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Fig. S1: Schematic of derivation 'lineages' for drug-sensitive and -resistant cell lines used in this study Fig. S2: Characterization of T790M+ cell lines Fig. S3: Characterization of A+C-resistant cell lines Fig. S4: Characterization of AZD9291-resistant cell lines

The solution of using a collector well was investigated to take additional water, overcome the weaknesses of the conventional weir that are prone to sediment accumulation intake structures of a weir, especially at the location where weir is not needed. The solution was considered using a laboratory model at Hydraulic Construction Institute - Vietnam Academy for Water Resources. The results of model research and construction of a pilot project in Muong Lan commune, Muong Ang district, Dien Bien province show that collector well can collect water over 31 l/s ensuring water supply for more than 20 ha of rice field.

Forest fires are always a problem around the world because of its great harm. Especially in Vietnam, the prevention and detection of forest fires are mainly based on the patrolling forest rangers; the warning board is not automatically controlled. In this article, we will present the design of an automatic controller, which was applied fuzzy logic to control the forest fire - level forecast warning signage. The controller relies on regional meteorological information to control the signage on the spot, and to directly inform the manager about the forest fire forecasting situation via text message. The experienced results of some forest protection units in Thai Nguyen prove that the application of fuzzy logic has met the requirements of the forest protection sector in improving the forest fire forecasting system.

The primary goal of this paper is to develop a smart system for short-term price prediction for various products using time series models. The system includes a series of processes, e.g., extracting sales data from a website, pre-processing raw data, and using an Autoregressive Integrated Moving Average (ARIMA) model We investigate that traditional ARIMA techniques suffer with performance issues due to identifying the parameter settings therefore, we use auto ARIMA for our project. To evaluate the prediction accuracy of our approach, we use the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) as performance metrics. We test on two seasonal products while considering different brands of each product. The sales data are taken from the PriceMe website. Furthermore, we also compare the ARIMA model with Moving Average (MA) model. In the case of the MA model, we find that the forecast trends are represented by a flat line. Also, the auto ARIMA model is not appropriate for predicting long-term trends.

Agriculture is a primary source of livelihood in the Vietnamese Mekong River Delta with different land use activities. There are different potential conflicts in agricultural land use, including: water resources use and interests. Therefore, establishing feasible land use planning (LUP) in an area needs an in-depth participation and discussion among stakeholders (including: governments at different levels, local farmers, and national and international scientists). Indigenous knowledge of local farmers plays an important role in making land use decision, which can support solving land-use conflicts to achieve the most sustainable management of land resources. In this study, we integrated local knowledge into a land use planning process to assess the suitability of land use types and to project potential impacts of climate change conditions on land use decisions in the Tra Hat hamlet, one of the climate-smart village (CSV) sites in the Vietnamese Mekong River Delta. A total of two consultation meetings, using simple tools (resources mapping, timeline, seasonal calendar, wealth ranking, Venn diagram, pair wise ranking, and key informants’ interviews), were conducted, and 130 households were interviewed during a field visit in 2015. As a result, a participatory land use planning (PLUP) was implemented with active participation of the concerned communities to establish a suitable land use planning and identify the potential adaptation techniques in the near future (2020). Land use planning at the hamlet was discussed to get the agreement and the coordination among local governments, farmers and scientists on adaptation practices.

Path planning for Unmanned Aerial Vehicle (UAV) targets at generating an optimal global path to the target, avoiding collisions and optimizing the given cost function under constraints. In this paper, the path planning problem for UAV in pre-known 3D environment is presented. Particle Swarm Optimization (PSO) was proved the efficiency for various problems. PSO has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. In this paper, the improved PSO with adaptive mutation to overcome its drawback in order to applied PSO the UAV path planning in real 3D environment which composed of mountains and constraints. The effectiveness of the proposed PSO algorithm is compared to Genetic Algorithm, standard PSO and other improved PSO using 3D map of Daklak, Dakrong and Langco Beach. The results have shown the potential for applying proposed algorithm in optimizing the 3D UAV path planning. Keywords: UAV, Path planning, PSO, Optimization.

Deep-seated landslides occurring near roads and building areas cause serious damages. High groundwater level due to long periods of rainfall or infiltration from reservoir saturates and softens soils landslides. This article introduces an approach which combines the restraining effects of stabilizing piles and the dewatering effect from large diameter vertical wells, to increase the stability of landslides. Effectiveness in resisting sliding force and lowering the groundwater level was evaluated using the finite-elements method. Our results show that the saturation line drops significantly, and the safety factor of the slope increases by approximately 20% compared to the case without the wells.

TDD large scale MIMO recently has emerged as a groundbreaking technology which enables many antennas can be equipped at a Base Station without overhead for downlink pilot. In such systems, the uplink channel estimation plays a critical role since it is needed for both MU-MIMO detection and precoding weight calculation. To facilitate the implementation, the channel estimation should be as low complex as possible. In addition, to support multiusers, the channel estimation should be robust to long delay spread. Previous methods usually consists of FFT/IFFT transformation and windows for separating channel impulse response of multiusers. Another method estimates channel transfer function of each user directly in frequency domain by using a simple sliding window, however, this method is susceptible to long delay spread. To remove FFT/IFFT transformation, and mitigate long delay spread, we propose a method for channel estimation in frequency domain using raised-cosine based filter. Simulation results shown that the proposal outperforms previous methods. Under 3.7 μs delay spread of ITU-B channel, the proposal achieves bit error ratio of 10−3 with 2dB additional carrier to noise ratio compared to perfect channel estimation. To further validate the proposal, experiments are conducted with 16 antennas at base station and 4 single antenna users using 16, 64, and 256 QAM.

Whole exome sequencing (WES) and RNA sequencing (RNA-Seq) are two main platforms used for next-generation sequencing (NGS). While WES is primarily for DNA variant discovery and RNA-Seq is mainly for measurement of gene expression, both can be used for detection of genetic variants, especially single nucleotide variants (SNVs). How consistently variants can be detected from WES and RNA-Seq has not been systematically evaluated. In this study, we examined the technical and biological inconsistencies in SNV detection using WES and RNA-Seq data from 27 pairs of tumor and matched normal samples. We analyzed SNVs in three categories: WES unique - those only detected in WES, RNA-Seq unique - those only detected in RNA-Seq, and shared - those detected in both. We found a small overlap (average ∼14%) between the SNVs called in WES and RNA-Seq. The WES unique SNVs were mainly due to low coverage, low expression, or their location on the non-transcribed strand in RNA-Seq data, while the RNA-Seq unique SNVs were primarily due to their location out of the WES-capture boundary regions (accounting ∼71%), as well as low coverage of the regions, low coverage of the mutant alleles or RNA-editing. The shared SNVs had high locus-specific coverage in both WES and RNA-Seq and high gene expression levels. Additionally, WES unique and RNA-Seq unique SNVs showed different nucleotide substitution patterns, e.g., ∼55% of RNA-Seq unique variants were A:T→G:C, a hallmark of RNA editing. This study provides an important evaluation on the inconsistencies of somatic SNVs called in WES and RNA-Seq data.

In this study, a geochemical model is proposed to analyze of strength increase mechanism of stabilized soils incorporating natural pozzolan, cement and lime. Modelling considered kinetics and thermodynamics of mineralogical transformations. The pozzolanic reaction between lime, cement, natural pozzolan and soil is successfully simulated by the geochemical model. The model shows that the quantity of C-S-H and C-A-S-H precipitation are suitable for the strength development of stabilized soils. The compressive and tensile strength are depended on the stabilized soils mixing ratios for soil, natural pozzolan, cement and lime. The good agreement between the numerical and experimental results for optimal mixing ratio shows that the geochemical model could be considered as a tool to assess the optimal mixing ratio, optimal content of natural pozzolan is \(15\%\). Moreover, the obtained results demonstrate that the soil of Dak Nong province in Central Highlands of Vietnam is inert in the process of soil stabilization, the natural pozzolan and lime are necessary to increase the amount of C-S-H as well as the strength.

Alterations in cellular phosphorylation patterns have been implicated in a number of diseases, including cancer, through multiple mechanisms. Herein we present a survey of the phosphorylation profiles of an isogenic pair of human cancer cell lines with opposite metastatic phenotype. Phosphopeptides were enriched from tumor cell lysates with titanium dioxide and zirconium dioxide, and identified with nano-LC-MS/MS using an automatic cross-validation of MS/MS and MS/MS/MS (MS2+MS3) data-dependent neutral loss method. A spectral counting quantitative strategy was applied to the two cell line samples on the MS2-only scan, which was implemented successively after each MS2+MS3 scan in the same sample. For all regulated phosphopeptides reported by spectral counting analysis, sequence and phosphorylation site assignments were validated by a MS2+MS3 data-dependent neutral loss method. With this approach, we identified over 70 phosphorylated sites on 27 phosphoproteins as being differentially expressed with respect to tumor cell phenotype. The altered expression levels of proteins identified by LC-MS/MS were validated using Western blotting. Using network pathway analysis, we observed that the majority of the differentially expressed proteins were highly interconnected and belong to two major intracellular signaling pathways. Our findings suggest that the phosphorylation of isoform A of lamin A/C and GTPase activating protein binding protein 1 is associated with metastatic propensity. The study demonstrates a quantitative and comparative proteomics strategy to identify differential phosphorylation patterns in complex biological samples.

Background Somatic mutation is the key element of tumorigenesis as these changes in nucleotide sequence of the cancer genome in somatic cells acquired throughout life can lead to protein alteration, cellular damage and thus cause cancer. The advent of next generation sequencing has significantly improved our ability to identify somatic mutations in cancer genomes paving the way for the comprehensive online catalogue for somatic mutation in human cancer (COSMIC) 1 which contains more than 820,000 mutations so far. Nevertheless, there are still many challenges in detecting somatic mutation in cancer especially for low frequency mutation due to either tumor heterogeneity or contamination with normal cells. Here, in this short tutorial, I will present a recent somatic mutation caller tool developed by the Broad Institute called Mutect 2 as part of the GATK (Genome Analysis Toolkit). I will use my own NGS dataset to demonstrate the tools and address some issues of troubleshooting input data and interpreting output.

Point mutation, often referred to as single-nucleotide polymorphism (SNP), does not occur randomly and is dependent on the GC content and sequence context in the human genome. The spectrum of mutations in the human genome can be reliably inferred by comparing SNPs with their ancestral sequences. The direction of mutations inferred by SNPs may be measured by three approaches: directly observed frequencies of nucleotide changes, relative frequencies of nucleotide changes by normalising the nucleotide content and frequencies of nucleotide changes after excluding the CpG effect. An examination of the spectrum of mutations indicates that point mutation varies between the categorised genomic regions and has been strongly influenced by the CpG effect. There is no major difference in the mutational spectrum among the common and rare variants or among major human subpopulations; however, rare variants have higher frequency of CT mutations, reflecting the CpG effect in the human genome. Key Concepts: Point mutation does not occur randomly in the human genome and is dependent on the GC content and sequence context. Single nucleotide polymorphisms (SNPs) are the most abundant genetic variants in the human genome. Mutational spectrum in the human genome and genomic regions is important for our understanding of the mechanisms of mutations, genome sequence evolution and causes of disease. The direction of mutations can be reliably inferred by comparing SNPs with their ancestral sequences. CpG effect has a significant influence on mutational spectrum. Spectrum of mutations can be measured by three approaches: the observed frequencies of nucleotide changes, the relative frequencies and exclusion of the CpG effect. Rare variants more likely reflect the recent mutational events in human populations. Keywords: spectrum of mutations; mutation direction; single-nucleotide polymorphisms (SNPs); nucleotide change; CpG effect; the 1000 genomes project; common variants; rare variants

An active area in cancer biomarker research is the development of statistical methods to identify expression signatures reflecting the heterogeneity of cancer across affected individuals. Tomlins et al. [5] observed heterogeneous patterns of oncogene activation within several cancer types, and introduced a statistical method called Cancer Outlier Profile Analysis (COPA) to identify "cancer outlier genes". Several related statistical approaches have since been developed, but the operating characteristics of these procedures (e.g. power, false positive rate), have not yet been fully characterized, especially in a proteomics setting. Here, we use simulation to identify the degree to which an outlier pattern of differential expression must hold in order for outlier-based approaches to be more effective than mean-based approaches. We also propose a diagnostic procedure that characterizes the potentially unequal levels of differential expression in the tails and in the center of a distribution of expression values. We find that for sample sizes and effect sizes typical of proteomics studies, the outlier pattern must be strong in order for outlier-based analysis to provide a meaningful benefit. This is corroborated by analysis of proteomics data from a melanoma study, in which the differential expression is most often present throughout the distribution, rather than being concentrated in the tails, albeit with a few proteins showing expression patterns consistent with outlier expression.

The immunogenic nature of cancer can be explored to distinguish pancreatic cancer from related non-cancer conditions. We describe a liquid-based microarray approach followed by statistical analysis and confirmation for discovery of auto-immune biomarkers for pancreatic cancer. Proteins from the Panc-1 pancreatic cancer cell line were fractionated using a 2-D liquid separation method into over 1052 fractions and spotted onto nitrocellulose coated glass slides. The slides were hybridized with 37 pancreatic cancer sera, 24 chronic pancreatitis sera and 23 normal sera to detect elevated levels of reactivity against the proteins in spotted fractions. The response data obtained from protein microarrays was first analyzed by Wilcoxon Rank-Sum Tests to generate two lists of fractions that positively responded to the cancer sera and showed p-values less than 0.02 in the pairwise comparison between cancer specimens and normal and chronic pancreatitis specimens. The top 3 fractions with the lowest correlations were combined in receiver operating characteristic analyses. The area-under-the-curve (AUC) values are 0.813 and 0.792 for cancer vs. normal and cancer vs. pancreatitis respectively. Outlier-Sum statistics were then applied to the microarray data to determine the existence of outliers exclusive in cancer sera. The selected fractions were identified by LC-MS/MS. We further confirmed the occurrence of outliers with three proteins among cancer samples in a confirmation experiment using a separate dataset of 165 serum samples containing 48 cancer sera and 117 non-cancer controls. Phosphoglycerate kinase 1 (PGK1) elicited greater reactivity in 20.9% (10 in 48) of the samples in the cancer group, while no outlier was present in the non-cancer groups.

The membrane glycoprotein component of the cellular proteome represents a promising source for potential disease biomarkers and therapeutic targets. Here we describe the development of a method that facilitates the analysis of membrane glycoproteins and apply it to the differential analysis of breast tumor cells with distinct malignant phenotypes. The approach combines two membrane extraction procedures, and enrichment using ConA and WGA lectin affinity columns, prior to digestion and analysis by LC-MS/MS. The glycoproteins are identified and quantified by spectral counting. Although the distribution of glycoprotein expression as a function of MW and p I was very similar between the two related cell lines tested, the approach enabled the identification of several distinct membrane glycoproteins with an expression index correlated with either a precancerous (MCF10AT1), or a malignant, metastatic cellular phenotype (MCF10CA1a). Among the proteins associated with the malignant phenotype, Gamma-glutamyl hydrolase, CD44, Galectin-3-binding protein, and Syndecan-1 protein have been reported as potential biomarkers of breast cancer.

Background Recently, a number of large-scale cancer genome sequencing projects have generated a large volume of somatic mutations; however, identifying the functional consequences and roles of somatic mutations in tumorigenesis remains a major challenge. Researchers have identified that protein pocket regions play critical roles in the interaction of proteins with small molecules, enzymes, and nucleic acid. As such, investigating the features of somatic mutations in protein pocket regions provides a promising approach to identifying new genotype-phenotype relationships in cancer. Methods In this study, we developed a protein pocket-based computational approach to uncover the functional consequences of somatic mutations in cancer. We mapped 1.2 million somatic mutations across 36 cancer types from the COSMIC database and The Cancer Genome Atlas (TCGA) onto the protein pocket regions of over 5,000 protein three-dimensional structures. We further integrated cancer cell line mutation profiles and drug pharmacological data from the Cancer Cell Line Encyclopedia (CCLE) onto protein pocket regions in order to identify putative biomarkers for anticancer drug responses. Results We found that genes harboring protein pocket somatic mutations were significantly enriched in cancer driver genes. Furthermore, genes harboring pocket somatic mutations tended to be highly co-expressed in a co-expressed protein interaction network. Using a statistical framework, we identified four putative cancer genes (RWDD1, NCF1, PLEK, and VAV3), whose expression profiles were associated with overall poor survival rates in melanoma, lung, or colorectal cancer patients. Finally, genes harboring protein pocket mutations were more likely to be drug-sensitive or drug-resistant. In a case study, we illustrated that the BAX gene was associated with the sensitivity of three anticancer drugs (midostaurin, vinorelbine, and tipifarnib). Conclusions This study provides novel insights into the functional consequences of somatic mutations during tumorigenesis and for anticancer drug responses. The computational approach used might be beneficial to the study of somatic mutations in the era of cancer precision medicine. Electronic supplementary material The online version of this article (doi:10.1186/s13073-014-0081-7) contains supplementary material, which is available to authorized users.