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  1. Free, publicly-accessible full text available March 1, 2023
  2. Transposable elements (TEs) are genomic parasites that can propagate throughout host genomes. Mammalian genomes are typically dominated by LINE retrotransposons and their associated SINEs, and germline mobilization is a challenge to genome integrity. There are defenses against TE proliferation and the PIWI/piRNA defense is among the most well understood. However, the PIWI/piRNA system has been investigated largely in animals with actively mobilizing TEs and it is unclear how the PIWI/piRNA system functions in the absence of mobilizing TEs. The 13-lined ground squirrel provides the opportunity to examine PIWI/piRNA and TE dynamics within the context of minimal, and possibly nonexistent, TEmore »accumulation. To do so, we compared the PIWI/piRNA dynamics in squirrels to observations from the rabbit and mouse. Despite a lack of young insertions in squirrels, TEs were still actively transcribed at higher levels compared to mouse and rabbit. All three Piwi genes were not expressed, prior to P8 in squirrel testis, and there was little TE expression change with the onset of Piwi expression. We also demonstrated there was not a major expression change in the young squirrel LINE families in the transition from juvenile to adult testis in contrast to young mouse and rabbit LINE families. These observations lead us to conclude that PIWI suppression, was weaker for squirrel LINEs and SINEs and did not strongly reduce their transcription. We speculate that, although the PIWI/piRNA system is adaptable to novel TE threats, transcripts from TEs that are no longer threatening receive less attention from PIWI proteins.« less
    Free, publicly-accessible full text available March 16, 2023
  3. Using presence/absence data from over 10,000 Ves SINE insertions, we reconstructed a phylogeny for 11 Myotis species. With nearly one-third of individual Ves gene trees discordant with the overall species tree, phylogenetic conflict appears to be rampant in this genus. From the observed conflict, we infer that ILS is likely a major contributor to the discordance. Much of the discordance can be attributed to the hypothesized split between the Old World and New World Myotis clades and with the first radiation of Myotis within the New World. Quartet asymmetry tests reveal signs of introgression between Old and New World taxamore »that may have persisted until approximately 8 MYA. Our introgression tests also revealed evidence of both historic and more recent, perhaps even contemporary, gene flow among Myotis species of the New World. Our findings suggest that hybridization likely played an important role in the evolutionary history of Myotis and may still be happening in areas of sympatry. Despite limitations arising from extreme discordance, our SINE-based phylogeny better resolved deeper relationships (particularly the positioning of M. brandtii) and was able to identify potential introgression pathways among the Myotis species sampled.« less
    Free, publicly-accessible full text available March 1, 2023
  4. Free, publicly-accessible full text available February 1, 2023
  5. Human activity recognition (HAR) from wearable sensors data has become ubiquitous due to the widespread proliferation of IoT and wearable devices. However, recognizing human activity in heterogeneous environments, for example, with sensors of different models and make, across different persons and their on-body sensor placements introduces wide range discrepancies in the data distributions, and therefore, leads to an increased error margin. Transductive transfer learning techniques such as domain adaptation have been quite successful in mitigating the domain discrepancies between the source and target domain distributions without the costly target domain data annotations. However, little exploration has been done when multiplemore »distinct source domains are present, and the optimum mapping to the target domain from each source is not apparent. In this paper, we propose a deep Multi-Source Adversarial Domain Adaptation (MSADA) framework that opportunistically helps select the most relevant feature representations from multiple source domains and establish such mappings to the target domain by learning the perplexity scores. We showcase that the learned mappings can actually reflect our prior knowledge on the semantic relationships between the domains, indicating that MSADA can be employed as a powerful tool for exploratory activity data analysis. We empirically demonstrate that our proposed multi-source domain adaptation approach achieves 2% improvement with OPPORTUNITY dataset (cross-person heterogeneity, 4 ADLs), whereas 13% improvement on DSADS dataset (cross-position heterogeneity, 10 ADLs and sports activities).« less
  6. Annotated IMU sensor data from smart devices and wearables are essential for developing supervised models for fine-grained human activity recognition, albeit generating sufficient annotated data for diverse human activities under different environments is challenging. Existing approaches primarily use human-in-the-loop based techniques, including active learning; however, they are tedious, costly, and time-consuming. Leveraging the availability of acoustic data from embedded microphones over the data collection devices, in this paper, we propose LASO, a multimodal approach for automated data annotation from acoustic and locomotive information. LASO works over the edge device itself, ensuring that only the annotated IMU data is collected, discardingmore »the acoustic data from the device itself, hence preserving the audio-privacy of the user. In the absence of any pre-existing labeling information, such an auto-annotation is challenging as the IMU data needs to be sessionized for different time-scaled activities in a completely unsupervised manner. We use a change-point detection technique while synchronizing the locomotive information from the IMU data with the acoustic data, and then use pre-trained audio-based activity recognition models for labeling the IMU data while handling the acoustic noises. LASO efficiently annotates IMU data, without any explicit human intervention, with a mean accuracy of 0.93 ($\pm 0.04$) and 0.78 ($\pm 0.05$) for two different real-life datasets from workshop and kitchen environments, respectively.« less
  7. Precise and eloquent label information is fundamental for interpreting the underlying data distributions distinctively and training of supervised and semi-supervised learning models adequately. But obtaining large amount of labeled data demands substantial manual effort. This obligation can be mitigated by acquiring labels of most informative data instances using Active Learning. However labels received from humans are not always reliable and poses the risk of introducing noisy class labels which will degrade the efficacy of a model instead of its improvement. In this paper, we address the problem of annotating sensor data instances of various Activities of Daily Living (ADLs) inmore »smart home context. We exploit the interactions between the users and annotators in terms of relationships spanning across spatial and temporal space which accounts for an activity as well. We propose a novel annotator selection model SocialAnnotator which exploits the interactions between the users and annotators and rank the annotators based on their level of correspondence. We also introduce a novel approach to measure this correspondence distance using the spatial and temporal information of interactions, type of the relationships and activities. We validate our proposed SocialAnnotator framework in smart environments achieving ≈ 84% statistical confidence in data annotation« less