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FIGURE 8 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 8. Muscular structures of Concavicaris woodfordi (Cooper, 1932). A, tomogram. B, tomogram (colourmarked). C, gastric muscles (3D rendering). D, cross-section (3D rendering). E, adductor muscles (3D rendering). Abbreviations: am, adductor muscle; gm, gastric muscles; il, inner layer; lvp, latero-ventral pouch; s, shield; sto, stomach. Scales: A, B, D, 5 mm; C, E, 2 mm.

FIGURE 1 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 1. Position and geology of the fossil locality. A, Map of USA with the position of Oklahoma (red area) and of Arbuckle Mountains (grey area). B, Map of Arbuckle mountains with the position of the type locality of Concavicaris woodfordi (Cooper, 1932). C, Section of the upper Woodford Shale at Interstate 35 road-cut section (I-35) (sec. 25, T2S, R2E, Arbuckle Mountains, Oklahoma, USA; redrawn after Over [1992]).

FIGURE 4 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 4. Anatomy of Concavicaris woodfordi (Cooper, 1932). A, longitudinal virtual section. B, longitudinal virtual section (colour-marked). C, dorsal view (3D rendering). D, lateral view (3D rendering). Abbreviations: am, adductor muscles; cs, cylindrical structure; g2–7, gills; gm, gastric muscles;ila, anterior part of the inner layer; ilp, posterior part of the inner layer; lvp, latero-ventral pouch; pta, posterior trunk appendages; ra1–3, raptorial appendages; so, shield outline; sto, stomach. Arrows indicate the anterior side of the specimen. Scales: 10 mm.

FIGURE 3 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 3. Marginal fold of Concavicaris woodfordi (Cooper, 1932). A, B, anterior part of the shield (tomogram and drawing). C, close-up of marginal fold in the anterior part of the shield (tomogram). D, E, middle part of the shield (tomogram and drawing). F, close-up of marginal fold in the middle part of the shield (tomogram). Abbreviations: il, inner layer; mf; marginal fold; so, shield outline. Scales: A, B, D, E, 5 mm; C, F, 1 mm.

FIGURE 7 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 7. Digestive, reproductive, and circulatory systems of Concavicaris woodfordi (Cooper, 1932). A, longitudinal virtual section. B, longitudinal virtual section (colour-marked). C, D, digestive and reproductive systems (3D rendering). C, anterior view. D, right lateral view. E, F, G, circulatory system. E, tomogram. F, tomogram (colour-marked). G, 3D rendering of the left part. Abbreviations: cs, cylindrical structure; g1–8, gills; go, gonads; il, inner layer; lvp, lateroventral pouch; sto, stomach. Arrows indicate the anterior side of the specimen. Scales: A, B, 10 mm; C–G, 5 mm.

FIGURE 6 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 6. Internal anatomy of Concavicaris woodfordi (Cooper, 1932). A, right lateral view (3D rendering). B, anterior view (3D rendering). C, dorsal view (3D rendering). D, longitudinal section (3D rendering). Abbreviations: am, adductor muscles; cs, cylindrical structure; g1–8, gills; gm, gastric muscles; go, gonads; lvp, latero-ventral pouch; pta1–3, posterior trunk appendages; r, rostrum; ra1, 3, raptorial appendages; s, shield; so, shield outline; sto, stomach. Arrows indicate the anterior side of the specimen. Scales: A, C, D, 10 mm; B, 5 mm.

FIGURE 10 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 10. Hypothetical reconstruction of Concavicaris woodfordi (Cooper, 1932). Morphology of anterior and posterior sides of the shield, eyes and number of posterior trunk appendages are reconstructed based on Concavicaris submarinus (Jobbins et al., 2020). Abbreviations: ce, compound eyes; g, gills; go, gonads; il, inner layer: lvp, lateroventral pouch; pt, posterior trunk; pta, posterior trunk appendages; r, rostrum; ra, raptorial appendages; s, shield; sto, stomach. Arrow indicates the anterior side of the specimen. Scales: 10 mm.

FIGURE 9 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 9. Appendages of Concavicaris woodfordi (Cooper, 1932). A, longitudinal section (3D rendering). B, raptorial appendages (3D rendering). C, tomogram. D, close-up of third posterior trunk appendage. E, posterior trunk appendages (3D rendering). Arrow indicates the third posterior trunk appendage. Abbreviations: il, inner layer; pta1–3, posterior trunk appendages; ra1–3, raptorial appendages; s, shield. Arrow indicates the anterior side of the specimen. Scales: A–C, 10 mm; D, G, 2 mm; E, 5 mm; F, 1 mm.

FIGURE 4 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms

FIGURE 4. Ordovician bryozoan colony. One-half of hemispherical bryozoan, interior of object, bearing probably oldest boring attributable to ichnogenus Entobia Bronn, 1837. Besides semi-radial tunnels and exploratory threads, three bulbous chambers discovered near the center of the hemisphere. Darriwilian (middle Ordovician), Khrevitsa locality, St. Petersburg Region, Russia. Scale bar equals 1 cm.

FIGURE 7 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms

FIGURE 7. Three-dimensional visualization of a shell of the recent Foraminifera Amphistegina sp. illustrating the potential of micro-CT in investigations of recent marine shelled organisms. (A) A surface view of the whole shell. (B) A transversal section through the whole shell (C, D) Details of the shells´s surface.

FIGURE 5 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms

FIGURE 5. Minute conulariid specimen. (A) Conulariid specimen of Archaeoconularia fecunda and trepostome bryozoan colony; coated with ammonium chloride, no. NMP L21990, locality Loděnice, Upper Ordovician, Zahořany Formation (lower Katian) (B) Micro-CT visualizing of inner surfaces. Scale bar equals 5 mm.

FIGURE 3 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms

FIGURE 3. Siliceous nodules of the Šárka Formation. (A, B) Pricyclopyge binodosa, complete trilobite, no. NMP L 35055, locality Praha-Šárka, Middle Ordovician (Darriwilian), (A) Enrolled trilobite coated with ammonium chloride, exterior of objects. (B) Micro-CT image showing dense burrows, interior of objects. (C, D) Rostrum with eyes of a trilobite P. binodosa, no. NMP L46892, locality Praha-Šárka, Middle Ordovician (Darriwilian). (C) Rostrum coated with ammonium chloride, exterior of objects. (D) Micro-CT visualization of tunnels, interior of objects. (E) Bivalve Redonia deshayesi, micro-CT image showing trace fossils, interior of objects, no. NMP L 51722, locality Osek, Middle Ordovician (Darriwilian). All scale bars equal 5 mm.

FIGURE 2 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms

FIGURE 2. Custom-made holders specially adapted for each scanned specimen. (A) Plastic cup. (B) Polystyrene holder. (C) Aluminum holder for small specimens. (D) Plastic tube filled with polystyrene.

FIGURE 1 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms

FIGURE 1. (A) Single x-ray projection. Schematic representation of positioning of the investigated object inside x-ray device. (B) Multiple x-ray projections as the object rotates. Positioning of investigated object inside micro-CT device. (C) Example of 3D dataset, i.e., a group of 2D slice images acquired by the MicroCT scanner. (D) Examples of Volume rendering; technique in visualization and computer graphics, used to display object from 3D data set in different aspects and orientations.

FIGURE 6 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms

FIGURE 6. Tube fragments of the serpulid polychaete Pyrgopolon (Pyrgopolon) deforme. Left images show exterior of objects; right images show interior of objects. (A) Specimen encrusted with bryozoan colonies and serpulid worms, boreholes assigned to Entobia Bronn, 1837, representing the most common ichnogenus in the examined serpulid tubes, no. MHNLM EMV 2016.3.14. (B) Intensely bored specimen preserving tunnels of ichnogenera Entobia and Trypanites Mägdefrau, 1932, no. MHNLM EMV 2016.3.44. (C) Serpulid tube with Entobia boreholes and encrusting juvenile oyster, no. MHNLM EMV 2016.3.40. Scale bar equals 1 cm.

opencc-by-4.0Apr 2024View details →

Computer simulations of photorelaxation dynamics of fluorazene

Description This dataset contains the results of nonadiabatic molecular dynamics (NAMD) simulations of the photorelaxation dynamics of fluorazene in acetonitrile solution. The dilute solution phase is modeled as a single fluorazene molecule at the center of a spherical 500-molecule acetonitrile nanodroplet. The nuclear wavepacket is represented by an ensemble of 60 trajectories, which are numbered trajectory_0001.xyz to trajectory_0060.xyz Each trajectory is formatted as a standard XYZ file, and it can be viewed with molecular editing software such as Jmol, GDIS, or VMD. The atomic coordinates are given in units of Ångström. The frames are written at intervals of 10 fs. Each trajectory lasts 1.5 ps. Each trajectory is accompanied by a CSV file (trajectory_0001.csv etc.) which contains information on the state energies during the given trajectory. The first line is a header: "t(fs),E0(Eh),E1(Eh),E2(Eh),E3(Eh),E4(Eh),Eocc(Eh),Etot(Eh)". The subsequent lines give the time t (in units of fs), the energies of states S0 to S4 in units of Eh (hartree), the energy of the occupied state at time t, and the total energy at time t. (Total energy is not perfectly conserved.) Acknowledgement This research was supported by the  Alexander von Humboldt Foundation, and by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 847413.

opencc-by-4.0Nov 2024View details →

quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data

This page contains the code and datasets used in "quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data". File test_datasets.zip containes three datasets: <ul> <li>D1.RData: scRNA-seq omics data derived from Salcher et. al (2022)</li> <li>D2.RData: scRNA-seq omics data derived from Pineda et al. (2024)</li> <li>D3.RData: in silico WGS SNP data.</li> </ul> File test_scripts.zip containes the code to reproduce the results.

opencc-by-4.0Sep 2024View details →

Data analysis Protocol for a Joint Study into the Impacts of AI on professional Competencies of IT Professionals and Implications for Computing Students. ITiCSE 2024 Working Group 02.

<h1><a name="_Toc169648661"></a>Overview</h1>   The purpose of this protocol is to help us define a common protocol for sharing and analysing data for the ITiCSE 2024 working group: “WG02: A Multi-Institutional-Multi-National Study into the Impacts of AI on Work Practices of IT Professionals and Implications for Computing Students”.  Excerpts from the working group plan to place the protocol in context (Clear et al., 2024) are given below.   Background and Related Work As Artificial Intelligence (AI) continues to make its presence felt in transforming workplaces around the world [1,10], and the Information Technology industry in particular, it is essential to understand its impact on the work practices of IT professionals, and the implications for computing students and curricula. This research project builds on work initiated jointly, in Sweden, New Zealand and Scotland, investigating concerns about the increasing impacts of Artificial Intelligence in IT Sector workplaces for employee work engagement [11,13,1] and the implications for tertiary study, assessment and curricula in computing [4, 8, 10, 9].  “Work engagement”, has been defined as the positive inner state where employees are fully present and engaged in their work, and is closely linked to motivation, learning, productivity, and accountability [11, 13]. Within the context of (Generative) AI at work, IT professionals have been noted as early adopters of AI [10, 1]. Their involvement in implementing and utilising AI technologies can provide valuable insights into the interplay between AI and work engagement.  The implications for students are significant as future IT professionals, who must acquire and enhance competencies to adapt and thrive in digital workplaces.   2           Goals of the Working Group By exploring the relationship between work engagement and learning, this study aims to shed light on the dynamics that drive employee engagement and its connection to the professional development of competencies. The previous study has interviewed IT professionals with the following research questions (RQ):   RQ1: How does AI influence work engagement for IT professionals? RQ2: How does AI affect the socio-technical work dynamics for IT professionals? RQ3: What are the implications of integrating AI on the acquisition and enhancement of professional competencies and the learning processes of IT professionals?   3           Methodology This working group aims to analyse the corpus of interview data collected from multiple countries to better understand the implications for computing students, tertiary computing education curricula and assessment of the new professional competencies emerging from this work. This study informed by the literature on work engagement, automation and motivation for IT professionals [11, 13], will use a combination of multi-vocal literature review [7] and qualitative research methods [2, 5], including thematic analysis of the interviews, to investigate the state of the practice in and challenges IT Professionals face within their local/global work contexts. The literature on professional competencies in computing [4, 3, 6] will be drawn upon to characterise the new needs identified in this analysis.  Further implications for computing curricula design and assessment will be developed from this analysis. REFERENCES [1]    ACM Technology Policy Council. 2023. Principles for the development, deployment, and use of generative AI technologies, ACM New York. [2]    Braun, V. and Clarke, V. 2021. One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative research in psychology, 18 (3). 328-352. [3]    Clear, A., Clear, T., Vichare, A., Charles, T., Frezza, S., Gutica, M., Lunt, B., Maiorana, F., Pears, A. and Pitt, F. 2020. Designing Computer Science  Competency Statements: A Process and Curriculum Model for the 21st Century in Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education, ACM, New York. [4]    Clear, A., Parrish, A. and CC2020 Task Force. 2020. Computing Curricula 2020 - CC2020 - Paradigms for Future Computing Curricula ACM and IEEE-CS eds. A Computing Curricula Series Report ACM, New York. [5]    Cruzes, D.S. and Dyba, T. 2011. Recommended steps for thematic synthesis in software engineering. in 2011 international symposium on empirical software engineering and measurement, IEEE, 2011, 275-284. [6]    Frezza, S., Clear, T. and Clear, A. 2020. Unpacking Dispositions in the CC2020 Computing Curriculum Overview Report in 2020 IEEE Frontiers in Education Conference (FIE), IEEE, Uppsala, Sweden. [7]    Garousi, V., Felderer, M., & Mäntylä, M. V. 2019. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. Information and Software Technology, 106. 101-121 [8]    Jacques, L. 2023. Teaching CS-101 at the Dawn of ChatGPT. ACM Inroads, 14 (2). 40-46. [9]    Liffiton, M., Sheese, B., Savelka, J. and Denny, P. 2023. CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes. arXiv preprint arXiv:2308.06921. [10]  Prather, J., Denny, P., Leinonen, J., Becker, B.A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T. and Luxton-Reilly, A. 2023. The robots are here: Navigating the generative ai revolution in computing education. arXiv preprint arXiv:2310.00658. [11]  Roto, V., Palanque, P. and Karvonen, H., 2019. Engaging automation at work–a literature review. in Human Work Interaction Design. Designing Engaging Automation: 5th IFIP WG 13.6 Working Conference, HWID 2018, Espoo, Finland, August 20-21, 2018, Revised Selected Papers 5, Springer, 158-172. [12]  SFIA Foundation. 2023. SFIA skills aligned to EU ICT Profiles, SFIA Institute, London. [13]  Sharp, H., Baddoo, N., Beecham, S., Hall, T. and Robinson, H. 2009. Models of motivation in software engineering. Information and software technology, 51 (1). 219-233.

opencc-by-4.0Nov 2024View details →

Supplementary data frames, AlphaFold models, Normal Mode Analysis (NMA) Data, and NMA of Corresponding NMR Ensembles in the S2RCI, MD, and S2 Datasets for "Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics"

<h1>Changes applied to V2</h1> In addition to the supplementary dataframes and AlphaFold models from each dataset in V1, V2 includes the additional data outlined below. The S2RCI and MD datasets include comprehensive analyses of AlphaFold2 models (both before and after truncation). These datasets feature:   <ul> <li>AlphaFold2 Models: Both original and truncated structures.  </li> <li>WEBnma Modes: `modes.txt` files generated from WEBnma analysis, available for both non-truncated and truncated AF2 models.  </li> <li>Root-Mean-Square-Fluctuations (RMSF): Profiles calculated before and after truncation of AF2 models.  </li> <li>NMR Data: Normal Mode Analysis (NMA): Performed on corresponding NMR ensembles (see below).  </li> </ul>   The NMR Data of NMA in these datasets includes:   <ul> <li>NMR ensembles  </li> <li>Individual NMR models extracted from each ensemble  </li> <li>STRIDE secondary structure calculations per-individual NMR models</li> <li>RMSF profiles per-individual NMR models</li> </ul> For detailed information, please refer to the `Readme.txt` file within each corresponding folder.   The S2 dataset includes all the features listed above, except for the NMR analysis.