328 research outputs found
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
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
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