The Zymoseptoria tritici white collar-1 gene, ZtWco-1, is required for development and virulence on wheat.

The fungus Zymoseptoria tritici causes Septoria Tritici Blotch (STB), which is one of the most devastating diseases of wheat in Europe. There are currently no fully durable methods of control against Z. tritici, so novel strategies are urgently required. One of the ways in which fungi are able to respond to their surrounding environment is through the use of photoreceptor proteins which detect light signals. Although previous evidence suggests that Z. tritici can detect light, no photoreceptor genes have been characterised in this pathogen. This study characterises ZtWco-1, a predicted photoreceptor gene in Z. tritici. The ZtWco-1 gene is a putative homolog to the blue light photoreceptor from Neurospora crassa, wc-1. Z. tritici mutants with deletions in ZtWco-1 have defects in hyphal branching, melanisation and virulence on wheat. In addition, we identify the putative circadian clock gene ZtFrq in Z. tritici. This study provides evidence for the genetic regulation of light detection in Z. tritici and it open avenues for future research into whether this pathogen has a circadian clock.

Constructing an artificial boundary to regulate solid electrolyte interface formation and synergistically enhance stability of nano-Si anodes.

Low coulombic efficiency and poor cyclic stability are two common problems for silicon anodes. Therefore, it is of great significance to improve cycling performance and initial coulombic efficiency (ICE) via rational surface engineering on nano-Si anodes. Herein, a new nano-silicon anode is obtained by straightforward constructing a multifunctional polypyrrole protective layer on the surface of silicon nanoparticles, which is further used as the inner boundary of solid electrolyte interface (SEI) film. Specifically, the Li salt decomposition reaction between the electrolyte and silicon surface is effectively inhibited under the protection of the compact artificial boundary. The transfer of Li+ for forming the SEI film is selectively slower than that of lithiation/delithiation reaction. This further reduces the amount of SEI film, leading to a high ICE of 93.2% at 0.5 A g-1 for modified nano-Si anodes. In addition, the flexible SEI precursor combined with the high proportion of organic components in SEIs not only accommodates the volume change of nano-silicon, but also suppresses accumulation of "waste SEI", so the electrode can maintain a reversible capacity of 1153.2 mAh g-1 at 1 A g-1 after 500 cycles. This work provides important guidance for surface structural optimization of alloy-type anodes with high volume change.

Hollow double-layer carbon nanocage confined Si nanoparticles for high performance lithium-ion batteries.

The huge volume variation and the unstable solid electrolyte interface (SEI) of Si (Si) during the lithiation and delithiation process severely obstruct its practical application as lithium-ion battery anodes. Here, we design and fabricate a hollow structure of double-layer hybrid carbon nanocage encapsulated Si nanoparticles to address these challenges. The double-layer hybrid carbon-Si nanoarchitecture is obtained by integrating electrostatic self-assembly, seed-induced growth and heterogeneous shrinkage. The internal layer of hollow N-doped carbon of the hybrid nanoarchitecture (Si@H-NC@GC) provides limited inner space for controlling volume changes of Si nanoparticles, while the outer graphite carbon layer facilitates the formation of a stable SEI. When evaluated as anode materials for LIBs, the Si@H-NC@GC nanoarchitecture exhibits greatly enhanced electrochemical performance compared with the bare Si, Si@NC and H-NC@GC electrodes. Notably, Si@H-NC@GC delivers a reversible capacity retention of 92.5% after 550 cycles at a high current density of 1 A g-1 and a high capacity of 1081 mA h g-1 after 500 cycles at 0.5 A g-1.

[Detection of epileptic waves in EEG based on wavelet transform].

Detection of epileptic waves in EEG is particularly helpful in the interpretation of the underlying process in seizures. This study is aimed at providing a new method for automatic detection of epileptic waves through the wavelet analysis of EEGs. It mainly deals with the detection of spikes or spike-waves based on wavelet transform. Since spikes and spike-waves contain high frequency energy, they will be represented in a particular scale localized in a small time window. According to these feature waveforms of epileptic waves, a continuous processing system for epileptic waveforms detection is constructed. We apply discrete wavelet transform on EEGs. Because of the time-frequency domain localization of wavelet transforms, we can get the local maximal positions across several successive dyadic scales of wavelet transform. And these positions indicate the points of sharp transitions in EEGs. Then we calculate the distance between every two successive maximal positions in each scale. This distance stands for the period of subwave. Furthermore, the distribution of subwave periods of each scale can be worked out. Then, comparing the distribution of normal EEG's and epileptic EEG's. The difference between these two waveforms provides us the criteria for automatic detection and classification. In order to reduce the detection workload, we also compare the detection efficiency of each scale. The scale that provides highest accuracy is selected for our automatic detection system. The results presented in this study show that scale 3 provides the best detection accuracy. So, scale 3 is deemed to be the proper scale for automatic detection. This system has the following advantages: (1) Reduced the workload significantly by selecting proper scale(s) for automatic selection; (2) Enhanced the detection accuracy by selecting proper criteria and threshold; (3) Capable of continuous detection; (4) It is also fit for the detection of other biomedical signals. This system showed good performance, and the initial clinical results obtained are also encouraging.