Targeted antiangiogenic agents in combination with cytotoxic chemotherapy in preclinical and clinical studies in sarcoma.

Sarcomas are a heterogeneous group of mesenchymal malignancies. In recent years, studies have demonstrated that inhibition of angiogenic pathways or disruption of established vasculature can attenuate the growth of sarcomas. However, when used as monotherapy in the clinical setting, these targeted antiangiogenic agents have only provided modest survival benefits in some sarcoma subtypes, and have not been efficacious in others. Preclinical and early clinical data suggest that the addition of conventional chemotherapy to antiangiogenic agents may lead to more effective therapies for patients with these tumors. In the current review, the authors summarize the available evidence and possible mechanisms supporting this approach

Periodic orbits in general Glass networks

Glass networks are piecewise linear ODE systems that models an interactive system where there are 'switching points': the underlying dynamic changes qualitatively when a certain variable pass over a threshold. One of the most well-studied class of models of the original Glass network are the cyclic attractor in the orthants (a sequence of orthants where the flow from one orthant to another is unanimous), which was first defined and analysed by Glass and Pasternack in 1978. In that paper, the authors gave a complete classification of the topological features of the flow in a full-rank cyclic attractor, which is a cyclic attractor that cannot be contained in any sub-cube in the graph of orthants. In this paper, we will extend the definition of cyclic attractor to one generalisation of the Glass network, one that allows for multiple switching points in each variables, and give a complete classification of the topological features of the flow for any cyclic attractor, both in the extended network and the original network, including non full-rank ones. We will show that in any cyclic attractor, there is either a unique and asymptotically stable periodic orbit, or that all periodic orbits are degenerated.Comment: 16 pages, 5 figure

Effect of Pauli repulsion and transfer on fusion

The effect of the Pauli exclusion principle on the nucleus-nucleus bare potential is studied using a new density-constrained extension of the Frozen-Hartree-Fock (DCFHF) technique. The resulting potentials exhibit a repulsion at short distance. The charge product dependence of this Pauli repulsion is investigated. Dynamical effects are then included in the potential with the density-constrained time-dependent Hartree-Fock (DCTDHF) method. In particular, isovector contributions to this potential are used to investigate the role of transfer on fusion, resulting in a lowering of the inner part of the potential for systems with positive Q-value transfer channels.Comment: Proceedings of an invited talk given at FUSION17, Hobart, Tasmania, AU (20-24 February, 2017

A Planarity Test via Construction Sequences

Optimal linear-time algorithms for testing the planarity of a graph are well-known for over 35 years. However, these algorithms are quite involved and recent publications still try to give simpler linear-time tests. We give a simple reduction from planarity testing to the problem of computing a certain construction of a 3-connected graph. The approach is different from previous planarity tests; as key concept, we maintain a planar embedding that is 3-connected at each point in time. The algorithm runs in linear time and computes a planar embedding if the input graph is planar and a Kuratowski-subdivision otherwise

The effect of vaccinating S. mansoni–infected BALB/c mice either before or after treatment

In Schistosoma mansoni endemic areas, there are people with ongoing S. mansoni infection, others have been infected and treated while others have never been infected. What would happen if these different groups of people were vaccinated against S. mansoni? BALB/c mice were divided into five groups: Infected-Treated-Vaccinated; Infected-Vaccinated-Treated; Vaccinated-Treated Control; Challenge Control and Untreated challenge Control. Vaccination (500 20krad irradiated S. mansoni cercariae), Treatment (praziquantel), Infection and Challenge (150 S. mansoni cercariae) were carried out at specified times. Proliferation assay, Enzyme linked immunosorbent assay, gross pathology, histopathology and perfusion were performed. High protection levels were obtained in mice treated after vaccination: Vaccinated-Treated control, 96.5%; Infected-Vaccinated-Treated, 68.9%; and Infected-Treated-Vaccinated, 41%. A good correlation was obtained between proliferative responses and protective levels, implying cellular involvement in protection. Although all protected animals had high IgG levels, there was no strong correlation between the two. Specificity rather than amounts of IgG, seem more important in protection. Praziquantel seemed to boost protective immunity when administered after vaccination. Granuloma development and modulation in the two test groups was similar. It seems better to vaccinate infected patients before treatment, the ideal situation being vaccinating people who have not encountered S. mansoni. African Journal of Health Sciences Vol. 13 (1-2) 2008: pp. 55-6

