A machine learning toolbox for the analysis of sharp-wave ripples reveals common waveform features across species.

The study of sharp-wave ripples has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy is considered a biomarker of dysfunction. Sharp-wave ripples exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine-learning models for automatic detection and analysis of these events. The machine-learning architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of ripple features recorded in the dorsal hippocampus of mice across awake and sleep conditions. When applied to data from the macaque hippocampus, these models are able to generalize detection and reveal shared properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize analysis of sharp-wave ripples, lowering the threshold for its adoption in biomedical applications.

A machine learning toolbox for the analysis of sharp-wave ripples reveal common features across species.

The study of sharp-wave ripples (SWRs) has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy and Alzheimer's disease is considered a biomarker of dysfunction. SWRs exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine learning (ML) models for automatic detection and analysis of SWRs. The ML architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of SWR features recorded in the dorsal hippocampus of mice. When applied to data from the macaque hippocampus, these models were able to generalize detection and revealed shared SWR properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize SWR research, lowering the threshold for its adoption in biomedical applications.

Development of alternatives to estimate resting metabolic rate from anthropometric variables in paralympic swimmers.

The resting metabolic rate (RMR) is one of the most representative components of total daily energy expenditure (TDEE). Multiple equations have been developed to estimate RMR, but none have been described for Para-Athletes. This study aimed to; i) develop and validate new RMR estimation models from anthropometric variables; and ii) explore the level of agreement between the RMR determined by indirect calorimetry (IC) and the developed models, as well as a selection of existent estimation models in Para-Athletes. Fifteen young Paralympic swimmers (age, 18.7±6.5 years) underwent assessments of RMR by IC and anthropometric batteries. Four RMR estimation models (M1-M4) were developed. The anthropometric variables which explained most of the variance were biacromial breadth (M3-37%), stature (M1-45%; M2-49%), and estimated stature from half arm span (M4-24%). However, the neck girth corrected by the submandibular skinfold entered in all four models. The 95% limits of agreement between IC and M3 equation (best performance model) ranged from -142.02 to 172.39 kcal×day-1 (bias 15.19 kcal×day-1). Concerning the commonly used equations, Harris & Benedict equation was the most consistent when compared to IC. The results of this study suggest four novel RMR equations that may assist in the estimation of energy requirements in elite Para-Athletes.

Unraveling how the Gly526Ser mutation arrests prostaglandin formation from arachidonic acid catalyzed by cyclooxygenase-2: a combined molecular dynamics and QM/MM study.

Cyclooxygenases (COXs) are the enzymes responsible for the biosynthesis of prostaglandins, eicosanoids that play a major role in many physiological processes. Particularly, prostaglandins are known to trigger inflammation, and COX-2, the enzyme isoform associated with this inflammatory response, catalyzes the cyclooxidation of arachidonic acid, leading to prostaglandin G2. For this reason, COX-2 has been a very important pharmacological target for several decades now. The catalytic mechanism of COX-2, a so-called all-radical mechanism, consists of six chemical steps. One of the most intriguing aspects of this mechanism is how COX-2 manages to control the regio- and stereospecificity of the products formed at each step. Mutagenesis experiments have previously been performed in an attempt to find those hot-spot residues that make such control possible. In this context, it is worth mentioning that in experiments with the Gly526Ser COX-2 mutant, prostaglandins were not detected. In this paper, we have combined molecular dynamics simulations and quantum mechanics/molecular mechanics calculations to analyze how the COX-2 catalytic mechanism is modified in the Gly526Ser mutant. Therefore, this study provides new insights into the COX-2 catalytic function.

The effect of relational training on intelligence quotient: a case study.

BACKGROUND: Relational training protocols based on Relational Frame Theory (RFT) are showing promising results in increasing intelligence quotient. This case study aimed at analyzing the effect of a training protocol in fluency and flexibility in relational responding on intelligence quotient with a 4-year-old child. METHOD: The child’s cognitive and psychomotor development was evaluated before and after the implementation of the training protocol using the McCarthy’s Aptitudes and Psychomotricity Scale (MSCA). The training protocol consisted of a multiple-exemplar-training (MET) in relational framing in accordance with COORDINATION (Phases 1 and 2), OPPOSITION (Phase 3 and 4), and COMPARISON (Phases 5 and 6). The MET protocol was implemented in approximately 12 hours throughout five and one half months. RESULTS: The training was effective in establishing relational responding in OPPOSITION and COMPARISON frames as well as in promoting fluency and flexibility in all the three types of trained relations. After this training, the child showed an increase above 1.5 SD in the General Cognitive Index of the MSCA (from 106 to 131). CONCLUSIONS: This case study adds further empirical evidence of the potential of RFT training to improve cognitive abilities and intelligence.