Recent Advances in Health Biotechnology During Pandemic

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in 2019, cut the epoch that will make profound fluctuates in the history of the world in social, economic, and scientific fields. Urgent needs in public health have brought with them innovative approaches, including diagnosis, prevention, and treatment. To exceed the coronavirus disease 2019 (COVID-19) pandemic, various scientific authorities in the world have procreated advances in real time polymerase chain reaction (RT-PCR) based diagnostic tests, rapid diagnostic kits, the development of vaccines for immunization, and the purposing pharmaceuticals for treatment. Diagnosis, treatment, and immunization approaches put for- ward by scientific communities are cross-fed from the accrued knowledge of multidisciplinary sciences in health biotechnology. So much so that the pandemic, urgently prioritized in the world, is not only viral infections but also has been the pulsion in the development of novel approaches in many fields such as diagnosis, treatment, translational medicine, virology, mi- crobiology, immunology, functional nano- and bio-materials, bioinformatics, molecular biol- ogy, genetics, tissue engineering, biomedical devices, and artificial intelligence technologies. In this review, the effects of the COVID-19 pandemic on the development of various scientific areas of health biotechnology are discussed

Create a Country Perception and Image by International Public Relations: An Empirical Research about Perception and Image of Turkey in Germany (2010-2013)

Önder, Hatice Burcu (Arel Author)For centuries, countries have always communicated with each other, such as wars, political and economic power struggles, outbreaks, migrations, border changes, technical developments. This communication is sometimes caused by reasons such as political or economic power struggles. Today, countries have to deal with the developments, trade, social, cultural exchanges that are happening within the borders of each other. It is necessary for one country to have a good place in the international arena. Counties need to gain a positive place in international communication. Providing a positive perception in the international arena will feed countries in commercial, economic and political sense. Positive perception will lead to greater investment in the country, increased cooperation and ultimately achieving country interests at the final point

Create a Country Perception and Image by International Public Relations: An Empirical Research about Perception and Image of Turkey in Germany (2010-2013)

Önder, Hatice Burcu (Arel Author)For centuries, countries have always communicated with each other, such as wars, political and economic power struggles, outbreaks, migrations, border changes, technical developments. This communication is sometimes caused by reasons such as political or economic power struggles. Today, countries have to deal with the developments, trade, social, cultural exchanges that are happening within the borders of each other. It is necessary for one country to have a good place in the international arena. Counties need to gain a positive place in international communication. Providing a positive perception in the international arena will feed countries in commercial, economic and political sense. Positive perception will lead to greater investment in the country, increased cooperation and ultimately achieving country interests at the final point

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis

A toolbox of machine learning software to support microbiome analysis

Peer reviewed: TrueAcknowledgements: This article is based upon work from COST Action ML4Microbiome “Statistical and machine learning techniques in human microbiome studies,” CA18131, supported by COST (European Cooperation in Science and Technology), www.cost.eu.The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.</jats:p