RhoT1 and Smad4 Are Correlated with Lymph Node Metastasis and Overall Survival in Pancreatic Cancer

Cancer cell invasion and metastasis are the most important adverse prognostic factors for pancreatic cancer. Identification of biomarkers associated with outcome of pancreatic cancer may provide new approaches and targets for anticancer therapy. The aim of this study is to examine the relationship between the expression of RhoT1, Smad4 and p16 and metastasis and survival in patients with pancreatic cancer. The analysis showed that the high cytoplasmic expression levels of RhoT1, Smad4 and p16 in pancreatic cancer tissues had significantly negative correlation with lymph node metastasis (LNM) (P = 0.017, P = 0.032, P = 0.042, respectively). However, no significant association was observed between perineural invasion (PNI) and the expression of above three proteins (all P>0.05). Additionally, the survival analysis showed that the low expression levels of RhoT1 and Smad4 were significantly associated with worse survival (P = 0.034, P = 0.047, respectively). In conclusion, these results indicated that the low-expression levels of RhoT1 and Smad4 were significantly associated with LNM and shorter survival. RhoT1 may be considered as a potential novel marker for predicting the outcome in patients with pancreatic cancer.     

Robustness of Oscillatory Behavior in Correlated Networks

Understanding network robustness against failures of network units is useful for preventing large-scale breakdowns and damages in real-world networked systems. The tolerance of networked systems whose functions are maintained by collective dynamical behavior of the network units has recently been analyzed in the framework called dynamical robustness of complex networks. The effect of network structure on the dynamical robustness has been examined with various types of network topology, but the role of network assortativity, or degree–degree correlations, is still unclear. Here we study the dynamical robustness of correlated (assortative and disassortative) networks consisting of diffusively coupled oscillators. Numerical analyses for the correlated networks with Poisson and power-law degree distributions show that network assortativity enhances the dynamical robustness of the oscillator networks but the impact of network disassortativity depends on the detailed network connectivity. Furthermore, we theoretically analyze the dynamical robustness of correlated bimodal networks with two-peak degree distributions and show the positive impact of the network assortativity.     

Fast Fragmentation of Networks Using Module-Based Attacks

In the multidisciplinary field of Network Science, optimization of procedures for efficiently breaking complex networks is attracting much attention from a practical point of view. In this contribution, we present a module-based method to efficiently fragment complex networks. The procedure firstly identifies topological communities through which the network can be represented using a well established heuristic algorithm of community finding. Then only the nodes that participate of inter-community links are removed in descending order of their betweenness centrality. We illustrate the method by applying it to a variety of examples in the social, infrastructure, and biological fields. It is shown that the module-based approach always outperforms targeted attacks to vertices based on node degree or betweenness centrality rankings, with gains in efficiency strongly related to the modularity of the network. Remarkably, in the US power grid case, by deleting 3% of the nodes, the proposed method breaks the original network in fragments which are twenty times smaller in size than the fragments left by betweenness-based attack.     

Optimal Allocation of Node Capacity in Cascade-Robustness Networks

The robustness of large scale critical infrastructures, which can be modeled as complex networks, is of great significance. One of the most important means to enhance robustness is to optimize the allocation of resources. Traditional allocation of resources is mainly based on the topology information, which is neither realistic nor systematic. In this paper, we try to build a framework for searching for the most favorable pattern of node capacity allocation to reduce the vulnerability to cascading failures at a low cost. A nonlinear and multi-objective optimization model is proposed and tackled using a particle swarm optimization algorithm (PSO). It is found that the network becomes more robust and economical when less capacity is left on the heavily loaded nodes and the optimized network performs better resisting noise. Our work is helpful in designing a robust economical network.     

Range-Wide Piping Plover Survival: Correlated Patterns and Temporal Declines

ABSTRACT Geographically isolated breeding populations of migratory shorebirds may be demographically connected through shared nonbreeding habitats. We used long-term (1998–2008) mark-recapture data on piping plovers (Charadrius melodus) collected from 7 separate studies located throughout North America to conduct a range-wide analysis of after hatch year apparent survival (φ_AHY). Our objectives were to compare concurrent survival estimates from disparate breeding sites and determine whether estimates followed similar trends or were correlated among breeding populations with shared wintering grounds. Average survival estimates were higher for Great Plains populations (range = 0.69–0.81) than for Great Lakes and Atlantic Coast populations (range = 0.56–0.71). Linear trend models indicated that apparent survival declined in 4 out of 7 populations, was unchanged in 3, and was generally highest among Great Plains populations. Based on a post hoc analysis, we found evidence of correlated year-to-year fluctuations in annual survival among populations wintering primarily along the southeastern United States Atlantic Coast and Gulf Coast. Our results indicate shared overwintering or stopover sites may influence annual variation in survival among geographically disparate breeding populations. Declines in piping plover survival are a cause for concern, and our results highlight the need for conservation efforts to include habitat used during the migratory and wintering periods.     

