Principal Component Analysis Research Articles

Evaluating genomic inbreeding of two Chinese yak (Bos grunniens) populations

BackgroundYaks are a vital livestock in the Qinghai-Tibetan Plateau area for providing food products, maintaining sustainable ecosystems, and promoting cultural heritage. Because of uncontrolled mating, it is impossible to estimate inbreeding level of yak populations using the pedigree-based approaches. With the aims to accurately evaluate inbreeding level of two Chinese yak populations (Maiwa and Jiulong), we obtained genome-wide single nucleotide polymorphisms (SNPs) by DNA sequencing and calculated five SNP-by-SNP estimators (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{HOM}$$\\end{document}, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{L\\&H}$$\\end{document}, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{VR1}$$\\end{document}, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{VR2}$$\\end{document}, and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{YAN}$$\\end{document}), as well as two segment-based estimators of runs of homozygosity (ROH, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{ROH}$$\\end{document}) and homozygous-by-descent (HBD, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{HBD}$$\\end{document}). Functional implications were analyzed for the positional candidate genes located within the related genomic regions.ResultsA total of 151,675 and 190,955 high-quality SNPs were obtained from 71 Maiwa and 30 Jiulong yaks, respectively. Jiulong had greater genetic diversity than Maiwa in terms of allele frequency and nucleotide diversity. The two populations could be genetically distinguished by principal component analysis, with the mean differentiation index (Fst) of 0.0054. The greater genomic inbreeding levels of Maiwa yaks were consistently supported by all five SNP-by-SNP estimators. Based on simple proportion of homozygous SNPs (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{HOM}$$\\end{document}), a lower inbreeding level was indicated by three successfully sequenced old leather samples that may represent historical Maiwa yaks about five generations ago. There were 3304 ROH detected among all samples, with mean and median length of 1.97 Mb and 1.0 Mb, respectively. A total of 94 HBD segments were found among all samples, whereas 92 of them belonged to the shortest class with the mean length of 10.9 Kb. Based on the estimates of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{ROH}$$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{F}_{HBD}$$\\end{document}, however, there was no difference in inbreeding level between Maiwa and Jiulong yaks. Within the genomic regions with the significant Fst or enriched by ROH, we found several candidate genes and pathways that have been reported to be related to diverse production traits in farm animals.ConclusionsWe successfully evaluated the genomic inbreeding level of two Chinese yak populations. Although different estimators resulted in inconsistent conclusions on their genomic inbreeding levels, our results may be helpful to implement the genetic conservation and utilization programs for the two yak populations.

A novel immunoassay technique using principal component analysis for enhanced detection of emerging viral variants.

Rapid diagnostics are critical infectious disease tools that are designed to detect a known biomarker using antibodies specific to that biomarker. However, a way to detect unknown disease variants has not yet been achieved in a paper test format. We describe here a route to make an adaptable paper immunoassay that can detect an unknown biomarker, demonstrating it on SARS-CoV-2 variants. The immunoassay repurposes cross reactive antibodies raised against the alpha variant. Gold nanoparticles of two different colors conjugated to two different antibodies create a colorimetric signal, and machine learning of the resulting colorimetric pattern is used to train the assay to discriminate between variants of alpha and Omicron BA.5. By using principal component analysis, the colorimetric test patterns can pick up and discriminate an unknown variant that it has not encountered before, Omicron BA.1. The test has an accuracy of 100% and a potential calculated discriminatory power of 900. We show that it can be used adaptively and that it can be used to pick up emerging variants without the need to raise new antibodies.

Fertility and quality of arable soils in Poland: spatial–temporal analysis of long-term monitoring

Soil quality is a globally important issue from the point of view of food production, maintenance of appropriate environmental quality and ecological balance. Assessing its quality and fertility using indices is one of the methods that make it possible to formulate recommendations for good soil management. The aim of the study was, therefore, to determine the spatial and temporal distribution of soil properties in the context of soil quality assessment (SQI) and soil fertility assessment (SFI, SEF, NIV, and SSFI) using the example of a Central European country (Poland). The inverse distance-weighted spatial interpolation technique was used to estimate the spatial distribution of soil quality and fertility indicators. So far, no research has been carried out that would include a comprehensive analysis of soil fertility and quality indicators in such a large research area over many years. The results of 216 points of monitoring the chemistry of arable soils in Poland from the period 1995–2020 for 28 physicochemical soil parameters were considered. The analyses carried out showed significant variability in the properties of the described soils in Poland, as evidenced by the principal component analysis (PCA) and the coefficient of variation (exceptions: bulk density, pH). It was found that heavy metals (Zn, Cd, Cu) were responsible for the deterioration of soil properties. The results obtained for different indices were different (SFI – most soils with good fertility, SEF – high fertility, NIV – soils highly enriched with nutrients, SQI – average soil quality). It was found that the SSFI is the most appropriate indicator among the analyzed indicators for Polish conditions in relation to arable soils, as it is adapted and calibrated to the specifics of this area. The Spearman correlation matrix showed that the largest group of statistically highly significant parameter pairs belonged to heavy metals (Ni/Zn, Zn/Pb, Zn/Cu, Ni/Cu). In contrast, the strongest correlations were recorded for Ni (with effective cation exchange capacity (ECEC), Mn, Mgav, Zn, Cu). The assessment revealed limiting factors and temporal trends in the overall evaluation of the quality of the country’s agricultural soils, which are important from the point of view of both sustainable development and agricultural production.

Effect of Soil Tillage Systems and Crop Seasonality on the Sediment Geochemical Properties Transferred into a Headwater: A Case of Study in Tobacco Plantation

ABSTRACT The global impacts of agricultural land conversion on soil erosion and pollution, particularly in tobacco cultivation areas, are well-recognized as significant contributors to soil degradation. These areas are identified as hotspots for environmental concerns due to practices that lead to increased erosion and pollution. From this perspective, this case of study explores fine sediment samples from two areas with tobacco cultivation under different tillage systems and seasonal variations, transport into a headwater, and evaluates, on a local scale: (1) the impact of tillage systems on the geochemical signature of sediments; (2) if whether crop seasonality affects these sediment geochemical signatures. The Conventional Ridge Tillage (CRT) system involves extensive soil exposure and machinery for soil management, while the Mulch Ridge Tillage (MRT) system prioritizes soil conservation and relies on herbicides for weed control. The analytical methodology used to assess the sample element characteristics was Energy Dispersive X-ray Fluorescence (EDXRF). It was applied on the twenty fine sediments (ten of harvest and ten of inter-harvest season of tobacco) to quantitatively assess their inorganic composition. Additionally, Pearson correlation analysis, Hierarchical Cluster Analysis (HCA), and Principal Component Analysis (PCA) were applied on the EDXRF data to highlight the similarities and, thus, providing information to assess the complex data clustering patterns. As a result, the sediment compositions from the two studied soil systems are not similar. The PCA showed that the CRT sediments are characterized by the P, S, K, Ca, and Mn content, presenting a geochemical signature related to manure and fertilizer compared to the MRT, which is correlated with Al, Ti, Fe, Cu, and Zn contents, exhibiting a geochemical signature characterized by the natural soil composition. Therefore, the sediment geochemical signatures might be affected by two phases in the study area: a) tillage system characteristics and b) seasonal soil erosion. These findings underscore the importance of managing soil nutrients to mitigate soil pollution and nutrient exportation to aquatic systems. Moreover, the results emphasize the recommendations for sustainable agricultural practices in tobacco-growing areas to protect environmental quality.

Machine learning and remote sensing-based lithological mapping of the Duwi Shear-Belt area, Central Eastern Desert, Egypt

Machine learning and remote sensing techniques are widely accepted as valuable, cost-effective tools in lithological discrimination and mineralogical investigations. The current study represents an attempt to use machine learning classification along with several remote sensing techniques being applied to Landsat-8/9 satellite data to discriminate the various outcropping lithological rock units at the Duwi Shear Belt (DSB) area in the Central Eastern Desert of Egypt. Multi-class machine learning classification, multiple conventional remote sensing mapping techniques, spectral separability analysis based on the Jeffries-Matusita (J-M) distance measure, fieldwork, and petrographic investigations were integrated to enhance the lithological discrimination of the exposed rock units at DSB area. The well-recognized machine learning classifier (Support Vector Machine—SVM) was adopted in this study, with training data determined carefully based on enhancing the lithological discrimination attained from various remote sensing techniques of False Color Composites (FCC), Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF), along with the fieldwork data and the previously published geologic maps. High overall accuracy of the SVM classification was obtained, however, inspection of the individual rock unit classes’ accuracies revealed lower accuracy for certain types of rock units which were also found associated with lower separability scores as well. Among the least separable rock units were; metagabbro rocks that showed high spectral similarity with the volcaniclastic metasediments rocks, and the metaultramafics of the ophiolitic mélange showed spectral attitude of high correlation to that of the Hammamat volcanosedimentary rocks. Target-oriented Color Ratio Composites (CRC) technique was implemented to better discriminate these hardly separable rock units. A final integrated geological map was obtained comprising the various discriminated Neoproterozoic basement rock units of the DSB area. The successfully mapped litho-units include; Meatiq Group (amphibolites, gneissic granitoids, and mylonitized granitoids), ophiolitic mélange (metaultramafics, metagabbro-amphibolites, and volcaniclastic metasediments), Dokhan volcanics, Hammamat sediments, and granites. An adequate description of these rock units was also given in light of the conducted intense fieldwork and petrographic investigations.

Elucidation of anti-pneumonia pharmacodynamic material basis and potential mechanisms of Xiebai San by combining spectrum–efficacy relationship and surface plasmon resonance

Ethnopharmacological relevanceXiebai San (XBS), a classic Chinese prescription, has been used for the clinical treatment of pneumonia-related diseases for thousands of years. However, the anti-pneumonia pharmacodynamic material basis of XBS and its underlying mechanisms remain unclear. Aim of the studyThis study aimed to comprehensively investigate and verify the anti-pneumonia pharmacodynamic material basis and mechanisms of XBS. Materials and methodsThis study explored the anti-pneumonia activity and key pneumonia targets of XBS in lipopolysaccharide (LPS)-induced zebrafish and RAW264.7 cells in vivo and in vitro through transcriptomics, western blotting, and reverse transcription–quantitative polymerase chain reaction (RT–qPCR). The chemical fingerprint of XBS was established using high-performance liquid chromatography, and the similarities and areas of characteristic peaks of 15 batches of XBS were analyzed. Based on the spectrum–efficacy relationship, the potential anti-inflammatory components were screened according to their peak areas and efficacy using principal component analysis (PCA), bivariate correlation, and partial least squares regression analysis. Active components that bind to core targets were further screened based on surface plasmon resonance (SPR). The binding mode of proteins and components was simulated via molecular docking, which enabled the identification of the primary active components of XBS, thereby elucidating its anti-pneumonia properties. Finally, the anti-inflammatory activities of these components were verified in vitro. ResultsXBS decreased neutrophil aggregation in zebrafish and nitric oxide (NO) secretion in RAW264.7 cells as well as suppressed the release of downstream inflammatory cytokines such as iNOS, TNF-α, IL-1β, IL-18, and CXCL10 related to TNF and JAK–STAT signaling pathways. The phosphorylation of IκBα, Akt, and Stat3 was alleviated after XBS in cells. The fingerprint similarities of 15 batches of XBS ranged from 0.381 to 0.994, with a large difference. A total of 15 characteristic peaks were identified, and the relative standard deviation of their peak areas ranged from 24.1% to 70.7%. The results of in vitro anti-inflammatory activities of 15 batches of XBS showed that all samples inhibited the expression levels of NO and nine inflammatory markers. The anti-inflammatory index of 15 batches of XBS was determined to be 0.69–0.96 based on transformation of the anti-inflammatory rate and composite index method via PCA. The spectrum–efficacy relationship model of 15 characteristic peak areas and the anti-inflammatory index showed that 7 main potential active components were related to the anti-inflammatory activity of XBS. Moreover, four components (mulberroside A, isoquercitrin, liquiritigenin, and glycyrrhizic acid) screened based on SPR had different affinities toward TNFR1, Akt1, and Stat3 proteins, and the binding modes were elucidated via molecular docking. Finally, in LPS-induced RAW264.7 cells, all four active components (at a concentration of 60 μM) significantly inhibited the expression levels of NO and inflammatory markers. ConclusionsBased on the comprehensive strategy of spectrum–efficacy relationship and SPR, mulberroside A, isoquercitrin, liquiritigenin, and glycyrrhizic acid were identified as the primary pharmacodynamic active components involved in the anti-pneumonia activity of XBS and were found to intervene in TNF and JAK–STAT signaling pathways.

