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Agrobacterium tumefaciens is the causal agent of crown gall disease in nature; in the laboratory the bacterium is widely used for plant genetic modification. The bacterium delivers a single-stranded transferred DNA (T-DNA) and a group of crucial virulence proteins into host cells. A putative T-complex is formed inside host cells that is composed of T-DNA and virulence proteins VirD2 and VirE2, which protect the foreign DNA from degradation and guide its way into the host nucleus. However, little is known about how the T-complex is assembled inside host cells. We combined the split-GFP and split-sfCherry labeling systems to study the interaction of Agrobacterium-delivered VirE2 and VirE3 in host cells. Our results indicated that VirE2 co-localized with VirE3 on the cytoplasmic side of the host cellular membrane upon the delivery. We identified and characterized two tandem domains at the VirE3 C-terminus that interacted with VirE2 in vitro. Deletion of these two domains abolished the VirE2 accumulation on the host plasma membrane and affected the transformation. Furthermore, the two VirE2-interacting domains of VirE3 exhibited different affinities with VirE2. Collectively, this study demonstrates that the anchorage protein VirE3 uses the two tandem VirE2-interacting domains to facilitate VirE2 protection for T-DNA at the cytoplasmic side of the host cell entrance.

Osteoarthritis (OA) is a chronic degenerative disease wherein the articular cartilage exhibits inflammation and degradation. Scutellarin (SCU) is a flavonoid glycoside with a range of pharmacological activities, as shown in previous studies demonstrating its anti-inflammatory activity. How SCU impacts the progression of OA, however, has not been explored to date. Herein, we assessed the impact of SCU on murine chondrocytes in an OA model system. In in vitro assays, we measured chondrocyte expression of key OA-associated factors such as matrix metalloproteinase 13 (MMP-13), a disintegrin and metalloproteinase with thrombospondin motifs 5 (ADAMTS-5), cyclooxygenase-2 (COX-2), and inducible nitric oxide synthase (iNOS) via qRT-PCR and Western blotting, the expression of interleukin 6 (IL-6), tumor necrosis factor-α (TNF-α), and prostaglandin E2 (PGE2) were detected by qRT-PCR. Our results showed that the downregulation of MMP-13, ADAMTS-5, COX-2, and iNOS expression by SCU and the overproduction of IL-6, TNF-α, and PGE2 induced by IL-1β were all inhibited by SCU in a concentration-dependent manner. Moreover, SCU was able to reverse aggrecan and collagen II degradation and nuclear factor-κB (NF-κB) and nuclear factor erythroid-derived 2-like 2 (Nrf2) signaling pathway activation both in vivo and in vitro. We further used a destabilization of the medial meniscus (DMM) murine model of OA to explore the therapeutic benefits of SCU in vivo. Together, our findings suggest SCU to be a potentially valuable therapeutic agent useful for treating OA.

Previous studies showed that low PPARG expression was associated with poor prognosis of lung adenocarcinoma (LA) with limited mechanisms identified. We first conducted a large-scale literature-based data mining to identify potential molecular pathways where PPARG could exert influence on the pathological development of LA. Then a mega-analysis using 13 independent LA expression datasets and a Pathway Enrichment Analysis (PEA) was conducted to study the gene expression levels and the functionalities of PPARG and the PPARG-driven triggers within the molecular pathways. Finally, a protein-protein interaction (PPI) network was established to reveal the functional connection between PPARG and its driven molecules. We identified 25 PPARG-driven molecule triggers forming multiple LA-regulatory pathways. Mega-analysis using 13 LA datasets supported these pathways and confirmed the downregulation of PPARG in the case of LA (p=1.07e−05). Results from the PEA and PPI analysis suggested that PPARG might inhibit the development of LA through the regulation of tumor cell proliferation and transmission-related molecules, including an LA tumor cell suppressor MIR145. Our results suggested that increased expression of PPARG could drive multiple molecular triggers against the pathologic development and prognosis of LA, indicating PPARG as a valuable therapeutic target for LA treatment.

Summary: Integration of stem cell-derived neurons into the central nervous system (CNS) remains a challenge. A co-culture system is developed to understand whether mouse embryonic stem cell (ESC)-derived spiral ganglion neuron (SGN)-like cells (ESNs) synapse with native mouse cochlear nucleus (CN) neurons. A Cre system is used to generate Cop-GFP ESCs, which are induced into ESNs expressing features similar to auditory SGNs. Neural connections are observed between ESNs and CN neurons 4–6 days after co-culturing, which is stimulated by thrombospondin-1 (TSP1). Antagonist and loss-of-function small hairpin RNA studies indicate that the α2δ1 receptor is critical for TSP1-induced synaptogenesis effects. Newly generated synapse-like structures express pre- and post-synaptic proteins. Synaptic vesicle recycling, pair recording, and blocker electrophysiology suggest functional synaptic vesicles, transsynaptic activities, and formation of glutamatergic synapses. These results demonstrate the synaptogenesis capability of ESNs, which is important for pluripotent ESC-derived neurons to form functional synaptic connections to CNS neurons. : Integration of stem cell-derived neurons (ESNs) into native cellular environment remains a major challenge. This research develops methods to study ESNs and brain nerve cells in the culture dish. ESNs form functional nerve connections with brain nerve cells in morphology, protein expression, and electrophysiology studies. The results are critical for stem cell-based neural pathway regeneration. Keywords: α2δ1 receptor, auditory, cochlear nucleus, embryonic stem cell, regeneration, spiral ganglion neuron, synapse, thrombospondin-1

Distribution mismatch can be easily found in multi-sensor systems, which may be caused by different shoot angles, weather conditions and so on. Domain adaptation aims to build robust classifiers using the knowledge from a well-labeled source domain, while applied on a related but different target domain. Pseudo labeling is a prevalent technique for class-wise distribution alignment. Therefore, numerous efforts have been spent on alleviating the issue of mislabeling. In this paper, unlike existing selective hard labeling works, we propose a fuzzy labeling based graph learning framework for matching conditional distribution. Specifically, we construct the cross-domain affinity graph by considering the fuzzy label matrix of target samples. In order to solve the problem of representation shrinkage, the paradigm of sparse filtering is introduced. Finally, a unified optimization method based on gradient descent is proposed. Extensive experiments show that our method achieves comparable or superior performance when compared to state-of-the-art works.

Degradation modeling and remaining useful life (RUL) prediction for products with multiple degradation features are hot topics in the prognostic and health management. The key to this problem is to describe the dependence among multiple degradation features effectively. In this paper, a multivariate degradation modeling approach based on the Bayesian dynamic linear model (BDLM) is proposed to calculate the RULs of degradation features, and the Copula function is employed to capture the dependence among RUL distributions. A combined BDLM is used to establish the multivariate degradation model, which includes two typical BDLMs, namely, the linear growth model and seasonal factors model. After the model parameters get calibrated by the maximum likelihood estimation, the model can predict the degradation process of features. Once the failure thresholds are given, the probability density function and cumulative distribution function (CDF) of RUL for each degradation feature can be obtained. Since these RUL distributions are not independent of each other, the Copula function is adopted herein to couple the CDFs. Finally, some practical testing data of a microwave component, which has two degradation features, are utilized to validate our proposed method. This paper provides a new idea for the multivariate degradation modeling and RUL prediction.

Accelerated degradation testing (ADT) is commonly used to obtain degradation data of products by exerting loads over usage conditions. Such data can be used for estimating component lifetime and reliability under usage conditions. The design of ADT entails to establish a model of the degradation process and define the test plan to satisfy given criteria under the constraint of limited test resources. Bayesian optimal design is a method of decision theory under uncertainty, which uses historical data and expert information to find the optimal test plan. Different expected utility functions can be selected as objectives. This paper presents a method for Bayesian optimal design of ADT, based on the inverse Gaussian process and considering three objectives for the optimization: relative entropy, quadratic loss function, and Bayesian D-optimality. The Markov chain Monte Carlo and the surface fitting methods are used to obtain the optimal plan. By sensitivity analysis and a proposed efficiency factor, the Bayesian D-optimality is identified as the most robust and appropriate objective for Bayesian optimization of ADT.

Ginsenoside Rg1 (Rg1), the major effective component of ginseng, has been shown to have multiple bioactivities, but low oral bioavailability. The aim of this study was to develop a simple, sensitive and rapid high performance liquid chromatography–tandem mass spectrometry (LC–MS/MS) method, which could be used to validate and quantify the concentrations of Rg1 and its metabolites in Sprague-Dawley rat bile, urine, and feces after oral administration (25 mg/kg). Calibration curves offered satisfactory linearity (r>0.995) within the determined ranges. Both intra-day and inter-day variances were less than 15%, and the accuracy was within 80–120%. The excretion recoveries of Rg1, ginsenoside Rh1 (Rh1), and protopanaxatriol (Ppt) in bile, urine, and feces combined were all greater than 70%. The fecal excretion recoveries of Rg1, Rh1, and Ppt were 40.11%, 22.19%, and 22.88%, respectively, whereas 6.88% of Rg1 and 0.09% of Rh1 were excreted in bile. Urinary excretion accounted for only 0.04% of Rg1. In conclusion, the observed excretion profiles for Rg1 and its metabolites after oral administration are helpful for understanding the poor oral bioavailability of Rg1 and will aid further investigations of Rg1 as a pharmacologically active component.

Inhibition of histone deacetylases (HDACs) has been an important emerging therapy for the treatment of multiple cancers. However, the application of HDAC inhibitors is restricted by the limited potency against solid tumors. In order to discover novel HDAC inhibitors with potent antitumor activities, nitrogen mustard group was introduced to the structure of CI994. The derived molecule N-(2-aminophenyl)-4-(bis(2-chloroethyl)amino)benzamide (NA) exhibited enzyme inhibitory pattern of class I selectivity with IC50 values of 95.2, 260.7, and 255.7 nM against HDAC1, HDAC2, and HDAC3, respectively. In the antiproliferative assay, NA exhibited 10.3-fold (2.66 μM) and 11.3-fold (1.73 μM) higher potency than did suberoylanilide hydroxamic acid (SAHA) (27.3 and 19.5 μM) in inhibition of A2780 and HepG2 cell growth, respectively. Further HepG2 cell-based cell cycle and apoptosis studies revealed that induction of the G2/M phase arrest and cell apoptosis contributes to the antitumor effects of NA. It is suggested that NA could be utilized as a lead compound in the development of bifunctional HDAC inhibitors for the treatment of solid tumors.

The Asian citrus psyllid (Diaphorina citri) is a major pest of citrus trees as it transmits Candidatus Liberibacter asiaticus (CLas). The composition of a host’s microbiota can affect the evolution and ecological distribution of the host. This study monitored the compositional shifts in the citrus psyllid microbiota through all the life stages (egg, nymph 1–5 stages, and adult) by next-generation sequencing (NGS) and quantitative real-time PCR. There were clear differences in both α- and β-diversity of microbiota through the psyllid life stages. Microbiota diversity was markedly higher in the nymph 2–5 stages than in the adult, egg, and nymph 1 stages. Proteobacteria were dominant in all the life stages of D. citri, representing >97.5% of the total bacterial community, and Candidatus Profftella armature was the dominant genus in all the life stages. Data from the qPCR analysis showed an exponential increase in the populations of three D. citri endosymbionts: Candidatus Profftella armature, Candidatus Carsonella ruddii, and Wolbachia. The gut bacterium Pantoea was present in all the life stages, but it was markedly higher in the nymph 2–5 stages. The microbiota composition substantially differed among the egg–nymph 1, nymphs 2–5, and adult stages. Therefore, we successfully characterized the microbiota dynamics and thus identified a microbiota shift during the life cycle of D. citri by 16S rRNA gene sequencing and quantitative PCR. Moreover, 16S rRNA gene sequencing suggested that D. citri acquired the ability to bear CLas in the nymph 1 stage. This study enhances our understanding of microbial establishment in the developing D. citri and provides a reference resource for the identification of potential biocontrol approaches against this pest.

