cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27211994.v1
title: TMEM164 promotes ferroptosis by selectively mediating ATG5-dependent autophagosome formation to inhibit the progression of LUAD

description: The study focuses on lung adenocarcinoma (LUAD), a predominant type of lung cancer. Despite advancements in diagnostics and molecular therapies, treatment remains challenging due to its low five-year survival rate. This study aims to investigate the role of the transmembrane protein TMEM164 in ferroptosis and anti-tumor immunity in LUAD, and to evaluate its potential as a therapeutic target. Through cellular experiments (such as QPCR, WB, CCK-8, EdU, Transwell, flow cytometry, CO-IP) and animal model experiments (including HE staining and IHC analysis), the relationship between TMEM164 expression and LUAD progression was explored, with particular attention to its mechanisms in ferroptosis and autophagy. The results show that TMEM164 expression is downregulated in LUAD and is associated with poor prognosis. Increasing TMEM164 expression significantly inhibits cell proliferation, migration, and invasion, while promoting an autophagy process dependent on ATG5 for autophagosome formation, thus facilitating ferroptosis. In mouse models, high TMEM164 expression combined with anti-PD-1 antibodies demonstrated synergistic anti-tumor effects. These findings highlight the critical role of TMEM164 in LUAD, suggesting that modulating TMEM164 expression could open new avenues for LUAD treatment.
DOI: 10.6084/m9.figshare.27958395
title: Dataset and Supplementary Material for the Manuscript entitled "Desert Ants Avoid Ambush Predator Pits Depending on Position, Visual Landmarks and Trapped Nestmates" Published in Royal Society Open Science

description: We investigated the factors influencing the likelihood of desert ant foragers falling into pitfall traps, simulating common predators such as antlions or spiders. We varied the spatial configuration of the pitfall traps, the presence of trapped nestmates, and the availability of signs to study the workers' susceptibility to falling into pits and their foraging success. Our results showed that a zigzag configuration of pits led to the fewest workers reaching the food reward, compared to straight-line and right-angled-triangle configurations. Over successive runs, workers became more efficient, with fewer falls into pits and faster food discovery, suggesting they either learned the locations of the pits or became more cautious. In any case, the ants learned about important landscape features close to their nest and the best way of navigating around it. Additionally, we examined the impact of trapped nestmates and signs. We found that pits marked with signs resulted in fewer falls. However, pits containing trapped nestmates attracted more ants, impaired food discovery, and increased falling rates into pits.
DOI: 10.6084/m9.figshare.27958395.v1
title: Dataset and Supplementary Material for the Manuscript entitled "Desert Ants Avoid Ambush Predator Pits Depending on Position, Visual Landmarks and Trapped Nestmates" Published in Royal Society Open Science

description: We investigated the factors influencing the likelihood of desert ant foragers falling into pitfall traps, simulating common predators such as antlions or spiders. We varied the spatial configuration of the pitfall traps, the presence of trapped nestmates, and the availability of signs to study the workers' susceptibility to falling into pits and their foraging success. Our results showed that a zigzag configuration of pits led to the fewest workers reaching the food reward, compared to straight-line and right-angled-triangle configurations. Over successive runs, workers became more efficient, with fewer falls into pits and faster food discovery, suggesting they either learned the locations of the pits or became more cautious. In any case, the ants learned about important landscape features close to their nest and the best way of navigating around it. Additionally, we examined the impact of trapped nestmates and signs. We found that pits marked with signs resulted in fewer falls. However, pits containing trapped nestmates attracted more ants, impaired food discovery, and increased falling rates into pits.
DOI: 10.15468/dl.6r5wqq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Caballerocotyla llewelyni Kohn & Justo, 2006",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016355-241126133413365/datasets/export for details.
DOI: 10.5281/zenodo.14272176
title: METHODS OF TRAINING AND ADAPTATION OF AI AGENTS IN COMPLEX PROCESS CONTROL SYSTEMS

description: The article presents a study of modern methods of training and adaptation of artificial agents used in managing complex processes, which are characterized by a high level of uncertainty and the need for prompt response to changes. Key methodological approaches such as machine learning and neuroevolution are discussed. These approaches allow AI agents to accumulate knowledge about the behavior of systems continuously, analyze external changes, and adjust the management strategy depending on environmental conditions, which significantly increases their ability to predict and prevent possible failures in management.

