Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Core-coupled states and split proton-neutron quasi-particle multiplets in 122-126Ag

Neutron-rich silver isotopes were populated in the fragmentation of a 136Xe beam and the relativistic fission of 238U. The fragments were mass analyzed with the GSI Fragment separator and subsequently implanted into a passive stopper. Isomeric transitions were detected by 105 HPGe detectors. Eight isomeric states were observed in 122-126Ag nuclei. The level schemes of 122,123,125Ag were revised and extended with isomeric transitions being observed for the first time. The excited states in the odd-mass silver isotopes are interpreted as core-coupled states. The isomeric states in the even-mass silver isotopes are discussed in the framework of the proton-neutron split multiplets. The results of shell-model calculations, performed for the most neutron-rich silver nuclei are compared to the experimental data

Response of AGATA Segmented HPGe Detectors to Gamma Rays up to 15.1 MeV

The response of AGATA segmented HPGe detectors to gamma rays in the energy range 2-15 MeV was measured. The 15.1 MeV gamma rays were produced using the reaction d(11B,ng)12C at Ebeam = 19.1 MeV, while gamma-rays between 2 to 9 MeV were produced using an Am-Be-Fe radioactive source. The energy resolution and linearity were studied and the energy-to-pulse-height conversion resulted to be linear within 0.05%. Experimental interaction multiplicity distributions are discussed and compared with the results of Geant4 simulations. It is shown that the application of gamma-ray tracking allows a suppression of background radiation following neutron capture by Ge nuclei. Finally the Doppler correction for the 15.1 MeV gamma line, performed using the position information extracted with Pulse-shape Analysis, is discussed.Comment: 10 pages, 11 figure

FoxP3+ T regulatory cells in cancer : prognostic biomarkers and therapeutic targets

T Regulatory cells (Tregs) can have both protective and pathological roles. They maintain immune homeostasis and inhibit immune responses in various diseases, including cancer. Proportions of Tregs in the peripheral blood of some cancer patients increase by five-to ten-folds, compared to those in healthy individuals. Tregs contribute to cancer development and progression by suppressing T effector cell functions, thereby compromising tumor killing and promoting tumor growth. Highly immunosuppressive Tregs express upregulated levels of the transcription factor, Forkhead box protein P3 (FoxP3). Elevated levels of FoxP3+ Tregs within the tumor microenvironment (TME) showed a positive correlation with poor prognosis in various cancer patients. Despite the success of immunotherapy, including the use of immune checkpoint inhibitors, a significant proportion of patients show low response rates as a result of primary or acquired resistance against therapy. Some of the mechanisms which underlie the development of therapy resistance are associated with Treg suppressive function. In this review, we describe Treg contribution to cancer development/progression, and the mechanisms of Treg-mediated immunosuppression. We discuss the prognostic significance of FoxP3+ Tregs in different cancers and their potential use as prognostic biomarkers. We also describe potential therapeutic strategies to target Tregs in combination with other types of immunotherapies aiming to overcome tumor resistance and improve clinical outcomes in cancer patients. Overall, understanding the prognostic significance of FoxP3+ Tregs in various cancers and their contribution to therapeutic resistance could help in the development of more effective targeted therapeutic strategies to enhance the clinical outcomes in cancer patients