The interplay between viral-derived miRNAs and host immunity during infection

MicroRNAs are short non-coding RNAs that play a crucial role in the regulation of gene expression during cellular processes. The host-encoded miRNAs are known to modulate the antiviral defense during viral infection. In the last decade, multiple DNA and RNA viruses have been shown to produce miRNAs known as viral miRNAs (v-miRNAs) so as to evade the host immune response. In this review, we highlight the origin and biogenesis of viral miRNAs during the viral lifecycle. We also explore the role of viral miRNAs in immune evasion and hence in maintaining chronic infection and disease. Finally, we offer insights into the underexplored role of viral miRNAs as potential targets for developing therapeutics for treating complex viral diseases

Investigation of Membrane Receptors’ Oligomers Using Fluorescence Resonance Energy Transfer and Multiphoton Microscopy in Living Cells

Investigating quaternary structure (oligomerization) of macromolecules (such as proteins and nucleic acids) in living systems (in vivo) has been a great challenge in biophysics, due to molecular diffusion, fluctuations in several biochemical parameters such as pH, quenching of fluorescence by oxygen (when fluorescence methods are used), etc. We studied oligomerization of membrane receptors in living cells by means of Fluorescence (Förster) Resonance Energy Transfer (FRET) using fluorescent markers and two photon excitation fluorescence micro-spectroscopy. Using suitable FRET models, we determined the stoichiometry and quaternary structure of various macromolecular complexes. The proteins of interest for this work are : (1) sigma-1 receptor and (2) rhodopsin, are described as below. (1) Sigma-1 receptors are molecular chaperone proteins, which also regulate ion channels. S1R seems to be involved in substance abuse, as well as several diseases such as Alzheimer’s. We studied S1R in the presence and absence of its ligands haloperidol (an antagonist) and pentazocine +/- (an agonist), and found that at low concentration they reside as a mixture of monomers and dimers and that they may form higher order oligomers at higher concentrations. (2) Rhodopsin is a prototypical G protein coupled receptor (GPCR) and is directly involved in vision. GPCRs form a large family of receptors that participate in cell signaling by responding to external stimuli such as drugs, thus being a major drug target (more than 40% drugs target GPCRs). Their oligomerization has been largely controversial. Understanding this may help to understand the functional role of GPCRs oligomerization, and may lead to the discovery of more drugs targeting GPCR oligomers. It may also contribute toward finding a cure for Retinitis Pigmentosa, which is caused by a mutation (G188R) in rhodopsin, a disease which causes blindness and has no cure so far. Comparing healthy rhodopsin’s oligomeric structure with that of the mutant may give clues to find the cure

Millennial-scale rates of erosion and change in relief in north Queensland using cosmogenic nuclide ¹⁰Be

