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The capability to obtain detailed motility information of model organisms is fundamental to reveal their functional and social behavior characteristics. Zebrafish is a powerful vertebrate model organism. Despite recent success in the automatic quantification of adult zebrafish movement, it remains a laborious task for group zebrafish larval tracking due to their similar appearance, frequent occlusions, and highly discontinuous kinematics. This article presents DanioSense (DS), an automatic tracker for group larval zebrafish, to overcome these tracking challenges. The integration of a light convolutional neural network and a centerline extraction algorithm enables the tracker to localize individuals even in occlusion cases where objects' identities are prone to switch. With reliable detections, an adaptive Kalman filter is designed to optimally estimate locomotive parameters, which is also used for object reidentification accomplished by a two-stage data association protocol. Experimental results demonstrated a tracking accuracy of over 97%, median errors of 102 μm, and 8.8° for the position and orientation measurement, and a processing speed of over 30 frames/s with a normal computer configuration. DS provides detailed quantitative data for a large-scale larvae group in nearly real time, highly boosting the efficiency of characterizing individual phenotypes and analyzing social interactions.

Supplementary Figure S1 PDF file - 69K,Combination Indexes (CIs) for interaction between PP242 and TRAIL in NSCLC cells

Caspase-mediated cleavage of proteins ensures the irreversible commitment of cells to undergo apoptosis, and is thus a hallmark of apoptosis. Rapamycin-insensitive companion of mTOR (rictor) functions primarily as a core and essential component of mTOR complex 2 (mTORC2) to critically regulate cellular homeostasis. However, its role in the regulation of apoptosis is largely unknown. In the current study, we found that rictor was cleaved to generate two small fragments at ~ 50 kD and ~ 130 kD in cells undergoing apoptosis upon treatment with different stimuli such as the death ligand, TRAIL, and the small molecule, AZD9291. This cleavage was abolished when caspases were inhibited and could be reproduced when directly incubating rictor protein and caspase-3 in vitro. Furthermore, the cleavage site of caspase-3 on rictor was mapped at D1244 (VGVD). These findings together robustly demonstrate that rictor is a substrate of caspase-3 and undergoes cleavage during apoptosis. These results add new information for understanding the biology of rictor in the regulation of cell survival and growth.

Using a Viskomat XL mortar rheometer, parameters related to the rheological properties of machine-sprayed mortar were studied, and the effects of cement, fly ash (FA), and additives such as hydroxy...

Calmodulin 3 (CaM3) is a key component of heat shock (HS) signal transduction in Arabidopsis (Arabidopsis thaliana). We previously reported that nitric oxide (NO) acts as an upstream signal of AtCaM3 in plant thermotolerance. In this study, we demonstrated the effect of AtCaM3 on NO accumulation in the tolerance of Arabidopsis plants to HS. In vivo and in vitro protein–protein interaction assays displayed a direct binding between AtCaM3 and S-nitrosoglutathione reductase (GSNOR), inducing an increased GSNOR activity and an inhibited NO level. The cam3 mutant seedlings exhibited a high internal NO accumulation under HS. A gsnor deficiency decreased the survival ratio of cam3 plants accompanied by an increased internal NO level. However, treatment with 2-phenyl-4,4,5,5-tetramethyl-imidazoline-1-oxyl-3-oxide (cPTIO, a special NO scavenger) elevated the survival ratio of cam3 mutant seedlings under HS conditions. Collectively, these data suggest an interesting finding that AtCaM3 serves as a signal in plant thermotolerance by reducing excessive NO accumulation through the binding and promoting of GSNOR, suggesting a feedback inhibition between AtCaM3 and NO in thermotolerance.

BACKGROUND Lung cancer is one of the leading causes of cancer-related mortalities worldwide and majority of these deaths result from non-small cell lung cancer (NSCLC). The primary objective of this research was to determine the anticancer potential of carnosic acid, a plant derived abietane diterpene, against human lung cancer cells, as well as to determine its effects on cell migration and invasion, apoptosis, and the PI3K/AKT/m-TOR signaling pathway. MATERIAL AND METHODS Cell viability was evaluated by Cell Counting Kit-8 (CCK-8) assay; fluorescence microscopy using acridine orange/ethidium bromide stain and Comet assay were used to study cellular apoptosis. In vitro wound healing assay was used to study effects on cell migration; Transwell assay was used to study cell invasion after drug treatment. Western blot assay was used to study effects of carnosic acid on the PI3K/AKT/m-TOR signaling pathway. RESULTS It was shown that carnosic acid could inhibit the growth of A-549 human non-small cell lung carcinoma cells dose-dependently showing an IC₅₀ value of 12.5 μM. This growth inhibition of A-549 cells was mediated via apoptotic cell death as observed by fluorescence microscopy showing nuclear fragmentation and chromatin condensation. Carnosic acid, dose-dependently, also inhibited cell migration and invasion. Finally, western blot assay revealed that carnosic acid also led to inhibition of the PI3K/AKT/m-TOR signaling pathway. CONCLUSIONS In conclusion, our results showed that Carnosic acid has the potential to inhibit cancer cell growth in A-549 lung cancer cells by activating apoptotic death, inhibiting cell migration and invasion and suppressing PI3K/AKT/m-TOR signaling pathway.

Introduction: Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. The mechanism of AF still remains unknown. In case - control study, we found that rs11091046 located in 3' - UTR of angiotensin II receptor 2 (AGTR2) gene was related to non - familial AF in Chinese Han population. It is necessary to figure out the mechanism of rs11091046 in non - familial AF. Methods: We predicted cohesion change between rs11091046 and miR-208a by RNAhybrid software and confirmed the prediction of cohesion change between rs11091046 and miR-208a by double luciferase reporter system. We detected the expression of AGTR2 gene in right atrium of patients with AF and subjects with sinus rhythm by quantitative real-time PCR. Results: RNAhybrid software showed that the cohesion between 3' - UTR of AGTR2 gene and seed region of miR - 208a was reduced because of the polymorphism of rs11091046 (wide type C mfe: -16.4kcal/mol, mutant type A mfe: -13.7kcal/mol). We also observed that the luciferase activity of transfected cells with miR - 208a and wild - type rs11091046 - C was significantly decreased by 59% while it was relatively enhanced by 22% with miR - 208a and mutant - type rs11091046 - A (Figure 1 up panel). The mRNA level of AGTR2 gene in right atrium of patients with AF was significantly increased compared with subjects with sinus rhythm. The mRNA level of AGTR2 gene in cardiomyocyte transfected by miR - 208a mimic was higher than cardiomyocyte transfected by miR - 208a (Figure 1 down panel). Conclusion: The polymorphism of rs11091046 in AGTR2 gene reduced the cohesion between 3 '- UTR of AGTR2 gene and seed region of miR - 208a, which increased the expression level of AGTR2 gene leading to occurrence of AF. Figure 1 Up panel The relative luciferase activity of transfected cells with wild - type rs11091046 - C and mutant - type rs11091046 - A (BK389: pmirGLO - rs11091046 - C, BK390: pmirGLO - rs11091046 - A). ** P < 0.01. Down panel The mRNA level of AGTR2 gene in cardiomyocyte transfected by miR - 208a mimic and miR - 208a. * P < 0.05.

The wall-associated kinase (WAK) multigene family plays critical roles in various cellular processes and stress responses in plants, however, whether WAKs are involved in salt tolerance is obscure. Herein, we report the functional characterization of a rice WAK, WAK112, whose expression is suppressed by salt. Overexpression of OsWAK112 in rice and heterologous expression of OsWAK112 in Arabidopsis significantly decreased plant survival under conditions of salt stress, while knocking down the OsWAK112 in rice increased plant survival under salt stress. OsWAK112 is universally expressed in plant and associated with cell wall. Meanwhile, in vitro kinase assays and salt tolerance analyses showed that OsWAK112 possesses kinase activity and that it plays a negative role in the response of plants to salt stress. In addition, OsWAK112 interacts with S-adenosyl-L-methionine synthetase (SAMS) 1/2/3, which catalyzes SAM synthesis from ATP and L-methionine, and promotes OsSAMS1 degradation under salt stress. Furthermore, in OsWAK112-overexpressing plants, there is a decreased SAMS content and a decreased ethylene content under salt stress. These results indicate that OsWAK112 negatively regulates plant salt responses by inhibiting ethylene production, possibly via direct binding with OsSAMS1/2/3.

We previously reported the involvement of cyclic nucleotide-gated ion channel 6 (CNGC6) and hydrogen peroxide (H2O2) in plant responses to heat shock (HS). To demonstrate their relationship with plant thermotolerance, we assessed the effect of HS on several groups of Arabidopsis (Arabidopsis thaliana) seedlings: wild-type, cngc6 mutant, and its complementation line. Under exposure to HS, the level of H2O2 was lower in the cngc6 mutant seedlings than in the wild-type (WT) seedlings but obviously increased in the complementation line. The treatment of Arabidopsis seeds with calcium ions (Ca2+) increased the H2O2 levels in the seedlings under HS treatment, whereas treatment with a Ca2+ chelator (EGTA) inhibited it, indicating that CNGC6 may stimulate the accumulation of H2O2 in a manner dependent on an increase in cytosolic Ca2+ ([Ca2+]cyt). This point was verified by phenotypic observations and thermotolerance testing with transgenic plants overexpressing AtRbohB and AtRbohD (two genes involved in HS-responsive H2O2 production), respectively, in a cngc6 background. Real-time reverse transcription-polymerase chain reactions and Western blotting suggested that CNGC6 enhanced the gene transcription of HS factors (HSFs) and the accumulation of HS proteins (HSPs) via H2O2. These upon results indicate that H2O2 acts downstream of CNGC6 in the HS signaling pathway, increasing our understanding of the initiation of plants responses to high temperatures.

Heart sound is one of the common medical signals for diagnosing cardiovascular diseases. This paper studies the binary classification between normal or abnormal heart sounds, and proposes a heart sound classification algorithm based on the joint decision of extreme gradient boosting (XGBoost) and deep neural network, achieving a further improvement in feature extraction and model accuracy. First, the preprocessed heart sound recordings are segmented into four status, and five categories of features are extracted from the signals based on segmentation. The first four categories of features are sieved through recursive feature elimination, which is used as the input of the XGBoost classifier. The last category is the Mel-frequency cepstral coefficient (MFCC), which is used as the input of long short-term memory network (LSTM). Considering the imbalance of the data set, these two classifiers are both improved with weights. Finally, the heterogeneous integrated decision method is adopted to obtain the prediction. The algorithm was applied to the open heart sound database of the PhysioNet Computing in Cardiology(CINC) Challenge in 2016 on the PhysioNet website, to test the sensitivity, specificity, modified accuracy and F score. The results were 93%, 89.4%, 91.2% and 91.3% respectively. Compared with the results of machine learning, convolutional neural networks (CNN) and other methods used by other researchers, the accuracy and sensibility have been obviously improved, which proves that the method in this paper could effectively improve the accuracy of heart sound signal classification, and has great potential in the clinical auxiliary diagnosis application of some cardiovascular diseases.心音是诊断心血管疾病常用的医学信号之一。本文对心音正常/异常的二分类问题进行了研究，提出了一种基于极限梯度提升（XGBoost）和深度神经网络共同决策的心音分类算法，实现了对特征的选择和模型准确率的进一步提升。首先，本文对预处理后的心音信号进行心音分割，在此基础上提取了 5 个大类的特征，前 4 类特征采用递归特征消除法进行特征选择，作为 XGBoost 分类器的输入，最后一类为梅尔频率倒谱系数（MFCC），作为长短时记忆网络（LSTM）的输入。考虑到数据集的不平衡性，本文在两种分类器中皆使用了加权改进的方法。最后采用异质集成决策方法得到预测结果。将本文所提心音分类算法应用于 PhysioNet 网站在 2016 年发起的 PhysioNet 心脏病学挑战赛（CINC）所用公开心音数据库，以测试灵敏度、特异性、修正后的准确率以及 F 得分，结果分别为 93%、89.4%、91.2%、91.3%，通过与其他研究者应用机器学习、卷积神经网络（CNN）等方法的结果比较，在准确率和灵敏度上有明显提高，证明了本文方法能有效地提高心音信号分类的准确性，在部分心血管疾病的临床辅助诊断应用中有很大的潜力。.

