Multitrophic communities that maintain the functionality of the extreme Antarctic terrestrial ecosystems, while the simplest of any natural community, are still challenging our knowledge about the limits to life on earth. In this study, we describe and interpret the linkage between the diversity of different trophic level communities to the geological morphology and soil geochemistry in the remote Transantarctic Mountains (Darwin Mountains, 80°S). We examined the distribution and diversity of biota (bacteria, cyanobacteria, lichens, algae, invertebrates) with respect to elevation, age of glacial drift sheets, and soil physicochemistry. Results showed an abiotic spatial gradient with respect to the diversity of the organisms across different trophic levels. More complex communities, in terms of trophic level diversity, were related to the weakly developed younger drifts (Hatherton and Britannia) with higher soil C/N ratio and lower total soluble salts content (thus lower conductivity). Our results indicate that an increase of ion concentration from younger to older drift regions drives a succession of complex to more simple communities, in terms of number of trophic levels and diversity within each group of organisms analysed. This study revealed that integrating diversity across multi-trophic levels of biotic communities with abiotic spatial heterogeneity and geological history is fundamental to understand environmental constraints influencing biological distribution in Antarctic soil ecosystems.

This survey of 206 forensic psychologists tested the “filtering” effects of preexisting expert attitudes in adversarial proceedings. Results confirmed the hypothesis that evaluator attitudes toward capital punishment influence willingness to accept capital case referrals from particular adversarial parties. Stronger death penalty opposition was associated with higher willingness to conduct evaluations for the defense and higher likelihood of rejecting referrals from all sources. Conversely, stronger support was associated with higher willingness to be involved in capital cases generally, regardless of referral source. The findings raise the specter of skewed evaluator involvement in capital evaluations, where evaluators willing to do capital casework may have stronger capital punishment support than evaluators who opt out, and evaluators with strong opposition may work selectively for the defense. The results may provide a partial explanation for the “allegiance effect” in adversarial legal settings such that preexisting attitudes may contribute to partisan participation through a self-selection process.

Background: Immunosignaturing is a new peptide microarray based technology for profiling of humoral immune responses. Despite new challenges, immunosignaturing gives us the opportunity to explore new and fundamentally different research questions. In addition to classifying samples based on disease status, the complex patterns and latent factors underlying immunosignatures, which we attempt to model, may have a diverse range of applications.

Methods: We investigate the utility of a number of statistical methods to determine model performance and address challenges inherent in analyzing immunosignatures. Some of these methods include exploratory and confirmatory factor analyses, classical significance testing, structural equation and mixture modeling.

Results: We demonstrate an ability to classify samples based on disease status and show that immunosignaturing is a very promising technology for screening and presymptomatic screening of disease. In addition, we are able to model complex patterns and latent factors underlying immunosignatures. These latent factors may serve as biomarkers for disease and may play a key role in a bioinformatic method for antibody discovery.

Conclusion: Based on this research, we lay out an analytic framework illustrating how immunosignatures may be useful as a general method for screening and presymptomatic screening of disease as well as antibody discovery.

Background: High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data.

Results: We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found ‘Naïve Bayes’ far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy.

Conclusions: ‘Naïve Bayes’ algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.

Portrayals of the US Southwest's Native American inhabitants as “primitive” relics have been shaped by the intertwining practices of archaeological collection and museum display. Focusing on the Pueblo Grande Museum in Phoenix, Arizona, this essay analyzes the interpellation of museum visitors as citizen archaeologists, a process that re/produces racialized discourses through rhetorics of science and time. It is argued that as visitors excavate remnants of the past they engage an archaeological vision that reinforces dominant constructions of “modern” citizenship. This vision maintains colonial histories by disallowing Native peoples both authorship of the past and belonging in the present.

MALDI-TOF MS has been shown capable of rapidly and accurately characterizing bacteria. Highly reproducible spectra are required to ensure reliable characterization. Prior work has shown that spectra acquired manually can have higher reproducibility than those acquired automatically. For this reason, the objective of this study was to optimize automated data acquisition to yield spectra with reproducibility comparable to those acquired manually. Fractional factorial design was used to design experiments for robust optimization of settings, in which values of five parameters (peak selection mass range, signal to noise ratio (S:N), base peak intensity, minimum resolution and number of shots summed) commonly used to facilitate automated data acquisition were varied. Pseudomonas aeruginosa was used as a model bacterium in the designed experiments, and spectra were acquired using an intact cell sample preparation method. Optimum automated data acquisition settings (i.e., those settings yielding the highest reproducibility of replicate mass spectra) were obtained based on statistical analysis of spectra of P. aeruginosa. Finally, spectrum quality and reproducibility obtained from non-optimized and optimized automated data acquisition settings were compared for P. aeruginosa, as well as for two other bacteria, Klebsiella pneumoniae and Serratia marcescens. Results indicated that reproducibility increased from 90% to 97% (p-value [~ over =] 0.002) for P. aeruginosa when more shots were summed and, interestingly, decreased from 95% to 92% (p-value [~ over =] 0.013) with increased threshold minimum resolution. With regard to spectrum quality, highly reproducible spectra were more likely to have high spectrum quality as measured by several quality metrics, except for base peak resolution. Interaction plots suggest that, in cases of low threshold minimum resolution, high reproducibility can be achieved with fewer shots. Optimization yielded more reproducible spectra than non-optimized settings for all three bacteria.

Immunosignaturing shows promise as a general approach to diagnosis. It has been shown to detect immunological signs of infection early during the course of disease and to distinguish Alzheimer’s disease from healthy controls. Here we test whether immunosignatures correspond to clinical classifications of disease using samples from people with brain tumors. Blood samples from patients undergoing craniotomies for therapeutically naïve brain tumors with diagnoses of astrocytoma (23 samples), Glioblastoma multiforme (22 samples), mixed oligodendroglioma/astrocytoma (16 samples), oligodendroglioma (18 samples), and 34 otherwise healthy controls were tested by immunosignature. Because samples were taken prior to adjuvant therapy, they are unlikely to be perturbed by non-cancer related affects. The immunosignaturing platform distinguished not only brain cancer from controls, but also pathologically important features about the tumor including type, grade, and the presence or absence of O⁶-methyl-guanine-DNA methyltransferase methylation promoter (MGMT), an important biomarker that predicts response to temozolomide in Glioblastoma multiformae patients.

Aquatic vertebrates that emerge onto land to spawn, feed, or evade aquatic predators must return to the water to avoid dehydration or asphyxiation. How do such aquatic organisms determine their location on land? Do particular behaviors facilitate a safe return to the aquatic realm? In this study, we asked: will fully-aquatic mosquitofish (Gambusia affinis) stranded on a slope modulate locomotor behavior according to body position to facilitate movement back into the water? To address this question, mosquitofish (n = 53) were placed in four positions relative to an artificial slope (30° inclination) and their responses to stranding were recorded, categorized, and quantified.

We found that mosquitofish may remain immobile for up to three minutes after being stranded and then initiate either a “roll” or a “leap”. During a roll, mass is destabilized to trigger a downslope tumble; during a leap, the fish jumps up, above the substrate. When mosquitofish are oriented with the long axis of the body at 90° to the slope, they almost always (97%) initiate a roll. A roll is an energetically inexpensive way to move back into the water from a cross-slope body orientation because potential energy is converted back into kinetic energy. When placed with their heads toward the apex of the slope, most mosquitofish (>50%) produce a tail-flip jump to leap into ballistic flight. Because a tail-flip generates a caudually-oriented flight trajectory, this locomotor movement will effectively propel a fish downhill when the head is oriented up-slope. However, because the mass of the body is elevated against gravity, leaps require more mechanical work than rolls. We suggest that mosquitofish use the otolith-vestibular system to sense body position and generate a behavior that is “matched” to their orientation on a slope, thereby increasing the probability of a safe return to the water, relative to the energy expended.

The probiotic effects of Lactobacillus reuteri have been speculated to partly depend on its capacity to produce the antimicrobial substance reuterin during the reduction of glycerol in the gut. In this study, the potential of this process to protect human intestinal epithelial cells against infection with Salmonella enterica serovar Typhimurium was investigated. We used a three-dimensional (3-D) organotypic model of human colonic epithelium that was previously validated and applied to study interactions between S. Typhimurium and the intestinal epithelium that lead to enteric salmonellosis. Using this model system, we show that L. reuteri protects the intestinal cells against the early stages of Salmonella infection and that this effect is significantly increased when L. reuteri is stimulated to produce reuterin from glycerol. More specifically, the reuterin-containing ferment of L. reuteri caused a reduction in Salmonella adherence and invasion (1 log unit), and intracellular survival (2 log units). In contrast, the L. reuteri ferment without reuterin stimulated growth of the intracellular Salmonella population with 1 log unit. The short-term exposure to reuterin or the reuterin-containing ferment had no observed negative impact on intestinal epithelial cell health. However, long-term exposure (24 h) induced a complete loss of cell-cell contact within the epithelial aggregates and compromised cell viability. Collectively, these results shed light on a potential role for reuterin in inhibiting Salmonella-induced intestinal infections and may support the combined application of glycerol and L. reuteri. While future in vitro and in vivo studies of reuterin on intestinal health should fine-tune our understanding of the mechanistic effects, in particular in the presence of a complex gut microbiota, this the first report of a reuterin effect on the enteric infection process in any mammalian cell type.

This study presents the first global transcriptional profiling and phenotypic characterization of the major human opportunistic fungal pathogen, Candida albicans, grown in spaceflight conditions. Microarray analysis revealed that C. albicans subjected to short-term spaceflight culture differentially regulated 452 genes compared to synchronous ground controls, which represented 8.3% of the analyzed ORFs. Spaceflight-cultured C. albicans–induced genes involved in cell aggregation (similar to flocculation), which was validated by microscopic and flow cytometry analysis. We also observed enhanced random budding of spaceflight-cultured cells as opposed to bipolar budding patterns for ground samples, in accordance with the gene expression data. Furthermore, genes involved in antifungal agent and stress resistance were differentially regulated in spaceflight, including induction of ABC transporters and members of the major facilitator family, downregulation of ergosterol-encoding genes, and upregulation of genes involved in oxidative stress resistance.

Finally, downregulation of genes involved in actin cytoskeleton was observed. Interestingly, the transcriptional regulator Cap1 and over 30% of the Cap1 regulon was differentially expressed in spaceflight-cultured C. albicans. A potential role for Cap1 in the spaceflight response of C. albicans is suggested, as this regulator is involved in random budding, cell aggregation, and oxidative stress resistance; all related to observed spaceflight-associated changes of C. albicans. While culture of C. albicans in microgravity potentiates a global change in gene expression that could induce a virulence-related phenotype, no increased virulence in a murine intraperitoneal (i.p.) infection model was observed under the conditions of this study. Collectively, our data represent an important basis for the assessment of the risk that commensal flora could play during human spaceflight missions. Furthermore, since the low fluid-shear environment of microgravity is relevant to physical forces encountered by pathogens during the infection process, insights gained from this study could identify novel infectious disease mechanisms, with downstream benefits for the general public.