29 research outputs found
Practical Quantum Simulation of Non-Hermitian Dynamics
Non-Hermitian quantum systems have recently attracted considerable attentions
due to their exotic properties. Though many experimental realizations of
non-Hermitian systems have been reported, the non-Hermiticity usually resorts
to the hard-to-control environments. An alternative approach is to use quantum
simulation with the closed system, whereas how to simulate general
non-Hermitian Hamiltonian dynamics remains a great challenge. To tackle this
problem, we propose a protocol by combining a dilation method with the
variational quantum algorithm. The dilation method is used to transform a
non-Hermitian Hamiltonian into a Hermitian one through an exquisite quantum
circuit, while the variational quantum algorithm is for efficiently
approximating the complex entangled gates in this circuit. As a demonstration,
we apply our protocol to simulate the dynamics of an Ising chain with nonlocal
non-Hermitian perturbations, which is an important model to study quantum phase
transition at nonzero temperatures. The numerical simulation results are highly
consistent with the theoretical predictions, revealing the effectiveness of our
protocol. The presented protocol paves the way for practically simulating
general non-Hermitian dynamics in the multi-qubit case.Comment: 9 pages, 5 figure
Measuring Quantum Entanglement from Local Information by Machine Learning
Entanglement is a key property in the development of quantum technologies and
in the study of quantum many-body simulations. However, entanglement
measurement typically requires quantum full-state tomography (FST). Here we
present a neural network-assisted protocol for measuring entanglement in
equilibrium and non-equilibrium states of local Hamiltonians. Instead of FST,
it can learn comprehensive entanglement quantities from single-qubit or
two-qubit Pauli measurements, such as R\'enyi entropy, partially-transposed
(PT) moments, and coherence. It is also exciting that our neural network is
able to learn the future entanglement dynamics using only single-qubit traces
from the previous time. In addition, we perform experiments using a nuclear
spin quantum processor and train an adoptive neural network to study
entanglement in the ground and dynamical states of a one-dimensional spin
chain. Quantum phase transitions (QPT) are revealed by measuring static
entanglement in ground states, and the entanglement dynamics beyond measurement
time is accurately estimated in dynamical states. These precise results
validate our neural network. Our work will have a wide range of applications in
quantum many-body systems, from quantum phase transitions to intriguing
non-equilibrium phenomena such as quantum thermalization.Comment: 5 pages, 4 figures. All comments are welcom
TP53-related signature for predicting prognosis and tumor microenvironment characteristics in bladder cancer: A multi-omics study
Background: The tumor suppressor gene TP53 is frequently mutated or inactivated in bladder cancer (BLCA), which is implicated in the pathogenesis of tumor. However, the cellular mechanisms of TP53 mutations are complicated, yet well-defined, but their clinical prognostic value in the management of BLCA remains controversial. This study aimed to explore the role of TP53 mutation in regulating the tumor microenvironment (TME), elucidate the effects of TP53 activity on BLCA prognosis and immunotherapy response.Methods: A TP53-related signature based on TP53-induced and TP53-repressed genes was used to construct a TP53 activity-related score and classifier. The abundance of different immune cell types was determined using CIBERSORT to estimate immune cell infiltration. Moreover, the heterogeneity of the tumor immune microenvironment between the high and low TP53 score groups was further evaluated using single-cell mass cytometry (CyTOF) and imaging mass cytometry (IMC). Moreover, pathway enrichment analysis was performed to explore the differential biological functions between tumor epithelial cells with high and low TP53 activity scores. Finally, the receptor–ligand interactions between immune cells and tumor epithelial cells harboring distinct TP53 activity were analyzed by single-cell RNA-sequencing.Results: The TP53 activity-related gene signature differentiated well between TP53 functional retention and inactivation in BLCA. BLCA patients with low TP53 scores had worse survival prognosis, more TP53 mutations, higher grade, and stronger lymph node metastasis than those with high TP53 scores. Additionally, CyTOF and IMC analyses revealed that BLCA patients with low TP53 scores exhibited a potent immunosuppressive TME. Consistently, single-cell sequencing results showed that tumor epithelial cells with low TP53 scores were significantly associated with high cell proliferation and stemness abilities and strongly interacted with immunosuppressive receptor–ligand pairs.Conclusion: BLCA patients with low TP53 scores have a worse prognosis and a more immunosuppressive TME. This TP53 activity-related signature can serve as a potential prognostic signature for predicting the immune response, which may facilitate the development of new strategies for immunotherapy in BLCA
TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
Tables are prevalent in real-world databases, requiring significant time and
effort for humans to analyze and manipulate. The advancements in large language
models (LLMs) have made it possible to interact with tables using natural
language input, bringing this capability closer to reality. In this paper, we
present TableGPT, a unified fine-tuned framework that enables LLMs to
understand and operate on tables using external functional commands. It
introduces the capability to seamlessly interact with tables, enabling a wide
range of functionalities such as question answering, data manipulation (e.g.,
insert, delete, query, and modify operations), data visualization, analysis
report generation, and automated prediction. TableGPT aims to provide
convenience and accessibility to users by empowering them to effortlessly
leverage tabular data. At the core of TableGPT lies the novel concept of global
tabular representations, which empowers LLMs to gain a comprehensive
understanding of the entire table beyond meta-information. By jointly training
LLMs on both table and text modalities, TableGPT achieves a deep understanding
of tabular data and the ability to perform complex operations on tables through
chain-of-command instructions. Importantly, TableGPT offers the advantage of
being a self-contained system rather than relying on external API interfaces.
Moreover, it supports efficient data process flow, query rejection (when
appropriate) and private deployment, enabling faster domain data fine-tuning
and ensuring data privacy, which enhances the framework's adaptability to
specific use cases.Comment: Technical Repor
Natural Coevolution of Tumor and Immunoenvironment in Glioblastoma.
Isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) has a dismal prognosis. A better understanding of tumor evolution holds the key to developing more effective treatment. Here we study GBM\u27s natural evolutionary trajectory by using rare multifocal samples. We sequenced 61,062 single cells from eight multifocal IDH wild-type primary GBMs and defined a natural evolution signature (NES) of the tumor. We show that the NES significantly associates with the activation of transcription factors that regulate brain development, including MYBL2 and FOSL2. Hypoxia is involved in inducing NES transition potentially via activation of the HIF1A-FOSL2 axis. High-NES tumor cells could recruit and polarize bone marrow-derived macrophages through activation of the FOSL2-ANXA1-FPR1/3 axis. These polarized macrophages can efficiently suppress T-cell activity and accelerate NES transition in tumor cells. Moreover, the polarized macrophages could upregulate CCL2 to induce tumor cell migration.
SIGNIFICANCE: GBM progression could be induced by hypoxia via the HIF1A-FOSL2 axis. Tumor-derived ANXA1 is associated with recruitment and polarization of bone marrow-derived macrophages to suppress the immunoenvironment. The polarized macrophages promote tumor cell NES transition and migration. This article is highlighted in the In This Issue feature, p. 2711
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
Modelling human choices: MADeM and decision‑making
Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)
Modeling Electrophysiological Dynamics in Transient Deep Brain Stimulation of the Subcallosal Cingulate
Deep brain stimulation (DBS) is a promising investigational treatment for patients with treatment resistant depression (TRD). However, the exact mechanism of action of SCCwm-DBS is unknown and its effects on the electrophysiology of brain networks are poorly understood despite high clinical efficacy.
Recent hardware advances have enabled stimulation and recording in clinical populations. Local field potentials (LFPs) recorded from patients under transient stimulation demonstrate strong oscillatory features that change over time. Three scales are explored in order to understand the network-level contributions to chirp generation. It was found that a single Wilson Cowan population could generate a transient down chirp when the parameters are near a homoclinic bifurcation. In a network of Wilson Cowan models informed by network connections seen in diffusion tensor imaging (DTI) of SCCwm-DBS and connected via glutamatergic excitatory-excitatory connection, a modeled stimulation on the connections between regions showed the appearance of transient down chirps in Wilson Cowan populations downstream from the populations directly connected by the edge of excitation. The further addition of inhibitory connections between Wilson Cowan populations showed more consistent appearances of transient down chirps in the modeled right temporal pole, a feature which suggests an importance of future LFP recordings from the temporal lobe.
The results of this thesis will be used to interpret empirical data collected from patient populations and can be objectively validated in patients through future experiments. The larger implications of this work may lead to identification of electrophysiological biometrics of SCCwm-DBS targeting and efficacy.Undergraduat
Data from: Statistical structure of locomotion and its modulation by odors
Most behaviors such as making tea are not stereotypical but have an obvious structure. However, analytical methods to objectively extract structure from non-stereotyped behaviors are immature. In this study, we analyze the locomotion of fruit flies and show that this non-stereotyped behavior is well-described by a Hierarchical Hidden Markov Model (HHMM). HHMM shows that a fly's locomotion can be decomposed into a few locomotor features, and odors modulate locomotion by altering the time a fly spends performing different locomotor features. Importantly, although all flies in our dataset use the same set of locomotor features, individual flies vary considerably in how often they employ a given locomotor feature, and how this usage is modulated by odor. This variation is so large that the behavior of individual flies is best understood as being grouped into at least 3-5 distinct clusters, rather than variations around an average fly