MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, https://doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph)

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses.

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase ( www.facebase.org , https://doi.org/10.25550/3-HXMC ) and GitHub ( https://github.com/jaydevine/MusMorph )

Threshold behavior in a Boolean Network model for SAT

sanp3px y (SAT)is the canonical NP-com lete roblem that lays an im ortant role in AI and has many ractical a lications in Com uter Science in general. Boolean networks (BN) are dynamicals ysmic that have recently been roos as an algorithm forsrp3 SATroblems [7]. We have carried out a detailed inves37px3 of the dynamical roerties of BNcorres onding to random SAT roblems of di#erents ize. We varied the roblemsbl by changing the number of variables and the number of claus7 in the Boolean formula

Integrating geographic information systems, spatial digital libraries and information spaces for conducting humanitarian assistance and disaster relief operations in urban environments

The GeoWorlds system integrates geographic information systems, spatial digital libraries and other information analysis, retrieval and collaboration tools. It supports multiple applications ranging from intelligence gathering to urban planning, to crisis management and response. Teams can rapidly assemble collections of document-based information from the World-Wide Web and other specialized information sources, visualize geo-spatial distribution of these collections and monitor events that might change conclusions or decisions formed on the basis of an initial information set. This functionality is provided within a framework that supports both synchronous and asynchronous collaboration over finding, filtering and organizing information and presenting it in a rich visualization environment