30,633 research outputs found
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EEG microstates: Functional significance and short-term test-retest reliability
Appendix A: Supplementary data to this article can be found online at https://doi. org/10.1016/j.ynirp.2022.100089.Copyright /© 2022 The Authors. EEG signal power, may have clinical relevance; however, their functional significance and test-retest reliability remain unclear. To investigate the functional significance of the canonical EEG microstate classes and their pairwise transitions, and to establish their within-session test-retest reliability, we recorded 36-channel EEGs in 20 healthy volunteers during three eyes-closed conditions: mind-wandering, verbalization (silently repeating the word ‘square’ every 2 s), and visualization (visualizing a square every 2 s). Each condition lasted 3 min and the sequence of three conditions was repeated four times (two runs of two sequence repetitions). The participants' alertness and their sense of effort during the experiment were rated using visual-analogue scales. The EEG data were 2–20 Hz bandpass-filtered and analysed into the four canonical microstate classes: A, B, C, and D. EEG microstate classes C and D were persistently more dominant than classes A and B in all conditions. Of the first-order microstate parameters, average microstate duration was most reliable. The duration of class D microstate was longer during mind-wandering (106.8 ms) than verbalization (102.2 ms) or visualization (99.8 ms), with a concomitantly higher coverage (36.4% vs. 34.7% and 35.2%), but otherwise there was no clear association of the four microstate classes to particular mental states. The test-retest reliability was higher at the beginning of each run, together with higher average alpha power and subjective ratings of alertness. Only the transitions between classes C and D (from C to D in particular) were significantly higher than what would be expected from the respective microstates' occurrences. The transition probabilities, however, did not distinguish between conditions, and their test-retest reliability was overall lower than that of the first-order parameters such as duration and coverage. Further studies are needed to establish the functional significance of the canonical EEG microstate classes. This might be more fruitfully achieved by looking at their complex syntax beyond pairwise transitions. To ensure greater test-retest reliability of microstate parameters, study designs should allow for shorter experimental runs with regular breaks, particularly when using EEG microstates as clinical biomarkers.BIAL Foundation (grant number: 183/16)
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Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts.
BACKGROUND: Although 10 000 steps per day is widely promoted to have health benefits, there is little evidence to support this recommendation. We aimed to determine the association between number of steps per day and stepping rate with all-cause mortality. METHODS: In this meta-analysis, we identified studies investigating the effect of daily step count on all-cause mortality in adults (aged ≥18 years), via a previously published systematic review and expert knowledge of the field. We asked participating study investigators to process their participant-level data following a standardised protocol. The primary outcome was all-cause mortality collected from death certificates and country registries. We analysed the dose-response association of steps per day and stepping rate with all-cause mortality. We did Cox proportional hazards regression analyses using study-specific quartiles of steps per day and calculated hazard ratios (HRs) with inverse-variance weighted random effects models. FINDINGS: We identified 15 studies, of which seven were published and eight were unpublished, with study start dates between 1999 and 2018. The total sample included 47 471 adults, among whom there were 3013 deaths (10·1 per 1000 participant-years) over a median follow-up of 7·1 years ([IQR 4·3-9·9]; total sum of follow-up across studies was 297 837 person-years). Quartile median steps per day were 3553 for quartile 1, 5801 for quartile 2, 7842 for quartile 3, and 10 901 for quartile 4. Compared with the lowest quartile, the adjusted HR for all-cause mortality was 0·60 (95% CI 0·51-0·71) for quartile 2, 0·55 (0·49-0·62) for quartile 3, and 0·47 (0·39-0·57) for quartile 4. Restricted cubic splines showed progressively decreasing risk of mortality among adults aged 60 years and older with increasing number of steps per day until 6000-8000 steps per day and among adults younger than 60 years until 8000-10 000 steps per day. Adjusting for number of steps per day, comparing quartile 1 with quartile 4, the association between higher stepping rates and mortality was attenuated but remained significant for a peak of 30 min (HR 0·67 [95% CI 0·56-0·83]) and a peak of 60 min (0·67 [0·50-0·90]), but not significant for time (min per day) spent walking at 40 steps per min or faster (1·12 [0·96-1·32]) and 100 steps per min or faster (0·86 [0·58-1·28]). INTERPRETATION: Taking more steps per day was associated with a progressively lower risk of all-cause mortality, up to a level that varied by age. The findings from this meta-analysis can be used to inform step guidelines for public health promotion of physical activity. FUNDING: US Centers for Disease Control and Prevention
Exploring the effects of spinal cord stimulation for freezing of gait in parkinsonian patients
Dopaminergic replacement therapies (e.g. levodopa) provide limited to no response for axial motor symptoms including gait dysfunction and freezing of gait (FOG) in Parkinson’s disease (PD) and Richardson’s syndrome progressive supranuclear palsy (PSP-RS) patients. Dopaminergic-resistant FOG may be a sensorimotor processing issue that does not involve basal ganglia (nigrostriatal) impairment. Recent studies suggest that spinal cord stimulation (SCS) has positive yet variable effects for dopaminergic-resistant gait and FOG in parkinsonian patients. Further studies investigating the mechanism of SCS, optimal stimulation parameters, and longevity of effects for alleviating FOG are warranted. The hypothesis of the research described in this thesis is that mid-thoracic, dorsal SCS effectively reduces FOG by modulating the sensory processing system in gait and may have a dopaminergic effect in individuals with FOG. The primary objective was to understand the relationship between FOG reduction, improvements in upper limb visual-motor performance, modulation of cortical activity and striatal dopaminergic innervation in 7 PD participants. FOG reduction was associated with changes in upper limb reaction time, speed and accuracy measured using robotic target reaching choice tasks. Modulation of resting-state, sensorimotor cortical activity, recorded using electroencephalography, was significantly associated with FOG reduction while participants were OFF-levodopa. Thus, SCS may alleviate FOG by modulating cortical activity associated with motor planning and sensory perception. Changes to striatal dopaminergic innervation, measured using a dopamine transporter marker, were associated with visual-motor performance improvements. Axial and appendicular motor features may be mediated by non-dopaminergic and dopaminergic pathways, respectively. The secondary objective was to demonstrate the short- and long-term effects of SCS for alleviating dopaminergic-resistant FOG and gait dysfunction in 5 PD and 3 PSP-RS participants without back/leg pain. SCS programming was individualized based on which setting best improved gait and/or FOG responses per participant using objective gait analysis. Significant improvements in stride velocity, step length and reduced FOG frequency were observed in all PD participants with up to 3-years of SCS. Similar gait and FOG improvements were observed in all PSP-RS participants up to 6-months. SCS is a promising therapeutic option for parkinsonian patients with FOG by possibly influencing cortical and subcortical structures involved in locomotion physiology
Comparative study of classification algorithms for quality assessment of resistance spot welding joints from preand post-welding inputs
Resistance spot welding (RSW) is a widespread manufacturing process in the automotive industry. There are different approaches for assessing the quality level of RSW joints. Multi-input-single-output methods, which take as inputs either the intrinsic parameters of the welding process or ultrasonic nondestructive testing variables, are commonly used. This work demonstrates that the combined use of both types of inputs can significantly improve the already competitive approach based exclusively on ultrasonic analyses. The use of stacking of tree ensemble models as classifiers dominates the classification results in terms of accuracy, F-measure and area under the receiver operating characteristic curve metrics. Through variable importance analyses, the results show that although the welding process parameters are less relevant than the ultrasonic testing variables, some of the former provide marginal information not fully captured by the latter
Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process
In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov-
ernment through the ELKARTEK program (OILTWIN project, ref. KK-
2020/00052)
Unraveling the effect of sex on human genetic architecture
Sex is arguably the most important differentiating characteristic in most mammalian
species, separating populations into different groups, with varying behaviors, morphologies,
and physiologies based on their complement of sex chromosomes, amongst other factors. In
humans, despite males and females sharing nearly identical genomes, there are differences
between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides
the genome with a distinct hormonal milieu, differential gene expression, and environmental
pressures arising from gender societal roles. This thus poses the possibility of observing
gene by sex (GxS) interactions between the sexes that may contribute to some of the
phenotypic differences observed. In recent years, there has been growing evidence of GxS,
with common genetic variation presenting different effects on males and females. These
studies have however been limited in regards to the number of traits studied and/or
statistical power. Understanding sex differences in genetic architecture is of great
importance as this could lead to improved understanding of potential differences in
underlying biological pathways and disease etiology between the sexes and in turn help
inform personalised treatments and precision medicine.
