Self-Organization in Multimode Microwave Phonon Laser (Phaser): Experimental Observation of Spin-Phonon Cooperative Motions

An unusual nonlinear resonance was experimentally observed in a ruby phonon laser (phaser) operating at 9 GHz with an electromagnetic pumping at 23 GHz. The resonance is manifested by very slow cooperative self-detunings in the microwave spectra of stimulated phonon emission when pumping is modulated at a superlow frequency (less than 10 Hz). During the self-detuning cycle new and new narrow phonon modes are sequentially ``fired'' on one side of the spectrum and approximately the same number of modes are ``extinguished'' on the other side, up to a complete generation breakdown in a certain final portion of the frequency axis. This is usually followed by a short-time refractority, after which the generation is fired again in the opposite (starting) portion of the frequency axis. The entire process of such cooperative spectral motions is repeated with high degree of regularity. The self-detuning period strongly depends on difference between the modulation frequency and the resonance frequency. This period is incommensurable with period of modulation. It increases to very large values (more than 100 s) when pointed difference is less than 0.05 Hz. The revealed phenomenon is a kind of global spin-phonon self- organization. All microwave modes of phonon laser oscillate with the same period, but with different, strongly determined phase shifts - as in optical lasers with antiphase motions.Comment: LaTeX2e file (REVTeX4), 5 pages, 5 Postscript figures. Extended and revised version of journal publication. More convenient terminology is used. Many new bibliographic references are added, including main early theoretical and experimental papers on microwave phonon lasers (in English and in Russian

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.

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

A comparison of the hypoglycemic effect of insulin with systemic venous and portal venous administration

The hyperglycemic effect of insulin by prolonged intraportal and systemic infusion was measured in unanesthetized dogs with a modified portacaval transposition. There was no significant difference in response with the two routes of administration. The relation of these results to research directed to surgical therapy of diabetes is discussed. © 1963 W. B. Saunders Company

Citizen Entrepreneurship: A Conceptual Picture of the Inclusion, Integration and Engagement of Citizens in the Entrepreneurial Process

This conceptual and exploratory article aims to present a rationale for the engagement of citizens with the process and practice of, and research on new civic forms of entrepreneurship. We argue that this form of citizen engagement could enable a better alignment of entrepreneurial initiatives with economic, social and community priorities, and to address issues of global significance of local interest in uncertain environments. To this end, we posit that engaging citizens in the entrepreneurial process could facilitate agency at the collective level of people with their rights, duties and responsibilities, to identify, participate in and govern with existing institutions, in meaningful economic and social activity in defined spatial environments. Our normative understanding of entrepreneurial process involves the creation of business, social and public enterprises, the formation of which is led by entrepreneurs. Entrepreneurs are of course citizens of specific nation states, but their endeavours do not necessarily warrant the participation of the wider community of citizens in the entrepreneurial process beyond their receiving function as users of goods and services. We consider whether pro-active engagement in a variety of ways, as nurtured in the practice of Citizen Science or Citizen Economics projects, could strengthen the profile and substance of entrepreneurship to resolve critical economic, social and environmental concerns of our times. We use the concept of the ‘commons’ and collective efficacy to argue for an understanding of entrepreneurship and innovation as a social good. We argue that Citizen Entrepreneurship (CE) is able to create new forms of collective organisation and governance, and derive economic and social value by addressing local issues arising from wide-spread phenomena such as climate change, ecological and environmental challenges, inequality, social polarisation, populism, migration and the gradual erosion of democratic institutions. To do so, citizens need to develop capabilities for engagement in the entrepreneurship process, especially when traditional public and market institutions fail to satisfy their existential needs. Indeed, active engagement could lead to the achievement of capabilities for well-being and fulfilling lives which go beyond the acquisition of skills and competencies necessary to pursue a vocation or a career. We refer to and interpret three examples of collective entrepreneurial activity in different urban environments in European countries as models of CE highlighting what we see as a growing trend in the entrepreneurial substance of the ‘urban commons’. We work towards the creation of a conceptual model with which to develop an understanding of a unique formulation of entrepreneurship

Comprehensive Brain MRI Segmentation in High Risk Preterm Newborns

Most extremely preterm newborns exhibit cerebral atrophy/growth disturbances and white matter signal abnormalities on MRI at term-equivalent age. MRI brain volumes could serve as biomarkers for evaluating the effects of neonatal intensive care and predicting neurodevelopmental outcomes. This requires detailed, accurate, and reliable brain MRI segmentation methods. We describe our efforts to develop such methods in high risk newborns using a combination of manual and automated segmentation tools. After intensive efforts to accurately define structural boundaries, two trained raters independently performed manual segmentation of nine subcortical structures using axial T2-weighted MRI scans from 20 randomly selected extremely preterm infants. All scans were re-segmented by both raters to assess reliability. High intra-rater reliability was achieved, as assessed by repeatability and intra-class correlation coefficients (ICC range: 0.97 to 0.99) for all manually segmented regions. Inter-rater reliability was slightly lower (ICC range: 0.93 to 0.99). A semi-automated segmentation approach was developed that combined the parametric strengths of the Hidden Markov Random Field Expectation Maximization algorithm with non-parametric Parzen window classifier resulting in accurate white matter, gray matter, and CSF segmentation. Final manual correction of misclassification errors improved accuracy (similarity index range: 0.87 to 0.89) and facilitated objective quantification of white matter signal abnormalities. The semi-automated and manual methods were seamlessly integrated to generate full brain segmentation within two hours. This comprehensive approach can facilitate the evaluation of large cohorts to rigorously evaluate the utility of regional brain volumes as biomarkers of neonatal care and surrogate endpoints for neurodevelopmental outcomes

The genetic basis of a social polymorphism in halictid bees

The emergence of eusociality represents a major evolutionary transition from solitary to group reproduction. The most commonly studied eusocial species, honey bees and ants, represent the behavioral extremes of social evolution but lack close relatives that are non-social. Unlike these species, the halictid bee Lasioglossum albipes produces both solitary and eusocial nests and this intraspecific variation has a genetic basis. Here, we identify genetic variants associated with this polymorphism, including one located in the intron of syntaxin 1a (syx1a), a gene that mediates synaptic vesicle release. We show that this variant can alter gene expression in a pattern consistent with differences between social and solitary bees. Surprisingly, syx1a and several other genes associated with sociality in L. albipes have also been implicated in autism spectrum disorder in humans. Thus, genes underlying behavioral variation in L. albipes may also shape social behaviors across a wide range of taxa, including humans

Linked read technology for assembling large complex and polyploid genomes

Background: Short read DNA sequencing technologies have revolutionized genome assembly by providing high accuracy and throughput data at low cost. But it remains challenging to assemble short read data, particularly for large, complex and polyploid genomes. The linked read strategy has the potential to enhance the value of short reads for genome assembly because all reads originating from a single long molecule of DNA share a common barcode. However, the majority of studies to date that have employed linked reads were focused on human haplotype phasing and genome assembly. Results: Here we describe a de novo maize B73 genome assembly generated via linked read technology which contains ~ 172,000 scaffolds with an N50 of 89 kb that cover 50% of the genome. Based on comparisons to the B73 reference genome, 91% of linked read contigs are accurately assembled. Because it was possible to identify errors with \u3e 76% accuracy using machine learning, it may be possible to identify and potentially correct systematic errors. Complex polyploids represent one of the last grand challenges in genome assembly. Linked read technology was able to successfully resolve the two subgenomes of the recent allopolyploid, proso millet (Panicum miliaceum). Our assembly covers ~ 83% of the 1 Gb genome and consists of 30,819 scaffolds with an N50 of 912 kb. Conclusions: Our analysis provides a framework for future de novo genome assemblies using linked reads, and we suggest computational strategies that if implemented have the potential to further improve linked read assemblies, particularly for repetitive genomes

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