291 research outputs found
Normal table of Xenopus development: a new graphical resource
© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Zahn, N., James-Zorn, C., Ponferrada, V. G., Adams, D. S., Grzymkowski, J., Buchholz, D. R., Nascone-Yoder, N. M., Horb, M., Moody, S. A., Vize, P. D., & Zorn, A. M. Normal table of Xenopus development: a new graphical resource. Development, 149(14), (2022): dev200356, https://doi.org/10.1242/dev.200356.Normal tables of development are essential for studies of embryogenesis, serving as an important resource for model organisms, including the frog Xenopus laevis. Xenopus has long been used to study developmental and cell biology, and is an increasingly important model for human birth defects and disease, genomics, proteomics and toxicology. Scientists utilize Nieuwkoop and Faber's classic ‘Normal Table of Xenopus laevis (Daudin)’ and accompanying illustrations to enable experimental reproducibility and reuse the illustrations in new publications and teaching. However, it is no longer possible to obtain permission for these copyrighted illustrations. We present 133 new, high-quality illustrations of X. laevis development from fertilization to metamorphosis, with additional views that were not available in the original collection. All the images are available on Xenbase, the Xenopus knowledgebase (http://www.xenbase.org/entry/zahn.do), for download and reuse under an attributable, non-commercial creative commons license. Additionally, we have compiled a ‘Landmarks Table’ of key morphological features and marker gene expression that can be used to distinguish stages quickly and reliably (https://www.xenbase.org/entry/landmarks-table.do). This new open-access resource will facilitate Xenopus research and teaching in the decades to come.This work was supported by grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development [P41 HD064556 to A.M.Z. and P.D.V. (Xenbase)] and the National Institute of Child Health and Human Development [P40-OD010997 and R24-OD030008 to M.H. (National Xenopus Resource)]. Open Access funding provided by Cincinnati Children's Hospital Medical Center. Deposited in PMC for immediate release
The Seven-sphere and its Kac-Moody Algebra
We investigate the seven-sphere as a group-like manifold and its extension to
a Kac-Moody-like algebra. Covariance properties and tensorial composition of
spinors under are defined. The relation to Malcev algebras is
established. The consequences for octonionic projective spaces are examined.
Current algebras are formulated and their anomalies are derived, and shown to
be unique (even regarding numerical coefficients) up to redefinitions of the
currents. Nilpotency of the BRST operator is consistent with one particular
expression in the class of (field-dependent) anomalies. A Sugawara construction
is given.Comment: 22 pages. Macropackages used: phyzzx, epsf. Three epsf figure files
appende
Bostonia: The Boston University Alumni Magazine. Volume 10
Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs
Academic team formation as evolving hypergraphs
This paper quantitatively explores the social and socio-semantic patterns of
constitution of academic collaboration teams. To this end, we broadly underline
two critical features of social networks of knowledge-based collaboration:
first, they essentially consist of group-level interactions which call for
team-centered approaches. Formally, this induces the use of hypergraphs and
n-adic interactions, rather than traditional dyadic frameworks of interaction
such as graphs, binding only pairs of agents. Second, we advocate the joint
consideration of structural and semantic features, as collaborations are
allegedly constrained by both of them. Considering these provisions, we propose
a framework which principally enables us to empirically test a series of
hypotheses related to academic team formation patterns. In particular, we
exhibit and characterize the influence of an implicit group structure driving
recurrent team formation processes. On the whole, innovative production does
not appear to be correlated with more original teams, while a polarization
appears between groups composed of experts only or non-experts only, altogether
corresponding to collectives with a high rate of repeated interactions
Bowling Together: Scientific Collaboration Networks of Demographers at European Population Conferences
Studies of collaborative networks of demographers are relatively scarce. Similar studies in other social sciences provide insight into scholarly trends of both the fields and characteristics of their successful scientists. Exploiting a unique database of metadata for papers presented at six European Population Conferences, this report explores factors explaining research collaboration among demographers. We find that (1) collaboration among demographers has increased over the past 10 years, however, among co-authored papers, collaboration across institutions remains relatively unchanged over the period, (2) papers based on core demographic subfields such as fertility, mortality, migration and data and methods are more likely to involve multiple authors and (3) multiple author teams that are all female are less likely to co-author with colleagues in different institutions. Potential explanations for these results are discussed alongside comparisons with similar studies of collaboration networks in other related social sciences
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Casting a wider net: Immunosurveillance by nonclassical MHC molecules
Most studies of T lymphocytes focus on recognition of classical major histocompatibility complex (MHC) class I or II molecules presenting oligopeptides, yet there are numerous variations and exceptions of biological significance based on recognition of a wide variety of nonclassical MHC molecules. These include αβ and γδ T cells that recognize different class Ib molecules (CD1, MR-1, HLA-E, G, F, et al.) that are nearly monomorphic within a given species. Collectively, these T cells can be considered “unconventional,” in part because they recognize lipids, metabolites, and modified peptides. Unlike classical MHC-specific cells, unconventional T cells generally exhibit limited T-cell antigen receptor (TCR) repertoires and often produce innate immune cell-like rapid effector responses. Exploiting this system in new generation vaccines for human immunodeficiency virus (HIV), tuberculosis (TB), other infectious agents, and cancer was the focus of a recent workshop, “Immune Surveillance by Non-classical MHC Molecules: Improving Diversity for Antigens,” sponsored by the National Institute of Allergy and Infectious Diseases. Here, we summarize salient points presented regarding the basic immunobiology of unconventional T cells, recent advances in methodologies to measure unconventional T-cell activity in diseases, and approaches to harness their considerable clinical potential
Evaluation of the current knowledge limitations in breast cancer research: a gap analysis
BACKGROUND
A gap analysis was conducted to determine which areas of breast cancer research, if targeted by researchers and funding bodies, could produce the greatest impact on patients.
