Search

Scene parsing, or semantic segmentation, aims at labeling all pixels in an image with the predefined categories of things and stuff. Learning a robust representation for each pixel is crucial for this task. Existing state-of-the-art (SOTA) algorithms employ deep neural networks to learn (discover) the representations needed for parsing from raw data. Nevertheless, these networks discover desired features or representations only from the given image (content), ignoring more generic knowledge contained in the dataset. To overcome this deficiency, we make the first attempt to explore the meaningful supportive knowledge, including general visual concepts (i.e., the generic representations for objects and stuff) and their relations from the whole dataset to enhance the underlying representations of a specific scene for better scene parsing. Specifically, we propose a novel supportive knowledge mining module (SKMM) and a knowledge augmentation operator (KAO), which can be easily plugged into modern scene parsing networks. By taking image-specific content and dataset-level supportive knowledge into full consideration, the resulting model, called knowledge augmented neural network (KANN), can better understand the given scene and provide greater representational power. Experiments are conducted on three challenging scene parsing and semantic segmentation datasets: Cityscapes, Pascal-Context, and ADE20K. The results show that our KANN is effective and achieves better results than all existing SOTA methods.

Mobile edge computing is a new paradigm in which the computing and storage resources are placed at the edge of the Internet. Data placement and retrieval are fundamental services of mobile edge computing when a network of edge clouds collaboratively provide data services. However existing methods such as distributed hash tables (DHTs) are not enough to achieve efficient data placement and retrieval services for cooperative edge clouds. This paper presents GRED, a novel data placement and retrieval service for mobile edge computing, which is efficient in not only the load balance but also routing path lengths and forwarding table sizes. GRED utilizes the programmable switches to support a virtual-space based DHT with only one overlay hop. Data location can be easily implemented on top of the GRED. We implement GRED in a P4 prototype, which provides a simple and efficient solution. Results from theoretical analysis, simulations, and experiments show that GRED can efficiently balance the load of edge clouds, and can fast answer data queries due to its low routing stretch.

Identifying the development trends of emerging technologies with disruptive potential as early as possible is crucial for enterprise research and development (R&D) investment planning and government R&D strategic planning. Many researchers have used academic papers and patent data to identify trends in emerging technologies, although they rarely have made use of web news data focused on emerging technologies. Web news contains a large amount of social awareness data that represents the public's sense of and response to emerging technologies, and the social awareness data is of great significance for identifying the development trends of emerging technologies. Therefore, this article presented a research framework to identify the development trends of emerging technologies using patent analysis and web news data mining. In the research framework, first, we have used patent analysis to analyze the evolution topics of these emerging technologies, and have applied the topic model to study the changing patterns of publics’ topics of concern regarding emerging technologies contained in web news. Second, we have made a comparison analysis of the results of patent analysis and the public’ sense of and response to emerging technologies contained in web news. Then, we have applied an improved sentiment analysis to study the changing patterns of publics’ expectations for emerging technologies contained in web news. Finally, by studying the changes in publics’ topics of concern and publics’ expectations for emerging technologies over time, we have identified the development trends of emerging technologies. The perovskite solar cells technology is used as a case study to analyze the effectiveness and feasibility of the framework. This article provides a new research perspective to identify technology trends, and contribute to the technology forecasting methodology.

In recent years, data-driven learning methods have been widely studied for autonomous robot skill learning. However, these methods rely on large amounts of robot-environment interaction data for training, which largely prevents them from being applied to real-world robots. To address this problem, this paper proposes a novel simulation-reality closed-loop learning framework for autonomous robot skill learning that can improve data efficiency, enhance policy stability and achieve effective policy simulation-to-reality (sim2real) transfer. First, a hybrid control model combining the asymmetric deep deterministic policy gradients (Asym-DDPG) model and the forward prediction control (FPC) model is proposed to learn vision-based manipulation policies in simulations, which can decompose complex tasks to improve learning efficiency. Second, a novel pixel-level domain adaptation method named Position-CycleGAN is designed to translate real images to simulated images while also preserving the task-related information. The policy trained in simulations can be directly migrated into real robots in a reverse reality-to-simulation manner using the Position-CycleGAN model. The experimental results validate the effectiveness of the proposed framework. This work provides an efficient and feasible path for achieving autonomous skill learning.

Background: Tumor regression grade (TRG) correlates with prognosis in patients with locally advanced rectal cancer (LARC), but there is controversy regarding the use of magnetic resonance imaging (MRI) for determining TRG. This study to evaluate the diagnostic value of change rate in signal intensity (SI) and volume (V) from MRI to TRG following preoperative chemoradiotherapy (CRT) in patiens with LARC.Materials and methods: This retrospective analysis examined 82 LARC patients who were admitted to our institution between Oct 2017 and Oct 2019. Patients underwent pre- and post-CRT T2-weighted (T2W), diffusion-weighted (DW)/apparent diffusion coefficient (ADC), and contrast-enhanced T1-weighted (ceT1W) MRI. Change rate of volume and relative SI ratio(%△V and %△SIR) from each sequence were determined. All LARCs were confirmed pathologically and classified into TRG 0, 1, 2 and 3. Descriptive statistics and receiver operating characteristic (ROC) analysis, with calculation of area under the curve (AUC), were used to compare the diagnostic performances. Results: Sixteen patients had TRG-0, 15 had TRG-1, 35 had TRG-2, and 16 had TRG-3. Except for ADC-%△SIR, the remaining%△V and %△SIR on T1W, DWI, and ceT1W had significant differences among the four groups. %△V and/or %△SIR did not distinguish TRG-1 from TRG-2 nor TRG-2 from TRG-3, but differences between other TRGs were identified by %△V and/or %△SIR on T2W, DWI, and ceT1W. The combined use of DW-%△V and T2W-%△SIR provided the best diagnostic performance in distinguishing of TRG-0 from TRG-2 (AUC: 0.954) and from TRG-3 (AUC: 1.000).Conclusions: Preoperative MRI of LARC patients can determine TRG and may improve selection of the preoperative therapy.

