Predicting Risk to Estuary Water Quality and Patterns of Benthic Environmental DNA in Queensland, Australia using Bayesian Networks

Predictive modeling can inform natural resource management by demonstrating stressor-response pathways and quantifying the effects on selected endpoints. This study develops a risk assessment model using the Bayesian network-relative risk model (BN-RRM) approach, and, for the first time, incorporates eukaryote environmental DNA data as a measure of benthic community structure into an ecological risk assessment context. Environmental DNA sampling is a relatively new technique for biodiversity measurements that involves extracting DNA from environmental samples, sequencing a region of the 18s rDNA gene, and matching the sequences to organisms. Using a network of probability distributions, the BN-RRM model predicts risk to water quality objectives and also the richness of benthic taxa in the Noosa, Pine, and Logan Estuaries in South East Queensland (SEQ), Australia. The model is more accurate at predicting Dissolved Oxygen than it is the Chlorophyll-a water quality endpoint, and it predicts photosynthesizing benthos more accurately than heterotrophs. Results of BN-RRM modeling indicate that the water quality and benthic assemblages of the Noosa are relatively homogenous across all sub risk regions, and that the Noosa has a high probability (73 - 92% probability) of achieving water quality objectives, which indicates low relative risk. On the other hand, the Middle Logan, Middle Pine, and Lower Pine regions are much less likely to meet objectives (15 – 55% probability), indicating a relatively high risk to water quality in those regions. The benthic community richness patterns associated with low relative risk in the Noosa are high Diatom relative richness and low Green Algae richness. The only benthic pattern consistently associated with high relative risk to water quality is the high Fungi richness state. The BN-RRM predicts current conditions in SEQ based on available monitoring data, and provides a basis for future predictions and adaptive management at the direction of resource managers. As new data are made available or more questions are asked, this BN-RRM model can be updated and improved

Using Bayesian Networks to Predict Risk to Estuary Water Quality and Patterns of Benthic Environmental DNA in Queensland

Predictive modeling can inform natural resource management by representing stressor-response pathways in a logical way and quantifying the effects on selected endpoints. This study demonstrates a risk assessment model using the Bayesian network-relative risk model (BNRRM) approach to predict water quality and; for the first time, eukaryote environmental DNA (eDNA) data as a measure of benthic community structure. Environmental DNA sampling is a technique for biodiversity measurements that involves extracting DNA from environmental samples, amplicon sequencing a targeted gene, in this case the 18s rDNA gene which targets eukaryotes, and matching the sequences to organisms. Using a network of probability distributions, the BN-RRM model predicts risk to water quality objectives and the relative richness of benthic taxa groups in the Noosa, Pine, and Logan estuaries in South East Queensland (SEQ), Australia. The model predicts Dissolved Oxygen more accurately than the Chlorophyll-a water quality endpoint, and photosynthesizing benthos more accurately than heterotrophs. Results of BN-RRM modeling given current inputs indicate that the water quality and benthic assemblages of the Noosa are relatively homogenous across all sub risk regions, and that the Noosa has a 73 – 92 percent probability of achieving water quality objectives, indicating a low relative risk. Conversely, the Middle Logan, Middle Pine, and Lower Pine regions are much less likely to meet objectives (15 – 55 percent probability), indicating a relatively higher risk to water quality in those regions. The benthic community richness patterns associated with risk in the Noosa are high Diatom relative richness and low Green Algae relative richness. The only benthic pattern consistently associated with the relatively higher risk to water quality is high richness of fungi species. The BN-RRM model provides a basis for future predictions and adaptive management at the direction of resource managers

Dataset for the Environmental Risk Assessment of Chlorpyrifos to Chinook Salmon in four Rivers of Washington State, United States

Data files available below. This data set is in support of Landis et al (in press) The integration of chlorpyrifos acetylcholinesterase inhibition, water temperature and dissolved oxygen concentration into a regional scale multiple stressor risk assessment estimating risk to Chinook salmon in four rivers in Washington State, USA. DOI: 10.1002/ieam.4199. In this research We estimated the risk to populations of Chinook salmon (Oncorhynchus tshawytscha) due to chlorpyrifos (CH), water temperature (WT) and dissolved oxygen concentrations (DO) in four watersheds in Washington State, USA. The watersheds included the Nooksack and Skagit Rivers in the Northern Puget Sound, the Cedar River in the Seattle -Tacoma corridor, and the Yakima River, a tributary of the Columbia River. The Bayesian network relative risk model (BN-RRM) was used to conduct this ecological risk assessment and was modified to contain an AChE inhibition pathway parameterized using data from chlorpyrifos toxicity datasets. The completed BN-RRM estimated risk at a population scale to Chinook salmon employing classical matrix modeling run up to 50 year timeframes. There were 4 primary conclusions drawn from the model building process and the risk calculations. First, the incorporation of an AChE inhibition pathway and the output from a population model can be combined with environmental factors in a quantitative fashion. Second, the probability of not meeting the management goal of no loss to the population ranges from 65 to 85 percent. Environmental conditions contributed to a larger proportion of the risk compared to chlorpyrifos. Third, the sensitivity analysis describing the influence of the variables on the predicted risk varied depending on seasonal conditions. In the summer, WT and DO were more influential that CH. In the winter, when the seasonal conditions are more benign, CH was the driver. Fourth, in order to reach the management-goal, we calculated the conditions that would increase in juvenile survival, adult survival, and a reduction in toxicological effects. The same process in this example should be applicable to the inclusion of multiple pesticides and to more descriptive population models such as those describing metapopulations. This research was supported by USEPA STAR Grant RD-83579501. Excel spreadsheet, model in Netica

Integration of Chlorpyrifos Acetylcholinesterase Inhibition, Water Temperature, and Dissolved Oxygen Concentration into a Regional Scale Multiple Stressor Risk Assessment Estimating Risk to Chinook Salmon

We estimated the risk to populations of Chinook salmon (Oncorhynchus tshawytscha) due to chlorpyrifos (CH), water temperature (WT), and dissolved oxygen concentration (DO) in 4 watersheds in Washington State, USA. The watersheds included the Nooksack and Skagit Rivers in the Northern Puget Sound, the Cedar River in the Seattle–Tacoma corridor, and the Yakima River, a tributary of the Columbia River. The Bayesian network relative risk model (BN‐RRM) was used to conduct this ecological risk assessment and was modified to contain an acetylcholinesterase (AChE) inhibition pathway parameterized using data from CH toxicity data sets. The completed BN‐RRM estimated risk at a population scale to Chinook salmon employing classical matrix modeling runs up to 50‐y timeframes. There were 3 primary conclusions drawn from the model‐ building process and the risk calculations. First, the incorporation of an AChE inhibition pathway and the output from a population model can be combined with environmental factors in a quantitative fashion. Second, the probability of not meeting the management goal of no loss to the population ranges from 65% to 85%. Environmental conditions contributed to a larger proportion of the risk compared to CH. Third, the sensitivity analysis describing the influence of the variables on the predicted risk varied depending on seasonal conditions. In the summer, WT and DO were more influential than CH. In the winter, when the seasonal conditions are more benign, CH was the driver. Fourth, in order to reach the management goal, we calculated the conditions that would increase juvenile survival, adult survival, and a reduction in toxicological effects. The same process in this example should be applicable to the inclusion of multiple pesticides and to more descriptive population models such as those describing metapopulations. Integr Environ Assess Manag 2020;16:28–42. © 2019 SETA

Quantum interference of electromechanically stabilized emitters in nanophotonic devices

Photon-mediated coupling between distant matter qubits may enable secure communication over long distances, the implementation of distributed quantum computing schemes, and the exploration of new regimes of many-body quantum dynamics. Nanophotonic devices coupled to solid-state quantum emitters represent a promising approach towards realization of these goals, as they combine strong light-matter interaction and high photon collection efficiencies. However, the scalability of these approaches is limited by the frequency mismatch between solid-state emitters and the instability of their optical transitions. Here we present a nano-electromechanical platform for stabilization and tuning of optical transitions of silicon-vacancy (SiV) color centers in diamond nanophotonic devices by dynamically controlling their strain environments. This strain-based tuning scheme has sufficient range and bandwidth to alleviate the spectral mismatch between individual SiV centers. Using strain, we ensure overlap between color center optical transitions and observe an entangled superradiant state by measuring correlations of photons collected from the diamond waveguide. This platform for tuning spectrally stable color centers in nanophotonic waveguides and resonators constitutes an important step towards a scalable quantum network

Central nervous system inflammation induces muscle atrophy via activation of the hypothalamic–pituitary–adrenal axis

Systemic and CNS-delimited inflammation triggers skeletal muscle catabolism in a manner dependent on glucocorticoid signaling

Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020

We show the distribution of SARS-CoV-2 genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three available genomic nomenclature systems for SARS-CoV-2 to all sequence data from the WHO European Region available during the COVID-19 pandemic until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation. We provide a comparison of the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2.Peer reviewe

Targeting DNA Damage Response and Replication Stress in Pancreatic Cancer

Background and aims: Continuing recalcitrance to therapy cements pancreatic cancer (PC) as the most lethal malignancy, which is set to become the second leading cause of cancer death in our society. The study aim was to investigate the association between DNA damage response (DDR), replication stress and novel therapeutic response in PC to develop a biomarker driven therapeutic strategy targeting DDR and replication stress in PC. Methods: We interrogated the transcriptome, genome, proteome and functional characteristics of 61 novel PC patient-derived cell lines to define novel therapeutic strategies targeting DDR and replication stress. Validation was done in patient derived xenografts and human PC organoids. Results: Patient-derived cell lines faithfully recapitulate the epithelial component of pancreatic tumors including previously described molecular subtypes. Biomarkers of DDR deficiency, including a novel signature of homologous recombination deficiency, co-segregates with response to platinum (P < 0.001) and PARP inhibitor therapy (P < 0.001) in vitro and in vivo. We generated a novel signature of replication stress with which predicts response to ATR (P < 0.018) and WEE1 inhibitor (P < 0.029) treatment in both cell lines and human PC organoids. Replication stress was enriched in the squamous subtype of PC (P < 0.001) but not associated with DDR deficiency. Conclusions: Replication stress and DDR deficiency are independent of each other, creating opportunities for therapy in DDR proficient PC, and post-platinum therapy

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe