The Consequences of Preferential Trade Agreements: Lessons for U.S.-Latin America Trade Relations

While academic and popular debates tend to focus on differential benefits and costs of trade across countries or industries, this brief highlights winners and losers at the level of individual firms. The authors demonstrate that preferential liberalization produces concentrated benefits among a relatively small number of very large and productive firms

Effect of probiotics on oral candidiasis: A systematic review and meta-analysis

Oral candidiasis (OC) is an increasing health problem due to the introduction of new drugs, population aging, and increasing prevalence of chronic illness. This study systematically reviews the effects of the oral intake of probiotics, prebiotics, and synbiotics on Candida spp. counts (colony-forming units (CFU)/mL) in oral and palatal samples. A literature search was conducted. Twelve studies, eight randomized clinical trials (RCTs), and four pre-post studies, resulted as eligible for the meta-analysis, which was performed through a Bayesian random-effects model. All studies analyzed probiotics, and none of them analyzed prebiotics or synbiotics. The treatments effects were measured in terms of odds ratio (OR) of OC (CFU/mL >102, 103, or 104). The meta-analytic OR was 0.71 (95% credibility interval (CrI): 0.37, 1.32), indicating a beneficial effect of treatment; the I2 index was 56.3%. Focusing only on RCTs, the OR was larger and more precise at 0.53 (95% CrI: 0.27, 0.93). The effect of treatment appeared to be larger on denture wearers. Our findings indicate that the intake of probiotics can have a beneficial effect on OC and that the effects could vary according to the patients’ characteristics. Due to the presence of medium–high-risk studies, the results should be interpreted with caution

Incremental Material Flow Analysis with Bayesian Inference

Material Flow Analysis (MFA) is widely used to study the life-cycles of materials from production, through use, to reuse, recycling or disposal, in order to identify environmental impacts and opportunities to address them. However, development of this type of analysis is often constrained by limited data, which may be uncertain, contradictory, missing or over-aggregated. This article proposes a Bayesian approach, in which uncertain knowledge about material flows is described by probability distributions. If little data is initially available, the model predictions will be rather vague. As new data is acquired, it is systematically incorporated to reduce the level of uncertainty. After reviewing previous approaches to uncertainty in MFA, the Bayesian approach is introduced, and a general recipe for its application to Material Flow Analysis is developed. This is applied to map global production of steel, using Markov Chain Monte Carlo simulations. As well as aiding the analyst, who can get started in the face of incomplete data, this incremental approach to MFA also supports efforts to improve communication of results by transparently accounting for uncertainty throughout.ngineering and Physical Sciences Research Council. Grant Numbers: EP/K039326/1, EP/N02351x/

Peer review and citation data in predicting university rankings, a large-scale analysis

Most Performance-based Research Funding Systems (PRFS) draw on peer review and bibliometric indicators, two different method- ologies which are sometimes combined. A common argument against the use of indicators in such research evaluation exercises is their low corre- lation at the article level with peer review judgments. In this study, we analyse 191,000 papers from 154 higher education institutes which were peer reviewed in a national research evaluation exercise. We combine these data with 6.95 million citations to the original papers. We show that when citation-based indicators are applied at the institutional or departmental level, rather than at the level of individual papers, surpris- ingly large correlations with peer review judgments can be observed, up to r <= 0.802, n = 37, p < 0.001 for some disciplines. In our evaluation of ranking prediction performance based on citation data, we show we can reduce the mean rank prediction error by 25% compared to previous work. This suggests that citation-based indicators are sufficiently aligned with peer review results at the institutional level to be used to lessen the overall burden of peer review on national evaluation exercises leading to considerable cost savings

Characterization of plug and slug multiphase flows by means of image analysis

Multiphase flow is involved in a wide range of applications, and among the flow patterns that a multiphase mixture may develop in its flow, the intermittent one is particularly complex both in behaviour and for analysis. Experimental analysis about the characteristics of the flow structures (plugs and slugs) is therefore still mandatory for a detailed description of the phenomenon. In this work an image-based technique for the determination of the plug/slug characteristics was applied to air-water, oil-air and three-phase oil-water-air flows in horizontal ducts with different diameters, with superficial velocities of the phases in the range 0.2-2.1 m/s. The technique is based on the acquisition of a video of the flow and the conversion of each frame (or part of it) into a Boolean signal, in which the non-zero part represents the structure of interest. Concatenation of such signals along the singleton dimension creates a space-time representation of the flow, from which information about the flow velocities, the structure lengths and frequencies and the void fraction can be extracted. Focus here is particularly on the performances of the technique when using high-speed videos. The results were also compared with the predictions of the drift-flux model

Missing not at random in end of life care studies : multiple imputation and sensitivity analysis on data from the ACTION study

Background: Missing data are common in end-of-life care studies, but there is still relatively little exploration of which is the best method to deal with them, and, in particular, if the missing at random (MAR) assumption is valid or missing not at random (MNAR) mechanisms should be assumed. In this paper we investigated this issue through a sensitivity analysis within the ACTION study, a multicenter cluster randomized controlled trial testing advance care planning in patients with advanced lung or colorectal cancer. Methods: Multiple imputation procedures under MAR and MNAR assumptions were implemented. Possible violation of the MAR assumption was addressed with reference to variables measuring quality of life and symptoms. The MNAR model assumed that patients with worse health were more likely to have missing questionnaires, making a distinction between single missing items, which were assumed to satisfy the MAR assumption, and missing values due to completely missing questionnaire for which a MNAR mechanism was hypothesized. We explored the sensitivity to possible departures from MAR on gender differences between key indicators and on simple correlations. Results: Up to 39% of follow-up data were missing. Results under MAR reflected that missingness was related to poorer health status. Correlations between variables, although very small, changed according to the imputation method, as well as the differences in scores by gender, indicating a certain sensitivity of the results to the violation of the MAR assumption. Conclusions: The findings confirmed the importance of undertaking this kind of analysis in end-of-life care studies

Remote Working and Home Learning: How the Italian Academic Population Dealt with Changes Due to the COVID-19 Pandemic Lockdown

The COVID-19 pandemic introduced changes in people's lives that affected their mental health. Our study aimed to explore the level of psychological distress in the academic population during the lockdown period and investigate its association with the new working or studying conditions. The study sample included 9364 students and 2159 employees from five Italian universities from the study IO CONTO 2020. We applied linear regression models to investigate the association between home learning or remote working conditions and psychological distress, separately for students and employees. Psychological distress was assessed using the Hospital Anxiety and Depression Scale (HADS). In both students and employees, higher levels of distress were significantly associated with study/work-family conflicts, concerns about their future careers, and inadequacy of equipment; in employees, higher levels of distress were significantly associated with a lack of clarity on work objectives. Our results are in line with previous research on the impact of spaces and equipment in remote working/studying from home. Moreover, the study contributes to deepening the association between well-being and telework-family conflict, which in the literature is still equivocal. Practical implications require academic governance to promote sustainable environments both in remote and hybrid work conditions, by referring to a specific management by objectives approach