National Evaluation of the Capacity Building Programme in English Local Government: Annex 4: Follow On Study of Progress in Seven Case Study Improvement Partnerships

This report is one of a series of outputs from the national evaluation of the Capacity Building Programme for local government in England (CBP), being undertaken by a team of researchers at the Policy Research Institute (PRI) at Leeds Metropolitan University and the Cities Research Unit at the University of West of England. This report summarises the findings from the second phase of fieldwork with regional and sub-regional Improvement Partnerships, established to facilitate capacity building and improvement activity in local authorities. The research underpinning this report was undertaken in seven case study Improvement Partnerships (see Section 2) in October and November 2006 and follows a similar – baseline – exercise undertaken during the same period during 2005. It thus both draws on the earlier research (see Section 3) and identifies evidence of progress and impact (see Section 10) since the baseline phase

National Evaluation of the Capacity Building Programme in English Local Government: Annex 1. Seven Case Studies: The Pilot Programme

This report presents the findings of the evaluation of the Capacity Building pilots. The evaluation of the pilots is part of the on-going evaluation of the Capacity Building Programme and has been undertaken in the early scoping phase of the main evaluation in order to capture key learning points and insights into the programme. It is intended that the ongoing evaluation of the Capacity Building Programme will be formative and assist in the development of the programme over time. In what follows the background to the pilots programme is discussed, highlighting the kinds of capacity building activities the pilots are engaged with and the link between the Capacity Building Programme and the CPA. The report outlines the structure and purpose of the pilots programme and the nature and characteristics of the different pilots that were undertaken. The seven case studies are then discussed in more detail in the main body of 1 Introduction | 5 the report. These are drawn together in key themes and findings which can be used to strengthen future project development

National Evaluation of the Capacity Building Programme in English Local Government: Overall Final Report

This report is one of a series of outputs from the National Evaluation of the Capacity Building Programme for local government in England (CBP), undertaken by a team of researchers at the Policy Research Institute (PRI) at Leeds Metropolitan University and the Cities Research Unit at the University of West of England. This report summarises the findings from all four key strands of the evaluation. Because of the difficulties associated with quantifying the capacity of local authorities, much less the sector, in relation to the dynamic roles and objectives that they pursue, the report focuses on what has worked, why and in what circumstances, rather than providing a definitive assessment of the extent of change of capacity building enabled by the CBP. The CBP was launched in 2003 as a joint Department for Communities and Local Government/Local Government Association (LGA) initiative to support capacity building and improvement activities within local authorities in England. The CBP has supported four main streams of improvement and capacity building activity in local authorities (see Section 1.2; p13)

Social structure varies with elevation in an Alpine ant.

Insect societies vary greatly in social organization, yet the relative roles of ecological and genetic factors in driving this variation remain poorly understood. Identifying how social structure varies along environmental gradients can provide insights into the ecological conditions favouring alternative social organizations. Here, we investigate how queen number variation is distributed along elevation gradients within a socially polymorphic ant, the Alpine silver ant Formica selysi. We sampled low- and high-elevation populations in multiple Alpine valleys. We show that populations belonging to different drainage basins are genetically differentiated. In contrast, there is little genetic divergence between low- and high-elevation populations within the same drainage basin. Thus, elevation gradients in each of the drainage basins represent independent contrasts. Whatever the elevation, all well-sampled populations are socially polymorphic, containing both monogynous (= one queen) and polygynous (= multiple queen) colonies. However, the proportion of monogynous colonies per population increases at higher elevation, while the effective number of queens in polygynous colonies decreases, and this pattern is replicated in each drainage basin. The increased prevalence of colonies with a single queen at high elevation is correlated with summer and winter average temperature, but not with precipitation. The colder, unpredictable and patchy environment encountered at higher elevations may favour larger queens with the ability to disperse and establish incipient monogynous colonies independently, while the stable and continuous habitat in the lowlands may favour large, fast-growing polygynous colonies. By highlighting differences in the environmental conditions favouring monogynous or polygynous colonies, this study sheds light on the ecological factors influencing the distribution and maintenance of social polymorphism

Conditions inside fisher dens during prescribed fires; what is the risk posed by spring underburns?

The use of spring prescribed fires to reduce accumulated fuel loads in western forests and facilitate the return of natural fire regimes is a controversial topic. While spring burns can be effective at reducing fuel loads and restoring heterogeneous landscapes, concerns exist over the potential impacts of unnaturally timed fires to native species. To protect native wildlife from disturbance during critical periods, limited operating periods (LOPs) are often implemented. However when LOPs for multiple species are combined into an integrated management plan, very few time windows for implementing prescribed fires remain. The use of spring burns is often effectively eliminated, thereby reducing land managers’ opportunities to implement what can be their most effective tool for forest restoration. To help guide the design of LOPs for fishers in the western United States, and to help identify opportunities to mitigate the risks posed by spring prescribed burns, we evaluated conditions within tree cavities during five prescribed fires in the Sierra National Forest and Yosemite National Park, CA. This relatively simple experiment was designed to provide much-needed and timely answers to crucial questions regarding the short-term impacts of prescribed fire on fishers and other wildlife species using cavities. Temperatures were remarkably stable within cavities, averaging 20.03 degrees C during burns. Carbon monoxide accumulation posed a greater threat, averaging a maximum of 170.8 ppm during burns and remaining elevated for \u3e30 min, conditions potentially hazardous to fisher neonates. We discuss how these risks can be interpreted, and recommend that measures to mitigate smoke accumulation in tree cavities be implemented where spring burns are conducted in areas potentially occupied by fishers

High-fat meals rich in EPA plus DHA compared with DHA only have differential effects on postprandial lipemia and plasma 8-isoprostane F-2 alpha concentrations relative to a control high-oleic acid meal: a randomized controlled trial

Background: Eicosapentaenoic acid (EPA) plus docosahexaenoic acid (DHA) supplementation has beneficial cardiovascular effects, but postprandial influences of these individual fatty acids are unclear. Objectives: The primary objective was to determine the vascular effects of EPA + DHA compared with DHA only during postprandial lipemia relative to control high–oleic acid meals; the secondary objective was to characterize the effects of linoleic acid–enriched high-fat meals relative to the control meal. Design: We conducted a randomized, controlled, double-blind crossover trial of 4 high-fat (75-g) meals containing 1) high–oleic acid sunflower oil (HOS; control), 2) HOS + fish oil (FO; 5 g EPA and DHA), 3) HOS + algal oil (AO; 5 g DHA), and 4) high–linoleic acid sunflower oil (HLS) in 16 healthy men (aged 35–70 y) with higher than optimal fasting triacylglycerol concentrations (mean ± SD triacylglycerol, 1.9 ± 0.5 mmol/L). Results: Elevations in triacylglycerol concentration relative to baseline were slightly reduced after FO and HLS compared with the HOS control (P < 0.05). The characteristic decrease from baseline in plasma nonesterified fatty acids after a mixed meal was inhibited after AO (Δ 0–3 h, P < 0.05). HLS increased the augmentation index compared with the other test meals (P < 0.05), although the digital volume pulse–reflection index was not significantly different. Plasma 8-isoprostane F(2α) analysis revealed opposing effects of FO (increased) and AO (reduced) compared with the control (P < 0.05). No differences in nitric oxide metabolites were observed. Conclusions: These data show differential postprandial 8-isoprostane F(2α) responses to high-fat meals containing EPA + DHA–rich fish oil compared with DHA-rich AO, but these differences were not associated with consistent effects on postprandial vascular function or lipemia. More detailed analyses of polyunsaturated fatty acid–derived lipid mediators are required to determine possible divergent functional effects of single meals rich in either DHA or EPA. This trial was registered at clinicaltrials.gov as NCT01618071

National Evaluation of the Capacity Building Programme in English Local Government: Evaluation of the National Programmes: Annex 2: Evaluation of the National Programmes

The report is one of a series of outputs from the national evaluation of the CBP, being undertaken by a team of researchers at the Policy Research Institute (PRI) at Leeds Metropolitan University and the Cities Research Unit at the University of West of England. The Capacity Building Programme for local government was launched in 2003 as a joint Department for Communities and Local Government (DCLG) / Local Government Association (LGA) initiative to support capacity building and improvement activities within local authorities in England. The evaluation of the Capacity Building Programme has been underway since late 2004. A scoping phase was conducted until May 2005, including a short evaluation of the Pilot Programmes. The main phase of the evaluation commenced in September 2005 and encompassed four main phases (see Section 1.3: p10)

Phenotypic heterogeneity of peripheral monocytes in healthy dogs

Monocytes are key cells of the innate immune system. Their phenotypic and functional roles have been investigated in humans, mice and other animals, such as the rat, pig and cow. To date, detailed phenotypic analysis of monocytes has not been undertaken in dogs. Two important surface markers in human monocytes are CD14 and MHC class II (MHC II). By flow cytometry, we demonstrated that canine monocytes can be subdivided into three separate populations: CD14posMHC IIneg, CD14posMHC IIpos and CD14negMHC IIpos. Both light and transmission electron microscopy confirmed the monocytic identity of all three populations. The CD14posMHC IIneg population could be distinguished on an ultrastructural level by their smaller size, the presence of more numerous, larger granules, and more pseudopodia than both of the other populations

SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparisifying Operator Approach

The sure independence screening and sparsifying operator (SISSO) approach (Ouyang et al., 2018) is an algorithm belonging to the field of artificial intelligence and more specifically a combination of symbolic regression and compressed sensing. As a symbolic regression method, SISSO is used to identify mathematical functions, i.e. the descriptors, that best predict the target property of a data set. Furthermore, the compressed sensing aspect of SISSO, allows it to find sparse linear models using tens to thousands of data points. SISSO is introduced for both regression and classification tasks. In practice, SISSO first constructs a large and exhaustive feature space of trillions of potential descriptors by taking in a set of user-provided primary features as a dataframe, and then iteratively applying a set of unary and binary operators, e.g. addition, multiplication, exponentiation, and squaring, according to a user-defined specification. From this exhaustive pool of candidate descriptors, the ones most correlated to a target property are identified via sure-independence screening, from which the low-dimensional linear models with the lowest error are found via an l0 regularization. Because symbolic regression generates an interpretable equation, it has become an increasingly popular concept across scientific disciplines (Neumann et al., 2020; Udrescu & Tegmark, 2020; Wang et al., 2019). A particular advantage of these approaches are their capability to model complex phenomena using relatively simple descriptors. SISSO has been used successfully in the past to model, explore, and predict important material properties, including the stability of different phases (Bartel et al., 2018; Schleder et al., 2020); the catalytic activity and reactivity (Andersen et al., 2019; Andersen & Reuter, 2021; Han et al., 2021; W. Xu et al., 2021); and glass transition temperatures (Pilania et al., 2019). Beyond regression problems, SISSO has also been used successfully to classify materials into different crystal prototypes (Ouyang et al., 2019), or whether a material crystallizes in its ground state as a perovskite (Bartel et al., 2019), or to determine whether a material is a topological insulator or not (Cao et al., 2020). The SISSO++ package is an open-source (Apache-2.0 licence), modular, and extensible C++ implementation of the SISSO method with Python bindings. Specifically, SISSO++ applies this methodology for regression, log regression, and classification problems. Additionally, the library includes multiple Python functions to facilitate the post-processing, analyzing, and visualizing of the resulting models

Hierarchical Symbolic Regression for Identifying Key Physical Parameters Correlated with Bulk Properties of Perovskites

Symbolic regression identifies nonlinear, analytical expressions relating materials properties and key physical parameters. However, the pool of expressions grows rapidly with complexity, compromising its efficiency. We tackle this challenge hierarchically: identified expressions are used as inputs for further obtaining more complex expressions. Crucially, this framework can transfer knowledge among properties, as demonstrated using the sure-independence-screening-and-sparsifying-operator approach to identify expressions for lattice constant and cohesive energy, which are then used to model the bulk modulus of ABO3 perovskites