Neurospora msh4 ortholog confirmed by split-marker deletion

Although most eukaryotes have both MSH4 and MSH5 orthologs, Neurospora was initially thought to lack msh-4. We have deleted the most likely msh-4 candidate and observed a delay in the sexual cycle, disruption to meiosis and a reduction in fertility. Deletion is dominant, showing msh-4 is subject to MSUD. We conclude that Neurospora has a MSH4 ortholog and that it may have remained undetected because of an unusually high number of introns

Client-centred therapeutic relationship conditions and authenticity: a prospective study

The aim was to investigate the association between experiencing a therapeutic relationship and subsequent authenticity. Forty-six clients completed the Barrett-Lennard Relationship Inventory (B-LRI, 1968), Relational Depth Inventory (RDI, Wiggins, Elliott, & Cooper, 2012), a measure of the therapeutic alliance (ARM-5, Agnew-Davies et al, 1998) and the Authenticity Scale (AS: Wood et al, 2008) at intervals over ten therapy sessions. Higher scores on the B-LRI at sessions three, five and ten were associated with an increase in authenticity over the ten sessions. These results provide some initial evidence in support of Rogers’ (1957) theory that it is the conditions of the therapeutic relationship that leads to greater authenticity

Exile Vol. IX No. 1

FICTION The Locust Season by Patterson Bouic 5-11 The Breughel by Hugh K. Duffield II 15-22 Then I Raised My Hand by Les Overlock 25-28 Berry Stew by Barbara Thiele 32-35 The Spectator by Sue Burton 36-37 ESSAY Dialogue by Paul Pottinger 38-40 POETRY Poem by Judith Pistor 11 Poem by Albert Werder 12-13 Salvage by Mary McCarthy 14 Poem by Barbara Thiele 23 Teatime by Hugh K. Duffield II 23 Aeschylus and the Turtle by Robert Hoyt 29 Turning by Sarah Conway 29 A Lucrezia by Christine Cooper 30 GRAPHICS Pen and Wash by Patricia Thomas 4 Pen and Ink by Elizabeth Surbeck 12 Action Drawing by Elizabeth Surbeck 14 Pen and Wash by Beverly Erbacher 24 Wash by Barbara Purdy 31 Expressionistic Head by Ramona Gibbs 3

Optimising health literacy and access of service provision to community dwelling older people with diabetes receiving home nursing support

Background. Health literacy is the ability to access, understand, and use information and services for good health. Among people with chronic conditions, health literacy requirements for effective self-management are high. The Optimising Health Literacy and Access (Ophelia) study engaged diverse organisations in the codesign of interventions involving the Health Literacy Questionnaire (HLQ) needs assessment, followed by development and evaluation of interventions addressing identified needs. This study reports the process and outcomes of one of the nine organisations, the Royal District Nursing Service (RDNS). Methods. Participants were home nursing clients with diabetes. The intervention included tailored diabetes self-management education according to preferred learning style, a standardised diabetes education tool, resources, and teach-back method. Results. Needs analysis of 113 quota-sampled clients showed difficulties managing health and finding and appraising health information. The service-wide diabetes education intervention was applied to 24 clients. The intervention was well received by clients and nurses. Positive impacts on clients\u27 diabetes knowledge and behaviour were seen and nurses reported clear benefits to their practice. Conclusion. A structured method that supports healthcare services to codesign interventions that respond to the health literacy needs of their clients can lead to evidence-informed, sustainable practice changes that support clients to better understand effective diabetes self-management

Heritage Quay: What Will You Discover? Transforming the Archives of the University of Huddersfield, Yorkshire, UK

The Heritage Quay project is changing how archive services at the University of Huddersfield are delivered. This article examines how the Staff/Space/Collections dependency model and Customer Service Excellence framework have been used, and what lessons can be drawn for other archives

