The perennial selenium (Se) hyperaccumulator Cardamine hupingshanensis (Brassicaceae) thrives in aquatic and subaquatic Se-rich environments along the Wuling Mountains, China. Using bright-field and epifluorescence microscopy, the present study determined the anatomical structures and histochemical features that allow this species to survive in Se-rich aquatic environments. The roots of C. hupingshanensis have an endodermis with Casparian walls, suberin lamellae, and lignified secondary cell walls; the cortex and hypodermal walls have phi (Φ) thickenings; and the mature taproots have a secondary structure with a periderm. The stems possess a lignified sclerenchymal ring and an endodermis, and the pith and cortex walls have polysaccharide-rich collenchyma. Air spaces are present in the intercellular spaces and aerenchyma in the cortex and pith of the roots and shoots. The dense fine roots with lignified Φ thickenings and polysaccharide-rich collenchyma in the shoots may allow C. hupingshanensis to hyperaccumulate Se. Overall, our study elucidated the anatomical features that permit C. hupingshanensis to thrive in Se-rich aquatic environments.

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Background: Little is known about the impacts of schizophrenia on different types of caregiving burden. Aim: This study aims to examine how the severity of schizophrenia, social functioning and aggressive behavior are associated with caregiving burden across different kinship types. Method: The analytic sample included 300 dyads of persons with schizophrenia and their family caregivers in Xinjin, Chengdu, China. The 10th edition of the International Classification of Diseases (ICD-10) was utilized to identify the patients, whose symptom severity, social functioning and aggressive behavior were measured. Caregiving burden was estimated using the Burden Scale for Family Caregivers–short (BSFC-s). Results: A higher level of burden was significantly associated with female caregivers, larger family size, lower income, worse symptoms, poorer functional status and more aggressive behaviors. Parent caregivers showed greater burden if the patients had better functioning of social interest and concern or more aggression toward property. Mother caregivers showed greater burden than fathers. Spouses tended to perceive greater burden if the patients had better marital functioning, poorer occupational functioning or more aggressive behaviors toward property. Patients attacking others or a father with schizophrenia was related to a higher burden of child caregivers. A heavier burden of other relatives was correlated with patients’ more verbal aggression and self-harm. Conclusion: This study shows the distinct impacts of disease-related factors on the caregiving burden across different kinship types. Our findings have implications for health-care professionals and practitioners in terms of developing more targeted family-based or individualized intervention to ameliorate burden according to kinship types and deal with behavioral and functional problems in schizophrenia.

In this paper, we propose to exploit the interactions between non-associable tracklets to facilitate multi-object tracking. We introduce two types of tracklet interactions, close interaction and distant interaction. The close interaction imposes physical constraints between two temporally overlapping tracklets and more importantly, allows us to learn local classifiers to distinguish targets that are close to each other in the spatiotemporal domain. The distant interaction, on the other hand, accounts for the higher-order motion and appearance consistency between two temporally isolated tracklets. Our approach is modeled as a binary labeling problem and solved using the efficient Quadratic Pseudo-Boolean Optimization (QPBO). It yields promising tracking performance on the challenging PETS09 and MOT16 dataset. Our code will be made publicly available upon the acceptance of the manuscript.

Entity linking refers to the task of aligning mentions of entities in the text to their corresponding entries in a specific knowledge base, which is of great significance for many natural language process applications such as semantic text understanding and knowledge fusion. The pivotal of this problem is how to make effective use of contextual information to disambiguate mentions. Moreover, it has been observed that, in most cases, mention has similar or even identical strings to the entity it refers to. To prevent the model from linking mentions to entities with similar strings rather than the semantically similar ones, in this paper, we introduce the advanced language representation model called BERT (Bidirectional Encoder Representations from Transformers) and design a hard negative samples mining strategy to fine-tune it accordingly. Based on the learned features, we obtain the valid entity through computing the similarity between the textual clues of mentions and the entity candidates in the knowledge base. The proposed hard negative samples mining strategy benefits entity linking from the larger, more expressive pre-trained representations of BERT with limited training time and computing sources. To the best of our knowledge, we are the first to equip entity linking task with the powerful pre-trained general language model by deliberately tackling its potential shortcoming of learning literally, and the experiments on the standard benchmark datasets show that the proposed model yields state-of-the-art results.INDEX TERMS Entity linking, natural language processing (NLP), bidirectional encoder representations from transformers (BERT), deep neural network (DNN).