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The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a revolutionary breakthrough in our comprehension of the Universe. However, due to the unprecedented sensitivity required to make such observations, gravitational-wave detectors also capture disruptive noise sources called glitches, potentially masking or appearing as gravitational wave signals themselves. To address this problem, a community-science project, Gravity Spy, incorporates human insight and machine learning to classify glitches in LIGO data. The machine learning classifier, integrated into the project since 2017, has evolved over time to accommodate increasing numbers of glitch classes. Despite its success, limitations have arisen in the ongoing LIGO fourth observing run (O4) due to its architecture’s simplicity, which led to poor generalization and inability to handle multi-time window inputs effectively. We propose an advanced classifier for O4 glitches. Our contributions include evaluating fusion strategies for multi-time window inputs, using label smoothing to counter noisy labels, and enhancing interpretability through attention module-generated weights. This development seeks to enhance glitch classification, aiding in the ongoing exploration of gravitational-wave phenomena. 

Microorganisms play essential roles in the health and resilience of cnidarians. Understanding the factors influencing cnidarian microbiomes requires cross study comparisons, yet the plethora of protocols used hampers dataset integration. We unify 16S rRNA gene sequences from cnidarian microbiome studies under a single analysis pipeline. We reprocess 12,010 cnidarian microbiome samples from 186 studies, alongside 3,388 poriferan, 370 seawater samples, and 245 cultured Symbiodiniaceae, unifying ~6.5 billion sequence reads. Samples are partitioned by hypervariable region and sequencing platform to reduce sequencing variability. This systematic review uncovers an incredible diversity of 86 archaeal and bacterial phyla associated with Cnidaria, and highlights key bacteria hosted across host sub-phylum, depth, and microhabitat. Shallow (< 30 m) water Alcyonacea and Actinaria are characterized by highly shared and relatively abundant microbial communities, unlike Scleractinia and most deeper cnidarians. Utilizing the V4 region, we find that cnidarian microbial composition, richness, diversity, and structure are primarily influenced by host phylogeny, sampling depth, and ocean body, followed by microhabitat and sampling date. We identify host and geographical generalist and specificEndozoicomonasclades within Cnidaria and Porifera. This systematic review forms a framework for understanding factors governing cnidarian microbiomes and creates a baseline for assessing stress associated dysbiosis.

 

Citizen scientists make valuable contributions to science but need to learn about the data they are working with to be able to perform more advanced tasks. We present a set of design principles for identifying the kinds of background knowledge that are important to support learning at different stages of engagement, drawn from a study of how free/libre open source software developers are guided to create and use documents. Specifically, we suggest that newcomers require help understanding the purpose, form and content of the documents they engage with, while more advanced developers add understanding of information provenance and the boundaries, relevant participants and work processes. We apply those principles in two separate but related studies. In study 1, we analyze the background knowledge presented to volunteers in the Gravity Spy citizen-science project, mapping the resources to the framework and identifying kinds of knowledge that were not initially provided. In study 2, we use the principles proactively to develop design suggestions for Gravity Spy 2.0, which will involve volunteers in analyzing more diverse sources of data. This new project extends the application of the principles by seeking to use them to support understanding of the relationships between documents, not just the documents individually. We conclude by discussing future work, including a planned evaluation of Gravity Spy 2.0 that will provide a further test of the design principles. 

This work focuses on creating a controller for the hip joint of a rat using a canonical motor microcircuit. It is thought that this circuit acts to modulate motor neuron activity at the output stage. We first created a simplified biomechanical model of a rat hindlimb along with a neural model of the circuit in a software tool called Animatlab. The canonical motor microcircuit controller was then tuned such that the trajectory of the hip joint was similar to that of a rat during locomotion. This work describes a successful method for hand-tuning the various synaptic parameters and the influence of Ia feedback on motor neuron activity. The neuromechanical model will allow for further analysis of the circuit, specifically, the function and significance of Ia feedback and Renshaw cells. 

We present a tomographic imaging technique for the D‐region electron density using a set of spatially distributed very low frequency (VLF) remote sensing measurements. The D‐region ionosphere plays a critical role in many long‐range and over‐the‐horizon communication systems; however, it is unreachable by most direct measurement techniques such as balloons and satellites. Fortunately, the D region, combined with Earth's surface, forms what is known as the Earth‐Ionosphere waveguide allowing VLF and low frequency (LF) radio waves to propagate to global distances. By measuring these signals, we can estimate a path measurement of the electron density, which we assume to be a path‐averaged electron density profile of the D region. In this work, we use path‐averaged inferences from lightning‐generated radio atmospherics (sferics) with a tomographic inversion to produce 3D models of electron density over the Southeastern United States and the Gulf of Mexico. The model begins with two‐dimensional great circle path observations, each of which is parameterized so it includes vertical profile information. The tomography is then solved in two dimensions (latitude and longitude) at arbitrarily many altitude slices to construct the 3D electron density. We examine the model's performance in the synthetic case and determine that we have an expected percent error better than 10% within our area of interest. We apply our model to the 2017 “Great American Solar Eclipse” and find a clear relationship between sunlight percentage and electron density at different altitudes.

 

Despite long‐standing knowledge of the benefits of riparian buffers for mitigating nonpoint source pollution, many streams are unprotected by buffers. Even landowners who understand ecological values of buffers mow riparian vegetation to the streambank. Do trends in rural riparian conditions reflect the development of riparian forest science? What motivates residential riparian management actions? Using high‐resolution orthoimagery, we quantified riparian conditions and trends between 1998 and 2015 in the rural upper Little Tennessee River basin in Macon County, North Carolina and explored how landowners view riparian zone management and riparian restoration programs. Buffer composition in 2015 was as follows:no buffer(32.5%),narrow(19.3%),forested(26.7%),shrub(7.2%), andintermediate(7.0%). Relative to 1998, the greatest decrease occurred in theno bufferclass (−17.7%, 46 km) and the largest increases occurred in theshrub(+72.5%, 20 km) and narrow (12.6%, 14 km) classes.Forestedbuffer marginally increased. Semi‐structured interview data suggest that landowners prioritize recreational and scenic aspects of riparian buffers over ecological functions such as filtration and bank stabilization. Riparian restoration programs might be made more enticing to non‐adopters if outreach language appealed to landowner priorities, design elements demonstrated intentional management, and program managers highlighted areas where ecological goals and landowner values align.

 

Although many new species of the millipede genus Nannaria Chamberlin, 1918 have been known from museum collections for over half a century, a systematic revision has not been undertaken until recently. There are two species groups in the genus: the minor species group and the wilsoni species group. In this study, the wilsoni species group was investigated. Specimens were collected from throughout its distribution in the Appalachian Mountains of the eastern United States and used for a multi-gene molecular phylogeny. The phylogenetic tree recovered Nannaria and the two species groups as monophyletic, with Oenomaea pulchella as its sister group. Seventeen new species were described, bringing the composition of the wilsoni species group to 24 species, more than tripling its known diversity, and increasing the total number of described Nannaria species to 78. The genus now has the greatest number of species in the family Xystodesmidae. Museum holdings of Nannaria were catalogued, and a total of 1,835 records used to produce a distribution map of the species group. Live photographs, illustrations of diagnostic characters, ecological notes, and conservation statuses are given. The wilsoni species group is restricted to the Appalachian region, unlike the widely-distributed minor species group (known throughout eastern North America), and has a distinct gap in its distribution in northeastern Tennessee and adjacent northwestern North Carolina. The wilsoni species group seems to be adapted to mesic microhabitats in middle to high elevation forests in eastern North America. New species are expected to be discovered in the southern Appalachian Mountains.