Antibiotic residues that enter the soil through swine manure could disturb the number, community structure and functions of microbiota which could also degrade antibiotics in soil. Five different concentrations of doxycycline (DOX) incorporated into swine manure were added to soil to explore the effects of DOX on microbiota in soil and degradation itself. The results showed that the soil microbiome evolved an adaptation to the soil containing DOX by generating resistance genes. Moreover, some of the organisms within the soil microbiome played crucial roles in the degradation of DOX. The average degradation half-life of DOX in non-sterile groups was 13.85 ± 0.45 days, which was significantly shorter than the 29.26 ± 0.98 days in the group with sterilized soil (P < 0.01), indicating that the soil microbiome promoted DOX degradation. DOX addition affected the number of tetracycline resistance genes, depending on the type of gene and the DOX concentration. Among these genes, tetA, tetM, tetW, and tetX had significantly higher copy numbers when the concentration of DOX was higher. In contrast, a lower concentration of DOX had an inhibitory effect on tetG. At the same time, the microbial compositions were affected by the initial concentration of DOX and the different experimental periods. The soil chemical indicators also affected the microbial diversity changes, mainly because some microorganisms could survive in adversity and become dominant bacterial groups, such as the genera Vagococcus and Enterococcus (which were associated with electrical conductivity) and Caldicoprobacter spp. (which were positively correlated with pH). Our study mainly revealed soil microbiota and DOX degradation answered differently under variable concentrations of DOX mixed with swine manure in soil.

Summary: Agrobacterium tumefaciens transfers oncogenic DNA (T-DNA) and effector proteins into various host plants. T-DNA is generated inside the bacteria and subsequently delivered into plant cells along with the companion effectors VirD2, VirE2, and VirE3. However, it is not clear how the T-complex consisting of VirD2 and VirE2 is assembled inside plant cells. Here, we report that the effector protein VirE3 localized to plant plasma membranes as an anchorage through a conserved α-helical-bundle domain. VirE3 interacted with itself and enabled VirE2 accumulation at host entry sites through direct interactions. VirE3 was critical for VirE2 function in T-DNA protection. Our data indicate that VirE3 functions as a previously unrecognized anchorage protein consisting of membrane-binding, self-interacting, and VirE2-interacting domains. Both VirE2 and VirE3 are conserved among Agrobacterium and rhizobia species but not other organisms, suggesting that a group of anchorage proteins have been generated through evolution to facilitate the nucleoprotein assembly at plant membranes. : Agrobacterium transfers T-DNA and virulence proteins into host cells. Li et al. show that a virulence protein, VirE3, anchors its companion protein VirE2 on host membranes at the entrance to facilitate T-DNA protection. VirE3 represents a class of proteins, anchorage proteins, which are present only in agrobacteria and rhizobia. Keywords: anchorage protein, Agrobacterium, VirE3, VirE2, T-complex, T-DNA, plasma membrane

In an increasingly globalized and warming world, drought can have devastating impacts on regional agriculture, water resources, and the ecological environment. Reliable prediction of future drought changes is especially important within the context of rapid warming. However, the extent and future trends of drought changes are variable and incomplete in the CMIP6 forcing scenarios. Based on the CMIP6 data, we chose the standardized precipitation-evapotranspiration index to predict future global drought. The results show that when emissions increase under the three shared socioeconomic pathway (SSP) scenarios (SSP126, SSP245 and SSP585), the global climate environment becomes drier and drought grow more severe and longer-lasting. Regions already classified as arid will suffer even more severe drought under high-emission SSPs. Specifically, 36.2% of global land will experience increased drought under SSP126, including 67.0% of regions designated as arid, with droughts intensifying significantly. Under SSP585, 68.3% of global land will suffer increased drought, with 93.2% of the arid regions experiencing significant drought intensification. Furthermore, the global duration of drought is estimated to be 4.4 months, 5.7 months, and 8.6 months for the time periods 1960–2000, 2021–2060, and 2061–2100, respectively. Notably, for the SSP585 scenario, regions that are already arid may become universally drought-stricken by the late 21st century. The most severe aridification trends may occur in the arid regions of Australia, Middle East, South Africa, Amazon basin, North Africa, Europe, and Central Asia. Additionally, Europe and the Amazon River Basin are also facing the threat of future drought. Increased aridification will put these regions and countries at risk of further land and ecological degradation, as well as increased poverty. The results of this study have far-ranging implications not only for how we deal with the impacts of climate warming-induced drought disaster, but also for how these impacts affect socio-economic and ecological security.

Deep neural networks have evolved significantly in the past decades and are now able to achieve better progression of sensor data. Nonetheless, most of the deep models verify the ruling maxim in deep learning—bigger is better—so they have very complex structures. As the models become more complex, the computational complexity and resource consumption of these deep models are increasing significantly, making them difficult to perform on resource-limited platforms, such as sensor platforms. In this paper, we observe that different layers often have different pruning requirements, and propose a differential evolutionary layer-wise weight pruning method. Firstly, the pruning sensitivity of each layer is analyzed, and then the network is compressed by iterating the weight pruning process. Unlike some other methods that deal with pruning ratio by greedy ways or statistical analysis, we establish an optimization model to find the optimal pruning sensitivity set for each layer. Differential evolution is an effective method based on population optimization which can be used to address this task. Furthermore, we adopt a strategy to recovery some of the removed connections to increase the capacity of the pruned model during the fine-tuning phase. The effectiveness of our method has been demonstrated in experimental studies. Our method compresses the number of weight parameters in LeNet-300-100, LeNet-5, AlexNet and VGG16 by 24×, 14×, 29× and 12×, respectively.

Domain adaptation aims to handle the distribution mismatch of training and testing data, which achieves dramatic progress in multi-sensor systems. Previous methods align the cross-domain distributions by some statistics, such as the means and variances. Despite their appeal, such methods often fail to model the discriminative structures existing within testing samples. In this paper, we present a sample-guided adaptive class prototype method, which consists of the no distribution matching strategy. Specifically, two adaptive measures are proposed. Firstly, the modified nearest class prototype is raised, which allows more diversity within same class, while keeping most of the class wise discrimination information. Secondly, we put forward an easy-to-hard testing scheme by taking into account the different difficulties in recognizing target samples. Easy samples are classified and selected to assist the prediction of hard samples. Extensive experiments verify the effectiveness of the proposed method.

Abstract We demonstrate the synergistic antitumor effect of oridonin and the PI3K/mTOR inhibitor NVP-BEZ235 on the non-germinal center B cell-like subtype of diffuse large B cell lymphoma (non-GCB DLBCL) both in vitro and in vivo. The underlying mechanism may be multifunctional, involving apoptosis, AKT/mTOR and NF-kB inactivation, and ROS-mediated DNA damage response. Our findings pave the way for a new potential treatment option for non-GCB DLBCL with the combination of oridonin and NVP-BEZ235.

With the advantages of uniform stress transfer and weight reduction, adhesive joints are widely used in engineering. The propagation of non-linear Lamb waves in an adhesive joint with micro-cracks distributing in a random way is systematically investigated by using the numerical simulation method in this paper. A finite element model of the tri-layer adhesive structure with micro-cracks distributing randomly is established, and the Lamb wave mode pair with a matching condition of the phase velocity is chosen to examine the interaction of the micro-cracks with Lamb waves. The results show that the micro-cracks within the adhesive layer will lead to the generation of second harmonics. We also find that the Acoustic Non-linearity Parameters (ANP) increase with the propagation distance in the micro-crack damage zone and the density of the micro-cracks. However, ANPs are less concerned with the friction coefficients of the surface of micro-cracks. This numerical research reveals that non-linear Lamb waves can be employed to effectively characterize the micro-cracks related damages within an adhesive joint.

