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opencc-zeroMar 2023View details →

Convergent genomic signatures of local adaptation across a continental-scale environmental gradient

Convergent local adaptation offers a glimpse into the role of constraint and stochasticity in adaptive evolution, in particular the extent to which similar genetic mechanisms drive adaptation to common selective forces. Here, we investigated the genomics of local adaptation in two non-sister woodpeckers that are co-distributed across an entire continent and exhibit remarkably convergent patterns of geographic variation. We sequenced the genomes of 140 individuals of Downy (Dryobates pubescens) and Hairy (D. villosus) woodpeckers and employed a suite of genomic approaches to identify loci under selection. We showed evidence that convergent genes have been targeted by selection in response to shared environmental pressures, such as temperature and precipitation. Among candidates, we found multiple genes putatively linked to key phenotypic adaptations to climate, including differences in body size (e.g., IGFPB) and plumage (e.g., MREG). These results are consistent with genetic constraints limiting the pathways of adaptation to broad climatic gradients, even after genetic backgrounds diverge.

opencc-by-4.0Jan 2023View details →

Defectors: A Large Scale Python Dataset for Defect Prediction

Defect prediction has been a major research problem in the software engineering domain for the last five decades. In recent years, large deep-learning models have shifted the performance of software engineering tasks to new limits and are gaining usage in defect prediction. However, these defect prediction models are often limited by the quality of their datasets, which are not large or diverse enough. In this paper, we present Defectors, a large dataset for both line-level and just-in-time defect prediction. Defectors consist of $\approx$ 213K source code files ($\approx$ 93K defective and $\approx$ 120K defect-free files) from 25 popular python projects from various domains and organizations. These projects come from a diverse set of domains including machine learning, automation, and internet-of-things. Such a scale and diversity make Defectors a suitable dataset for deep learning models, especially transformer models that require large and diverse datasets to effectively generalize defect-inducing patterns to predict future defects. <table> <caption>Dataset Description</caption> <tbody> <tr> <td>File Name</td> <td>Description</td> </tr> <tr> <td>defectors.zip</td> <td>The original Dataset. Find its description in Section II of the paper.</td> </tr> <tr> <td>bug_inducing_commits.zip</td> <td>Each yaml file contains a map of bugfix commits to bug-inducing commits.</td> </tr> <tr> <td>filtered_bug_inducing_commits.yaml</td> <td>A map in structure {repo_name: {bug_inducing_commits: [list of python files in the commit]}}. This file only contains the bug-inducing commits that match the filtering criteria from Section III.D.</td> </tr> <tr> <td>repo_links.yaml</td> <td>Links to the repositories we used to construct the dataset.</td> </tr> </tbody> </table>

Simulation outputs required to generate figures for "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework"

This file contains all of the model simulation outputs necessary to produce the figures for the paper "Modeling Multi-Scale Deformation Cycles in Subduction Zones with a Continuum Visco-Elastic-Brittle Framework". Below are the details of which file is required to produce which figure:   Figure 5 (De_dam_fields.pdf) <a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz">De_dam_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz</a>   Figure 6 (convergence.pdf) <a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz">comp_dt_We_0_001_th_10_10_alpha_4_ddam10.tar.gz </a> Contains 4 files, each one for a different temporal resolution (delta t).   Figure C1 (convergence2.pdf, appendix) <a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz">comp_dt_We_0_1_th_10_9_alpha_4_ddam10.tar.gz </a> <a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz">comp_dt_We_10_th_10_8_alpha_4_ddam10.tar.gz </a> Each contains 4 files, one for each temporal resolution (delta t).   Figure 7 (CPU_time.pdf) No simulation output file: all of the necessary information (CPU times) are included in the associated MATLAB code, available in the Github repository.   Figure 8 (T_h.pdf) Left panels, a, c, e <a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz">comp_th_We_0_001_dt_10_5_alpha_4_ddam_10.tar.gz </a> <a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz">comp_th_We_0_1_dt_10_4_alpha_4_ddam_10.tar.gz </a> <a href="https://zenodo.org/api/files/3150cf22-7ee4-4f5a-9cd4-b420ee0dbd91/comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz">comp_th_We_10_dt_10_3_alpha_4_ddam_10.tar.gz </a> Each contains 4 files, one for each healing time (T_h) Right panels, b, d, f comp_th_We_0_001_dt_10_5_th_10_11_alpha_4_ddam_10.tar.gz comp_th_We_0_001_dt_10_5_th_10_10_alpha_4_ddam_10.tar.gz comp_th_We_0_001_dt_10_5_th_10_9_alpha_4_ddam_10.tar.gz comp_th_We_0_001_dt_10_5_th_10_8_alpha_4_ddam_10.tar.gz comp_th_We_0_1_dt_10_4_th_10_11_alpha_4_ddam_10.tar.gz comp_th_We_0_1_dt_10_4_th_10_10_alpha_4_ddam_10.tar.gz comp_th_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz comp_th_We_0_1_dt_10_4_th_10_8_alpha_4_ddam_10.tar.gz comp_th_We_10_dt_10_3_th_10_11_alpha_4_ddam_10.tar.gz comp_th_We_10_dt_10_3_th_10_10_alpha_4_ddam_10.tar.gz comp_th_We_10_dt_10_3_th_10_9_alpha_4_ddam_10.tar.gz comp_th_We_10_dt_10_3_th_10_8_alpha_4_ddam_10.tar.gz Each contains 5 files, for 5 different realisations of the model simulations (same parameters, different initial noise on cohesion)   Figure 9 (comp_ddam_We_0_001.pdf) comp_ddam_We_0_001_dt_10_5_th_10_10.tar.gz One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)   Figure 10 (comp_ddam_We_0_1.pdf) comp_ddam_We_0_1_dt_10_4_th_10_9.tar.gz One file for each alpha value (2, 3, 4, 6, 8), one file for each delta d value (0.1, 0.3, 0.5, 0.7, 0.9)   Figure 11 (discussion.pdf) u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_10.tar.gz u_sfc_We_0_1_dt_10_4_th_10_9_alpha_4_ddam_50.tar.gz   SI movie SI_movie.tar.gz

opencc-zeroMar 2023View details →

Evolution of wing scales in the Diptera

Among the insects with wings clad in scales, the butterflies are most known and show the greatest variety of scales. In the Diptera, only some families or particular genera of two large groups are known to bear scales on wings, i.e., mosquitoes (Culicomorpha) and moth flies (Psychodomorpha). From among another large dipteran group, the crane-flies (Tipulomorpha), the scales on wings are present only in one small genus, Maietta Alexander, now endemic to the southwestern coast of South America. Herewith, we describe the Eocene ancestor of Maietta, embedded in Baltic amber, Maietta (Hoffeinsetta ) christelae, n. subgen., n. sp. This species and its recent congeners document the evolution of scale cover from scarce, restricted only to the anterior portion of the wing, to complete and dense. A similar parallel evolutionary route was previously described in the Culicidae. The fossil representative of Maietta provides also a further example of biogeographical relationships of Baltic fauna with recent congeners distributed today far from Europe. The present finding prompts a discussion on a possible protective role of scales against sticky surfaces of resin and the same proposes an explanation to a question: why some insects are astonishingly rare in fossil resins; a problem unsolved since 19th century.

Chromosome-scale genome assembly of the African spiny mouse (Acomys cahirinus)

Genomic DNA was extracted from blood from a single male A. cahirinus animal using a Monarch HMW DNA Extraction Kit for Cells & Blood (T3050, New England Biolabs, Ipswich MA) following the manufacturer’s recommended protocol. DNA was quantified prior to library construction using the Qubit DNA HS Assay (ThermoFischer, Waltham MA) and DNA fragment lengths were assessed using the Agilent Femto Pulse System (Santa Clara, CA). Libraries were prepared for sequencing using the Oxford Nanopore ligation kit (SQK-LSK110) following the manufacturers’ instructions, except that DNA repair and A-tailing was performed for 30 min and the ligation was allowed to continue for 1 hr. Prepared libraries were quantified using a Qubit fluorometer and 30 fmol of the library was loaded onto a Nanopore version R.9.4.1 flow cell and loaded on a PromethION running MinKNOW version (21.05.20). To increase output, the flow cell was washed after approximately 24 hr of sequencing then an additional 12 fmol of library was added to the flow cell and run for an additional 48 hr. Basecalling was performed using Guppy 5.0.12 (Oxford Nanopore) using the superior model (dna_r9.4.1_450bps_sup_prom.cfg). FASTQ files for assembly were extracted from unaligned bam files using samtools (Li et al. 2009) then Flye version 2.9 for assembly using the --nano-hq flag (Kolmogorov et al. 2019). Haplotigs and overlaps in the assembly were purged using purge_dups (https://github.com/dfguan/purge_dups). The assembly was then polished using Medaka version 1.4.2 (https://github.com/nanoporetech/medaka) followed by a second polishing step with pilon version 1.24 (Walker et al. 2014). Assembly statistics at each step were generated using Quast (Gurevich et al. 2013) and BUSCO (Simão et al. 2015) (Table S2). The primary contigs assembled from the Nanopore data were anchored to chromosomes using 505,210,505 read pairs of a Hi-C library isolated from another A. cahirinus individual of unknown sex downloaded from the NCBI Short Read Archive (SRX13258644) (Wang et al. 2022). After aligning the Hi-C reads with the ArimaHi-C Mapping Pipeline (https://github.com/ArimaGenomics/mapping_pipeline), YaHS v1.0 (Zhou et al. 2023) was used with default error correction for scaffolding, and Juicebox v1.11.08 (Dudchenko et al. 2018) was used to generate a Hi-C contact map. Progressive Cactus was used (Armstrong et al. 2020) to perform a whole-genome alignment of the A. cahirinus draft assembly to the Mus musculus GRCm39 reference genome (RefSeq GCF_000001635.27_GRCm39). Comparative annotation of the draft genomes was then performed using the Comparative Annotation Toolkit (CAT) (Fiddes et al. 2018). Briefly, the M. musculus RefSeq annotation GFF was parsed and validated with the “parse_ncbi_gff3” and “validate_gff3” programs (respectively) from CAT. The M. musculus reference transcript cDNA sequences were downloaded and mapped to the M. musculus draft genome with minimap2 (Li 2018) and provided to CAT as long-read RNA-seq reads in the “[ISO_SEQ_BAM]” field of the configuration file. For A. cahirinus, bulk RNA-seq data obtained from multiple pooled organs were downloaded from NCBI SRA BioProject PRJNA342864 (Bellofiore et al. 2017) and mapped to the draft assembly with STAR (Dobin et al. 2013) then provided to CAT in the “[BAMS]” field. CpG islands were identified using the cpg_lh utility from the UCSC suite of tools (Kent et al. 2002).

opencc-by-4.0Dec 2022View details →

Data Availibility for "Susceptibility of microseismic triggering to small sinusoidal stress perturbations at the laboratory scale"

List of data contained within this repository. Total weight = ~ 40GB --- Raw data :   -For all experiments (control and oscillation experiments): Mechanical data files (460 MB); Acoustic emission waveforms (40 GB). --- Derived data:   -For the control experiment: RSFit3000 Rate and State parameters (30 MB);   -For oscillation experiments: RMS magnitudes of acoustic emissions ; (1 MB) Phase of arrival of acoustic emissions (500 KB); Response amplitudes of fused and unfused steps (1 KB); Susceptibility to Pc oscillation amplitude (1 KB); b-values as a function of stress oscillation phase (100 KB).

opencc-zeroMar 2023View details →

Chromosome VCF files and 1Mb recombination rate estimations for: Fine-scale recombination rate variation and association with genomic features in a butterfly

