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The i‐motif DNA, also known as i‐DNA, is a non‐canonical DNA secondary structure formed by cytosine‐rich sequences, consisting of two intercalated parallel‐stranded duplexes held together by hemi‐protonated cytosine–cytosine+ (C:C+) base pairs. The growing interest in the i‐DNA structure as a target in anticancer therapy increases the need for tools for a rapid and meaningful interpretation of the spectroscopic data of i‐DNA samples. Herein, we analyzed the circular dichroism (CD) and thermal difference UV‐absorbance spectra (TDS) of 255 DNA sequences by means of multivariate data analysis, aiming at unveiling peculiar spectral regions that could be used as diagnostic features during the analysis of i‐DNA‐forming sequences., i‐DNA is an emerging non‐canonical DNA secondary structure that represents a suitable target in anticancer therapy. A multivariate data analysis unveiled peculiar TDS and CD spectral regions that could be used for the structural determination of i‐DNA‐forming sequences.

Recent studies indicate that i-DNA, a four-stranded cytosine-rich DNA also known as the i-motif, is actually formed in vivo ; however, a systematic study on sequence effects on stability has been missing. Herein, an unprecedented number of different sequences (271) bearing four runs of 3-6 cytosines with different spacer lengths has been tested. While i-DNA stability is nearly independent on total spacer length, the central spacer plays a special role on stability. Stability also depends on the length of the C-tracts at both acidic and neutral pHs. This study provides a global picture on i-DNA stability thanks to the large size of the introduced data set; it reveals unexpected features and allows to conclude that determinants of i-DNA stability do not mirror those of G-quadruplexes. Our results illustrate the structural roles of loops and C-tracts on i-DNA stability, confirm its formation in cells, and allow establishing rules to predict its stability.

International audience; The influence of the G-quartet structural integrity on the catalytic activity of the G-quadruplex (G4) was investigated by comparing the G4-DNAzyme performances of a series of G4s with a G-vacancy site and a G-triplex (G-tri). The results presented herein not only confirm that the structural integrity of the 3’-end G-quartet is necessary for G4s to be catalytically competent but also show how to remediate G-vacancy-mediated catalytic activity losses via the addition of guanine surrogates in an approach referred to as G-vacancy complementation strategy that is applicable to parallel G4s only. Furthermore, this study demonstrates that the terminal G-quartet could act as a proximal coordinating group and cooperate with the flanking nucleotide to activate the hemin cofactor.

International audience; Massive efforts are currently being invested to improve the performance, versatility, and scope of applications of nucleic acid catalysts. G-quadruplex (G4)/hemin DNAzymes are of particular interest owing to their structural programmability and chemical robustness. However, optimized catalytic efficiency is still bottleneck and the activation mechanism is unclear. Herein, we have designed a series of parallel G4s with different proximal cytosine (dC) derivatives to fine-tune the hemin-binding pocket for G4-DNAzymes. Combining theoretical and experimental methods, we have assessed the dependence of catalytic enhancement on the electronic properties of proximal dCs and demonstrated how proximal dCs activate catalytic proficiency. These results provide interesting clues in recapitulating the push–pull mechanism as the basis of peroxidase activity and help to devise a new strategy to design highly competent DNA catalysts whose performances are of the same order as protease.

Aiming at the problem of high power consumption and slow operation speed of neural network in embedded system, this paper presents an automatic design method of convolutional neural network (CNN) accelerator based on field programmable gate array (FPGA) to meet the requirements of embedded terminal for operation performance and power consumption. Firstly, in order to reduce the storage resources of the FPGA and the time required for network computing, the weight quantization method is used to convert network parameters from floating-point data to binary data. Secondly, in order to improve the speed and throughput of the whole system, coarse-grained and fine-grained parallel computing optimization methods are used. In this paper, the above acceleration scheme will be applied to the scene of unmanned aerial vehicle (UAV) object detection. The test results shows that the power consumption of the system is 2.38W and the calculated power consumption ratio is 29.3GOPS/W. Compared with the work of related literature in recent years, the proposed scheme can provide higher speed and efficiency. The experimental results show that the accuracy of the IOU is 21% and the speed is 7FPS.

In recent years, Discriminant Correlation Filters (DCF) based methods have performed well on online object tracking. And Fully-convolutional Siamese network becomes a dominant approach to real-time object tracking. In this work, we build a two-fold Siamese network, namely SiamDCF, to learn the convolutional features and perform the correlation tracking process with channel attention simultaneously. We train these two branches of SiamDCF separately, ensuring their heterogeneous features. We treat DCF as a correlation filter layer, and the layer outputs the response map of object location. This branch learns filters which extract semantic features and perform well in situations, such as deformation and motion blur, as a complement to the original SiamFC. In particular, we introduce the channel attention module to the network. The architecture and channel attention mechanism improve the tracking performance. The network is trained on the ILSVRC15 dataset for object detection in video. The proposed architecture is end-to-end and operates at frame-rates beyond real-time. We perform comprehensive experiments on OTB2013 benchmark, and the proposed tracker achieves high performance.