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Foreign body responses in central nervous system mimic natural wound responses and alter biomaterial functions
Biomaterials hold promise for diverse therapeutic applications in the central nervous system (CNS). Little is known about molecular factors that determine CNS foreign body responses (FBRs) in vivo , or about how such responses influence biomaterial function. Here, we probed these factors using a platform of injectable hydrogels readily modified to present interfaces with different representative physiochemical properties to host cells. We show that biomaterial FBRs mimic specialized multicellular CNS wound responses not present in peripheral tissues, which serve to isolate damaged neural tissue and restore barrier functions. Moreover, we found that the nature and intensity of CNS FBRs are determined by definable properties. For example, cationic, anionic or nonionic interfaces with CNS cells elicit quantifiably different levels of stromal cell infiltration, inflammation, neural damage and amyloid production. The nature and intensity of FBRs significantly influenced hydrogel resorption and molecular delivery functions. These results characterize specific molecular mechanisms that drive FBRs in the CNS and have important implications for developing effective biomaterials for CNS applications
Analysis the Role Conflict of Trade Union Chairman and Its Types
With the development of the market economy, the trade union work has become increasingly complex. As a special group, trade union chairman plays multiple roles. On the one hand, the trade union chairman should safeguard the rights and interests of enterprise employees, on the other hand, as the workers of the enterprise, they have to create benefits for the enterprise, this kind of dual role trigger a trade union chairman role conflict. In this paper, through reviewing the relevant literature, from the personal angle of the trade union chairman, trade union chairman of role conflict from three dimensions divided responsibility, time, interests. Through the division of the three dimension classifying trade union chairman of role conflict, and explain the causes of different types of role conflict and the corresponding countermeasures and suggestions, so as to eliminate the role of the trade union chairman conflict, give full play to the role of the trade union chairman
Better Explain Transformers by Illuminating Important Information
Transformer-based models excel in various natural language processing (NLP)
tasks, attracting countless efforts to explain their inner workings. Prior
methods explain Transformers by focusing on the raw gradient and attention as
token attribution scores, where non-relevant information is often considered
during explanation computation, resulting in confusing results. In this work,
we propose highlighting the important information and eliminating irrelevant
information by a refined information flow on top of the layer-wise relevance
propagation (LRP) method. Specifically, we consider identifying syntactic and
positional heads as important attention heads and focus on the relevance
obtained from these important heads. Experimental results demonstrate that
irrelevant information does distort output attribution scores and then should
be masked during explanation computation. Compared to eight baselines on both
classification and question-answering datasets, our method consistently
outperforms with over 3\% to 33\% improvement on explanation metrics, providing
superior explanation performance. Our anonymous code repository is available
at: https://github.com/LinxinS97/Mask-LR
Fraction Constraint in Partial Wave Analysis
To resolve the non-convex optimization problem in partial wave analysis, this
paper introduces a novel approach that incorporates fraction constraints into
the likelihood function. This method offers significant improvements in both
the efficiency of pole searching and the reliability of resonance selection
within partial wave analysis
PWACG: Partial Wave Analysis Code Generator supporting Newton-conjugate gradient method
This paper introduces a novel Partial Wave Analysis Code Generator (PWACG)
that automatically generates high-performance partial wave analysis codes. This
is achieved by leveraging the JAX automatic differentiation library and the
jinja2 template engine. The resulting code is constructed using the
high-performance API of JAX, and includes support for the Newton's Conjugate
Gradient optimization method, as well as the full utilization of parallel
computing capabilities offered by GPUs. By harnessing these advanced computing
techniques, PWACG demonstrates a significant advantage in efficiently
identifying global optimal points compared to conventional partial wave
analysis software packages
Event Generation and Consistence Test for Physics with Sliced Wasserstein Distance
In the field of modern high-energy physics research, there is a growing
emphasis on utilizing deep learning techniques to optimize event simulation,
thereby expanding the statistical sample size for more accurate physical
analysis. Traditional simulation methods often encounter challenges when
dealing with complex physical processes and high-dimensional data
distributions, resulting in slow performance. To overcome these limitations, we
propose a solution based on deep learning with the sliced Wasserstein distance
as the loss function. Our method shows its ability on high precision and
large-scale simulations, and demonstrates its effectiveness in handling complex
physical processes. By employing an advanced transformer learning architecture,
we initiate the learning process from a Monte Carlo sample, and generate
high-dimensional data while preserving all original distribution features. The
generated data samples have passed the consistence test, that is developed to
calculate the confidence of the high-dimentional distributions of the generated
data samples through permutation tests. This fast simulation strategy, enabled
by deep learning, holds significant potential not only for increasing sample
sizes and reducing statistical uncertainties but also for applications in
numerical integration, which is crucial in partial wave analysis,
high-precision sample checks, and other related fields. It opens up new
possibilities for improving event simulation in high-energy physics research
tau-FPL: Tolerance-Constrained Learning in Linear Time
Learning a classifier with control on the false-positive rate plays a
critical role in many machine learning applications. Existing approaches either
introduce prior knowledge dependent label cost or tune parameters based on
traditional classifiers, which lack consistency in methodology because they do
not strictly adhere to the false-positive rate constraint. In this paper, we
propose a novel scoring-thresholding approach, tau-False Positive Learning
(tau-FPL) to address this problem. We show the scoring problem which takes the
false-positive rate tolerance into accounts can be efficiently solved in linear
time, also an out-of-bootstrap thresholding method can transform the learned
ranking function into a low false-positive classifier. Both theoretical
analysis and experimental results show superior performance of the proposed
tau-FPL over existing approaches.Comment: 32 pages, 3 figures. This is an extended version of our paper
published in AAAI-1
Cell-specific and region-specific transcriptomics in the multiple sclerosis model: Focus on astrocytes.
Changes in gene expression that occur across the central nervous system (CNS) during neurological diseases do not address the heterogeneity of cell types from one CNS region to another and are complicated by alterations in cellular composition during disease. Multiple sclerosis (MS) is multifocal by definition. Here, a cell-specific and region-specific transcriptomics approach was used to determine gene expression changes in astrocytes in the most widely used MS model, experimental autoimmune encephalomyelitis (EAE). Astrocyte-specific RNAs from various neuroanatomic regions were attained using RiboTag technology. Sequencing and bioinformatics analyses showed that EAE-induced gene expression changes differed between neuroanatomic regions when comparing astrocytes from spinal cord, cerebellum, cerebral cortex, and hippocampus. The top gene pathways that were changed in astrocytes from spinal cord during chronic EAE involved decreases in expression of cholesterol synthesis genes while immune pathway gene expression in astrocytes was increased. Optic nerve from EAE and optic chiasm from MS also showed decreased cholesterol synthesis gene expression. The potential role of cholesterol synthesized by astrocytes during EAE and MS is discussed. Together, this provides proof-of-concept that a cell-specific and region-specific gene expression approach can provide potential treatment targets in distinct neuroanatomic regions during multifocal neurological diseases
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