Sci Rep. 2023 Feb 6;13(1):2117. doi: 10.1038/s41598-023-29102-0.
NO ABSTRACT
PMID:36747076 | DOI:10.1038/s41598-023-29102-0
Category: Nevin Manimala
Sci Rep. 2023 Feb 6;13(1):2088. doi: 10.1038/s41598-023-28965-7.
ABSTRACT
In this study, we investigate how an organism’s codon usage bias can serve as a predictor and classifier of various genomic and evolutionary traits across the domains of life. We perform secondary analysis of existing genetic datasets to build several AI/machine learning models. When trained on codon usage patterns of nearly 13,000 organisms, our models accurately predict the organelle of origin and taxonomic identity of nucleotide samples. We extend our analysis to identify the most influential codons for phylogenetic prediction with a custom feature ranking ensemble. Our results suggest that the genetic code can be utilized to train accurate classifiers of taxonomic and phylogenetic features. We then apply this classification framework to open reading frame (ORF) detection. Our statistical model assesses all possible ORFs in a nucleotide sample and rejects or deems them plausible based on the codon usage distribution. Our dataset and analyses are made publicly available on GitHub and the UCI ML Repository to facilitate open-source reproducibility and community engagement.
PMID:36747072 | DOI:10.1038/s41598-023-28965-7
Sci Rep. 2023 Feb 6;13(1):2095. doi: 10.1038/s41598-023-28855-y.
ABSTRACT
Satellite clock bias is the key factor affecting the accuracy of the single point positioning of a global navigation satellite system. The traditional model back propagation (BP) neural network is prone to local optimum problems. This paper presents a prediction model and algorithm for the clock bias of the BP neural network based on the optimization of the mind evolutionary algorithm (MEA), which is used to optimize the initial weights and thresholds of the BP neural network. The accuracy of the comparison between clock bias data is verified with and without one-time difference processing. Compared with grey model (GM (1,1)) and BP neural network, this paper discusses the advantages and general applicability of this method from different constellation satellites, different atomic clock type satellites, and the amount of modeling data. The accuracy of the grey model (GM(1,1)), BP, and MEA-BP models for satellite clock bias prediction is analyzed and the root mean square error, range difference error, and the mean of the clock bias data compared. The results demonstrate that the prediction accuracy of the three satellites significantly increased after one-time difference processing and that they have good stability. The prediction accuracy of four sessions of 2 h, 3 h, 6 h, and 12 h obtained using the MEA-BP model was better than 0.74, 0.80, 1.12, and 0.87 ns, respectively. The MEA-BP model has a specific degree of improvement in the prediction accuracy of the different sessions. Additionally, the prediction accuracy of different models has a specific relationship with the length of the original modeling sequence, of which BP model is the most affected, and MEABP is relatively less affected by the length of the modeling sequence, indicating that the MEA-BP model has strong anti-interference ability.
PMID:36747070 | DOI:10.1038/s41598-023-28855-y
Sci Rep. 2023 Feb 6;13(1):2108. doi: 10.1038/s41598-023-28973-7.
ABSTRACT
The effects of the curvature parameters on the energy eigenvalues and thermodynamic properties of quantum pseudoharmonic oscillator are investigated within the framework of nonrelativistic quantum mechanics. By employing Nikiforov-Uvarov method, the energy spectra are obtained and used to study the ordinary statistics and q-deformed superstatistics as a function of temperature in the presence and absence of the curvature parameters. It is shown that the q-deformed supertatistics properties of the quantum pseudoharmonic oscillator reduce to the ordinary statistical properties in the absence of the deformation parameter. Finally, our results are illustrated graphically to show the behaviour of the energy spectra and thermodynamic properties for the three curvature parameters:[Formula: see text].
PMID:36747069 | DOI:10.1038/s41598-023-28973-7
Sci Rep. 2023 Feb 6;13(1):2105. doi: 10.1038/s41598-023-28785-9.
ABSTRACT
Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million “in-stock” molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.
PMID:36747041 | DOI:10.1038/s41598-023-28785-9
Sci Rep. 2023 Feb 6;13(1):2131. doi: 10.1038/s41598-023-29373-7.
ABSTRACT
This study aims to describe the epidemiological characteristics of scrub typhus, detect the spatio-temporal patterns of scrub typhus at county level, and explore the associations between the environmental variables and scrub typhus cases in Anhui Province. Time-series analysis, spatial autocorrelation analysis, and space-time scan statistics were used to explore the characteristics and spatiotemporal patterns of the scrub typhus in Anhui Province. Negative binomial regression analysis was used to explore the association between scrub typhus and environmental variables. A total of 16,568 clinically diagnosed and laboratory-confirmed cases were reported from 104 counties of 16 prefecture-level cities. The number of female cases was higher than male cases, with a proportion of 1.32:1. And the proportion of cases over 65 years old was the highest, accounting for 33.8% of the total cases. Two primary and five secondary high-risk clusters were detected in the northwestern, northeastern, and central-eastern parts of Anhui Province. The number of cases in primary and secondary high-risk clusters accounted for 60.27% and 3.00%, respectively. Scrub typhus incidence in Anhui Province was positively correlated with the population density, normalized difference vegetation index, and several meteorological variables. The mean monthly sunshine duration with 3 lags (SSD_lag3), mean monthly ground surface temperature with 1 lag (GST_lag1), and mean monthly relative humidity with 3 lags (RHU_lag3) had the most significant association with increased cases of scrub typhus. Our findings indicate that public health interventions need to be focused on the elderly farmers in north of the Huai River in Anhui Province.
PMID:36747027 | DOI:10.1038/s41598-023-29373-7
Eur J Hum Genet. 2023 Feb 7. doi: 10.1038/s41431-023-01307-x. Online ahead of print.
ABSTRACT
Haploinsufficiency of TRIP12 causes a neurodevelopmental disorder characterized by intellectual disability associated with epilepsy, autism spectrum disorder and dysmorphic features, also named Clark-Baraitser syndrome. Only a limited number of cases have been reported to date. We aimed to further delineate the TRIP12-associated phenotype and objectify characteristic facial traits through GestaltMatcher image analysis based on deep-learning algorithms in order to establish a TRIP12 gestalt. 38 individuals between 3 and 66 years (F = 20, M = 18) – 1 previously published and 37 novel individuals – were recruited through an ERN ITHACA call for collaboration. 35 TRIP12 variants were identified, including frameshift (n = 15) and nonsense (n = 6) variants, as well as missense (n = 5) and splice (n = 3) variants, intragenic deletions (n = 4) and two multigene deletions disrupting TRIP12. Though variable in severity, global developmental delay was noted in all individuals, with language deficit most pronounced. About half showed autistic features and susceptibility to obesity seemed inherent to this disorder. A more severe expression was noted in individuals with a missense variant. Facial analysis showed a clear gestalt including deep-set eyes with narrow palpebral fissures and fullness of the upper eyelids, downturned corners of the mouth and large, often low-set ears with prominent earlobes. We report the largest cohort to date of individuals with TRIP12 variants, further delineating the associated phenotype and introducing a facial gestalt. These findings will improve future counseling and patient guidance.
PMID:36747006 | DOI:10.1038/s41431-023-01307-x
Sci Rep. 2023 Feb 6;13(1):2127. doi: 10.1038/s41598-023-28724-8.
ABSTRACT
As a specific predictor of ovarian reserve, serum anti-Müllerian hormone (AMH) has become an area of intense research interest in the field of assisted reproductive technology. We assessed the relationship between AMH levels and pregnancy outcomes in Chinese patients and investigate the influencing factors of cumulative live birth in patients with high AMH levels. A total of 1379 patients starting their IVF/ICSI cycle were divided into normal (Group A, 1.1-4.0 ng/ml, n = 639) and high (Group B, > 4.0 ng/ml, n = 740) groups by serum AMH levels. Live birth rate (LBR), cumulative live birth rate (CLBR) and cumulative clinical pregnancy rate (CCPR) were also investigated. Compared with Group A, Group B had a significantly higher CLBR (65.80% vs. 43.95%) and CCPR (76.77% vs. 57.14%), respectively. Binomial logistic regression analysis showed that age over 40 years, LH/FSH > 2.5, total Gn dose and Gn duration, and greater than 4000 ng/ml serum E2 levels on HCG day were significantly associated with CLBR in Group B. The AUC value of CLBR averaged 0.664 (ranging from 0.621 to 0.706) (p < 0.001). The patients with high AMH levels had higher CPR, higher LBR, and lower MR with no statistically significant differences, although there were significant improvements in CLBR. Advanced age (> 40 years) still impacted CLBR, even in women with good ovarian reserves. Consequently, it is still recommended that patients over 40 years old with high AMH levels actively receive IVF treatment if they seek to become pregnant. PCOS diagnoses did not influence the CLBR. In summary, this study showed that serum AMH levels could positively predict patient ovarian responses and further affect pregnancy outcomes.
