Evaluation of the Rhenium-Osmium geochronometer in the Phosphoria Petroleum System, Bighorn Basin of Wyoming and Montana, USA

Rhenium–osmium (Re–Os) geochronometry is applied to crude oils derived from the Permian Phosphoria Formation of the Bighorn Basin in Wyoming and Montana to determine whether the radiogenic age reflects the timing of petroleum generation, timing of migration, age of the source rock, or the timing of thermochemical sulfate reduction (TSR). The oils selected for this study are interpreted to be derived from the Meade Peak Phosphatic Shale and Retort Phosphatic Shale Members of the Phosphoria Formation based on oil–oil and oil–source rock correlations utilizing bulk properties, elemental composition, δ13C and δ34S values, and biomarker distributions. The δ34S values of the oils range from −6.2‰ to +5.7‰, with oils heavier than −2‰ interpreted to be indicative of TSR. The Re and Os isotope data of the Phosphoria oils plot in two general trends: (1) the main trend (n = 15 oils) yielding a Triassic age (239 ± 43 Ma) with an initial 187Os/188Os value of 0.85 ± 0.42 and a mean square weighted deviation (MSWD) of 1596, and (2) the Torchlight trend (n = 4 oils) yielding a Miocene age (9.24 ± 0.39 Ma) with an initial 187Os/188Os value of 1.88 ± 0.01 and a MSWD of 0.05. The scatter (high MSWD) in the main-trend regression is due, in part, to TSR in reservoirs along the eastern margin of the basin. Excluding oils that have experienced TSR, the regression is significantly improved, yielding an age of 211 ± 21 Ma with a MSWD of 148. This revised age is consistent with some studies that have proposed Late Triassic as the beginning of Phosphoria oil generation and migration, and does not seem to reflect the source rock age (Permian) or the timing of re-migration (Late Cretaceous to Eocene) associated with the Laramide orogeny. The low precision of the revised regression (±21 Ma) is not unexpected for this oil family given the long duration of generation from a large geographic area of mature Phosphoria source rock, and the possible range in the initial 187Os/188Os values of the Meade Peak and Retort source units. Effects of re-migration may have contributed to the scatter, but thermal cracking and biodegradation likely have had minimal or no effect on the main-trend regression. The four Phosphoria-sourced oils from Torchlight and Lamb fields yield a precise Miocene age Re–Os isochron that may reflect the end of TSR in the reservoir due to cooling below a threshold temperature in the last 10 m.y. from uplift and erosion of overlying rocks. The mechanism for the formation of a Re–Os isotopic relationship in a family of crude oils may involve multiple steps in the petroleum generation process. Bitumen generation from the source rock kerogen may provide a reset of the isotopic chronometer, and incremental expulsion of oil over the duration of the oil window may provide some of the variation seen in 187Re/188Os values from an oil family

Subventricular zone stem cells are heterogeneous with respect to their embryonic origins and neurogenic fates in the adult olfactory bulb

Wedetermined the embryonic origins of adult forebrain subventricular zone (SVZ) stem cells by Cre-lox fate mapping in transgenic mice. We found that all parts of the telencephalic neuroepithelium, including the medial ganglionic eminence and lateral ganglionic eminence (LGE) and the cerebral cortex, contribute multipotent, self-renewing stem cells to the adult SVZ. Descendants of the embryonic LGE and cortex settle in ventral and dorsal aspects of the dorsolateral SVZ, respectively. Both populations contribute new (5-bromo-2(')-deoxyuridine- labeled) tyrosine hydroxylase- and calretinin-positive interneurons to the adult olfactory bulb. However, calbindin-positive interneurons in the olfactory glomeruli were generated exclusively by LGE- derived stem cells. Thus, different SVZ stem cells have different embryonic origins, colonize different parts of the SVZ, and generate different neuronal progeny, suggesting that some aspects of embryonic patterning are preserved in the adult SVZ. This could have important implications for the design of endogenous stem cell-based therapies in the future

Enhanced ionization of the Martian nightside ionosphere during solar energetic particle events

Electron densities in the Martian nightside ionosphere are more than 90% of time too low to be detected by the Mars Advanced Radar for Subsurface and Ionosphere Sounding radar sounder on board the Mars Express spacecraft. However, the relative number of ionograms with peak electron density high enough to be detected represents a good statistical proxy of the ionospheric density. We focus on solar energetic particle (SEP) events, and we analyze their effects on ionospheric formation. SEP time intervals were identified in situ using the background counts recorded by the ion sensor of the ASPERA-3 instrument on board Mars Express. We show that peak electron densities during the SEP events are large enough to be detected in more than 30% of measurements, and, moreover, the reflections of the sounding signal from the ground almost entirely disappear. Nightside electron densities during SEP events are thus substantially increased as compared to normal nightside conditions

