High-dimensional simulation of the shape-space model for the immune system

1992 ◽  
Vol 180 (1-2) ◽  
pp. 42-52 ◽  
Author(s):  
Dietrich Stauffer ◽  
Gérard Weisbuch
Keyword(s):  
Immune System ◽  
Shape Space ◽  
High Dimensional ◽  
Space Model ◽  
Dimensional Simulation

A STUDY OF IMMUNITY BASED ON PENNA AGING MODEL

2006 ◽  
Vol 17 (04) ◽  
pp. 479-492
Author(s):  
MINGFENG HE ◽  
SHUANG WANG
Keyword(s):  
Immune System ◽  
Evolutionary Model ◽  
Shape Space ◽  
Aging Model ◽  
Space Model ◽  
Model Based ◽  
Penna Model

This paper describes an evolutionary model based on sexual Penna model and shape space model with infection and immunity. Each individual is represented by Penna model with an immune system. In order to study how the infection and immunity influence the survival process, we modify the Verhulst factor. Then, we present the results of our simulations, and discuss the evolution of population and the effect of immunity respectively. In addition, we study the effect of the memory of the immune system and the effect of vaccination under different conditions.

MONTE CARLO SIMULATION OF ISING-LIKE IMMUNOLOGICAL SHAPE SPACE

1994 ◽  
Vol 05 (03) ◽  
pp. 513-518 ◽  
Author(s):  
DIETRICH STAUFFER
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Immune System ◽  
Response Curve ◽  
Nearest Neighbor ◽  
Shape Space ◽  
High Dimensional ◽  
Immune Systems ◽  
Probabilistic Generalization ◽  
Dimensional Shape

The high-dimensional shape space for the antibodies of the immune system is simulated with an Ising-like interaction. However, instead of the molecular field being linear in the sum of the neighbor spins, we take it as quadratic and negative. In this way the bell-shaped response curve of biological immune systems is approximated, as a probabilistic generalization of window automata. We find phase transitions only in five and more dimensions, not in two to four, for nearest-neighbor interactions.

P-067: High dimensional CyTOF analysis of the immune system of multiple myeloma patients with different responses to lenalidomide maintenance

2021 ◽  
Vol 21 ◽  
pp. S75-S76
Author(s):  
Raija Silvennoinen ◽  
Komal Kumar Javarappa ◽  
Sini Luoma ◽  
Philipp Sergeev ◽  
Pekka Anttila ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Immune System ◽  
High Dimensional

scholarly journals LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4112 ◽  
Author(s):  
Se-Min Lim ◽  
Hyeong-Cheol Oh ◽  
Jaein Kim ◽  
Juwon Lee ◽  
Jooyoung Park
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
State Space Model ◽  
Skill Assessment ◽  
Series Data ◽  
High Dimensional ◽  
Table Tennis ◽  
Space Model ◽  
Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

scholarly journals High-dimensional immune-profiling in cancer: implications for immunotherapy

2020 ◽  
Vol 8 (1) ◽  
pp. e000363 ◽  
Author(s):  
Samuel Chuah ◽  
Valerie Chew
Keyword(s):  
Immune System ◽  
Immune Cell ◽  
Positive Impact ◽  
Cell Types ◽  
High Dimensional ◽  
Host Immune System ◽  
Cancer Therapies ◽  
Quality Of Data ◽  
New Information ◽  
Immune Profiling

Immunotherapy is a rapidly growing field for cancer treatment. In contrast to conventional cancer therapies, immunotherapeutic strategies focus on reactivating the immune system to mount an antitumor response. Despite the encouraging outcome in clinical trials, a large proportion of patients still do not respond to treatment and many experience different degrees of immune-related adverse events. Furthermore, it is now increasingly appreciated that even many conventional cancer therapies such as radiotherapy could have a positive impact on the host immune system for better clinical response. Hence, there is a need to better understand tumor immunity in order to design immunotherapeutic strategies, especially evidence-based combination therapies, for improved clinical outcomes. With this aim, cancer research turned its attention to profiling the immune contexture of either the tumor microenvironment (TME) or peripheral blood to uncover mechanisms and biomarkers which might aid in precision immunotherapeutics. Conventional technologies used for this purpose were limited by the depth and dimensionality of the data. Advances in newer techniques have, however, greatly improved the breadth and depth, as well as the quantity and quality of data that can be obtained. The result of these advances is a wealth of new information and insights on how the TME could be affected by various immune cell-types, and how this might in turn impact the clinical outcome of cancer patients . We highlight herein some of the high-dimensional technologies currently employed in immune profiling in cancer and summarize the insights and potential benefits they could bring in designing better cancer immunotherapies.

A Short-Text Oriented Clustering Method for Hot Topics Extraction

2015 ◽  
Vol 25 (03) ◽  
pp. 453-471 ◽  
Author(s):  
Yan Zheng ◽  
Zhaopeng Meng ◽  
Chao Xu
Keyword(s):  
Feature Vector ◽  
Vector Space Model ◽  
Analysis Approach ◽  
High Dimensional ◽  
Short Text ◽  
Sample Matrix ◽  
Space Model ◽  
Three Stages ◽  
Low Dimensional ◽  
Value Decomposition

A major challenge in document clustering is the extremely high dimensionality as well as the sparsity of the sample matrix. In this paper, we propose a new short-text oriented analysis approach to cluster short text automatically and extract the hot topics from each cluster. Different from the previous studies focused on long text, our analysis approach mainly focused on short-text cases. The approach consists of three stages: Firstly, generate feature vector for each sample so as to obtain the whole high-dimensional Vector Space Model; Secondly, use Singular Value Decomposition to achieve the dimensions reduction; Lastly, apply cosine similarity and k-means method to cluster samples on the low-dimensional matrix and extract the hot topics for each cluster. The experimental results show that our analysis approach can deal with the short-text samples and find out the hot topics efficiently and effectively.

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