained-k-means to decide the number of topics, and finally get the sub-topic sets by k-Means clustering. This algorithm can dynamically generate the number of k-means clustering, and the experiment result indicates that the accuracy of clustering is improved

Sub-topic detecting is an important step in the abstracting of multi-documents.This paper describes a new method for sub-topic detecting based on semi-supervised learning:it firstly gets the primal sets of topics by hierarchy clustering,and labels the sentences which have high scores in the topics,then use the method of constrained-kMeans to decide the number of topics(k),and finally get the topic sets by k-Means clustering.The experiment result indicates that its value is stable.

Automatic summarization is an important issue in natural language processing and becomes more and more important in some domains such as web information retrieval. This paper describes an automatic summarization system based on topic partition: Firstly, it analyzes the text structure of the web pages, then proposes an algorithm for multi-topic text partitioning based on semantic distance between paragraphs, and finally get the summarization by extracting the key sentences from different topics. This method can makes the abstract of multi-topic article have more general content and more balanced structure. The experimental result shows that its value is stable.

Automatic abstracting is a practical and difficult branch in natural language processing, which becomes an important problem in some domains such as Internet information retrieval. This paper presents a new automatic abstracting method on the basis of traditional method, which adds the sentence weight based on user-demand into the sentence importance. The user-demand consists of the keywords that user queried. The experimental results show that this method can improve the accuracy of searching information.

Nonstationary random signals have been the hot topic in domestic and overseas research recently. Mostly various signals studied in EMC are random jammer and nonstationary signals. The paper discusses stability judgement and feature extraction, proceeds to time-frequency analysis based on the Wigner-Ville distribution, and presents a practical example of radar jammer signals. The study can be applied to suppress jammer signals in EMC and offer jammer signal sources for experimental research by means of stimulation and emulation.