Source and sink carbon dynamics and carbon allocation in the Amazon basin

Changes to the carbon cycle in tropical forests could affect global climate, but predicting such changes has been previously limited by lack of field-based data. Here we show seasonal cycles of the complete carbon cycle for 14, 1ha intensive carbon cycling plots which we separate into three regions: humid lowland, highlands, and dry lowlands. Our data highlight three trends: (1) there is differing seasonality of total net primary productivity (NPP) with the highlands and dry lowlands peaking in the dry season and the humid lowland sites peaking in the wet season, (2) seasonal reductions in wood NPP are not driven by reductions in total NPP but by carbon during the dry season being preferentially allocated toward either roots or canopy NPP, and (3) there is a temporal decoupling between total photosynthesis and total carbon usage (plant carbon expenditure). This decoupling indicates the presence of nonstructural carbohydrates which may allow growth and carbon to be allocated when it is most ecologically beneficial rather than when it is most environmentally available

Population Recovery of Nicobar Long-Tailed Macaque Macaca fascicularis umbrosus following a Tsunami in the Nicobar Islands, India

Natural disasters pose a threat to isolated populations of species with restricted distributions, especially those inhabiting islands. The Nicobar long tailed macaque.Macaca fascicularis umbrosus, is one such species found in the three southernmost islands (viz. Great Nicobar, Little Nicobar and Katchal) of the Andaman and Nicobar archipelago, India. These islands were hit by a massive tsunami (Indian Ocean tsunami, 26 December 2004) after a 9.2 magnitude earthquake. Earlier studies [Umapathy et al. 2003; Sivakumar, 2004] reported a sharp decline in the population of M. f. umbrosus after thetsunami. We studied the distribution and population status of M. f. umbrosus on thethree Nicobar Islands and compared our results with those of the previous studies. We carried out trail surveys on existing paths and trails on three islands to get encounter rate as measure of abundance. We also checked the degree of inundation due to tsunami by using Normalized Difference Water Index (NDWI) on landsat imageries of the study area before and after tsunami. Theencounter rate of groups per kilometre of M. f. umbrosus in Great Nicobar, Little Nicobar and Katchal was 0.30, 0.35 and 0.48 respectively with the mean group size of 39 in Great Nicobar and 43 in Katchal following the tsunami. This was higher than that reported in the two earlier studies conducted before and after the tsunami. Post tsunami, there was a significant change in the proportion of adult males, adult females and immatures, but mean group size did not differ as compared to pre tsunami. The results show that population has recovered from a drastic decline caused by tsunami, but it cannot be ascertained whether it has reached stability because of the altered group structure. This study demonstrates the effect of natural disasters on island occurring species

ON DERIVING SPATIAL PROTEIN-STRUCTURE FROM NMR OR X-RAY-DIFFRACTION DATA

During the last decade it has become possible to derive the spatial structure of small proteins in solution using multidimensional NMR spectroscopy measurements and interpreting the data in terms of a chemical atomic model. The NMR experiments generate a set of interproton distance constraints, which is subsequently used to generate spatial structures that satisfy the experimental data. Correspondingly, crystallographic least-squares and molecular dynamics refinement is routinely applied to obtain a protein structure that is compatible with the observed structure factor amplitudes. The quality of the structure obtained will depend on the number and quality of the experimental data and on the searching power of the refinement method and protocol. The potential energy annealing conformational search (PEACS) algorithm is shown to be an improvement over standard molecular dynamics search methods. The use of time-dependent distance or structure factor restraints in molecular dynamics refinement yields a much better representation of experimental information than the fixed, static restraints which have generally been used until now. Conventional structure refinement methods lead to a too static and rigid picture of a protein in solution or in the crystalline state

Using transfer learning-based causality extraction to mine latent factors for Sjögren's syndrome from biomedical literature

Understanding causality is a longstanding goal across many different domains. Different articles, such as those published in medical journals, disseminate newly discovered knowledge that is often causal. In this paper, we use this intuition to build a model that leverages causal relations to unearth factors related to Sjögren's syndrome from biomedical literature. Sjögren's syndrome is an autoimmune disease affecting up to 3.1 million Americans. Due to the uncommon nature of the illness, symptoms across different specialties coupled with common symptoms of other autoimmune conditions such as rheumatoid arthritis, it is difficult for clinicians to diagnose the disease timely. Due to the lack of a dedicated dataset for causal relationships built from biomedical literature, we propose a transfer learning-based approach, where the relationship extraction model is trained on a wide variety of datasets. We conduct an empirical analysis of numerous neural network architectures and data transfer strategies for causal relation extraction. By conducting experiments with various contextual embedding layers and architectural components, we show that an ELECTRA-based sentence-level relation extraction model generalizes better than other architectures across varying web-based sources and annotation strategies. We use this empirical observation to create a pipeline for identifying causal sentences from literature text, extracting the causal relationships from causal sentences, and building a causal network consisting of latent factors related to Sjögren's syndrome. We show that our approach can retrieve such factors with high precision and recall values. Comparative experiments show that this approach leads to 25% improvement in retrieval F1-score compared to several state-of-the-art biomedical models, including BioBERT and Gram-CNN. We apply this model to a corpus of research articles related to Sjögren's syndrome collected from PubMed to create a causal network for Sjögren's syndrome. The proposed causal network for Sjögren's syndrome will potentially help clinicians with a holistic knowledge base for faster diagnosis