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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]

Containing the largest number of species, the orchid family provides not only materials for studying plant evolution and environmental adaptation, but economically and culturally important ornamental plants for human society. Previously, we collected genome and transcriptome information of Dendrobium catenatum, Phalaenopsis equestris, and Apostasia shenzhenica which belong to two different subfamilies of Orchidaceae, and developed user-friendly tools to explore the orchid genetic sequences in the OrchidBase 4.0. The OrchidBase 4.0 offers the opportunity for plant science community to compare orchid genomes and transcriptomes and retrieve orchid sequences for further study. In the year 2022, two whole-genome sequences of Orchidoideae species, Platanthera zijinensis and Platanthera guangdongensis, were de novo sequenced, assembled and analyzed. In addition, systemic transcriptomes from these two species were also established. Therefore, we included these datasets to develop the new version of OrchidBase 5.0. In addition, three new functions including synteny, gene order, and miRNA information were also developed for orchid genome comparisons and miRNA characterization. OrchidBase 5.0 extended the genetic information to three orchid subfamilies (including five orchid species) and provided new tools for orchid researchers to analyze orchid genomes and transcriptomes. The online resources can be accessed at https://cosbi.ee.ncku.edu.tw/orchidbase5/ [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]

Background: One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. Results: To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference's distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/. Conclusions: RnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization. [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]

The study aims to develop a new control chart model suitable for monitoring the process quality of multistage manufacturing systems. Considering both the auto-correlated process outputs and the correlation occurring between neighboring stages in a multistage manufacturing system, we first propose a new multiple linear regression model to describe their relationship. Then, the multistage residual EWMA and CUSUM control charts are used to monitor the overall process quality of multistage systems. Moreover, an overall run length (ORL) concept is adopted to compare the detecting performance for various multistage residual control charts. Finally, a numerical example with oxide thickness measurements of a three-stage silicon wafer manufacturing process is given to demonstrate the usefulness of our proposed multistage residual control charts in the Phase II monitoring. A computerized algorithm can also be written based on our proposed scheme for the multistage residual EWMA/CUSUM control charts and it may be further converted to an expert and intelligent system. Hopefully, the results of this study can provide a better alternative for detecting process change and serve as a useful guideline for quality practitioners when monitoring and controlling the process quality of multistage systems with auto-correlated data. [ABSTRACT FROM AUTHOR]

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]

Measurement system analysis (MSA) plays an important role in helping organizations to improve their product quality. Usually, a gauge repeatability and reproducibility (GRR) study needs to be conducted prior to the process capability analysis for assessing the adequacy of gauge variation. In performing multivariate MSA for multivariate measurement systems, the existing multivariate precision-to-tolerance (P/T) ratio does not take the correlation coefficients among tolerances into account. Moreover, the optimal allocation of several parameters such as the number of quality characteristics ( v ), sample size of parts ( p ), number of operators ( o ) and replicate measurements ( r ) have not been considered in the multivariate GRR study either. As the total number of measurements ( n ) increase, the estimated total variation becomes more precise, but the related inspection time and costs will be increased as well. Striking the right balance between the precision of measurement system while still maintaining cost-effectiveness in determining the optimal allocation of opr parameters for correlated quality characteristics becomes an important issue in practical applications. In this paper, a revised precision-to-tolerance (P/T) ratio for multivariate MSA with correlated quality characteristics is proposed. The simulation results show that our revised P/T ratio outperforms the existing ones in terms of MSE and MAPE. We have also found that two parameters (number of parts p and a total number of measurements n ) significantly affect the expected length of confidence interval of P / T R . A reference table with the optimal allocation of por parameters is constructed accordingly if the inspection cost is limited. Finally, a numerical example with a step-by-step procedure for conducting a MGRR study is given to illustrate the appropriateness of our proposed P/T ratio. Hopefully, it can be served as a useful guideline for quality practitioners when performing a multivariate measurement system analysis in industries. [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]

Highlights: [•] We propose three novel capability indices for multidimensional machining processes. [•] Consider the variances of machining results on different directions may not be equal. [•] The statistical properties of the point estimators for the new indices are derived. [•] The simulation results show that the new indices outperform the predecessors. [•] Two numerical examples further demonstrate the usefulness of our proposed indices. [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]

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]

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]

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]

Prior research has shown that the deconvolution of cell-free RNA can uncover the tissue origin. The conventional deconvolution approaches rely on constructing a reference tissue-specific gene panel, which cannot capture the inherent variation present in actual data. To address this, we have developed a novel method that utilizes a neural network framework to leverage the entire training dataset. Our approach involved training a model that incorporated 15 distinct tissue types. Through one semi-independent and two complete independent validations, including deconvolution using a semi in silico dataset, deconvolution with a custom normal tissue mixture RNA-seq data, and deconvolution of longitudinal circulating tumor cell RNA-seq (ctcRNA) data from a cancer patient with metastatic tumors, we demonstrate the efficacy and advantages of the deep-learning approach which were exerted by effectively capturing the inherent variability present in the dataset, thus leading to enhanced accuracy. Sensitivity analyses reveal that neural network models are less susceptible to the presence of missing data, making them more suitable for real-world applications. Moreover, by leveraging the concept of organotropism, we applied our approach to trace the migration of circulating tumor cell-derived RNA (ctcRNA) in a cancer patient with metastatic tumors, thereby highlighting the potential clinical significance of early detection of cancer metastasis. [ABSTRACT FROM AUTHOR]

Background: Improper endotracheal tube (ETT) positioning is frequently observed and potentially hazardous in the intensive care unit. The authors developed a deep learning-based automatic detection algorithm detecting the ETT tip and carina on portable supine chest radiographs to measure the ETT-carina distance. This study investigated the hypothesis that the algorithm might be more accurate than frontline critical care clinicians in ETT tip detection, carina detection, and ETT-carina distance measurement.Methods: A deep learning-based automatic detection algorithm was developed using 1,842 portable supine chest radiographs of 1,842 adult intubated patients, where two board-certified intensivists worked together to annotate the distal ETT end and tracheal bifurcation. The performance of the deep learning-based algorithm was assessed in 4-fold cross-validation (1,842 radiographs), external validation (216 radiographs), and an observer performance test (462 radiographs) involving 11 critical care clinicians. The performance metrics included the errors from the ground truth in ETT tip detection, carina detection, and ETT-carina distance measurement.Results: During 4-fold cross-validation and external validation, the median errors (interquartile range) of the algorithm in ETT-carina distance measurement were 3.9 (1.8 to 7.1) mm and 4.2 (1.7 to 7.8) mm, respectively. During the observer performance test, the median errors (interquartile range) of the algorithm were 2.6 (1.6 to 4.8) mm, 3.6 (2.1 to 5.9) mm, and 4.0 (1.7 to 7.2) mm in ETT tip detection, carina detection, and ETT-carina distance measurement, significantly superior to that of 6, 10, and 7 clinicians (all P < 0.05), respectively. The algorithm outperformed 7, 3, and 0, 9, 6, and 4, and 5, 5, and 3 clinicians (all P < 0.005) regarding the proportions of chest radiographs within 5 mm, 10 mm, and 15 mm error in ETT tip detection, carina detection, and ETT-carina distance measurement, respectively. No clinician was significantly more accurate than the algorithm in any comparison.Conclusions: A deep learning-based algorithm can match or even outperform frontline critical care clinicians in ETT tip detection, carina detection, and ETT-carina distance measurement.Editor’s Perspective: [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]

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]

Background: The Orchid family is the largest families of the monocotyledons and an economically important ornamental plant worldwide. Given the pivotal role of this plant to humans, botanical researchers and breeding communities should have access to valuable genomic and transcriptomic information of this plant. Previously, we established OrchidBase, which contains expressed sequence tags (ESTs) from different tissues and developmental stages of Phalaenopsis as well as biotic and abiotic stress-treated Phalaenopsis. The database includes floral transcriptomic sequences from 10 orchid species across all the five subfamilies of Orchidaceae. Description: Recently, the whole-genome sequences of Apostasia shenzhenica, Dendrobium catenatum, and Phalaenopsis equestris were de novo assembled and analyzed. These datasets were used to develop OrchidBase 4.0, including genomic and transcriptomic data for these three orchid species. OrchidBase 4.0 offers information for gene annotation, gene expression with fragments per kilobase of transcript per millions mapped reads (FPKM), KEGG pathways and BLAST search. In addition, assembled genome sequences and location of genes and miRNAs could be visualized by the genome browser. The online resources in OrchidBase 4.0 can be accessed by browsing or using BLAST. Users can also download the assembled scaffold sequences and the predicted gene and protein sequences of these three orchid species. Conclusions: OrchidBase 4.0 is the first database that contain the whole-genome sequences and annotations of multiple orchid species. OrchidBase 4.0 is available at http://orchidbase.itps.ncku.edu.tw/ [ABSTRACT FROM AUTHOR]

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]

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]

Motivation: Exome capture kits have capture efficiencies that range from 40 to 60%. A significant amount of off-target reads are from the mitochondrial genome. These unintentionally sequenced mitochondrial reads provide unique opportunities to study the mitochondria genome.Results: MitoSeek is an open-source software tool that can reliably and easily extract mitochondrial genome information from exome and whole genome sequencing data. MitoSeek evaluates mitochondrial genome alignment quality, estimates relative mitochondrial copy numbers and detects heteroplasmy, somatic mutation and structural variants of the mitochondrial genome. MitoSeek can be set up to run in parallel or serial on large exome sequencing datasets.Availability: https://github.com/riverlee/MitoSeekContact: yan.guo@vanderbilt.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract: Background and purpose: To determine if dose and/or dose-volume parameters to anatomic swallowing structures are predictive of gastrostomy tube (PEG) dependence from chemotherapy-intensity modulated radiotherapy (IMRT) in locally advanced head and neck cancer (LAHNC). Methods and materials: A retrospective study was performed on 141 consecutive patients with LAHNC (squamous cell) treated with definitive chemoIMRT with weekly concurrent carboplatin and paclitaxel. Late dysphagia was assessed by length of PEG requirement. Analysis of IMRT dose was retrospectively performed for critical swallowing structures. Results: Approximately 62% of patients required PEG, the majority placed during treatment. Mean and median time for PEG was 7.7 and 4.4months respectively (range 1.4–43.8). Only IMRT dose to the inferior constrictor was significantly associated with length of PEG. Mean dose (of individual mean doses) was 47Gy for prolonged PEG use versus 41Gy for PEG ⩽12months. V40 to the inferior constrictor also correlated with PEG >12months (p =0.02) with a mean V40 of 48% versus 41% for PEG ⩽12months. Conclusions: IMRT dose to the inferior constrictor correlated with persistent dysphagia requiring prolonged PEG use. Maintaining mean inferior constrictor dose to ⩽41Gy and V40 to ⩽41% may help minimize gastrostomy tube dependence. [Copyright &y& Elsevier]

Abstract: The human mitochondrial genome has an exclusively maternal mode of inheritance. Mitochondrial DNA (mtDNA) is particularly vulnerable to environmental insults due in part to an underdeveloped DNA repair system, limited to base excision and homologous recombination repair. Radiation exposure to the ovaries may cause mtDNA mutations in oocytes, which may in turn be transmitted to offspring. We hypothesized that the children of female cancer survivors who received radiation therapy may have an increased rate of mtDNA heteroplasmy mutations, which conceivably could increase their risk of developing cancer and other diseases. We evaluated 44 DNA blood samples from 17 Danish and 1 Finnish families (18 mothers and 26 children). All mothers had been treated for cancer as children and radiation doses to their ovaries were determined based on medical records and computational models. DNA samples were sequenced for the entire mitochondrial genome using the Illumina GAII system. Mother''s age at sample collection was positively correlated with mtDNA heteroplasmy mutations. There was evidence of heteroplasmy inheritance in that 9 of the 18 families had at least one child who inherited at least one heteroplasmy site from his or her mother. No significant difference in single nucleotide polymorphisms between mother and offspring, however, was observed. Radiation therapy dose to ovaries also was not significantly associated with the heteroplasmy mutation rate among mothers and children. No evidence was found that radiotherapy for pediatric cancer is associated with the mitochondrial genome mutation rate in female cancer survivors and their children. [Copyright &y& Elsevier]

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]