Деградація земель у Калуському районі внаслідок сольового забруднення

Показано, що джерелами деградації ґрунтів внаслідок, їх засолення, є солевідвали Домбровського кар’єру. Основними чинниками, що призводять до деградації є вітрова і водна ерозія. Досліджено, що основну роль в засоленні ґрунтового покриву відіграють процеси дифузії. Встановлено, що площа засолення (деградація) ґрунтів у декілька разів перевищує площу солевідводів.Показано, что источниками деградации почв вследствие их засоления, являются солеотвалы Домбровского карьера. Основными факторами, которые приводят к деградации является ветровая и водная эрозия. Доказано, что основную роль в засолении почвенного покрова играют процессы диффузии. Установлено, что площадь засоления (деградация) почв в несколько раз превышает площадь солеотвалов.In the article is shown that the sources of land degradation occurs because of their salinity and salt piles from Dombrowsky career. The main factors that lead to the degradation are wind and water erosion. It is investigated that the main role in the salinity of soil processes play diffusion. It is established that the area of salinity (degradation) of soil several times salt piles are

Additional file 1 of Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network

Supplementary figures and tables. Figure S1. The AUCs of precision recall curves of our proposed method when the number of dimensions K increases. Figure S2. Performance comparison of our proposed method and existing network-based methods, evaluated by IntOGen list. Figure S3. Performance of our proposed method when the parameters for sparseness (or robustness) are fixed and the parameters for prior knowledge varies, where λ RV , λ LV and λ RU are fixed and λ LU varies. Figure S4. Performance of our proposed method when the parameters for sparseness (or robustness) are fixed and the parameters for prior knowledge varies, where λ RU , λ LU and λ RV are fixed and λ LV varies. Figure S5. Performance comparison of our proposed method and existing network-based methods, applied on GBM, COADREAD and BRCA datasets and evaluated by IntOGen list. Figure S6. Performance comparison of our proposed method and existing network-based methods, applied on KIRC, THCA and PRAD datasets and evaluated by CGC list. Figure S7. Performance comparison of our proposed method and existing network-based methods, applied on KIRC, THCA and PRAD datasets and evaluated by IntOGen list. Figure S8. Performance comparison of our proposed method and existing network-based methods with network information from both iRefIndex and String v10. Table S1. Fisher’s exact test on the top scored candidates of BRCA results for CGC and IntOGen benchmarking genes. Table S2. Fisher’s exact test on the top scored candidates of GBM results for CGC and IntOGen benchmarking genes. Table S3. The full list of the top 200 genes detected by our model on GBM dataset. Table S4. The full list of the top 200 genes detected by our model on COADREAD dataset. Table S5. The full list of the top 200 genes detected by our model on BRCA dataset. Table S6. Functional enrichment analysis results for KEGG pathways of the top 200 genes of the proposed method on COADREAD dataset. Table S7. Functional enrichment analysis results for KEGG pathways of the top 200 genes of the proposed method on BRCA dataset. (PDF 5670kb

Additional file 3: Table S2. of CLImAT-HET: detecting subclonal copy number alterations and loss of heterozygosity in heterogeneous tumor samples from whole-genome sequencing data

This file contains Supplementary Table S2. (XLS 26 kb

Additional file 2: of CLImAT-HET: detecting subclonal copy number alterations and loss of heterozygosity in heterogeneous tumor samples from whole-genome sequencing data

This file contains Supplementary Figures. (PDF 1702 kb

Additional file 4: of CloneCNA: detecting subclonal somatic copy number alterations in heterogeneous tumor samples from whole-exome sequencing data

Simulated mixtures with combinations of four genomes. (XLS 26 kb

TAFFYS: An Integrated Tool for Comprehensive Analysis of Genomic Aberrations in Tumor Samples

<div><p>Background</p><p>Tumor single nucleotide polymorphism (SNP) array is a common platform for investigating the cancer genomic aberration and the functionally important altered genes. Original SNP array signals are usually corrupted by noise, and need to be de-convoluted into absolute copy number profile by analytical methods. Unfortunately, in contrast with the popularity of tumor Affymetrix SNP array, the methods that are specifically designed for this platform are still limited. The complicated characteristics of noise in signals is one of the difficulties for dissecting tumor Affymetrix SNP array data, as they inevitably blur the distinction between aberrations and create an obstacle for the copy number aberration (CNA) identification.</p><p>Results</p><p>We propose a tool named TAFFYS for comprehensive analysis of tumor Affymetrix SNP array data. TAFFYS introduce a wavelet-based de-noising approach and copy number-specific signal variance model for suppressing and modelling the noise in signals. Then a hidden Markov model is employed for copy number inference. Finally, by using the absolute copy number profile, statistical significance of each aberration region is calculated in term of different aberration types, including amplification, deletion and loss of heterozygosity (LOH). The result shows that copy number specific-variance model and wavelet de-noising algorithm fits well with the Affymetrix SNP array signals, leading to more accurate estimation for diluted tumor sample (even with only 30% of cancer cells) than other existed methods. Results of examinations also demonstrate a good compatibility and extensibility for different Affymetrix SNP array platforms. Application on the 35 breast tumor samples shows that TAFFYS can automatically dissect the tumor samples and reveal statistically significant aberration regions where cancer-related genes locate.</p><p>Conclusions</p><p>TAFFYS provide an efficient and convenient tool for identifying the copy number alteration and allelic imbalance and assessing the recurrent aberrations for the tumor Affymetrix SNP array data.</p></div

Kinase Identification with Supervised Laplacian Regularized Least Squares

<div><p>Phosphorylation is catalyzed by protein kinases and is irreplaceable in regulating biological processes. Identification of phosphorylation sites with their corresponding kinases contributes to the understanding of molecular mechanisms. Mass spectrometry analysis of phosphor-proteomes generates a large number of phosphorylated sites. However, experimental methods are costly and time-consuming, and most phosphorylation sites determined by experimental methods lack kinase information. Therefore, computational methods are urgently needed to address the kinase identification problem. To this end, we propose a new kernel-based machine learning method called Supervised Laplacian Regularized Least Squares (SLapRLS), which adopts a new method to construct kernels based on the similarity matrix and minimizes both structure risk and overall inconsistency between labels and similarities. The results predicted using both Phospho.ELM and an additional independent test dataset indicate that SLapRLS can more effectively identify kinases compared to other existing algorithms.</p></div

Compared AUC values of the four algorithms: SLapRLS, SVM, BDT and KNN.

<p>Compared AUC values of the four algorithms: SLapRLS, SVM, BDT and KNN.</p

Pterostilbene–Nicotinamide Cocrystal: A Case Report of Single Cocrystals Grown from Melt Microdroplets

Screening and single-crystal growth of cocrystals is a time-consuming process, typically performed by solution crystallization. Here we reported a case that a single pterostilbene–nicotinamide cocrystal was efficiently cultivated from melt within 30 min, resulting in successful structure elucidation. This new cocrystal was discovered from melts and can be prepared on a gram scale within 10 min by simply seeding melts at 80 °C. This work demonstrates the possibility of growing single cocrystal from the melts and highlights the high efficiency of melt crystallization in research of pterostilbene–nicotinamide cocrystals

Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme-7

<p><b>Copyright information:</b></p><p>Taken from "Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme"</p><p>BMC Bioinformatics 2006;7():32-32.</p><p>Published online 22 Jan 2006</p><p>PMCID:PMC1403803.</p><p></p>to data C to construct the five noisy datasets