Bayesian modelling and quantification of Raman spectroscopy

Raman spectroscopy can be used to identify molecules such as DNA by the characteristic scattering of light from a laser. It is sensitive at very low concentrations and can accurately quantify the amount of a given molecule in a sample. The presence of a large, nonuniform background presents a major challenge to analysis of these spectra. To overcome this challenge, we introduce a sequential Monte Carlo (SMC) algorithm to separate each observed spectrum into a series of peaks plus a smoothly-varying baseline, corrupted by additive white noise. The peaks are modelled as Lorentzian, Gaussian, or pseudo-Voigt functions, while the baseline is estimated using a penalised cubic spline. This latent continuous representation accounts for differences in resolution between measurements. The posterior distribution can be incrementally updated as more data becomes available, resulting in a scalable algorithm that is robust to local maxima. By incorporating this representation in a Bayesian hierarchical regression model, we can quantify the relationship between molecular concentration and peak intensity, thereby providing an improved estimate of the limit of detection, which is of major importance to analytical chemistry

Focusing on the Big Picture: Insights into a Systems Approach to Deep Learning for Satellite Imagery

Deep learning tasks are often complicated and require a variety of components working together efficiently to perform well. Due to the often large scale of these tasks, there is a necessity to iterate quickly in order to attempt a variety of methods and to find and fix bugs. While participating in IARPA's Functional Map of the World challenge, we identified challenges along the entire deep learning pipeline and found various solutions to these challenges. In this paper, we present the performance, engineering, and deep learning considerations with processing and modeling data, as well as underlying infrastructure considerations that support large-scale deep learning tasks. We also discuss insights and observations with regard to satellite imagery and deep learning for image classification.Comment: Accepted to IEEE Big Data 201

Broadband Magnetometry and Temperature Sensing with a Light Trapping Diamond Waveguide

Solid-state quantum sensors are attracting wide interest because of their exceptional sensitivity at room temperature. In particular, the spin properties of individual nitrogen vacancy (NV) color centers in diamond make it an outstanding nanoscale sensor of magnetic fields, electric fields, and temperature, under ambient conditions. Recent work on ensemble NV-based magnetometers, inertial sensors, and clocks have employed $N$ unentangled color centers to realize a factor of up to $\sqrt{N}$ improvement in sensitivity. However, to realize fully this signal enhancement, new techniques are required to excite efficiently and to collect fluorescence from large NV ensembles. Here, we introduce a light-trapping diamond waveguide (LTDW) geometry that enables both high fluorescence collection ($\sim20\%$) and efficient pump absorption achieving an effective path length exceeding $1$ meter in a millimeter-sized device. The LTDW enables in excess of $2\%$ conversion efficiency of pump photons into optically detected magnetic resonance (ODMR) fluorescence, a \textit{three orders of magnitude} improvement over previous single-pass geometries. This dramatic enhancement of ODMR signal enables broadband measurements of magnetic field and temperature at less than $1$ Hz, a frequency range inaccessible by dynamical decoupling techniques. We demonstrate \sim 1~\mbox{nT}/\sqrt{\mbox{Hz}} magnetic field sensitivity for $0.1$ Hz to $10$ Hz and a thermal sensitivity of \sim 400 ~\mu\mbox{K}/\sqrt{\mbox{Hz}} and estimate a spin projection limit at $\sim 0.36$ fT/\sqrt{\mbox{Hz}} and \sim 139~\mbox{pK}/\sqrt{\mbox{Hz}}, respectively.Comment: 8 pages, 5 figure

Readily accessible sp3-rich cyclic hydrazine frameworks exploiting nitrogen fluxionality

Increased molecular complexity correlates with improved chances of success in the drug development process. Here, a strategy for the creation of sp3-rich, non-planar heterocyclic scaffolds suitable for drug discovery is described that obviates the need to generate multiple stereogenic centers with independent control. Asymmetric transfer hydrogenation using a tethered Ru-catalyst is used to efficiently produce a range of enantiopure cyclic hydrazine building blocks (up to 99% ee). Iterative C–N functionalization at the two nitrogen atoms of these compounds produces novel hydrazine and hydrazide based chemical libraries. Wide chemical diversification is possible through variation in the hydrazine structure, use of different functionalization chemistries and coupling partners, and controlled engagement of each nitrogen of the hydrazine in turn. Principal Moment of Inertia (PMI) analysis of this small hydrazine library reveals excellent shape diversity and three-dimensionality. NMR and crystallographic studies confirm these frameworks prefer to orient their substituents in three-dimensional space under the control of a single stereogenic center through exploitation of the fluxional behavior of the two nitrogen atoms

Semantic Memory Functional MRI and Cognitive Function After Exercise Intervention in Mild Cognitive Impairment

Mild cognitive impairment (MCI) is associated with early memory loss, Alzheimer\u27s disease (AD) neuropathology, inefficient or ineffective neural processing, and increased risk for AD. Unfortunately, treatments aimed at improving clinical symptoms or markers of brain function generally have been of limited value. Physical exercise is often recommended for people diagnosed with MCI, primarily because of its widely reported cognitive benefits in healthy older adults. However, it is unknown if exercise actually benefits brain function during memory retrieval in MCI. Here, we examined the effects of exercise training on semantic memory activation during functional magnetic resonance imaging (fMRI). Seventeen MCI participants and 18 cognitively intact controls, similar in sex, age, education, genetic risk, and medication use, volunteered for a 12-week exercise intervention consisting of supervised treadmill walking at a moderate intensity. Both MCI and control participants significantly increased their cardiorespiratory fitness by approximately 10% on a treadmill exercise test. Before and after the exercise intervention, participants completed an fMRI famous name discrimination task and a neuropsychological battery, Performance on Trial 1 of a list-learning task significantly improved in the MCI participants. Eleven brain regions activated during the semantic memory task showed a significant decrease in activation intensity following the intervention that was similar between groups (p-values ranged 0.048 to 0.0001). These findings suggest exercise may improve neural efficiency during semantic memory retrieval in MCI and cognitively intact older adults, and may lead to improvement in cognitive function. Clinical trials are needed to determine if exercise is effective to delay conversion to AD

Smoking cessation and tobacco prevention in Indigenous populations

This article systematically reviews 91 smoking cessation and tobacco prevention studies tailored for Indigenous populations around the world, with a particular focus on Aboriginal and Torres Strait Islander populations in Australia. We identified several components of effective interventions, including the use of multifaceted programs that simultaneously address the behavioural, psychological and biochemical aspects of addiction, using resources culturally tailored for the needs of individual Indigenous populations. Pharmacotherapy for smoking cessation was effective when combined with culturally tailored behavioural interventions and health professional support, though it is generally underused in clinical practice. From a policy perspective, interventions of greater intensity, with more components, were more likely to be effective than those of lower intensity and shorter duration. For any new policy it is important to consider community capacity building, development of knowledge, and sustainability of the policy beyond guided implementation. Future research should address how the intervention can be supported into standard practice, policy, or translation into the front-line of clinical care. Investigations are also required to determine the efficacy of emerging therapies (such as e-cigarettes and the use of social media to tackle youth smoking), and under-researched interventions that hold promise based on non-Indigenous studies, such as the use of Champix. We conclude that more methodologically rigorous investigations are required to determine components of the less-successful interventions to aid future policy, practice and research initiatives. Evidence Base, issue 3, 201

Does Physical Activity Influence Semantic Memory Activation in Amnestic Mild Cognitive Impairment?

The effect of physical activity (PA) on functional brain activation for semantic memory in amnestic mild cognitive impairment (aMCI) was examined using event-related functional magnetic resonance imaging during fame discrimination. Significantly greater semantic memory activation occurred in the left caudate of High- versus Low-PA patients, (P=0.03), suggesting PA may enhance memory-related caudate activation in aMCI

Repurposing Albendazole: new potential as a chemotherapeutic agent with preferential activity against HPV-negative head and neck squamous cell cancer.

Albendazole is an anti-helminthic drug that has been shown to exhibit anti-cancer properties, however its activity in head and neck squamous cell cancer (HNSCC) was unknown. Using a series of in vitro assays, we assessed the ability of albendazole to inhibit proliferation in 20 HNSCC cell lines across a range of albendazole doses (1 nM-10 μM). Cell lines that responded to treatment were further examined for cell death, inhibition of migration and cell cycle arrest. Thirteen of fourteen human papillomavirus-negative HNSCC cell lines responded to albendazole, with an average IC50 of 152 nM. In contrast, only 3 of 6 human papillomavirus-positive HNSCC cell lines responded. Albendazole treatment resulted in apoptosis, inhibition of cell migration, cell cycle arrest in the G2/M phase and altered tubulin distribution. Normal control cells were not measurably affected by any dose tested. This study indicates that albendazole acts to inhibit the proliferation of human papillomavirus-negative HNSCC cell lines and thus warrants further study as a potential chemotherapeutic agent for patients suffering from head and neck cancer