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Abstract Metastasis is one of the main causes of failure in the treatment of triple-negative breast cancer (TNBC). Abnormally estrogen level and activated platelets are the key driving forces for TNBC metastasis. Herein, an “ion/gas” bioactive nanogenerator (termed as IGBN), comprising a copper-based MOF and loaded cisplatin-arginine (Pt-Arg) prodrug is developed for metastasis-promoting tumor microenvironment reprogramming and TNBC therapy. The copper-based MOF not only serves as a drug carrier, but also specifically produces Cu2+ in tumors, which catalytic oxidizing estrogen to reduce estrogen levels in situ. Meanwhile, the rationally designed Pt-Arg prodrug reduced into cisplatin to significantly promote the generation of H2O2 in the tumor, then permitting self-augmented cascade NO gas generation by oxidizing Arg through a H2O2 self-supplied way, thus blocking platelet activation in tumor. We clarified that IGBN inhibited TNBC metastasis through local estrogen deprivation and platelets blockade, affording 88.4% inhibition of pulmonary metastasis in a 4T1 mammary adenocarcinoma model. Notably, the locally copper ion interference, NO gas therapy and cisplatin chemotherapy together resulted in an enhanced therapeutic efficacy in primary tumor ablation without significant toxicity. This “ion/gas” bioactive nanogenerator offers a robust and safe strategy for TNBC therapy. Graphical Abstract

Abstract Background and objective Moderate exercise contributes to good health. However, excessive exercise may lead to cardiac fatigue, myocardial damage and even exercise sudden death. Monitoring the heart health has important implication to prevent exercise sudden death. Diagnosis methods such as electrocardiogram, echocardiogram, blood pressure and histological analysis have shown that arrhythmia and left ventricular fibrosis are early warning symptoms of exercise sudden death. Heart sounds (HS) can reflect the changes of cardiac valve, cardiac blood flow and myocardial function. Deep learning has drawn wide attention because of its ability to recognize disease. Therefore, a deep learning method combined with HS was proposed to predict exercise sudden death in New Zealand rabbits. The objective is to develop a method to predict exercise sudden death in New Zealand rabbits. Methods This paper proposed a method to predict exercise sudden death in New Zealand rabbits based on convolutional neural network (CNN) and gated recurrent unit (GRU). The weight-bearing exhaustive swimming experiment was conducted to obtain the HS of exercise sudden death and surviving New Zealand rabbits (n = 11/10) at four different time points. Then, the improved Viola integral method and double threshold method were employed to segment HS signals. The segmented HS frames at different time points were taken as the input of a combined CNN and GRU called CNN–GRU network to complete the prediction of exercise sudden death. Results In order to evaluate the performance of proposed network, CNN and GRU were used for comparison. When the fourth time point segmented HS frames were taken as input, the result shows that the proposed network has better performance with an accuracy of 89.57%, a sensitivity of 89.38% and a specificity of 92.20%. In addition, the segmented HS frames at different time points were input into CNN–GRU network, and the result shows that with the progress of the experiment, the prediction accuracy of exercise sudden death in New Zealand rabbits increased from 50.98 to 89.57%. Conclusion The proposed network shows good performance in classifying HS, which proves the feasibility of deep learning in exploring exercise sudden death. Further, it may have important implications in helping humans explore exercise sudden death.

Abstract Background Heart failure (HF) is a type of cardiovascular disease caused by abnormal cardiac structure and function. Early screening of HF has important implication for treatment in a timely manner. Heart sound (HS) conveys relevant information related to HF; this study is therefore based on the analysis of HS signals. The objective is to develop an efficient tool to identify subjects of normal, HF with preserved ejection fraction and HF with reduced ejection fraction automatically. Methods We proposed a novel HF screening framework based on gated recurrent unit (GRU) model in this study. The logistic regression-based hidden semi-Markov model was adopted to segment HS frames. Normalized frames were taken as the input of the proposed model which can automatically learn the deep features and complete the HF screening without de-nosing and hand-crafted feature extraction. Results To evaluate the performance of proposed model, three methods are used for comparison. The results show that the GRU model gives a satisfactory performance with average accuracy of 98.82%, which is better than other comparison models. Conclusion The proposed GRU model can learn features from HS directly, which means it can be independent of expert knowledge. In addition, the good performance demonstrates the effectiveness of HS analysis for HF early screening.

Abstract The adhesion of two-dimensional (2D) materials onto other surfaces is usually considered a solid-solid mechanical contact. Here, we conduct both atomistic simulations and theoretical modeling to show that there in fact exists an energy conversion between heat and mechanical work in the attachment/detachment of two-dimensional materials on/off solid surfaces, indicating two-dimensional materials adhesion is a gas-like adsorption rather than a pure solid-solid mechanical adhesion. We reveal that the underlying mechanism of this intriguing gas-like adhesion is the configurational entropy difference between the freestanding and adhered states of the two-dimensional materials. Both the theoretical modeling and atomistic simulations predict that the adhesion induced entropy difference increases with increasing adhesion energy and decreasing equilibrium binding distance. Our findings provide a fundamental understanding of the adhesion of two-dimensional materials, which is important for designing two-dimensional materials based devices and may have general implications for nanoscale efficient actuators.

Knowledge of the mechanical behaviors of polymer film in humid environments is of great significance for predicting the long-term performance of emulsion polymer isocyanate (EPI) as a high-performance wood adhesive. A tri-copolymer latex was cross-linked by the general polymeric methylene diisocyanate (p-MDI) and aqueous emulsified isocyanate (EMDI) at different loadings for preparing EPI. Furthermore, a series of uniaxial tension tests under different relative humidity (RH) were carried out on cured EPI samples before and after post-curing treatment, and the corresponding chemical structure, as well as the microstructure of polymers, was investigated in detail. In addition, a constitutive equation was formulated to calculate the viscoelastic characteristics of the adhesive layer. The results indicate that the EPI films reveal various kinds of intrinsic deformation as RH increases, and the tensile rupture stress and stiffness would obviously decrease, even at cross-linker weight ratios of up to 20%. Furthermore, the moisture resistance could be markedly improved by increasing the isocyanate content and post-cure. Importantly, EMDI-cross-linked film not only exhibits much better mechanical properties than that containing p-MDI at 0–80% RH, but is also more sensitive to post-cure. Finally, the derived viscoelastic model could efficiently track moisture-dependent stress-strain curves of EPI films, and the obtained relaxation time further reveals the influence mechanism of isocyanate and post-cure on the mechanical response of the cured polymer under moist conditions.

Analysis of heart sound is of great importance to the diagnosis of heart diseases. Most of the feature extraction methods about heart sound have focused on linear time-variant or timeinvariant models. While heart sound is a kind of highly nonstationary and nonlinear vibration signal, traditional methods cannot fully reveal its essential properties. In this paper, a novel feature extraction approach is proposed for heart sound classification and recognition. The ensemble empirical mode decomposition (EEMD) method is used to decompose the heart sound into a finite number of intrinsic mode functions (IMFs), and the correlation dimensions of the main IMF components (IMFl~dMF4) are calculated as feature set. Then the classical Binary Tree Support Vector Machine (BT-SVM) classifier is employed to classify the heart sounds which include the normal heart sounds (NHSs) and three kinds of abnormal signals namely mitral stenosis (MT), ventricular septal defect (VSD) and aortic stenosis (AS). Finally, the performance of the new feature set is compared with the correlation dimensions of original signals and the main IMF components obtained by the EMD method. The results showed that, for NHSs, the feature set proposed in this paper performed the best with recognition rate of 98.67%. For the abnormal signals, the best recognition rate of 91.67% was obtained. Therefore, the proposed feature set is more superior to two comparative feature sets, which has potential application in the diagnosis of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Discusses two indicators for cardiac reserve, the receiver operator characteristic curve and likelihood ratio of cardiac contractility change trend and the ratio of diastolic to systolic phase duration. Basic points for heart sound quantitative analysis; Phonocardiogram exercise test; Instruments.