Heavy metal impacts on antioxidants in cow blood from wastewater-irrigated areas

The aim of the present research was to investigate the presence of heavy metals such as lead (Pb), copper (Cu), chromium (Cr), and cadmium (Cd) in blood samples from cows raised with irrigated wastewater, as well as in the wastewater itself, in the North-western region of Pakistan. A total of 60 blood samples were collected from five different locations in Kohat, namely Tappi Road (TR), Pindi Road (PR), Gul Malik Road (GMR), Markaz Road (MR), and a control group. The samples of both i.e. cow blood and wastewater were analyzed for the concentrations of heavy metals. The highest concentration of Cd was detected in the MR site with a mean value of 0.03 mg/L, and the highest concentration of Cu (0.04 mg/L) was recorded in the TR site, while the lowest level was found in the control group with a mean of 0.002 mg/L in blood samples. The highest Cr and Pb concentrations were found at the PR site, with mean values of 0.03 and 0.07 mg/L, respectively, whereas the control group had the lowest concentrations, with mean values of 0.002 and 0.01 mg/L. Similarly, heavy metal concentrations were analyzed in wastewater used for irrigation in the study area. Results indicated elevated concentrations of Cu and Cr in wastewater, although they remained below the World Health Organization (WHO) recommended values except for Cr (0.13 mg/L) in the GMR site, which exceeded permissible limits. Cd and Pb concentrations in wastewater were relatively low, but Cd concentration surpassed WHO limits, particularly with a mean concentration of 0.08 mg/L in the TR site. Comparison between heavy metal concentrations in blood and wastewater revealed higher values of Cd and Pb in blood samples than in wastewater, while Cu and Cr concentrations were higher in water compared to blood. Additionally, elevated levels of Super Oxide Dismutase (SOD), antioxidant enzyme Catalase (CAT), and oxidative stress marker malondialdehyde (MDA) were detected in blood samples. Cluster and principal component analyses were employed to assess heavy metal toxicity among the groups, indicating potential long-term adverse health effects on animals, transfer to humans, and toxicity in living organisms.

Olfactory-chemical establishment using odour wheels and fingerprints to manage odor pollution for the petrochemical industry

The odor nuisance may cause major environmental issues, so identifying the odor characteristics is important for odor management and traceability in the petrochemical industry. In this study, odour wheels and fingerprints were applied to manage odor pollution in the petrochemical industry. First, the odor profile method and portable electronic nose evaluation were applied to analyze the flavour sensory profiles of five upstream and downstream petrochemical plants, in order to grasp the odor natures of plants. Second, 375 organized and unorganized samples of 5 plants were analyzed by gas chromatography-mass spectrometry (GC–MS) technology, and a total of 583 odorants were obtained, including sulfur compounds, alcohols, esters, aldehydes, ketones, acids, alkanes, alkenes, halogenated hydrocarbons, aromatic hydrocarbons, nitrogen compounds, and other compounds. Third, the key odorants were identified by gas chromatography-olfactory (GC-O) and odor activity value (OAV) analysis. Fourth, the fingerprint spectras were developed by the identification of key odorants and process sources through principal component analysis. For plant A, 21 odorants were confirmed, including naphthalene, 1-methylnaphthalene, 2-methylnaphthalene, 1-methylindene, and 2-methylindene. Methyl methacrylate, acetophenone, isobutene, 4-vinyl-1-cyclohexene, acrylonitrile, and styrene were key element for plant B. The fingerprint factor of plant C had 20 odorants, including methanol, butanol, 2-ethylhexanol, acetic acid, and ammonia. And there were 21 odorants for plant D and 14 odorants for plant E, respectively, with similar substance types, including 2-ethyl-2-hexenal, 3-methyl-4-heptanone, isobutyl butyrate, and 1,2,3,5-tetraethylbenzene. Finally, the odor wheels were established by the correlation analysis between odorants and malodor, and compounds in fingerprint spectra weaker related to malodor of plants were removed. Hence, the characteristic odorants of each plant were further clarified, and the odors were associated with compounds, achieving rapid diagnosis and traceability of odor pollution in the petrochemical park. This provided new ideas and technical methods for odor pollution monitoring and identification.

Spurious negative eigenvalues of numerical variance-covariance matrices in many-body systems correlate with the existence of frozen degrees of freedom

The principal component analysis (PCA) is widely used to reduce the dimensionality of a dataset to its essential components. To perform PCA, the covariance matrix is constructed and its eigenvalues and eigenvectors are computed. In practical numerical applications, the tail of the sorted eigenvalues is sometimes found to contain negative eigenvalues, which are prohibited mathematically and are a pure consequence of finite-accuracy numerics. The present study suggests that in the case of a many-body dynamical system, the spurious negative eigenvalues of the covariance matrix may in fact be related to the frozen degrees of freedom in the system. Here, we outline the mathematical connection between the eigenvalues of the covariance matrix and the frozen degrees of freedom and validate the connection through two case studies: a model system of coupled harmonic oscillators and a molecular dynamics simulation of a small protein in solution.

Identification of N6-Methyladenosine-Associated lncRNAs and Analysis of Prognostic Signature in Breast Cancer.

Breast cancer represents the predominant malignant neoplasm in women, posing significant threats to both life and health. N6-methyladenosine (m6A) methylation, the most prevalent RNA modification, plays a crucial role in cancer development. This study aims to delineate the prognostic implications of m6A-associated long non-coding RNAs (m6AlncRNAs) and identify potential m6AlncRNA candidates as novel therapeutic targets for breast cancer. Through univariate Cox, Least Absolute Shrinkage and Selection Operator and multiple Cox regression analysis, m6AlncRNA was analyzed and a risk-prognosis model was constructed. Kaplan-Meier analysis, principal component analysis and nomogram were used to evaluate the risk model. Finally, we screened candidate lncRNAs and validated them in breast cancer cell lines. m6AlncRNAs were stratified into three subtypes, and their associations with survival outcomes and immune infiltrating capacities were systematically analyzed. Subsequently, breast cancer patients were stratified into high and low-risk groups based on median risk scores, revealing distinct clinical characteristics, tumor immunoinvasive profiles, tumor mutation burden, and survival probabilities. Additionally, a prognostic model was established, highlighting three promising candidate lncRNAs: ECE1-AS1, NDUFA6-DT, and COL4A2-AS1. This study investigated the prognostic implications of m6A-associated long non-coding RNAs (m6AlncRNAs) and developed a prognostic risk model to identify three potential m6AlncRNA candidates. These findings provide valuable insights into the potential application of these m6AlncRNAs in guiding immunotherapeutic strategies for breast cancer.

Real-time prediction of postoperative spinal shape with machine learning models trained on finite element biomechanical simulations.

Adolescent idiopathic scoliosis is a chronic disease that may require correction surgery. The finite element method (FEM) is a popular option to plan the outcome of surgery on a patient-based model. However, it requires considerable computing power and time, which may discourage its use. Machine learning (ML) models can be a helpful surrogate to the FEM, providing accurate real-time responses. This work implements ML algorithms to estimate post-operative spinal shapes. The algorithms are trained using features from 6400 simulations generated using the FEM from spine geometries of 64 patients. The features are selected using an autoencoder and principal component analysis. The accuracy of the results is evaluated by calculating the root-mean-squared error and the angle between the reference and predicted position of each vertebra. The processing times are also reported. A combination of principal component analysis for dimensionality reduction, followed by the linear regression model, generated accurate results in real-time, with an average position error of 3.75 mm and orientation angle error below 2.74 degrees in all main 3D axes, within 3 ms. The prediction time is considerably faster than simulations based on the FEM alone, which require seconds to minutes. It is possible to predict post-operative spinal shapes of patients with AIS in real-time by using ML algorithms as a surrogate to the FEM. Clinicians can compare the response of the initial spine shape of a patient with AIS to various target shapes, which can be modified interactively. These benefits can encourage clinicians to use software tools for surgical planning of scoliosis.

Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning

Protein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved in DNA bending, bridging, looping, and compaction (≥ 1 kbp) are challenging to address experimentally or by all-atom molecular dynamics simulations, making coarse-grained simulations a natural approach. Here we present a simple and generic coarse-grained model for the DNA-protein and protein-protein interactions, and investigate the role of the latter in the protein-induced compaction of DNA. Our approach models the DNA as a discrete worm-like chain. The proteins are treated in the grand-canonical ensemble and the protein-DNA binding strength is taken from experimental measurements. Protein-DNA interactions are modeled as an isotropic binding potential with an imposed binding valency, without specific assumptions about the binding geometry. To systematically and quantitatively classify DNA-protein complexes, we present an unsupervised machine learning pipeline that receives a large set of structural order parameters as input, reduces the dimensionality via principal component analysis, and groups the results using a Gaussian mixture model. We apply our method to recent data on the compaction of viral genome-length DNA by HIV integrase and we find that protein-protein interactions are critical to the formation of looped intermediate structures seen experimentally. Our methodology is broadly applicable to DNA-binding proteins and to protein-induced DNA compaction and provides a systematic and semi-quantitative approach for analyzing their mesoscale complexes.

Revealing the evolutionary history and contemporary population structure of Pacific salmon in the Fraser River through genome resequencing.

The Fraser River once supported massive salmon returns. However, over the last century, the largest returns have consistently been less than half of the recorded historical maximum. There is substantial interest from surrounding communities and governments to increase salmon returns for both human use and functional ecosystems. To generate resources for this endeavor, we resequenced genomes of Chinook (Oncorhynchus tshawytscha), coho (O. kisutch), and sockeye salmon (O. nerka) from the Fraser River at moderate coverage (∼16x). A total of 954 resequenced genomes were analyzed, with 681 collected specifically for this study from tissues sampled between 1997 and 2021. An additional 273 were collected from previous studies. At the species level, Chinook salmon appeared to have 1.6-2.1x more SNPs than coho or sockeye salmon, respectively. This difference may be attributable to large historical declines of coho and sockeye salmon. At the population level, three Fraser River genetic groups were identified for each species using principal component and admixture analyses, which is consistent with previous research and supports the continued use of these groups in conservation and management efforts. Environmental factors and a migration barrier were identified as major factors influencing the boundaries of these genetic groups. Additionally, 20 potentially adaptive loci were identified among the genetic groups. This information may be valuable in new management and conservation efforts. Furthermore, the resequenced genomes are an important resource for contemporary genomics research on Fraser River salmon and have been made publicly available.

Exploring transformation of dissolved organic matters and dissolved organic nitrogen in full-scale anammox wastewater treatment: Temperature and microbial roles

Variation of dissolved organic matters (DOM) in mainstream anammox process has received limited attention. This study systematically characterized DOM and dissolved organic nitrogen (DON) in a full-scale mainstream anammox wastewater treatment plant (WWTP) using spectroscopy and Fourier transform-ion cyclotron resonance mass spectrometry. Roles of bacterial community structures related with temperatures on DOM and DON transformations were analyzed. Results indicated that the WWTP removed highly bioavailable, S-containing DOM while producing more unsaturated, aromatic, and N-containing DOM. Higher relative abundances of Proteobacteria and Chloroflexi at low temperature resulted in greater removal rates of proteins, SMP-like and humic acid-like substances. At high temperature, higher relative abundance of Actinobacteriota increased lignin production. Principal component analysis revealed that temperature significantly impacted DOM characteristics compared to DON. These findings are crucial for understanding DOM and DON transformation during mainstream anammox WWTP

Noise-reduction techniques for 1H-FID-MRSI at 14.1 T: Monte Carlo validation and in vivo application.

Proton magnetic resonance spectroscopic imaging (1H-MRSI) is a powerful tool that enables the multidimensional non-invasive mapping of the neurochemical profile at high resolution over the entire brain. The constant demand for higher spatial resolution in 1H-MRSI has led to increased interest in post-processing-based denoising methods aimed at reducing noise variance. The aim of the present study was to implement two noise-reduction techniques, Marchenko-Pastur principal component analysis (MP-PCA) based denoising and low-rank total generalized variation (LR-TGV) reconstruction, and to test their potential with and impact on preclinical 14.1 T fast in vivo 1H-FID-MRSI datasets. Since there is no known ground truth for in vivo metabolite maps, additional evaluations of the performance of both noise-reduction strategies were conducted using Monte Carlo simulations. Results showed that both denoising techniques increased the apparent signal-to-noise ratio (SNR) while preserving noise properties in each spectrum for both in vivo and Monte Carlo datasets. Relative metabolite concentrations were not significantly altered by either method and brain regional differences were preserved in both synthetic and in vivo datasets. Increased precision of metabolite estimates was observed for the two methods, with inconsistencies noted for lower-concentration metabolites. Our study provided a framework for how to evaluate the performance of MP-PCA and LR-TGV methods for preclinical 1H-FID MRSI data at 14.1 T. While gains in apparent SNR and precision were observed, concentration estimations ought to be treated with care, especially for low-concentration metabolites.

PCA-IFNNR: Principal component analysis based image fusion directed nearest neighbor representation for dark face image super resolution

PCA-IFNNR: Principal component analysis based image fusion directed nearest neighbor representation for dark face image super resolution

Extensive genetic diversity in plasmodium vivax from Sudan and its genetic relationships with other geographical isolates

Plasmodium vivax, traditionally overlooked has experienced a notable increase in cases in East Africa. This study investigated the geographical origin and genetic diversity of P. vivax in Sudan using 14 microsatellite markers. A total of 113 clinical P. vivax samples were collected from two different ecogeographical zones, New Halfa and Khartoum, in Sudan. Additionally, 841 geographical samples from the database were incorporated for a global genetic analysis to discern genetic relationships among P. vivax isolates on regional and worldwide scales. On the regional scale, our findings revealed 91 unique and 8 shared haplotypes among the Sudan samples, showcasing a remarkable genetic diversity compared to other geographical isolates and supporting the hypothesis that P. vivax originated from Africa. On a global scale, distinct genetic clustering of P. vivax isolates from Africa, South America, and Asia (including Papua New Guinea and Solomon Island) was observed, with limited admixture among the three clusters. Principal component analysis emphasized the substantial contribution of African isolates to the observed global genetic variation. The Sudanese populations displayed extensive genetic diversity, marked by significant multi-locus linkage disequilibrium, suggesting an ancestral source of P. vivax variation globally and frequent recombination among the isolates. Notably, the East African P. vivax exhibited similarity with some Asian isolates, indicating potential recent introductions. Overall, our results underscore the effectiveness of utilizing microsatellite markers for implementing robust control measures, given their ability to capture extensive genetic diversity and linkage disequilibrium patterns.

Optimizing the Topology of Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) for Brain Tumor diagnosis (BTD) through MRIs

The use of MRI analysis for BTD and tumor type detection has considerable importance within the domain of machine vision. Numerous methodologies have been proposed to address this issue, and significant progress has been achieved in this domain via the use of deep learning (DL) approaches. While the majority of offered approaches using artificial neural networks (ANNs) and deep neural networks (DNNs) demonstrate satisfactory performance in Bayesian Tree Descent (BTD), none of these research studies can ensure the optimality of the employed learning model structure. Put simply, there is room for improvement in the efficiency of these learning models in BTD. This research introduces a novel approach for optimizing the configuration of Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) to address the BTD issue. The suggested approach employs Convolutional Neural Networks (CNN) for the purpose of segmenting brain MRIs. The model's configurable hyper-parameters are tuned using a genetic algorithm (GA). The Multi-Linear Principal Component Analysis (MPCA) is used to decrease the dimensionality of the segmented features in the pictures after they have been segmented. Ultimately, the segmentation procedure is executed using an Artificial Neural Network (ANN). In this artificial neural network (ANN), the genetic algorithm (GA) sets the ideal number of neurons in the hidden layer and the appropriate weight vector. The effectiveness of the suggested approach was assessed by utilizing the BRATS2014 and BTD20 databases. The results indicate that the proposed method can classify samples from these two databases with an average accuracy of 98.6% and 99.1%, respectively, which represents an accuracy improvement of at least 1.1% over the preceding methods.

Energy poverty and health in Turkey: Evidence from Longitudinal data

This study critically examines the effect of energy poverty on health in Turkey between 2018 and 2021 using the Income and Living Conditions Survey Longitudinal Micro Data Set. It considers the multidimensional structure of household energy poverty and its individual effects on health, resulting in several significant findings. Firstly, the multidimensional energy poverty index at the household level is obtained as a continuous variable using principal component analysis, considering subjective and objective indicators of energy poverty. Secondly, the impact of energy poverty on health at the individual level and the effect of several socioeconomic variables, including unobserved heterogeneity, are estimated with the random effects ordered logit model. Finally, commonly used measures of energy poverty in the literature and their health impacts are compared. The robustness analysis results show that the model's goodness of fit is highest when the multidimensional energy poverty index is constructed using principal component analysis. In addition, the analysis results show that unobserved heterogeneity across individuals significantly impacts health. These results indicate that decentralized policies should be implemented to increase policy effectiveness in combating energy poverty.

Genomic evidence of reproductive isolation among the Semisulcospira snails radiated in the ancient Lake Biwa.

Determining species boundaries within rapidly evolving species flocks is essential to understanding their evolutionary history but is often difficult to achieve due to the lack of clear diagnostic features. Ancient Lake Biwa harbors endemic snails in the genus Semisulcospira, a species flock with 19 described species. However, their morphological and genetic similarity cast doubt on the validity of their species status and their histories of explosive speciation. To evaluate their species boundaries, we examine patterns of gene flow among the sympatric or parapatric nominal Semisulcospira species in Lake Biwa. The principal component analysis and Bayesian structure analysis based on the genome-wide genotyping dataset demonstrated no gene flow between five pairs of the Semisulcospira species. However, we found the hybrids between the closely related species pair, Semisulcospira decipiens and S. rugosa. Despite the presence of hybrids, these nominal species still formed their own genetic clusters. There are variations in the chromosome numbers among these species, potentially providing an intrinsic barrier to panmictic gene flow. Our study showed complete or partial reproductive isolation among the sympatric or parapatric Semisulcospira species, demonstrating that the Semisulcospira snails are real species assemblages radiated in Lake Biwa. Our study provides significant implications for establishing species boundaries among rapidly evolving freshwater species in ancient lakes.