A compact spectroradiometer for solar simulator measurements

Compact spectral irradiance probe has been designed and built which uses wedge filter in conjunction with silicon cell and operational amplifier. Probe is used to monitor spectral energy distribution of solar simulators and other high intensity sources

Sky localization of complete inspiral-merger-ringdown signals for nonspinning massive black hole binaries

We investigate the capability of LISA to measure the sky position of equal-mass, nonspinning black hole binaries, combining for the first time the entire inspiral-merger-ringdown signal, the effect of the LISA orbits, and the complete three-channel LISA response. We consider an ensemble of systems near the peak of LISA's sensitivity band, with total rest mass of 2\times10^6 M\odot, a redshift of z = 1, and randomly chosen orientations and sky positions. We find median sky localization errors of approximately \sim3 arcminutes. This is comparable to the field of view of powerful electromagnetic telescopes, such as the James Webb Space Telescope, that could be used to search for electromagnetic signals associated with merging massive black holes. We investigate the way in which parameter errors decrease with measurement time, focusing specifically on the additional information provided during the merger-ringdown segment of the signal. We find that this information improves all parameter estimates directly, rather than through diminishing correlations with any subset of well- determined parameters. Although we have employed the baseline LISA design for this study, many of our conclusions regarding the information provided by mergers will be applicable to alternative mission designs as well.Comment: 9 pages, 5 figures, submitted to Phys. Rev.

Observing mergers of non-spinning black-hole binaries

Advances in the field of numerical relativity now make it possible to calculate the final, most powerful merger phase of binary black-hole coalescence for generic binaries. The state of the art has advanced well beyond the equal-mass case into the unequal-mass and spinning regions of parameter space. We present a study of the nonspinning portion of parameter space, primarily using an analytic waveform model tuned to available numerical data, with an emphasis on observational implications. We investigate the impact of varied mass ratio on merger signal-to-noise ratios (SNRs) for several detectors, and compare our results with expectations from the test-mass limit. We note a striking similarity of the waveform phasing of the merger waveform across the available mass ratios. Motivated by this, we calculate the match between our 1:1 (equal mass) and 4:1 mass-ratio waveforms during the merger as a function of location on the source sky, using a new formalism for the match that accounts for higher harmonics. This is an indicator of the amount of degeneracy in mass ratio for mergers of moderate-mass-ratio systems.Comment: 13 pages, 11 figures, submitted to Phys. Rev.

Life in lights:tracking mitochondrial delivery to lysosomes in vivo

The past decade has seen an intensive and concerted research effort into the molecular regulation of mitophagy, the selective autophagy of mitochondria. Cell-based studies have implicated mitophagy in the pathology of diverse conditions ranging from cancer to neurodegeneration. However, a definitive link between mitophagy and the etiology of human disease remains to be demonstrated. Moreover, we do not know how pervasive mammalian mitophagy is in vivo and fundamental questions remain unanswered. For example, is mitophagy common to all tissues under basal conditions or does it only occur in highly oxidative tissues under stress? This paucity of knowledge is largely due to a lack of experimentally tractable tools that can measure and monitor mitophagy in tissues. Our recent work describes the development of mito-QC, a mouse model to study mitophagy at single cell resolution in vivo

Mitophagy in the aging nervous system

Aging is characterised by the progressive accumulation of cellular dysfunction, stress, and inflammation. A large body of evidence implicates mitochondrial dysfunction as a cause or consequence of age-related diseases including metabolic disorders, neuropathies, various forms of cancer and neurodegenerative diseases. Because neurons have high metabolic demands and cannot divide, they are especially vulnerable to mitochondrial dysfunction which promotes cell dysfunction and cytotoxicity. Mitophagy neutralises mitochondrial dysfunction, providing an adaptive quality control strategy that sustains metabolic homeostasis. Mitophagy has been extensively studied as an inducible stress response in cultured cells and short-lived model organisms. In contrast, our understanding of physiological mitophagy in mammalian aging remains extremely limited, particularly in the nervous system. The recent profiling of mitophagy reporter mice has revealed variegated vistas of steady-state mitochondrial destruction across different tissues. The discovery of patients with congenital autophagy deficiency provokes further intrigue into the mechanisms that underpin neural integrity. These dimensions have considerable implications for targeting mitophagy and other degradative pathways in age-related neurological disease.Peer reviewe

Monitoring autophagy in cancer : From bench to bedside

Autophagy refers to an essential mechanism that evolved to sustain eukaryotic homeostasis and metabolism during instances of nutrient deprivation. During autophagy, intracellular cargo is encapsulated and delivered to the lysosome for elimination. Loss of basal autophagy in vivo negatively impacts cellular proteostasis, metabolism and tissue integrity. Accordingly, many drug development strategies are focused on modulating autophagic capacity in various pathophysiological states, from cancer to neurodegenerative disease. The role of autophagy in cancer is particularly complicated, as either augmenting or attenuating this process can have variable outcomes on cellular survival, proliferation and transformation. This complexity is compounded by the emergence of several selective autophagy pathways, which act to eliminate damaged or superfluous cellular components in a targeted fashion. The advent of sensitive tools to monitor autophagy pathways in vivo holds promise to clarify their importance in cancer pathophysiology. In this review, we provide an overview of autophagy in cancer biology and outline how the development of tools to study autophagy in vivo could enhance our understanding of its function for translational benefit.Peer reviewe

Lipid droplets promote efficient mitophagy

Mitophagy neutralizes defective mitochondria via lysosomal elimination. Increased levels of mitophagy hallmark metabolic transitions and are induced by iron depletion, yet its metabolic basis has not been studied in-depth. How mitophagy integrates with different homeostatic mechanisms to support metabolic integrity is incompletely understood. We examined metabolic adaptations in cells treated with deferiprone (DFP), a therapeutic iron chelator known to induce PINK1-PRKN-independent mitophagy. We found that iron depletion profoundly rewired the cellular metabolome, remodeling lipid metabolism within minutes of treatment. DGAT1-dependent lipid droplet biosynthesis occurs upstream of mitochondrial turnover, with many LDs bordering mitochondria upon iron chelation. Surprisingly, DGAT1 inhibition restricts mitophagy in vitro by lysosomal dysfunction. Genetic depletion of mdy/DGAT1 in vivo impairs neuronal mitophagy and locomotor function in Drosophila, demonstrating the physiological relevance of our findings.Non peer reviewe

Consistency of post-Newtonian waveforms with numerical relativity

General relativity predicts the gravitational wave signatures of coalescing binary black holes. Explicit waveform predictions for such systems, required for optimal analysis of observational data, have so far been achieved using the post-Newtonian (PN) approximation. The quality of this treatment is unclear, however, for the important late-inspiral portion. We derive late-inspiral waveforms via a complementary approach, direct numerical simulation of Einstein's equations. We compare waveform phasing from simulations of the last $\sim 14$ cycles of gravitational radiation from equal-mass, nonspinning black holes with the corresponding 2.5PN, 3PN, and 3.5PN orbital phasing. We find phasing agreement consistent with internal error estimates based on either approach, suggesting that PN waveforms for this system are effective until the last orbit prior to final merger.Comment: Replaced with published version -- one figure removed, text and other figures updated for clarity of discussio

A systematic correlation between two-dimensional flow topology and the abstract statistics of turbulence

Velocity differences in the direct enstrophy cascade of two-dimensional turbulence are correlated with the underlying flow topology. The statistics of the transverse and longitudinal velocity differences are found to be governed by different structures. The wings of the transverse distribution are dominated by strong vortex centers, whereas, the tails of the longitudinal differences are dominated by saddles. Viewed in the framework of earlier theoretical work this result suggests that the transfer of enstrophy to smaller scales is accomplished in regions of the flow dominated by saddles.Comment: 4 pages, 4 figure

Analysis of Bayesian classification-based approaches for Android malware detection

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware