Cell Therapy in Huntington’s Disease

Huntington’s disease (HD) is a rare neurodegenerative disease inherited in an autosomal dominant pattern. Expanded cytosine-adenine-guanine (CAG) repeats (polyQ) in the huntingtin gene cause the aggregates of abnormally expanded polyQ-containing huntingtin protein, and striatal medium spiny neurons are shown to be the most vulnerable. Affected patients develop cognitive, motor, and psychiatric symptoms typically in middle age, and several pharmacological drugs are currently used for symptomatic relief. Since the effect of current therapies is very limited and there is no way to modify disease progression, there is an unmet need for developing new therapies for HD. Toxin or genetic rodent models are widely used for drug development, and large animal models are also available. Previous studies transplanting cells originating from embryonic or fetal striatal tissues, neural stem cells, mesenchymal stem cells, and induced pluripotent stem cells (iPSCs) in HD animal models have shown the possibilities of clinical trials. Because clinical trials performed using human fetal striatal cells have shown variable outcomes, future directions of cell therapy in HD should consider the reconstitution of a functional dynamic information-processing circuit without ectopic connections. Another major challenge is to achieve controlled differentiation of embryonic stem cells or iPSCs into specific neuronal phenotypes

I Know What's Moved!

poste

Nanomechanical characterization of quantum interference in a topological insulator nanowire

The discovery of two-dimensional gapless Dirac fermions in graphene and topological insulators (TI) has sparked extensive ongoing research toward applications of their unique electronic properties. The gapless surface states in three-dimensional insulators indicate a distinct topological phase of matter with a non-trivial Z2 invariant that can be verified by angle-resolved photoemission spectroscopy or magnetoresistance quantum oscillation. In TI nanowires, the gapless surface states exhibit Aharonov-Bohm (AB) oscillations in conductance, with this quantum interference effect accompanying a change in the number of transverse one-dimensional modes in transport. Thus, while the density of states (DOS) of such nanowires is expected to show such AB oscillation, this effect has yet to be observed. Here, we adopt nanomechanical measurements that reveal AB oscillations in the DOS of a topological insulator. The TI nanowire under study is an electromechanical resonator embedded in an electrical circuit, and quantum capacitance effects from DOS oscillation modulate the circuit capacitance thereby altering the spring constant to generate mechanical resonant frequency shifts. Detection of the quantum capacitance effects from surface-state DOS is facilitated by the small effective capacitances and high quality factors of nanomechanical resonators, and as such the present technique could be extended to study diverse quantum materials at nanoscale.Comment: 15+16 pages, 4+11 figure

Self-Feedback DETR for Temporal Action Detection

Temporal Action Detection (TAD) is challenging but fundamental for real-world video applications. Recently, DETR-based models have been devised for TAD but have not performed well yet. In this paper, we point out the problem in the self-attention of DETR for TAD; the attention modules focus on a few key elements, called temporal collapse problem. It degrades the capability of the encoder and decoder since their self-attention modules play no role. To solve the problem, we propose a novel framework, Self-DETR, which utilizes cross-attention maps of the decoder to reactivate self-attention modules. We recover the relationship between encoder features by simple matrix multiplication of the cross-attention map and its transpose. Likewise, we also get the information within decoder queries. By guiding collapsed self-attention maps with the guidance map calculated, we settle down the temporal collapse of self-attention modules in the encoder and decoder. Our extensive experiments demonstrate that Self-DETR resolves the temporal collapse problem by keeping high diversity of attention over all layers.Comment: Accepted to ICCV 202