In the course of the study, models were considered that allow automating the execution of complex, multitasking processes, minimizing human intervention, and reducing the likelihood of errors. In addition, the presented methods provide high flexibility and scalability of systems, which is especially important in industrial and technological industries, where stability and reliability are critical. The results showed that AI agents with adaptive learning capabilities can increase operational efficiency while reducing costs and optimizing resource use. The conclusion highlights the prospects of using artificial intelligence to build highly autonomous control systems capable of responding to dynamic challenges, which opens up new horizons for automation and intellectual support in industrial production, logistics, and other key areas.

Thus, the article makes a significant contribution to understanding the role of AI in management modernization, offering practical recommendations on the implementation of intelligent agents in real-world scenarios to increase productivity and sustainability.
DOI: 10.5281/zenodo.14272177
title: METHODS OF TRAINING AND ADAPTATION OF AI AGENTS IN COMPLEX PROCESS CONTROL SYSTEMS

description: The article presents a study of modern methods of training and adaptation of artificial agents used in managing complex processes, which are characterized by a high level of uncertainty and the need for prompt response to changes. Key methodological approaches such as machine learning and neuroevolution are discussed. These approaches allow AI agents to accumulate knowledge about the behavior of systems continuously, analyze external changes, and adjust the management strategy depending on environmental conditions, which significantly increases their ability to predict and prevent possible failures in management.

In the course of the study, models were considered that allow automating the execution of complex, multitasking processes, minimizing human intervention, and reducing the likelihood of errors. In addition, the presented methods provide high flexibility and scalability of systems, which is especially important in industrial and technological industries, where stability and reliability are critical. The results showed that AI agents with adaptive learning capabilities can increase operational efficiency while reducing costs and optimizing resource use. The conclusion highlights the prospects of using artificial intelligence to build highly autonomous control systems capable of responding to dynamic challenges, which opens up new horizons for automation and intellectual support in industrial production, logistics, and other key areas.

Thus, the article makes a significant contribution to understanding the role of AI in management modernization, offering practical recommendations on the implementation of intelligent agents in real-world scenarios to increase productivity and sustainability.
DOI: 10.15468/dl.mycyc8
title: Occurrence Download

description: A dataset containing 10220 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sula leucogaster (Boddaert, 1783)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 10220 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016357-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9yansp
title: Occurrence Download

description: A dataset containing 833 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nezumia sclerorhynchus (Valenciennes, 1838)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 833 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016358-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14271781
title: An annual 30 m cultivated pasture dataset of the Tibetan Plateau from 1988 to 2021

description: Cultivated pastures have rapidly developed across the Tibetan Plateau over the past several decades, raising concerns about grassland degradation. Accordingly, considerable attention is focused on the protection of grassland ecosystems. However, the high-resolution spatial distribution of cultivated pastures on the Tibetan Plateau remains poorly understood, primarily due to the difficulty of discriminating cultivated pastures from non-cultivated pastures using remote sensing techniques. The absence of such information hinders efficient agricultural and livestock husbandry management, making it challenging to support ecological protection and restoration efforts. Here, we mapped the cultivated pastures on the Tibetan Plateau at a 30-m resolution for the years 1988 to 2021 using the Landsat data on the Google Earth Engine (GEE) cloud computing platform. We built a Random Forest (RF) binary classification model with inputs of the spectral-temporal metrics of Landsat images acquired in the growing season, as well as ancillary topographic data. The model was trained using carefully selected training samples and validated against 2,000 independent random reference points. The model achieved an overall accuracy of 97.05% ± 0.4% and an F1 spatial consistency score of 82.51% ± 14.22% (Precision: 90.04% ± 6.18%, Recall: 76.74% ± 9.91%), suggesting high confidence in mapping the distribution of cultivated pastures. We produced a dataset of cultivated pasture maps for the years from 1988 to 2021 for Qinghai Province and the Tibet Autonomous Region on the Tibetan Plateau, covering 77% of the plateau. To our knowledge, we are the first to map cultivated pastures on the Tibetan Plateau, and our RF binary classification approach holds promise in identifying cultivated pastures in other regions of the world, which could prove invaluable for scientists, policymakers, ecological conservation practitioners, and herdsmen.
DOI: 10.15468/dl.xabyg4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pelecanus leucogaster Boddaert, 1783",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016359-241126133413365/datasets/export for details.
DOI: 10.48677/7d4f40d6-c002-4b0d-a0e7-3c5a2bccbc1f
title: Land Cover (LC) Operat 2022160

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.15468/dl.kjdqan
title: Occurrence Download

description: A dataset containing 255 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepidotrigla microptera Günther, 1873",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 255 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016360-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48677/78a95d85-609d-4083-af6e-936e255f8ccc
title: Land Cover (LC) Operat 2022250

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.48677/14952440-c1ae-4c3f-a602-75319c7cb59e
title: Land Cover (LC) Operat 2022260

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.48677/2b644352-9a82-447a-b996-24a862f71bdd
title: Land Cover (LC) Operat 2022350

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.48677/633db2c3-7e43-4bb0-9be1-786b5b1ca9a7
title: Land Cover (LC) Operat 2022360

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.15468/dl.y44mdc
title: Occurrence Download

description: A dataset containing 3335 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (NSW DPI Biosecurity Collections, EOD – eBird Observation Dataset, Snow Entomological Museum Collection, iNaturalist Research-grade Observations, Natural History Museum, Vienna - Herbarium W, INSDC Sequences, International Barcode of Life project (iBOL), A global database for the distributions of crop wild relatives, Pl@ntNet automatically identified occurrences, Lichens at Herbarium Berolinense, Berlin (B), RBINS DaRWIN, RBINS Mammal collection, Centre for Genetic Resources, the Netherlands, PGR passport data, Bioversity Collecting Mission Database, Pl@ntNet observations, The System-wide Information Network for Genetic Resources (SINGER))",
 "Geometry POLYGON((50.2779 37.14477,50.03153 36.76401,50.82664 36.30486,51.09541 36.71362,50.2779 37.14477))",
 "IucnRedListCategory is one of (CR, EN, VU, NT, LC)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 3335 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016361-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kfk3mu
title: Occurrence Download

description: A dataset containing 299 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grus nigricollis Prjevalsky, 1876",
 "Year 1981-2010"
 ]
}

 The dataset includes 299 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016364-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5z3s7n
title: Occurrence Download

description: A dataset containing 249 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepidotrigla guentheri Hilgendorf, 1879",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 249 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016363-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48677/25c3c6d3-e656-4ced-9da7-3415fcbebd69
title: Land Cover (LC) Operat 2022150

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.48677/3b2a6053-a6e1-4592-bf67-18b967ff4da2
title: Land Cover (LC) Operat 2022370

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.48610/1a19d80
title: Data associated with publication: O'Brien et al. "The genetic architecture of polygenic adaptation under a network-derived trait"

description: This paper investigates the evolutionary outcomes of populations adapting towards a phenotypic optimum when the phenotype is a polygenic trait described by either an additive quantitative genetics model or a simple gene regulatory network, the negative autoregulation (NAR) motif. Under the network model, the trait is constructed by solving an ordinary differential equation, which is modified by mutations at loci along the genome. Two treatments of the NAR are constructed, with different coefficients in the equation being modified by mutations. Simulations were carried out in a custom version of SLiM 4.1 available at https://github.com/nobrien97/SLiM/releases/tag/PolygenicNAR2024. This dataset contains csv files measuring trait responses (detailing how the mean population trait value changed during adaptation), mutation information (allelic effect sizes, origin times, and frequencies), linkage disequilibrium, epistasis strength, and genetic variance.
DOI: 10.48677/70326ef8-5b46-41ef-949b-2c4301b554ac
title: Land Cover (LC) Operat 2022450

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.48677/fefa3eee-70de-4ac1-851e-0e601c45b9d5
title: Land Cover (LC) Operat 2022460

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.15468/dl.m58ms7
title: Occurrence Download

description: A dataset containing 201 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lagenorhynchus obliquidens Gill, 1865",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 201 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016366-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48677/0e1b302e-7933-42a2-8c13-b3cee386c4e5
title: Land Cover (LC) Operat 2022470

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.48677/ad108962-bea4-4950-b41f-930bf08391a4
title: Land Cover (LC) Operat 2022650

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.48677/253590eb-69a7-4f5c-b6e8-ed734fd11964
title: Land Cover (LC) Operat 2023370

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.48677/7841eea3-5058-4e4a-a29f-2179bfc6defc
title: Land Cover (LC) Operat 2023470

description: Das Produkt Land Cover liefert österreichweit Informationen zu 6 Bodenbedeckungsklassen 
(hohe, mittlere und niedrige Vegetation, Gebäude, Bodenflächen, Gewässer). 
Der Datensatz Land Cover wird aus Fernerkundungsdaten (Digitalen Orthophotos, Digitalem Oberflächenmodell aus Luftbildern, Digitalem Geländehöhenmodell aus ALS-Befliegung) mittels automatisierter Prozesse abgeleitet und liegt als Raster mit einer Auflösung von 20 cm vor.
DOI: 10.15468/dl.ty8skw
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sagmatias obliquidens (Gill, 1865)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 38 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016368-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.