The global genetic diversity of planktonic foraminifera reveals the structure of cryptic speciation in plankton.

The nature and extent of diversity in the plankton has fascinated scientists for over a century. Initially, the discovery of many new species in the remarkably uniform and unstructured pelagic environment appeared to challenge the concept of ecological niches. Later, it became obvious that only a fraction of plankton diversity had been formally described, because plankton assemblages are dominated by understudied eukaryotic lineages with small size that lack clearly distinguishable morphological features. The high diversity of the plankton has been confirmed by comprehensive metabarcoding surveys, but interpretation of the underlying molecular taxonomies is hindered by insufficient integration of genetic diversity with morphological taxonomy and ecological observations. Here we use planktonic foraminifera as a study model and reveal the full extent of their genetic diversity and investigate geographical and ecological patterns in their distribution. To this end, we assembled a global data set of ~7600 ribosomal DNA sequences obtained from morphologically characterised individual foraminifera, established a robust molecular taxonomic framework for the observed diversity, and used it to query a global metabarcoding data set covering ~1700 samples with ~2.48 billion reads. This allowed us to extract and assign 1 million reads, enabling characterisation of the structure of the genetic diversity of the group across ~1100 oceanic stations worldwide. Our sampling revealed the existence of, at most, 94 distinct molecular operational taxonomic units (MOTUs) at a level of divergence indicative of biological species. The genetic diversity only doubles the number of formally described species identified by morphological features. Furthermore, we observed that the allocation of genetic diversity to morphospecies is uneven. Only 16 morphospecies disguise evolutionarily significant genetic diversity, and the proportion of morphospecies that show genetic diversity increases poleward. Finally, we observe that MOTUs have a narrower geographic distribution than morphospecies and that in some cases the MOTUs belonging to the same morphospecies (cryptic species) have different environmental preferences. Overall, our analysis reveals that even in the light of global genetic sampling, planktonic foraminifera diversity is modest and finite. However, the extent and structure of the cryptic diversity reveals that genetic diversification is decoupled from morphological diversification, hinting at different mechanisms acting at different levels of divergence.

Patterns of eukaryotic diversity from the surface to the deep-ocean sediment.

Remote deep-ocean sediment (DOS) ecosystems are among the least explored biomes on Earth. Genomic assessments of their biodiversity have failed to separate indigenous benthic organisms from sinking plankton. Here, we compare global-scale eukaryotic DNA metabarcoding datasets (18S-V9) from abyssal and lower bathyal surficial sediments and euphotic and aphotic ocean pelagic layers to distinguish plankton from benthic diversity in sediment material. Based on 1685 samples collected throughout the world ocean, we show that DOS diversity is at least threefold that in pelagic realms, with nearly two-thirds represented by abundant yet unknown eukaryotes. These benthic communities are spatially structured by ocean basins and particulate organic carbon (POC) flux from the upper ocean. Plankton DNA reaching the DOS originates from abundant species, with maximal deposition at high latitudes. Its seafloor DNA signature predicts variations in POC export from the surface and reveals previously overlooked taxa that may drive the biological carbon pump.

Scientific and budgetary trade-offs between morphological and molecular methods for deep-sea biodiversity assessment.

Deep-sea biodiversity, a source of critical ecological functions and ecosystem services, is increasingly subject to the threat of disturbance from existing practices (e.g., fishing, waste disposal, oil and gas extraction) as well as emerging industries such as deep-seabed mining. Current scientific tools may not be adequate for monitoring and assessing subsequent changes to biodiversity. In this paper, we evaluate the scientific and budgetary trade-offs associated with morphology-based taxonomy and metabarcoding approaches to biodiversity surveys in the context of nascent deep-seabed mining for polymetallic nodules in the Clarion-Clipperton Zone, the area of most intense interest. For the dominant taxa of benthic meiofauna, we discuss the types of information produced by these methods and use cost-effectiveness analysis to compare their abilities to yield biological and ecological data for use in environmental assessment and management. On the basis of our evaluation, morphology-based taxonomy is less cost-effective than metabarcoding but offers scientific advantages, such as the generation of density, biomass, and size structure data. Approaches that combine the two methods during the environmental assessment phase of commercial activities may facilitate future biodiversity monitoring and assessment for deep-seabed mining and for other activities in remote deep-sea habitats, for which taxonomic data and expertise are limited. Integr Environ Assess Manag 2022;18:655-663. © 2021 SETAC.

Microbial community diversity and composition in river sediments contaminated with tetrabromobisphenol A and copper.

The microbial community composition in aquatic ecosystems have received increased attention. However, the knowledge gap relative to the responses of bacterial, archaeal and fungal communities in co-contaminated river sediments remain poorly studied. Here, we investigated the changes of tetrabromobisphenol A (TBBPA) and copper (Cu) concentrations and the responses of microbial communities in co-contaminated sediments during long-term incubation. TBBPA concentrations significantly decreased over time, whereas Cu concentrations remained relatively stable over the 60-day incubation. Abundances of the bacterial 16S rRNA, archaeal 16S rRNA and fungal ITS genes ranged from 6.53 × 106 to 1.26 × 109 copies g-1, 1.12 × 106 to 5.47 × 106 copies g-1 and 5.33 × 103 to 7.51 × 104 copies g-1 in the samples, respectively. A total of 11, 6 and 5 bacterial, archaeal and fungal phyla were identified across all samples. Bacterial, archaeal and fungal communities mainly consisted of members from the phyla Proteobacteria and Acidobacteria, Methanomicrobia and Woesearchaeia as well as Agaricales and Helotiales, respectively. Fungal communities showed a stronger response to pollutant addition after a long incubation compared with bacterial and archaeal communities. The variance analysis results revealed that the bacterial, archaeal and fungal microbial communities of all treatments were distinctly distributed into two separated clusters according to incubation time. However, the three microbial communities did not significantly change in response to pollutant types, which was consistent with variation in relative abundances of the three microbial communities. These findings improve our understanding of the ecotoxicological effects of co-exposure on sediment microbial communities.

Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes.

Increasing anthropogenic impact and global change effects on natural ecosystems has prompted the development of less expensive and more efficient bioassessments methodologies. One promising approach is the integration of DNA metabarcoding in environmental monitoring. A critical step in this process is the inference of ecological quality (EQ) status from identified molecular bioindicator signatures that mirror environmental classification based on standard macroinvertebrate surveys. The most promising approaches to infer EQ from biotic indices (BI) are supervised machine learning (SML) and the calculation of indicator values (IndVal). In this study we compared the performance of both approaches using DNA metabarcodes of bacteria and ciliates as bioindicators obtained from 152 samples collected from seven Norwegian salmon farms. Results from standard macroinvertebrate-monitoring of the same samples were used as reference to compare the accuracy of both approaches. First, SML outperformed the IndVal approach to infer EQ from eDNA metabarcodes. The Random Forest (RF) algorithm appeared to be less sensitive to noisy data (a typical feature of massive environmental sequence data sets) and uneven data coverage across EQ classes (a typical feature of environmental compliance monitoring scheme) compared to a widely used method to infer IndVals for the calculation of a BI. Second, bacteria allowed for a more accurate EQ assessment than ciliate eDNA metabarcodes. For the implementation of DNA metabarcoding into routine monitoring programmes to assess EQ around salmon aquaculture cages, we therefore recommend bacterial DNA metabarcodes in combination with SML to classify EQ categories based on molecular signatures.

Benthic monitoring of oil and gas offshore platforms in the North Sea using environmental DNA metabarcoding.

Since 2010, considerable efforts have been undertaken to monitor the environmental status of European marine waters and ensuring the development of methodological standards for the evaluation of this status. However, the current routine biomonitoring implicates time-consuming and costly manual sorting and morphological identification of benthic macrofauna. Environmental DNA (eDNA) metabarcoding represents an alternative to the traditional monitoring method with very promising results. Here, we tested it further by performing eDNA metabarcoding of benthic eukaryotic communities in the vicinity of two offshore oil and gas platforms in the North Sea. Three different genetic markers (18S V1V2, 18S V9 and COI) were used to assess the environmental pressures induced by the platforms. All markers showed patterns of alpha and beta diversity consistent with morphology-based macrofauna analyses. In particular, the communities' structure inferred from metabarcoding and morphological data significantly changed along distance gradients from the platforms. The impact of the operational discharges was also detected by the variation of biotic index values, AMBI index showing the best correlation between morphological and eDNA data sets. Finally, the sediment physicochemical parameters were used to build a local de novo pressure index that served as benchmark to test the potential of a taxonomy-free approach. Our study demonstrates that metabarcoding approach outperforms morphology-based approach and can be used as a cost and time-saving alternative solution to the traditional morphology-based monitoring in order to monitor more efficiently the impact of industrial activities on marine biodiversity.

Monitoring the ecological status of rivers with diatom eDNA metabarcoding: A comparison of taxonomic markers and analytical approaches for the inference of a molecular diatom index.

Recently, several studies demonstrated the usefulness of diatom eDNA metabarcoding as an alternative to assess the ecological quality of rivers and streams. However, the choice of the taxonomic marker as well as the methodology for data analysis differ between these studies, hampering the comparison of their results and effectiveness. The aim of this study was to compare two taxonomic markers commonly used in diatom metabarcoding and three distinct analytical approaches to infer a molecular diatom index. We used the values of classical morphological diatom index as a benchmark for this comparison. We amplified and sequenced both a fragment of the rbcL gene and the V4 region of the 18S rRNA gene for 112 epilithic samples from Swiss and French rivers. We inferred index values using three analytical approaches: by computing it directly from taxonomically assigned sequences, by calibrating de novo the ecovalues of all metabarcodes, and by using a supervised machine learning algorithm to train predictive models. In general, the values of index obtained using the two "taxonomy-free" approaches, encompassing molecular assignment and machine learning, were closer correlated to the values of the morphological index than the values based on taxonomically assigned sequences. The correlations of the three analytical approaches were higher in the case of rbcL compared to the 18S marker, highlighting the importance of the reference database which is more complete for the rbcL marker. Our study confirms the effectiveness of diatom metabarcoding as an operational tool for rivers ecological quality assessment and shows that the analytical approaches by-passing the taxonomic assignments are particularly efficient when reference databases are incomplete.

Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap.

A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Planktonic foraminifera eDNA signature deposited on the seafloor remains preserved after burial in marine sediments.

Environmental DNA (eDNA) metabarcoding of marine sediments has revealed large amounts of sequences assigned to planktonic taxa. How this planktonic eDNA is delivered on the seafloor and preserved in the sediment is not well understood. We address these questions by comparing metabarcoding and microfossil foraminifera assemblages in sediment cores taken off Newfoundland across a strong ecological gradient. We detected planktonic foraminifera eDNA down to 30 cm and observed that the planktonic/benthic amplicon ratio changed with depth. The relative proportion of planktonic foraminiferal amplicons remained low from the surface down to 10 cm, likely due to the presence of DNA from living benthic foraminifera. Below 10 cm, the relative proportion of planktonic foraminifera amplicons rocketed, likely reflecting the higher proportion of planktonic eDNA in the DNA burial flux. In addition, the microfossil and metabarcoding assemblages showed a congruent pattern indicating that planktonic foraminifera eDNA is deposited without substantial lateral advection and preserves regional biogeographical patterns, indicating deposition by a similar mechanism as the foraminiferal shells. Our study shows that the planktonic eDNA preserved in marine sediments has the potential to record climatic and biotic changes in the pelagic community with the same spatial and temporal resolution as microfossils.

Benthic foraminiferal metabarcoding and morphology-based assessment around three offshore gas platforms: Congruence and complementarity.

Since the 1960 s, there has been a rapid expansion of drilling activities in the central and northern Adriatic Sea to meet the increasing global energy demand. The discharges of organic and inorganic pollutants, as well as the alteration of the sediment substrate, are among the main impacts associated with these activities. In the present study, we evaluate the response of benthic foraminifera to the activities of three gas platforms in the northwestern Adriatic Sea, with a special focus on the Armida A platform for which extensive geochemical data (organic matter, trace elements, polycyclic aromatic hydrocarbons, other hydrocarbons, and volatile organic compounds) are available. The response to disturbance is assessed by analyzing the foraminiferal diversity using the traditional morphology-based approach and by 18S rDNA-based metabarcoding. The two methods give congruent results, showing relatively lower foraminiferal diversity and higher dominance values at stations closer to the platforms (<50 m). The taxonomic compositions of the morphological and metabarcoding datasets are very different, the latter being dominated by monothalamous, mainly soft-walled species. However, compositional changes consistently occur at 50 m from the platform and can be related to variations in sediment grain-size variation and higher concentrations of Ni, Zn, Ba, hydrocarbons and total organic carbon. Additionally, several morphospecies and Molecular Operational Taxonomic Units (MOTUs) show strong correlations with distance from the platform and with environmental parameters extracted from BIOENV analysis. Some of these MOTUs have the potential to become new bioindicators, complementing the assemblage of hard-shelled foraminiferal species detected through microscopic analyses. The congruence and complementarity between metabarcoding and morphological approaches support the application of foraminiferal metabarcoding in routine biomonitoring surveys as a reliable, time- and cost-effective methodology to assess the environmental impacts of marine industries.

Health Care Utilization and Medical Cost Outcomes from a Digital Diabetes Prevention Program in a Medicare Advantage Population.

This study examined the effects of a digital diabetes prevention program (DPP) on health care costs and utilization among Medicare Advantage participants. Patients (n = 501) received access to a plan-sponsored, digitally-delivered DPP accessible through computer, tablet, or smartphone. Prior research demonstrated a 7.5% reduction in body weight at 12 months. A comparison group who did not participate in the DPP was constructed by matching on demographic, health plan, health status, and health care costs and utilization. The authors assessed effects on cost and utilization outcomes using difference-in-differences regressions, controlling for propensities to participate and engage in the DPP, in the 12 months prior to DPP enrollment and 24 months after. Though post-enrollment data showed trends in decreased drug spending and emergency department use, increased inpatient utilization, and no change in total nondrug costs or outpatient utilization, the findings did not reach statistical significance, potentially because of sample size. The population had low costs and utilization at baseline, which may be responsible for the lack of observed effects in the short time frame. This study demonstrates the challenges of studying the effectiveness of preventive programs in a population with low baseline costs and the importance of using a large enough sample and follow-up period, but remains an important contribution to exploring the effects of digital DPPs in a real-world sample of individuals who were eligible and willing to participate.

Primary Care Provider Encounter Cadence and HbA1c Control in Older Patients With Diabetes.

INTRODUCTION: Primary care provider encounters are associated with health and well-being; however, limited evidence guides optimal primary care provider rate of visit, referred to as encounter cadence. This study measures associations between primary care provider encounter cadence and diabetes outcomes among individuals newly diagnosed with type 2 diabetes mellitus. METHODS: In this retrospective cohort study, 7,106 people enrolled in Medicare Advantage and newly diagnosed with type 2 diabetes mellitus between July 1, 2012 and June 30, 2013 were identified and followed for 36 months. Two methods measured primary care provider encounter cadence: total primary care provider encounters (frequency) and quarters with primary care provider encounter (regularity). Logistic regression measured relationships between primary care provider encounter cadence and non-insulin diabetes medication adherence, HbA1c control, emergency department visits, and inpatient admissions. Non-insulin diabetes medication adherence was defined according to the National Committee for Quality Assurance, Healthcare Effectiveness Data and Information Set specifications and measured using healthcare claims data. Post-hoc models examined adherence and diabetes control among those nonadherent (n=5,212) and with noncontrolled HbA1c (n=326) during the encounter/cadence period. Data were extracted and analyzed in 2017. RESULTS: Adjusted models indicated that both frequency (AOR=1.08, 95% CI=1.06, 1.10) and regularity (AOR=1.18, 95% CI=1.13, 1.22) of primary care provider encounters were associated with increased odds of adherence. Post-hoc analyses indicated that more frequent (AOR=1.12, 95% CI=1.10, 1.15) and regular (AOR=1.27, 95% CI=1.22, 1.33) primary care provider encounters were associated significantly with adherence and were associated directionally with HbA1c control. CONCLUSIONS: More frequent and regular primary care provider encounters are associated with an increased likelihood of non-insulin diabetes medication adherence. These findings contribute to data needed to establish evidence-based guidelines for primary care provider encounter cadence for those newly diagnosed with type 2 diabetes mellitus.

Charlson Comorbidity Index: ICD-9 Update and ICD-10 Translation.

BACKGROUND: The original Charlson Comorbidity Index (CCI) encompassed 19 categories of medical conditions that were identifiable in medical records. Subsequent publications provided scoring algorithms based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. The recent adoption of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes in the United States created a need for a new scoring scheme. In addition, a review of existing claims-based scoring systems suggested 3 areas for improvement: the lack of explicit identification of secondary diabetes, the lack of differentiation between HIV infection and AIDS, and insufficient guidance on scoring hierarchy. In addition, addressing the third need raised the issue of disease severity in renal disease. OBJECTIVES: This initiative aimed to create an expanded and refined ICD-9 scoring system for CCI, addressing the classification of issues noted above, create a corresponding ICD-10 system, assess the comparability of ICD-9- and ICD-10-based scores, and validate the new scoring scheme. METHODS: We created ICD-9 and ICD-10 code tables for 19 CCI medical conditions. The new scoring scheme was labeled CDMF CCI and was tested using claims-based data for individuals aged ≥65 years who participated in a Humana Medicare Advantage plan during at least 1 of 3 consecutive 12-month periods. Two 12-month periods were during the ICD-9 era and the third 12-month period was during the ICD-10 era. Because many individuals were counted in more than one 12-month period, we described the study population as comprising 3 panels. We used regression models to analyze the association between the CCI score and same-year inpatient admissions and near-term (90-day) mortality. Additional testing was done by comparing the mean CCI score or disease prevalence in the 3 subpopulations of people with HIV/AIDS, renal disease, or diabetes. Finally, we calculated area under the receiver operating characteristics (AUC-ROC) curve values by applying the Deyo system and our ICD-9 and ICD-10 scoring systems. RESULTS: The CDMF ICD-9 and ICD-10 scoring scheme yielded comparable scores across the 3 panels, and inpatient admissions and mortality rates consistently increased in each panel as the CCI score increased. Comparisons of the performance of the Deyo system and our proposed CDMF ICD-9 system in the 3 key subpopulations showed that the CDMF ICD-9 system produced a lower CCI score in the presence of HIV infection without AIDS, achieved similar detection ability of diabetes, and allowed good differentiation between mild-to-moderate and severe renal disease. AUC-ROC values were similar between the CDMF ICD-9 coding system and the Deyo system. CONCLUSION: Our results support the implementation of the CDMF CCI scoring instrument to triage individual patients for disease- and care-management programs. In addition, the CDMF scheme allows for a more precise understanding of chronic disease at a population level, thus allowing health systems and plans to design services and benefits to meet multifactorial clinical needs. Preliminary validation sets the stage for further testing using long-term follow-up data and for the adaptation of this coding scheme to a chart review instrument.

Multi-marker eDNA metabarcoding survey to assess the environmental impact of three offshore gas platforms in the North Adriatic Sea (Italy).

The environmental DNA (eDNA) metabarcoding represents a new promising tool for biomonitoring and environmental impact assessment. One of the main advantages of eDNA metabarcoding, compared to the traditional morphotaxonomy-based methods, is to provide a more holistic biodiversity information that includes inconspicuous morphologically non-identifiable taxa. Here, we use eDNA metabarcoding to survey marine biodiversity in the vicinity of the three offshore gas platforms in North Adriatic Sea (Italy). We isolated eDNA from 576 water and sediment samples collected at 32 sampling sites situated along four axes at increasing distances from the gas platforms. We obtained about 46 million eDNA sequences for 5 markers from nuclear 18S V1V2, 18S V4, 18S 37F and mitochondrial 16S and COI genes that cover a wide diversity of benthic and planktonic eukaryotes. Our results showed some impact of platform activities on benthic and pelagic communities at very close distance (<50 m), while communities for intermediate (125 m, 250 m, 500 m) and reference (1000 m, 2000 m) sites did not show any particular biodiversity changes that could be related to platforms activities. The most significant community change along the distance gradient was obtained with the 18S V1V2 marker targeting benthic eukaryotes, even though other markers showed similar trends, but to a lesser extent. These results were congruent with the AMBI index inferred from the eDNA sequences assigned to benthic macrofauna. We finally explored the relation between various physicochemical parameters, including hydrocarbons, on benthic community in the case of one of the platforms. Our results showed that these communities were not significantly impacted by most of hydrocarbons, but rather by macro-elements and sediment texture.

SLIM: a flexible web application for the reproducible processing of environmental DNA metabarcoding data.

BACKGROUND: High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) has become a routine tool for biodiversity survey and ecological studies. By including sample-specific tags in the primers prior PCR amplification, it is possible to multiplex hundreds of samples in a single sequencing run. The analysis of millions of sequences spread into hundreds to thousands of samples prompts for efficient, automated yet flexible analysis pipelines. Various algorithms and software have been developed to perform one or multiple processing steps, such as paired-end reads assembly, chimera filtering, Operational Taxonomic Unit (OTU) clustering and taxonomic assignment. Some of these software are now well established and widely used by scientists as part of their workflow. Wrappers that are capable to process metabarcoding data from raw sequencing data to annotated OTU-to-sample matrix were also developed to facilitate the analysis for non-specialist users. Yet, most of them require basic bioinformatic or command-line knowledge, which can limit the accessibility to such integrative toolkits. Furthermore, for flexibility reasons, these tools have adopted a step-by-step approach, which can prevent an easy automation of the workflow, and hence hamper the analysis reproducibility. RESULTS: We introduce SLIM, an open-source web application that simplifies the creation and execution of metabarcoding data processing pipelines through an intuitive Graphic User Interface (GUI). The GUI interact with well-established software and their associated parameters, so that the processing steps are performed seamlessly from the raw sequencing data to an annotated OTU-to-sample matrix. Thanks to a module-centered organization, SLIM can be used for a wide range of metabarcoding cases, and can also be extended by developers for custom needs or for the integration of new software. The pipeline configuration (i.e. the modules chaining and all their parameters) is stored in a file that can be used for reproducing the same analysis. CONCLUSION: This web application has been designed to be user-friendly for non-specialists yet flexible with advanced settings and extensibility for advanced users and bioinformaticians. The source code along with full documentation is available on the GitHub repository ( https://github.com/yoann-dufresne/SLIM ) and a demonstration server is accessible through the application website ( https://trtcrd.github.io/SLIM/ ).

A Comparison of Different Ciliate Metabarcode Genes as Bioindicators for Environmental Impact Assessments of Salmon Aquaculture.

