Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases. In the last decade, studies on AD diagnosis has attached great significance to artificial intelligence-based diagnostic algorithms. Among the diverse modalities of imaging data, T1-weighted MR and FDG-PET are widely used for this task. In this paper, we propose a convolutional neural network (CNN) to integrate all the multi-modality information included in both T1-MR and FDG-PET images of the hippocampal area, for the diagnosis of AD. Different from the traditional machine learning algorithms, this method does not require manually extracted features, instead, it utilizes 3D image-processing CNNs to learn features for the diagnosis or prognosis of AD. To test the performance of the proposed network, we trained the classifier with paired T1-MR and FDG-PET images in the ADNI datasets, including 731 cognitively unimpaired (labeled as CN) subjects, 647 subjects with AD, 441 subjects with stable mild cognitive impairment (sMCI) and 326 subjects with progressive mild cognitive impairment (pMCI). We obtained higher accuracies of 90.10% for CN vs. AD task, 87.46% for CN vs. pMCI task, and 76.90% for sMCI vs. pMCI task. The proposed framework yields a state-of-the-art performance. Finally, the results have demonstrated that (1) segmentation is not a prerequisite when using a CNN for the classification, (2) the combination of two modality imaging data generates better results.

Crop weediness, especially that of weedy rice (Oryza sativa f. spontanea), remains mysterious. Weedy rice possesses robust ecological adaptability; however, how this strain originated and gradually formed proprietary genetic features remains unclear. Here, we demonstrate that weedy rice at Asian high latitudes (WRAH) is phylogenetically well defined and possesses unselected genomic characteristics in many divergence regions between weedy and cultivated rice. We also identified novel quantitative trait loci underlying weedy-specific traits, and revealed that a genome block on the end of chromosome 1 is associated with rice weediness. To identify the genomic modifications underlying weedy rice evolution, we generated the first de novo assembly of a high-quality weedy rice genome (WR04-6), and conducted a comparative genomics study between WR04-6 with other rice reference genomes. Multiple lines of evidence, including the results of demographic scenario comparisons, suggest that differentiation between weedy rice and cultivated rice was initiated by genetic improvement of cultivated rice and that the essence of weediness arose through semi-domestication. A plant height model further implied that the origin of WRAH can be modeled as an evolutionary game and indicated that strategy-based selection driven by fitness shaped its genomic diversity.

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Long Short-Term Memory (LSTM) neural network has been widely used in many applications, but its application in classification of vehicle movement patterns is still limited. In this paper, LSTM is applied to the vehicle behavior recognition problem to identify the left turn, right turn and straight behavior of the vehicle at the intersection. On the basis of the traditional LSTM classification model, this paper transversely merges the input features and then inputs into a LSTM cell to get an improved model. The improved model can make full use of the input information and reduce unnecessary calculations, and the output of a single LSTM cell model can filter out interference information and retain important information, so it has better classification effect and faster training speed. The experimental results show that the proposed improved LSTM network classification model in this paper has a significant improvement in recognition accuracy and training speed compared with the improved model, the accuracy is increased by 1.6%, and the training time is reduced by 3.96 s. In addition, this paper also applies the improved model to regression problems, emotion classification and handwritten digit recognition and all of them have a good improvement effect, which improves the applicability and stability of LSTM in classification problems and provides a new way to deal with classification problems.