In-situ borehole temperature measurements confirm dynamics of the gas hydrate stability zone at the upper Danube deep sea fan, Black Sea

Highlights • In-situ temperature measurements were conducted at the Danube deep sea fan. • Operations were performed with the MARUM-MeBo200 seafloor drill rig. • The BSR is located ∼20 m below the current gas hydrate stability zone. • Seismic data suggest presence of shallower BSR-like events. Abstract Coring, geophysical logging, and in-situ temperature measurements were performed with the MARUM-MeBo200 seafloor rig to characterize gas hydrate occurrences in sediments of the Danube deep sea fan, off Romania, Black Sea. The new drilling data showed no evidence for significant gas hydrate saturations within the sediments but the presence of free gas at the depth of the bottom-simulating reflector (BSR). In-situ temperature and core-derived geochemical data suggest that the current base of the gas hydrate stability zone (BGHSZ) is ∼20 m shallower than the BSR. Investigation of the seismic data around the drill sites shows several locations where free gas previously trapped at a former BGHSZ migrated upwards forming a new reflection above the BSR. This shows that the gas hydrate system in the Danube deep sea fan is still responding to climate changes initiated at the end of the last glacial maximum

Contamination tracer testing with seabed drills: IODP Expedition 357

IODP Expedition 357 utilized seabed drills for the first time in the history of the ocean drilling program, with the aim of collecting intact sequences of shallow mantle core from the Atlantis Massif to examine serpentinization processes and the deep biosphere. This novel drilling approach required the development of a new remote seafloor system for delivering synthetic tracers during drilling to assess for possible sample contamination. Here, we describe this new tracer delivery system, assess the performance of the system during the expedition, provide an overview of the quality of the core samples collected for deep biosphere investigations based on tracer concentrations, and make recommendations for future applications of the system

CTD (MARUM 13894, SBE 37) data collected during the DAUNE experiment near Helgoland

This data set contains CTD data collected during the DAUNE experiment using the given sensor. The goal of this experiment was to reach a common understanding of how measurement uncertainty can be derived initially focusing on temperature data. Data collection was performed using the AWI O2A infrastructure (https://epic.awi.de/id/eprint/37171/) which performs automatized near real time quality control. During the data ingest and archival process, the hereby assigned quality flags used by the O2A system have been transformed into the pangaea flagging scheme as follows, flagging symbols are shown in brackets: O2A Flag ->PANGAEA Flag No quality control (0) ->unknown (*) Good data (1) ->valid () Probably good (2) ->questionable (?) Probably bad (3) ->questionable (?) Bad (4) ->not valid (/

Data collected during the Data UNcertainty Evaluation (DAUNE) experiment near Helgoland

This data set contains raw data synchronously collected by various CTD instrument during the DAUNE experiment. The goal of this experiment was to reach a common understanding of how measurement uncertainty can be derived initially focusing on temperature data. After developing a strategy for measuring and implementing high-quality data, the collected data was analysed for the purpose of estimating measurement uncertainties. The resulting procedures were defined in a step-by-step process, taking into account the different perspectives of the authors as well as the special conditions for environmental measurements, which are collected while the observed volume/area is undergoing a constant change. In this context, the data available here were used to provide a universally applicable method for the systematic evaluation of in-situ measurement data with regard to the quantification of the uncertainty of the measurement results

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd