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Sleep spindles (SP) are one of the few known electrophysiological neuronal biomarkers of interindividual differences in cognitive abilities and aptitudes. Recent simultaneous electroencephalography with functional magnetic resonance imaging (EEG-fMRI) studies suggest that the magnitude of the activation of brain regions recruited during spontaneous spindle events is specifically related to Reasoning abilities. However, it is not known if the relationship with cognitive abilities differs between uncoupled spindles, uncoupled slow waves (SW), and coupled SW–SP complexes, nor have the functional-neuroanatomical substrates that support this relationship been identified. Here, we investigated the functional significance of activation of brain areas recruited during SW-coupled spindles, uncoupled spindles, and uncoupled slow waves. We hypothesize that brain activations time locked to SW-coupled spindle complexes will be primarily associated to Reasoning abilities, especially in subcortical areas. Our results provide direct evidence that the relationship between Reasoning abilities and sleep spindles depends on spindle coupling status. Specifically, we found that the putamen and thalamus, recruited during coupled SW–SP events were positively correlated with Reasoning abilities. In addition, we found a negative association between Reasoning abilities and hippocampal activation time-locked to uncoupled SWs that might reflect a refractory mechanism in the absence of new, intensive hippocampal-dependent memory processing.

As we age, the added benefit of sleep for memory consolidation is lost. One of the hallmark age-related changes in sleep is the reduction of sleep spindles and slow waves. Gray matter neurodegeneration is related to both age-related changes in sleep and age-related changes in memory, including memory for problem-solving skills. Here, we investigated whether spindles and slow waves might serve as biological markers for neurodegeneration of gray matter and for the related memory consolidation deficits in older adults. Forty healthy young adults (20–35 yr) and 30 healthy older adults (60–85 yr) were assigned to either nap or wake conditions. Participants were trained on the Tower of Hanoi in the morning, followed by either a 90-min nap opportunity or period of wakefulness, and were retested afterward. We found that age-related changes in sleep spindles and slow waves were differentially related to gray matter intensity in young and older adults in brain regions that support sleep-dependent memory consolidation for problem-solving skills. Specifically, we found that spindles were related to gray matter in neocortical areas (e.g., somatosensory and parietal cortex), and slow waves were related to gray matter in the anterior cingulate, hippocampus, and caudate, all areas known to support problem-solving skills. These results suggest that both sleep spindles and slow waves may serve as biological markers of age-related neurodegeneration of gray matter and the associated reduced benefit of sleep for memory consolidation in older adults.

Background Abnormalities in heart rate during sleep linked to impaired neuro-cardiac modulation may provide new information about physiological sleep signatures of depression. This study assessed the validity of an algorithm using patterns of heart rate changes during sleep to discriminate between individuals with depression and healthy controls. Methods A heart rate profiling algorithm was modeled using machine-learning based on 1203 polysomnograms from individuals with depression referred to a sleep clinic for the assessment of sleep abnormalities, including insomnia, excessive daytime fatigue, and sleep-related breathing disturbances (n = 664) and mentally healthy controls (n = 529). The final algorithm was tested on a distinct sample (n = 174) to categorize each individual as depressed or not depressed. The resulting categorizations were compared to medical record diagnoses. Results The algorithm had an overall classification accuracy of 79.9% [sensitivity: 82.8, 95% CI (0.73–0.89), specificity: 77.0, 95% CI (0.67–0.85)]. The algorithm remained highly sensitive across subgroups stratified by age, sex, depression severity, comorbid psychiatric illness, cardiovascular disease, and smoking status. Conclusions Sleep-derived heart rate patterns could act as an objective biomarker of depression, at least when it co-occurs with sleep disturbances, and may serve as a complimentary objective diagnostic tool. These findings highlight the extent to which some autonomic functions are impaired in individuals with depression, which warrants further investigation about potential underlying mechanisms. Electronic supplementary material The online version of this article (10.1186/s12888-019-2152-1) contains supplementary material, which is available to authorized users.

© 2019 Elsevier Inc. Sleep is known to be beneficial to the strengthening of two distinct forms of procedural memory: memory for novel, cognitively simple series of motor movements, and memory for novel, cognitively complex strategies required to solve problems. However, these two types of memory are intertwined, since learning a new cognitive procedural strategy occurs through practice, and thereby also requires the execution of a series of simple motor movements. As a result, it is unclear whether the benefit of sleep results from the enhancement of the cognitive strategy, or the motor skills required to execute the solution. To disentangle the role of sleep in these aspects of procedural memory, we employed two tasks: (1) the Tower of Hanoi (ToH), and, (2) a modified version of the ToH, akin to an implicit Motor Sequence Learning (MSL) task. The MSL task involved the identical series of motor movements as the ToH, but without access to the information necessary to execute the task according to the underlying cognitive procedural strategy. Participants (n = 28) were trained on the 3-disk ToH, then retested on 5-disk versions of both ToH and MSL tasks. Half (n = 15) were trained and immediately tested at 8 PM and retested at 8 AM after a night of sleep. They were retested again at 8 PM after a day of wake (PM-AM-PM condition). The other half (n = 13) were trained and immediately tested at 8 AM, retested at 8 PM after a day of wake, and retested again at 8 AM after a night of sleep (AM-PM-AM condition). ToH performance only improved following a period of sleep. There was no benefit of sleep to implicit MSL. Our results show that sleep, but not wake, allowed individuals to extrapolate what was learned on a simpler 3-disk version of the task to the larger 5-disk problem, which included new elements to which they had not yet been exposed. Here, we isolate the specific role sleep plays for cognitive procedural memory: sleep benefits the cognitive strategy, rather than strengthening implicitly acquired motor sequences required to learn and execute the underlying strategy itself.

