The FHA domain protein ArnA functions as a global DNA damage response repressor in the hyperthermophilic archaeon Saccharolobus islandicus.

Forkhead-associated (FHA) domain proteins specifically recognize phosphorylated threonine via the FHA domain and are involved in signal transduction in various processes especially DNA damage response (DDR) and cell cycle regulation in eukaryotes. Although FHA domain proteins are found in prokaryotes, archaea, and bacteria, their functions are far less clear as compared to the eukaryotic counterparts, and it has not been studied whether archaeal FHA proteins play a role in DDR. Here, we have characterized an FHA protein from the hyperthermophilic Crenarchaeon Saccharolobus islandicus (SisArnA) by genetic, biochemical, and transcriptomic approaches. We find that ΔSisarnA exhibits higher resistance to DNA damage agent 4-nitroquinoline 1-oxide (NQO). The transcription of ups genes, encoding the proteins for pili-mediated cell aggregation and cell survival after DDR, is elevated in ΔSisarnA. The interactions of SisArnA with two predicted partners, SisvWA1 (SisArnB) and SisvWA2 (designated as SisArnE), were enhanced by phosphorylation in vitro. ΔSisarnB displays higher resistance to NQO than the wild type. In addition, the interaction between SisArnA and SisArnB, which is reduced in the NQO-treated cells, is indispensable for DNA binding in vitro. These indicate that SisArnA and SisArnB work together to inhibit the expression of ups genes in vivo. Interestingly, ΔSisarnE is more sensitive to NQO than the wild type, and the interaction between SisArnA and SisArnE is strengthened after NQO treatment, suggesting a positive role of SisArnE in DDR. Finally, transcriptomic analysis reveals that SisArnA represses a number of genes, implying that archaea apply the FHA/phospho-peptide recognition module for extensive transcriptional regulation. IMPORTANCE Cellular adaption to diverse environmental stresses requires a signal sensor and transducer for cell survival. Protein phosphorylation and its recognition by forkhead-associated (FHA) domain proteins are widely used for signal transduction in eukaryotes. Although FHA proteins exist in archaea and bacteria, investigation of their functions, especially those in DNA damage response (DDR), is limited. Therefore, the evolution and functional conservation of FHA proteins in the three domains of life is still a mystery. Here, we find that an FHA protein from the hyperthermophilic Crenarchaeon Saccharolobus islandicus (SisArnA) represses the transcription of pili genes together with its phosphorylated partner SisArnB. SisArnA derepression facilitates DNA exchange and repair in the presence of DNA damage. The fact that more genes including a dozen of those involved in DDR are found to be regulated by SisArnA implies that the FHA/phosphorylation module may serve as an important signal transduction pathway for transcriptional regulation in archaeal DDR.

Learning Transformation-Invariant Local Descriptors With Low-Coupling Binary Codes.

Despite the great success achieved by prevailing binary local descriptors, they are still suffering from two problems: 1) vulnerable to the geometric transformations; 2) lack of an effective treatment to the highly-correlated bits that are generated by directly applying the scheme of image hashing. To tackle both limitations, we propose an unsupervised Transformation-invariant Binary Local Descriptor learning method (TBLD). Specifically, the transformation invariance of binary local descriptors is ensured by projecting the original patches and their transformed counterparts into an identical high-dimensional feature space and an identical low-dimensional descriptor space simultaneously. Meanwhile, it enforces the dissimilar image patches to have distinctive binary local descriptors. Moreover, to reduce high correlations between bits, we propose a bottom-up learning strategy, termed Adversarial Constraint Module, where low-coupling binary codes are introduced externally to guide the learning of binary local descriptors. With the aid of the Wasserstein loss, the framework is optimized to encourage the distribution of the generated binary local descriptors to mimic that of the introduced low-coupling binary codes, eventually making the former more low-coupling. Experimental results on three benchmark datasets well demonstrate the superiority of the proposed method over the state-of-the-art methods. The project page is available at https://github.com/yoqim/TBLD.

Graphene/MoS2/FeCoNi(OH)x and Graphene/MoS2/FeCoNiPx multilayer-stacked vertical nanosheets on carbon fibers for highly efficient overall water splitting.

Development of excellent and cheap electrocatalysts for water electrolysis is of great significance for application of hydrogen energy. Here, we show a highly efficient and stable oxygen evolution reaction (OER) catalyst with multilayer-stacked hybrid structure, in which vertical graphene nanosheets (VGSs), MoS2 nanosheets, and layered FeCoNi hydroxides (FeCoNi(OH)x) are successively grown on carbon fibers (CF/VGSs/MoS2/FeCoNi(OH)x). The catalyst exhibits excellent OER performance with a low overpotential of 225 and 241 mV to attain 500 and 1000 mA cm-2 and small Tafel slope of 29.2 mV dec-1. Theoretical calculation indicates that compositing of FeCoNi(OH)x with MoS2 could generate favorable electronic structure and decrease the OER overpotential, promoting the electrocatalytic activity. An alkaline water electrolyzer is established using CF/VGSs/MoS2/FeCoNi(OH)x anode for overall water splitting, which generates a current density of 100 mA cm-2 at 1.59 V with excellent stability over 100 h. Our highly efficient catalysts have great prospect for water electrolysis.

First record of gregarine protists (Apicomplexa: Sporozoa) in Asian fungus-growing termite Macrotermes barneyi (Blattaria: Termitidae).

Macrotermes barneyi, widely distributed in southern China, is the major fungus-growing termite in the subfamily Macrotermitinae. It has no flagellated protists in the guts. Here, we report occurrence of gregarine, a protozoan parasite in the digestive tract of M. barneyi. The general morphology and ultrastructure of the gregarine gamonts and syzygies by light micrograph and scanning electron micrograph are presented. SSU rDNA sequence analysis showed that the termite gregarine has the highest identity (90.10%) to that of Gregarina blattarum from cockroaches. Phylogenetic analysis based on the SSU rDNA sequences from diverse insect eugregarines indicated that the gregarine from M. barneyi is phylogenetically close to G. blattarus, L. erratica and G. tropica from Gregarinidae and Leidyanidae families, and may represent a novel species. This study expands our knowledge about the diversity of terrestrial eugregarines parasitizing in termites.

Phosphoproteomic Analysis Reveals Rio1-Related Protein Phosphorylation Changes in Response to UV Irradiation in Sulfolobus islandicus REY15A.

DNA damage response (DDR) in eukaryotes is largely regulated by protein phosphorylation. In archaea, many proteins are phosphorylated, however, it is unclear how the cells respond to DNA damage through global protein phosphorylation. We previously found that Δrio1, a Rio1 kinase homolog deletion strain of Sulfolobus islandicus REY15A, was sensitive to UV irradiation. In this study, we showed that Δrio1 grew faster than the wild type. Quantitative phosphoproteomic analysis of the wild type and Δrio1, untreated and irradiated with UV irradiation, revealed 562 phosphorylated sites (with a Ser/Thr/Tyr ratio of 65.3%/23.8%/10.9%) of 333 proteins in total. The phosphorylation levels of 35 sites of 30 proteins changed with >1.3-fold in the wild type strain upon UV irradiation. Interestingly, more than half of the UV-induced changes in the wild type did not occur in the Δrio1 strain, which were mainly associated with proteins synthesis and turnover. In addition, a protein kinase and several transcriptional regulators were differentially phosphorylated after UV treatment, and some of the changes were dependent on Rio1. Finally, many proteins involved in various cellular metabolisms exhibited Riol-related and UV-independent phosphorylation changes. Our results suggest that Rio1 is involved in the regulation of protein recycling and signal transduction in response to UV irradiation, and plays regulatory roles in multiple cellular processes in S. islandicus.

Porous Cu Film Enables Thick Slurry-Cast Anodes with Enhanced Charge Transfer Efficiency for High-Performance Li-Ion Batteries.

The ever-growing demand for energy in the consumer market has put higher requirements on the energy density of Li-ion batteries. Many researchers have strived to discover new electrode materials with higher capacity, while little attention has been focused on improving the cell structure. How to increase the thickness of conventional slurry-cast electrodes as well as decrease the charge transfer resistance by improving the electrode structure is an urgent problem for enhancing the energy density of Li-ion batteries. Here, a porous Cu film is developed to replace the conventional Cu foil current collector, and a thick graphite anode (300 µm) is engineered by two-side slurry casting. The anode delivers a maximum capacity of 18 mAh cm-2 or 301.3 mAh g-1 under a highly active mass loading of 60 mg cm-2, much higher than that fabricated on Cu foil. The assembled full cell with the graphite anode and the LiFePO4 cathode achieves high energy densities of 36.2 mWh cm-2 and 283.3 Wh kg-1. Systematic experimental and simulation investigations reveal the enhanced performance benefits from the facilitated charge transfer efficiency by the porous Cu current collector. This work provides a new strategy for engineering thick electrodes for high-energy Li-ion batteries by improving the conventional electrode structure.

On Aggregation of Unsupervised Deep Binary Descriptor with Weak Bits.

Despite the thrilling success achieved by existing binary descriptors, most of them are still in the mire of three limitations: 1) vulnerable to the geometric transformations; 2) incapable of preserving the manifold structure when learning binary codes; 3) NO guarantee to find the true match if multiple candidates happen to have the same Hamming distance to a given query. All these together make the binary descriptor less effective, given large-scale visual recognition tasks. In this paper, we propose a novel learning-based feature descriptor, namely Unsupervised Deep Binary Descriptor (UDBD), which learns transformation invariant binary descriptors via projecting the original data and their transformed sets into a joint binary space. Moreover, we involve a ℓ2,1-norm loss term in the binary embedding process to gain simultaneously the robustness against data noises and less probability of mistakenly flipping bits of the binary descriptor, on top of it, a graph constraint is used to preserve the original manifold structure in the binary space. Furthermore, a weak bit mechanism is adopted to find the real match from candidates sharing the same minimum Hamming distance, thus enhancing matching performance. Extensive experimental results on public datasets show the superiority of UDBD in terms of matching and retrieval accuracy over state-of-the-arts.

End-to-End Feature-Aware Label Space Encoding for Multilabel Classification With Many Classes.

To make the problem of multilabel classification with many classes more tractable, in recent years, academia has seen efforts devoted to performing label space dimension reduction (LSDR). Specifically, LSDR encodes high-dimensional label vectors into low-dimensional code vectors lying in a latent space, so as to train predictive models at much lower costs. With respect to the prediction, it performs classification for any unseen instance by recovering a label vector from its predicted code vector via a decoding process. In this paper, we propose a novel method, namely End-to-End Feature-aware label space Encoding (E2FE), to perform LSDR. Instead of requiring an encoding function like most previous works, E2FE directly learns a code matrix formed by code vectors of the training instances in an end-to-end manner. Another distinct property of E2FE is its feature awareness attributable to the fact that the code matrix is learned by jointly maximizing the recoverability of the label space and the predictability of the latent space. Based on the learned code matrix, E2FE further trains predictive models to map instance features into code vectors, and also learns a linear decoding matrix for efficiently recovering the label vector of any unseen instance from its predicted code vector. Theoretical analyses show that both the code matrix and the linear decoding matrix in E2FE can be efficiently learned. Moreover, similar to previous works, E2FE can be specified to learn an encoding function. And it can also be extended with kernel tricks to handle nonlinear correlations between the feature space and the latent space. Comprehensive experiments conducted on diverse benchmark data sets with many classes show consistent performance gains of E2FE over the state-of-the-art methods.

Cross-View Retrieval via Probability-Based Semantics-Preserving Hashing.

For efficiently retrieving nearest neighbors from large-scale multiview data, recently hashing methods are widely investigated, which can substantially improve query speeds. In this paper, we propose an effective probability-based semantics-preserving hashing (SePH) method to tackle the problem of cross-view retrieval. Considering the semantic consistency between views, SePH generates one unified hash code for all observed views of any instance. For training, SePH first transforms the given semantic affinities of training data into a probability distribution, and aims to approximate it with another one in Hamming space, via minimizing their Kullback-Leibler divergence. Specifically, the latter probability distribution is derived from all pair-wise Hamming distances between to-be-learnt hash codes of the training data. Then with learnt hash codes, any kind of predictive models like linear ridge regression, logistic regression, or kernel logistic regression, can be learnt as hash functions in each view for projecting the corresponding view-specific features into hash codes. As for out-of-sample extension, given any unseen instance, the learnt hash functions in its observed views can predict view-specific hash codes. Then by deriving or estimating the corresponding output probabilities with respect to the predicted view-specific hash codes, a novel probabilistic approach is further proposed to utilize them for determining a unified hash code. To evaluate the proposed SePH, we conduct extensive experiments on diverse benchmark datasets, and the experimental results demonstrate that SePH is reasonable and effective.

Sequential Discrete Hashing for Scalable Cross-Modality Similarity Retrieval.

With the dramatic development of the Internet, how to exploit large-scale retrieval techniques for multimodal web data has become one of the most popular but challenging problems in computer vision and multimedia. Recently, hashing methods are used for fast nearest neighbor search in large-scale data spaces, by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. Inspired by this, in this paper, we introduce a novel supervised cross-modality hashing framework, which can generate unified binary codes for instances represented in different modalities. Particularly, in the learning phase, each bit of a code can be sequentially learned with a discrete optimization scheme that jointly minimizes its empirical loss based on a boosting strategy. In a bitwise manner, hash functions are then learned for each modality, mapping the corresponding representations into unified hash codes. We regard this approach as cross-modality sequential discrete hashing (CSDH), which can effectively reduce the quantization errors arisen in the oversimplified rounding-off step and thus lead to high-quality binary codes. In the test phase, a simple fusion scheme is utilized to generate a unified hash code for final retrieval by merging the predicted hashing results of an unseen instance from different modalities. The proposed CSDH has been systematically evaluated on three standard data sets: Wiki, MIRFlickr, and NUS-WIDE, and the results show that our method significantly outperforms the state-of-the-art multimodality hashing techniques.