A computational study on altered theta-gamma coupling during learning and phase coding

There is considerable interest in the role of coupling between theta and gamma oscillations in the brain in the context of learning and memory. Here we have used a neural network model which is capable of producing coupling of theta phase to gamma amplitude firstly to explore its ability to reproduce reported learning changes and secondly to memory-span and phase coding effects. The spiking neural network incorporates two kinetically different GABAA receptor-mediated currents to generate both theta and gamma rhythms and we have found that by selective alteration of both NMDA receptors and GABAA,slow receptors it can reproduce learning-related changes in the strength of coupling between theta and gamma either with or without coincident changes in theta amplitude. When the model was used to explore the relationship between theta and gamma oscillations, working memory capacity and phase coding it showed that the potential storage capacity of short term memories, in terms of nested gamma-subcycles, coincides with the maximal theta power. Increasing theta power is also related to the precision of theta phase which functions as a potential timing clock for neuronal firing in the cortex or hippocampus

On the Energy Transfer Performance of Mechanical Nanoresonators Coupled with Electromagnetic Fields

We study the energy transfer performance in electrically and magnetically coupled mechanical nanoresonators. Using the resonant scattering theory, we show that magnetically coupled resonators can achieve the same energy transfer performance as for their electrically coupled counterparts, or even outperform them within the scale of interest. Magnetic and electric coupling are compared in the Nanotube Radio, a realistic example of a nano-scale mechanical resonator. The energy transfer performance is also discussed for a newly proposed bio-nanoresonator composed of a magnetosomes coated with a net of protein fibers.Comment: 9 Pages, 3 Figure

Modeling concept drift: A probabilistic graphical model based approach

An often used approach for detecting and adapting to concept drift when doing classi cation is to treat the data as i.i.d. and use changes in classi cation accuracy as an indication of concept drift. In this paper, we take a different perspective and propose a framework, based on probabilistic graphical models, that explicitly represents concept drift using latent variables. To ensure effcient inference and learning, we resort to a variational Bayes inference scheme. As a proof of concept, we demonstrate and analyze the proposed framework using synthetic data sets as well as a real fi nancial data set from a Spanish bank

Unsupervised Multi-Omic Data Fusion: the Neural Graph Learning Network

In recent years, due to the high availability of omic data, data-driven biology has greatly expanded. However, the analysis of different data sources is still an open challenge. A few multi-omics approaches have been proposed in the literature, none of which takes into consideration the intrinsic topology of each omic, though. In this work, an unsupervised learning method based on a deep neural network is proposed. Foreach omic, a separate network is trained, whose outputs are fused into a single graph; at this purpose, an innovative loss function has been designed to better represent the data cluster manifolds. The graph adjacency matrix is exploited to determine similarities among samples. With this approach, omics having a different number of features are merged into a unique representation. Quantitative and qualitative analyses show that the proposed method has comparable results to the state of the art. The method has great intrinsic flexibility as it can be customized according to the complexity of the tasks and it has a lot of room for future improvements compared to more fine-tuned methods, opening the way for future research

Hospital contacts for injuries and musculoskeletal diseases among seamen and fishermen: A population-based cohort study

<p>Abstract</p> <p>Background</p> <p>We studied musculoskeletal diseases (MSD) and injuries among fishermen and seamen with focus on low back disorders, carpal tunnel syndrome (CTS), rotator cuff syndrome and arthrosis.</p> <p>Methods</p> <p>Cohorts of all male Danish seamen (officers and non-officers) and fishermen employed 1994 and 1999 with at least six months employment history were linked to the Occupational Hospitalisation Register. We calculated standardised incidence ratios (SIR) for the two time periods, using rates for the entire Danish workforce as a reference.</p> <p>Results</p> <p>Among fishermen, we found high SIRs for knee arthrosis, thoraco-lumbar disc disorders, injuries and statistically significant SIRs above 200 were seen for both rotator cuff syndrome and CTS. The SIR was augmented for injuries and reduced for hip arthrosis between the two time periods. The SIRs for injuries and CTS were high for non-officers. A sub-analysis revealed that the highest risk for CTS was found among male non-officers working as deck crew, SIR 233 (95% CI: 166–317) based on 40 cases. Among officers, the SIRs for injuries and MSDs were low. The number of employed Danish fishermen declined with 25% 1994–1999 to 3470. Short-term employments were common. None of the SIRs increased with increasing length of employment.</p> <p>Conclusion</p> <p>Both fishermen and non-officers have high SIRs for injuries and fishermen also for MSD. Only the SIR for injuries among fishermen was augmented between 1994 and 1999. Our findings suggest an association between the incidence of rotator cuff syndrome and CTS and work within fishery. Long-term cumulative effects of employment were not shown for any of the disease outcomes. Other conditions may play a role.</p

Identifying the favored mutation in a positive selective sweep.

Most approaches that capture signatures of selective sweeps in population genomics data do not identify the specific mutation favored by selection. We present iSAFE (for "integrated selection of allele favored by evolution"), a method that enables researchers to accurately pinpoint the favored mutation in a large region (∼5 Mbp) by using a statistic derived solely from population genetics signals. iSAFE does not require knowledge of demography, the phenotype under selection, or functional annotations of mutations