Use of a UAV for Water Sampling to Assist Remote Sensing of Bacterial Flora in Freshwater Environments

Ground truth data collection in bodies of water traditionally relies on the use of watercraft and manual sampling. The transport and cost associated with the use of this type of equipment, as well as the time required to reach the site of collection, may all be significantly reduced by the use of small unmanned aerial vehicles (UAV) or drones. In this project we evaluate the implementation of a modified UAV with the ability to collect a small volume of surface water up to 400m offshore. The bacterial flora found in the water of several locations in the Lake Ontario-Rochester Embayment area is then entered into a multi-year database that attempts to correlate hyperspectral data obtained by the Landsat 8 Operational Land Imager with the isolated bacterial species. We found that water collection using a consumer grade UAV facilitated sampling efforts, saving time and providing easy access to otherwise difficult to reach collection sites

Parameter Setting in Quantum Approximate Optimization of Weighted Problems

Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers. However, in many cases QAOA requires computationally intensive parameter optimization. The challenge of parameter optimization is particularly acute in the case of weighted problems, for which the eigenvalues of the phase operator are non-integer and the QAOA energy landscape is not periodic. In this work, we develop parameter setting heuristics for QAOA applied to a general class of weighted problems. First, we derive optimal parameters for QAOA with depth $p=1$ applied to the weighted MaxCut problem under different assumptions on the weights. In particular, we rigorously prove the conventional wisdom that in the average case the first local optimum near zero gives globally-optimal QAOA parameters. Second, for $p\geq 1$ we prove that the QAOA energy landscape for weighted MaxCut approaches that for the unweighted case under a simple rescaling of parameters. Therefore, we can use parameters previously obtained for unweighted MaxCut for weighted problems. Finally, we prove that for $p=1$ the QAOA objective sharply concentrates around its expectation, which means that our parameter setting rules hold with high probability for a random weighted instance. We numerically validate this approach on general weighted graphs and show that on average the QAOA energy with the proposed fixed parameters is only $1.1$ percentage points away from that with optimized parameters. Third, we propose a general heuristic rescaling scheme inspired by the analytical results for weighted MaxCut and demonstrate its effectiveness using QAOA with the XY Hamming-weight-preserving mixer applied to the portfolio optimization problem. Our heuristic improves the convergence of local optimizers, reducing the number of iterations by 7.2x on average

Quantum computing for finance

Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. We present a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning. This Review is aimed at physicists, so it outlines the classical techniques used by the financial industry and discusses the potential advantages and limitations of quantum techniques. Finally, we look at the challenges that physicists could help tackle

Parameter Setting in Quantum Approximate Optimization of Weighted Problems

Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers. However, in many cases QAOA requires computationally intensive parameter optimization. The challenge of parameter optimization is particularly acute in the case of weighted problems, for which the eigenvalues of the phase operator are non-integer and the QAOA energy landscape is not periodic. In this work, we develop parameter setting heuristics for QAOA applied to a general class of weighted problems. First, we derive optimal parameters for QAOA with depth $p=1$ applied to the weighted MaxCut problem under different assumptions on the weights. In particular, we rigorously prove the conventional wisdom that in the average case the first local optimum near zero gives globally-optimal QAOA parameters. Second, for $p\geq 1$ we prove that the QAOA energy landscape for weighted MaxCut approaches that for the unweighted case under a simple rescaling of parameters. Therefore, we can use parameters previously obtained for unweighted MaxCut for weighted problems. Finally, we prove that for $p=1$ the QAOA objective sharply concentrates around its expectation, which means that our parameter setting rules hold with high probability for a random weighted instance. We numerically validate this approach on general weighted graphs and show that on average the QAOA energy with the proposed fixed parameters is only $1.1$ percentage points away from that with optimized parameters. Third, we propose a general heuristic rescaling scheme inspired by the analytical results for weighted MaxCut and demonstrate its effectiveness using QAOA with the XY Hamming-weight-preserving mixer applied to the portfolio optimization problem. Our heuristic improves the convergence of local optimizers, reducing the number of iterations by 7.4x on average

Quantum Deep Hedging

Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-critic algorithms that use quantum neural network architectures with orthogonal and compound layers for the policy and value functions. We prove that the quantum neural networks we use are trainable, and we perform extensive simulations that show that quantum models can reduce the number of trainable parameters while achieving comparable performance and that the distributional approach obtains better performance than other standard approaches, both classical and quantum. We successfully implement the proposed models on a trapped-ion quantum processor, utilizing circuits with up to $16$ qubits, and observe performance that agrees well with noiseless simulation. Our quantum techniques are general and can be applied to other reinforcement learning problems beyond hedging

The effectiveness of neuromuscular warm-up strategies, that require no additional equipment, for preventing lower limb injuries during sports participation: a systematic review

PMCID: PMC3408383The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1741-7015/10/75. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Development of a broadly active influenza intranasal vaccine adjuvanted with self-assembled particles composed of mastoparan-7 and CpG

Currently licensed vaccine adjuvants offer limited mucosal immunity, which is needed to better combat respiratory infections such as influenza. Mast cells (MCs) are emerging as a target for a new class of mucosal vaccine adjuvants. Here, we developed and characterized a nanoparticulate adjuvant composed of an MC activator [mastoparan-7 (M7)] and a TLR ligand (CpG). This novel nanoparticle (NP) adjuvant was co-formulated with a computationally optimized broadly reactive antigen (COBRA) for hemagglutinin (HA), which is broadly reactive against influenza strains. M7 was combined at different ratios with CpG and tested for in vitro immune responses and cytotoxicity. We observed significantly higher cytokine production in dendritic cells and MCs with the lowest cytotoxicity at a charge-neutralizing ratio of nitrogen/phosphate = 1 for M7 and CpG. This combination formed spherical NPs approximately 200 nm in diameter with self-assembling capacity. Mice were vaccinated intranasally with COBRA HA and M7-CpG NPs in a prime–boost–boost schedule. Vaccinated mice had significantly higher antigen-specific antibody responses (IgG and IgA) in serum and mucosa compared with controls. Splenocytes from vaccinated mice had significantly increased cytokine production upon antigen recall and the presence of central and effector memory T cells in draining lymph nodes. Finally, co-immunization with NPs and COBRA HA induced influenza H3N2-specific HA inhibition antibody titers across multiple strains and partially protected mice from a challenge against an H3N2 virus. These results illustrate that the M7-CpG NP adjuvant combination can induce a protective immune response with a broadly reactive influenza antigen via mucosal vaccination

A2ML1 and otitis media : novel variants, differential expression, and relevant pathways

A genetic basis for otitis media is established, however, the role of rare variants in disease etiology is largely unknown. Previously a duplication variant within A2ML1 was identified as a significant risk factor for otitis media in an indigenous Filipino population and in US children. In this report exome and Sanger sequencing was performed using DNA samples from the indigenous Filipino population, Filipino cochlear implantees, US probands, Finnish, and Pakistani families with otitis media. Sixteen novel, damaging A2ML1 variants identified in otitis media patients were rare or low-frequency in population-matched controls. In the indigenous population, both gingivitis and A2ML1 variants including the known duplication variant and the novel splice variant c.4061 + 1 G>C were independently associated with otitis media. Sequencing of salivary RNA samples from indigenous Filipinos demonstrated lower A2ML1 expression according to the carriage of A2ML1 variants. Sequencing of additional salivary RNA samples from US patients with otitis media revealed differentially expressed genes that are highly correlated with A2ML1 expression levels. In particular, RND3 is upregulated in both A2ML1 variant carriers and high-A2ML1 expressors. These findings support a role for A2ML1 in keratinocyte differentiation within the middle ear as part of otitis media pathology and the potential application of ROCK inhibition in otitis media.Peer reviewe

Early Cretaceous vegetation and climate change at high latitude: Palynological evidence from Isachsen Formation, Arctic Canada

Quantitative palynology of the marginal marine and deltaic-fluvial Isachsen Formation of the Sverdrup Basin, Canadian Arctic, provides insight into high latitude climate during much of the Early Cretaceous (Valanginian to early Aptian). Detrended Correspondence Analysis of main pollen and spore taxa is used to derive three ecological groupings influenced by moisture and disturbance based on the botanical affinities of palynomorphs: 1) a mixed coniferous assemblage containing both lowland and upland components; 2) a conifer-filicopsid community that likely grew in dynamic lowland habitats; and, 3) a mature dry lowland community composed of Cheirolepidiaceans. Stratigraphic changes in the relative abundance of pollen and spore taxa reflect climate variability in this polar region during the ~20 Mya history of the Isachsen Formation. The late Valanginian was relatively cool and moist and promoted lowland conifer-filicopsid communities. Warming in the Hauterivian resulted in the expansion coniferous communities in well-drained or arid hinterlands. A return to relatively cool and moist conditions in the Barremian resulted in the expansion of mixed lowland communities. This work demonstrates the utility of a multivariate statistical approach to palynology to provide insight into the composition and dynamics of ecosystems and climate of high latitude regions during the Early Cretaceous

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes