The brachial plexus is a set of nerves that innervate the upper extremity and may become injured during the birthing process through an injury known as Neonatal Brachial Plexus Palsy. Studying the mechanisms of these injuries on infant cadavers is challenging due to the justifiable sensitivity surrounding testing. Thus, these specimens are generally unavailable to be used to investigate variations in brachial plexus injury mechanisms. Finite Element Models are an alternative way to investigate the response of the neonatal brachial plexus to loading. Finite Element Models allow a virtual representation of the neonatal brachial plexus to be developed and analyzed with dimensions and mechanical properties determined from experimental studies. Using ABAQUS software, a two-dimensional brachial plexus model was created to analyze how stresses and strains develop within the brachial plexus. The main objectives of this study were (1) to develop a model of the brachial plexus and validate it against previous literature, and (2) to analyze the effect of stress on the nerve roots based on variations in the angles between the nerve roots and the spinal cord. The predicted stress for C5 and C6 was calculated as 0.246 MPa and 0.250 MPa, respectively. C5 and C6 nerve roots experience the highest stress and the largest displacement in comparison to the lower nerve roots, which correlates with clinical patterns of injury. Even small (+/- 3 and 6 degrees) variations in nerve root angle significantly impacted the stress at the proximal nerve root. This model is the first step towards developing a complete three-dimensional model of the neonatal brachial plexus to provide the opportunity to more accurately assess the effect of the birth process on the stretch within the brachial plexus and the impact of biological variations in structure and properties on the risk of Neonatal Brachial Plexus Palsy.

Data curation encompasses a range of actions undertaken to ensure that research data are fit for purpose and available for discovery and reuse, and can help to improve the likelihood that data is more FAIR (Findable, Accessible, Interoperable, and Reusable). The Data Curation Network (DCN) has taken a collaborative approach to data curation, sharing curation expertise across a network of partner institutions and data repositories, and enabling those member institutions to provide expert curation for a wide variety of data types and discipline-specific datasets. This study sought to assess the satisfaction of researchers who had received data curation services, and to learn more about what curation actions were most valued by researchers. By surveying researchers who had deposited data into one of six academic generalist data repositories between 2019-2021, this study set out to collect feedback on the value of curation from the researchers themselves. A total of 568 researchers were surveyed; 42% (238) responded. Respondents were positive in their evaluation of the importance and value of curation, indicating that the participants not only value curation services, but are largely satisfied with the services provided. An overwhelming majority 97% of researchers agreed that data curation adds value to the data sharing process, 96% agreed it was worth the effort, and 90% felt more confident sharing their data due to the curation process. We share these results to provide insights into researchers' perceptions and experience of data curation, and to contribute evidence of the positive impact of curation on repository depositors. From the perspective of researchers we surveyed, curation is worth the effort, increases their comfort with data sharing, and makes data more findable, accessible, interoperable, and reusable.

Over 95% of plastid proteins are nuclear-encoded as their precursors containing an N-terminal extension known as the transit peptide (TP). Although highly variable, TPs direct the precursors through a conserved, posttranslational mechanism involving translocons in the outer (TOC) and inner envelope (TOC). The organelle import specificity is mediated by one or more components of the Toc complex. However, the high TP diversity creates a paradox on how the sequences can be specifically recognized. An emerging model of TP design is that they contain multiple loosely conserved motifs that are recognized at different steps in the targeting and transport process. Bioinformatics has demonstrated that many TPs contain semi-conserved physicochemical motifs, termed FGLK. In order to characterize FGLK motifs in TP recognition and import, we have analyzed two well-studied TPs from the precursor of RuBisCO small subunit (SStp) and ferredoxin (Fdtp). Both SStp and Fdtp contain two FGLK motifs. Analysis of large set mutations (∼85) in these two motifs using in vitro, in organello, and in vivo approaches support a model in which the FGLK domains mediate interaction with TOC34 and possibly other TOC components. In vivo import analysis suggests that multiple FGLK motifs are functionally redundant. Furthermore, we discuss how FGLK motifs are required for efficient precursor protein import and how these elements may permit a convergent function of this highly variable class of targeting sequences.

Data Information Literacy (DIL) project team worked with a faculty member and graduate students in natural resources.The faculty member's lab collects data on longitudinal changes in fish species and zooplankton-namely Lake Ontario.After interviewing the faculty member, a former student, and a lab technician, we determined that the DIL needs for this area were primarily data management and organization and data quality and documentation, including metadata and data description.We also placed a secondary focus on databases and data formats, data visualization and representation, and cultures of practice, including data sharing.To address these needs, the Cornel series of DIL workshops, open to the whole Cornell community, which was an introduction to data management and data management plans (DMPs), data organization, and data documentation.The second was a 6-week credit course on data management for graduate students in natural re taught by the faculty member and the data librarian, Sarah J. Wright, in the spring of 2013.The course built on the previous workshop topics and also include analysis, and visualization.Assessment for the workshops involved using post included formative "1-minute papers," very short, anonymous exercises per class; instructor feed-back on active learning exercises (including an optional DMP exercise graded by a rubric-see Appendix A to this chapter); and a final survey that asked students to self perceived skills before and after taking the class.The feedback was generally very positive, with the majority of students in the credit course indicating that they would recommend it to other graduate students in natural resources.They also reported an increase in their skill level This chapter will discuss the Cornell case study and our instructional approaches.The strengths of our program were that we • introduced graduate students to major • built and gathered modules, exercises

The RDMSG is co-sponsored by the Cornell University Library and Cornell's Office of the Vice-Provost for Research.

The Data Information Literacy Toolkit was the interview instrument developed and used by the Data Information Literacy project to better understand the educational needs of graduate students in managing, working with and curating their data sets. Results from the interviews conducted at Purdue University, Cornell University, the University of Minnesota and the University of Oregon were used to inform the educational programs offered by librarians at each institution in 2012 or 2013. The interview is based in part on the 12 data information literacy competencies as defined by Carlson, Fosmire, Miller and Sapp Nelson in an article that appeared in the April 2011 edition of portal: Libraries and the academy (doi: 10.1353/pla.2011.0022).

A computational modeling study was conducted using multinomial logistic regression to predict whether exposure to an unfamiliar regional accent of English would influence vowel categorization in (1) the exposure accent, (2) the native accent, and (3) another unfamiliar accent. We manipulated the number of talkers in the exposure data to determine whether talker variability influenced the efficacy of the training. Results showed a multiple-talker training benefit for the categorization of some vowels. Training also transferred to an untrained accent. Finally, the models predicted that exposure to an unfamiliar accent has a negative impact on vowel categorization in the native accent.

Faculty saw this as a critical competency for students to master. In considering this competency, faculty identified a need for more advanced instruction for students to maximize the effectiveness of their representations. Several of the faculty reported that students were able to learn the mechanical aspects of using visualization tools, but were not as skilled in the conceptual aspects of what makes a good visualization. As one faculty member stated “visualization is communication”. Students also struggle in making use of representations to evaluate the quality of their data or to “impact a specific decision.”