Advanced Statistical Analysis Research Articles

Abstract 3598: Classification of the collagen ECM in normal human tissue as a biomarker for future breast cancer incidence

Abstract Because we do not know who will develop breast cancer, or who will have a relapse of cancer following surgery, there is an urgent need for the development of biomarkers that can be detected specifically in normal, nondiseased tissue. For 30 years there has been a search for the genetic markers that will predict risk of developing breast cancer, but even while family history is a known risk factor, only 10% of breast cancer risk can be specifically linked to a genetic mechanism (BRCA-1, BRCA-2). Furthermore, individuals who are at high risk due to genetics or family history do not always develop breast cancer. Breast density is another indicator of future incidence, as high-density tissue is correlated with a 4-6 fold increased risk of developing cancer. We hypothesized that a population analysis of the properties of the individual collagen fibers that comprise the extracellular matrix (ECM) in nondiseased breast tissue is a biomarker for the onset of disease. We used multiphoton second-harmonic generation (SHG) imaging to generate high-contrast images of the collagen ECM that can be gathered from common histologic slides that require no additional staining. Our goal was to determine the range of heterogeneity in the properties of the collagen matrix in normal samples. We imaged samples from 141 normal patient donors to comprise 4 different cohorts that define this ground truth state of the matrix. The cohorts were from donors who had never been diagnosed with breast cancer; who had previously been diagnosed with breast cancer but now were disease free; who had never been diagnosed with breast cancer at the time of tissue donation but later went on to develop breast cancer; and those who had donated tissue while in remission from an initial breast cancer diagnosis but later went on to have a recurrence of breast cancer. The SHG images of each patient sample were then analyzed using the curvelet transform-based ctFIRE and CurveAlign software platforms to generate data (fiber length, width, straightness, density, angle with respect to a boundary of epithelial ducts/lobules, and the relative alignment of fibers to each other) on each individual collagen fiber. Advanced statistical measurements and principal component analysis were used to classify the nature of each of the cohorts. Based on our measurements of the structure and organization of collagen fibers, we found that while measurements within a cohort were consistent, there were unique attributes of the collagen matrix that defined each cohort. Furthermore, we performed regression analysis of our measurements against the standard clinical features (age, race, BMI, etc.). Because our various cohorts delineate the nondiseased, involuted, precancer, and prerecurrence normal matrix, respectively, we feel that these data serve as a highly useful, novel classifier, one that describes the clinical impact of the timbre of the matrix in nondiseased women. Citation Format: Ryan J. Gigstad, Tianjie Wang, Yifei Liu, Menggang Yu, Yuming Liu, Adib Keikhosravi, Kevin W. Eliceiri, Patricia J. Keely, Matthew W. Conklin. Classification of the collagen ECM in normal human tissue as a biomarker for future breast cancer incidence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3598.

Redundancy analysis for determination of the main physicochemical characteristics of filtration membranes explaining their fouling by peptides

Peptide fouling is a technological drawback in filtration membrane processes. The impact of membrane characteristics on fouling by peptides from a complex whey protein hydrolysate (WPH) fouling was assessed based on advanced statistical redundancy analysis. Six membranes were characterized and tested: PES, PVDF, CF55, S11, S11+ and S11-. Among the eight physicochemical characteristics analyzed, zeta-potential (ZP) and roughness (Rz) were highly correlated with total fouling quantity (TFQ), suggesting that peptide fouling was mainly due to electrostatic interactions over wider surfaces. Concerning peptide sequences, redundancy analysis indicated that at least one characteristic among ZP, Rz, contact angle and thickness contributed significantly to the fouling of ALMPHIR, LIVTQTMK, TKIPAVFK, VLVLDTDYK, TPEVDDEALEK, TPEVDDEALEFDK or SLAMAASDISLLDAQSAPLR. It appeared that membranes with hydrophilic surfaces were more likely to be fouled by WPH regardless the peptide hydrophilicity. Besides, wider surface would enable more contact area for peptides, increasing consequently fouling. The TFQ was described by a statistical predictive model using the combined effect of ZP and Rz, of the form: ln(TFQ) = 8.64 + 0.072.XRz + 0.088.XZP (R2 = 0.9098). Moreover, statistical models were also established for each peptide, enabling to predict their specific fouling on a variety of membranes with different physicochemical characteristics.

Sodium-cooled fast reactor pin model for predicting pin failure during a power excursion

Within the framework of the Generation IV Sodium-cooled Fast Reactor (SFR) in which the CEA (French Commissariat à l’Energie Atomique et aux Energies Alternatives) is involved, the French innovative reactor design behavior under severe accidents conditions has to be assessed. Such accidents have mainly been simulated with mechanistic calculation tools (such as SAS-SFR and SIMMER-III). As a complement to these codes, which provide reference accidental transient calculations, a new physico-statistical approach is being developed at CEA; its final objective being to derive the variability of the main results of interest to quantify the safety margins. This approach requires fast-running tools to simulate extended accident sequences, by coupling models of the main physical phenomena with advanced statistical analysis techniques. The tool enables to perform a large number of simulations in a reasonable computational time and to describe all the possible scenario progressions of the hypothetical accidents. This general approach, combining mechanistic codes and evaluation tools, has already been conducted for some accidental initiator families (USAF – Unprotected SubAssembly Fault (Marie et al., 2016) and ULOF – Unprotected Loss Of Flow (Droin et al., 2017). In this context, this paper presents a physical tool (numerical models and result’s assessment) dedicated to the simulation of the beginning of the primary phase of the Unprotected Transient OverPower accidents (i.e. before failure of sub-assembly wrapper).At the beginning of this primary phase, the fast increase of nuclear power induces a strong temperature rise in the fuel pellets leading to strong mechanical and thermal loads on the cladding which could lead to clad failure or/and fuel meltdown. These phenomena are described and modelled analytically in single pin geometry in accordance to the level of details required to catch all the decisive phenomena.Slow power increase transients, such as control rod withdrawal, and fast power increase transients have been investigated in the past. Experimental validation on CABRI (experimental reactor dedicated to safety studies) and CESAR (Circuit d’Etude de l’ébullition du Sodium lors d’un Accident de Réactivité) experiments were carried out focusing on the amount of molten fuel formed during the transient, on the propagation of the void front in the channel in case of sodium boiling and on the pin failure mechanisms. Furthermore, a comparison of the physical tool calculation results was performed against reference accident SIMMER-III calculations.The tool is demonstrated to be able to predict the radial propagation of the molten zone in the fuel pin, the pin failure mechanism and void front propagation with a discrepancy of less than 10%. In the future, this physical tool, associated with a point kinetic neutronic model, will be used to simulate the global core behavior under an UTOP transient following a ramp excursion.

Automated Advanced Calibration and Optimization of Thermochemical Models Applied to Biomass Gasification and Pyrolysis

This paper presents a methodology that combines physicochemical modeling with advanced statistical analysis algorithms as an efficient workflow, which is then applied to the optimization and design of biomass pyrolysis and gasification processes. The goal was to develop an automated flexible approach for the analyses and optimization of such processes. The approach presented here can also be directly applied to other biomass conversion processes and, in general, to all those processes for which a parametrized model is available. A flexible physicochemical model of the process is initially formulated. Within this model, a hierarchy of sensitive model parameters and input variables (process conditions) is identified, which are then automatically adjusted to calibrate the model and to optimize the process. Through the numerical solution of the underlying mathematical model of the process, we can understand how species concentrations and the thermodynamic conditions within the reactor evolve for the two proce...

Using Machine Learning to Predict Success or Failure in Chapter 13 Bankruptcy Cases

Obtaining a chapter 13 bankruptcy discharge is notoriously difficult. Past empirical studies conclude that only one-third of chapter 13 debtors complete their obligations under their plans and obtain a chapter 13 discharge. Many cases end up dismissed, or converted to a case under chapter 7. New data recently made available by the Federal Judicial Center, shows that in recent years only about 39% of chapter 13 filers successfully obtain their chapter 13 discharges. These are low numbers. In this project I examined a public case level database made available in 2017 by the US Federal Judicial Center, based on information collected by the Administrative Office of the United States Courts. The project examines the extent and quality of this data, and the steps needed to use it for advanced statistical analysis and application of machine learning models. This project goes beyond such descriptive statistics. Using machine learning algorithms – so-called artificial intelligence – it describes a model that can predict, using data from the Federal Judicial Center's Integrated Database, whether a debtor will obtain a chapter 13 discharge based only on information provided in the initial petition and summary of schedules. The model is able to predict case results with 70% accuracy overall – and for about 25% of cases can predict results with more than 90% accuracy. When case predictions are cross-referenced against actual case results, the model can assign to specific cases a highly accurate probability of success. The model uses a random forest decision tree algorithm to achieve its results, although nearly similar results were also obtained using a neural network. The model, relevant scripts, and related files and instructions for use are available online through Github at /warrenagin/Ch13Learner.

Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review

ABSTRACTBig data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

A web-based system for neural network based classification in temporomandibular joint osteoarthritis

ObjectiveThe purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). MethodsThis study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. ResultsThe DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. ConclusionsThe findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis.

Nuclear proliferomics: A new field of study to identify signatures of nuclear materials as demonstrated on alpha-UO3

The use of a limited set of signatures in nuclear forensics and nuclear safeguards may reduce the discriminating power for identifying unknown nuclear materials, or for verifying processing at existing facilities. Nuclear proliferomics is a proposed new field of study that advocates for the acquisition of large databases of nuclear material properties from a variety of analytical techniques. As demonstrated on a common uranium trioxide polymorph, α-UO3, in this paper, nuclear proliferomics increases the ability to improve confidence in identifying the processing history of nuclear materials. Specifically, α-UO3 was investigated from the calcination of unwashed uranyl peroxide at 350, 400, 450, 500, and 550 °C in air. Scanning electron microscopy (SEM) images were acquired of the surface morphology, and distinct qualitative differences are presented between unwashed and washed uranyl peroxide, as well as the calcination products from the unwashed uranyl peroxide at the investigated temperatures. Differential scanning calorimetry (DSC), UV–Vis spectrophotometry, powder X-ray diffraction (p-XRD), and thermogravimetric analysis-mass spectrometry (TGA-MS) were used to understand the source of these morphological differences as a function of calcination temperature. Additionally, the SEM images were manually segmented using Morphological Analysis for MAterials (MAMA) software to identify quantifiable differences in morphology for three different surface features present on the unwashed uranyl peroxide calcination products. No single quantifiable signature was sufficient to discern all calcination temperatures with a high degree of confidence; therefore, advanced statistical analysis was performed to allow the combination of a number of quantitative signatures, with their associated uncertainties, to allow for complete discernment by calcination history. Furthermore, machine learning was applied to the acquired SEM images to demonstrate automated discernment with at least 89% accuracy.

Exploring the longevity risk using statistical tools derived from the Shiryaev–Roberts procedure

Longevity and mortality risks are daily issues for the actuarial community. To monitor this risk, various, accurate and efficient tools have been developed (e.g. Shiryaev in Theory Probab Appl 8(1):22–46, 1963; Roberts in Technometrics 8(3):411–430, 1966; Polunchenko and Tartakovsky in Ann Stat 38(6):3445–3457, 2010). A particular attention is usually spent on the detection of a change-point, i.e. the time where the level of mortality changes (El Karoui et al. in Minimax optimality in robust detection of a disorder time in poisson rate (working paper or preprint), 2015; Croix et al. in Bulletin Francais d’Actuariat 15(29):75–112, 2015). A common assumption in all these works is that the distribution of the deaths is well known not only before the change-point but also after. In the present paper, we consider a parametric framework for the distribution after the changer and we suppose that we do not know its parameter after the change-point. Thus we focus on its estimation. Our method is derived from the sequential Shiryaev–Roberts procedure. The paper starts with a presentation of this procedure and our methodology in a general framework. We provide a specific Poisson model, designed here for the study of the mortality as in Rhodes and Freitas (Advanced statistical analysis of mortality. AFIR papers. Boston Colloquia. MIB inc., Westwood, 2004) and Planchet and Tomas (Insur Math Econ 63:169–190, 2015). Two versions are analysed: in the first one, we assume that the current mortality is stable and we look for a sudden but persistent change of level. In the second model, we introduce a new set-up: the mortality evolves at a steady pace and we look for a change of the trend. Variants of these approaches are also widely expressed and are compared to benchmark methodologies. An important part of this work is devoted to the application of our methodology on real data, in a context where the change is obvious, using specific methodologies to adjust the data as in Mei et al. (Stat Sin 597–624, 2011). We also study a real insurance portfolio where no specific information might help us to understand the change, and where the change itself does not seem perceptible. For the given examples, the main results allow us to identify the change-points of the mortality when they happen and to measure the lag before clear identification of the phenomena.

Identifying the Absence of Effective Internal Controls: An Alternative Approach for Internal Control Audits

ABSTRACTAuditors face new challenges when auditing internal controls due to the increasing integration of information systems for transaction processing and the growing amount of data. Traditional manual control testing procedures become inefficient or require highly specialized and scarce technical knowledge. This study presents audit procedures that follow a new approach. Instead of manually testing internal controls, automated procedures search for the absence of those controls. Process mining techniques are combined with advanced statistical analysis where process mining serves as a data analysis technique to create process models from the recorded transaction data. These are searched for critical data constellations in combination with an exploratory factor analysis to identify systematic deficiencies in the internal control system. The manual and time-intensive inspection of individual controls is replaced by automated audit procedures that cover the totality of recorded transactions. The study follows a design science approach and uses case study data for illustration.

Experimental and statistical investigation of the compressive strength anisotropy in structural concrete

This paper offers a new and closer look into the strength anisotropy of concrete by presenting the so far largest experimental programme (290 tests) and by presenting an advanced statistical analysis of the results. The experimental investigation sheds light on the influence of several important design parameters and conditions on the anisotropy. This includes the influence of reinforcement, w/c-ratio, curing time, load history and structural geometry. For this purpose, cores were drilled out at different angles from beam- and slab specimens for compressive testing. The main findings include: a) the reference cylinder strength (i.e. w/c-ratio) does not have a significant influence on the anisotropy when the anisotropy is quantified as an absolute difference between the strength of cores drilled in the two directions; b) the anisotropy in structural members without load history is <5 MPa; c) the anisotropy amounts to 5–10 MPa for members with load history.

Development of Growth Charts of Pakistani Children Aged 4-15 Years Using Quantile Regression: A Cross-sectional Study.

BackgroundGrowth charts are essential tools used by pediatricians as well as public health researchers in assessing and monitoring the well-being of pediatric populations. Development of these growth charts, especially for children above five years of age, is challenging and requires current anthropometric data and advanced statistical analysis. These growth charts are generally presented as a series of smooth centile curves. A number of modeling approaches are available for generating growth charts and applying these on national datasets is important for generating country-specific reference growth charts.ObjectiveTo demonstrate that quantile regression (QR) as a viable statistical approach to construct growth reference charts and to assess the applicability of the World Health Organization (WHO) 2007 growth standards to a large Pakistani population of school-going children.MethodologyThis is a secondary data analysis using anthropometric data of 9,515 students from a Pakistani survey conducted between 2007 and 2014 in four cities of Pakistan. Growth reference charts were created using QR as well as the LMS (Box-Cox transformation (L), the median (M), and the generalized coefficient of variation (S)) method and then compared with WHO 2007 growth standards.ResultsCentile values estimated by the LMS method and QR procedure had few differences. The centile values attained from QR procedure of BMI-for-age, weight-for-age, and height-for-age of Pakistani children were lower than the standard WHO 2007 centile.ConclusionQR should be considered as an alternative method to develop growth charts for its simplicity and lack of necessity to transform data. WHO 2007 standards are not suitable for Pakistani children.

Membrane Leakage: Further Insight by Advanced Statistical Analysis of Vesicle Leakage

The action of membrane-active compounds involves permeabilizing the lipid portion of membranes. For example, this applies to antimicrobial, anticancer and cell penetrating peptides and their synthetic mimics, transfection agents, and drug delivery vehicles. Membrane permeabilization is modelled by the release of an entrapped self-quenching fluorescent dye from lipid vesicles. This allows to determine the total leakage but also the distribution of leakage over the vesicle population. By a statistical concept and kinetic studies, we characterize the strength of an individual leakage event and discuss the molecular mechanisms such as asymmetry stress, curvature stabilization (toroidal pores, defects), large (oligomeric) pores and combinations of these. We apply the concept to leakage induced by detergents, antimicrobial compounds and leakage upon phase transition of lipids. Surprisingly, the strength of the individual leakage event can vary for a given molecular mechanism. For example, the strength of leakage in asymmetric binding and cracking-in can depend on the ratio of electrostatic to hydrophobically driven interactions and varies with both, active agent and lipid composition. The superposition or succession of different mechanisms (e.g. asymmetry stress and formation of defined pores at higher concentrations of active agent) can be assessed and may contribute to selectivity and activity. We better relate results from model studies to biological cases and improve our understanding of processes impossible to study directly, such as membrane-mediated antimicrobial action.

Business model innovation: towards a conceptual framework

Business model innovation (BMI) is core for entrepreneurship in industrial or service sectors, including tourism. Our submission focuses on the development of the field of BMI. We perform systematic literature review based on 316 valid papers indexed on ISI Current Contents, published between 2000 and 2015. Bibliometric results indicate a significant increase in the number of papers, journals and authors, and lack of specialized journals/authors publishing regularly in the BMI field. Thematic analysis reveals that BMI has been extensively studied, however there is rarely any cumulative knowledge effect between authors. Most researches were conceptual or qualitative, however few constructs have been defined and tested to enable quantitative studies. We conclude that there had been little evolution in terms of theoretically developing the BMI field. We argue that there is a need to adopt already existing conceptual models, develop constructs and validate them with primary quantitative data and advanced statistical analysis.

Natalizumab Changes the Peripheral Profile of the Th17 Panel in MS Patients: New Mechanisms of Action.

Natalizumab (NAT) is an effective treatment for relapsing remitting multiple sclerosis (RRMS), as it makes the blood-brain-barrier impenetrable by binding to the α4integrin subunit. The objectives of our study were to find new peripheral mechanisms of action of NAT and new biomarkers of treatment response. We prospectively assessed the serum levels of 15 cytokines from the Th17 Cytokine Panel using Bio-plex Pro Human in a group of 29 RRMS patients treated with NAT and 29 healthy subjects (HS) at inclusion and after 8 months of NAT treatment. For each patient, demographic data, number of relapses and Expanded Disability Status Scale (EDSS) were collected and compared with the initial and final values of each cytokine. Moreover, the Th17/Treg shift was assessed using the interleukine (IL)-17F/IL-10 ratio and the cytokine signature (the sum of all the cytokines). Advanced statistical analysis was used. RRMS patients had significantly lower serum levels of IL-23, IL-17F, IL-1β and IL-31 compared to HS. Serum sCD40L, IL-17F, IL-31 and cytokine signature levels significantly decreased after 8 months of NAT treatment. Positively correlations were found between the relapse number and IL- 17F, IL-1β, IL-31 serum levels and between EDSS and tumor necrotic factor-α, IL-1β and IL-17/IL-10 serum levels. IL-10 serum levels correlated negatively with the EDSS score. In evaluating the mode of action of NAT, it is important to determine the value of each cytokine, the Th17/Treg shift and the cytokine signature. NAT significantly decreased peripheral serum levels of some pro-inflammatory cytokines as a novel mechanism of action. IL-17F, sCD40L and IL-31 were the best biomarkers to assess the effectiveness of NAT.

Prediction of Extended Red Blood Cell Phenotype from Exome Next Generation Sequencing Data

Introduction:Accurate typing of patient and donor red blood cell (RBC) antigens is critical for safe transfusion practice. Although blood typing is traditionally accomplished by serology, genotyping methods to predict RBC antigens have proven valuable in a growing number of situations such as recently-transfused patients, scarcity of typing reagents, and indeterminate serologic results. However current RBC genotyping assays address a limited number of blood group genes and associated variants, and may not detect novel genetic changes and certain rare but clinically-significant variants. Next generation sequencing (NGS) technology provides an appealing alternative technology, allowing the user to examine a patient's entire genome or exome in a high-throughput manner. Whereas efforts are underway in multiple fields to apply exome sequencing (ES) for diagnostic, prognostic, and treatment purposes, Transfusion Medicine, with its extensive clinical genomic database, should find ready application from this approach. We describe here the creation of an algorithm to interpret NGS into a predicted extended RBC phenotype, and its application to analyze ES data from 245 participants of the ClinSeq® sequencing cohort.Methods:RyLAN (Red Cell and Lymphocyte Antigen prediction from NGS) was created as an open-source Python application that takes an NGS sorted binary alignment matrix (.bam) file and index as input. The software interacts with a non-relational database that encodes genomic blood group coordinates and phenotype interpretation rules, and yields a predicted extended RBC phenotype and quality parameters. Hard filters for mapping quality, depth, vcf QUAL, and fraction of alternate allele can be modified per individual genomic coordinate. The output is provided as a MongoDB document to facilitate advanced bulk queries and statistical analysis. We employed RyLAN to analyze 245 ES NGS files from the ClinSeq® cohort, using a database of 176 known antigenic, null, and weak blood group single nucleotide variants in 27 blood group genes as input.Results:The cohort consisted of 115 females and 130 males; 89% of participants self-described as white race, non- Hispanic ethnicity. Three percent of participants self-described as Hispanic or Latino, 4% as Asian, 2% with African ancestry, and the remaining as mixed or unknown race. From the total 176 genomic positions analyzed, 160 were not addressed by current commercially-available RBC genotyping platforms. The average read depth for the positions of interest was 78.2, and the average vcf QUAL value was 968. The highest variant nucleotide frequency was observed at the Fya/Fyb and Jka/Jkb loci (275 and 223 total haplotype variant calls, respectively).Among other phenotypes, RyLAN predicted 4 instances of heterozygosity for the KEL*02N.17 allele, 5 heterozygous individuals for the weak FY* X allele, 32 total heterozygous samples for various weak Kidd alleles, 2 homozygous individuals for weak Kidd expression, 1 heterozygosity for Lu6/Lu9, 1 SC:1,2 case, 1 Co(a-b+) predicted phenotype, and a total of 19 RHAG*01.04 and 47 KLF1*BGM12 alleles. Limited areas of the BCAM, KLF1, KEL, FUT7, ERMAP and CR1 genes failed quality filters repeatedly, and careful review indicated that these regions were not captured in the ES libraries. The ACKR1 promoter GATA-binding site variant was present in every sample and predicted all cases of self-reported African ancestry.Conclusions:We describe a new, open-source informatics tool to translate NGS data into a predicted extended RBC phenotype, and demonstrate its application through the analysis of 245 ClinSeq® ES files. Most predicted antigen frequencies were as expected for the ethnic composition of our cohort. We detected a higher frequency of the RHAG p.V270I and KLF1 p.S102P variants than expected, findings that are in agreement with the 1000 Genomes Project and warrant further study. Our analysis also corroborates the relative frequency of the JK*01W.01 allele, and the presence of the JK*01W.03 and JK*01W.04 alleles in the Caucasian population, which can lead to serologic discrepancies in other genotyping platforms. Serologic confirmation of these findings is being conducted. Further study of genomic data across multiple ethnic groups can help refine knowledge of blood group gene polymorphisms and their clinical association. DisclosuresNo relevant conflicts of interest to declare.

VISIBE AND INFRARED SPECTRAL CHARACTERISATION OF CHINESE CABBAGE (BRASSICA RAPA L. SUBSPECIES CHINENSIS), GROWN UNDER DIFFERENT NITROGEN, POTASSIUM AND PHOSPHORUS CONCENTRATIONS

Abstract. There is a need to intensify research efforts on improving productivity of indigenous vegetables in South Africa. One research avenue is operationalizing remote sensing techniques to monitor crop health status. This study aimed at characterising the spectral properties of Chinese cabbage (Brassica Rapa L. subspecies Chinensis) grown under varying fertilizer treatments: nitrogen (0 kg/ha, 75 kg/ha, 125 kg/ha, 175 kg/ha and 225 kg/ha), phosphorus (0 kg/ha, 9.4 kg/ha, 15.6, 21.9 kg/ha and 28.1 kg/ha) and potassium (0 kg/ha, 9.4 kg/ha, 15.6 kg/ha, 21.9 kg/ha and 28.1 kg/ha). Visible and infrared spectral measurements were taken from a total of 60 samples inside the laboratory. Contiguous spectral regions were plotted to show spectral profiles of the different fertilizer treatments and then classified using gradient boosting and random forest classifiers. ANOVA revealed the potential of spectral reflectance data in discriminating different fertiliser treatments from crops. There was also a significant difference between the capabilities of the two classifiers. Gradient boost model (GBM) yielded higher classification accuracies than random forest (RF). The important variables identified by each model improved the classification accuracy. Overall, the results indicate a potential for the use of spectroscopy in monitoring food quality parameters, thereby reducing the cost of traditional methods. Further research into advanced statistical analysis techniques is needed to improve the accuracy with which fertiliser concentrations in crops could be quantified. The random forest model particularly requires improvements.

MGA repository: a curated data resource for ChIP-seq and other genome annotated data.

The Mass Genome Annotation (MGA) repository is a resource designed to store published next generation sequencing data and other genome annotation data (such as gene start sites, SNPs, etc.) in a completely standardised format. Each sample has undergone local processing in order the meet the strict MGA format requirements. The original data source, the reformatting procedure and the biological characteristics of the samples are described in an accompanying documentation file manually edited by data curators. 10 model organisms are currently represented: Homo sapiens, Mus musculus, Danio rerio, Drosophila melanogaster, Apis mellifera, Caenorhabditis elegans, Arabidopsis thaliana, Zea mays, Saccharomyces cerevisiae and Schizosaccharomyces pombe. As of today, the resource contains over 24 000 samples. In conjunction with other tools developed by our group (the ChIP-Seq and SSA servers), it allows users to carry out a great variety of analysis task with MGA samples, such as making aggregation plots and heat maps for selected genomic regions, finding peak regions, generating custom tracks for visualizing genomic features in a UCSC genome browser window, or downloading chromatin data in a table format suitable for local processing with more advanced statistical analysis software such as R. Home page: http://ccg.vital-it.ch/mga/.

Impact of Child’s Cancer on Family Life Case Study: Children with Cancer Newly admitted to the National Cancer Institute (NCI), Cairo University

The diagnosis of a family member with any disease certainly affects the family life in general. Hence, this study aims at exploring how the diagnosis of a child with cancer affects family life. Additionally, the study seeks to identify the extent to which the life of family can change when they have a child diagnosed with cancer. The study used the Advanced Statistical Analysis technique through conducting a random sampling field study of (804) individuals, of which (402) were mothers of children diagnosed with cancer (as an case group), and (402) were mothers whose children’s samples gave negative results when tested for cancer (as a control group). Through a Questionnaire Form designed to track the extent to which the lives of families covered by the two study samples (the case group and the control group) have changed, the researcher used a number of indicators, which she believes to have an effect on family status, to compare the changes experienced by families in both groups. The 11 indicators used by the study measure the effects of having a child with cancer in each family individually. Results revealed significant differences in the values of indicators between the group having a child with cancer and the group whose children tested negative for cancer before and after diagnosis/examination. A composite indicator consisting of the previous indicators is developed using Factor Analysis, which calculated the said indicator based on four factors. These factors are then multiplied by their corresponding weights and aggregated into the new composite indicator (Family Status Indicator). The indicator was clustered using the K-Mean Cluster Analysis into 3 clusters, i.e. low, medium, and high. Conclusion: The child cancer has an impact on the Family Status indicator, demographics and socioeconomic determinants affect the Family Status indicator like (mother work and Education, Education of the father, Place of Residence, ...etc.) after child examination or disease.

A modified Winkler’s method for determination of dissolved oxygen concentration in water: Dependence of method accuracy on sample volume

A modification to the standard Winkler’s method (WM) for measurement of dissolved oxygen (DO) concentration in aqueous samples enabled DO measurements in 1mL sample volume. However, advanced statistical analysis indicated that the proposed method (PM) showed a small positive bias, giving results that were on average about 6% higher than corresponding values measured using the WM. In this study, DO measurements were carried out in sample volumes ranging from 2mL to 10mL using the PM. Statistical analysis of these results indicate that the bias reported in the PM could be eliminated (i.e., limited to a±2% range) using sample volumes of 7mL or higher. Use of a larger sample volume also enables lowering the least count of the PM to 0.1mg/L O2 as compared to 0.25mg/L O2 reported previously.