scholarly journals Dengue geography in Vientiane Capital, 2012–2019: Combining multiple datasets to understand virus spread in an endemic city

2020 ◽  
Vol 101 ◽  
pp. 234
Author(s):  
O. Telle ◽  
M. Choisy ◽  
S. Somlor ◽  
S. Benkimoun ◽  
V. Pommelet ◽  
...  
Keyword(s):  
Virus Spread ◽  
Multiple Datasets

Olfactomedin-like 2A, 2B and 3 are differentially expressed in breast cancer metastases.

2019 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Breast Cancer ◽  
Genetically Engineered ◽  
Differentially Expressed ◽  
Genetically Engineered Mouse Models ◽  
Breast Cancer Metastases ◽  
Multiple Datasets ◽  
Cancer Metastases ◽  
Distant Organ ◽  
Metastatic Process ◽  
Differential Gene

Differential gene expression analysis of multiple datasets, in mice and in men revealed that transcripts of the olfactomedin-like family are differentially expressed in metastases, both in patients with breast cancer and in genetically engineered mouse models of breast cancer. The expression of olfactomedin-like genes was perturbed in metastases to the bone, brain and the lung, suggesting that these molecules function in the metastatic process rather than having tissue-specific associations with the site of dissemination. The olfactomedin-like family may play a role in the progression of breast cancer from frank tumor to colonization of distant organ sites.

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

Measles Virus Spread by Cell-Cell Contacts: Uncoupling of Contact-Mediated Receptor (CD46) Downregulation from Virus Uptake

1999 ◽  
Vol 73 (12) ◽  
pp. 10556-10556
Author(s):  
Ruth firsching ◽  
christian j. buchholz ◽  
urs schneider ◽  
roberto cattaneo ◽  
volker ter meulen ◽  
...  
Keyword(s):  
Measles Virus ◽  
Virus Spread ◽  
Cell Contacts ◽  
Cell Cell

Locally temperature - driven mathematical model of West Nile virus spread in Germany

2020 ◽  
Vol 488 ◽  
pp. 110117
Author(s):  
Suman Bhowmick ◽  
Jörn Gethmann ◽  
Franz J. Conraths ◽  
Igor M. Sokolov ◽  
Hartmut H.K. Lentz
Keyword(s):  
Mathematical Model ◽  
West Nile Virus ◽  
Virus Spread ◽  
West Nile

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

scholarly journals Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3046
Author(s):  
Shervin Minaee ◽  
Mehdi Minaei ◽  
Amirali Abdolrashidi
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Visualization Technique ◽  
Convolutional Network ◽  
The Past ◽  
Multiple Datasets ◽  
The Face ◽  
Traditional Approaches

Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.

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