478 research outputs found
Synergies for Improving Oil Palm Production and Forest Conservation in Floodplain Landscapes
Lowland tropical forests are increasingly threatened with conversion to oil palm as global demand and high profit drives crop expansion throughout the world’s tropical regions. Yet, landscapes are not homogeneous and regional constraints dictate land suitability for this crop. We conducted a regional study to investigate spatial and economic components of forest conversion to oil palm within a tropical floodplain in the Lower Kinabatangan, Sabah, Malaysian Borneo. The Kinabatangan ecosystem harbours significant biodiversity with globally threatened species but has suffered forest loss and fragmentation. We mapped the oil palm and forested landscapes (using object-based-image analysis, classification and regression tree analysis and on-screen digitising of high-resolution imagery) and undertook economic modelling. Within the study region (520,269 ha), 250,617 ha is cultivated with oil palm with 77% having high Net-Present-Value (NPV) estimates (637/ha?yr); but 20.5% is under-producing. In fact 6.3% (15,810 ha) of oil palm is commercially redundant (with negative NPV of -65/ha?yr) due to palm mortality from flood inundation. These areas would have been important riparian or flooded forest types. Moreover, 30,173 ha of unprotected forest remain and despite its value for connectivity and biodiversity 64% is allocated for future oil palm. However, we estimate that at minimum 54% of these forests are unsuitable for this crop due to inundation events. If conversion to oil palm occurs, we predict a further 16,207 ha will become commercially redundant. This means that over 32,000 ha of forest within the floodplain would have been converted for little or no financial gain yet with significant cost to the ecosystem. Our findings have globally relevant implications for similar floodplain landscapes undergoing forest transformation to agriculture such as oil palm. Understanding landscape level constraints to this crop, and transferring these into policy and practice, may provide conservation and economic opportunities within these seemingly high opportunity cost landscapes
Health effects of identifying patients with undiagnosed obstructive sleep apnea in the preoperative clinic: a follow-up study
Machine learning on normalized protein sequences
<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have been widely applied to biological sequences, e.g. to predict drug resistance in HIV-1 from sequences of drug target proteins and protein functional classes. As deletions and insertions are frequent in biological sequences, a major limitation of current methods is the inability to handle varying sequence lengths.</p> <p>Findings</p> <p>We propose to normalize sequences to uniform length. To this end, we tested one linear and four different non-linear interpolation methods for the normalization of sequence lengths of 19 classification datasets. Classification tasks included prediction of HIV-1 drug resistance from drug target sequences and sequence-based prediction of protein function. We applied random forests to the classification of sequences into "positive" and "negative" samples. Statistical tests showed that the linear interpolation outperforms the non-linear interpolation methods in most of the analyzed datasets, while in a few cases non-linear methods had a small but significant advantage. Compared to other published methods, our prediction scheme leads to an improvement in prediction accuracy by up to 14%.</p> <p>Conclusions</p> <p>We found that machine learning on sequences normalized by simple linear interpolation gave better or at least competitive results compared to state-of-the-art procedures, and thus, is a promising alternative to existing methods, especially for protein sequences of variable length.</p
Informing primary care reform in Greece: patient expectations and experiences (the QUALICOPC study)
Potential determinants of obesity among children and adolescents in Germany: results from the cross-sectional KiGGS study
<p>Abstract</p> <p>Background</p> <p>Obesity among children and adolescents is a growing public health problem. The aim of the present paper is to identify potential determinants of obesity and risk groups among 3- to 17-year old children and adolescents to provide a basis for effective prevention strategies.</p> <p>Methods</p> <p>Data were collected in the German Health Interview and Examination Survey for Children and Adolescents (KiGGS), a nationally representative and comprehensive data set on health behaviour and health status of German children and adolescents. Body height and weight were measured and body mass index (BMI) was classified according to IOTF cut-off points. Statistical analyses were conducted on 13,450 non-underweight children and adolescents aged 3 to 17 years. The association between overweight, obesity and several potential determinants was analysed for this group as well as for three socio-economic status (SES) groups. A multiple logistic regression model with obesity as the dependent variable was also calculated.</p> <p>Results</p> <p>The strongest association with obesity was observed for parental overweight and for low SES. Furthermore, a positive association with both overweight (including obesity) and obesity was seen for maternal smoking during pregnancy, high weight gain during pregnancy (only for mothers of normal weight), high birth weight, and high media consumption. In addition, high intakes of meat and sausages, total beverages, water and tea, total food and beverages, as well as energy-providing food and beverages were significantly associated with overweight as well as with obesity. Long sleep time was negatively associated with obesity among 3- to 10-year olds. Determinants of obesity occurred more often among children and adolescents with low SES.</p> <p>Conclusion</p> <p>Parental overweight and a low SES are major potential determinants of obesity. Families with these characteristics should be focused on in obesity prevention.</p
The degree of segmental aneuploidy measured by total copy number abnormalities predicts survival and recurrence in superficial gastroesophageal adenocarcinoma
Background: Prognostic biomarkers are needed for superficial gastroesophageal adenocarcinoma (EAC) to predict clinical outcomes and select therapy. Although recurrent mutations have been characterized in EAC, little is known about their clinical and prognostic significance. Aneuploidy is predictive of clinical outcome in many malignancies but has not been evaluated in superficial EAC. Methods: We quantified copy number changes in 41 superficial EAC using Affymetrix SNP 6.0 arrays. We identified recurrent chromosomal gains and losses and calculated the total copy number abnormality (CNA) count for each tumor as a measure of aneuploidy. We correlated CNA count with overall survival and time to first recurrence in univariate and multivariate analyses. Results: Recurrent segmental gains and losses involved multiple genes, including: HER2, EGFR, MET, CDK6, KRAS (recurrent gains); and FHIT, WWOX, CDKN2A/B, SMAD4, RUNX1 (recurrent losses). There was a 40-fold variation in CNA count across all cases. Tumors with the lowest and highest quartile CNA count had significantly better overall survival (p = 0.032) and time to first recurrence (p = 0.010) compared to those with intermediate CNA counts. These associations persisted when controlling for other prognostic variables. Significance: SNP arrays facilitate the assessment of recurrent chromosomal gain and loss and allow high resolution, quantitative assessment of segmental aneuploidy (total CNA count). The non-monotonic association of segmental aneuploidy with survival has been described in other tumors. The degree of aneuploidy is a promising prognostic biomarker in a potentially curable form of EAC. © 2014 Davison et al
Malaria vaccines and their potential role in the elimination of malaria
Research on malaria vaccines is currently directed primarily towards the development of vaccines that prevent clinical malaria. Malaria elimination, now being considered seriously in some epidemiological situations, requires a different vaccine strategy, since success will depend on killing all parasites in the community in order to stop transmission completely
Multidimensional prognostic indices for use in COPD patient care. A systematic review
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98117.pdf (publisher's version ) (Open Access)BACKGROUND: A growing number of prognostic indices for chronic obstructive pulmonary disease (COPD) is developed for clinical use. Our aim is to identify, summarize and compare all published prognostic COPD indices, and to discuss their performance, usefulness and implementation in daily practice. METHODS: We performed a systematic literature search in both Pubmed and Embase up to September 2010. Selection criteria included primary publications of indices developed for stable COPD patients, that predict future outcome by a multidimensional scoring system, developed for and validated with COPD patients only. Two reviewers independently assessed the index quality using a structured screening form for systematically scoring prognostic studies. RESULTS: Of 7,028 articles screened, 13 studies comprising 15 indices were included. Only 1 index had been explored for its application in daily practice. We observed 21 different predictors and 7 prognostic outcomes, the latter reflecting mortality, hospitalization and exacerbation. Consistent strong predictors were FEV1 percentage predicted, age and dyspnoea. The quality of the studies underlying the indices varied between fairly poor and good. Statistical methods to assess the predictive abilities of the indices were heterogenic. They generally revealed moderate to good discrimination, when measured. Limitations: We focused on prognostic indices for stable disease only and, inevitably, quality judgment was prone to subjectivity. CONCLUSIONS: We identified 15 prognostic COPD indices. Although the prognostic performance of some of the indices has been validated, they all lack sufficient evidence for implementation. Whether or not the use of prognostic indices improves COPD disease management or patients' health is currently unknown; impact studies are required to establish this
Serum Metabolome and Lipidome Changes in Adult Patients with Primary Dengue Infection
10.1371/journal.pntd.0002373PLoS Neglected Tropical Diseases78
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