Mutants only Partially Represent Characteristics of Calcium-ReleaseActivated Calcium Channel Gating

Calcium-release-activated calcium(CARC)channels are one of the major pathways of calcium entry in non-excitable cells.Despite a decade or two of research,its regulatory mechanism is not yet thoroughly understood.The slow progress is due to the complexity of its pores(i.e.,Orai)on one hand and the difficulty in capturing its regulatory complex on the other hand.As a result,possible gating mechanisms have often been speculated by exploring the structure and properties of constitutive open mutants.However,there is much debate about how they can truly reflect the gating of CRAC channels under physiological conditions.In the present study,we combined molecular dynamics simulations with free energy calculations to study three dOrai mutants(G170P,H206A,and P288A),and further calculated their current-voltage curves.Results show that these constructs adopt different approaches to maintain their conductive state.Meanwhile they have unique pore structures and distinctive rectification properties and ion selectivity for cations compared to wild-type pores.We conclude that although the mutants may partially capture the gating motion characteristics of wild-type pores,the information obtained from these mutants is likely not a true reflection of CRAC channel gating under physiological conditions.

The Coincidence of Critical Day Length Recognition for Florigen Gene Expression and Floral Transition under Long-Day Conditions in Rice

The photoperiodic control of flowering time is essential for the adaptation of plants to variable environments and for successful reproduction. The identification of genes encoding florigens, which had been elusive but were supposedly synthesized in leaves and then transmitted to shoot apices to induce floral transitions, has greatly advanced our understanding of the photoperiodic regulation of flowering. Studies on the photoperiodism of Arabidopsis, a model long-day plant, revealed the molecular mechanisms regulating the expression of the Arabidopsis florigen gene FT, which is gradually induced in response to increase in day length. By contrast, in rice, a model short-day plant, the expression of the florigen gene Hd3a （an FTortholog in rice） is regulated in an on/off fashion, with strong induction under short-day conditions and repression under long-day conditions. This critical day length dependence of Hd3a expression enables rice to recognize a slight change in the photoperiod as a trigger to initiate floral induction. Rice possesses a second florigen gene, RFT1, which can be expressed to induce floral transition under non-inductive long-day conditions. The complex transcriptional regulation of florigen genes and the resulting precise control over flowering time provides rice with the adaptability required for a crop species of increasing global importance.

Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration

It is of great significance to guarantee the efficient statistics of high-speed railway on-board equipment fault information,which also improves the efficiency of fault analysis. Considering this background, this paper presents an empirical exploration of named entity recognition(NER) of on-board equipment fault information. Based on the historical fault records of on-board equipment, a fault information recognition model based on multi-neural network collaboration is proposed. First, considering Chinese recorded data characteristics, a method of constructing semantic features and additional features based on character granularity is proposed. Then, the two feature representations are concatenated and passed into the gated convolutional layer to extract the dependencies from multiple different subspaces and adjacent characters in parallel. Next, the local features are transmitted to the bidirectional long short-term memory(BiLSTM) to learn long-term dependency information. On top of BiLSTM, the sequential conditional random field(CRF) is used to jointly decode the optimized tag sequence of the whole sentence. The model is tested and compared with other representative baseline models. The results show that the proposed model not only considers the language characteristics of on-board fault records, but also has obvious advantages on the performance of fault information recognition.

Defectivity control of aluminum chemical mechanical planarization in replacement metal gate process of MOSFET

The replacement metal gate（RMG） defectivity performance control is very challenging in high-k metal gate（HKMG） chemical mechanical polishing（CMP）. In this study, three major defect types, including fall-on particles, micro-scratch and corrosion have been investigated. The research studied the effects of polishing pad,pressure, rotating speed, flow rate and post-CMP cleaning on the three kinds of defect, which finally eliminated the defects and achieved good surface morphology. This study will provide an important reference value for the future research of aluminum metal gate CMP.

Learning Single-Shot Detector with Mask Prediction and Gate Mechanism

Detection efficiency plays an increasingly important role in object detection tasks.One-stage methods are widely adopted in real life because of their high efficiency especially in some real-time detection tasks such as face recognition and self-driving cars.RetinaMask achieves significant progress in the field of one-stage detectors by adding a semantic segmentation branch,but it has limitation in detecting multi-scale objects.To solve this problem,this paper proposes RetinaMask with Gate(RMG)model,consisting of four main modules.It develops RetinaMask with a gate mechanism,which extracts and combines features at different levels more effectively according to the size of objects.It firstly extracted multi-level features from input image by ResNet.Secondly,it constructed a fused feature pyramid through feature pyramid network,then gate mechanism was employed to adaptively enhance and integrate features at various scales with the respect to the size of object.Finally,three prediction heads were added for classification,localization and mask prediction,driving the model to learn with mask prediction.The predictions of all levels were integrated during the post-processing.The augment network shows better performance in object detection without the increase of computation cost and inference time,especially for small objects.