The Flynn Effect and U.S. Policies: The Impact of Rising IQ Scores on American Society Via Mental Retardation Diagnoses.

Over the last century, IQ scores have been steadily rising, a phenomenon dubbed the Flynn effect. Because of the Flynn effect, IQ tests are periodically renormed, making them harder. Given that eligibility for mental retardation (MR) services relies heavily on IQ scores, renormed tests could have a significant impact on MR placements. In longitudinal IQ records from 9 sites around the country, students in the borderline and mild MR range lost an average of 5.6 points when retested on a renormed test and were more likely to be classified MR compared with peers retested on the same test. The magnitude of the effect is large and affects national policies on education, social security, the death penalty, and the military. This paper reports the perceptions of professionals as they relate to IQ score fluctuations in normal, borderline, and/or MR populations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Network regression with predictive clustering trees

Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a data mining algorithm, called NCLUS, that explicitly considers autocorrelation when building regression models from network data. The algorithm is based on the concept of predictive clustering trees (PCTs) that can be used for clustering, prediction and multi-target prediction, including multi-target regression and multi-target classification. We evaluate our approach on several real world problems of network regression, coming from the areas of social and spatial networks. Empirical results show that our algorithm performs better than PCTs learned by completely disregarding network information, as well as PCTs that are tailored for spatial data, but do not take autocorrelation into account, and a variety of other existing approaches.     

PRILJ: an efficient two-step method based on embedding and clustering for the identification of regularities in legal case judgments

In an era characterized by fast technological progress that introduces new unpredictable scenarios every day, working in the law field may appear very difficult, if not supported by the right tools. In this respect, some systems based on Artificial Intelligence methods have been proposed in the literature, to support several tasks in the legal sector. Following this line of research, in this paper we propose a novel method, called PRILJ, that identifies paragraph regularities in legal case judgments, to support legal experts during the redaction of legal documents. Methodologically, PRILJ adopts a two-step approach that first groups documents into clusters, according to their semantic content, and then identifies regularities in the paragraphs for each cluster. Embedding-based methods are adopted to properly represent documents and paragraphs into a semantic numerical feature space, and an Approximated Nearest Neighbor Search method is adopted to efficiently retrieve the most similar paragraphs with respect to the paragraphs of a document under preparation. Our extensive experimental evaluation, performed on a real-world dataset provided by EUR-Lex, proves the effectiveness and the efficiency of the proposed method. In particular, its ability of modeling different topics of legal documents, as well as of capturing the semantics of the textual content, appear very beneficial for the considered task, and make PRILJ very robust to the possible presence of noise in the data.

     

Suggestibility of the child witness: a historical review and synthesis

The field of children's testimony is in turmoil, but a resolution to seemingly intractable debates now appears attainable. In this review, we place the current disagreement in historical context and describe psychological and legal views of child witnesses held by scholars since the turn of the 20th century. Although there has been consistent interest in children's suggestibility over the past century, the past 15 years have been the most active in terms of the number of published studies and novel theorizing about the causal mechanisms that underpin the observed findings. A synthesis of this research posits three "families" of factors--cognitive, social, and biological--that must be considered if one is to understand seemingly contradictory interpretations of the findings. We conclude that there are reliable age differences in suggestibility but that even very young children are capable of recalling much that is forensically relevant. Findings are discussed in terms of the role of expert witnesses.     

Bridging logic and kernel machines

We propose a general framework to incorporate first-order logic (FOL) clauses, that are thought of as an abstract and partial representation of the environment, into kernel machines that learn within a semi-supervised scheme. We rely on a multi-task learning scheme where each task is associated with a unary predicate defined on the feature space, while higher level abstract representations consist of FOL clauses made of those predicates. We re-use the kernel machine mathematical apparatus to solve the problem as primal optimization of a function composed of the loss on the supervised examples, the regularization term, and a penalty term deriving from forcing real-valued constraints deriving from the predicates. Unlike for classic kernel machines, however, depending on the logic clauses, the overall function to be optimized is not convex anymore. An important contribution is to show that while tackling the optimization by classic numerical schemes is likely to be hopeless, a stage-based learning scheme, in which we start learning the supervised examples until convergence is reached, and then continue by forcing the logic clauses is a viable direction to attack the problem. Some promising experimental results are given on artificial learning tasks and on the automatic tagging of bibtex entries to emphasize the comparison with plain kernel machines.     

A proactive and resilient seismic risk mitigation strategy for existing school buildings

Different methods and procedures have been developed to define prioritisation strategies of retrofit interventions aimed at reducing the seismic risk of school buildings on a large territorial scale. However, these approaches fail to demonstrate how risk analysis has been used successfully to quantitatively assess and select the optimal risk management decision. This article proposes innovative and useful metrics to measure the potential costs and benefits related to the prioritisation of retrofit intervention and the resilience of the analysed school system by directly integrating engineering, organisational, socio-economic and political aspects in the realm of seismic resilience assessment. Based on probabilistic risk assessments considering the new vulnerability of the prioritised school buildings, these measures could predict the expected economic and functional losses associated with a disastrous seismic event, as well as the possible post-disaster recovery of the system. In order to help decision-makers in selecting the optimal mitigation strategy with a multidisciplinary and multidimensional perspective, different political scenarios, the relative prioritisations of interventions and their intervention options are also defined. The proposed framework is demonstrated in a complex case study of 1,825 public schools in the Lima metropolitan area, Peru. Policymakers, planners and engineering professionals could benefit from results.     

Differential Metabotypes in Synovial Fibroblasts and Synovial Fluid in Hip Osteoarthritis Patients Support Inflammatory Responses

Changes in cellular metabolism have been implicated in mediating the activated fibroblast phenotype in a number of chronic inflammatory disorders, including pulmonary fibrosis, renal disease and rheumatoid arthritis. The aim of this study was therefore to characterise the metabolic profile of synovial joint fluid and synovial fibroblasts under both basal and inflammatory conditions in a cohort of obese and normal-weight hip OA patients. Furthermore, we sought to ascertain whether modulation of a metabolic pathway in OA synovial fibroblasts could alter their inflammatory activity. Synovium and synovial fluid was obtained from hip OA patients, who were either of normal-weight or obese and were undergoing elective joint replacement surgery. The synovial fluid metabolome was determined by 1H NMR spectroscopy. The metabolic profile of isolated synovial fibroblasts in vitro was characterised by lactate secretion, oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) using the Seahorse XF Analyser. The effects of a small molecule pharmacological inhibitor and siRNA targeted at glutaminase-1 (GLS1) were assessed to probe the role of glutamine metabolism in OA synovial fibroblast function. Obese OA patient synovial fluid (n = 5) exhibited a different metabotype, compared to normal-weight patient fluid (n = 6), with significantly increased levels of 1, 3-dimethylurate, N-Nitrosodimethylamine, succinate, tyrosine, pyruvate, glucose, glycine and lactate, and enrichment of the glutamine–glutamate metabolic pathway, which correlated with increasing adiposity. In vitro, isolated obese OA fibroblasts exhibited greater basal lactate secretion and aerobic glycolysis, and increased mitochondrial respiration when stimulated with pro-inflammatory cytokine TNFα, compared to fibroblasts from normal-weight patients. Inhibition of GLS1 attenuated the TNFα-induced expression and secretion of IL-6 in OA synovial fibroblasts. These findings suggest that altered cellular metabolism underpins the inflammatory phenotype of OA fibroblasts, and that targeted inhibition of glutamine–glutamate metabolism may provide a route to reducing the pathological effects of joint inflammation in OA patients who are obese.     

Plasma Metabolomics Profile of “Insulin Sensitive” Male Hypogonadism after Testosterone Replacement Therapy

Male hypogonadism is a disorder characterized by low levels of testosterone, but patients can either show normal insulin (insulin-sensitive (IS)) or over time they can become insulin-resistant (IR). Since the two groups showed different altered metabolisms, testosterone replacement therapy (TRT) could achieve different results. In this paper, we analyzed plasma from 20 IS patients with low testosterone (<8 nmol/L) and HOMAi < 2.5. The samples, pre- and post-treatment with testosterone for 60 days, were analyzed by UHPLC and mass spectrometry. Glycolysis was significantly upregulated, suggesting an improved glucose utilization. Conversely, the pentose phosphate pathway was reduced, while the Krebs cycle was not used. Branched amino acids and carnosine metabolism were positively influenced, while β-oxidation of fatty acids (FFA) was not activated. Cholesterol, HDL, and lipid metabolism did not show any improvements at 60 days but did so later in the experimental period. Finally, both malate and glycerol shuttle were reduced. As a result, both NADH and ATP were significantly lower. Interestingly, a significant production of lactate was observed, which induced the activation of the Cori cycle between the liver and muscles, which became the main source of energy for these patients without involving alanine. Thus, the treatment must be integrated with chemicals which are not restored in order to reactivate energy production.     

Improved Second Generation Iron Pincer Complexes for Effective Ester Hydrogenation

Hydrogenation of esters to alcohols with a well‐defined iron ^iPr2PNP pincer complex has been recently reported by us and other groups. We now introduce a novel and sterically less hindered ^Et2PNP congener that provides superior catalytic activity in the hydrogenation of various carboxylic acid esters and lactones compared to the known complex. Successful hydrogenation proceeds under relatively mild conditions (60 °C) with lower catalyst loadings.