Early Social Networks Predict Survival in Wild Bottlenose Dolphins

A fundamental question concerning group-living species is what factors influence the evolution of sociality. Although several studies link adult social bonds to fitness, social patterns and relationships are often formed early in life and are also likely to have fitness consequences, particularly in species with lengthy developmental periods, extensive social learning, and early social bond-formation. In a longitudinal study of bottlenose dolphins (Tursiops sp.), calf social network structure, specifically the metric eigenvector centrality, predicted juvenile survival in males. Additionally, male calves that died post-weaning had stronger ties to juvenile males than surviving male calves, suggesting that juvenile males impose fitness costs on their younger counterparts. Our study indicates that selection is acting on social traits early in life and highlights the need to examine the costs and benefits of social bonds during formative life history stages.     

Multi-Parametric Clustering for Sensor Node Coordination in Cognitive Wireless Sensor Networks

The deployment of wireless sensor networks for healthcare applications have been motivated and driven by the increasing demand for real-time monitoring of patients in hospital and large disaster response environments. A major challenge in developing such sensor networks is the need for coordinating a large number of randomly deployed sensor nodes. In this study, we propose a multi-parametric clustering scheme designed to aid in the coordination of sensor nodes within cognitive wireless sensor networks. In the proposed scheme, sensor nodes are clustered together based on similar network behaviour across multiple network parameters, such as channel availability, interference characteristics, and topological characteristics, followed by mechanisms for forming, joining and switching clusters. Extensive performance evaluation is conducted to study the impact on important factors such as clustering overhead, cluster joining estimation error, interference probability, as well as probability of reclustering. Results show that the proposed clustering scheme can be an excellent candidate for use in large scale cognitive wireless sensor network deployments with high dynamics.     

Node vulnerability under finite perturbations in complex networks

A measure to quantify vulnerability under perturbations (attacks, failures, large fluctuations) in ensembles (networks) of coupled dynamical systems is proposed. Rather than addressing the issue of how the network properties change upon removal of elements of the graph (the strategy followed by most of the existing methods for studying the vulnerability of a network based on its topology), here a dynamical definition of vulnerability is introduced, referring to the robustness of a collective dynamical state to perturbing events occurring over a fixed topology. In particular, we study how the collective (synchronized) dynamics of a network of chaotic units is disrupted under the action of a finite size perturbation on one of its nodes. Illustrative examples are provided for three systems of identical chaotic oscillators coupled according to three distinct well-known network topologies. A quantitative comparison between the obtained vulnerability rankings and the classical connectivity/centrality rankings is made that yields conclusive results. Possible applications of the proposed strategy and conclusions are also discussed.     

Internal Node and Shortcut Based Routing with Guaranteed Delivery in Wireless Networks

Several localized position based routing algorithms for wireless networks were described recently. In greedy routing algorithm (that has close performance to the shortest path algorithm, if successful), sender or node S currently holding the message m forwards m to one of its neighbors that is the closest to destination. The algorithm fails if S does not have any neighbor that is closer to destination than S. FACE algorithm guarantees the delivery of m if the network, modeled by unit graph, is connected. GFG algorithm combines greedy and FACE algorithms. Greedy algorithm is applied as long as possible, until delivery or a failure. In case of failure, the algorithm switches to FACE algorithm until a node closer to destination than last failure node is found, at which point greedy algorithm is applied again. Past traffic does not need to be memorized at nodes. In this paper we further improve the performance of GFG algorithm, by reducing its average hop count. First we improve the FACE algorithm by adding a sooner-back procedure for earlier escape from FACE mode. Then we perform a shortcut procedure at each forwarding node S. Node S uses the local information available to calculate as many hops as possible and forwards the packet to the last known hop directly instead of forwarding it to the next hop. The second improvement is based on the concept of dominating sets. Each node in the network is classified as internal or not, based on geographic position of its neighboring nodes. The network of internal nodes defines a connected dominating set, i.e., and each node must be either internal or directly connected to an internal node. In addition, internal nodes are connected. We apply several existing definitions of internal nodes, namely the concepts of intermediate, inter-gateway and gateway nodes. We propose to run GFG routing, enhanced by shortcut procedure, on the dominating set, except possibly the first and last hops. The performance of proposed algorithms is measured by comparing its average hop count with hop count of the basic GFG algorithm and the benchmark shortest path algorithm, and very significant improvements were obtained for low degree graphs. More precisely, we obtained localized routing algorithm that guarantees delivery and has very low excess in terms of hop count compared to the shortest path algorithm. The experimental data show that the length of additional path (in excess of shortest path length) can be reduced to about half of that of existing GFG algorithm.     

Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms

We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN) are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox) is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart), which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart’s predicted risk groups turn out to be inverted, an example of crossing survival curves. Cross-validation shows that all three models exhibit crossing of some survival curves on this small data set but that the ANN model manages the best separation of groups in terms of median survival time before such crossings. The conclusion is that optimizing the area under the survival curve is a viable approach to identify risk groups. Training ANNs to optimize this area combines two key strengths from both prognostic indices and Rpart. First, a desired minimum group size can be specified, as for a prognostic index. Second, the ability to utilize non-linear effects among the covariates, which Rpart is also able to do.     

Beclin 1 Deficiency Correlated with Lymph Node Metastasis,Predicts a Distinct Outcome in Intrahepatic and Extrahepatic Cholangiocarcinoma

Autophagy can be tumor suppressive as well as promotive in regulation of tumorigenesis and disease progression. Accordingly, the prognostic significance of autophagy key regulator Beclin 1 was varied among different tumors. Here, we detected the clinicopathological and prognostic effect of Beclin 1 in the subtypes of intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC). Beclin 1 expression level was detected by immunohistochemistry staining in 106 ICC and 74 ECC patients. We found that Beclin 1 was lowly expressed in 126 (70%) cholangiocarcinoma patients, consist of 72 ICC and 54 ECC. Moreover, the cholangiocarcinoma patients with lymph node metastasis (N1) had a lower Beclin 1 level than that of N0 subgroup (P=0.012). However, we did not detect any correlations between Beclin 1 and other clinicopathological features, including tumor subtypes, vascular invasion, HBV infection, liver cirrhosis, cholecystolithiasis and TNM stage. Survival analysis showed that, compared with the high expression subset, Beclin 1 low expression was correlated with a poorer 3-year progression-free survival (PFS, 69.1% VS 46.8%, P=041) for cholangiocarcinoma. Importantly, our stratified univariate and multivariate analysis confirmed that Beclin 1 lowly expressed ICC had an inferior PFS as well as overall survival than ECC, particularly than that of Beclin 1 highly expressed ECC patients. Thus, our study demonstrated that Beclin 1low expression, correlated with lymph node metastasis, and might be a negative prognostic biomarker for cholangiocarcinoma. Combined Beclin 1 level with the anatomical location might lead to refined prognosis for the subtypes of ICC and ECC.     

Regulation of Survival Networks in Senescent Cells: From Mechanisms to Interventions

Cellular senescence is a state of stable cell cycle arrest arising in response to DNA and mitochondrial damages. Senescent cells undergo morphological, structural and functional changes that are influenced by a number of variables, including time, stress, tissue, and cell type. The heterogeneity of the senescent phenotype is exemplified by the many biological properties that senescent cells can cover. The advent of innovative model organisms has demonstrated a functional role of senescent cells during embryogenesis, tissue remodeling, tumorigenesis and aging. Importantly, prolonged and aberrant persistence of senescent cells is often associated with tissue dysfunction and pathology, and is partially the consequence of mechanisms that enhance survival and resistance to cell death. Here, we describe the main molecular players involved in promoting survival of senescent cells, with particular emphasis on the regulation of senescence-associated anti-apoptotic pathways. We discuss the consequences these pathways have in providing resistance to intrinsic and extrinsic pro-apoptotic signals. Finally, we highlight the importance of these pathways in the development of targets for senolytic interventions.     

Modulation of HJURP (Holliday Junction-Recognizing Protein) Levels Is Correlated with Glioblastoma Cells Survival

Background

Diffuse astrocytomas are the most common type of primary brain cancer in adults. They present a wide variation in differentiation and aggressiveness, being classified into three grades: low-grade diffuse astrocytoma (grade II), anaplastic astrocytoma (grade III) and glioblastoma multiforme (grade IV), the most frequent and the major lethal type. Recent studies have highlighted the molecular heterogeneity of astrocytomas and demonstrated that large-scale analysis of gene expression could help in their classification and treatment. In this context, we previously demonstrated that HJURP, a novel protein involved in the repair of DNA double-strand breaks, is highly overexpressed in glioblastoma.

Methodology/Principal Findings

Here we show that HJURP is remarkably overexpressed in a cohort composed of 40 patients with different grade astrocytomas. We also observed that tumors presenting the higher expression levels of HJURP are associated with poor survival prognosis, indicating HJURP overexpression as an independent prognostic factor of death risk for astrocytoma patients. More importantly, we found that HJURP knockdown strongly affects the maintenance of glioblastoma cells in a selective manner. Glioblastoma cells showed remarkable cell cycle arrest and premature senescence that culminated in elevated levels of cell death, differently from non-tumoral cells that were minimally affected.

Conclusions

These data suggest that HJURP has an important role in the maintenance of extremely proliferative cells of high-grade gliomas and point to HJURP as a potential therapeutic target for the development of novel treatments for glioma patients.     

The Activation Pattern of Blood Leukocytes in Head and Neck Squamous Cell Carcinoma Is Correlated to Survival

Head and neck squamous cell carcinoma (HNSCC) is known to cause substantial immunosuppression. The present study was designed to characterize blood leukocyte activation in HNSCC and to investigate if the individual activation pattern could be related to tumor progress and survival. The leukocyte activation profile of HNSCC patients and healthy controls was assessed with flow cytometry. HNSCC patients displayed increased numbers of monocytes, neutrophils and total leukocytes as well as an enhanced neutrophil/lymphocyte ratio. In addition, patients had a higher percentage of CD69⁺, CD71⁺ and CD98⁺ T cell subsets and NK cells, and a reduced expression of L-selectin in CD14^highCD16⁺ monocytes and neutrophils, when compared to controls. These changes could be correlated to both tumor burden and spread to lymph nodes. Among the cancer patients an increased neutrophil/lymphocyte ratio, a low neutrophil and CD14^high CD16⁺ monocyte activation state and an elevated CD4/CD8 ratio were related to poor survival. In contrast, a high percentage of CD98⁺ Th cells appeared to be associated with a better outcome. Taken together, the present data indicate that HNSCC causes activation of blood leukocytes and that the individual activation pattern can be linked to prognosis.     

Piwil 2 Expression Is Correlated with Disease-Specific and Progression-Free Survival of Chemotherapy-Treated Bladder Cancer Patients

Piwi-like 2 (Piwil 2) belongs to the family of Argonaute genes/proteins. The expression of Piwil 2 is associated with stem cells. A role in tumorigenesis and/or tumor progression is proposed for different cancers but not yet for bladder cancer (BCa). We investigated Piwil 2 expression by immunohistochemistry in a cohort of 202 BCa patients treated by cystectomy and adjuvant chemotherapy. The association between Piwil 2 expression and disease-specific (DSS) or progression-free survival (PFS) was calculated using Kaplan-Meier analyses and univariate/multivariate Cox regression hazard models. In a multivariate Cox regression analysis, Piwil 2 expression, either in the cytoplasm or the nucleus, was significantly associated with DSS and PFS. A weak cytoplasmic staining pattern was associated with poor DSS and tumor progression (relative risk [RR] = 2.7, P = 0.004, and RR = 2.4, P = 0.027). Likewise, absent nuclear Piwil 2 immunoreactivity was associated with poor DSS and tumor progression (RR = 2.3, P = 0.023, and RR = 2.2, P = 0.022). BCa patients whose tumors exhibited a combination of weak cytoplasmic and absent nuclear immunoreactivity had a 6-fold increased risk of tumor-related death (P = 0.005) compared with patients with strong expression. Considering only patients with high-grade G3 tumors, a 7.8-fold risk of tumor-associated death and a 3.6-fold risk of tumor progression were detected independently of the histologic tumor subtype or the chemotherapy regimen. In summary, a combination of weak cytoplasmic and absent nuclear expression of Piwil 2 is significantly associated with an increased risk of DSS and tumor progression. This indicates that Piwil 2 could be a valuable prognostic marker for high-risk BCa patients.     

Terrorist Attacks Escalate in Frequency and Fatalities Preceding Highly Lethal Attacks

Highly lethal terrorist attacks, which we define as those killing 21 or more people, account for 50% of the total number of people killed in all terrorist attacks combined, yet comprise only 3.5% of terrorist attacks. Given the disproportionate influence of these incidents, uncovering systematic patterns in attacks that precede and anticipate these highly lethal attacks may be of value for understanding attacks that exact a heavy toll on life. Here we examined whether the activity of terrorist groups escalates–both in the number of people killed per attack and in the frequency of attacks–leading up to highly lethal attacks. Analyses of terrorist attacks drawn from a state-of-the-art international terrorism database (The Global Terrorism Database) showed evidence for both types of escalation leading up to highly lethal attacks, though complexities to the patterns emerged as well. These patterns of escalation do not emerge among terrorist groups that never commit a highly lethal attack.