El estudio y el uso sustentable de la biota austral : Un programa del Museo de La Plata

El impacto de la intervención humana sobre los componentes de la biodiversidad, desde los genes hasta los ecosistemas, ha estimulado la inquietud de muchos biólogos en el sentido de intentar revertir los efectos disruptivos de origen antropogénico sobre dichos componentes, a través de medidas de efectiva mitigación y/o compensación. Pero es indudable que esta genuina aspiración sólo puede basarse en el conocimiento previo de la composición y el estatus de conservación de los componentes de la biota que se pretenden preservar y aun utilizar de modo ecológicamente compatible. Es cierto, por otra parte, que en la consideración de todos los niveles biológicos de organización, desde los átomos y macromoléculas, basta la biosfera, en el contexto de la biodiversidad, la especie biológica es el más destacado, no sólo por su condición de entidad biológica real y definible, sino también por ser una de las pruebas más fehacientes de la pérdida de la diversidad biótica.Fundación Museo La Plat

El estudio y el uso sustentable de la biota austral : Un programa del Museo de La Plata

El impacto de la intervención humana sobre los componentes de la biodiversidad, desde los genes hasta los ecosistemas, ha estimulado la inquietud de muchos biólogos en el sentido de intentar revertir los efectos disruptivos de origen antropogénico sobre dichos componentes, a través de medidas de efectiva mitigación y/o compensación. Pero es indudable que esta genuina aspiración sólo puede basarse en el conocimiento previo de la composición y el estatus de conservación de los componentes de la biota que se pretenden preservar y aun utilizar de modo ecológicamente compatible. Es cierto, por otra parte, que en la consideración de todos los niveles biológicos de organización, desde los átomos y macromoléculas, basta la biosfera, en el contexto de la biodiversidad, la especie biológica es el más destacado, no sólo por su condición de entidad biológica real y definible, sino también por ser una de las pruebas más fehacientes de la pérdida de la diversidad biótica.Fundación Museo La Plat

Active Selection of Classification Features

Some data analysis applications comprise datasets, where explanatory variables are expensive or tedious to acquire, but auxiliary data are readily available and might help to construct an insightful training set. An example is neuroimaging research on mental disorders, specifically learning a diagnosis/prognosis model based on variables derived from expensive Magnetic Resonance Imaging (MRI) scans, which often requires large sample sizes. Auxiliary data, such as demographics, might help in selecting a smaller sample that comprises the individuals with the most informative MRI scans. In active learning literature, this problem has not yet been studied, despite promising results in related problem settings that concern the selection of instances or instance-feature pairs. Therefore, we formulate this complementary problem of Active Selection of Classification Features (ASCF): Given a primary task, which requires to learn a model f: x-> y to explain/predict the relationship between an expensive-to-acquire set of variables x and a class label y. Then, the ASCF-task is to use a set of readily available selection variables z to select these instances, that will improve the primary task's performance most when acquiring their expensive features z and including them to the primary training set. We propose two utility-based approaches for this problem, and evaluate their performance on three public real-world benchmark datasets. In addition, we illustrate the use of these approaches to efficiently acquire MRI scans in the context of neuroimaging research on mental disorders, based on a simulated study design with real MRI data.Comment: Accepted for publication at the 19th Intelligent Data Analysis Symposium, 2021. The final authenticated publication will be made available online at springer.co

Semantic and Acoustic Markers in Schizophrenia-Spectrum Disorders:A Combinatory Machine Learning Approach

BACKGROUND AND HYPOTHESIS: Speech is a promising marker to aid diagnosis of schizophrenia-spectrum disorders, as it reflects symptoms like thought disorder and negative symptoms. Previous approaches made use of different domains of speech for diagnostic classification, including features like coherence (semantic) and form (acoustic). However, an examination of the added value of each domain when combined is lacking as of yet. Here, we investigate the acoustic and semantic domains separately and combined. STUDY DESIGN: Using semi-structured interviews, speech of 94 subjects with schizophrenia-spectrum disorders (SSD) and 73 healthy controls (HC) was recorded. Acoustic features were extracted using a standardized feature-set, and transcribed interviews were used to calculate semantic word similarity using word2vec. Random forest classifiers were trained for each domain. A third classifier was used to combine features from both domains; 10-fold cross-validation was used for each model. RESULTS: The acoustic random forest classifier achieved 81% accuracy classifying SSD and HC, while the semantic domain classifier reached an accuracy of 80%. Joining features from the two domains, the combined classifier reached 85% accuracy, significantly improving on separate domain classifiers. For the combined classifier, top features were fragmented speech from the acoustic domain and variance of similarity from the semantic domain. CONCLUSIONS: Both semantic and acoustic analyses of speech achieved ~80% accuracy in classifying SSD from HC. We replicate earlier findings per domain, additionally showing that combining these features significantly improves classification performance. Feature importance and accuracy in combined classification indicate that the domains measure different, complementing aspects of speech.</p

Predicting future suicidal behaviour in young adults, with different machine learning techniques: a population-based longitudinal study