Evaluation of a Conversation Management Toolkit for Multi Agent Programming

The Agent Conversation Reasoning Engine (ACRE) is intended to aid agent developers to improve the management and reliability of agent communication. To evaluate its effectiveness, a problem scenario was created that could be used to compare code written with and without the use of ACRE by groups of test subjects. This paper describes the requirements that the evaluation scenario was intended to meet and how these motivated the design of the problem. Two experiments were conducted with two separate sets of students and their solutions were analysed using a combination of simple objective metrics and subjective analysis. The analysis suggested that ACRE by default prevents some common problems arising that would limit the reliability and extensibility of conversation-handling code. As ACRE has to date been integrated only with the Agent Factory multi agent framework, it was necessary to verify that the problems identified are not unique to that platform. Thus a comparison was made with best practice communication code written for the Jason platform, in order to demonstrate the wider applicability of a system such as ACRE.Comment: appears as Programming Multi-Agent Systems - 10th International Workshop, ProMAS 2012, Valencia, Spain, June 5, 2012, Revised Selected Paper

Probing upper thermospheric neutral densities at Mars using electron reflectometry

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95666/1/grl20594.pd

Enhancing legal argument mining with domain pre-training and neural networks

The contextual word embedding model, BERT, has proved its ability on downstream tasks with limited quantities of annotated data. BERT and its variants help to reduce the burden of complex annotation work in many interdisciplinary research areas, for example, legal argument mining in digital humanities. Argument mining aims to develop text analysis tools that can automatically retrieve arguments and identify relationships between argumentation clauses. Since argumentation is one of the key aspects of case law, argument mining tools for legal texts are applicable to both academic and non-academic legal research. Domain-specific BERT variants (pre-trained with corpora from a particular background) have also achieved strong performance in many tasks. To our knowledge, previous machine learning studies of argument mining on judicial case law still heavily rely on statistical models. In this paper, we provide a broad study of both classic and contextual embedding models and their performance on practical case law from the European Court of Human Rights (ECHR). During our study, we also explore a number of neural networks when being combined with different embeddings. Our experiments provide a comprehensive overview of a variety of approaches to the legal argument mining task. We conclude that domain pre-trained transformer models have great potential in this area, although traditional embeddings can also achieve strong performance when combined with additional neural network layers

A decade of legal argumentation mining: datasets and approaches

The growing research field of argumentation mining (AM) in the past ten years has made it a popular topic in Natural Language Processing. However, there are still limited studies focusing on AM in the context of legal text (Legal AM), despite the fact that legal text analysis more generally has received much attention as an interdisciplinary field of traditional humanities and data science. The goal of this work is to provide a critical data-driven analysis of the current situation in Legal AM. After outlining the background of this topic, we explore the availability of annotated datasets and the mechanisms by which these are created. This includes a discussion of how arguments and their relationships can be modelled, as well as a number of different approaches to divide the overall Legal AM task into constituent sub-tasks. Finally we review the dominant approaches that have been applied to this task in the past decade, and outline some future directions for Legal AM research

Argument mining with graph representation learning

Argument Mining (AM) is a unique task in Natural Language Processing (NLP) that targets arguments: a meaningful logical structure in human language. Since the argument plays a significant role in the legal field, the interdisciplinary study of AM on legal texts has significant promise. For years, a pipeline architecture has been used as the standard paradigm in this area. Although this simplifies the development and management of AM systems, the connection between different parts of the pipeline causes inevitable shortcomings such as cascading error propagation. This paper presents an alternative perspective of the AM task, whereby legal documents are represented as graph structures and the AM task is undertaken as a hybrid approach incorporating Graph Neural Networks (GNNs), graph augmentation and collective classification. GNNs have been demonstrated to be an effective method for representation learning on graphs, and they have been successfully applied to many other NLP tasks. In contrast to previous pipeline-based architecture, our approach results in a single end-to-end classifier for the identification and classification of argumentative text segments. Experiments based on corpora from both the European Court of Human Rights (ECHR) and the Court of Jus- tice of the European Union (CJEU) show that our approach achieves strong results compared to state-of-the-art baselines. Both the graph augmentation and collective classification steps are shown to improve performance on both datasets when compared to using GNNs alone

Four Martian years of nightside upper thermospheric mass densities derived from electron reflectometry: Method extension and comparison with GCM simulations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95498/1/jgre2766.pd

Decreasing the Peril of Antimicrobial Resistance Through Enhanced Health Literacy in Outpatient Settings: An Underrecognized Approach to Advance Antimicrobial Stewardship

© 2020, The Author(s). Globally, antimicrobial resistance (AMR) is a serious problem causing 700,000 deaths annually. By 2050, AMR is expected to cause approximately 10 million deaths globally each year if allowed to increase at the present rate. Many individuals have limited knowledge regarding appropriate antibiotic use and AMR. Most antibiotic use occurs in the outpatient setting, with approximately 30% of antibiotics prescribed deemed unnecessary. Antimicrobial stewardship (AMS) is a means to reduce inappropriate antibiotic use and AMR. While existing AMS efforts generally focus on the inpatient setting, a significant gap is present in the outpatient setting. A common theme across various national action plans to reduce AMR is the need for education and awareness. The importance of communicating information in a manner easily comprehended by the patient in addition to productive clinician–patient dialogue cannot be overestimated. Enhancing the public’s and patients’ AMS health literacy is an underrecognized approach to help address AMR. We describe Four Core Elements of Enhancing AMS Health Literacy in the Outpatient Setting, utilizing the Centers for Disease Control and Prevention’s framework: (1) leadership commitment, (2) intervention/action, (3) tracking/reporting, and (4) education/expertise. We call upon leaders in outpatient settings to embrace this approach to curb inappropriate antimicrobial use