Although water is one of the main agents of erosion in many environmental settings, many erosion rates derived from beryllium-10 (¹⁰Be) suggests that a relationship between precipitation and erosion rate is statistically non-significant on a global scale. This might be because of the strong influence of other variables on erosion rate. The first chapter of this thesis contains global ¹⁰Be compilation, in which I examine if mean annual precipitation has a statistically significant secondary control on erosion rate. My secondary variable assessment suggests a significant secondary influence of precipitation on erosion rate. This is the first time that the influence of precipitation on ¹⁰Be-derived erosion rate is recognized on global scale. In fact, in areas where slope is <200m/km (~11°), precipitation influences erosion rate as much as mean basin slope, which has been recognized as the most important variable in previous ¹⁰Be compilations. In areas where elevation is <1000m and slope is <11°, the correlation between precipitation and erosion rate improves considerably. These results also suggest that erosion rate responds to change in mean annual precipitation nonlinearly and in three regimes: 1) it increases with an increase in precipitation until ~1000 mm/yr; 2) erosion rate stabilizes at ~1000 mm/yr and decreases slightly with increased precipitation until ~2200 mm/yr; and 3) it increases again with further increases in precipitation. This complex relationship between erosion rate and mean annual precipitation is best explained by the interrelationship between mean annual precipitation and vegetation. Increased vegetation, particularly the presence of trees, is widely recognized to lower erosion rate. Our results suggest that tree cover of 40% or more reduces erosion rate enough to outweigh the direct erosive effects of increased rainfall. Thus, precipitation emerges as a stronger secondary control on erosion rate in hyper-arid areas, as well as in hyper-wet areas. In contrast, the regime between ~1000 and ~2200 mm/yr is dominated by opposing relationships where higher rainfall acts to increase erosion rate, but more water also increases vegetation/tree cover, which slows erosion. These results suggest that when interpreting the sedimentological record, high sediment fluxes are expected to occur when forests transition to grasslands/savannahs; however, aridification of grasslands or savannahs into deserts will result in lower sediment fluxes. This study also implies that anthropogenic deforestation, particularly in regions with high rainfall, can greatly increase erosion. Quantification of long-term erosion rates is important in north Queensland, which is proximal to the Great Barrier Reef. One of the main threats to the Great Barrier Reef is sediment generated by erosion. Recent applications of ¹⁰Be in north Queensland has contributed significantly towards understanding erosion rates in the region. However, the existing information has a limited spatial distribution and information on bedrock erosion rates in north Queensland are very limited. Here, I focus on quantifying erosion rates across north Queensland and investigating how erosion rate varies across different slopes, rock types, and precipitation values. I also determined paleoerosion rates for the Burdekin River, and quantified erosion rates from bedrock samples and compared these to adjacent basins to explore the implications for rates of relief generation and landscape evolution. The erosion rates of basins in north Queensland range from 2.2 to 53.6m/My and bedrock rates range from 3.6 to 70.2m/My. These rates are slow, compared to basins in other parts of the world that experience similar precipitation. Basins in northern part of north Queensland are eroding faster than the southern part, because the northern part experiences higher rainfall. Precipitation has strong influence on basin erosion rate (R²=0.71), whereas the influence of mean basin slope is negligible (R²=0.09). The strong influence of precipitation and weak influence of slope on erosion rates in north Queensland is consistent with the fact that most sampling sites are from areas where slope was low (~<13°). The bedrock erosion rates in north Queensland are mainly governed by lithology; sedimentary rocks are eroding faster than granites, and precipitation has no influence on bedrock erosion rate. Unlike most places in the world, bedrock in north Queensland erodes faster than basins. This implies that relief is being lost over time. My results also suggests minimal temporal variation for erosion rates in north Queensland, and that these erosion rates were consistently slow for ~120,000 years, implying that landscapes in north Queensland have most likely attained steady state. In order to obtain the paleoerosion rate of Burdekin River, sediments buried under the Toomba flow were collected. The Toomba flow is the youngest flow of the Nulla volcanic province, located in north Queensland. This 120 km long flow has a published ⁴⁰Ar/³⁹Ar age of 21,000 ± 3000 years. In contrast, seven conventional radiocarbon (¹⁴C) analyses of carbon-bearing material beneath the flow yielded radiocarbon ages of 16,000 to <2500 BP. Published radiocarbon ages are younger than the ⁴⁰Ar/³⁹Ar age, potentially due to contamination of the charcoal by younger carbon that was not removed by acid-base pre-treatment methodology used. I have, therefore, re-examined the radiocarbon age of the Toomba flow using newly sampled charcoal buried beneath the Toomba flow in combination with hydrogen pyrolysis pre-treatment and accelerated mass spectrometer (AMS) measurements. I determined a calibrated radiocarbon age of 20,815–19,726 calBP (2σ) for the material beneath the Toomba flow. Our radiocarbon age, therefore: (1) is older than previous radiocarbon ages for the Toomba flow, (2) provides the most precise age yet available for the Toomba flow, (3) is in agreement with the ⁴⁰Ar/³⁹Ar age, and (4) validates that hydrogen pyrolysis is a robust and effective pre-treatment method for subtropical conditions where samples are susceptible to contamination by younger carbon. The Toomba flow erupted during the Last Glacial Maximum, but the preserved surface suggests that the rate of weathering and soil formation has been almost negligible on this flow, despite being situated in a subtropical climate that experiences highly variable, often intense rainfall. The age of Toomba flow also allowed determination of paleoerosion rates for the Burdekin River, and the present erosion rate derived from modern Burdekin samples is not very different from the erosion rate derived from sediment deposited ~20,000 years ago

Generalized Zero-Shot Learning via Synthesized Examples

We present a generative framework for generalized zero-shot learning where the training and test classes are not necessarily disjoint. Built upon a variational autoencoder based architecture, consisting of a probabilistic encoder and a probabilistic conditional decoder, our model can generate novel exemplars from seen/unseen classes, given their respective class attributes. These exemplars can subsequently be used to train any off-the-shelf classification model. One of the key aspects of our encoder-decoder architecture is a feedback-driven mechanism in which a discriminator (a multivariate regressor) learns to map the generated exemplars to the corresponding class attribute vectors, leading to an improved generator. Our model's ability to generate and leverage examples from unseen classes to train the classification model naturally helps to mitigate the bias towards predicting seen classes in generalized zero-shot learning settings. Through a comprehensive set of experiments, we show that our model outperforms several state-of-the-art methods, on several benchmark datasets, for both standard as well as generalized zero-shot learning.Comment: Accepted in CVPR'1

Morpheus: Automated Safety Verification of Data-Dependent Parser Combinator Programs

Parser combinators are a well-known mechanism used for the compositional construction of parsers, and have shown to be particularly useful in writing parsers for rich grammars with data-dependencies and global state. Verifying applications written using them, however, has proven to be challenging in large part because of the inherently effectful nature of the parsers being composed and the difficulty in reasoning about the arbitrarily rich data-dependent semantic actions that can be associated with parsing actions. In this paper, we address these challenges by defining a parser combinator framework called Morpheus equipped with abstractions for defining composable effects tailored for parsing and semantic actions, and a rich specification language used to define safety properties over the constituent parsers comprising a program. Even though its abstractions yield many of the same expressivity benefits as other parser combinator systems, Morpheus is carefully engineered to yield a substantially more tractable automated verification pathway. We demonstrate its utility in verifying a number of realistic, challenging parsing applications, including several cases that involve non-trivial data-dependent relations