Deformation of the Nankai Trough inner accretionary prism: The role of inherited structures

Accretionary prisms commonly grow seaward, with the strata of the inner prism consisting of older, previously accreted outer prism rocks overlain by thick fore-arc basin strata. We focus on the Nankai Trough inner accretionary prism using three-dimensional (3-D) seismic data and logging data from the Integrated Ocean Drilling Program (IODP). We update the 3-D seismic volume using well velocity data to better constrain deeper horizons. Interpretation of these horizons reveals multiple folds with axial surfaces that strike near parallel to modern outer prism thrust faults, and we interpret that these folds formed as a result of thrust faulting. Reactivation of one inner prism thrust fault continued until at least ∼0.44 Ma, after the modern fore-arc basin formed, indicating that the inner prism had continued deformation until that time. Structural restorations of these folded seismic horizons demonstrate that ∼580 m of slip occurred on this steeply dipping reactivated thrust after fore-arc basin formation. Structural interpretation and analysis of logging-while-drilling data, including borehole images, in the deep inner prism revealed intense deformation of a generally homogenous lithology characterized by bedding that dips steeply (60°–90°), intersected by faults and fractures that have a range of dips and densities. Our study of the deep Kumano Basin provides new insights into the structure of the inner prism and reveals that although the inner prism has partially preserved inherited outer prism structures, these older folds and faults are steeply rotated and cut by multiple fracture populations during subsequent deformation.Funding for this research was provided by the National Science Foundation (grant OCE-0451790) and a U.S. Science Support Program Post-Expedition Award.Peer reviewe

Beyond summative evaluation: The Instructional Quality Assessment as a professional development tool

In order to improve students' opportunities to learn, educators need tools that can assist them to reflect on and analyze their own and others' teaching practice. Many available observation tools and protocols for studying student work are inadequate because they do not directly engage educators in core issues about rigorous content and pedagogy. In this conceptual paper, we argue that the Instructional Quality Assessment (IQA)--a formal toolkit for rating instructional quality that is based primarily on classroom observations and student assignments--has strong potential to support professional development within schools at multiple levels. We argue that the IQA could be useful to "teachers" for analyzing their own and their colleagues' practice; additionally, the IQA could aid the efforts of "principals" in their work as instructional leaders, identifying effective practitioners to help lead professional development within a school and targeting professional development needs that would require external support. Although the IQA was designed for summative, external evaluation, we argue that the steps taken to improve the reliability of the instrument--particularly the efforts to make the rubric descriptors for gradations of instructional quality as transparent as possible--also serve to make the tool a resource for professional growth among educators. The following are appended: (1) Abridged Version of the Principles of Learning; (2) Relationship between Checklist Ratings and Rubric Scores; and (3) Accountable Talk Function Checklist. (Contains 3 notes, 1 table, and 1 figure.

Chiasma

Newspaper reporting on events at the Boston University School of Medicine in the 1960s

New Insights from Seafloor Mapping of a Hawaiian Marine Monument

On 15 June 2006, when U.S. President George W. Bush signed the proclamation creating the Papahānaumokuākea Marine National Monument (PMNM), he probably wasn’t thinking about underwater morphology. To fully understand the coral reefs and marine ecosystems that the monument was created to protect, however, scientists need to have a detailed picture of the seafloor features, home to corals and other species, as well as the geologic history of the area. Thanks to a recent, multi-institution expedition, such a seafloor features that will not only inform conservation efforts but also enable geologists and geophysicists to revise their understanding of Hawaii’s complex geologic past. Specifically, data should help scientists answer fundamental questions about the area’s regional geology. For instance, which seamounts were truly formed because of Hawaiian hotspot volcanism, and which seamounts were not

Individual snag detection using neighborhood attribute filtered airborne lidar data

The ability to estimate and monitor standing dead trees (snags) has been difficult due to their irregular and sparse distribution, often requiring intensive sampling methods to obtain statistically significant estimates. This study presents a new method for estimating and monitoring snags using neighborhood attribute filtered airborne discrete-return lidar data. The method first develops and then applies an automated filtering algorithm that utilizes three dimensional neighborhood lidar point-based intensity and density statistics to remove lidar points associated with live trees and retain lidar points associated with snags. A traditional airborne lidar individual-tree detection procedure is then applied to the snag-filtered lidar point cloud, resulting in stem map of identified snags with height estimates. The filtering algorithm was developed using training datasets comprised of four different forest types in wide range of stand conditions, and then applied to independent data to determine successful snag detection rates. Detection rates ranged from 43 to 100%, increasing as the size of snags increased. The overall detection rate for snags with DBH ≥ 25 cm was 56% (± 2.9%) with low commission error rates. The method provides the ability to estimate snag density and stem map a large proportion of snags across the landscape. The resulting information can be used to analyze the spatial distribution of snags, provide a better understanding of wildlife snag use dynamics, assess achievement of stocking standard requirements, and bring more clarity to snag stocking standards.Keywords: Snag detection, Snag density, Airborne lidar, Forestry, Snags, Neighborhood attribute lidar filtering, Lidar filterin

Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest

Forest understory communities are important components in forest ecosystems providing wildlife habitat and influencing nutrient cycling, fuel loadings, fire behavior and tree species composition over time. One of the most widely utilized understory component metrics is understory vegetation cover, often used as a measure of vegetation abundance. To date, understory vegetation cover estimation and prediction has proven to be inherently difficult using traditional explanatory variables such as: leaf area index, basal area, slope, and aspect. We introduce airborne lidar-derived metrics into the modeling framework for understory vegetation cover. A new airborne lidar metric, understory lidar cover density, created by filtering understory lidar points using intensity values increased traditional explanatory power from non-lidar understory vegetation cover estimation models (non-lidar R²-values: 0.2–0.45 vs. lidar R²-values: 0.7–0.8). Beta regression, a relatively new modeling technique for this type of data, was compared with a traditional weighted linear regression model using a leave-one-out cross‐validation procedure. Both models provided similar understory vegetation cover accuracies (± 22%) and biases (~ 0%) using 40.5 m² circular plots (n = 154). The method presented in this paper provides the ability to accurately obtain census understory vegetation cover information at fine spatial resolutions over a broad range of stand conditions for the interior ponderosa pine forest type. Additional model enhancement and the extension of the method into other forest types warrant further investigation.This is the publisher’s final pdf. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/remote-sensing-of-environment/Keywords: Weighted regression, Lidar, Intensity, Understory vegetation cover, Beta regressionKeywords: Weighted regression, Lidar, Intensity, Understory vegetation cover, Beta regressio

Workshop report: Exploring deep oceanic crust off Hawai‘i

For more than half a century, exploring a complete sequence of the oceanic crust from the seafloor through the Mohorovičić discontinuity (Moho) and into the uppermost mantle has been one of the most challenging missions of scientific ocean drilling. Such a scientific and technological achievement would provide humankind with profound insights into the largest realm of our planet and expand our fundamental understanding of Earth's deep interior and its geodynamic behavior. The formation of new oceanic crust at mid-ocean ridges and its subsequent aging over millions of years, leading to subduction, arc volcanism, and recycling of some components into the mantle, comprise the dominant geological cycle of matter and energy on Earth. Although previous scientific ocean drilling has cored some drill holes into old (> 110 Ma) and young (< 20 Ma) ocean crust, our sampling remains relatively shallow (< 2 km into intact crust) and unrepresentative of average oceanic crust. To date, no hole penetrates more than 100 m into intact average-aged oceanic crust that records the long-term history of seawater–basalt exchange (60 to 90 Myr). In addition, the nature, extent, and evolution of the deep subseafloor biosphere within oceanic crust remains poorly unknown. To address these fundamentally significant scientific issues, an international workshop “Exploring Deep Oceanic Crust off Hawai`i” brought together 106 scientists and engineers from 16 countries that represented the entire spectrum of disciplines, including petrologists, geophysicists, geochemists, microbiologists, geodynamic modelers, and drilling/logging engineers. The aim of the workshop was to develop a full International Ocean Discovery Program (IODP) proposal to drill a 2.5 km deep hole into oceanic crust on the North Arch off Hawai`i with the drilling research vessel Chikyu. This drill hole would provide samples down to cumulate gabbros of mature (∼ 80 Ma) oceanic crust formed at a half spreading rate of ∼ 3.5 cm a−1. A Moho reflection has been observed at ∼ 5.5 km below the seafloor at this site, and the workshop concluded that the proposed 2.5 km deep scientific drilling on the North Arch off Hawai`i would provide an essential “pilot hole” to inform the design of future mantle drilling

Cancer immunology and canine malignant melanoma: A comparative review

Oral canine malignant melanoma (CMM) is a spontaneously occurring aggressive tumour with relatively few medical treatment options, which provides a suitable model for the disease in humans. Historically, multiple immunotherapeutic strategies aimed at provoking both innate and adaptive anti-tumour immune responses have been published with varying levels of activity against CMM. Recently, a plasmid DNA vaccine expressing human tyrosinase has been licensed for the adjunct treatment of oral CMM. This article reviews the immunological similarities between CMM and the human counterpart; mechanisms by which tumours evade the immune system; reasons why melanoma is an attractive target for immunotherapy; the premise of whole cell, dendritic cell (DC), viral and DNA vaccination strategies alongside preliminary clinical results in dogs. Current “gold standard” treatments for advanced human malignant melanoma are evolving quickly with remarkable results being achieved following the introduction of immune checkpoint blockade and adoptively transferred cell therapies. The rapidly expanding field of cancer immunology and immunotherapeutics means that rational targeting of this disease in both species should enhance treatment outcomes in veterinary and human clinics

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Fine Mapping and Identification of BMI Loci in African Americans

Genome-wide association studies (GWASs) primarily performed in European-ancestry (EA) populations have identified numerous loci associated with body mass index (BMI). However, it is still unclear whether these GWAS loci can be generalized to other ethnic groups, such as African Americans (AAs). Furthermore, the putative functional variant or variants in these loci mostly remain under investigation. The overall lower linkage disequilibrium in AA compared to EA populations provides the opportunity to narrow in or fine-map these BMI-related loci. Therefore, we used the Metabochip to densely genotype and evaluate 21 BMI GWAS loci identified in EA studies in 29,151 AAs from the Population Architecture using Genomics and Epidemiology (PAGE) study. Eight of the 21 loci (SEC16B, TMEM18, ETV5, GNPDA2, TFAP2B, BDNF, FTO, and MC4R) were found to be associated with BMI in AAs at 5.8 × 10−5. Within seven out of these eight loci, we found that, on average, a substantially smaller number of variants was correlated (r2 > 0.5) with the most significant SNP in AA than in EA populations (16 versus 55). Conditional analyses revealed GNPDA2 harboring a potential additional independent signal. Moreover, Metabochip-wide discovery analyses revealed two BMI-related loci, BRE (rs116612809, p = 3.6 × 10−8) and DHX34 (rs4802349, p = 1.2 × 10−7), which were significant when adjustment was made for the total number of SNPs tested across the chip. These results demonstrate that fine mapping in AAs is a powerful approach for both narrowing in on the underlying causal variants in known loci and discovering BMI-related loci