Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon

Peru has the fourth largest area of peatlands in the Tropics. Its most representative land cover on peat is a Mauritia flexuosa dominated palm swamp (thereafter called dense PS), which has been under human pressure over decades due to the high demand for the M. flexuosa fruit often collected by cutting down the entire palm. Degradation of these carbon dense forests can substantially affect emissions of greenhouse gases and contribute to climate change. The first objective of this research was to assess the impact of dense PS degradation on forest structure and biomass carbon stocks. The second one was to explore the potential of mapping the distribution of dense PS with different degradation levels using remote sensing data and methods. Biomass stocks were measured in 0.25 ha plots established in areas of dense PS with low (n = 2 plots), medium (n = 2) and high degradation (n = 4). We combined field and remote sensing data from the satellites Landsat TM and ALOS/PALSAR to discriminate between areas typifying dense PS with low, medium and high degradation and terra firme, restinga and mixed PS (not M. flexuosa dominated) forests. For this we used a Random Forest machine learning classification algorithm. Results suggest a shift in forest composition from palm to woody tree dominated forest following degradation. We also found that human intervention in dense PS translates into significant reductions in tree carbon stocks with initial (above and below-ground) biomass stocks (135.4 ± 4.8 Mg C ha−1) decreased by 11 and 17% following medium and high degradation. The remote sensing analysis indicates a high separability between dense PS with low degradation from all other categories. Dense PS with medium and high degradation were highly separable from most categories except for restinga forests and mixed PS. Results also showed that data from both active and passive remote sensing sensors are important for the mapping of dense PS degradation. Overall land cover classification accuracy was high (91%). Results from this pilot analysis are encouraging to further explore the use of remote sensing data and methods for monitoring dense PS degradation at broader scales in the Peruvian Amazon. Providing precise estimates on the spatial extent of dense PS degradation and on biomass and peat derived emissions is required for assessing national emissions from forest degradation in Peru and is essential for supporting initiatives aiming at reducing degradation activities

Microarray analysis of Xenopus endoderm expressing Ptf1a

Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Genesis 50 (2012): 853-870, doi:10.1002/dvg.22048.Pancreas specific transcription factor 1a (Ptf1a), a bHLH transcription factor, has two temporally distinct functions during pancreas development; initially it is required for early specification of the entire pancreas, while later it is required for proper differentiation and maintenance of only acinar cells. The importance of Ptf1a function was revealed by the fact that loss of Ptf1a leads to pancreas agenesis in humans. While Ptf1a is one of the most important pancreatic transcription factors, little is known about the differences between the regulatory networks it controls during initial specification of the pancreas as opposed to acinar cell development, and to date no comprehensive analysis of its downstream targets has been published. In this paper, we use Xenopus embryos to identify putative downstream targets of Ptf1a. We isolated anterior endoderm tissue overexpressing Ptf1a at two early stages, NF32 and NF36, and compared their gene expression profiles using microarrays. Our results revealed that Ptf1a regulates genes with a wide variety of functions, providing insight into the complexity of the regulatory network required for pancreas specification.National Institutes of Health (DK077197

Effect of energy restriction and physical exercise intervention on phenotypic flexibility as examined by transcriptomics analyses of mRNA from adipose tissue and whole body magnetic resonance imaging

Overweight and obesity lead to changes in adipose tissue such as inflammation and reduced insulin sensitivity. The aim of this study was to assess how altered energy balance by reduced food intake or enhanced physical activity affect these processes. We studied sedentary subjects with overweight/obesity in two intervention studies, each lasting 12 weeks affecting energy balance either by energy restriction (~20% reduced intake of energy from food) in one group, or by enhanced energy expenditure due to physical exercise (combined endurance- and strength-training) in the other group. We monitored mRNA expression by microarray and mRNA sequencing from adipose tissue biopsies. We also measured several plasma parameters as well as fat distribution with magnetic resonance imaging and spectroscopy. Comparison of microarray and mRNA sequencing showed strong correlations, which were also confirmed using RT-PCR. In the energy restricted subjects (body weight reduced by 5% during a 12 weeks intervention), there were clear signs of enhanced lipolysis as monitored by mRNA in adipose tissue as well as plasma concentration of free-fatty acids. This increase was strongly related to increased expression of markers for M1-like macrophages in adipose tissue. In the exercising subjects (glucose infusion rate increased by 29% during a 12-week intervention), there was a marked reduction in the expression of markers of M2-like macrophages and T cells, suggesting that physical exercise was especially important for reducing inflammation in adipose tissue with insignificant reduction in total body weight. Our data indicate that energy restriction and physical exercise affect energy-related pathways as well as inflammatory processes in different ways, probably related to macrophages in adipose tissue.</p

Improving cardiovascular magnetic resonance access in low- and middle-income countries for cardiomyopathy assessment: rapid cardiovascular magnetic resonance

AIMS: To evaluate the impact of a simplified, rapid cardiovascular magnetic resonance (CMR) protocol embedded in care and supported by a partner education programme on the management of cardiomyopathy (CMP) in low- and middle-income countries (LMICs). METHODS AND RESULTS: Rapid CMR focused particularly on CMP was implemented in 11 centres, 7 cities, 5 countries, and 3 continents linked to training courses for local professionals. Patients were followed up for 24 months to assess impact. The rate of subsequent adoption was tracked. Five CMR conferences were delivered (920 attendees-potential referrers, radiographers, reporting cardiologists, or radiologists) and five new centres starting CMR. Six hundred and one patients were scanned. Cardiovascular magnetic resonance indications were 24% non-contrast T2* scans [myocardial iron overload (MIO)] and 72% suspected/known cardiomyopathies (including ischaemic and viability). Ninety-eighty per cent of studies were of diagnostic quality. The average scan time was 22 ± 6 min (contrast) and 12 ± 4 min (non-contrast), a potential cost/throughput reduction of between 30 and 60%. Cardiovascular magnetic resonance findings impacted management in 62%, including a new diagnosis in 22% and MIO detected in 30% of non-contrast scans. Nine centres continued using rapid CMR 2 years later (typically 1-2 days per week, 30 min slots). CONCLUSIONS: Rapid CMR of diagnostic quality can be delivered using available technology in LMICs. When embedded in care and a training programme, costs are lower, care is improved, and services can be sustained over time. KEY QUESTION: KEY FINDING: TAKE-HOME MESSAGE

Association and interaction of PPAR-complex gene variants with latent traits of left ventricular diastolic function

<p>Abstract</p> <p>Background</p> <p>Abnormalities in myocardial metabolism and/or regulatory genes have been implicated in left ventricular systolic dysfunction. However, the extent to which these modulate left ventricular diastolic function (LVDF) is uncertain.</p> <p>Methods</p> <p>Independent component analysis was applied to extract latent LVDF traits from 14 measured echocardiography-derived endophenotypes of LVDF in 403 Caucasians. Genetic association was assessed between measured and latent LVDF traits and 64 single nucleotide polymorphisms (SNPs) in three peroxisome proliferator-activated receptor <it>(PPAR)</it>-complex genes involved in the transcriptional regulation of fatty acid metabolism.</p> <p>Results</p> <p>By linear regression analysis, 7 SNPs (4 in <it>PPARA</it>, 2 in <it>PPARGC1A</it>, 1 in <it>PPARG</it>) were significantly associated with the latent LVDF trait, whereas a range of 0-4 SNPs were associated with each of the 14 measured echocardiography-derived endophenotypes. Frequency distribution of <it>P </it>values showed a greater proportion of significant associations with the latent LVDF trait than for the measured endophenotypes, suggesting that analyses of the latent trait improved detection of the genetic underpinnings of LVDF. Ridge regression was applied to investigate within-gene and gene-gene interactions. In the within-gene analysis, there were five significant pair-wise interactions in <it>PPARGC1A </it>and none in <it>PPARA </it>or <it>PPARG</it>. In the gene-gene analysis, significant interactions were found between rs4253655 in <it>PPARA </it>and rs1873532 (p = 0.02) and rs7672915 (p = 0.02), both in <it>PPARGC1A</it>, and between rs1151996 in <it>PPARG </it>and rs4697046 in <it>PPARGC1A </it>(p = 0.01).</p> <p>Conclusions</p> <p>Myocardial metabolism <it>PPAR</it>-complex genes, including within and between genes interactions, may play an important role modulating left ventricular diastolic function.</p

Synthetic sex pheromone in a long-lasting lure attracts the visceral leishmaniasis vector, lutzomyia longipalpis, for up to 12 weeks in Brazil

Current control methodologies have not prevented the spread of visceral leishmaniasis (VL) across Brazil. Here, we describe the development of a new tool for controlling the sand fly vector of the disease: a long-lasting lure, which releases a synthetic male sex pheromone, attractive to both sexes of Lutzomyia longipalpis. This device could be used to improve the effectiveness of residual insecticide spraying as a means of sand fly control, attracting L. longipalpis to insecticide-treated animal houses, where they could be killed in potentially large numbers over a number of weeks. Different lure designs releasing the synthetic pheromone (±)-9-methylgermacrene-B (CAS 183158-38-5) were field-tested in Araçatuba, São Paulo (SP). Experiments compared numbers of sand flies caught overnight in experimental chicken sheds with pheromone lures, to numbers caught in control sheds without pheromone. Prototype lures, designed to last one night, were first used to confirm the attractiveness of the pheromone in SP, and shown to attract significantly more flies to test sheds than controls. Longer-lasting lures were tested when new, and at fortnightly intervals. Lures loaded with 1 mg of pheromone did not attract sand flies for more than two weeks. However, lures loaded with 10 mg of pheromone, with a releasing surface of 15 cm2 or 7.5 cm2, attracted female L. longipalpis for up to ten weeks, and males for up to twelve weeks. Approximately five times more sand flies were caught with 7.5 cm2 10 mg lures when first used than occurred naturally in non-experimental chicken resting sites. These results demonstrate that these lures are suitably long-lasting and attractive for use in sand fly control programmes in SP. To our knowledge, this is the first sex pheromone-based technology targeting an insect vector of a neglected human disease. Further studies should explore the general applicability of this approach for combating other insect-borne diseases

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio