Mutual detoxification of mercury and selenium in unicellular Tetrahymena

Selenium（Se） is commonly recognized as a protective element with an antagonistic effect against mercury（Hg） toxicity. However, the mechanisms of this Hg–Se antagonism are complex and remain controversial. To gain insight into the Hg–Se antagonism, a type of unicellular eukaryotic protozoa（Tetrahymena malaccensis, T. malaccensis） was selected and individually or jointly exposed to two Hg and three Se species. We found that Se species showed different toxic effects on the proliferation of T. malaccensis with the toxicity following the order：selenite（Se（IV）） 〉 selenomethionine（SeMeth） 〉 selenate（Se（VI））. The Hg–Se antagonism in Tetrahymena was observed because the joint toxicity significantly decreased under co-exposure to highly toxic dosages of Hg and Se versus individual toxicity. Unlike Se（IV） and Se（VI）, non-toxic dosage of SeM eth significantly decreased the Hg toxicity, revealing the influence of the Se species and dosages on the Hg–Se antagonism. Unexpectedly, inorganic divalent Hg（Hg2＋） and monomethylmercury（MeHg） also displayed detoxification towards extremely highly toxic dosages of Se, although their detoxifying efficiency was discrepant. These results suggested mutual Hg–Se detoxification in T. malaccensis, which was highly dependent on the dosages and species of both elements. As compared to other species, SeM eth and MeHg promoted the Hg–Se joint effects to a higher degree. Additionally, the Hg contents decreased for all the Hg–Se co-exposed groups, revealing a sequestering effect of Se towards Hg in T. malaccensis.

Efficient Visual Recognition:A Survey on Recent Advances and Brain-inspired Methodologies

Visual recognition is currently one of the most important and active research areas in computer vision,pattern recognition,and even the general field of artificial intelligence.It has great fundamental importance and strong industrial needs,particularly the modern deep neural networks(DNNs)and some brain-inspired methodologies,have largely boosted the recognition performance on many concrete tasks,with the help of large amounts of training data and new powerful computation resources.Although recognition accuracy is usually the first concern for new progresses,efficiency is actually rather important and sometimes critical for both academic research and industrial applications.Moreover,insightful views on the opportunities and challenges of efficiency are also highly required for the entire community.While general surveys on the efficiency issue have been done from various perspectives,as far as we are aware,scarcely any of them focused on visual recognition systematically,and thus it is unclear which progresses are applicable to it and what else should be concerned.In this survey,we present the review of recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related and brain-inspired visual recognition approaches,including efficient network compression and dynamic brain-inspired networks.We investigate not only from the model but also from the data point of view(which is not the case in existing surveys)and focus on four typical data types(images,video,points,and events).This survey attempts to provide a systematic summary via a comprehensive survey that can serve as a valuable reference and inspire both researchers and practitioners working on visual recognition problems.