Evolution of opinions on social networks in the presence of competing committed groups

Public opinion is often affected by the presence of committed groups of individuals dedicated to competing points of view. Using a model of pairwise social influence, we study how the presence of such groups within social networks affects the outcome and the speed of evolution of the overall opinion on the network. Earlier work indicated that a single committed group within a dense social network can cause the entire network to quickly adopt the group's opinion (in times scaling logarithmically with the network size), so long as the committed group constitutes more than about 10% of the population (with the findings being qualitatively similar for sparse networks as well). Here we study the more general case of opinion evolution when two groups committed to distinct, competing opinions $A$ and $B$, and constituting fractions $p_A$ and $p_B$ of the total population respectively, are present in the network. We show for stylized social networks (including Erd\H{o}s-R\'enyi random graphs and Barab\'asi-Albert scale-free networks) that the phase diagram of this system in parameter space $(p_A,p_B)$ consists of two regions, one where two stable steady-states coexist, and the remaining where only a single stable steady-state exists. These two regions are separated by two fold-bifurcation (spinodal) lines which meet tangentially and terminate at a cusp (critical point). We provide further insights to the phase diagram and to the nature of the underlying phase transitions by investigating the model on infinite (mean-field limit), finite complete graphs and finite sparse networks. For the latter case, we also derive the scaling exponent associated with the exponential growth of switching times as a function of the distance from the critical point.Comment: 23 pages: 15 pages + 7 figures (main text), 8 pages + 1 figure + 1 table (supplementary info

The Effects of Mechanical Stress on the Growth, Differentiation, and Paracrine Factor Production of Cardiac Stem Cells

Stem cell therapies have been clinically employed to repair the injured heart, and cardiac stem cells are thought to be one of the most potent stem cell candidates. The beating heart is characterized by dynamic mechanical stresses, which may have a significant impact on stem cell therapy. The purpose of this study is to investigate how mechanical stress affects the growth and differentiation of cardiac stem cells and their release of paracrine factors. In this study, human cardiac stem cells were seeded in a silicon chamber and mechanical stress was then induced by cyclic stretch stimulation (60 cycles/min with 120% elongation). Cells grown in non-stretched silicon chambers were used as controls. Our result revealed that mechanical stretching significantly reduced the total number of surviving cells, decreased Ki-67-positive cells, and increased TUNEL-positive cells in the stretched group 24 hrs after stretching, as compared to the control group. Interestingly, mechanical stretching significantly increased the release of the inflammatory cytokines IL-6 and IL-1β as well as the angiogenic growth factors VEGF and bFGF from the cells in 12 hrs. Furthermore, mechanical stretching significantly reduced the percentage of c-kit-positive stem cells, but increased the expressions of cardiac troponin-I and smooth muscle actin in cells 3 days after stretching. Using a traditional stretching model, we demonstrated that mechanical stress suppressed the growth and proliferation of cardiac stem cells, enhanced their release of inflammatory cytokines and angiogenic factors, and improved their myogenic differentiation. The development of this in vitro approach may help elucidate the complex mechanisms of stem cell therapy for heart failure

Learning Moore Machines from Input-Output Traces

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample

Detection of multipartite entanglement with two-body correlations

We show how to detect entanglement with criteria built from simple two-body correlation terms. Since many natural Hamiltonians are sums of such correlation terms, our ideas can be used to detect entanglement by energy measurement. Our criteria can straightforwardly be applied for detecting different forms of multipartite entanglement in familiar spin models in thermal equilibrium.Comment: 5 pages including 2 figures, LaTeX; for the proceedings of the DPG spring meeting, Berlin, March 200

New directions in cellular therapy of cancer: a summary of the summit on cellular therapy for cancer

A summit on cellular therapy for cancer discussed and presented advances related to the use of adoptive cellular therapy for melanoma and other cancers. The summit revealed that this field is advancing rapidly. Conventional cellular therapies, such as tumor infiltrating lymphocytes (TIL), are becoming more effective and more available. Gene therapy is becoming an important tool in adoptive cell therapy. Lymphocytes are being engineered to express high affinity T cell receptors (TCRs), chimeric antibody-T cell receptors (CARs) and cytokines. T cell subsets with more naïve and stem cell-like characteristics have been shown in pre-clinical models to be more effective than unselected populations and it is now possible to reprogram T cells and to produce T cells with stem cell characteristics. In the future, combinations of adoptive transfer of T cells and specific vaccination against the cognate antigen can be envisaged to further enhance the effectiveness of these therapies

Virus-free induction of pluripotency and subsequent excision of reprogramming factors

Reprogramming of somatic cells to pluripotency, thereby creating induced pluripotent stem (iPS) cells, promises to transform regenerative medicine. Most instances of direct reprogramming have been achieved by forced expression of defined factors using multiple viral vectors1-7. However, such iPS cells contain a large number of viral vector integrations1,8, any one of which could cause unpredictable genetic dysfunction. While c-Myc is dispensable for reprogramming9,10, complete elimination of the other exogenous factors is also desired since ectopic expression of either Oct4 or Klf4 can induce dysplasia11,12. Two transient transfection reprogramming methods have been published to address this issue13,14. However, the efficiency of either approach is extremely low, and neither has thus far been applied successfully to human cells. Here we show that non-viral transfection of a single multiprotein expression vector, which comprises the coding sequences of c-Myc​,​ Klf4​,​ Oct4 and Sox2 linked with 2A peptides, can reprogram both mouse and human fibroblasts. Moreover, the transgene can be removed once reprogramming has been achieved. iPS cells produced with this non-viral vector show robust expression of pluripotency markers, indicating a reprogrammed state confirmed functionally by in vitro differentiation assays and formation of adult chimeric mice. When the single vector reprogramming system was combined with a piggyBac transposon15,16 we succeeded in establishing reprogrammed human cell lines from embryonic fibroblasts with robust expression of pluripotency markers. This system minimizes genome modification in iPS cells and enables complete elimination of exogenous reprogramming factors, efficiently providing iPS cells that are applicable to regenerative medicine, drug screening and the establishment of disease models

Complement activation capacity in plasma before and during high-dose prednisolone treatment and tapering in exacerbations of Crohn's disease and ulcerative colitis

BACKGROUND: Ulcerative colitis (UC) and Crohn's disease (CD) are characterized by intestinal inflammation mainly caused by a disturbance in the balance between cytokines and increased complement (C) activation. Our aim was to evaluate possible associations between C activation capacity and prednisolone treatment. METHODS: Plasma from patients with exacerbations of UC (n = 18) or CD (n = 18) were collected before and during high dose prednisolone treatment (1 mg/kg body weight) and tapering. Friedman's two way analysis of variance, Mann-Whitney U test and Wilcoxon signed-rank sum test were used RESULTS: Before treatment, plasma from CD patients showed significant elevations in all C-mediated analyses compared to the values obtained from 38 healthy controls (p < 0.02), and in mannan binding lectin (MBL)-concentration and MBL-C4-activation capacity (AC) values compared to UC patients (p < 0.02). Before treatment, plasma from UC patients showed significant elevations only in the classical pathway-mediated C3-AC compared to values obtained from healthy controls (p < 0.01). After treatment was initiated, significant reductions, which persisted during follow-up, were observed in the classical pathway-mediated C3-AC and MBL-C4-AC in plasma from CD patients (p < 0.05). CONCLUSION: Our findings indicate that C activation capacity is up-regulated significantly in plasma from CD patients. The decreases observed after prednisolone treatment reflect a general down-regulation in immune activation

Fast 3D shape screening of large chemical databases through alignment-recycling

<p>Abstract</p> <p>Background</p> <p>Large chemical databases require fast, efficient, and simple ways of looking for similar structures. Although such tasks are now fairly well resolved for graph-based similarity queries, they remain an issue for 3D approaches, particularly for those based on 3D shape overlays. Inspired by a recent technique developed to compare molecular shapes, we designed a hybrid methodology, alignment-recycling, that enables efficient retrieval and alignment of structures with similar 3D shapes.</p> <p>Results</p> <p>Using a dataset of more than one million PubChem compounds of limited size (< 28 heavy atoms) and flexibility (< 6 rotatable bonds), we obtained a set of a few thousand diverse structures covering entirely the 3D shape space of the conformers of the dataset. Transformation matrices gathered from the overlays between these diverse structures and the 3D conformer dataset allowed us to drastically (100-fold) reduce the CPU time required for shape overlay. The alignment-recycling heuristic produces results consistent with <it>de novo </it>alignment calculation, with better than 80% hit list overlap on average.</p> <p>Conclusion</p> <p>Overlay-based 3D methods are computationally demanding when searching large databases. Alignment-recycling reduces the CPU time to perform shape similarity searches by breaking the alignment problem into three steps: selection of diverse shapes to describe the database shape-space; overlay of the database conformers to the diverse shapes; and non-optimized overlay of query and database conformers using common reference shapes. The precomputation, required by the first two steps, is a significant cost of the method; however, once performed, querying is two orders of magnitude faster. Extensions and variations of this methodology, for example, to handle more flexible and larger small-molecules are discussed.</p

Mechanism of Cancer Cell Death Induced by Depletion of an Essential Replication Regulator

Background: Depletion of replication factors often causes cell death in cancer cells. Depletion of Cdc7, a kinase essential for initiation of DNA replication, induces cancer cell death regardless of its p53 status, but the precise pathways of cell death induction have not been characterized. Methodology/Principal Findings: We have used the recently-developed cell cycle indicator, Fucci, to precisely characterize the cell death process induced by Cdc7 depletion. We have also generated and utilized similar fluorescent cell cycle indicators using fusion with other cell cycle regulators to analyze modes of cell death in live cells in both p53-positive and-negative backgrounds. We show that distinct cell-cycle responses are induced in p53-positive and-negative cells by Cdc7 depletion. p53-negative cells predominantly arrest temporally in G2-phase, accumulating CyclinB1 and other mitotic regulators. Prolonged arrest at G2-phase and abrupt entry into aberrant M-phase in the presence of accumulated CyclinB1 are followed by cell death at the post-mitotic state. Abrogation of cytoplasmic CyclinB1 accumulation partially decreases cell death. The ATR-MK2 pathway is responsible for sequestration of CyclinB1 with 14-3-3s protein. In contrast, p53-positive cancer cells do not accumulate CyclinB1, but appear to die mostly through entry into aberrant S-phase after Cdc7 depletion. The combination of Cdc7 inhibition with known anti-cancer agents significantly stimulates cell death effects in cancer cells in a genotype-dependent manner, providing a strategic basis for future combination therapies