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ObjectiveWe aimed to identify imaging biomarkers to assess predictive capacity of radiomics nomogram regarding treatment response status (responder/non-responder) in patients with advanced NSCLC undergoing anti-PD1 immunotherapy.Methods197 eligible patients with histologically confirmed NSCLC were retrospectively enrolled from nine hospitals. We carried out a radiomics characterization from target lesions (TL) approach and largest target lesion (LL) approach on baseline and first follow-up (TP1) CT imaging data. Delta-radiomics feature was calculated as the relative net change in radiomics feature between baseline and TP1. Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression were applied for feature selection and radiomics signature construction.ResultsRadiomics signature at baseline did not show significant predictive value regarding response status for LL approach (P = 0.10), nor in terms of TL approach (P = 0.27). A combined Delta-radiomics nomogram incorporating Delta-radiomics signature with clinical factor of distant metastasis for target lesions had satisfactory performance in distinguishing responders from non-responders with AUCs of 0.83 (95% CI: 0.75–0.91) and 0.81 (95% CI: 0.68–0.95) in the training and test sets respectively, which was comparable with that from LL approach (P = 0.92, P = 0.97). Among a subset of those patients with available pretreatment PD-L1 expression status (n = 66), models that incorporating Delta-radiomics features showed superior predictive accuracy than that of PD-L1 expression status alone (P <0.001).ConclusionEarly response assessment using combined Delta-radiomics nomograms have potential advantages to identify patients that were more likely to benefit from immunotherapy, and help oncologists modify treatments tailored individually to each patient under therapy.

Objectives To retrospectively investigate whether radiological and clinicopathological characteristics were associated with the presence of stage IA–IIA lung adenocarcinoma in patients at high risk for a postoperative recurrence. Materials and Methods Three hundred twelve patients with biopsy-proven node-negative early-stage (IA–IIA) lung adenocarcinoma met the inclusion criteria for this study. Demographics data and histopathological findings were collected from medical records. Computed tomography (CT) performed approximately 1 month before surgery was manually scored using 23 CT descriptors. Univariate analyses were applied to demonstrate an association between clinicopathological and radiological features and 2-/5-year recurrences. Multivariate logistic regression was performed to assess the ability of radiological and clinicopathological features to discriminate low and high-risk factors for recurrence. A ROC curve was used to evaluate prediction performance. Results Univariate analysis revealed that the 2-year recurrence was associated with six radiological features and two clinicopathological features, while 5-year recurrence was associated with five radiological features and two clinicopathological features. A multivariate logistic regression model of combined clinicopathological and radiological features showed that stage IIA (OR = 2.87), solid texture (solid part > 50 %: OR = 4.81; solid part = 100 %: OR = 3.61), pleural attachment (OR = 3.97) and bronchovascular bundle thickening (OR = 2.16) were associated with the independent predictors of 2-year recurrence, and stage IIA (OR = 3.52), solid texture (solid part > 50 %: OR = 3.56; solid part = 100 %: OR = 2.44) and pleural attachment (OR = 4.57) were associated with 5-year recurrence. Combined radiological and clinicopathological features could be significant indicators of 2- and 5-year recurrences (AUC = 0.784 and AUC = 0.815, respectively). Conclusions The combination of radiological and clinicopathological features has the potential to help predict postoperative recurrence in patients with stage IA–IIA lung adenocarcinomas and guide oncologists and patients whether to undergo additional treatment after surgery.

The purpose of this study was to explore the differences in interhemispheric functional connectivity in patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) based on a triple network model consisting of the default mode network (DMN), salience network (SN), and executive control network (ECN). The technique of voxel-mirrored homotopic connectivity (VMHC) analysis was applied to explore the aberrant connectivity of all patients. The results showed that: (1) the statistically significant connections of interhemispheric brain regions included DMN-related brain regions (i.e. precuneus, calcarine, fusiform, cuneus, lingual gyrus, temporal inferior gyrus, and hippocampus), SN-related brain regions (i.e. frontoinsular cortex), and ECN-related brain regions (i.e. frontal middle gyrus and frontal inferior); (2) the precuneus and frontal middle gyrus in the AD group exhibited lower VMHC values than those in the aMCI and healthy control (HC) groups, but no significant difference was observed between the aMCI and HC groups; and (3) significant correlations were found between peak VMHC results from the precuneus and Mini Mental State Examination (MMSE) and Montreal Cognitive Scale (MOCA) scores and their factor scores in the AD, aMCI, and AD plus aMCI groups, and between the results from the frontal middle gyrus and MOCA factor scores in the aMCI group. These findings indicated that impaired interhemispheric functional connectivity was observed in AD and could be a sensitive neuroimaging biomarker for AD. More specifically, the DMN was inhibited, while the SN and ECN were excited. VMHC results were correlated with MMSE and MOCA scores, highlighting that VMHC could be a sensitive neuroimaging biomarker for AD and the progression from aMCI to AD.

Objective This study aimed to identify misdiagnosed or undiagnosed psychiatric disorders and the factors associated with these disorders in patients with sleep problems who are referred to a consultation-liaison service. Method Records of all inpatients receiving a consultation from the Psychiatry Department between January and December 2016 were retrospectively reviewed. Psychiatric diagnoses were analyzed using descriptive statistics, and the factors associated with the risk of these disorders in patients with sleep problems were determined by multiple logistic regression analysis. Results Of the 331 referral patients whose referral reason was simply having trouble in sleeping, only 97 patients were diagnosed with primary sleep disorder after consultation. The recognition rate of psychiatric disorders in inpatients with sleep problems among nonpsychiatric physicians was 29.3%. Anxiety (107, 45.7%) was the most common psychiatric diagnosis in patients with sleep problems followed by organic mental disorder (83, 35.5%), depression (37, 15.8%), and other mental disorders (8, 3.4%). Multiple logistic regression analysis revealed that a course >1 month (OR = 3.656, 95% CI = 2.171–6.156, p = 0.000) and sleep–wake rhythm disturbances (OR = 25.008, 95% CI = 5.826–107.341, p = 0.000) were associated with increased risks of psychiatric disorders. Conclusions The study showed that recognition rate of psychiatric disorders in inpatients with sleep problems was very low. A course >1 month and sleep–wake rhythm disturbances were associated with increased risks of disorders and could be used as indicators by nonpsychiatric physicians to improve diagnoses.

Local administration has many advantages for treating diseases. However, the surface mucus layer becomes a major obstacle that easily traps and fast removes local administrated drugs and genes in mucosal tissues. Fortunately, the rapidly developing nanocarriers with special physical and chemical properties may help to refine the treatment of mucosal tissues via delivering drugs and genes to the target tissue, and prolong the drug action time. Therefore, this review focuses on the strategies to apply different nanocarriers for drug-delivery in mucosal tissues, including mucoadhesive and mucus-penetrating types. Delivering drugs and genes to anatomical sites with high mucus turnover becomes more feasible and effective, and maintains sufficient local drug concentration to improve treatment efficacy.

Worldwide, urban rivers suffer various degrees of ecological degradation. Rehabilitating heavily modified urban rivers requires holistic approaches, including environmental flow management. We examine the case of Lower Yongding River, Beijing’s mother river, which had dried up since the 1980s and is undergoing a flow replenishment experiment, receiving 342 million m3 of water during 2019–2020 for ecosystem enhancement. Considering the massive cost of replenishment, we address the urgent need to evaluate its outcomes and inform future management through an interdisciplinary modeling approach under the circumstance of severe data shortage. We simulated the study reach’s landscape evolution under five flow scenarios and assessed their ecological effects using the CAESAR-Lisflood model and habitat suitability index method. Despite overall minor morphological differences across scenarios, individual reaches presented pronounced physical changes. Higher-flow scenarios shaped a distinct channel in certain reaches, but historic channel modifications by mining and farming caused minimal responses in others. Additionally, higher-flow scenarios generally created larger and more evenly distributed habitat areas but showed a low payback given the higher flow volumes needed. Targeted channel-floodplain geomorphological restoration is essential for flows to generate desired ecological outcomes. The demonstrated modeling framework offers great promise, informing future rehabilitation actions for heavily modified urban streams.

The waves and currents acting near a monopile foundation will potentially lead to scour, which may affect the stability of the wind turbine. The design of scour protection against the seabed lowering around a wind turbine monopile foundation is an important issue for wind energy industries. Many laboratory tests have been carried out to investigate the relationship between the hydrodynamic conditions and the monopile foundation scour protection layer damage, and various design criteria have been proposed. However, the experimental uncertainty of the underlying test results has not been discussed in detail. In the present research, small scale wave flume experiments of a 5m diameter monopile foundation scour protection under waves combined with currents in shallow water are described. Two groups of repetitive experiments are completed under the same wave and current conditions. The erosion development of the scour protection armor layer is measured by using a laser profiler and is evaluated based on three dimensional damage numbers. Together with visualization of the damage pattern, the damage analysis discusses the erosion in different subareas and the variances of the subarea damage number. The analysis of the uncertainty of the erosion results based on two sets of repetitive tests has been carried out. Using the uncertainty analysis methodology stated in ISO GUM standard: JCGM 100-2008, the Type A uncertainty, calibration uncertainty and combined uncertainty of the experiment are evaluated separately. The Type A uncertainty gives an overall uncertainty level and it shows that higher uncertainty occurs in the regions where stronger vortices exist. The combined uncertainty is analyzed based on scour protection dynamic stability design formula. Analysis result shows that the uncertainty due to modelling is a major source of the total uncertainty. The study gives a preliminary result of uncertainty level in wave flume test of monopile scour protection and provides a reference for future experimental research.