Ciliates are powerful indicators for monitoring the impact of aquaculture and other industrial activities in the marine environment. Here, we tested the efficiency of four different genetic markers (V4 and V9 regions of the SSU rRNA gene, D1 and D2 regions of the LSU rRNA gene, obtained from environmental (e)DNA and environmental (e)RNA) of benthic ciliate communities for environmental monitoring. We obtained these genetic metabarcodes from sediment samples collected along a transect extending from below salmon cages toward the open sea. These data were compared to benchmark data from traditional macrofauna surveys of the same samples. In beta diversity analyses of ciliate community structures, the V4 and V9 markers had a higher resolution power for sampling sites with different degrees of organic enrichment compared to the D1 and D2 markers. The eDNA and eRNA V4 markers had a higher discriminatory power than the V9 markers. However, results obtained with the eDNA V9 marker corroborated better with the traditional macrofauna monitoring. This allows for a more direct comparison of ciliate metabarcoding with the traditional monitoring. We conclude that the ciliate eDNA V9 marker is the best choice for implementation in routine monitoring programs in marine aquaculture.

Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring.

Genomics is fast becoming a routine tool in medical diagnostics and cutting-edge biotechnologies. Yet, its use for environmental biomonitoring is still considered a futuristic ideal. Until now, environmental genomics was mainly used as a replacement of the burdensome morphological identification, to screen known morphologically distinguishable bioindicator taxa. While prokaryotic and eukaryotic microbial diversity is of key importance in ecosystem functioning, its implementation in biomonitoring programs is still largely unappreciated, mainly because of difficulties in identifying microbes and limited knowledge of their ecological functions. Here, we argue that the combination of massive environmental genomics microbial data with machine learning algorithms can be extremely powerful for biomonitoring programs and pave the way to fill important gaps in our understanding of microbial ecology.

Determinants of outcomes following outpatient placement of implantable cardioverter defibrillators in a Medicare Advantage population.

BACKGROUND: Recipients of ICD are likely to have several risk factors that could interfere with successful use of implantable cardioverter defibrillators (ICDs). HYPOTHESIS: Age, sex, and factors indicated in claims are associated with one-year mortality and complications after ICD placement. METHODS: Adult Medicare Advantage patients who underwent outpatient ICD implantation from January 2014 to September 2015 were included. Age, sex, Charlson Comorbidity Index (CCI), prior year hospitalization and emergency department (ED) visit, diabetes, heart failure, ischemic heart disease, and indicators of the need for pacing were evaluated as risk factors. Mortality and device-related complications (lead and nonlead) were assessed at one-year post-procedure using Kaplan-Meier and Cox Proportional Hazard analysis. RESULTS: Among 8450 patients who underwent implantation, 1-year event-free survival was 80.1%, based on an overall composite measure of complications and mortality. Adjusted survival analysis showed that age ≥ 65, male sex, incremental increase in CCI, heart failure, prior year hospitalization, ED visit, and prior year pacing procedure were significant predictors of mortality. Age ≥ 65, male sex, and prior year hospitalization were significant predictors of a composite measure of device-related complications. CCI and prior hospitalization were significant predictors of a composite measure of any adverse outcome. CONCLUSIONS: Results suggest most patients in an older population do not experience adverse outcomes in the year following ICD implantation. The risk of mortality may be greater in men, patients over the age of 65, and patients with greater general morbidity, heart failure, or a history of a pacing procedure.

Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring.

Biodiversity monitoring is the standard for environmental impact assessment of anthropogenic activities. Several recent studies showed that high-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) could overcome many limitations of the traditional morphotaxonomy-based bioassessment. Recently, we demonstrated that supervised machine learning (SML) can be used to predict accurate biotic indices values from eDNA metabarcoding data, regardless of the taxonomic affiliation of the sequences. However, it is unknown to which extent the accuracy of such models depends on taxonomic resolution of molecular markers or how SML compares with metabarcoding approaches targeting well-established bioindicator species. In this study, we address these issues by training predictive models upon five different ribosomal bacterial and eukaryotic markers and measuring their performance to assess the environmental impact of marine aquaculture on independent data sets. Our results show that all tested markers are yielding accurate predictive models and that they all outperform the assessment relying solely on taxonomically assigned sequences. Remarkably, we did not find any significant difference in the performance of the models built using universal eukaryotic or prokaryotic markers. Using any molecular marker with a taxonomic range broad enough to comprise different potential bioindicator taxa, SML approach can overcome the limits of taxonomy-based eDNA bioassessment.