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 ($413/ha?yr–$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 $-299/ha?yr-$-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

The condition-dependent transcriptional landscape of Burkholderia pseudomallei

This is the final version of the article. Available from the publisher via the DOI in this record.Burkholderia pseudomallei (Bp), the causative agent of the often-deadly infectious disease melioidosis, contains one of the largest prokaryotic genomes sequenced to date, at 7.2 Mb with two large circular chromosomes (1 and 2). To comprehensively delineate the Bp transcriptome, we integrated whole-genome tiling array expression data of Bp exposed to >80 diverse physical, chemical, and biological conditions. Our results provide direct experimental support for the strand-specific expression of 5,467 Sanger protein-coding genes, 1,041 operons, and 766 non-coding RNAs. A large proportion of these transcripts displayed condition-dependent expression, consistent with them playing functional roles. The two Bp chromosomes exhibited dramatically different transcriptional landscapes--Chr 1 genes were highly and constitutively expressed, while Chr 2 genes exhibited mosaic expression where distinct subsets were expressed in a strongly condition-dependent manner. We identified dozens of cis-regulatory motifs associated with specific condition-dependent expression programs, and used the condition compendium to elucidate key biological processes associated with two complex pathogen phenotypes--quorum sensing and in vivo infection. Our results demonstrate the utility of a Bp condition-compendium as a community resource for biological discovery. Moreover, the observation that significant portions of the Bp virulence machinery can be activated by specific in vitro cues provides insights into Bp's capacity as an "accidental pathogen", where genetic pathways used by the bacterium to survive in environmental niches may have also facilitated its ability to colonize human hosts.This work was funded by a core grant provided by the Agency for Science, Technology and Research to the Genome Institute of Singapore, and funding from the Defence Medical and Environmental Research Institute, Singapore. This work was supported in part through NIAID contract HHSN266200400035C to BWS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Exploiting physico-chemical properties in string kernels

<p>Abstract</p> <p>Background</p> <p>String kernels are commonly used for the classification of biological sequences, nucleotide as well as amino acid sequences. Although string kernels are already very powerful, when it comes to amino acids they have a major short coming. They ignore an important piece of information when comparing amino acids: the physico-chemical properties such as size, hydrophobicity, or charge. This information is very valuable, especially when training data is less abundant. There have been only very few approaches so far that aim at combining these two ideas.</p> <p>Results</p> <p>We propose new string kernels that combine the benefits of physico-chemical descriptors for amino acids with the ones of string kernels. The benefits of the proposed kernels are assessed on two problems: MHC-peptide binding classification using position specific kernels and protein classification based on the substring spectrum of the sequences. Our experiments demonstrate that the incorporation of amino acid properties in string kernels yields improved performances compared to standard string kernels and to previously proposed non-substring kernels.</p> <p>Conclusions</p> <p>In summary, the proposed modifications, in particular the combination with the RBF substring kernel, consistently yield improvements without affecting the computational complexity. The proposed kernels therefore appear to be the kernels of choice for any protein sequence-based inference.</p> <p>Availability</p> <p>Data sets, code and additional information are available from <url>http://www.fml.tuebingen.mpg.de/raetsch/suppl/aask</url>. Implementations of the developed kernels are available as part of the Shogun toolbox.</p

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Background: The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk. / Methods: We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies. / Findings: We estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from 66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from <1% of those younger than 20 years to approximately 20% of those aged 70 years or older). We estimated 6% (3–12) of males to be at high risk compared with 3% (2–7) of females. The share of the population at increased risk was highest in countries with older populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence. Estimates of the number of individuals at increased risk were most sensitive to the prevalence of chronic kidney disease, diabetes, cardiovascular disease, and chronic respiratory disease. / Interpretation: About one in five individuals worldwide could be at increased risk of severe COVID-19, should they become infected, due to underlying health conditions, but this risk varies considerably by age. Our estimates are uncertain, and focus on underlying conditions rather than other risk factors such as ethnicity, socioeconomic deprivation, and obesity, but provide a starting point for considering the number of individuals that might need to be shielded or vaccinated as the global pandemic unfolds

Children grow and horses race: is the adiposity rebound a critical period for later obesity?

BACKGROUND: The adiposity rebound is the second rise in body mass index that occurs between 3 and 7 years. An early age at adiposity rebound is known to be a risk factor for later obesity. The aim here is to clarify the connection between the age at rebound and the corresponding pattern of body mass index change, in centile terms, so as to better understand its ability to predict later fatness. DISCUSSION: Longitudinal changes in body mass index during adiposity rebound, measured both in original (kg/m(2)) and standard deviation (SD) score units, are studied in five hypothetical subjects. Two aspects of the body mass index curve, the body mass index centile and the rate of body mass index centile crossing, determine a child's age at rebound. A high centile and upward centile crossing are both associated separately with an early rebound, while a low centile and/or downward centile crossing correspond to a late rebound. Early adiposity rebound is a risk factor for later fatness because it identifies children whose body mass index centile is high and/or crossing upwards. Such children are likely to have a raised body mass index later in childhood and adulthood. This is an example of Peto's "horse racing effect". The association of centile crossing with later obesity is statistical not physiological, and it applies at all ages not just at rebound, so adiposity rebound cannot be considered a critical period for future obesity. Body mass index centile crossing is a more direct indicator of the underlying drive to fatness. SUMMARY: An early age at adiposity rebound predicts later fatness because it identifies children whose body mass index centile is high and/or crossing upwards. Such children are likely to have a raised body mass index later. Body mass index centile crossing is more direct than the timing of adiposity rebound for predicting later fatness

The impact of immunosuppressive therapy on secondary infections and antimicrobial use in COVID-19 inpatients: a retrospective cohort study.

BACKGROUND: Immunosuppressive therapies have become a cornerstone of the management of severe COVID-19. The impact of these therapies on secondary infections and antimicrobial prescribing remains unclear. We sought to assess antimicrobial use and the incidence of bacterial and fungal infections in patients with severe COVID-19, and to explore their associations with receipt of immunosuppressive therapies. METHODS: Our retrospective cohort study included 715 hospitalised, adult patients with severe COVID-19 admitted to St George's Hospital, London, UK, during the first UK pandemic wave (1st March-10th June 2020). Co-infections (occurring within 48 h of admission) and secondary infections (≥ 48 h) were defined as a positive microbiological culture with supporting clinical, radiological or laboratory data to suggest true infection. Cox regression models with time-dependent covariates were used to explore the association between immunosuppressant use and secondary infection. RESULTS: Microbiologically confirmed co-infection occurred in 4.2% (n = 30) and secondary infection in 9.3% (n = 66) of the cohort (n = 715) and were associated with in-hospital mortality (48% vs 35%, OR 1.8, 95%CI 1.1-2.7, p = 0.01). Respiratory (n = 41, 39%) and bloodstream infections (n = 38, 36%) predominated, with primarily Gram-negative pathogens. 606 (84.7%) patients received an antimicrobial, amounting to 742 days of therapy per 1000 patient-days (DOTs). In multivariable models, receipt of high-dose steroids (≥ 30 mg prednisolone or equivalent) or tocilizumab was significantly associated with increased antimicrobial consumption (+ 5.5 DOTs, 95%CI 3.4-7.7 days) but not secondary infection (HR 0.56, 95%CI 0.26-1.18). CONCLUSIONS: Bacterial and fungal infections in severe COVID-19 were uncommon. Receipt of steroids or tocilizumab was independently associated with antimicrobial consumption despite its lack of association with secondary infection. These findings should galvanise efforts to promote antimicrobial stewardship in patients with COVID-19

Asymptomatic papillary fibroelastoma of the Aortic valve in a young woman - a case report

Echocardiography represents an invaluable diagnostic tool for the detection of intracardiac masses while simultaneously provides information about their size, location, mobility and attachment site as well as the presence and extent of any consequent hemodynamic derangement