Boise State University

Abstract: In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure flux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagy-related protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the field.

Abstract: Why are intentions interesting to those who care about new venture formation? Entrepreneurship is a way of thinking, a way of thinking that emphasizes opportunities over threats. The opportunity identification process is clearly an intentional process, and, therefore, entrepreneurial intentions clearly merit our attention. Equally important, they offer a means to better explain—and predict—entrepreneurship. We don't start a business as a reflex, do we? We may respond to the conditions around us, such as an intriguing market niche, by starting a new venture. Yet, we think about it first; we process the cues from the environment around us and set about constructing the perceived opportunity into a viable business proposition. In the psychological literature, intentions have proven the best predictor of planned behavior, particularly when that behavior is rare, hard to observe, or involves unpredictable time lags. New businesses emerge over time and involve considerable planning. Thus, entrepreneurship is exactly the type of planned behavior Bird 1988 , Katz and Gartner 1988 for which intention models are ideally suited. If intention models prove useful in understanding business venture formation intentions, they offer a coherent, parsimonious, highly-generalizable, and robust theoretical framework for understanding and prediction. Empirically, we have learned that situational (for example, employment status or informational cues) or individual (for example, demographic characteristics or personality traits) variables are poor predictors. That is, predicting entrepreneurial activities by modeling only situational or personal factors usually resulted in disappointingly small explanatory power and even smaller predictive validity. Intentions models offer us a significant opportunity to increase our ability to understand and predict entrepreneurial activity. The current study compares two intention-based models in terms of their ability to predict entrepreneurial intentions: Ajzen's theory of planned behavior (TPB) and Shapero's model of the entrepreneurial event (SEE). Ajzen argues that intentions in general depend on perceptions of personal attractiveness, social norms, and feasibility. Shapero argues that entrepreneurial intentions depend on perceptions of personal desirability, feasibility, and propensity to act. We employed a competing models approach, comparing regression analyses results for the two models. We tested for overall statistical fit and how well the results supported each component of the models. The sample consisted of student subjects facing imminent career decisions. Results offered strong statistical support for both models. (1) Intentions are the single best predictor of any planned behavior, including entrepreneurship. Understanding the antecedents of intentions increases our understanding of the intended behavior. Attitudes influence behavior by their impact on intentions. Intentions and attitudes depend on the situation and person. Accordingly, intentions models will predict behavior better than either individual (for example, personality) or situational (for example, employment status) variables. Predictive power is critical to better post hoc explanations of entrepreneurial behavior; intentions models provide superior predictive validity. (2) Personal and situational variables typically have an indirect influence on entrepreneurship through influencing key attitudes and general motivation to act. For instance, role models will affect entrepreneurial intentions only if they change attitudes and beliefs such as perceived self-efficacy. Intention-based models describe how exogenous influences (for eample, perceptions of resource availability) change intentions and, ultimately, venture creation. (3) The versatility and robustness of intention models support the broader use of comprehensive, theory-driven, testable process models in entrepreneurship research (MacMillan and Katz 1992) . Intentional behavior helps explain and model why many entrepreneurs decide to start a business long before they scan for opportunities. Understanding intentions helps researchers and theoreticians to understand related phenomena. These include: what triggers opportunity scanning, the sources of ideas for a business venture, and how the venture ultimately becomes a reality. Intention models can describe how entrepreneurial training molds intentions in subsequent venture creation (for example, how does training in business plan writing change attitudes and intentions?). Past research has extensively explored aspects of new venture plans once written. Intentionality argues instead that we study the planning process itself for determinants of venturing behavior. We can apply intentions models to other strategic decisions such as the decision to grow or exit a business. Researchers can model the intentions of critical stakeholders in the venture, such as venture capitalists' intentions toward investing in a given company. Finally, management researchers can explore the overlaps between venture formation intentions and venture opportunity identification. Entrepreneurs themselves (and those who teach and train them) should benefit from a better understanding of their own motives. The lens provided by intentions affords them the opportunity to understand why they made certain choices in their vision of the new venture. Intentions-based models provide practical insight to any planned behavior. This allows us to better encourage the identification of personally-viable, personally-credible opportunities. Teachers, consultants, advisors, and entrepreneurs should benefit from a better general understanding of how intentions are formed, as well as a specific understanding of how founders' beliefs, perceptions, and motives coalesce into the intent to start a business. This understanding offers sizable diagnostic power, thus entrepreneurship educators can use this model to better understand the motivations and intentions of students and trainees and to help students and trainees understand their own motivations and intentions. Carefully targeted training becomes possible. For example, ethnic and gender differences in career choice are largely explained by self-efficacy differences. Applied work in psychology and sociology tells us that we already know how to remediate self-efficacy differences. Raising entrepreneurial efficacies will raise perceptions of venture feasibility, thus increasing the perception of opportunity. Economic and community development hinges not on chasing smokestacks, but on growing new businesses. To encourage economic development in the form of new enterprises we must first increase perceptions of feasibility and desirability. Policy initiatives will increase business formations if those initiatives positively influence attitudes and thus influence intentions. The growing trends of downsizing and outsourcing make this more than a sterile academic exercise. Even if we successfully increase the quantity and quality of potential entrepreneurs, we must also promote such perceptions among critical stakeholders including suppliers, financiers, neighbors, government officials, and the larger community. The findings of this study argue that promoting entrepreneurial intentions by promoting public perceptions of feasibility and desirability is not just desirable; promoting entrepreneurial intentions is also thoroughly feasible.

Abstract: Six DNA regions were evaluated as potential DNA barcodes for Fungi, the second largest kingdom of eukaryotic life, by a multinational, multilaboratory consortium. The region of the mitochondrial cytochrome c oxidase subunit 1 used as the animal barcode was excluded as a potential marker, because it is difficult to amplify in fungi, often includes large introns, and can be insufficiently variable. Three subunits from the nuclear ribosomal RNA cistron were compared together with regions of three representative protein-coding genes (largest subunit of RNA polymerase II, second largest subunit of RNA polymerase II, and minichromosome maintenance protein). Although the protein-coding gene regions often had a higher percent of correct identification compared with ribosomal markers, low PCR amplification and sequencing success eliminated them as candidates for a universal fungal barcode. Among the regions of the ribosomal cistron, the internal transcribed spacer (ITS) region has the highest probability of successful identification for the broadest range of fungi, with the most clearly defined barcode gap between inter- and intraspecific variation. The nuclear ribosomal large subunit, a popular phylogenetic marker in certain groups, had superior species resolution in some taxonomic groups, such as the early diverging lineages and the ascomycete yeasts, but was otherwise slightly inferior to the ITS. The nuclear ribosomal small subunit has poor species-level resolution in fungi. ITS will be formally proposed for adoption as the primary fungal barcode marker to the Consortium for the Barcode of Life, with the possibility that supplementary barcodes may be developed for particular narrowly circumscribed taxonomic groups.

Abstract: Matrix-matched calibration by natural zircon standards and analysis of natural materials as a reference are the principle methods for achieving accurate results in inicrobeam U-Pb dating and Hf isotopic analysis. We describe a new potential zircon reference material for laser ablation ICP-MS that was extracted from a potassic granulite facies rock collected in the southern part of the Bohemian Massif (Plesovice, Czech Republic). Data from different techniques (ID-TIMS, SIMS and LA ICP-MS) and several laboratories suggest that this zircon has a concordant U-Pb age with a weighted mean Pb-206/U-238 date of 337.13 +/- 0.37 Ma (ID-TIMS, 95% confidence limits, including tracer calibration uncertainty) and U-Pb age homogeneity on the scale used in LA ICP-MS dating. Inhomogeneities in trace element composition due to primary growth zoning prevent its use as a calibration standard for trace element analysis. The content of U varies from 465 ppm in pristine parts of the grains to similar to 3000 ppm in actinide-rich sectors that correspond to pyramidal faces with a high degree of metamictization (present in ca. 30% of the grains). These domains are easily recognized from high intensities on BSE images and should be avoided during the analysis. Hf isotopic composition of the Plesovice zircon (>0.9 wt.% Hf) is homogenous within and between the grains with a mean Hf-176/Hf-177 value of 0.282492 +/- 0.000013 (2SD). The age and Hf isotopic homogeneity of the Plesovice zircon together with its relatively high U and Pb contents make it an ideal calibration and reference material for laser ablation ICP-MS measurements, especially when using low laser energies and/or small diameters of laser beam required for improved spatial resolution.

Abstract: A comprehensive phylogenetic classification of the kingdom Fungi is proposed, with reference to recent molecular phylogenetic analyses, and with input from diverse members of the fungal taxonomic community. The classification includes 195 taxa, down to the level of order, of which 16 are described or validated here: Dikarya subkingdom nov.; Chytridiomycota, Neocallimastigomycota phyla nov.; Monoblepharidomycetes, Neocallimastigomycetes class. nov.; Eurotiomycetidae, Lecanoromycetidae, Mycocaliciomycetidae subclass. nov.; Acarosporales, Corticiales, Baeomycetales, Candelariales, Gloeophyllales, Melanosporales, Trechisporales, Umbilicariales ords. nov. The clade containing Ascomycota and Basidiomycota is classified as subkingdom Dikarya, reflecting the putative synapomorphy of dikaryotic hyphae. The most dramatic shifts in the classification relative to previous works concern the groups that have traditionally been included in the Chytridiomycota and Zygomycota. The Chytridiomycota is retained in a restricted sense, with Blastocladiomycota and Neocallimastigomycota representing segregate phyla of flagellated Fungi. Taxa traditionally placed in Zygomycota are distributed among Glomeromycota and several subphyla incertae sedis, including Mucoromycotina, Entomophthoromycotina, Kickxellomycotina, and Zoopagomycotina. Microsporidia are included in the Fungi, but no further subdivision of the group is proposed. Several genera of 'basal' Fungi of uncertain position are not placed in any higher taxa, including Basidiobolus, Caulochytrium, Olpidium, and Rozella.