Cryptic species and Miocene diversification of Palaearctic naked-toed geckos (Squamata: Gekkonidae) in the Indian dry zone

We sampled Palaearctic naked-toed geckos from across their range in India and used two mitochondrial and two nuclear genes to reconstruct relationships within a global phylogeny. Published sequences of Peninsular Indian Hemidactylus allow us to contrast these two groups in dating analyses - providing insights into the history of the Indian dry zone. Palaearctic naked-toed geckos first moved onto the Indian Plate in the Oligocene, with higher-level diversification probably linked to collision of the Indian and Eurasian plates, and subsequent dispersal into-India and diversification with increasing Miocene aridity. An independent gekkonid radiation with species in the dry zone, Hemidactylus diversified during the same period in Peninsular India. Our results demonstrate that dry zone taxa across India may date back to at least the Miocene, with a potential historical climatic barrier between the Indus and Peninsular Indian Divisions. `Cyrtopodion' aravallense is revealed to be a complex with seven genetically and environmentally divergent lineages that began diversifying in the late Miocene, congruent with increased aridity in north-western India. This discovery of cryptic diversity in the Indian dry zone represents the first terrestrial vertebrate radiation from north-western central India and highlights how little we understand of the regions' biodiversity, emphasizing the need for systematic geographic sampling and multiline evidence to reveal true patterns of diversity. The ancestor of `Cyrtopodion' aravallense came into the nascent Indian dry zone in the Miocene and has since diversified, potentially in the absence of any sympatric scansorial rupicolous geckos. Cyrtopodion scabrum represents a unique case of commensalism and shows phylogeographic structure in its presumed native range. The taxonomic implications of our study include a number of undescribed species, recognition of `Cyrtopodion' as a distinct lineage and the non-monophyly of Altiphylax.

Consumption and portfolio rules whit stochastic hyperbolic discounting

We extend the classic Merton (1969, 1971) problem that investigates the joint consumption-savings and portfolio-selection problem under capital risk by assuming sophisticated but time-inconsistent agents. We introduce stochastic hyperbolic preferences as in Harris and Laibson (2013) and find closed-form solutions for Merton's optimal consumption and portfolio selection problem in continuous time. We find that the portfolio rule remains identical to the time-consistent solution with power utility and no borrowing constraints. However,the marginal propensity to consume out of wealth is unambiguously greater than the time-consistent, exponential case and,importantly, it is also more responsive to changes in risk. These results suggest that hyperbolic discounting with sophisticated agents offers promise for contributing to explaining important aspects of asset market data.

Global movements, national grievances. Mobilizing for “real democracy” and social justice

In the ashes of political and socio-economic collapse, social movements sometimes rise like a phoenix. Little more than a year has passed since the Tunisian uprisings, the spark that ignited a series of “mobilizations of the indignant” that spread like wildfire around the world. Many observers have reported on these unprecedented global protests. They have portrayed citizens who declare feeling marginalized if not scapegoated, and who reject the increasing inequalities between rich and poor, the declining mobility of most, and the “disclassment” of many. They have shown, as well, massive protests against governments and politicians that are perceived as indifferent at best, duplicitous at worst, and in any event as blatantly closed to popular concerns. Many journalists have indeed asked what took so long for people to protest given this fatal combination. For the social scientist, however, the questions of who, why and how mobilizes are not so simple. There are specific problematics of mediation between structure, culture and individual or collective agency that need to be addressed.

Duplication and functional diversification of pancreatic ribonuclease (RNASE1) gene

Adaptation is one of the most fundamental issues in the studies of organismal evolution. Pancreatic ribonuclease is a very important digestive enzyme and secreted by the pancreas. Numerous studies have suggested that RNASE1 gene duplication is closely related to the functional adaptation of the digestive system in the intestinal fermentation herbivores. RNASE1 gene thus becomes one of the most important candidate genetic markers to study the molecular mechanism of adaptation of organisms to the feeding habit. Interestingly, RNASE1 gene duplication has also been found in some non-intestinal fermentation mammals, suggesting that RNASE1 gene may have produced novel tissue specificity or functions in these species. In this review, RNASE1 gene and its implications in adaptive evolution, especially in association with the feeding habit of organisms, are summarized.

Frequency locked micro disk resonator for real time and precise monitoring of refractive index.

We experimentally demonstrate a frequency modulation locked servo loop, locked to a resonance line of an on-chip microdisk resonator in a silicon nitride platform. By using this approach, we demonstrate real-time monitoring of refractive index variations with a precision approaching 10(-7) RIU, using a moderate Q factor of 10(4). The approach can be applied for intensity independent, dynamic and precise index of refraction monitoring for biosensing applications.

Urbanization effects on welfare and income diversification strategies of peri-urban farm households in Tigray, Northern Ethiopia: An empirical analysis

Urban areas in many developing countries are expanding rapidly by incorporating nearby subsistence farming communities. This has a direct effect on the consumption and production behaviours of the farm households but empirical evidence is sparse. This thesis investigated the effects of rapid urbanization and the associated policies on welfare of subsistence farm households in peri-urban areas using a panel dataset from Tigray, Ethiopia. The study revealed a number of important issues emerging with the rapid urban expansion. Firstly, private asset holdings and consumption expenditure of farm households, that have been incorporated into urban administration, has decreased. Secondly, factors that influence the farm households’ welfare and vulnerability depend on the administration they belong to, urban or rural. Gender and literacy of the household head have significant roles for the urban farm households to fall back into and/or move out of poverty. However, livestock holding and share of farm income are the most important factors for rural households. Thirdly, the study discloses that farming continues to be important source of income and income diversification is the principal strategy. Participation in nonfarm employment is less for farm households in urban than rural areas. Adult labour, size of the local market and past experience in the nonfarm sector improves the likelihood of engaging in skilled nonfarm employment opportunities. But money, given as compensation for the land taken away, is not crucial for the household to engage in better paying nonfarm employments. Production behaviour of the better-off farm households is the same, regardless of the administration they belong to. However, the urban poor participate less in nonfarm employment compared to the rural poor. These findings signify the gradual development of urban-induced poverty in peri-urban areas. In the case of labour poor households, introducing urban safety net programmes could improve asset productivity and provide further protection.

Elucidation of hepatitis C virus transmission and early diversification by single genome sequencing.

A precise molecular identification of transmitted hepatitis C virus (HCV) genomes could illuminate key aspects of transmission biology, immunopathogenesis and natural history. We used single genome sequencing of 2,922 half or quarter genomes from plasma viral RNA to identify transmitted/founder (T/F) viruses in 17 subjects with acute community-acquired HCV infection. Sequences from 13 of 17 acute subjects, but none of 14 chronic controls, exhibited one or more discrete low diversity viral lineages. Sequences within each lineage generally revealed a star-like phylogeny of mutations that coalesced to unambiguous T/F viral genomes. Numbers of transmitted viruses leading to productive clinical infection were estimated to range from 1 to 37 or more (median = 4). Four acutely infected subjects showed a distinctly different pattern of virus diversity that deviated from a star-like phylogeny. In these cases, empirical analysis and mathematical modeling suggested high multiplicity virus transmission from individuals who themselves were acutely infected or had experienced a virus population bottleneck due to antiviral drug therapy. These results provide new quantitative and qualitative insights into HCV transmission, revealing for the first time virus-host interactions that successful vaccines or treatment interventions will need to overcome. Our findings further suggest a novel experimental strategy for identifying full-length T/F genomes for proteome-wide analyses of HCV biology and adaptation to antiviral drug or immune pressures.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.