The sleep spindle, a waxing and waning oscillation in the sigma frequency range, has been shown to correlate with fluid intelligence; i.e. the ability to use logic, learn novel rules/patterns, and solve problems. Using simultaneous EEG and fMRI, we previously identified the neural correlates of this relationship, including activation of the thalamus, bilateral putamen, medial frontal gyrus, middle cingulate cortex, and precuneus. However, research to date has focussed primarily on non-rapid eye movement (NREM) sleep, and spindles per se, thus overlooking the possibility that brain activity that occurs in other sleep–wake states might also be related to cognitive abilities. In our current study, we sought to investigate whether brain activity across sleep/wake states is also related to human intelligence in N = 29 participants. During NREM sleep, positive correlations were observed between fluid intelligence and blood oxygen level dependent (BOLD) activations in the bilateral putamen and the paracentral lobule/precuneus, as well as between short-term memory (STM) abilities and activity in the medial frontal cortex and inferior frontal gyrus. During wake, activity in bilateral postcentral gyri and occipital lobe was positively correlated with short-term memory abilities. In participants who experienced REM sleep in the scanner, fluid intelligence was positively associated with midbrain activation, and verbal intelligence was associated with right postcentral gyrus activation. These findings provide evidence that the relationship between sleep and intellectual abilities exists beyond sleep spindles.

Fourteen patients with severe brain injuries and chronic disorders of consciousness underwent polysomnographic recordings for a 24-h period. Their electrophysiological data were scored using a modified sleep staging system employed in a previous study of similar patients (J Head Trauma Rehabil 30:334-346, 2015). In addition to sleep scoring, the patients' data were compared with a sample of approximately age-matched healthy volunteers in the spectral domain. All patients demonstrated some form of a sleep-wake cycle; however, the integrity of normal sleep features was remarkably heterogenous across individuals, and in some cases, sleep was significantly impoverished. In three patients, these cycles were biphasic and comprised of only alternating periods of wakefulness and sleep-like electrophysiological activity. Two patients demonstrated a sleep-wake cycle that included all sleep stages aside from non-REM stage 3, and another two patients demonstrated a sleep-wake cycle that included all sleep stages aside from REM sleep. The remaining seven patients, which included patients diagnosed as being in a minimally conscious state and patients diagnosed as being in a vegetative state (unresponsive wakefulness syndrome), demonstrated full sleep architecture, including k-complexes, REMs, and slow wave sleep. However, three of the patients with full sleep architecture did not generate sleep spindles. Altogether, these findings highlight the heterogeneity of brain function among patients with disorders of consciousness, regardless of their diagnostic category. Polysomnography is a useful tool to complement other behavioural and physiological assessments that characterize the abilities of each patient.

STUDY OBJECTIVES: The effects of serotonergic agents on respiration neuromodulation may vary according to differences in the serotonin system, such as those linked to depression. This study investigated how sleep-related respiratory disturbances relate to depression and the use of medications commonly prescribed for depression. METHODS: Retrospective polysomnography was collated for all 363 individuals who met selection criteria out of 2,528 consecutive individuals referred to a specialized sleep clinic (Ottawa, Canada) between 2006 and 2016. The apnea-hypopnea index (AHI), oxygen saturation nadir, and oxygen desaturation index during REM and NREM sleep were analyzed using mixed analyses of covariance comparing 3 main groups: (1) medicated individuals with depressive disorders (antidepressant group; subdivided into the selective serotonin reuptake inhibitor and norepinephrine-dopamine reuptake inhibitor subgroups), (2) non-medicated individuals with depressive disorders (non-medicated group), and (3) mentally healthy control patients (control group). RESULTS: Individuals with depressive disorders (on antidepressants or not) had significantly higher AHIs compared to control patients (both P ≤ .007). The antidepressant group had a lower NREM sleep oxygen saturation nadir and a higher NREM sleep oxygen desaturation index than the control and non-medicated groups (all P ≤ .009). Within individuals with depressive disorders, independent of depression severity, the selective serotonin reuptake inhibitor group had a lower oxygen saturation nadir and a higher oxygen desaturation index during NREM sleep than the norepinephrine-dopamine reuptake inhibitor (both P ≤ .045) and non-medicated groups (both P < .001) and a higher NREM sleep AHI than the non-medicated group (P = .014). CONCLUSIONS: These findings suggest that the use of selective serotonin reuptake inhibitors may be associated with impaired breathing and worse nocturnal oxygen saturation in individuals with depressive disorders and sleep complaints, but this needs to be confirmed by prospective studies. CITATION: Robillard R, Saad M, Ray RB, et al. Selective serotonin reuptake inhibitor use is associated with worse sleep-related breathing disturbances in individuals with depressive disorders and sleep complaints: a retrospective study. J Clin Sleep Med. 2021;17(3):505–513.

This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video-electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.

EEG studies have shown that interindividual differences in the electrophysiological properties of sleep spindles (e.g., density, amplitude, duration) are highly correlated with trait-like “reasoning” abilities (i.e., “fluid intelligence”; problem-solving skills; the ability to employ logic or identify complex patterns), but not interindividual differences in STM or “verbal” intellectual abilities. Previous simultaneous EEG-fMRI studies revealed brain activations time-locked to spindles. Our group has recently demonstrated that the extent of activation in a subset of these regions was related to interindividual differences in reasoning intellectual abilities, specifically. However, spindles reflect communication between spatially distant and functionally distinct brain areas. The functional communication among brain regions related to spindles and their relationship to reasoning abilities have yet to be investigated. Using simultaneous EEG-fMRI sleep recordings and psychophysiological interaction analysis, we identified spindle-related functional communication among brain regions in the thalamo-cortical-BG system, the salience network, and the default mode network. Furthermore, the extent of the functional connectivity of the cortical–striatal circuitry and the thalamo-cortical circuitry was specifically related to reasoning abilities but was unrelated to STM or verbal abilities, thus suggesting that individuals with higher fluid intelligence have stronger functional coupling among these brain areas during spontaneous spindle events. This may serve as a first step in further understanding the function of sleep spindles and the brain network functional communication, which support the capacity for fluid intelligence.

