Mesoscopic description of the annealed Ising model and Multiplicative noise

A new type of Langevin equation exhibiting a non trivial phase transition associated with the presence of multiplicative noise is introduced. The equation is derived as a mesoscopic representation of the microscopic annealed Ising model (AIM) proposed by Thorpe and Beeman, and reproduces perfectly its basic phenomenology. The AIM exhibits a non-trivial behavior as the temperature is increased, in particular it presents a disorder-to-order phase transition at low temperatures, and a order-to-disorder transition at higher temperatures. This behavior resembles that of some Langevin equations with multiplicative noise, which exhibit also two analogous phase transitions as the noise-amplitude is increased. By comparing the standard models for noise-induced transitions with our new Langevin equation we elucidate that the mechanisms controlling the disorder-to-order transitions in both of them are essentially different, even though for both of them the presence of multiplicative noise is a key ingredient.Comment: Submitted to Phys. Rev.

NIDCAP improves brain function and structure in preterm infants with severe intrauterine growth restriction

Objective: The effect of NIDCAP (Newborn Individualized Developmental Care and Assessment Program) was examined on the neurobehavioral, electrophysiological and neurostructural development of preterm infants with severe intrauterine growth restriction (IUGR). Study Design: A total of 30 infants, 27–33 weeks gestation, were randomized to control (C; N=17) or NIDCAP/experimental (E; N=13) care. Baseline health and demographics were assessed at intake; electroencephalography (EEG) and magnetic resonance imaging (MRI) at 35 and 42 weeks postmenstrual age; and health, growth and neurobehavior at 42 weeks and 9 months corrected age (9 months). Results: C and E infants were comparable in health and demographics at baseline. At follow-up, E infants were healthier, showed significantly improved brain development and better neurobehavior. Neurobehavior, EEG and MRI discriminated between C and E infants. Neurobehavior at 42 weeks correlated with EEG and MRI at 42 weeks and neurobehavior at 9 months. Conclusion: NIDCAP significantly improved IUGR preterm infants' neurobehavior, electrophysiology and brain structure. Longer-term outcome assessment and larger samples are recommended

Recent results on multiplicative noise

Recent developments in the analysis of Langevin equations with multiplicative noise (MN) are reported. In particular, we: (i) present numerical simulations in three dimensions showing that the MN equation exhibits, like the Kardar-Parisi-Zhang (KPZ) equation both a weak coupling fixed point and a strong coupling phase, supporting the proposed relation between MN and KPZ; (ii) present dimensional, and mean field analysis of the MN equation to compute critical exponents; (iii) show that the phenomenon of the noise induced ordering transition associated with the MN equation appears only in the Stratonovich representation and not in the Ito one, and (iv) report the presence of a new first-order like phase transition at zero spatial coupling, supporting the fact that this is the minimum model for noise induced ordering transitions.Comment: Some improvements respect to the first versio

Infant Brain Atlases from Neonates to 1- and 2-Year-Olds

Background: Studies for infants are usually hindered by the insufficient image contrast, especially for neonates. Prior knowledge, in the form of atlas, can provide additional guidance for the data processing such as spatial normalization, label propagation, and tissue segmentation. Although it is highly desired, there is currently no such infant atlas which caters for all these applications. The reason may be largely due to the dramatic early brain development, image processing difficulties, and the need of a large sample size. Methodology: To this end, after several years of subject recruitment and data acquisition, we have collected a unique longitudinal dataset, involving 95 normal infants (56 males and 39 females) with MRI scanned at 3 ages, i.e., neonate, 1-yearold, and 2-year-old. State-of-the-art MR image segmentation and registration techniques were employed, to construct which include the templates (grayscale average images), tissue probability maps (TPMs), and brain parcellation maps (i.e., meaningful anatomical regions of interest) for each age group. In addition, the longitudinal correspondences between agespecific atlases were also obtained. Experiments of typical infant applications validated that the proposed atlas outperformed other atlases and is hence very useful for infant-related studies. Conclusions: We expect that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains. These atlases are publicly available in our website

Price shocks in regional markets: Japan's great Kantō Earthquake of 1923

Japan’s Great Kantō Earthquake of September 1st 1923 devastated the area around Tokyo and the country’s main port of Yokohama. This paper uses the earthquake as a case study to inform our understanding of the economics of disasters and the history of market integration. It seeks to test two main assumptions: firstly, that shifting demand and supply curves consequent on a disaster will have some impact on prices; and secondly, that any local changes in the disaster region are likely to be diffused across a wider geographical area. We make use of a unique monthly wholesale price dataset for a number of cities across Japan, and our analysis suggests three main findings: that price changes in the affected areas immediately following the disaster were in most cases reflected in price changes in Japan’s provincial cities; that cities further away from the devastation witnessed smaller price changes than those nearer to the affected area; and that the observed pattern of price changes reflects the regional heterogeneity identified by scholars who have worked on market integration in Japan

SEGMA: an automatic SEGMentation Approach for human brain MRI using sliding window and random forests

Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38–42 weeks gestational age), children and adolescents (4–17 years) and adults (35–71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course

LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8 months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy

The adaptive significance of cultural behavior

In this article, I argue that human social behavior is a product of the coevolution of human biology and culture. While critical of attempts by anthropologists to explain cultural practices as if they were independent of the ability of individual human beings to survive and reproduce, I am also leery of attempts by biologists to explain the consistencies between neo-Darwinian theory and cultural behavior as the result of natural selection for that behavior. Instead, I propose that both biological and cultural attributes of human beings result to a large degree from the selective retention of traits that enhance the inclusive fitnesses of individuals in their environments. Aspects of human biology and culture may be adaptive in the same sense despite differences between the mechanisms of selection and regardless of their relative importance in the evolution of a trait. The old idea that organic and cultural evolution are complementary can thus be used to provide new explanations for why people do what they do .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44491/1/10745_2005_Article_BF01531215.pd