Background: The predictive accuracy of suicidal behaviour has not improved over the last decades. We aimed to explore the potential of machine learning to predict future suicidal behaviour using population-based longitudinal data. Method: Baseline risk data assessed within the Scottish wellbeing study, in which 3508 young adults (18-34 years) completed a battery of psychological measures, were used to predict both suicide ideation and suicide attempts at one-year follow-up. The performance of the following algorithms was compared: regular logistic regression, K-nearest neighbors, classification tree, random forests, gradient boosting and support vector machine. Results: At one year follow up, 2428 respondents (71%) finished the second assessment. 336 respondents (14%) reported suicide ideation between baseline and follow up, and 50 (2%) reported a suicide attempt. All performance metrics were highly similar across methods. The random forest algorithm was the best algorithm to predict suicide ideation (AUC 0.83, PPV 0.52, BA 0.74) and the gradient boosting to predict suicide attempt (AUC 0.80, PPV 0.10, BA 0.69). Limitations: The number of respondents with suicidal behaviour at follow up was small. We only had data on psychological risk factors, limiting the potential of the more complex machine learning algorithms to outperform regular logistical regression. Conclusions: When applied to population-based longitudinal data containing multiple psychological measurements, machine learning techniques did not significantly improve the predictive accuracy of suicidal behavior. Adding more detailed data on for example employment, education or previous health care uptake, might result in better performance of machine learning over regular logistical regression

Contributing factors to advanced brain aging in depression and anxiety disorders

Depression and anxiety are common and often comorbid mental health disorders that represent risk factors for aging-related conditions. Brain aging has shown to be more advanced in patients with major depressive disorder (MDD). Here, we extend prior work by investigating multivariate brain aging in patients with MDD, anxiety disorders, or both, and examine which factors contribute to older-appearing brains. Adults aged 18–57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pretrained brain-age prediction model based on >2000 samples from the ENIGMA consortium was applied to obtain brain-predicted age differences (brain PAD, predicted brain age minus chronological age) in 65 controls and 220 patients with current MDD and/or anxiety. Brain-PAD estimates were associated with clinical, somatic, lifestyle, and biological factors. After correcting for antidepressant use, brain PAD was significantly higher in MDD (+2.78 years, Cohen’s d = 0.25, 95% CI −0.10-0.60) and anxiety patients (+2.91 years, Cohen’s d = 0.27, 95% CI −0.08-0.61), compared with controls. There were no significant associations with lifestyle or biological stress systems. A multivariable model indicated unique contributions of higher severity of somatic depression symptoms (b = 4.21 years per unit increase on average sum score) and antidepressant use (−2.53 years) to brain PAD. Advanced brain aging in patients with MDD and anxiety was most strongly associated with somatic depressive symptomatology. We also present clinically relevant evidence for a potential neuroprotective antidepressant effect on the brain-PAD metric that requires follow-up in future research

Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study:a machine learning approach

Schizophrenia and related disorders have heterogeneous outcomes. Individualized prediction of long-term outcomes may be helpful in improving treatment decisions. Utilizing extensive baseline data of 523 patients with a psychotic disorder and variable illness duration, we predicted symptomatic and global outcomes at 3-year and 6-year follow-ups. We classified outcomes as (1) symptomatic: in remission or not in remission, and (2) global outcome, using the Global Assessment of Functioning (GAF) scale, divided into good (GAF &gt;= 65) and poor (GAF &lt; 65). Aiming for a robust and interpretable prediction model, we employed a linear support vector machine and recursive feature elimination within a nested cross-validation design to obtain a lean set of predictors. Generalization to out-of-study samples was estimated using leave-one-site-out cross-validation. Prediction accuracies were above chance and ranged from 62.2% to 64.7% (symptomatic outcome), and 63.5-67.6% (global outcome). Leave-one-site-out cross-validation demonstrated the robustness of our models, with a minor drop in predictive accuracies of 2.3% on average. Important predictors included GAF scores, psychotic symptoms, quality of life, antipsychotics use, psychosocial needs, and depressive symptoms. These robust, albeit modestly accurate, long-term prognostic predictions based on lean predictor sets indicate the potential of machine learning models complementing clinical judgment and decision-making. Future model development may benefit from studies scoping patient's and clinicians' needs in prognostication.</p

Does having a twin-brother make for a bigger brain?

Objective: Brain volume of boys is larger than that of girls by ∼10%. Prenatal exposure to testosterone has been suggested in the masculinization of the brain. For example, in litter-bearing mammals intrauterine position increases prenatal testosterone exposure through adjacent male fetuses, resulting in masculinization of brain morphology. Design: The influence of intrauterine presence of a male co-twin on masculinization of human brain volume was studied in 9-year old twins. Methods: Magnetic resonance imaging brain scans, current testosterone, and estradiol levels were acquired from four groups of dizygotic (DZ) twins: boys from same-sex twin-pairs (SSM), boys from opposite-sex twin-pairs (OSM), girls from opposite-sex twin-pairs (OSF), and girls from same-sex twin-pairs (SSF; n=119 individuals). Data on total brain, cerebellum, gray and white matter volumes were examined. Results: Irrespective of their own sex, children with a male co-twin as compared to children with a female co-twin had larger total brain (+2.5%) and cerebellum (+5.5%) volumes. SSM, purportedly exposed to the highest prenatal testosterone levels, were found to have the largest volumes, followed by OSM, OSF and SSF children. Birth weight partly explained the effect on brain volumes. Current testosterone and estradiol levels did not account for the volumetric brain differences. However, the effects observed in children did not replicate in adult twins. Conclusions: Our study indicates that sharing the uterus with a DZ twin brother increases total brain volume in 9-year olds. The effect may be transient and limited to a critical period in childhood. © 2009 European Society of Endocrinology