Background Numerous studies with varying associations between domestic use of solid biomass fuels (wood, dung, crop residue, charcoal) and respiratory diseases have been reported. Objective To present the current data systematically associating use of biomass fuels with respiratory outcomes in rural women and children. Methods Systematic searches were conducted in 13 electronic databases. Data were abstracted from original articles that satisfied selection criteria for meta-analyses. Publication bias and heterogeneity of samples were tested. Studies with common diagnoses were analysed using random-effect models. Results A total of 2717 studies were identified. Fifty-one studies were selected for data extraction and 25 studies were suitable for meta-analysis. The overall pooled ORs indicate significant associations with acute respiratory infection in children (OR 3.53, 95% CI 1.94 to 6.43), chronic bronchitis in women (OR 2.52, 95% CI 1.88 to 3.38) and chronic obstructive pulmonary disease in women (OR 2.40, 95% CI

The accurate relay of electrical signals within cortical networks is key to perception and cognitive function. Theoretically, it has long been proposed that temporal coordination of neuronal spiking activity controls signal transmission and behavior. However, whether and how temporally precise neuronal coordination in population activity influences perception is unknown. Here, we recorded from neuronal populations in early and mid-level visual cortex (areas V1 and V4) simultaneously to discover that the precise temporal coordination between the spiking activity of three or more cells carries information about visual perception in the absence of firing rate modulation. The accuracy of perceptual responses was correlated with high-order spiking coordination within V4, but not V1, and with feedforward coordination between V1 and V4. These results indicate that while visual stimuli are encoded in the discharge rates of neurons, perceptual accuracy is related to the temporally precise spiking coordination within and between cortical networks.

Neural responses in the cerebral cortex change dramatically between the 'synchronized' state during sleep and 'desynchronized' state during wakefulness. Our understanding of cortical state emerges largely from experiments performed in sensory areas of head-fixed or tethered rodents due to technical limitations of recording from larger freely-moving animals for several hours. Here, we report a system integrating wireless electrophysiology, wireless eye tracking, and real-time video analysis to examine the dynamics of population activity in a high-level, executive areadorsolateral prefrontal cortex (dlPFC) of unrestrained monkey. This technology allows us to identify cortical substates during quiet and active wakefulness, and transitions in population activity during rest. We further show that narrow-spiking neurons exhibit stronger synchronized fluctuations in population activity than broad-spiking neurons regardless of state. Our results show that cortical state is controlled by behavioral demands and arousal by asymmetrically modulating the slow response fluctuations of local excitatory and inhibitory cell populations.

Animals forage within their environment to extract valuable resources at lowest cost. Previous studies have suggested that animals simply maximize the current flow of reward without predicting the future outcomes of their actions and that this recent reward rate is represented in various brain areas. To test this, we devised a foraging task in which the relevant reward dynamics were hidden from the animal, and wirelessly record population activity in dorsolateral prefrontal cortex (dlPFC) while monkeys forage freely in their environment. We discover that their brains indeed contain predictions of future rewards and plans of their next actions. By decoding the dynamic reward probability and the memory of recent outcomes from the dlPFC population response, we show that monkeys create an internal representation of reward dynamics. The decoded variables predicted animal’s subsequent actions better than either the true experimental variables or the raw neural responses. Our results suggest that the relevant task variables and behavioral decisions are dynamically encoded in prefrontal cortex during the time course of foraging.

The dichotomy of excitation and suppression is one of the canonical mechanisms explaining the complexity of the neural activity. Computational models of the interplay of excitation and suppression in single neurons aim at investigating how this interaction affects a neuron's spiking responses and shapes, for example, the encoding of sensory stimuli. Here, we compare the performance of three filter-based stimulus-encoding models in predicting retinal ganglion cell responses recorded from axolotl, mouse, and marmoset retina to different types of temporally varying visual stimuli. Suppression in these models is implemented via subtractive or divisive interactions of stimulus filters or by a response-driven feedback module. For the majority of ganglion cells, the subtractive and divisive models perform similarly and outperform the feedback model as well as a linear-nonlinear (LN) model with no suppression. Comparison between the subtractive and the divisive model depended on cell type, species, and stimulus components, with the divisive model generalizing best across temporal stimulus frequencies and visual contrast and the subtractive model capturing in particular responses for slow temporal stimulus dynamics and for slow axolotl cells. Overall, we conclude that the divisive and subtractive models are well suited for capturing interactions of excitation and suppression in ganglion cells and emphasize different temporal regimes of these interactions.