304 research outputs found
Exact semiclassical dynamics of generic Lipkin-Meshkov-Glick model
Lipkin-Meshkov-Glick model is paradigmatic to study quantum phase transition
in equilibrium or non-equilibrium systems and entanglement dynamics for a
variety of disciplines. In thermodynamics limit, quantum fluctuations are
negligible, its semiclassical dynamics in presence of only one nonlinear
couplings, as a good benchmark to study quantum fluctuation in finite-size
system, can be well obtained in terms of Jacobi elliptic functions. In this
work, we extend this semiclassical analysis into the regime where both
nonlinear interactions are present, and successfully obtain its exact solutions
of semiclassical equations by constructing an auxiliary function that is a
linear combination of the and component of the classical spin in
thermodynamic limit. Taking implementation of Lipkin-Meshkov-Glick model in a
Bose-Einstein condensate setup as an example, we figure out all classical
dynamical modes, specially find out mesoscopic self-trapping mode in population
and phase-difference space even persists in presence of both nonlinear
couplings. Our results would be useful to analyze dynamical phase transitions
and entanglement dynamics of Lipkin-Meshkov-Glick model in presence of both
nonlinear couplings.Comment: 12 pages, 10 figure
Selective killing of HIV-1-positive macrophages and T cells by the Rev-dependent lentivirus carrying anthrolysin O from Bacillus anthracis
<p>Abstract</p> <p>Background</p> <p>The ability of Human Immunodeficiency Virus (HIV) to persist in the body has proven to be a long-standing challenge to virus eradication. Current antiretroviral therapy cannot selectively destroy infected cells; it only halts active viral replication. With therapeutic cessation or interruption, viral rebound occurs, and invariably, viral loads return to pre-treatment levels. The natural reservoirs harboring replication-competent HIV-1 include CD4 T cells and macrophages. In particular, cells from the macrophage lineage resist HIV-1-mediated killing and support sustained viral production. To develop a complementary strategy to target persistently infected cells, this proof-of-concept study explores an HIV-1 Rev-dependent lentiviral vector carrying a bacterial hemolysin, <it>anthrolysin O </it>(<it>anlO</it>) from <it>Bacillus anthracis</it>, to achieve selective killing of HIV-1- infected cells.</p> <p>Results</p> <p>We demonstrate that in the Rev-dependent lentiviral vector, <it>anlO </it>expression is exclusively dependent on Rev, a unique HIV-1 protein present only in infected cells. Intracellular expression and oligomerization of AnlO result in membrane pore formation and cytolysis. We have further overcome a technical hurdle in producing a Revdependent AnlO lentivirus, through the use of β-cyclodextrin derivatives to inhibit direct killing of producer cells by AnlO. Using HIV-1-infected macrophages and T cells as a model, we demonstrate that this Rev-dependent AnlO lentivirus diminishes HIV-1- positive cells.</p> <p>Conclusion</p> <p>The Rev-dependent lentiviral vector has demonstrated its specificity in targeting persistently infected cells. The choice of <it>anlO </it>as the first suicidal gene tested in this vector is based on its cytolytic activity in macrophages and T cells. We conclude that Rev-regulated expression of suicidal genes in HIV-1-positive cells is possible, although future <it>in vivo </it>delivery of this system needs to address numerous safety issues.</p
Cofilin Activation in Peripheral CD4 T Cells of HIV-1 Infected Patients: A Pilot Study
Cofilin is an actin-depolymerizing factor that regulates actin dynamics critical for T cell migration and T cell activation. In unstimulated resting CD4 T cells, cofilin exists largely as a phosphorylated inactive form. Previously, we demonstrated that during HIV-1 infection of resting CD4 T cells, the viral envelope-CXCR4 signaling activates cofilin to overcome the static cortical actin restriction. In this pilot study, we have extended this in vitro observation and examined cofilin phosphorylation in resting CD4 T cells purified from the peripheral blood of HIV-1-infected patients. Here, we report that the resting T cells from infected patients carry significantly higher levels of active cofilin, suggesting that these resting cells have been primed in vivo in cofilin activity to facilitate HIV-1 infection. HIV-1-mediated aberrant activation of cofilin may also lead to abnormalities in T cell migration and activation that could contribute to viral pathogenesis.Department of Defense (National Defense Science and Engineering Fellowship); National Institute of Allergy and Infectious Diseases (AI069981
OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation
This paper presents OmniDataComposer, an innovative approach for multimodal
data fusion and unlimited data generation with an intent to refine and
uncomplicate interplay among diverse data modalities. Coming to the core
breakthrough, it introduces a cohesive data structure proficient in processing
and merging multimodal data inputs, which include video, audio, and text. Our
crafted algorithm leverages advancements across multiple operations such as
video/image caption extraction, dense caption extraction, Automatic Speech
Recognition (ASR), Optical Character Recognition (OCR), Recognize Anything
Model(RAM), and object tracking. OmniDataComposer is capable of identifying
over 6400 categories of objects, substantially broadening the spectrum of
visual information. It amalgamates these diverse modalities, promoting
reciprocal enhancement among modalities and facilitating cross-modal data
correction. \textbf{The final output metamorphoses each video input into an
elaborate sequential document}, virtually transmuting videos into thorough
narratives, making them easier to be processed by large language models. Future
prospects include optimizing datasets for each modality to encourage unlimited
data generation. This robust base will offer priceless insights to models like
ChatGPT, enabling them to create higher quality datasets for video captioning
and easing question-answering tasks based on video content. OmniDataComposer
inaugurates a new stage in multimodal learning, imparting enormous potential
for augmenting AI's understanding and generation of complex, real-world data
MovePose: A High-performance Human Pose Estimation Algorithm on Mobile and Edge Devices
We present MovePose, an optimized lightweight convolutional neural network
designed specifically for real-time body pose estimation on CPU-based mobile
devices. The current solutions do not provide satisfactory accuracy and speed
for human posture estimation, and MovePose addresses this gap. It aims to
maintain real-time performance while improving the accuracy of human posture
estimation for mobile devices. The network produces 17 keypoints for each
individual at a rate exceeding 11 frames per second, making it suitable for
real-time applications such as fitness tracking, sign language interpretation,
and advanced mobile human posture estimation. Our MovePose algorithm has
attained an Mean Average Precision (mAP) score of 67.7 on the COCO
\cite{cocodata} validation dataset. The MovePose algorithm displayed efficiency
with a performance of 69+ frames per second (fps) when run on an Intel
i9-10920x CPU. Additionally, it showcased an increased performance of 452+ fps
on an NVIDIA RTX3090 GPU. On an Android phone equipped with a Snapdragon 8 + 4G
processor, the fps reached above 11. To enhance accuracy, we incorporated three
techniques: deconvolution, large kernel convolution, and coordinate
classification methods. Compared to basic upsampling, deconvolution is
trainable, improves model capacity, and enhances the receptive field. Large
kernel convolution strengthens these properties at a decreased computational
cost. In summary, MovePose provides high accuracy and real-time performance,
marking it a potential tool for a variety of applications, including those
focused on mobile-side human posture estimation. The code and models for this
algorithm will be made publicly accessible
SkeletonGait: Gait Recognition Using Skeleton Maps
The choice of the representations is essential for deep gait recognition
methods. The binary silhouettes and skeletal coordinates are two dominant
representations in recent literature, achieving remarkable advances in many
scenarios. However, inherent challenges remain, in which silhouettes are not
always guaranteed in unconstrained scenes, and structural cues have not been
fully utilized from skeletons. In this paper, we introduce a novel skeletal
gait representation named skeleton map, together with SkeletonGait, a
skeleton-based method to exploit structural information from human skeleton
maps. Specifically, the skeleton map represents the coordinates of human joints
as a heatmap with Gaussian approximation, exhibiting a silhouette-like image
devoid of exact body structure. Beyond achieving state-of-the-art performances
over five popular gait datasets, more importantly, SkeletonGait uncovers novel
insights about how important structural features are in describing gait and
when they play a role. Furthermore, we propose a multi-branch architecture,
named SkeletonGait++, to make use of complementary features from both skeletons
and silhouettes. Experiments indicate that SkeletonGait++ outperforms existing
state-of-the-art methods by a significant margin in various scenarios. For
instance, it achieves an impressive rank-1 accuracy of over 85% on the
challenging GREW dataset. All the source code is available at
https://github.com/ShiqiYu/OpenGait
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