In this thesis we provide insights into both the scope and mechanism of GxS across the
genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK
Biobank. We found small yet widespread differences in genetic architecture across traits
through the calculation of sex-specific heritability, genetic correlations, and sex-stratified
genome-wide association studies (GWAS). We further investigated whether sex-agnostic
(non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we
studied the potential functional role of sex differences in genetic architecture through sex
biased expression quantitative trait loci (eQTL) and gene-level analyses.
Overall, this study marks a broad examination of the genetics of sex differences. Our findings
parallel previous reports, suggesting the presence of sexual genetic heterogeneity across
complex traits of generally modest magnitude. Furthermore, our results suggest the need to
consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms
Structure and adsorption properties of gas-ionic liquid interfaces
Supported ionic liquids are a diverse class of materials that have been considered
as a promising approach to design new surface properties within solids for gas
adsorption and separation applications. In these materials, the surface morphology and
composition of a porous solid are modified by depositing ionic liquid. The resulting
materials exhibit a unique combination of structural and gas adsorption properties
arising from both components, the support, and the liquid. Naturally, theoretical and
experimental studies devoted to understanding the underlying principles of exhibited
interfacial properties have been an intense area of research. However, a complete
understanding of the interplay between interfacial gas-liquid and liquid-solid
interactions as well as molecular details of these processes remains elusive.
The proposed problem is challenging and in this thesis, it is approached from
two different perspectives applying computational and experimental techniques. In
particular, molecular dynamics simulations are used to model gas adsorption in films
of ionic liquids on a molecular level. A detailed description of the modeled systems is
possible if the interfacial and bulk properties of ionic liquid films are separated. In this
study, we use a unique method that recognizes the interfacial and bulk structures of
ionic liquids and distinguishes gas adsorption from gas solubility. By combining
classical nitrogen sorption experiments with a mean-field theory, we study how liquid-solid interactions influence the adsorption of ionic liquids on the surface of the porous
support.
The developed approach was applied to a range of ionic liquids that feature
different interaction behavior with gas and porous support. Using molecular
simulations with interfacial analysis, it was discovered that gas adsorption capacity
can be directly related to gas solubility data, allowing the development of a predictive
model for the gas adsorption performance of ionic liquid films. Furthermore, it was
found that this CO2 adsorption on the surface of ionic liquid films is determined by the
specific arrangement of cations and anions on the surface. A particularly important
result is that, for the first time, a quantitative relation between these structural and
adsorption properties of different ionic liquid films has been established. This link
between two types of properties determines design principles for supported ionic
liquids.
However, the proposed predictive model and design principles rely on the
assumption that the ionic liquid is uniformly distributed on the surface of the porous
support. To test how ionic liquids behave under confinement, nitrogen physisorption
experiments were conducted for micro‐ and mesopore analysis of supported ionic
liquid materials. In conjunction with mean-field density functional theory applied to
the lattice gas and pore models, we revealed different scenarios for the pore-filling
mechanism depending on the strength of the liquid-solid interactions.
In this thesis, a combination of computational and experimental studies provides
a framework for the characterization of complex interfacial gas-liquid and liquid-solid
processes. It is shown that interfacial analysis is a powerful tool for studying
molecular-level interactions between different phases. Finally, nitrogen sorption
experiments were effectively used to obtain information on the structure of supported
ionic liquids
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Drivers and Direct Impacts of Lean Mass Dynamics on the Stopover Ecology and Migratory Pace of Nearctic-Neotropical Migrant Songbirds in Spring
Annual migration in songbirds is one of the most demanding life-history stages. It represents a period of high mortality, yet there is still much unknown about the ecological correlates that influence its successful completion. After long non-stop migratory flights, birds require a stopover period to rest and replenish depleted energy reserves. Birds use fat as the primary fuel to power long-distance flights. However, birds also burn lean tissue, which results in significant reductions in muscle and organ masses. The discovery and quantification of lean mass catabolism represented a paradigm shift in migration ecology because non-fat components were thought to remain homeostatic. Because rebuilding protein is slow, muscle and organ breakdown during migration may dramatically prolong stopover periods and delay overall migration time, which in turn dramatically reduces breeding success. Therefore, the breakdown of lean tissue, the conditions that lead to it, and its consequences are important considerations in understanding the migration strategies of birds.
Through this dissertation research, I aim to understand the impact of weather on body condition and how physiological condition impacts subsequent migratory performance. I investigate (1) how weather impacts the lean mass of songbirds after crossing an ecological barrier, and (2) how body condition after crossing an ecological barrier affects stopover duration, refueling rate, and habitat use. My predictions are that higher nightly temperatures or drier conditions experienced during migratory flight will correspond with lower lean body mass on arrival; and that birds with lower lean body mass will require longer stopovers, different habitat, or higher foraging effort to continue migration.
I used an integrative approach, combining the field and lab, to better understand how weather experienced during flight can impact the body condition of migratory birds and how this can influence the entire migratory cycle. By using Quantitative Magnetic Resonance (QMR) technology in combination with a novel automated radio-telemetry system, my research provides unprecedented access to detailed physiological and movement data for small migratory songbirds. This research underlines that successfully crossing the Gulf of Mexico may be a key driver of physiological and morphological adaptations. My findings challenge the current paradigm that birds with low lean mass require longer stopover and demonstrates that species under time constraints may shorten stopover even when in poor condition, departing in sub-optimal body condition
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Quantitative Character and the Composite Account of Phenomenal Content
I advance an account of quantitative character, a species of phenomenal character that presents as an intensity (cf. a quality) and includes experience dimensions such as loudness, pain intensity, and visual pop-out. I employ psychological and neuroscientific evidence to demonstrate that quantitative characters are best explained by attentional processing, and hence that they do not represent external qualities. Nonetheless, the proposed account of quantitative character is conceived as a compliment to the reductive intentionalist strategy toward qualitative states; I argue that an account of perceptual experience that combines a tracking account of qualitative character with my functionalist proposal of quantitative character permits replies to some notoriously difficult problems for tracking representationalism without sacrificing its chief virtues
The applied psychology of addictive orientations : studies in a 12-step treatment context.
The clinical data for the studies was collected at The PROMIS Recovery Centre, a Minnesota Model treatmentc entre for addictions,w hich encouragesth e membership and use of the 12 step Anonymous Fellowships, and is abstinence based. The area of addiction is contextualised in a review chapter which focuses on research relating to the phenomenon of cross addiction. A study examining the concept of "addictive orientations" in male and female addicts is described, which develops a study conductedb y StephensonM, aggi, Lefever, & Morojele (1995). This presents study found a four factor solution which appeared to be subdivisions of the previously found Hedonism and Nurturance factors. Self orientated nurturance (both food dimensions, shopping and caffeine), Other orientated nurturance (both compulsive helping dimensions and work), Sensation seeking hedonism (Drugs, prescription drugs, nicotine and marginally alcohol), and Power related hedonism (Both relationship dimensions, sex and gambling. This concept of "addictive orientations" is further explored in a non-clinical population, where again a four factor solution was found, very similar to that in the clinical population. This was thought to indicate that in terms of addictive orientation a pattern already exists in this non-clinical population and that consideration should be given to why this is the case. These orientations are examined in terms of gender differences. It is suggested that the differences between genders reflect power-related role relationships between the sexes. In order to further elaborate the significance and meaning behind these orientations, the next two chapters look at the contribution of personality variables and how addictive orientations relate to psychiatric symptomatology. Personality variables were differentially, and to a considerable extent predictably involved with the four factors for both males and females.Conscientiousness as positively associated with "Other orientated Nurturance" and negatively associated with "Sensation seeking hedonism" (particularly for men). Neuroticism had a particularly strong association with the "Self orientated Nurturance" factor in the female population. More than twice the symptomatology variance was explained by the factor scores for females than it was for males. The most important factorial predictors for psychiatric symptomatology were the "Power related hedonism" factor for males, and "Self oriented nurturance" for females. The results are discussed from theoretical and treatment perspectives
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