cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6843527
title: R script 1. R code used to create Fig. 1 and Fig. S1. from Morphological variation under domestication: how variable are chickens?

description: The process of domestication has long fascinated evolutionary biologists, yielding insights into the rapidity with which selection can alter behaviour and morphology. Previous studies on dogs, cattle and pigeons have demonstrated that domesticated forms show greater magnitudes of morphological variation than their wild ancestors. Here, we quantify variation in skull morphology, modularity and integration in chickens and compare those to the wild fowl using three-dimensional geometric morphometrics and multivariate statistics. Similar to other domesticated species, chickens exhibit a greater magnitude of variation in shape compared with their ancestors. The most variable part of the chicken skull is the cranial vault, being formed by dermal and neural crest-derived bones, its form possibly related to brain shape variation in chickens, especially in crested breeds. Neural crest-derived portions of the skull exhibit a higher amount of variation. Further, we find that the chicken skull is strongly integrated, confirming previous studies in birds, in contrast to the presence of modularity and decreased integration in mammals.
DOI: 10.6084/m9.figshare.6843527.v1
title: R script 1. R code used to create Fig. 1 and Fig. S1. from Morphological variation under domestication: how variable are chickens?

description: The process of domestication has long fascinated evolutionary biologists, yielding insights into the rapidity with which selection can alter behaviour and morphology. Previous studies on dogs, cattle and pigeons have demonstrated that domesticated forms show greater magnitudes of morphological variation than their wild ancestors. Here, we quantify variation in skull morphology, modularity and integration in chickens and compare those to the wild fowl using three-dimensional geometric morphometrics and multivariate statistics. Similar to other domesticated species, chickens exhibit a greater magnitude of variation in shape compared with their ancestors. The most variable part of the chicken skull is the cranial vault, being formed by dermal and neural crest-derived bones, its form possibly related to brain shape variation in chickens, especially in crested breeds. Neural crest-derived portions of the skull exhibit a higher amount of variation. Further, we find that the chicken skull is strongly integrated, confirming previous studies in birds, in contrast to the presence of modularity and decreased integration in mammals.
DOI: 10.6084/m9.figshare.6843521
title: Matrix of raw landmark data analysed in this study. from Morphological variation under domestication: how variable are chickens?

description: The process of domestication has long fascinated evolutionary biologists, yielding insights into the rapidity with which selection can alter behaviour and morphology. Previous studies on dogs, cattle and pigeons have demonstrated that domesticated forms show greater magnitudes of morphological variation than their wild ancestors. Here, we quantify variation in skull morphology, modularity and integration in chickens and compare those to the wild fowl using three-dimensional geometric morphometrics and multivariate statistics. Similar to other domesticated species, chickens exhibit a greater magnitude of variation in shape compared with their ancestors. The most variable part of the chicken skull is the cranial vault, being formed by dermal and neural crest-derived bones, its form possibly related to brain shape variation in chickens, especially in crested breeds. Neural crest-derived portions of the skull exhibit a higher amount of variation. Further, we find that the chicken skull is strongly integrated, confirming previous studies in birds, in contrast to the presence of modularity and decreased integration in mammals.
DOI: 10.6084/m9.figshare.6843521.v1
title: Matrix of raw landmark data analysed in this study. from Morphological variation under domestication: how variable are chickens?

description: The process of domestication has long fascinated evolutionary biologists, yielding insights into the rapidity with which selection can alter behaviour and morphology. Previous studies on dogs, cattle and pigeons have demonstrated that domesticated forms show greater magnitudes of morphological variation than their wild ancestors. Here, we quantify variation in skull morphology, modularity and integration in chickens and compare those to the wild fowl using three-dimensional geometric morphometrics and multivariate statistics. Similar to other domesticated species, chickens exhibit a greater magnitude of variation in shape compared with their ancestors. The most variable part of the chicken skull is the cranial vault, being formed by dermal and neural crest-derived bones, its form possibly related to brain shape variation in chickens, especially in crested breeds. Neural crest-derived portions of the skull exhibit a higher amount of variation. Further, we find that the chicken skull is strongly integrated, confirming previous studies in birds, in contrast to the presence of modularity and decreased integration in mammals.
DOI: 10.6084/m9.figshare.6843518
title: R script 3. R code used to conduct modularity and integration analyses. from Morphological variation under domestication: how variable are chickens?

description: The process of domestication has long fascinated evolutionary biologists, yielding insights into the rapidity with which selection can alter behaviour and morphology. Previous studies on dogs, cattle and pigeons have demonstrated that domesticated forms show greater magnitudes of morphological variation than their wild ancestors. Here, we quantify variation in skull morphology, modularity and integration in chickens and compare those to the wild fowl using three-dimensional geometric morphometrics and multivariate statistics. Similar to other domesticated species, chickens exhibit a greater magnitude of variation in shape compared with their ancestors. The most variable part of the chicken skull is the cranial vault, being formed by dermal and neural crest-derived bones, its form possibly related to brain shape variation in chickens, especially in crested breeds. Neural crest-derived portions of the skull exhibit a higher amount of variation. Further, we find that the chicken skull is strongly integrated, confirming previous studies in birds, in contrast to the presence of modularity and decreased integration in mammals.
DOI: 10.6084/m9.figshare.6843518.v1
title: R script 3. R code used to conduct modularity and integration analyses. from Morphological variation under domestication: how variable are chickens?

description: The process of domestication has long fascinated evolutionary biologists, yielding insights into the rapidity with which selection can alter behaviour and morphology. Previous studies on dogs, cattle and pigeons have demonstrated that domesticated forms show greater magnitudes of morphological variation than their wild ancestors. Here, we quantify variation in skull morphology, modularity and integration in chickens and compare those to the wild fowl using three-dimensional geometric morphometrics and multivariate statistics. Similar to other domesticated species, chickens exhibit a greater magnitude of variation in shape compared with their ancestors. The most variable part of the chicken skull is the cranial vault, being formed by dermal and neural crest-derived bones, its form possibly related to brain shape variation in chickens, especially in crested breeds. Neural crest-derived portions of the skull exhibit a higher amount of variation. Further, we find that the chicken skull is strongly integrated, confirming previous studies in birds, in contrast to the presence of modularity and decreased integration in mammals.
DOI: 10.6084/m9.figshare.6843506
title: Blue whale dive profile analyses from Context-dependent variability in blue whale acoustic behaviour

description: The 'BmDiveProfileAnalyses' workbook contains 121 tabs, each describing the blue whale dives recorded from an individual deployment. A more detailed description of the dive profile data is included on the first tab.
DOI: 10.6084/m9.figshare.6843506.v1
title: Blue whale dive profile analyses from Context-dependent variability in blue whale acoustic behaviour

description: The 'BmDiveProfileAnalyses' workbook contains 121 tabs, each describing the blue whale dives recorded from an individual deployment. A more detailed description of the dive profile data is included on the first tab.
DOI: 10.6084/m9.figshare.6843503
title: Blue whale call detections and determinations data from Context-dependent variability in blue whale acoustic behaviour

description: The 'BmCallDeterminations' workbook contains 42 tabs, each logging the occurrence of all blue whale calls detected from an individual deployment and used in the analysis based on caller determination. A more detailed description of the call data is included on the first tab.
DOI: 10.6084/m9.figshare.6843503.v1
title: Blue whale call detections and determinations data from Context-dependent variability in blue whale acoustic behaviour

description: The 'BmCallDeterminations' workbook contains 42 tabs, each logging the occurrence of all blue whale calls detected from an individual deployment and used in the analysis based on caller determination. A more detailed description of the call data is included on the first tab.
DOI: 10.6084/m9.figshare.6843500
title: Blue whale GEE - R code from Context-dependent variability in blue whale acoustic behaviour

description: The R-code used to assess variability in blue whale call and phrase production rates using Generalized Estimating Equations (GEEs).
DOI: 10.6084/m9.figshare.6843500.v1
title: Blue whale GEE - R code from Context-dependent variability in blue whale acoustic behaviour

description: The R-code used to assess variability in blue whale call and phrase production rates using Generalized Estimating Equations (GEEs).
DOI: 10.6084/m9.figshare.6843410
title: Figure 5A.pzfx

description: Effect of ET-1 on mRNA expression of NHE1 in rat PASMCs
DOI: 10.6084/m9.figshare.6843413
title: Fig 5C.pzfx

description: Effect of ET-1 on NHE1 protein expression in rat PASMCs
DOI: 10.6084/m9.figshare.6843410.v1
title: Figure 5A.pzfx

description: Effect of ET-1 on mRNA expression of NHE1 in rat PASMCs
DOI: 10.6084/m9.figshare.6843413.v1
title: Fig 5C.pzfx

description: Effect of ET-1 on NHE1 protein expression in rat PASMCs
DOI: 10.6084/m9.figshare.6843401
title: Fig 3.pzfx

description: Effect ET-1, NHE, and Rho kinase on rat PASMC proliferation.
DOI: 10.6084/m9.figshare.6843404
title: Figure 4.pzfx

description: Effect of ET-1, NHE, and Rho kinase on rat PASMC migration
DOI: 10.6084/m9.figshare.6843401.v1
title: Fig 3.pzfx

description: Effect ET-1, NHE, and Rho kinase on rat PASMC proliferation.
DOI: 10.6084/m9.figshare.6843404.v1
title: Figure 4.pzfx

description: Effect of ET-1, NHE, and Rho kinase on rat PASMC migration
DOI: 10.6084/m9.figshare.6843317
title: Fig 2B-C.pzfx

description: Effect of ET-1 on resting pH and NHE activity in HEPES-buffered solution in rat PASMCs
DOI: 10.6084/m9.figshare.6843317.v1
title: Fig 2B-C.pzfx

description: Effect of ET-1 on resting pH and NHE activity in HEPES-buffered solution in rat PASMCs
DOI: 10.6084/m9.figshare.6842750
title: Fig 2A.pzfx

description: Effect of ET-1 on rat PASMC resting pH in bicarbonate-buffered solution.
DOI: 10.6084/m9.figshare.6840707
title: Glucose-Induced beta-cell Dysfunction In Vivo: Evidence for a Causal Role of C-jun N-terminal Kinase Pathway

description: Glucose-Induced beta-cell Dysfunction In Vivo: Evidence for a Causal Role of C-jun N-terminal Kinase Pathway<br><br>Christine Tang • Lucy Shu Nga Yeung • Khajag Koulajian • Liling Zhang • Kevin Tai • Allen Volchuk • Adria Giacca<br><br><br><br>
DOI: 10.6084/m9.figshare.6842750.v1
title: Fig 2A.pzfx

description: Effect of ET-1 on rat PASMC resting pH in bicarbonate-buffered solution.
DOI: 10.6084/m9.figshare.6840707.v2
title: Glucose-Induced beta-cell Dysfunction In Vivo: Evidence for a Causal Role of C-jun N-terminal Kinase Pathway

description: Glucose-Induced beta-cell Dysfunction In Vivo: Evidence for a Causal Role of C-jun N-terminal Kinase Pathway<br><br>Christine Tang • Lucy Shu Nga Yeung • Khajag Koulajian • Liling Zhang • Kevin Tai • Allen Volchuk • Adria Giacca<br><br><br><br>
DOI: 10.6084/m9.figshare.6840707.v1
title: Glucose-Induced beta-cell Dysfunction In Vivo: Evidence for a Causal Role of C-jun N-terminal Kinase Pathway

description: Glucose-Induced beta-cell Dysfunction In Vivo: Evidence for a Causal Role of C-jun N-terminal Kinase Pathway<br><br>Christine Tang • Lucy Shu Nga Yeung • Khajag Koulajian • Liling Zhang • Kevin Tai • Allen Volchuk • Adria Giacca<br><br><br><br>
DOI: 10.6084/m9.figshare.6840599
title: Categorization and counts of NMJs following in-vivo two-color BTX experimentation

