Programming error messages play an important role in learning to program. The cycle of program input and error message response completes a loop between the programmer and the compiler/interpreter and is a fundamental interaction between human and computer. However, error messages are notoriously problematic, especially for novices. Despite numerous guidelines citing the importance of message readability, there is little empirical research dedicated to understanding and assessing it. We report three related experiments investigating factors that influence programming error message readability. In the first two experiments we identify possible factors, and in the third we ask novice programmers to rate messages using scales derived from these factors. We find evidence that several key factors significantly affect message readability: message length, jargon use, sentence structure, and vocabulary. This provides novel empirical support for previously untested long-standing guidelines on message design, and informs future efforts to create readability metrics for programming error messages.

Sense of belonging, or belongingness, describes how accepted one feels in their academic community and is an important factor in creating inclusive learning environments.Belongingness is influenced by many factors including: students' backgrounds and experiences; other people; environments (physical and virtual); academic discipline; external factors such as local, regional, and global issues; and time.2020 has been dominated by several major events including the COVID-19 pandemic which dramatically impacted education.The Black Lives Matter movement has further raised global awareness of equality, diversity and inclusion not just in society, but in educational contexts.Climate change concerns, and politicallycharged news are also increasingly affecting our students.We have been monitoring our undergraduate computing students' sense of belonging for over three years, providing us with a unique opportunity to gauge recent changes during the pandemic.Our results surprised us.We found statistically significant reductions in the belongingness of students identifying as men as well as those not identifying as being part of a minority.However, investigating intersectionality of self-identified gender and minority status revealed more complicated and nuanced trends, illustrating important shifts in the belongingness of our students that we are only beginning to understand.

Student sense of belonging has been shown to be associated with many attributes such as motivation and persistence. However, sense of belonging can show variations according to factors such as race and gender. In this study, we examine the relationship between undergraduate Computer Science students' participation in networking, outreach, and mentoring activities and their sense of belonging. Results reveal lower levels of sense of belonging in women and self-identified minorities. However, we observed a higher sense of belonging in female students who participated in networking, outreach, and mentoring activities.

MOTIVATION: The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins. RESULTS: Here, we present a neural network-based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with a Matthews correlation coefficient of 0.75-0.86 outperforming the other state-of-the-art web servers we tested. AVAILABILITY AND IMPLEMENTATION: SCLpred-EMS is freely available for academic users at http://distilldeep.ucd.ie/SCLpred2/. CONTACT: [email protected].

UNLABELLED: MicroRNAs are short non-coding RNA which function to fine-tune protein levels in all cells. This is achieved mainly by sequence-specific binding to 3' untranslated regions of target mRNA. The result is post-transcriptional interference in gene expression which reduces protein levels either by promoting destabilisation of mRNA or translational repression. Research published since 2010 shows that microRNAs are important regulators of gene expression in epilepsy. A series of microRNA profiling studies in rodent and human tissue has revealed that epilepsy is associated with wide ranging changes to microRNA levels in the brain. These are thought to influence processes including cell death, inflammation and re-wiring of neuronal networks. MicroRNAs have also been identified in the blood after injury to the brain and therefore may serve as biomarkers of epilepsy. EpimiRBase is a manually curated database for researchers interested in the role of microRNAs in epilepsy. The fully searchable database includes information on up- and down-regulated microRNAs in the brain and blood, as well as functional studies, and covers both rodent models and human epilepsy. AVAILABILITY AND IMPLEMENTATION: EpimiRBase is available at http://www.epimirbase.eu CONTACT: [email protected].

Abstract Summary: Cell penetrating peptides (CPPs) are attracting much attention as a means of overcoming the inherently poor cellular uptake of various bioactive molecules. Here, we introduce CPPpred, a web server for the prediction of CPPs using a N-to-1 neural network. The server takes one or more peptide sequences, between 5 and 30 amino acids in length, as input and returns a prediction of how likely each peptide is to be cell penetrating. CPPpred was developed with redundancy reduced training and test sets, offering an advantage over the only other currently available CPP prediction method. Availability and Implementation: CPPpred is freely available to non-commercial users at http://bioware.ucd.ie/cpppred. Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Peptides play important roles in signalling, regulation and immunity within an organism. Many have successfully been used as therapeutic products often mimicking naturally occurring peptides. Here we present PeptideLocator for the automated prediction of functional peptides in a protein sequence. RESULTS: We have trained a machine learning algorithm to predict bioactive peptides within protein sequences. PeptideLocator performs well on training data achieving an area under the curve of 0.92 when tested in 5-fold cross-validation on a set of 2202 redundancy reduced peptide containing protein sequences. It has predictive power when applied to antimicrobial peptides, cytokines, growth factors, peptide hormones, toxins, venoms and other peptides. It can be applied to refine the choice of experimental investigations in functional studies of proteins. AVAILABILITY AND IMPLEMENTATION: PeptideLocator is freely available for academic users at http://bioware.ucd.ie/.

SUMMARY: Knowledge of the subcellular location of a protein provides valuable information about its function and possible interaction with other proteins. In the post-genomic era, fast and accurate predictors of subcellular location are required if this abundance of sequence data is to be fully exploited. We have developed a subcellular localization predictor (SCLpred), which predicts the location of a protein into four classes for animals and fungi and five classes for plants (secreted, cytoplasm, nucleus, mitochondrion and chloroplast) using machine learning models trained on large non-redundant sets of protein sequences. The algorithm powering SCLpred is a novel Neural Network (N-to-1 Neural Network, or N1-NN) we have developed, which is capable of mapping whole sequences into single properties (a functional class, in this work) without resorting to predefined transformations, but rather by adaptively compressing the sequence into a hidden feature vector. We benchmark SCLpred against other publicly available predictors using two benchmarks including a new subset of Swiss-Prot Release 2010_06. We show that SCLpred surpasses the state of the art. The N1-NN algorithm is fully general and may be applied to a host of problems of similar shape, that is, in which a whole sequence needs to be mapped into a fixed-size array of properties, and the adaptive compression it operates may shed light on the space of protein sequences. AVAILABILITY: The predictive systems described in this article are publicly available as a web server at http://distill.ucd.ie/distill/. CONTACT: [email protected].

BACKGROUND: Proteins, especially larger ones, are often composed of individual evolutionary units, domains, which have their own function and structural fold. Predicting domains is an important intermediate step in protein analyses, including the prediction of protein structures. RESULTS: We describe novel systems for the prediction of protein domain boundaries powered by Recursive Neural Networks. The systems rely on a combination of primary sequence and evolutionary information, predictions of structural features such as secondary structure, solvent accessibility and residue contact maps, and structural templates, both annotated for domains (from the SCOP dataset) and unannotated (from the PDB). We gauge the contribution of contact maps, and PDB and SCOP templates independently and for different ranges of template quality. We find that accurately predicted contact maps are informative for the prediction of domain boundaries, while the same is not true for contact maps predicted ab initio. We also find that gap information from PDB templates is informative, but, not surprisingly, less than SCOP annotations. We test both systems trained on templates of all qualities, and systems trained only on templates of marginal similarity to the query (less than 25% sequence identity). While the first batch of systems produces near perfect predictions in the presence of fair to good templates, the second batch outperforms or match ab initio predictors down to essentially any level of template quality.We test all systems in 5-fold cross-validation on a large non-redundant set of multi-domain and single domain proteins. The final predictors are state-of-the-art, with a template-less prediction boundary recall of 50.8% (precision 38.7%) within +/- 20 residues and a single domain recall of 80.3% (precision 78.1%). The SCOP-based predictors achieve a boundary recall of 74% (precision 77.1%) again within +/- 20 residues, and classify single domain proteins as such in over 85% of cases, when we allow a mix of bad and good quality templates. If we only allow marginal templates (max 25% sequence identity to the query) the scores remain high, with boundary recall and precision of 59% and 66.3%, and 80% of all single domain proteins predicted correctly. CONCLUSION: The systems presented here may prove useful in large-scale annotation of protein domains in proteins of unknown structure. The methods are available as public web servers at the address: http://distill.ucd.ie/shandy/ and we plan on running them on a multi-genomic scale and make the results public in the near future.

BACKGROUND: Structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. Structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. Nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio. RESULTS: Here we develop high-throughput machine learning systems for the prediction of protein secondary structure and solvent accessibility that exploit homology to proteins of known structure, where available, in the form of simple structural frequency profiles extracted from sets of PDB templates. We compare these systems to their state-of-the-art ab initio counterparts, and with a number of baselines in which secondary structures and solvent accessibilities are extracted directly from the templates. We show that structural information from templates greatly improves secondary structure and solvent accessibility prediction quality, and that, on average, the systems significantly enrich the information contained in the templates. For sequence similarity exceeding 30%, secondary structure prediction quality is approximately 90%, close to its theoretical maximum, and 2-class solvent accessibility roughly 85%. Gains are robust with respect to template selection noise, and significant for marginal sequence similarity and for short alignments, supporting the claim that these improved predictions may prove beneficial beyond the case in which clear homology is available. CONCLUSION: The predictive system are publicly available at the address http://distill.ucd.ie.

BACKGROUND: We describe Distill, a suite of servers for the prediction of protein structural features: secondary structure; relative solvent accessibility; contact density; backbone structural motifs; residue contact maps at 6, 8 and 12 Angstrom; coarse protein topology. The servers are based on large-scale ensembles of recursive neural networks and trained on large, up-to-date, non-redundant subsets of the Protein Data Bank. Together with structural feature predictions, Distill includes a server for prediction of Calpha traces for short proteins (up to 200 amino acids). RESULTS: The servers are state-of-the-art, with secondary structure predicted correctly for nearly 80% of residues (currently the top performance on EVA), 2-class solvent accessibility nearly 80% correct, and contact maps exceeding 50% precision on the top non-diagonal contacts. A preliminary implementation of the predictor of protein Calpha traces featured among the top 20 Novel Fold predictors at the last CASP6 experiment as group Distill (ID 0348). The majority of the servers, including the Calpha trace predictor, now take into account homology information from the PDB, when available, resulting in greatly improved reliability. CONCLUSION: All predictions are freely available through a simple joint web interface and the results are returned by email. In a single submission the user can send protein sequences for a total of up to 32k residues to all or a selection of the servers. Distill is accessible at the address: http://distill.ucd.ie/distill/.