Research on Target Detection Algorithm of Radar and Visible Image Fusion Based on Wavelet Transform

The target detection rate of unmanned surface vehicle is low because of waves, fog, background clutter and other environmental factors on the interference. Therefore, the paper studies the target detection algorithm of radar and visible image fusion based on wavelet transform. The visible image is preprocessed to ensure the detection effect. The multi-scale fractal model is used to extract the target features, and the difference between the fractal features of the target and the background is used to detect the target. The radar image is denoised by a combination of median filtering and wavelet transform. The processed visible light and radar image are fused with wavelet transform strategy. The coefficients of the low frequency sub-band are processed by the average fusion strategy. The coefficients of the high frequency sub-band are processed using a strategy with a higher absolute value. The standard deviation, the spatial frequency and the contrast resolution of the image fusion result are compared. The simulation results show that the processed image is better than the unprocessed image after the fusion

Computable features required to evaluate the efficacy of drugs and a universal algorithm to find optimally effective drug in a drug complex

Background The H1N1 pandemic in 2009 and the H5N1 pandemic in 2005 demonstrated that the drugs approved to treat influenza A viruses have low efficacy. This provided a stimulus for new studies of influenza A viruses in the context of the methods used in drug design developed over the past 100 years. Finding new universal drugs is the ultimate goal but its long time horizon is incompatible with emergency situations created by reoccurring influenza outbreaks. Therefore, we propose a computer-aided method for finding efficacious drugs and drug complexes based on the use of the DrugBank database. Methods (1) We start by assembling a panel of target proteins. (2) We then assemble a panel of drugs. (3) This is followed by a selection of benchmark binding pockets based on the panel of target proteins and the panel of drugs. (4) We generate a set of computational features, which measure the efficacy of a drug. (5) We propose a universal program to search for drugs and drug complexes. (6) A case study we report here illustrates how to use this universal program for finding an optimal drug and a drug complex for a given target. (7) Validation of the Azirchromycin and Aspirin complex is provided mathematically. (8) Finally, we propose a simple strategy to validate our computational prediction that the Azirchromycin and Aspirin complex should prove clinically effective. Result A set of computable features are mined and then based on these features, a universal program for finding the potential drug &drug complexes is proposed. Using this universal program, the Azirchromycin and Aspirin complex is selected and its efficacy is predicted mathematically. For clinical validation of this finding, future work is still required

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Uneven Distribution of Ecosystem Services along the Yarlung Zangbo River Basin in Tibet Reveals the Quest for Multi-Target Policies of Rural Development in Less-Favored Areas

This study was conducted in the Qinghai-Tibet Plateau which is a typical less-favored ecologically fragile area. First, we constructed a GIS-based spatial gridding structure over the study area, the Yarlung Zangbo river basin in China’s Tibet, and used a value-assessment model to measure supply, support, regulation, and culture ecosystem services in each study grid. We then analyzed the spatiotemporal patterns of different ecosystem services in the region from 2000 to 2020. In addition, we conducted a spatial visualized analysis of the trade-off and synergies of multiple ecosystem services in each study grid. We found that: (1) On the temporal scale, from 2000 to 2020, the values of the four ecosystem services for supply, support, regulation, and culture along the basin demonstrated an upward trend. (2) On the spatial scale, the values of ecosystem services showed an uneven distribution, with a decline trend from east to west along the basin. (3) From the perspective of land use types, due to the large areas of water, grassland, and forest along the river basin, the ecosystem service values of the three types of land use ranked among the top levels. (4) The trade-offs and synergies between different ecosystem services and their spatial distribution along the river basin showed an uneven distribution pattern. The ecosystem services zoning revealed that the policies in guiding rural sustainability in the less-favored areas should adjust the measures to local conditions, it’s necessary to establish multiple targets across the entire region

PSIONplus: Accurate Sequence-Based Predictor of Ion Channels and Their Types

<div><p>Ion channels are a class of membrane proteins that attracts a significant amount of basic research, also being potential drug targets. High-throughput identification of these channels is hampered by the low levels of availability of their structures and an observation that use of sequence similarity offers limited predictive quality. Consequently, several machine learning predictors of ion channels from protein sequences that do not rely on high sequence similarity were developed. However, only one of these methods offers a wide scope by predicting ion channels, their types and four major subtypes of the voltage-gated channels. Moreover, this and other existing predictors utilize relatively simple predictive models that limit their accuracy. We propose a novel and accurate predictor of ion channels, their types and the four subtypes of the voltage-gated channels called PSIONplus. Our method combines a support vector machine model and a sequence similarity search with BLAST. The originality of PSIONplus stems from the use of a more sophisticated machine learning model that for the first time in this area utilizes evolutionary profiles and predicted secondary structure, solvent accessibility and intrinsic disorder. We empirically demonstrate that the evolutionary profiles provide the strongest predictive input among new and previously used input types. We also show that all new types of inputs contribute to the prediction. Results on an independent test dataset reveal that PSIONplus obtains relatively good predictive performance and outperforms existing methods. It secures accuracies of 85.4% and 68.3% for the prediction of ion channels and their types, respectively, and the average accuracy of 96.4% for the discrimination of the four ion channel subtypes. Standalone version of PSIONplus is freely available from <a href="https://sourceforge.net/projects/psion/" target="_blank">https://sourceforge.net/projects/psion/</a></p></div

Workflow of the PSIONplus model.

<p>SS: secondary structure, RSA: relative solvent accessibility.</p

Summary of results based on the jackknife and 5-fold cross validation (5-cv) tests on the training datasets TRAIN_ION, TRAIN_VLG and TRAIN_VGS.

<p>Summary of results based on the jackknife and 5-fold cross validation (5-cv) tests on the training datasets TRAIN_ION, TRAIN_VLG and TRAIN_VGS.</p