The Lantern Vol. 52, No. 1, Fall 1985

• Nudes • Orion • Fragments of an Epic • Sunrise • The Planting Season • Nursing Home • Hope Chest • Childhood Swing • Relationships • Elroy, Leopold, and Max • Urban Dragon • The Farmer\u27s Wife • A Ballad of Two Lovers • Betrayal • Choices • Letting Go • Emergence of a Butterfly • Poem for Every Man • Friction • Genesis • All\u27s Well • The Willow Tree • White Wasteland • Moe\u27s Happy Christmas • Rare Bird • Carnivalhttps://digitalcommons.ursinus.edu/lantern/1127/thumbnail.jp

Mapping regional risks from climate change for rainfed rice cultivation in India

Global warming is predicted to increase in the future, with detrimental consequences for rainfed crops that are dependent on natural rainfall (i.e. non-irrigated). Given that many crops grown under rainfed conditions support the livelihoods of low-income farmers, it is important to highlight the vulnerability of rainfed areas to climate change in order to anticipate potential risks to food security. In this paper, we focus on India, where ~ 50% of rice is grown under rainfed conditions, and we employ statistical models (climate envelope models (CEMs) and boosted regression trees (BRTs)) to map changes in climate suitability for rainfed rice cultivation at a regional level (~ 18 × 18 km cell resolution) under projected future (2050) climate change (IPCC RCPs 2.6 and 8.5, using three GCMs: BCC-CSM1.1, MIROC-ESM-CHEM, and HadGEM2-ES). We quantify the occurrence of rice (whether or not rainfed rice is commonly grown, using CEMs) and rice extent (area under cultivation, using BRTs) during the summer monsoon in relation to four climate variables that affect rice growth and yield namely ratio of precipitation to evapotranspiration (PER), maximum and minimum temperatures (Tmax and Tmin), and total rainfall during harvesting. Our models described the occurrence and extent of rice very well (CEMs for occurrence, ensemble AUC = 0.92; BRTs for extent, Pearson's r = 0.87). PER was the most important predictor of rainfed rice occurrence, and it was positively related to rainfed rice area, but all four climate variables were important for determining the extent of rice cultivation. Our models project that 15%–40% of current rainfed rice growing areas will be at risk (i.e. decline in climate suitability or become completely unsuitable). However, our models project considerable variation across India in the impact of future climate change: eastern and northern India are the locations most at risk, but parts of central and western India may benefit from increased precipitation. Hence our CEM and BRT models agree on the locations most at risk, but there is less consensus about the degree of risk at these locations. Our results help to identify locations where livelihoods of low-income farmers and regional food security may be threatened in the next few decades by climate changes. The use of more drought-resilient rice varieties and better irrigation infrastructure in these regions may help to reduce these impacts and reduce the vulnerability of farmers dependent on rainfed cropping

Communications Biophysics

Contains reports on eight research projects split into four sections.National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 5 K04 NS00113)National Institutes of Health (Training Grant 5 T32 NS07047)National Science Foundation (Grant BNS80-06369)National Institutes of Health (Grant 5 ROl NS11153)National Institutes of Health (Fellowship 1 F32 NS06544)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 R01 NS10916)National Institutes of Health (Grant 5 RO1 NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 R01 NS14092)National Institutes of Health (Grant 2 R01 NS11680)National Institutes of Health (Grant 5 ROl1 NS11080)National Institutes of Health (Training Grant 5 T32 GM07301

Communications Biophysics

Contains reports on nine research projects split into four sections.National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 5 K04 NS00113)National Institutes of Health (Training Grant 5 T32 NS07047)National Institutes of Health (Grant 5 ROl NS11153-03)National Institutes of Health (Fellowship 1 T32 NS07099-01)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 5 ROl NS10916)National Institutes of Health (Grant 5 ROl NS12846)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 1 RO1 NS14092)Health Sciences FundNational Institutes of Health (Grant 2 R01 NS11680)National Institutes of Health (Grant 2 RO1 NS11080)National Institutes of Health (Training Grant 5 T32 GM07301