Usability of Error Messages for Introductory Students

Error messages are an important tool programmers use to help find and fix mistakes or issues in their code. When an error message is unhelpful, it can be difficult to find the issue and may impose additional challenges in learning the language and concepts. Error messages are especially critical for introductory programmers in understanding problems with their code. Unfortunately, not all error messages in programming are beneficial for novice programmers. This paper discusses the general usability of error messages for introductory programmers, analyses of error messages in compilers and DrRacket, and two methodologies intended to improve error handling

Developing Beginner-Friendly Programming Error Messages

The motivation for our work is to introduce a recently developed programming language, Clojure, in a beginner computer science (CSci) class at the University of Minnesota, Morris. Clojure is an industryaccepted programming language that provides significant benefits for beginner programmers, such as focus on a functional approach to programming which, in UMM experience, provides a good foundation for subsequent CSci curriculum. Learning Clojure in an introductory class opens opportunities for students to collaborate on numerous worldwide projects, as well as take advantage of improvements in modern computing hardware. However, Clojure is challenging to use because of its complicated handling of programmers’ mistakes. Mistakes in computer programming are a natural part of developing software. When a mistake happens, there is a system to notify the programmer of an error. The specific information that the programmer receives, known as an error message, may or may not be helpful in identifying the issue. Clojure error messages are notorious for being confusing to beginners. We are developing a system that intercepts the existing Clojure error messages and automatically rephrases them for beginner programmers. We will conduct usability tests by observing the interactions between beginner programmers and our system, and the feedback we receive will be used to further improve our project. We present our new error message handling and discuss testing our system with new programmers.https://digitalcommons.morris.umn.edu/urs_2015/1005/thumbnail.jp

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/

Drivers of genetic diversity in secondary metabolic gene clusters within a fungal species

Drivers of genetic diversity in secondary metabolic gene clusters within a fungal speciesFilamentous fungi produce a diverse array of secondary metabolites (SMs) critical for defense, virulence, and communication. The metabolic pathways that produce SMs are found in contiguous gene clusters in fungal genomes, an atypical arrangement for metabolic pathways in other eukaryotes. Comparative studies of filamentous fungal species have shown that SM gene clusters are often either highly divergent or uniquely present in one or a handful of species, hampering efforts to determine the genetic basis and evolutionary drivers of SM gene cluster divergence. Here, we examined SM variation in 66 cosmopolitan strains of a single species, the opportunistic human pathogen Aspergillus fumigatus. Investigation of genome-wide within-species variation revealed 5 general types of variation in SM gene clusters: nonfunctional gene polymorphisms; gene gain and loss polymorphisms; whole cluster gain and loss polymorphisms; allelic polymorphisms, in which different alleles corresponded to distinct, nonhomologous clusters; and location polymorphisms, in which a cluster was found to differ in its genomic location across strains. These polymorphisms affect the function of representative A. fumigatus SM gene clusters, such as those involved in the production of gliotoxin, fumigaclavine, and helvolic acid as well as the function of clusters with undefined products. In addition to enabling the identification of polymorphisms, the detection of which requires extensive genome-wide synteny conservation (e.g., mobile gene clusters and nonhomologous cluster alleles), our approach also implicated multiple underlying genetic drivers, including point mutations, recombination, and genomic deletion and insertion events as well as horizontal gene transfer from distant fungi. Finally, most of the variants that we uncover within A. fumigatus have been previously hypothesized to contribute to SM gene cluster diversity across entire fungal classes and phyla. We suggest that the drivers of genetic diversity operating within a fungal species shown here are sufficient to explain SM cluster macroevolutionary patterns.National Science Foundation (grant number DEB-1442113). Received by AR. U.S. National Library of Medicine training grant (grant number 2T15LM007450). Received by ALL. Conselho Nacional de Desenvolvimento Cientı´fico e 573 Tecnológico. Northern Portugal Regional Operational Programme (grant number NORTE-01- 0145-FEDER-000013). Received by FR. Fundação de Amparo à Pesquisa do 572 Estado de São Paulo. Received by GHG. National Institutes of Health (grant number R01 AI065728-01). Received by NPK. National Science Foundation (grant number IOS-1401682). Received by JHW. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Plant-symbiotic fungi as chemical engineers: multi-genome analysis of the Clavicipitaceae reveals dynamics of alkaloid Loci

The fungal family Clavicipitaceae includes plant symbionts and parasites that produce several psychoactive and bioprotective alkaloids. The family includes grass symbionts in the epichloae clade (Epichloë and Neotyphodium species), which are extraordinarily diverse both in their host interactions and in their alkaloid profiles. Epichloae produce alkaloids of four distinct classes, all of which deter insects, and some—including the infamous ergot alkaloids—have potent effects on mammals. The exceptional chemotypic diversity of the epichloae may relate to their broad range of host interactions, whereby some are pathogenic and contagious, others are mutualistic and vertically transmitted (seed-borne), and still others vary in pathogenic or mutualistic behavior. We profiled the alkaloids and sequenced the genomes of 10 epichloae, three ergot fungi (Claviceps species), a morning-glory symbiont (Periglandula ipomoeae), and a bamboo pathogen (Aciculosporium take), and compared the gene clusters for four classes of alkaloids. Results indicated a strong tendency for alkaloid loci to have conserved cores that specify the skeleton structures and peripheral genes that determine chemical variations that are known to affect their pharmacological specificities. Generally, gene locations in cluster peripheries positioned them near to transposon-derived, AT-rich repeat blocks, which were probably involved in gene losses, duplications, and neofunctionalizations. The alkaloid loci in the epichloae had unusual structures riddled with large, complex, and dynamic repeat blocks. This feature was not reflective of overall differences in repeat contents in the genomes, nor was it characteristic of most other specialized metabolism loci. The organization and dynamics of alkaloid loci and abundant repeat blocks in the epichloae suggested that these fungi are under selection for alkaloid diversification. We suggest that such selection is related to the variable life histories of the epichloae, their protective roles as symbionts, and their associations with the highly speciose and ecologically diverse cool-season grasses

One thousand DNA barcodes of piranhas and pacus reveal geographic structure and unrecognised diversity in the Amazon

Piranhas and pacus (Characiformes: Serrasalmidae) are a charismatic but understudied family of Neotropical fishes. Here, we analyse a DNA barcode dataset comprising 1,122 specimens, 69 species, 16 genera, 208 localities, and 34 major river drainages in order to make an inventory of diversity and to highlight taxa and biogeographic areas worthy of further sampling effort and conservation protection. Using four methods of species discovery - incorporating both tree and distance based techniques - we report between 76 and 99 species-like clusters, i.e. between 20% and 33% of a priori identified taxonomic species were represented by more than one mtDNA lineage. There was a high degree of congruence between clusters, with 60% supported by three or four methods. Pacus of the genus Myloplus exhibited the most intraspecific variation, with six of the 13 species sampled found to have multiple lineages. Conversely, piranhas of the Serrasalmus rhombeus group proved difficult to delimit with these methods due to genetic similarity and polyphyly. Overall, our results recognise substantially underestimated diversity in the serrasalmids, and emphasise the Guiana and Brazilian Shield rivers as biogeographically important areas with multiple cases of across-shield and within-shield diversifications. We additionally highlight the distinctiveness and complex phylogeographic history of rheophilic taxa in particular, and suggest multiple colonisations of these habitats by different serrasalmid lineages. © 2018 The Author(s)

Accounting for spatiotemporal sampling variation in joint species distribution models

Estimating relative abundance is critical for informing conservation and management efforts and for making inferences about the effects of environmental change on populations. Freshwater fisheries span large geographic regions, occupy diverse habitats and consist of varying species assemblages. Monitoring schemes used to sample these diverse populations often result in populations being sampled at different times and under different environmental conditions. Varying sampling conditions can bias estimates of abundance when compared across time, location and species, and properly accounting for these biases is critical for making inferences. We develop a joint species distribution model (JSDM) that accounts for varying sampling conditions due to the environment and time of sampling when estimating relative abundance. The novelty of our JSDM is that we explicitly model sampling effort as the product of known quantities based on time and gear type and an unknown functional relationship to capture seasonal variation in species life history. We use the model to study relative abundance of six freshwater fish species across the state of Minnesota, USA. Our model enables estimates of relative abundance to be compared both within and across species and lakes, and captures the inconsistent sampling present in the data. We discuss how gear type, water temperature and day of the year impact catchability for each species at the lake level and throughout a year. We compare our estimates of relative abundance to those obtained from a model that assumes constant catchability to highlight important differences within and across lakes and species. Synthesis and applications: Our method illustrates that assumptions relating indices of abundance to observed catch data can greatly impact model inferences derived from JSDMs. Specifically, not accounting for varying sampling conditions can bias inference of relative abundance, restricting our ability to detect responses to management interventions and environmental change. While our focus is on freshwater fisheries, this model architecture can be adopted to other systems where catchability may vary as a function of space, time and species