Fueling ab initio folding with marine metagenomics enables structure and function predictions of new protein families

Abstract Introduction The ocean microbiome represents one of the largest microbiomes and produces nearly half of the primary energy on the planet through photosynthesis or chemosynthesis. Using recent advances in marine genomics, we explore new applications of oceanic metagenomes for protein structure and function prediction. Results By processing 1.3 TB of high-quality reads from the Tara Oceans data, we obtain 97 million non-redundant genes. Of the 5721 Pfam families that lack experimental structures, 2801 have at least one member associated with the oceanic metagenomics dataset. We apply C-QUARK, a deep-learning contact-guided ab initio structure prediction pipeline, to model 27 families, where 20 are predicted to have a reliable fold with estimated template modeling score (TM-score) at least 0.5. Detailed analyses reveal that the abundance of microbial genera in the ocean is highly correlated to the frequency of occurrence in the modeled Pfam families, suggesting the significant role of the Tara Oceans genomes in the contact-map prediction and subsequent ab initio folding simulations. Of interesting note, PF15461, which has a majority of members coming from ocean-related bacteria, is identified as an important photosynthetic protein by structure-based function annotations. The pipeline is extended to a set of 417 Pfam families, built on the combination of Tara with other metagenomics datasets, which results in 235 families with an estimated TM-score over 0.5. Conclusions These results demonstrate a new avenue to improve the capacity of protein structure and function modeling through marine metagenomics, especially for difficult proteins with few homologous sequences.https://deepblue.lib.umich.edu/bitstream/2027.42/152239/1/13059_2019_Article_1823.pd

WDN: A One-Stage Detection Network for Wheat Heads with High Performance

The counting of wheat heads is labor-intensive work in agricultural production. At present, it is mainly done by humans. Manual identification and statistics are time-consuming and error-prone. With the development of machine vision-related technologies, it has become possible to complete wheat head identification and counting with the help of computer vision detection algorithms. Based on the one-stage network framework, the Wheat Detection Net (WDN) model was proposed for wheat head detection and counting. Due to the characteristics of wheat head recognition, an attention module and feature fusion module were added to the one-stage backbone network, and the formula for the loss function was optimized as well. The model was tested on a test set and compared with mainstream object detection network algorithms. The results indicate that the mAP and FPS indicators of the WDN model are better than those of other models. The mAP of WDN reached 0.903. Furthermore, an intelligent wheat head counting system was developed for iOS, which can present the number of wheat heads within a photo of a crop within 1 s

WDN: A One-Stage Detection Network for Wheat Heads with High Performance

The counting of wheat heads is labor-intensive work in agricultural production. At present, it is mainly done by humans. Manual identification and statistics are time-consuming and error-prone. With the development of machine vision-related technologies, it has become possible to complete wheat head identification and counting with the help of computer vision detection algorithms. Based on the one-stage network framework, the Wheat Detection Net (WDN) model was proposed for wheat head detection and counting. Due to the characteristics of wheat head recognition, an attention module and feature fusion module were added to the one-stage backbone network, and the formula for the loss function was optimized as well. The model was tested on a test set and compared with mainstream object detection network algorithms. The results indicate that the mAP and FPS indicators of the WDN model are better than those of other models. The mAP of WDN reached 0.903. Furthermore, an intelligent wheat head counting system was developed for iOS, which can present the number of wheat heads within a photo of a crop within 1 s

Retraction: Sun et al. WDN: A One-Stage Detection Network for Wheat Heads with High Performance. Information 2022, 13, 153

The journal retracts the article “WDN: A One-Stage Detection Network for Wheat Heads with High Performance” [...

Pear Defect Detection Method Based on ResNet and DCGAN

To address the current situation, in which pear defect detection is still based on a workforce with low efficiency, we propose the use of the CNN model to detect pear defects. Since it is challenging to obtain defect images in the implementation process, a deep convolutional adversarial generation network was used to augment the defect images. As the experimental results indicated, the detection accuracy of the proposed method on the 3000 validation set was as high as 97.35%. Variant mainstream CNNs were compared to evaluate the model’s performance thoroughly, and the top performer was selected to conduct further comparative experiments with traditional machine learning methods, such as support vector machine algorithm, random forest algorithm, and k-nearest neighbor clustering algorithm. Moreover, the other two varieties of pears that have not been trained were chosen to validate the robustness and generalization capability of the model. The validation results illustrated that the proposed method is more accurate than the commonly used algorithms for pear defect detection. It is robust enough to be generalized well to other datasets. In order to allow the method proposed in this paper to be applied in agriculture, an intelligent pear defect detection system was built based on an iOS device

Retraction: Sun et al. WDN: A One-Stage Detection Network for Wheat Heads with High Performance. <i>Information</i> 2022, <i>13</i>, 153

The journal retracts the article “WDN: A One-Stage Detection Network for Wheat Heads with High Performance” [...

Vision-assisted mmWave beam management for next-generation wireless systems: concepts, solutions and open challenges

Beamforming techniques have been widely used in the millimeter wave (mmWave) bands to mitigate the path loss of mmWave radio links as the narrow straight beams by directionally concentrating the signal energy. However, traditional mmWave beam management algorithms usually require excessive channel state information overhead, leading to extremely high computational and communication costs. This hinders thewidespread deployment of mmWave communications. By contrast, the revolutionary vision-assisted beam management system concept employed at base stations (BSs) can select the optimal beam for the target user equipment (UE) based on its locationinformation determined by machine learning (ML) algorithms applied to visual data, without requiring channel information. In this paper, we present a comprehensive framework for a vision-assisted mmWave beam management system, its typicaldeployment scenarios as well as the specifics of the framework. Then, some of the challenges faced by this system and their efficient solutions are discussed from the perspective of ML. Next, a new simulation platform is conceived to provide both visual and wireless data for model validation and performance evaluation. Our simulation results indicate that the vision-assisted beam management is indeed attractive for next-generation wireless systems

Research on a New Adaptive Integral Sliding Mode Controller Based on a Small BLDC

A new adaptive saturation Integral Sliding Mode Controller (ADSA-ISMC) was presented to solve the speed fluctuation problem caused by a small brushless DC motor (S-BLDC) under variable loads. First, according to the characteristics of the S-BLDC and Kalman filter, a Kalman-based S-BLDC load disturbance observer was established, and an adaptive integrated sliding mode controller (AD-ISMC) was constructed based on the motor load obtained by the observer. Then, we introduce the saturation function into the input error link of the traditional integral sliding mode function, to propose a new ADSA-ISMC to improve the windup phenomenon produced by the integral sliding mode controller and prove the stability and convergence of the controller using the Lyapunov method. Finally, the experimental results show that the Kalman load disturbance observer can effectively observe the motor load disturbance. In addition, when the load of the S-BLDC changes, many speed control performance indexes of the ADSA-ISMC are significantly better than those of the SMC and AD-ISMC. The theoretical analysis and experimental results show that the ADSA-ISMC proposed in this study exhibit excellent control performance in S-BLDC speed control

Development of a parallel computing-based Futureland model for multiple land-use simulation: a case study in Shanghai

Predicting future urban land-use scenarios and assessing their encroachment on ecological land is important for sustainability policy formulation. This study develops a new Futureland model using cellular automata with user-defined transformation rules, graphical neighborhood configuration and matrixed transformation cost to simulate multiple land-use changes. Compared with existing models, Futureland applies different factors to different land-use types in a single simulation experiment. Futureland was developed using Geospatial Data Abstraction Library and parallel computing, which significantly improve the implementation efficiency. The case study of Shanghai 2010–2020 illustrates an overall accuracy of 86.6% and a Kappa simulation of 0.79. The land-use scenarios for 2020–2035 were projected under greenspace planning constraints using Futureland. The results indicate that Shanghai’s urban sprawl will gradually slow down by 2030, and the increased urban areas will be mainly in the urban fringes and suburban regions. Futureland can help decision-makers to manage future land-use and optimize urban planning policies

Spatial planning-constrained modeling of urban growth in the Yangtze River Delta considering the element flows

Urban growth models that consider spatial control and factor flows are important for the integrated management of urban agglomerations, and to date, no simulation method has been available to consider both thoroughly. We proposed a new cellular automaton (CAGBDT) model based on gradient boosting decision trees (GBDT) to simulate urban growth in the Yangtze River Delta (YRD) area of China. In this model, GBDT was used to retrieve land transition rules by integrating multiple weak learners and a feature expansion approach was employed to remove high correlations among urban growth drivers and then expand the factor features. We applied the final urban pattern as the dependent variable and selected nine drivers as the independent variables of urban growth in YRD. The simulation results show that the overall accuracies exceeded 89% and the figure-of-merits (FOMs) exceeded 27%, about 10% higher than other similar simulations in YRD, indicating the strong ability of CAGBDT to simulate urban growth in YRD. Although the inclusion of inter-city material and information element flows in the model improves the accuracy by only 1%, it reveals the different development patterns in Hangzhou Bay in the south of YRD and the Taihu Lake basin in the center of YRD. By considering urban scenarios under different strategies of spatial control, it shows that the simulated FOMs declined by 5% with the stronger enforcement of spatial regulations, reflecting that the actual urban growth in fully non-developable areas and 20% partly developable areas of YRD has already violated the regulations. The results can help urban planners and local authorities to develop solutions for the development of a high-quality YRD urban agglomeration. The proposed model is applicable to diagnose urban land-use change elsewhere, especially in rapidly developing cities that need balancing urban growth and ecological protection