Sublethal responses to endrin in sediment by Stylodrilus heringianus (Lumbriculidae) as measured by a 137cesium marker layer technique

Sediment reworking rates of Stylodrilus heringianus (Oligochaeta: Lumbriculidae) were measured in microcosms containing sediments dosed with the chlorinated pesticide, endrin. Reworking rates were measured at 10[deg]C by monitoring a 137cesium marker layer burial in contaminated and uncontaminated microcosms. Endrin concentrations ranged from 3.1 to 42 000 ng/g dry sediment. Alterations in reworking rates were observed at sediment concentrations five and one half orders of magnitude below the LC50 (1 650 [mu]g/g). For the lower concentrations, marker layer burial rate data suggested possible stimulatory effects in the first 300 to 600 h, followed by significant rate decreases relative to controls. For higher concentrations, rates were equal to or slower than control rates in the first 600 h, followed by dramatic decreases in the last 600 h. High final surficial sediment endrin concentrations at the end of experiments implied worm mediated upward transport. Worm mortalities were 9.3 to 28% for the two highest concentrations (42 000 and 11 500 ng/g) and 0 to 6.7% for all other concentrations including controls. Post experimental worm dry weights were inversely related to high concentrations. Bioaccumulation factors ranged from 34 to 67 on a g dry organism to g dry sediment basis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27083/1/0000074.pd

Sublethal responses to endrin in sediment by Limnodrilus hoffmeisteri (Tubificidae), and in mixed-culture with Stylodrilus heringianus (Lumbriculidae)

Sediment reworking by Limnodrilus hoffmeisteri (Tubificidae) alone, and with Stylodrilus heringianus (Lumbriculidae) were measured in sediments dosed with endrin by monitoring the burial of a 137cesium marker layer. Endrin concentrations ranged from 16.1 to 81 400 ng/g dry sediment weight. Alterations in reworking rates were observed at sediment concentrations two to five orders of magnitude below LC50 values. In single species experiments with L. hoffmeisteri at low endrin concentrations, marker layer burial rate data did not suggest stimulation of reworking, as had previously been found for S. heringianus. At higher concentrations, reworking rates were equal to or slower than control rates early in experiments, followed by dramatic decreases thereafter. Reworking rates with mixed species (1:1 species ratio) suggested that the presence of S. heringianus enhanced the reworking response of L. hoffmeisteri.Post-experimental worm dry weights were inversely related to high sediment concentrations for both species. Reductions in post-experimental L. hoffmeisteri mortalities and increases in L. hoffmeisteri dry weights in mixed species tests at high endrin concentrations implied that L. hoffmeisteri benefits from the presence of S. heringianus, although the reverse was not observed.High final sediment endrin concentrations in the upper three cm implied worm mediated upward contaminant transport. Bioaccumulation factors for S. heringianus ranged from 9.7 to 43.8 and were consistently three to four times greater than bioaccumulation factors for L. hoffmeisteri (1.7 to 13.6).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27082/1/0000073.pd

An Experimental Investigation of Applying Mica2 Motes in Pavement Condition Monitoring

Pavement maintenance is vital for travel safety, thus detecting dangerous road conditions in a real-time fashion is desirable. Using an off-the-shelf wireless sensor network to detect such conditions at a low cost poses many challenges. In order to meet these challenges, a Mica2 Mote sensor network is adopted in this study to process and transmit data collected from three external analog sensors. Consequentially, several hardware and software interfaces are developed to complete a pavement monitoring system that uses temperature and moisture presence to detect hazardous road conditions. Surge Time Synchronization is explored in this specific application to enable the wireless sensor network to operate in a low power consumption mode. A fairly simplistic pattern classification algorithm is embedded into the motes to create the smart wireless sensing application. A series of outdoor tests are conducted in this study paying special attention to the survivability of fragile analog sensors in harsh roadway conditions. In this regard, a novel solution called the ``Sensor-Road Button''(SRB) is developed and validated experimentally. This is one of several exercises made in this study to enable the application of sensor technologies in intelligent transportation systems (ITS). The size of the wireless sensor network in this study is relatively small, utilizing a total of five motes in order to fully exploit the transmitting range of each mote. Long testing periods (i.e., uninterrupted 12-hour time frames for each period of data collection) add an additional advantage, allowing for the evaluation of the selected wireless sensor network for long-term monitoring using the low power consumption mode under Surge Time Synchronization. Many performance metrics of the adopted small-size, large-interval Mica2 Motes wireless sensor network are revealed in this study through a series of data processing efforts. Results are presented to examine (i) inter-node connectivity and transmitting range, (ii) battery life, (iii) the length of the initial network connection time as affected by methods of setting up tests under practical conditions, (iv) error rate and analysis of different error types (showing the importance of the subsequent data cleansing step), and (v) other network routing properties including the parent time histories for each mote. The results and analysis form a database for future efforts to better understand, appreciate, and improve the performance of Mica2 Motes. This study will thus benefit robust real-world implementation of off-the-shelf sensor network products such as Mica2 Motes in terms of hardware development and data processing.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Targeted genetic analysis in a large cohort of familial and sporadic cases of aneurysm or dissection of the thoracic aorta

PURPOSE: Thoracic aortic aneurysm/aortic dissection (TAAD) is a disorder with highly variable age of onset and phenotype. We sought to determine the prevalence of pathogenic variants in TAAD-associated genes in a mixed cohort of sporadic and familial TAAD patients and identify relevant genotype–phenotype relationships. METHODS: We used a targeted polymerase chain reaction and next-generation sequencing–based panel for genetic analysis of 15 TAAD-associated genes in 1,025 unrelated TAAD cases. RESULTS: We identified 49 pathogenic or likely pathogenic (P/LP) variants in 47 cases (4.9% of those successfully sequenced). Almost half of the variants were in nonsyndromic cases with no known family history of aortic disease. Twenty-five variants were within FBN1 and two patients were found to harbor two P/LP variants. Presence of a related syndrome, younger age at presentation, family history of aortic disease, and involvement of the ascending aorta increased the risk of carrying a P/LP variant. CONCLUSION: Given the poor prognosis of TAAD that is undiagnosed prior to acute rupture or dissection, genetic analysis of both familial and sporadic cases of TAAD will lead to new diagnoses, more informed management, and possibly reduced mortality through earlier, preclinical diagnosis in genetically determined cases and their family members

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Mapping genetic variations to three- dimensional protein structures to enhance variant interpretation: a proposed framework

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods