Project description:There is considerable evidence that labeling supports infants' object categorization. Yet in daily life, most of the category exemplars that infants encounter will remain unlabeled. Inspired by recent evidence from machine learning, we propose that infants successfully exploit this sparsely labeled input through "semi-supervised learning." Providing only a few labeled exemplars leads infants to initiate the process of categorization, after which they can integrate all subsequent exemplars, labeled or unlabeled, into their evolving category representations. Using a classic novelty preference task, we introduced 2-year-old infants (n = 96) to a novel object category, varying whether and when its exemplars were labeled. Infants were equally successful whether all exemplars were labeled (fully supervised condition) or only the first two exemplars were labeled (semi-supervised condition), but they failed when no exemplars were labeled (unsupervised condition). Furthermore, the timing of the labeling mattered: when the labeled exemplars were provided at the end, rather than the beginning, of familiarization (reversed semi-supervised condition), infants failed to learn the category. This provides the first evidence of semi-supervised learning in infancy, revealing that infants excel at learning from exactly the kind of input that they typically receive in acquiring real-world categories and their names.
Project description:Consciousness is currently a thriving area of research in psychology and neuroscience. While this is often attributed to events that took place in the early 1990s, consciousness studies today are a continuation of research that started in the late 19th century and that continued throughout the 20th century. From the beginning, the effort built on studies of animals to reveal basic principles of brain organization and function, and of human patients to gain clues about consciousness itself. Particularly important and our focus here is research in the 1950s, 1960s, and 1970s involving three groups of patients-amnesia, split brain, and blindsight. Across all three groups, a similar pattern of results was found-the patients could respond appropriately to stimuli that they denied seeing (or in the case of amnesiacs, having seen before). These studies paved the way for the current wave of research on consciousness. The field is, in fact, still grappling with the implications of the findings showing that the ability to consciously know and report the identity of a visual stimulus can be dissociated in the brain from the mechanisms that underlie the ability to behave in a meaningful way to the same stimulus.
Project description:Zoonotic pathogens such as Ebola and rabies pose a major health risk to humans. One proven approach to minimizing the impact of a pathogen relies on reducing its prevalence within animal reservoir populations using mass vaccination. However, two major challenges remain for vaccination programs that target free-ranging animal populations. First, limited or challenging access to wild hosts, and second, expenses associated with purchasing and distributing the vaccine. Together, these challenges constrain a campaign's ability to maintain adequate levels of immunity in the host population for an extended period of time. Transmissible vaccines could lessen these constraints, improving our ability to both establish and maintain herd immunity in free-ranging animal populations. Because the extent to which vaccine transmission could augment current wildlife vaccination campaigns is unknown, we develop and parameterize a mathematical model that describes long-term mass vaccination campaigns in the US that target rabies in wildlife. The model is used to investigate the ability of a weakly transmissible vaccine to (1) increase vaccine coverage in campaigns that fail to immunize at levels required for herd immunity, and (2) decrease the expense of campaigns that achieve herd immunity. When parameterized to efforts that target rabies in raccoons using vaccine baits, our model indicates that, with current vaccination efforts, a vaccine that transmits to even one additional host per vaccinated individual could sufficiently augment US efforts to preempt the spread of the rabies virus. Higher levels of transmission are needed, however, when spatial heterogeneities associated with flight-line vaccination are incorporated into the model. In addition to augmenting deficient campaigns, our results show that weak vaccine transmission can reduce the costs of vaccination campaigns that are successful in attaining herd immunity.
Project description:Individuals change their behavior during an epidemic in response to whether they and/or those they interact with are healthy or sick. Healthy individuals may utilize protective measures to avoid contracting a disease. Sick individuals may utilize preemptive measures to avoid spreading a disease. Yet, in practice both protective and preemptive changes in behavior come with costs. This paper proposes a stochastic network disease game model that captures the self-interests of individuals during the spread of a susceptible-infected-susceptible disease. In this model, individuals strategically modify their behavior based on current disease conditions. These reactions influence disease spread. We show that there is a critical level of concern, i.e., empathy, by the sick individuals above which disease is eradicated rapidly. Furthermore, we find that risk averse behavior by the healthy individuals cannot eradicate the disease without the preemptive measures of the sick individuals. Empathy is more effective than risk-aversion because when infectious individuals change behavior, they reduce all of their potential infections, whereas when healthy individuals change behavior, they reduce only a small portion of potential infections. This imbalance in the role played by the response of the infected versus the susceptible individuals on disease eradication affords critical policy insights.
Project description:Repetitive transcranial magnetic stimulation (rTMS) is a widespread technique in neuroscience and medicine, however its mechanisms are not well known. In this review, we consider intensity as a key therapeutic parameter of rTMS, and review the studies that have examined the biological effects of rTMS using magnetic fields that are orders of magnitude lower that those currently used in the clinic. We discuss how extensive characterisation of "low intensity" rTMS has set the stage for translation of new rTMS parameters from a mechanistic evidence base, with potential for innovative and effective therapeutic applications. Low-intensity rTMS demonstrates neurobiological effects across healthy and disease models, which include depression, injury and regeneration, abnormal circuit organisation, tinnitus etc. Various short and long-term changes to metabolism, neurotransmitter release, functional connectivity, genetic changes, cell survival and behaviour have been investigated and we summarise these key changes and the possible mechanisms behind them. Mechanisms at genetic, molecular, cellular and system levels have been identified with evidence that low-intensity rTMS and potentially rTMS in general acts through several key pathways to induce changes in the brain with modulation of internal calcium signalling identified as a major mechanism. We discuss the role that preclinical models can play to inform current clinical research as well as uncover new pathways for investigation.
Project description:This article conforms to a recent trend of developing an energy-efficient Spiking Neural Network (SNN), which takes advantage of the sophisticated training regime of Convolutional Neural Network (CNN) and converts a well-trained CNN to an SNN. We observe that the existing CNN-to-SNN conversion algorithms may keep a certain amount of residual current in the spiking neurons in SNN, and the residual current may cause significant accuracy loss when inference time is short. To deal with this, we propose a unified framework to equalize the output of the convolutional or dense layer in CNN and the accumulated current in SNN, and maximally align the spiking rate of a neuron with its corresponding charge. This framework enables us to design a novel explicit current control (ECC) method for the CNN-to-SNN conversion which considers multiple objectives at the same time during the conversion, including accuracy, latency, and energy efficiency. We conduct an extensive set of experiments on different neural network architectures, e.g., VGG, ResNet, and DenseNet, to evaluate the resulting SNNs. The benchmark datasets include not only the image datasets such as CIFAR-10/100 and ImageNet but also the Dynamic Vision Sensor (DVS) image datasets such as DVS-CIFAR-10. The experimental results show the superior performance of our ECC method over the state-of-the-art.
Project description:It is currently unclear if damping plays a functional role in legged locomotion, and simple models often do not include damping terms. We present a new model with a damping term that is isolated from other parameters: that is, the damping term can be adjusted without retuning other model parameters for nominal motion. We systematically compare how increased damping affects stability in the face of unexpected ground-height perturbations. Unlike most studies, we focus on task-level stability: instead of observing whether trajectories converge towards a nominal limit-cycle, we quantify the ability to avoid falls using a recently developed mathematical measure. This measure allows trajectories to be compared quantitatively instead of only being separated into a binary classification of 'stable' or 'unstable'. Our simulation study shows that increased damping contributes significantly to task-level stability; however, this benefit quickly plateaus after only a small amount of damping. These results suggest that the low intrinsic damping values observed experimentally may have stability benefits and are not simply minimized for energetic reasons. All Python code and data needed to generate our results are available open source.
Project description:To determine if oral dosing with the CFTR-potentiator ivacaftor (VX-770, Kalydeco) improves CFTR-dependent sweating in CF subjects carrying G551D or R117H-5T mutations, we optically measured sweat secretion from 32-143 individually identified glands in each of 8 CF subjects; 6 F508del/G551D, one G551D/R117H-5T, and one I507del/R117H-5T. Two subjects were tested only (-) ivacaftor, 3 only (+) ivacaftor and 3 (+/-) ivacaftor (1-5 tests per condition). The total number of gland measurements was 852 (-) ivacaftor and 906 (+) ivacaftor. A healthy control was tested 4 times (51 glands). For each gland we measured both CFTR-independent (M-sweat) and CFTR-dependent (C-sweat); C-sweat was stimulated with a β-adrenergic cocktail that elevated [cAMP]i while blocking muscarinic receptors. Absent ivacaftor, almost all CF glands produced M-sweat on all tests, but only 1/593 glands produced C-sweat (10 tests, 5 subjects). By contrast, 6/6 subjects (113/342 glands) produced C-sweat in the (+) ivacaftor condition, but with large inter-subject differences; 3-74% of glands responded with C/M sweat ratios 0.04%-2.57% of the average WT ratio of 0.265. Sweat volume losses cause proportionally larger underestimates of CFTR function at lower sweat rates. The losses were reduced by measuring C/M ratios in 12 glands from each subject that had the highest M-sweat rates. Remaining losses were estimated from single channel data and used to correct the C/M ratios, giving estimates of CFTR function (+) ivacaftor = 1.6%-7.7% of the WT average. These estimates are in accord with single channel data and transcript analysis, and suggest that significant clinical benefit can be produced by low levels of CFTR function.
Project description:Integrating theory on close relationships and intergroup relations, we construct a manipulation of similarity that we demonstrate can improve interracial interactions across different settings. We find that manipulating perceptions of similarity on self-revealing attributes that are peripheral to the interaction improves interactions in cross-race dyads and racially diverse task groups. In a getting-acquainted context, we demonstrate that the belief that one's different-race partner is similar to oneself on self-revealing, peripheral attributes leads to less anticipatory anxiety than the belief that one's partner is similar on peripheral, nonself-revealing attributes. In another dyadic context, we explore the range of benefits that perceptions of peripheral, self-revealing similarity can bring to different-race interaction partners and find (a) less anxiety during interaction, (b) greater interest in sustained contact with one's partner, and (c) stronger accuracy in perceptions of one's partners' relationship intentions. By contrast, participants in same-race interactions were largely unaffected by these manipulations of perceived similarity. Our final experiment shows that among small task groups composed of racially diverse individuals, those whose members perceive peripheral, self-revealing similarity perform superior to those who perceive dissimilarity. Implications for using this approach to improve interracial interactions across different goal-driven contexts are discussed.
Project description:In acidic media, many transition-metal phosphides are reported to be stable catalysts for the hydrogen evolution reaction (HER) but typically exhibit poor stability toward the corresponding oxygen evolution reaction (OER). A notable exception appears to be Rh2P/C nanoparticles, reported to be active and stable toward both the HER and OER. Previously, we investigated base-metal-substituted Rh2P, specifically Co2-xRhxP and Ni2-xRhxP, for HER and OER as a means to reduce the noble-metal content and tune the reactivity for these disparate reactions. In alkaline media, the Rh-rich phases were found to be most active for the HER, while base-metal-rich phases were found to be the most active for the OER. However, Co2-xRhxP was not stable in acidic media due to the dissolution of Co. In this study, the activity and stability of our previously synthesized Ni2-xRhxP nanoparticle catalysts (x = 0, 0.25, 0.50, 1.75) toward the HER and OER in acidic electrolyte are probed. For the HER, the Ni0.25Rh1.75P phase was found to have comparable geometric activity (overpotential at 10 mA/cmgeo2) and stability to Rh2P. In contrast, for OER, all of the tested Ni2-xRhxP phases had similar overpotential values at 10 mA/cmgeo2, but these were >2x the initial value for Rh2P. However, the activity of Rh2P fades rapidly, as does Ni2P and Ni-rich Ni2-xRhxP phases, whereas Ni0.25Rh1.75P shows only modest declines. Overall water splitting (OWS) conducted using Ni0.25Rh1.75P as a catalyst relative to the state-of-the-art (RuO2||20% Pt/C) revealed comparable stabilities, with the Ni0.25Rh1.75P system demanding an additional 200 mV to achieve 10 mA/cmgeo2. In contrast, a Rh2P||Rh2P OWS cell had a similar initial overpotential to RuO2||20% Pt/C, but is unstable, completely deactivating over 140 min. Thus, Rh2P is not a stable anode for the OER in acidic media, but can be stabilized, albeit with a loss of activity, by incorporation of nominally modest amounts of Ni.