Background: Fear of negative evaluation (FNE), referring to negative expectation and feelings toward other people’s social evaluation, is closely associated with social anxiety that plays an important role in our social life. Exploring the neural markers of FNE may be of theoretical and practical significance to psychiatry research (e.g., studies on social anxiety).

Methods: To search for potentially relevant biomarkers of FNE in human brain, the current study applied multivariate relevance vector regression, a machine-learning and data-driven approach, on brain morphological features (e.g., cortical thickness) derived from structural imaging data; further, we used these features as indexes to predict self-reported FNE score in each participant.

Results: Our results confirm the predictive power of multiple brain regions, including those engaged in negative emotional experience (e.g., amygdala, insula), regulation and inhibition of emotional feeling (e.g., frontal gyrus, anterior cingulate gyrus), and encoding and retrieval of emotional memory (e.g., posterior cingulate cortex, parahippocampal gyrus).

Conclusions: The current findings suggest that anxiety represents a complicated construct that engages multiple brain systems, from primitive subcortical mechanisms to sophisticated cortical processes.

Background: China is a multi-ethnic country. It is of great significance for the skull identification to realize the skull ethnic classification through computers, which can promote the development of forensic anthropology and accelerate the exploration of national development.

Methods: In this paper, the 3D skull model is transformed into 2D auxiliary image including curvature, depth and elevation information, and then the deep learning method of the 2D auxiliary image is used for ethnic classification. We construct a convolution neural network structure inspired by VGGNet16 which has achieved excellent performance on image classification. In order to optimize the network, Adam algorithm is adopted to avoid falling into local minimum, and to ensure the stability of the algorithm with regularization terms.

Results: Experiments on 400 skull models have been conducted for ethnic classification by our method. We set different learning rates to compare the performance of the model, the highest accuracy of ethnic classification is 98.75%, which have better performance than other five classical neural network structures.

Conclusions: Deep learning based on skull auxiliary image for skull ethnic classification is an automatic and effective method with great application significance.

Background: As one of the leading causes of global disability, major depressive disorder (MDD) places a noticeable burden on individuals and society. Despite the great expectation on finding accurate biomarkers and effective treatment targets of MDD, studies in applying functional magnetic resonance imaging (fMRI) are still faced with challenges, including the representational ambiguity, small sample size, low statistical power, relatively high false positive rates, etc. Thus, reviewing studies with solid methodology may help achieve a consensus on the pathology of MDD.

Methods: In this systematic review, we screened fMRI studies on MDD through strict criteria to focus on reliable studies with sufficient sample size, adequate control of head motion, and a proper multiple comparison control strategy.

Results: We found consistent evidence regarding the dysfunction within and among the default mode network (DMN), the frontoparietal network (FPN), and other brain regions. However, controversy remains, probably due to the heterogeneity of participants and data processing strategies.

Conclusion: Future studies are recommended to apply a comprehensive set of neuro-behavioral measurements, consider the heterogeneity of MDD patients and other potentially confounding factors, apply surface-based neuroscientific network fMRI approaches, and advance research transparency and open science by applying state-of-the-art pipelines along with open data sharing.

Background: Cows actions are important factors of cows health and their well-being. By monitoring the individual cows actions, we prevent cows diseases and realize modern precision cows rearing. However, traditional cows actions monitoring is usually conducted through video recording or direct visual observation, which is time-consuming and laborious, and often lead to misjudgement due to the subjective consciousness or negligence.

Methods: This paper proposes a method of cows actions recognition based on tracked trajectories to automatically recognize and evaluate the actions of cows. First, we construct a dataset including 60 videos to describe the popular actions existing in the daily life of cows, providing the basic data for designing our actions recognition method. Second, eight famous trackers are used to track and obtain temporal and spatial information of targets. Third, after studying and analysing the tracked trajectories of different actions about cows, a rigorous and effective constraint method is designed to realize actions recognition by us.

Results: Many experiments demonstrate that our method of actions recognition performs favourably in detecting the actions of cows, and the proposed dataset basically satisfies the actions evaluation for farmers.

Conclusion: The proposed tracking guided actions recognition provides a feasible way to maintain and promote cows health and welfare.

Background: Physiological signal-based research has been a hot topic in affective computing. Previous works mainly focus on some strong, short-lived emotions (e.g., joy, anger), while the attention, which is a weak and long-lasting emotion, receives less attraction. In this paper, we present a study of attention recognition based on electrocardiogram (ECG) signals, which contain a wealth of information related to emotions.

Methods: The ECG dataset is derived from 10 subjects and specialized for attention detection. To relieve the impact of noise of baseline wondering and power-line interference, we apply wavelet threshold denoising as preprocessing and extract rich features by pan-tompkins and wavelet decomposition algorithms. To improve the generalized ability, we tested the performance of a variety of combinations of different feature selection algorithms and classifiers.

Results: Experiments show that the combination of generic algorithm and random forest achieve the highest correct classification rate (CCR) of 86.3%.

Conclusion: This study indicates the feasibility and bright future of ECG-based attention research.

Background: Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing. Traditionally, size uniformity is one of the significant features of superpixels. However, in medical images, in which subjects scale varies greatly and background areas are often flat, size uniformity rarely conforms to the varying content. To obtain the fewest superpixels with retaining important details, the size of superpixel should be chosen carefully.

Methods: We propose a scale-adaptive superpixel algorithm relaxing the size-uniformity criterion for medical images, especially pathological images. A new path-based distance measure and superpixel region growing schema allow our algorithm to generate superpixels with different scales according to the complexity of image content, that is smaller (larger) superpixels in color-riching areas (flat areas).

Results: The proposed superpixel algorithm can generate superpixels with boundary adherence, insensitive to noise, and with extremely big sizes and extremely small sizes on one image. The number of superpixels is much smaller than size-uniformly superpixel algorithms while retaining more details of images.

Conclusion: With the proposed algorithm, the choice of superpixel size is automatic, which frees the user from the predicament of setting suitable superpixel size for a given application. The results on the nuclear dataset show that the proposed superpixel algorithm superior to the respective state-of-the-art algorithms on both quantitative and quantitative comparisons.

Background: Images of anatomical regions and neuron type distribution, as well as their related literature are valuable assets for neuroscience research. They are vital evidence and vehicles in discovering new phenomena and knowledge refinement through image and text big data. The knowledge acquired from image data generally echoes with the literature accumulated over the years. The knowledge within the literature can provide a comprehensive context for a deeper understanding of the image data. However, it is quite a challenge to manually identify the related literature and summarize the neuroscience knowledge in the large-scale corpus. Thus, neuroscientists are in dire need of an automated method to extract neuroscience knowledge from large-scale literature.

Methods: A proposed deep learning model named BioBERT-CRF extracts brain region entities from the WhiteText dataset. This model takes advantage of BioBERT and CRF to predict entity labels while training.

Results: The proposed deep learning model demonstrated comparable performance against or even outperforms the previous models on the WhiteText dataset. The BioBERT-CRF model has achieved the best average precision, recall, and F1 score of 81.3%, 84.0%, and 82.6%, respectively. We used the BioBERT-CRF model to predict brain region entities in a large-scale PubMed abstract dataset and used a rule-based method to normalize all brain region entities to three neuroscience dictionaries.

Conclusions: Our work shows that the BioBERT-CRF model can be well-suited for brain region entity extraction. The rankings of different brain region entities by their appearance in the large-scale corpus indicate the anatomical regions that researchers are most concerned about.

Background: Image-based automatic diagnosis of field diseases can help increase crop yields and is of great importance. However, crop lesion regions tend to be scattered and of varying sizes, this along with substantial intra-class variation and small inter-class variation makes segmentation difficult.

Methods: We propose a novel end-to-end system that only requires weak supervision of image-level labels for lesion region segmentation. First, a two-branch network is designed for joint disease classification and seed region generation. The generated seed regions are then used as input to the next segmentation stage where we design to use an encoder-decoder network. Different from previous works that use an encoder in the segmentation network, the encoder-decoder network is critical for our system to successfully segment images with small and scattered regions, which is the major challenge in image-based diagnosis of field diseases. We further propose a novel weakly supervised training strategy for the encoder-decoder semantic segmentation network, making use of the extracted seed regions.

Results: Experimental results show that our system achieves better lesion region segmentation results than state of the arts. In addition to crop images, our method is also applicable to general scattered object segmentation. We demonstrate this by extending our framework to work on the PASCAL VOC dataset, which achieves comparable performance with the state-of-the-art DSRG (deep seeded region growing) method.

Conclusion: Our method not only outperforms state-of-the-art semantic segmentation methods by a large margin for the lesion segmentation task, but also shows its capability to perform well on more general tasks.

Background: The use of image analysis to understand the structure of chromosome and chromatin is critical to the study of genetic evolution and diversification. Furthermore, a detailed chromosome map and the structure of chromatin in the nucleus may contribute to the plant breeding and the study of fundamental biology and genetics in organisms.

Results: In plants with a fully annotated genome project, such as the Leguminosae species, the integration of genetic information, including DNA sequence data, a linkage map, and the cytological quantitative chromosome map could further improve their genetic value. The numerical parameters of chromocenters in 3D can provide useful genetic information for phylogenetic studies of plant diversity and heterochromatic markers whose epigenetic changes may explain the developmental and environmental changes in the plant genome. Extended DNA fibers combined with fluorescence in situ hybridization revealed the highest spatial resolution of the obtained genome structure. Moreover, image analysis at the nano-scale level using a helium ion microscope revealed the surface structure of chromatin, which consists of chromatin fibers compacted into plant chromosomes.

Conclusions: The studies described in this review sought to measure and evaluate chromosome and chromatin using the image analysis method, which may reduce measurement time and improve resolution. Further, we discussed the development of an effective image analysis to evaluate the structure of chromosome and chromatin. An effective application study of cell biology and the genetics of plants using image analysis methods is expected to be a major propeller in the development of new applications.