European Mixed Forests: definition and research perspectives

peer-reviewedAim of study: We aim at (i) developing a reference definition of mixed forests in order to harmonize comparative research in mixed forests and (ii) briefly review the research perspectives in mixed forests. Area of study: The definition is developed in Europe but can be tested worldwide. Material and methods: Review of existent definitions of mixed forests based and literature review encompassing dynamics, management and economic valuation of mixed forests. Main results: A mixed forest is defined as a forest unit, excluding linear formations, where at least two tree species coexist at any developmental stage, sharing common resources (light, water, and/or soil nutrients). The presence of each of the component species is normally quantified as a proportion of the number of stems or of basal area, although volume, biomass or canopy cover as well as proportions by occupied stand area may be used for specific objectives. A variety of structures and patterns of mixtures can occur, and the interactions between the component species and their relative proportions may change over time. The research perspectives identified are (i) species interactions and responses to hazards, (ii) the concept of maximum density in mixed forests, (iii) conversion of monocultures to mixed-species forest and (iv) economic valuation of ecosystem services provided by mixed forests. Research highlights: The definition is considered a high-level one which encompasses previous attempts to define mixed forests. Current fields of research indicate that gradient studies, experimental design approaches, and model simulations are key topics providing new research opportunities.The networking in this study has been supported by COST Action FP1206 EuMIXFOR

Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

<p>Abstract</p> <p>Background</p> <p>Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.</p> <p>Results</p> <p>We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.</p> <p>Conclusions</p> <p>Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.</p

Predicting Bevirimat resistance of HIV-1 from genotype

<p>Abstract</p> <p>Background</p> <p>Maturation inhibitors are a new class of antiretroviral drugs. Bevirimat (BVM) was the first substance in this class of inhibitors entering clinical trials. While the inhibitory function of BVM is well established, the molecular mechanisms of action and resistance are not well understood. It is known that mutations in the regions CS p24/p2 and p2 can cause phenotypic resistance to BVM. We have investigated a set of p24/p2 sequences of HIV-1 of known phenotypic resistance to BVM to test whether BVM resistance can be predicted from sequence, and to identify possible molecular mechanisms of BVM resistance in HIV-1.</p> <p>Results</p> <p>We used artificial neural networks and random forests with different descriptors for the prediction of BVM resistance. Random forests with hydrophobicity as descriptor performed best and classified the sequences with an area under the Receiver Operating Characteristics (ROC) curve of 0.93 ± 0.001. For the collected data we find that p2 sequence positions 369 to 376 have the highest impact on resistance, with positions 370 and 372 being particularly important. These findings are in partial agreement with other recent studies. Apart from the complex machine learning models we derived a number of simple rules that predict BVM resistance from sequence with surprising accuracy. According to computational predictions based on the data set used, cleavage sites are usually not shifted by resistance mutations. However, we found that resistance mutations could shorten and weaken the <it>α</it>-helix in p2, which hints at a possible resistance mechanism.</p> <p>Conclusions</p> <p>We found that BVM resistance of HIV-1 can be predicted well from the sequence of the p2 peptide, which may prove useful for personalized therapy if maturation inhibitors reach clinical practice. Results of secondary structure analysis are compatible with a possible route to BVM resistance in which mutations weaken a six-helix bundle discovered in recent experiments, and thus ease Gag cleavage by the retroviral protease.</p

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

High-mass star-forming cloud G0.38+0.04 in the Galactic center dust ridge contains H2CO and SiO masers

We have discovered a new H2CO (formaldehyde) 11,0−11,1 4.82966 GHz maser in Galactic center Cloud C, G0.38+0.04. At the time of acceptance, this is the eighth region to contain an H2CO maser detected in the Galaxy. Cloud C is one of only two sites of confirmed high-mass star formation along the Galactic center ridge, affirming that H2CO masers are exclusively associated with high-mass star formation. This discovery led us to search for other masers, among which we found new SiO vibrationally excited masers, making this the fourth star-forming region in the Galaxy to exhibit SiO maser emission. Cloud C is also a known source of CH3OH Class-II and OH maser emission. There are now two known regions that contain both SiO and H2CO masers in the CMZ, compared to two SiO and six H2CO in the Galactic disk, while there is a relative dearth of H2O and CH3OH Class-II masers in the CMZ. SiO and H2CO masers may be preferentially excited in the CMZ, perhaps because of higher gas-phase abundances from grain destruction and heating, or alternatively H2O and CH3OH maser formation may be suppressed in the CMZ. In any case, Cloud C is a new testing ground for understanding maser excitation conditions

Immune-escape mutations and stop-codons in HBsAg develop in a large proportion of patients with chronic HBV infection exposed to anti-HBV drugs in Europe

Background: HBsAg immune-escape mutations can favor HBV-transmission also in vaccinated individuals, promote immunosuppression-driven HBV-reactivation, and increase fitness of drug-resistant strains. Stop-codons can enhance HBV oncogenic-properties. Furthermore, as a consequence of the overlapping structure of HBV genome, some immune-escape mutations or stop-codons in HBsAg can derive from drug-resistance mutations in RT. This study is aimed at gaining insight in prevalence and characteristics of immune-associated escape mutations, and stop-codons in HBsAg in chronically HBV-infected patients experiencing nucleos(t)ide analogues (NA) in Europe. Methods: This study analyzed 828 chronically HBV-infected European patients exposed to ≥ 1 NA, with detectable HBV-DNA and with an available HBsAg-sequence. The immune-associated escape mutations and the NA-induced immune-escape mutations sI195M, sI196S, and sE164D (resulting from drug-resistance mutation rtM204 V, rtM204I, and rtV173L) were retrieved from literature and examined. Mutations were defined as an aminoacid substitution with respect to a genotype A or D reference sequence. Results: At least one immune-associated escape mutation was detected in 22.1% of patients with rising temporal-trend. By multivariable-analysis, genotype-D correlated with higher selection of ≥ 1 immune-associated escape mutation (OR[95%CI]:2.20[1.32-3.67], P = 0.002). In genotype-D, the presence of ≥ 1 immune-associated escape mutations was significantly higher in drug-exposed patients with drug-resistant strains than with wild-type virus (29.5% vs 20.3% P = 0.012). Result confirmed by ana

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives