Barriers to infection of human cells by feline leukemia virus: insights into resistance to zoonosis

The human genome displays a rich fossil record of past gamma-retrovirus infections, yet no current epidemic is evident, despite environmental exposure to viruses that infect human cells in vitro. Feline leukemia viruses (FeLVs) rank high on this list, but domestic or workplace exposure has not been associated with detectable serological responses. Non-specific inactivation of gamma-retroviruses by serum factors appears insufficient to explain these observations. To investigate further we explored the susceptibility of primary and established human cell lines to FeLV-B, the most likely zoonotic variant. Fully permissive infection was common in cancer-derived cell lines, but was also a feature of non-transformed keratinocytes and lung fibroblasts. Cells of haematopoietic origin were less generally permissive and formed discrete groups on the basis of high or low intracellular protein expression and virion release. Potent repression was observed in primary human blood mononuclear cells and a subset of leukemia cell lines. However, the early steps of reverse transcription and integration appear to be unimpaired in non-permissive cells. FeLV-B was subject to G->A hypermutation with a predominant APOBEC3G signature in partially permissive cells but was not mutated in permissive cells or in non-permissive cells that block secondary viral spread. Distinct cellular barriers that protect primary human blood cells are likely to be important in protection against zoonotic infection with FeLV

Mobile Weather Satellite Receiver

The Mobile Weather Satellite Receiver project aims to create a portable, user-friendly device that receives and displays weather information from geostationary satellites, addressing the limitations of traditional weather sources like the Internet and weather radio, particularly in remote areas. This device will collect and demodulate satellite data, including imagery and Emergency Managers Weather Information Network (EMWIN) forecasts, to provide users with detailed local forecasts and real-time alerts. Designed with a user-centric approach, the system includes a satellite dish, Software Defined Radio (SDR), a Raspberry Pi, and a custom software interface for ease of use. Its portability and ability to function without an internet connection make it particularly valuable for users in remote areas such as RV travelers and off-grid homesteaders, where timely weather data is crucial for safety. This project leverages existing satellite communication technology to offer a cost-effective, all-in-one solution, making satellite weather data accessible and easy to use for people in isolated locations. I will be working on the antenna mount, parabolic dish, and the horn antenna

Quantifying the differences in avian attack rates on reptiles between an infrastructure and a control site

Acknowledgements We would like to thank Professor Bob Furness for his comments on an earlier draft of this manuscript, and the staff of Camster Wind Farm for their cooperation. Two anonymous reviewers provided comments that greatly improved the text.Peer reviewedPostprin

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Predicting pairwise preferences between TTS audio stimuli using parallel ratings data and anti-symmetric twin neural networks

Automatically predicting the outcome of subjective listening tests is a challenging task. Ratings may vary from person to person even if preferences are consistent across listeners. While previous work has focused on predicting listeners' ratings (mean opinion scores) of individual stimuli, we focus on the simpler task of predicting subjective preference given two speech stimuli for the same text. We propose a model based on anti-symmetric twin neural networks, trained on pairs of waveforms and their corresponding preference scores. We explore both attention and recurrent neural nets to account for the fact that stimuli in a pair are not time aligned. To obtain a large training set we convert listeners' ratings from MUSHRA tests to values that reflect how often one stimulus in the pair was rated higher than the other. Specifically, we evaluate performance on data obtained from twelve MUSHRA evaluations conducted over five years, containing different TTS systems, built from data of different speakers. Our results compare favourably to a state-of-the-art model trained to predict MOS scores

Syllable-Level Representations of Suprasegmental Features for DNN-Based Text-to-Speech Synthesis

A top-down hierarchical system based on deep neural networks is investigated for the modeling of prosody in speech synthesis. Suprasegmental features are processed separately from segmental features and a compact distributed representation of high-level units is learned at syllable-level. The suprasegmental representation is then integrated into a frame-level network. Objective measures show that balancing segmental and suprasegmental features can be useful for the frame-level network. Additional features incorporated into the hierarchical system are then tested. At the syllable-level, a bag-of-phones representation is proposed and, at the word-level, embeddings learned from text sources are used. It is shown that the hierarchical system is able to leverage new features at higher-levels more efficiently than a system which exploits them directly at the frame-level. A perceptual evaluation of the proposed systems is conducted and followed by a discussion of the results

Parallel and cascaded deep neural networks for text-to-speech synthesis

An investigation of cascaded and parallel deep neural networks for speech synthesis is conducted. In these systems, suprasegmental linguistic features (syllable-level and above) are processed separately from segmental features (phone-level and below). The suprasegmental component of the networks learns compact distributed representations of high-level linguistic units without any segmental influence. These representations are then integrated into a frame-level system using a cascaded or a parallel approach. In the cascaded network, suprasegmental representations are used as input to the framelevel network. In the parallel network, segmental and suprasegmental features are processed separately and concatenated at a later stage. These experiments are conducted with a standard set of high-dimensional linguistic features as well as a hand-pruned one. It is observed that hierarchical systems are consistently preferred over the baseline feedforward systems. Similarly, parallel networks are preferred over cascaded networks.<br/