Caspase-mediated cleavage of proteins ensures the irreversible commitment of cells to undergo apoptosis, and is thus a hallmark of apoptosis. Rapamycin-insensitive companion of mTOR (rictor) functions primarily as a core and essential component of mTOR complex 2 (mTORC2) to critically regulate cellular homeostasis. However, its role in the regulation of apoptosis is largely unknown. In the current study, we found that rictor was cleaved to generate two small fragments at ~ 50 kD and ~ 130 kD in cells undergoing apoptosis upon treatment with different stimuli such as the death ligand, TRAIL, and the small molecule, AZD9291. This cleavage was abolished when caspases were inhibited and could be reproduced when directly incubating rictor protein and caspase-3 in vitro. Furthermore, the cleavage site of caspase-3 on rictor was mapped at D1244 (VGVD). These findings together robustly demonstrate that rictor is a substrate of caspase-3 and undergoes cleavage during apoptosis. These results add new information for understanding the biology of rictor in the regulation of cell survival and growth.

The electrocardiogram (ECG) is an important tool for detecting arrhythmia. To solve the limitations of visual inspection, computer-aided diagnosis appears and grows rapidly. Most of the reported researches for heartbeat classification were based on intra-patient dataset. Moreover, existing inter-patient researches were usually conducted for superclasses of arrhythmia. To classify specific types of arrhythmia, this study proposed an inter-patient heartbeat classification method based on region feature extraction and ensemble classifier. The proposed method is composed of four stages. In preprocessing stage, the ECG signal is filtered and proportionally segmented. Afterwards, heartbeats are divided into three regions and region features are extracted. Subsequently, the dimension of features is reduced and all the features are fused and normalized. Eventually, an ensemble classifier is employed for the classification of Normal (N), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). The method was applied to a new dataset divided from MIT-BIH arrhythmia database. The obtained sensitivities for Normal, LBBB, RBBB, APV and VPC were 95.0%, 27.9%, 79.6%, 81.8% and 88.1%. A comparative experiment demonstrated that the proposed region feature extraction method improves the accuracy of arrhythmia classification. The new division of MIT-BIH arrhythmia database is also advised to other researchers.

Activation of death receptor 5 (DR5) to induce apoptosis in cancer cells is an attractive strategy for cancer therapy. However, many tumor cell lines and primary tumors are resistant to DR5 targeted agents including recombinant tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) and anti-DR5 agonistic antibodies. Here we identify tubulin proteins - primarily consisting of α and β subunits folded into microtubule polymers - as a crucial modulator of DR5 mediated apoptosis. Using affinity purification coupled with mass spectrometry, we found that DR5 interacts with both α- and β-tubulin proteins in cancer cells. Pharmacological disruption of microtubules increased DR5 protein expression and subsequently sensitized the cells to TRAIL-induced apoptosis. Similar results were observed by selectively silencing tubulin transcript using small RNA interference. We also demonstrate that tubulin/microtubule blockade augments TRAIL induced apoptosis by stabilizing DR5 protein. Together, our results link the tubulin/microtubule network to the stringent regulation of DR5 mediated apoptosis, which could lead to potential therapeutic strategies to enhance cancer therapy efficacy.

Epitope peptides are not suitable for nasal administration immunity due to their poor immunogenicity and low delivery efficiency. Here, we reported an intranasal self-assembled nanovaccine (I-OVA NE), which was loaded with the peptides IKVAV-OVA

Plants have evolved sophisticated mechanisms to perceive and respond to environmental signal stimuli and coordinate growth and development accordingly. As a ubiquitous second messenger, Ca2+ (calcium ion) participates widely in the responses to stress and the regulation of growth and development in plants. As one of the target proteins of CaM, CAMTA (calmodulin-binding transcription activator) is a type of transcription factor with a CaM binding site, also known as SR (signal responsive), CAMTA is highly conserved throughout evolution and plays important roles in many aspects of plant life, including growth, development, and responses to biotic and abiotic stresses. In this chapter, we summarize the structural characteristics of CAMTAs, as well as the functional significance in plant growth and development, defenses against biotic stress, and tolerance to abiotic stresses including cold, salt, drought, and metal stress. Future research on CAMTA is also prospected based on our understanding.

Synthetic epitope peptide are not suitable for nasal administration due to its weak immunogenic and low delivery efficiency. In this work, we developed a intranasal epitope nanovaccine (I-OVA NE) which can prolong mucosal retention and enhance CTL activity induced by epitopes. I-OVA NE was a nanoemulsion system that assembled with IKVAV-OVA257-264(I-OVA) conjugated peptides.This nanovaccine with I-OVA at a concentration of 4 mg/mL showed the average particle size of 30.37±2.49 nm, zeta potential of -16.67±1.76 mV, and encapsulation rate of 84.07±7.59%. I-OVA NE particles exhibit smooth and spherical surfaces, good dispersibility and no obvious aggregation. Moreover, the physicochemical characteristics (size, PdI and zeta potential) of this vaccine did not significantly change in the condition of mucin exist. I-OVA NE had no significant cytotoxic effects on BEAS-2B cells, and no obvious acute pathological changes were observed on nasal mucosa or lung tissue in the mice after nasal immunization. We found that I-OVA NE prolonged the nasal residence time, promoted the cellular uptake of the epitope peptide and improved the antigen uptake efficiency of BEAS-2B cells, but this effect was significantly decreased after integrin blockade. Importantly, the level of Th1 cytokines and the proportion of epitope-specific CD8+ T cells increased significantly, and thus I-OVA NE protected E.G7/OVA tumor-bearing mice by suppressing tumour growth and provoking anti-tumour immune activation. Overall, these data indicate that I-OVA NE can be an applicable strategy for tumor vaccine design.

In this paper, the effect of HPMC (hydroxypropyl methyl cellulose ether) on the cement mortar water retention (WR) and composition was studied. The relationship between the plastic viscosity and water retention of cement mortar was revealed. The results showed that HPMC formed a colloidal film with a 3D network structure in water, which changed the ability of water to migrate. The HPMC colloid adsorbed on the surface of cement and sand particles and played a bridging role due to the influence of the spatial network structure of the thin film. Fine particles formed a grid-like distribution, and the hydration products formed a unique fibrous tree-like structure. A positive correlation was observed between the plastic viscosity and the water holding capacity of cement mortar. Finally, the mechanism responsible for the improved water retention of cement mortar by HPMC was analyzed using the changing water migration capacity, migration channels, and mortar cohesion.

LCZ696 is a novel treatment for patients suffering from heart failure that combines the two active pharmaceutical ingredients sacubitril and valsartan in a single chemical compound. While valsartan is an established drug substance, a new manufacturing process suitable for large-scale commercial production had to be developed for sacubitril. The use of chemocatalysis, biocatalysis, and flow chemistry as state-of-the-art technologies allowed to efficiently build up the structure of sacubitril and achieve the defined performance targets.

We reported a rare case of pheochromocytoma presenting as the alternation of hypertension and hypotension, which highlighted the rare event of an extreme and cyclical blood pressure pattern in a pheochromocytoma hypertensive crisis. Early recognition is critical for selection of the appropriate treatment to terminate the effects of catecholamine storm, which prepares for surgical resection of the tumor.

With age, our blood vessels are prone to aging, which induces cardiovascular disease. As an important basis for diagnosing heart disease and evaluating heart function, the electrocardiogram (ECG) records cardiac physiological electrical activity. Abnormalities in cardiac physiological activity are directly reflected in the ECG. Thus, ECG research is conducive to heart disease diagnosis. Considering the complexity of arrhythmia detection, we present an improved convolutional neural network (CNN) model for accurate classification. Compared with the traditional machine learning methods, CNN requires no additional feature extraction steps due to the automatic feature processing layers. In this paper, an improved CNN is proposed to automatically classify the heartbeat of arrhythmia. Firstly, all the heartbeats are divided from the original signals. After segmentation, the ECG heartbeats can be inputted into the first convolutional layers. In the proposed structure, kernels with different sizes are used in each convolution layer, which takes full advantage of the features in different scales. Then a max-pooling layer followed. The outputs of the last pooling layer are merged and as the input to fully-connected layers. Our experiment is in accordance with the AAMI inter-patient standard, which included normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F), and unknown beats (Q). For verification, the MIT arrhythmia database is introduced to confirm the accuracy of the proposed method, then, comparative experiments are conducted. The experiment demonstrates that our proposed method has high performance for arrhythmia detection, the accuracy is 99.06%. When properly trained, the proposed improved CNN model can be employed as a tool to automatically detect different kinds of arrhythmia from ECG.

Application of dexmedetomidine combined with sufentanil in colon cancer resection and its effect on immune and coagulation function of patients was studied. Colon cancer cases (n=176) admitted to Xiangya Hospital Central South University were selected into the study. They were divided into group A (n=92) and group B (n=84). In group A, patients underwent surgery anesthesia with dexmedetomidine combined with sufentanil. In group B, patients underwent surgery anesthesia only with sufentanil. The anesthesia induced intubation, operation time and incidence of postoperative adverse reaction of patients were compared between the two groups. Heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) were observed and recorded before induction, before intubation and after intubation. Blood coagulation analyzer was used to detect four items of coagulation before and after operation. FACSCalibur flow cytometry was used to detect T lymphocyte subsets in peripheral blood of patients in the two groups. The pain scores (VAS) of patients in the two groups were measured and recorded after surgery at 4, 24 and 48 h. There was a difference in anesthesia induced intubation and operation time of patients in both groups (P

Device-to-device (D2D) communications underlying cellular networks are a novel approach for providing IoT services, in which D2D communication links and cellular communication links share the same channels. Unfortunately, various kinds of devices are randomly distributed in cellular networks and potentially result in critical interferences. To solve the interference problem, we propose a novel greedy-based channel assignment algorithm in this paper. First, we construct a novel interference graph that describes the interferences between pairs of communication links when they simultaneously share the same channel. Then, a novel channel assignment algorithm is proposed to alleviate system interferences by greedily assigning the best channel to each communication link, which is simple and feasible to implement. Moreover, we convert the channel assignment problem into a robust graph coloring problem and search for a near-optimal solution to minimize the system interferences. Numerical results indicate that the proposed algorithm not only dramatically improves the network capacity but also enhances the fairness among devices.

The Guangxi Zhuang Autonomous Region is an important manganese ore district in Southwest China, with manganese ore resource reserves accounting for 23% of the total manganese ore resource reserves in China. The Xialei manganese deposit (Daxin County, Guangxi) is the first super-large manganese deposit discovered in China. The Mn oxide in the supergene oxidation zone of the Xialei deposit was characterized using scanning electron microscopy (SEM), energy spectrometer (EDS), transmission electron microscopy (TEM, HRTEM), and X-ray diffraction analysis (XRD). The Mn oxides have a gray-black/steel-gray color, a semi-metallic-earthy luster, and appear as oolitic, pisolitic, banded, massive, and cellular textures. Scanning electron microscopy images show that the manganese oxide minerals are present as fine-spherical particles with an earthy surface. TEM and HRTEM indicate the presence of oriented bundled and staggered nanorods, and nanopores between the crystals. The Mn oxide ore can be classified into two textural types: (1) oolitic and pisolitic (often with annuli) Mn oxide, and (2) massive Mn oxide. Pyrolusite, cryptomelane, and hollandite are the main Mn oxide minerals. The potassium contents of cryptomelane and pyrolusite are discussed. The unit cell parameters of pyrolusite are refined.

In this paper, a novel shape detecting method is proposed for continuum soft manipulator to detect the shape in real time, so as to achieve high-precision position and attitude control. The method uses three cable encoders to measure the length of the manipulator per section, and they are distributed at 120 degrees apart from the endplate of the manipulator. Based on the theoretical hypothesis that the soft manipulator has infinite degrees of freedom (DOF), the kinematic model for the single-section manipulator is developed by the piecewise constant curvature method. Further, the kinematic model is extended to the multi-section manipulator via improved D-H method, which achieves the mapping from the length information to the pose of the manipulator. Finally, the three-dimensional shape of the soft manipulator is simulated in real time by MATLAB, which verified the effectiveness of the proposed shape detection method.

This paper presents a high precision and low computational complexity premature ventricular contraction (PVC) assessment method for the ECG human-machine interface device. The original signals are preprocessed by integrated filters. Then, R points and surrounding feature points are determined by corresponding detection algorithms. On this basis, a complex feature set and feature matrices are obtained according to the position feature points. Finally, an exponential Minkowski distance method is proposed for PVC recognition. Both public dataset and clinical experiments were utilized to verify the effectiveness and superiority of the proposed method. The results show that our R peak detection algorithm can substantially reduce the error rate, and obtained 98.97% accuracy for QRS complexes. Meanwhile, the accuracy of PVC recognition was 98.69% for the MIT-BIH database and 98.49% for clinical tests. Moreover, benefiting from the lightweight of our model, it can be easily applied to portable healthcare devices for human-computer interaction.

Arrhythmia indicates abnormal sources of electrical impulses in the heart or abnormalities in any conductive process. The electrocardiogram (ECG) is the most popular tool for detecting arrhythmia as it is non-invasive and effective. As visual inspection is difficult and tedious, computer-aided diagnosis (CAD) is developed to provide medical decision supports. In this study, a method based on convolutional neural network (CNN) is proposed to automated classify heartbeat of arrhythmia. Compared with conventional machine learning methods, CNN requires no additional feature extraction or feature selection processes. The ECG signal is segmented and then the heartbeats are inputted to the model directly. The structure of CNN in this study is improved. In one convolution layer, kernels with different sizes are used simultaneously. Afterward, the max-pooling operation is applied to the feature maps. Finally, the feature maps generated by kernels with different sizes are concatenated and inputted to the fully-connected layers. The method was employed on the MIT-BIH arrhythmia database and an accuracy of 98.73% was obtained. A comparative experiment was conducted to demonstrate the effectiveness of the proposed method.

A novel approach for detecting feature points of ECG signals is proposed, which is based on an adaptive wavelet-threshold-slope method. Biorthogonal spline wavelet is applied for QRS complex detection. The original signals are decomposed with the equivalent filters of a biorthogonal wavelet by Mallat algorithm. In addition, threshold and slope methods are used for auxiliary monitoring of QRS complex. A fully connected neural network is proposed for arrhythmia detection, after morphological and statistics features are calculated by the position of feature points. MIT-BIH Arrhythmia Databases are used to verify the recognition accuracy of the proposed method, and comparative experiments are conducted. Experimental results demonstrated that adaptive threshold and slope detection methods are robust against noise, and the fully connected neural network has high performance for arrhythmia detection.

Figure S1. [Ca2+]cyt analysis using Ca2+ sensor protein aequorin in wild-type, cngc6, noa1, cngc6/35S::NOA1â 1, and cngc6/35S::NIA2â 1 seedlings. Table S1. Primers used for real-time quantitative RT-PCR. (PDF 91 kb)

Background and objective Electrocardiogram (ECG) is a useful tool for detecting heart disease. Automated ECG diagnosis allows for heart monitoring on small devices, especially on wearable devices. In order to recognize arrhythmias automatically, accurate classification method for electrocardiogram (ECG) heartbeats was studied in this paper. Methods Based on weighted extreme gradient boosting (XGBoost), a hierarchical classification method is proposed. A large number of features from 6 categories are extracted from the preprocessed heartbeats. Then recursive feature elimination is used for selecting features. Afterwards, a hierarchical classifier is constructed in classification stage. The hierarchical classifier is composed of threshold and XGBoost classifiers. And the XGBoost classifiers are improved with weights. Results The method was applied to an inter-patient experiment conforming AAMI standard. The obtained sensitivities for normal (N), supraventricular (S), ventricular (V), fusion (F), and Unknown beats (Q) were 92.1%, 91.7%, 95.1%, and 61.6%. Positive predictive values of 99.5%, 46.2%, 88.1%, and 15.2% were also provided for the four classes. Conclusions XGBoost was improved and firstly introduced in single heartbeat classification. A comparison showed the effectiveness of the novel method. The method was more suitable for clinical application as both high positive predictive value for N class and high sensitivities for abnormal classes were provided.

Pseudomonas aeruginosa (PA) is a major cause of nosocomial infections, which remain an unsolved problem in the clinic despite conventional antibiotic treatment. A PA vaccine could be both an effective and economical strategy to address this issue. Many studies have shown that PcrV, a structural protein of the type 3 secretion system (T3SS) from PA, is an ideal target for immune prevention and therapy. However, difficulties in the production of high-quality PcrV likely hinder its further application in the vaccine industry. Thus, we hypothesized that an optimized PcrV derivative with a rational design could be produced. In this study, the full-length PcrV was divided into four domains with the guidance of its structure, and the Nter domain (Met1-Lys127) and H12 domain (Leu251-Ile294) were found to be immunodominant. Subsequently, Nter and H12 were combined with a flexible linker to generate an artificial PcrV derivative (PcrVNH). PcrVNH was successfully produced in E. coli and behaved as a homogenous monomer. Moreover, immunization with PcrVNH elicited a multifactorial immune response and conferred broad protection in an acute PA pneumonia model and was equally effective to full-length PcrV. In addition, passive immunization with anti-PcrVNH antibodies alone also showed significant protection, at least based on inhibition of the T3SS and mediation of opsonophagocytic killing activities. These results provide an additional example for the rational design of antigens and suggest that PcrVNH is a promising vaccine candidate for the control of PA infection.

Background and objective Atrial fibrillation (AF) is a type of arrhythmia with high incidence. Automatic AF detection methods have been studied in previous works. However, a model cannot be used all the time without any improvement. And updating model requires adequate data and cost. Therefore, this study aims at finding a low-cost way to choose learning samples and developing an incremental learning system for AF detection. Methods Based on transfer learning and active learning, this paper proposed a loop-locked framework integrating AF diagnose, label query, and model fine-tuning. In the pre-training stage, a novel multiple-input deep neural network (MIDNN) is pre-trained using labeled samples from an original training set. In practical application, the model can be used for AF detection. Meanwhile, continuous data is collected to form the candidate set. In the incremental learning stage, the model was fine-tuned continuously by the most informative samples in the candidate set. These samples are selected from the candidate set based on the pre-trained model and a new active learning strategy. The strategy combines the features and the uncertainty of the predicted results. Results In order to evaluate the method, the MIT-BIH atrial fibrillation database was used for pre-training and samples of the MIT-BIH arrhythmia database were taken as candidate set. The initial values of Acc, Sen, and PPV were 87.40%, 97.46%, and 81.11%. These indexes reached to the top values of 97.53%, 100.00%, and 95.29% after 14 iterations. Hence, the number of queries was saved by 90.67%. Conclusions The proposed system is able to update the model continuously and reduce the labeling cost over 90%. The comparisons demonstrated the effectiveness of MIDNN model and the suitability of novel learning strategy for AF. Moreover, this framework can be extended to other biomedical applications.

The arrhythmia is an important group of cardiovascular disease. Electrocardiogram (ECG) is commonly used for detecting arrhythmias. Computer-aided diagnosis system can diagnose ECG automatically without the limitations of visual inspection. In order to improve the performance of ECG heartbeat classification, this paper proposes a novel automatic classification system. Based on convolutional neural network (CNN) and long short-term memory (LSTM) network, a deep structure with multiple input layers is proposed. Four input layers are constructed based on different regions of a heartbeat and RR interval features. The first three inputs are convolved using different strides. The three outputs of CNN are then concatenated and go through an LSTM network. Two fully-connected layers follow and the output is concatenated with the fourth input. Eventually, the last fully-connected layer outputs the predicted label. The proposed system was evaluated by two division schemes of the MIT-BIH arrhythmia database. Class-oriented scheme achieved an overall accuracy of 99.26% and subject-oriented scheme obtained an accuracy of 94.20%. The comparison with previous works showed the excellent performance of the novel network. The combination of automatic features and handcraft features was demonstrated to be helpful in heartbeat classification. Hence, the system can be used for clinical application.

Device-to-Device communication is a key technique in future cellular sensor networks since it provides short range communications between two adjacent devices in terms of power consumption, green communication, and system capacity as compared to conventional homogeneous cellular network. What is more, the D2D protocol not only provides direct communication to various kinds of devices but also bridges together two devices of wireless sensor and cellular device. However, the sensors reuse licensed channels with cellular devices and potential result in severe interference from each other. In this paper, we investigate the problem of optimal spectrum partitioning and the impacts of device density on outage probability in cellular sensor networks. We convert the throughput maximization problem in to an optimal spectrum partitioning problem with signal to interference plus noise ratio constraints. Simulation results show that the proposed algorithm achieves the higher throughput.

TNF-related apoptosis inducing ligand (TRAIL) selectively induces apoptosis in cancer cells without harming most normal cells. Currently, multiple clinical trials are underway to evaluate the antitumor activity of recombinant human TRAIL (rhTRAIL) and agonistic antibodies that target death receptors (DRs) 4 or 5. It is encouraging that these products have shown a tolerated safety profile in early phase studies. However, their therapeutic potential is likely limited by the emergence of tumor drug resistance phenomena. Increasing evidence indicates that TRAIL DRs are deficient on the plasma membrane of some cancer cells despite their total protein expression. Notably, the lack of surface DR4/DR5 is sufficient to render cancers resistant to TRAIL-induced apoptosis, regardless of the status of other apoptosis signaling components. The current review highlights recent findings on the dynamic expression of TRAIL death receptors, including the regulatory roles of endocytosis, autophagy, and Ras GTPase-mediated signaling events. This information could aid in the identification of novel predictive biomarkers of tumor response as well as the development of combinational drugs to overcome or bypass tumor drug resistance to TRAIL receptor-targeted therapies.