HFSCCD: A Hybrid Neural Network for Fetal Standard Cardiac Cycle Detection in Ultrasound Videos.

In the fetal cardiac ultrasound examination, standard cardiac cycle (SCC) recognition is the essential foundation for diagnosing congenital heart disease. Previous studies have mostly focused on the detection of adult CCs, which may not be applicable to the fetus. In clinical practice, localization of SCCs needs to recognize end-systole (ES) and end-diastole (ED) frames accurately, ensuring that every frame in the cycle is a standard view. Most existing methods are not based on the detection of key anatomical structures, which may not recognize irrelevant views and background frames, results containing non-standard frames, or even it does not work in clinical practice. We propose an end-to-end hybrid neural network based on an object detector to detect SCCs from fetal ultrasound videos efficiently, which consists of 3 modules, namely Anatomical Structure Detection (ASD), Cardiac Cycle Localization (CCL), and Standard Plane Recognition (SPR). Specifically, ASD uses an object detector to identify 9 key anatomical structures, 3 cardiac motion phases, and the corresponding confidence scores from fetal ultrasound videos. On this basis, we propose a joint probability method in the CCL to learn the cardiac motion cycle based on the 3 cardiac motion phases. In SPR, to reduce the impact of structure detection errors on the accuracy of the standard plane recognition, we use XGBoost algorithm to learn the relation knowledge of the detected anatomical structures. We evaluate our method on the test fetal ultrasound video datasets and clinical examination cases and achieve remarkable results. This study may pave the way for clinical practices.

Thiolated Mesoporous Silica Nanoparticles as an Immunoadjuvant to Enhance Efficacy of Intravesical Chemotherapy for Bladder Cancer.

The characteristics of global prevalence and high recurrence of bladder cancer has led numerous efforts to develop new treatments. The spontaneous voiding and degradation of the chemodrug hamper the efficacy and effectiveness of intravesical chemotherapy following tumor resection. Herein, the externally thiolated hollow mesoporous silica nanoparticles (MSN-SH(E)) is fabricated to serve as a platform for improved bladder intravesical therapy. Enhanced mucoadhesive effect of the thiolated nanovector is confirmed with porcine bladder. The permeation-enhancing effect is also verified, and a fragmented distribution pattern of a tight junction protein, claudin-4, indicates the opening of tight junction. Moreover, MSN-SH(E)-associated reprogramming of M2 macrophages to M1-like phenotype is observed in vitro. The antitumor activity of the mitomycin C (MMC)-loaded nanovector (MMC@MSN-SH(E)) is more effective than that of MMC alone in both in vitro and in vivo. In addition, IHC staining is used to analyze IFN-γ, TGF-ß1, and TNF-α. These observations substantiated the significance of MMC@MSN-SH(E) in promoting anticancer activity, holding the great potential for being used in intravesical therapy for non-muscle invasive bladder cancer (NMIBC) due to its mucoadhesivity, enhanced permeation, immunomodulation, and prolonged and very efficient drug exposure.

City-scale holographic traffic flow data based on vehicular trajectory resampling.

Despite abundant accessible traffic data, researches on traffic flow estimation and optimization still face the dilemma of detailedness and integrity in the measurement. A dataset of city-scale vehicular continuous trajectories featuring the finest resolution and integrity, as known as the holographic traffic data, would be a breakthrough, for it could reproduce every detail of the traffic flow evolution and reveal the personal mobility pattern within the city. Due to the high coverage of Automatic Vehicle Identification (AVI) devices in Xuancheng city, we constructed one-month continuous trajectories of daily 80,000 vehicles in the city with accurate intersection passing time and no travel path estimation bias. With such holographic traffic data, it is possible to reproduce every detail of the traffic flow evolution. We presented a set of traffic flow data based on the holographic trajectories resampling, covering the whole city, including stationary average speed and flow data of 5-minute intervals and dynamic floating car data (FCD).

A YOLOX-based Deep Instance Segmentation Neural Network for Cardiac Anatomical Structures in Fetal Ultrasound Images.