METHODS
Fifty-six Breast Cancer Campaign grant holders and prominent UK breast cancer researchers participated in a gap analysis of current breast cancer research. Before, during and following the meeting, groups in seven key research areas participated in cycles of presentation, literature review and discussion. Summary papers were prepared by each group and collated into this position paper highlighting the research gaps, with recommendations for action.
RESULTS
Gaps were identified in all seven themes. General barriers to progress were lack of financial and practical resources, and poor collaboration between disciplines. Critical gaps in each theme included: (1) genetics (knowledge of genetic changes, their effects and interactions); (2) initiation of breast cancer (how developmental signalling pathways cause ductal elongation and branching at the cellular level and influence stem cell dynamics, and how their disruption initiates tumour formation); (3) progression of breast cancer (deciphering the intracellular and extracellular regulators of early progression, tumour growth, angiogenesis and metastasis); (4) therapies and targets (understanding who develops advanced disease); (5) disease markers (incorporating intelligent trial design into all studies to ensure new treatments are tested in patient groups stratified using biomarkers); (6) prevention (strategies to prevent oestrogen-receptor negative tumours and the long-term effects of chemoprevention for oestrogen-receptor positive tumours); (7) psychosocial aspects of cancer (the use of appropriate psychosocial interventions, and the personal impact of all stages of the disease among patients from a range of ethnic and demographic backgrounds).
CONCLUSION
Through recommendations to address these gaps with future research, the long-term benefits to patients will include: better estimation of risk in families with breast cancer and strategies to reduce risk; better prediction of drug response and patient prognosis; improved tailoring of treatments to patient subgroups and development of new therapeutic approaches; earlier initiation of treatment; more effective use of resources for screening populations; and an enhanced experience for people with or at risk of breast cancer and their families. The challenge to funding bodies and researchers in all disciplines is to focus on these gaps and to drive advances in knowledge into improvements in patient care
Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor
Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues
Association between co-authorship network and scientific productivity and impact indicators in academic medical research centers: A case study in Iran
<p>Abstract</p> <p>Background</p> <p>We aimed to examine the co-authorship networks in three successful Iranian academic research centers, in order to find the association between the scientific productivity and impact indicators with network features in a case study.</p> <p>Methods</p> <p>We searched for English articles of the three research centers. We drew co-authorship maps of each center and calculated social network measures.</p> <p>Results</p> <p>The collaboration networks in centers shared many structural features, including a "star-like" pattern of relations. Centers with more successful scientific profile showed denser and more cooperative networks. Key figures in each center were interviewed for their understandings of the reasons for the emergence of these patterns.</p> <p>Conclusion</p> <p>Star shape network structure and dependency on a single big member is a common feature observed in our case study. Scientific output measures correlate with the network structure of research centers. Network analysis seems a useful method to explore the subtle scientific contexts in research organizations.</p
VISUAL PPINOT: A Graphical Notation for Process Performance Indicators
Process performance indicators (PPIs) allow the quantitative evaluation of business processes, providing essential information for decision making. It is common practice today that business processes and PPIs are usually modelled separately using graphical notations for the former and natural language for the latter. This approach makes PPI definitions simple to read and write, but it hinders maintenance consistency between business processes and PPIs. It also requires their manual translation into lower-level implementation languages for their operationalisation, which is a time-consuming, error-prone task because of the ambiguities inherent to natural language definitions. In this article, Visual ppinot, a graphical notation for defining PPIs together with business process models, is presented. Its underlying formal metamodel allows the automated processing of PPIs. Furthermore, it improves current state-of-the-art proposals in terms of expressiveness and in terms of providing an explicit visualisation of the link between PPIs and business processes, which avoids inconsistencies and promotes their co-evolution. The reference implementation, developed as a complete tool suite, has allowed its validation in a multiple-case study, in which five dimensions of Visual ppinot were studied: expressiveness, precision, automation, understandability, and traceability
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