Dynamical effects in fusion with exotic nuclei

[Background] Reactions with stable beams have demonstrated a strong interplay between nuclear structure and fusion. Exotic beam facilities open new perspectives to understand the impact of neutron skin, large isospin, and weak binding energies on fusion. Microscopic theories of fusion are required to guide future experiments. [Purpose] To investigate new effects of exotic structures and dynamics in near-barrier fusion with exotic nuclei. [Method] Microscopic approaches based on the Hartree-Fock (HF) mean-field theory are used for studying fusion barriers in $^{40-54}$Ca+$^{116}$Sn reactions for even isotopes. Bare potential barriers are obtained assuming frozen HF ground-state densities. Dynamical effects on the barrier are accounted for in time-dependent Hartree-Fock (TDHF) calculations of the collisions. Vibrational couplings are studied in the coupled-channel framework and near-barrier nucleon transfer is investigated with TDHF calculations. [Results] The development of a neutron skin in exotic calcium isotopes strongly lowers the bare potential barrier. However, this static effect is not apparent when dynamical effects are included. On the contrary, a fusion hindrance is observed in TDHF calculations with the most neutron rich calcium isotopes which cannot be explained by vibrational couplings. Transfer reactions are also important in these systems due to charge equilibration processes. [Conclusions] Despite its impact on the bare potential, the neutron skin is not seen as playing an important role in the fusion dynamics. However, the charge transfer with exotic projectiles could lead to an increase of the Coulomb repulsion between the fragments, suppressing fusion. The effect of transfer and dissipative mechanisms on fusion with exotic nuclei deserve further studies.The authors are grateful to M. Dasgupta, D. J. Hinde, and A. S. Umar for stimulating discussions during this work. This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government. This research was supported under Australian Research Council’s Future Fellowship (Project No. FT120100760), Discovery Projects (Project No. DP140101337), and Laureate Fellowship (Project No. FL110100098) funding schemes

Subject-independent P300 BCI using ensemble classifier, dynamic stopping and adaptive learning

© 2017 IEEE. Brain-computer interfaces (BCIs) are used to assist people, especially those with verbal or physical disabilities, communicate with the computer to indicate their selections, control a device or answer questions only by their mere thoughts. Due to the noisy nature of brain signals, the required time for each experimental session must be lengthened to reach satisfactory accuracy. This is the trade-off between the speed and the precision of a BCI system. In this paper, we propose a unified method which is the integration of ensemble classifier, dynamic stopping, and adaptive learning. We are able to both increase the accuracy, as well as to reduce the spelling time of the P300-Speller. Another merit of our study is that it does not require the training phase for any new subject, hence eliminates the extensively time-consuming process for learning purposes. Experimental results show that we achieve the averaged bit rate boost up of 182% on 15 subjects. Our best achieved accuracy is 95.95% by using 7.49 flashing iterations and our best achieved bit rate is 40.87 bits/min with 83.99% accuracy and 3.64 iterations. To the best of our knowledge, these results outperformed most of the related P300-based BCI studies

A Stationary Wavelet Entropy-Based Clustering Approach Accurately Predicts Gene Expression

Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation

Memory-Augmented Graph Neural Networks: A Neuroscience Perspective

Graph neural networks (GNNs) have been extensively used for many domains where data are represented as graphs, including social networks, recommender systems, biology, chemistry, etc. Recently, the expressive power of GNNs has drawn much interest. It has been shown that, despite the promising empirical results achieved by GNNs for many applications, there are some limitations in GNNs that hinder their performance for some tasks. For example, since GNNs update node features mainly based on local information, they have limited expressive power in capturing long-range dependencies among nodes in graphs. To address some of the limitations of GNNs, several recent works started to explore augmenting GNNs with memory for improving their expressive power in the relevant tasks. In this paper, we provide a comprehensive review of the existing literature of memory-augmented GNNs. We review these works through the lens of psychology and neuroscience, which has established multiple memory systems and mechanisms in biological brains. We propose a taxonomy of the memory GNN works, as well as a set of criteria for comparing the memory mechanisms. We also provide critical discussions on the limitations of these works. Finally, we discuss the challenges and future directions for this area