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Knowledge about the biogeographic affinities of the world’s tropical forests helps to better understand regional differences in forest structure, diversity, composition, and dynamics. Such understanding will enable anticipation of region-specific responses to global environmental change. Modern phylogenies, in combination with broad coverage of species inventory data, now allow for global biogeographic analyses that take species evolutionary distance into account. Here we present a classification of the world’s tropical forests based on their phylogenetic similarity. We identify five principal floristic regions and their floristic relationships: (i) Indo-Pacific, (ii) Subtropical, (iii) African, (iv) American, and (v) Dry forests. Our results do not support the traditional neo- versus paleotropical forest division but instead separate the combined American and African forests from their Indo-Pacific counterparts. We also find indications for the existence of a global dry forest region, with representatives in America, Africa, Madagascar, and India. Additionally, a northern-hemisphere Subtropical forest region was identified with representatives in Asia and America, providing support for a link between Asian and American northern-hemisphere forests.

Semantic knowledge may be organized in terms of similarity relations based on shared features and/or complementary relations based on co-occurrence in events. Thus, relationships between manipulable objects such as tools may be defined by their functional properties (what the objects are used for) or thematic properties (e.g., what the objects are used with or on). A recent study from our laboratory used eye-tracking to examine incidental activation of semantic relations in a word-picture matching task and found relatively early activation of thematic relations (e.g., broom-dustpan), later activation of general functional relations (e.g., broom-sponge), and an intermediate pattern for specific functional relations (e.g., broom-vacuum cleaner). Combined with other recent studies, these results suggest that there are distinct semantic systems for thematic and similarity-based knowledge and that the \"specific function\" condition drew on both systems. This predicts that left hemisphere stroke that damages either system (but not both) may spare specific function processing. The present experiment tested these hypotheses using the same experimental paradigm with participants with left hemisphere lesions (N = 17). The results revealed that, compared to neurologically intact controls (N = 12), stroke participants showed later activation of thematic and general function relations, but activation of specific function relations was spared and was significantly earlier for stroke participants than controls. Across the stroke participants, activation of thematic and general function relations was negatively correlated, further suggesting that damage tended to affect either one semantic system or the other. These results support the distinction between similarity-based and complementarity-based semantic relations and suggest that relations that draw on both systems are relatively more robust to damage.

In addition to the effect of litter quality (LQ) on decomposition, increasing evidence is demonstrating that carbon mineralisation can be influenced by the past resource history, mainly through following two processes: (i) decomposer communities from recalcitrant litter environments may have a wider functional ability to decompose a wide range of litter species than those originating from richer environments, i.e. the functional breadth (FB) hypothesis; and/or (ii) decomposer communities may be specialized towards the litter they most frequently encounter, i.e. the home-field advantage (HFA) hypothesis. Nevertheless, the functional dissimilarities among contrasting microbial communities, which are generated by the FB and the HFA, have rarely been simultaneously quantified in the same experiment, and their relative contributions over time have never been assessed. To test these hypotheses, we conducted a reciprocal transplant decomposition experiment under controlled conditions using litter and soil originating from four ecosystems along a land-use gradient (forest, plantation, grassland and cropland) and one additional treatment using 13C labelled flax litter allowing us to assess the priming effect (PE) in each ecosystem. We found substantial effects of LQ on carbon mineralisation (more than two-thirds of the explained variance), whereas the contribution of the soil type was fairly low (less than one-tenth), suggesting that the contrasting soil microbial communities play only a minor role in regulating decomposition rates. Although the results on PE showed that we overestimated litter-derived CO2 fluxes, litter-microbe interactions contributed significantly to the unexplained variance observed in carbon mineralisation models. The magnitudes of FB and HFA were relatively similar, but the directions of these mechanisms were sometimes opposite depending on the litter and soil types. FB and HFA estimates calculated on parietal sugar mass loss were positively correlated with those calculated on enzymatic activity, confirming the idea that the interaction between litter quality and microbial community structure may modify the trajectory of carbon mineralisation via enzymatic synthesis. We conclude that although litter quality was the predominant factor controlling litter mineralisation, the local microbial communities and interactions with their substrates can explain a small (< 5%) but noticeable portion of carbon fluxes

Background It has been widely accepted that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human diseases. Many association prediction models have been proposed for predicting lncRNA functions and identifying potential lncRNA-disease associations. Nevertheless, among them, little effort has been attempted to measure lncRNA functional similarity, which is an essential part of association prediction models. Results In this study, we presented an lncRNA functional similarity calculation model, IDSSIM for short, based on an improved disease semantic similarity method, highlight of which is the introduction of information content contribution factor into the semantic value calculation to take into account both the hierarchical structures of disease directed acyclic graphs and the disease specificities. IDSSIM and three state-of-the-art models, i.e., LNCSIM1, LNCSIM2, and ILNCSIM, were evaluated by applying their disease semantic similarity matrices and the lncRNA functional similarity matrices, as well as corresponding matrices of human lncRNA-disease associations coming from either lncRNADisease database or MNDR database, into an association prediction method WKNKN for lncRNA-disease association prediction. In addition, case studies of breast cancer and adenocarcinoma were also performed to validate the effectiveness of IDSSIM. Conclusions Results demonstrated that in terms of ROC curves and AUC values, IDSSIM is superior to compared models, and can improve accuracy of disease semantic similarity effectively, leading to increase the association prediction ability of the IDSSIM-WKNKN model; in terms of case studies, most of potential disease-associated lncRNAs predicted by IDSSIM can be confirmed by databases and literatures, implying that IDSSIM can serve as a promising tool for predicting lncRNA functions, identifying potential lncRNA-disease associations, and pre-screening candidate lncRNAs to perform biological experiments. The IDSSIM code, all experimental data and prediction results are available online at https://github.com/CDMB-lab/IDSSIM .

Home‐field advantage (HFA) is a commonly used sports analogy, which has seen recent growth within the ecosystem ecology literature. It is most often invoked in litter transplant studies, where local adaptation (HFA) explains higher decomposition rates of leaf litter on ‘home’ soil communities. In exploring the mechanisms driving functional differences among soil decomposer communities, a consistent quantitative framework is lacking. We review methods for calculating HFA, propose a consolidated regression approach and demonstrate why HFA must be calculated along with a new ‘ability’ metric if we are to test definitively the competing hypotheses that soil decomposer communities are functionally equivalent versus dissimilar. We demonstrate that qualitative interpretations of HFA differ when the ability of a decomposer community is calculated simultaneously with HFA. For example, communities may differ in their ability to degrade litter in the absence of HFA, or apparent HFA may instead be caused by differing abilities, changing our ecological interpretation of the factors generating functional differences among decomposer communities. Synthesis: We propose a single, statistical approach to help evaluate how and why soil decomposer communities differ in functional abilities. Our approach should help formalize mechanistic interpretations of why soil community composition commonly influences litter decomposition rates.

Aim Woody ecosystems provide critical ecosystem functions and services but are increasingly threatened as invasive pathogens spread globally. Myrtle rust, caused by Austropuccinia psidii, arrived in New Zealand in 2017 and infects at least 12 of 18 species in the susceptible Myrtaceae plant family. Among these are species of structural, successional and cultural importance. We aim to assess whether the functional consequences of Myrtaceae loss could be mitigated if co‐occurring species with shared functional attributes are able to replace them. Location New Zealand (but with concepts and methodologies that apply globally). Methods Using a nationwide forest and shrubland plot data set, we assessed community vulnerability to the loss of Myrtaceae species by analysing proportional changes in average trait values when they are absent and produced spatial predictions indicating where species loss might have the greatest impact on community functionality. We then assessed whether compensatory infilling by co‐occurring species would mediate community vulnerability. Results Forests and shrublands containing Kunzea ericoides and Leptospermum scoparium are highly vulnerable to their loss. Areas most vulnerable overall are the central and south‐eastern North Island, north‐eastern South Island and Stewart Island. For all species, compensatory infilling moderated the impact of their loss. However, if co‐occurring Myrtaceae were unable to respond, possibly if they were also infected, community vulnerability almost always increased because infilling species had different functional attributes, compounding the functional impact. Main Conclusions Early successional woody plant communities and Myrtaceae‐dominated old‐growth forests are at most risk. Our spatial assessment of species‐level functional impacts from myrtle rust will facilitate better‐informed landscape‐level responses. Management actions and monitoring can now be targeted to areas and communities at greatest risk of losing ecosystem‐level processes.

Based on the framework of attribute diversity (a generalization of Hill numbers of order q), we develop a class of functional diversity measures sensitive not only to species abundances but also to trait-based species-pairwise functional distances. The new method refines and improves on the conventional species-equivalent approach in three areas: (1) the conventional method often gives similar values (close to unity) to assemblages with contrasting levels of functional diversity; (2) when a distance metric is unbounded, the conventional functional diversity depends on the presence/absence of other assemblages in the study; (3) in partitioning functional gamma diversity into alpha and beta components, the conventional gamma is sometimes less than alpha. To resolve these issues, we add to the attribute-diversity framework a novel concept: τ, the threshold of functional distinctiveness between any two species; here, τ can be chosen to be any positive value. Any two species with functional distance ≥ τ are treated as functionally equally distinct. Our functional diversity quantifies the effective number of functionally equally distinct species (or \"virtual functional groups\") with all pairwise distances at least s for different species pairs. We advocate the use of two complementary diversity profiles (τ profile and q profile), which depict functional diversity with varying levels of τ and q, respectively. Both the conventional species-equivalent method (i.e., τ is the maximum of species-pairwise distances) and classic taxonomic diversity (i.e., τ is the minimum of non-zero species-pairwise distances) are incorporated into our proposed τ profile for an assemblage. For any type of species-pairwise distance matrices, our attribute-diversity approach allows proper diversity partitioning, with the desired property gamma ≥ alpha and thus avoids all the restrictions that apply to the conventional diversity decomposition. Our functional alpha and gamma are interpreted as the effective numbers of functionally equally distinct species, respectively, in an assemblage and in the pooled assemblage, while beta is the effective number of equally large assemblages with no shared species and all species in the assemblages being equally distinct. The resulting beta diversity can be transformed to obtain abundance-sensitive Sørensen-and Jaccard-type functional (dis)similarity profiles. Hypothetical and real examples are used to illustrate the framework. Online software and R codes are available to facilitate computations.

Rare species reintroductions are an increasingly common conservation strategy, but often result in poor survival of reintroduced individuals. Reintroduction sites are chosen primarily based on historical occupancy and/or abiotic properties of the site, with much less consideration given to properties of the larger biotic community. However, ecological niche theory suggests that the ability to coexist with other species is determined in part by the degree of functional similarity between species. The degree to which functional similarity affects the survival of reintroduced plants is poorly understood, but has important implications for the allocation of limited conservation resources. We collected a suite of abiotic, biotic, and functional trait variables centered on outplanted individuals from four reintroduced rare plant species and used logistic regression and model selection to assess their influence on individual survival. We show that higher functional similarity between reintroduced individuals and the local community, measured by differences between their multivariate functional traits and the community-weighted mean traits of their immediate neighbors, increases survival and is a stronger predictor of survival than local variation in abiotic factors, suggesting that the functional composition of the biotic community should be incorporated into site selection to improve reintroduction success.

Microarray data analysis needs utmost care as it plays a significant role in cancer study. Due to the excessive complexity of the data extraction process, it loses some relevant information (missing values) which leads to a significant irrecoverable disruption from the actual scenario. The imputation of missing values is a crucial preprocessing step in analyzing microarray data. Currently, numerous methodologies have been designed to resolve the problem, but the unsatisfactory outcome is obtained with high missing rates of data. In order to estimate the missing expression to complete the dataset, a novel method has been proposed based on the similarity index and generative adversarial network (Sim-GAN). Firstly, the raw dataset has been divided into two subsets, i.e., the target set (which contains genes with missing expression values) and the candidate set (contains without missing values). In the next step, the similarity index between target genes and candidate genes has been obtained. As microarray data represents several biological factors, three similarity matrices (structural similarity, functional similarity, and semantic similarity) have been derived to find the small subset of candidate genes for each target gene. In structural similarity, a novel approach has been used to reduce the time complexity is O(1) as well as tackle the nonlinearity. Now, the obtained subsets are fed into a generative adversarial network to compute the missing values of the targeted genomes. The experimental outcomes consolidate the claim that the proposed methodology gives a satisfactory performance in terms of meaningful expression values. A detailed comparative study based on several statistical (i.e., NRMSE, AUROC, etc.) and biological (i.e., CPP, BLCI) metrics to confirm that the proposed Sim-GAN outperforms the existing missing value estimation techniques.

Background More and more evidence shows that circRNA plays an important role in various biological processes and human health. Therefore, inferring the circRNA’s potential functions and obtaining circRNA functional similarity has become more and more significant. However, there is no effective approach to explore the functional similarity of circRNAs. Methods In this paper, we propose a new approach, called MSCFS, to calculate the functional similarity of circRNA by integrating multiple data sources. We combine circRNA-disease association, circRNA-gene-Gene Ontology association, and circRNA sequence information to explore the functional similarity of circRNA. Firstly, we employ different learning representation methods from three data sources to establish three circRNA functional similarity networks. Then we integrate the three networks to obtain the final circRNA functional similarity. Results We utilize circRNA–miRNA association similarity and circRNA co-expression similarity to evaluate the performance of MSCFS. The results show a positive correlation with miRNA association ( R = 0.213 ) and circRNA co-expression similarity ( R = 0.8991 ). Finally, we construct a circRNA functional similarity network and perform case analysis. The result shows our method can be applied to infer new potential functions of circRNA and other associations. Conclusions MSCFS combines multiple data sources related to circRNA functions. Correlation analysis and case analyses prove that MSCFS is a useful method to explore circRNA functional similarity.