scholarly journals Comparison of Models for Spatial Distribution and Prediction of Cadmium in Subtropical Forest Soils, Guangdong, China

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 906
Author(s):  
Xiaogang Ding ◽  
Zhengyong Zhao ◽  
Zisheng Xing ◽  
Shengting Li ◽  
Xiaochuan Li ◽  
...  
Keyword(s):  
Forest Soils ◽  
Large Scale ◽  
Prediction Models ◽  
Soil Depth ◽  
Toxic Metal ◽  
Principal Component ◽  
Clay Content ◽  
Slope Position ◽  
Elevation Model ◽  
Soil Cd

Cadmium (Cd) is a toxic metal and found in various soils, including forest soils. The great spatial heterogeneity in soil Cd makes it difficult to determine its distribution. Both traditional soil surveys and spatial modeling have been used to study the natural distribution of Cd. However, traditional methods are highly labor-intensive and expensive, while modeling is often encumbered by the need to select the proper predictors. In this study, based on intensive soil sampling (385 soil pits plus 64 verification soil pits) in subtropical forests in Yunfu, Guangdong, China, we examined the impacting factors and the possibility of combining existing soil information with digital elevation model (DEM)-derived variables to predict the Cd concentration at different soil depths along the landscape. A well-developed artificial neural network model (ANN), multi-variate analysis, and principal component analysis were used and compared using the same dataset. The results show that soil Cd concentration varied with soil depth and was affected by the top 0–20 cm soil properties, such as soil sand or clay content, and some DEM-related variables (e.g., slope and vertical slope position, varying with depth). The vertical variability in Cd content was found to be correlated with metal contents (e.g., Cu, Zn, Pb, Ni) and Cd contents in the layer immediately above. The selection of candidate predictors differed among different prediction models. The ANN models showed acceptable accuracy (around 30% of predictions have a relative error of less than 10%) and could be used to assess the large-scale Cd impact on environmental quality in the context of intensifying industrialization and climate change, particularly for ecosystem management in this region or other regions with similar conditions.

Evaluating soil erosion and sediment deposition rates by  137Cs fingerprinting technique at different karst hillslope positions  in Yunnan Province, southwest China

2021 ◽  
Author(s):  
Yanqing Li ◽  
yang Yu ◽  
funing Lan ◽  
peng Liu
Keyword(s):  
Soil Erosion ◽  
Water Conservation ◽  
Large Scale ◽  
Yunnan Province ◽  
Southwest China ◽  
Soil Depth ◽  
Principal Component ◽  
Delivery Ratio ◽  
Deposition Rates ◽  
Small Watersheds

<p>In karst environments, soil erosion is a  prominent environmental issue that can cause many other problems. Researching the erosion and deposition rates at the hillslope scale in small watersheds is important for designing efficient soil and water conservation measures for the small watersheds even the large scale areas. In our research, the closed watershed, a representative depression in karst gabin basin, located in the Yunnan province, Southwest China, was selected to assess the soil erosion and sediment mobilisation at different hillslope positions using the <sup>137</sup>Cs tracing technique. The results showed that the soil erosion rates in the shoulders, backslopes and footslopes were 0.87, 0.35 and 0.49 cm a<sup>-1</sup>, respectively, meanwhile the soil sediment rate in depression bottom was 2.68 cm a<sup>-1</sup>. The average annual soil erosion modulus of the complete hillslope was 632 t km<sup>-2</sup>a<sup>-1</sup>, which confirmed the serious gradation according to karst soil erosion standards. The sediment delivery ratio would summarize 0.82 in the whole catchment according to the square of hillslope and depression bottom. To identify which factor could play the most important role in influencing the estimations using <sup>137</sup>Cs, a linear correlation and Principal Component Analysis were conducted. The results showed <sup>137</sup>Cs concentration of different soil depth at different hillslope positions were significantly correlated with soil organic matter (SOM) and total nitrogen (TN) (P<0.05). As the typical karst geomorphological types, these findings are expected to provide data support for the whole watershed soil erosion management and ecological restoration in  fragile karst ecosystem.</p>

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

scholarly journals Principal component analysis tomography in near-infrared integral field spectroscopy of young stellar objects – I. Revisiting the high-mass protostar W33A

2021 ◽  
Vol 503 (1) ◽  
pp. 270-291
Author(s):  
F Navarete ◽  
A Damineli ◽  
J E Steiner ◽  
R D Blum
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Rotation Axis ◽  
Principal Component ◽  
Young Stellar Objects ◽  
High Mass ◽  
Continuum Emission ◽  
Rotating Disc ◽  
Stellar Objects ◽  
K Band

ABSTRACT W33A is a well-known example of a high-mass young stellar object showing evidence of a circumstellar disc. We revisited the K-band NIFS/Gemini North observations of the W33A protostar using principal components analysis tomography and additional post-processing routines. Our results indicate the presence of a compact rotating disc based on the kinematics of the CO absorption features. The position–velocity diagram shows that the disc exhibits a rotation curve with velocities that rapidly decrease for radii larger than 0.1 arcsec (∼250 au) from the central source, suggesting a structure about four times more compact than previously reported. We derived a dynamical mass of 10.0$^{+4.1}_{-2.2}$ $\rm {M}_\odot$ for the ‘disc + protostar’ system, about ∼33 per cent smaller than previously reported, but still compatible with high-mass protostar status. A relatively compact H2 wind was identified at the base of the large-scale outflow of W33A, with a mean visual extinction of ∼63 mag. By taking advantage of supplementary near-infrared maps, we identified at least two other point-like objects driving extended structures in the vicinity of W33A, suggesting that multiple active protostars are located within the cloud. The closest object (Source B) was also identified in the NIFS field of view as a faint point-like object at a projected distance of ∼7000 au from W33A, powering extended K-band continuum emission detected in the same field. Another source (Source C) is driving a bipolar $\rm {H}_2$ jet aligned perpendicular to the rotation axis of W33A.

scholarly journals Farmers’ Perceptions of Commercial Insect-Based Feed for Sustainable Livestock Production in Kenya

2021 ◽  
Vol 13 (10) ◽  
pp. 5359
Author(s):  
Afrika Onguko Okello ◽  
Jonathan Makau Nzuma ◽  
David Jakinda Otieno ◽  
Michael Kidoido ◽  
Chrysantus Mbi Tanga
Keyword(s):  
Food Systems ◽  
Large Scale ◽  
Principal Component ◽  
Farm Income ◽  
Purchase Decisions ◽  
Wealth Status ◽  
Feed Formulation ◽  
Alternative Protein ◽  
Livestock Products ◽  
The Impact

The utilization of insect-based feeds (IBF) as an alternative protein source is increasingly gaining momentum worldwide owing to recent concerns over the impact of food systems on the environment. However, its large-scale adoption will depend on farmers’ acceptance of its key qualities. This study evaluates farmer’s perceptions of commercial IBF products and assesses the factors that would influence its adoption. It employs principal component analysis (PCA) to develop perception indices that are subsequently used in multiple regression analysis of survey data collected from a sample of 310 farmers. Over 90% of the farmers were ready and willing to use IBF. The PCA identified feed performance, social acceptability of the use of insects in feed formulation, feed versatility and marketability of livestock products reared on IBF as the key attributes that would inform farmers’ purchase decisions. Awareness of IBF attributes, group membership, off-farm income, wealth status and education significantly influenced farmers’ perceptions of IBF. Interventions such as experimental demonstrations that increase farmers’ technical knowledge on the productivity of livestock fed on IBF are crucial to reducing farmers’ uncertainties towards acceptability of IBF. Public partnerships with resource-endowed farmers and farmer groups are recommended to improve knowledge sharing on IBF.

scholarly journals Large-Scale Precipitation Variability over Northwest China Inferred from Tree Rings

2011 ◽  
Vol 24 (13) ◽  
pp. 3457-3468 ◽  
Author(s):  
Keyan Fang ◽  
Xiaohua Gou ◽  
Fahu Chen ◽  
Edward Cook ◽  
Jinbao Li ◽  
...  
Keyword(s):  
Tree Ring ◽  
Large Scale ◽  
Principal Component ◽  
Northwest China ◽  
Poor Quality ◽  
Contour Method ◽  
Precipitation Reconstruction ◽  
Nw China ◽  
Climate Station ◽  
Instrumental Records

Abstract A preliminary study of a point-by-point spatial precipitation reconstruction for northwestern (NW) China is explored, based on a tree-ring network of 132 chronologies. Precipitation variations during the past ~200–400 yr (the common reconstruction period is from 1802 to 1990) are reconstructed for 26 stations in NW China from a nationwide 160-station dataset. The authors introduce a “search spatial correlation contour” method to locate candidate tree-ring predictors for the reconstruction data of a given climate station. Calibration and verification results indicate that most precipitation reconstruction models are acceptable, except for a few reconstructions (stations Hetian, Hami, Jiuquan, and Wuwei) with degraded quality. Additionally, the authors compare four spatial precipitation factors in the instrumental records and reconstructions derived from a rotated principal component analysis (RPCA). The northern and southern Xinjiang factors from the instrumental and reconstructed data agree well with each other. However, differences in spatial patterns between the instrumentation and reconstruction data are also found for the other two factors, which probably result from the relatively poor quality of a few stations. Major drought events documented in previous studies—for example, from the 1920s through the 1930s for the eastern part of NW China—are reconstructed in this study.

scholarly journals Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Daniel J. Panyard ◽  
Kyeong Mo Kim ◽  
Burcu F. Darst ◽  
Yuetiva K. Deming ◽  
Xiaoyuan Zhong ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Drug Targets ◽  
Large Scale ◽  
Prediction Models ◽  
Genome Wide Association ◽  
Large Samples ◽  
Genome Wide ◽  
Metabolomic Data ◽  
Related Phenotype ◽  
Omic Data

AbstractThe study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

scholarly journals Codon usage bias and environmental adaptation in microbial organisms

2021 ◽  
Author(s):  
Davide Arella ◽  
Maddalena Dilucca ◽  
Andrea Giansanti
Keyword(s):  
Codon Usage ◽  
Codon Usage Bias ◽  
Pathogenic Bacteria ◽  
Large Scale ◽  
Synonymous Codon ◽  
Principal Component ◽  
Gene Copy ◽  
Phenotypic Traits ◽  
Translational Efficiency ◽  
Microbial Organisms

AbstractIn each genome, synonymous codons are used with different frequencies; this general phenomenon is known as codon usage bias. It has been previously recognised that codon usage bias could affect the cellular fitness and might be associated with the ecology of microbial organisms. In this exploratory study, we investigated the relationship between codon usage bias, lifestyles (thermophiles vs. mesophiles; pathogenic vs. non-pathogenic; halophilic vs. non-halophilic; aerobic vs. anaerobic and facultative) and habitats (aquatic, terrestrial, host-associated, specialised, multiple) of 615 microbial organisms (544 bacteria and 71 archaea). Principal component analysis revealed that species with given phenotypic traits and living in similar environmental conditions have similar codon preferences, as represented by the relative synonymous codon usage (RSCU) index, and similar spectra of tRNA availability, as gauged by the tRNA gene copy number (tGCN). Moreover, by measuring the average tRNA adaptation index (tAI) for each genome, an index that can be associated with translational efficiency, we observed that organisms able to live in multiple habitats, including facultative organisms, mesophiles and pathogenic bacteria, are characterised by a reduced translational efficiency, consistently with their need to adapt to different environments. Our results show that synonymous codon choices might be under strong translational selection, which modulates the choice of the codons to differently match tRNA availability, depending on the organism’s lifestyle needs. To our knowledge, this is the first large-scale study that examines the role of codon bias and translational efficiency in the adaptation of microbial organisms to the environment in which they live.

57 Precision neoantigen discovery using novel algorithms and expanded HLA-ligandome datasets

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A62-A62
Author(s):  
Dattatreya Mellacheruvu ◽  
Rachel Pyke ◽  
Charles Abbott ◽  
Nick Phillips ◽  
Sejal Desai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Lines ◽  
Antigen Processing ◽  
Large Scale ◽  
Prediction Models ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
High Quality ◽  
Tissue Samples

BackgroundAccurately identified neoantigens can be effective therapeutic agents in both adjuvant and neoadjuvant settings. A key challenge for neoantigen discovery has been the availability of accurate prediction models for MHC peptide presentation. We have shown previously that our proprietary model based on (i) large-scale, in-house mono-allelic data, (ii) custom features that model antigen processing, and (iii) advanced machine learning algorithms has strong performance. We have extended upon our work by systematically integrating large quantities of high-quality, publicly available data, implementing new modelling algorithms, and rigorously testing our models. These extensions lead to substantial improvements in performance and generalizability. Our algorithm, named Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™), is integrated into the ImmunoID NeXT Platform®, our immuno-genomics and transcriptomics platform specifically designed to enable the development of immunotherapies.MethodsIn-house immunopeptidomic data was generated using stably transfected HLA-null K562 cells lines that express a single HLA allele of interest, followed by immunoprecipitation using W6/32 antibody and LC-MS/MS. Public immunopeptidomics data was downloaded from repositories such as MassIVE and processed uniformly using in-house pipelines to generate peptide lists filtered at 1% false discovery rate. Other metrics (features) were either extracted from source data or generated internally by re-processing samples utilizing the ImmunoID NeXT Platform.ResultsWe have generated large-scale and high-quality immunopeptidomics data by using approximately 60 mono-allelic cell lines that unambiguously assign peptides to their presenting alleles to create our primary models. Briefly, our primary ‘binding’ algorithm models MHC-peptide binding using peptide and binding pockets while our primary ‘presentation’ model uses additional features to model antigen processing and presentation. Both primary models have significantly higher precision across all recall values in multiple test data sets, including mono-allelic cell lines and multi-allelic tissue samples. To further improve the performance of our model, we expanded the diversity of our training set using high-quality, publicly available mono-allelic immunopeptidomics data. Furthermore, multi-allelic data was integrated by resolving peptide-to-allele mappings using our primary models. We then trained a new model using the expanded training data and a new composite machine learning architecture. The resulting secondary model further improves performance and generalizability across several tissue samples.ConclusionsImproving technologies for neoantigen discovery is critical for many therapeutic applications, including personalized neoantigen vaccines, and neoantigen-based biomarkers for immunotherapies. Our new and improved algorithm (SHERPA) has significantly higher performance compared to a state-of-the-art public algorithm and furthers this objective.

scholarly journals Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid Line to Line Analysis Tool

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4436
Author(s):  
Mohammad Al Ktash ◽  
Mona Stefanakis ◽  
Barbara Boldrini ◽  
Edwin Ostertag ◽  
Marc Brecht
Keyword(s):  
Hyperspectral Imaging ◽  
Large Scale ◽  
Solid Phase ◽  
Ultra Violet ◽  
Principal Component ◽  
Ccd Camera ◽  
Conveyor Belt ◽  
Hyperspectral Data ◽  
Analysis Tool ◽  
Sample Set

A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application.

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