Effects of impurities on radiation damage of silicon solar cells

Impurities effects on radiation damage of silicon solar cell

Fractal-like Distributions over the Rational Numbers in High-throughput Biological and Clinical Data

Recent developments in extracting and processing biological and clinical data are allowing quantitative approaches to studying living systems. High-throughput sequencing, expression profiles, proteomics, and electronic health records are some examples of such technologies. Extracting meaningful information from those technologies requires careful analysis of the large volumes of data they produce. In this note, we present a set of distributions that commonly appear in the analysis of such data. These distributions present some interesting features: they are discontinuous in the rational numbers, but continuous in the irrational numbers, and possess a certain self-similar (fractal-like) structure. The first set of examples which we present here are drawn from a high-throughput sequencing experiment. Here, the self-similar distributions appear as part of the evaluation of the error rate of the sequencing technology and the identification of tumorogenic genomic alterations. The other examples are obtained from risk factor evaluation and analysis of relative disease prevalence and co-mordbidity as these appear in electronic clinical data. The distributions are also relevant to identification of subclonal populations in tumors and the study of the evolution of infectious diseases, and more precisely the study of quasi-species and intrahost diversity of viral populations

The Electrochemistry of Simple Inorganic Molecules in Room Temperature Ionic Liquids

The electrochemistry of simple inorganic compounds in room temperature ionic liquids (RTILs) is reviewed and some new work in this area is presented. This paper focuses on the comparison between electrochemical behaviour in RTILs and in conventional aprotic solvents. Some compounds (iodides, O2, NO2, SO2, NH3) display similar reactions and mechanisms in RTILs as in aprotic solvents (as is observed for organic compounds). However other species (nitrates, PCl3, POCl3) show remarkably different behaviour to traditional solvents. This makes RTILs very promising media for the study of inorganic compounds, and highlights the need for more investigations in this exciting area

Clustering and preferential attachment in growing networks

We study empirically the time evolution of scientific collaboration networks in physics and biology. In these networks, two scientists are considered connected if they have coauthored one or more papers together. We show that the probability of scientists collaborating increases with the number of other collaborators they have in common, and that the probability of a particular scientist acquiring new collaborators increases with the number of his or her past collaborators. These results provide experimental evidence in favor of previously conjectured mechanisms for clustering and power-law degree distributions in networks.Comment: 13 pages, 2 figure

Surgeon’s and Caregivers’ Appraisals of Primary Cleft Lip Treatment with and without Nasoalveolar Molding: A Prospective Multicenter Pilot Study

Despite the increasing use of nasoalveolar molding (NAM) in early cleft treatment, questions remain about its effectiveness. This study examines clinician and caregiver appraisals of primary cleft lip and nasal reconstruction with and without NAM in a non-randomized, prospective multicenter study

Universal Behavior of Load Distribution in Scale-free Networks

We study a problem of data packet transport in scale-free networks whose degree distribution follows a power-law with the exponent $\gamma$. We define load at each vertex as the accumulated total number of data packets passing through that vertex when every pair of vertices send and receive a data packet along the shortest path connecting the pair. It is found that the load distribution follows a power-law with the exponent $\delta \approx 2.2(1)$, insensitive to different values of $\gamma$ in the range, $2 < \gamma \le 3$, and different mean degrees, which is valid for both undirected and directed cases. Thus, we conjecture that the load exponent is a universal quantity to characterize scale-free networks.Comment: 5 pages, 5 figures, revised versio

Colorectal Cancer Prevention: Perspectives of Key Players from Social Networks in a Low-Income Rural US Region

Social networks influence health behavior and health status. Within social networks, “key players” often influence those around them, particularly in traditionally underserved areas like the Appalachian region in the USA. From a total sample of 787 Appalachian residents, we identified and interviewed 10 key players in complex networks, asking them what comprises a key player, their role in their network and community, and ideas to overcome and increase colorectal cancer (CRC) screening. Key players emphasized their communication skills, resourcefulness, and special occupational and educational status in the community. Barriers to CRC screening included negative perceptions of the colonoscopy screening procedure, discomfort with the medical system, and misinformed perspectives on screening. Ideas to improve screening focused on increasing awareness of women\u27s susceptibility to CRC, providing information on different screening tests, improving access, and the key role of health-care providers and key players themselves. We provide recommendations to leverage these vital community resources

A Comparison of Three Child OHRQoL Measures.

Comparing oral health-related quality of life (OHRQoL) measures can facilitate selecting the most appropriate one for a particular research question/setting. Three child OHRQoL measures Child Perceptions Questionnaire (CPQ11⁻14), the Child Oral Health Impact Profile (COHIP) and the Caries Impacts and Experiences Questionnaire for Children (CARIES-QC) were used with 335 10- to 13-year-old participants in a supervised tooth-brushing programme in New Zealand. The use of global questions enabled their validity to be examined. Assessments were conducted at baseline and after 12 months. All three measures had acceptable internal consistency reliability. There were moderate, positive correlations among their scores, and all showed differences in the impact of dental caries on OHRQoL, with children with the highest caries experience having the highest scale scores. Effect sizes were used to assess meaningful change. The CPQ11⁻14 and the CARIES-QC showed meaningful change. The COHIP-SF score showed no meaningful change. Among children reporting improved OHRQoL, baseline and follow-up scores differed significantly for the CPQ11⁻14 and CARIES-QC measures, although not for the COHIP-SF. The three scales were broadly similar in their conceptual basis, reliability and validity, but responsiveness of the COHIP-SF was questionable, and the need to compute two different scores for the CARIES-QC meant that its administrative burden was considerably greater than for the other two measures. Replication and use of alternative approaches to measuring meaningful change are suggested

Network robustness and fragility: Percolation on random graphs

Recent work on the internet, social networks, and the power grid has addressed the resilience of these networks to either random or targeted deletion of network nodes. Such deletions include, for example, the failure of internet routers or power transmission lines. Percolation models on random graphs provide a simple representation of this process, but have typically been limited to graphs with Poisson degree distribution at their vertices. Such graphs are quite unlike real world networks, which often possess power-law or other highly skewed degree distributions. In this paper we study percolation on graphs with completely general degree distribution, giving exact solutions for a variety of cases, including site percolation, bond percolation, and models in which occupation probabilities depend on vertex degree. We discuss the application of our theory to the understanding of network resilience.Comment: 4 pages, 2 figure

Finding and evaluating community structure in networks

We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.Comment: 16 pages, 13 figure