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To resolve the problem of ductile fracturing of beam flanges in traditional connections with concrete-filled steel tube columns using site-welded side plates, an alternative connection design requiring minimal site welding was designed and studied. An explicit analysis method, considering the fracture strain criterion developed by the stress triaxiality path, was adopted to study the seismic performance and fracture resistance of the proposed connection. The results of a verified finite-element model indicated that the steel beam flange in the connection never fractured. The connection exhibited a notably higher maximum bending moment and ductility than the traditional connection. The side plates in the proposed connection were tension-bending or compression-bending elements, unlike in the traditional connection. The failure modes, maximum bending moment, ductility and stress distribution of the side plates were compared and analysed parametrically. Based on the stress distribution of the side plates according to the finite-element results, a fitting formula related to the width and thickness of the side plates was obtained. A formula to calculate the flexural capacity was also developed according to the force model of the proposed connection. The calculations and finite-element results were in good agreement.

In multiple coordinated views (MCVs), visualizations across views update their content in response to users' interactions in other views. Interactive systems provide direct manipulation to create coordination between views, but are restricted to limited types of predefined templates. By contrast, textual specification languages enable flexible coordination but expose technical burden. To bridge the gap, we contribute Nebula, a grammar based on natural language for coordinating visualizations in MCVs. The grammar design is informed by a novel framework based on a systematic review of 176 coordinations from existing theories and applications, which describes coordination by demonstration, i.e., how coordination is performed by users. With the framework, Nebula specification formalizes coordination as a composition of user- and coordination-triggered interactions in origin and destination views, respectively, along with potential data transformation between the interactions. We evaluate Nebula by demonstrating its expressiveness with a gallery of diverse examples and analyzing its usability on cognitive dimensions.

Vehicle Re-ID has recently attracted enthusiastic attention due to its potential applications in smart city and urban surveillance. However, it suffers from large intra-class variation caused by view variations and illumination changes, and inter-class similarity especially for different identities with the similar appearance. To handle these issues, in this paper, we propose a novel deep network architecture, which guided by meaningful attributes including camera views, vehicle types and colors for vehicle Re-ID. In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes (i.e., camera view, vehicle type and vehicle color). Moreover, to overcome the shortcomings of limited vehicle images of different views, we design a view-specified generative adversarial network to generate the multi-view vehicle images. For network training, we annotate the view labels on the VeRi-776 dataset. Note that one can directly adopt the pre-trained view (as well as type and color) subnetwork on the other datasets with only ID information, which demonstrates the generalization of our model. Extensive experiments on the benchmark datasets VeRi-776 and VehicleID suggest that the proposed approach achieves the promising performance and yields to a new state-of-the-art for vehicle Re-ID.

Cartoon is a common form of art in our daily life and automatic generation of cartoon images from photos is highly desirable. However, state-of-the-art single-style methods can only generate one style of cartoon images from photos and existing multi-style image style transfer methods still struggle to produce high-quality cartoon images due to their highly simplified and abstract nature. In this article, we propose a novel multi-style generative adversarial network (GAN) architecture, called MS-CartoonGAN, which can transform photos into multiple cartoon styles. MS-CartoonGAN uses only unpaired photos and cartoon images of multiple styles for training. To achieve this, we propose to use (1) a hierarchical semantic loss with sparse regularization to retain semantic content and recover flat shading in different abstract levels, (2) a new edge-promoting adversarial loss for producing fine edges, and (3) a style loss to enhance the difference between output cartoon styles and make training process more stable. We also develop a multi-domain architecture, where the generator consists of a shared encoder and multiple decoders for different cartoon styles, along with multiple discriminators for individual styles. By observing that cartoon images drawn by different artists have their unique styles while sharing some common characteristics, our shared network architecture exploits the common characteristics of cartoon styles, achieving better cartoonization and being more efficient than single-style cartoonization. We show that our multi-domain architecture can theoretically guarantee to output desired multiple cartoon styles. Through extensive experiments including a user study, we demonstrate the superiority of the proposed method, outperforming state-of-the-art single-style and multi-style image style transfer methods.

A concern with the isolation transformer in a dual active bridge (DAB) dc–dc converter is the dc bias in magnetization. This article proposes a fluxgate-based current sensor to measure the dc component mixed with a large, high-frequency ac current. Compared with a commercial Hall effect current sensor, the proposed sensor significantly reduces measurement error. This article presents the working principle and design considerations. A prototype is demonstrated for dc bias elimination control in a DAB converter.\ud

Contains fulltext : 248871.pdf (Publisher’s version ) (Open Access) BACKGROUND: Previous studies showed that lifestyle behaviors (cigarette smoking, alcohol, coffee) are inversely associated with Parkinson's disease (PD). The prodromal phase of PD raises the possibility that these associations may be explained by reverse causation. OBJECTIVE: To examine associations of lifestyle behaviors with PD using two-sample Mendelian randomisation (MR) and the potential for survival and incidence-prevalence biases. METHODS: We used summary statistics from publicly available studies to estimate the association of genetic polymorphisms with lifestyle behaviors, and from Courage-PD (7,369 cases, 7,018 controls; European ancestry) to estimate the association of these variants with PD. We used the inverse-variance weighted method to compute odds ratios (ORIVW) of PD and 95%confidence intervals (CI). Significance was determined using a Bonferroni-corrected significance threshold (p = 0.017). RESULTS: We found a significant inverse association between smoking initiation and PD (ORIVW per 1-SD increase in the prevalence of ever smoking = 0.74, 95%CI = 0.60-0.93, p = 0.009) without significant directional pleiotropy. Associations in participants ≤67 years old and cases with disease duration ≤7 years were of a similar size. No significant associations were observed for alcohol and coffee drinking. In reverse MR, genetic liability toward PD was not associated with smoking or coffee drinking but was positively associated with alcohol drinking. CONCLUSION: Our findings are in favor of an inverse association between smoking and PD that is not explained by reverse causation, confounding, and survival or incidence-prevalence biases. Genetic liability toward PD was positively associated with alcohol drinking. Conclusions on the association of alcohol and coffee drinking with PD are hampered by insufficient statistical power.

With the worldwide rapidly increasing urbanization, the demand for heating increases too. To meet the substantially higher heating-demand, traditionally, the number of gas-fired boilers is simply increased. Unfortunately, this also causes severe air pollution. A viable alternative is solar heating systems that could be added to the existing gas-fired boilers system. This study investigates a heating system, which combines solar heating and existing gas-fired boiler heating into one system. The optimal individual capacity of the system is determined and the operation characteristics, energy saving, and economics are analyzed. The results showed that the combined heating system has a clear advantage with respect to both energy saving and economics over the traditional solution (gas-fired boiler-capacity expansion). For an expansion rate of 20%, for example, the average energy-saving rate is as high as 69%. With increasing capacity, the difference per unit heating-area cost between the two systems increases. This means that the combined heating system is even more beneficial when the capacity increase is high. This study shows a new way to meet increased heating demand effectively, which is especially useful for cities that require a significantly-increased heating capacity.

Objectives: This study was to investigate the expression and clinical significance of long intergenic noncoding RNA 00665 (LINC00665) in ovarian cancer , as well as its effect on malignant biological behavior of ovarian cancer cells. Methods: The expressions of LINC00665, miR-148b-3p and KLF5 in ovarian cancer (OC) tissues and cells were detected by RT-qPCR. Western blotting was used to detect KLF5 expression. The expression patterns of LINC00665, U6 and GAPDH in nuclear and cytoplasm fractions were detected using qRT-PCR. Besides, CCK-8 assay, clone formation assay, transwell, scratch test, and flow cytometry were respectively used to detect the cell activity, proliferation, invasiveness, healing of cells, and apoptosis rate of OC cells. Furthermore, the interactions between LINC00665 and miR-148b-3p and between miR-148b-3p and KLF5 were verified by luciferase reporter assay, and the correlations among these three genes were analyzed.Results: LINC00665 expression was up-regulated in ovarian cancer cell lines and tissues. Si-LINC00665 inhibited cell proliferation, invasion and migration, and induced the apoptosis to a certain extent. Subcellular fraction assay revealed the location of LINC00665 was mainly situated in the cytoplasm. Besides, miR-148b-3p was a target of LINC00665, and KLF5 was directly targeted by miR-148b-3p. Si-LINC00665 inhibited KLF5 expression, miR-148b-3p inhibitor promoted KLF5 expression, and si-KLF5 inhibited LINC00665 expression. Interestingly, expressions of LINC00665 and miR-148b-3p, and expressions of miR-148b-3p and KLF5 were negatively correlated, while expressions of LINC00665 and KLF5 were positively correlated. In addition, si-KLF5 inhibited the malignant biological behavior of ovarian cancer cells, while miR-148b-3p inhibitor had the opposite effect. Most importantly, the si-LINC00665 could reverse the promotion effect of miR-148b-3p inhibitor on the malignant biological behavior of ovarian cancer cells.Conclusions: LINC00665 can be used as an effective prognostic indicator of ovarian cancer, which has the potential to be a new drug target.

The intersection is a critical traffic problem from the perspective of safety and traffic efficiency. As wireless communication technology advances, vehicle infrastructure cooperative approaches have received increased attention. In this paper, we propose a cooperative critical turning point method to help the cooperation between vehicles and infrastructures to improve the traffic efficiencies. The idea of cooperative critical turning point improves the cooperation between connected automated vehicles, the surrounding traffic and roadside infrastructures in order to provide high efficiencies of the intersection. An intersection management system using such a method is implemented based on the framework of the connected automated vehicle highway system. Such system can efficiently allow a roadside infrastructure receives state information from vehicles, reserve the associated intersection time-space occupancy, and then provide decision-making and planning feedback to the vehicles. The vehicles covered by the system then adjust their trajectories to meet their assigned time slot. The study validates the proposed system that considers the uncertainties of the driving environment by formulating the problem into a POMDP problem and solves it using an online solver. Based on preliminary simulation experiments, the proposed strategy can significantly reduce travel delays, decrease stops and improve the sustainability of the traffic system.

Background Reports have suggested an association between the development of myocarditis and the receipt of messenger RNA (mRNA) vaccines against coronavirus disease 2019 (Covid-19), but the frequency and severity of myocarditis after vaccination have not been extensively explored. Methods We searched the database of Clalit Health Services, the largest health care organization (HCO) in Israel, for diagnoses of myocarditis in patients who had received at least one dose of the BNT162b2 mRNA vaccine (Pfizer–BioNTech). The diagnosis of myocarditis was adjudicated by cardiologists using the case definition used by the Centers for Disease Control and Prevention. We abstracted the presentation, clinical course, and outcome from the patient’s electronic health record. We performed a Kaplan–Meier analysis of the incidence of myocarditis up to 42 days after the first vaccine dose. Results Among more than 2.5 million vaccinated HCO members who were 16 years of age or older, 54 cases met the criteria for myocarditis. The estimated incidence per 100,000 persons who had received at least one dose of vaccine was 2.13 cases (95% confidence interval [CI], 1.56 to 2.70). The highest incidence of myocarditis (10.69 cases per 100,000 persons; 95% CI, 6.93 to 14.46) was reported in male patients between the ages of 16 and 29 years. A total of 76% of cases of myocarditis were described as mild and 22% as intermediate; 1 case was associated with cardiogenic shock. After a median follow-up of 83 days after the onset of myocarditis, 1 patient had been readmitted to the hospital, and 1 had died of an unknown cause after discharge. Of 14 patients who had left ventricular dysfunction on echocardiography during admission, 10 still had such dysfunction at the time of hospital discharge. Of these patients, 5 underwent subsequent testing that revealed normal heart function. Conclusions Among patients in a large Israeli health care system who had received at least one dose of the BNT162b2 mRNA vaccine, the estimated incidence of myocarditis was 2.13 cases per 100,000 persons; the highest incidence was among male patients between the ages of 16 and 29 years. Most cases of myocarditis were mild or moderate in severity. (Funded by the Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute.)

Registration between phases in 4D cardiac MRI is essential for reconstructing high-quality images and appreciating the dynamics. Complex motion and limited image quality make it challenging to design regularization functionals. We propose to introduce a motion representation model (MRM) into a registration network to impose customized, site-specific, and spatially variant prior for cardiac motion.We propose a novel approach to regularize deep registration with a deformation vextor field (DVF) representation model using computed tomography angiography (CTA). In the form of a convolutional auto-encoder, the MRM was trained to capture the spatially variant pattern of feasible DVF Jacobian. The CTA-derived MRM was then incorporated into an unsupervised network to facilitate MRI registration. In the experiment, 10 CTAs were used to derive the MRM. The method was tested on 10 0.35 T scans in long-axis view with manual segmentation and 15 3 T scans in short-axis view with tagging-based landmarks.Introducing the MRM improved registration accuracy and achieved 2.23, 7.21, and 4.42 mm 80% Hausdorff distance on left ventricle, right ventricle, and pulmonary artery, respectively, and 2.23 mm landmark registration error. The results were comparable to carefully tuned SimpleElastix, but reduced the registration time from 40 s to 0.02 s. The MRM presented good robustness to different DVF sample generation methods.The model enjoys high accuracy as meticulously tuned optimization model and the efficiency of deep networks.The method enables model to go beyond the quality limitation of MRI. The robustness to training DVF generation scheme makes the method attractive to adapting to the available data and software resources in various clinics.

This study aimed to determine the pooled incidence, risk factors, and clinical prognosis of tricuspid regurgitation (TR) deterioration after implantation of a cardiac implantable electronic device (CIED). The study was designed as a meta-analysis of randomized controlled trials and observational studies. Patients with indications for CIEDs were selected as participants and CIED implantation was the intervention. PubMed, EMBASE, the Cochrane Library, China National Knowledge Infrastructure, Wanfang Data, and China Science and Technology Journal Database were searched systematically to identify studies. Thirty-seven studies with 8,144 patients were included. The pooled incidence of TR deterioration of at least one grade was 25.1% (95% confidence interval [CI], 20.9-29.3; Z = 11.60; p one year after pacemaker implantation) was higher in patients with TR deterioration (hazard ratio, 1.598; 95% CI, 1.275-2.002; Z = 4.07; p

Cross-spectral recognition is still an open challenge in iris recognition. In cross-spectral iris recognition, there exist distinct device-specific bands between near-infrared (NIR) and visible (VIS) images, resulting in the distribution gap between samples from different spectra and thus severe degradation in recognition performance. To tackle this problem, we propose a new cross-spectral iris recognition method to learn spectral-invariant features by estimating device-specific bands. In the proposed method, Gabor Trident Network (GTN) first utilizes the Gabor function’s priors to perceive iris textures under different spectra, and then codes the device-specific band as the residual component to assist the generation of spectral-invariant features. By investigating the device-specific band, GTN effectively reduces the impact of device-specific bands on identity features. Besides, we make three efforts to further reduce the distribution gap. First, Spectral Adversarial Network (SAN) adopts a class-level adversarial strategy to align feature distributions. Second, Sample-Anchor (SA) loss upgrades triplet loss by pulling samples to their class center and pushing away from other class centers. Third, we develop a higher-order alignment loss to measures the distribution gap according to space bases and distribution shapes. Extensive experiments on five iris datasets demonstrate the efficacy of our proposed method for cross-spectral iris recognition.

The purpose of this study was to investigate whether differences in the pattern of the lingual plate split in sagittal split ramus osteotomy (SSRO) affect the remodelling of the split site. Sixty-one patients with mandibular prognathism (122 sides) underwent SSRO. Computed tomography (CT) was performed at 1 week and 1 year after SSRO. Bone splits were classified according to the lingual split scale (LSS) and the lateral bone cut end (LBCE) by evaluating CT images at 1 week. The remodelling at the split sites was evaluated by superimposing the CT images obtained at 1 week and 1 year. Regarding the LSS pattern, significant differences were observed in the distance between anteroposterior ramus points (P = 0.033) and the ramus area in the axial image (P = 0.011). The LBCE pattern also showed a significant difference in the distance between anteroposterior ramus points (P = 0.043). In conclusion, the differences in the lingual plate split and ramus cut end of the SSRO influence the postoperative remodelling in the anteroposterior region of the split site.

BackgroundCalcific aortic valve disease (CAVD) is the most frequent pathogeny of aortic valve replacement in developed countries. Iron deposits are found in the intraleaflet hemorrhage (IH) areas of calcific aortic valves. Ferroptosis is a form of regulated cell death that involves metabolic dysfunction resulting from iron overload-dependent excessive lipid peroxidation. In this research, we attempted to clarify the role of ferroptosis in CAVD.MethodsThe level of ferroptosis in tissue and valvular interstitial cells (VICs) was assessed by the contents of 4-HNE, NADPH, ROS, and GSH, lipid peroxidation and mitochondrial morphology. The levels of calcification, iron accumulation and Slc7a11 expression in surgical aortic valve specimens were detected by Alizarin red or Von Kossa, Perl’s blue and immunohistochemical staining. The osteogenic differentiation of VICs was assessed by PCR and western blot analyses. Furthermore, RNA sequencing was used to detect potential differentially expressed genes between normal and osteogenic medium-treated (OM-treated) VICs.ResultsOur experiments demonstrated that ferroptosis occurred in the IH areas of calcific aortic valves. We also found that Slc7a11 was expressed at low levels in OM-treated VICs and IH areas. Finally, we demonstrated that iron promoted Slc7a11-deficient VICs osteogenic differentiation by aggravating ferroptosis in vitro.ConclusionIn conclusion, iron promotes Slc7a11-deficient VIC osteogenic differentiation by aggravating ferroptosis in vitro, thereby accelerating the progression of aortic valve calcification.StatementStatements and opinions expressed in the articles and communications herein are those of the author(s) and not necessarily those of the Editor(s), Society, or publisher, and the Editor(s), Society, and publisher disclaim any responsibility or liability for such material.Brief SummaryIn this work, two novel notions have been proposed. First, we reported that ferroptosis participated in the progression of CAVD. Second, this is the first cytology experiment of valvular interstitial cells (VICs) to clarify the mechanism by which intraleaflet hemorrhage aggravates calve calcification. This research provides new ideas and targets for alleviating the progression of CAVD, especially in patients who have calcified aortic valves without severe stenosis.

To compare the rates of schizophrenia among 1st and 2nd generation immigrants from two distinct backgrounds and across sequential periods of immigration.A 30-years retrospective cohort study (187,184 individuals) of 1st and 2nd generation East-African immigrants (EAIs) and former Soviet-Union immigrants (FSUIs) who migrated to Israel between 1980 and 2012. EAIs were further divided according to waves of immigration. Period prevalence was calculated between the years 2002-2012. Multivariate logistic regression models were used to examine the association between immigration-related factors and prevalence of schizophrenia (Native-Born Israelis serving as reference group).The prevalence of schizophrenia in 1st generation EAIs and FSUIs was 1.8% and 1.2%, respectively, compared to 1.0% among NBIs (p0.001). The prevalence of schizophrenia among 2nd generation EAIs and FSUIs was 1.3% and 0.8%, respectively, compared to 0.6% among NBIs (p0.001). Adjusted odds ratios for developing schizophrenia compared to NBIs were 1.6 (95%CI:1.4-1.8) and 2.1 (95%CI:1.6-2.7), among 1st and 2nd generation EAIs and 1.1 (95%CI:0.9-1.2) and 1.3 (95%CI:1.0-1.8) among 1st and 2nd generation FSUIs respectively. Among EAIs, we observed the highest rate of schizophrenia in the pioneer wave of immigrants with gradual decline across subsequent waves: 2.4%, 1.9% and 1.0% for the 1st, 2nd and 3rd waves of immigration, respectively (p0.001).The increased risk for developing schizophrenia among 2nd generation immigrants and among pioneer groups of immigrants emphasizes the importance of persistent investment in acculturation. Further studies elucidating the impact of country of origin and ethnic density on the risk for developing schizophrenia are warranted.

Prefabricated pavement is increasingly applied worldwide due to the rapid construction on-site. This paper presents a state-of-the-art review of the precast systems and assembly connection of concrete slabs, as well as the precast pavement features. The previous research indicates that (1) Both Super-Slab and Michigan systems are recommended with the satisfied road performance close to the cast-in-place. (2) Flexible base material is suggested for the fabricated pavement for the satisfied leveling and stress distribution. (3) To prevent the voiding phenomenon of the fabricated pavement, the sand cushion course, dry mixed mortar or self-leveling mortar, and other flowable materials could be used for the secondary leveling of the base after the pavement splicing. (4) Two-direction dowel bars are recommended for slab connections of the fabricated pavement, which helps to improve the load transfer capacity of the joints and enhance the durability of the fabricated pavement structure. (5) The sealing treatment of precast slab joints needs strengthening to reduce the impact of surface runoff on the base course. (6) The further research focuses are designing with functional, composite, mechanized, intelligent, lightweight, and flexible pavement slabs. Besides, pavement mechanical properties induced by temperature overlapping traffic loads need to be revealed.

Lane-level traffic state prediction is one of the most essential issues in the connected automated vehicle highway systems. Accurate and timely traffic state prediction of the lane sections can assist the connected automated vehicles in planning the optimal route and making lane selection. In this article, we tackle the problem of forecasting lane-level short-term traffic speed and propose a novel mixed deep learning (MDL) model by coordinating the convolutional long short-term memory (Conv-LSTM) layers, convolutional layers, and a dense layer in an end-to-end structure. The introduction of the Conv-LSTM neural network enables the proposed MDL model to better capture the spatio-temporal characteristics and correlations of the dynamic lane-based traffic flow synchronously. To improve the efficiency of the proposed model, a feature correlation analysis method based on the maximum information coefficient is presented to measure the relevance between the historical traffic flows and the traffic speeds to be forecasted. Validated by the ground-truth traffic flow data collected by the remote traffic microwave sensors installed on the expressways in Beijing, the MDL model is capable of capturing the fluctuation of the lane-level traffic speeds at different types of lanes effectively during the whole day. Furthermore, the results confirm that the MDL model achieves better predictive performance than several state-of-the-art benchmark models in terms of prediction accuracy and space-time distributions. Our code and data are available at https://github.com/lwqs93/MDL.

In this communication, we propose glide-symmetric double-layer spoof surface plasmon polaritons (SSPPs) structures for controlling the field confinement of SSPPs to achieve antenna beam manipulation with a large frequency-scanning angle. By changing the shape of the glide-symmetric double-layer SSPPs, we can control the electric field confinement of the SSPPs to emerge symmetric or asymmetric distribution. Hence, the proposed SSPPs structure can produce electromagnetic leakage on both sides or single side, furtherly achieving dual-beam or single-beam radiation. Two SSPPs antennas with dual beams and single beam are designed, fabricated, and measured. Both the dual-beam antenna and single-beam antenna operate from 9 GHz to 24 GHz. Within the working band, the dual-beam antenna accomplishes 134.3° (-90° to +44.3°) beam-scanning angle with the realized gain of 8.03-12.17 dBi, while the single-beam antenna achieves 119° (-77.9° to 41.1°) beam-scanning angle with 10.31-14.24 dBi realized gain. The simulated and experimental results verified the beam manipulation and large frequency-scanning angle in antenna design.

Refereed/Peer-reviewed This study employs Q methodology to identify responses to wildfire imagery and climate change concern. Using photographs shared on Twitter during California’s 2018 Camp Fire, findings reveal three different perspectives on what images evoked the most concern among individuals: catastrophic destruction, smokescapes, and human or animal suffering. Results also reveal the images that evoke the least concern, such as scenes of aid and support and officials’ rescue meetings. These results extend literature on the role of visuals in climate change communication and have implications for the selection of images that appeal to the various perspectives of the broader public.

Energy harvesting technology has been applied to power nodes in a wireless sensor networks (WSNs). However, it suffers from the dynamic nature of the ambient energy. Wireless charging, on the contrary, offers an additional choice to charge the sensor nodes (SNs) with a mobile charger (MC) which is often implemented to move within the network. Nevertheless, the energy spent on moving among sensors accounts for a considerable proportion of the whole energy the MC can take. To this end, we consider combining these two sources of energy to carry out the energy neutral operation for the WSNs. In this paper, we formulate the problem of minimizing the number of nodes for perpetual coverage of targets under the constraint of the energy capacity of a MC and the distribution of the energy harvesting rate across the monitoring field which generates great difficulty. We designed two approximation algorithms to tackle this problem. We conduct extensive simulations to evaluate the performance of our proposed algorithms. The results showed performance of these two algorithms with respect to impact of different parameters.

Introduction: Immune checkpoint blockade of programmed death-1 (PD-1) receptor and its ligand (PD-L1) has contributed to efficacy in many tumor types, with clinical responses observed in a proportion of patients (pts) with Hodgkin lymphoma and rare non-Hodgkin lymphoma subtypes. A recent study demonstrated that combination treatment with anti-PD-L1 and anti-CD38 agents contributed to a stronger anti-tumor immune response compared with anti-PD-L1 monotherapy. Isatuximab, an anti-CD38 monoclonal antibody, is approved for use in multiple myeloma. Cemiplimab, an anti-PD-1 monoclonal antibody, is approved for use in cutaneous squamous cell carcinoma, basal cell carcinoma, and non-small cell lung cancer. Methods: This Phase 1/2 open-label study (NCT03769181) was designed to assess the safety, tolerability, and efficacy of isatuximab in combination with cemiplimab (Isa+Cemi) in pts with relapsed and refractory classic Hodgkin's lymphoma (cHL), diffuse large B-cell lymphoma (DLBCL), and peripheral T-cell lymphoma (PTCL). The primary objectives of Phase 1 were to characterize the safety and tolerability of Isa+Cemi and to confirm the recommended Phase 2 dose. Phase 2 used a Simon's 2-stage design to assess the complete response rate in Cohort A1 (anti-PD-1/PD-L1 naïve cHL; n=18; median age, 36 years; 55.6% male; ≥2 prior regimens, 100%) and to assess the objective response rate in Cohorts A2 (cHL progressing after PD-1/PD-L1 therapy; n=12; median age, 33 years; 58.3% male; ≥2 prior regimens, 100%), B (DLBCL; n=17; median age, 64 years; 70.6% male; ≥2 prior regimens, 100%), and C (PTCL; n=11; median age, 69 years; 63.6% male; ≥2 prior regimens, 9.1%). Pts received Isa+Cemi for up to 96 weeks. In Phase 1, the isatuximab dose was 10 mg/kg every week (Cycle 1), every 2 weeks (Cycle 2-6), or every 3 weeks (Cycle 7+). The cemiplimab dose was 250 mg every 2 weeks (Cycle 1-6) or 350 mg every 3 weeks (Cycle 7+). An interim analysis was performed when the last pt in Phase 2 was followed up for 24 weeks. The efficacy evaluation was based on Simon's 2-stage design with 85% power at a 5% 1-sided alpha level for each cohort. At least 8 (44.4%) and 3 (30.0%) responses were required in Cohorts B and C, respectively, in Phase 2 Stage 1 to advance to Phase 2 Stage 2. Results: Isa+Cemi demonstrated a manageable safety profile with no new safety signals. No dose-limiting toxicities were observed, confirming the recommended Phase 2 dose. Treatment-emergent adverse events (TEAEs) were reported in 83.3% (Cohort A1) and 100% (Cohorts A2, B, C) of pts. Grade ≥3 TEAEs occurred in 5.6%, 8.3%, 70.6%, and 81.8% of pts in Cohorts A1, A2, B, and C, respectively. There were no pts in Cohorts A1 or A2 who reported TEAEs leading to definitive discontinuation; 5.9% and 27.3% of pts in Cohorts B and C experienced TEAEs leading to definitive discontinuation. No Grade 5 TEAEs with fatal outcome were reported in Cohorts A1 or A2. There were 4 deaths reported during the on-treatment period in Cohort B (progressive disease, n=2; intestinal perforation, n=1; urinary tract infection, n=1) and 2 in Cohort C (unknown, n=1; progressive disease, n=1). Infusion reactions were reported in 38.9%, 75.0%, 52.9%, and 72.7% of pts in Cohorts A1, A2, B, and C, respectively; there was 1 (9.1%) Grade ≥3 infusion reaction reported in Cohort C. Pharmacokinetics (PK) analyses suggested no effect of cemiplimab on isatuximab PK, and vice versa. Based on Lugano 2014 criteria, 55.6% (Cohort A1), 33.3% (Cohort A2), 5.9% (Cohort B), and 9.1% (Cohort C) of pts in the all-treated population achieved a complete or partial response. Median progression-free survival was 8.38 months (95% CI: 2.72-not calculable [NC]), 8.28 months (95% CI: 2.6-NC), 2.37 months (95% CI: 0.46-2.69), and 2.66 months (95% CI: 0.43-2.99) in Cohorts A1, A2, B, and C, respectively. Conclusion: In this study, Isa+Cemi had a manageable safety profile. Clinical efficacy was observed in pts with cHL, with increased responses observed in pts who had not previously received anti-PD-1/PD-L1 therapy compared with those who progressed on anti-PD-1/PD-L1 therapy. For Cohorts B (DLBCL) and C (PTCL), results of the interim efficacy analysis did not meet prespecified criteria to continue enrollment in Phase 2 Stage 2. Most pts with DLBCL were primary refractory/bulky and discontinued rapidly, which may have contributed to the lack of activity with this combination. Disclosures Carlo-Stella: ADC Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Honoraria; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen Oncology: Honoraria; Celgene: Membership on an entity's Board of Directors or advisory committees; Karyopharm Therapeutics: Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy, Research Funding. Zinzani: TG Therapeutics Inc: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kyowa Kirin: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sandoz: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; ImmuneDesign: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Verastem: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Servier: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celltrion: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Merck Sharp & Dohme: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; EUSA Pharma: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; ADC Therapeutics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; BeiGene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Portola: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Sureda: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Support for attending meetings and/or travel, Research Funding, Speakers Bureau; GSK: Consultancy, Honoraria, Speakers Bureau; Roche: Other: Support for attending meetings and/or travel; Bluebird: Membership on an entity's Board of Directors or advisory committees; Mundipharma: Consultancy; Kite, a Gilead Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; BMS/Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Support for attending meetings and/or travel, Speakers Bureau; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Casasnovas: TAKEDA: Consultancy, Research Funding; Gilead/Kite: Consultancy, Research Funding; BMS: Consultancy; Janssen: Consultancy; Amgen: Consultancy; ROCHE: Consultancy, Research Funding. Carpio: Regeneron, TAKEDA, Celgene, Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travels and accommodation. Bouabdallah: Abbvie: Honoraria; Sandoz: Consultancy, Honoraria; Kite/Gilead: Consultancy, Honoraria, Other: Travel/Accommodations/Expenses; Roche: Consultancy, Honoraria, Other: Travel/Accommodations/Expenses; Takeda: Consultancy, Honoraria, Other: Travel/Accommodations/Expenses. Cartron: Roche, Celgene-BMS: Consultancy; Danofi, Gilead, Novartis, Jansen, Roche, Celgene-BMS, Abbvie, Takeda: Honoraria. Kim: Celltrion: Research Funding; Dong-A Pharmaceutical: Research Funding; Kyowa Kirin: Research Funding; Sanofi: Research Funding; IGM Biosciences: Research Funding; Eisai: Research Funding; Johnson & Johnson: Research Funding; Roche: Research Funding. Cordoba: Incyte: Membership on an entity's Board of Directors or advisory committees; Pfizer: Research Funding; Kyowa-Kirin: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kite: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; ADCTherapeutics: Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Koh: Pfizer: Consultancy; Jassen: Honoraria; AstraZeneca: Honoraria; Novartis: Honoraria; GSK: Honoraria; Roche: Honoraria; Takeda: Honoraria. Alves: Janssen, Cilag, Gilead, Takeda, Astrazeneca, Roche, Abbvie: Consultancy, Honoraria. Chamuleau: Gilead: Research Funding; Genmab: Research Funding; Celgene: Research Funding. Lopez-Guillermo: Roche, Gilead/Kite, Celgene, Novartis, Janssen, AbbVie, Spectrum: Consultancy, Honoraria, Research Funding. Van Der Poel: Roche, Janssen, Abbvie: Honoraria. Abbadessa: Sanofi: Current Employment. Meng: Sanofi: Current Employment. Ji: Sanofi: Current Employment. Lepine: Sanofi: Other: Contractual relationship. Saleem: Sanofi: Current Employment. Ribrag: PharmaMar: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Research Funding; Epizyme: Honoraria, Research Funding; Servier: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; MSD Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Argen-X: Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Nanostring: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Astex Pharmaceuticals: Research Funding; Infinity Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees. OffLabel Disclosure: Based on the Phase III ICARIA-MM study, isatuximab (Sarclisa) is approved in a number of countries in combination with pomalidomide and dexamethasone for the treatment of adult patients with relapsed/refractory multiple myeloma who have received at least two prior therapies, including lenalidomide and a proteasome inhibitor. Based on the Phase III IKEMA study, isatuximab in combination with carfilzomib and dexamethasone is approved in the United States for the treatment of adult patients with relapsed or refractory multiple myeloma who have received one to three prior lines of therapy, and in the European Union for the treatment of adult patients with multiple myeloma who have received at least one prior therapy. Cemiplimab (Libtayo) is an anti-PD-1 antibody approved for the treatment of the following: 1) patients with metastatic cutaneous squamous cell carcinoma or locally advanced cutaneous squamous cell carcinoma who are not candidates for curative surgery or curative radiation; 2) patients with locally advanced or metastatic basal cell carcinoma previously treated with a hedgehog pathway inhibitor or for whom a hedgehog pathway inhibitor is not appropriate; and 3) patients with NSCLC and high tumor PD-L1 expression as determined by an FDA-approved test, with no EGFR, ALK, or ROS1 aberrations, and is locally advanced where patients are not candidates for surgical resection or definitive chemoradiation or metastatic.

Takotsubo cardiomyopathy (TCMP) is an important, though under-recognised, syndrome which mimics acute coronary syndrome (ACS) presenting with similar clinical, biochemical and ECG features. A 68-year-old man was referred as ACS for emergency coronary angiography; however, a history of lethargy, weight loss and electrolyte abnormalities prompted further investigations. Angiography was postponed, adrenal insufficiency confirmed and steroid replacement commenced. Echocardiography demonstrated reduced left ventricular (LV) function (45%) with regional wall motion abnormalities, although angiography confirmed unobstructed arteries. Steroid replacement induced a rapid improvement in symptoms and LV function. Few cases of TCMP associated with adrenal insufficiency have been reported. This appears to be the first case describing TCMP precipitated by new-onset secondary adrenal insufficiency following long-term steroid use in a male patient, and highlights the importance of considering TCMP in patients presenting with suspected ACS. Here, prompt recognition and treatment of a serious underlying disorder prevented a potentially life-threatening Addisonian crisis.

Purpose: The scoring system for traumatic liver injury (SSTLI) was developed in 2015 to predict mortality in patients with polytraumatic liver injury. This study aimed to validate the SSTLI as a prognostic factor in patients with polytrauma and liver injury through a generalized estimating equation analysis. Methods: The medical records of 521 patients with traumatic liver injury from January 2015 to December 2019 were reviewed. The primary outcome variable was in-hospital mortality. All the risk factors were analyzed using multivariate logistic regression analysis. The SSTLI has five clinical measures (age, Injury Severity Score, serum total bilirubin level, prothrombin time, and creatinine level) chosen based on their predictive power. Each measure is scored as 0–1 (age and Injury Severity Score) or 0–3 (serum total bilirubin level, prothrombin time, and creatinine level). The SSTLI score corresponds to the total points for each item (0–11 points). Results: The areas under the curve of the SSTLI to predict mortality on post-traumatic days 0, 1, 3, and 5 were 0.736, 0.783, 0.830, and 0.824, respectively. A very good to excellent positive correlation was observed between the probability of mortality and the SSTLI score (γ=0.997, P

As the most common cause of dementia, the study of Alzheimer’s disease (AD) faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnosis and effective treatment. Rapid and accurate identification of AD biomarkers in the brain is critical to provide key insights into AD and facilitate the development of early diagnosis methods. In this work, we developed a platform that enables a rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, the accuracy was significantly increased from 77% to 98% in machine learning classification. Further, using linear supporting vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra. Based on this spectral feature importance map, we identified AD biomarkers including Aβ and tau proteins, and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies. Our Raman-machine learning integrated method with interpretability is promising to greatly accelerate the study of AD and can be extended to other tissues, biofluids, and for various other diseases.

Inertial navigation system (INS) is a practical method for indoor pedestrian navigation without pre-installation of infrastructure. Based on the fundamentals of human bipedal motion, zero velocity update (ZUPT) is a pervasive approach to tackle the accumulated error of inertial measurement units (IMU). While zero velocity detection plays a vital role in the algorithm, existing fixed-threshold methods to pick these pseudo-measurements of error-state Kalman Filter (ESKF) have the doubtful capability to fit various individuals in different motions. To address this issue, we propose the Symmetrical-Net leveraging deep Recurrent Convolutional Neural Networks (RCNNs) to detect the zero velocity interval adaptively. Additionally, two RCNNs are constructed in the symmetrical framework considering bidirectional IMU readings, which further improves the performance of the model. A comprehensive evaluation containing 87 different trajectories from 27 individuals has been conducted. The results show that the detection accuracy is up to 99.5% and 96.5% on the training and validation sets, respectively. It is verified that the precise and robust Symmetrical-Net can be a viable approach for the ZUPT-aided INS system.

In order to improve the detection effect and simplify the number of sensors of electronic nose system for wound infection detection, the sensor arrays are optimized based on Hilbert-Schmidt independence criterion (HSIC) in this paper. Specifically, the HSIC optimization algorithm of Gaussian kernel function is exploited. The criterion of Hilbert-Schmidt independence is obtained by empirical estimation of norm of Hilbert-Schmidt cross-covariance operator. The main idea of its application in sensor array optimization is that the larger the empirical norm estimates of Hilbert-Schmidt cross-covariance operator, the stronger the correlation between sensor features and labels, the greater the role of sensor features in classification and recognition. The experimental results show that the HSIC optimization method (Gaussian kernel function) performs better than the linear discriminant analysis (LDA) method and the principal component analysis (PCA).

Ports are major energy consumers and carbon emission producers. Developing integrated port energy systems (IPES) is the key to solve this problem. The energy forms in an IPES include electricity, hydrogen, heat and natural gas. Hydrogen can be generated from surplus renewable energies via water electrolysis, and thus power the previously oil-fueled equipment. Meanwhile, the remaining hydrogen can be converted to natural gas by the methanation reaction. By using fuel cells, gas turbine, hydrogen boiler and gas boiler, hydrogen and natural gas can supply the electrical and heat loads in the port area, forming a closed-loop of energy flow. Based on the above energy coupling structure, a day-ahead optimal economic dispatching model for IPES is established. The model takes the energy conversion loss and carbon dioxide emissions into account. The second order conic relaxation is used to deal with the nonlinear part of power flow calculation. The linearization model for connecting heat source and heat load is built. Numerical results demonstrate that the proposed economic dispatching model effectively reduces the total energy cost and carbon dioxide emissions of IPES.

BACKGROUND Emerging data suggest increased arterial thrombosis risk in the months preceding a cancer diagnosis. OBJECTIVES Assess whether patients without documented vascular risk factors or pre-existent cardiovascular disease have a higher relative risk of cancer 12 months after arterial thrombotic events (ATE), compared to unselected patients. PATIENTS/METHODS A population-based cohort study of Clalit Health Services (CHS) database included CHS members ≥25 years without prior cancer or ATE (n=2804584). An iterative matching process selected 10 potential controls chronologically for each consecutive exposed, age- and sex-matched (actual controls drawn 1:1 from a lot). Study exposure, ATE, was defined as ischemic stroke, transient ischemic attack, myocardial infarction or systemic arterial thromboembolism during hospitalization. The outcome was newly-diagnosed cancer within 12 months, based on Israeli national cancer registry. Cox proportional hazards multivariate regression calculated hazard ratio (HR) for outcomes, adjusted for cancer risk factors. Analysis also performed for 3 subgroups: age≤50 years; no cardiovascular risk factors; no prior cardiovascular disease. RESULTS The full ATE and matched control cohorts included 43108 patients. The 12-month cumulative incidence of cancer (95% confidence interval) was 0.020 (0.019-0.022) in the ATE cohort and 0.012 (0.011-0.013) in controls, corresponding to an adjusted HR of 1.665 (1.489-1.862). The relative risk of cancer was high in all subgroups up to a HR of 3.754 (1.912-7.372) in patients without cardiovascular risk factors. CONCLUSION There is an increased risk of previously undiagnosed cancer at 12 months after ATE, especially in patients without documented vascular risk factors or pre-existent cardiovascular disease.

As a constrained optimization strategy, model predictive control (MPC) is widely used in the optimal dispatch of microgrids. Rolling optimization and feedback correction strategy can effectively deal with the impact due to the system uncertainty and model mismatch which ensures the stable operation of the microgrid. Due to the uncertainty of distributed power sources, MPC can be applied to solve the problem of renewable energy output in microgrid for fishery. This paper reviews the optimal dispatch of microgrid for fishery based on model predictive control. Firstly, this article introduces the basic principles and classification of model predictive control, and then explains the remaining problems and research directions. The characteristics of the microgrid optimal dispatching problem are described, and the analysis are focused on how MPC solves the uncertain problems in the microgrid optimal dispatching. Finally, perspectives and the future research direction of model predictive control in microgrid energy dispatch is reviewed.

Understanding the underlying patterns of the urban mobility dynamics is essential for both the traffic state estimation and management of urban facilities and services. Due to the coupling relationship of generative factors in spatial-temporal domain, it is challenging to model the citywide traffic dynamics under a structural pattern of critical features such as hours of days, days of weeks and weather conditions. To address this challenge, this article develops a disentangled representation learning framework to learn an interpretable factorized representation of the independent data generative factors. In order to make full use of the knowledge on generative factors, this article proposes spatial-temporal generative adversarial network (ST-GAN) to assign the generative factors of traffic flow to the feature vector in latent space and reconstructs the high-dimensional citywide traffic flow from the given factors. With the help of the disentangled representations, the decomposed feature vector in latent space discloses the relationship between underlying patterns and citywide traffic dynamics. Several comprehensively experiments show that ST-GAN not only effectively improves the prediction accuracy but also promisingly characterize structural properties of the traffic evolution process.

Silicosis is a leading cause of occupational disease-related morbidity and mortality worldwide, but the molecular basis underlying its development remains unclear. An accumulating body of evidence supports gasdermin D (GSDMD)-mediated pyroptosis as a key component in the development of various pulmonary diseases. However, there is little experimental evidence connecting silicosis and GSDMD-driven pyroptosis. In this work, we investigated the role of GSDMD-mediated pyroptosis in silicosis. Single-cell RNA sequencing of healthy and silicosis human and murine lung tissues indicated that GSDMD-induced pyroptosis in macrophages was relevant to silicosis progression. Through microscopy we then observed morphological alterations of pyroptosis in macrophages treated with silica. Measurement of interleukin-1β release, lactic dehydrogenase activity, and real-time propidium iodide staining further revealed that silica induced pyroptosis of macrophages. Additionally, we verified that both canonical (caspase-1-mediated) and non-canonical (caspase-4/5/11-mediated) signaling pathways mediated silica-induced pyroptosis activation, in vivo and in vitro. Notably, Gsdmd knockout mice exhibited dramatically alleviated silicosis phenotypes, which highlighted the pivotal role of pyroptosis in this disease. Taken together, our results demonstrated that macrophages underwent GSDMD-dependent pyroptosis in silicosis and inhibition of this process could serve as a viable clinical strategy for mitigating silicosis.

With the development of high power converters, the safe operation of large IGBT modules with parallel chips is of increasing importance. In a multichip module, uneven solder layer degradation translates into higher thermal resistance for some of the chips, leading to junction temperature differences between them. However, the specific meaning of a virtual junction temperature extracted from any external electric characteristic parameter of the module is not yet clear. This paper studies the physical meanings of the temperatures estimated by the threshold voltage and the Miller platform duration as well as the quasi turn-off delay time. Experimental results show that the temperature estimated from the threshold voltage is close to the highest temperature of all the chips, while that estimated from the Miller platform duration or quasi turn-off delay time is close to the average chip temperature. Making use of the difference between the two, this paper proposes a new method to monitor the initial solder layer degradation in the multichip IGBT module. Such a method using combined temperature sensitive electric parameters (TSEPs) is verified for different levels of degradation.

Inverse design has long been an efficient and powerful design tool in the aircraft industry. In this paper, a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learning. A Conditional Variational AutoEncoder (CVAE) and an integrated generative network CVAE-GAN that combines the CVAE with the Wasserstein Generative Adversarial Networks (WGAN), are conducted as generative models. They are used to generate target wall Mach distributions for the inverse design that matches specified features, such as locations of suction peak, shock and aft loading. Qualitative and quantitative results show that both adopted generative models can generate diverse and realistic wall Mach number distributions satisfying the given features. The CVAE-GAN model outperforms the CVAE model and achieves better reconstruction accuracies for all the samples in the dataset. Furthermore, a deep neural network for nonlinear mapping is adopted to obtain the airfoil shape corresponding to the target wall Mach number distribution. The performances of the designed deep neural network are fully demonstrated and a smoothness measurement is proposed to quantify small oscillations in the airfoil surface, proving the authenticity and accuracy of the generated airfoil shapes.

Using deep learning to solve garbage classification has become a hot topic in computer version. The most widely used garbage dataset Trashnet only has garbage images with a white board as background. Previous studies based on Trashnet focus on using different networks to achieve a higher classification accuracy without considering the complex backgrounds which might encounter in practical applications. To solve this problem, we propose a framework that combines saliency detection and image classification to improve the generalization performance and robustness. A saliency network Salinet is adopted to obtain the garbage target area. Then, a smallest rectangle containing this area is created and used to segment the garbage. A classification network Inception V3 is used to identify the segmented garbage image. Images of the original Trashnet are fused with complex backgrounds of the other saliency detection datasets. The fused and original Trashnet are used together for training to improve the robustness to noises and complex backgrounds. Compared with the image classification networks and classic target detection algorithms, the proposed framework improves the accuracy of 0.50% − 15.79% on the testing sets fused with complex backgrounds. In addition, the proposed framework achieves the best performance with a gain of 4.80% in accuracy on the collected actual dataset. The comparisons prove that our framework is more robust to garbage classification in complex backgrounds. This method can be applied to smart trash cans to achieve automatic garbage classification.

As the circuit feature size continuously shrinks down, hotspot detection has become a more challenging problem in modern DFM flows. Developed deep learning techniques have recently shown their superiorities on hotspot detection tasks. However, existing hotspot detectors can only handle defect detection from one small layout clip each time, thus may be very time-consuming when dealing with a large full-chip layout. In this paper, we develop a new end-to-end framework that can detect multiple hotspots in a large region at a time and promise a better hotspot detection performance. We design a joint auto-encoder and inception module for efficient feature extraction. A two-stage classification and regression framework is designed to detect hotspot with progressive accurate localization, which provides a promising performance improvement. Experimental results show that our framework enables a significant speed improvement over existing methods with higher accuracy and fewer false alarms.

To provide a detailed description of the sonographic appearance and development of various fetal structures of the midbrain and hindbrain (MBHB) during the early second trimester, and to evaluate the impact of the frequency of the transvaginal sonography (TVS) transducer on the early recognition of these structures.This was a retrospective analysis of three-dimensional volumetric datasets of the MBHB from apparently normal fetuses at 14-19 gestational weeks, acquired by TVS in the midsagittal view through the posterior fontanelle. Using a multiplanar approach, we measured the tectal thickness and length, aqueductal thickness, tegmental thickness and width and height of the Blake's pouch (BP) neck. In addition, we assessed the existence of early vermian fissures, the linear shape of the brainstem and the components of the fastigium. The correlation between gestational age according to last menstrual period and sonographic measurements of MBHB structures was evaluated using Pearson's correlation (r). A subanalysis was performed to assess the performance of a 5-9-MHz vs a 6-12-MHz TVS transducer in visualizing the MBHB structures in the early second trimester.Sixty brain volumes were included in the study, obtained at a mean gestational age of 16.2 weeks (range, 14.1-19.0 weeks), with a transverse cerebellar diameter range of 13.0-19.8 mm. We found a strong correlation between gestational age and all MBHB measurements, with the exception of the tectal, tegmental and aqueductal thicknesses, for which the correlation was moderate. There was good-to-excellent intraobserver and moderate-to-good interobserver correlation for most MBHB measurements. We observed that the BP neck was patent in all fetuses between 14 and 18 weeks with decreasing diameter, and that the aqueductal thickness was significantly smaller at ≥ 18 weeks compared with at 16 weeks. The early vermian fissures and the linear shape of the brainstem were present in all fetuses from 14 weeks. We found that, in the early second trimester, the horizontal arm of the presumed 'fastigium' evolves from the fourth ventricular choroid plexus and not the posterior vermis, indicating that this is not the fastigium. Standard- and high-resolution TVS transducers performed similarly in the assessment of MBHB anatomy.Detailed early second-trimester assessment of the MBHB is feasible by transvaginal neurosonography and provides reference data which may help in the early detection of brain pathology involving the MBHB. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

The key to hyperspectral anomaly detection is to effectively distinguish anomalies from the background, especially in the case that background is complex and anomalies are weak. Hyperspectral imagery (HSI) as an image-spectrum merging cube data can be intrinsically represented as a third-order tensor that integrates spectral information and spatial information. In this article, a prior-based tensor approximation (PTA) is proposed for hyperspectral anomaly detection, in which HSI is decomposed into a background tensor and an anomaly tensor. In the background tensor, a low-rank prior is incorporated into spectral dimension by truncated nuclear norm regularization, and a piecewise-smooth prior on spatial dimension can be embedded by a linear total variation-norm regularization. For anomaly tensor, it is unfolded along spectral dimension coupled with spatial group sparse prior that can be represented by the l2,1-norm regularization. In the designed method, all the priors are integrated into a unified convex framework, and the anomalies can be finally determined by the anomaly tensor. Experimental results validated on several real hyperspectral data sets demonstrate that the proposed algorithm outperforms some state-of-the-art anomaly detection methods.

Epstein-Barr virus–positive (EBV-positive) B-cell lymphomas are common in immunocompromised patients and remain an unmet medical need. Here we report that MDM2 inhibitors (MDM2is) navtemadlin and idasanutlin have potent in vivo activity in EBV-positive B-cell lymphoma established in immunocompromised mice. Tumor regression was observed in all 5 EBV-positive xenograft–associated B-cell lymphomas treated with navtemadlin or idasanutlin. Molecular characterization showed that treatment with MDM2is resulted in activation of p53 pathways and downregulation of cell cycle effectors in human lymphoma cell lines that were either EBV-positive or had undetectable expression of BCL6, a transcriptional inhibitor of the TP53 gene. Moreover, treatment with navtemadlin resulted in tumor regression and prevented systemic dissemination of EBV-positive lymphoma derived from 2 juvenile patients with posttransplant lymphoproliferative diseases, including 1 whose tumor was resistant to virus-specific T-cell therapy. These results provide proof-of-concept for targeted therapy of EBV-positive lymphoma with MDM2is and the feasibility of using EBV infection or loss of BCL6 expression to identify responders to MDM2is.

