201 research outputs found
Interaction of Sedlin with chloride intracellular channel proteins
AbstractSedlin is an evolutionarily conserved protein encoded by the causative gene SEDL for spondyloepiphyseal dysplasia tarda. Nevertheless, how Sedlin mutations cause the disease remains unknown. Here, the intracellular chloride channel protein CLIC1 was shown to associate with Sedlin by yeast two-hybrid screening. Green fluorescence protein-CLIC1 readily co-immunoprecipitated with FLAG-Sedlin. In addition, both proteins colocalized extensively in cytoplasmic vesicular/reticular structures in COS-7 cells, suggesting their interaction at intracellular membranous organelles. Sedlin also associated with CLIC2 in yeast two-hybrid assays. The link between Sedlin and the intracellular chloride channels is the first step to understand their functional interplays
Restricted Strong Convexity of Deep Learning Models with Smooth Activations
We consider the problem of optimization of deep learning models with smooth
activation functions. While there exist influential results on the problem from
the ``near initialization'' perspective, we shed considerable new light on the
problem. In particular, we make two key technical contributions for such models
with layers, width, and initialization variance. First,
for suitable , we establish a
upper bound on the spectral norm of the Hessian of such models, considerably
sharpening prior results. Second, we introduce a new analysis of optimization
based on Restricted Strong Convexity (RSC) which holds as long as the squared
norm of the average gradient of predictors is
for the square loss. We also present
results for more general losses. The RSC based analysis does not need the
``near initialization" perspective and guarantees geometric convergence for
gradient descent (GD). To the best of our knowledge, ours is the first result
on establishing geometric convergence of GD based on RSC for deep learning
models, thus becoming an alternative sufficient condition for convergence that
does not depend on the widely-used Neural Tangent Kernel (NTK). We share
preliminary experimental results supporting our theoretical advances
Time-resolved Measurement of Quadrupole Wakefields in Corrugated Structures
Corrugated structures have recently been widely used for manipulating
electron beam longitudinal phase space and for producing THz radiation. Here we
report on time-resolved measurements of the quadrupole wakefields in planar
corrugated structures. It is shown that while the time- dependent quadrupole
wakefield produced by a planar corrugated structure causes significant growth
in beam transverse emittance, it can be effectively canceled with a second
corrugated structure with orthogonal orientation. The strengths of the
time-dependent quadrupole wakefields for various corrugated structure gaps are
also measured and found to be in good agreement with theories. Our work should
forward the applications of corrugated structures in many accelerator based
scientific facilities
Evaluation of anti-smoking television advertising on tobacco control among urban community population in Chongqing, China
Background
China is the largest producer and consumer of tobacco in the world. Considering the constantly growing urban proportion, persuasive tobacco control measures are important in urban communities. Television, as one of the most pervasive mass media, can be used for this purpose.
Methods
The anti-smoking advertisement was carried out in five different time slots per day from 15 May to 15 June in 2011 across 12 channels of Chongqing TV. A cross-sectional study was conducted in the main municipal areas of Chongqing. A questionnaire was administered in late June to 1,342 native residents aged 18–45, who were selected via street intercept survey.
Results
Respondents who recognized the advertisement (32.77 %) were more likely to know or believe that smoking cigarettes caused impotence than those who did not recognize the advertisement (26.11 %). According to 25.5 % of smokers, the anti-smoking TV advertising made them consider quitting smoking. However, females (51.7 %) were less likely to be affected by the advertisement to stop and think about quitting smoking compared to males (65.6 %) (OR = 0.517, 95 % CI [0.281–0.950]). In addition, respondents aged 26–35 years (67.4 %) were more likely to try to persuade others to quit smoking than those aged 18–25 years (36.3 %) (OR = 0.457, 95 % CI [0.215–0.974]). Furthermore, non-smokers (87.4 %) were more likely to find the advertisement relevant than smokers (74.8 %) (OR = 2.34, 95 % CI [1.19–4.61]).
Conclusions
This study showed that this advertisement did not show significant differences on smoking-related knowledge and attitude between non-smokers who had seen the ad and those who had not. Thus, this form may not be the right tool to facilitate change in non-smokers. The ad should instead be focused on the smoking population. Gender, smoking status, and age influenced the effect of anti-smoking TV advertising on the general population in China
Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI
Brain network analysis is a useful approach to studying human brain disorders
because it can distinguish patients from healthy people by detecting abnormal
connections. Due to the complementary information from multiple modal
neuroimages, multimodal fusion technology has a lot of potential for improving
prediction performance. However, effective fusion of multimodal medical images
to achieve complementarity is still a challenging problem. In this paper, a
novel hierarchical structural-functional connectivity fusing (HSCF) model is
proposed to construct brain structural-functional connectivity matrices and
predict abnormal brain connections based on functional magnetic resonance
imaging (fMRI) and diffusion tensor imaging (DTI). Specifically, the prior
knowledge is incorporated into the separators for disentangling each modality
of information by the graph convolutional networks (GCN). And a disentangled
cosine distance loss is devised to ensure the disentanglement's effectiveness.
Moreover, the hierarchical representation fusion module is designed to
effectively maximize the combination of relevant and effective features between
modalities, which makes the generated structural-functional connectivity more
robust and discriminative in the cognitive disease analysis. Results from a
wide range of tests performed on the public Alzheimer's Disease Neuroimaging
Initiative (ADNI) database show that the proposed model performs better than
competing approaches in terms of classification evaluation. In general, the
proposed HSCF model is a promising model for generating brain
structural-functional connectivities and identifying abnormal brain connections
as cognitive disease progresses.Comment: 4 figure
MotionBERT: A Unified Perspective on Learning Human Motion Representations
We present a unified perspective on tackling various human-centric video
tasks by learning human motion representations from large-scale and
heterogeneous data resources. Specifically, we propose a pretraining stage in
which a motion encoder is trained to recover the underlying 3D motion from
noisy partial 2D observations. The motion representations acquired in this way
incorporate geometric, kinematic, and physical knowledge about human motion,
which can be easily transferred to multiple downstream tasks. We implement the
motion encoder with a Dual-stream Spatio-temporal Transformer (DSTformer)
neural network. It could capture long-range spatio-temporal relationships among
the skeletal joints comprehensively and adaptively, exemplified by the lowest
3D pose estimation error so far when trained from scratch. Furthermore, our
proposed framework achieves state-of-the-art performance on all three
downstream tasks by simply finetuning the pretrained motion encoder with a
simple regression head (1-2 layers), which demonstrates the versatility of the
learned motion representations. Code and models are available at
https://motionbert.github.io/Comment: ICCV 2023 Camera Read
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