description: Mice were anesthetized via isoflurane inhalation and placed in a supine position. Neck hair was removed using a depilatory and the surgical site sterilized with povidone-iodine. An incision was made from chin to sternum and the skin retracted. The salivary glands were moved to the side to expose the STM. Surrounding connective tissue was gently removed and a non-saturating concentration (diluted 2 ug/mL in sterile lactated Ringers) of α-bungarotoxin conjugated to Alexa FluorTM 555 (ThermoFisher Scientific, Cat# 35451) applied to the surface of the muscles for 5 minutes (BTX-1). The neck cavity was then washed copiously with lactated Ringer’s ten times. For muscle injury experiments, superficial muscle fibers at areas close to the site of entry of the accessory nerve into the muscle were cut with a sterile #15 surgical scalpel. The contralateral muscle was also labeled with BTX-1, but not injured and used as an internal control (IC). For denervation experiments both STM muscles were labeled with BTX-1 and the accessory nerve of one muscle was severed near its insertion site into the muscle. The other muscle was spared and used as an IC. The salivary glands were moved back into place following labeling, washing, and injury. The skin was sutured with 8-0 silk and treated with 2% lidocaine hydrochloride jelly. Animals were allowed to convalesce for ten days post-surgery. Endpoints for the studies were P38, P66, P160, and P450. were counted and categorized using a Leica DMRX epifluorescence microscope and a 40X Oil objective (NA 1.4). Junctions were considered fragmented if they had 5 or more non-connected clusters of AChRs in close proximity to each other on the same myofiber. Junctions were categorized based of the robustness of a primary fluorescent bungarotoxin stain and a second label, as well as if they were continuous (&lt; 5 AChR clusters), or fragmented (&gt; 5 AChR clusters). They were classified as stable if BTX-1 and BTX-2 labels were equally robust, or as dynamic if BTX-1 label was mostly absent and BTX-2 label intense. In many cases dynamic junctions did not completely lose BTX-1, and small puncta of fluorescence was seen. It is likely that these puncta are BTX-1 labelled receptors tethered to the basal lamina and demarcate the initial synaptic site before myofiber degeneration. If both the BTX-1 and BTX-2 labels were faint, junctions were classified as lost, but this was rarely observed.
DOI: 10.6084/m9.figshare.6840575
title: Confocal micrograph measurements of GRMD and WT canine nueromuscular junctions

description: Confocal micrographs were collected on either a Leica TPS II SP5 or a Zeiss LSM780 microscope using a 40X Oil objective (NA 1.4). Step size was set between 300-500 nm. Laser lines 405 nm, 458 nm, 488 nm, 563 nm, and 633 nm were used as necessary. Images were analyzed in ImageJ (ImageJ, RRID:SCR_003070) by creating maximum projection images from collected stacks. Junctional Area was calculated by thresholding the fluorescent bungarotoxin labeled channel and drawing a polygon around the object and measuring the shape’s area. Receptor Area was measured as the thresholded area of the BTX label within the circumscribed region of the AChR. Dispersion Index was calculated as Receptor Area/Junctional Area. The number of AChR fragments (fragmentation) was measured using the objects counter in ImageJ by setting the minimum area for counts as 5 μm2. tSC counts were made by counting only fluorescently labeled Schwann cells in proximity to the endplate having coincident nuclear label via DAPI. Axon branch points were measured by creating 3D rotations of the confocal images of fluorescently labeled motor neurons, and then counting the number of times the axon split. Small varicosities of the motor terminal were not counted as branches.<br>Included is a lif file of confocal collections. Best viewed in ImageJ
DOI: 10.6084/m9.figshare.6840599.v1
title: Categorization and counts of NMJs following in-vivo two-color BTX experimentation

description: Mice were anesthetized via isoflurane inhalation and placed in a supine position. Neck hair was removed using a depilatory and the surgical site sterilized with povidone-iodine. An incision was made from chin to sternum and the skin retracted. The salivary glands were moved to the side to expose the STM. Surrounding connective tissue was gently removed and a non-saturating concentration (diluted 2 ug/mL in sterile lactated Ringers) of α-bungarotoxin conjugated to Alexa FluorTM 555 (ThermoFisher Scientific, Cat# 35451) applied to the surface of the muscles for 5 minutes (BTX-1). The neck cavity was then washed copiously with lactated Ringer’s ten times. For muscle injury experiments, superficial muscle fibers at areas close to the site of entry of the accessory nerve into the muscle were cut with a sterile #15 surgical scalpel. The contralateral muscle was also labeled with BTX-1, but not injured and used as an internal control (IC). For denervation experiments both STM muscles were labeled with BTX-1 and the accessory nerve of one muscle was severed near its insertion site into the muscle. The other muscle was spared and used as an IC. The salivary glands were moved back into place following labeling, washing, and injury. The skin was sutured with 8-0 silk and treated with 2% lidocaine hydrochloride jelly. Animals were allowed to convalesce for ten days post-surgery. Endpoints for the studies were P38, P66, P160, and P450. were counted and categorized using a Leica DMRX epifluorescence microscope and a 40X Oil objective (NA 1.4). Junctions were considered fragmented if they had 5 or more non-connected clusters of AChRs in close proximity to each other on the same myofiber. Junctions were categorized based of the robustness of a primary fluorescent bungarotoxin stain and a second label, as well as if they were continuous (&lt; 5 AChR clusters), or fragmented (&gt; 5 AChR clusters). They were classified as stable if BTX-1 and BTX-2 labels were equally robust, or as dynamic if BTX-1 label was mostly absent and BTX-2 label intense. In many cases dynamic junctions did not completely lose BTX-1, and small puncta of fluorescence was seen. It is likely that these puncta are BTX-1 labelled receptors tethered to the basal lamina and demarcate the initial synaptic site before myofiber degeneration. If both the BTX-1 and BTX-2 labels were faint, junctions were classified as lost, but this was rarely observed.
DOI: 10.6084/m9.figshare.6840575.v1
title: Confocal micrograph measurements of GRMD and WT canine nueromuscular junctions

description: Confocal micrographs were collected on either a Leica TPS II SP5 or a Zeiss LSM780 microscope using a 40X Oil objective (NA 1.4). Step size was set between 300-500 nm. Laser lines 405 nm, 458 nm, 488 nm, 563 nm, and 633 nm were used as necessary. Images were analyzed in ImageJ (ImageJ, RRID:SCR_003070) by creating maximum projection images from collected stacks. Junctional Area was calculated by thresholding the fluorescent bungarotoxin labeled channel and drawing a polygon around the object and measuring the shape’s area. Receptor Area was measured as the thresholded area of the BTX label within the circumscribed region of the AChR. Dispersion Index was calculated as Receptor Area/Junctional Area. The number of AChR fragments (fragmentation) was measured using the objects counter in ImageJ by setting the minimum area for counts as 5 μm2. tSC counts were made by counting only fluorescently labeled Schwann cells in proximity to the endplate having coincident nuclear label via DAPI. Axon branch points were measured by creating 3D rotations of the confocal images of fluorescently labeled motor neurons, and then counting the number of times the axon split. Small varicosities of the motor terminal were not counted as branches.<br>Included is a lif file of confocal collections. Best viewed in ImageJ
DOI: 10.6084/m9.figshare.6840536
title: The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014

description: Over the past half century, countries of Mainland Southeast Asia (MSEA) – Cambodia, Laos, Myanmar, Thailand, and Vietnam – have witnessed increases in commercialized agriculture with rapid expansions of boom-crop plantations. We used MODIS EVI and SWIR time-series from 2001–2014 to classify tree-cover changes across MSEA and performed a supervised change detection using an upscaling approach by deriving samples from existing Landsat classifications. We used the random forest classifier and distinguished 24 classes (16 representing boom-crops) with an accuracy of 82.2%. Boom-crops occupy about 18% of the landscape (8% of which is rubber). Since 2003 74,960 km<sup>2</sup> of rubber have been planted; 70% of rubber is planted on former forest land, and 30% on low vegetation area (mainly former croplands). Timing, patterns of change, and deforestation rates, however, differ among the MSEA countries and the high spatial and temporal detail of our classification allowed us to quantify dynamics and discuss political and socio-economic drivers of change.
DOI: 10.6084/m9.figshare.6840536.v1
title: The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014

description: Over the past half century, countries of Mainland Southeast Asia (MSEA) – Cambodia, Laos, Myanmar, Thailand, and Vietnam – have witnessed increases in commercialized agriculture with rapid expansions of boom-crop plantations. We used MODIS EVI and SWIR time-series from 2001–2014 to classify tree-cover changes across MSEA and performed a supervised change detection using an upscaling approach by deriving samples from existing Landsat classifications. We used the random forest classifier and distinguished 24 classes (16 representing boom-crops) with an accuracy of 82.2%. Boom-crops occupy about 18% of the landscape (8% of which is rubber). Since 2003 74,960 km<sup>2</sup> of rubber have been planted; 70% of rubber is planted on former forest land, and 30% on low vegetation area (mainly former croplands). Timing, patterns of change, and deforestation rates, however, differ among the MSEA countries and the high spatial and temporal detail of our classification allowed us to quantify dynamics and discuss political and socio-economic drivers of change.