Budding yeast Spc110, a member of γ-tubulin complex receptor family (γ-TuCR), recruits γ-tubulin complexes to microtubule (MT) organizing centers (MTOCs). Biochemical studies suggest that Spc110 facilitates higher-order γ-tubulin complex assembly (Kollman et al., 2010). Nevertheless the molecular basis for this activity and the regulation are unclear. Here we show that Spc110 phosphorylated by Mps1 and Cdk1 activates γ-TuSC oligomerization and MT nucleation in a cell cycle dependent manner. Interaction between the N-terminus of the γ-TuSC subunit Spc98 and Spc110 is important for this activity. Besides the conserved CM1 motif in γ-TuCRs (Sawin et al., 2004), a second motif that we named Spc110/Pcp1 motif (SPM) is also important for MT nucleation. The activating Mps1 and Cdk1 sites lie between SPM and CM1 motifs. Most organisms have both SPM-CM1 (Spc110/Pcp1/PCNT) and CM1-only (Spc72/Mto1/Cnn/CDK5RAP2/myomegalin) types of γ-TuCRs. The two types of γ-TuCRs contain distinct but conserved C-terminal MTOC targeting domains.DOI: http://dx.doi.org/10.7554/eLife.02208.001

In this large series of patients with the diagnosis of US who have undergone cochlear implantation, CT and MRI imaging were normal. Significant delay in independent ambulation was present in this population secondary to abnormal vestibular function associated with US Type I. A majority of children developed significant open-set speech perception and oral communication skills. Implantation of US children provides them with the opportunity to develop useful hearing and oral communication.

Scholarship over the past three decades has generated considerable insight into the roles of the media, politicians, and law enforcement officials in constructing images of criminal justice; still, that body of research has rarely ventured into the realm of corrections. Filling this void, we drew a sample of 206 newspaper articles on corrections published in the New York Times for the purpose of examining news sources and their quoted statements. Our findings reveal that the New York Times relies heavily on political and government sources who—not surprisingly—express support for the prevailing correctional policies and practices. Whereas the New York Times also quoted sources critical of the government's correctional strategies, the dominance of political sources in the press offers evidence of agenda setting in the debate over corrections.

Secretion analysis is a useful tool in forensic genetics, since it establishes the (cellular) origin of the DNA prior in addition to the identification of the DNA donor. This information can be crucial for the construction of the crime sequence or verification of statements of people involved in the crime. For some secretions, rapid/pretests already exist (blood, semen, urine, and saliva) or can be determined via published methylation analyses or expression analyses (blood, saliva vaginal secretions, menstrual blood, and semen). To discriminate nasal secretion/blood from other secretions (like oral mucosa/saliva, blood, vaginal secretion, menstrual blood, and seminal fluid), assays based on specific methylation patterns at several CpGs were set up in this study. Out of an initial 54 different CpG markers tested, two markers showed a specific methylation value for nasal samples: N21 and N27 with a methylation mean value of 64.4% ± 17.6% and 33.2% ± 8.7%, respectively. Although identification or discrimination was not possible for all nasal samples (due to partial overlap in methylation values to other secretions), 63% and 26% of the nasal samples could be unambiguously identified and distinguished from the other secretions using the CpG marker N21 and N27, respectively. In combination with a blood pretest/rapid test, a third marker (N10) was able to detect nasal cells in 53% of samples. Moreover, the employment of this pretest increases the proportion of identifiable or discriminable nasal secretion samples using marker N27 to 68%. In summary, our CpG assays proved to be promising tools in forensic analysis for the detection of nasal cells in samples from a crime scene.

Abstract. In quantitative analyses of biological processes, one may use many different scales of models (e.g., spatial or non-spatial, deterministic or stochastic, time varying or at steady state) or many different approaches to match models to experimental data (e.g., model fitting or parameter uncertainty/sloppiness quantification with different experiment designs). These different analyses can lead to surprisingly different results, even when applied to the same data and the same model. We use a simplified gene regulation model to illustrate many of these concerns, especially for ODE analyses of deterministic processes, chemical master equation and finite state projection analyses of heterogeneous processes, and stochastic simulations. For each analysis, we employ Matlab and Python software to consider a time-dependent input signal (e.g., a kinase nuclear translocation) and several model hypotheses, along with simulated single-cell data. We illustrate different approaches (e.g., deterministic and stochastic) to identify the mechanisms and parameters of the same model from the same simulated data. For each approach, we explore how uncertainty in parameter space varies with respect to the chosen analysis approach or specific experiment design. We conclude with a discussion of how our simulated results relate to the integration of experimental and computational investigations to explore signal-activated gene expression models in yeast [1] and human cells [2] ‡.