Additional Sample Sets Research Articles

Effects of a single oral dose of azithromycin in laboring women on antimicrobial resistance (AMR) and the microbiome: a protocol for the Antimicrobial Resistance Sub-Study of the A-PLUS trial

Background The Azithromycin Prevention in Labor Use Study (A-PLUS), a large, multi-national randomized trial, was performed to evaluate the improvement of maternal and newborn outcomes following a single dose of azithromycin (AZ) given during labor. However, the immediate and long-term impact of this single dose on the microbiome and the development or prevalence of antimicrobial resistance are unknown. We designed a sub-study to assess AZ resistance of bacterial isolates from clinical infections and of three target bacteria (Staphylococcus aureus, Streptococcus pneumoniae, and Escherichia coli) from the serial surveillance of the nasopharynx and rectum. Additionally, the serially surveilled samples will be evaluated for changes to the microbiome and the resistome of the nasopharynx and rectum. Methods and Analysis As part of the large, randomized APLUS trial, maternal and neonatal clinical infections were monitored for up to 42 days postpartum, and samples collected for culture using site-specific routine methods. For this sub-study, cultured bacterial isolates will be assessed for AZ resistance using an antibiotic susceptibility method. Additionally, a random subset of maternal-neonatal dyads from the main trial will be selected for serial surveillance with aseptic swab collections of the nasopharynx and the rectum at baseline, and subsequently at 1-week, 6-weeks, 3-months, 6-months, and 12-months postpartum. The serial samples will undergo selective culturing of sentinel bacterial species and screened for AZ resistance. An additional set of serial samples will be stored for future microbiome and resistome analyses. Ethics and Dissemination The study protocol was reviewed, and ethics approval obtained from all the relevant ethical review boards at each research site. All participants will provide informed written consent prior to their enrollment. Results of the trial will be disseminated through peer-reviewed publications, presentations at relevant conferences, and at dissemination meetings with the ministries of health and local stakeholders at each research site. Trial Registration NCT03871491.

Deuterium retention in displacement-damaged tungsten-rhenium alloys: Influence of rhenium concentration and irradiation temperature

Tungsten (W) samples containing 0, 1, 3, and 5 at.% rhenium (Re) were irradiated by 20 MeV W ions to 0.5 dpa at 290 K and to 0.3 dpa at 1350 K. An additional set of samples was irradiated to 0.5 dpa at 290 K and then annealed at 1350 K. The irradiated samples were exposed to a deuterium (D) plasma at 370 K. D concentration profiles in the samples were measured using D(He3,p)α nuclear reaction analysis. The D binding states in the defects were analysed using thermal desorption spectroscopy (TDS). In the case of irradiation at 290 K, the trapped D concentration monotonically increases with increasing Re concentration: in W-5%Re it is 17% higher than in pure W. On the contrary, for irradiation at 1350 K the trapped D concentration monotonically decreases with increasing Re concentration: in W-5%Re it is 50 times smaller than in pure W. In the case of annealing at 1350 K of the samples irradiated at 290 K, the Re presence yields only up to three times reduction of trapped D concentration compared with pure W. TDS shows that the nature of D trapping sites is different for the irradiations at 290 K and 1350 K. We attribute the reduced D trapping in W-Re alloys irradiated at 1350 K to the reduction of cavity growth caused by the presence of Re.

MEASUREMENT OF MICROBIOLOGICAL QUALITY OF RAW GOAT´S MILK BY LASER FLOW CYTOMETRY

The popularity of goat's milk and its products is growing in Slovakia. Among the mandatory health safety features of raw goat's milk is the total bacterial count. It can be determined by the anchor plate-cultivation method, which is, however, tedious, and laborious. Laser flow cytometry is a modern alternative method for measuring the microbiological quality of raw milk. To use the results from laser flow cytometry method for legislative purposes, it is necessary to create a representative conversion of the primarily measured results obtained by this method into the CFU/mL scale. This work describes the creation of such a conversion for raw goat's milk, which will be valid for the Slovak region. After measuring several hundred individual and bulk tank samples of raw goat's milk on a BactoScan FC laser flow cytometer and at the same time using the anchor method according to STN EN ISO 4833-1 and after their subsequent logarithmic transformation, such a conversion was created in the form of log10(CFU/mL) = 1.0174 x log10(IBC/ml) + 2.7483. Subsequently, this was verified by comparing the recalculated and directly measured results in CFU/mL, using additional data set of samples. The influence of the somatic cell count in raw goat's milk, as a possible interfering factor, was also assessed.