Abstract: Background: Long noncoding RNAs (LncRNAs) represent a large category of functional RNA molecules that play a significant role in human cancers. lncRNAs can be genes modulators to affect the biological process of multiple cancers. Methods: Here, we developed a computational framework that uses lncRNA-mRNA network and mutations in individual genes of 9 cancers from TCGA to prioritize cancer lncRNA modulators. Our method screened risky cancer lncRNA regulators based on integrated multiple lncRNA functional networks and 3 calculation methods in network. Results: Validation analyses revealed that our method was more effective than prioritization based on a single lncRNA network. This method showed high predictive performance and the highest ROC score was 0.836 in breast cancer. It’s worth noting that we found that 5 lncRNAs scores were abnormally high and these lncRNAs appeared in 9 cancers. By consulting the literatures, these 5 lncRNAs were experimentally supported lncRNAs. Analyses of prioritizing lncRNAs reveal that these lncRNAs are enriched in various cancer-related biological processes and pathways. Conclusions: Together, these results demonstrated the ability of this method identifying candidate lncRNA molecules and improved insights into the pathogenesis of cancer.

Aims TASK-1 (K2P3.1) two-pore domain potassium channels are atrial-specific and significantly upregulated in atrial fibrillation (AF) patients, contributing to AF-related electrical remodelling. Inhibition of TASK-1 in cardiomyocytes of AF patients was shown to counteract AF-related action potential duration shortening. Doxapram was identified as a potent inhibitor of the TASK-1 channel. In the present study, we investigated the antiarrhythmic efficacy of doxapram in a porcine model of AF. Methods and results Doxapram successfully cardioverted pigs with artificially induced episodes of AF. We established a porcine model of persistent AF in domestic pigs via intermittent atrial burst stimulation using implanted pacemakers. All pigs underwent catheter-based electrophysiological investigations prior to and after 14 d of doxapram treatment. Pigs in the treatment group received intravenous administration of doxapram once per day. In doxapram-treated AF pigs, the AF burden was significantly reduced. After 14 d of treatment with doxapram, TASK-1 currents were still similar to values of sinus rhythm animals. Doxapram significantly suppressed AF episodes and normalized cellular electrophysiology by inhibition of the TASK-1 channel. Patch-clamp experiments on human atrial cardiomyocytes, isolated from patients with and without AF could reproduce the TASK-1 inhibitory effect of doxapram. Conclusions Repurposing doxapram might yield a promising new antiarrhythmic drug to treat AF in patients. Translational perspective Pharmacological suppression of atrial TASK 1 potassium currents prolongs atrial refractoriness with no effects on ventricular repolarization, resulting in atrial-specific class III antiarrhythmic effects. In our preclinical pilot study the respiratory stimulant doxapram was successfully administered for cardioversion of acute AF as well as rhythm control of persistent AF in a clinically relevant porcine animal model.

This paper proposes a new 3D multi-object tracker to more robustly track objects that are temporarily missed by detectors. Our tracker can better leverage object features for 3D Multi-Object Tracking (MOT) in point clouds. The proposed tracker is based on a novel data association scheme guided by prediction confidence, and it consists of two key parts. First, we design a new predictor that employs a constant acceleration (CA) motion model to estimate future positions, and outputs a prediction confidence to guide data association through increased awareness of detection quality. Second, we introduce a new aggregated pairwise cost to exploit features of objects in point clouds for faster and more accurate data association. The proposed cost consists of geometry, appearance and motion components. Specifically, we formulate the geometry cost using resolutions (lengths, widths and heights), centroids, and orientations of 3D bounding boxes (BBs), the appearance cost using appearance features from the deep learning-based detector backbone network, and the motion cost by associating different motion vectors. Extensive multi-object tracking experiments on the KITTI tracking benchmark demonstrated that our method outperforms, by a large margin, the state-of-the-art methods in both tracking accuracy and speed.

Temporal and spatial contexts, characterizing target appearance variations and target-background differences, respectively, are crucial for improving the online adaptive ability and instance-level discriminative ability of object tracking. However, most existing trackers focus on either the temporal context or the spatial context during tracking and have not exploited these contexts simultaneously and effectively. In this paper, we propose a Spatial-TEmporal Memory (STEM) network to exploit these contexts jointly for object tracking. Specifically, we develop a key-value structured memory model equipped with a key-value index-based memory reading mechanism to model the spatial and temporal contexts simultaneously. To update the memory with new target states and ensure the diversity of the memory, we introduce a similarity-aware memory update scheme. In addition, we construct an entropy-guided ensemble strategy to fuse the prediction models based on these two contexts, such that these two contexts can be exploited to estimate the target state jointly. Extensive experimental results on eight challenging datasets, including OTB2015, TC128, UAV123, VOT2018, LaSOT, TrackingNet, GOT-10k, and OxUvA, demonstrate that the proposed method performs favorably against state-of-the-art trackers.

Non-destructive thermography is a high-speed, low-cost, and safe solution for subsurface defects detection of carbon fiber reinforced polymer (CFRP) materials, providing essential quality control in aerospace, automobile, and sports industries. In this paper, we build a reflective lock-in thermography system and construct a dataset that contains real-captured thermal image sequences of CFRP samples with various simulated internal defects under different excitation frequencies. Then, we present a novel 3D Convolutional Neural Network (CNN) model incorporating a combination of spatial and temporal convolutional filters and batch-size independent Group Normalization (GN) as a unified framework to process thermal image sequences captured by lock-in thermography for simultaneous subsurface defect detection and depth estimation. Finally, we define a multi-task loss function to perform end-to-end training of both defect detection and depth estimation tasks based on the real-captured infrared sequences. Comparative experiments are carried out on CFRP specimens with artificial defects of various sizes/shapes and at different depths. Qualitative and quantitative results illustrate that our 3D CNN model is capable of predicting accurate locations and depths of subsurface defects and performs favorably against the hand-crafted and CNN-based methods in lock-in thermography for individual defect detection and depth estimation tasks. The captured dataset and the source codes will be made publicly available.

In visual tracking, it is challenging to distinguish the target from similar objects called noises in the background. As deep trackers use convolutional neural networks for image classification as feature extractors, the extracted features are insensitive to different instances in the same class, which is prone to make prediction models confuse the target and the similar noises in the background. To this end, we propose a noise-suppressing algorithm to learn the discriminative representation for distinguishing the target from the noises in the background. First, we learn polynomial kernels for a search patch under the semantic guidance to increase the difference between representations of the target and the noises in the background. Second, we formulate the online foreground-background functions for the target and the noises in the background to learn an adaptive kernel, which suppresses the features positive for the noises and promotes the features positive for the target. We evaluate the proposed method on seven public datasets including OTB-2013, OTB-2015, VOT-2018, LaSOT, TrackingNet, GOT10k, and NFS. The comprehensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods, while running at real-time speed.

Face detection generally requires prior boxes and an extra non-maximum suppression(NMS) post-processing in modern deep learning methods. However, anchor design and anchor matching strategy significantly affect the performance of face detectors, so we have to spend a lot of time on anchor designing for different business scenarios. The other issue is that NMS cannot be easily parallelized and it may become a bottleneck of detection speed. In this paper, we propose a simple yet efficient pure convolutional neural network face detection method, named dual-branch center face detector(DBCFace for short), which solve face detection via a dual branch fully convolutional framework without extra anchor design and NMS. Extensive experiments are conducted on four popular face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE, demonstrating that our method is comparable with state-of-the-art methods while the speed is faster.

Recently in nanophotonics, a rigorous evolution from passive to active metasurfaces has been witnessed. This advancement not only brings forward interesting physical phenomena but also elicits opportunities for practical applications. However, active metasurfaces operating at visible frequencies often exhibit low performance due to design and fabrication restrictions at the nanoscale. In this work, we demonstrate electrochemically controlled metasurfaces with high intensity contrast, fast switching rate, and excellent reversibility at visible frequencies. We use a conducting polymer, polyaniline (PANI), that can be locally conjugated on preselected gold nanorods to actively control the phase profiles of the metasurfaces. The optical responses of the metasurfaces can be in situ monitored and optimized by controlling the PANI growth of subwavelength dimension during the electrochemical process. We showcase electrochemically controlled anomalous transmission and holography with good switching performance. Such electrochemically powered optical metasurfaces lay a solid basis to develop metasurface devices for real-world optical applications.

Objectives To map travel policies implemented due to COVID-19 during 2020, and conduct a mixed-methods systematic review of health effects of such policies, and related contextual factors. Design Policy mapping and systematic review. Data sources and Eligibility Criteria for the policy mapping, we searched websites of relevant government bodies and used data from the Oxford COVID-19 Government Response Tracker for a convenient sample of 31 countries across different regions. For the systematic review, we searched Medline (Ovid), PubMed, EMBASE, the Cochrane Central Register of Controlled Trials and COVID-19 specific databases. We included randomized controlled trial, non-randomized studies, modeling studies, and qualitative studies. Two independent reviewers selected studies, abstracted data and assessed risk of bias. Results Most countries adopted a total border closure at the start of the pandemic. For the remainder of the year, partial border closure banning arrivals from some countries or regions was the most widely adopted measure, followed by mandatory quarantine and screening of travelers. The systematic search identified 69 eligible studies, including 50 modeling studies. Both observational and modeling evidence suggest that border closure may reduce the number of COVID-19 cases, disease spread across countries and between regions, and slow the progression of the outbreak. These effects are likely to be enhanced when implemented early, and when combined with measures reducing transmission rates in the community. Quarantine of travelers may decrease the number of COVID-19 cases but its effectiveness depends on compliance and enforcement and is more effective if followed by testing, especially when less than 14 day-quarantine is considered. Screening at departure and/or arrival is unlikely to detect a large proportion of cases or to delay an outbreak. Effectiveness of screening may be improved with increased sensitivity of screening tests, awareness of travelers, asymptomatic screening, and exit screening at country source. While four studies on contextual evidence found that the majority of the public is supportive of travel restrictions, they uncovered concerns about the unintended harms of those policies. Conclusion Most countries adopted full or partial border closure in response to COVID-19 in 2020. Evidence suggests positive effects on controlling the COVID-19 pandemic for border closure (particularly when implemented early), as well as quarantine of travelers (particularly with higher levels of compliance). While these positive effects are enhanced when implemented in combination with other public health measures, they are associated with concerns by the public regarding some unintended effects.

In wireless power transfer (WPT) systems, the relative position between the transmitting and receiving coils could change suddenly, which is one of the most significant distinctions between wireless and wired power transfer systems. To the best of the authors knowledge, no theoretical analysis has been reported on the dynamic behavior of the WPT systems under the relative position transient (RPT). This paper proposes the concept of coupling-coefficient susceptibility to describe this dynamic behavior. The extended describing function method is applied to extract the mathematical model of the coupling-coefficient susceptibility. Finally, an RPT testing platform, composed of a series-series compensated WPT system and a coil motion mechanism is designed and fabricated to verify the theoretical analysis. The experimental results exhibit a good agreement with the proposed model.

Adolescence is an important stage for brain maturation. To investigate the effect of different exercise doses on inhibitory control in adolescents aged 12 to 14-year old, an after-school exercise program was offered 5 days per week for 12 weeks during a school semester. Thirsty-four adolescents (17 boys) from the first six classes were randomly divided into low-dose exercise group (LE group, one 30-min aerobic exercise bout per day, n ​= ​16) and high-dose exercise group (HE group, two 30-min aerobic exercise bouts per day, n ​= ​18), while 23 adolescents (10 boys) in the control group (CON, zero 30-min exercise bout, n ​= ​23) were from the last two classes. All the participants in different classes received the same physical education with the same contents, duration, and intensity at class. All the participants completed flanker tests and cardiorespiratory tests before and after exercise intervention. The HE group showed more significant improvements on inhibitory control and VO2peak than CON (p ​

The emerging energy harvesting technology facilitates the development of ubiquitous and everlasting battery-free motion detectors. This paper introduces a robust design of the transient-motion-powered motion detector, which is called ViPSN-pluck. “ViPSN” is the acronym for vibration-powered sensing node while “pluck” stands for the plucking-motion energy harvester. By using a piezo-magneto-elastic structure, ViPSN-pluck can efficiently harvest energy from a transient motion. By properly making good use of this tiny harvested energy, ViPSN-pluck can effectively carry out motion detection and Bluetooth low-energy (BLE) wireless communication. Given the concurrency of mechanical potential energy pre-charging and motion detection, the transient-motion plucking energy harvester used in ViPSN-pluck has the merit of high energy reliability. This unique feature is unprecedented in the solar and RF energy harvesting cases, which might suffer from energy outages under fluctuating irradiance or RF signal strength, respectively. The working principle of ViPSN-pluck, in particular, the dynamic characteristics of the plucking energy harvester and the energy matching between generation and utilization, are discussed in detail to demonstrate the robustness in operation. The cyber-electro-mechanical synergy among the mechanical dynamics, power conditioning circuit, and low-power embedded system is highlighted. The design methodology of ViPSN-pluck provides a valuable reference for the developments of future motion-powered IoT devices.

How to understand the innovation pathways of emerging technologies emerge from technology to market and identifing the future changes and trends in innovation pathways is crucial for enterprises’ research and development (R&D) strategic planning, and new production development decision making. The technology roadmapping (TRM) is considered as an effective method to study innovation pathways, and provides a structured approach to map how emerging technologies have evolved from technology to market, and visualize the development relationships among markets, products, and technologies. However, the traditional TRM methods to visualize the innovation pathways, and identify the relationships among markets, products, and technologies mainly relied on experts’ opinions, which are considered as time consuming and costly. The technology-relationship-technology (TRT) semantic analysis can semiautomatically extract technology information and the relationships among products, functions, and technologies from patents, which can significantly reduce the bias due to using experts’ opinions that are based on intuitive knowledge when roadmapping the innovation pathways from technology to market activities. Therefore, this article integrates TRT semantic analysis and TRM method to propose a framework for monitoring the innovation pathways of emerging technologies and identifying future changes and trends in innovation pathways of emerging technologies. In this framework, first, we use the TRT semantic analysis to extract the TRT structures containing various structural relationships from the patents, and determine the analytical dimensions of TRM based on the TRT structures’ characteristics and experts’ knowledge. Second, we analyze the various structural relationships in-depth to explore their particular contributions in the process of TRM construction, and construct the technology roadmap to monitor the innovation pathways. Finally, after completing the construction of the TRT-based technology roadmap, we in-depth analyze the technology, function, product evolution paths in the TRM to identify the future changes and trends in innovation pathways with the help of domain experts. To illustrate the implementation of the research framework, we employ the elderly smart wear technology as the case study. This article will contribute to the TRM methodology, and will be of interest to elderly smart wear technology R&D experts and new production development experts.

Synthesizing photo-realistic images based on text descriptions is a challenging image generation problem. Although many recent approaches have significantly advanced the performance of text-to-image generation, to guarantee semantic matchings between the text description and synthesized image remains very challenging. In this paper, we propose a new model, Cross-modal Semantic Matching Generative Adversarial Networks (CSM-GAN), to improve the semantic consistency between text description and synthesized image for a fine-grained text-to-image generation. Two new modules are proposed in CSM-GAN: Text Encoder Module (TEM) and Textual-Visual Semantic Matching Module (TVSMM). TVSMM is aimed at making \textcolor{red}{the distance of the pairs of synthesized image and its corresponding text description closer}, in global semantic embedding space, than those of mismatched pairs. This improves the semantic consistency and consequently, the generalizability of CSM-GAN. In TEM, we introduce Text Convolutional Neural Networks (Text\_CNNs) to capture and highlight local visual features in textual descriptions. Thorough experiments on two public benchmark datasets demonstrated the superiority of CSM-GAN over other representative state-of-the-art methods.

Becoming a vital position in the interconnection industry of the Internet of Things, IIoT has promoted the conversion of traditional industries to intelligent industries. However, it is necessary further to solve the security and privacy threats in IIoT while reducing communication bandwidth and computing resource consumption to carry out research. In order to solve the problem of user privacy leakage caused by the access structure, a fog computing-oriented access control structure hiding scheme is proposed in the paper. The Paillier homomorphic filter algorithm is introduced in the fog calculation. The Paillier homomorphic algorithm hides the mapping function in the access structure during the data upload process, achieving the effect of completely hiding the access structure. Moreover, the ciphertext is stored separately from the access structure, and the Paillier homomorphic algorithm is run through the fog node during the decryption process to detect whether the attributes of the data user exist in the hidden access structure. If it exists, reconstruct the mapping function and send it to the data user, who then downloads and decrypts the ciphertext. If it does not exist, it means that the data user does not meet the access conditions of the data, and there is no need to download and decrypt the ciphertext. Meanwhile, a simulation experiment platform is constructed in the paper to distinguish the performance of the method proposed in the paper from other similar methods, proving the efficiency and practicability of the scheme.

Alloys present the great potential in catalysis because of their adjustable compositions, structures and element distributions, which unfortunately also limit the fast screening of the potential alloy catalysts. Machine learning methods are able to tackle the multi-variable issues but still cannot yet predict the complex alloy catalysts from the properties of pure metals due to the lack of universal descriptors. Herein we propose a transferable machine-learning model based on the intrinsic properties of substrates and adsorbates, which can predict the adsorption energies of single-atom alloys (SAAs), AB intermetallics (ABs) and high-entropy alloys (HEAs), simply by training the properties of transition metals (TMs). Furthermore, this model builds the structure-activity relationship of the adsorption energies on alloys from the perspective of machine learning, which reveals the role of the surface atoms’ valence, electronegativity and coordination and the adsorbates’ valence in determining the adsorption energies. This transferable scheme advances the understanding of the adsorption mechanism on alloys and the rapid design of alloy catalysts.

Triple cation perovskite photodetectors with different feature bandgaps were stacked and sealed for multi-color fluorescence detection.

Person re-identification (Re-ID) is a challenging research topic which aims to retrieve the pedestrian images of the same person that captured by non-overlapping cameras. Existing methods either assume the body parts of the same person are well-aligned, or use attention selection mechanisms to constrain the effective region of feature learning. But these methods concentrate only on coarse feature representation and cannot model complex real scenes effectively. We propose a novel Local Attention Guided Network (LAG-Net) to not only exploit the most salient area among different people, but also extract important local detail through a Local Attention System (LAS). LAS is an attention selection unit that could extract approximate semantic local features of human body parts without extra supervision. To learn discriminative attention feature representation, we explore an attention feature regularization scheme to enhance the relevance of body part features that belong to same personal identity. Considering the effectiveness of feature augmentation in the Re-ID task and the defect of the existing methods, we propose a Batch Attention DropBlock (BA-DropBlock) to further improve DropBlock by combining the attention selection mechanism. Results on mainstream datasets demonstrate the superiority of our model over the state-of-the-art. Especially, our approach exceeds the current best method by a large margin of 4.6 % on the most challenging dataset CUHK03.

BACKGROUND Penetrating neck injuries require prompt recognition, diagnosis and management of critical airways. This case demonstrates an emergent situation that a “medical negligence” was avoided with the aid of end-tidal carbon dioxide (ETCO2) waveform. CASE SUMMARY We report a case of malposition of the endotracheal tube into the right hemithoracic cavity for cervical knife trauma, resulting in pneumothorax. Tube placement was not confirmed during emergency airway management, and the patient was directly transferred to the emergency operation room. Assisted by ETCO2 and imaging examinations, the anesthetist timely noticed the absence of ETCO2 waveform and resolved this urgent situation before anesthesia induction. CONCLUSION This case emphasizes the necessity of ETCO2 waveform and/or X-ray confirmation of endotracheal intubation even in emergent situations.

Background Strain analysis has become commonly used in clinical practice in various heart diseases. Purpose To explore whether late gadolinium enhancement (LGE)-negative areas with coronary artery chronic total occlusion (CTO) appear normal when analyzed for longitudinal strain using cardiac magnetic resonance (CMR) imaging. Material and Methods A total of 16 patients and 31 healthy controls who underwent 1.5-T MR at our hospital between January 2015 and July 2017 were included in the study. The LGE-CMR of patients with CTO was negative. Left ventricular functional parameters, segmental longitudinal strain/strain rate, and perfusion parameters were measured using CVI42 software. Results For myocardial segments supplied by CTO vessels, systolic longitudinal strain rate (SLSR)was significantly lower than that of healthy controls, and diastolic longitudinal strain rate (DLSR) was significantly higher (1.19 1/s vs. 1.02 1/s; P = 0.018). Moreover, longitudinal strain (LS), SLSR, and DLSR did not differ between good and poor collateral circulation. Perfusion index of CTO territory segments was lower than non-CTO territory segments (0.20 vs. 0.22; P = 0.027). No correlation was found between longitudinal strain parameters and perfusion parameters. Conclusion Although LGE-CMR was negative in patients with CTO, the myocardial SLSR of CTO territory segments was significantly lower than that of healthy controls.

Combining plasmonic and magnetic properties, namely magneto-plasmonic coupling, inspires great research interest and the search for magneto-plasmonic nanostructure becomes considerably critical. Here we designed a nanopillar-in-matrix structure with core–shell alloyed nanopillars for both BaTiO3 (BTO)-Au0.5Co0.5 (AuCo) and BTO-Au0.25Cu0.25Co0.25Ni0.25 (AuCuCoNi) hybrid systems, i.e., ferromagnetic alloy cores (e.g., Co or CoNi) with plasmonic shells (e.g., Au or Au/Cu). These core–shell alloy nanopillars are uniformly embedded into a dielectric BTO matrix to form a vertically aligned nanocomposite (VAN) structure. Both hybrid systems present excellent epitaxial quality and interesting multi-functionality, e.g., high magnetic anisotropy, magneto-optical coupling response, tailorable plasmonic resonance wavelength, tunable hyperbolic properties and strong optical anisotropy. These alloyed nanopillars-in-matrix designs provide enormous potential for complex hybrid material designs with multi-functionality and demonstrate strong interface enabled magneto-plasmonic coupling along with plasmonic and magnetic performance.

Pharmacometrics is an emerging science that interprets drug, disease, and trial information in a mathematical fashion to inform and facilitate efficient drug development and/or regulatory decisions. Pharmacometrics study is increasingly adopted in the regulatory review of new antimicrobial agents. We summarized the 31 antimicrobial agents approved by the US Food and Drug Administration (FDA) and the 26 antimicrobial agents approved by European Medicines Agency (EMA) from January 2001 to May 2019. We also reviewed recent examples of utilizing pharmacometrics to support antimicrobial agent’s registration in China, including modeling and simulation methods, effects of internal/external factors on pharmacokinetic (PK) parameters, safety and efficacy evaluation in terms of exposure‐response analysis, refinement of the wording of product labeling and package leaflet, and possible postmarketing clinical trial. Ongoing communication among regulator, academia, and industry regarding pharmacometrics is encouraged to streamline and facilitate the development of new antimicrobial agents. The industry can maximize its benefit in drug development through continued pharmacometrics education/training.

Advanced Non-alcoholic fatty liver disease (NAFLD) is associated with increased risk of cardiovascular disease (CVD).We determine whether combinations of ultrasound graphic steatosis grades, fibrosis scores and apolipoprotein levels add value to CVD risk prediction in NAFLD patients.The retrospective cohort study enrolled 10,453 individuals (3519 NAFLD; 6934 non NAFLD) from 2004 to 2018. Hepatic ultrasound measurements, lipid and apolipoprotein profiles, Fibrosis-4 and the NAFLD fibrosis scores (NFS) were assessed. The primary outcome included both clinical and subclinical CVD.During 116-month follow-up period, there were 957 clinical and 752 subclinical CVD events. NAFLD patients had a higher incidence of CVD than non NAFLD patients as the steatosis degree, NFS, and FIB4 scores increased (25.1% vs 11.9%, Log Rank: p 0.001). For the lipid and apolipoprotein profiles excluding triglyceride or ApoE, subjects with varied steatosis severity in the upper two tertiles had different risk of CVD (p for interaction 0.001). A nomogram model combination of Framingham Risk Score (FRS), NFS and apolipoprotein profiles presented a higher AUC than FRS in a time-dependent ROC curve (0.816 vs 0.752, p 0.001).The novel risk score considering ultrasonography-defined steatosis grades, non-invasive liver fibrosis scores and apolipoprotein profiles accurately predicted the 10-year risk of CVD.

The aim of the studies was to evaluate the safety, tolerability, and efficacy of tafolecimab, a novel proprotein convertase subtilisin/kexin type 9 (PCSK9) monoclonal antibody, in Chinese healthy volunteers and patients with hypercholesterolemia. Fifty-eight healthy volunteers (phase 1a) were randomized to receive a single dose of 25, 75, 150, 300, 450, or 600 mg tafolecimab subcutaneously, 75 or 450 mg intravenously, or placebo. Sixty patients with hypercholesterolemia (phase 1b) were randomized to receive 75 or 140 mg tafolecimab every 2 weeks, 300 or 420 mg every 4 weeks, or 450 or 600 mg every 6 weeks subcutaneously or placebo for 12 weeks. Tafolecimab was well tolerated. Adverse events in both studies were either mild or moderate. In the phase 1a study, a single dose of tafolecimab reduced low-density lipoprotein-cholesterol (LDL-C) levels up to 72% in healthy volunteers. In the phase 1b study, tafolecimab reduced LDL-C levels up to 71.6% and by more than 50% consistently to week 12 for all tafolecimab dose regimens. Tafolecimab is a safe PCSK9 monoclonal antibody with significant and potential long-acting LDL-C–lowering effect. (Single Ascending Dose Study of PCSK-9 Inhibitor [IBI306] in Healthy Subjects; NCT03366688 ) (Multiple Ascending Dose Study of PCSK-9 Inhibitor [IBI306] in Chinese Patients With Hypercholesterolemia; NCT03815812 )

Data driven method based on big data and deep model is an effective tool to solve the problem of online power system transient stability assessment. Nevertheless, when the system suffers from some large disturbances such as the changes of topology, there is no proper way for a model to supplement the original knowledge base but to retrain it from scratch. Meanwhile, with the continuous emergence of new scenarios, data management has become a new dilemma reflected in the increase of data accumulation and retraining time. To address these issues, a novel TSA framework with continual learning ability is proposed. First, the improved convolutional neural network-based orthogonal weight modification algorithm is selected as a transient stability predictor. Then, large disturbance is regarded as a new recognition task which can be continuously learned by the predictor. Through the proposed updating process, the original knowledge base can be updated and supplemented only by the data corresponding to new scenario. The test results on two power systems show that the proposed scheme can realize the tradeoff between the integrity of knowledge base and the update speed without losing too much accuracy in a limited capacity.

In order to realize high-sensitivity real-time monitoring of acid-base neutral solutions in the industrial field, it is very important to study how to improve the sensitivity of a pH sensor. Herein, the sensitivity of the pH sensor was improved by using both monolithic integrated planar multi-channel and photoconductive properties of gallium nitride (GaN) high electron-mobility transistors (HEMTs) as a core of the pH sensor. The sensitive area of aluminum gallium nitride/gallium nitride (Al $_{0.25}$ Ga $_{0.75}$ N/GaN) heterojunction HEMT channel surface was increased, and the channel effective resistance was reduced by planar multi-channels. And the photogenerated current was generated by 365-nm ultraviolet (UV) light based on the photoconductive effect. This investigation mainly shows as the sensitivity of the tri-channel was 37.04 μA/pH, which increased 30 times compared with that of the single-channel device under the dual action of planar multi-channel and 365-nm UV light coupling in potassium hydrogen phthalate, mixed phosphate, and borax pH correction buffer solutions. The concept of using the GaN HEMT-based pH sensor for direct quantification of pH values in waste water provides a simple and high-performance method for industrial applications.

A major challenge to the treatment of advanced prostate cancer (PCa) is the development of resistance to androgen-deprivation therapy (ADT) and chemotherapy. It is imperative to discover effective therapies to overcome drug resistance and improve clinical outcomes. We have developed a novel class of silicon-containing compounds and evaluated the anticancer activities and mechanism of action using cellular and animal models of drug-resistant PCa. Five organosilicon compounds were evaluated for their anticancer activities in the NCI-60 panel and established drug-resistant PCa cell lines. GH1504 exhibited potent in vitro cytotoxicity in a broad spectrum of human cancer cells, including PCa cells refractory to ADT and chemotherapy. Molecular studies identified several potential targets of GH1504, most notably androgen receptor (AR), AR variant 7 (AR-v7) and survivin. Mechanistically, GH1504 may promote the protein turnover of AR, AR-v7 and survivin, thereby inducing apoptosis in ADT-resistant and chemoresistant PCa cells. Animal studies demonstrated that GH1504 effectively inhibited the in vivo growth of ADT-resistant CWR22Rv1 and chemoresistant C4-2B-TaxR xenografts in subcutaneous and intraosseous models. These preclinical results indicated that GH1504 is a promising lead that can be further developed as a novel therapy for drug-resistant PCa.

In an effort to promote rational drug pricing and relieve the pressure of drug shortages, the Chinese government implemented a low-price medicine (LPM) policy in July 2014, and abolished price regulations for most medications in June 2015. This study examines trends in the availability and pricing of LPMs since policy implementation. Data on price and availability of 752 LPMs during 2013-2017 were obtained from the Jiangsu Institute of Medicine Information. Availability was defined as the proportion of facilities in which a medicine was in inventory during each survey period. A price index was constructed based on purchasing prices in 40 public healthcare facilities, using a standard method developed by the International Labour Organization. Mean availability fluctuated slightly but held at low levels (

Background: Fibrovascular polyps (FVPs) with hypopharyngeal pedicles (hFVPs) are the rare intraluminal benign tumours of the upper aerodigestive tract, and their accurate diagnosis and optimal management are challenging. Purpose: The present retrospective study attempted to explore the optimal diagnosis and treatment of hFVPs. Research Design: The clinical records of 2 patients with giant, irregularly shaped hFVPs, who underwent several failed surgical procedures after inaccurate diagnosis, were reviewed. Finally, the patients were correctly diagnosed and successfully treated at Capital Medical University Beijing Friendship Hospital in different years, 2018 and 2020. Results: Case 1 was of a 43-year-old woman with 2 months of progressive dysphagia. Gastroenterologists overlooked the origin of her FVP, and decided to sever its narrowest point in the oesophagus through endoscopy. However, upon unsuccessful removal of the mass, a gastrotomy procedure was performed to extract the mass 7 days later. Symptoms recurred 3 months after the treatment, and a fibreoptic laryngoscopy confirmed hFVP in the patient at our department. A transcervical approach was used to sever the hypopharyngeal pedicle, achieve haemostasis and remove the oesophageal tumour. No recurrence was detected during the 2-year follow-up period after the treatment. Case 2 was of a 32-year-old man with dysphagia who had previously undergone transthoracic and transcervical oesophagotomy procedures within a gap of 3 months for the removal of FVP causing dysphagia. The hypopharyngeal pedicle was not diagnosed in the patient. The symptoms of dysphagia recurred 4 years after the treatment, and a fibreoptic laryngoscope confirmed hFVP at our department. The tumour was removed successfully through the transcervical approach. No recurrence was detected during the 6-months follow-up after surgery. Conclusion: In conclusion, the transcervical approach is suitable for achieving haemostasis and removing giant, irregularly shaped hFVPs.

Parkinson's disease (PD), as the second most common neurodegenerative disease, is seriously affecting the life quality of the elderly. However, there is still a lack of efficient medical methods to diagnosis PD before apparent symptoms occur. In recent years, clinical biomarkers including genetic, imaging, and tissue markers have exhibited remarkable benefits in assisting PD diagnoses. Due to the advantages of high-throughput detection of metabolites and almost non-invasive sample collection, metabolomics research of PD is widely used for diagnostic biomarker discovery. However, there are also a few shortages for those identified biomarkers, such as the scarcity of verifications regarding the sensitivity and specificity. Thus, reviewing the research progress of PD biomarkers based on metabolomics techniques is of great significance for developing PD diagnosis. To comprehensively clarify the progress of current metabolic biomarker studies in PD, we reviewed 20 research articles regarding the discovery and validation of biomarkers for PD diagnosis from three mainstream academic databases (NIH PubMed, ISI Web of Science, and Elsevier ScienceDirect). By analyzing those materials, we summarized the metabolic biomarkers identified by those metabolomics studies and discussed the potential approaches used for biomarker verifications. In conclusion, this review provides a comprehensive and updated overview of PD metabolomics research in the past two decades and particularly discusses the validation of disease biomarkers. We hope those discussions might provide inspiration for PD biomarker discovery and verification in the future.

Purpose: The optimal treatment for isolated abdominal aortic dissection (IAAD) is currently unknown. We compared the effects of straight and bifurcated aortic stent grafts on postoperative aortic remodeling in patients with IAAD. Materials and Methods: From February 2012 to December 2019, 57 patients with IAAD were treated using endovascular methods, including either a bifurcated or a straight aortic stent graft. The clinical features, risk factors, computed tomography angiograms, midterm follow-up results, and aortic remodeling of these patients were reviewed and analyzed. Results: In total, 44 (77%) patients were treated with a bifurcated graft and 13 (23%) patients were treated with a straight graft. Patients treated with straight grafts had fewer common iliac arteries involved (38% vs 73%, p=0.023), the dissection length was shorter (76.3 ± 40.0 vs 116.2 ± 56.7 mm, p=0.011), and the preoperative aortic diameter (26.0 ± 5.6 vs 35.2 ± 12.1 mm) and the false lumen diameter (13.1 ± 5.2 vs 21.2 ± 11.3 mm) were smaller. During the procedure, there were 3 (5.3%) type I endoleaks, 1 (1.8%) surgical conversion and 1 (1.8%) partial renal artery coverage without perioperative mortality. Patients with straight grafts had shorter operative time (96.5 ± 24.4 vs 144.2 ± 49.0 minutes, pConclusions: Endovascular treatment provides a safe, minimally invasive treatment for IAAD in midterm follow-up. Compression of the true lumen at the aortic bifurcation is the main concern after treatment with a bifurcated graft. Straight grafts are an excellent alternative for some patients, with the benefit of reduced procedural time, effective aortic remodeling, and excellent clinical prognosis. More experience is needed to offer clear recommendations for making treatment decisions as well as determine long-term effectiveness and durability.

INTRODUCTION Double-eyelid blepharoplasty and epicanthoplasty is usually performed concurrently in Asian patients to achieve aesthetically pleasing eye appearance, but the epicanthal area is prone to scarring. This article introduces an integrated four-step technique that enlarges the eyes with minimal scarring risk. METHODS One hundred and thirty-five patients who underwent Asian upper blepharoplasty and dual-plane epicanthoplasty between January 2019 and September 2020 were retrospectively reviewed. Palpebral fissure distance (PFD), inner canthal distance (ICD), and vertical height of the palpebral fissure (VHPF) were measured based on photographs taken pre- and postoperatively, with the diameter of the iris as the reference value, D. Patient-reported outcome measures (PROMs) were collected to evaluate patient satisfaction and complications. RESULTS A total of 129 female and 6 male patients were included. The average follow-up duration was 12.9 months. The average increasing ratio of PFD/D was 8.1%, whereas the average shortening ratio of ICD/D was 3.5%. The average VHPF/D increased by 10.6%. Statistical analysis was conducted using a paired t-test which demonstrated statistically significant results. A total of 96 patients responded to our patient-reported outcome measures (PROMs) at 6 months postoperatively, with an average patient satisfaction score of 62.9 out of 70 (89.8%). Barely visible scarring for the upper eyelid and the inner canthus were reported by 90.6% and 85.4% of patients, respectively. Three (3.1%) patients were not satisfied with the scar in the medial canthal area 6 months after surgery. CONCLUSIONS The integrated four-step blepharoplasty and dual-plane epicanthoplasty is an effective, reliable, and reproducible method with high patient satisfaction and minimal scarring risk.

Artificial intelligence technology is an important transformative force for teaching innovation in the intelligent era. It is being widely used in American school teaching, including the design of intelligent tutoring systems to achieve precise problem solving, the machine learning technology to ensure personalized activity design, the creation of intelligent virtual reality to promote classroom teaching contextualization, and the development of intelligent evaluation systems to ensure the scientific evaluation of capabilities. In the process of advancing the teaching and application of artificial intelligence technology, the United States has built a linkage mechanism of federal leadership, university follow-up, and social collaboration and implemented the smart technology in school teaching and professors’ academic governance. This paper is aimed at studying the professors’ academic governance of American research universities by Internet data mining, historical analysis method, documentary method, survey method, and other methods. Professors’ academic governance is a vital part of the modern university system that causes the institutional reform of the internal governance structure of modern universities. The United States is a powerful country in higher education, and professors in American research universities have always participated in university academic governance for centuries. By studying the definition, history, and development and mode of operation of professors’ academic governance in American research universities, the results indicate a clear division of power and responsibility between the professors and administrators based on an artificial intelligence decision system in American research universities. Also, there is a good communication platform based on artificial intelligence environment for professors to discuss their opinions on academic affairs. Third, professors exercise academic power under the guarantee of diversified guaranteed systems based on the artificial intelligence evaluation system and the ideology of mutual respect based on the artificial intelligence management and service system. Studying the application of artificial intelligence techniques in operating mode and enlightenment of professors’ academic governance in an American research university is of great significance to promote the construction of other modern universities’ professors’ academic governance system.