PMID:36746984 | DOI:10.1038/s41598-023-28724-8
Sci Rep. 2023 Feb 6;13(1):2132. doi: 10.1038/s41598-023-28937-x.
ABSTRACT
Quantifying relationships between animal behavior and habitat use is essential to understanding animal decision-making. High-resolution location and acceleration data allows unprecedented insights into animal movement and behavior. These data types allow researchers to study the complex linkages between behavioral plasticity and habitat distribution. We used a novel Markov model in a Bayesian framework to quantify the influence of behavioral state frequencies and environmental variables on transitions among landcover types through joint use of location and tri-axial accelerometer data. Data were collected from 56 greater white-fronted geese (Anser albifrons frontalis) across seven ecologically distinct winter regions over two years in midcontinent North America. We showed that goose decision-making varied across landcover types, ecoregions, and abiotic conditions, and was influenced by behavior. We found that time spent in specific behaviors explained variation in the probability of transitioning among habitats, revealing unique behavioral responses from geese among different habitats. Combining GPS and acceleration data allowed unique study of potential influences of an ongoing large-scale range shift in the wintering distribution of a migratory bird across midcontinent North America. We anticipate that behavioral adaptations among variable landscapes is a likely mechanism explaining goose use of highly variable ecosystems during winter in ways which optimize their persistence.
PMID:36746981 | DOI:10.1038/s41598-023-28937-x
Transl Psychiatry. 2023 Feb 6;13(1):47. doi: 10.1038/s41398-023-02326-4.
ABSTRACT
Extracellular vesicles (EVs) are present in numerous peripheral bodily fluids and function in critical biological processes, including cell-to-cell communication. Most relevant to the present study, EVs contain microRNAs (miRNAs), and initial evidence from the field indicates that miRNAs detected in circulating EVs have been previously associated with mental health disorders. Here, we conducted an exploratory longitudinal and cross-sectional analysis of miRNA expression in serum EVs from adolescent participants. We analyzed data from a larger ongoing cohort study, evaluating 116 adolescent participants at two time points (wave 1 and wave 2) separated by three years. Two separate data analyses were employed: A cross-sectional analysis compared individuals diagnosed with Major Depressive Disorder (MDD), Anxiety disorders (ANX) and Attention deficit/Hyperactivity disorder (ADHD) with individuals without psychiatric diagnosis at each time point. A longitudinal analysis assessed changes in miRNA expression over time between four groups showing different diagnostic trajectories (persistent diagnosis, first incidence, remitted and typically developing/control). Total EVs were isolated, characterized by size distribution and membrane proteins, and miRNAs were isolated and sequenced. We then selected differentially expressed miRNAs for target prediction and pathway enrichment analysis. In the longitudinal analysis, we did not observe any statistically significant results. In the cross-sectional analysis: in the ADHD group, we observed an upregulation of miR-328-3p at wave 1 only; in the MDD group, we observed a downregulation of miR-4433b-5p, miR-584-5p, miR-625-3p, miR-432-5p and miR-409-3p at wave 2 only; and in the ANX group, we observed a downregulation of miR-432-5p, miR-151a-5p and miR-584-5p in ANX cases at wave 2 only. Our results identified previously observed and novel differentially expressed miRNAs and their relationship with three mental health disorders. These data are consistent with the notion that these miRNAs might regulate the expression of genes associated with these traits in genome-wide association studies. The findings support the promise of continued identification of miRNAs contained within peripheral EVs as biomarkers for mental health disorders.
PMID:36746925 | DOI:10.1038/s41398-023-02326-4