We consider the process by which the syntactic parameters of human language are set. Previous work has shown that for natural languages there can be no instant "automatic" triggering of parameters because the trigger properties in natural languages are often deep properties, not recognizable without parsing the input sentence. There are parametric algorithms that learn by parsing, but they are inefficient because they do not respect the Parametric Principle, they evaluate millions of grammars, rather than establishing the values of a few dozen parameters. They do so because they cannot tell in advance which input sentences are pertinent to which parameters, and because they have no protection against misleaming due to parametric ambiguity of the input. There is one model that does implement the Parametric Principle. This is the Structural Triggers Learner (STL). For an STL, a parameter value and its trigger are one and the same thing; they are what we call a structural trigger or treelet (a subtree or in the limiting case a single feature). These structural triggers are made available by UG and adopted into the learner's grammar just in case they prove essential for parsing input sentences. This permits efficient recognition of the parameter values entailed by input sentences and allows the learner to avoid errors by discarding ambiguous input. However, the high degree of ambiguity inlierent in natural language impedes learning even for this efTicient system. An STL must wait a long time between unambiguous inputs. As we explain, this problem is particularly acute in the early stages of learning. In this paper we give a computational analysis of the performance of an STL. We then identify an important factor - the parametric expression rate - that holds promise of a solution to this early learning problem.

AbstractBackgroundRapid detection and therapeutic intervention for infectious and emerging diseases is a major scientific goal in biodefense and public health. Toward this end, cytokine profiles in human blood were investigated using a human whole blood ex vivo exposure model, called WEEM.ResultsSamples of whole blood from healthy volunteers were incubated with seven pathogens including Yersinia pseudotuberculosis, Yersinia enterocolitica, Bacillus anthracis, and multiple strains of Yersinia pestis, and multiplexed protein expression profiling was conducted on supernatants of these cultures with an antibody array to detect 30 cytokines simultaneously. Levels of 8 cytokines, IL-1α, IL-1β, IL-6, IL-8, IL-10, IP-10, MCP-1 and TNFα, were significantly up-regulated in plasma after bacterial exposures of 4 hours. Statistical clustering was applied to group the pathogens based on the host response protein expression profiles. The nearest phylogenetic neighbors clustered more closely than the more distant pathogens, and all seven pathogens were clearly differentiated from the unexposed control. In addition, the Y. pestis and Yersinia near neighbors were differentiated from the B. anthracis strains.ConclusionsCluster analysis, based on host response cytokine profiles, indicates that distinct patterns of immunomodulatory proteins are induced by the different pathogen exposures and these patterns may enable further development into biomarkers for diagnosing pathogen exposure.

Increasing global CO2 emissions have profound consequences for plant biology, not least because of direct influences on carbon gain. However, much remains uncertain regarding how our major crops will respond to a future high CO2 world. Crop model inter-comparison studies have identified large uncertainties and biases associated with climate change. The need to quantify uncertainty has drawn the fields of plant molecular physiology, crop breeding and biology, and climate change modeling closer together. Comparing data from different models that have been used to assess the potential climate change impacts on soybean and maize production, future yield losses have been predicted for both major crops. When CO2 fertilization effects are taken into account significant yield gains are predicted for soybean, together with a shift in global production from the Southern to the Northern hemisphere. Maize production is also forecast to shift northwards. However, unless plant breeders are able to produce new hybrids with improved traits, the forecasted yield losses for maize will only be mitigated by agro-management adaptations. In addition, the increasing demands of a growing world population will require larger areas of marginal land to be used for maize and soybean production. We summarize the outputs of crop models, together with mitigation options for decreasing the negative impacts of climate on the global maize and soybean production, providing an overview of projected land-use change as a major determining factor for future global crop production.

In order for human microbiome studies to translate into actionable outcomes for health, meta-analysis of reproducible data from population-scale cohorts is needed. Achieving sufficient reproducibility in microbiome research has proven challenging. We report a baseline investigation of variability in taxonomic profiling for the Microbiome Quality Control (MBQC) project baseline study (MBQC-base). Blinded specimen sets from human stool, chemostats, and artificial microbial communities were sequenced by 15 laboratories and analyzed using nine bioinformatics protocols. Variability depended most on biospecimen type and origin, followed by DNA extraction, sample handling environment, and bioinformatics. Analysis of artificial community specimens revealed differences in extraction efficiency and bioinformatic classification. These results may guide researchers in experimental design choices for gut microbiome studies.

Comparison of human sequences with the DNA of other mammals is an excellent means of identifying functional elements in the human genome. Here we describe the utility of high-density oligonucleotide arrays as a rapid approach for comparing human sequences with the DNA of multiple species whose sequences are not presently available. High-density arrays representing approximately 22.5 Mb of nonrepetitive human chromosome 21 sequence were synthesized and then hybridized with mouse and dog DNA to identify sequences conserved between humans and mice (human-mouse elements) and between humans and dogs (human-dog elements). Our data show that sequence comparison of multiple species provides a powerful empiric method for identifying actively conserved elements in the human genome. A large fraction of these evolutionarily conserved elements are present in regions on chromosome 21 that do not encode known genes.