Child Poverty Monitor: Technical Report 2017

The Child Poverty Monitor and this Technical Report provide data on a set of indicators that assess aspects of child poverty in New Zealand and their implications for child wellbeing. In it are data on income and non-income measures of poverty, including measures that reflect increasing levels of severity. Other data include indicators related to health, living conditions, education, and a selection of economic measures used to assess how well we are doing as a nation that are relevant to the wellbeing of children and their families. The Child Poverty Monitor is a partnership comprising the Office of the Children’s Commissioner, the University of Otago’s New Zealand Child and Youth Epidemiology Service (NZCYES) and the J R McKenzie Trust. The purpose is to compile and share robust information on child poverty measures that are publicly available and easily accessible. Only by having the essential measures on child poverty in New Zealand compiled, published and disseminated annually can we tell how well we are progressing in effectively reducing child poverty in our nation

Health behaviours of Australian men and the likelihood of attending a dedicated men's health service

Background: Redesigning primary health services may enhance timely and effective uptake by men. The primary aim of this study was to assess the likelihood of Australian men attending a dedicated men's health service (DMHS). The further aims were to better understand the reasons for their preferences and determine how health behaviours influence likelihood. Methods: A survey on health service use and preferences, health help-seeking behaviours, and the likelihood of attending a DMHS was administered by telephone to 1506 randomly selected men (median age 56 years, range 19-95). Likelihood of attending a DMHS was rated using a single item Likert scale where 0 was not at all likely and 10 highly likely. Respondents were classified by age ( = 65 years) and health status. Principal component analyses were used to define health behaviours, specifically help-seeking and delay/avoidance regarding visiting a doctor. Multivariable linear and logistic regression analyses were used to examine predictors of likelihood of attending a DMHS. Results: The mean likelihood of attending a DMHS was 5.8 (SD 3.3, median 6, moderate likelihood) and 21%, 26% and 23% of men rated likelihood as moderate, high and very high respectively. Being happy with their existing doctor was the most common reason (52%) for being less likely to attend a DMHS. In unadjusted analyses, younger men reported being more likely to attend a DMHS (p < 0.001) with older-sick men reporting being least likely (p < 0.001). Younger men were more likely than older men to score higher on delay/avoidance and were more likely to self-monitor. In the full model, men with current health concerns (p ≤ 0.01), who scored higher on delay/avoidance (p ≤ 0.0006), who were more likely to be information-seekers (p < 0.0001) and/or were motivated to change their health (p ≤ 0.0001) reported a higher likelihood of attending a DMHS irrespective of age and health status. Conclusions: Seventy percent of men reported a moderate or higher likelihood of attending a DMHS. As young healthy men are more likely than older men to display health behaviours that are associated with a higher likelihood of attending a DHMS, such as delay/avoidance, marketing a DMHS to such men may be of value.Andrew D. Vincent, Phoebe G. Drioli-Phillips, Jana Le, Lynette Cusack, Timothy J. Schultz, Margaret A. McGee, Deborah A. Turnbull, and Gary A. Witter

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy

Methane (CH) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH. To address this limitation, we developed novel CH prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH production (g CH/animal·d, ANIM-B models) and CH yield (g CH/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH emissions from sheep, providing valuable insights for future research and mitigation strategies.Te authors gratefully acknowledge funding for this project from the USDA National Institute of Food and Agriculture (Award number: 2014-67003-21979). Te animal and microbial data originated from a study funded by the Pastoral Greenhouse Gas Research Consortium (www.pggrc.co.nz)

Full adoption of the most effective strategies to mitigate methane emissions by ruminants can help meet the 1.5 °C target by 2030 but not 2050

To meet the 1.5 °C target, methane (CH) from ruminants must be reduced by 11 to 30% by 2030 and 24 to 47% by 2050 compared to 2010 levels. A meta-analysis identified strategies to decrease product-based (PB; CH per unit meat or milk) and absolute (ABS) enteric CH emissions while maintaining or increasing animal productivity (AP; weight gain or milk yield). Next, the potential of different adoption rates of one PB or one ABS strategy to contribute to the 1.5 °C target was estimated. The database included findings from 430 peer-reviewed studies, which reported 98 mitigation strategies that can be classified into three categories: animal and feed management, diet formulation, and rumen manipulation. A random-effects meta-analysis weighted by inverse variance was carried out. Three PB strategies—namely, increasing feeding level, decreasing grass maturity, and decreasing dietary forage-to-concentrate ratio—decreased CH per unit meat or milk by on average 12% and increased AP by a median of 17%. Five ABS strategies—namely CH inhibitors, tanniferous forages, electron sinks, oils and fats, and oilseeds—decreased daily methane by on average 21%. Globally, only 100% adoption of the most effective PB and ABS strategies can meet the 1.5 °C target by 2030 but not 2050, because mitigation effects are offset by projected increases in CH due to increasing milk and meat demand. Notably, by 2030 and 2050, low- and middle-income countries may not meet their contribution to the 1.5 °C target for this same reason, whereas high-income countries could meet their contributions due to only a minor projected increase in enteric CH emissions.We thank the GLOBAL NETWORK project for generating part of the database. The GLOBAL NETWORK project (https://globalresearchalliance.org/research/livestock/collaborative-activities/global-research-project/; accessed 20 June 2020) was a multinational initiative funded by the Joint Programming Initiative on Food Security, Agriculture, and Climate Change and was coordinated by the Feed and Nutrition Network (https://globalresearchalliance.org/research/livestock/networks/feed-nutrition-network/; accessed 20 June 2020) within the Livestock Research Group of the Global Research Alliance on Agricultural GHG (https://globalresearchalliance.org; accessed 20 June 2020). We thank MitiGate, which was part of the Animal Change project funded by the EU under Grant Agreement FP7-266018 for sharing their database with us (http://mitigate.ibers.aber.ac.uk/, accessed 1 July 2017). Part of C.A., A.N.H., and S.C.M.’s time in the early stages of this project was funded by the Kravis Scientific Research Fund (New York) and a gift from Sue and Steve Mandel to the Environmental Defense Fund. Another part of C.A.’s work on this project was supported by the National Program for Scientific Research and Advanced Studies - PROCIENCIA within the framework of the "Project for the Improvement and Expansion of the Services of the National System of Science, Technology and Technological Innovation" (Contract No. 016-2019) and by the German Federal Ministry for Economic Cooperation and Development (issued through Deutsche Gesellschaft für Internationale Zusammenarbei) through the research “Programme of Climate Smart Livestock” (Programme 2017.0119.2). Part of A.N.H.’s work was funded by the US Department of Agriculture (Washington, DC) National Institute of Food and Agriculture Federal Appropriations under Project PEN 04539 and Accession no. 1000803. E.K. was supported by the Sesnon Endowed Chair Fund of the University of California, Davis

Immunogenicity of AGS-004 Dendritic Cell Therapy in Patients Treated during Acute HIV Infection

AGS-004 consists of matured autologous dendritic cells co-electroporated with in vitro transcribed RNA encoding autologous HIV antigens. In an open-label, single arm sub-study of AGS-004-003, AGS-004 was administered monthly to suppressed participants who started antiretroviral therapy (ART) during acute HIV infection. HIV-1 specific T cell responses were measured by multicolor flow cytometry after 3-4 doses. The frequency of resting CD4+ T-cell infection (RCI) was measured by quantitative viral outgrowth assay. Participants demonstrating increased immune response postvaccination were eligible for analytic treatment interruption (ATI). AGS-004 induced a positive immune response defined as ≥2-fold increase from baseline in the number of multifunctional HIV-1 specific CD28+/CD45RA- CD8+ effector/memory cytoxic T-lymphocytes (CTLs) in all six participants. All participants underwent ATI with rebound viremia at a median of 29 days. Immune correlates between time to viral rebound and the induction of effector CTLs were determined. Baseline RCI was low in most participants (0.043-0.767 IUPM). One participant had a &gt;2-fold decrease (0.179-0.067 infectious units per million [IUPM]) in RCI at week 10. One participant with the lowest RCI had the longest ATI. AGS-004 dendritic cell administration increased multifunctional HIV-specific CD28+/CD45RA- CD8+ memory T cell responses in all participants, but did not permit sustained ART interruption. However, greater expansion of CD28-/CCR7-/CD45RA- CD8+ effector T cell responses correlated with a longer time to viral rebound. AGS-004 may be a useful tool to augment immune responses in the setting of latency reversal and eradication strategies

A robust microparticle platform for a STING-targeted adjuvant that enhances both humoral and cellular immunity during vaccination

Most FDA-approved adjuvants for infectious agents boost humoral but not cellular immunity, and have poorly-understood mechanisms. Stimulator of interferon genes (STING, also known as MITA, MPYS, or ERIS) is an exciting adjuvant target due to its role in cyclic dinucleotide (CDN)-driven anti-viral immunity; however, a major hindrance is STING's cytosolic localization which requires intracellular delivery of its agonists. As a result, STING agonists administered in a soluble form have elicited suboptimal immune responses. Delivery of STING agonists via particle platforms has proven a more successful strategy, but the opportunity for improved formulations and bioactivity remains. In this study we evaluated the adjuvant activity of the potent STING agonist, CDN 3′3′-cGAMP (cGAMP), encapsulated in acid-sensitive acetalated dextran (Ace-DEX) polymeric microparticles (MPs) which passively target antigen-presenting cells for intracellular release. This formulation was superior to all particle delivery systems evaluated and maintained its bioactivity following a sterilizing dose of gamma irradiation. Compared to soluble cGAMP, the Ace-DEX cGAMP MPs enhanced type-I interferon responses nearly 1000-fold in vitro and 50-fold in vivo, caused up to a 104-fold boost in antibody titers, increased Th1-associated responses, and expanded germinal center B cells and memory T cells. Furthermore, the encapsulated cGAMP elicited no observable toxicity in animals and achieved protective immunity against a lethal influenza challenge seven months post-immunization when using CDN adjuvant doses up to 100-fold lower than previous reports. For these reasons, Ace-DEX MP-encapsulated cGAMP represents a potent vaccine adjuvant of humoral and cellular immunity

CHEMICAL CHANGES IN BEAN GRAINS DURING STORAGE IN CONTROLLED CONDITIONS

Banjarmasin, Borneo (Kalimantan, Indonesia) 195-GrayscaleForman Safety Negatives, Box

Does technology and Innovation Management improve Market Position? Empirical Evidence from Innovating Firms in South Africa

There is a growing recognition of the central role of technology and knowledge management for market success of organizations. Little is empirically know, however, about this relationship. Drawing on the South African Innovation Survey, a unique dataset on innovative behavior of South African firms in manufacturing and services, this paper investigates the question to what extent and in which ways do technology and innovation management activities affect firms’ market position. Findings show that conducting technology strategy activities pays out. Moreover, especially a combination of internal and external technology audits seems to be beneficial for organizational performance

Narrative inquiry into (re)imagining alternative schools: a case study of Kevin Gonzales.

Although there are many alternative schools that strive for the successful education for their students, negative images of alternative schools persist. While some alternative schools are viewed as “idealistic havens,” many are viewed as “dumping grounds,” or “juvenile detention centers.” Employing narrative inquiry, this article interrogates how a student, Kevin Gonzales, experiences his alternative education and raises questions about the role of alternative schools. Kevin Gonzales’s story is presented in a literary form of biographical journal to provide a “metaphoric loft” that helps us imagine other students like Kevin. This, in turn, provokes us to examine our current educational practice, and to (re)imagine ways in which alternative education can provide the best possible educational experiences for disenfranchised students who are increasingly underserved by the public education system