Variations of Biogeography-based Optimization and Markov Analysis

Biogeography-based optimization (BBO) is a new evolutionary algorithm that is inspired by biogeography. Previous work has shown that BBO is a competitive optimization algorithm, and it demonstrates good performance on various benchmark functions and real-world optimization problems. Motivated by biogeography theory and previous results, three variations of BBO migration are introduced in this paper. We refer to the original BBO algorithm as partial immigration-based BBO. The new BBO variations that we propose are called total immigration-based BBO, partial emigration-based BBO, and total emigration-based BBO. Their corresponding Markov chain models are also derived based on a previously-derived BBO Markov model. The optimization performance of these BBO variations is analyzed, and new theoretical results that are confirmed with simulation results are obtained. Theoretical results show that total emigration-based BBO and partial emigration-based BBO perform the best for three-bit unimodal problems, partial immigration-based BBO performs the best for three-bit deceptive problems, and all these BBO variations have similar results for three-bit multimodal problems. Performance comparison is further investigated on benchmark functions with a wide range of dimensions and complexities. Benchmark results show that emigration-based BBO performs the best for unimodal problems, and immigration-based BBO performs the best for multimodal problems. In addition, BBO is compared with a stud genetic algorithm (SGA), standard particle swarm optimization (SPSO 07), and adaptive differential evolution (ADE) on real-world optimization problems. The numerical results demonstrate that BBO outperforms SGA and SPSO 07, and performs similarly to ADE for the real-world problems

TFCP2 Genetic Polymorphism Is Associated with Predisposition to and Transplant Prognosis of Hepatocellular Carcinoma

TFCP2 is an oncogene and plays crucial roles in the incidence and progression of hepatocellular carcinoma (HCC). However, no reports are available on the impact of TFCP2 genetic polymorphism on the susceptibility to and the transplant prognosis of HCC. Here, we genotyped 7 SNPs of TFCP2 in a case-control study of 119 patients with HCC and 200 patients with chronic liver disease. Of the 7 SNPs in TFCP2, rs7959378 distributed differentially between patients with versus patients without HCC. The patients with the CA (OR = 0.58, 95% CI = 0.35–0.96), the CC (OR = 0.39, 95% CI = 0.20–0.76), and the CA/CC (OR = 0.52, 95% CI = 0.32–0.83) genotypes had significantly decreased risk for HCC compared with those carrying the rs7959378 AA genotype. After adjusting for confounding factors, rs7959378 still conferred significant risk for HCC. Furthermore, the patients who carried rs7959378 AC/CC had a higher overall survival and lower relapse-free survival than those with the rs7959378 AA genotype. Similar results were found in the multivariate analysis adjusted by AFP, tumor size and tumor number, and differentiation. These findings indicate that rs7959378 is associated with the risk of HCC in patient with chronic liver disease and prognosis of HCC patients after liver transplantation

LEPREL1 Expression in Human Hepatocellular Carcinoma and Its Suppressor Role on Cell Proliferation

Background. Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies worldwide. It is characterized by its high invasive and metastatic potential. Leprecan-like 1 (LEPREL1) has been demonstrated to be downregulated in the HCC tissues in previous proteomics studies. The present study is aimed at a new understanding of LEPREL1 function in HCC. Methods. Quantitative RT-PCR, immunohistochemical analysis, and western blot analysis were used to evaluate the expression of LEPREL1 between the paired HCC tumor and nontumorous tissues. The biology function of LEPREL1 was investigated by Cell Counting Kit-8 (CCK8) assay and colony formation assay in HepG2 and Bel-7402 cells. Results. The levels of LEPREL1 mRNA and protein were significantly lower in the HCC tissues as compared to those of the nontumorous tissues. Reduced LEPREL1 expression was not associated with conventional clinical parameters of HCC. Overexpression of LEPREL1 in HepG2 and Bel-7402 cells inhibited cell proliferation (P<0.01) and colony formation (P<0.05). LEPREL1 suppressed tumor cell proliferation through regulation of the cell cycle by downregulation of cyclins. Conclusions. Clinical parameters analysis suggested that LEPREL1 was an independent factor in the development of HCC. The biology function experiments showed that LEPREL1 might serve as a potential tumor suppressor gene by inhibiting the HCC cell proliferation

Full-length single-cell RNA-seq applied to a viral human cancer:applications to HPV expression and splicing analysis in HeLa S3 cells

Background: Viral infection causes multiple forms of human cancer, and HPV infection is the primary factor in cervical carcinomas Recent single-cell RNA-seq studies highlight the tumor heterogeneity present in most cancers, but virally induced tumors have not been studied HeLa is a well characterized HPV+ cervical cancer cell line Result: We developed a new high throughput platform to prepare single-cell RNA on a nanoliter scale based on a customized microwell chip Using this method, we successfully amplified full-length transcripts of 669 single HeLa S3 cells and 40 of them were randomly selected to perform single-cell RNA sequencing Based on these data, we obtained a comprehensive understanding of the heterogeneity of HeLa S3 cells in gene expression, alternative splicing and fusions Furthermore, we identified a high diversity of HPV-18 expression and splicing at the single-cell level By co-expression analysis we identified 283 E6, E7 co-regulated genes, including CDC25, PCNA, PLK4, BUB1B and IRF1 known to interact with HPV viral proteins Conclusion: Our results reveal the heterogeneity of a virus-infected cell line It not only provides a transcriptome characterization of HeLa S3 cells at the single cell level, but is a demonstration of the power of single cell RNA-seq analysis of virally infected cells and cancers

Comparative analysis of sequencing technologies for single-cell transcriptomics.

Single-cell RNA-seq technologies require library preparation prior to sequencing. Here, we present the first report to compare the cheaper BGISEQ-500 platform to the Illumina HiSeq platform for scRNA-seq. We generate a resource of 468 single cells and 1297 matched single cDNA samples, performing SMARTer and Smart-seq2 protocols on two cell lines with RNA spike-ins. We sequence these libraries on both platforms using single- and paired-end reads. The platforms have comparable sensitivity and accuracy in terms of quantification of gene expression, and low technical variability. Our study provides a standardized scRNA-seq resource to benchmark new scRNA-seq library preparation protocols and sequencing platforms

Effects of Land Use Changes on the Ecosystem Service Values of Lowland Savannas in Belize

Accurately identifying the extents and nature of changes within savanna ecosystems is important for planning their conservation and development. In this study, we explore using Sentinel-2 multispectral imagery and an object-based classification to classify the ecosystem of Lowland savannas in Belize at both landscape level and patch level, and investigate the land use changes in this ecosystem and differences in ecosystem service values (ESVs) between 2010-2019. The overall mapping of the savanna extent has an estimated accuracy of 88% and 76% at landscape level and patch level, respectively, and for the specific sub-classes of open savanna and dense tree savanna the accuracy is estimated at 80% and 70%. Perceivable changes in land use have occurred across the country. Agriculturalization and urbanization are commonly observed, however, there are also cases of afforestation. The total area of the savanna ecosystem within the whole Belize has only decreased slightly from 1705 km2 in 2010 to 1696 km2 in 2019, representing a consequent ESV decrease from US$273.46 billion to US$ 272.57 billion. The small net change balances an ESV decrease caused by expansion of agricultural or urban land, with an ESV increase resulting from a growing area of tree savanna that restores some of the degraded ecosystem services. This study demonstrates that the higher spatial resolution imagery from Sentinel-2 enabled more subtle land cover changes within the savanna ecosystem to be detected more accurately, which presents a complex picture at the patch level with different areas changing in their biodiversity, carbon storage and economic value

Variations of Biogeography-based Optimization and Markov Analysis

Biogeography-based optimization (BBO) is a new evolutionary algorithm that is inspired by biogeography. Previous work has shown that BBO is a competitive optimization algorithm, and it demonstrates good performance on various benchmark functions and real-world optimization problems. Motivated by biogeography theory and previous results, three variations of BBO migration are introduced in this paper. We refer to the original BBO algorithm as partial immigration-based BBO. The new BBO variations that we propose are called total immigration-based BBO, partial emigration-based BBO, and total emigration-based BBO. Their corresponding Markov chain models are also derived based on a previously-derived BBO Markov model. The optimization performance of these BBO variations is analyzed, and new theoretical results that are confirmed with simulation results are obtained. Theoretical results show that total emigration-based BBO and partial emigration-based BBO perform the best for three-bit unimodal problems, partial immigration-based BBO performs the best for three-bit deceptive problems, and all these BBO variations have similar results for three-bit multimodal problems. Performance comparison is further investigated on benchmark functions with a wide range of dimensions and complexities. Benchmark results show that emigration-based BBO performs the best for unimodal problems, and immigration-based BBO performs the best for multimodal problems. In addition, BBO is compared with a stud genetic algorithm (SGA), standard particle swarm optimization (SPSO 07), and adaptive differential evolution (ADE) on real-world optimization problems. The numerical results demonstrate that BBO outperforms SGA and SPSO 07, and performs similarly to ADE for the real-world problems

Is HF really necessary? Revisiting silica etching protocols in particle brushes

Particle brushes are an emerging type of hybrid polymer material at present, among which the polymer brush with silica as the core is a the most common type. Normally, hydrofluoric acid is used to etch these particles prior to chromatographic analysis, but hydrofluoric acid is particularly hazardous to humans and the environment. Herein we reports the use of NH4HF2 instead of hydrofluoric acid for the etching process and is extended to particle brushes with other inorganic cores such as barium titanate

Estimating Venous Thromboembolism Risk in Metastatic Colorectal Cancer Inpatients: Validation of Existing Risk Scores and Development of New Risk Scores

Metastatic colorectal cancer (mCRC) patients are predisposed to venous thromboembolism (VTE). This study aimed to (1) evaluate the efficacy of 4 existing cancer-specific VTE models in predicting VTE incidence among hospitalized mCRC patients, and (2) examine the influence of incorporating mCRC molecular subtypes into these models. We conducted an evaluation of 4 cancer-specific VTE models, including Khorana, Vienna CATS, Protecht, and CONKO in a dataset involving 1392 mCRC patients. To evaluate the predictive performance, we utilized receiver operating characteristic (ROC) curves for both the original models and the modified models that incorporated microsatellite instability status or KRAS / NRAS / BRAF mutations. Moreover, we computed the net reclassification improvement (NRI) to quantify the enhancements made to the modified VTE risk models. All models demonstrated a moderate area under the ROC curve (ROC-AUC) when predicting the occurrence of VTE: Khorana (0.550), Vienna CATS (0.671), Protecht (0.652), and CONKO (0.578). The incorporation of KRAS and BRAF mutations significantly improved the ROC-AUC of all 4 existing models (modified Khorana: 0.796, modified Vienna CATS: 0.832, modified Protecht: 0.834, and modified CONKO: 0.809). After dichotomizing the risk using a threshold of 3 points and comparing them with the original models, NRI values for the 4 modified models were 0.97, 0.95, 1.11, and 0.98, respectively. All 4 cancer-specific VTE models exhibit moderate performance when identifying mCRC patients at high risk of VTE. Incorporating KRAS and BRAF mutations may enhance the prediction of VTE in hospitalized mCRC patients