Dataset Information

Configuration-Specific Insight into Single-Molecule Conductance and Noise Data Revealed by the Principal Component Projection Method.

ABSTRACT: We explore the merits of neural network boosted, principal-component-projection-based, unsupervised data classification in single-molecule break junction measurements, demonstrating that this method identifies highly relevant trace classes according to the well-defined and well-visualized internal correlations of the data set. To this end, we investigate single-molecule structures exhibiting double molecular configurations, exploring the role of the leading principal components in the identification of alternative junction evolution trajectories. We show how the proper principal component projections can be applied to separately analyze the high- or low-conductance molecular configurations, which we exploit in 1/f-type noise measurements on bipyridine molecules. This approach untangles the unclear noise evolution of the entire data set, identifying the coupling of the aromatic ring to the electrodes through the π orbitals in two distinct conductance regions, and its subsequent uncoupling as these configurations are stretched.

SUBMITTER: Balogh Z

PROVIDER: S-EPMC10258846 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Configuration-Specific Insight into Single-Molecule Conductance and Noise Data Revealed by the Principal Component Projection Method.

Balogh Z Z Mezei G G Tenk N N Magyarkuti A A Halbritter A A

The journal of physical chemistry letters 20230530 22

We explore the merits of neural network boosted, principal-component-projection-based, unsupervised data classification in single-molecule break junction measurements, demonstrating that this method identifies highly relevant trace classes according to the well-defined and well-visualized internal correlations of the data set. To this end, we investigate single-molecule structures exhibiting double molecular configurations, exploring the role of the leading principal components in the identifica ...[more]

PMID: 37249493

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Project description:BackgroundGender differences in dietary patterns and physical activity are known to influence metabolic health, but research exploring these differences using principal component analysis (PCA) is limited. This study aims to identify distinct patterns of eating behaviour, body composition and physical activity between men and women, in order to develop tailored interventions.MethodsA cross-sectional study was conducted on 2,509 adults attending a metabolic health centre. Data on eating habits, physical activity and body composition were collected by means of questionnaires and bioimpedance analysis. PCA was used to identify patterns of eating behaviour and physical activity. Statistical analyses were performed to explore gender-specific differences.ResultsBased on the PCA, five distinct groups of participants were identified: Balanced Eaters, Focused on Home Cooking, Routine Eaters, Restaurant Lovers and Varied Eaters. Significant gender differences in food preferences were observed, with men consuming more meat and women more vegetables. Men also reported greater participation in strength and endurance sports, while women showed a more structured eating routine.ConclusionsThis study, using principal component analysis (PCA), revealed gender-specific patterns in diet, physical activity and body composition. PCA identified five distinct behavioural groups, revealing that men tended to consume more meat and engage in strength training, while women adhered to structured, vegetable-rich diets. The application of PCA provided more insight than traditional analysis, highlighting the complexity of gender-specific behaviour. These results emphasize the need for tailored interventions, focusing on increasing vegetable intake in men and encouraging strength training in women. Future research should exploit PCA to explore behavioural patterns longitudinally for more refined and personalised health strategies.Clinical trials registeredThis study is registered on ClinicalTrials.gov (NCT06654674).

Project description:BackgroundIdentifying subpopulations within a study and inferring intercontinental ancestry of the samples are important steps in genome wide association studies. Two software packages are widely used in analysis of substructure: Structure and Eigenstrat. Structure assigns each individual to a population by using a Bayesian method with multiple tuning parameters. It requires considerable computational time when dealing with thousands of samples and lacks the ability to create scores that could be used as covariates. Eigenstrat uses a principal component analysis method to model all sources of sampling variation. However, it does not readily provide information directly relevant to ancestral origin; the eigenvectors generated by Eigenstrat are sample specific and thus cannot be generalized to other individuals.ResultsWe developed FastPop, an efficient R package that fills the gap between Structure and Eigenstrat. It can: 1, generate PCA scores that identify ancestral origins and can be used for multiple studies; 2, infer ancestry information for data arising from two or more intercontinental origins. We demonstrate the use of FastPop using 2318 SNP markers selected from the genome based on high variability among European, Asian and West African (African) populations. We conducted an analysis of 505 Hapmap samples with European, African or Asian ancestry along with 19661 additional samples of unknown ancestry. The results from FastPop are highly consistent with those obtained by Structure across the 19661 samples we studied. The correlations of the results between FastPop and Structure are 0.99, 0.97 and 0.99 for European, African and Asian ancestry scores, respectively. Compared with Structure, FastPop is more efficient as it finished ancestry inference for 19661 samples in 16 min compared with 21-24 h required by Structure. FastPop also provided scores based on SNP weights so the scores of reference population can be applied to other studies provided the same set of markers are used. We also present application of the method for studying four continental populations (European, Asian, African, and Native American).ConclusionsWe developed an algorithm that can infer ancestries on data involving two or more intercontinental origins. It is efficient for analyzing large datasets. Additionally the PCA derived scores can be applied to multiple data sets to ensure the same ancestry analysis is applied to all studies.

Dataset Information

Configuration-Specific Insight into Single-Molecule Conductance and Noise Data Revealed by the Principal Component Projection Method.

Publications

Configuration-Specific Insight into Single-Molecule Conductance and Noise Data Revealed by the Principal Component Projection Method.

Similar Datasets

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