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Figure 4 in Relationships of the haematophagous marine snail Colubraria (Rachiglossa: Colubrariidae), within the neogastropod phylogenetic framework
Figure 4. Reproductive system of Colubraria muricata. A, a male with the body wall dissected dorsally. B, distal penis with papilla penialis of Colubraria tenera. C, distal penis with papilla penialis of Colubraria reticulata. D, distal portion of the spermiduct. E, semischematic drawings of the male reproductive system in C. muricata. F, semischematic drawings of the male reproductive system in some neogastropods [e.g. Nucella canaliculata (Duclos, 1832) or Anachis lyrata (Sowerby, 1832); modified after Houston (1976) and deMaintenon (1999)]. H, semischematic drawings of the female reproductive system in some neogastropods [e.g. Buccinum undatum Linnaeus, 1758, or Nucella emarginata (Deshayes, 1839); modifiend after Houston (1976) and deMaintenon (1999)]. I, semischematic drawings of the female reproductive system in C. muricata. Abbreviations: ag, albumen gland; bc, bursa copulatrix; cg, capsule gland; fgo, female genital opening; gpcd, gonopericardial duct; ig, ingesting gland; ki, kidney; ov, ovarium; pc, pericardium; pe, penis; ped, penial duct pp, papilla penialis; pro, prostatic opening in the mantle cavity; prs, prostate; te, testis; vd, vas deferens; ve, vestibulum. Scale bars: A, 1 cm; D, G, 1 mm.
Figure 1 in Relationships of the haematophagous marine snail Colubraria (Rachiglossa: Colubrariidae), within the neogastropod phylogenetic framework
Figure 1. Colubraria muricata (Lightfoot, 1786), the type species of the genus. A, Colubraria muricata feeding on a Siganus sp. at Santo Island (Palliculo Bay; depth, 11 m; photo S. Schiaparelli). B, the cephalic region of C. muricata from Santo Island. C, shells of C. muricata from the Philippines (photo G. & P. Poppe).
Figure 3 in Relationships of the haematophagous marine snail Colubraria (Rachiglossa: Colubrariidae), within the neogastropod phylogenetic framework
Figure 3. Digestive system of Colubraria muricata. A, radula. B, radular central tooth. C, the foregut from the proboscis tip to the mid-posterior oesophagus. D, the dissected stomach. E, longitudinal section of the proboscis tip, showing the buccal mass. F, transversal section of the proboscis. G, enlargement of the anterior oesophagus of F. H, transversal section of the anterior oesophagus at the base of the proboscis. Abbreviations: aoe, anterior oesophagus; bm, buccal mass; in, intestine; lsg, left salivary gland; mo, mouth; mpoe, mid-posterior oesophagus; nf, nerve fibre; nr, nerve ring; od, odontophore; pa, proboscideal artery; pr, proboscis; prr, proboscis retractor muscle; pw, proboscis wall; ra, radula; re, rectum; rsg, right salivary gland; sd, salivary gland duct; stl, stomach lumen. Scale bars: A, 50 mm; B, 25 mm; C, D, 10 mm; E–H, 100 mm.
Figure 2 in Relationships of the haematophagous marine snail Colubraria (Rachiglossa: Colubrariidae), within the neogastropod phylogenetic framework
Figure 2. Mantle organs of a dissected male specimen of Colubraria muricata. A, mantle dissected medially. B, body wall dissected dorsally. Abbreviations: an, anus; ct, ctenidium; dg, digestive gland; fo, foot; go, gonad; hyg, hypobranchial gland; lsg, left salivary gland; mtl, mantle; os, osphradium; pe, penis; pr, proboscis; re, rectum; ry, rhynchodaeum; st, stomach. Scale bar: 1 cm.
Extension of Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks
<p>This provides the dataset and the corresponding parsing scripts that extend the "Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks" study.</p>
A unified DTI prediction framework based on knowledge graph and recommendation system
<p>## A unified DTI prediction framework based on knowledge graph and recommendation system</p> <p> </p> <p># Code and data description</p> <p>## Scripts</p> <p>- `kge_nfm.py`: the complement of the KGE_NFM & NFM methods.</p> <p>- `kge_rf.py`: the complement of the KGE_RF & RF methods.</p> <p>- `deepdit.py`: the complement of the MPNN_CNN & DeepDTI methods.</p> <p>- the complement of DTINet and DTiGEMS is tested based on their source packages (more in Prerequisites)</p> <p><br> </p> <p>## `data/` directory</p> <p>#### `yamanishi_08/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_1/`</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg_data/`: supporting knowledge graph data</p> <p>- `dt_all_08.csv`: whole DTI dataset</p> <p>- `791drug_struc.csv`: drugbank id and smiles of drugs</p> <p>- `989proseq.csv`: kegg id and sequences of proteins</p> <p>- `morganfp.txt`: list of drug morgan fingerprints</p> <p>- `pro_ctd.txt`: list of protein descriptors</p> <p> </p> <p>#### `BioKG/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg.csv`: supporting knowledge graph data</p> <p>- `dti.csv`: whole DTI dataset</p> <p>- `comp_struc.csv`: drugbank id and smiles of drugs</p> <p>- `pro_seq.csv`: sequences of proteins</p> <p>- `fp_df.csv`: list of drug morgan fingerprints</p> <p>- `prodes_df.csv`: list of protein descriptors</p> <p> </p> <p>#### `hetionet/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg.csv`: supporting knowledge graph data</p> <p>- `dti.csv`: whole DTI dataset</p> <p>- `map_drugs_df`: drugbank id and smiles of drugs</p> <p>- `pro_seq.csv`: sequences of proteins</p> <p>- `fp_df.csv`: list of drug morgan fingerprints</p> <p>- `prodes_df.csv`: list of protein descriptors</p> <p> </p> <p>#### `luo's_dataset/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_1/`</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `mapping/`: related mappings and similarity matrix (https://github.com/luoyunan/DTINet)</p> <p>- `protein.txt`: list of protein names</p> <p>- `disease.txt`: list of disease names</p> <p>- `se.txt`: list of side effect names</p> <p>- `drug_dict_map`: a complete ID mapping between drug names and DrugBank ID</p> <p>- `protein_dict_map`: a complete ID mapping between protein names and UniProt ID</p> <p>- `Similarity_Matrix_Drugs.txt` : Drug similarity scores based on chemical structures of drugs</p> <p>- `Similarity_Matrix_Proteins.txt` : Protein similarity scores based on primary sequences of proteins</p> <p>- `feature/`: related features used in methods</p> <p>- `drug_smiles.csv`: drugbank id and smiles</p> <p>- `seq.txt`: list of protein sequences</p> <p>- `morganfp.txt`: list of drug morgan fingerprints</p> <p>- `pro_ctd.txt`: list of protein descriptors</p> <p> </p> <p>#### `eg_model/` directory</p> <p>We provided a pre-trained kge model for example.</p> <p>- `dismult_400_warm_1_10.pkl`</p> <p><br> </p> <p># Prerequisites</p> <p>#### Operating system: Linux</p> <p>#### Programing language: python</p> <p>#### KGE_NFM & NFM dependencies</p> <p>```</p> <p>- python 3.6</p> <p>- pandas '1.1.5'</p> <p>- numpy '1.18.4'</p> <p>- scikit-learn '0.24.1'</p> <p>- tensorflow '1.15.0'</p> <p>- ampligraph '1.3.2'</p> <p>- deepctr '0.8.4'</p> <p>```</p> <p>#### baseline dependencies</p> <p>- RF & KGE_RF (included in KGE_NFM&NFM dependencies)</p> <p>- MPNN_CNN & DeepDTI:</p> <p>- source: https://github.com/kexinhuang12345/DeepPurpose</p> <p>```</p> <p>- deeppurpose '0.0.9'</p> <p>- torch '1.6.0+cu101'</p> <p>```</p> <p>- DTINet:</p> <p>- source: https://github.com/luoyunan/DTINet</p> <p>- note: in this work, we run the DTINet in a python environment, which need Linux system and python2. Importantly, this method requires the [Inductive Matrix Completion](http://bigdata.ices.utexas.edu/software/inductive-matrix-completion/) (IMC) library. More detailed information about the installation of this method could be found in the source code of the DTINet.</p> <p>- DTiGEMS:</p> <p>- source: https://github.com/MahaThafar/DTiGEMSplus</p> <p>- TriModel:</p> <p>- source: http://drugtargets.insight-centre.org/</p> <p><br> <br> </p> <p># Example (kge_nfm.py)</p> <p> </p> <p>#### A brief presentation of the results:</p> <p>- return average loss when training kge model</p> <p>```</p> <p>Average Loss: 0.475181: 2%|###3 | 1/50 [01:10<57:31, 70.44s/epoch]</p> <p>```</p> <p>- return performance(mrr) on training set of DTI for early stopping (kge_model in `eg_model/`)</p> <p>```</p> <p>In [35]: roc = roc_auc(test_label,test_score)</p> <p>...: pr = pr_auc(test_label,test_score)</p> <p>...: print(roc)</p> <p>...: print(pr)</p> <p>0.8731770833333332</p> <p>0.44079654835037246</p> <p>```</p> <p> </p> <p>- nfm training process (`patience=10`)</p> <p> </p> <p>```</p> <p>In [45]: roc_nfm,pr_nfm,pred_y = train_nfm(feature_columns,train_model_input,train_label,test_model_input,test_label,patience)</p> <p>Train on 44851 samples</p> <p>Epoch 1/2000</p> <p>44851/44851 - 2s - loss: 0.5332 - precision: 0.0976</p> <p>Epoch 2/2000</p> <p>44851/44851 - 1s - loss: 0.4143 - precision: 0.0000e+00</p> <p>Epoch 3/2000</p> <p>44851/44851 - 1s - loss: 0.3456 - precision: 0.0000e+00</p> <p>Epoch 4/2000</p> <p>44851/44851 - 1s - loss: 0.3443 - precision: 0.0000e+00</p> <p>Epoch 5/2000</p> <p>44851/44851 - 1s - loss: 0.3470 - precision: 0.0000e+00</p> <p>Epoch 6/2000</p> <p>44851/44851 - 1s - loss: 0.3382 - precision: 0.0000e+00</p> <p>......</p> <p>Epoch 279/2000</p> <p>44851/44851 - 1s - loss: 0.0758 - precision: 0.9248</p> <p>Epoch 280/2000</p> <p>44851/44851 - 1s - loss: 0.0753 - precision: 0.9327</p> <p>Epoch 281/2000</p> <p>44851/44851 - 1s - loss: 0.0796 - precision: 0.9155</p> <p>Epoch 282/2000</p> <p>44851/44851 - 1s - loss: 0.0764 - precision: 0.9276</p> <p>Epoch 283/2000</p> <p>44851/44851 - 1s - loss: 0.0739 - precision: 0.9127</p> <p>```</p> <p> </p> <p>- reutrn results as type of roc_auc & pr_auc</p> <p>```</p> <p>0.9812476679104477</p> <p>0.8803416284646345</p> <p>```</p>
From Sintering to Particle Discrimination: New Opportunities in Metal–Organic Frameworks Scintillators
<p>Dataset from publication</p>
A framework for the quantification of soundscape diversity using Hill numbers
<p>This is the data underlying the case study and supplementary material described in Luypaert et al. (2021): A framework for the quantification of soundscape diversity using Hill numbers. </p>
Integrating venom peptide libraries into a phylogenetic and broader biological framework
<p>The venomous marine snails are conventionally divided into three groups, the cone snails (family Conidae), the auger snails (family Terebridae) and the turrids (formerly all assigned to a single family, Turridae). In this study, a library of venom peptides from species conventionally assigned to the genus Turris was correlated to a phylogenetic analysis. Nucleotide sequences of multiple genes from transcriptomes were used to assess the phylogenic relationships across a diverse set of species. The resulting tree shows that as conventionally defined, the Conoidean genus Turris, is polyphyletic. We describe a new genus, Purpuraturris gen. nov., that comprises the outlier species. In addition to morphological distinctions, molecular data reveal that this group are more closely related to Unedogemmula and Turridrupa than to Turris sensu stricto. The correlation between phylogenetic information and multiple peptide sequences from the library of venom peptides was used to highlight those peptides mostly likely to be unique and intimately associated with biological diversity. The plethora of peptide sequences available requires two prioritization decisions: which subset of peptides to initially characterize, and after these are characterized, which to comprehensively investigate for potential biomedical applications such as drug developments.</p>
Code from: A theoretical framework for trait-based eco-evolutionary dynamics: population structure, intraspecific variation, and community assembly
<p>How is trait diversity in a community apportioned between and within co-evolving species? Disruptive selection may result in either a few species with large intraspecific trait variation (ITV) or many species with different mean traits but little ITV. Similar questions arise in spatially structured communities: heterogeneous environments could result in either a few species that exhibit local adaptation or many species with different mean traits but little local adaptation. To date, theory has been well-equipped to either include ITV or to dynamically determine the number of coexisting species, but not both. Here, we devise a theoretical framework that combines these facets, and apply it to the above questions of how trait variation is apportioned within and between species in unstructured and structured populations, using two simple models of Lotka-Volterra competition. For unstructured communities, we find that as the breadth of the resource spectrum increases, ITV goes from being unimportant to crucial for characterizing the community. For spatially structured communities on two patches, we find no local adaptation, symmetric local adaptation, or asymmetric local adaptation depending on how much the patches differ. Our framework provides a general approach to incorporate ITV in models of eco-evolutionary community assembly.</p>