Background: Although a very close relationship between the amplitude of the first heart sound (S1) and the cardiac contractility have been proven by previous studies, the absolute value of S1 can not be applied for evaluating cardiac contractility. However, we were able to devise some indicators with relative values for evaluating cardiac function. Methods: Tests were carried out on a varied group of volunteers. Four indicators were devised: (1) the increase of the amplitude of the first heart sound after accomplishing different exercise workloads, with respect to the amplitude of the first heart sound (S1)recorded at rest was defined as cardiac contractility change trend (CCCT). When the subjects completed the entire designed exercise workload (7000 J), the resulting CCCT was defined as CCCT(1); when only 1/4 of the designed exercise workload was completed, the result was defined as CCCT(1/4). (2) The ratio of S1 amplitude to S2 amplitude (S1/S2). (3) The ratio of S1 amplitude at tricuspid valve auscultation area to that at mitral auscultation area T1/M1 (4) the ratio of diastolic to systolic duration (D/S). Data were expressed as mean ± SD. Results: CCCT(1/4) was 6.36 ± 3.01 (n = 67), CCCT(1) was 10.36 ± 4.2 (n = 33), S1/S2 was 1.89 &plumsn; 0.94 (n = 140), T1/M1 was 1.44 ± 0.99 (n = 144), and D/S was 1.68 ± 0.27 (n = 172). Conclusions: Using indicators CCCT(1/4) and CCCT(1) may be beneficial for evaluating cardiac contractility and cardiac reserve mobilization level, S1/S2 for considering the factor for hypotension, T1/M1 for evaluating the right heart load, and D/S for evaluating diastolic cardiac blood perfusion time. [ABSTRACT FROM AUTHOR]

Using Brenner’s second generation reactive empirical bond order potential, we show by molecular dynamics that the single-walled carbon nanotube with a diameter of about 5 nm under axial strain possesses excellent reversible mechanical bistability. This feature provides a high potential of using only one single-walled carbon nanotube to realize bistate functions in nanomechanical systems which will benefit from smaller size significantly. [ABSTRACT FROM AUTHOR]

An analytical molecular mechanics model is established to relate the chirality- and size-dependent elastic properties of a single-walled carbon nanotube to its atomic structure. Properties at different length scales are directly connected by the derived closed-form expressions. The effects of tube chirality and tube diameter are investigated. The present analytical results are helpful to the understanding of elastic properties of carbon nanotubes, and thus are important for the application of carbon nanotubes as building blocks of nanomechanical devices. [ABSTRACT FROM AUTHOR]

Controlling surface patterns are useful in a wide range of applications including flexible electronics, biological templates, microelectromechanical systems and device fabrication. The present paper investigates the wrinkling and fracture of graphene subjected to in-plane shear. It is found that the size of a graphene sheet has significant effect on the wrinkle and fracture based on both molecular dynamics simulation and nonlocal plate theory. The analytical expressions for wrinkle amplitude and wavelength are deduced. The nonlocal parameter of nonlocal plate theory is evaluated. Furthermore, the higher aspect ratio has enhanced the wrinkle resistance and shear strength of graphene. Temperature and chirality have insignificant impact on the wrinkling, but significantly influence the fracture of the graphene sheet. This work is expected to provide a better understanding of the mechanism of nanometer scale wrinkles. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations reveal that thermal-induced edge barriers and forces can govern the interlayer interaction of double walled carbon nanotubes. As a result, friction in such systems depends on both the area of contact and the length of the contact edges. The latter effect is negligible in macroscopic friction and provides a feasible explanation for the seemingly contradictory laws of interlayer friction, which have been reported in the literature. The temperature-dependent edge forces can be utilized as a driving force in carbon nanotube thermal actuators, and has general implications for nanoscale friction and contact. [ABSTRACT FROM AUTHOR]

Insect chitin deacetylases (CDAs) catalyze the removal of acetyl groups from chitin and modify this polymer during its synthesis and reorganization. CDAs are essential for insect survival and therefore represent promising targets for insecticide development. However, the structural and biochemical characteristics of insect CDAs have remained elusive. Here, we report the crystal structures of two insect CDAs from the silk moth Bombyx mori: BmCDA1, which may function in cuticle modification, and BmCDA8, which may act in modifying peritrophic membranes in the midgut. Both enzymes belong to the carbohydrate esterase 4 (CE4) family. Comparing their overall structures at 1.98-2.4 Å resolution with those from well-studied microbial CDAs, we found that two unique loop regions in BmCDA1 and BmCDA8 contribute to the distinct architecture of their substrate-binding clefts. These comparisons revealed that both BmCDA1 and BmCDA8 possess a much longer and wider substrate-binding cleft with a very open active site in the center than the microbial CDAs, including VcCDA from Vibrio cholerae and ArCE4A from Arthrobacter species AW19M34-1. Biochemical analyses indicated that BmCDA8 is an active enzyme that requires its substrates to occupy subsites 0,+1, and +2 for catalysis. In contrast, BmCDA1 also required accessory proteins for catalysis. To the best of our knowledge, our work is the first to unveil the structural and biochemical features of insect proteins belonging to the CE4 family. [ABSTRACT FROM AUTHOR]

How to induce nanoscale directional motion via some intrinsic mechanisms pertaining to a nanosystem remains a challenge in nanotechnology. Here we show via molecular dynamics simulations that there exists a fundamental driving force for a nanoscale object to move from a region of lower stiffness toward one of higher stiffness on a substrate. Such nanoscale directional motion is induced by the difference in effective van der Waals potential energy due to the variation in stiffness of the substrate; i.e., all other conditions being equal, a nanoscale object on a stiffer substrate has lower van der Waals potential energy. This fundamental law of nanoscale directional motion could lead to promising routes for nanoscale actuation and energy conversion. [ABSTRACT FROM AUTHOR]

In this investigation, the collection efficiency of particulate emission control devices (PECDs), particulate matter (PM) emissions, and PM size distribution were determined experimentally at the inlet and outlet of PECDs at five coal-fired power plants. Different boilers, coals, and PECDs are used in these power plants. Measurement in situ was performed by an electrical low-pressure impactor with a sampling system, which consisted of an isokinetic sampler probe, precut cyclone, and two-stage dilution system with a sample line to the instruments. The size distribution was measured over a range from 0.03 to 10 µm. Before and after all of the PECDs, the particle number size distributions display a bimodal distribution. The PM2.5 fraction emitted to atmosphere includes a significant amount of the mass from the coarse particle mode. The controlled and uncontrolled emission factors of total PM, inhalable PM (PM10), and fine PM P(M2.5) were obtained. Electrostatic precipitator (ESP) and baghouse total collection efficiencies are 96.38-99.89% and 99.94%, respectively. The minimum collection efficiency of the ESP and the baghouse both appear in the particle size range of 0.1-1 µm. In this size range, ESP and baghouse collection efficiencies are 85.79-98.6% and 99.54%. Real-time measurement shows that the mass and number concentration of PM10 will be greatly affected by the operating conditions of the PECDs. The number of emitted particles increases with increasing boiler load level because of higher combustion temperature. During test run periods, the data reproducibility is satisfactory. [ABSTRACT FROM AUTHOR]

Chronic heart failure (CHF) is a cardiac condition caused by various of cardiac diseases in the end stage. This paper employed the linear and nonlinear approaches to analyze the heart sound (HS) signals from the patients with CHF. The linear approaches include the time and frequency domain analysis. The nonlinear parameters include largest Lyapunov exponent, correlation dimension, sample entropy and the width of multifractal spectrum, which describe the chaos, fractal characteristics and complexity of the HS signals. Statistical test and receiver operating characteristic (ROC) curve analysis have been applied to the characteristic parameters extracted from the HS signals of the healthy subjects and CHF patients. The results show that the statistically significant differences of linear and nonlinear features between the healthy and CHF groups can be observed. Compared to the healthy people, the cardiac mechanical activity of the patients with CHF has a decreased chaotic characteristic, complexity and randomness, and it indicates the HS features could be the measure to distinguish the CHF patients from the healthy subjects. Hence, our study suggests the proposed features could be as supplementary indexes or efficient clues for the diagnosis of CHF.

Time synchronization is necessary to a Wireless Sensor Network (WSN). Existing time synchronization methods are mostly designed for large scale ad hoc network and are not suitable for small star network like Wireless Physiological Information Sensor Network (WPISN). In this article we present a simple and power-efficient time synchronization method designed specially for WPISN, Reference on Demand Time Synchronization method (RDTS), which employs the Near-Field Magnetic Communication (NFMC) technology. NFMC offers significant power, cost, and size advantages over RF communication.