Semantic effects in the word\u2013word interference task: a comment on Roelofs, Piai, and Schriefers (2013)

Roelofs, Piai, and Schriefers (Language and Cognitive Processes) test both the WEAVER++ model of word production and the response-exclusion account of performance in Stroop-like tasks against data from the word-word interference (WWI) task, and conclude that whereas the WEAVER++ successfully accounts for those data, the response-exclusion hypothesis fails. Here we show that once recent data from the WWI task are considered, both models fail

Multi-photon corrections to W boson mass determination at hadron colliders

The impact of higher-order final-state photonic corrections on the precise determination of the W-boson mass at the Tevatron and LHC colliders is evaluated. The W-mass shift from a fit to the transverse mass distribution is found to be about 10 MeV in the W --> mu nu channel and a few MeV in the W --> e nu channel. The calculation, which is implemented in the Monte Carlo event generator HORACE for data analysis, can contribute to reduce the uncertainty associated to the W mass measurement at present and future hadron collider experiments.Comment: 3 pages, 2 figures, to appear in the proceedings of International Europhysics Conference on High-Energy Physics (EPS 2003), Aachen, Germany, 17-23 Jul 200

SINGLE BUILDING POINT CLOUD SEGMENTATION: TOWARDS URBAN DATA MODELING AND MANAGEMENT

To manage urban areas, a key step is the development of a geometric survey and its subsequent analysis and processing in order to provide useful information, and to become a good basis for urban modeling. Surveys of urban areas can be developed with various technologies, such as Aerial Laser Scanning, Unmanned Aerial Systems photogrammetry, and Mobile Mapping Systems. To make the resulting point clouds useful for subsequent steps, it is necessary to segment them into classes representing urban elements. On the other hand, there are 2D land representations that provide a variety of information related to the elements in the urban environment, which are linked to databases that have information content related to them. In this context, the element identified as interesting for urban management of the built heritage is the individual building unit. This paper presents an automated method for using map datasets to segment individual building units on a point cloud of an urban area. A unique number is then assigned to the segmented points, linking them directly to the corresponding element in the map database. The resulting point cloud thus becomes a container of the information in the map database, and a basis for possible city modeling. The method was successfully tested on the historic city of Sabbioneta (northern Italy), using two point clouds, one obtained through the use of a Mobile Mapping System and one obtained with Unmanned Aerial System photogrammetry. Two cartographic databases were used, one opensource (OpenStreetMap) and one provided by the regional authorities (regional cartographic database)

Mentalità stratigrafica e progetti per la conoscenza e per la conservazione

Gli autori presentano i risultati più significativi di una ricerca svolta presso le Università di Milano e di Brescia, nell'ambito dei corsi di Restauro architettonico. Fra i principali obiettivi dello studio e delle esperienze didattiche correlate vi è il tentativo non solo di utilizzare le procedure dell'archeologia stratigrafica, per quanto utile e possibile, nella complessa serie di operazioni che segna la redazione di un progetto di conservazione e di riuso, ma di trasferirvi anche quella che si può definire come "mentalità stratigrafica". In particolare considerando nuovi codici, quali le Interfacce di Fase e le Unità Stratigrafiche Associate. A corredo di queste argomentazioni vengono anche svolte alcune considerazioni in merito al cosiddetto "Restauro archeologico

The BioVRPi project: a valuable and sustainable alternative for genomic analysis

Since 2012, the Raspberry Pi Foundation has started developing pocket-sized and low-cost devices, originally meant to teach computer science in developing Countries. Its growing interest and constant improvement led Raspberry Pi devices to find different applications and to suit the needs of various research areas. In the previous years, different researchers already reported applications of Raspberry Pi devices in bioinformatics, such as basic train- ing and proteomics. In the beginning of 2021, we gave birth to BioVRPi, a project which aims to develop and offer a low-cost and stable bioinformatic environment for students and re- searchers involved in the genomics and transcriptomics fields. We evaluated performances and software compatibilities of different scenarios, focusing on Genome-Wide Association Studies for complex traits in Homo sapiens, transcriptomic analyses on RNA-seq data from Strongyloides stercoralis samples and alignment of small organisms, such as SARS-CoV-2 (virus), Escherichia Coli (bacterium) and Caenorhabditis elegans (nematode). Results from both the bioinformatic and benchmarking analyses showed that Raspberry Pi devices are capable of accomplishing different bioinformatic tasks in terms of results and performances. Moreover, they proved to be a valuable low-cost and sustainable alternative, in accordance with the United Nation 2030 Agenda, to answer the needs and the challenges of the current socio-economic situation

Towards pocket-sized genomic analyses: cross-platform benchmark of multi-organism genomic data indexing and alignment

The current socio-economic situation as well as international objectives set by the United Nation (2030 Sustainable Agenda) underline the urgency of low-cost and environmental-friendly computational alternatives. Moreover, in recent years the bioinformatic community has shown renewed interest for Raspberry Pi (RPi) application in teaching and research projects. In the context of the BioVRPi project - which aims to develop and offer a low-cost, stable and tested bioinformatic environment - we propose an exploratory cross-platform benchmarking of multi-organism genomic analyses. The benchmark of indexing and alignment processes was carried out on the following devices: RPi 4 (Raspberry Pi OS 04-04-2022) RAM 8GB HDD storage, laptop (MacOS Big Sur v11.2.3) Intel Core i5 2GHz quad-core processor RAM 16GB SSD, and desktop (Ubuntu 20.04.4 LTS) Intel Core i7 3GHz octa-core processor RAM 32GB HDD storage. Performance assessment was evaluated on SARS-CoV-2 virus, Escherichia coli and Caenorhabditis elegans genome sequences (respective RefSeq accessions: GCF_009858895.2, GCF_000005845.2, GCF_000002985.6) since they present different degrees of genomic complexity: virus, bacterium, and nematode. To minimize variability and possible biases due to sequencing technologies used, sample reads were generated in silico from their respective reference genomes using ART Illumina v2.5.8 with the following parameters: read length 150, paired end, coverage 30X, mean fragment length 200, standard deviation 10, HiSeqX v2.5 TruSeq built-in profile. Indexing and alignment were performed with 3 alignment tools: BWA v0.7.17-r1188, Bowtie2 v2.4.5, and Minimap2 v2.17, using default parameters and scaling from 1 up to 4 threads. Benchmarking was evaluated using Hyperfine v1.13.0 with a warmup step of 3 simulations and 10 runs for each process. We performed a cross-platform benchmark of multi-organism genomic indexing and short reads alignment to evaluate RPi as a viable alternative to common bioinformatic devices. To assess its performance, we tested some of the most widely used alignment tools on SARS-CoV-2, E. coli and C. elegans genomic data (respective genome sizes: 29.9Kbp, 4.6Mbp, 100.3Mbp). The computational times for indexing and alignment are reported in Table 1. As regards indexing, we observed comparable runtimes among RPi and other platforms using BWA and Bowtie2 for SARS-CoV-2 and E. coli, whereas Minimap2 indexing showed an increase of one order of magnitude in runtimes for RPi. Nonetheless, Minimap2 showed the fastest runtimes for indexing overall. In addition, we found an increase of one order of magnitude in RPi runtimes for C. elegans for all considered tools, even though differences in runtimes across platforms showed to be stable across organisms. As regards the alignment process, we observed consistency in runtimes differences across all organisms and tools. Overall, Minimap2 performances proved to be the fastest whereas Bowtie2 displayed poor performances across all platforms, exacerbating its inefficiency on RPi. Even though BWA seems to work more efficiently on RPi than on desktop for SARS-COV-2 data, desktop and laptop showed better performances on more complex organisms as expected. Benchmarking analyses considered multi-threading up to 4 threads, the maximum available on RPi. As regards indexing on Bowtie2, multi-threading proved to be effective for C. elegans data, showing no improvement in runtimes for SARS-CoV-2 and E. coli. Conversely, alignment showed the best performances using multi-threading as expected. In conclusion, RPi showed promising results, proved to be a viable low-cost and environmental-friendly alternative to perform genomic data analysis on different organisms and turned out to be particularly efficient for microorganisms. Further advances and tools optimization for RPi ARM architecture will lead to a greater scalability for complex organisms and will be carried out by the BioVRPi project in future exploratory analyses

The interplay between microbiota and human complex traits

Microorganisms have been one of the most influential drivers propelling some of the greatest environmental and evolutionary changes in the landscape and biology of the entire planet [...]