Student and School Performance Across Countries:a Machine Learning Approach

In this paper, we develop and apply novel machine learning and statistical methods to analyse the determinants of students' PISA 2015 test scores in nine countries: Australia, Canada, France, Germany, Italy, Japan, Spain, UK and USA. The aim is to find out which student characteristics are associated with test scores and which school characteristics are associated to school value-added (measured at school level). A specific aim of our approach is to explore non-linearities in the associations between covariates and test scores, as well as to model interactions between school-level factors in affecting results. In order to address these issues, we apply a two-stage methodology using flexible tree-based methods. We first run multilevel regression trees in the first stage, to estimate school value-added. In the second stage, we relate the estimated school value- added to school level variables by means of regression trees and boosting. Results show that while several student and school level characteristics are significantly associated to students' achievements, there are marked differences across countries. The proposed approach allows an improved description of the structurally different educational production functions across countries

Using regression tree ensembles to model interaction effects: a graphical approach

Multiplicative interaction terms are widely used in economics to identify heterogeneous effects and to tailor policy recommendations. The execution of these models is often flawed due to specification and interpretation errors. This article introduces regression trees and regression tree ensembles to model and visualize interaction effects. Tree-based methods include interactions by construction and in a nonlinear manner. Visualizing nonlinear interaction effects in a way that can be easily read overcomes common interpretation errors. We apply the proposed approach to two different datasets to illustrate its usefulness

Semiparametric mixed-effects models for unsupervised classification of Italian schools

The main purpose of the paper is to improve research on school effectiveness by applying a new strategy for uncovering subpopulations of schools that differ in terms of distributionof student outcomes. We propose a semiparametric mixed effects model with an expectation–maximization algorithm to estimate its parameters and we apply it to the Italian Institute for theEducational Evaluation of Instruction and Training data of 2013–2014 as a tool for the identification of latent subpopulations of schools. The semiparametric assumption provides the random effects of the mixed effects model to be distributed according to a discrete distribution with an(a priori) unknown number of support points. This modelling induces an automatic clustering of schools (the higher level of hierarchy), where schools within the same cluster share the same random effects. The latent subpopulations of schools identified may then be exploited through the use of multinomial models that include school level features. The novelties introduced by this paper are twofold: first, the semiparametric expectation–maximization algorithm is an innovative method that could be used in many classification problems; second, its application to education data represents a new approach to study school effectiveness

Psychological Health Status of Psychiatric Patients Living in Treatment Communities before and during the COVID-19 Lockdown: A Brief Report

Many studies investigated the psychological impact of lockdown measures on the general population, while few studies focused on the psychiatric population. This study aimed to investigate the role of therapeutic communities in the management and containment of symptoms of patients with psychosis living in psychiatric residential facilities. Data were collected at two different points: November 2019 (Coronavirus disease 19 had not yet spread) and April 2020 (during the lockdown in Italy). Twenty-two study participants were recruited from three residential accredited psychiatric facilities. During lockdown, the patients showed a small increase in symptomatology in terms of emotional isolation. In addition, it was been observed significant differences in certain functional areas of the behavior, measured as lower inclination towards violent behaviors during lockdown, and higher scores in substance abuse and medical impairment. The lockdown condition could represent a form of containment; daily routines, along with adequate social support, are important aspects of the stability and the level of behavioral functioning of psychiatric patients. Social support and continuity of care offered by psychiatric communities can be an effective safeguard against the psychological impact of the COVID-19 epidemic

Novel N-(Heterocyclylphenyl)benzensulfonamide Sharing an Unreported Binding Site with T-Cell Factor 4 at the β-Catenin Armadillo Repeats Domain as an Anticancer Agent

Despite intensive efforts, no inhibitors of the Wnt/beta-catenin signaling pathway have been approved so far for the clinical treatment of cancer. We synthesized novel N-(heterocyclylphenyl)benzenesulfonamides as beta-catenin inhibitors. Compounds 5-10 showed strong inhibition of the luciferase activity. Compounds 5 and 6 inhibited the MDA-MB-231, HCC1806, and HCC1937 TNBC cells. Compound 9 induced in vitro cell death in SW480 and HCT116 cells and in vivo tumorigenicity of a human colorectal cancer line HCT116. In a co-immunoprecipitation study in HCT116 cells transfected with Myc-tagged T-cell factor 4 (Tcf-4), compound 9 abrogated the association between beta-catenin and Tcf-4. The crystallographic analysis of the beta-catenin Armadillo repeats domain revealed that compound 9 and Tcf-4 share a common binding site within the hotspot binding region close to Lys508. To our knowledge, compound 9 is the first small molecule ligand of this region to be reported. These results highlight the potential of this novel class of beta-catenin inhibitors as anticancer agents

The suppression of TdMRP3 genes reduces the phytic acid and increases the nutrient accumulation in durum wheat grain

Micronutrient malnutrition affects more than half of the world population. Reduced bioavailability of microelements in the raw materials is considered one of the main causes of mineral deficiency in populations whose diet is largely based on the consumption of staple crops. In this context, the production of low phytic acid (lpa) cereals is a main goal of the breeding programs, as phytic acid (PA) binds essential mineral cations such as iron (Fe), zinc (Zn), manganese (Mn), potassium (K), calcium (Ca) and magnesium (Mg) precipitating in the form of phytate salts poorly digested by monogastric animals, including humans, due to the lack of phytases in the digestive tract. Since PA limits the bioavailability of microelements, it is widely recognized as an anti-nutritional compound. A Targeting Induced Local Lesions IN Genomes (TILLING) approach has been undertaken to silence the genes encoding the TdABCC13 proteins, known as Multidrug-Resistance associated Proteins 3 (TdMRP3), transporters involved in the accumulation of PA inside the vacuole in durum wheat. The TdMRP3 complete null genotypes showed a significant reduction in the content of PA and were able to accumulate a higher amount of essential micronutrients (Fe, Zn, Mn) compared to the control. The number of spikelets and seeds per spike, traits associated with the agronomic performances, were reduced compared to the control, but the negative effect was in part balanced by the increased grain weight. The TdMRP3 mutant lines showed morphological differences in the root apparatus such as a significant decrease in the number of root tips, root length, volume and surface area and an increase in root average diameter compared to the control plants. These materials represent a promising basis for obtaining new commercial durum wheats with higher nutritional value

Mitochondria and Energetic Depression in Cell Pathophysiology

Mitochondrial dysfunction is a hallmark of almost all diseases. Acquired or inherited mutations of the mitochondrial genome DNA may give rise to mitochondrial diseases. Another class of disorders, in which mitochondrial impairments are initiated by extramitochondrial factors, includes neurodegenerative diseases and syndromes resulting from typical pathological processes, such as hypoxia/ischemia, inflammation, intoxications, and carcinogenesis. Both classes of diseases lead to cellular energetic depression (CED), which is characterized by decreased cytosolic phosphorylation potential that suppresses the cell’s ability to do work and control the intracellular Ca2+ homeostasis and its redox state. If progressing, CED leads to cell death, whose type is linked to the functional status of the mitochondria. In the case of limited deterioration, when some amounts of ATP can still be generated due to oxidative phosphorylation (OXPHOS), mitochondria launch the apoptotic cell death program by release of cytochrome c. Following pronounced CED, cytoplasmic ATP levels fall below the thresholds required for processing the ATP-dependent apoptotic cascade and the cell dies from necrosis. Both types of death can be grouped together as a mitochondrial cell death (MCD). However, there exist multiple adaptive reactions aimed at protecting cells against CED. In this context, a metabolic shift characterized by suppression of OXPHOS combined with activation of aerobic glycolysis as the main pathway for ATP synthesis (Warburg effect) is of central importance. Whereas this type of adaptation is sufficiently effective to avoid CED and to control the cellular redox state, thereby ensuring the cell survival, it also favors the avoidance of apoptotic cell death. This scenario may underlie uncontrolled cellular proliferation and growth, eventually resulting in carcinogenesis