Asymmetrical crossing barriers in angiosperms

Patterns of reproductive isolation between species may provide insight into the mechanisms and evolution of barriers to interspeci¢c gene exchange. We used data from published interspeci¢c hybridization experiments from 14 genera of angiosperms in order to test for the presence of asymmetrical barriers to gene exchange. Reproductive isolation was examined at three life-history stages: the ability of interspeci¢c crosses to produce seeds, the viability of F 1 hybrids, and the fertility of F 1 hybrids. Statistically signi¢cant asymmetries in the strength of reproductive isolation between species were detected in all genera and at each of the three life-history stages. Asymmetries in seed production may be caused by a variety of mechanisms including di¡erences in stigma/style lengths, self compatibility and di¡erential fruit abortion. Asymmetries in post-zygotic isolation are probably caused by nuclear^cytoplasmic interactions. Asymmetrical reproductive isolation between plant taxa may have important implications for the dynamics of hybrid zones, the direction of genetic introgression and the probability of reinforcement

Barriers to women entering surgical careers: a global study into medical student perceptions

Background Barriers to female surgeons entering the field are well documented in Australia, the USA and the UK, but how generalizable these problems are to other regions remains unknown. Methods A cross-sectional survey was developed by the International Federation of Medical Students' Associations (IFMSA)'s Global Surgery Working Group assessing medical students' desire to pursue a surgical career at different stages of their medical degree. The questionnaire also included questions on students' perceptions of their education, resources and professional life. The survey was distributed via IFMSA mailing lists, conferences and social media. Univariate analysis was performed, and statistically significant exposures were added to a multivariate model. This model was then tested in male and female medical students, before a further subset analysis by country World Bank income strata. Results 639 medical students from 75 countries completed the survey. Mentorship [OR 3.42 (CI 2.29–5.12) p = 0.00], the acute element of the surgical specialties [OR 2.22 (CI 1.49–3.29) p = 0.00], academic competitiveness [OR 1.61 (CI 1.07–2.42) p = 0.02] and being from a high or upper-middle-income country (HIC and UMIC) [OR 1.56 (CI 1.021–2.369) p = 0.04] all increased likelihood to be considering a surgical career, whereas perceived access to postgraduate training [OR 0.63 (CI 0.417–0.943) p = 0.03], increased year of study [OR 0.68 (CI 0.57–0.81) p = 0.00] and perceived heavy workload [OR 0.47 (CI 0.31–0.73) p = 0.00] all decreased likelihood to consider a surgical career. Perceived quality of surgical teaching and quality of surgical services in country overall did not affect students' decision to pursue surgery. On subset analysis, perceived poor access to postgraduate training made women 60% less likely to consider a surgical career [OR 0.381 (CI 0.217–0.671) p = 0.00], whilst not showing an effect in the men [OR 1.13 (CI 0.61–2.12) p = 0.70. Concerns about high cost of training halve the likelihood of students from low and low-middle-income countries (LICs and LMICs) considering a surgical career [OR 0.45 (CI 0.25–0.82) p = 0.00] whilst not demonstrating a significant relationship in HIC or UMIC countries. Women from LICs and LMICs were 40% less likely to consider surgical careers than men, when controlling for other factors [OR 0.59 CI (0.342–1.01 p = 0.053]. Conclusion Perceived poor access to postgraduate training and heavy workload dissuade students worldwide from considering surgical careers. Postgraduate training in particular appears to be most significant for women and cost of training an additional factor in both women and men from LMICs and LICs. Mentorship remains an important and modifiable factor in influencing student's decision to pursue surgery. Quality of surgical education showed no effect on student decision-making

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Coherent diffraction of single Rice Dwarf virus particles using hard X-rays at the Linac Coherent Light Source

Single particle diffractive imaging data from Rice Dwarf Virus (RDV) were recorded using the Coherent X-ray Imaging (CXI) instrument at the Linac Coherent Light Source (LCLS). RDV was chosen as it is a wellcharacterized model system, useful for proof-of-principle experiments, system optimization and algorithm development. RDV, an icosahedral virus of about 70 nm in diameter, was aerosolized and injected into the approximately 0.1 mu m diameter focused hard X-ray beam at the CXI instrument of LCLS. Diffraction patterns from RDV with signal to 5.9 angstrom ngstrom were recorded. The diffraction data are available through the Coherent X-ray Imaging Data Bank (CXIDB) as a resource for algorithm development, the contents of which are described here.11Ysciescopu

Evaluation of a Bayesian inference network for ligand-based virtual screening

Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity. Results Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought. Conclusion A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening

A self-organized model for cell-differentiation based on variations of molecular decay rates

Systemic properties of living cells are the result of molecular dynamics governed by so-called genetic regulatory networks (GRN). These networks capture all possible features of cells and are responsible for the immense levels of adaptation characteristic to living systems. At any point in time only small subsets of these networks are active. Any active subset of the GRN leads to the expression of particular sets of molecules (expression modes). The subsets of active networks change over time, leading to the observed complex dynamics of expression patterns. Understanding of this dynamics becomes increasingly important in systems biology and medicine. While the importance of transcription rates and catalytic interactions has been widely recognized in modeling genetic regulatory systems, the understanding of the role of degradation of biochemical agents (mRNA, protein) in regulatory dynamics remains limited. Recent experimental data suggests that there exists a functional relation between mRNA and protein decay rates and expression modes. In this paper we propose a model for the dynamics of successions of sequences of active subnetworks of the GRN. The model is able to reproduce key characteristics of molecular dynamics, including homeostasis, multi-stability, periodic dynamics, alternating activity, differentiability, and self-organized critical dynamics. Moreover the model allows to naturally understand the mechanism behind the relation between decay rates and expression modes. The model explains recent experimental observations that decay-rates (or turnovers) vary between differentiated tissue-classes at a general systemic level and highlights the role of intracellular decay rate control mechanisms in cell differentiation.Comment: 16 pages, 5 figure

Unanticipated Insights into Biomedicine from the Study of Acupuncture

Research into acupuncture has had ripple effects beyond the field of acupuncture. This paper identifies five exemplars to illustrate that there is tangible evidence of the way insights gleaned from acupuncture research have informed biomedical research, practice, or policy. The first exemplar documents how early research into acupuncture analgesia has expanded into neuroimaging research, broadening physiologic understanding and treatment of chronic pain. The second describes how the acupuncture needle has become a tool to enhance biomedical knowledge of connective tissue. The third exemplar, which illustrates use of a modified acupuncture needle as a sham device, focuses on emergent understanding of placebo effects and, in turn, on insights into therapeutic encounters in treatments unrelated to acupuncture. The fourth exemplar documents that two medical devices now in widespread use were inspired by acupuncture: transcutaneous electrical nerve stimulators for pain control and antinausea wrist bands. The final exemplar describes how pragmatic clinical trial designs applied in acupuncture research have informed current general interest in comparative effectiveness research. In conclusion, these exemplars of unanticipated outcomes of acupuncture research comprise an additional rationale for continued support of basic and clinical research evaluating acupuncture and other under-researched therapies

Neutral tumor evolution?

No abstract available

A cre-inducible DUX4 transgenic mouse model for investigating facioscapulohumeral muscular dystrophy

The Double homeobox 4 (DUX4) gene is an important regulator of early human development and its aberrant expression is causal for facioscapulohumeral muscular dystrophy (FSHD). The DUX4-full length (DUX4-fl) mRNA splice isoform encodes a transcriptional activator; however, DUX4 and its unique DNA binding preferences are specific to old-world primates. Regardless, the somatic cytotoxicity caused by DUX4 expression is conserved when expressed in cells and animals ranging from fly to mouse. Thus, viable animal models based on DUX4-fl expression have been difficult to generate due in large part to overt developmental toxicity of low DUX4-fl expression from leaky transgenes. We have overcome this obstacle and here we report the generation and initial characterization of a line of conditional floxed DUX4-fl transgenic mice, FLExDUX4, that is viable and fertile. In the absence of cre, these mice express a very low level of DUX4-fl mRNA from the transgene, resulting in mild phenotypes. However, when crossed with appropriate cre-driver lines of mice, the double transgenic offspring readily express DUX4-fl mRNA, protein, and target genes with the spatiotemporal pattern of nuclear cre expression dictated by the chosen system. When cre is expressed from the ACTA1 skeletal muscle-specific promoter, the double transgenic animals exhibit a developmental myopathy. When crossed with tamoxifen-inducible cre lines, DUX4-mediated pathology can be induced in adult animals. Thus, the appearance and progression of pathology can be controlled to provide readily screenable phenotypes useful for assessing therapeutic approaches targeting DUX4-fl mRNA and protein. Overall, the FLExDUX4 line of mice is quite versatile and will allow new investigations into mechanisms of DUX4-mediated pathophysiology as well as much-needed pre-clinical testing of DUX4-targeted FSHD interventions in vivo

Catalyst preparation for CMOS-compatible silicon nanowire synthesis

Metallic contamination was key to the discovery of semiconductor nanowires, but today it stands in the way of their adoption by the semiconductor industry. This is because many of the metallic catalysts required for nanowire growth are not compatible with standard CMOS (complementary metal oxide semiconductor) fabrication processes. Nanowire synthesis with those metals which are CMOS compatible, such as aluminium and copper, necessitate temperatures higher than 450 C, which is the maximum temperature allowed in CMOS processing. Here, we demonstrate that the synthesis temperature of silicon nanowires using copper based catalysts is limited by catalyst preparation. We show that the appropriate catalyst can be produced by chemical means at temperatures as low as 400 C. This is achieved by oxidizing the catalyst precursor, contradicting the accepted wisdom that oxygen prevents metal-catalyzed nanowire growth. By simultaneously solving material compatibility and temperature issues, this catalyst synthesis could represent an important step towards real-world applications of semiconductor nanowires.Comment: Supplementary video can be downloaded on Nature Nanotechnology websit