Publications by authors named "Wenrui Dai"

29 Publications

  • Page 1 of 1

From Micropores to Ultra-micropores inside Hard Carbon: Toward Enhanced Capacity in Room-/Low-Temperature Sodium-Ion Storage.

Nanomicro Lett 2021 Mar 30;13(1):98. Epub 2021 Mar 30.

Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou, 350207, People's Republic of China.

Highlights: Hard-carbon anode dominated with ultra-micropores (< 0.5 nm) was synthesized for sodium-ion batteries via a molten diffusion-carbonization method. The ultra-micropores dominated carbon anode displays an enhanced capacity, which originates from the extra sodium-ion storage sites of the designed ultra-micropores. The thick electrode (~ 19 mg cm) with a high areal capacity of 6.14 mAh cm displays an ultrahigh cycling stability and an outstanding low-temperature performance. Pore structure of hard carbon has a fundamental influence on the electrochemical properties in sodium-ion batteries (SIBs). Ultra-micropores (< 0.5 nm) of hard carbon can function as ionic sieves to reduce the diffusion of slovated Na but allow the entrance of naked Na into the pores, which can reduce the interficial contact between the electrolyte and the inner pores without sacrificing the fast diffusion kinetics. Herein, a molten diffusion-carbonization method is proposed to transform the micropores (> 1 nm) inside carbon into ultra-micropores (< 0.5 nm). Consequently, the designed carbon anode displays an enhanced capacity of 346 mAh g at 30 mA g with a high ICE value of ~ 80.6% and most of the capacity (~ 90%) is below 1 V. Moreover, the high-loading electrode (~ 19 mg cm) exhibits a good temperature endurance with a high areal capacity of 6.14 mAh cm at 25 °C and 5.32 mAh cm at - 20 °C. Based on the in situ X-ray diffraction and ex situ solid-state nuclear magnetic resonance results, the designed ultra-micropores provide the extra Na storage sites, which mainly contributes to the enhanced capacity. This proposed strategy shows a good potential for the development of high-performance SIBs.
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http://dx.doi.org/10.1007/s40820-020-00587-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010088PMC
March 2021

Automatic Image Selection Model Based on Machine Learning for Endobronchial Ultrasound Strain Elastography Videos.

Front Oncol 2021 31;11:673775. Epub 2021 May 31.

School of Electronic Information & Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Background: Endoscopic ultrasound (EBUS) strain elastography can diagnose intrathoracic benign and malignant lymph nodes (LNs) by reflecting the relative stiffness of tissues. Due to strong subjectivity, it is difficult to give full play to the diagnostic efficiency of strain elastography. This study aims to use machine learning to automatically select high-quality and stable representative images from EBUS strain elastography videos.

Methods: LNs with qualified strain elastography videos from June 2019 to November 2019 were enrolled in the training and validation sets randomly at a quantity ratio of 3:1 to train an automatic image selection model using machine learning algorithm. The strain elastography videos in December 2019 were used as the test set, from which three representative images were selected for each LN by the model. Meanwhile, three experts and three trainees selected one representative image severally for each LN on the test set. Qualitative grading score and four quantitative methods were used to evaluate images above to assess the performance of the automatic image selection model.

Results: A total of 415 LNs were included in the training and validation sets and 91 LNs in the test set. Result of the qualitative grading score showed that there was no statistical difference between the three images selected by the machine learning model. Coefficient of variation (CV) values of the four quantitative methods in the machine learning group were all lower than the corresponding CV values in the expert and trainee groups, which demonstrated great stability of the machine learning model. Diagnostic performance analysis on the four quantitative methods showed that the diagnostic accuracies were range from 70.33% to 73.63% in the trainee group, 78.02% to 83.52% in the machine learning group, and 80.22% to 82.42% in the expert group. Moreover, there were no statistical differences in corresponding mean values of the four quantitative methods between the machine learning and expert groups (p >0.05).

Conclusion: The automatic image selection model established in this study can help select stable and high-quality representative images from EBUS strain elastography videos, which has great potential in the diagnosis of intrathoracic LNs.
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http://dx.doi.org/10.3389/fonc.2021.673775DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201408PMC
May 2021

3D-Assembled rutile TiO spheres with c-channels for efficient lithium-ion storage.

Nanoscale 2021 Jul 16;13(25):11104-11111. Epub 2021 Jun 16.

Department of Chemistry, National University of Singapore, Singapore 117543, Singapore.

