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Nicotine use can lead to dependence through complex processes that are regulated by both its rewarding and aversive effects. Recent studies show that aversive nicotine doses activate excitatory inputs to the interpeduncular nucleus (IPN) from the medial habenula (MHb), but the downstream targets of the IPN that mediate aversion are unknown. Here we show that IPN projections to the laterodorsal tegmentum (LDTg) are GABAergic using optogenetics in tissue slices from mouse brain. Selective stimulation of these IPN axon terminals in LDTg in vivo elicits avoidance behavior, suggesting that these projections contribute to aversion. Nicotine modulates these synapses in a concentration-dependent manner, with strong enhancement only seen at higher concentrations that elicit aversive responses in behavioral tests. Optogenetic inhibition of the IPN–LDTg connection blocks nicotine conditioned place aversion, suggesting that the IPN–LDTg connection is a critical part of the circuitry that mediates the aversive effects of nicotine. Despite its known effects in brain reward centers, nicotine can be aversive in high doses. Here, the authors show that nicotine aversion depends on low-affinity nicotinic acetylcholine receptors expressed on projections from the interpeduncular nucleus to the laterodorsal tegmentum.

Annotating and interpreting the results of genome-wide association studies (GWAS) remains challenging. Assigning function to genetic variants as expression quantitative trait loci is an expanding and useful approach, but focuses exclusively on mRNA rather than protein levels. Many variants remain without annotation. To address this problem, we measured the steady state abundance of 441 human signaling and transcription factor proteins from 68 Yoruba HapMap lymphoblastoid cell lines to identify novel relationships between inter-individual protein levels, genetic variants, and sensitivity to chemotherapeutic agents. Proteins were measured using micro-western and reverse phase protein arrays from three independent cell line thaws to permit mixed effect modeling of protein biological replicates. We observed enrichment of protein quantitative trait loci (pQTLs) for cellular sensitivity to two commonly used chemotherapeutics: cisplatin and paclitaxel. We functionally validated the target protein of a genome-wide significant trans-pQTL for its relevance in paclitaxel-induced apoptosis. GWAS overlap results of drug-induced apoptosis and cytotoxicity for paclitaxel and cisplatin revealed unique SNPs associated with the pharmacologic traits (at p<0.001). Interestingly, GWAS SNPs from various regions of the genome implicated the same target protein (p<0.0001) that correlated with drug induced cytotoxicity or apoptosis (p ≤ 0.05). Two genes were functionally validated for association with drug response using siRNA: SMC1A with cisplatin response and ZNF569 with paclitaxel response. This work allows pharmacogenomic discovery to progress from the transcriptome to the proteome and offers potential for identification of new therapeutic targets. This approach, linking targeted proteomic data to variation in pharmacologic response, can be generalized to other studies evaluating genotype-phenotype relationships and provide insight into chemotherapeutic mechanisms.

Over the past decade, the development of the MC21 Monte Carlo radiation transport solver has focused on extending the functionality of the code beyond static calculations of reactivity and reaction rate distributions, as well as improving accuracy, performance, and scalability. Notable improvements include enhanced interaction physics models, efficient model representation and tracking algorithms, support for coupled physics calculations using both in-line and externally coupled feedback modules, and the development of native visualization and results post-processing tools. As a result of these improvements, MC21 v.10 provides a comprehensive analysis framework that allows complicated engineering problems to be solved with minimal reliance on external tools.

In this work, we describe a method for converging nonlinear feedback during the convergence of the neutron fission source in a Monte Carlo reactor simulation. This approach involves updating feedback physics during discard batches in the Monte Carlo simulation rather than fully (or partially) converging the neutronics prior to the nonlinear update. This approach is demonstrated for a single PWR pin with thermal feedback and with both thermal and xenon feedback. Converging these feedbacks inline with the fission source is shown to have the benefit of reducing numerical instability by effectively underrelaxing the tallied quantities in the Monte Carlo simulation and improving computational performance by converging feedback within (or near to) the number of discard batches required to converge the fission source even without any feedback.

Annotating and interpreting the results of genome-wide association studies (GWAS) remains challenging. Assigning function to genetic variants as expression quantitative trait loci is an expanding and useful approach, but focuses exclusively on mRNA rather than protein levels. Many variants remain without annotation. To address this problem, we measured the steady state abundance of 441 human signaling and transcription factor proteins from 68 Yoruba HapMap lymphoblastoid cell lines to identify novel relationships between inter-individual protein levels, genetic variants, and sensitivity to chemotherapeutic agents. Proteins were measured using micro-western and reverse phase protein arrays from three independent cell line thaws to permit mixed effect modeling of protein biological replicates. We observed enrichment of protein quantitative trait loci (pQTLs) for cellular sensitivity to two commonly used chemotherapeutics: cisplatin and paclitaxel. We functionally validated the target protein of a genome-wide significant trans-pQTL for its relevance in paclitaxel-induced apoptosis. GWAS overlap results of drug-induced apoptosis and cytotoxicity for paclitaxel and cisplatin revealed unique SNPs associated with the pharmacologic traits (at p<0.001). Interestingly, GWAS SNPs from various regions of the genome implicated the same target protein (p<0.0001) that correlated with drug induced cytotoxicity or apoptosis (p≤0.05). Two genes were functionally validated for association with drug response using siRNA: SMC1A with cisplatin response and ZNF569 with paclitaxel response. This work allows pharmacogenomic discovery to progress from the transcriptome to the proteome and offers potential for identification of new therapeutic targets. This approach, linking targeted proteomic data to variation in pharmacologic response, can be generalized to other studies evaluating genotype-phenotype relationships and provide insight into chemotherapeutic mechanisms.

In this work we propose using Newton's method, specifically the inexact Newton-GMRES formulation, to solve the k-eigenvalue problem in both transport and diffusion neutronics problems. This is achieved by choosing a nonlinear function whose roots are the eigenpairs of the k-eigenvalue calculation and then using Newton's method to solve the nonlinear system. The exibility resulting from the use of a Krylov subspace method to solve the linear Newton step can be further extended via the use of the Jacobian-Free Newton-Krylov (JFNK) approximation, which requires no knowledge of the system's Jacobian; instead only the ability to evaluate the system residual is necessary. For the diffusion approximation, the nonlinear function is written in the form of the generalized eigenvalue problem and a set of preconditioners is developed and applied to the GMRES iterations that are used to solve the linearized Newton problem. Most of the developed methods can be implemented as either Newton-Krylov (NK) methods, where the Jacobian-vector product is formed using the explicitly constructed Jacobian, or via the JFNK approximation, where a finite-difference perturbation is used to approximate the Jacobian-vector product. One particularly effective preconditioning option comprises the use of the standard power iteration to precondition the GMRES iteration on either the right or the left. Pre-conditioning on the left, denoted JFNK(PI), results in a modified nonlinear system whose implementation only requires the ability to perform a single traditional outer iteration, making this approach relatively simple to wrap around an existing diffusion theory k-eigenvalue problem solver. Similar methods were developed for transport theory, cast using an operator notation that greatly simplifies their presentation. All of the nonlinear functions developed are written in terms of a generic fixed-point iteration, with a number of specific fixed-point formulations considered. Each fixed-point scheme represents a viable k-eigenvalue problem solution method, with two of the techniques corresponding to traditionally used iterative schemes. The new methods developed can also be wrapped around existing software in most instances, simplifying the implementation process. Ultimately it is seen that the most effective of the Newton formulations in transport theory is wrapped around a k-eigenvalue formulation that is a very special instance of traditional methods: no upscattering iterations are performed, only one inner iteration completed per outer, using source iteration with the previous outer iterate as the initial guess. In the Newton approach an extra degree of freedom is introduced by including the eigenvalue as an unknown, meaning an additional relation is necessary to close the system. In the diffusion theory case a normalization condition on the eigenvector was generally used, however in transport theory a number of so-called constraint relations were considered. These fall into two categories: normalization relations and eigenvalue update formulations. It was observed that the most effective of these constraint relations is the fission-rate eigenvalue update, derived directly from the eigenvalue update formula traditionally used to solve the k-eigenvalue problem. Numerical results, including measured performance quantified in number of iterations and execution time, were generated for suites of benchmark problems using the various Newton's Method formulations for the k-eigenvalue problem in both transport and diffusion theories. These results showed that the choice of the perturbation parameter in the JFNK approximation has very little impact on the calculation while the choice of GMRES stopping criterion significantly affects the total cost of the calculation. Overall, the numerical results showed that the Newton formulation of the k-eigenvalue problem in diffusion theory is competitive with the Chebyshev accelerated power iteration, with the JFNK(PI) formulation generally resulting in quicker execution times. The transport results showed that a number of the Newton formulations developed result in methods that are significantly less computationally expensive than traditional techniques. Results for the well-known C5G7-MOX benchmark problem demonstrate that the Newton approach reduces by a factor of 5 the total number of sweeps necessary to converge the point-wise fission source error to 10-4. (Abstract shortened by UMI.)

Global AML efforts are only as strong as their weakest link. One bank officer who fails in his or her AML compliance obligations - whether maliciously or to avoid asking a customer uncomfortable questions - can undermine efforts at other institutions by not presenting a unified front and by making their institution more inviting to both money launderers and to otherwise law-abiding customers who find AML obligations cumbersome. \"Know your customer\" is one of the most important elements in the prevention of laundering, but it is an element lacking at many Middle East banks, which instead follow local traditions of accommodating customers' requests without what might be perceived as intrusive questions. The absence of sound customer information has a significant impact on other facets of anti-laundering programs, including transaction monitoring and the bank's ability to apply a risk-based approach to its customer base. Consider how the mafia long held sway in Sicily and how the Colombian cocaine kingpin Pablo Escobar waged a war of terror against his nation's financial institutions. Combating money laundering isn't just about preventing mobsters from buying mansions - it's about life and death.

Older people have been reported to be at higher risk of COVID-19 mortality. This study explored the factors mediating this association and whether older age was associated with increased mortality risk in the absence of other risk factors. In UK Biobank, a population cohort study, baseline data were linked to COVID-19 deaths. Poisson regression was used to study the association between current age and COVID-19 mortality. Among eligible participants, 438 (0.09%) died of COVID-19. Current age was associated exponentially with COVID-19 mortality. Overall, participants aged ≥75 years were at 13-fold (95% CI 9.13-17.85) mortality risk compared with those <65 years. Low forced expiratory volume in 1 second, high systolic blood pressure, low handgrip strength, and multiple long-term conditions were significant mediators, and collectively explained 39.3% of their excess risk. The associations between these risk factors and COVID-19 mortality were stronger among older participants. Participants aged ≥75 without additional risk factors were at 4-fold risk (95% CI 1.57-9.96, P = 0.004) compared with all participants aged <65 years. Higher COVID-19 mortality among older adults was partially explained by other risk factors. 'Healthy' older adults were at much lower risk. Nonetheless, older age was an independent risk factor for COVID-19 mortality.

Objective To investigate the association between active commuting and incident cardiovascular disease (CVD), cancer, and all cause mortality.Design Prospective population based study. Setting UK Biobank.Participants 263 450 participants (106 674 (52%) women; mean age 52.6), recruited from 22 sites across the UK. The exposure variable was the mode of transport used (walking, cycling, mixed mode v non-active (car or public transport)) to commute to and from work on a typical day.Main outcome measures Incident (fatal and non-fatal) CVD and cancer, and deaths from CVD, cancer, or any causes.Results 2430 participants died (496 were related to CVD and 1126 to cancer) over a median of 5.0 years (interquartile range 4.3-5.5) follow-up. There were 3748 cancer and 1110 CVD events. In maximally adjusted models, commuting by cycle and by mixed mode including cycling were associated with lower risk of all cause mortality (cycling hazard ratio 0.59, 95% confidence interval 0.42 to 0.83, P=0.002; mixed mode cycling 0.76, 0.58 to 1.00, P<0.05), cancer incidence (cycling 0.55, 0.44 to 0.69, P<0.001; mixed mode cycling 0.64, 0.45 to 0.91, P=0.01), and cancer mortality (cycling 0.60, 0.40 to 0.90, P=0.01; mixed mode cycling 0.68, 0.57 to 0.81, P<0.001). Commuting by cycling and walking were associated with a lower risk of CVD incidence (cycling 0.54, 0.33 to 0.88, P=0.01; walking 0.73, 0.54 to 0.99, P=0.04) and CVD mortality (cycling 0.48, 0.25 to 0.92, P=0.03; walking 0.64, 0.45 to 0.91, P=0.01). No statistically significant associations were observed for walking commuting and all cause mortality or cancer outcomes. Mixed mode commuting including walking was not noticeably associated with any of the measured outcomes.Conclusions Cycle commuting was associated with a lower risk of CVD, cancer, and all cause mortality. Walking commuting was associated with a lower risk of CVD independent of major measured confounding factors. Initiatives to encourage and support active commuting could reduce risk of death and the burden of important chronic conditions.