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Scientific Reports Jun 2024High-resolution digital elevation models are commonly utilized for detecting and classifying landslides. In this study, we aim to refine landslide detection and...
High-resolution digital elevation models are commonly utilized for detecting and classifying landslides. In this study, we aim to refine landslide detection and classification by analyzing the geometry of landslides using slope and aspect, coupled with descriptive statistics up to the fourth central moment (kurtosis). Employing the Monte Carlo method for creating terrain topography probability distributions and ANOVA tests for statistical validation, we analyzed 364 landslides in Gorce National Park, Poland, revealing significant kurtosis differences across landslide types and lithologies. This methodology offers a novel approach to landslide classification based on surface geometry, with implications for enhancing scientific research and improving landslide risk management strategies.
PubMed: 38898211
DOI: 10.1038/s41598-024-65026-z -
Communications Biology Jun 2024Gene set enrichment analysis is foundational to the interpretation of high throughput biology. Identifying enriched Gene Ontology (GO) terms or disease-associated gene...
Gene set enrichment analysis is foundational to the interpretation of high throughput biology. Identifying enriched Gene Ontology (GO) terms or disease-associated gene sets within a list of gene effect sizes that represent experimental outcomes is an everyday task in life science that crucially depends on robust and sensitive statistical tools. We here present GOAT, a parameter-free algorithm for gene set enrichment analysis of preranked gene lists. The algorithm can precompute null distributions from standardized gene scores, enabling enrichment testing of the GO database in one second. Validations using synthetic data show that estimated gene set p-values are well calibrated under the null hypothesis and invariant to gene list length and gene set size. Application to various real-world proteomics and gene expression studies demonstrates that GOAT identifies more significant GO terms as compared to current methods. GOAT is freely available as an R package and user-friendly online tool for gene set enrichment analyses that includes interactive data visualizations: https://ftwkoopmans.github.io/goat .
Topics: Algorithms; Gene Ontology; Humans; Gene Expression Profiling; Animals; Computational Biology; Software; Proteomics; Databases, Genetic
PubMed: 38898151
DOI: 10.1038/s42003-024-06454-5 -
Scientific Reports Jun 2024Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and...
Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models.
Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.
PubMed: 38898067
DOI: 10.1038/s41598-024-64667-4 -
CoDAS 2024To describe and analyze auditory and academic complaints of students and employees of a federal public university.
OBJECTIVE
To describe and analyze auditory and academic complaints of students and employees of a federal public university.
METHODS
The study was carried out using a non-probabilistic. The EAPAC Scale with adaptations was used to fulfill the research objectives. It has 14 questions about complaints related to listening skills and 12 questions related to the academic environment. Descriptive data analysis was performed through the frequency distribution of categorical variables and Pearson's chi-square test was used for association analyses.
RESULTS
646 individuals aged between 17 and 67 years old participated in the research. The most prevalent complaints were academic difficulty related to memory, concentration, and planning, hearing and understanding speech in noise, and memorization of tasks that were only heard. There was an association with bidirectional statistical significance between academic and auditory complaints.
CONCLUSION
It was possible to observe that there is an association between auditory and academic complaints in adults, marked by the relationship between cognitive and auditory aspects. It is relevant that these factors are considered when performing assessments of Central Auditory Processing when intervening in patients with auditory complaints, and in student life.
Topics: Humans; Adult; Adolescent; Male; Female; Young Adult; Middle Aged; Aged; Auditory Perception; Self Concept; Students; Brazil; Surveys and Questionnaires; Universities; Cross-Sectional Studies
PubMed: 38896744
DOI: 10.1590/2317-1782/20242023098pt -
BioRxiv : the Preprint Server For... Jun 2024Accurate prediction of complex traits is an important task in quantitative genetics that has become increasingly relevant for personalized medicine. Genotypes have...
Accurate prediction of complex traits is an important task in quantitative genetics that has become increasingly relevant for personalized medicine. Genotypes have traditionally been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regressions, dimension reductions, and machine learning methods. Recent studies have shown that gene expression levels can produce higher prediction accuracy than genotypes. However, only a few prediction methods were used in these studies. Thus, a comprehensive assessment of methods is needed to fully evaluate the potential of gene expression as a predictor of complex trait phenotypes. Here, we used data from the Genetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance from gene expression in the two sexes separately. The methods considered differ in assumptions about the distribution of gene effect sizes - ranging from models that assume that every gene affects the trait to more sparse models - and their ability to capture gene-gene interactions. We also used functional annotation (, Gene Ontology (GO)) as an external source of biological information to inform prediction models. The results show that differences in prediction accuracy between methods exist, although they are generally not large. Methods performing variable selection gave higher accuracy in females while methods assuming a more polygenic architecture performed better in males. Incorporating GO annotations further improved prediction accuracy for a few GO terms of biological significance. Biological significance extended to the genes underlying highly predictive GO terms with different genes emerging between sexes. Notably, the Insulin-like Receptor () was prevalent across methods and sexes. Our results confirmed the potential of transcriptomic prediction and highlighted the importance of selecting appropriate methods and strategies in order to achieve accurate predictions.
PubMed: 38895364
DOI: 10.1101/2024.06.01.596951 -
BioRxiv : the Preprint Server For... Jun 2024Understanding genetic variation at the single-cell level is crucial for insights into cellular heterogeneity, clonal evolution, and gene expression regulation, but there...
MOTIVATION
Understanding genetic variation at the single-cell level is crucial for insights into cellular heterogeneity, clonal evolution, and gene expression regulation, but there is a scarcity of tools for visualizing and analyzing cell-level genetic variants.
RESULTS
We introduce scSNViz, a comprehensive R-based toolset for visualization and analysis of cell-specific expressed Single Nucleotide Variants (sceSNVs) within cell-barcoded single-cell RNA-sequencing (scRNA-seq) data. ScSNViz offers 3D sceSNV visualization capabilities for dimensionally reduced scRNA-seq gene expression data, compatibility with popular scRNA-seq processing tools like Seurat, cell-type classification tools such as SingleR and scType, and trajectory inference computation using Slingshot. Furthermore, scSNViz conducts estimation, summary, and graphical representation of statistical metrics pertaining to sceSNVs distribution and expression across individual cells. It also provides support for the analysis of individual sceSNVs as well as sets comprising multiple expressed sceSNVs of interest.
AVAILABILITY
ScSNViz is implemented as user-friendly R-scripts, freely available on https://horvathlab.github.io/NGS/scSNViz , supported by help utilities, and requiring no specialized bioinformatics skills for use.
PubMed: 38895293
DOI: 10.1101/2024.05.31.596816 -
Sensors (Basel, Switzerland) May 2024Road safety is a serious concern worldwide, and traffic signs play a critical role in confirming road safety, particularly in the context of AVs. Therefore, there is a...
Road safety is a serious concern worldwide, and traffic signs play a critical role in confirming road safety, particularly in the context of AVs. Therefore, there is a need for ongoing advancements in traffic sign evaluation methodologies. This paper comprehensively analyzes the relationship between traffic sign retroreflectivity and LiDAR intensity to enhance visibility and communication on road networks. Using Python 3.10 programming and statistical techniques, we thoroughly analyzed handheld retroreflectivity coefficients alongside LiDAR intensity data from two LiDAR configurations: 2LRLiDAR and 1CLiDAR systems. The study focused specifically on RA1 and RA2 traffic sign classes, exploring correlations between retroreflectivity and intensity and identifying factors that may impact their performance. Our findings reveal variations in retroreflectivity compliance rates among different sign categories and color compositions, emphasizing the necessity for targeted interventions in sign design and production processes. Additionally, we observed distinct patterns in LiDAR intensity distributions, indicating the potential of LiDAR technology for assessing sign visibility. However, the limited correlations between retroreflectivity and LiDAR intensity underscore the need for further investigation and standardization efforts. This study provides valuable insights into optimizing traffic sign effectiveness, ultimately contributing to improved road safety conditions.
PubMed: 38894097
DOI: 10.3390/s24113304 -
Sensors (Basel, Switzerland) May 2024Interactions between mobile robots and human operators in common areas require a high level of safety, especially in terms of trajectory planning, obstacle avoidance and...
Interactions between mobile robots and human operators in common areas require a high level of safety, especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection, the crossings of planned trajectories and their uncertainty based on model fluctuations, system noise and sensor noise play an outstanding role. This paper discusses the calculation of the expected areas of interactions during human-robot navigation with respect to fuzzy and noisy information. The expected crossing points of the possible trajectories are nonlinearly associated with the positions and orientations of the robots and humans. The nonlinear transformation of a noisy system input, such as the directions of the motion of humans and robots, to a system output, the expected area of intersection of their trajectories, is performed by two methods: statistical linearization and the sigma-point transformation. For both approaches, fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from the given output distribution parameters.
Topics: Robotics; Humans; Algorithms; Fuzzy Logic
PubMed: 38894096
DOI: 10.3390/s24113303 -
Diagnostics (Basel, Switzerland) May 2024The aim of this study was to compare the characteristics of breast microcalcification on digital mammography (DM) with the histological and molecular subtypes of breast...
The aim of this study was to compare the characteristics of breast microcalcification on digital mammography (DM) with the histological and molecular subtypes of breast cancer and to identify the predictive value of DM and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing microcalcifications for radiologic-pathologic correlation. We relied on our prospectively maintained database of suspicious microcalcifications on DM, from which data were retrospectively collected between January 2020 and April 2023. We enrolled 158 patients, all of whom were subjected to biopsy. Additionally, 63 patients underwent breast DCE-MRI. Microcalcifications with a linear branched morphology were correlated with malignancies ( < 0.001), among which an association was highlighted between triple negatives (TNs) and segmental distribution ( < 0.001). Amorphous calcifications were correlated with atypical ductal hyperplasia (ADH) ( = 0.013), coarse heterogeneous ( < 0.001), and fine-pleomorphic ( = 0.008) with atypical lobular hyperplasia (ALH) and fine pleomorphic ( = 0.009) with flat epithelial atypia (FEA). Regarding DCE-MRI, no statistical significance was observed between non-mass lesions and ductal carcinoma in situ (DCIS). Concerning mass lesions, three were identified as DCIS and five as invasive ductal carcinoma (IDC). In conclusion, microcalcifications assessed in DM exhibit promising predictive characteristics concerning breast lesion subtypes, leading to a reduction in diagnostic times and further examination costs, thereby enhancing the clinical management of patients.
PubMed: 38893590
DOI: 10.3390/diagnostics14111063 -
Molecules (Basel, Switzerland) Jun 2024CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were...
CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were identified by using Gaussian accelerated molecular dynamics (GaMD), deep learning (DL), and free energy landscapes (FELs). DL finds that the binding pocket as well as the T-loop binding to the Vcyclin protein are involved in obvious differences of conformation contacts. This result suggests that the binding pocket of inhibitors (LQQ and AP9) and the binding interface of CDK6 to the Vcyclin protein play a key role in the function of CDK6. The analyses of FELs reveal that the binding pocket and the T-loop of CDK6 have disordered states. The results from principal component analysis (PCA) indicate that the binding of the Vcyclin protein affects the fluctuation behavior of the T-loop in CDK6. Our QM/MM-GBSA calculations suggest that the binding ability of LQQ to CDK6 is stronger than AP9 with or without the binding of the Vcyclin protein. Interaction networks of inhibitors with CDK6 were analyzed and the results reveal that LQQ contributes more hydrogen binding interactions (HBIs) and hot interaction spots with CDK6. In addition, the binding pocket endures flexibility changes from opening to closing states and the Vcyclin protein plays an important role in the stabilizing conformation of the T-loop. We anticipate that this work could provide useful information for further understanding the function of CDK6 and developing new promising inhibitors targeting CDK6.
Topics: Cyclin-Dependent Kinase 6; Molecular Dynamics Simulation; Protein Binding; Deep Learning; Humans; Protein Conformation; Binding Sites; Protein Kinase Inhibitors; Principal Component Analysis; Thermodynamics; Normal Distribution
PubMed: 38893554
DOI: 10.3390/molecules29112681