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Motivation As an important player in transcriptome regulation, microRNAs may effectively diffuse somatic mutation impacts to broad cellular processes and ultimately manifest disease and dictate prognosis. Previous studies that tried to correlate mutation with gene expression dysregulation neglected to adjust for the disparate multitudes of false positives associated with unequal sample sizes and uneven class balancing scenarios. Results To properly address this issue, we developed a statistical framework to rigorously assess the extent of mutation impact on microRNAs in relation to a permutation-based null distribution of a matching sample structure. Carrying out the framework in a pan-cancer study, we ascertained 9008 protein-coding genes with statistically significant mutation impacts on miRNAs. Of these, the collective miRNA expression for 83 genes showed significant prognostic power in nine cancer types. For example, in lower-grade glioma, 10 genes' mutations broadly impacted miRNAs, all of which showed prognostic value with the corresponding miRNA expression. Our framework was further validated with functional analysis and augmented with rich features including the ability to analyze miRNA isoforms; aggregative prognostic analysis; advanced annotations such as mutation type, regulator alteration, somatic motif, and disease association; and instructive visualization such as mutation OncoPrint, Ideogram, and interactive mRNA–miRNA network. Availability and implementation The data underlying this article are available in MutMix, at http://innovebioinfo.com/Database/TmiEx/MutMix.php. [ABSTRACT FROM AUTHOR]

Introduction: Cystic periventricular leukomalacia (PVL) is the most common white matter injury and a common cause of cerebral palsy in preterm infants. Postnatal epilepsy may occur after cystic PVL, but their causal relationship remains uncertain. Our aim was to validate the contribution of cystic PVL to postnatal epilepsy in very preterm infants and demonstrate their seizure characteristics. Methods: This prospective cohort study enrolled 1,342 preterm infants (birth weight <1,500 g and gestational age <32 weeks) from 2003 to 2015. Cystic PVL was diagnosed by serial cerebral ultrasound, and other comorbidities were recorded during hospitalization. Neurological developments and consequences, including epilepsy, were serially accessed until the age of 5. Results: A total of 976 preterm infants completed a 5-year neurological follow-up; 47 (4.8%) had cystic PVL. Preterm infants with cystic PVL were commonly associated with other comorbidities, including necrotizing enterocolitis stage III, neonatal seizures, and intraventricular hemorrhage during hospitalization. At age 5, 14 of the 47 (29.8%) preterm infants with cystic PVL had postnatal epilepsy. After adjusting for gender, gestational age, and three common comorbidities, cystic PVL was an independent risk factor for postnatal epilepsy (adjust OR: 16.2; 95% CI: 6.8–38.4; p < 0.001). Postnatal epilepsy after cystic PVL was commonly the generalized type (13 of 14, 92.9%), not intractable and most occurred after 1 year of age. Discussion/Conclusion: Cystic PVL would independently lead to postnatal epilepsy. Preterm infants with cystic PVL are at risk of postnatal epilepsy after age 1 in addition to cerebral palsy. [ABSTRACT FROM AUTHOR]

In some manufacturing processes, the quality of a product can be characterized by the functional relationship between the response variable and the explanatory variables. Profile monitoring is a tool used to examine the stability of the functional relationship over time. Many studies have pointed out that the measurements within a profile are uncorrelated. In this research, to take advantage of the ability to provide a comprehensive description of a wide variety of data, the Tweedie exponential dispersion process model is used to account for the within-profile correlation. Then, two exponentially weighted moving average control charts are developed to detect process changes during a Phase II application. In the simulation studies, we showed that our proposed control charts outperform the existing methods for monitoring linear and nonlinear profiles. Finally, two numerical examples are given to demonstrate the usefulness of our proposed control charts. [ABSTRACT FROM AUTHOR]

Simple Summary: Using the covariate-adjusted tensor classification in the high-dimension (CATCH) model, we integrated adjusted radiomics-based CT images into RNA immune genomic expression data to achieve the accurate classification of recurrent CRC. The correlation between radiomic features and immune gene expression identifies potential therapeutic targets in CRC. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. To evaluate whether adjusted computed tomography (CT) scan image-based radiomics combined with immune genomic expression can achieve accurate stratification of cancer recurrence and identify potential therapeutic targets in stage III colorectal cancer (CRC), this cohort study enrolled 71 patients with postoperative stage III CRC. Based on preoperative CT scans, radiomic features were extracted and selected to build pixel image data using covariate-adjusted tensor classification in the high-dimension (CATCH) model. The differentially expressed RNA genes, as radiomic covariates, were identified by cancer recurrence. Predictive models were built using the pixel image and immune genomic expression factors, and the area under the curve (AUC) and F1 score were used to evaluate their performance. Significantly adjusted radiomic features were selected to predict recurrence. The association between the significantly adjusted radiomic features and immune gene expression was also investigated. Overall, 1037 radiomic features were converted into 33 × 32-pixel image data. Thirty differentially expressed genes were identified. We performed 100 iterations of 3-fold cross-validation to evaluate the performance of the CATCH model, which showed a high sensitivity of 0.66 and an F1 score of 0.69. The area under the curve (AUC) was 0.56. Overall, ten adjusted radiomic features were significantly associated with cancer recurrence in the CATCH model. All of these methods are texture-associated radiomics. Compared with non-adjusted radiomics, 7 out of 10 adjusted radiomic features influenced recurrence-free survival. The adjusted radiomic features were positively associated with PECAM1, PRDM1, AIF1, IL10, ISG20, and TLR8 expression. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. Adjusted CT scan image-based radiomics with immune genomic expression covariates using the CATCH model can efficiently predict cancer recurrence. The correlation between adjusted radiomic features and immune genomic expression can provide biological relevance and individualized therapeutic targets. [ABSTRACT FROM AUTHOR]

Orchid gynostemium, the fused organ of the androecium and gynoecium, and ovule development are unique developmental processes. Two DROOPING LEAF / CRABS CLAW (DL / CRC) genes, PeDL1 and PeDL2, were identified from the Phalaenopsis orchid genome and functionally characterized. Phylogenetic analysis indicated that the most recent common ancestor of orchids contained the duplicated DL/CRC -like genes. Temporal and spatial expression analysis indicated that PeDL genes are specifically expressed in the gynostemium and at the early stages of ovule development. Both PeDL s could partially complement an Arabidopsis crc-1 mutant. Virus-induced gene silencing (VIGS) of PeDL1 and PeDL2 affected the number of protuberant ovule initials differentiated from the placenta. Transient overexpression of PeDL1 in Phalaenopsis orchids caused abnormal development of ovule and stigmatic cavity of gynostemium. PeDL1, but not PeDL2, could form a heterodimer with Phalaenopsis equestris CINCINNATA 8 (PeCIN8). Paralogous retention and subsequent divergence of the gene sequences of PeDL1 and PeDL2 in P. equestris might result in the differentiation of function and protein behaviors. These results reveal that the ancestral duplicated DL/CRC -like genes play important roles in orchid reproductive organ innovation. [ABSTRACT FROM AUTHOR]

Abstract Background When using Illumina high throughput short read data, sometimes the genotype inferred from the positive strand and negative strand are significantly different, with one homozygous and the other heterozygous. This phenomenon is known as strand bias. In this study, we used Illumina short-read sequencing data to evaluate the effect of strand bias on genotyping quality, and to explore the possible causes of strand bias. Result We collected 22 breast cancer samples from 22 patients and sequenced their exome using the Illumina GAIIx machine. By comparing the consistency between the genotypes inferred from this sequencing data with the genotypes inferred from SNP chip data, we found that, when using sequencing data, SNPs with extreme strand bias did not have significantly lower consistency rates compared to SNPs with low or no strand bias. However, this result may be limited by the small subset of SNPs present in both the exome sequencing and the SNP chip data. We further compared the transition and transversion ratio and the number of novel non-synonymous SNPs between the SNPs with low or no strand bias and those with extreme strand bias, and found that SNPs with low or no strand bias have better overall quality. We also discovered that the strand bias occurs randomly at genomic positions across these samples, and observed no consistent pattern of strand bias location across samples. By comparing results from two different aligners, BWA and Bowtie, we found very consistent strand bias patterns. Thus strand bias is unlikely to be caused by alignment artifacts. We successfully replicated our results using two additional independent datasets with different capturing methods and Illumina sequencers. Conclusion Extreme strand bias indicates a potential high false-positive rate for SNPs.

Abstract Background Exome sequencing using next-generation sequencing technologies is a cost efficient approach to selectively sequencing coding regions of human genome for detection of disease variants. A significant amount of DNA fragments from the capture process fall outside target regions, and sequence data for positions outside target regions have been mostly ignored after alignment. Result We performed whole exome sequencing on 22 subjects using Agilent SureSelect capture reagent and 6 subjects using Illumina TrueSeq capture reagent. We also downloaded sequencing data for 6 subjects from the 1000 Genomes Project Pilot 3 study. Using these data, we examined the quality of SNPs detected outside target regions by computing consistency rate with genotypes obtained from SNP chips or the Hapmap database, transition-transversion (Ti/Tv) ratio, and percentage of SNPs inside dbSNP. For all three platforms, we obtained high-quality SNPs outside target regions, and some far from target regions. In our Agilent SureSelect data, we obtained 84,049 high-quality SNPs outside target regions compared to 65,231 SNPs inside target regions (a 129% increase). For our Illumina TrueSeq data, we obtained 222,171 high-quality SNPs outside target regions compared to 95,818 SNPs inside target regions (a 232% increase). For the data from the 1000 Genomes Project, we obtained 7,139 high-quality SNPs outside target regions compared to 1,548 SNPs inside target regions (a 461% increase). Conclusions These results demonstrate that a significant amount of high quality genotypes outside target regions can be obtained from exome sequencing data. These data should not be ignored in genetic epidemiology studies.

Background and Objective. The main purpose of this study was to develop a simple automatic diagnostic classification scheme for chemotherapy-induced peripheral neuropathy. Methods. This was a prospective cohort study that enrolled patients with colorectal or gynecologic cancer post chemotherapy for more than 1 year. The patients underwent laboratory examinations (nerve conduction studies and quantitative sensory tests), and a questionnaire about the quality of life. An unsupervised classification algorithm was used to classify the patients into groups using a small number of variables derived from the laboratory tests. A panel of five neurologists also diagnosed the types of neuropathies according to the laboratory tests. The results by the unsupervised classification algorithm and the neurologists were compared. Results. The neurologists' diagnoses showed much higher rates of entrapment syndromes (66.1%) and radiculopathies (55.1%) than polyneuropathy (motor/sensory: 33.1%/29.7%). A multivariate analysis showed that the questionnaire was not significantly correlated with the results of quantitative sensory tests (r = 0.27) or the neurologists' diagnoses (r = 0.2). All of the patients were classified into four groups by the unsupervised classification algorithm. The classification corresponded to the severity of neuropathy and correlated well with the neurologists' diagnoses and the scales of neurological examinations. The overall correct rate of classification by the unsupervised classification algorithm was 78.8% (95% confidence interval: 73.1%-88.3%). Conclusion. The results of our unsupervised classification algorithm based on three variables of laboratory tests correlated well with the neurologists' diagnoses. [ABSTRACT FROM AUTHOR]

Profile monitoring is a technique to test the stability of the relationship between a response variable and explanatory variables over time. The most relevant linear profile monitoring methods have been constructed using the normality assumption. However, the normality assumption could be violated in many quality control applications. In this study, we consider a situation in which the random errors in a linear profile model follow a skew‐normal distribution. The skew‐normal distribution is a generalized version of the normal distribution. A new Shewhart‐type chart and exponentially weighted moving average (EWMA) chart, named the ShewhartR and EWMAR charts, respectively, are constructed based on residuals to monitor the parameters of linear profile model. The simulation results show that the multivariate EWMA chart is sensitive to the normality assumption and that the proposed ShewhartR and EWMAR charts have good ability to detect big and small‐to‐moderate process shifts, respectively. An example using photo mask techniques in semiconductor manufacturing is provided to illustrate the applications of the ShewhartR and EWMAR charts. [ABSTRACT FROM AUTHOR]

In profile monitoring for a multivariate manufacturing process, the functional relationship of the multivariate profiles rarely occurs in linear form, and the real data usually do not follow a multivariate normal distribution. Thus, in this paper, the functional relationship of multivariate nonlinear profile data is described via a nonparametric regression model. We first fit the multivariate nonlinear profile data and obtain the reference profiles through support vector regression (SVR) model. The differences between the observed multivariate nonlinear profiles and the reference profiles are used to calculate the vector of metrics. Then, a nonparametric revised spatial rank exponential weighted moving average (RSREWMA) control chart is proposed in the phase II monitoring. Moreover, a simulation study is conducted to evaluate the detecting performance of our proposed nonparametric RSREWMA control chart under various process shifts using out‐of‐control average run length (ARL1). The simulation results indicate that the SREWMA control chart coupled with the metric of mean absolute deviation (MAD) can be used to monitor the multivariate nonlinear profile data when a common fixed design (CFD) is not applicable in the phase II study. Finally, a realistic multivariate nonlinear profile example is used to demonstrate the usefulness of our proposed RSREWMA control chart and its monitoring schemes. [ABSTRACT FROM AUTHOR]

In today's manufacturing industries, if the quality characteristic of a product or a process is assumed to be represented by a functional relationship between the response variable and one or more explanatory variables, then the data generated from such a relationship are called profile data. Generally speaking, the functional relationship of the profile data rarely occurs in linear form, and the real data usually do not follow normal distribution. Thus, in this paper, the functional relationship of profile data is described via a nonparametric regression model and a nonparametric exponentially weighted moving average (EWMA) control chart is developed for detecting the process shifts for nonlinear profile data in the Phase II monitoring. We first fit the nonlinear profile data via a support vector regression model and use the fitted values to calculate the five metrics. Then, the nonparametric EWMA control chart with the five metrics can be constructed accordingly. Moreover, a simulation study is conducted to evaluate the detecting performance of the new control chart under various process shifts using the out‐of‐control average run length. Finally, a realistic nonlinear profile example is used to demonstrate the usefulness of our proposed nonparametric EWMA control chart and its monitoring schemes. It is expected that the proposed nonparametric EWMA control chart can enhance the monitoring efficiency for nonlinear profile data in the phase II study. [ABSTRACT FROM AUTHOR]

Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type. [ABSTRACT FROM AUTHOR]

In some applications, the quality of the process or product is characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. Profile monitoring is a technique for checking the stability of the relationship over time. Existing linear profile monitoring methods usually assumed the error distribution to be normal. However, this assumption may not always be true in practice. To address this situation, we propose a method for profile monitoring under the framework of generalized linear models when the relationship between the mean and variance of the response variable is known. Two multivariate exponentially weighted moving average control schemes are proposed based on the estimated profile parameters obtained using a quasi-likelihood approach. The performance of the proposed methods is evaluated by simulation studies. Furthermore, the proposed method is applied to a real data set, and the R code for profile monitoring is made available to users. [ABSTRACT FROM AUTHOR]

To assess the effect of chemotherapy on mitochondrial genome mutations in cancer survivors and their offspring, a study sequenced the full mitochondrial genome and determined the mitochondrial DNA heteroplasmic (mtDNA) mutation rate. To build a model for counts of heteroplasmic mutations in mothers and their offspring, bivariate Poisson regression was used to examine the relationship between mutation count and clinical information while accounting for the paired correlation. However, if the sequencing depth is not adequate, a limited fraction of the mtDNA will be available for variant calling. The classical bivariate Poisson regression model treats the offset term as equal within pairs; thus, it cannot be applied directly. In this research, we propose an extended bivariate Poisson regression model that has a more general offset term to adjust the length of the accessible genome for each observation. We evaluate the performance of the proposed method with comprehensive simulations, and the results show that the regression model provides unbiased parameter estimations. The use of the model is also demonstrated using the paired mtDNA dataset. [ABSTRACT FROM AUTHOR]

Purpose – In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions. Design/methodology/approach – In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMCp, RNMCpm and RNMCpu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMCp and NMCpm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case. Findings – A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices. Practical implications – Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system. Originality/value – Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions. [ABSTRACT FROM AUTHOR]

The Shewhart-type control chart is traditionally developed under the normality assumption. In practice, however, this assumption may not hold. Because the skew normal distribution represents a broad distribution class and is more flexible than is the normal distribution, we propose two new control charts to monitor process mean and spread for skew normal distributed data. Moreover, to facilitate practical implementation, tables of charting constants are provided. We conducted simulation studies to compare the false alarm rates, and the results show that new proposed charts perform better than others as skewness increases. Finally, an illustrative example is provided. [ABSTRACT FROM PUBLISHER]

Recent studies have emphasized a role of adaptive immunity, and particularly T cells, in the genesis of hypertension. We sought to determine the T-cell subtypes that contribute to hypertension and renal inflammation in angiotensin II-induced hypertension. Using T-cell receptor spectratyping to examine T-cell receptor usage, we demonstrated that CD8(+) cells, but not CD4(+) cells, in the kidney exhibited altered T-cell receptor transcript lengths in Vβ3, 8.1, and 17 families in response to angiotensin II-induced hypertension. Clonality was not observed in other organs. The hypertension caused by angiotensin II in CD4(-/-) and MHCII(-/-) mice was similar to that observed in wild-type mice, whereas CD8(-/-) mice and OT1xRAG-1(-/-) mice, which have only 1 T-cell receptor, exhibited a blunted hypertensive response to angiotensin II. Adoptive transfer of pan T cells and CD8(+) T cells but not CD4(+)/CD25(-) cells conferred hypertension to RAG-1(-/-) mice. In contrast, transfer of CD4(+)/CD25(+) cells to wild-type mice receiving angiotensin II decreased blood pressure. Mice treated with angiotensin II exhibited increased numbers of kidney CD4(+) and CD8(+) T cells. In response to a sodium/volume challenge, wild-type and CD4(-/-) mice infused with angiotensin II retained water and sodium, whereas CD8(-/-) mice did not. CD8(-/-) mice were also protected against angiotensin-induced endothelial dysfunction and vascular remodeling in the kidney. These data suggest that in the development of hypertension, an oligoclonal population of CD8(+) cells accumulates in the kidney and likely contributes to hypertension by contributing to sodium and volume retention and vascular rarefaction. [ABSTRACT FROM AUTHOR]

This trial was designed to assess efficacy and safety of erlotinib with sorafenib in the treatment of patients with advanced pancreatic adenocarcinoma. An exploratory correlative study analyzing pretreatment serum samples using a multivariate protein mass spectrometry-based test (VeriStrat®), previously shown to correlate with outcomes in lung cancer patients treated with erlotinib, was performed. Patients received sorafenib 400 mg daily along with erlotinib 150 mg daily with a primary endpoint of 8-week progression free survival ( PFS) rate. Pretreatment serum sample analysis by VeriStrat was done blinded to clinical and outcome data; the endpoints were PFS and overall survival ( OS). Difference between groups (by VeriStrat classification) was assessed using log-rank P values; hazard ratios ( HR) were obtained from Cox proportional hazards model. Thirty-six patients received study drug and were included in the survival analysis. Eight-week PFS rate of 46% (95% confidence interval ( CI): 0.32-0.67) did not meet the primary endpoint of a rate ≥70%. Thirty-two patients were included in the correlative analysis, and VeriStrat 'Good' patients had superior PFS (HR = 0.18, 95% CI: 0.06-0.57; P = 0.001) and OS (HR = 0.31 95% CI: 0.13-0.77, P = 0.008) compared to VeriStrat 'Poor' patients. Grade 3 toxicities of this regimen included fever, anemia, diarrhea, dehydration, rash, and altered liver function. This study did not meet the primary endpoint, and this combination will not be further pursued. In this small retrospective analysis, the proteomic classification was significantly associated with clinical outcomes and is being further evaluated in ongoing studies. [ABSTRACT FROM AUTHOR]

With the advent of modern technology, manufacturing processes have become very sophisticated; a single quality characteristic can no longer reflect a product's quality. In order to establish performance measures for evaluating the capability of a multivariate manufacturing process, several new multivariate capability (NMC) indices, such as NMC p and NMC pm , have been developed over the past few years. However, the sample size determination for multivariate process capability indices has not been thoroughly considered in previous studies. Generally, the larger the sample size, the more accurate an estimation will be. However, too large a sample size may result in excessive costs. Hence, the trade-off between sample size and precision in estimation is a critical issue. In this paper, the lower confidence limits of NMC p and NMC pm indices are used to determine the appropriate sample size. Moreover, a procedure for conducting the multivariate process capability study is provided. Finally, two numerical examples are given to demonstrate that the proper determination of sample size for multivariate process indices can achieve a good balance between sampling costs and estimation precision. [ABSTRACT FROM AUTHOR]

Background: Neonatal hypoxic-ischemic encephalopathy (HIE) is the most common cause of mortality and neurological disability in infancy after perinatal asphyxia. Reliable biomarkers to predict neurological outcomes of neonates after perinatal asphyxia are still not accessible in clinical practice. Methods: A prospective cohort study enrolled neonates with perinatal asphyxia. Biochemical blood tests and cerebral Doppler ultrasound were measured within 6 h of age and at the 4th day old. Neurological outcomes were assessed at 1 year old. Results: Sixty-four neonates with perinatal asphyxia were enrolled. Fifty-eight (90%) had hypoxic-ischemic encephalopathy (HIE) including 20 (34%) Stage I, 21 (36%) Stage II, and 17 (29%) Stage III. In the asphyxiated infants without therapeutic hypothermia, HIE stage, PH, and base excess levels within 6 h of age were the predictors of adverse outcomes. In the asphyxiated infants receiving therapeutic hypothermia, HIE stage failed to predict outcomes. Instead, blood lactate levels and pulsatility index (PI) of medial cerebral arteries (MCA) either in 6 h of age or at the 4th day old independently predicted adverse outcomes. Conclusions: Blood lactate, which is a common accessible test at the hospital and MCA PI on cerebral ultrasound could predict adverse outcomes in asphyxiated infants receiving therapeutic hypothermia. [ABSTRACT FROM AUTHOR]

3-Monochloropropane-1,2-diol (3-MCPD), 2,3-epoxy-1-propanol (glycidol), and their esters are well-known food contaminants mainly formed by the heat processing of certain refined oils and coexist in various kinds of foodstuffs. However, the combined health effect and the underlying mechanism of 3-MCPD and glycidol coexposure are not well-understood. In this study, we investigated the systemic toxicity effects and the nephrotoxicity mechanisms of 3-MCPD and glycidol coexposure with in vitro and in vivo models, and next-generation sequencing (NGS) analysis. It was found that 3-MCPD and glycidol coexposure for 28 days synergistically induced toxicity in the kidney, lung, testis, and heart in C57BL/6 mice. Kidney was the most sensitive organ to coexposure, and the coexposure had a synergistic effect on inflammation and cytotoxicity through activation of the NLRP3 inflammasome, and the induction of necroptosis, and autophagic cell death in NRK-52E cells. Moreover, the NGS results revealed the genes changes associated with nephrotoxicity, inflammation and with the broad toxicity effects induced by 3-MCPD or glycidol alone or in combination, which were consistent with the results of in vitro and in vivo models. In summary, we report for the first time of the comprehensive toxicity effects and the mechanisms caused by 3-MCPD and glycidol coexposure. ga1 • This is the first study to reveal the toxicity mechanisms of coexposure to food contaminants 3-MCPD and glycidol. • Coexposure conditions induced a more toxic response in the organs including kidney, lung, testis, and heart. • Synergistic nephrotoxicity was a significant effect after coexposure. • NLRP3 inflammasome activation, necroptosis, and autophagic cell death contributed to synergistic nephrotoxicity. • This study raised the awareness of the toxicity effects of 3-MCPD and glycidol coexposure. [ABSTRACT FROM AUTHOR]

Traditionally, somatic mutations are detected by examining DNA sequence. The maturity of sequencing technology has allowed researchers to screen for somatic mutations in the whole genome. Increasingly, researchers have become interested in identifying somatic mutations through RNAseq data. With this motivation, we evaluated the practicability of detecting somatic mutations from RNAseq data. Current somatic mutation calling tools were designed for DNA sequencing data. To increase performance on RNAseq data, we developed a somatic mutation caller GLMVC based on bias reduced generalized linear model for both DNA and RNA sequencing data. Through comparison with MuTect and Varscan we showed that GLMVC performed better for somatic mutation detection using exome sequencing or RNAseq data. GLMVC is freely available for download at the following website: https://github.com/shengqh/GLMVC/wiki . [ABSTRACT FROM AUTHOR]