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Communities of color have been historically excluded and marginalized in the ongoing conversations about climate preparedness and resilience at local, national, and global levels. Using focus groups composed of Boston communities of color (Asian American, Black, Latino, and Native American), this study aimed to understand their perspectives on climate change, providing in-depth knowledge of its impact and their views on preparedness and resilience. Research shows that these communities have long been concerned about climate change and emphasize the urgent need to improve climate preparedness. A multi-pronged approach is crucial: listening to communities of color to leverage local knowledge and leadership, engaging in community organizing, advocating for policy change, redirecting attention to institutional resources, and addressing systemic inequalities that exacerbate vulnerabilities. The findings of this study highlight the need for policy changes driven by collaboration and collective action, which can benefit those most negatively impacted by climate change and the lack of preparedness and resilience in Boston and beyond. 

Abstract Recent evidence indicates that individual behavioural variation in animals, defined as consistent individual differences in behaviour across contexts and time, influence ecological and evolutionary processes, and a growing number of studies demonstrate that individual behavioural variation can play a large role in shaping grouping dynamics among social animals. We studied the common degu,Octodon degus, a social rodent, to evaluate whether individual behavioural variation underlies social organization and the reproductive success of individuals within groups. We examined social groups in a population in central-north Chile during one breeding season, tested 67 adults in an open field test (i.e., the propensity to explore an unfamiliar environment) and 62 adults in a poke test (i.e., the propensity to charge an object) to quantify individual behavioural variation, determined assortment based on individual behavioural differences across 19 social groups, and performed genetic analyses to assess reproductive success. We found that the response to the poke test was repeatable, while none of the behaviours from an open field test were. The repeatable behaviour during the poke test was not associated to components of social organization (group composition), or to reproductive success. These findings imply that individual behavioural variation did not affect grouping patterns or direct fitness in this degu population. 

A key player in brain and neural crest development is the gastrulation-brain-homeobox (Gbx) transcription factor family member, Gbx2. During the early stages of gastrulation, Gbx2 RNA is broadly expressed in the prospective hindbrain and posterior region of the embryo. Later it becomes restricted to a sharp transverse band at the interface between the prospective midbrain and hindbrain, and is maintained in the anterior hindbrain in the developing neuroaxis (Bouillet et al. 1995; Li & Joyner 2001; Martinez-Barbera et al. 2001). Gbx2 regulates diverse developmental processes, including anteroposterior patterning within the mid-/hindbrain boundary and anterior hindbrain (Burroughs-Garcia et al. 2011). Expression of Gbx2 is required for the correct formation of rhombomeres r1-r3 (Wassarman et al. 1997). Loss of Gbx2 function in mouse embryos (Gbx2-/-), results in aberrant neural crest cell patterning leading to defects in neural crest derivatives and to abnormalities in the central nervous system, craniofacial, and cardiovascular components (Byrd & Meyers 2005). Li et al. (2009) demonstrated that Gbx2 is a direct target of the neural crest inducer Wnt, and is essential for neural crest induction. Together, these studies show that Gbx2 resides upstream in the genetic cascade controlling neural crest development and directly regulates the expression of key molecules involved in the migration and survival of neural crest cells that differentiate into neural and other components (e.g., connective tissue) of the head and heart. It was shown that Gbx2neo/neo mouse embryos, in which wild-type levels of Gbx2 expression is reduced to 6-10% of normal, are useful to further elucidate the complexity concerning the role of Gbx2 in anterior hindbrain development (Waters & Lewandoski 2006). Among other malformations, in Gbx2neo/neo embryos the mandibular branch of the trigeminal nerve (CNV3) is absent. CNV3 innervates the muscles of mastication (e.g., pterygoids, masseter, temporalis). However these muscles are needed to suckle and neonate (P0) Gbx2neo/neo mice are not able to suckle and die perinatally (Langenbach & van Eijden 2001). Here we describe the anatomy of the trigeminal ganglion and the trigeminal nerves in neonate Gbx2neo/neo mice and evaluate if there are differences in the muscles of mastication in these mice as compared to wildtype specimens. We expected that we find clear abnormalities in the thickness of the masseter, temporalis, and other muscles innervated by CNV3. However, this is not the case, indicating that the innervation of a muscle is not, as previously thought, needed for the differentiation of the muscles. Histological analyses will give insights into the muscle cell structure and if this is altered in the Gbx2neo/neo mice, which could be related to the loss of motor innervation. The research was funded by NSF EiR HBUC 18-522 awarded to JMZ (#2000005) and STW (#1956450). Bouillet et al. (1995). Dev Dyn, 204: 372-82. Burroughs-Garcia et al. (2011). Dev Dyn, 240: 828-38. Byrd & Meyers (2005). Dev Biol, 284: 233-45. Langenbach & van Eijden(2001). Am Zool, 41: 1338-51. Li et al. (2009). Development, 136: 3267-78. Li & Joyner (2001). Development, 128: 4979-91. Martinez-Barbera et al. (2001). Development, 128: 4789-800. Wassarman et al. (1997). Development, 124: 2923-34. Waters & Lewandoski (2006) Development, 133: 1991-2000. Funding or Support Information: The research was funded by NSF EiR HBUC 18-522 awarded to JMZ (#2000005) and STW(#1956450). 

Abstract PremiseThe selection ofArabidopsisas a model organism played a pivotal role in advancing genomic science. The competing frameworks to select an agricultural‐ or ecological‐based model species were rejected, in favor of building knowledge in a species that would facilitate genome‐enabled research. MethodsHere, we examine the ability of models based onArabidopsisgene expression data to predict tissue identity in other flowering plants. Comparing different machine learning algorithms, models trained and tested onArabidopsisdata achieved near perfect precision and recall values, whereas when tissue identity is predicted across the flowering plants using models trained onArabidopsisdata, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64. ResultsThe identity of belowground tissue can be predicted more accurately than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance fromArabidopsis.k‐nearest neighbors is the most successful algorithm, suggesting that gene expression signatures, rather than marker genes, are more valuable to create models for tissue and cell type prediction in plants. DiscussionOur data‐driven results highlight that the assertion that knowledge fromArabidopsisis translatable to other plants is not always true. Considering the current landscape of abundant sequencing data, we should reevaluate the scientific emphasis onArabidopsisand prioritize plant diversity. 

iCn3D was initially developed as a web-based 3D molecular viewer. It then evolved from visualization into a full-featured interactive structural analysis software. It became a collaborative research instrument through the sharing of permanent, shortened URLs that encapsulate not only annotated visual molecular scenes, but also all underlying data and analysis scripts in a FAIR manner. More recently, with the growth of structural databases, the need to analyze large structural datasets systematically led us to use Python scripts and convert the code to be used in Node. js scripts. We showed a few examples of Python scripts at https://github.com/ncbi/icn3d/tree/master/icn3dpython to export secondary structures or PNG images from iCn3D. Users just need to replace the URL in the Python scripts to export other annotations from iCn3D. Furthermore, any interactive iCn3D feature can be converted into a Node. js script to be run in batch mode, enabling an interactive analysis performed on one or a handful of protein complexes to be scaled up to analysis features of large ensembles of structures. Currently available Node. js analysis scripts examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode . This development will enable ensemble analyses on growing structural databases such as AlphaFold or RoseTTAFold on one hand and Electron Microscopy on the other. In this paper, we also review new features such as DelPhi electrostatic potential, 3D view of mutations, alignment of multiple chains, assembly of multiple structures by realignment, dynamic symmetry calculation, 2D cartoons at different levels, interactive contact maps, and use of iCn3D in Jupyter Notebook as described at https://pypi.org/project/icn3dpy .