Echocardiography is an essential procedure for the prenatal examination of the fetus for congenital heart disease (CHD). Accurate segmentation of key anatomical structures in a four-chamber view is an essential step in measuring fetal growth parameters and diagnosing CHD. Currently, most obstetricians perform segmentation tasks manually, but the pixel-level operation is labor-intensive and requires extensive anatomical knowledge and clinical experience. As such, efficiently and accurately detecting structures from real-world fetal ultrasound images is a key challenge. In this paper, we propose a YOLOX-based deep instance segmentation neural network (i.e., IS-YOLOX) for cardiac anatomical structure location and segmentation in fetal ultrasound images. Specifically, we reconstruct a new instance segmentation branch based on a multi-task deep learning framework. We then design a new multi-level non-maximum suppression (NMS) mechanism to further improve the segmentation performance that consists of three levels of selection. Moreover, unlike two-stage instance segmentation approaches, our method does not rely on object detection results. To the best of our knowledge, this is the first study regarding instance segmentation on 13 types of anatomical structures in the fetal four-chamber view. Extensive experiments were carried out on clinical datasets, and the experimental results show that our method outperforms nine competitive baselines.

MobileUNet-FPN: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber Segmentation in Edge Computing Environments.

The apical four-chamber (A4C) view in fetal echocardiography is a prenatal examination widely used for the early diagnosis of congenital heart disease (CHD). Accurate segmentation of A4C key anatomical structures is the basis for automatic measurement of growth parameters and necessary disease diagnosis. However, due to the ultrasound imaging arising from artefacts and scattered noise, the variability of anatomical structures in different gestational weeks, and the discontinuity of anatomical structure boundaries, accurately segmenting the fetal heart organ in the A4C view is a very challenging task. To this end, we propose to combine an explicit Feature Pyramid Network (FPN), MobileNet and UNet, i.e., MobileUNet-FPN, for the segmentation of 13 key heart structures. To our knowledge, this is the first AI-based method that can segment so many anatomical structures in fetal A4C view. We split the MobileNet backbone network into four stages and use the features of these four phases as the encoder and the upsampling operation as the decoder. We build an explicit FPN network to enhance multi-scale semantic information and ultimately generate segmentation masks of key anatomical structures. In addition, we design a multi-level edge computing system and deploy the distributed edge nodes in different hospitals and city servers, respectively. Then, we train the MobileUNet-FPN model in parallel at each edge node to effectively reduce the network communication overhead. Extensive experiments are conducted and the results show the superior performance of the proposed model on the fetal A4C and femoral-length images.

High-power tunable dual-wavelength diode laser with a composite external cavity.

A high-power tunable dual-wavelength composite external cavity architecture obtained by means of a holographic grating and a volume Bragg grating is proposed and demonstrated. The tunable frequency difference of the dual-wavelength output is from 0.41 THz to 3.89 THz. We obtain an output power of 2.1 W when the frequency difference is 1.86 THz. The side-mode suppression ratio of more than 29 dB is suppressed over the entire tunable dual-wavelength output range. The two corresponding wavelengths of the dual-wavelength output basically maintain the same intensity with the smallest power difference of only 0.10%.

Online Trajectory Estimation Based on a Network-Wide Cellular Fingerprint Map.

Cellular signaling data is widely available in mobile communications and contains abundant movement sensing information of individual travelers. Using cellular signaling data to estimate the trajectories of mobile users can benefit many location-based applications, including infectious disease tracing and screening, network flow sensing, traffic scheduling, etc. However, conventional methods rely too much on heuristic hypotheses or hardware-dependent network fingerprinting approaches. To address the above issues, NF-Track (Network-wide Fingerprinting based Tracking) is proposed to realize accurate online map-matching of cellular location sequences. In particular, neither prior assumptions such as arterial preference and less-turn preference or extra hardware-relevant parameters such as RSS and SNR are required for the proposed framework. Therefore, it has a strong generalization ability to be flexibly deployed in the cloud computing environment of telecom operators. In this architecture, a novel segment-granularity fingerprint map is put forward to provide sufficient prior knowledge. Then, a real-time trajectory estimation process is developed for precise positioning and tracking. In our experiments implemented on the urban road network, NF-Track can achieve a recall rate of 91.68% and a precision rate of 90.35% in sophisticated traffic scenes, which are superior to the state-of-the-art model-based unsupervised learning approaches.

A resampling approach to disaggregate analysis of bus-involved crashes using panel data with excessive zeros.

Public bus constitutes more than 70% of the overall road-based public transport patronage in Hong Kong, and its crash involvement rate has been the highest among all public transport modes. Though previous studies had identified explanatory factors that affect the crash risk of buses, use of considerably imbalanced crash data with excessive zero observations could lead to inaccurate parameter estimation. This study aims to resolve the excess zero problem of disaggregate analysis of bus-involved crashes based on synthetic data using a Synthetic Minority Over-Sampling Technique for panel data (SMOTE-P). Dataset comprising crash, traffic, and road inventory data of 88 road segments in Hong Kong during the period from 2014 to 2017 is used. To assess the data balancing performance, other common data generation approaches such as Random Under-sampling of the Majority Class (RUMC) technique, Cluster-Based Under-Sampling (CBUS), and mixed resampling, are also considered. Random effect Poisson (REP) models based on synthetic data and random effect zero-inflated Poisson (REZIP) model based on original data are estimated. Results indicate that REP model based on synthetic data using SMOTE-P outperforms REZIP model based on original data and REP models based on synthetic data using RUMC, CBUS and mixed approaches, in terms of statistical fit, prediction error, and explanatory factors identified. Results of model estimation based on SMOTE-P suggest that factors including morning peak, evening peak, hourly traffic flow, average lane width, road length, bus stop density, percentage of bus in the traffic stream, and presence of bus priority lane all affect the bus-involved crash frequency. More importantly, this study provides a feasible solution for disaggregate crash analysis with imbalanced panel data.

Nitrogen-doped C60 as a robust catalyst for CO oxidation.

The O2 activation and CO oxidation on nitrogen-doped C59 N fullerene are investigated using first-principles calculations. The calculations indicate that the C59 N fullerene is able to activate O2 molecules resulting in the formation of superoxide species ( O2-) both kinetically and thermodynamically. The active superoxide can further react with CO to form CO2 via the Eley-Rideal mechanism by passing a stepwise reaction barrier of only 0.20 eV. Ab initio molecular dynamics (AIMD) simulation is carried out to evidence the feasibility of the Eley-Rideal mechanism. In addition, the second CO oxidation takes place with the remaining atomic O without any activation energy barrier. The full catalytic reaction cycles can occur energetically favorable and suggest a two-step Eley-Rideal mechanism for CO oxidation with O2 catalyzed by the C59 N fullerene. The catalytic properties of high percentage nitrogen-doped fullerene (C48 N12 ) is also examined. This work contributes to designing higher effective carbon-based materials catalysts by a dependable theoretical insight into the catalytic properties of the nitrogen-doped fullerene. © 2017 Wiley Periodicals, Inc.

Nitrogen-doped carbon nanotube as a potential metal-free catalyst for CO oxidation.

We elucidate the possibility of nitrogen-doped carbon nanotube as a robust catalyst for CO oxidation. We have performed first-principles calculations considering the spin-polarization effect to demonstrate the reaction of CO oxidation catalyzed by the nitrogen-doped carbon nanotube. The calculations show that O2 species can be partially reduced with charge transfer from the nitrogen-doped carbon nanotube and directly chemisorbed on the C-N sites of the nitrogen-doped carbon nanotube. The partially reduced O2 species at the C-N sites can further directly react with a CO molecule via the Eley-Rideal mechanism with the barriers of 0.45-0.58 eV for the different diameter of nanotube. Ab initio molecular dynamics (AIMD) simulations were performed and showed that the oxidation of CO occurs by the Eley-Rideal mechanism. The relationship between the curvature and reactivity of the nitrogen doped carbon nanotube was also unraveled. It appears that the barrier height of the rate-limiting step depends on the curvature of the nitrogen-doped carbon nanotube in the trend of (3,3)-NCNT < (4,4)-NCNT < (5,5)-NCNT (decreases with increased curvature). Using this relationship, we can predict the barriers for other N-doped carbon nanotubes with different tube diameters. Our results reveal that the nitrogen doped carbon nanomaterials can be a good, low-cost, and metal-free catalyst for CO oxidation.

To Transfer or Not to Transfer? Development of a Dinitrosyl Iron Complex as a Nitroxyl Donor for the Nitroxylation of an Fe(III) -Porphyrin Center.

A positive myocardial inotropic effect achieved using HNO/NO(-) , compared with NO⋅, triggered attempts to explore novel nitroxyl donors for use in clinical applications in vascular and myocardial pharmacology. To develop M-NO complexes for nitroxyl chemistry and biology, modulation of direct nitroxyl-transfer reactivity of dinitrosyl iron complexes (DNICs) is investigated in this study using a Fe(III) -porphyrin complex and proteins as a specific probe. Stable dinuclear {Fe(NO)2 }(9) DNIC [Fe(µ-(Me) Pyr)(NO)2 ]2 was discovered as a potent nitroxyl donor for nitroxylation of Fe(III) -heme centers through an associative mechanism. Beyond the efficient nitroxyl transfer, transformation of DNICs into a chemical biology probe for nitroxyl and for pharmaceutical applications demands further efforts using in vitro/in vivo studies.

Hydrogen generation by the reaction of H2O with Al2O3-based materials: a computational analysis.

The mechanisms for H2O adsorption on γ-Al2O3(110) surface were investigated to illustrate the influence of oxide modifiers on the hydrogen generation reaction. Periodic density functional theory (DFT) calculations with the projected augmented wave (PAW) approach were carried out to study the adsorption of H2O, OH, O and H species, as well as the reaction mechanisms of H2O splitting and H2 generation. Their corresponding structures and adsorption energies are also reported. The calculation results show that H2O, OH, O and H are preferably bound at Al(I)-top, Al(III)-bridge, Al(I,II)-bridge and Al(III)-bridge sites with adsorption energies of -0.42, -5.01, -8.70 and -2.38 eV, respectively. The potential energy profiles for water splitting and hydrogen generation on the γ-Al2O3(110) surface are mapped out. We find that hydrogen generation on the surface occurs via two processes, namely, H2O dehydrogenation and direct H2 generation with an overall exothermicity of 2.24 eV. The nonexistence of intrinsic transition-state barriers and the high exothermicity for the reaction of H2O(g) + γ-Al2O3(110) → O(ads)/γ-Al2O3(110) + H2(g) result in rapid H2 generation. The stepwise H2 generation mechanism of adsorption on the γ-Al2O3(110) surface was also demonstrated using first-principles molecular dynamics simulations. In addition, the nature of the interaction between the adsorbate and the surface during the reaction was also analyzed by the local density of states and by Bader charge calculations.

Computational study of the kinetics and mechanisms for the HCO + O3 reaction.

The mechanisms of radical-molecule reactions between HCO (formyl radical) and O3 (ozone) have been investigated by using BH&HLYP and QCISD methods with the 6-311++G(3df,2p) basis set. The energetics have been refined with CCSD(T) and QCISD(T) theoretical approaches with the same basis set based on the geometries calculated at the QCISD method. The intermediates of hydrogen-bonded complexes and the critical transition states are also examined with the multireference methods. Two possible reaction pathways containing hydrogen-abstraction and association-elimination processes for the interaction of HCO with O3 are proposed. Both reaction mechanisms can occur via the prereactive hydrogen-bonded complex, O3-HCO, with 2.45 kcal/mol stability at the CCSD(T) approach with respect to the reactants; even so, the hydrogen-abstraction mechanism exhibits a lower energy barrier. The rate constants for both processes are also predicted. The total rate constant at 298 K is calculated to be in close agreement with the experimental value of 8.3 × 10(-13) cm(3) molecule(-1) s(-1).

Computational study on reaction mechanisms and kinetics of RNCN (R = H, F, Cl, Br, CH3) radicals with NO.

We carried out a computational study of radical reactions of RNCN (R = H, F, Cl, Br, CH(3)) + NO to investigate how the substitution can influence their corresponding energy barriers and rate coefficients. The preferable reactive sites of RNCN radicals with various substituents are calculated by employing the Fukui functions and hard-and-soft acid-and-base theory, which were generally proved to be successful in the prediction and interpretation of regioselectivity in various types of electrophilic and nucleophilic reactions. Our calculated results clearly show that if the substituted RNCN radical has electron-donating substituent (for R = CH(3)), its corresponding barrier heights for transition states will be substantially decreased. The possible explanations of the observed increase and/or decrease in the energy barriers for the varied substituted RNCN radicals are also analyzed in this article.