Genetic recombination is a key molecular mechanism that has profound implications on both micro- and macro-evolutionary processes. However, the determinants of recombination rate variation in holocentric organisms are poorly understood, in particular in Lepidoptera (moths and butterflies). The wood white butterfly (Leptidea sinapis) shows considerable intraspecific variation in chromosome numbers and is a suitable system for studying regional recombination rate variation and its potential molecular underpinnings. Here, we developed a large whole-genome resequencing data set from a population of wood whites to obtain high-resolution recombination maps using linkage disequilibrium information. The analyses revealed that larger chromosomes had a bimodal recombination landscape, potentially due to interference between simultaneous chiasmata. The recombination rate was significantly lower in subtelomeric regions, with exceptions associated with segregating chromosome rearrangements, showing that fissions and fusions can have considerable effects on the recombination landscape. There was no association between the inferred recombination rate and base composition, supporting a negligible influence of GC-biased gene conversion in butterflies. We found significant but variable associations between the recombination rate and the density of different classes of transposable elements (TEs), most notably a significant enrichment of SINEs in genomic regions with higher recombination rate. Finally, the analyses unveiled significant enrichment of genes involved in farnesyltranstransferase activity in recombination cold-spots, potentially indicating that expression of transferases can inhibit formation of chiasmata during meiotic division. Our results provide novel information about recombination rate variation in holocentric organisms and has particular implications for forthcoming research in population genetics, molecular/genome evolution and speciation.

Brazilian Starter Data Kit Update to Support the Paper "Integrated Long-Term Expansion Planning and Short-Term Operation Assessment for Decarbonisation Pathways in Brazil Considering Utility-Scale Storage"

This dataset presents updates on the Brazilian starter data kit, considering current load profile data and specific renewable energy sources production, using public data from official Brazilian power system agents. The dataset contains the input data and respective files to run OSeMOSYS and Flextool, including the utility-scale storage model. Additionally, this dataset contains the results of the study "Integrated Long-Term Expansion Planning and Short-Term Operation Assessment for Decarbonisation Pathways in Brazil Considering Utility-Scale Storage". This study evaluates the increase in variable renewable energy in Brazil and its integration with Storage systems.

CRLC: Cross-resolution national-scale land-cover mapping based on noisy label learning: a case study of China

CRLC are the 10-meter resolution land cover maps for China in 2020 achieved by a deep classification network. The maps include eight land cover classes: 1: Cropland 2: Forest 3: Grass/shrubland 5: Wetland 6: Water bodies 8: Impervious 9: Bareland 10: Snow/ice   Reference: <pre>@article{liu2023cross, title={Cross-resolution national-scale land-cover mapping based on noisy label learning: A case study of China}, author={Liu, Yinhe and Zhong, Yanfei and Ma, Ailong and Zhao, Ji and Zhang, Liangpei}, journal={International Journal of Applied Earth Observation and Geoinformation}, volume={118}, pages={103265}, year={2023}, publisher={Elsevier} }</pre>

A plea for scale, and why it matters for invasive species management, biodiversity, and conservation

<ol> <li>Invasive species are suspected to be major contributors to biodiversity declines worldwide. Counterintuitively, however, invasive species effects are likely scale-dependent, and are hypothesized to be positively related to biodiversity at large spatial scales. Past studies investigating the effect of invasion on biodiversity have been mostly conducted at small scales (<100 m2) that cannot represent large-dynamic landscapes by design. Therefore, replicated experimental evidence supporting a negative effect of invasive plants on biodiversity is lacking across many landscape types, including large grasslands.</li> <li>We collected data across eight large (333–809 ha) grassland landscapes managed with pyric herbivory—i.e., the recoupling of fire and grazing—to test how an invasive legume (Lespedeza cuneata) affected plant and bird communities at spatial grains ranging from 0.1 m2 to >3,000,000 m2.</li> <li> L. cuneata invasion effects on grassland plant diversity and composition changed with scale; being negative at small spatial grains (0.1 m2) and neutral or positive at large spatial grains (>3,000,000 m2).</li> <li> L. cuneata abundance did not significantly affect bird diversity at any spatial grain measured.</li> <li> L. cuneata may negatively affect biodiversity if abundances are greater than those observed in this study. However, previous research suggests that L. cuneata may not be capable of exceeding 20% canopy cover across large landscapes (>400 ha). Control and eradication strategies can be costly and are fraught with risk. If data do not clearly support a negative L. cuneata abundance-biodiversity relationship, and if invasion is spatially limited across large landscapes, ongoing control and eradication efforts may be unwarranted and ineffective.</li> <li> Synthesis and applications: Invasive species effects gleaned from small-scale studies may not reliably predict their effects at larger scales. Although we recognize the importance of small-scale studies in potentially isolating individual mechanisms, management strategies based solely on results from small-scale studies of invasion are unlikely to increase or conserve biodiversity across large landscapes. Rather, processes that generate landscape heterogeneity—like pyric herbivory—are probably more important for promoting biodiversity across all scales. Scale is a central problem in ecology, and defining scale in management objectives is essential for effective biodiversity conservation.</li> </ol>

Large-scale saliva pooling as a screening strategy to control transmission_raw data

This dataset include the raw data of our experience after building a bespoke laboratory to control critical areas by pooling samples of saliva. From August 2020 to February 2022; 928,528 individual self-samples of saliva were processed in 52,580 pools, 4,935 of which were positive and helped us detect 5,806 nonsymptomatic individuals.  The dataset was collected directly from the laboratory information system and was processed to delete the columns with sensitive information. The Dataset was charged as XLSX file and only need a compatible software. To facilitate the understanding of the data set, which uses the Galician language and an internal nomenclature of our laboratory, we include a sheet named "description" and a README file in which it defines the terms used and their equivalence with those used in the article.

A laboratory-scale simulation framework for analyzing wildfire hydrologic and water quality effects

Datasets containing information on experimental conditions for each tested soil sample, measured hydrologic and water quality responses, and calculated response metrics.

Input data and model implementation from: How do terrestrial wildlife communities respond to small-scale Acacia plantations embedded in harvested tropical forest?

To offset the declining timber supply from shifting towards more sustainable forestry practices, industrial tree plantations are expanding in tropical production forests. The conversion of natural forest to tree plantation is generally associated with loss of biodiversity and shifts toward more generalist and disturbance tolerant communities; but effects of mixed-landuse landscapes integrating natural and plantation forest remain little understood. Using camera traps, we surveyed the medium-to-large bodied terrestrial wildlife community across two mixed-land-use forest management areas in Sarawak, Malaysia Borneo which include areas dedicated for logging of natural forest and adjacent planted Acacia forests. We analysed data from a 25-wildlife species community using a Bayesian community occupancy model to assess species richness and species-specific occurrence responses to Acacia plantations at a broad scale, and to remote-sensed local habitat conditions within the different forest land-use types. All species were estimated to occur in both land-use types, but species-level percent area occupied and predicted average local species richness were slightly higher in the natural forest management areas compared to licensed planted forest. Similarly, occupancy-based species diversity profiles and defaunation indices for both a full community and only threatened and endemic species suggested the diversity and occurrence were slightly higher in the natural forest management areas. At the local scale, forest quality was the most prominent predictor of species occurrence. These associations with forest quality varied among species but were predominantly positive. Our results highlight the ability of a mixed-land-use landscape with small-scale Acacia plantations embedded in natural forest to retain terrestrial wildlife communities while providing an alternate source of timber. Nonetheless, there was a tendency towards reduced biodiversity in planted forests, which would likely be more pronounced in plantations that are larger or embedded in a less natural matrix.

LiftWEC deliverable 3.6 - Part II: Dataset from numerical simulations of full-scale LiftWEC device using a high-fidelity RANS model

This dataset contains the numerical simulation results for a full-scale LiftWEC device in regular and irregular wave conditions. The regular wave cases occur at static pitch and fixed rotational velocity. The parameters used for these cases are described in the corresponding *CaseParameters.csv file. These simulations were used to derive a first estimate of the maximum conversion efficiency of the rotor from wave power to shaft power. A detailed description of the employed numerical model and the case setup can be found in LiftWEC deliverable 3.6 Hydrodynamic Validation of Final Design, which was also uploaded to the LiftWEC community on zenodo. In accordance with the coordinate system definition used in the validation case, a relative phase angle of 270° degree corresponds to foil1 at 3 o'clock position while the wave crest passes over the rotor axis position. Rotation is clockwise, Rotor rotates in the direction of orbital wave particle velocities. The control reference case describes the first test case of a cyclorotor in irregular waves under active control of angular velocity and foil pitch. This case is also documented in deliverable D3.6. The case files can be used to recreate wave conditions and motion signal. The load-file can be used to validate the obtained tangential and radial forces on foil 1 as well as the total power output over time. More information on the control model can be found in the corresponding project deliverables D5.X. All loads given as values per unit span length (e.g. for forces [N/m]).

'Projected Landscape-scale Repercussions of Global Action for Climate and Biodiversity Protection' - model outputs

Archive of model outputs produced for the MAgPIE v4.3.5 paper 'Projected Landscape-scale Repercussions of Global Action for Climate and Biodiversity Protection'. The model code of the MAgPIE and SEALS models can be accessed via: MAgPIE model code: <a href="https://doi.org/10.5281/zenodo.5394196">https://doi.org/10.5281/zenodo.5394196</a> and <a href="https://github.com/magpiemodel/magpie">https://github.com/magpiemodel/magpie</a> MAgPIE model documentation: <a href="https://rse.pik-potsdam.de/doc/magpie/4.3.5/">https://rse.pik-potsdam.de/doc/magpie/4.3.5/</a> SEALS model code: <a href="https://doi.org/10.5281/zenodo.7795957">https://doi.org/10.5281/zenodo.7795957</a> Data descriptions: glosem_input.zip contains the spatially-explicit RLSK and C-factor data for each of the modelled scenarios at 10 arcseconds and the R code to estimate C-factor values based on the MAgPIE-SEALS outputs. glosem_output.zip contains the spatially-explicit soil loss estimates for all scenarioso and the R code used to process the input data. The data was used to create Fig. 7. magpie_ouput.zip contains the MAgPIE model outputs of all scenarios. The data is shown in Figs. 2, 3, 4, & 5. pollination_sufficiency.zip contains the spatially-explicit pollination sufficiency estimates for all modelled scenarios and the R code used to derive the pollination sufficiency scores. The data is displayed in Fig. 6.

openother-openApr 2023View details →

Data from: Chilean bee diversity: Contrasting patterns of species and phylogenetic turnover along a large-scale ecological gradient

Title of dataset Data from: Chilean bee diversity: Contrasting patterns of species and phylogenetic turnover along a large-scale ecological gradient Authors of dataset Leon Marshall1,2, John S. Ascher3, Cristian Villagra4, Amaury Beaugendre1, Valentina Herrera4, Patricia Henríquez-Piskulich4, Alejandro Vera5, Nicolas J. Vereecken1 <ol> <li>Agroecology Lab, Université libre de Bruxelles (ULB), Boulevard du Triomphe CP 264/2, B 1050 Brussels, Belgium</li> <li>Naturalis Biodiversity Center, Darwinweg 2, 2333 CR Leiden, The Netherlands</li> <li>Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543, Singapore</li> <li>Instituto de Entomología, Universidad Metropolitana de Ciencias de la Educación, Santiago, Región Metropolitana, Chile</li> <li>Departamento de Biología, Universidad Metropolitana de Ciencias de la Educación, Santiago, Región Metropolitana, Chile</li> </ol> Abstract Chile's isolation and varied climates have driven the evolution of a unique biodiversity with a high degree of endemism. As a result, Chile encompasses diverse environments, including the Mediterranean-type ecosystem, a global biodiversity hotspot. These environments are currently threatened by anthropogenic land use change impacting the integrity of local biomes and associated species. This area is the most intensively sampled of the country with high endemicity of native bee species. Characterising habitat requirements of bees is a pressing priority to safeguard these insects and the ecosystem services they provide. We investigated broad-scale patterns of bee (Hymenoptera: Apoidea: Anthophila) diversity using newly accessible expert-validated datasets comprising digitized specimen records from Chilean and US collections, and novel expert-validated type specimen data for the bees of Chile. We used a generalised dissimilarity modelling (GDM) approach to explore both compositional and phylogenetic β-diversity patterns across latitudinal, altitudinal, climate and habitat gradients in well-sampled bee assemblages in Central Chile. Using the GDM measures of increasing compositional and environmental dissimilarity we categorised and compared the most important drivers of these patterns and used them to classify 'wild bee ecoregions' (WBE) representing unique assemblages. Turnover of bee assemblages was explained primarily by latitudinal variation (proxy for climate) from south to north in Chile. However, temperature variations, precipitation and the presence of bare soil also significantly explained turnover in bee assemblages. In comparison, we observed less turnover in phylogenetic biodiversity corresponding to spatial gradients. We identified six de novo ecoregions (WBE), all with distinct taxa, endemic lineages, and representative species. The WBE represent distinct spatial classifications but have similarities to existing biogeographical classifications, ecosystems and bioclimatic zones. This approach establishes the baseline needed to prioritise bee species conservation efforts across this global biodiversity hotspot. We discuss the novelty of this classification considering previous biogeographical characterisations and their relevance in assessing conservation priorities for bee conservation. We argue that Chile's WBE highlight areas in need of funding for bee species surveys and description, distribution mapping and strengthening of conservation policies. Usage notes The dataset contains species occurrence data of chilean bees aggregated to a 5 x 5 km grid shapefile. The shapefile of the grid and the raster mask of the Central Chilean study area are also included. Finally, a database of type specimen data used to supplement the dataset is included here.  The code for the analysis can be found at: <a href="https://github.com/lmar116/ChileanBeeDiversity">https://github.com/lmar116/ChileanBeeDiversity</a>. Shapefiles, rasters, CSV files and code were all loaded and analyzed using R statistics software.  Four files are included: <ol> <li>Marshall-et-al-2023_Ecosphere_DataTable_bee_grid: contains all species occurrence data used in GDM analysis, Grid column refers to cl.5km.shp.</li> <li>cl.5km.shp (and associated files): 5 x 5 km grid shapefile of the Central Chilean study area</li> <li>chile.mask.tif: raster outline of the Central Chilean study area</li> <li>Marshall-et-al-2023_Ecosphere_CentralChileTypeSpecimens.xlsx: contains type specimen data used to supplement species occurrence dataset.</li> </ol>

Data and codes from: Taxon-specific responses to landscape-scale and long-term implementation of environmentally friendly rice farming

<ol> <li> Rice feeds about half of the world's population. The intensification of rice farming has threatened biodiversity and ecosystem services, such as pest control. A growing body of evidence suggests that environmentally friendly rice farming (EFF) can be effective in biodiversity conservation. However, its dependence on spatial and temporal scales of EFF implementation and surrounding seminatural habitats has remained largely unknown. </li> <li> We evaluated the effects of three types of EFF (organic farming, winter flooding and Integrated Pest Management [IPM]) against conventional farming on plants, spiders, and waterbirds in Japan. Through systematic surveys of 254 rice fields across rice production areas, we monitored EFF implementation at field and landscape scales, considering the time since transition to EFF and the surrounding seminatural areas. </li> <li> EFF implementation at the field scale, such as organic farming, was found to have positive effects on plant richness and spider abundance, whereas the percent area of EFF fields within a 200-m radius positively affected Ardeidae abundance. However, as the percent area of surrounding semi-natural habitats increased, the difference in the plant richness between organic and other fields decreased. </li> <li>Prolonged implementation periods of IPM or winter flooding, together with a reduction of more than 50% in synthetic fertilisers and pesticides from conventional agriculture, led to an increase in the spider abundance. Conversely, no clear positive effect of longer implementation periods of organic farming resulted in similar spider abundance in the three EFF types after 20 years.</li> <li> Synthesis and applications. This suggests that the benefits of environmentally friendly rice farming depend on their spatial and temporal scales and the surrounding seminatural areas. We propose that such a context dependency should be considered in future policy. Our findings also provide a starting point for future research to test the mixed biodiversity benefits of EFF across cropland types and around the world. </li> </ol>

MMpedia: A Large-scale Multi-modal Knowledge Graph

List of files: <ul> <li>entity2image.json: The entity to map images file</li> <li>MMpedia_triples.ttl: The triples file</li> <li>EntlistXX.tar: The image file</li> </ul> MMpedia dataset is split into 132 subsets and each subset is compressed into a <code>.tar</code> file. After unziping files under the folder "MMpedia", the data structure are as following: <pre><code>|-MMpedia |-Entitylist1 |-Entity1 |-1.jpg |-2.jpg |-3.jpg ... |-Entity2 |-Entity3 ... |-Entitylist2 |-Entitylist3</code></pre> For example, the path <code>MMpedia/Entlist141/Bart Tanski/Bart Tanski+1.jpg</code> means the image corresponding to the entity "Bart Tanski" Other image files can be found in following URLs: <a href="https://zenodo.org/record/7854781#.ZEU7Uc7iu38">MMpedia2 | Zenodo</a> <a href="https://zenodo.org/record/7855010">MMpedia3 | Zenodo</a> <a href="https://zenodo.org/record/7855226">MMpedia4 | Zenodo</a>  For more details, please refer to <a href="https://github.com/Delicate2000/MMpedia">Delicate2000/MMpedia (github.com)</a>

opencc-by-4.0Nov 2022View details →

READ: Large-Scale Neural Scene Rendering for \\ Autonomous Driving

With the development of advanced driver assistance systems~(ADAS) and autonomous vehicles, conducting experiments in various scenarios becomes an urgent need. Although having been capable of synthesizing photo-realistic street scenes, conventional image-to-image translation methods cannot produce coherent scenes due to the lack of 3D information. In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to generate large-scale driving scenes in real time on a PC through a variety of sampling schemes. In order to effectively represent driving scenarios, we propose an ω rendering network to learn neural descriptors from sparse point clouds. Our model can not only synthesize photo-realistic driving scenes but also stitch and edit them. The promising experimental results show that our model performs well in large-scale driving scenarios.   Source Code: https://github.com/JOP-Lee/READ

Evidence of cospeciation between termites and their gut bacteria on a geological time scale

Termites host diverse communities of gut microbes, including many bacterial lineages only found in this habitat. The bacteria endemic to termite guts are transmitted via two routes: a vertical route from parent colonies to daughter colonies and a horizontal route between colonies sometimes belonging to different termite species. The relative importance of both transmission routes in shaping the gut microbiota of termites remains unknown. Using bacterial marker genes derived from the gut metagenomes of 197 termites and one Cryptocercus cockroach, we show that bacteria endemic to termite guts are mostly acquired by vertical transfers. We identified eighteen lineages of gut bacteria showing cophylogenetic patterns with termites over tens of millions of years. Horizontal transfer rates estimated for these lineages compared to those of fifteen mitochondrial genes suggested that sixteen bacterial lineages present cophylogenetic patterns that could be explained by a model involving no horizontal transfers. Some of these associations probably date back more than 150 million years and are an order of magnitude older than the cophylogenetic patterns between mammalian hosts and their gut bacteria. Our results suggest that termites have cospeciated with their gut bacteria since first appearing in the geological record.