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Exposure to early life adversity (ELA), including childhood maltreatment, is one of the most significant risk factors for the emergence of neuropsychiatric disorders in adolescence and adulthood. Despite this relationship being well established, the underlying mechanisms remain unclear. One way to achieve this understanding is to identify molecular pathways and processes that are perturbed as a consequence of childhood maltreatment. Ideally, these perturbations would be evident as changes in DNA, RNA or protein profiles in easily accessible biological samples collected in the shadow of childhood maltreatment. In this study, we isolated circulating extracellular vesicles (EVs) from plasma collected from adolescent rhesus macaques that had either experienced nurturing maternal care (CONT) or maternal maltreatment (MALT) in infancy. RNA sequencing of RNA in plasma EVs and gene enrichment analysis revealed that genes related to translation, ATP synthesis, mitochondrial function and immune response were downregulated in MALT samples, while genes involved in ion transport, metabolism and cell differentiation were upregulated. Interestingly, we found that a significant proportion of EV RNA aligned to the microbiome and that MALT altered the diversity of microbiome-associated RNA signatures found in EVs. Part of this altered diversity suggested differences in prevalence of bacterial species in CONT and MALT animals noted in the RNA signatures of the circulating EVs. Our findings provide evidence that immune function, cellular energetics and the microbiome may be important conduits via which infant maltreatment exerts effects on physiology and behavior in adolescence and adulthood. As a corollary, perturbations of RNA profiles related to immune function, cellular energetics and the microbiome may serve as biomarkers of responsiveness to ELA. Our results demonstrate that RNA profiles in EVs can serve as a powerful proxy to identify biological processes that might be perturbed by ELA and that may contribute to the etiology of neuropsychiatric disorders in the aftermath of ELA.

RESUMEN

Early life adversity/stress (ELA/ELS), particularly adverse caregiving experiences such as child maltreatment (MALT), is a main risk factor for psychopathology, including psychiatric disorders such as anxiety, depression, ADHD, and substance abuse. Yet how these alterations unfold during development and the underlying mechanisms remain poorly understood, as it is difficult to prospectively and longitudinally study early developmental phases in humans, and nearly impossible to disentangle postnatal caregiving effects from heritable traits. This study examined the specific effects of "nurture" (maternal care) versus "nature" (heritable, biological maternal factors) on nonhuman primate infant socioemotional, stress neuroendocrine, and physical development. For this we used a translational and naturalistic macaque model of infant maltreatment by the mother with randomized assignment at birth to either mothers with a history of maltreating their infants (MALT group, n = 22) or to competent mothers (Control group, n = 20). Over the first 6 months of life (roughly equivalent to 2 years in humans), we examined the development of the mother-infant relationship, as well as infants' social behavior and emotional reactivity. In parallel, we assessed hypothalamic-pituitary-adrenal (HPA) axis function longitudinally, using measures of hair cortisol accumulation, and basal morning plasma cortisol. We identified broad impairments in maternal care exhibited by MALT foster mothers, beyond maltreatment (physical abuse, rejection) events, suggesting that MALT foster mothers provide an overall lower quality of care to their infants compared to Controls. MALT infants exhibited alterations in their initiations and breaks of proximity towards their mothers, as well as heightened emotional reactivity in comparison to Controls. Most striking are the HPA axis findings, with MALT infants showing higher levels of plasma cortisol across the first 6 postnatal months as well as higher hair cortisol accumulation from birth through month 6 (a signature of chronic stress) than Controls. No caregiving effects were detected on physical growth, which ruled out confounding effects of maternal nutrition, metabolism, etc. Taken together, these results suggest that the developmental trajectory of MALT and Control infants is different, marked by heightened levels of emotional reactivity, increased HPA activity and alterations in mother-infant interactions in MALT animals. These findings appear to be due to specific effects of postnatal maternal care, and not to biological/ behavioral traits inherited from the mother, or due to prenatal programming caused by prenatal stress, as the cross-fostering design controlled for these potential factors. However, we also detected a couple of interesting biological effects suggesting heritable transmission of some phenotypes. The prolonged HPA axis activation during the first 6 postnatal months of life is expected to have long-term consequences for brain, physiological, and behavioral development in MALT offspring.

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For application to military and civilian needs, Defence Research and Development Canada--Toronto contracted Queen's University, Kingston to develop a suite of biomechanical assessment and analytical tools to supplement human-based load carriage system assessment methods. This suite of tools permitted efficient objective evaluation of biomechanical aspects of load-bearing webbing, vests, packs and their components, and therefore contributed to early system assessment and a rapid iterative design process. This paper is a summary of five assessment and analytical tools. A dynamic load carriage simulator was developed to simulate cadence of walking, jogging and running. The simulator comprised a computer-controlled pneumatic platform that oscillated anthropometrically weighted mannequins of varying dimensions from which measures of skin contact pressure, hip reaction forces and moments and relative pack-person displacements were taken. A stiffness tester for range of motion provided force-displacement data on pack suspension systems. A biomechanical model was used to determine forces and moments on the shoulders and hips, and validated using a static load distribution mannequin. Subjective perceptual rating systems were used gather soldier feedback during a standardized mobility circuit. Objective outcome measures were validated by means of other objective measures (e.g., Optotrak, video, Instron, etc.) and then compared to subjective ratings. This approach led to development of objective performance criteria for load carriage systems and to improvements in load carriage designs that could be used both in the military and in general.

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This paper reviews data acquisition and signal processing issues relative to producing an amplitude estimate of surface EMG. The paper covers two principle areas. First, methods for reducing noise, artefact and interference in recorded EMG are described. Wherever possible noise should be reduced at the source via appropriate skin preparation, and the use of well designed active electrodes and signal recording instrumentation. Despite these efforts, some noise will always accompany the desired signal, thus signal processing techniques for noise reduction (e.g. band-pass filtering, adaptive noise cancellation filters and filters based on the wavelet transform) are discussed. Second, methods for estimating the amplitude of the EMG are reviewed. Most advanced, high-fidelity methods consist of six sequential stages: noise rejection/filtering, whitening, multiple-channel combination, amplitude demodulation, smoothing and relinearization. Theoretical and experimental research related to each of the above topics is reviewed and the current recommended practices are described.

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A pattern classification system, designed to separate myoelectric signal records based on contraction tasks, is described. The amplitude of the myoelectric signal during the first 200 ms following the onset of a contraction has a non-random structure that is specific to the task performed. This permits the application of advanced pattern recognition techniques to separate these signals. The pattern classification system described consists of a spectrographic preprocessor, a feature extraction stage and a classifier stage. The preprocessor creates a spectrogram by generating a series of power spectral densities over adjacent time segments of the input signal. The feature extraction stage reduces the dimensionality of the spectrogram by identifying features that correspond to subtle underlying structures in the input signal data. This is realised by a self-organising artificial neural network (ANN) that performs an advanced statistical analysis procedure known as exploratory projection pursuit. The extracted features are then classified by a supervised-learning ANN. An evaluation of the system, in terms of system performance and the complexity of the ANNs, is presented.

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It has recently been shown that it is possible to discriminate accurately among myoelectric signals underlying different muscle contraction types, specifically elbow flexion and extension and forearm pronation and supination. It was reported that once a number of distinctive features had been extracted from the myoelectric signals, a neural network could be trained to distinguish the contraction types with an impressively high accuracy. In the present paper, we show that a technique known as parallel cascade identification can be used to construct classifiers that can also accurately differentiate the contraction types. The use of parallel cascades has the benefit of dispensing with the need for feature extraction, so that raw myoelectric signal data can be used directly. In addition, very little data are required to train the parallel cascades to distinguish accurately novel incoming myoelectric signals. Results of using parallel cascades to distinguish forearm pronation, supination, and elbow flexion are presented.

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