2995 research outputs found
Sort by
The Evolutionary Dynamics of Cooperation in Collective Search
No full textHow does cooperation arise in an evolutionary context? We approach this problem using a collective search paradigm where interactions are dynamic and there is competition for rewards. Using evolutionary simulations, we find that the unconditional sharing of information can be an evolutionary advantageous strategy without the need for conditional strategies or explicit reciprocation. Shared information acts as a recruitment signal and facilitates the formation of a self-organized group. Thus, the improved search efficiency of the collective bestows byproduct benefits onto the original sharer. A key mechanism is a visibility radius, where individuals have unconditional access to information about neighbors within a limited distance. Our results show that for a variety of initial conditions—including populations initially devoid of prosocial individuals—and across both static and dynamic fitness landscapes, we find strong selection pressure to evolve unconditional sharing
Towards Digital Manufacturing of Smart Multimaterial Fibers
No full textFibers are ubiquitous and usually passive. Optoelectronics realized in a fiber could revolutionize multiple application areas, including biosynthetic and wearable electronics, environmental sensing, and energy harvesting. However, the realization of high-performance electronics in a fiber remains a demanding challenge due to the elusiveness of a material processing strategy that would allow the wrapping of devices made in crystalline semiconductors, such as silicon, into a fiber in an ordered, addressable, and scalable manner. Current fiber-sensor fabrication approaches either are non-scalable or limit the choice of semiconductors to the amorphous ones, such as chalcogenide glasses, inferior to silicon in their electronic performance, resulting in limited bandwidth and sensitivity of such sensors when compared to a standard silicon photodiode. Our group substantiates a universal in-fiber manufacturing of logic circuits and sensory systems analogous to very large-scale integration (VLSI), which enabled the emergence of the modern microprocessor. We develop a versatile hybrid-fabrication methodology that assembles in-fiber material architectures typical to integrated microelectronic devices and systems in silica, silicon, and high-temperature metals. This methodology, dubbed “VLSI for Fibers,” or “VLSI-Fi,” combines 3D printing of preforms, a thermal draw of fibers, and post-draw assembly of fiber-embedded integrated devices by means of material-selective spatially coherent capillary breakup of the fiber cores. We believe that this method will deliver a new class of durable, low cost, pervasive fiber devices, and sensors, enabling integration of fabrics met with human-made objects, such as furniture and apparel, into the Internet of Things (IoT). Furthermore, it will boost innovation in 3D printing, extending the digital manufacturing approach into the nanoelectronics realm
Accounting for Variance in Concussion Tolerance Between Individuals: Comparing Head Accelerations Between Concussed and Physically Matched Control Subjects.
No full textResearchers have been collecting head impact data from instrumented football players to characterize the biomechanics of concussion for the past 15 years, yet the link between biomechanical input and clinical outcome is still not well understood. We have previously shown that even though concussive biomechanics might be unremarkable in large datasets of head impacts, the impacts causing injury are of high magnitude for the concussed individuals relative to their impact history. This finding suggests a need to account for differences in tolerance at the individual level. In this study, we identified control subjects for our concussed subjects who demonstrated traits we believed were correlated to factors thought to affect injury tolerance, including height, mass, age, race, and concussion history. A total of 502 college football players were instrumented with helmet- mounted accelerometer arrays and provided complete base- line assessment data, 44 of which sustained a total of 49 concussion. Biomechanical measures quantifying impact frequency and acceleration magnitude were compared between groups. On average, we found that concussed subjects experienced 93.8 more head impacts (p = 0.0031), 10.2 more high magnitude impacts (p = 0.0157), and 1.9 9 greater risk-weighted exposure (p = 0.0175) than their physically matched controls. This finding provides further evidence that head impact data need to be considered at the individual level and that cohort wide assessments may be of little value in the context of concussion
Accelerometry data in health research: challenges and opportunities
No full textWearable accelerometers provide detailed, objective, and continuous measurements of physical activity (PA). Recent advances in technology and the decreasing cost of wearable devices led to an explosion in the popularity of wearable technology in health research. An ever-increasing number of studies collect high-throughput, sub-second level raw acceleration data. In this paper, we discuss problems related to the collection and analysis of raw accelerometry data and refer to published solutions. In particular, we describe the size and complexity of the data, the within- and between-subject variability, and the effects of sensor location on the body. We also discuss challenges related to sampling frequency, device calibration, data labeling, and multiple PA monitors synchronization. We illustrate these points using the Developmental Epidemiological Cohort Study (DECOS), which collected raw accelerometry data on individuals both in a controlled and the free-living environment
Brain Connectivity-Informed Regularization Methods for Regression
No full textOne of the challenging problems in brain imaging research is a principled incorporation of information from different imaging modalities. Frequently, each modality is analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization-method to estimate the association between the brain structure features and a scalar outcome within the linear regression framework. Our regularization technique provides a principled approach to use external information from the structural brain connectivity and inform the estimation of the regression coefficients. Our proposal extends the classical Tikhonov regularization framework by defining a penalty term based on the structural connectivity-derived Laplacian matrix. Here, we address both theoretical and computational issues. The approach is first illustrated using simulated data and compared with other penalized regression methods. We then apply our regularization method to study the associations between the alcoholism phenotypes and brain cortical thickness using a diffusion imaging derived measure of structural connectivity. Using the proposed methodology in 148 young male subjects with a risk for alcoholism, we found a negative associations between cortical thickness and drinks per drinking day in bilateral caudal anterior cingulate cortex, left lateral OFC and left precentral gyrus
Perceiving happiness in an intergroup context: The role of race and attention to the eyes in differentiating between true and false smiles.
No full textThe present research comprises six experiments that investigated racial biases in the perception of positive emotional expressions. In an initial study, we demonstrated that White participants distinguished more in their happiness ratings of Duchenne (“true”) and non-Duchenne (“false”) smiles on White compared with Black faces (Experiment 1). In a subsequent study we replicated this effect using a different set of stimuli and non-Black participants (Experiment 2). As predicted, this bias was not demonstrated by Black participants, who did not significantly differ in happiness ratings between smile types on White and Black faces (Experiment 3). Furthermore, in addition to happiness ratings, we demonstrated that non-Black participants were also more accurate when categorizing true versus false expressions on White compared with Black faces (Experiment 4). The final two studies provided evidence for the mediating role of attention to the eyes in intergroup emotion identification. In particular, eye tracking data indicated that White participants spent more time attending to the eyes of White than Black faces and that attention to the eyes predicted biases in happiness ratings between true and false smiles on White and Black faces (Experiment 5). Furthermore, an experimental manipulation focusing participants on the eyes of targets eliminated the effects of target race or perceptions of happiness (Experiment 6). Together, the findings provide novel evidence for racial biases in the identification of positive emotions and highlight the critical role of visual attention in this process. (APA PsycInfo Database Record (c) 2019 APA, all rights reserved
Lactate dehydrogenase and glycerol-3-phosphate dehydrogenase cooperatively regulate growth and carbohydrate metabolism during Drosophila melanogaster larval development
No full textThe dramatic growth that occurs during Drosophila larval development requires rapid conversion of nutrients into biomass. Many larval tissues respond to these biosynthetic demands by increasing carbohydrate metabolism and lactate dehydrogenase (LDH) activity. The resulting metabolic program is ideally suited for synthesis of macromolecules and mimics the manner by which cancer cells rely on aerobic glycolysis. To explore the potential role of Drosophila LDH in promoting biosynthesis, we examined how Ldh mutations influence larval development. Our studies unexpectedly found that Ldh mutants grow at a normal rate, indicating that LDH is dispensable for larval biomass production. However, subsequent metabolomic analyses suggested that Ldh mutants compensate for the inability to produce lactate by generating excess glycerol-3-phosphate (G3P), the production of which also influences larval redox balance. Consistent with this possibility, larvae lacking both LDH and G3P dehydrogenase (GPDH1) exhibit growth defects, synthetic lethality and decreased glycolytic flux. Considering that human cells also generate G3P upon inhibition of lactate dehydrogenase A (LDHA), our findings hint at a conserved mechanism in which the coordinate regulation of lactate and G3P synthesis imparts metabolic robustness to growing animal tissues
Residential Proximity to Agricultural Fields and Neurological and Mental Health Outcomes in Rural Adults in Matlab, Bangladesh
No full textPesticide exposure is an important rural public health concern that is linked to a spectrum of health outcomes in farmers. However, little is known about these effects on residents living in close proximity to agricultural fields and who are not involved in regular farming. This paper compared the effects of residential proximity to farming lands on a number of neurological and mental health outcomes in adults. A cross-sectional study was performed on 57 adults involved in farming only occasionally in rural Matlab in Bangladesh. A health and demographic surveillance system (HDSS) and geocoding were used to define proximity to the agricultural field. Neurological health was measured using the trail making test, vibrotactile threshold measurement, and dominant ulnar nerve conduction velocity (NCV) amplitude. An adapted Center for Epidemiological Studies Depression scale (CES-D) questionnaire was used to evaluate mental health. Results indicated that respondents living near agricultural fields had significantly higher vibrotactile threshold in big toes (p < 0.004) and needed a longer time to complete the trail making test (p < 0.004) than those living far from fields after accounting for the covariates. Results of this pilot study suggest further investigations to establish the impact of pesticide exposure among occasional and non-farmers on neurological health outcomes
Aging relates to a disproportionately weaker functional architecture of brain networks during rest and task states
No full textFunctional connectivity – the co-activation of brain regions – forms the basis of the brain’s functional architecture. Often measured during resting-state (i.e., in a task-free setting), patterns of functional connectivity within and between brain networks change with age. These patterns are of interest to aging researchers because age differences in resting-state connectivity relate to older adults’ relative cognitive declines. Less is known about age differences in large-scale brain networks during directed tasks. Recent work in younger adults has shown that patterns of functional connectivity are highly correlated between rest and task states. Whether this finding extends to older adults remains largely unexplored. To this end, we assessed younger and older adults’ functional connectivity across the whole brain using fMRI while participants underwent resting-state or completed directed tasks (e.g., a reasoning judgement task). Resting-state and task functional connectivity were less strongly correlated in older as compared to younger adults. This age-dependent difference could be attributed to significantly lower consistency in network organization between rest and task states among older adults. Older adults had less distinct or segregated networks during resting-state. This more diffuse pattern of organization was exacerbated during directed tasks. Finally, the default mode network, often implicated in neurocognitive aging, contributed strongly to this pattern. These findings establish that age differences in functional connectivity are state-dependent, providing greater insight into the mechanisms by which aging may lead to cognitive declines
A generic imperative language for polynomial time
No full textThe ramification method in Implicit Computational Complexity has been associated with functional programming, but adapting it to generic imperative programming is highly desirable, given the wider algorithmic applicability of imperative programming. We introduce a new approach to ramification which, among other benefits, adapts readily to fully general imperative programming. The novelty is in ramifying finite second-order objects, namely finite structures, rather than ramifying elements of free algebras. In so doing we bridge between Implicit Complexity's type theoretic characterizations of feasibility, and the data-flow approach of Static Analysis