A new approach to generating research-quality data through citizen science: The USA National Phenology Monitoring System

Phenology is one of the most sensitive biological responses to climate change, and recent changes in phenology have the potential to shake up ecosystems. In some cases, it appears they already are. Thus, for ecological reasons it is critical that we improve our understanding of species’ phenologies and how these phenologies are responding to recent, rapid climate change. Phenological events like flowering and bird migrations are easy to observe, culturally important, and, at a fundamental level, naturally inspire human curiosity— thus providing an excellent opportunity to engage citizen scientists. The USA National Phenology Network has recently initiated a national effort to encourage people at different levels of expertise—from backyard naturalists to professional scientists—to observe phenological events and contribute to a national database that will be used to greatly improve our understanding of spatio-temporal variation in phenology and associated phenological responses to climate change.

Traditional phenological observation protocols identify specific dates at which individual phenological events are observed. The scientific usefulness of long-term phenological observations could be improved with a more carefully structured protocol. At the USA-NPN we have developed a new approach that directs observers to record each day that they observe an individual plant, and to assess and report the state of specific life stages (or phenophases) as occurring or not occurring on that plant for each observation date. Evaluation is phrased in terms of simple, easy-to-understand, questions (e.g. “Do you see open flowers?”), which makes it very appropriate for a citizen science audience. From this method, a rich dataset of phenological metrics can be extracted, including the duration of a phenophase (e.g. open flowers), the beginning and end points of a phenophase (e.g. traditional phenological events such as first flower and last flower), multiple distinct occurrences of phenophases within a single growing season (e.g multiple flowering events, common in drought-prone regions), as well as quantification of sampling frequency and observational uncertainties. These features greatly enhance the utility of the resulting data for statistical analyses addressing questions such as how phenological events vary in time and space, and in response to global change. This new protocol is an important step forward, and its widespread adoption will increase the scientific value of data collected by citizen scientists.
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Dark Matter-Induced Baryonic Feedback in Galaxies

We demonstrate that non-gravitational interactions between dark matter and baryonic matter can affect structural properties of galaxies. Detailed galaxy simulations and analytic estimates demonstrate that dark matter which collects inside white dwarf stars and ignites Type Ia supernovae can substantially alter star formation, stellar feedback, and the halo density profile through a dark matter-induced baryonic feedback process, distinct from usual supernova feedback in galaxies.Comment: 5+12 pages, 15 figure

Superconducting correlations in metallic nanoparticles: exact solution of the BCS model by the algebraic Bethe ansatz

Superconducting pairing of electrons in nanoscale metallic particles with discrete energy levels and a fixed number of electrons is described by the reduced BCS model Hamiltonian. We show that this model is integrable by the algebraic Bethe ansatz. The eigenstates, spectrum, conserved operators, integrals of motion, and norms of wave functions are obtained. Furthermore, the quantum inverse problem is solved, meaning that form factors and correlation functions can be explicitly evaluated. Closed form expressions are given for the form factors that describe superconducting pairing.Comment: revised version, 5 pages, revtex, no figure

De novo assembly and characterization of the invasive Northern Pacific Seastar transcriptome

Invasive species are a major threat to global biodiversity but can also serve as valuable model systems to examine important evolutionary processes. While the ecological aspects of invasions have been well documented, the genetic basis of adaptive change during the invasion process has been hampered by a lack of genomic resources for the majority of invasive species. Here we report the first larval transcriptomic resource for the Northern Pacific Seastar, Asterias amurensis, an invasive marine predator in Australia. Approximately 117.5 million 100 base-pair (bp) paired-end reads were sequenced from a single RNA-Seq library from a pooled set of full-sibling A. amurensis bipinnaria larvae. We evaluated the efficacy of a pre-assembly error correction pipeline on subsequent de novo assembly. Error correction resulted in small but important improvements to the final assembly in terms of mapping statistics and core eukaryotic genes representation. The error-corrected de novo assembly resulted in 115,654 contigs after redundancy clustering. 41,667 assembled contigs were homologous to sequences from NCBI\u27s non-redundant protein and UniProt databases. We assigned Gene Ontology, KEGG Orthology, Pfam protein domain terms and predicted protein-coding sequences to > 36,000 contigs. The final transcriptome dataset generated here provides functional information for 18,319 unique proteins, comprising at least 11,355 expressed genes. Furthermore, we identified 9,739 orthologs to P. miniata proteins, evaluated our annotation pipeline and generated a list of 150 candidate genes for responses to several environmental stressors that may be important for adaptation of A. amurensis in the invasive range. Our study has produced a large set of A. amurensis RNA contigs with functional annotations that can serve as a resource for future comparisons to other echinoderm transcriptomes and gene expression studies. Our data can be used to study the genetic basis of adaptive change and other important evolutionary processes during a successful invasion

Deciphering the Plant Splicing Code: Experimental and Computational Approaches for Predicting Alternative Splicing and Splicing Regulatory Elements

Extensive alternative splicing (AS) of precursor mRNAs (pre-mRNAs) in multicellular eukaryotes increases the protein-coding capacity of a genome and allows novel ways to regulate gene expression. In flowering plants, up to 48% of intron-containing genes exhibit AS. However, the full extent of AS in plants is not yet known, as only a few high-throughput RNA-Seq studies have been performed. As the cost of obtaining RNA-Seq reads continues to fall, it is anticipated that huge amounts of plant sequence data will accumulate and help in obtaining a more complete picture of AS in plants. Although it is not an onerous task to obtain hundreds of millions of reads using high-throughput sequencing technologies, computational tools to accurately predict and visualize AS are still being developed and refined. This review will discuss the tools to predict and visualize transcriptome-wide AS in plants using short-reads and highlight their limitations. Comparative studies of AS events between plants and animals have revealed that there are major differences in the most prevalent types of AS events, suggesting that plants and animals differ in the way they recognize exons and introns. Extensive studies have been performed in animals to identify cis-elements involved in regulating AS, especially in exon skipping. However, few such studies have been carried out in plants. Here, we review the current state of research on splicing regulatory elements (SREs) and briefly discuss emerging experimental and computational tools to identify cis-elements involved in regulation of AS in plants. The availability of curated alternative splice forms in plants makes it possible to use computational tools to predict SREs involved in AS regulation, which can then be verified experimentally. Such studies will permit identification of plant-specific features involved in AS regulation and contribute to deciphering the splicing code in plants

Fintech for Psychological and Financial Resilience: Determinants of Financial Data Sharing Behavior for Individuals with Bipolar Disorder

Financial stability is a key challenge for individuals with bipolar disorder, a serious mental illness requiring life-long management. Symptomatic periods often lead to poor financial decision-making, including compulsive spending and risky behaviors. Widespread consumer adoption of financial technologies ("fintech") has accelerated in recent years, with numerous consumer-centric applications providing insight into personal financial behavior in exchange for access to financial data. We believe these technologies can be applied to meaningfully support individual resilience in this population and, potentially, the resilience of families and surrounding networks of care. However, little is known about this population's unique perspectives, expectations, or privacy preferences related to financial data sharing for these purposes. To this end, we deployed an online survey (N=480) to assess the privacy expectations of individuals with bipolar disorder surrounding the use of financial data as an early-warning indicator of symptoms. A factorial vignette design allowed us to vary vignette dimensions across the granularity of financial data types, context of potential data use, and recipient of data insights. This exploratory analysis demonstrates that individuals are most comfortable sharing financial data when they were the only party to receive algorithmically-generated insights, while factors such as context of use and granularity of data types were less significant. Individuals who were most willing to engage creditors or other financial technologies for assistance were significantly more willing to share with family members and clinicians.Comment: 4 pages, 1 figure, conference workshop paper (DIS 2023 - Designing for and Reflecting upon Resilience in Health and Wellbeing

Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods

Background: Count data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary "library size" by the sequencing depth of the assay, and typically must be normalized prior to statistical analysis. The constrained nature of these data means one could alternatively use a log-ratio transformation in lieu of normalization, as often done when testing for differential abundance (DA) of operational taxonomic units (OTUs) in 16S rRNA data. Therefore, we benchmark how well the ALDEx2 package, a transformation-based DA tool, detects differential expression in high-throughput RNA-sequencing data (RNA-Seq), compared to conventional RNA-Seq methods such as edgeR and DESeq2. Results: To evaluate the performance of log-ratio transformation-based tools, we apply the ALDEx2 package to two simulated, and two real, RNA-Seq data sets. One of the latter was previously used to benchmark dozens of conventional RNA-Seq differential expression methods, enabling us to directly compare transformation-based approaches. We show that ALDEx2, widely used in meta-genomics research, identifies differentially expressed genes (and transcripts) from RNA-Seq data with high precision and, given sufficient sample sizes, high recall too (regardless of the alignment and quantification procedure used). Although we show that the choice in log-ratio transformation can affect performance, ALDEx2 has high precision (i.e., few false positives) across all transformations. Finally, we present a novel, iterative log-ratio transformation (now implemented in ALDEx2) that further improves performance in simulations. Conclusions: Our results suggest that log-ratio transformation-based methods can work to measure differential expression from RNA-Seq data, provided that certain assumptions are met. Moreover, these methods have very high precision (i.e., few false positives) in simulations and perform well on real data too. With previously demonstrated applicability to 16S rRNA data, ALDEx2 can thus serve as a single tool for data from multiple sequencing modalities

Final Report of the AFIT Quality Initiative Internal Discovery Committee

This document contains results of a study designed to document the key elements for student success at AFIT in our continuing education and graduate programs and discover to what degree they exist at AFIT. The effort represents an attempt to guide improvement of our graduate and continuing education programs through experience available from our faculty, staff and students. The process outlined herein was designed to achieve success by allowing the participants to define what it means to succeed and then self-assess the presence of these factors at AFIT. It’s therefore a true internal discovery process since its output reflects the state of our internal understanding of teaching and learning excellence. This inclusive approach, which garnered participation from 400 people across AFIT’s schools, will be used in conjunction with the external committee\u27s recommendations to determine a course of action to invest into AFIT\u27s instructional capabilities

Beam test results for the FiberGLAST instrument

The FiberGLAST scintillating fiber telescope is a large-area instrument concept for NASA\u27s GLAST program. The detector is designed for high-energy gamma-ray astronomy, and uses plastic scintillating fibers to combine a photon pair tracking telescope and a calorimeter into a single instrument. A small prototype detector has been tested with high energy photons at the Thomas Jefferson National Accelerator Facility. We report on the result of this beam test, including scintillating fiber performance, photon track reconstruction, angular resolution, and detector efficiency