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opencc-by-4.0Apr 2024View details →

Dataset for the article 'Beyond Human Perception: Challenges in AI Interpretability of Orangutan Artwork'

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2024View details →

Thread-Modular Abstract Interpretation: The Local Perspective

<div> <h1>Thread-Modular Abstract Interpretation: The Local Perspective</h1> <div>This artifact contains the software and data necessary to reproduce the results described in the chapter entitled Experimental Evaluation of the manuscript Thread-Modular Abstract Interpretation: The Local Perspective by Michael Schwarz.</div> <div>It is structured as follows:</div> <div>- The folder analyzer contains the Goblint analyzer in the version used for the experiments (Branch michael-schwarz-dissertation).</div> <div>- The folder bench contains the Goblint benchmark repository in the version used for the experiments which also contains scripts to run the experiments (Branch michael-schwarz-dissertation).</div> <div>- The archive bench_raw.tar.xz contains the raw benchmark results.</div> <div>- The folder data contains spreadsheets, CSV files, and scripts necessary to extract relevant information from the raw benchmark results.</div> <div>This artifact is unfortunately documented relatively poorly at the moment. A good starting point for understanding the various scripts etc. are our previous artifacts on which this evaluation builds and their rather comprehensive READMEs.</div> <div>- ESOP '23: https://zenodo.org/records/7767778</div> <div>- SAS '21: https://zenodo.org/records/5148608</div> <div>If you want to reproduce these results but have trouble getting started, you are very much encouraged to bother me, e.g., by emailing me at `m DOT schwarz AT tum DOT de`</div> <div>The contents of `analyzer` and `bench` all are published under the respective licenses mentioned in folders or individual files.</div> </div>

opencc-zeroNov 2021View details →

The precautionary principle and dietary DNA metabarcoding: commonly used abundance thresholds change ecological interpretation

Dietary DNA metabarcoding enables researchers to identify and characterize trophic interactions with a high degree of taxonomic precision. It is also sensitive to sources of bias and contamination in the field and lab. One of the earliest and most common strategies for dealing with such sensitivities has been to filter resulting sequence data to remove low-abundance sequences before conducting ecological analyses based on the presence or absence of food taxa. Although this step is now often perceived to be both necessary and sufficient for cleaning up datasets, evidence to support this perception is lacking and more attention needs to be paid to the related risk of introducing other undesirable errors. Using computer simulations, we demonstrate that common strategies to remove low-abundance sequences can erroneously eliminate true dietary sequences in ways that impact downstream dietary inferences. Using real data from well-studied wildlife populations in Yellowstone National Park, we further show how these strategies can markedly alter the composition of individual dietary profiles in ways that scale-up to obscure ecological interpretations about dietary generalism, specialism, and niche partitioning. Although the practice of removing low-abundance sequences may continue to be a useful strategy to address a subset of research questions that focus on a subset of relatively abundant food resources, its continued widespread use risks generating misleading perceptions about the structure of trophic networks. Researchers working with dietary DNA metabarcoding data—or similar data such as environmental DNA, microbiomes, or pathobiomes—should be aware of potential drawbacks and consider alternative bioinformatic, experimental, and statistical solutions.

opennotspecifiedDec 2021View details →

FIGURE 3. Jurodes ignoramus Ponomarenko, 1985 interpretative line drawings. A and B, Holotype. C and D, Paratype. E–G in Jurodidae (Coleoptera: Archostemata) from Transbaikalia: new findings and redescription of type material

FIGURE 3. Jurodes ignoramus Ponomarenko, 1985 interpretative line drawings. A and B, Holotype. C and D, Paratype. E–G, Original line drawings of the holotype from Ponomarenko, 1985 with minor changes. H, Details of holotype's head. I, Details of holotype's pronotum. K and J, Specimens from Khasurty locality. Abbreviations: alc, alacrista; an.cl, anapleural cleft; ant.g, antennal groove; ant.s, antennal socket; c.e, compound eye; clp, clypeus; cx3, metacoxae; em3, metepimeron; epl, epipleuron; eps2, mesanepisternum; es3, metanepisternum; fm1,2,3, pro-, meso-, metafemur; gen, genae; lbr, labrum; lts, laterosternite; md.p, pronotal median protuberance; P2, supraocular protuberance; P3, posteromesal protuberance; P4, posterolateral protuberance; po.e, postocular extension; ppl, propleuron; SIII, third abdominal sternite; sc2, mesoscutum; scl2, mesoscutellum; s.prc, paracoxal suture; TIX-X, abdominal tergites; tb1, protibia; tr1,2,3, pro- meso-, metatrochantin; trc1, protrochanter; v2, mesoventrite. All scale bars are equal to 1 mm.

opennotspecifiedDec 2021View details →

FIGURE 5. Jurodes minor Ponomarenko, 1990 interpretative line drawings. A and B, Holotype. C and D in Jurodidae (Coleoptera: Archostemata) from Transbaikalia: new findings and redescription of type material

FIGURE 5. Jurodes minor Ponomarenko, 1990 interpretative line drawings. A and B, Holotype. C and D, Original line drawings of the holotype from Ponomarenko, 1990 with minor changes. E, Details of paratype's PIN, 3063/115 head. F–H, Paratypes PIN, 3063/115, 3063/1770 parts and counterparts, hindwings. I, Details of paratype's PIN, 3015/372 metathorax and abdomen. J, Paratype PIN, 3063/115. K, Paratype PIN, 3063/1262, metaventrite. L, Paratype PIN, 3015/365. Abbreviations: 11, apical antennomere; AA, anal vein; aed, aedeagus; alc, alacrista; an.cl, anapleural cleft; ant, antennae; ant.g, antennal groove; apm, apical membrane; C+ScA, costa fused with subcosta anterior; clp, clypeus; CuA, cubitus anterior; CuA2, cubitus anterior, second branch; cx3, metacoxae; em3, metepimeron; es3, metanepisternum; lbr, labrum; md.p, median protuberance; mpn3, metapostnotum; msi, median scutal impression; o, oblongum cell; P3, posteromesal protuberance; P4, posterolateral protuberance; po.e, postocular extension; RA, radius anterior; rc, radial cell; sc2, 3, meso- and metascutum; scl2, mesoscutellum; ScP, subcostal posterior; s.prc, paracoxal sutures; s.scl3, metathoracic scutoscutellar suture; TI-VIII, abdominal tergites; tb2, mesotibia; tr3, metatrochantin; trh3, metatrochanter. Figures G and H are given with the single scale bar. Scale bars = 1 mm.

opencc-zeroFeb 2022View details →

Code for: Comparison and interpretability of machine learning models to predict severity of chest injury

Objective: Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at non-trauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting. Materials and Methods: A level I trauma center was queried for patients presenting after injury between 2014 and 2018. Prediction modeling was performed to classify severe chest injury using a reference dataset labeled by certified registrars. Clinical documents from trauma encounters were processed into concept unique identifiers for inputs to ML models: logistic regression with elastic net regularization (EN), extreme gradient boosted machines (XGB), and convolutional neural networks (CNN). The optimal model was identified by examining predictive and face validity metrics using global explanations. Results: Of 8,952 encounters, 542 (6.1%) had a severe chest injury. CNN and EN had the highest discrimination, with an area under the receiver operating characteristic curve of 0.93 and calibration slopes between 0.88 and 0.97. CNN had better performance across risk thresholds with fewer discordant cases. Examination of global explanations demonstrated the CNN model had better face validity, with top features including "contusion of lung" and "hemopneumothorax." Discussion: The CNN model featured optimal discrimination, calibration, and clinically relevant features selected. Conclusion: NLP and ML methods to populate trauma registries for quality analyses are feasible.

opencc-by-4.0Mar 2022View details →

Data for "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection"

Once decompressed, the file contains a folder which contains: <ul> <li>The files "s100_Nth.fasta" (where "N" is 5, 6, 7 or 8), which are the output of the SELEX experiment described in the paper with DOI: <a href="https://doi.org/10.1002/cbic.201900265">10.1002/cbic.201900265</a>. They are standard fasta files, and the descriptor of each sequence is of the form "seqX-Y", where "X" is an increasing label, and "Y" is the number of times "seqX" has been obtained (number of counts of "seqX").</li> <li>The file "Aptamer_Exp_Results.csv", which contains the sequences tested experimentally for the paper "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection" (preprint available at https://doi.org/10.1101/2022.03.12.484094), with the following experimental results for each sequence: (i) whether the sequence was able to bind thrombin ('B' for binders, 'NB' for non-binders); (ii) the thrombin exosite used for binding ('I' for exosite I, 'II' for exosite II, 'n/a' for sequences not tested).</li> </ul> Examples of usage of the data are available at https://github.com/adigioacchino/RBMsForAptamers.

opencc-by-4.0Mar 2022View details →

Codes and results for self-learning entropic population annealing for interpretable materials design

Codes that can reproduce the results in the paper entitled "self-learning entropic population annealing for interpretable materials design". Results, when the number of particles is 50, are included.

opencc-zeroApr 2022View details →

Patch-clamp dataset (SyncroPatch384PE) for control variants used in a calibrated functional patch clamp assay to enhance clinical variant interpretation in KCNH2-related long QT syndrome

Modern sequencing technologies have revolutionised our detection of gene variants. However, in most genes, including KCNH2, the majority of missense variants are currently classified as variants of uncertain significance (VUS). The aim of this study was to investigate the utility of an automated patch-clamp assay for aiding clinical variant classification in the KCNH2 gene. The assay was designed according to recommendations proposed by the ClinGen Sequence Variant Interpretation Working Group. Thirty-one variants (17 pathogenic/likely pathogenic, 14 benign/likely benign) were classified internally as variant controls. They were heterozygously expressed in Flp-In HEK293 cells for assessing the effects of variant on current density and channel gating in order to determine the sensitive and specificity of the assay. All 17 pathogenic variant controls had reduced current density and 13/14 benign variant controls had normal current density, which enabled determination of normal and abnormal ranges for applying moderate or supporting evidence strength for VUS reclassification. Inclusion of KCNH2 functional assay evidence enabled us to reclassify 6 out of 44 VUS as likely pathogenic. The high-throughput patch clamp assay can provide moderate strength evidence for clinical interpretation of clinical KCNH2 variants and demonstrates the value of developing automated patch clamp assays for functional characterisation of ion channel gene variants.

opencc-by-4.0May 2022View details →

Data underlying the study on "Grammatical and contextual factors affecting the interpretation of superordinate collectives in child and adult Mandarin"

Dataset underlying the study on  "Grammatical and contextual factors affecting the interpretation of superordinate collectives in child and adult Mandarin", published in Linguistics

opencc-zeroJun 2022View details →

Medical data formatting to improve physician interpretation speed in the military healthcare system

Objective: The purpose of this project was to improve the ease and speed of physician comprehension when interpreting daily laboratory data for patients admitted within the Military Healthcare System (MHS). Materials and Methods: A JavaScript program was created to convert the laboratory data obtained via the outpatient electronic medical record (EMR) into a "fishbone diagram" format that is familiar to most physicians. Using a balanced crossover design, 35 internal medicine trainees and staff were asked to complete timed comprehension tests for laboratory data sets formatted in the outpatient EMR's format and in fishbone diagram format. The number of responses per second and error rate per response were measured for each format. Participants were asked to rate relative ease of use for each format and indicate which format they preferred. Results: Comprehension speed increased 37% (6.28 seconds per interpretation) with the fishbone diagram format with no observed increase in errors. Using a Likert scale of 1 to 5 (1 being hard, 5 easy), participants indicated the new format was easier to use (4.14 for fishbone vs 2.14 for table) with 89% expressing a preference for the new format. Discussion: The publically available web application that converts tabular lab data to fishbone diagram format is currently used 10,000-12,000 times per month across the MHS, delivering significant benefit to the enterprise in terms of time saved and improved physician experience. Conclusions: This study supports the use of fishbone diagram formatting for laboratory data for inpatients within the MHS.

opencc-zeroDec 2014View details →

Data from: Interpreting the FLOCK algorithm from a statistical perspective

We show that the algorithm in the program FLOCK (Duchesne & Turgeon 2009) can be interpreted as an estimation procedure based on a model essentially identical to the STRUCTURE (Pritchard et al. 2000) model with no admixture and non-correlated allele frequency priors. Rather than using MCMC, the FLOCK algorithm searches for the maximum-a-posteriori estimate of this STRUCTURE model via a simulated annealing algorithm with a rapid cooling schedule (namely, the exponent on the objective function --> ∞). We demonstrate the similarities between the two programs in a two step approach. First, to enable rapid batch processing of many simulated data sets, we modified the source code of STRUCTURE to use the FLOCK algorithm, producing the program FLOCKTURE. With simulated data we confirmed that results obtained with FLOCK and FLOCKTURE are very similar (though ockture is some 200 times faster). Second, we simulated multiple large data sets under varying levels of population differentiation for both microsatellite and SNP genotypes. We analyzed them with FLOCKTURE and STRUCTURE and assessed each program on its ability to cluster individuals to their correct subpopulation. We show that FLOCKTURE yields results similar to STRUCTURE albeit with greater variability from run to run. FLOCKTURE did perform better than STRUCTURE when genotypes were composed of SNPs and differentiation was moderate (FST = 0.022 - 0.032). When differentiation was low, STRUCTURE outperformed FLOCKTURE for both marker types. On large data sets like those we simulated, it appears that FLOCK's reliance on inference rules regarding its "plateau record" are not helpful. Interpreting FLOCK's algorithm as a special case of the model in STRUCTURE should aid in understanding the program's output and behavior.

opencc-by-4.0Aug 2022View details →

Effects of topicality in the Interpretation of implicit consequentiality: Evidence from offline and online referential processing in Korean

This supplementary material contains data files and R scripts that accompany the article to be published in Linguistics (volume 61, 2023).

Figure 10 in The first cave associated genus of Berothidae (Insecta: Neuroptera), and a new interpretation of the subfamily Cyrenoberothinae

Figure 10. Male genitalia of Speleoberotha mineira sp. nov. (Berothidae: Cyrenoberothinae). A, B, ventral views. C, dorsal view. D, genital complex, dorsal view. Abbreviations: ect, ectoproct; gst10, tenth gonostyli; gx9–gx11, ninth–eleventh gonocoxites; S8, S9, eighth and ninth sternites; T8, T9, eighth and ninth tergites.

Figure 6 in The first cave associated genus of Berothidae (Insecta: Neuroptera), and a new interpretation of the subfamily Cyrenoberothinae

Figure 6. Male genitalia of Speleoberotha palomae sp. nov. (Berothidae: Cyrenoberothinae). A, B, ventral views. C, caudal view. D, genital complex, ventral view. E, genital complex, caudal view. Abbreviations: ect, ectoproct; gph10, tenth gonapophyses; gst10, tenth gonostyli; gx9–gx11, ninth–eleventh gonocoxites; S9, ninth sternite; T9, ninth tergite.

Figure 5 in The first cave associated genus of Berothidae (Insecta: Neuroptera), and a new interpretation of the subfamily Cyrenoberothinae

Figure 5. Male genitalia of Speleoberotha palomae sp. nov. (Berothidae: Cyrenoberothinae). A, B, lateral views. C, oblique view. D, E, genital complex, oblique view. Abbreviations: ect, ectoproct; gph10, tenth gonapophyses; gst10, tenth gonostyli; gx9–gx11, ninth–eleventh gonocoxites; S8, S9, eighth and ninth sternites; T8, T9, eighth and ninth tergites.

Figure 4 in The first cave associated genus of Berothidae (Insecta: Neuroptera), and a new interpretation of the subfamily Cyrenoberothinae

Figure 4. Speleoberotha palomae sp. nov. (Berothidae: Cyrenoberothinae). A, habitus, lateral. B, wings. C, head, frontal view. D, head and thorax, dorsal view. Abbreviations: A1, A2, anal veins; CuA, cubitus anterior; CuP, cubitus posterior; MA, media anterior; MP, media posterior; RA, radius anterior; RP, radius posterior; Sc, subcostal; 1r-m, first radial–medial crossvein.

Figure 9 in The first cave associated genus of Berothidae (Insecta: Neuroptera), and a new interpretation of the subfamily Cyrenoberothinae

Figure 9. Male genitalia of Speleoberotha mineira sp. nov. (Berothidae: Cyrenoberothinae). A, B, lateral views. C, caudal view. D, genital complex, lateral view. E, genital complex, caudal view. Abbreviations: ect, ectoproct; gst10, tenth gonostyli; gx9–gx11, ninth–eleventh gonocoxites; S8, S9, eighth and ninth sternites; T8, T9, eighth and ninth tergites.

Figure 1 in The first cave associated genus of Berothidae (Insecta: Neuroptera), and a new interpretation of the subfamily Cyrenoberothinae

Figure 1. Phylogenetic relationship within Berothidae (Insecta: Neuroptera) (after Aspöck & Randolf, 2014). Most parsimonious and unique tree (number of steps = 217, consistency index = 32, retention index = 59) under implied weight analysis using values of k = 9–15. Character changes are marked on branches: red above = character numbers; and black below = character state. Forward unequivocal changes (black circles) and reversed or multiple changes (white circles) are shown. Bremer values of branch support are presented below nodes in blue. Names for taxa that contain only extinct species are preceded by a dagger (†) (Supporting Information, Table S1).

Figure 3 in The first cave associated genus of Berothidae (Insecta: Neuroptera), and a new interpretation of the subfamily Cyrenoberothinae

Figure 3. Distribution of Cyrenoberothinae (Neuroptera: Berothidae). A, world map showing the distribution of all genera included here in Cyrenoberothinae. B, map of South America showing the records of Speleoberotha. C, maps showing in detail the three Brazilian states where Speleoberotha were collected: Ceará, Minas Gerais and Pernambuco.