Coexistent Charcot-Marie-Tooth type 1A and type 2 diabetes mellitus neuropathies in a Chinese family

Charcot-Marie-Tooth disease type 1A（CMT1A） is caused by duplication of the peripheral myelin protein 22（PMP22） gene on chromosome 17. It is the most common inherited demyelinating neuropathy. Type 2 diabetes mellitus is a common metabolic disorder that frequently causes predominantly sensory neuropathy. In this study, we report the occurrence of CMT1 A in a Chinese family affected by type 2 diabetes mellitus. In this family, seven individuals had duplication of the PMP22 gene, although only four had clinical features of polyneuropathy. All CMT1 A patients with a clinical phenotype also presented with type 2 diabetes mellitus. The other three individuals had no signs of CMT1 A or type 2 diabetes mellitus. We believe that there may be a genetic link between these two diseases.

The coupling loss factors for non-conservatively coupled structures

The method to calculate the coupling loss factors for non-conservatively coupled structures by using the mobilities of substructures is studied. By using the form of the energy balance equations of conservatively coupled systems, the relationship between the coupling loss factors and the energy ratios in non-conservatively coupled systems is derived. The method to calculate the energy ratios by using the mobilities of substructures is introduced, and experiment verification is carried out. The test data agree well with the predicted results.

A flat-topped etched diffraction grating demultiplexer with low polarization-dependent loss using a tapered MMI structure

A flat-topped etched diffraction grating (EDG) demultiplexer with a low polarization-dependent loss (PDL) is designed. A design and simulation method based on the method of moment (MoM) is proposed. A 65-channcl EDG demultiplexer with channel spacing of 100 GHz is considered as a design example. A tapered multi-mode interferometer (MMI) is used to flatten the passband of the EDG demultiplexer. The numerical results show that the exit width of the tapered waveguide impacts the loss of the TE case more than that of the TM case. Based on this fact, the exit width of the taper is optimized to obtain the lowest PDL. The tapering angle is also optimized where the minimal ripple is obtained. The designed EDG demultiplexer has an excellent flat-topped spectral response and a very low PDL.

Low Loss S-Bend Structure With Tapered Curved Waveguides

A novel S-bend with tapered curved waveguides is proposed. The normalized transmitted power is greater than the conventional bend with weakly guided waveguides. Small size and low loss can be reached by the proposed S-bend.

A novel coupled quantum well structure with large field-induced refractive index change and low absorption loss at 1.55 μm

A novel coupled quantum well structure - quasi-symmetric coupled quantum well （QSCQW） is proposed. In the case of low applied electric field （F = 25 kV/cm） and low absorption loss （a ≈ 100 cm^-1）, a large field-induced refractive index change （for TE mode, △n = 0.0106; for TM mode, △n = 0.0115） is obtained in QSCQW structure at operating wavelength λ = 1550 nm. The value is larger by over one to two order of magnitude compared to that in a rectangular quantum well （RQW） and about 50% larger than that of five-step asymmetric coupled quantum well （FACQW） structure under the above work conditions.

Towards Effective Author Name Disambiguation by Hybrid Attention

Author name disambiguation(AND)is a central task in academic search,which has received more attention recently accompanied by the increase of authors and academic publications.To tackle the AND problem,existing studies have proposed various approaches based on different types of information,such as raw document features(e.g.,co-authors,titles,and keywords),the fusion feature(e.g.,a hybrid publication embedding based on multiple raw document features),the local structural information(e.g.,a publication's neighborhood information on a graph),and the global structural information(e.g.,interactive information between a node and others on a graph).However,there has been no work taking all the above-mentioned information into account and taking full advantage of the contributions of each raw document feature for the AND problem so far.To fill the gap,we propose a novel framework named EAND(Towards Effective Author Name Disambiguation by Hybrid Attention).Specifically,we design a novel feature extraction model,which consists of three hybrid attention mechanism layers,to extract key information from the global structural information and the local structural information that are generated from six similarity graphs constructed based on different similarity coefficients,raw document features,and the fusion feature.Each hybrid attention mechanism layer contains three key modules:a local structural perception,a global structural perception,and a feature extractor.Additionally,the mean absolute error function in the joint loss function is used to introduce the structural information loss of the vector space.Experimental results on two real-world datasets demonstrate that EAND achieves superior performance,outperforming state-of-the-art methods by at least+2.74%in terms of the micro-F1 score and+3.31%in terms of the macro-F1 score.