Proper patient selection for immunotherapy is critical, as certain tumor microenvironments are more permissible to therapy than others. Currently, the use of programmed cell death ligand-1 (PD-L1) and microsatellite instability high and/or mismatch repair deficiency are used as biomarkers for immunotherapy response. To improve tumor characterization, methodologies are being developed to combine imaging with tumor immune environment characterization. Imaging of tumors from immunotherapy responders and nonresponders with various imaging modalities has led to the development of criteria that could predict patient response to immunotherapy. Additionally, radiomics-based artificial intelligence methods are being used to characterize tumor microenvironments to predict and evaluate immunotherapy responses, as well as to predict risk of immune-related adverse events. Molecular imaging techniques are also being developed for various modalities to observe tumor expression of immunotherapy targets, such as PD-L1 and, to confirm the target is being expressed on resident tumors. In all, the advancements of imaging techniques to define tumor immunologic characteristics will help to stratify patients who are more likely to respond to immunotherapies. Keywords: Computer Aided Diagnosis (CAD), Computer Applications-Virtual Imaging, Efficacy Studies, MR-Imaging, Molecular Imaging-Cancer, Molecular Imaging-Immunotherapy, Molecular Imaging-Nanoparticles, Molecular Imaging-Probe Development, Molecular Imaging-Target Development, SPECT/CT © RSNA, 2019.

In vehicular ad-hoc network (VANET), the reliability of communication is associated with driving safety. However, research shows that the safety-message transmission in VANET may be congested under some urgent communication cases. More spectrum resource is an effective way to solve transmission congestion. Hence, we introduce cognitive radio (CR) enabled VANET (CR-VANET), where CR device can detect possible idle spectrum for VANET communications and assist to timely broadcast safety-message. Given high-speed mobility of vehicles and dynamically-changing availability of channels, a novel prediction algorithm is proposed to pick out the channel with the greatest probability of availability, which can meet the quality of service (QoS) requirement of urgent communications and effectively avoid conflict with licensed users. Specifically, the spatiotemporal correlations among historical spectrum sensing data are exploited to form prior knowledge of channel availability probability, and Bayesian inference is used to derive posterior probability of channel availability. Comparing with other spectrum detection methods, the proposed algorithm has more than 8 percent detection performance improvement at false alarm probability 0.2, and thus can avoid access conflict with licensed users dramatically. Furthermore, the proposed algorithm always has larger packet reception probability (PRP) and lower transmission delay compared with conventional VANET broadcasting. Hence, the proposed algorithm can improve reliability of safety-message transmission and enhance driving safety significantly.

The wave loads and the resulting motions of floating wave energy converters are traditionally computed using linear radiation–diffraction methods. Yet for certain cases such as survival conditions, phase control and wave energy conver- ters operating in the resonance region, more complete mathematical models such as computational fluid dynamics are preferred and over the last 5 years, computational fluid dynamics has become more frequently used in the wave energy field. However, rigorous estimation of numerical errors, convergence rates and uncertainties associated with computa- tional fluid dynamics simulations have largely been overlooked in the wave energy sector. In this article, we apply formal verification and validation techniques to computational fluid dynamics simulations of a passively controlled point absor- ber. The phase control causes the motion response to be highly nonlinear even for almost linear incident waves. First, we show that the computational fluid dynamics simulations have acceptable agreement to experimental data. We then present a verification and validation study focusing on the solution verification covering spatial and temporal discretiza- tion, iterative and domain modelling errors. It is shown that the dominating source of errors is, as expected, the spatial discretization, but temporal and iterative errors cannot be neglected. Using hexahedral cells with low aspect ratio and 30 cells per wave height, we obtain results with less than 5% uncertainty in motion response (except for surge) and restraining forces for the buoy without phase control. The amplified nonlinear response due to phase control caused a large increase in numerical uncertainty, illustrating the difficulty to obtain reliable solutions for highly nonlinear responses, and that much denser meshes are required for such cases.

Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora. In this work, we test this claim by proposing a new method for exploiting handcrafted features as part of a novel hybrid learning approach, incorporating a feature auto-encoder loss component. We evaluate on the task of named entity recognition (NER), where we show that including manual features for part-of-speech, word shapes and gazetteers can improve the performance of a neural CRF model. We obtain a $F_1$ of 91.89 for the CoNLL-2003 English shared task, which significantly outperforms a collection of highly competitive baseline models. We also present an ablation study showing the importance of auto-encoding, over using features as either inputs or outputs alone, and moreover, show including the autoencoder components reduces training requirements to 60\%, while retaining the same predictive accuracy., Comment: Accepted to EMNLP 2018 (camera-ready)

Based on the function level of a gas turbine generator, this paper presents a method for the quantitative assessment of the health condition of a gas turbine generator by using fuzzy comprehensive assessment (Fuzzy) and an analysis hierarchy process (AHP). First, the failure mode, effects and criticality analysis method (FMECA) is used to construct the assessment framework and obtain the indicator set for the health assessment. The membership degree of each indicator is realized by using triangular and trapezoidal membership functions. Second, the fuzzy comprehensive assessment model with analysis hierarchy process (Fuzzy-AHP) is established to implement the quantitative assessment of the health condition of the gas turbine generator, and the man-made influence on the weight assignment is decreased. Finally, an example of a three-shaft gas turbine generator health assessment is provided to validate and prove that the method is effective and feasible.

The open-source code DualSPHysics is applied to simulate the interaction of sea waves with floating offshore structures, which are typically moored to the seabed, such as vessels, boats, floating breakwaters and wave energy converters (WECs). The goal is to develop a numerical tool that allows the study of the survivability of floating moored devices under highly energetic sea states, obtaining the optimum mooring layout to increase lifetime. The moorings are modelled by coupling DualSPHysics with MoorDyn, a lumped-mass mooring dynamics model. MoorDyn represents mooring line behaviour subject to axial elasticity, hydrodynamic forces in quiescent water, and vertical contact forces with the seabed. Calculated mooring tensions at the fairlead are added as external forces in order to compute the resulting response and motions of the floating structures in DualSPHysics. The coupled model has been validated against data from scale model tests generated during the experimental campaigns for the European MaRINET2 EsflOWC project. In order to evaluate the accuracy of the coupling implementation with the lumped-mass mooring model, free-surface elevation, motions of the floater and mooring tensions are numerically computed and compared to experimental data. Overall, the results demonstrate the accuracy of the coupling between DualSPHysics and MoorDyn to simulate the motion of a moored floating structure under the action of regular waves. Going forward, this modelling approach can be employed to simulate more complex floating structures such as floating wind turbines, buoys, WECs, offshore platforms, etc.

By inserting a thin AlOδ barrier layer between the electrode and tantalum oxide resistive switching layer, the triple-layer ReRAM devices with improved resistive switching performance are demonstrated. Pulsed programming measurements without external current compliance show 1011 cycle endurance without performance degradation. The ON/OFF ratio exceeds 1000. Low operation current is achieved using a smaller SET compliance current. Multilevel states are obtained through adjusting SET and RESET conditions, and retention longer than 104 s at 125°C is reported.

Al/W:AlO x /WO y /W and Pt/AlO δ /Ta 2 O 5-x /TaO y /Pt multiple layers ReRAM devices have been fabricated and carefully studied. Experimental results exhibit significant performance improvement through the insertion of AlO x layer between the switching layer and the top electrode. Operation current is remarkably reduced, ON/OFF ratio is greatly increased, and stable multi-level operations have been successfully achieved. Multiple layers stack engineering has been proved as an efficient method to improve the performances of ReRAM devices.

Resistive random access memories (RRAM) often show large variation due to the stochastic nature of the switching process. Bilayer AlOx/WOx based RRAM cells were investigated for key parameters variations. Those AlOx/WOx memory devices were fabricated in a commercial CMOS foundry. Yield of forming process is studied with different transistor size and forming voltage. The tradeoff between array size, speed and power consumption are discussed.

We report the design and fabrication of W:AlOx/WOx bilayer based resistive switching cells in a standard 0.18 μm CMOS process with only one extra mask. The devices show excellent performance with low power consumption. Low operation voltages (SET voltage

The recently developed inexact Lyapunov inverse iteration method is presented for the analysis of aeroelastic and fluid stability problems with Hopf bifurcations when using computational fluid dynamics in the modelling. The idea is to take the Jacobian matrix and its derivative with respect to an independent parameter, both evaluated at an equilibrium point, to obtain estimates of the critical eigenpair in addition to the critical value of the independent parameter. Prior knowledge of a frequency estimate, required as shift in standard inverse iteration, is not needed. The test cases presented include a two–degrees– of–freedom aerofoil and a flexible wing encountering flutter, and unsteady vortex shedding behind a circular cylinder in low Reynolds number flow.