Differential Spatial Distribution of TSPO or Amino Acid PET Signal and MRI Contrast Enhancement in Gliomas

In this study, dual PET and contrast enhanced MRI were combined to investigate their correlation per voxel in patients at initial diagnosis with suspected glioblastoma. Correlation with contrast enhancement (CE) as an indicator of BBB leakage was further used to evaluate whether PET signal is likely caused by BBB disruption alone, or rather attributable to specific binding after BBB passage. PET images with [18F]GE180 and the amino acid [18F]FET were acquired and normalized to healthy background (tumor-to-background ratio, TBR). Contrast enhanced images were normalized voxel by voxel with the pre-contrast T1-weighted MRI to generate relative CE values (rCE). Voxel-wise analysis revealed a high PET signal even within the sub-volumes without detectable CE. No to moderate correlation of rCE with TBR voxel-values and a small overlap as well as a larger distance of the hotspots delineated in rCE and TBR-PET images were detected. In contrast, voxel-wise correlation between both PET modalities was strong for most patients and hotspots showed a moderate overlap and distance. The high PET signal in tumor sub-volumes without CE observed in voxel-wise analysis as well as the discordant hotspots emphasize the specificity of the PET signals and the relevance of combined differential information from dual PET and MRI images.

Differential Spatial Distribution of TSPO or Amino Acid PET Signal and MRI Contrast Enhancement in Gliomas

In this study dual PET and contrast enhanced MRI were combined to investigate their correlation per voxel in patients at initial diagnosis with suspected glioblastoma. Correlation with contrast enhancement (CE) as an indicator of BBB leakage was further used to evaluate whether PET signal is likely caused by BBB disruption alone, or rather attributable to specific binding after BBB passage. PET images with [18F]GE180 and the amino acid [18F]FET were acquired and normalized to healthy background (TBR). Contrast enhanced images were normalized voxel by voxel with the pre-contrast T1-weighted MRI to generate relative CE values (rCE). Voxel-wise analysis revealed a high PET signal even within the sub-volumes without detectable CE. No to moderate correlation of rCE with TBR voxel-values and a small overlap as well as a larger distance of the hotspots delineated in rCE and TBR-PET images were detected. In contrast, voxel-wise correlation between both PET modalities was strong for most patients and hotspots showed a moderate overlap and distance. The high PET signal in tumor sub-volumes without CE observed in voxel-wise analysis as well as the discordant hotspots emphasize the specificity of the PET signals and the relevance of combined differential information from dual PET and MRI images.

Population Genetic Structures of Puccinia striiformis f. sp. tritici in the Gansu-Ningxia Region and Hubei Province, China

Wheat stripe rust, caused by the fungal pathogen Puccinia striiformis f. sp. tritici (Pst), is a destructive wheat disease in China. The Gansu–Ningxia region (GN) is a key area for pathogen over-summering in China, and northwestern Hubei (HB) is an important region for pathogen over-wintering, serving as a source of inoculum in spring epidemic regions. The spatiotemporal population genetic structure of Pst in HB and the pathogen population exchanges between GN and HB are important for estimating the risk of interregional epidemics. Here, 567 isolates from GN and HB were sampled from fall 2016 to spring 2018 and were genotyped using simple sequence repeat markers. The genotypic and genetic diversity of Pst subpopulations in HB varied among seasons and locations. Greater genetic diversification levels were found in the spring compared with fall populations using principal coordinate analysis and Bayesian assignments. In total, there were 17 common genotypes among the 208 determined, as shown by a small overlap of genotypes in the principal coordinate analysis and dissimilar Bayesian assignments in both regions, which revealed the limited genotype exchange between the populations of GN and HB.

TOWARDS EFFICIENT INDOOR/OUTDOOR REGISTRATION USING PLANAR POLYGONS

The registration of indoor and outdoor scans with a precision reaching the level of geometric noise represents a major challenge for Indoor/Outdoor building modeling. The basic idea of the contribution presented in this paper consists in extracting planar polygons from indoor and outdoor LiDAR scans, and then matching them. In order to cope with the very small overlap between indoor and outdoor scans of the same building, we propose to start by extracting points lying in the buildings’ interior from the outdoor scans as points where the laser ray crosses detected façades. Since, within a building environment, most of the objects are bounded by a planar surface, we propose a new registration algorithm that matches planar polygons by clustering polygons according to their normal direction, then by their offset in the normal direction. We use this clustering to find possible polygon correspondences (hypotheses) and estimate the optimal transformation for each hypothesis. Finally, a quality criteria is computed for each hypothesis in order to select the best one. To demonstrate the accuracy of our algorithm, we tested it on real data with a static indoor acquisition and a dynamic (Mobile Laser Scanning) outdoor acquisition.

Multi-peak Graph-based Multi-instance Learning for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD), aiming to detect objects with only image-level annotations, has become one of the research hotspots over the past few years. Recently, much effort has been devoted to WSOD for the simple yet effective architecture and remarkable improvements have been achieved. Existing approaches using multiple-instance learning usually pay more attention to the proposals individually, ignoring relation information between proposals. Besides, to obtain pseudo-ground-truth boxes for WSOD, MIL-based methods tend to select the region with the highest confidence score and regard those with small overlap as background category, which leads to mislabeled instances. As a result, these methods suffer from mislabeling instances and lacking relations between proposals, degrading the performance of WSOD. To tackle these issues, this article introduces a multi-peak graph-based model for WSOD. Specifically, we use the instance graph to model the relations between proposals, which reinforces multiple-instance learning process. In addition, a multi-peak discovery strategy is designed to avert mislabeling instances. The proposed model is trained by stochastic gradients decent optimizer using back-propagation in an end-to-end manner. Extensive quantitative and qualitative evaluations on two publicly challenging benchmarks, PASCAL VOC 2007 and PASCAL VOC 2012, demonstrate the superiority and effectiveness of the proposed approach.

Analyzing Biomarker Discovery: Estimating the Reproducibility of Biomarker Sets

Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible - subsequent studies often fail to identify the same sets. Indeed, there is often a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by an arbitrary fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. We have also created a publicly available website, hosted at <a href="https://biomarker.shinyapps.io/BiomarkerReprod/">https://biomarker.shinyapps.io/BiomarkerReprod/</a>, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels).

Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC®)

Objective Our study consists in aligning the interface terminology of the Bordeaux university hospital (TLAB) to the Logical Observation Identifiers Names and Codes (LOINC). The objective was to facilitate the shared and integrated use of biological results with other health information systems. Materials and Methods We used an innovative approach based on a decomposition and re-composition of LOINC concepts according to the transversal relations that may be described between LOINC concepts and their definitional attributes. TLAB entities were first anchored to LOINC attributes and then aligned to LOINC concepts through the appropriate combination of definitional attributes. Finally, using laboratory results of the Bordeaux data-warehouse, an instance-based filtering process has been applied. Results We found a small overlap between the tokens constituting the labels of TLAB and LOINC. However, the TLAB entities have been easily aligned to LOINC attributes. Thus, 99.8% of TLAB entities have been related to a LOINC analyte and 61.0% to a LOINC system. A total of 55.4% of used TLAB entities in the hospital data-warehouse have been mapped to LOINC concepts. We performed a manual evaluation of all 1-1 mappings between TLAB entities and LOINC concepts and obtained a precision of 0.59. Conclusion We aligned TLAB and LOINC with reasonable performances, given the poor quality of TLAB labels. In terms of interoperability, the alignment of interface terminologies with LOINC could be improved through a more formal LOINC structure. This would allow queries on LOINC attributes rather than on LOINC concepts only.

Meta Distribution and Secrecy of Partial Non-Orthogonal Multiple Access (NOMA) in Poisson Networks

<div>This work studies the meta distribution in a partial-NOMA network to obtain fine-grained information about the network performance. As the meta distribution is approximated using the beta distribution via moment matching of the first two moments, reduced integral expressions are derived for the first two moments of the meta distribution. Accurate approximate moments are also proposed to further simplify the calculation. Security is an issue in partial-NOMA because the strong user may decode the weak user’s message in the process of decoding its own message using flexible successive interference cancellation (FSIC). Therefore, a measure of secrecy is defined in this context and the secrecy probability is derived for the case of: 1) a malicious strong user that prioritizes eavesdropping, 2) an innocent strong user that decodes the weak user’s message only when it is required to do so. The obtained results highlight the superiority of partial-NOMA over traditional NOMA in terms of secrecy. They also show that receive filtering and FSIC have a significant positive impact on the secrecy of partial- OMA. Furthermore, partial-NOMA with a small overlap of the resource-block can secure the network from the additional deterioration a malicious eavesdropper may cause.</div>

Meta Distribution and Secrecy of Partial Non-Orthogonal Multiple Access (NOMA) in Poisson Networks

<div>This work studies the meta distribution in a partial-NOMA network to obtain fine-grained information about the network performance. As the meta distribution is approximated using the beta distribution via moment matching of the first two moments, reduced integral expressions are derived for the first two moments of the meta distribution. Accurate approximate moments are also proposed to further simplify the calculation. Security is an issue in partial-NOMA because the strong user may decode the weak user’s message in the process of decoding its own message using flexible successive interference cancellation (FSIC). Therefore, a measure of secrecy is defined in this context and the secrecy probability is derived for the case of: 1) a malicious strong user that prioritizes eavesdropping, 2) an innocent strong user that decodes the weak user’s message only when it is required to do so. The obtained results highlight the superiority of partial-NOMA over traditional NOMA in terms of secrecy. They also show that receive filtering and FSIC have a significant positive impact on the secrecy of partial- OMA. Furthermore, partial-NOMA with a small overlap of the resource-block can secure the network from the additional deterioration a malicious eavesdropper may cause.</div>

Revealing the biodiversity of Chilean birds through the COI barcode approach

The mitochondrial cytochrome c oxidase subunit I (COI) gene is an effective molecular tool for the estimation of genetic variation and the identification of bird species. This molecular marker is used to differentiate among Chilean bird species by analyzing barcodes for 76 species (197 individuals), comprising 28 species with no previous barcode data and 48 species with sequences retrieved from the BOLD and GenBank databases. The DNA barcodes correctly identified 94.7% of the species analyzed (72 of 76 species). Mean intraspecific K2P distance was 0.3% (range 0–8.7%). Within the intraspecific divergence range, three species, Phrygilus gayi, Sephanoides sephanoides and Curaeus curaeus, showed relatively high intraspecific divergence (1.5–8.7%), possibly due to the presence of a species complex or geographic isolation of sub-populations. Mean interspecific K2P distance was 24.7% (range 1.3–43.5%). Consequently, the intraspecific K2P distance showed limited overlap with interspecific K2P distance. The mean intraspecific divergence in our study was similar to that found in temperate regions of South America (0.24%). However, it was approximately one order of magnitude lower than values reported for bird species in tropical regions of northern South America (1.8–2.13%). This result suggests that bird species from Chile show low levels of genetic structure and divergence. The small overlap between intra- and inter-specific distances implies that COI barcodes could be used as an effective tool to identify nearly all the Chilean bird species analyzed.