Integration of auditory and visual communication information in the primate ventrolateral prefrontal cortex

The integration of auditory and visual stimuli is crucial for recognizing objects, communicating effectively, and navigating through our complex world. Although the frontal lobes are involved in memory, communication, and language, there has been no evidence that the integration of communication information occurs at the single-cell level in the frontal lobes. Here, we show that neurons in the macaque ventrolateral prefrontal cortex (VLPFC) integrate audiovisual communication stimuli. The multisensory interactions included both enhancement and suppression of a predominantly auditory or a predominantly visual response, although multisensory suppression was the more common mode of response. The multisensory neurons were distributed across the VLPFC and within previously identified unimodal auditory and visual regions (O’Scalaidhe et al., 1997; Romanski and Goldman-Rakic, 2002). Thus, our study demonstrates, for the first time, that single prefrontal neurons integrate communication information from the auditory and visual domains, suggesting that these neurons are an important node in the cortical network responsible for communication

Design of domestic photovoltaics manufacturing systems under global constraints and uncertainty

As global political discourse is taking place where the need for a cleaner energy mix is constantly highlighted, manufacturing strategies are becoming more relevant. Thus, the photovoltaics system design is a crucial aspect related with the overall sustainability. In fact, various countries are considering the potential to locally manufacture different elements of the photovoltaics (PV) value chain and the strategies to incentivize a local manufacturing base. This paper develops a mathematical programming approach for the optimal design of a PV manufacturing value chain considering diverse criteria linked to economic and environmental performance such as minimum sustainable price, transportation capacity, among others, and considering uncertainty. In addition, the proposed methodology involves the dependence over time of supply chain variables and economic parameters such as inflation, electricity cost, and weighted average cost of capital, to determine the manufacturing system topology under uncertain conditions. Our results highlight the importance of planning models to develop markets policies related to supply chains, production level changes and imposed tariffs all while involving uncertainty in economic parameters, which is an improvement compared to planning models that use deterministic formulations. Finally, the proposed methodology and results can encourage decision-making considering probable variations in different parameters

Bird pollinators, seed storage and cockatoo granivores explain large woody fruits as best seed defense in Hakea

Nutrient-impoverished soils with severe summer drought and frequent fire typify many Mediterranean-type regions of the world. Such conditions limit seed production and restrict opportunities for seedling recruitment making protection from granivores paramount. Our focus was on Hakea, a genus of shrubs widespread in southwestern Australia, whose nutritious seeds are targeted by strong-billed cockatoos. We assessed 56 Hakea species for cockatoo damage in 150 populations spread over 900 km in relation to traits expected to deter avian granivory: dense spiny foliage; large, woody fruits; fruit crypsis via leaf mimicry and shielding; low seed stores; and fruit clustering. We tested hypothesises centred on optimal seed defenses in relation to (a) pollination syndrome (bird vs insect), (b) fire regeneration strategy (killed vs resprouting) and (c) on-plant seed storage (transient vs prolonged).Twenty species in 50 populations showed substantial seed loss from cockatoo granivory. No subregional trends in granivore damage or protective traits were detected, though species in drier, hotter areas were spinier. Species lacking spiny foliage around the fruits (usually bird-pollinated) had much larger (4–5 times) fruits than those with spiny leaves and cryptic fruits (insect-pollinated). Species with woody fruits weighing >1 g were rarely attacked, unlike those with spiny foliage and small cryptic fruits. Fire-killed species were just as resistant to granivores as resprouters but with much greater seed stores. Strongly serotinous species with prolonged seed storage were rarely attacked, with an order of magnitude larger fruits but no difference in seed store compared with weakly/non-serotinous species. Overall, the five traits examined could be ranked in success at preventing seed loss from large woody fruits (most effective), fruit clustering, low seed stores, spinescence, to crypsis (least effective). We conclude that the evolution of large woody fruits is contingent on pollinator type (dictates flower/fruit location, thus apparency to granivores), level of serotiny (response to poor soils and fire that requires prolonged seed defense) and presence of a formidable granivore (that promotes strong defense)

Patterns in high-frequency FX data: Discovery of 12 empirical scaling laws

We have discovered 12 independent new empirical scaling laws in foreign exchange data-series that hold for close to three orders of magnitude and across 13 currency exchange rates. Our statistical analysis crucially depends on an event-based approach that measures the relationship between different types of events. The scaling laws give an accurate estimation of the length of the price-curve coastline, which turns out to be surprisingly long. The new laws substantially extend the catalogue of stylised facts and sharply constrain the space of possible theoretical explanations of the market mechanisms.Comment: 26 pages, 3 figures, 23 tables,2nd version (text made more concise and readable, algorithm pseudocode, results unchanged), 5-year datasets (USD-JPY, EUR-USD) provided at http://www.olsen.ch/more/datasets

Role of maternity waiting homes in the reduction of maternal death and stillbirth in developing countries and its contribution for maternal death reduction in Ethiopia: a systematic review and meta-analysis.

BACKGROUND: Every family expect to have a healthy mother and new born baby after pregnancy. Especially for parents, pregnancy is a time of great anticipation. Access to maternal and child health care insures safer pregnancy and its outcome. MWHs is one the strategy. The objective was to synthesize the best available evidence on effectiveness of maternity waiting homes on the reduction of maternal mortality and stillbirth in developing countries. METHODS: Before conducting this review non-occurrences of the same review is verified. To avoid introduction of bias because of errors, two independent reviewers appraised each article. Maternal death and stillbirth were the primary outcomes. Review Manager 5 were used to produce a random-effect meta-analysis. Grade Pro software were used to produce risk of bias summary and summary of findings. RESULT: In developing countries, maternity waiting homes users were 80% less likely to die than non-users (OR = 0. 20, 95% CI [0.08, 0.49]) and there was 73% less occurrence of stillbirth among users (OR = 0.27, 95% CI [0.09, 0.82]). In Ethiopia, there was a 91% reduction of maternal death among maternity waiting homes users unlike non-users (OR = 0.09, 95% CI [0.04, 0.19]) and it contributes to the reduction of 83% stillbirth unlike non-users (OR = 0.17, 95% CI [0.05, 0.58]). CONCLUSION: Maternity waiting home contributes more than 80% to the reduction of maternal death among users in developing countries and Ethiopia. Its contribution for reduction of stillbirth is good. More than 70% of stillbirth is reduced among the users of maternity waiting homes. In Ethiopia maternity waiting homes contributes to the reduction of more than two third of stillbirths

Histopathology of gill, liver, muscle and brain of Cyprinus carpio communis L. exposed to sublethal concentration of lead and cadmium

Histological studies in organs like gill, liver, muscle and brain of Cyprinus carpio communis were made to assess tissue damage due to sublethal concentration of heavy metals lead and cadmium after 28 days of exposure. In lead treated gill, disintegration and fusion of primary lamellae, extensive vacuolization with disruption of epithelial lining was observed, whereas on sublethal exposure to cadmium, hyperplasia of branchial arch, vacuolization and congestion of blood vessels were well marked. Metal accumulation was clearly visible in treated liver with degeneration and severe necrosis. Both lead and cadmium treated fish showed marked thickening and separation of muscle bundles with severe intramuscular oedema more pronounced in sublethal treatment of cadmium. Neuronal cell degeneration, swelling of pyramidal cells, vacuolization and dystrophic changes were characteristic features observed in treated brain.Key words: Lead, cadmium, histopathology, Cyprinus carpio communis

Global and Multiplexed Dendritic Computations under In Vivo-like Conditions.

Dendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to the overall input-output transformation of single neurons. We developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex, in vivo-like spatiotemporal synaptic input patterns. We used the hLN to predict the somatic membrane potential of an in vivo-validated detailed biophysical model of a L2/3 pyramidal cell. Linear input integration with a single global dendritic nonlinearity achieved above 90% prediction accuracy. A novel hLN motif, input multiplexing into parallel processing channels, could improve predictions as much as conventionally used additional layers of local nonlinearities. We obtained similar results in two other cell types. This approach provides a data-driven characterization of a key component of cortical circuit computations: the input-output transformation of neurons during in vivo-like conditions

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Fiber Orientation Estimation Guided by a Deep Network

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for fiber tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs with a relatively small number of diffusion gradients. However, accurate FO estimation in regions with complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent the diffusion signals. To estimate the mixture fractions of the dictionary atoms (and thus coarse FOs), a deep network is designed specifically for solving the sparse reconstruction problem. Here, the smaller dictionary is used to reduce the computational cost of training. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding dense basis FOs is used and a weighted l1-norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and real dMRI data, and the results demonstrate the benefit of using a deep network for FO estimation.Comment: A shorter version is accepted by MICCAI 201

Determination of Lipophilic Extractives in Ionic Liquid Extracts of Eucalyptus Pulp by Gas Chromatography - Mass Spectrometry

Lipophilic wood extractives composition is currently a big concern of pulp and paper industries as well as for the environmentalists due to their negative impacts on the quality of pulp and the environment. Because of the shortcomings of different extraction procedures using volatile organic solvents in capturing residual lipophilic extractives in pulp, this study reports on the use of ionic liquids as an effective approach for such extraction. The capacity of two ionic liquids; 1- butyl-3-methylimidazolium acetate and 1-butyl-3-methylimidazolium chloride to recover wood extractives was compared and it was observed that ionic liquid with chloride anion recovered a higher amount of extractives. The effect of temperature of the added precipitating solvent during cellulose regeneration on the recovery of extractives was also studied. Recovery of extractives increased with increasing temperature of the added precipitating solvent and equilibrium was reached at 90oC. Fatty acids (saturated, unsaturated and α-hydroxyl acids), sterols (β-sitosterol and stigmastanol), steroid hydrocarbons and ketones were the main compounds determined from bleached pulp using gas chromatography mass spectrometry. On the basis of the fact that ionic liquids are biodegradable and non-volatile, this approach of analysis is definitely a highly green process for the determination of lipophilic extractives in pulp.Key words: Dissolving pulp, Extractives, Gas chromatography-mass spectrometry, Green solvent, Ionic liqui