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Species of the tribe Delphinieae (Ranunculaceae) have long been the focus of morphological, ecological, and evolutionary studies due to their highly specialized, nearly zygomorphic (bilaterally symmetrical) spiral flowers with nested petal and sepal spurs and reduced petals. The mechanisms underlying the development and evolution of Delphinieae flowers, however, remain unclear. Here, by conducting extensive phylogenetic, comparative transcriptomic, expression, and functional studies, we clarified the evolutionary histories, expression patterns, and functions of floral organ identity and symmetry genes in Delphinieae. We found that duplication and/or diversification of APETALA3-3 (AP3-3), AGAMOUS-LIKE6 (AGL6), CYCLOIDEA (CYC), and DIVARICATA (DIV) lineage genes was tightly associated with the origination of Delphinieae flowers. Specifically, an AGL6-lineage member (such as the Delphinium ajacis AGL6-1a) represses sepal spur formation and petal development in the lateral and ventral parts of the flower while determining petal identity redundantly with AGL6-1b. By contrast, two CYC2-like genes, CYC2b and CYC2a, define the dorsal and lateral-ventral identities of the flower, respectively, and form complex regulatory links with AP3-3, AGL6-1a, and DIV1. Therefore, duplication and diversification of floral symmetry genes, as well as co-option of the duplicated copies into the preexisting floral regulatory network, have been key for the origin of Delphinieae flowers.

Plasma cell-free DNA (cfDNA) was elevated in patients with acute myocardial infarction (AMI) or heart failure (HF). However, whether cfDNA could serve as a predictor for risk of HF after AMI remains unknown. In this study, we conducted a pilot prospective cohort study in which 98 AMI patients were enrolled from a single center to assess the association between cfDNA levels at admission and risk of HF in an AMI population. Patients with cfDNA above the median level (14.39 ng/mL) showed higher low-density lipoprotein cholesterol, cardiac troponin I (cTnI), and soluble suppression of tumorigenicity 2 (sST2) levels compared with patients below the median. cfDNA was positively correlated with cTnI (

The deterioration of aging population has seriously hindered the development of society. Medical cloud platform has been widely used to alleviate the pressure of aging population on social economy. Most of them collect the user’s sign information through the edge node and complete the disease prediction and diagnosis function combined with the cloud platform. However, the limited resources prevent the edge node from deploying the corresponding security policy after completing the data collection, storage, and calculation, which makes the edge data easy to be stolen. This paper proposes a security architecture of medical cloud platform based on lightweight algorithm model, which not only satisfies the needs of medical cloud platform to complete disease prediction and diagnosis accurately, but also creates a more secure edge node environment combined with other security strategies and hardware design. Finally, the prediction of cerebrovascular disease is used to verify the effectiveness of the proposed algorithm model.

Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. We choose the classical particle swarm optimization (PSO) and improve it, including the introduction of a biological population model to control the population size, and the addition of a parallel mechanism for further tuning. The proposed RS-PPSO algorithm was successfully used to optimize the initial weights and biases of back propagation neural network (BPNN), shortening the training time and raising the prediction accuracy. Wireless sensor networks (WSN) has become the key supporting platform of Internet of Things (IoT). The correctness of the data collected by the sensor nodes has a great influence on the reliability, real-time performance and energy saving of the entire network. The optimized machine learning technology scheme given in this paper can effectively identify the fault data, so as to ensure the effective operation of WSN.

Wireless sensor networks (WSN) have gradually integrated into the concept of the Internet of Things (IoT) and become one of the key technologies. This paper studies the optimization algorithm in the field of artificial intelligence (AI) and effectively solves the problem of node location in WSN. Specifically, we propose a hybrid algorithm WOA-QT based on the whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm. It skillfully combines the strengths of the two algorithms, not only retaining the WOA’s distinctive framework advantages but also having QUATRE’s excellent coevolution ability. In order to further save optimization time, an auxiliary strategy for dynamically shrinking the search space (DSS) is introduced in the algorithm. To ensure the fairness of the evaluation, this paper selects 30 different types of benchmark functions and conducts experiments from multiple angles. The experiment results demonstrate that the optimization quality and efficiency of WOA-QT are very prominent. We use the proposed algorithm to optimize the weighted centroid location (WCL) algorithm based on received signal strength indication (RSSI) and obtain satisfactory positioning accuracy. This reflects the high value of the algorithm in practical applications.

The self-powered sensors are more and more important in current society. However, detecting both light and temperature signals simultaneously without energy waste and signal interference is still a challenge. Here, we report a ZnO/graphene nanocomposite foam-based self-powered sensor, which can realize the simultaneous detection of light and temperature by using the conjuncted photo-thermoelectric effect in ZnO–graphene nanocomposite foam sensor. The output current under light, heating and cooling of the device with the best ZnO/graphene ratio (8:1) for the foam can reach 1.75 µA, 1.02 µA and 0.70 µA, respectively, which are approximately three fold higher than them of devices with other ZnO/graphene ratios. The ZnO–graphene nanocomposite foam device also possesses excellent thermoelectric and photoelectric performances for conjuncted lighting and heating detection without mutual interference. The ZnO–graphene nanocomposite foam device exhibits a new designation on the road towards the fabrication of low cost and one-circuit-based multifunction sensors and systems.

As the reproductive structures of angiosperms, flowers show tremendous diversity in morphology and structure. The diversification of flowers in basic structure and other features has greatly contributed to the success of angiosperms. In recent years, thanks to the rise of, as well as the rapid progress in evolutionary developmental biology (evo-devo), our knowledge of the molecular basis for the development and diversification of flowers in basic structure has advanced considerably. In this review, we attempt to summarize the developmental and evolutionary mechanisms underlying the diversification of flowers in terms of types, fusion, arrangement patterns, and number of floral organs, as well as floral symmetry. We suggest that future studies should be focused on the establishment of new model systems, application of advanced approaches and integration of multiple disciplines to elucidate the processes, patterns, causes, and molecular bases of the diversification of flowers in basic structure.

The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

The etiology of cardiovascular disease is still an unsolved world problem, and high morbidity, disability, and mortality are the main characteristics of cardiovascular diseases. There is, therefore, a need for effective and rapid early prediction of likely outcomes in patients with cardiovascular disease using artificial intelligence (AI) techniques. The Internet of Things (IoT) is becoming a catalyst for enhancing the capabilities of AI applications. Data are collected through IoT sensors and analyzed and predicted using machine learning (ML). Existing traditional ML models do not handle data inequities well and have relatively low model prediction accuracy. To address this problem, considering the data observation mechanism and training methods of different algorithms, this paper proposes an ensemble framework based on stacking model fusion, from Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Extra Tree (ET), Gradient Boosting Decision Tree (GBDT), XGBoost, LightGBM, CatBoost, and Multilayer Perceptron (MLP) (10 classifiers to select the optimal base learners). In order to avoid the overfitting phenomenon generated by the base learners, we use the Logistic Regression (LR) simple linear classifier as the meta learner. We validated the proposed algorithm using a fused Heart Dataset from several UCI machine learning repositories and another publicly available Heart Attack Dataset, and compared it with 10 single classifier models. The experimental results show that the proposed stacking classifier outperforms other classifiers in terms of accuracy and applicability.

Pseudonectaries, or false nectaries, the glistening structures that resemble nectaries or nectar droplets but do not secrete nectar, show considerable diversity and play important roles in plant-animal interactions. The morphological nature, optical features, molecular underpinnings and ecological functions of pseudonectaries, however, remain largely unclear. Here, we show that pseudonectaries of Nigella damascena (Ranunculaceae) are tiny, regional protrusions covered by tightly arranged, non-secretory polygonal epidermal cells with flat, smooth and reflective surface, and are clearly visible even under ultraviolet light and bee vision. We also show that genes associated with cell division, chloroplast development and wax formation are preferably expressed in pseudonectaries. Specifically, NidaYABBY5, an abaxial gene with ectopic expression in pseudonectaries, is indispensable for pseudonectary development: knockdown of it led to complete losses of pseudonectaries. Notably, when flowers without pseudonectaries were arrayed beside those with pseudonectaries, clear differences were observed in the visiting frequency, probing time and visiting behavior of pollinators (i.e., honey bees), suggesting that pseudonectaries serve as both visual attractants and nectar guides.

Pseudonectaries, or false nectaries, the glistening structures that resemble nectaries or nectar droplets but do not secrete nectar, show considerable diversity and play important roles in plant-animal interactions. The morphological nature, optical features, molecular underpinnings and ecological functions of pseudonectaries, however, remain largely unclear. Here, we show that pseudonectaries of Nigella damascena (Ranunculaceae) are tiny, regional protrusions covered by tightly arranged, non-secretory polygonal epidermal cells with flat, smooth and reflective surface, and are clearly visible even under ultraviolet light and bee vision. We also show that genes associated with cell division, chloroplast development and wax formation are preferably expressed in pseudonectaries. Specifically, NidaYABB5, an abaxial gene with ectopic expression in pseudonectaries, is indispensable for pseudonectary development: knockdown of it led to complete losses of pseudonectaries. Notably, when flowers without pseudonectaries were arrayed beside those with pseudonectaries, clear differences were observed in the visiting frequency, probing time and visiting behavior of pollinators (i.e., honey bees), suggesting that pseudonectaries serve as both visual attractants and nectar guides., Interspecies interactions, including those between plants and pollinators, can involve deception. The authors characterize the molecular development of Nigella damascena pseudonectaries, and their adaptive function in attracting specific pollinators to concealed nectaries with visual cues.

Image denoising is an important research direction of image restoration. With the increasing requirements of image quality, image denoising has been widely concerned by scholars at home and abroad. This paper studies the problem of noise image denoising, using improved sparse representation algorithm and deep learning technology to denoise the noise image. In this paper, a de-noising model based on generative countermeasure network is proposed. The residual algorithm and dense connection are added to the network layer of learning image noise information features. At the same time, the discriminator is trained to judge whether the generated image is true or false, so as to avoid boundary blur while de-noising. Zero filling convolution is used to ensure the consistency of input and output characteristic dimensions of each layer. The output of network model is image noise information. Simulation results show that the algorithm has good denoising performance.

By 60 Co-γ irradiation method, the Chitosan-oligosaccharide (COS) was grafted onto the inner surface of the polystyrene (PS) microtiter, which was soaked with COS solution before the irradiation. To evaluated the effect of COS concentration on the properties of the PS microtiter, FTIR, XPS, AFM, Contact angle tester and enzyme-linked analyser was used to measure the surface properties and BSA adsorption of PS-COS plates. The results shows that, with the increase of COS concentration, the contact angle clearly decreased at the dose of 12 kGy. The absorbance variances of the COS modified plate is less than 5% while the BSA adsorption is higher than the PS plates. The COS-modified microtiter has the potential applications in biochemical analysis.

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