Santa Fe Institute

About: Santa Fe Institute is a nonprofit organization based out in Santa Fe, New Mexico, United States. It is known for research contribution in the topics: Population & Context (language use). The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.

Noisy: identification of problematic columns in multiple sequence alignments.

Abstract: Motivation: Sequence-based methods for phylogenetic reconstruction from (nucleic acid) sequence data are notoriously plagued by two effects: homoplasies and alignment errors. Large evolutionary distances imply a large number of homoplastic sites. As most protein-coding genes show dramatic variations in substitution rates that are not uncorrelated across the sequence, this often leads to a patchwork pattern of (i) phylogenetically informative and (ii) effectively randomized regions. In highly variable regions, furthermore, alignment errors accumulate resulting in sometimes misleading signals in phylogenetic reconstruction. Results: We present here a method that, based on assessing the distribution of character states along a cyclic ordering of the taxa, allows the identification of phylogenetically uninformative homoplastic sites in a multiple sequence alignment. Removal of these sites appears to improve the performance of phylogenetic reconstruction algorithms as measured by various indices of "tree quality". In particular, we obtain more stable trees due to the exclusion of phylogenetically incompatible sites that most likely represent strongly randomized characters. Software: The computer program noisy implements this approach. It can be employed to improving phylogenetic reconstruction capability with quite a considerable success rate whenever (1) the average bootstrap support obtained from the original alignment is low, and (2) there are sufficiently many taxa in the data set – at least, say, 12 to 15 taxa. The software can be obtained under the GNU Public License from http://www.bioinf.uni-leipzig.de/Software/noisy/.

Evaluating the causes of cost reduction in photovoltaic modules

Abstract: Photovoltaic (PV) module costs have declined rapidly over forty years but the reasons remain elusive. Here we advance a conceptual framework and quantitative method for quantifying the causes of cost changes in a technology, and apply it to PV modules. Our method begins with a cost model that breaks down cost into variables that changed over time. Cost change equations are then derived to quantify each variable's contribution. We distinguish between changes observed in variables of the cost model – which we term low-level mechanisms of cost reduction – and research and development, learning-by-doing, and scale economies, which we refer to as high-level mechanisms. We find that increased module efficiency was the leading low-level cause of cost reduction in 1980–2012, contributing almost 25% of the decline. Government-funded and private R&D was the most important high-level mechanism over this period. After 2001, however, scale economies became a more significant cause of cost reduction, approaching R&D in importance. Policies that stimulate market growth have played a key role in enabling PV's cost reduction, through privately-funded R&D and scale economies, and to a lesser extent learning-by-doing. The method presented here can be adapted to retrospectively or prospectively study many technologies, and performance metrics besides cost.

Thermodynamic depth of causal states: Objective complexity via minimal representations

Abstract: Thermodynamic depth is an appealing but flawed structural complexity measure. It depends on a set of macroscopic states for a system, but neither its original introduction by Lloyd and Pagels nor any follow-up work has considered how to select these states. Depth, therefore, is at root arbitrary. Computational mechanics, an alternative approach to structural complexity, provides a definition for a system{close_quote}s minimal, necessary causal states and a procedure for finding them. We show that the rate of increase in thermodynamic depth, or {ital dive}, is the system{close_quote}s reverse-time Shannon entropy rate, and so depth only measures degrees of macroscopic randomness, not structure. To fix this, we redefine the depth in terms of the causal state representation{emdash}{epsilon}-machines{emdash}and show that this representation gives the minimum dive consistent with accurate prediction. Thus, {epsilon}-machines are optimally shallow. {copyright} {ital 1999} {ital The American Physical Society}

Observed forest sensitivity to climate implies large changes in 21st century North American forest growth.

Abstract: Predicting long-term trends in forest growth requires accurate characterisation of how the relationship between forest productivity and climatic stress varies across climatic regimes. Using a network of over two million tree-ring observations spanning North America and a space-for-time substitution methodology, we forecast climate impacts on future forest growth. We explored differing scenarios of increased water-use efficiency (WUE) due to CO2 -fertilisation, which we simulated as increased effective precipitation. In our forecasts: (1) climate change negatively impacted forest growth rates in the interior west and positively impacted forest growth along the western, southeastern and northeastern coasts; (2) shifting climate sensitivities offset positive effects of warming on high-latitude forests, leaving no evidence for continued 'boreal greening'; and (3) it took a 72% WUE enhancement to compensate for continentally averaged growth declines under RCP 8.5. Our results highlight the importance of locally adapted forest management strategies to handle regional differences in growth responses to climate change.

Evolution of Vault RNAs

Abstract: Vault RNAs (vtRNAs) are small, about 100 nt long, polymerase III transcripts contained in the vault particles of eukaryotic cells. Presumably due to their enigmatic function, they have received little attention compared with most other noncoding RNA (ncRNA) families. Their poor sequence conservation makes homology search a complex and tedious task even within vertebrates. Here we report on a systematic and comprehensive analysis of this rapidly evolving class of ncRNAs in deuterostomes, providing a comprehensive collection of computationally predicted vtRNA genes. We find that all previously described vtRNAs are located at a conserved genomic locus linked to the protocadherin gene cluster, an association that is conserved throughout gnathostomes. Lineage-specific expansions to small vtRNA gene clusters are frequently observed in this region. A second vtRNA locus is syntenically conserved across eutherian mammals. The vtRNAs at the two eutherian loci exhibit substantial differences in their promoter structures, explaining their differential expression patterns in several human cancer cell lines. In teleosts, expression of several paralogous vtRNA genes, most but not all located at the syntenically conserved protocadherin locus, was verified by reverse transcriptase-polymerase chain reaction.