A Glass Polyalkenoate Cement Carrier for Bone Morphogenetic Proteins

This work considers a glass polyalkenoate cement (GPC)-based carrier for the effective delivery of bone morphogenetic proteins (BMPs) at an implantation site. A 0.12 CaO–0.04 SrO–0.36 ZnO–0.48 SiO2 based glass and poly(acrylic acid) (PAA, Mw 213,000) were employed for the fabrication of the GPC. The media used for the water source in the GPC reaction was altered to produce a series of GPCs. The GPC liquid media was either 100 % distilled water with additions of albumin at 0, 2, 5 and 8 wt% of the glass content, 100 % formulation buffer (IFB), and 100 % BMP (150 µg rhBMP-2/ml IFB). Rheological properties, compressive strength, ion release profiles and BMP release were evaluated. Working times (Tw) of the formulated GPCs significantly increased with the addition of 2 % albumin and remained constant with further increases in albumin content or IFB solutions. Setting time (Ts) experienced an increase with 2 and 5 % albumin content, but a decrease with 8 % albumin. Changing the liquid source to IFB containing 5 % albumin had no significant effect on Ts compared to the 8 % albumin-containing BT101. Replacing the albumin with IFB/BMP-2 did not significantly affect Tw. However, Ts increased for the BT101_BMP-2 containing GPCs, compared to all other samples. The compressive strength evaluated 1 day post cement mixing was not affected significantly by the incorporation of BMPs, but the ion release did increase from the cements, particularly for Zn and Sr. The GPCs released BMP after the first day, which decreased in content during the following 6 days. This study has proven that BMPs can be immobilized into GPCs and may result in novel materials for clinical applications

An Analysis of the Abstracts Presented at the Annual Meetings of the Society for Neuroscience from 2001 to 2006

Annual meeting abstracts published by scientific societies often contain rich arrays of information that can be computationally mined and distilled to elucidate the state and dynamics of the subject field. We extracted and processed abstract data from the Society for Neuroscience (SFN) annual meeting abstracts during the period 2001–2006 in order to gain an objective view of contemporary neuroscience. An important first step in the process was the application of data cleaning and disambiguation methods to construct a unified database, since the data were too noisy to be of full utility in the raw form initially available. Using natural language processing, text mining, and other data analysis techniques, we then examined the demographics and structure of the scientific collaboration network, the dynamics of the field over time, major research trends, and the structure of the sources of research funding. Some interesting findings include a high geographical concentration of neuroscience research in the north eastern United States, a surprisingly large transient population (66% of the authors appear in only one out of the six studied years), the central role played by the study of neurodegenerative disorders in the neuroscience community, and an apparent growth of behavioral/systems neuroscience with a corresponding shrinkage of cellular/molecular neuroscience over the six year period. The results from this work will prove useful for scientists, policy makers, and funding agencies seeking to gain a complete and unbiased picture of the community structure and body of knowledge encapsulated by a specific scientific domain

Deciphering the Preference and Predicting the Viability of Circular Permutations in Proteins

Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology

Workflow and Atlas System for Brain-Wide Mapping of Axonal Connectivity in Rat

Detailed knowledge about the anatomical organization of axonal connections is important for understanding normal functions of brain systems and disease-related dysfunctions. Such connectivity data are typically generated in neuroanatomical tract-tracing experiments in which specific axonal connections are visualized in histological sections. Since journal publications typically only accommodate restricted data descriptions and example images, literature search is a cumbersome way to retrieve overviews of brain connectivity. To explore more efficient ways of mapping, analyzing, and sharing detailed axonal connectivity data from the rodent brain, we have implemented a workflow for data production and developed an atlas system tailored for online presentation of axonal tracing data. The system is available online through the Rodent Brain WorkBench (www.rbwb.org; Whole Brain Connectivity Atlas) and holds experimental metadata and high-resolution images of histological sections from experiments in which axonal tracers were injected in the primary somatosensory cortex. We here present the workflow and the data system, and exemplify how the online image repository can be used to map different aspects of the brain-wide connectivity of the rat primary somatosensory cortex, including not only presence of connections but also morphology, densities, and spatial organization. The accuracy of the approach is validated by comparing results generated with our system with findings reported in previous publications. The present study is a contribution to a systematic mapping of rodent brain connections and represents a starting point for further large-scale mapping efforts

Substituted 2‑(Dimethylamino)biphenyl-2′-carboxaldehydes as Substrates for Studying n→π* Interactions and as a Promising Framework for Tracing the Bürgi–Dunitz Trajectory

The Bürgi–Dunitz trajectory traces points along the pathway of bond formation between a nucleophile and electrophile. Previous X-ray crystallographic studies of some molecules containing a nucleophilic nitrogen atom and electrophilic carbonyl group provided some initial evidence for various degrees of bond formation via initial n→π* interactions. Observation of a complete set of points along the trajectory, however, has not yet been attained. In this paper, we present a DFT computational study investigating substituted 2-(dimethylamino)­biphenyl-2′-carboxaldehydes as substrates for further examination of n→π* interactions and as a potential framework for more complete tracing of the Bürgi–Dunitz trajectory. These compounds are particulary suitable for study because of the rotational freedom granted by the C–C bond connecting the two aromatic rings allowing the molecule to choose the degree of interaction between the two complementary groups. The extent of interaction is measured by interatomic distance, NBO second-order perturbative analysis energies, volume of transferred electron density as provided by ETS-NOCV analysis, and differences in energies between models that allow for n→π* interactions and those that do not. A series of substituted biphenyls are ultimately identified as future synthetic targets that have maximum potential for providing improved tracing of the Bürgi–Dunitz trajectory

Fostering shared intentionality for diverse learners through cross-sensory interaction design

As the theme of this year’s conference suggests, cognitive diversity among learners and educators is increasingly acknowledged. However, in our societies that increasingly require advanced education, training, and technical skills, the pressure to standardize learning objectives, delivery techniques and delivery tools, especially online, is high. In these situations, learners and educators of diverse cognitive phenotypes and abilities experience learning environments that are a poor match for their abilities, making effective delivery of educational content challenging. In addition to learning about our work developing cross-sensory interaction design principles, workshop participants will share lived experiences of the pandemic-induced experimentation in online learning over the past two years to co-design prototypes that address pain points identified by participants