Feedback-enhanced algorithm for aberration correction of holographic atom traps

We show that a phase-only spatial light modulator can be used to generate non-trivial light distributions suitable for trapping ultracold atoms, when the hologram calculation is included within a simple and robust feedback loop that corrects for imperfect device response and optical aberrations. This correction reduces the discrepancy between target and experimental light distribution to the level of a few percent (RMS error). We prove the generality of this algorithm by applying it to a variety of target light distributions of relevance for cold atomic physics.Comment: 5 pages, 4 figure

Children\u27s experiences of companion animal maltreatment in households characterized by intimate partner violence

Cruelty toward companion animals is a well-documented, coercive tactic used by abusive partners to intimidate and control their intimate partners. Experiences of co-occurring violence are common for children living in families with intimate partner violence (IPV) and surveys show that more than half are also exposed to abuse of their pets. Given children\u27s relationships with their pets, witnessing such abuse may be traumatic for them. Yet little is known about the prevalence and significance of this issue for children. The present study examines the experiences of children in families with co-occurring pet abuse and IPV. Using qualitative methods, 58 children ages 7–12 who were exposed to IPV were asked to describe their experiences of threats to and harm of their companion animals. Following the interviews, template analysis was employed to systematically develop codes and themes. Coding reliability was assessed using Randolph\u27s free-marginal multirater kappa (kfree = .90). Five themes emerged from the qualitative data, the most common being children\u27s exposure to pet abuse as a power and control tactic against their mother in the context of IPV. Other themes were animal maltreatment to discipline or punish the pet, animal cruelty by a sibling, children intervening to prevent pet abuse, and children intervening to protect the pet during a violent episode. Results indicate that children\u27s experiences of pet abuse are multifaceted, potentially traumatic, and may involve multiple family members with diverse motives

Using surveillance data to determine treatment rates and outcomes for patients with chronic hepatitis C virus infection

The aim of this work was to develop and validate an algorithm to monitor rates of, and response to, treatment of patients infected with hepatitis C virus (HCV) across England using routine laboratory HCV RNA testing data. HCV testing activity between January 2002 and December 2011 was extracted from the local laboratory information systems of a sentinel network of 23 laboratories across England. An algorithm based on frequency of HCV RNA testing within a defined time period was designed to identify treated patients. Validation of the algorithm was undertaken for one center by comparison with treatment data recorded in a clinical database managed by the Trent HCV Study Group. In total, 267,887 HCV RNA test results from 100,640 individuals were extracted. Of these, 78.9% (79,360) tested positive for viral RNA, indicating an active infection, 20.8% (16,538) of whom had a repeat pattern of HCV RNA testing suggestive of treatment monitoring. Annual numbers of individuals treated increased rapidly from 468 in 2002 to 3,295 in 2009, but decreased to 3,110 in 2010. Approximately two thirds (63.3%; 10,468) of those treated had results consistent with a sustained virological response, including 55.3% and 67.1% of those with a genotype 1 and non-1 virus, respectively. Validation against the Trent clinical database demonstrated that the algorithm was 95% sensitive and 93% specific in detecting treatment and 100% sensitive and 93% specific for detecting treatment outcome. Conclusions: Laboratory testing activity, collected through a sentinel surveillance program, has enabled the first country-wide analysis of treatment and response among HCV-infected individuals. Our approach provides a sensitive, robust, and sustainable method for monitoring service provision across Englan

A novel transcriptional signature identifies T-cell infiltration in high-risk paediatric cancer.

BACKGROUND: Molecular profiling of the tumour immune microenvironment (TIME) has enabled the rational choice of immunotherapies in some adult cancers. In contrast, the TIME of paediatric cancers is relatively unexplored. We speculated that a more refined appreciation of the TIME in childhood cancers, rather than a reliance on commonly used biomarkers such as tumour mutation burden (TMB), neoantigen load and PD-L1 expression, is an essential prerequisite for improved immunotherapies in childhood solid cancers. METHODS: We combined immunohistochemistry (IHC) with RNA sequencing and whole-genome sequencing across a diverse spectrum of high-risk paediatric cancers to develop an alternative, expression-based signature associated with CD8+ T-cell infiltration of the TIME. Furthermore, we explored transcriptional features of immune archetypes and T-cell receptor sequencing diversity, assessed the relationship between CD8+ and CD4+ abundance by IHC and deconvolution predictions and assessed the common adult biomarkers such as neoantigen load and TMB. RESULTS: A novel 15-gene immune signature, Immune Paediatric Signature Score (IPASS), was identified. Using this signature, we estimate up to 31% of high-risk cancers harbour infiltrating T-cells. In addition, we showed that PD-L1 protein expression is poorly correlated with PD-L1 RNA expression and TMB and neoantigen load are not predictive of T-cell infiltration in paediatrics. Furthermore, deconvolution algorithms are only weakly correlated with IHC measurements of T-cells. CONCLUSIONS: Our data provides new insights into the variable immune-suppressive mechanisms dampening responses in paediatric solid cancers. Effective immune-based interventions in high-risk paediatric cancer will require individualised analysis of the TIME.Chelsea Mayoh, Andrew J. Gifford, Rachael Terry, Loretta M. S. Lau, Marie Wong, Padmashree Rao, Tyler Shai, Hee, Federica Saletta, Dong, Anh Khuong, Quang, Vicky Qin, Marion K. Mateos, Deborah Meyran, Katherine E. Miller, Aysen Yuksel, Emily V. A. Mould, Rachel Bowen, James, Dinisha Govender, Akanksha Senapati, Nataliya Zhukova, Natacha Omer, Hetal Dholaria, Frank Alvaro, Heather Tapp, Yonatan Diamond, Luciano Dalla Pozza, Andrew S. Moore, Wayne Nicholls, Nicholas G. Gottardo, Geoffrey McCowage, Jordan R. Hansford, Seong, Lin Khaw, Paul J. Wood, Daniel Catchpoole, Catherine E. Cottrell, Elaine R. Mardis, Glenn M. Marshall, Vanessa Tyrrell, Michelle Haber, David S. Ziegler, Orazio Vittorio, Joseph A. Trapani, Mark J. Cowley, Paul J. Neeson, and Paul G. Eker