Quality control implementation for universal characterization of DNA and RNA viruses in clinical respiratory samples using single metagenomic next-generation sequencing workflow.

BACKGROUND: In recent years, metagenomic Next-Generation Sequencing (mNGS) has increasingly been used for an accurate assumption-free virological diagnosis. However, the systematic workflow evaluation on clinical respiratory samples and implementation of quality controls (QCs) is still lacking. METHODS: A total of 3 QCs were implemented and processed through the whole mNGS workflow: a no-template-control to evaluate contamination issues during the process; an internal and an external QC to check the integrity of the reagents, equipment, the presence of inhibitors, and to allow the validation of results for each sample. The workflow was then evaluated on 37 clinical respiratory samples from patients with acute respiratory infections previously tested for a broad panel of viruses using semi-quantitative real-time PCR assays (28 positive samples including 6 multiple viral infections; 9 negative samples). Selected specimens included nasopharyngeal swabs (n = 20), aspirates (n = 10), or sputums (n = 7). RESULTS: The optimal spiking level of the internal QC was first determined in order to be sufficiently detected without overconsumption of sequencing reads. According to QC validation criteria, mNGS results were validated for 34/37 selected samples. For valid samples, viral genotypes were accurately determined for 36/36 viruses detected with PCR (viral genome coverage ranged from 0.6 to 100%, median = 67.7%). This mNGS workflow allowed the detection of DNA and RNA viruses up to a semi-quantitative PCR Ct value of 36. The six multiple viral infections involving 2 to 4 viruses were also fully characterized. A strong correlation between results of mNGS and real-time PCR was obtained for each type of viral genome (R2 ranged from 0.72 for linear single-stranded (ss) RNA viruses to 0.98 for linear ssDNA viruses). CONCLUSIONS: Although the potential of mNGS technology is very promising, further evaluation studies are urgently needed for its routine clinical use within a reasonable timeframe. The approach described herein is crucial to bring standardization and to ensure the quality of the generated sequences in clinical setting. We provide an easy-to-use single protocol successfully evaluated for the characterization of a broad and representative panel of DNA and RNA respiratory viruses in various types of clinical samples.

Interpreting experiments on egg production--statistical considerations.

A given data set can be analyzed many ways, but only one is the correct analysis based on the design actually used when running the experiment. This work gives a tutorial-like illustration of the effects of the presence of a regression variable (or covariate) on the recorded responses in an experiment set up as a standard factorial design and shows how the analysis results are to be adjusted for the presence of covariates. An underlying assumption of a factorial model is that each of the treatments (e.g., diets) is randomly allocated to different subjects (hens). When many measurements (e.g., over time) are made on the same subject (hen), this independence assumption is violated; in these cases, the design is an example from the class of repeated measures designs. The difference in analysis between factorial designs and repeated measures designs is also discussed. Then, the 2 concepts are merged wherein the results for a repeated measures analysis have to be adjusted for the presence of covariates. The paper concludes with analyses on the results of egg production responses from an experiment in which repeated measurements were made on the same hens and in which an unanticipated temperature covariate was present.

Effects of balanced dietary protein levels on egg production and egg quality parameters of individual commercial layers.

The effects of a series of balanced dietary protein levels on egg production and egg quality parameters of laying hens from 18 through 74 wk of age were investigated. One hundred forty-four pullets (Bovans) were randomly assigned to individual cages with separate feeders including 3 different protein level series of isocaloric diets. Diets were separated into 4 phases of 18-22, 23-32, 33-44, and 45-74 wk of age. The high protein (H) series contained 21.62, 19.05, 16.32, and 16.05% CP, respectively. Medium protein (M) and low protein (L) series were 2 and 4% lower in balanced dietary protein. The results clearly demonstrated that the balanced dietary protein level was a limiting factor for BW, ADFI, egg weight, hen day egg production (HDEP), and feed per kilogram of eggs. Feeding with the L series resulted in lower ADFI and HDEP (90.33% peak production) and more feed per kilogram of eggs compared with the H or M series (HDEP; 93.23 and 95.68% peak production, monthly basis). Egg weight responded in a linear manner to balanced dietary protein level (58.78, 55.94, and 52.73 g for H, M, and L, respectively). Feed intake of all hens, but especially those in the L series, increased considerably after wk 54 when the temperature of the house decreased due to winter conditions. Thus, hens fed the L series seemed particularly dependent on house temperature to maintain BW, ADFI, and HDEP. For egg quality parameters, percent yolk, Haugh units, and egg specific gravity were similar regardless of diets. Haugh units were found to be greatly affected by the variation of housing temperature (P = 0.025). Maximum performance cannot always be expected to lead to maximum profits. Contrary to the idea of a daily amino acid requirement for maximum performance, these results may be used to determine profit-maximizing levels of balanced dietary protein based on the cost of protein and returns from different possible protein levels that may be fed.

Partition of variation for predicting experimental power with a broiler chicken example.

A 1932 editorial in Poultry Science stated that sampling theory, or experimental power, could be useful for "the investigator to know how many … birds to put into each experimental pen." Nevertheless, in the past 90 yr, appropriate experimental power estimates have rarely been applied to research with poultry. To estimate the overall variation and appropriate use of resources with animals in pens, a nested analysis should be conducted. Bird-to-bird and separate pen-to-pen variances were separated for 2 datasets, one from Australia and one from North America. The implications of using variances for birds per pen and pens per treatments are detailed. With 5 pens per treatment, increasing birds per pen from 2 to 4 decreased the SD from 183 to 154, but increasing birds/pen from 100 to 200 only decreased the SD from 70 to 60. With 15 birds per treatment, increasing pens/treatment from 2 to 3 decreased SD from 140 to 126, but increasing pens/treatment from 11 to 12 only decreased the SD from 91 to 89. Choosing the number of birds to include in any study should be based on expectations from historical data and the amount of risk investigators are prepared to accept. Too little replication will not allow relatively small differences to be detected. On the other hand, too much replication is wasteful in terms of birds and resources, and violates the fundamental principles of the ethical use of animals in research. Two general conclusions can be made from this analysis. First, it is very difficult to detect 1% to 3% differences in broiler chicken body weight with only one experiment consistently because of inherent genetic variability. Second, increasing either birds per pen or pens per treatment decreased the SD in a diminishing returns fashion. The example presented here is body weight, of primary importance to production agriculture, but it is applicable whenever a nested design is used (multiple samples from the same bird or tissue, etc.).

New heterogeneous test statistics for the unbalanced fixed-effect nested design.

When the underlying variances are unknown or/and unequal, using the conventional F test is problematic in the two-factor hierarchical data structure. Prompted by the approximate test statistics (Welch and Alexander-Govern methods), the authors develop four new heterogeneous test statistics to test factor A and factor B nested within A for the unbalanced fixed-effect two-stage nested design under variance heterogeneity. The actual significance levels and statistical power of the test statistics were compared in a simulation study. The results show that the proposed procedures maintain better Type I error rate control and have greater statistical power than those obtained by the conventional F test in various conditions. Therefore, the proposed test statistics are recommended in terms of robustness and easy implementation.

A comparison of methods to estimate nutritional requirements from experimental data.

1. Research papers use a variety of methods for evaluating experiments designed to determine nutritional requirements of poultry. Growth trials result in a set of ordered pairs of data. Often, point-by-point comparisons are made between treatments using analysis of variance. This approach ignores that response variables (body weight, feed efficiency, bone ash, etc.) are continuous rather than discrete. Point-by-point analyses harvest much less than the total amount of information from the data. Regression models are more effective at gleaning information from data, but the concept of "requirements" is poorly defined by many regression models. 2. Response data from a study of the lysine requirements of young broilers was used to compare methods of determining requirements. In this study, multiple range tests were compared with quadratic polynomials (QP), broken line models with linear (BLL) or quadratic (BLQ) ascending portions, the saturation kinetics model (SK) a logistic model (LM) and a compartmental (CM) model. 3. The sum of total residuals squared was used to compare the models. The SK and LM were the best fit models, followed by the CM, BLL, BLQ, and QP models. A plot of the residuals versus nutrient intake showed clearly that the BLQ and SK models fitted the data best in the important region where the ascending portion meets the plateau. 4. The BLQ model clearly defines the technical concept of nutritional requirements as typically defined by nutritionists. However, the SK, LM and CM models better depict the relationship typically defined by economists as the "law of diminishing marginal productivity". The SK model was used to demonstrate how the law of diminishing marginal productivity can be applied to poultry nutrition, and how the "most economical feeding level" may replace the concept of "requirements".

Choosing sample sizes for various blood parameters of broiler chickens with normal and non-normal observations.

Experimental power is a measure of the ability of an experiment to detect differences between treatment means. Researchers design experiments and then calculate the probability that differences are simply due to chance, the null hypothesis. The objective of the analyses reported here was to determine the appropriate number of samples to demonstrate significant differences of various magnitudes from broiler chicken blood constituents. Over 800 samples were taken for a study of the effects of sample storage time, serum vs. plasma, light intensity, and fed vs. fasted birds on blood cholesterol, triglycerides, uric acid, glucose, total protein (TP), albumin, globulin, alanine aminotransferase (ALT), aspartate aminotransferase, gammaGT, creatinine, alkaline phosphatase, Ca and P. Various transformations increased the QQ plot R2 values from 0.000 to 0.149 or 0.00 to 17.62%. Most of the QQ plot R2 values were at or above 0.90. The 1/x2 transformation of blood P data showed the biggest increase in QQ plot R2 (0.846 to 0.995). The different standard deviations and coefficients of variation (CVs) found for each variable resulted in widely different numbers of replicates needed to detect differences in 2 treatment means. The extremes were glucose with a CV of 6.9% and ALT with a CV of 39.7%. For glucose, 15 replicates are needed to find a 10% difference in 97% of experiments; for ALT, 15 replicates would detect a 50% difference 91% of the time. The use of parameters such as cholesterol, glucose, TP, albumin, and globulin showed low CVs, indicating they may be considered as stable parameters. The lower CVs make it possible to find differences with a smaller number of replicates used in studies. As reported, the phosphorus values did not have a normal distribution of the data, so a transformation of these data could be an alternative to better discuss the results found.

Three-stage stochastic epidemic model: an application to AIDS.

A three-stage stochastic epidemic model extending the so-called classical epidemic process to one that includes time-dependent transition probabilities is described, and a solution to the appropriate set of forward differential-difference equations is given. When an individual can move from being a susceptible to one infected with the HIV virus to one diagnosed as having AIDS, we can use this general model to describe an AIDS epidemic process. We obtain expressions for the mean and variance of the number of AIDS cases for some special cases. By comparing these with actual data, it is suggested that, for some categories of cases (in particular, children), this model might be a plausible model to describe the underlying mechanism of the AIDS epidemic.

A review and synthesis of the HIV/AIDS epidemic as a multi-stage process.

As our understanding of the dynamics of the transmission of the HIV virus toward diagnosis of AIDS increases, numerous models are being developed to help explain the underlying mechanism. We show that many of the models established so far in the literature are examples of a general class of multi-stage epidemic models that are themselves right-shift processes as developed in 1969 by Severo. We focus attention on those models that can be described as three-stage, four-stage, five-stage, etc. models.

A multi-stage compartmental model for HIV-infected individuals: I--waiting time approach.

Traditionally, epidemic processes have focused on establishing systems of differential-difference equations governing the number of individuals at each stage of the epidemic. Except for simple situations such as when transition rates are linear, these equations are notoriously intractable mathematically. In this work, the process is described as a compartmental model. The model also allows for individuals to go directly from any prior compartment directly to a final stage corresponding to death. This allows for the possibility that individuals can die earlier due to some non-disease related cause. Then, the model is based on waiting times in each compartment. Survival probabilities of moving from a given compartment to another compartment are established. While our approach can be used for general epidemic processes, our framework is for the HIV/AIDS process. It is then possible to establish the impact of the HIV/AIDS epidemic process on, e.g., insurance premiums and payouts and health-care costs. The effect of changing model parameter values on these entities is investigated.

A multi-stage compartmental model for HIV-infected individuals: II--application to insurance functions and health-care costs.

Stochastic population processes have received a lot of attention over the years. One approach focuses on compartmental modeling. Billard and Dayananda (2012) developed one such multi-stage model for epidemic processes in which the possibility that individuals can die at any stage from non-disease related causes was also included. This extra feature is of particular interest to the insurance and health-care industries among others especially when the epidemic is HIV/AIDS. Rather than working with numbers of individuals in each stage, they obtained distributional results dealing with the waiting time any one individual spent in each stage given the initial stage. In this work, the impact of the HIV/AIDS epidemic on several functions relevant to these industries (such as adjustments to premiums) is investigated. Theoretical results are derived, followed by a numerical study.

Classification of hospital pathways in the management of cancer: application to lung cancer in the region of burgundy.

CONTEXT: The evaluation of national cancer plans is an important aspect of their implementation. For this evaluation, the principal actors in the field (doctors, nurses, etc.) as well as decision-makers must have access to information that is reliable, synthetic and easy to interpret, and which reflects the implementation process in the field. We propose here a methodology to make this type of information available in the context of reducing inequalities with regard to access to healthcare for patients with lung cancer in the region of Burgundy. METHODS: We used the national medico-administrative DRG-type database, which gathers together all hospital stays. By using this database, it was possible to identify and reconstruct the care management history of these patients. That is, by linking together all attended hospitals, sorted chronologically. Eligible patients were at least 18 years old, whatever the gender and had undergone surgery for their lung cancer. They had to be residents of Burgundy at the time of the first operation between 2006 and 2008. Patient's pathway was defined as the sequence of all attended hospitals (hospital stays) during the year of follow up linked together using an anonymised patient identifier. We then constructed a pathway typology of pathway using an unsupervised clustering method, and conducted a spatial analysis of this typology. RESULTS: Between 2006 and 2008, we selected 495 patients in the 4 administrative departments of the Burgundy region. They accounted for a total of 3821 stays during the year of follow-up. There were 393 men (79%) and the mean age was 64 (95% confidence interval: 63-65) years. We reconstructed 94 pathways (about five per patient). Here, neighbourhood's cares accounted for 41% of them, while 44% included a surgical intervention outside the region of Burgundy. We constructed a pathway typology with five classes. Spatial analysis showed that the vast majority of initial surgeries took place in the major regional centres. CONCLUSION: The construction of a pathway typology leads to better understanding of the reasoning that lies behind the movements of patients. It opens the way for analysis of the collaboration between the different healthcares establishments attended, which should bring to light associations that need to be developed.

Unmasking of epigenetically silenced candidate tumor suppressor genes by removal of methyl-CpG-binding domain proteins.

Methyl-cytosine-phosphate-guanine (CpG)-binding domain (MBD) proteins are bound to hypermethylated promoter CpG islands of tumor suppressor genes in human cancer cells, although a direct causal relationship at the genome-wide level between MBD presence and gene silencing remains to be demonstrated. To this end, we have inhibited the expression of MBD proteins in HeLa cells by short hairpin RNAs; and studied the functional consequences of MBD depletion using microarray-based expression analysis in conjunction with extensive bisulfite genomic sequencing and chromatin immunoprecipitation. The removal of MBDs results in a release of gene silencing associated with a loss of MBD occupancy in 5'-CpG islands without any change in the DNA methylation pattern. Our results unveil new targets for epigenetic inactivation mediated by MBDs in transformed cells, such as the cell adhesion protein gamma-parvin and the fibroblast growth factor 19, where we also demonstrate their bona fide tumor suppressor features. Our data support a fundamental role for MBD proteins in the direct maintenance of transcriptional repression of tumor suppressors and identify new candidate genes for epigenetic disruption in cancer cells.

The world of biometry.

The International Biometric Society is an international society for the advancement of biological science through the development of quantitative theories and the application, development and dissemination of effective mathematical and statistical techniques. We consider some of the nonscientific and scientific issues being addressed by researchers in and across Regions and Groups of our Biometric world. These run the gamut of theoretical to applied mathematics and statistics with the applications spanning many fields and, not surprisingly, forming a rich source of new theoretical developments.

The stochastic general epidemic model revisited and a generalization.

While the mathematical theory of epidemics has its origins with Ross (1911), it was not until Kryscio (1975) that explicit expressions for the state probabilities of the classical general epidemic model established by Bartlett (1949) were found. However, these formulae were of limited practical use when the population size was of even moderate size. By shifting the focus from the bivariate pair representing the number of susceptibles and infectives to that for the number of infectives and removals, one is able to obtain solutions that are considerably simpler and easier to manage than those previously derived and which are not restricted by the size of the population. The results are obtained for a generalized general epidemic process in which transition probabilities are arbitrary functions of the state space, and then applied to the classical model. An extension to time-dependent transition rates is also considered.

Incubation period of AIDS in patients infected via blood transfusion.

Temporal trends in the prevalence of HIV (human immunodeficiency virus) infection are uncertain because of the reluctance of most governments to embark on large-scale programmes of serological surveillance. In the absence of such data, attempts have been made to relate the number of reported cases of AIDS (acquired immune deficiency syndrome) in a defined population to the proportion of that population infected with the virus as a specified time point. One crucial determinant of this relationship is the probability distribution of the incubation period of the disease, with the period defined as the time interval from infection to diagnosis. Recent statistical analysis suggests a mean incubation period of 4.5 years with wide confidence limits, whereas a more heuristic study reports a mean of 15 years. Here we report on a new analysis which reveals age-related differences in the mean (and median) incubation period: 1.97 (1.90) years for children (0-4 yrs old at infection), 8.23 (7.97) years for adults (5-59 yrs old), 5.50 (5.44) years for elderly patients (60 yrs and older).