Impact assessment of metabolite instability in the development and validation of LC-MS/MS bioanalytical assays for measurement of rosuvastatin in human plasma and urine samples.

During bioanalytical assay development and validation, maintaining the stability of the parent drug and metabolites of interest is critical. While stability of the parent drug has been thoroughly investigated, the stability of unanalyzed metabolites is often overlooked. When an unstable metabolite is known or suspected to interfere with measurement of the parent drug or other metabolites of interest through back-conversion or other routes, additional tests with these unstable metabolites should be conducted. Here, the development and validation of two assays for quantification of rosuvastatin, one in human plasma and one in human urine, was reported. To this end, additional sets of quality control samples were added during assay validation to ensure the reliability of the assays. Acid treatment of samples is shown to be necessary for rosuvastatin quantification. In this regard, stability issues caused by the metabolite, rosuvastatin lactone, may have been overlooked if assay development and validation had only considered the parent drug, rosuvastatin. These assays represent a case study for how to develop and validate assays with unstable metabolites. Taken together, unstable metabolites should be included in all applicable stability tests.

A novel ensemble of metamodels using improved stepwise metamodel selection and two-layer pointwise ensemble

Although an independent metamodel may be used to accurately describe certain response relationships, it can be challenging to find suitable metamodels for various problems. The ensembled metamodel, in contrast, is a more universal method. The key to enhancing the performance of the ensembled metamodel is determining how to select the optimal set of metamodels to be ensembled (MBEs) and how to ensemble them to obtain a better fit. However, existing methods face challenges in exploring the fitting ability of individual metamodels within a pre-set sample set, often requiring prior knowledge or additional sample sets for assistance to get optimal MBEs, and they struggle to fully utilize the fitting ability of individual metamodels at each sample point. To address these challenges, this paper proposes a novel ensemble of metamodels using improved stepwise metamodel selection and two-layer pointwise ensemble (OSF-TLPE) through research in three aspects: model selection, weight calculation, and residual compensation. Firstly, the Leave-P-Out Prediction (LPOP) proposed in this paper reconstructs the response of the metamodel to the problem, which reduces the complexity of model selection. Then, considering the redundancy of the metamodel during ensemble, the Out-of-Bag Estimation (OOBE) based Stepwise Fit (OSF) eliminates redundancy as feature selection, which is a globally optimal method for metamodel selection. The two-layer pointwise ensemble of metamodels (TLPE), which consists of weight metamodels (WMs) and a residual metamodel (RM) provide a more robust ensemble strategy by assigning weights point by point and compensating for residuals. Finally, the performances of OSF and TLPE are evaluated on 12 benchmark functions using 6 and 13 typical metamodels, respectively. A performance comparison was also conducted on an aircraft final assembly line performance prediction problem. The results demonstrate that OSF-TLPE can deliver more flexible and reliable performances compared to common ensemble methods.

Critical assessment of chemometric models employed for varietal authentication of wine based on UHPLC-HRMS data

The use of metabolic fingerprinting combined with advanced chemometric tools for wine authentication has increased in recent years. Although numerous studies, showing different authentication strategies, have been published, rarely any attention has been paid to the stability of used classification models over a longer time period.Here, we present a reliable and robust metabolic fingerprinting-based multiclass strategy for varietal authentication of wine. Analysis was conducted using ultra-high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry. Two sets of commercial wine samples, one for the creation of classification models (201 wines, five red and five white grape varieties) and one for the verification of their validity over a longer time period (138 wines, three white varieties), were analysed. The generated data from the first sample set were subjected to orthogonal partial least squares discriminant analysis (OPLS-DA). The resulting models were validated and used to build decision trees, which enabled the classification of wine samples according to the grape variety. The individual classification rates of the OPLS-DA models were 90–100%. Overall classification rates of the decision trees were 94 and 96% for red and white wines, respectively. In case of the white wine decision tree, verification of its validity over a longer time period was performed using an additional sample set, analysed four months after the original sample set. From the additional sample set, 87% of samples were correctly classified, thus, the stability of the OPLS-DA classification models over a longer time period was verified. In addition, 25 varietal markers of significant statistical importance, mostly flavonoids, phenolic acids and their derivatives, were tentatively identified.

Biomarker Exploration for neoadjuvant chemotherapy with gemcitabine combined with cisplatin in muscle-invasive bladder cancer.

e16556 Background: Neoadjuvant chemotherapy (NCT) is a standard treatment for muscle-invasive bladder cancer (MIBC). However, there is still a considerable proportion of patients who respond suboptimally to available treatments or experience adverse effects. Thus, identifying novel biomarkers to predict response to NCT of MIBC is urgently needed. Methods: A total of 23 patients were enrolled in this study, all of whom were cT2-T4aNxM0 MIBC and received 2̃4 cycles CG (gemcitabine and cisplatin) NCT. Radiographic assessment was performed at the end of each cycle or at the time of progression. Tumor tissue were collected from all patients before NCT. Whole exome sequencing (WES) and RNA sequencing (RNA-seq) were performed for tissue samples. In addition, peripheral blood was collected as a control for WES. Treatment responses were assessed by comparing the imaging data before and after treatment using the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Patients with CR and PR were regarded as the responder group, and those with SD and PD were regarded as the non-responder group. Results: Number of patients received 2̃4 cycles NCT were 16 for the responder group and 7 for the non-responder group. Of 19 patients completed WES, 13 patients achieved response and 6 patients did not respond. In the responder group, 6 patients (46%) had variant in any of RB1, ERCC2 and ATM, which were previously reported to predict better response to NCT. Only a patient (16.7%) in the non-responder group had any of those variants. Additionally, our results showed that 3 patients in the non-responder group had MAST4 gene mutations, but not in the responder group, and there was a significant difference between the two groups (p < 0.05). There were no differences in Tumor Mutational Burden (TMB) and Microsatellite instability (MSI) between the two groups. RNA-seq was also completed in 18 patients, of which 14 patients achieved response and 4 patients did not respond. Significant differences in gene expression (eg. UPK3A, CD8B2, TUBB2B) between the two groups were found. The KEGG pathway enrichment analysis revealed that down-regulated DEGs were enriched in the Cytokine-cytokine receptor interaction pathway and up-regulated DEGs were enriched in the Steroid hormone biosynthesis. The infiltration of immune cells in the non-responder group was higher, and the CD4 cells, B cells, Th17 cells, and NK cells in the non-response group were significantly higher than those in the responder group (p < 0.05). Conclusions: Patients with MAST4 mutations may be insensitive to GC NCT. The non-response group had higher levels of immune cell infiltration and may be more sensitive to immunotherapy. These results require further validation in additional sample sets.

Integrated Analysis of Cancer Tissue and Vitreous Humor from Retinoblastoma Eyes Reveals Unique Tumor-Specific Metabolic and Cellular Pathways in Advanced and Non-Advanced Tumors.

Retinoblastoma (Rb) is a pediatric intraocular malignancy that is proposed to originate from maturing cone cell precursors in the developing retina. The molecular mechanisms underlying the biological and clinical behaviors are important to understand in order to improve the management of advanced-stage tumors. While the genetic causes of Rb are known, an integrated understanding of the gene expression and metabolic processes in tumors of human eyes is deficient. By integrating transcriptomic profiling from tumor tissues and metabolomics from tumorous eye vitreous humor samples (with healthy, age-matched pediatric retinae and vitreous samples as controls), we uncover unique functional associations between genes and metabolites. We found distinct gene expression patterns between clinically advanced and non-advanced Rb. Global metabolomic analysis of the vitreous humor of the same Rb eyes revealed distinctly altered metabolites, indicating how tumor metabolism has diverged from healthy pediatric retina. Several key enzymes that are related to cellular energy production, such as hexokinase 1, were found to be reduced in a manner corresponding to altered metabolites; notably, a reduction in pyruvate levels. Similarly, E2F2 was the most significantly elevated E2F family member in our cohort that is part of the cell cycle regulatory circuit. Ectopic expression of the wild-type RB1 gene in the Rb-null Y79 and WERI-Rb1 cells rescued hexokinase 1 expression, while E2F2 levels were repressed. In an additional set of Rb tumor samples and pediatric healthy controls, we further validated differences in the expression of HK1 and E2F2. Through an integrated omics analysis of the transcriptomics and metabolomics of Rb, we uncovered a significantly altered tumor-specific metabolic circuit that reduces its dependence on glycolytic pathways and is governed by Rb1 and HK1.

Genetic dissection of the powdery mildew resistance in wheat breeding line LS5082 using BSR-Seq

Powdery mildew of wheat is a destructive disease seriously threatening yield and quality worldwide. Comprehensive dissection of new resistance-related loci/genes is necessary to control this disease. LS5082 is a Chinese wheat breeding line with resistance to powdery mildew. Genetic analysis, using the populations of LS5082 and three susceptible parents (Shannong 29, Shimai 22 and Huixianhong), indicated that a single dominant gene, tentatively designated PmLS5082, conferred seedling resistance to different Blumeria graminis f. sp. tritici (Bgt) isolates. Bulked segregant RNA-Seq was carried out to map PmLS5082 and to profile differentially expressed genes associated with PmLS5082. PmLS5082 was mapped to a 0.7 cM genetic interval on chromosome arm 2BL, which was aligned to a 0.7 Mb physical interval of 710.3–711.0 Mb. PmLS5082 differs from the known powdery mildew (Pm) resistance genes on chromosome arm 2BL based on their origin, chromosome positions and/or resistance spectrum, suggesting PmLS5082 is most likely a new Pm gene/allele. Through clusters of orthologous groups and kyoto encyclopedia of genes and genomes analyses, differentially expressed genes (DEGs) associated with PmLS5082 were profiled. Six DEGs in the PmLS5082 interval were confirmed to be associated with PmLS5082 via qPCR analysis, using an additional set of wheat samples and time-course analysis post-inoculation with Bgt isolate E09. Ten closely linked markers, including two kompetitive allele-specific PCR markers, were confirmed to be suitable for marker-assisted selection of PmLS5082 in different genetic backgrounds, thus can be used to detect PmLS5082 and pyramid it with other genes in breeding programs.

Bulked Segregant RNA-Seq Provides Distinctive Expression Profile Against Powdery Mildew in the Wheat Genotype YD588.

Wheat powdery mildew, caused by the fungal pathogen Blumeria graminis f. sp. tritici (Bgt), is a destructive disease leading to huge yield losses in production. Host resistance can greatly contribute to the control of the disease. To explore potential genes related to the powdery mildew (Pm) resistance, in this study, we used a resistant genotype YD588 to investigate the potential resistance components and profiled its expression in response to powdery mildew infection. Genetic analysis showed that a single dominant gene, tentatively designated PmYD588, conferred resistance to powdery mildew in YD588. Using bulked segregant RNA-Seq (BSR-Seq) and single nucleotide polymorphism (SNP) association analysis, two high-confidence candidate regions were detected in the chromosome arm 2B, spanning 453,752,054-506,356,791 and 584,117,809-664,221,850 bp, respectively. To confirm the candidate region, molecular markers were developed using the BSR-Seq data and mapped PmYD588 to an interval of 4.2 cM by using the markers YTU588-004 and YTU588-008. The physical position was subsequently locked into the interval of 647.1–656.0 Mb, which was different from those of Pm6, Pm33, Pm51, Pm52, Pm63, Pm64, PmQ, PmKN0816, MlZec1, and MlAB10 on the same chromosome arm in its position, suggesting that it is most likely a new Pm gene. To explore the potential regulatory genes of the R gene, 2,973 differentially expressed genes (DEGs) between the parents and bulks were analyzed using gene ontology (GO), clusters of orthologous group (COG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Based on the data, we selected 23 potential regulated genes in the enriched pathway of plant-pathogen interaction and detected their temporal expression patterns using an additional set of wheat samples and time-course analysis postinoculation with Bgt. As a result, six disease-related genes showed distinctive expression profiles after Bgt invasion and can serve as key candidates for the dissection of resistance mechanisms and improvement of durable resistance to wheat powdery mildew.

Water Quality Monitoring in Inle Lake, Myanmar from the Floating Garden Activity

Inle Lake is the second largest inland lake in Myanmar. Floating gardens, mostly for tomato cultivation, are a unique and profitable method of agriculture used by people living on and around the lake. This study investigated the water quality of Inle Lake and how it has been affected by the different agricultural practices used in tomato cultivation on floating garden beds, by measuring pollution levels. Water samples were collected from the sites representing two types of agricultural practice from four villages. The first was designated as being grown under good agricultural practices (GAP), and the other as under non-good agricultural practices (non-GAP), with this study undertaken during wet season, 2019 and dry season, 2020. Two additional sets of water samples were collected as references. One of these sets was from the center of the lake and the other from an inlet stream to the lake. All water samples were analyzed for physical and chemical properties. The results found that Ca2+, Mg2+ and HCO3- were dominant in the lake surface water. The results showed significant differences in the mean values for some water quality parameters between the GAP and non-GAP of each study sites in both seasons. In particular, nutrient pollution from chemicals such as nitrogen and phosphorus from the non-GAP were significantly higher than those from GAP. Water quality index was calculated to describe the overall quality of lake surface water. It was observed that the water quality was almost threatened in the floating garden areas. In comparison between two practices, the non-GAP gave the higher water quality index value than the GAP. This investigated that poor management of fertilizers usage has had a negative effect on the water quality of the lake. The differences seen in water quality from the GAP and non-GAP areas, point to ways to successfully manage sources of water pollution in order to better conserve the lake by sustainable agricultural production.

How and when to stop the co-training process

Co-training is a semi-supervised learning approach used when only a small set of the data that is available for training is labeled. By using multiple classifiers, the co-training process utilizes the small set of labeled data in order to label an additional set of samples. During this process, the classifiers gradually augment the training data in an iterative process in which a new co-training model is derived and used for labeling the unlabeled samples in each iteration. A few of the newly labeled samples are added in each iteration to the training dataset to improve the performance of the classifiers. The main challenge in applying co-training is to make sure that the co-trainer assigns accurate labels to the unlabeled samples. Many empirical studies showed that the performance (accuracy) of the co-trainer could not be further improved when a certain number of iterations was reached, and in some cases, the performance even declined if the process (i.e., labeling) continued. Despite this, no general solution has been suggested for identifying the optimal final co-training model or number of iterations before this decline. In this work, we propose a novel method aimed at selecting the near-optimal final co-training model among all models created in the various iterations according to a predefined measurement based solely on the unlabeled data. Experiments on nine open, publicly available and real-life datasets demonstrate that the proposed method outputs a near-optimal final co-training model compared to other co-training models created in the various iterations.

Performance estimation of the state-of-the-art convolution neural networks for thermal images-based gender classification system

Gender classification has found many useful applications in the broader domain of computer vision systems including in-cabin driver monitoring systems, human–computer interaction, video surveillance systems, crowd monitoring, data collection systems for the retail sector, and psychological analysis. In previous studies, researchers have established a gender classification system using visible spectrum images of the human face. However, there are many factors affecting the performance of these systems including illumination conditions, shadow, occlusions, and time of day. Our study is focused on evaluating the use of thermal imaging to overcome these challenges by providing a reliable means of gender classification. As thermal images lack some of the facial definition of other imaging modalities, a range of state-of-the-art deep neural networks are trained to perform the classification task. For our study, the Tufts University thermal facial image dataset was used for training. This features thermal facial images from more than 100 subjects gathered in multiple poses and multiple modalities and provided a good gender balance to support the classification task. These facial samples of both male and female subjects are used to fine-tune a number of selected state-of-the-art convolution neural networks (CNN) using transfer learning. The robustness of these networks is evaluated through cross validation on the Carl thermal dataset along with an additional set of test samples acquired in a controlled lab environment using prototype uncooled thermal cameras. Finally, a new CNN architecture, optimized for the gender classification task, GENNet, is designed and evaluated with the pretrained networks.

Combining multivariate image analysis with high-performance thin-layer chromatography for development of a reliable tool for saffron authentication and adulteration detection.

In this work, high-performance thin-layer chromatography (HPTLC) coupled with multivariate image analysis (MIA) is proposed as a fast and reliable tool for authentication and adulteration detection of Iranian saffron samples based on their HPTLC fingerprints. At first, the secondary metabolites of saffron were extracted using ultrasonic-assisted solvent extraction (UASE) which was optimized using central composite design (CCD). Next, the RGB coordinates of HPTLC images were used for estimation of saffron origin based on principal component analysis (PCA). The PCA scores plot showed that saffron samples were clustered into two clear-cut groups which was 92% matched with the geographical origins of the samples. In the next step, common plant-derived adulterants of saffron including safflower, saffron style, calendula, and rubia were investigated with MIA analysis of HPTLC images using partial least squares-discriminant analysis (PLS-DA) at 5-35% (w/w) levels. The PLS-DA results showed proper classification of saffron and adulterants with sensitivity 99.14%, specificity 96.94%, error rate 1.96% and accuracy 98.04. Also, the effect of HPTLC injection volume on the performance of the proposed strategy was evaluated. The ability of the proposed method was then investigated by analyzing two additional sample sets including mixed samples of four plant-derived adulterants and adulterated commercial samples. Sensitivity and specificity of this model were 100% which confirmed its validity.

Characterizing the total within-sample variation in cotton fiber length using the High Volume Instrument fibrogram

Within-sample variation in cotton fiber length is important when explaining variation in yarn quality. However, typical High Volume Instrument (HVI) length parameters, the Upper Half Mean Length (UHML) and Uniformity Index (UI), do not characterize the total within-sample variation in fiber length. HVI fiber length measurements are based on the fibrogram principle where the HVI generates a curve called a fibrogram and reports the UHML and UI. Our results, based on 19,628 commercial bales, reveal that the typical HVI length measurements do not characterize unique types of length variation. Fibrograms from a subset of 538 commercial samples suggest that the fibrograms capture additional within-sample variation in fiber length that is not being currently reported. Two additional sets of samples were then used to evaluate the importance of this additional length variation. Partial Least Square Regression models and leave-one-out cross-validation reveal that the HVI fibrogram explains yarn quality better than current HVI length parameters and is comparable with the Advanced Fiber Information System (AFIS) length distribution by number. The validation results show that the models built with the HVI fibrogram are better than models with the current HVI length parameters and at least as good as the AFIS length distribution by number when predicting yarn quality. Fiber length variation captured by the whole fibrogram could provide a new tool to breeders for selecting breeding lines and spinners for purchasing cotton bales.

Abstract A63: Examination of ctDNA false positive variants reported from commercial vendors by ultrasensitive orthogonal testing

Abstract Noninvasive circulating tumor DNA (ctDNA) assays have the potential to revolutionize clinical trial design; however, challenges still remain in using ctDNA for the molecular profiling of cancer with next-generation sequencing (NGS). Our previous work has defined some of the technical and biologic factors that contribute to these challenges, in particular for variants below 1% allele frequency (AF). To continue to examine these factors, we have designed a study to investigate tumor-plasma discordant variants reported from commercial vendors with an orthogonal ultrasensitive NGS assay focusing primarily on false positive variant calls. The study used 24 commercially purchased matched FFPE tumor-plasma samples from breast, prostate, lung, and ovarian cancer. The FFPE tumor-normal pairs were sent to Foundation Medicine (FMI) for sequencing, while replicate sets of plasma were shipped to three commercial vendors for ctDNA sequencing using the currently available panel at the time of shipment. An additional set of samples, which included tumor, normal, and plasma samples, was sent to a fourth vendor for ultrasensitive NGS assay design and sequencing. The returned variants from three commercial vendors were compared to the tissue variants to define truth. True positive variants were defined as an alteration in the plasma that matches either the tissue or another vendors plasma result, while a false positive is a reported variant identified by only one vendor. The comparative analysis identified that Vendor A had the highest sensitivity at 87%, while Vendor B had the highest PPV at 91%. It should be noted that Vendor B had the smallest targeted panel. Vendors A and C contributed the majority of reported false positive variants with Vendor B only having two false positive variants. A total of 54 false positive variant calls were identified, collated along with several driver mutations and germline SNPs, and sent to Vendor D in a blinded fashion for assay design and sequencing. The orthogonal NGS data returned from Vendor D found that two false positive variants had read support to be tissue false negatives. The tissue AF of both variants was found to be just below the standard cutoff for FMI of 5% AF. Both variants were from Vendor A, which improved the sensitivity of Vendor A to 88%. The additional false positive calls from Vendor A were found in the plasma only, with no supporting evidence in the tissue. Vendor B’s false positive calls had no support from the orthogonal assay in either plasma or tissue. Vendor C had variants that had no support in either the tissue or plasma and variants that have support in the plasma. Overall, the false positive variants tested with the ultrasensitive NGS assay could be grouped into three general categories: variant was not found in either the tissue or plasma, variant was found in only the plasma, and variant had support in both the tissue and plasma. The false positive variants found in plasma or in both tissue and plasma may represent true positive variants and need to be investigated further. Citation Format: Daniel Stetson, Ambar Ahmed, Barrett Nuttall, Tristan Lubinski, J. Carl Barrett, Brian Dougherty. Examination of ctDNA false positive variants reported from commercial vendors by ultrasensitive orthogonal testing [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A63.

Protein Signature in Saliva of Temporomandibular Disorders Myalgia.

In the last years, several attempts have been made to study specific biological markers of temporomandibular disorders (TMD). So far, no laboratory tests have been appropriately validated for the diagnosis and prognosis of these disorders. This study aimed to investigate the proteomic profile of the whole stimulated saliva of TMD myalgia patients in order to evaluate potential diagnostic and/or prognostic salivary candidate proteins which could be useful for the management of TMD. Twenty patients diagnosed with TMD myalgia according to the validated Diagnostic Criteria for TMD (DC/TMD) and 20 matched healthy pain-free controls were enrolled. Saliva samples were collected in the morning. Comparative proteomic analysis was performed with two-dimensional gel electrophoresis followed by identification with liquid chromatography–tandem mass spectrometry. Statistical analysis of the quantitative proteomics data revealed that 20 proteins were significantly altered in patients compared to controls. Among these proteins, 12 showed significantly increased levels, and 8 showed significantly decreased levels in patients with TMD myalgia compared to controls. The identified proteins are involved in metabolic processes, immune response, and stress response. This proteomic study shows that the salivary protein profile can discriminate patients with TMD myalgia from healthy subjects, but the protein signature has no correlation with the clinical features of TMD myalgia. Additional studies are needed to validate our observations in additional sample sets and to continue assessing the utility of saliva as a suitable sample for studying processes related to TMD myalgia.

DNA methylation clocks as a predictor for ageing and age estimation in naked mole-rats, Heterocephalus glaber.

