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Dataset for paper "GRN-Transformer: Predicting Single Cell Gene Regulatory Network based on Axial Transformer"

Dataset for paper "GRN-Transformer: Predicting Single Cell Gene Regulatory Network based on Axial Transformer"

opennotspecifiedDec 2021View details →

Additional data, Table 2. — Forewing and proboscis lengths (cm) in Xanthopan praedicta stat. nov. – first 35 lines: MNHN specimens. – lines 36 (Ranomena) to 43 (Lakato): RCPB. – lines 44 (Ranomafana) to 47 (Amber Mountain): CJHL. – line 55 (female from Ranomafana) and last five lines: NHMUK. – last 30 lines: individuals captured and released by DCL, except for the six NHMUK specimens. in A new taxonomic status for Darwin's "predicted" pollinator: Xanthopan praedicta stat. nov. (Lepidoptera Sphingidae Sphinginae)

Additional data, Table 2. — Forewing and proboscis lengths (cm) in Xanthopan praedicta stat. nov. – first 35 lines: MNHN specimens. – lines 36 (Ranomena) to 43 (Lakato): RCPB. – lines 44 (Ranomafana) to 47 (Amber Mountain): CJHL. – line 55 (female from Ranomafana) and last five lines: NHMUK. – last 30 lines: individuals captured and released by DCL, except for the six NHMUK specimens.

opennotspecifiedDec 2021View details →

Additional data, Table 1. — Forewing and proboscis lengths (cm) in Xanthopan morganii. – last four specimens: CJHL (Rungwe, Hombo, and Convalescence) and RCPB (Tsoundzou). – all other specimens: MNHN. in A new taxonomic status for Darwin's "predicted" pollinator: Xanthopan praedicta stat. nov. (Lepidoptera Sphingidae Sphinginae)

Additional data, Table 1. — Forewing and proboscis lengths (cm) in Xanthopan morganii. – last four specimens: CJHL (Rungwe, Hombo, and Convalescence) and RCPB (Tsoundzou). – all other specimens: MNHN.

Visualization results of Kinase-Ligand complex based on deep learning prediction score

It contains all the validation results used in the manuscript "Global analysis of deep learning prediction using large-scale in-house kinome-wide profiling data". "ligand_list.smi" shows the compound structure information for each ligand, and the ligand atom weighting results for each target are stored in the "Targets" folder. PyMol can be used to visualize the results by using "load.py" (see readme for how to run it).

opencc-zeroJan 2022View details →

Predicting habitat suitability for wild deer in relation to threatened ecological communities in south-eastern New South Wales, Australia

Context. High density deer populations can cause ecological damage, yet their distribution and impacts are poorly known across much of Australia. As a result, land managers rely on anecdotal reports to make decisions about management and control measures. Aims. We aimed to model habitat suitability for deer in the South Coast of New South Wales (NSW), Australia, to be used as a baseline for future management and identify which threatened ecological communities (TECs) in the region are at greatest current risk of being occupied by deer. Methods. We compiled 678 presence-only records of wild deer from online databases, observations made by National Parks and Wildlife Service field staff and field-based surveys. We combined these observations with eight environmental variables to model and map habitat suitability for deer across our study area using maximum entropy. Three spatial models of habitat suitability across our study area were produced: one for all deer species; and two species-specific models for fallow and sambar deer. Key results. Our models indicate that suitable habitat for deer exists throughout much of the South Coast of NSW. Of the TECs examined, Coastal Saltmarsh, Themeda Grassland, and Swamp Sclerophyll Forest had the highest proportion of area likely to be extremely suitable for deer and thus should be prioritised for protection within our study area. Conclusions. Further systematic field-based surveys are needed to improve the quality of models in this region. Implications. We recommend that areas having high habitat suitability but are not yet occupied by deer be identified as sites where deer occupancy could be prevented.

Hell ant ecological niche prediction dataset

Data S1. Supplementary Data_Fossil Morphometrics (separate file) All fossil morphometric data for all fossil specimens included in this study. The Excel file has four tabs: one with all raw trait measurements measured in a functional morphological framework (“raw functional”); one with all raw trait measurements measured in a homologous morphological framework (“raw homologous”); one with all size-corrected ratio trait measurements measured in a functional morphological framework (“ratio functional”); and one with all size-corrected ratio trait measurements measured in a homologous morphological framework (“raw homologous”).    Data S2. Supplementary Data_ Trap Jaw Ecomorphospace (separate file) All body size and ecological niche data used for calculating ecomorphospace occupation in extinct and extant trapjaw ant taxa. The Excel file has tabs for each genus of trapjaw ants, which were combined for cases where multiple genera were treated as one lineage. “MinHaidomyrmecinae” is collated solely from predictions estimated under a raw trait measurement functional morphology framework, while “MaxHaidomyrmecinae” is collated from predictions estimated using all models.   Data S3. Supplementary Data_Random Forest Code (separate file) R code that may be used to reproduce the Random Forest analyses, commented for clarity.

opencc-zeroJan 2022View details →

Salmon louse infestation levels on sea trout can be predicted from a hydrodynamic lice dispersal model

The abundance of the parasitic salmon louse has increased with the growth in aquaculture of salmonids in open net pens. This represents a threat to wild salmonid populations as well as a key limiting factor for salmon farming. The Norwegian 'traffic light' management system for salmon farming aims to increase aquaculture production while securing sustainable wild salmonid populations. However, this system is at present solely focusing on mortality in wild Atlantic salmon, while responses of sea trout with different ecological characteristics are not included. We analyze lice counts on sea trout from surveillance data and use Bayesian statistical models to relate the observed lice infestations to the environmental lice infestation pressure, salinity, and current speed. These models can be used in risk assessment to predict when and where lice numbers surpass threshold levels for expected serious health effects in wild sea trout. We find that in production areas with the highest density of salmon farms (West coast), more than 50 % of the sea trout experienced lice infestations above levels of expected serious health effects. We also observed high lice infestations on sea trout in areas with salinities below louse tolerance levels, indicating that the fish had been infested elsewhere but were returning to low-saline waters to avoid lice or delouse. This behavioural response may over time disrupt anadromy in sea trout. The observed infestations on sea trout can be explained by the hydrodynamic lice dispersal model, which provides continuous estimates of lice exposure along the whole Norwegian coast. These estimates, which are used in Atlantic salmon research and management, can also be used for sea trout. Synthesis and policy implications: Wild sea trout, spending its entire feeding migration in fjords and coastal areas, is at higher risk than Atlantic salmon to lice infestations from aquaculture. The observed high levels of lice infestation on sea trout question the environmental sustainability of the current aquaculture industry in areas with intensive farming. We discuss the complex responses of sea trout to salmon lice and how the 'traffic light' management system may include data on this species.

Code and data for "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction" By Guan et al. Submitted to JGR Oceans.

This repository contains the code and data for the machine learning analysis of "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction”By Guan et al. Submitted to JGR Oceans. Specifically, this repository contains the following items: (1) Codes for assessing the representation skill of the machine learning and linear regression (LR) methods. Three machine learning methods are considered: random forest (RF), back-propagation neural network (BP), and convolutional neural network (CNN). (2) Codes for assessing the prediction skill of the machine learning and LR methods.   (3) Seasonal-mean and annual-mean input data to run these codes.   (4) The package needed to run the random forest code, i.e. the RF_MexStandalone-v0.02 program package from https://code.google.com/archive/p/randomforest-matlab/downloads .

Meta-learning an Intermediate Representation for Few-shot Block-wise Prediction of Landslide Susceptibility

This upload contains 1) the used thematic maps in the study, 2) the samples to train and validate the proposed model, and 3) the samples used to predict landslide susceptibility of Fengjie County and Fuling District. The code related to the methods is available on the website: <a href="https://github.com/Young-Excavator/Meta_LSM">https://github.com/Young-Excavator/Meta_LSM</a>.

Complete workflow for the prediction of the secretome of Fusarium oxysporum f. sp. albedinis, the causal agent of palm dieback

Custom scripts for mining the secretome of Fusarium oxysporum f. sp. albedinis, the causal agent of date palm dieback disease.

opencc-by-4.0Nov 2021View details →

Topographic deep neural networks predict the functional organization of the primate ventral visual pathway

Recording of presentation at the Neuroscience 2021 annual meeting (held virtually). The abstract follows:   The primate ventral visual pathway is organized into functional maps, including pinwheel-like arrangements of orientation-tuned neurons in primary visual cortex (V1) and patches of category-selective neurons in higher visual cortex. While deep convolutional neural networks (DCNNs) trained for object recognition accurately predict neural representations throughout the ventral pathway, they have no spatial layout for features at a given retinotopic location and are thus unable to predict the rich topographic organization of visual cortex. Here, we close this gap by first assigning each DCNN unit a position in a 2D cortical sheet, then training the network to minimize a cost function with two components: one encouraging accurate object recognition, and another favoring correlated responses among nearby units in each model layer (Figure 1A, 1B).  We find that training with this composite spatial loss produces brain-like topographic maps in both early and later model layers (Figure 1B). Early layers contain smooth orientation preference maps with pinwheels, clusters of units preferring the same spatial frequency, and color-preference domains resembling V1 “blobs”. In a later layer of the same model, we observe clusters of category-selective units, e.g., face patches, whose spatial organization largely matches that found in primate higher visual cortex. Our model thus leverages local response correlations, which have been linked to theories of wire-length minimization, to accurately predict neuron responses and functional organization throughout the ventral visual pathway. In support of the wire-length minimization hypothesis, we find that our topographic DCNN would require shorter connections than a standard DCNN to support connections between similarly-tuned neurons within early (38% reduction) and later (31% reduction) model layers (Figure 1D). These results suggest that the functional organization of visual cortex can be explained by two constraints: the need to perform object recognition and pressure for local populations of neurons to have correlated responses.

opencc-by-4.0Jun 2022View details →

SolProp Dataset for: Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures

The datasets and supplementary materials for the manuscript "Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures" by Florence H. Vermeire, Yunsie Chung, and William H. Green. Citations should refer directly to the manuscript (<a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1021%2Fjacs.2c01768&data=05%7C01%7CFlorence.Vermeire%40ugent.be%7C31f767a36cf547f91aef08da48bb3fcd%7Cd7811cdeecef496c8f91a1786241b99c%7C1%7C0%7C637902266316761522%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=1CKXFXvTtqOGDSBpSw8AeKCt25%2B27CC%2FNU7Ery276D0%3D&reserved=0">https://doi.org/10.1021/jacs.2c01768</a>). The preprint version of of the manuscript is available at: https://doi.org/10.26434/chemrxiv-2022-92hl1 The SolProp data collection contains thermodynamic properties related to the dissolution of organic solutes in organic solvents and water. Thermodynamic properties included are solvation free energy at 298K (dGsolv), solvation enthalpy at 298K (dHsolv), Abraham Solute Parameters (E,S,A,B,L),  aqueous solid solubility at 298K (logSaq), and solid solubility in organic solvents at various temperatures (logS). The dataset is limited to neutral solutes and solvents. Detailed information can be found in the "ReadMe.txt" file.

opencc-by-4.0Feb 2022View details →

Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps - data

Data from the article Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps published in Journal of Vision. code: https://zenodo.org/record/5999215 data: https://zenodo.org/record/5999046 Marek A. Pedziwiatr marek.pedziwi@gmail.com February 2022

opencc-zeroFeb 2022View details →

Data from: Dynamic body acceleration as a proxy to predict the cost of locomotion in bottlenose dolphins

Estimates of the energetic costs of locomotion (COL) at different activity levels are necessary to answer fundamental eco-physiological questions and to understand the impacts of anthropogenic disturbance to marine mammals. We combined estimates of energetic costs derived from breath-by-breath respirometry with measurements of overall dynamic body acceleration (ODBA) from biologging tags to validate ODBA as a proxy for COL in trained common bottlenose dolphins (Tursiops truncatus). We measured resting metabolic rate (RMR); mean individual RMR was 0.71–1.42 times that of a similarly sized terrestrial mammal and agreed with past measurements that used breath-by-breath and flow-through respirometry. We also measured energy expenditure during submerged swim trials, at primarily moderate exercise levels. We subtracted RMR to obtain COL, and normalized COL by body size to incorporate individual swimming efficiencies. We found both mass-specific energy expenditure and mass-specific COL were linearly related with ODBA. Measurements of activity level and cost of transport (the energy required to move a given distance) improve understanding of the COL in marine mammals. The strength of the correlation between ODBA and COL varied among individuals, but the overall relationship can be used at a broad scale to estimate the energetic costs of disturbance and daily locomotion costs to build energy budgets, and investigate the costs of diving in free-ranging animals where bio-logging data are available. We propose that a similar approach could be applied to other cetacean species.

opencc-zeroDec 2021View details →

Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement

Volition - the sense of control or agency over one's voluntary actions - is widely recognized as the basis of both human subjective experience and natural behavior in non-human animals. To date, several human studies have found peaks in neural activity preceding voluntary actions, e.g. the readiness potential (RP), and some have shown upcoming actions could be decoded even before awareness. While these findings may pose a challenge to traditional accounts of human volition, some have proposed that random processes underlie and explain pre-movement neural activity. Here we seek to address part of this controversy by evaluating whether pre-movement neural activity in mice contains structure beyond that present in random neural activity. Implementing a self-initiated water-rewarded lever pull paradigm in mice while recording widefield [Ca++] neural activity we find that cortical activity changes in variance seconds prior to movement and that upcoming lever pulls or spontaneous body movements could be predicted between 1 second to more than 10 seconds prior to movement, similar to but even earlier than in human studies. We show that mice, like humans, are biased towards initiation of voluntary actions during specific phases of neural activity oscillations but that the pre-movement neural code in mice changes over time and is widely distributed as behavior prediction improved when using all vs single cortical areas. These findings support the presence of structured multi-second neural dynamics preceding voluntary action beyond that expected from random processes. Our results also suggest that neural mechanisms underlying self-initiated voluntary action could be preserved between mice and humans.

opencc-by-4.0Feb 2022View details →

A validation sample (input, label, and prediction) for the network: "SeisLoca_Unet"

This file is a validation sample including the input, label, and prediction. The data of variable "pred" can be obtained by running the prediction file: "prediction.py". Please remember to modified the data directory.

opencc-by-4.0Feb 2022View details →

Data for "Explainable Machine Learning for Predicting Homicide Clearance in the United States"

These pickle files can be used to replicate the analyses carried out in the paper "Explainable Machine Learning for Predicting Homicide Clearance in the United States", currently under review.

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-zeroDec 2021View details →

Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape

Predicting forest recovery at landscape scales will aid forest restoration efforts. The first step in successful forest recovery is tree recruitment. Forecasts of tree recruit abundance, derived from the landscape-scale distribution of seed sources (i.e. adult trees), could assist efforts to identify sites with high potential for natural regeneration. However, previous work has revealed wide variation in the effect of seed sources on seedling abundance, from positive to no effect. We quantified the relationship between adult tree seed sources and tree recruits, and predicted where natural recruitment would occur in a fragmented tropical agricultural landscape. We integrated species-specific tree crown maps generated from hyperspectral imagery and property boundaries data on individual property ownership with field data on the spatial distribution of tree recruits from five species. We then developed hierarchical Bayesian models to predict landscape-scale recruit abundance. Our models revealed that species-specific maps of tree crowns improved recruit abundance predictions. Conspecific crown area had a much stronger impact on recruitment abundance (8.00% increase in recruit abundance when conspecific tree density increases from zero to one tree; 95% CI: 0.80 to 11.57%) than heterospecific crown area (0.03% increase with the addition of a single heterospecific tree, 95% CI: -0.60 to 0.68%).Individual property ownership was also an important predictor of recruit abundance: the best performing model had varying effects of conspecific and heterospecific crown area on recruit abundance, depending on individual property ownership. We demonstrate how novel remote sensing approaches and cadastral data can be used to generate high-resolution and landscape-level maps of tree recruit abundance. Spatial models parameterized with field, cadastral, and remote sensing data are poised to assist decision support for forest landscape restoration.