Austérité et gestion dans les universités québécoises: une analyse des perceptions de directeurs de département

This article discusses different aspects of the management of budgetary austerity in higher education. Its database consists of in-depth interviews with twenty-five department chairpersons from six Québec universities. In general, strategic choices imposed by university administration are reactive and aimed at meeting short-term government objectives. Among the strategies more dependent on departmental choice, some are proactive. Nevertheless, the range of choices is limited by overall constraints. Longer term solutions to cope with the crisis in higher education proposed by chairpersons are problematic since they stem from somewhat contradictory views on the major role of the university as an institution.Cet article présente différents aspects de la gestion de l'austérité budgétaire dans l'enseignement supérieur à partir d'entrevues en profondeur effectuées auprès de vingt-cinq directeurs de département de six universités québécoises. En général, il apparaît que les mesures imposées par la direction de l'Université pour faire face aux compressions budgétaires sont plutôt réactives et visent à rencontrer à court terme les objectifs gouvernementaux. Parmi les stratégies relevant plus directement des choix des départements, quelques unes sont proactives. Le poids des contraintes limite, cependant, les choix. Les solutions à plus long terme proposées par les directeurs pour surmonter la crise actuelle de l'enseignement supérieur sont problématiques car elles se basent sur des visions contradictoires de la mission institutionnelle de l'université

Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size

Abstract Background Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required. Thus, the performances of co-expression network analysis methods on large co-expression networks consisting of a few thousand nodes, with only a small number of profiles with a single perturbation, which more accurately reflect normal experimental conditions, are generally uncharacterized and unknown. Methods We proposed a novel network inference methods based on Relevance Low order Partial Correlation (RLowPC). RLowPC method uses a two-step approach to select on the high-confidence edges first by reducing the search space by only picking the top ranked genes from an intial partial correlation analysis and, then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association. Results We selected six co-expression-based methods with good performance in evaluation studies from the literature: Partial correlation, PCIT, ARACNE, MRNET, MRNETB and CLR. The evaluation of these methods was carried out on simulated time-series data with various network sizes ranging from 100 to 3000 nodes. Simulation results show low precision and recall for all of the above methods for large networks with a small number of expression profiles. We improved the inference significantly by refinement of the top weighted edges in the pre-inferred partial correlation networks using RLowPC. We found improved performance by partitioning large networks into smaller co-expressed modules when assessing the method performance within these modules. Conclusions The evaluation results show that current methods suffer from low precision and recall for large co-expression networks where only a small number of profiles are available. The proposed RLowPC method effectively reduces the indirect edges predicted as regulatory relationships and increases the precision of top ranked predictions. Partitioning large networks into smaller highly co-expressed modules also helps to improve the performance of network inference methods. The RLowPC R package for network construction, refinement and evaluation is available at GitHub: https://github.com/wyguo/RLowPC

Laparoscopy in management of appendicitis in high-, middle-, and low-income countries: a multicenter, prospective, cohort study.

BACKGROUND: Appendicitis is the most common abdominal surgical emergency worldwide. Differences between high- and low-income settings in the availability of laparoscopic appendectomy, alternative management choices, and outcomes are poorly described. The aim was to identify variation in surgical management and outcomes of appendicitis within low-, middle-, and high-Human Development Index (HDI) countries worldwide. METHODS: This is a multicenter, international prospective cohort study. Consecutive sampling of patients undergoing emergency appendectomy over 6 months was conducted. Follow-up lasted 30 days. RESULTS: 4546 patients from 52 countries underwent appendectomy (2499 high-, 1540 middle-, and 507 low-HDI groups). Surgical site infection (SSI) rates were higher in low-HDI (OR 2.57, 95% CI 1.33-4.99, p = 0.005) but not middle-HDI countries (OR 1.38, 95% CI 0.76-2.52, p = 0.291), compared with high-HDI countries after adjustment. A laparoscopic approach was common in high-HDI countries (1693/2499, 67.7%), but infrequent in low-HDI (41/507, 8.1%) and middle-HDI (132/1540, 8.6%) groups. After accounting for case-mix, laparoscopy was still associated with fewer overall complications (OR 0.55, 95% CI 0.42-0.71, p < 0.001) and SSIs (OR 0.22, 95% CI 0.14-0.33, p < 0.001). In propensity-score matched groups within low-/middle-HDI countries, laparoscopy was still associated with fewer overall complications (OR 0.23 95% CI 0.11-0.44) and SSI (OR 0.21 95% CI 0.09-0.45). CONCLUSION: A laparoscopic approach is associated with better outcomes and availability appears to differ by country HDI. Despite the profound clinical, operational, and financial barriers to its widespread introduction, laparoscopy could significantly improve outcomes for patients in low-resource environments. TRIAL REGISTRATION: NCT02179112

An HDG Method for Dirichlet Boundary Control of Convection Dominated Diffusion PDE

We first propose a hybridizable discontinuous Galerkin (HDG) method to approximate the solution of a \emph{convection dominated} Dirichlet boundary control problem. Dirichlet boundary control problems and convection dominated problems are each very challenging numerically due to solutions with low regularity and sharp layers, respectively. Although there are some numerical analysis works in the literature on \emph{diffusion dominated} convection diffusion Dirichlet boundary control problems, we are not aware of any existing numerical analysis works for convection dominated boundary control problems. Moreover, the existing numerical analysis techniques for convection dominated PDEs are not directly applicable for the Dirichlet boundary control problem because of the low regularity solutions. In this work, we obtain an optimal a priori error estimate for the control under some conditions on the domain and the desired state. We also present some numerical experiments to illustrate the performance of the HDG method for convection dominated Dirichlet boundary control problems

The Global Energy Transition: A Review of the Existing Literature

This chapter presents an overview of the existing literature on the geopolitics of the global energy transition. Notwithstanding its potentially re-defining role for international relations, this issue has, so far, not been analysed in a comprehensive manner but in a rather fragmented way. This chapter represents a useful summary to the state-of-the-art of knowledge in the field, and therefore a useful starting point for the book

A roadmap to inform development, validation and approval of digital mobility outcomes: the Mobilise-D approach

Health care has had to adapt rapidly to COVID-19, and this in turn has highlighted a pressing need for tools to facilitate remote visits and monitoring. Digital health technology, including body-worn devices, offers a solution using digital outcomes to measure and monitor disease status and provide outcomes meaningful to both patients and health care professionals. Remote monitoring of physical mobility is a prime example, because mobility is among the most advanced modalities that can be assessed digitally and remotely. Loss of mobility is also an important feature of many health conditions, providing a read-out of health as well as a target for intervention. Real-world, continuous digital measures of mobility (digital mobility outcomes or DMOs) provide an opportunity for novel insights into health care conditions complementing existing mobility measures. Accepted and approved DMOs are not yet widely available. The need for large collaborative efforts to tackle the critical steps to adoption is widely recognised. Mobilise-D is an example. It is a multidisciplinary consortium of 34 institutions from academia and industry funded through the European Innovative Medicines Initiative 2 Joint Undertaking. Members of Mobilise-D are collaborating to address the critical steps for DMOs to be adopted in clinical trials and ultimately health care. To achieve this, the consortium has developed a roadmap to inform the development, validation and approval of DMOs in Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease and recovery from proximal femoral fracture. Here we aim to describe the proposed approach and provide a high-level view of the ongoing and planned work of the Mobilise-D consortium. Ultimately, Mobilise-D aims to stimulate widespread adoption of DMOs through the provision of device agnostic software, standards and robust validation in order to bring digital outcomes from concept to use in clinical trials and health care

APOBEC3G and APOBEC3F Require an Endogenous Cofactor to Block HIV-1 Replication

APOBEC3G (A3G)/APOBEC3F (A3F) are two members of APOBEC3 cytidine deaminase subfamily. Although they potently inhibit the replication of vif-deficient HIV-1, this mechanism is still poorly understood. Initially, A3G/A3F were thought to catalyze C-to-U transitions on the minus-strand viral cDNAs during reverse transcription to disrupt the viral life cycle. Recently, it was found more likely that A3G/A3F directly interrupts viral reverse transcription or integration. In addition, A3G/A3F are both found in the high-molecular-mass complex in immortalized cell lines, where they interact with a number of different cellular proteins. However, there has been no evidence to prove that these interactions are required for A3G/A3F function. Here, we studied A3G/A3F-restricted HIV-1 replication in six different human T cell lines by infecting them with wild-type or vif-deficient HIV-1. Interestingly, in a CEM-derived cell line CEM-T4, which expresses high levels of A3G/A3F proteins, the vif-deficient virus replicated as equally well as the wild-type virus, suggesting that these endogenous antiretroviral genes lost anti-HIV activities. It was confirmed that these A3G/A3F genes do not contain any mutation and are functionally normal. Consistently, overexpression of exogenous A3G/A3F in CEM-T4 cells still failed to restore their anti-HIV activities. However, this activity could be restored if CEM-T4 cells were fused to 293T cells to form heterokaryons. These results demonstrate that CEM-T4 cells lack a cellular cofactor, which is critical for A3G/A3F anti-HIV activity. We propose that a further study of this novel factor will provide another strategy for a complete understanding of the A3G/A3F antiretroviral mechanism

Characterization of the Interaction of Full-Length HIV-1 Vif Protein with its Key Regulator CBFβ and CRL5 E3 Ubiquitin Ligase Components

Human immunodeficiency virus-1 (HIV-1) viral infectivity factor (Vif) is essential for viral replication because of its ability to eliminate the host's antiviral response to HIV-1 that is mediated by the APOBEC3 family of cellular cytidine deaminases. Vif targets these proteins, including APOBEC3G, for polyubiquitination and subsequent proteasome-mediated degradation via the formation of a Cullin5-ElonginB/C-based E3 ubiquitin ligase. Determining how the cellular components of this E3 ligase complex interact with Vif is critical to the intelligent design of new antiviral drugs. However, structural studies of Vif, both alone and in complex with cellular partners, have been hampered by an inability to express soluble full-length Vif protein. Here we demonstrate that a newly identified host regulator of Vif, core-binding factor-beta (CBFβ), interacts directly with Vif, including various isoforms and a truncated form of this regulator. In addition, carboxyl-terminal truncations of Vif lacking the BC-box and cullin box motifs were sufficient for CBFβ interaction. Furthermore, association of Vif with CBFβ, alone or in combination with Elongin B/C (EloB/C), greatly increased the solubility of full-length Vif. Finally, a stable complex containing Vif-CBFβ-EloB/C was purified in large quantity and shown to bind purified Cullin5 (Cul5). This efficient strategy for purifying Vif-Cul5-CBFβ-EloB/C complexes will facilitate future structural and biochemical studies of Vif function and may provide the basis for useful screening approaches for identifying novel anti-HIV drug candidates

Learning Transcriptional Regulatory Relationships Using Sparse Graphical Models

Understanding the organization and function of transcriptional regulatory networks by analyzing high-throughput gene expression profiles is a key problem in computational biology. The challenges in this work are 1) the lack of complete knowledge of the regulatory relationship between the regulators and the associated genes, 2) the potential for spurious associations due to confounding factors, and 3) the number of parameters to learn is usually larger than the number of available microarray experiments. We present a sparse (L1 regularized) graphical model to address these challenges. Our model incorporates known transcription factors and introduces hidden variables to represent possible unknown transcription and confounding factors. The expression level of a gene is modeled as a linear combination of the expression levels of known transcription factors and hidden factors. Using gene expression data covering 39,296 oligonucleotide probes from 1109 human liver samples, we demonstrate that our model better predicts out-of-sample data than a model with no hidden variables. We also show that some of the gene sets associated with hidden variables are strongly correlated with Gene Ontology categories. The software including source code is available at http://grnl1.codeplex.com