Bovine herpesvirus 1 (BoHV-1) is a highly contagious viral pathogen which causes infectious bovine rhinotracheitis in cattle worldwide. Currently, there is no antiviral prophylactic treatment available capable of mitigating the disease impact and facilitating recovery from latent infection. In this study, we have engineered a novel recombinant anti-BoHV-1 immunotoxin construct termed “BoScFv-PE38” that consists of a single-chain monoclonal antibody fragment (scFv) fused with an active domain of Pseudomonas exotoxin A as a toxic effector (PE38). The recombinant BoScFv-PE38 immunotoxin expressed in a prokaryotic expression system has specific binding affinity for BoHV-1 glycoprotein D (gD) with a dissociation constant (Kd) of 12.81 nM and for BoHV-1 virus particles with a Kd value of 97.63 nM. We demonstrate that the recombinant BoScFv-PE38 is internalized into MDBK cell compartments that inhibit BoHV-1 replication with a half-maximal inhibitory concentration (IC50) of 4.95 ± 0.33 nM and a selective index (SI) of 456 ± 31. Furthermore, the BoScFv-PE38 exerted a cytotoxic effect through the induction of ATP and ammonia, leading to apoptosis of BoHV-1-infected cells and the inhibition of BoHV-1 replication in MDBK cells. Collectively, we show that BoScFv-PE38 can potentially be employed as a therapeutic agent for the treatment of BoHV-1 infection.

In this paper, the stress distribution field in front of the crack tip was obtained by loading a modified WOL specimen using a bolt. Considering the relationship between microhardness and hydrogen content or internal stress in the metal, a model based on the change of microhardness increment is proposed to describe the trend of hydrogen concentration distribution in the stress environment. The agreement between theoretical model and experimental results is verified by the Vickers microhardness tester. Based on the model, there is a simple additive relationship between the hydrogen-induced microhardness increment and the stress-induced microhardness increment. Therefore, the microhardness tester can be employed to evaluate the hydrogen distribution in metals quantitatively. The experimental results demonstrated that the Vickers microhardness method has accurately revealed the hydrogen concentration behavior accurately in a known equibiaxial stress environment. The hydrogen distribution of specimens in the stress environment was analyzed by taking the change of the microhardness increment along the crack propagation direction of specimens as the indicator.

Fusarium verticillioides, an important maize pathogen produces fumonisins and causes stalk and ear rot; thus, we are aimed to clarify its infection cycle by assessing enhanced green fluorescent protein (EGFP) expression in stalk and ear rot strains. Maize seeds were inoculated with stable and strongly pathogenic transformants. To investigate the degree of infection, inoculated plants were observed under a stereo fluorescence microscope, and affected tissue strains were detected using PCR. We found that both transformants infected maize. Hyphae infected the plants from radical to the stem and extended to the ear and infected ear kernels caused a second infection. This process formed the infection cycle.

The weapon-target assignment (WTA) problem is a key issue in Command & Control (C2). Asset-based multiobjective static WTA (MOSWTA) problem is known as one of the notable issues of WTA. Since this is an NP-complete problem, multiobjective evolutionary algorithms (MOEAs) can be used to solve it effectively. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a practical and promising multiobjective optimization technique. However, MOEA/D is originally designed for continuous multiobjective optimization which loses its efficiency to discrete contexts. In this study, an improved MOEA/D is proposed to solve the asset-based MOSWTA problem. The defining characteristics of this problem are summarized and analyzed. According to these characteristics, an improved MOEA/D framework is introduced. A novel decomposition mechanism is designed. The mating restriction and selection operation are reformulated. Furthermore, a problem-specific population initialization method is presented to improve the efficiency of the proposed algorithm, and a novel nondominated solution-selection method is put forward to handle the constraints of Pareto front. Appropriate extensions of four MOEA variants are developed in comparison with the proposed algorithm on some generated scenarios. Extensive experiments demonstrate that the proposed method is effective and promising.

Dempster-Shafer evidence theory has been widely used in various applications. However, to solve the problem of counter-intuitive outcomes by using classical Dempster-Shafer combination rule is still an open issue while fusing the conflicting evidences. Many approaches based on discounted evidence and weighted average evidence have been investigated and have made significant improvements. Nevertheless, all of these approaches have inherent flaws. In this paper, a new weighting factor is proposed to address this problem. First, a modified dissimilarity measurement is proposed which is characterized by both distance and conflict between evidences. Second, a measurement of information volume of each evidence based on Deng entropy is introduced. Then two kinds of weight derived from aforementioned measurement are combined to obtain a new weighting factor and a weighted average method based on the new weighting factor is proposed. Numerical examples are used to illustrate the validity and effectiveness of the proposed method. In the end, the new method is applied to a real-life application of river water quality monitoring, which effectively identify the major land use activities contributing to river pollution.

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

The sensor-weapon−target assignment (S-WTA) problem is a crucial decision issue in C4ISR. The cooperative engagement capability (CEC) of sensors and weapons depends on the S-WTA schemes, which can greatly affect the operational effectiveness. In this paper, a mathematical model based on the synthetical framework of the S-WTA problem is established, combining the dependent and independent cooperative engagement modes of sensors and weapons. As this problem is a complex combinatorial optimization problem, a novel genetic algorithm is proposed to improve the solution of this formulated S-WTA model. Based on the prior knowledge of this problem, a problem-specific population initialization method and two novel repair operators are introduced. The performances of the proposed algorithm have been validated on the known benchmarks. Extensive experimental studies compared with three state-of-the-art approaches demonstrate that the proposed algorithm can generate better assignment schemes for the most of the benchmarks.

Time lapse microscopy is essential for quantifying the dynamics of cells, subcellular organelles and biomolecules. Biologists use different fluorescent tags to label and track the subcellular structures and biomolecules within cells. However, not all of them are compatible with time lapse imaging, and the labeling itself can perturb the cells in undesirable ways. We hypothesized that phase image has the requisite information to identify and track nuclei within cells. By utilizing both traditional blob detection to generate binary mask labels from the stained channel images and the deep learning Mask RCNN model to train a detection and segmentation model, we managed to segment nuclei based only on phase images. The detection average precision is 0.82 when the IoU threshold is to be set 0.5. And the mean IoU for masks generated from phase images and ground truth masks from experts is 0.735. Without any ground truth mask labels during the training time, this is good enough to prove our hypothesis. This result enables the ability to detect nuclei without the need for exogenous labeling.

Dempster-Shafer evidence theory (DST) has shown its great advantages to tackle uncertainty in a wide variety of applications. However, how to quantify the information-based uncertainty of basic probability assignment (BPA) with belief entropy in DST framework is still an open issue. The main work of this study is to define a new belief entropy for measuring uncertainty of BPA. The proposed belief entropy has two components. The first component is based on the summation of the probability mass function (PMF) of single events contained in each BPA, which are obtained using plausibility transformation. The second component is the same as the weighted Hartley entropy. The two components could effectively measure the discord uncertainty and non-specificity uncertainty found in DST framework, respectively. The proposed belief entropy is proved to satisfy the majority of the desired properties for an uncertainty measure in DST framework. In addition, when BPA is probability distribution, the proposed method could degrade to Shannon entropy. The feasibility and superiority of the new belief entropy is verified according to the results of numerical experiments.

Error factors in the encoded sun sensor (ESS) are analyzed and simulated. Based on the analysis results, an ESS error compensation model containing structural errors and fine-code algorithm errors is established, and the corresponding calibration method for model parameters is proposed. As external parameters, installation deviation between ESS and calibration equipment are introduced to the ESS calibration model, so that the model parameters can be calibrated accurately. The experimental results show that within plus/minus 60 degree of incident angle, the ESS measurement accuracy after compensation is three times higher on average than that before compensation.

Accelerated degradation testing (ADT) has been widely used for reliability prediction of highly reliable products. In many applications, ADT data consists of multiple degradation-related features, and these features are usually dependent. When dealing with such ADT data, it is important to fully utilize the multiple degradation features and take into account their inherent dependency. This paper proposes a novel reliability-assessment method that combines Brownian motion and copulas to model ADT data obtained from vibration signals. In particular, degradation feature extraction is first carried out using the raw vibration signals, and a feature selection method quantifying feature properties, such as trendability, monotonicity, and robustness, is adopted to determine the most suitable degradation features. Then, a multivariate s-dependent ADT model is developed, where a Brownian motion is used to depict the degradation path of each degradation feature and a copula function is employed to describe the dependence among these degradation features. Finally, the proposed ADT model is demonstrated using the vibration-based ADT data for an electric motor.

When optimizing an accelerated degradation testing (ADT) plan, the initial values of unknown model parameters must be pre-specified. However, it is usually difficult to obtain the exact values, since many uncertainties are embedded in these parameters. Bayesian ADT optimal design was presented to address this problem by using prior distributions to capture these uncertainties. Nevertheless, when the difference between a prior distribution and actual situation is large, the existing Bayesian optimal design might cause some over-testing or under-testing issues. For example, the implemented ADT following the optimal ADT plan consumes too much testing resources or few accelerated degradation data are obtained during the ADT. To overcome these obstacles, a Bayesian sequential step-down-stress ADT design is proposed in this article. During the sequential ADT, the test under the highest stress level is firstly conducted based on the initial prior information to quickly generate degradation data. Then, the data collected under higher stress levels are employed to construct the prior distributions for the test design under lower stress levels by using the Bayesian inference. In the process of optimization, the inverse Gaussian (IG) process is assumed to describe the degradation paths, and the Bayesian D-optimality is selected as the optimal objective. A case study on an electrical connector’s ADT plan is provided to illustrate the application of the proposed Bayesian sequential ADT design method. Compared with the results from a typical static Bayesian ADT plan, the proposed design could guarantee more stable and precise estimations of different reliability measures.

Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses.

Laser tracking system (LTS) has been widely used in energy industries to meet increasingly high accuracy requirements. However, its measuring accuracy is highly affected by the dynamic uncertainty errors of LTS, and also the current range and angle measurements of LTS are mainly weighted statically, obtaining the dynamic weighting model which is essential for LTS to improve the measuring accuracy. In this paper, a dynamic weighting model that describes not only the dynamic uncertainty but also the geometric variations of LTS is developed, which can be adjusted to the weighting factors of dynamic parameters according to the range observations relative to the angle observations. Intensive experimental studies are conducted to check validity of the theoretical results. The results show that the measuring accuracy of LTS has been increased 2 times after using this technique for correction.

Accelerated degradation testing (ADT) is an efficient technique for evaluating the lifetime of a highly reliable product whose underlying failure process may be traced by the degradation of the product’s performance parameters with time. However, most research on ADT mainly focuses on a single performance parameter. In reality, the performance of a modern product is usually characterized by multiple parameters, and the degradation paths are usually nonlinear. To address such problems, this paper develops a new s-dependent nonlinear ADT model for products with multiple performance parameters using a general Wiener process and copulas. The general Wiener process models the nonlinear ADT data, and the dependency among different degradation measures is analyzed using the copula method. An engineering case study on a tuner’s ADT data is conducted to demonstrate the effectiveness of the proposed method. The results illustrate that the proposed method is quite effective in estimating the lifetime of a product with s-dependent performance parameters.

From the aspect of human circulation system structure, a complete hemodynamic model requires consideration of the influence of microcirculation load effect. This paper selected the seepage in porous media as the simulant of microcirculation load. On the basis of a bi-directional liquid-solid coupling tube model, we built a liquid-solid-porous media seepage coupling model. The simulation parameters accorded with the physiological reality. Inlet condition was set as transient single-pulse velocity, and outlet as free outlet. The pressure in the tube was kept at the state of dynamic stability in the range of 80–120 mmHg. The model was able to simulate the entire propagating process of pulse wave. The pulse wave velocity simulated was 6.25 m/s, which accorded with the physiological reality. The complex pressure wave shape produced by reflections of pressure wave was also observed. After the model changed the cardiac cycle length, the pressure change according with actual human physiology was simulated successfully. The model in this paper is well-developed and reliable. It demonstrates the importance of microcirculation load in hemodynamic model. Moreover the properties of the model provide a possibility for the simulation of dynamic adjustment process of human circulation system, which indicates a promising prospect in clinical application.