A new approach to assess the degree of contamination and determine sources and risks related to PTEs in an urban environment: the case study of Santiago (Chile)

In 2017, a geochemical survey was carried out across the Commune of Santiago, a local administrative unit located at the center of the namesake capital city of Chile, and the concentration of a number of major and trace elements (53 in total) was determined on 121 topsoil samples. Multifractal IDW (MIDW) interpolation method was applied to raw data to generate geochemical baseline maps of 15 potential toxic elements (PTEs); the concentration–area (C-A) plot was applied to MIDW grids to highlight the fractal distribution of geochemical data. Data of PTEs were elaborated to statistically determine local geochemical baselines and to assess the spatial variation of the degree of soil contamination by means of a new method taking into account both the severity of contamination and its complexity. Afterwards, to discriminate the sources of PTEs in soils, a robust Principal Component Analysis (PCA) was applied to data expressed in isometric log-ratio (ilr) coordinates. Based on PCA results, a Sequential Binary Partition (SBP) was also defined and balances were determined to generate contrasts among those elements considered as proxies of specific contamination sources (Urban traffic, productive settlements, etc.). A risk assessment was finally completed to potentially relate contamination sources to their potential effect on public health in the long term. A probabilistic approach, based on Monte Carlo method, was deemed more appropriate to include uncertainty due to spatial variation of geochemical data across the study area. Results showed how the integrated use of multivariate statistics and compositional data analysis gave the authors the chance to both discriminate between main contamination processes characterizing the soil of Santiago and to observe the existence of secondary phenomena that are normally difficult to constrain. Furthermore, it was demonstrated how a probabilistic approach in risk assessment could offer a more reliable view of the complexity of the process considering uncertainty as an integral part of the results.

Compositional Closure—Its Origin Lies Not in Mathematics but Rather in Nature Itself

Compositional closure, spurious negative correlations in data sets of a fixed sum (e.g., fractions and percent), is often encountered in geostatistical analyses, particularly in mineralogy, petrology, and geochemistry. Techniques to minimize the effects of closure (e.g., log-ratio transformations) can provide consistent geostatistical results. However, such approaches do not remove these effects because closure does not result from mathematical operations but is an inherent property of the physical systems under study. The natural world causes physical closure; mathematics simply describes that closure and cannot alter it by manipulations. Here, we examine the distinct types of geologic systems and samples to determine in which situations closure (physical and mathematical) does or does not ensue and the reasons therefor. We parse compositional systems based on (1) types of components under study, immutable (e.g., elements) or reactive (minerals), and (2) whether the system is open or closed to component transfer. Further, open systems can be (1) displacive in which addition of a component physically crowds out others, or (2) accommodative in which addition or subtraction of components does not affect the others. Only displacive systems are subject to compositional closure. Accommodative systems, even with components expressed as percent or fractions, are not closed physically or, therefore, mathematically.

Change Detection in Water reservoir using Image Fusion and Supervised Classifier

The research provides a method for improving change detection in SAR images using a fusion object and a supervised classification system. To remove noise from the input image, we use the DnCNN denoising approach. The data from the first image is then processed with the mean ratio operator. The log ratio operator is used to process the second image. These two images are fused together using SWT-based image fusion, and the output is sent to a supervise classifier for change detection.

Differential Compositional Variation Feature Selection: A Machine Learning Framework with Log Ratios for Compositional Metagenomic Data

The demand for tight integration of compositional data analysis and machine learning methodologies for predictive modeling in high-dimensional settings has increased dramatically with the increasing availability of metagenomics data. We develop the differential compositional variation machine learning framework (DiCoVarML) with robust multi-level log ratio bio-marker discovery for metagenomic datasets. Our framework makes use of the full set of pairwise log ratios, scoring ratios according to their variation between classes and then selecting out a small subset of log ratios to accurately predict classes. Importantly, DiCoVarML supports a targeted feature selection mode enabling researchers to define the number of predictors used to develop models. We demonstrate the performance of our framework for binary classification tasks using both synthetic and real datasets. Selecting from all pairwise log ratios within the DiCoVarML framework provides greater flexibility that can in demonstrated cases lead to higher accuracy and enhanced biological insight.

HIV incidence, viremia, and the national response in Eswatini: Two sequential population-based surveys

With the highest HIV incidence and prevalence globally, the government of Eswatini started a substantial scale-up of HIV treatment and prevention services in 2011. Two sequential large population-based surveys were conducted before and after service expansion to assess the impact of the national response. Cross-sectional, household-based, nationally representative samples of adults, ages 18 to 49 years, were sampled in 2011 and 2016. We measured HIV prevalence, incidence (recent infection based on limiting antigen ≤1.5 optical density units and HIV RNA ≥1000 copies/mL), viral load suppression (HIV RNA <1000 copies/mL among all seropositive adults) and unsuppressed viremia (HIV RNA ≥1000 copies/mL among all, regardless of HIV status) and assessed for temporal changes by conducting a trend analysis of the log ratio of proportions, using a Z statistic distribution. HIV prevalence remained stable from 2011 to 2016 [32% versus 30%, p = 0.10]. HIV incidence significantly declined 48% [2.48% versus 1.30%, p = 0.01]. Incidence remained higher among women than men [2011: 3.16% versus 1.83%; 2016: 1.76% versus 0.86%], with a smaller but significant relative reduction among women [44%; p = 0.04] than men [53%; p = 0.09]. The proportion of seropositive adults with viral load suppression significantly increased from 35% to 71% [p < .001]. The proportion of the total adult population with unsuppressed viremia decreased from 21% to 9% [p < .001]. National HIV incidence in Eswatini decreased by nearly half and viral load suppression doubled over a five-year period. Unsuppressed viremia in the total population decreased 58%. These population-based findings demonstrate the national impact of expanded HIV services in a hyperendemic country.

Abstract 14407: Effect of Initial Defibrillation on Chest Compression Artifact in the ECG

Introduction: Chest compressions (CCs) during CPR cause electrical artifacts in the ECG. Prior work has found that the severity of CC artifact, quantified by the signal-to-noise ratio (SNR), affects the diagnostic sensitivity of defibrillator algorithms designed to detect shockable rhythms during CCs. Whether SNR is altered by defibrillation is unknown. We therefore compared SNR before and after defibrillation shocks. Methods: We evaluated patients with out-of-hospital cardiac arrest who received at least 1 defibrillation shock, had subsequent ventricular fibrillation (VF), and had a calculable SNR before and after initial shock. We measured the CC artifact during VF before and after the initial shock (and up to 3 subsequent shocks) using CC amplitude and SNR. CC amplitude was defined as the median peak-to-peak voltage of the ECG during CCs. SNR was calculated as the log ratio of the power of the CC-free VF signal to the power of the estimated noise caused by CC artifact (Figure). Differences in medians before and after the first 4 shocks were evaluated using Wilcoxon signed-rank test with Bonferroni correction (alpha = 0.0125). Results: A total of 192 patients had a calculable SNR during VF before and after initial shock. Of these, the median CC amplitude decreased after the initial shock (0.93 vs. 0.75 mV, p<0.001), and SNR improved (-2.30 vs. -1.07 dB, p=0.004). In contrast to the initial shock, both CC amplitude and SNR did not differ significantly before and after shock 2 (n=107), shock 3 (n=54), or shock 4 (n=32). Conclusion: Measures of CC artifact in the ECG were greater before initial shock than afterward. This could potentially be due to changes in CC characteristics, variations in physical perturbation of the defibrillator electrodes, degradation of VF over time, or effects of tissue electroporation on paddle conductivity and noise. These findings may have implications for selection of decision thresholds in algorithms to detect shockable rhythm during CCs.

Scalable estimation of microbial co-occurrence networks with Variational Autoencoders

Estimating microbe-microbe interactions is critical for understanding the ecological laws governing microbial communities. Rapidly decreasing sequencing costs have promised new opportunities to estimate microbe-microbe interactions across thousands of uncultured, unknown microbes. However, typical microbiome datasets are very high dimensional and accurate estimation of microbial correlations requires tens of thousands of samples, exceeding the computational capabilities of existing methodologies. Furthermore, the vast majority of microbiome studies collect compositional metagenomics data which enforces a negative bias when computing microbe-microbe correlations. The Multinomial Logistic Normal (MLN) distribution has been shown to be effective at inferring microbe-microbe correlations, however scalable Bayesian inference of these distributions has remained elusive. Here, we show that carefully constructed Variational Autoencoders (VAEs) augmented with the Isometric Log-ratio (ILR) transform can estimate low-rank MLN distributions thousands of times faster than existing methods. These VAEs can be trained on tens of thousands of samples, enabling co-occurrence inference across tens of thousands of microbes without regularization. The latent embedding distances computed from these VAEs are competitive with existing beta-diversity methods across a variety of mouse and human microbiome classification and regression tasks, with notable improvements on longitudinal studies.

14 Claudin-6 affects the cell cycle and p53 signaling in Gastric cancer

BackgroundClaudin-6 (CLDN6) differentially overexpressed in Gastric Cancer (GC) is associated with poor prognosis and survival of patients. Uncovering affected pathways and genes associated with CLDN6 in GC can help in the identification of novel prognostic targets.MethodsCBioPortal1 2 was used to extract and analyze The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma Pan-Cancer Atlas Data (STAD). FunRich tool3 and Gene Ontology molecular signature database of Gene Set Enrichment Analysis (GSEA) were used for functional enrichment. CBioPortal was used to identify differentially expressed genes between groups. Graph pad Prism 8 was used to generate the graphics.ResultsTCGA STAD PanCancer Atlas data was analyzed in terms of alterations in CLDN6. 34% of the GC samples (141 samples) were assigned to the CLDN6 alteration group while the rest of the samples (299) were assigned to the non-CLDN6 alteration group (figure 1). Major alterations of CLDN6 in GC included shallow deletion, diploid, gain, and differentially expressed mRNA (figure 2). Differentially overexpressed genes in the CLDN6 group with log-ratio cutoff≥1 were used for functional enrichment in different biological pathway categories (figure 3); 18.1% of genes were associated with the transport of small molecules through the membrane, 14.6% mediated transmembrane transport and 12.5% were associated with lipid metabolism (figure 4). The Gene ontology molecular signature database of GSEA also confirmed that these genes were involved in lipid metabolism, transport activity, and epithelium development processes (table 1). Moreover, GC samples with CLDN6 alterations have higher mutations in p53 signaling (29% in TP53, 7% in CDKN2A) gene signature (figure 5) over samples in the non-CLDN6 alteration group (figure 6). Similarly, genes related with cell cycle control like CCNE1, MYC, SRC, and STAT3 showed higher mutations in the CLDN6 alteration group while JAK1 and E2F8 showed lower mutations than non-CLDN6 GC samples (figure 7). These observations indicate that CLDN6 in GC affects the transport of small molecules and lipid metabolism and that it is associated to tumors with higher mutations in p53 and cell cycle-related genes.Abstract 14 Table 1Functional enrichment of differentially expressed genes (log-ratio ≥1) in CLDN6 group GC samples in Gene ontology molecular signature database of GSEAAbstract 14 Figure 1Distribution of GC samples and patients of TCGA STAD data between CLDN6 and non CLDN6 alteration groupsAbstract 14 Figure 2Major alterations in CLDN6 in GC (TCGA STAD data)Abstract 14 Figure 3Volcano plot of differentially expressed genes in CLDN6 and non CLDN6 groups. Log10 p-Value cutoff ≥2Abstract 14 Figure 4Functional enrichment of differentially expressed genes (log-ratio ≥1) in biological process categoryAbstract 14 Figure 5Overall mutations in several important gene signatures involved in cancer between CLDN6 and non-CLDN6 GC samplesAbstract 14 Figure 6Total mutations in p53 signaling genesAbstract 14 Figure 7Total mutations in genes involved in cell cycle control between CLDN6 and non-CLDN6 GC samplesConclusionsCLDN6 alterations in GC affect the cell cycle and p53 signaling pathways with higher mutations in TP53, CDKN2A, CCNE1, MYC, SRC, and STAT3 genes.AcknowledgementsThis research was supported by CONACYT CVU grant 871712.ReferencesCerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012;2:401–4.Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013;6:l1.Pathan M, Keerthikumar S, Ang C-S, Gangoda L, Quek CYJ, Williamson NA, et al. FunRich: An open access standalone functional enrichment and interaction network analysis tool. PROTEOMICS 2015;15:2597–601.