Bioavailable iron in the Southern Ocean: the significance of the iceberg conveyor belt

Productivity in the Southern Oceans is iron-limited, and the supply of iron dissolved from aeolian dust is believed to be the main source from outside the marine reservoir. Glacial sediment sources of iron have rarely been considered, as the iron has been assumed to be inert and non-bioavailable. This study demonstrates the presence of potentially bioavailable Fe as ferrihydrite and goethite in nanoparticulate clusters, in sediments collected from icebergs in the Southern Ocean and glaciers on the Antarctic landmass. Nanoparticles in ice can be transported by icebergs away from coastal regions in the Southern Ocean, enabling melting to release bioavailable Fe to the open ocean. The abundance of nanoparticulate iron has been measured by an ascorbate extraction. This data indicates that the fluxes of bioavailable iron supplied to the Southern Ocean from aeolian dust (0.01–0.13 Tg yr-1) and icebergs (0.06–0.12 Tg yr-1) are comparable. Increases in iceberg production thus have the capacity to increase productivity and this newly identified negative feedback may help to mitigate fossil fuel emissions

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.

PurposeMachine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction.MethodsWe collected 12 datasets (3496 patients) from prior studies on post-(chemo)radiotherapy toxicity, survival, or tumor control with clinical, dosimetric, or blood biomarker features from multiple institutions and for different tumor sites, that is, (non-)small-cell lung cancer, head and neck cancer, and meningioma. Six common classification algorithms with built-in feature selection (decision tree, random forest, neural network, support vector machine, elastic net logistic regression, LogitBoost) were applied on each dataset using the popular open-source R package caret. The R code and documentation for the analysis are available online (https://github.com/timodeist/classifier_selection_code). All classifiers were run on each dataset in a 100-repeated nested fivefold cross-validation with hyperparameter tuning. Performance metrics (AUC, calibration slope and intercept, accuracy, Cohen's kappa, and Brier score) were computed. We ranked classifiers by AUC to determine which classifier is likely to also perform well in future studies. We simulated the benefit for potential investigators to select a certain classifier for a new dataset based on our study (pre-selection based on other datasets) or estimating the best classifier for a dataset (set-specific selection based on information from the new dataset) compared with uninformed classifier selection (random selection).ResultsRandom forest (best in 6/12 datasets) and elastic net logistic regression (best in 4/12 datasets) showed the overall best discrimination, but there was no single best classifier across datasets. Both classifiers had a median AUC rank of 2. Preselection and set-specific selection yielded a significant average AUC improvement of 0.02 and 0.02 over random selection with an average AUC rank improvement of 0.42 and 0.66, respectively.ConclusionRandom forest and elastic net logistic regression yield higher discriminative performance in (chemo)radiotherapy outcome and toxicity prediction than other studied classifiers. Thus, one of these two classifiers should be the first choice for investigators when building classification models or to benchmark one's own modeling results against. Our results also show that an informed preselection of classifiers based on existing datasets can improve discrimination over random selection

Bivalve Assemblages as Hotspots for Biodiversity

Many bivalve species occur in aggregations, and locally cover large partsof the seafloor. Above a certain density they provide a distinct, three dimensional structure and the aggregations are called bivalve beds or reefs. These persistent aggregations form a biogenic habitat for many other species. Bivalve beds, therefore, often have, in comparison with the surrounding areas, a high biodiversity value and can be seen as hotspots for biodiversity. Bivalve have a wide global distribution, on rocky and sedimentary coasts. Different processes and mechanisms influence the presence of associated benthic fauna. This paper reviewed the main drivers that influence the biodiversity, such as the bivalve species involved, the density, the size and the age of the bed, the depth or height in the tidal zone and the substratum type. Bivalve beds not only occur naturally in many subtidal and intertidal areas around the world, but mussels and oysters are also extensively cultured. Addition of physical cultivation structures in the water column or on the bottom allows for development of substantial and diverse communities that have a structure similar to that of natural beds. Dynamics of culture populations may however differ from naturalbivalve reefs as a result of culture site and/or maintenance and operation likeharvesting of the bivalve cultures. We used the outcome of the review on the drivers for wild assemblages to evaluate trade-offs between bivalve aquaculture and biodiversity conservation. Studies comparing natural and cultured assemblages proved to allow for a better understanding of the effect of the culture strategies and, consequently, to forward sustainable bivalve cultures. This is illustrated by a case study in the Dutch Wadden Sea