A framework for 210Pb model selection and its application to 37 cores from Eastern Canada to identify the dynamics and drivers of lake sedimentation rates
<ul> <li>Radioisotopic and ancillary data (i.e., estimated temperature, precipitation and population in lake watersheds)</li> <li>R-code script used in the establishment of 210Pb framework along with example spreadsheet</li> </ul> <p>Original publication: Baud, A., Aulard, C., Ghanbari, H., Fradette, M., Antoniades, D., Del Giorgio, P., Huot, Y., Francus, P., Smol, J. & Gregory‐Eaves, I. (2022). A framework for 210Pb model selection and its application to 37 cores from Eastern Canada to identify the dynamics and drivers of lake sedimentation rates. Earth Surface Processes and Landforms. https:</p>
Data for: A hierarchical framework for unpacking the nitrogen challenge
<p>To feed the world population while mitigating pressing nitrogen (N) pollution problems, tremendous efforts have been devoted to developing and implementing N-efficient technologies in food production, but limited progress has been made. The N management improvement on a farm does not necessarily translate to N pollution reduction on a broader scale due to complex responses of natural and human systems and lack of coordination among stakeholders. Consequently, it is imperative to develop an N management framework that encompasses the complex N dynamics across systems and spatial scales, yet simple enough to guide policies and actions of various stakeholders. Here, we propose a new framework, <em>CAFE</em>, that defines four N management systems (Cropping, Animal-crop, Food, and Ecosystem) in a hierarchical manner, and apply it to 13 representative countries to partition N surpluses across systems in a simple and consistent manner, thereby facilitating the identification and prioritization of systems-based intervention strategies. Surprisingly, the Cropping system contributes less than half of the total N surplus within its Ecosystem for most countries, highlighting the importance of N management beyond croplands. As N surplus increases along the <em>CAFE</em> hierarchy, systems-based intervention strategies are revealed: coupling chemical fertilizers with other N sources by maintaining half of the N from manure and N bio-fixation; coupling animal-crop production by reducing animal density to lower than 1.2 livestock units, and increasing self-sufficiency of animal feed to above 50%; coupling food trade with domestic demand and production; and coupling population needs for economic opportunities with environmental capacity of the region.</p>
CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity
<p>Source code, datasets and outputs. </p>
An Integrated Framework for Real-time Intelligent Traffic Management of Smart Highways
<p>Raw data for the paper "An Integrated Framework for Real-time Intelligent Traffic Management of Smart Highways".</p>
Supplementary material 7 from: Herrera-Mesías F, Ep Jarboui IK, Weigand AM (2022) A metabarcoding framework for wild bee assessment in Luxembourg. Journal of Hymenoptera Research 94: 215-246. https://doi.org/10.3897/jhr.94.84617
Species delimitation congruence, comparing Linnaean species assignment of the original sequences retrieved from BOLD v/s results of MOTU clustering
Supplementary material 1 from: Herrera-Mesías F, Ep Jarboui IK, Weigand AM (2022) A metabarcoding framework for wild bee assessment in Luxembourg. Journal of Hymenoptera Research 94: 215-246. https://doi.org/10.3897/jhr.94.84617
In silico penalty scores, barcode coverage and congruency analysis of the wild bee species of Luxembourg
Supplementary material 3 from: Herrera-Mesías F, Ep Jarboui IK, Weigand AM (2022) A metabarcoding framework for wild bee assessment in Luxembourg. Journal of Hymenoptera Research 94: 215-246. https://doi.org/10.3897/jhr.94.84617
Summary and metadata of the wild bee samples from Luxembourg and Germany used in the mock communities
Relevant Datasets and Software Used for Paper "KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description"
<p>This repository contains relevant datasets and software used in a paper "KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description". They are used to run the code of <em>KGML-xDTD </em>stored on <a href="https://github.com/chunyuma/KGML-xDTD">Github</a> and support the results of this paper.</p> <p><strong>About the datasets</strong></p> <p>1. <em>bkg_rtxkg2c_v2.7.3.tar.gz</em></p> <p>This tar.gz file contains three sub-folders: tsv_files, scripts, and relevant_dbs. The "tsv_files" sub-folder has the input files that the neo4j software uses. The "scripts" sub-folder contains a shell script with a relevant python script to construct the biomedical knowledge graph. The "relevant_dbs" sub-folder stores two auxiliary databases that <em>KGML-xDTD</em> needs to use. </p> <p>2. <em>indication_paths.yaml</em></p> <p>This file contains the <a href="https://sulab.github.io/DrugMechDB">DrugMechDB</a> MOA paths that we used to evaluate the predicted MOA paths by <em>KGML-xDTD. </em>It is downloaded from the official <a href="https://github.com/SuLab/DrugMechDB">GitHub repository</a> of DrugMechDB.</p> <p>3. <em>training_data.tar.gz</em></p> <p>This tar.gz file contains the processed training data of four data sources (e.g., <a href="https://mychem.info">MyChem</a>, <a href="https://lhncbc.nlm.nih.gov/ii/tools/SemRep_SemMedDB_SKR/SemMedDB_download.html">SemMedDB</a>, <a href="https://bioportal.bioontology.org/ontologies/NDFRT">NDF-RT</a>, <a href="https://unmtid-shinyapps.net/shiny/repodb/">RepoDB</a>) mentioned in the paper. These processed drug-disease pairs have been matched to the identifiers of biological entities used in our biomedical knowledge graph and respectively split into true positive (tp) sets and true negative (tn) sets. We also provide the names of these drug identifiers and disease identifiers under a sub-folder "translated _to_name".</p> <p><strong>About the software</strong></p> <p><em>neo4j-community-3.5.26.tar.gz</em></p> <p>This tar.gz is the Neo4j community version 3.5.26 downloaded from <a href="https://neo4j.com/download-center/#community">Neo4j Download Center</a>. Although the newer versions are available, due to their big changes in the Neo4j setting that are not compatible with our scripts on Github, we provide the version that we used in our research. If you would like to use the newer version, modifications to our script will be required to import the biomedical knowledge graph into your local Neo4j database with the new setting.</p>
Framework of virtual platforms for learning and developing competencies
<p>Database used for the SLR of the "Framework of virtual platforms for learning and developing competencies" paper.</p>
A multifaceted isoneutral eddy transport diagnostic framework and its application in the Southern Ocean -- datasets
<p>We propose a multifaceted isoneutral eddy transport diagnostic framework and apply it in the Southern Ocean to analyze eddy potential vorticity (PV) transport dynamics. We leverage a thickness-weighted spatiotemporal scale separation to define a generalized form of eddy flux. A local sampling method is employed and validated to estimate the transport tensor. We introduce Leonard’s decomposition and stationary-transient decomposition to diagnose the role of multiscale interactions. The non-Reynolds term under Leonard’s decomposition and the stationary effect perform prominently in determining the transport property. The Reynolds and transient parts have distinctions, that show oceanic mesoscale dynamics are not synergistic in time and spatial scales. We investigate the spatial scale dependence of each part and find that the transient and stationary components of the Reynolds (non-Reynolds) term have consistent (dissimilar) scale dependence. The scale dependence of the total eddy flux and the transport eigenvalues is controlled by the non-Reynolds term. We further investigate the eigenvalues in terms of the potential enstrophy budget and anisotropy. The potential enstrophy in the Southern Ocean holistically has a forward cascade on the researched scales. The Reynolds and transient effects efficiently promote the forward enstrophy cascade. The non-Reynolds and stationary effects stimulate drastically anisotropic eddy transport. The PV gradient barrier mechanism dominates the drastic anisotropic case. When the anisotropy is weak, a special anisotropic case with a locally balanced potential enstrophy budget overwhelms the case of real isotropy. The results urge future diagnostic works to examine the generalized eddy flux form in a spatiotemporal sense.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.