Morphological characteristics of the lip grooves in citizens of the Republic of North Macedonia determined by Cheiloscopy

Introduction: Cheiloscopy is defined as the study of the sulci labiorum, known as "lip prints". Aim of the study: The aim of this study was to determine the morphological characteristics of the lip grooves in the three dominant nationalities in the Republic of North Macedonia (Macedonians, Albanians, Roma) and to compare the obtained results with the morphological characteristics of the lip grooves in three other populations from different geographical regions. Material and methods: In this research, we included 150 examinees aged 25-50 years and divided them into three groups: Macedonians (50), Albanians (50) and Roma (50). The lip prints were taken using microscopic slides and detected using the dactyloscopic powder and brush method. We used the Suzuki and Tsuchihashi classification to typify the lip prints. Results: The most common type of lip grooves in the population of the Republic of North Macedonia was the type II grooves. There was no significant difference in the presence of different types of lip grooves in the four quadrants between males and females, nor between Macedonians, Albanians and Roma. The comparative analysis showed that populations from different geographical areas had different anthropological and morphological characteristics of the lip grooves. Conclusion: Type II lip grooves are the most common in the population of the Republic of North Macedonia and there is no statistically significant difference between the prevalence of different types of lip grooves in the three nationalities in this study. Considering the large number of factors that can affect the quality of the lip print, we recommend that a swab should always be taken before collecting the lip print in order to attempt to extract DNA material from the found trace

Haplogroup Prediction Using Y-Chromosomal Short Tandem Repeats in the General Population of Bosnia and Herzegovina

Human Y-chromosomal haplogroups are an important tool used in population genetics and forensic genetics. A conventional method used for Y haplogroup assignment is based on a set of Y-single nucleotide polymorphism (SNP) markers deployed, which exploits the low mutation rate nature of these markers. Y chromosome haplogroups can be successfully predicted from Y-short tandem repeat (STR) markers using different software packages, and this method gained much attention recently due to its labor-, time-, and cost-effectiveness. The present study was based on the analysis of a total of 480 adult male buccal swab samples collected from different regions of Bosnia and Herzegovina. Y haplogroup prediction was performed using Whit Athey’s Haplogroup Predictor, based on haplotype data on 23 Y-STR markers contained within the PowerPlex® Y23 kit. The results revealed the existence of 14 different haplogroups, with I2a, R1a, and E1b1b being the most prevalent with frequencies of 43.13, 14.79, and 14.58%, respectively. Compared to the previously published studies on Bosnian-Herzegovinian population based on Y-SNP and Y-STR data, this study represents an upgrade of molecular genetic data with a significantly larger number of samples, thus offering more accurate results and higher probability of detecting rare haplogroups

Advancing microbiome research with machine learning : key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

Peer reviewed: TrueAcknowledgements: The authors are grateful to all COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies” members for their contribution to the COST Action objectives, and to COST (European Cooperation in Science and Technology) for the economic support, www.cost.eu. WG2 and WG3 thank Emmanuelle Le Chatelier and Pauline Barbet (Université Paris-Saclay, INRAE, MetaGenoPolis, 78350, Jouy-en-Josas, France) for preparing the shotgun CRC benchmark dataset.The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices

Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices