Teaching Year Nine Differently: A Poetic Inquiry into one Australian Middle Years Program

This thesis examined an Australian year nine program to consider the lived experience of being a year nine teacher and the current and future curriculum and pedagogies used by such programs. The findings are that year nine programs are better working with year nines and that teachers do a remarkable job despite the shortcomings of the current funding model

The expendable teacher in covid-19 times : a poetic inquiry into the reconfiguration of governmentality in victorian schools

The actualization of a neoliberal rationality has been widely explored in global education policy and Australian schools. This paper draws on engagements with neoliberalism as rationality made ‘real’ through government practices, specifically those that reify the teaching profession into one of risk-management and problem-solving at the expense of deliberation about purposes. In this paper, redacted policy poetry and participant-voiced poetry are employed in parallel to explore the COVID-19 crisis as it emerged in the State of Victoria, Australia with a specific focus on the reconfiguration of risk-management discourses through blanket policy directive. This paper identifies and explores three themes highlighted by this reconfiguration of risk discourse and shifts in modes of governance during this time that are magnified by a teacher’s affective and practical responses to the situation. They are: (1) collective teacher response to overt policy decisions that compel the teacher to embrace risk; (2) contradictions of expectation for schools to continue as usual; and (3) an explicit shift away from instrumental evidence-based pedagogies toward new purposes, pedagogies, and community engagement with little guidance. © 2021, Institute for Education Policy Studies. All rights reserved

Modulating energy metabolism to treat non-obstructive hypertrophic cardiomyopathy? Insights from IMPROVE-HCM

: Hypertrophic cardiomyopathy (HCM) is the most common genetic heart disease worldwide and may present with or without dynamic left ventricular outflow tract obstruction (LVOTO). Significant advances have been made in the management of obstructive HCM. On the other hand, despite their significant symptomatic burden, patients with non-obstructive HCM (nHCM) (i.e., without LVOTO) still do not have evidence-based therapeutical options. The recent IMPROVE-HCM study, a phase 2 randomized, double-blinded trial, aims to place a first step in filling this gap in knowledge. The study assessed the safety (primary endpoint) and efficacy (secondary endpoint) of ninerafaxstat, a novel cardiac mitotrope drug that increases adenosine triphosphate production. We highlighted the main findings of the trial, contextualizing these results within the larger landscape of completed and ongoing trials in nHCM

Abnormal liver function tests in acute heart failure: relationship with clinical characteristics and outcome in the PROTECT study

Aims: Episodes of acute heart failure (AHF) unfavourably affect multiple organs, which may have an adverse impact on the outcomes. We investigated the prevalence and clinical consequences of abnormal liver function tests (LFTs) in AHF patients enrolled in the PROTECT study. Methods and results: The LFTs comprised serial assessment of aspartate aminotransferase (AST), alanine aminotransferase (ALT) and albumin at baseline and during follow-up (daily until discharge, on days 7 and 14). The prevalence of abnormal LFTs (above upper limit of normal for AST and ALT or below lower limit of normal for albumin) was: at baseline AST 20%, ALT 12%, albumin 40%; and at day 14: AST 15%, ALT 9%, albumin 26%. Abnormal LFTs at baseline were associated with a higher risk of in-hospital death with odds ratios [95% confidence interval (CI)] of 3.5 (1.7–7.3) for AST, 3.9 (1.8–8.4) for ALT, and 2.8 (1.3–5.9) for albumin (all P < 0.01). Abnormal baseline and discharge LFTs had an unfavourable impact on 180-day mortality with hazard ratios (95% CI) for baseline AST, ALT, and albumin of 1.3 (1.0–1.7), 1.1 (1.0–1.2), 1.4 (1.1–1.8), respectively, and 1.5 (1.1–2.0), 1.5 (1.0–2.2), and 1.6 (1.2–2.1), for discharge AST, ALT, albumin, respectively (all P < 0.05). Analysis of LFTs trajectories (calculated as changes in LFTs over time) revealed that increasing AST and ALT on day 3 as well as decreasing albumin on day 4 were independent prognosticators of 180-day outcome (all P < 0.05). Conclusions: Abnormal LFTs are frequent in AHF at baseline and during hospital stay and predict worse outcomes. Whether this association is causal and what are the underlying mechanisms involved require further study

Improving Postdischarge Outcomes in Acute Heart Failure

The global burden that acute heart failure (AHF) carries has remained unchanged over the past several decades (1). European registries (2–5) showed that 1-year outcome rates remain unacceptably high (Table 1) and confirm that hospitalization for AHF represents a change in the natural history of the disease process(6). As patients hospitalized for HF have a bad prognosis, it is crucial to utilize hospitalization as an opportunity to: 1) assess the individual components of the cardiac substrate; 2) identify and treat comorbidities; 3) identify early, safe endpoints of therapy to facilitate timely hospital discharge and outpatient follow-up; and 4) implement and begin optimization guideline-directed medical therapies (GDMTs). As outcomes are influenced by many factors, many of which are incompletely understood, a systematic approach is proposed that should start with admission and continues through post-discharge (7)

Entrevista al Dr. Juan Martín López-Calva: El bien común, horizonte orientador para una educación e investigación educativa transformadora

Entrevista al Dr. Juan Martín López Calva sobre el tema de Educación para el bien común

Interpretable Survival Analysis for Heart Failure Risk Prediction

Survival analysis, or time-to-event analysis, is an important and widespread problem in healthcare research. Medical research has traditionally relied on Cox models for survival analysis, due to their simplicity and interpretability. Cox models assume a log-linear hazard function as well as proportional hazards over time, and can perform poorly when these assumptions fail. Newer survival models based on machine learning avoid these assumptions and offer improved accuracy, yet sometimes at the expense of model interpretability, which is vital for clinical use. We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models. Specifically, we use an improved version of survival stacking to transform a survival analysis problem to a classification problem, ControlBurn to perform feature selection, and Explainable Boosting Machines to generate interpretable predictions. To evaluate our pipeline, we predict risk of heart failure using a large-scale EHR database. Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure

Flexible Power Modeling of LTE Base Stations

With the explosion of wireless communications in number of users and data rates, the reduction of network power consumption becomes more and more critical. This is especially true for base stations which represent a dominant share of the total power in cellular networks. In order to study power reduction techniques, a convenient power model is required, providing estimates of the power consumption in different scenarios. This paper proposes such a model, accurate but simple to use. It evaluates the base station power consumption for different types of cells supporting the 3GPP LTE standard. It is flexible enough to enable comparisons between state-of-the-art and advanced configurations, and an easy adaptation to various scenarios. The model is based on a combination of base station components and sub-components as well as power scaling rules as functions of the main system parameters