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No full textThis thesis and exhibition are a tribute to the fragility and beauty of coral and to raise
awareness of the impact reefs have on human life and marine ecosystems. The ceramic
installation “The Final Reef” translates scientific research on living coral, coral bleaching, and
marine degradation, into a tactile, visual language. The work explores the tension between
beauty and loss, rendering coral forms in their living vibrancy and alternatively, when they are
bleak and bleached. Assembled to function symbiotically within the exhibition space, the
sculptures highlight the interconnectedness of reef systems while expressing their critical
endangerment. Through sculpture and hand building with clay, this installation encapsulates and
preserves the delicate essence of coral reefs, capturing their forms in both living and bleached
states.N
BatAnalysis -- A Comprehensive Python Pipeline for Swift BAT Time-Tagged Event Data Analysis
No full textThe Swift Burst Alert Telescope (BAT) is a coded aperture gamma-ray instrument with a large field of view that was designed to detect and localize transient events. When a transient is detected, either on-board or externally, the BAT saves time-tagged event (TTE) data which provides the highest quality information of the locations of the photons on the detector plane and their energies. This data can be used to produce spectra, lightcurves, and sky images of a transient event. While these data products are produced by the Swift Data Center and can be produced by current software, they are often preset to certain time and energy intervals which has limited their use in the current time domain and multi-messenger environment. Here, we introduce a new capability for the BatAnalysis python package to download and process TTE data under an open-source pythonic framework that allows for easy interfacing with other python packages. The new capabilities of the BatAnalysis software allows for TTE data to be used by the community in a variety of advanced customized analyses of astrophysical sources which BAT may have TTE data for, such as Fast Radio Bursts (FRBs), Gamma-ray Bursts (GRBs), Low Mass X-ray Binaries (LMXB), Soft Gamma-ray Repeaters, magnetars, and many other sources. We highlight the usefulness of the BatAnalysis package in analyzing TTE data produced by an on-board GRB trigger, a FRB external trigger, a sub-threshold detection of the LMXB EXO 0748-676, and an external trigger of a GRB that BAT detected during a slew.The material is based upon work supported by NASA under award number 80GSFC21M0002.http://arxiv.org/abs/2502.0027
The Problem of the 50-percent Democrat
No full textAfter 10 Democratic Senators including Minority Leader Chuck Schumer voted with Republicans to pass the Continuing Resolution to keep the government from shutting down, Democratic anger exploded. Sunil Dasgupta talks with Democratic political consultant and VP of Red Horse Strategies Michelle Ngwafon about the problem of Democrats unable to meet the moment. Music by Washington art-pop rock band Catscan!https://open.spotify.com/episode/1udikJxZrkjK73SOD9fpY
Application of Machine Learning for Aboveground Biomass Modeling in Tropical and Temperate Forests from Airborne Hyperspectral Imagery
No full textAccurate operational methods used to measure, verify, and report changes in biomass at large spatial scales are required to support conservation initiatives. In this study, we demonstrate that machine learning can be used to model aboveground biomass (AGB) in both tropical and temperate forest ecosystems when provided with a sufficiently large training dataset. Using wavelet-transformed airborne hyperspectral imagery, we trained a shallow neural network (SNN) to model AGB. An existing global AGB map developed as part of the European Space Agency’s DUE GlobBiomass project served as the training data for all study sites. At the temperate site, we also trained the model on airborne-LiDAR-derived AGB. In comparison, for all study sites, we also trained a separate deep convolutional neural network (3D-CNN) with the hyperspectral imagery. Our results show that extracting both spatial and spectral features with the 3D-CNN produced the lowest RMSE across all study sites. For example, at the tropical forest site the Tortuguero conservation area, with the 3D-CNN, an RMSE of 21.12 Mg/ha (R² of 0.94) was reached in comparison to the SNN model, which had an RMSE of 43.47 Mg/ha (R² 0.72), accounting for a ~50% reduction in prediction uncertainty. The 3D-CNN models developed for the other tropical and temperate sites produced similar results, with a range in RMSE of 13.5 Mg/ha–31.18 Mg/ha. In the future, as sufficiently large field-based datasets become available (e.g., the national forest inventory), a 3D-CNN approach could help to reduce the uncertainty between hyperspectral reflectance and forest biomass estimates across tropical and temperate bioclimatic domains.This research was funded by the Canadian Airborne Biodiversity Observatory CABO which was funded by a Discovery Frontiers grant from the Natural Sciences and Engineering Research Council of Canada NSERC grant number 509190 2017 the Mission Airborne Carbon 13 MAC 13 project which was funded by the Canadian Space Agency FAST AO grant number 11STFAMG16 and the Department of Geography Rathlyn GIS Award The APC was funded by the NSERC Discovery Grant RGPIN 2022 05288https://www.mdpi.com/1999-4907/16/3/47
Trans-synaptic modulation of cholinergic circuits tunes opioid reinforcement
No full textOpioids trigger structural and functional neural adaptations of the reward circuit that lead to dependence. Synaptic cell adhesion molecules (CAMs) play a pivotal role in circuit organization and present prime candidates for orchestrating remodeling of neural connections in response to drug exposure. However, the contribution of CAMs to opioid-induced rewiring of the reward circuit has not been explored. Here, we used unbiased molecular profiling to identify CAMs in the nucleus accumbens (NAc) modulated by morphine administration. We found that opioid exposure induces the expression of ELFN1, a CAM selectively expressed in cholinergic interneurons in the NAc. We determined that ELFN1 acts trans-synaptically to modulate the strength and plasticity of the glutamatergic inputs onto cholinergic neurons via the recruitment of presynaptic metabotropic glutamate receptor 4 (mGlu4). Disruption of Elfn1 diminished morphine reward and intake in self-administering mice. Together, our findings identify a key molecular factor responsible for adjusting the strength of opioid effects by modulating the configuration of striatal circuitry in an experience-dependent fashion and unveil potential therapeutic target for combating opioid abuse.This work was supported by NIH Grants DA048579 (S.Z.), DA056414 (K.A.M.), NSF-REU 2229342 (B.A.F.), Spanish Ministerio de Economía y Competitividad (PID2021-125875OB-I00), and Junta de Comunidades de Castilla-La Mancha (SBPLY/21/180501/000064) to R.L. We also would like to thank the reviewers and the Editor for their time and many excellent points and suggestions that helped improve this manuscript.https://www.pnas.org/doi/10.1073/pnas.240932512
Attack Detection and Optimal Deployment for Underwater Constrained Wireless Sensor Networks via Hybrid Trust Evidence
No full textUnderwater wireless sensor networks have been widely used in the acquisition and processing of oceanic information. The marine environment is complex and changeable, and the existence of obstacles is the main manifestation of the complex underwater environment, which affect the communication between underwater nodes. In addition, wireless sensor networks with obstacles are often more vulnerable to various attacks, making it more fragile. In order to address the aforementioned issues, we firstly propose a underwater wireless sensor deployment strategy with obstacle avoidance as the target (GEHO). After that, we use Tabtransformer algorithm to build trust model and detect attacks according to trust data set, which can enhance the robustness of the entire wireless sensor network. In the final stage, we collect the patterns of malicious attacks on nodes according to the detection results, which is convenient for us to make timely responses and reduce the losses of underwater acoustic sensor networks due to malicious attacks. The simulation results show that the trust model can effectively detect malicious nodes and attack types in the network, and has higher detection accuracy than the existing trust model.This work was supported in part by Taishan Scholar Project under Grant tsqnz20230602, Natural Science Foundation of Shandong Province under Grant ZR2024MF115 and ZR2023LZH010, National Natural Science Foundation of China under Grant 52171341 and Youth Innovation University Team Project in Shandong under Grant 2022KJ062.https://ieeexplore.ieee.org/abstract/document/1087679
The first analysis of the outward H fluxes measured by IBEX-Lo in 20–50 Rᴇ geocentric distances
No full textIn this study, we analyze the energetic neutral atom (ENA) observations measured in the lowest energy channel (10–21 eV) of the IBEX-Lo instrument on Interstellar Boundary Explorer (IBEX) during two spring seasons, day of year (DOY) 101–146, 2009, and DOY 88–178, 2013, confirming the existence of outward hydrogen (H) fluxes at 15 eV. The outward H flux decreases slightly with distance, showing an intensity of approximately 10⁶ cm⁻² s⁻¹ sr⁻¹ keV⁻¹. Results also suggest that the outward H fluxes are not influenced by solar radio flux. We compute the expected H ENA fluxes at 15 eV using ion flux measurements from the Helium, Oxygen, Proton, and Electron (HOPE) mass spectrometer aboard the Radiation Belt Storm Probes (RBSP) during the corresponding period of the 2013 spring season, combined with a simple exospheric density model (nʜ=nʜ₀(r₀/r)³, where r₀=10 Rᴇ). The expected ENA fluxes similarly show a decrease in the intensity with increasing geocentric distance, which is on the order of 10⁵–10⁶ cm⁻² s⁻¹ sr⁻¹ keV⁻¹. These consistent features suggest that the outward H fluxes observed by IBEX-Lo are closely related to escaping H ENAs produced within the inner exosphere (<4 Rᴇ).The author(s) declare that financial support was received for the research and/or publication of this article. This research was supported by the NASA Goddard Space Flight Center through Cooperative Agreement 80NSSC21M0180 to the University of Maryland Baltimore County, Partnership for Heliophysics and Space Environment Research (PHaSER), and the NASA Heliophysics Theory, Modeling, and Simulation (H-TMS) programhttps://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2025.1529064/ful
Inference and Social Proficiency: An argument for teaching social skills as rhetorical skills in First Year Composition
No full textThis study explores the relationship between inductive inferential reasoning, social proficiency, and audience construction in the context of First Year Composition (FYC). By centering social skills as rhetorical skills, this project presents an argument that the teaching of social proficiencies sits within the sphere of responsibility of FYC, even though composition scholars across the last four decades have repeatedly insisted otherwise. Finally, this project offers strategies for composition teachers to develop students’ social proficiencies that do not unduly disrupt other conventional composition course priorities.
Considering contemporary perspectives, I reexamine the seminal literature of the “social turn” of composition scholarship in search of unexamined and potentially problematic assumptions about social proficiency in students that have carried through decades of composition scholarship. I use evidence from the literature itself to support the argument that inductive reasoning, a form of inference in which principles, beliefs, and behaviors are derived from observations, is a socio-cognitive act of prediction that draws on social experience and social proficiency. Unlike deductive reasoning, which seeks to produce certainty, inductive reasoning produces conclusions that are probable. While probability can refer only to mathematical or statistical determinations, arguing by induction often involves convincing human audiences that something is probably true, or feels probably true enough to believe or act upon. Persuading a human audience involves predicting other people’s habits of inference in order to achieve rhetorical (persuasive) success. As such, social skills are rhetorical skills and fall within the sphere of responsibility of FYC. “Inference and Social Proficiency: An argument for teaching social skills as rhetorical skills in First Year Composition” presents a study of the relationships between social proficiency, social anxiety, and how students approach participation in collaborative practices in an FYC classroom and presents an argument that the teaching of social proficiencies sits within the sphere of responsibility of FYC, even though seminal composition scholars across the last four decades have repeatedly asserted otherwise. This project offers broad pedagogical considerations for composition teachers that foreground incoming students’ social proficiencies and ways of developing social skills as rhetorical tools without unduly disrupting other conventional composition course priorities
Optimized Preparation of Segmentally Labeled RNAs for NMR Structure Determination
No full textRNA structures are significantly underrepresented in public repositories (∼ 100-fold compared to proteins) despite their importance for mechanistic understanding and for development of structure prediction/validation tools. A substantial portion of deposited RNA structures have been determined by NMR (∼30%), but most comprise fewer than 60 nucleotides due to complications associated with NMR signal overlap. A promising approach for applying NMR to larger RNAs involves use of a mutated DNA polymerase (TGK) that can extend “primer” RNA strands generated independently by synthetic or enzymatic methods [Haslecker et al., Nature Commun. 2023]. In attempts to employ this technology, we uncovered sequence- and enzyme-dependent complications for most constructs examined that prohibited preparation of homogeneous samples. By using TGK extension efficiency and NMR as guides, we identified non-templated run-on by wild-type T7-RNA polymerase (RNAPWT) as the primary source of product heterogeneity. Use of 2′-O-methylated DNA templates did not prevent RNAPWT run-on for most constructs examined. However, primer RNAs with appropriate 3′ end homogeneity were obtained in high yield using a recently described T7 RNAP mutant designed for improved immunogenic behavior. Minor spectral heterogeneity sometimes observed for 3′ residues, caused by partial premature TGK termination, could be moved to sites downstream of the RNA region of interest by employing extended template DNAs that encode additional non-interacting 3′ nucleotides. We additionally present an approach for large-scale synthesis of homogeneous template DNA required for TGK extension. With these modifications, segmentally labeled RNAs appropriate for high resolution structural studies are now routinely obtainable.Funding from the NIH National Institute of Allergy and Infectious Diseases (R01 AI150498 to MFS; U54 AI17660 to MFS and JM) and the Howard Hughes Medical Institute is gratefully acknowledgedhttps://www.sciencedirect.com/science/article/pii/S002228362500139
Fiducial Confidence Intervals for Agreement Measures Among Raters Under a Generalized Linear Mixed Effects Model
No full textA generalization of the classical concordance correlation coefficient (CCC) is considered under a three-level design where multiple raters rate every subject over time, and each rater is rating every subject multiple times at each measuring time point. The ratings can be discrete or continuous. A methodology is developed for the interval estimation of the CCC based on a suitable linearization of the model along with an adaptation of the fiducial inference approach. The resulting confidence intervals have satisfactory coverage probabilities and shorter expected widths compared to the interval based on Fisher Z-transformation, even under moderate sample sizes. Two real applications available in the literature are discussed. The first application is based on a clinical trial to determine if various treatments are more effective than a placebo for treating knee pain associated with osteoarthritis. The CCC was used to assess agreement among the manual measurements of the joint space widths on plain radiographs by two raters, and the computer-generated measurements of digitalized radiographs. The second example is on a corticospinal tractography, and the CCC was once again applied in order to evaluate the agreement between a well-trained technologist and a neuroradiologist regarding the measurements of fiber number in both the right and left corticospinal tracts. Other relevant applications of our general approach are highlighted in many areas including artificial intelligence.http://arxiv.org/abs/2503.0411