Taxonomy Compilation & Curation Within R

Research projects in ecology or biodiversity either start with an area of study or a target species list. Working with these species lists or taxonomic lists is not as straightforward as it seems. The taxonomic names that are considered to be “standard,” are surprisingly dynamic. Over time, the names keep changing with ongoing research and advancements in taxonomy. Additionally, they undergo all sorts of reorganization, such as one species being split into multiple species and/or subspecies, the grouping of multiple species into a single species, and the reclassification of species from one genus to another. Compiling a consistent target species list can be very time consuming and tricky. However it is the initial step in most research projects and needs to be completed in order to continue the research. Advancements in biodiversity informatics are helping simplify and automate some of these tasks. There are several web services that provide taxonomic data with either a taxonomic or a geographic focus. An increasing number of experts are opening access to their carefully curated taxonomic lists. Even with the help of these services, a lot of time needs to be spent to create a working list of names that can be linked to data such as Global Biodiversity Information Facility (GBIF) mediated occurrence data. The package “taxotools” (Barve 2021) provides basic taxonomic list processing functions within the R programming environment (R Core Team 2021). Even though it is a work in progress, the functions available so far are applicable to diverse projects. The tools available can be categorized into the following broad areas: Name manipulation: A set of helper functions to check scientific names with global name resolution services like Global Names Architecture (GNA) & GBIF Name Parser, and to construct and deconstruct scientific names to and from components like genus, species and subspecific units. Name matching: Matches names either with global name services or with user-created master taxonomy lists using fuzzy matching, testing combinations of genus level synonyms, subspecies elevation to species, trying to match with higher level taxonomic entities like genus and family, and employing a user-defined lookup table to manually resolve names. List processing: Updates list fields such as unique identifiers (id), higher taxonomy and taxonomic ranks. List matching: Compares user generated lists with each other and finds differences in the two lists, then prepares the lists for merging together to form a masterlist. Format conversion: Converts taxolist to and from formats like HTML and Darwin Core (Wieczorek et al. 2021), which is useful in data exchange or checking the lists manually. Name harvesting functions: Acquires additional names from Integrated Taxonomic Information System (ITIS) and Wikipedia (taxonomy infobox). Name manipulation: A set of helper functions to check scientific names with global name resolution services like Global Names Architecture (GNA) & GBIF Name Parser, and to construct and deconstruct scientific names to and from components like genus, species and subspecific units. Name matching: Matches names either with global name services or with user-created master taxonomy lists using fuzzy matching, testing combinations of genus level synonyms, subspecies elevation to species, trying to match with higher level taxonomic entities like genus and family, and employing a user-defined lookup table to manually resolve names. List processing: Updates list fields such as unique identifiers (id), higher taxonomy and taxonomic ranks. List matching: Compares user generated lists with each other and finds differences in the two lists, then prepares the lists for merging together to form a masterlist. Format conversion: Converts taxolist to and from formats like HTML and Darwin Core (Wieczorek et al. 2021), which is useful in data exchange or checking the lists manually. Name harvesting functions: Acquires additional names from Integrated Taxonomic Information System (ITIS) and Wikipedia (taxonomy infobox). Detailed function listings under each category are listed in Table 1. This package has been effectively used in several biodiversity studies and projects like Map of Life, ButterflyNet, Terrestrial Parasite Tracker etc. It has been successfully tested on a masterlist constructed with ~1M names from World Flora Online and performs well. The package is available on The Comprehensive R Archive Network (CRAN) [https://CRAN.R-project.org/package=taxotools] and the developmental release is on GitHub [https://github.com/vijaybarve/taxotools].

Segments: An alternative rainfall problem

Elliot Soloway’s Rainfall problem is a well-known and well-studied problem to investigate the problem-solving strategies of programmers. Kathi Fisler investigated this programming challenge from the point of view of functional programmers. She showed that this particular challenge gives rise to five different high-level solution strategies, of which three are predominant and cover over 80% of all chosen solutions. In this study, we put forward the Segments problem as an alternative challenge to investigate the problem-solving skills of functional programmers. Analysis of the student solutions, their high-level solution strategies, and corresponding archetype solutions shows that the Segments problem gives rise to seven different high-level solution strategies that can be further divided into 17 subclasses. The Segments problem is particularly suited to investigate problem-solving skills that involve list processing and higher-order functions.

The expressivity dimension of speech is the basis of the expression dimension. Evidence from behavioural and neuroimaging studies

The modalities of communication are the sum of the expression dimension (linguistics) and the expressivity dimension (prosody), both being equally important in language communication. The expressivity dimension which comes first in the act of speech, is the basis on which phonemes, syllables, words, grammar and morphosyntax, i.e., the expression dimension of speech is superimposed. We will review evidence (1) revealing the importance of prosody in language acquisition and (2) showing that prosody triggers the involvement of specific brain areas dedicated to sentences and word-list processing. To support the first point, we will not only rely on experimental psychology studies conducted in newborns and young children but also on neuroimaging studies that have helped to validate these behavioral experiments. Then, neuroimaging data on adults will allow for concluding that the expressivity dimension of speech modulates both the right hemisphere prosodic areas and the left hemisphere network in charge of the expression dimension

Faster sequence alignment through GPU-accelerated restriction of the seed-and-extend search space

Motivation: In computing pairwise alignments of biological sequences, software implementations employ a variety of heuristics that decrease the computational effort involved in computing potential alignments. A key element in achieving high processing throughput is to identify and prioritize potential alignments where high-scoring mappings can be expected. These tasks involve list-processing operations that can be efficiently performed on GPU hardware. Results: We implemented a read aligner called A21 that exploits GPU-based parallel sort and reduction techniques to restrict the number of locations where potential alignments may be found. When compared with other high-throughput aligners, this approach finds more high-scoring mappings without sacrificing speed or accuracy. A21 running on a single GPU is about 10 times faster than comparable CPU-based tools; it is also faster and more sensitive in comparison with other recent GPU-based aligners.