Objective: Schizophrenia, a complex psychiatric disorder, is often associated with cognitive, neurological and neuroimaging abnormalities. The processes underlying these abnormalities, and whether a subset of people with schizophrenia have a neuroprogressive or neurodegenerative component to schizophrenia, remain largely unknown. Examining fluid biomarkers of diverse types of neuronal damage could increase our understanding of these processes, as well as potentially provide clinically useful biomarkers, for example with assisting with differentiation from progressive neurodegenerative disorders such as Alzheimer and frontotemporal dementias. Methods: This study measured plasma neurofilament light chain protein (NfL) using ultrasensitive Simoa technology, to investigate the degree of neuronal injury in a well-characterised cohort of people with treatment-resistant schizophrenia on clozapine ( n = 82), compared to first-degree relatives (an at-risk group, n = 37), people with schizophrenia not treated with clozapine ( n = 13), and age- and sex-matched controls ( n = 59). Results: We found no differences in NfL levels between treatment-resistant schizophrenia (mean NfL, M = 6.3 pg/mL, 95% confidence interval: [5.5, 7.2]), first-degree relatives (siblings, M = 6.7 pg/mL, 95% confidence interval: [5.2, 8.2]; parents, M after adjusting for age = 6.7 pg/mL, 95% confidence interval: [4.7, 8.8]), controls (M = 5.8 pg/mL, 95% confidence interval: [5.3, 6.3]) and not treated with clozapine (M = 4.9 pg/mL, 95% confidence interval: [4.0, 5.8]). Exploratory, hypothesis-generating analyses found weak correlations in treatment-resistant schizophrenia, between NfL and clozapine levels (Spearman’s r = 0.258, 95% confidence interval: [0.034, 0.457]), dyslipidaemia ( r = 0.280, 95% confidence interval: [0.064, 0.470]) and a negative correlation with weight ( r = −0.305, 95% confidence interval: [−0.504, −0.076]). Conclusion: Treatment-resistant schizophrenia does not appear to be associated with neuronal, particularly axonal degeneration. Further studies are warranted to investigate the utility of NfL to differentiate treatment-resistant schizophrenia from neurodegenerative disorders such as behavioural variant frontotemporal dementia, and to explore NfL in other stages of schizophrenia such as the prodome and first episode.

The aim of this study was to evaluate the pharmacokinetic bioequivalence of a generic budesonide nasal spray and a branded product in healthy Chinese subjects under fasting condition. A single-center, single-dose, randomized, open-label, crossover study was conducted in 32 healthy Chinese subjects under fasting condition. The subjects were administered 256 μg of generic budesonide nasal spray (test drug) or branded budesonide nasal spray (RHINOCORT AQUA, reference drug), respectively. For each period, the subjects were administered with 64 μg of budesonide per spray and 2 sprays for each nostril followed by a washout period of 7 days. Plasma concentration of budesonide was determined by a validated high-performance liquid chromatography-tandem mass spectrometry method. The pharmacokinetic (PK) parameters were calculated, and the bioequivalence was compared using the noncompartment model with the Phoenix WinNonlin 7.0 program. Results show that the 90% confidence intervals of the test/reference ratios of maximum concentration, area under the plasma concentration-time curve from time 0 to the last measurable concentration, and area under the plasma concentration-time curve from time 0 to infinity for the budesonide concentration were 84.8% to 102.7%, 84.6% to 94.4%, and 85.4% to 95.2%, respectively, all fall within the bioequivalent range of 80% to 125%. The test and reference budesonide nasal sprays were PK bioequivalents in healthy Chinese subjects with comparable PK parameters. No serious adverse events were reported, and the 2 products have a good and similar safety profile.

There has been recent interest in understanding the transport of nanoconfined fluids through single-digit nanopores (SDNs) or those smaller than 10 nm in diameters, where confinement alters the flu...

ObjectiveTo investigate the prevalence and clinical correlates of video polysomnography (vPSG)-confirmed rapid eye movement sleep behaviour disorder (RBD) in patients with major depressive disorder (MDD).MethodsThis is a clinic-based two-phase epidemiological study. In phase 1, patients with MDD were screened by a validated questionnaire, RBD Questionnaire-Hong Kong (RBDQ-HK). In phase 2, a subsample of both the screen-positive (RBDQ-HK >20) and screen–negative patients with MDD underwent further clinical and sleep assessment (vPSG) to confirm the diagnosis of RBD (MDD+RBD). Poststratification weighting method was used to estimate the prevalence of MDD+RBD. The total likelihood ratio and the probability of prodromal Parkinson’s disease (PD) were calculated from prodromal markers and risk factors, as per the Movement Disorder Society research criteria.ResultsA total of 455 patients with MDD were screened (median age (IQR)=52.66 (15.35) years, 77.58% woman, 43.74% positive). Eighty-one patients underwent vPSG and 12 of them were confirmed MDD+RBD. The prevalence of MDD+RBD was estimated to be 8.77% (95% CI: 4.33% to 16.93%), with possibly male predominance. MDD+RBD were associated with colour vision and olfaction deficit and a higher probability for prodromal PD.ConclusionsAlmost 9% of patients with MDD in the psychiatric outpatient clinic has vPSG-confirmed RBD. Comorbid MDD+RBD may represent a subtype of MDD with underlying α-synucleinopathy neurodegeneration. Systematic screening of RBD symptoms and necessity of vPSG confirmation should be highlighted for capturing this MDD subtype with a view to enhance personalised treatment and future neuroprotection to prevent neurodegeneration.

The present study is aimed at investigating the molecular mechanism of C1q/TNF-related protein 9 (CTRP9) and providing a new perspective in arteriovenous shunt-induced pulmonary arterial hypertension (PAH). PAH was established by an arteriovenous shunt placement performed in rats. Adenovirus(Ad)-CTRP9 and Ad-green fluorescent protein viral particles were injected into the rats through the tail vein. Following 12 weeks, the mean pulmonary arterial pressure (mPAP) and right ventricular systolic pressure (RVSP) were measured and morphological analysis was conducted to confirm the establishment of the PAH model. The systemic elevation of CTRP9 maintained pulmonary vascular homeostasis and protected the rats from dysfunctional and abnormal remodeling. CTRP9 attenuated the pulmonary vascular remodeling in the shunt group by decreasing the mPAP and RVSP, which was associated with suppressed inflammation, apoptosis, and extracellular matrix injury. In addition, CTRP9 dramatically increased the phosphorylation of AKT and p38-MAPK in the lung tissues of shunt-operated animals. These findings suggest a previously unrecognized effect of CTRP9 in pulmonary vascular homeostasis during PAH pathogenesis.

This paper explores the coordination problem of a socially responsible supply chain, in which the supplier contributes corporate social responsibility by donating a specific amount of money to a cause marketing and the retailer cares for consumer surplus in addition to its own profit. We consider two donation formats: absolute amount donation and percentage of profit donation. For each donation format, the optimal solutions for the centralized and decentralized decisions are derived, and the effects of system parameters on the optimal decisions and other outcome variables are investigated. Moreover, a two-part tariff contract and a quantity discount plus cost sharing contract are proposed to coordinate the socially responsible supply chain. This study may provide theoretical support and operational procedures for supply chain managers to integrate corporate social responsibility into business routines.