2018 Viczko et al. Sleep facilitates the consolidation (i.e., enhancement) of simple, explicit (i.e., conscious) motor sequence learning (MSL). MSL can be dissociated into egocentric (i.e., motor) or allocentric (i.e., spatial) frames of reference. The consolidation of the allocentric memory representation is sleep-dependent, whereas the egocentric consolidation process is independent of sleep or wake for explicit MSL. However, it remains unclear the extent to which sleep contributes to the consolidation of implicit (i.e., unconscious) MSL, nor is it known what aspects of the memory representation (egocentric, allocentric) are consolidated by sleep. Here, we investigated the extent to which sleep is involved in consolidating implicit MSL, specifically, whether the egocentric or the allocentric cognitive representations of a learned sequence are enhanced by sleep, and whether these changes support the development of explicit sequence knowledge across sleep but not wake. Our results indicate that egocentric and allocentric representations can be behaviorally dissociated for implicit MSL. Neither representation was preferentially enhanced across sleep nor were developments of explicit awareness observed. However, after a 1-wk interval performance enhancement was observed in the egocentric representation. Taken together, these results suggest that like explicit MSL, implicit MSL has dissociable allocentric and egocentric representations, but unlike explicit sequence learning, implicit egocentric and allocentric memory consolidation is independent of sleep, and the time-course of consolidation differs significantly.

Creativity represents the pinnacle of higher-level cognition, but exactly how it is achieved remains poorly understood, especially when simultaneously facing the opposing challenge of intractable complexity. The aims of the current study were (a) to examine how the brain may achieve the dual goals of creativity and complexity reduction, and (b) to begin developing higher-level cognition and creativity in robots. We address these aims by (a) modeling an example of insight problem solving and comparing it to empirical data, and (b) testing the model on a robot platform. Unlike other models, we propose a single mechanism for both creative problem solving and complexity reduction. Focusing on creativity, the computational mechanism leads to insightful problem solving by restructuring an internal belief representation based on evidence collected during an incubation period. Because insightful problem solving has been examined closely in nonhuman primates, providing detailed quantitative datasets lacking in humans, we tested the model by comparing simulations to insightful problem solving by rhesus monkeys. Results show that the proposed model accounts for both the discontinuous three stage problem-solving patterns and the spontaneous generalization to novel cases observed with the monkeys. To test the model in a physical environment, we implemented it in a vision-equipped robot, and the model solved the same insight problem from camera percepts. Our model shows how the creative brain may address the dual challenges of complex environmentsfinding unprecedented opportunity hidden within potentially intractable complexityand suggests that both challenges may be met by a single underlying computational mechanism. Proposes a single computational model for the dual challenge of complexity and creativity.Compares model simulation to quantitative data on insightful problem solving by rhesus monkeys.The model accounts for observed discontinuous problem-solving and spontaneous generalization.Implements model in a vision-equipped robot to solve the same insight problem from camera percepts.Shows how the creative brain may address the challenges of complex environments and creativity.

© Sleep Research Society 2018. Resting state network (RSN) functional connectivity (FC) has been investigated under a wealth of different healthy and compromised conditions. Such investigations are often dependent on the defined spatial boundaries and nodes of so-called canonical RSNs, themselves the product of extensive deliberations over distinctions between functional magnetic resonance imaging (fMRI) noise and neural signal, specifically in the context of the healthy waking state. However, a similar unbiased cataloging of noise and networks remains to be done in other states, particularly sleep, a healthy alternate mode of the brain that supports distinct operations from wakefulness, such as dreaming and memory consolidation. The purpose of this study was to explicitly test the hypothesis that there are RSNs unique to sleep. Simultaneous electroencephalography (EEG) and fMRI was used to record brain activity of non-sleep-deprived participants. Independent component analysis was performed on both rapid eye movement (REM; N = 7) and non-REM sleep stage fMRI data (non-REM2; N = 28, non-REM3; N = 11), with the resulting components spatially correlated with the canonical RSNs, for the purpose of identifying spatially distinct RSNs. Surprisingly, all low-correlation components were positively identified as noise, and all high-correlation components comprised the canonical set of RSNs typically observed in wake, indicating that sleep is supported by much the same RSN architecture as wakefulness, despite the unique operations performed during sleep. This further indicates that the implicit assumptions of prior studies, i.e. that the canonical RSNs apply to sleep FC analysis, are valid and have not overlooked sleep-specific RSNs.

This paper presents a generalized, multibody dynamics model for a tracked vehicle equipped with a towing winch and control strategies that enhance vehicle mobility by regulating track slip based on real-time terrain characterization and automating winch use. The vehicle model is validated under conditions where no action is taken by the winch. Thereafter, two mobility enhancing control strategies are outlined. The first strategy regulates track slip to a real-time estimated value that generates maximum net traction. This is done by computing state-force estimates from a Kalman filter that are compared to terrain traction models using a Bayesian hypothesis selection approach. If the vehicle is traction limited and the first strategy fails, a second strategy that automates winch use is activated. Simulation results are shown for both scenarios.

We introduce a new set of mechanisms for tracking entities through videos, at substantially less expense than required by standard methods. The approach combines inexpensive initial processing of individual frames together with integration of information across long time spans (multiple frames), resulting in the recognition and tracking of spatially and temporally contiguous entities, rather than focusing on the individual pixels that comprise those entities.

© 2018 Sleep Research Society. Study Objectives The behavioral and cognitive consequences of severe sleep deprivation are well understood. Surprisingly, relatively little is known about the neural correlates of mild and acute sleep restriction on tasks that require sustained vigilance for prolonged periods of time during the day. Methods and Results Event-related potential (ERP) paradigms can reveal insight into the neural correlates underlying visual processing and behavioral responding that is impaired with reduced alertness, as a consequence of sleep loss. Here, we investigated the impact of reduced vigilance following at-home mild sleep restriction to better understand the associated behavioral consequences and changes in information processing revealed by ERPs. As expected, vigilance was reduced (e.g. increased lapses and response slowing) that increased over the course of the experiment in the "sleep restricted" (5 hr sleep) compared with the "sleep-extension" (9 hr sleep) condition. Corresponding to these lapses, we found decreased positivity of visually evoked potentials in the Sleep Restriction vs. Sleep Extension condition emerging from 316 to 449 ms, maximal over parietal/occipital cortex. We also investigated electrophysiological signs of motor-related processing by comparing lateralized readiness potentials (LRPs) and found reduced positivity of LRPs in the Sleep Restriction vs. Sleep Extension condition at 70-40 ms before, and 115-158 ms after a response was made. Conclusions These results suggest that even a single night of mild sleep restriction can negatively affect vigilance, reflected by reduced processing capacity for decision making, and dulls motor preparation and execution.

The McMurdo shear zone (MSZ) is strip of heavily crevassed ice oriented in the south-north direction and moving northward. Previous airborne surveys revealed a chaotic crevasse structure superimposed on a set of expected crevasse orientations at 45 degrees to the south-north flow (due to shear stress mechanisms). The dynamics that produced this chaotic structure are poorly understood. Our purpose is to present our field methodology and provide field data that will enable validation of models of the MSZ evolution, and here, we present a method for deriving a local velocity field from ground penetrating radar (GPR) data towards that end. Maps of near-surface crevasses were derived from two annual GPR surveys of a 28 km2 region of the MSZ using Eulerian sampling. Our robot-towed and GPS navigated GPR enabled a dense survey grid, with transects of the shear zone at 50 m spacing. Each survey comprised multiple crossings of long (> 1 km) crevasses that appear in echelon on the western and eastern boundaries of the shear zone, as well as two or more crossings of shorter crevasses in the more chaotic zone between the western and eastern boundaries. From these maps, we derived a local velocity field based on the year-to-year movement of the same crevasses. Our velocity field varies significantly from fields previously established using remote sensing and provides more detail than one concurrently derived from a 29-station GPS network. Rather than a simple velocity gradient expected for crevasses oriented approximately 45 degrees to flow direction, we find constant velocity contours oriented diagonally across the shear zone with a wavy fine structure. Although our survey is based on near-surface crevasses, similar crevassing found in marine ice at 160 m depth leads us to conclude that this surface velocity field may hold through the body of meteoric and marine ice. Our success with robot-towed GPR with GPS navigation suggests we may greatly increase our survey areas.

This paper presents methods to automatically classify ground penetrating radar (GPR) images of crevasses on ice sheets. We use a combination of support vector machines (SVMs) and hidden Markov models (HMMs) with down sampling, a preprocessing step that is unbiased and suitable for real-time analysis and detection. We perform modified cross-validation experiments with 129 examples of Greenland GPR imagery from 2012, collected by a lightweight robot towing a GPR. In order to minimize false positives, an HMM classifier is trained to prescreen the data and mark locations in the GPR files to evaluate with an SVM, and we evaluate the classification results with a similar modified cross-validation technique. The combined HMM–SVM method retains all of the correct classifications by the SVM, and reduces the false positive rate to 0.0007. This method also reduces the computational burden in classifying GPR traces because the SVM is evaluated only on select prescreened traces. Our experiments demonstrate the promise, robustness, and reliability of real-time crevasse detection and classification with robotic GPR surveys.

© 2017 Elsevier B.V. Periodic limb movements (PLMs) during sleep increase with age and are associated with striatal neurodegeneration and dopamine deficiency. Limb movements are often associated with disruptions to non-rapid eye movement (NREM) sleep. Motor skill memory consolidation recruits the striatum, and learning-dependent striatal activation is associated with NREM sleep. Therefore, we investigated whether de novo individuals who significantly experience elevated levels of PLMs but have not been formally diagnosed with periodic limb movement disorder had learning and sleep-related memory deficits and whether these deficits were related to sleep quality and symptom severity. In total, 14 adults with significantly elevated PLMs (PLM condition), 15 age-matched controls (CTRL), and 14 age-matched “disturbed” sleep (through induced leg movements) controls (CTRL-ES) participated. The participants were trained (PM) and retested (AM) on procedural motor sequence learning (MSL) and declarative paired associates memory tasks. Baseline sleep quality was significantly worse in PLM than in CTRL. Despite the continued presence of PLMs in the PLM condition on the experimental night, remarkably, sleep quality improved and arousals decreased, vs. baseline, and did not differ from CTRL. MSL was significantly slower in the PLM condition than in CTRL at training but surprisingly exhibited overnight performance gains, which correlated with reduced arousals. As predicted, CTRL but not CTRL-ES had overnight gains in MSL. Taken together, this suggests that in the PLM condition, sleep quality was normalized following MSL, where they derived the same benefit of sleep to procedural memory consolidation as in CTRL. Sleep did not benefit declarative memory. Although preliminary, these results suggest that MSL in individuals with PLMs may provide a benefit to sleep, which in turn may benefit memory consolidation.

Inter-individual differences in sleep spindles are highly correlated with “Reasoning” abilities (problem solving skills; i.e., the ability to employ logic, identify complex patterns), but not Short Term Memory or Verbal abilities. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have revealed brain activations time-locked to spindles (e.g., thalamic, paralimbic, and motor cortical areas)–yet the functional significance of inter-individual differences in spindle-related brain activation remains to be investigated. Using EEG-fMRI during sleep, we identified, for the first time, the neural activation patterns time-locked to spindles that are correlated with cognitive abilities. Similar to previous studies, activations time-locked to spindles were observed in thalamocortical circuitry and basal ganglia regions. Importantly, spindle-related activation in a subset of these regions were specifically related to inter-individual differences in Reasoning, but not STM or Verbal abilities. These results may help elucidate the physiological mechanisms which support the function of sleep for the capacity for reasoning.

Sleep spindles—short, phasic, oscillatory bursts of activity that characterize non-rapid eye movement sleep—are one of the only electrophysiological oscillations identified as a biological marker of human intelligence (e.g., cognitive abilities commonly assessed using intelligence quotient tests). However, spindles are also important for sleep maintenance and are modulated by circadian factors. Thus, the possibility remains that the relationship between spindles and intelligence quotient may be an epiphenomenon of a putative relationship between good quality sleep and cognitive ability or perhaps modulated by circadian factors such as morningness–eveningness tendencies. We sought to ascertain whether spindles are directly or indirectly related to cognitive abilities using mediation analysis. Here, we show that fast (13.5–16 Hz) parietal but not slow (11–13.5 Hz) frontal spindles in both non-rapid eye movement stage 2 sleep and slow wave sleep are directly related to reasoning abilities (i.e., cognitive abilities that support “fluid intelligence,” such as the capacity to identify complex patterns and relationships and the use of logic to solve novel problems) but not verbal abilities (i.e., cognitive abilities that support “crystalized intelligence”; accumulated knowledge and experience) or cognitive abilities that support STM (i.e., the capacity to briefly maintain information in an available state). The relationship between fast spindles and reasoning abilities is independent of the indicators of sleep maintenance and circadian chronotype, thus suggesting that spindles are indeed a biological marker of cognitive abilities and can serve as a window to further explore the physiological and biological substrates that give rise to human intelligence.

This study examines the prevalence and correlates of poor glycemic control in Mexican Americans aged 75 years and older with diabetes.Data are from the 5(th) wave (2004-05) of the Hispanic Established Population for the Epidemiological Study of the Elderly (H-EPESE). A total of 2,069 Mexican Americans aged 75 and over were interviewed. Six hundred eighty nine subjects (33.5%) reported having been diagnosed with diabetes and 209 (30.3%) subjects agreed to a blood test of their HbA(1)c level.Of the 209 diabetic subjects with an HbA(1)c test, 73 (34.9%) had good glycemic control (HbA(1)c7%) and 136 (65.1%) had poor glycemic control (HbA(1)c7%). Bivariate analysis revealed that subjects with poor control had longer disease duration, had lower education, used the glucometer more frequently, and had more diabetes-complications when compared to those in the good glycemic control group. Multivariable logistic regression analysis found the following factors associated with poor glycemic control:8 years of education, foreign-born, smoking, obesity, longer disease duration, daily glucometer use, and having macro-complications.Prevalence of poor glycemic control is very high in this population with very high and rising prevalence of diabetes. Further studies are needed to explore the effect of these and other characteristics on glycemic control among older Mexican Americans and to develop appropriate interventions to improve diabetes outcomes and increase life-expectancy.

Pichia pastoris is a methylotrophic yeast that has been genetically engineered to express over one thousand heterologous proteins valued for industrial, pharmaceutical and basic research purposes. In most cases, the 5′ untranslated region (UTR) of the alcohol oxidase 1 (AOX1) gene is fused to the coding sequence of the recombinant gene for protein expression in this yeast. Because the effect of the AOX1 5′UTR on protein expression is not known, site-directed mutagenesis was performed in order to decrease or increase the length of this region. Both of these types of changes were shown to affect translational efficiency, not transcript stability. While increasing the length of the 5′UTR clearly decreased expression of a β-galactosidase reporter in a proportional manner, a deletion analysis demonstrated that the AOX1 5′UTR contains a complex mixture of both positive and negative cis-acting elements, suggesting that the construction of a synthetic 5′UTR optimized for a higher level of expression may be challenging.

This paper presents a supervised learning approach to improving the autonomous mobility of wheeled robots through sensing the robot's interaction with terrain `underfoot.' Mobility characterization is cast as a hierarchical task, in which pre-immobilization detection is achieved using support vector machines in time to prevent full immobilization, and if a pre-immobilization condition is detected, the associated terrain feature affecting mobility is identified using a Hidden Markov model. These methods are implemented using a hierarchical, layered control scheme developed for the Yeti robot, a 73-kg, four-wheeled robot designed to perform autonomous medium-range missions in polar terrain. The methodology is motivated by the difficultly of visually recognizing terrain features that impact mobility in low contrast terrain. The efficacy of the approach is evaluated using data from a suite of proprioceptive sensors. Real-time implementation shows that Yeti can consistently detect pre-immobilization conditions, stop in time to avoid unrecoverable immobilization, identify the terrain feature presenting the mobility challenge, and execute an escape sequence to retreat from the condition.

Purpose To describe trends in hypertension prevalence, awareness, treatment, and control among older Mexican Americans living in the Southwestern United States from 1993–1994 to 2004–2005. Methods This study is a comparison between two separate cross-sectional cohorts of non-institutionalized Mexican Americans 75 years of age or older from the Hispanic Established Population for the Epidemiological Study of the Elderly (919 subjects from the 1993–1994 cohort and 738 from the 2004–2005 cohort). Data were collected on self-reported hypertension, measured blood pressure, medications, as well as sociodemographic and other health-related factors. Results Hypertension prevalence increased from 73.0% in the period 1993–1994 to 78.4% in 2004–2005. Cross-cohort multivariate analyses showed that the higher odds of hypertension in the 2004–2005 cohort was attenuated by adding diabetes and obesity to the model. There was a significant increase in hypertension awareness among hypertensives (63.0% to 82.6%) and in control among treated hypertensives (42.5% to 55.4%). Cross-cohort multivariate analyses showed that the higher odds of control in 2004–2005 cohorts were accentuated by adding diabetes to the model. There were no significant changes in treatment rates (62.2% to 65.6%) Conclusion Hypertension prevalence in very old Mexican Americans residing in the Southwestern United States was higher in 2004–2005 than in 1993–1994 and was accompanied by a significant increase in awareness and control rates.

Extant literature on Urge Urinary Incontinence (UUI) focuses on women and non-Hispanic Whites and little is known about ethnic minority men. We analyzed 700 Mexican-American men aged 75 and older from the fifth Wave (2004/5) of the Hispanic Established Population for the Epidemiologic Study of the Elderly. Logistic regression analyses examined risk factors for self-reported UUI and the impact of UUI on mental health and social support. Twenty-nine percent reported having difficulty holding their urine until they could get to a toilet. Men with more co-morbid conditions and men with prostate problems were more likely to report UUI symptoms. Men with UUI were less likely to report having a confidant and had a higher risk of high depressive symptoms. This study is the first to examine risk factors for and consequences of self-reported UUI among older Mexican-American men using a large community-based survey.

The epidemiological transition has progressed through much of Mexico and, as a result, the population in Mexico is aging quickly.1 Mexico has now reached the juncture where health and quality of life in older age are a public health focus, yet little effort has been made to address the specific needs of this population.2,3 Most government programs to improve health are geared toward the poor in general, only benefiting poor older Mexicans. For example, one of the biggest efforts to improve the health of poor Mexicans, health care reform—a universal program in its infancy stage—primarily benefits infants and children.4 In addition to health care reform, municipalities and the federal government have set up social programs to alleviate the negative effects of poverty on nutrition for the poor and elderly. For instance, in Mexico City, a public health program for the elderly was established to provide a small pension and staple food allocation for those aged 65 years and older.5 Food banks are also becoming more pervasive, providing poor Mexicans free or low-cost food.6 Despite these efforts, much of the older Mexican population continues to live without any reliable source of health care or social resources when they become ill.2 In addition, no attempt has been made to narrow the disparities between rural and urban older Mexicans in health care access. In 2006, Mexico had a population of approximately 107 million people, mostly urban (75.7%).7 However, while younger individuals move to urban areas for employment; the elderly tend to remain in their traditional locations. Data from the 2000 census shows that nearly 40% (38.1%) of Mexicans aged 65 and over live in towns with populations of 14 999 people or fewer, a greater proportion than any other age group.8 Given the elevated levels of poverty and lack of services in smaller towns and villages, older Mexicans are at greater risk for experiencing the negative health consequences associated with material disadvantage.

The goal of the current investigation was to develop a systematic method to validate the accuracy of an automated method of sleep spindle detection that takes into consideration individual differences in spindle amplitude. The benchmarking approach used here could be employed more generally to validate automated spindle scoring from other detection algorithms. In a sample of Stage 2 sleep from 10 healthy young subjects, spindles were identified both manually and automatically. The minimum amplitude threshold used by the Prana (PhiTools, Strasbourg, France) software spindle detection algorithm to identify a spindle was subject-specific and determined based upon each subject's mean peak spindle amplitude. Overall sensitivity and specificity values were 98.96 and 88.49%, respectively, when compared to manual scoring. Selecting individual amplitude thresholds for spindle detection based on systematic benchmarking data may validate automated spindle detection methods and improve reproducibility of experimental results. Given that interindividual differences are accounted for, we feel that automatic spindle detection provides an accurate and efficient alternative approach for detecting sleep spindles.

OBJECTIVES: To examine an association between pain severity and functional disability in older Mexican Americans. DESIGN: Cross-sectional study (2005/06), a subsample of the Hispanic Established Population for Epidemiologic Study of the Elderly. SETTING: Community. PARTICIPANTS: One thousand thirteen Mexican American aged 74 to 100. MEASUREMENTS: Bilingual interviewers administered structured questionnaires and assessed physical measures of mobility and frailty (exhaustion, weight loss, walking speed, grip strength, and self-reported physical activity). Two items from the SF-36 questionnaire assessed pain experiences in the previous 4 weeks. RESULTS: Chi-square one-way analysis of variance and least square and negative binomial regressions were computed for 744 participants with complete data to investigate experience of pain and other dimensions of health and functioning. Close to two-thirds (64.7%) reported pain within 4 weeks of the interview, and 49.7% reported that pain interfered with performance of daily activities. Female sex; low education; frailty; reduced mobility; disability; and high comorbidity, body mass index, and depressive symptomatology were significantly associated with pain severity and interference. Regression coefficients revealed that pain severity was significantly related to disability in activities of daily living (0.22, P

Objective: To examine the association between frailty status and incidence of disability among non-disabled older Mexican Americans. Design: A 10-year prospective cohort study. Subjects: A total of 1645 non-institutionalized Mexican Americans aged 67 years and older from the Hispanic Established Population for the Epidemiological Study of the Elderly (H-EPESE), who reported no limitation in activities of daily living at baseline. Methods: Frailty was defined as meeting 3 or more of the following components: (i) unintentional weight loss of > 2.26 kg; (ii) weakness (lowest 20% in hand grip strength); (iii) self-reported exhaustion; (iv) slow walking speed; and (v) low physical activity level. Socio-demographic factors, Mini Mental State Examination, medical conditions, body mass index, and self-reported activities of daily living were obtained. Results: Of the 1645 non-disabled subjects at baseline, 820 (50%) were not frail, 749 (45.7%) were pre-frail, and 71 (4.3%) were frail. The hazard ratio of activities of daily living disability at 10-year follow-up for pre-frail subjects was 1.32 (95% confidence interval 1.10–1.58) and 2.42 (95% confidence interval 70–3.46) for frail subjects compared with not frail subjects. This association remained statistically significant after controlling for potential confounding factors at baseline. Conclusion: Pre-frail and frail status in older Mexican Americans was associated with an increased risk of activities of daily living disability over a 10-year period among non-disabled subjects.

© 2015 Elsevier Inc. How do you get to Carnegie Hall? Practice, sleep, practice. With enough practice - and sleep - we adopt new strategies that eventually become automatic, and subsequently require only the refinement of the existing skill to become an "expert". It is not known whether sleep is involved in the mastery and refinement of new skills that lead to expertise, nor is it known whether this may be primarily dependent on rapid eye movement (REM), non-REM stage 2 (NREM2) or slow wave sleep (SWS). Here, we employed behavioural and scalp-recorded electroencephalography (EEG) techniques to investigate the post-learning changes in the architecture (e.g., REM, NREM2 and SWS duration) and the electrophysiological features (e.g., rapid eye movements, sleep spindles and slow wave activity) that characterize these sleep states as individuals progress from night to night, from "Novice" to "Experts" on a cognitive procedural task (e.g., the Tower of Hanoi task). Here, we demonstrate that speed of movements improves over the course of training irrespective of whether sleep or wake intervenes training sessions, whereas accuracy improves gradually, but only significantly over a night of sleep immediately prior to mastery of the task. On the night that subjects are first exposed to the task, the density of fast spindles increased significantly during both NREM2 and SWS accompanied by increased NREM2 sigma power and SWS delta power, whereas, on the night that subjects become experts on the task, they show increased REM sleep duration and spindles became larger in terms of amplitude and duration during SWS. Re-exposure to the task one-week after it had already been mastered resulted in increased NREM sleep duration, and again, increased spindle density of fast spindles during SWS and NREM2 and increased NREM2 sigma power and SWS delta power. Importantly, increased spindle density was correlated with overnight improvement in speed and accuracy. Taken together, these results help to elucidate how REM and NREM sleep are uniquely involved in memory consolidation over the course of the mastery of a new cognitively complex skill, and help to resolve controversies regarding sequential nature of memory processing during sleep in humans, for which consistent evidence is currently lacking.

OBJECTIVE—The aim of this study was to examine the relationship between inconsistency in use of diabetes drugs and risk of renal, eye, and circulation problems and death over a 7-year period in community-dwelling older Mexican Americans. RESEARCH DESIGN AND METHODS—Data are from the four waves of the Hispanic Established Population for the Epidemiologic Study of the Elderly. In-home interviewers assessed consistency in use of diabetes medications among 908 diabetic Mexican Americans, aged ≥65 years. Diabetes and complications were by self-report. Subjects with poor consistency in use of medication were those who, at any time during the 7-year follow-up, discontinued or inconsistently used their diabetes medications and those who had no diabetic medications at home despite self-report of taking medicine for diabetes. RESULTS—Thirty-six percent of our sample were inconsistent with diabetes medication usage. Older age and lack of supplemental health insurance were significantly associated with inconsistency of use of medication. In a multivariate logistic regression model, subjects with poor consistency in use of medication were more likely to report kidney problems (odds ratio [OR] 1.59; 95% CI 1.13–2.23; P = 0.008) at follow-up compared with those with good consistency, after controlling for age, sex, medication type, duration of diabetes, education, income, marital status, language of interview, insurance status, cognitive function, presence of depressive symptoms, activities of daily living, and instrumental activities of daily living. In Cox regression models, poor consistency with diabetic medication was also associated with increased all-cause mortality (hazard ratio [HR] 1.43; 95% CI 1.13–1.82; P = 0.003) and diabetes-related deaths (1.66; 1.20–2.30; P = 0.002) over a 7-year period after adjusting for relevant confounders. CONCLUSIONS—Inconsistent use of diabetic medication was associated with an increased risk of kidney problems and deaths over a 7-year period in older Mexican Americans.

OBJECTIVE—To examine the separate and combined effects of depression and diabetes on the incidence of adverse health outcomes among older Mexican Americans. RESEARCH DESIGN AND METHODS—Longitudinal data from the Hispanic Established Population for the Epidemiologic Study of the Elderly (EPESE) survey were used to examine the main effects and interaction effects of diabetes and depressive symptoms (measured with the Center for Epidemiologic Study of Depression) or clinical diagnostic criteria (measured with the Composite International Diagnostic Interview Depression Module) on the development of macrovascular complications (including cardiovascular disease, stroke, and kidney disease), microvascular complications (including nephropathy, neuropathy, retinopathy, and amputations), functional disability, and mortality over 7 years in a sample of 2,830 Mexican Americans aged ≥65 years. RESULTS—The interaction of diabetes and depression was found to be synergistic, predicting greater mortality, greater incidence of both macro- and microvascular complications, and greater incidence of disability in activities of daily living, even when controlling for sociodemographic characteristics such as sex, age, education, acculturation, and marital status. Importantly, this interaction was found to predict not only greater incidence but also earlier incidence of adverse events in older adults. CONCLUSIONS—Whether a marker for underlying disease severity, an indicator of diminished self-care motivation, or the result of physiologic changes, the interaction of depression and diabetes has a synergistic effect on the health of older Mexican Americans, increasing the risk for poor outcomes. This is of particular clinical importance because although depression is often underrecognized in older adults, effective treatment is available and can result in improved medical outcomes.

An adaptive leaky normalized least-mean-square (NLMS) algorithm has been developed to optimize stability and performance of active noise cancellation systems. The research addresses LMS filter performance issues related to insufficient excitation, nonstationary noise fields, and time-varying signal-to-noise ratio. The adaptive leaky NLMS algorithm is based on a Lyapunov tuning approach in which three candidate algorithms, each of which is a function of the instantaneous measured reference input, measurement noise variance, and filter length, are shown to provide varying degrees of tradeoff between stability and noise reduction performance. Each algorithm is evaluated experimentally for reduction of low frequency noise in communication headsets, and stability and noise reduction performance are compared with that of traditional NLMS and fixed-leakage NLMS algorithms. Acoustic measurements are made in a specially designed acoustic test cell which is based on the original work of Ryan et al. ["Enclosure for low frequency assessment of active noise reducing circumaural headsets and hearing protection," Can. Acoust. 21, 19-20 (1993)] and which provides a highly controlled and uniform acoustic environment. The stability and performance of the active noise reduction system, including a prototype communication headset, are investigated for a variety of noise sources ranging from stationary tonal noise to highly nonstationary measured F-16 aircraft noise over a 20 dB dynamic range. Results demonstrate significant improvements in stability of Lyapunov-tuned LMS algorithms over traditional leaky or nonleaky normalized algorithms, while providing noise reduction performance equivalent to that of the NLMS algorithm for idealized noise fields.

The high prevalence of musculoskeletal symptoms in elderly persons tends to obscure the recognition of specific medical conditions. The purpose of this study is to describe the prevalence of chronic rheumatologic symptoms and the associated measures of health status in an older population.This is a cross-sectional study of 507 noninstitutionalized tri-ethnic men and women aged 75 and older living in Galveston County, Texas. A home interview collected data on demographics, chronic medical conditions, cognition, depression, and functional status. Site, severity, and duration of rheumatologic symptoms such as morning stiffness, body tenderness, and body aching in the past month were also collected.There was a high prevalence of nonspecific rheumatologic symptoms such as morning stiffness (32% in the shoulder girdle, 31% in the hip girdle), tenderness to touch (9%), and generalized body aching (11%) in the study population. Twenty-one percent of the subjects reported either bilateral shoulder or hip girdle stiffness or tenderness lasting more than 30 minutes almost every day or every day or generalized body aching most of the time during the past month. Age-, gender-, and ethnicity-adjusted multivariate analyses showed that more than three self-reported chronic medical conditions, poor or fair self-reported health, impairment in instrumental activities of daily living (IADL), and the presence of depressive or anxiety symptoms were associated with the presence of these chronic rheumatologic symptoms.Chronic rheumatologic symptoms are common in people aged 75 and older. Such symptoms are markers for underlying poor health and for anxiety and depression among older subjects.

Non-model-based diagnostic methods typically rely on measured signals that must be empirically related to process behavior or incipient faults. The difficulty in interpreting a signal that is indirectly related to the fundamental process behavior is significant. This paper presents an integrated non-model and model-based approach to detecting when process behavior varies from a proposed model. The method, which is based on nonlinear filtering combined with maximum likelihood hypothesis testing, is applicable to dynamic systems whose constitutive model is well known, and whose process inputs are poorly known. Here, the method is applied to friction estimation and diagnosis during motion control in a rotating machine. A nonlinear observer estimates friction torque in a machine from shaft angular position measurements and the known input voltage to the motor. The resulting friction torque estimate can be analyzed directly for statistical abnormalities, or it can be directly compared to friction torque outputs of an applicable friction process model in order to diagnose faults or model variations. Nonlinear estimation of friction torque provides a variable on which to apply diagnostic methods that is directly related to model variations or faults. The method is evaluated experimentally by its ability to detect normal load variations in a closed-loop controlled motor driven inertia with bearing friction and an artificially-induced external line contact. Results show an ability to detect statistically significant changes in friction characteristics induced by normal load variations over a wide range of underlying friction behaviors.

BACKGROUND Joint pain is a very common complaint among elderly persons and may lead to functional disability. The purpose of this study is to estimate the prevalence of self-reported pain on weight bearing and its impact on the 2-year incidence of limitation in lower-body activities of daily living (ADL) in initially nondisabled Mexican American elderly subjects. METHODS We studied a probability sample of 2167 noninstitutionalized Mexican American men and women aged 65 or older residing in five Southwestern states. Subjects were asked about pain on weight bearing, ADL, depressive symptomatology, and the presence of chronic diseases. The body mass index was computed using measured height and weight. Finally, a three-task (tandem balance, 8-foot walk, and repeated chair stands), performance-based, lower-body function test was performed. RESULTS The overall prevalence of pain on weight bearing in the sample was 31.9%, with 37.7% for women versus 24.0% for men (p

An extended Kalman–Bucy filter (EKBF)-based friction compensation method is presented and validated. The method relies on an accurate model of system rigid-body dynamics and measured motion, rather than a structured nonlinear friction model, to estimate external friction torque. The estimate is used in a traditional friction compensator to cancel friction effects. The EKBF compensator is compared with other model-based and non-model-based friction compensation strategies through position tracking experiments. Results show that when motion is dominated by static and Stribeck friction, non-model-based friction estimation and compensation using the EKBF consistently provides equal or superior performance over model-based adaptive friction compensation methods.

Objective This study examines the prevalence of self-reported physician-diagnosed arthritis and arthritis symptoms and their relationship to functional limitations in Mexican American elderly. Methods We conducted a cross-sectional study using a probability sample of 2,873 non-institutionalized Mexican American men and women aged 65 or older, residing in the southwestern United States. Measures included self-reported physician-diagnosed arthritis, morning pain or stiffness, pain when standing, global health rating, activities of daily living (ADL), instrumental activities of daily living (IADL), depressive symptoms, presence of chronic diseases (diabetes mellitus, hypertension, heart attack, stroke), and body mass index. The Mantel-Haenszel chi-square statistic was used to test differences by arthritis status, and a logistic regression model was used to predict the odds of having arthritis. Results The overall prevalence of self-reported physician-diagnosed arthritis in the sample was 40.8 percent, 50.0 percent among women and 28.8 percent among men (P < 0.001). Morning pain or stiffness was reported by 37.7 percent of respondents and pain when standing or walking by 31.9 percent. All comorbid conditions, and both IADL and ADL limitations, were more prevalent in those with arthritis than in those without arthritis. Female sex and several medical conditions were independently associated with self-reported arthritis. Conclusions Self-reported physician-diagnosed arthritis is common among older Mexican Americans. Functional limitation and disability are more prevalent among subjects with arthritis than among those without arthritis.