Repurposing a clinically approved prescription Colquhounia root tablet to treat diabetic kidney disease via suppressing PI3K/AKT/NF-kB activation

Background Growing clinical evidences show the potentials of Colquhounia root tablet (CRT) in alleviating diabetic kidney disease (DKD). However, its pharmacological properties and underlying mechanisms remain unclear. Methods ‘Drug target-Disease gene’ interaction network was constructed and the candidate network targets were screened through evaluating node genes' topological importance. Then, a DKD rat model induced by high-fat diet/streptozotocin was established and used to determine pharmacological effects and network regulatory mechanisms of CRT against DKD, which were also verified using HK2 cell model induced by high glucose. Results The candidate network targets of CRT against DKD were involved into various type II diabetes-related and nephropathy-related pathways. Due to the topological importance of the candidate network targets and the important role of the imbalance between immunity and inflammation in the pathogenesis of DKD, PI3K/AKT/NF-кB signaling-mediated immune-modulatory and anti-inflammatory actions of CRT were selected to be experimentally verified. On the basis of high-fat diet (HFD) / streptozotocin (STZ)-induced DKD rat model, CRT effectively reduced the elevated level of blood glucose, decreased the accumulation of renal lipid, suppressed inflammation and the generation of ECM proteins, and ameliorated kidney function and the renal histopathology through inhibiting the activation of PI3K, AKT and NF-кB proteins, reducing the nuclear accumulation of NF-кB protein and the serum levels of downstream cytokines, which were in line with the in vitro findings. Conclusions Our data suggest that CRT may be the promising candidate drug for treating DKD via reversing the imbalance of immune-inflammation system mediated by the PI3K/AKT/NF-кB/IL-1β/TNF-α signaling.

Classification Method of Rape Root Swelling Disease Based on Convolution Neural Network

Both the seedling stage and the adult plant stage of rape can be infected with root edema, and the damaged roots swell to form tumors of different sizes and shapes. The incidence of rape root swelling at the seedling stage reached 17%, and the average incidence at the adult plant stage was 15%, resulting in a 10.2% reduction in rape production. The average plant height, number of siliques, number of kernels per horn, 1000-seed weight and yield per plant of healthy plants were significantly higher than those of diseased plants. Grading root lesions can help trace the root causes of root lesions. However, the method of grading is often performed manually by professionals at present, which has the problems of low speed and low efficiency. In order to solve this problem, a method for grading rape root swelling based on deep convolutional neural network is proposed in this paper. Firstly, a rape root swelling model based on convolutional neural network and regional candidate network was established, and then implement it on the deep learning Tensorflow framework Model, and finally compare and analyze the results. The rape root swelling model uses the VGG16 network to extract the characteristics of the rape root swelling image. The regional candidate network generates the preliminary position candidate frame of the rape root swelling, and Fast-RCNN realizes the classification and positioning of the candidate frame. The results show that this method can achieve rapid and accurate detection of healthy, first-level tumors, second-level tumors, and third-level tumors of four-level rape root swelling, with an average accuracy rate of 84.12%. The experimental results show that the accuracy rate can reach more than 90%.

Automatic Detection and Segmentation for Group-Housed Pigs Based on PigMS R-CNN

Instance segmentation is an accurate and reliable method to segment adhesive pigs’ images, and is critical for providing health and welfare information on individual pigs, such as body condition score, live weight, and activity behaviors in group-housed pig environments. In this paper, a PigMS R-CNN framework based on mask scoring R-CNN (MS R-CNN) is explored to segment adhesive pig areas in group-pig images, to separate the identification and location of group-housed pigs. The PigMS R-CNN consists of three processes. First, a residual network of 101-layers, combined with the feature pyramid network (FPN), is used as a feature extraction network to obtain feature maps for input images. Then, according to these feature maps, the region candidate network generates the regions of interest (RoIs). Finally, for each RoI, we can obtain the location, classification, and segmentation results of detected pigs through the regression and category, and mask three branches from the PigMS R-CNN head network. To avoid target pigs being missed and error detections in overlapping or stuck areas of group-housed pigs, the PigMS R-CNN framework uses soft non-maximum suppression (soft-NMS) by replacing the traditional NMS to conduct post-processing selected operation of pigs. The MS R-CNN framework with traditional NMS obtains results with an F1 of 0.9228. By setting the soft-NMS threshold to 0.7 on PigMS R-CNN, detection of the target pigs achieves an F1 of 0.9374. The work explores a new instance segmentation method for adhesive group-housed pig images, which provides valuable exploration for vision-based, real-time automatic pig monitoring and welfare evaluation.

A Generic Encapsulation to Unravel Social Spreading of a Pandemic: An Underlying Architecture

Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing. People naturally exist in dynamically cascading networks of social/spatial interactions. Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis. Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc. Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns. We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread. Such properties are essential in managing the rate of spread of viral infections. Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network. We derive mathematical formulations to demonstrate the viral spread in the network structures.

A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks

A heterogeneous wireless network (HWN) contains many kinds of wireless networks with overlapping areas of signal coverage. One of the research topics on HWNs is how to make users choose the most suitable network. This paper designs a user-oriented intelligent access selection algorithm in HWNs with five modules (input, user preference calculation, candidate network score calculation, output, and learning). Essentially, the input module uses a utility function to calculate the utility value of the judgment parameter; the user preference calculation module calculates the weight of the judgment parameter using the fuzzy analysis hierarchy process (FAHP) approach; the candidate network score calculation module calculates the network score through a fuzzy neural network; the output module calculates the error between the actual output value and the expected output value; and the learning module corrects the parameter of the membership function in the fuzzy neural network structure according to the error. Simulation results show that the algorithm proposed in this paper can enable users to select the most suitable network according to service characteristics and can enable users to obtain higher gains.

Multiattribute Access Selection Algorithm Supporting Service Characteristics and User Preferences in Heterogeneous Wireless Networks

In heterogeneous wireless networks (HWNs), various wireless networks have signal ranges that overlap and cover each other. Enabling mobile users to access the most suitable network is one of the research topics on HWNs. This paper designs a multiattribute access selection algorithm supporting service characteristics and user preferences in HWNs, which includes five calculation modules: network attribute utility value, network attribute weight, network attribute score, user preference value, and candidate network comprehensive score. In addition, the algorithm proposed in this paper integrates the utility theory, fuzzy analysis hierarchy process (FAHP), fuzzy logic, and multiattribute decision-making (MADM) methods for a complete access selection scheme that considers different network performances, service characteristics, and user preferences. The simulation results show that the algorithm proposed in this paper can allow users to select the most suitable network while obtaining higher gains and reducing user handover between different networks.

Well-Structured Committees

In the standard model of committee selection, we are given a set of ordinal votes over a set of candidates and a desired committee size, and the task is to select a committee that relates to the given votes. Motivated by possible interactions and dependencies between candidates, we study a generalization of committee selection in which the candidates are connected via a network and the task is to select a committee that relates to the given votes while also satisfy certain properties with respect to this candidate network. To accommodate certain correspondences to the voter preferences, we consider three standard voting rules (in particular, $k$-Borda, Chamberlin-Courant, and Gehrlein stability); to model different aspects of interactions and dependencies between candidates, we consider two graph properties (in particular, Independent Set and Connectivity). We study the parameterized complexity of the corresponding combinatorial problems and discuss certain implications of our algorithmic results.

Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography

Photogrammetry involves aerial photography of the Earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It is used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our main goal is the automatic design of deep neural network architectures with grammar-guided genetic programming. In this kind of evolutive algorithm, all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g., Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state-of-the-art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.