The naked mole-rat, Heterocephalus glaber (NMR), the longest-lived rodent, is of significance and interest in the study of biomarkers for ageing. Recent breakthroughs in this field have revealed ‘epigenetic clocks’ that are based on the temporal accumulation of DNA methylation at specific genomic sites. Here, we validate the hypothesis of an epigenetic clock in NMRs based on changes in methylation of targeted CpG sites. We initially analysed 51 CpGs in NMR livers spanning an age range of 39-1,144 weeks and found 23 to be significantly associated with age (p<0.05). We then built a predictor of age using these sites. To test the accuracy of this model, we analysed an additional set of liver samples, and were successfully able to predict their age with a root mean squared error of 166 weeks. We also profiled skin samples with the same age range, finding a striking correlation between their predicted age versus their actual age (R=0.93), but which was lower when compared to the liver, suggesting that skin ages slower than the liver in NMRs. Our model will enable the prediction of age in wild-caught and captive NMRs of unknown age, and will be invaluable for further mechanistic studies of mammalian ageing.

Interlaboratory test for stable carbon isotope analysis of dissolved inorganic carbon in geothermal fluids.

Stable carbon isotope ratios have many applications in natural sciences. In the first worldwide interlaboratory proficiency test, the discrepancies in measured δ13 CDIC values of natural waters were up to σ = ±3‰. Therefore, we continued the investigation on the analytical data quality assurance of individual laboratories and internal consistency among laboratories worldwide. We designed and performed an interlaboratory comparison exercise for δ13 C analyses of ten water and two solid samples (Na2 CO3 , CaCO3 ), including two synthetic samples prepared by dissolving the carbonates individually. Three laboratories analyzed an additional sample set to assess solution stability, at least one month after the first set analysis period. The δ13 C values were measured using dual inlet isotope ratio mass spectrometry (DI-IRMS) or continuous flow (CF)-IRMS. The δ13 C values of solid Na2 CO3 and its aqueous solution were -5.06 ± 0.21‰ and 5.32 ± 0.24‰, respectively, while the δ13 C value of solid CaCO3 was -4.49 ± 0.93‰. Similarly, the lake water has a consistent value (2.45 ± 0.19‰). The δ13 C values of geothermal water have a wide dispersion among individual laboratory measurements and among those of different laboratories; however, a trend exists in the δ13 C values measured at the three sampling points of each well. The δ13 C values of solid Na2 CO3 and its solution, and lake water (i.e. DIC concentration samples >100 mg/L carbon) are consistent among all the participating laboratories. The dispersion in the δ13 C values of solid CaCO3 is associated with its lower chemical affinity than that of Na2 CO3 . The poor reproducibility in the δ13 C values of geothermal fluids, collected at three points of a geothermal well, despite overall consistent trends regarding their collection points suggests inadequate sample handling (atmospheric CO2 exchange) and/or inappropriate analytical approaches (incomplete H3 PO4 acid reaction).

Screening for differential expression of genes for resistance to Sitodiplosis mosellana in bread wheat via BSR-seq analysis.

Five putative candidate genes for OWBM resistance in Chinese winter wheat 'Jimai 24' were identified via BSR-seq and differential expression analyses. Orange wheat blossom midge (OWBM), Sitodiplosis mosellana, is one of the most serious threats to wheat production worldwide. Conventional gene mapping methods to identify genes require significant amounts of financial support and time. Here, bulked segregant RNA-seq (BSR-seq) was applied to profile candidate genes and develop associated markers for OWBM resistance. Previously, we identified a major QTL (QSm.hebau-4A) for OWBM resistance on the long arm of chromosome 4A. In this study, we aimed at screening differentially expressed resistance genes associated with this QTL. Twelve differentially expressed genes (DEGs) were obtained based on BSR-seq and differential expression analyses. Among them, four were confirmed to be associated with OWBM resistance via quantitative reverse transcription PCR, using an additional set of wheat samples subjected to OWBM invasion. One SPI-like gene and one Malectin-like gene were revealed by gene annotation, respectively. Sequencing results confirmed that the four DEGs and the SPI gene had SNP polymorphisms between wheat parents. All these five resistance-related genes for OWBM were located in the same genomic region with QSm.hebau-4A. Furthermore, six new markers developed based on sequences of the five genes were also mapped in the same genomic region using genetic population. These five genes may be the candidate genes for OWBM resistance in Chinese wheat 'Jimai 24' and should be the targets for further positional isolation.