Multiple and heterogenous Earth observation (EO) platforms are broadly used for a wide array of applications, and the integration of these diverse modalities facilitates better extraction of information than using them individually. The detection capability of the multispectral unmanned aerial vehicle (UAV) and satellite imagery can be significantly improved by fusing with ground hyperspectral data. However, variability in spatial and spectral resolution can affect the efficiency of such dataset's fusion. In this study, to address the modality bias, the input data was projected to a shared latent space using cross-modal generative approaches or guided unsupervised transformation. The proposed adversarial networks and variational encoder-based strategies used bi-directional transformations to model the cross-domain correlation without using cross-domain correspondence. It may be noted that an interpolation-based convolution was adopted instead of the normal convolution for learning the features of the point spectral data (ground spectra). The proposed generative adversarial network-based approach employed dynamic time wrapping based layers along with a cyclic consistency constraint to use the minimal number of unlabeled samples, having cross-domain correlation, to compute a cross-modal generative latent space. The proposed variational encoder-based transformation also addressed the cross-modal resolution differences and limited availability of cross-domain samples by using a mixture of expert-based strategy, cross-domain constraints, and adversarial learning. In addition, the latent space was modelled to be composed of modality independent and modality dependent spaces, thereby further reducing the requirement of training samples and addressing the cross-modality biases. An unsupervised covariance guided transformation was also proposed to transform the labelled samples without using cross-domain correlation prior. The proposed latent space transformation approaches resolved the requirement of cross-domain samples which has been a critical issue with the fusion of multi-modal Earth observation data. This study also proposed a latent graph generation and graph convolutional approach to predict the labels resolving the domain discrepancy and cross-modality biases. Based on the experiments over different standard benchmark airborne datasets and real-world UAV datasets, the developed approaches outperformed the prominent hyperspectral panchromatic sharpening, image fusion, and domain adaptation approaches. By using specific constraints and regularizations, the network developed was less sensitive to network parameters, unlike in similar implementations. The proposed approach illustrated improved generalizability in comparison with the prominent existing approaches. In addition to the fusion-based classification of the multispectral and hyperspectral datasets, the proposed approach was extended to the classification of hyperspectral airborne datasets where the latent graph generation and convolution were employed to resolve the domain bias with a small number of training samples. Overall, the developed transformations and architectures will be useful for the semantic interpretation and analysis of multimodal data and are applicable to signal processing, manifold learning, video analysis, data mining, and time series analysis, to name a few., This research was partly supported by the Hebrew University of Jerusalem Intramural Research Found Career Development, Association of Field Crop Farmers in Israel and the Chief Scientist of the Israeli Ministry of Agriculture and Rural Development (projects 20-02-0087 and 12-01-0041).

Background H protons magnetic resonance spectroscopy (1H-MRS) has been used to detect abnormal biochemical changes and the mechanism of executive dysfunction in major depressive disorder (MDD). While, finding information associated with non-suicidal self-injury (NSSI) among adolescents with MDD is challenging. The present study aimed to examine the executive function and biochemical metabolism alterations as well as to elucidate their associations in depressed adolescents with NSSI. Methods A total of 86 adolescents with MDD (40 with NSSI, and 46 without NSSI) and 28 healthy controls were recruited in the current study. The executive function was assessed by Digital symbol test (DST), Wisconsin Card Sorting Test (WCST), Trail Making Test, part B (TMT-B), and Verbal fluency (VF). Bilateral metabolite levels of the prefrontal cortex (PFC), anterior cingulated cortex (ACC), lenticular nucleus (LN) of basal ganglia and thalamus were obtained by 1H-MRS at 3.0 T, and then the ratios of N-acetyl aspartate (NAA) and choline-containing compounds (Cho) to creatine (Cr) were determined, respectively. Finally, association analysis was conducted to investigate their relationships. Results The depressed adolescents with NSSI showed significantly lower VF scores than those without NSSI and healthy controls. We also found significantly higher NAA/Cr ratios in the right thalamus, while significantly lower Cho/Cr ratios in the right thalamusof NSSI group than the MDD without NSSI group and healthy controls. And NSSI group also showed lower NAA/Cr ratio in the right LNthan the MDD without NSSI group. For MDD with NSSI, the NAA/Cr ratios of the left thalamus was positively correlated with the time of TMTB and the Cho/Cr ratios of the left ACC was positively correlated with the VF scores. Conclusions Depressed adolescents with NSSI may have executive dysfunction and an abnormal NAA and Cho metabolism in thalamus. Andthe NAA/Cr ratios of the right LN could distinguish NSSI from depressed adolescents. Further, the executive dysfunction may associated with the abnormal NAA metabolism in the right thalamus and ACC.

This research discussed a deep learning method based on an improved generative adversarial network to segment the hippocampus. Different convolutional configurations were proposed to capture information obtained by a segmentation network. In addition, a generative adversarial network based on Pixel2Pixel was proposed. The generator was a codec structure combining a residual network and an attention mechanism to capture detailed information. The discriminator used a convolutional neural network to discriminate the segmentation results of the generated model and that of the expert. Through the continuously transmitted losses of the generator and discriminator, the generator reached the optimal state of hippocampus segmentation. T1-weighted magnetic resonance imaging scans and related hippocampus labels of 130 healthy subjects from the Alzheimer's disease Neuroimaging Initiative dataset were used as training and test data; similarity coefficient, sensitivity, and positive predictive value were used as evaluation indicators. Results showed that the network model could achieve an efficient automatic segmentation of the hippocampus and thus has practical relevance for the correct diagnosis of diseases, such as Alzheimer's disease.

To perform a systematic review and meta-analysis of randomized controlled trials (RCTs) in the literature to ascertain the extent to which platelet-rich plasma (PRP) and platelet-rich fibrin (PRF) improved patient outcomes in arthroscopic rotator cuff repair.Two independent reviewers performed the literature search based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, with a third author resolving any discrepancies. RCTs comparing PRP or PRF to a control in rotator cuff repair were included. Quality of evidence was assessed using the Cochrane Collaboration risk of bias tool. Clinical outcomes were compared using the risk ratio for dichotomous variables and the mean difference for continuous variables. A P value.05 was deemed statistically significant.Included in this review are 23 RCTs with 1440 patients. PRP resulted in significantly decreased rates of retear (15.9% versus 29.0%, respectively; P.0001). Significant results were noted in favor of PRP compared with control based on the Constant score (83.9 versus 81.2, respectively; P = .0006); the University of California, Los Angeles score (31.1 versus 30.2; P.00001); the American Shoulder and Elbow Surgeons score (87.3 versus 84.5; P = .04); and the visual analog scale score (1.3 versus 1.6; P = .01). PRF resulted in an improved Constant score (80.1 versus 80.0, respectively; P = .04) compared with control.The current evidence shows that using PRP in arthroscopic rotator cuff repair can improve pain levels and functional outcome scores while reducing the retear rate after surgery. PRF injection, on the other hand, improves only the Constant score.II; systematic review and meta-analysis of level I and II evidence.

Research Question To investigate which characteristics of patients with a thin endometrium (endometrial thickness [EMT] ≤7.5 mm on human chorionic gonadotrophin [HCG] trigger day) suggest the possibility of an EMT >7.5 mm in the subsequent frozen cycle, and to build a prediction nomogram. Design Data were collected from the university-affiliated Center for Reproductive Medicine between January 2013 and September 2019. Multivariable logistic regression was used to generate the final prediction model and construct the nomogram. Model performances were quantified by discrimination and calibration. Results The predictive variables that entered the final model were: hysteroscopic adhesiolysis history, polycystic ovary syndrome status, application of clomiphene in the ovarian stimulation process, the ovarian stimulation protocol, and the endometrial preparation protocol. The receiver operating characteristic curve for the final model and validation cohort was 0.76 (95% confidence interval [CI]: 0.722–0.797) and 0.71 (95% CI: 0.664–0.759), respectively. Discrimination performed well in both the modelling and validation cohorts. Conclusions We conclude that in women with a thin endometrium (EMT ≤7.5 mm on HCG trigger day), the absence of a hysteroscopic adhesiolysis history, the presence of polycystic ovary syndrome, the application of clomiphene in the ovarian stimulation process, the application of a GnRH agonist short protocol, mild stimulation protocol, natural cycle protocol, and natural cycle for endometrial preparation are prognostic for an increased possibility of an EMT >7.5 mm in the subsequent frozen cycle.