Three-dimensional (3D) TiO architectures have attracted significant attention recently as they can improve the electrochemical stability and realize the full potential of TiO-based anodes in lithium ion batteries. Here, flower-like rutile TiO spheres with radially assembled nanorods (c-channels) were fabricated via a simple hydrothermal method. The 3D radial architecture affords massive active sites to fortify the lithium storage. Moreover, the presence of c-channels facilitates electrolyte infiltration and offers facile pathways for efficient Li transport. As a result, this flower-like rutile TiO anode gives significantly enhanced specific capacities (615 mA h g at 1 C and 386 mA h g at 2 C after 400 cycles) and a superior long-term cyclability (up to 10 000 cycles with a specific capacity of 67 mA h g at 100 C). Kinetic analysis reveals that the enhanced diffusion-controlled and surface capacitive storage leads to the excellent electrochemical behavior. This work not only exhibits the enormous advantages of 3D architectures with c-channels, but also provides access to structural design and crystal phase selection for TiO-based anode materials.
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http://dx.doi.org/10.1039/d1nr02064aDOI Listing
July 2021

Monodispersed Ruthenium Nanoparticles on Nitrogen-Doped Reduced Graphene Oxide for an Efficient Lithium-Oxygen Battery.

ACS Appl Mater Interfaces 2021 May 21;13(17):19915-19926. Epub 2021 Apr 21.

Department of Chemistry, National University of Singapore, 3 Science Drive 3, 117543, Singapore.

Lithium-oxygen batteries with ultrahigh energy densities have drawn considerable attention as next-generation energy storage devices. However, their practical applications are challenged by sluggish reaction kinetics aimed at the formation/decomposition of discharge products on battery cathodes. Developing effective catalysts and understanding the fundamental catalytic mechanism are vital to improve the electrochemical performance of lithium-oxygen batteries. Here, uniformly dispersed ruthenium nanoparticles anchored on nitrogen-doped reduced graphene oxide are prepared by using an pyrolysis procedure as a bifunctional catalyst for lithium-oxygen batteries. The abundance of ruthenium active sites and strong ruthenium-support interaction enable a feasible discharge product formation/decomposition route by modulating the surface adsorption of lithium superoxide intermediates and the nucleation and growth of lithium peroxide species. Benefiting from these merits, the electrode provides a drastically increased discharge capacity (17,074 mA h g), a decreased charge overpotential (0.51 V), and a long-term cyclability (100 cycles at 100 mA g). Our observations reveal the significance of the dispersion and coordination of metal catalysts, shedding light on the rational design of efficient catalysts for future lithium-oxygen batteries.
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http://dx.doi.org/10.1021/acsami.0c23125DOI Listing
May 2021

General Bitwidth Assignment for Efficient Deep Convolutional Neural Network Quantization.

IEEE Trans Neural Netw Learn Syst 2021 Apr 8;PP. Epub 2021 Apr 8.

Model quantization is essential to deploy deep convolutional neural networks (DCNNs) on resource-constrained devices. In this article, we propose a general bitwidth assignment algorithm based on theoretical analysis for efficient layerwise weight and activation quantization of DCNNs. The proposed algorithm develops a prediction model to explicitly estimate the loss of classification accuracy led by weight quantization with a geometrical approach. Consequently, dynamic programming is adopted to achieve optimal bitwidth assignment on weights based on the estimated error. Furthermore, we optimize bitwidth assignment for activations by considering the signal-to-quantization-noise ratio (SQNR) between weight and activation quantization. The proposed algorithm is general to reveal the tradeoff between classification accuracy and model size for various network architectures. Extensive experiments demonstrate the efficacy of the proposed bitwidth assignment algorithm and the error rate prediction model. Furthermore, the proposed algorithm is shown to be well extended to object detection.
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http://dx.doi.org/10.1109/TNNLS.2021.3069886DOI Listing
April 2021

Efficient photocatalytic hydrogen peroxide generation coupled with selective benzylamine oxidation over defective ZrS nanobelts.

Nat Commun 2021 04 1;12(1):2039. Epub 2021 Apr 1.

Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, Singapore.

Photocatalytic hydrogen peroxide (HO) generation represents a promising approach for artificial photosynthesis. However, the sluggish half-reaction of water oxidation significantly limits the efficiency of HO generation. Here, a benzylamine oxidation with more favorable thermodynamics is employed as the half-reaction to couple with HO generation in water by using defective zirconium trisulfide (ZrS) nanobelts as a photocatalyst. The ZrS nanobelts with disulfide (S) and sulfide anion (S) vacancies exhibit an excellent photocatalytic performance for HO generation and simultaneous oxidation of benzylamine to benzonitrile with a high selectivity of >99%. More importantly, the S and S vacancies can be separately introduced into ZrS nanobelts in a controlled manner. The S vacancies are further revealed to facilitate the separation of photogenerated charge carriers. The S vacancies can significantly improve the electron conduction, hole extraction, and kinetics of benzylamine oxidation. As a result, the use of defective ZrS nanobelts yields a high production rate of 78.1 ± 1.5 and 32.0 ± 1.2 μmol h for HO and benzonitrile, respectively, under a simulated sunlight irradiation.
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http://dx.doi.org/10.1038/s41467-021-22394-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016833PMC
April 2021

iPool--Information-Based Pooling in Hierarchical Graph Neural Networks.

IEEE Trans Neural Netw Learn Syst 2021 Mar 31;PP. Epub 2021 Mar 31.

With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspective. The majority of these works, however, focus on adapting the convolution operator to graph representation. At the same time, the pooling operator also plays an important role in distilling multiscale and hierarchical representations, but it has been mostly overlooked so far. In this article, we propose a parameter-free pooling operator, called iPool, that permits to retain the most informative features in arbitrary graphs. With the argument that informative nodes dominantly characterize graph signals, we propose a criterion to evaluate the amount of information of each node given its neighbors and theoretically demonstrate its relationship to neighborhood conditional entropy. This new criterion determines how nodes are selected and coarsened graphs are constructed in the pooling layer. The resulting hierarchical structure yields an effective isomorphism-invariant representation of networked data on arbitrary topologies. The proposed strategy achieves superior or competitive performance in graph classification on a collection of public graph benchmark data sets and superpixel-induced image graph data sets.
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http://dx.doi.org/10.1109/TNNLS.2021.3067441DOI Listing
March 2021

Evoking ordered vacancies in metallic nanostructures toward a vacated Barlow packing for high-performance hydrogen evolution.

Sci Adv 2021 Mar 24;7(13). Epub 2021 Mar 24.

Department of Chemistry, City University of Hong Kong, Kowloon, Hong Kong, China.

Metallic nanostructures are commonly densely packed into a few packing variants with slightly different atomic packing factors. The structural aspects and physicochemical properties related with the vacancies in such nanostructures are rarely explored because of lack of an effective way to control the introduction of vacancy sites. Highly voided metallic nanostructures with ordered vacancies are however energetically high lying and very difficult to synthesize. Here, we report a chemical method for synthesis of hierarchical Rh nanostructures (Rh NSs) composed of ultrathin nanosheets, composed of hexagonal close-packed structure embedded with nanodomains that adopt a vacated Barlow packing with ordered vacancies. The obtained Rh NSs exhibit remarkably enhanced electrocatalytic activity and stability toward the hydrogen evolution reaction (HER) in alkaline media. Theoretical calculations reveal that the exceptional electrocatalytic performance of Rh NSs originates from their unique vacancy structures, which facilitate the adsorption and dissociation of HO in the HER.
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http://dx.doi.org/10.1126/sciadv.abd6647DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990340PMC
March 2021

Partially-Connected Neural Architecture Search for Reduced Computational Redundancy.

IEEE Trans Pattern Anal Mach Intell 2021 09 4;43(9):2953-2970. Epub 2021 Aug 4.

Differentiable architecture search (DARTS) enables effective neural architecture search (NAS) using gradient descent, but suffers from high memory and computational costs. In this paper, we propose a novel approach, namely Partially-Connected DARTS (PC-DARTS), to achieve efficient and stable neural architecture search by reducing the channel and spatial redundancies of the super-network. In the channel level, partial channel connection is presented to randomly sample a small subset of channels for operation selection to accelerate the search process and suppress the over-fitting of the super-network. Side operation is introduced for bypassing (non-sampled) channels to guarantee the performance of searched architectures under extremely low sampling rates. In the spatial level, input features are down-sampled to eliminate spatial redundancy and enhance the efficiency of the mixed computation for operation selection. Furthermore, edge normalization is developed to maintain the consistency of edge selection based on channel sampling with the architectural parameters for edges. Theoretical analysis shows that partial channel connection and parameterized side operation are equivalent to regularizing the super-network on the weights and architectural parameters during bilevel optimization. Experimental results demonstrate that the proposed approach achieves higher search speed and training stability than DARTS. PC-DARTS obtains a top-1 error rate of 2.55 percent on CIFAR-10 with 0.07 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.1 percent on ImageNet (under the mobile setting) within 2.8 GPU-days.
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http://dx.doi.org/10.1109/TPAMI.2021.3059510DOI Listing
September 2021

Deep learning with convex probe endobronchial ultrasound multimodal imaging: A validated tool for automated intrathoracic lymph nodes diagnosis.

Endosc Ultrasound 2021 Feb 9. Epub 2021 Feb 9.

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Background And Objectives: Along with the rapid improvement of imaging technology, convex probe endobronchial ultrasound (CP-EBUS) sonographic features play an increasingly important role in the diagnosis of intrathoracic lymph nodes (LNs). Conventional qualitative and quantitative methods for EBUS multimodal imaging are time-consuming and rely heavily on the experience of endoscopists. With the development of deep-learning (DL) models, there is great promise in the diagnostic field of medical imaging.

Materials And Methods: We developed DL models to retrospectively analyze CP-EBUS images of 294 LNs from 267 patients collected between July 2018 and May 2019. The DL models were trained on 245 LNs to differentiate benign and malignant LNs using both unimodal and multimodal CP-EBUS images and independently evaluated on the remaining 49 LNs to validate their diagnostic efficiency. The human comparator group consisting of three experts and three trainees reviewed the same test set as the DL models.

Results: The multimodal DL framework achieves an accuracy of 88.57% (95% confidence interval [CI] [86.91%-90.24%]) and area under the curve (AUC) of 0.9547 (95% CI [0.9451-0.9643]) using the three modes of CP-EBUS imaging in comparison to the accuracy of 80.82% (95% CI [77.42%-84.21%]) and AUC of 0.8696 (95% CI [0.8369-0.9023]) by experts. Statistical comparison of their average receiver operating curves shows a statistically significant difference (P < 0.001). Moreover, the multimodal DL framework is more consistent than experts (kappa values 0.7605 vs. 0.5800).

Conclusions: The DL models based on CP-EBUS imaging demonstrated an accurate automated tool for diagnosis of the intrathoracic LNs with higher diagnostic efficiency and consistency compared with experts.
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http://dx.doi.org/10.4103/EUS-D-20-00207DOI Listing
February 2021

Big Data Privacy in Biomedical Research.

IEEE Trans Big Data 2020 Jun 13;6(2):296-308. Epub 2016 Sep 13.

Department of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093.

Biomedical research often involves studying patient data that contain personal information. Inappropriate use of these data might lead to leakage of sensitive information, which can put patient privacy at risk. The problem of preserving patient privacy has received increasing attentions in the era of big data. Many privacy methods have been developed to protect against various attack models. This paper reviews relevant topics in the context of biomedical research. We discuss privacy preserving technologies related to (1) record linkage, (2) synthetic data generation, and (3) genomic data privacy. We also discuss the ethical implications of big data privacy in biomedicine and present challenges in future research directions for improving data privacy in biomedical research.
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http://dx.doi.org/10.1109/TBDATA.2016.2608848DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7258042PMC
June 2020

Gas-Phase Photoelectrocatalytic Oxidation of NO TiO Nanorod Array/FTO Photoanodes.

Environ Sci Technol 2020 05 14;54(9):5902-5912. Epub 2020 Apr 14.

Chinese Education Ministry Key Lab and International Joint Lab of Resource Chemistry, Shanghai Normal University, Shanghai 200234, P. R. China.

Most photoelectrocatalytic (PEC) reactions are performed in the liquid phase for convenient electron transfer in an electrolyte solution. Herein, a novel PEC reactor involving a tandem combination of TiO nanorod array/fluorine-doped tin oxide (TiO-NR/FTO) working electrodes and an electrochemical auxiliary cell was constructed to drive the highly efficient PEC oxidation of indoor gas (NO). With the aid of a low bias voltage (0.3 V), the as-formed PEC reactor exhibited an 80% removal rate for oxidizing NO (500 ppb) under light irradiation, which is much higher than that of the traditional photocatalytic (PC) process. Upon being irradiated by light, the photogenerated electrons are quickly separated from the holes and transferred to the counter electrode (Pt) owing to the applied bias voltage, leaving photogenerated holes in the TiO-NR/FTO electrode for oxidizing NO molecules. Moreover, both dry and humid NO could be effectively removed by the tandem TiO-NR/FTO-based gas-phase PEC reactor, indicating that the NO molecules could also be directly oxidized by photogenerated holes in addition to hydroxyl radicals. The presence of trace amounts of water could promote the PEC oxidation of NO owing to the formation of hydroxyl radicals induced by reactions between the water and holes, which could further oxidize NO. This PEC reactor offers an energy-saving, environmentally friendly, and efficient route to treat air polluted with low concentrations of gases (NO and SO).
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http://dx.doi.org/10.1021/acs.est.9b07757DOI Listing
May 2020

Potassium Doping Facilitated Formation of Tunable Superoxides in LiO for Improved Electrochemical Kinetics.

ACS Appl Mater Interfaces 2020 Jan 21;12(4):4558-4564. Epub 2020 Jan 21.

Department of Chemistry , National University of Singapore , 3 Science Drive 3 , 117543 Singapore.

Superoxide (O) species play a crucial role in determining the charge kinetics for aprotic lithium-oxygen (Li-O) batteries. However, the growth of O-rich lithium peroxide (LiO) is challenging since O is thermodynamically unfavorable and unstable in an O atmosphere. Herein, we reported the synthesis of defective LiO with tunable O via K doping. The K dopants can successfully stabilize O species and induce the coordination of Li with O, leading to increased Li vacancies. Compared to the pristine LiO, the as-prepared defective LiO can be charged at a lower overpotential in Li-O batteries, which is ascribed to further increased Li vacancies contributed by the depotassiation process at the onset of the charge process. Our findings suggest a new strategy to better control O species in LiO by K dopants and provide insights into the K effects on charge mechanism in Li-O batteries.
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http://dx.doi.org/10.1021/acsami.9b21554DOI Listing
January 2020

Privacy Policy and Technology in Biomedical Data Science.

Annu Rev Biomed Data Sci 2018 Jul;1:115-129

Department of Biomedical Informatics, School of Medicine, University of California, San Diego, La Jolla, California 92093, USA;

Privacyis an important consideration when sharing clinical data, which often contain sensitive information. Adequate protection to safeguard patient privacy and to increase public trust in biomedical research is paramount. This review covers topics in policy and technology in the context of clinical data sharing. We review policy articles related to () the Common Rule, HIPAA privacy and security rules, and governance; () patients' viewpoints and consent practices; and () research ethics. We identify key features of the revised Common Rule and the most notable changes since its previous version. We address data governance for research in addition to the increasing emphasis on ethical and social implications. Research ethics topics include data sharing best practices, use of data from populations of low socioeconomic status (SES), recent updates to institutional review board (IRB) processes to protect human subjects' data, and important concerns about the limitations of current policies to address data deidentification. In terms of technology, we focus on articles that have applicability in real world health care applications: deidentification methods that comply with HIPAA, data anonymization approaches to satisfy well-acknowledged issues in deidentified data, encryption methods to safeguard data analyses, and privacy-preserving predictive modeling. The first two technology topics are mostly relevant to methodologies that attempt to sanitize structured or unstructured data. The third topic includes analysis on encrypted data. The last topic includes various mechanisms to build statistical models without sharing raw data.
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http://dx.doi.org/10.1146/annurev-biodatasci-080917-013416DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497413PMC
July 2018

Monodispersed Ru Nanoparticles Functionalized Graphene Nanosheets as Efficient Cathode Catalysts for O-Assisted Li-CO Battery.

ACS Omega 2017 Dec 29;2(12):9280-9286. Epub 2017 Dec 29.

Department of Physics, National University of Singapore, 2 Science Drive 3, 117542 Singapore.

In Li-CO battery, due to the highly insulating nature of the discharge product of LiCO, the battery needs to be charged at a high charge overpotential, leading to severe cathode and electrolyte instability and hence poor battery cycle performance. Developing efficient cathode catalysts to effectively reduce the charge overpotential represents one of key challenges to realize practical Li-CO batteries. Here, we report the use of monodispersed Ru nanoparticles functionalized graphene nanosheets as cathode catalysts in Li-CO battery to significantly lower the charge overpotential for the electrochemical decomposition of LiCO. In our battery, a low charge voltage of 4.02 V, a high Coulomb efficiency of 89.2%, and a good cycle stability (67 cycles at a 500 mA h/g limited capacity) are achieved. It is also found that O plays an essential role in the discharge process of the rechargeable Li-CO battery. Under the pure CO environment, Li-CO battery exhibits negligible discharge capacity; however, after introducing 2% O (volume ratio) into CO, the O-assisted Li-CO battery can deliver a high capacity of 4742 mA h/g. Through an in situ quantitative differential electrochemical mass spectrometry investigation, the final discharge product LiCO is proposed to form via the reaction 4Li + 2CO + O + 4e → 2LiCO. Our results validate the essential role of O and can help deepen the understanding of the discharge and charge reaction mechanisms of the Li-CO battery.
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http://dx.doi.org/10.1021/acsomega.7b01495DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6645591PMC
December 2017

Increase in contralateral prophylactic mastectomy conversation online unrelated to decision-making.

J Surg Res 2017 10 7;218:253-260. Epub 2017 Jul 7.

Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, California.

Background: The increased uptake of contralateral prophylactic mastectomy (CPM) among breast cancer patients remains poorly understood. We hypothesized that the increased rate of CPM is represented in conversations on an online breast cancer community and may contribute to patients choosing this operation.

Methods: We downloaded 328,763 posts and their dates of creation from an online breast cancer community from August 1, 2000, to May 22, 2016. We then performed a keyword search to identify posts which mentioned breast cancer surgeries: contralateral prophylactic mastectomy (n = 7095), mastectomy (n = 10,889), and lumpectomy (n = 9694). We graphed the percentage of CPM-related, lumpectomy-related, and mastectomy-related conversations over time. We also graphed the frequency of posts which mentioned multiple operations over time. Finally, we performed a qualitative study to identify factors influencing the observed trends.

Results: Surgically related posts (e.g., mentioning at least one operation) made up a small percentage (n = 27,678; 8.4%) of all posts on this community. The percentage of surgically related posts mentioning CPM was found to increase over time, whereas the percentage of surgically related posts mentioning mastectomy decreased over time. Among posts that mentioned more than one operation, mastectomy and lumpectomy were the procedures most commonly mentioned together, followed by mastectomy and CPM. There was no change over time in the frequency of posts that mentioned more than one operation. Our qualitative review found that most posts mentioning a single operation were unrelated to surgical decision-making; rather the operation was mentioned only in the context of the patient's cancer history. Conversely, the most posts mentioning multiple operations centered around the patients' surgical decision-making process.

Conclusions: CPM-related conversation is increasing on this online breast cancer community, whereas mastectomy-related conversation is decreasing. These results appear to be primarily informed by patients reporting the types of operations they have undergone, and thus appear to correspond to the known increased uptake of CPM.
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http://dx.doi.org/10.1016/j.jss.2017.05.074DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5658786PMC
October 2017

Correction: Recent advances in understanding of the mechanism and control of LiO formation in aprotic Li-O batteries.

Chem Soc Rev 2017 10;46(19):6073

National University of Singapore (Suzhou) Research Institute, Suzhou, 215123, China.

Correction for 'Recent advances in understanding of the mechanism and control of LiO formation in aprotic Li-O batteries' by Zhiyang Lyu et al., Chem. Soc. Rev., 2017, DOI: 10.1039/c7cs00255f.
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http://dx.doi.org/10.1039/c7cs90095cDOI Listing
October 2017

Recent advances in understanding of the mechanism and control of LiO formation in aprotic Li-O batteries.

Chem Soc Rev 2017 Oct;46(19):6046-6072

National University of Singapore (Suzhou) Research Institute, Suzhou, 215123, China.

Aprotic Li-O batteries represent promising alternative devices for electrical energy storage owing to their extremely high energy densities. Upon discharge, insulating solid LiO forms on cathode surfaces, which is usually governed by two growth models, namely the solution model and the surface model. These LiO growth models can largely determine the battery performances such as the discharge capacity, round-trip efficiency and cycling stability. Understanding the LiO formation mechanism and controlling its growth are essential to fully realize the technological potential of Li-O batteries. In this review, we overview the recent advances in understanding the electrochemical and chemical processes that occur during the LiO formation. In the beginning, the oxygen reduction mechanisms, the identification of O/LiO intermediates, and their influence on the LiO morphology have been discussed. The effects of the discharge current density and potential on the LiO growth model have been subsequently reviewed. Special focus is then given to the prominent strategies, including the electrolyte-mediated strategy and the cathode-catalyst-tailoring strategy, for controlling the LiO growth pathways. Finally, we conclude by discussing the profound implications of controlling LiO formation for further development in Li-O batteries.
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http://dx.doi.org/10.1039/c7cs00255fDOI Listing
October 2017

PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension.

BMC Med Genomics 2017 07 26;10(Suppl 2):48. Epub 2017 Jul 26.

Department of Biomedical Informatics, University of California San Diego, La Jolla, 92093, CA, USA.

Background: Advances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data.

Methods: We present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing.

Results: We compared the performance of the proposed PRESAGE framework with the state-of-the-art homomorphic encryption scheme, as well as the plaintext implementation. The experimental results demonstrated significant performance over the homomorphic encryption methods and a small computational overhead in comparison to plaintext implementation.

Conclusions: The proposed PRESAGE provides an alternative solution for secure and efficient genomic data outsourcing in an untrusted cloud by using a hybrid framework that combines secure hardware and multiple crypto protocols.
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http://dx.doi.org/10.1186/s12920-017-0281-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5547453PMC
July 2017

Progressive Dictionary Learning With Hierarchical Predictive Structure for Low Bit-Rate Scalable Video Coding.

IEEE Trans Image Process 2017 Jun 12;26(6):2972-2987. Epub 2017 Apr 12.

Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers with a guarantee of reconstruction performance. The overcomplete dictionary is trained to adaptively capture local structures along motion trajectories as well as exploit the correlations between the neighboring layers of resolutions. Furthermore, progressive dictionary learning is developed to enable the scalability in temporal domain and restrict the error propagation in a closed-loop predictor. Under the hierarchical predictive structure, online learning is leveraged to guarantee the training and prediction performance with an improved convergence rate. To accommodate with the state-of-the-art scalable extension of H.264/AVC and latest High Efficiency Video Coding (HEVC), standardized codec cores are utilized to encode the base and enhancement layers. Experimental results show that the proposed method outperforms the latest scalable extension of HEVC and HEVC simulcast over extensive test sequences with various resolutions.
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http://dx.doi.org/10.1109/TIP.2017.2692882DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638692PMC
June 2017

Sparse Representation With Spatio-Temporal Online Dictionary Learning for Promising Video Coding.

IEEE Trans Image Process 2016 Oct 27;25(10):4580-4595. Epub 2016 Jul 27.

Classical dictionary learning methods for video coding suffer from high computational complexity and interfered coding efficiency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3D low-frequency and high-frequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data, such as batch learning methods, e.g., K-SVD. Since the selected volumes are supposed to be independent identically distributed samples from the underlying distribution, decomposition coefficients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL-based coding scheme achieves performance improvements than H.264/AVC or High Efficiency Video Coding as well as existing super-resolution-based methods in rate-distortion performance and visual quality.
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http://dx.doi.org/10.1109/TIP.2016.2594490DOI Listing
October 2016

Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE).

BMC Med Inform Decis Mak 2016 07 25;16 Suppl 3:89. Epub 2016 Jul 25.

Department of Biomedical Informatics, University of California, San Diego, CA, 92093, USA.

Background: In biomedical research, data sharing and information exchange are very important for improving quality of care, accelerating discovery, and promoting the meaningful secondary use of clinical data. A big concern in biomedical data sharing is the protection of patient privacy because inappropriate information leakage can put patient privacy at risk.

Methods: In this study, we deployed a grid logistic regression framework based on Secure Multi-party Computation (SMAC-GLORE). Unlike our previous work in GLORE, SMAC-GLORE protects not only patient-level data, but also all the intermediary information exchanged during the model-learning phase.

Results: The experimental results demonstrate the feasibility of secure distributed logistic regression across multiple institutions without sharing patient-level data.

Conclusions: In this study, we developed a circuit-based SMAC-GLORE framework. The proposed framework provides a practical solution for secure distributed logistic regression model learning.
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http://dx.doi.org/10.1186/s12911-016-0316-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959358PMC
July 2016

CNTs threaded (001) exposed TiO2 with high activity in photocatalytic NO oxidation.

Nanoscale 2016 Feb;8(5):2899-907

Education Ministry Key Lab of Resource Chemistry, Shanghai Key Laboratory of Rare Earth Functional Materials, International Joint Lab on Resource Chemistry SHNU-NUS-PU, Department of Chemistry, Shanghai Normal University, Shanghai 200234, China.

A microwave-ionothermal strategy was developed for in situ synthesis of CNTs threaded TiO2 single crystal with a tunable percentage of surface exposed (001) active facets. The CNTs were used as microwave antennas to create local "super hot" dots to induce Ti(3+) adsorption and hydrolysis, thereby leading to a good assembly of (001) facets exposed single crystalline TiO2 threaded by the CNTs in the presence of Hmim[BF4] ionic liquid. Due to the high percentage of the active (001) facets of single crystal TiO2 and the direct electron transfer property of the CNTs, the as-prepared CNTs-TiO2 composite showed a photocatalytic NO removal ratio of up to 76.8% under UV irradiation. In addition, with self-doped Ti(3+), the CNTs-TiO2 composite also exhibited an enhanced activity under irradiation with either solar lights or visible lights, showing good potential in practical applications for environmental remediation.
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http://dx.doi.org/10.1039/c5nr07589kDOI Listing
February 2016

FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption.

BMC Med Inform Decis Mak 2015 21;15 Suppl 5:S5. Epub 2015 Dec 21.

Background: The increasing availability of genome data motivates massive research studies in personalized treatment and precision medicine. Public cloud services provide a flexible way to mitigate the storage and computation burden in conducting genome-wide association studies (GWAS). However, data privacy has been widely concerned when sharing the sensitive information in a cloud environment.

Methods: We presented a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. We introduced two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics.

Results: The proposed framework was evaluated for the task of chi-square statistic computation with two case-control datasets from the 2015 iDASH genome privacy protection challenge. Experimental results show that the performance of FORESEE can be significantly improved through algorithmic optimization and parallel computation. Remarkably, the secure approximation division provides significant performance gain, but without missing any significance SNPs in the chi-square association test using the aforementioned datasets.

Conclusions: Unlike many existing HME based studies, in which final results need to be computed by the data owner due to the lack of the secure division operation, the proposed FORESEE framework support complete outsourcing to the cloud and output the final encrypted chi-square statistics.
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http://dx.doi.org/10.1186/1472-6947-15-S5-S5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4698942PMC
October 2016

HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS.

Bioinformatics 2016 Jan 6;32(2):211-8. Epub 2015 Oct 6.

Department of Biomedical Informatics, University of California, San Diego, CA 92093.

Motivation: Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual's privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size.

Results: We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets.

Availability And Implementation: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ CONTACT: [email protected]

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btv563DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4739182PMC
January 2016

Highly efficient and stable Au/CeO2-TiO2 photocatalyst for nitric oxide abatement: potential application in flue gas treatment.

Langmuir 2015 Oct;31(39):10822-30

Key Laboratory of Resource Chemistry of Ministry of Education, Shanghai Key Laboratory of Rare Earth Functional Materials, College of Life and Environmental Science, Shanghai Normal University , Shanghai 200234, China (PRC).

In the present work, highly efficient and stable Au/CeO2-TiO2 photocatalysts were prepared by a microwave-assisted solution approach. The Au/CeO2-TiO2 composites with optimal molar ratio of Au/Ce/Ti of 0.004:0.1:1 delivered a remarkably high and stable NO conversion rate of 85% in a continuous flow reactor system under simulated solar light irradiation, which far exceeded the rate of 48% over pure TiO2. The tiny Au nanocrystals (∼1.1 nm) were well stabilized by CeO2 via strong metal-support bonding even it was subjected to calcinations at 550 °C for 6 h. These Au nanocrystals served as the very active sites for activating the molecule of nitric oxide and reducing the transmission time of the photogenerated electrons to accelerate O2 transforming to reactive oxygen species. Moreover, the Au-Ce(3+) interface formed and served as an anchoring site of O2 molecule. Then more adsorbed oxygen could react with photogenerated electrons on TiO2 surfaces to produce more superoxide radicals for NO oxidation, resulting in the improved efficiency. Meanwhile, O2 was also captured at the Au/TiO2 perimeter site and the NO molecules on TiO2 sites were initially delivered to the active perimeter site via diffusion on the TiO2 surface, where they assisted O-O bond dissociation and reacted with oxygen at these perimeter sites. Therefore, these finite Au nanocrystals can consecutively expose active sites for oxidizing NO. These synergistic effects created an efficient and stable system for breaking down NO pollutants. Furthermore, the excellent antisintering property of the catalyst will allow them for the potential application in photocatalytic treatment of high-temperature flue gas from power plant.
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http://dx.doi.org/10.1021/acs.langmuir.5b02232DOI Listing
October 2015

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding.

IEEE Trans Circuits Syst Video Technol 2013 Nov;23(11):1941-1956

Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260 USA.

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.
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http://dx.doi.org/10.1109/TCSVT.2013.2269776DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4260422PMC
November 2013

Large Discriminative Structured Set Prediction Modeling With Max-Margin Markov Network for Lossless Image Coding.

IEEE Trans Image Process 2014 Feb;23(2):541-54

Inherent statistical correlation for context-based prediction and structural interdependencies for local coherence is not fully exploited in existing lossless image coding schemes. This paper proposes a novel prediction model where the optimal correlated prediction for a set of pixels is obtained in the sense of the least code length. It not only exploits the spatial statistical correlations for the optimal prediction directly based on 2D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Under the joint constraints for local coherence, max-margin Markov networks are incorporated to combine support vector machines structurally to make max-margin estimation for a correlated region. Specifically, it aims to produce multiple predictions in the blocks with the model parameters learned in such a way that the distinction between the actual pixel and all possible estimations is maximized. It is proved that, with the growth of sample size, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. Incorporated into the lossless image coding framework, the proposed model outperforms most prediction schemes reported.
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http://dx.doi.org/10.1109/TIP.2013.2293429DOI Listing
February 2014

An Adaptive Difference Distribution-based Coding with Hierarchical Tree Structure for DNA Sequence Compression.

Proc Data Compress Conf 2013;2013:371-380. Epub 2013 Mar 22.

Division of Biomedical Informatics University of California, San Diego San Diego, CA 92093, USA,

Previous reference-based compression on DNA sequences do not fully exploit the intrinsic statistics by merely concerning the approximate matches. In this paper, an adaptive difference distribution-based coding framework is proposed by the fragments of nucleotides with a hierarchical tree structure. To keep the distribution of difference sequence from the reference and target sequences concentrated, the sub-fragment size and matching offset for predicting are flexible to the stepped size structure. The matching with approximate repeats in reference will be imposed with the Hamming-like weighted distance measure function in a local region closed to the current fragment, such that the accuracy of matching and the overhead of describing matching offset can be balanced. A well-designed coding scheme will make compact both the difference sequence and the additional parameters, e.g. sub-fragment size and matching offset. Experimental results show that the proposed scheme achieves 150% compression improvement in comparison with the best reference-based compressor GReEn.
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http://dx.doi.org/10.1109/DCC.2013.45DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617277PMC
March 2013
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