Generating Robust and Efficient Networks Under Targeted Attacks

Much of our commerce and traveling depend on the efficient operation of large scale networks. Some of those, such as electric power grids, transportation systems, communication networks, and others, must maintain their efficiency even after several failures, or malicious attacks. We outline a procedure that modifies any given network to enhance its robustness, defined as the size of its largest connected component after a succession of attacks, whilst keeping a high efficiency, described in terms of the shortest paths among nodes. We also show that this generated set of networks is very similar to networks optimized for robustness in several aspects such as high assortativity and the presence of an onion-like structure

Towards designing robust coupled networks

Natural and technological interdependent systems have been shown to be highly vulnerable due to cascading failures and an abrupt collapse of global connectivity under initial failure. Mitigating the risk by partial disconnection endangers their functionality. Here we propose a systematic strategy of selecting a minimum number of autonomous nodes that guarantee a smooth transition in robustness. Our method which is based on betweenness is tested on various examples including the famous 2003 electrical blackout of Italy. We show that, with this strategy, the necessary number of autonomous nodes can be reduced by a factor of five compared to a random choice. We also find that the transition to abrupt collapse follows tricritical scaling characterized by a set of exponents which is independent on the protection strategy

A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model.

Background: Autosomal dominant polycystic kidney disease (ADPKD) is the leading inheritable cause of end-stage renal disease (ESRD); however, the natural course of disease progression is heterogeneous between patients. This study aimed to develop a natural history model of ADPKD that predicted progression rates and long-term outcomes in patients with differing baseline characteristics. Methods: The ADPKD Outcomes Model (ADPKD-OM) was developed using available patient-level data from the placebo arm of the Tolvaptan Efficacy and Safety in Management of ADPKD and its Outcomes Study (TEMPO 3:4; ClinicalTrials.gov identifier NCT00428948). Multivariable regression equations estimating annual rates of ADPKD progression, in terms of total kidney volume (TKV) and estimated glomerular filtration rate, formed the basis of the lifetime patient-level simulation model. Outputs of the ADPKD-OM were compared against external data sources to validate model accuracy and generalisability to other ADPKD patient populations, then used to predict long-term outcomes in a cohort matched to the overall TEMPO 3:4 study population. Results: A cohort with baseline patient characteristics consistent with TEMPO 3:4 was predicted to reach ESRD at a mean age of 52 years. Most patients (85%) were predicted to reach ESRD by the age of 65 years, with many progressing to ESRD earlier in life (18, 36 and 56% by the age of 45, 50 and 55 years, respectively). Consistent with previous research and clinical opinion, analyses supported the selection of baseline TKV as a prognostic factor for ADPKD progression, and demonstrated its value as a strong predictor of future ESRD risk. Validation exercises and illustrative analyses confirmed the ability of the ADPKD-OM to accurately predict disease progression towards ESRD across a range of clinically-relevant patient profiles. Conclusions: The ADPKD-OM represents a robust tool to predict natural disease progression and long-term outcomes in ADPKD patients, based on readily available and/or measurable clinical characteristics. In conjunction with clinical judgement, it has the potential to support decision-making in research and clinical practice

Attack Resilience of the Evolving Scientific Collaboration Network

Stationary complex networks have been extensively studied in the last ten years. However, many natural systems are known to be continuously evolving at the local (“microscopic”) level. Understanding the response to targeted attacks of an evolving network may shed light on both how to design robust systems and finding effective attack strategies. In this paper we study empirically the response to targeted attacks of the scientific collaboration networks. First we show that scientific collaboration network is a complex system which evolves intensively at the local level – fewer than 20% of scientific collaborations last more than one year. Then, we investigate the impact of the sudden death of eminent scientists on the evolution of the collaboration networks of their former collaborators. We observe in particular that the sudden death, which is equivalent to the removal of the center of the egocentric network of the eminent scientist, does not affect the topological evolution of the residual network. Nonetheless, removal of the eminent hub node is exactly the strategy one would adopt for an effective targeted attack on a stationary network. Hence, we use this evolving collaboration network as an experimental model for attack on an evolving complex network. We find that such attacks are ineffectual, and infer that the scientific collaboration network is the trace of knowledge propagation on a larger underlying social network. The redundancy of the underlying structure in fact acts as a protection mechanism against such network attacks

Are we there yet?:An update on transitional care in rheumatology

Abstract Significant progress has been made in the understanding of transitional care in rheumatology over the last few decades, yet universal implementation has not been realised and unmet needs continue to be reported. Possible explanations for this include lack of evidence as to which model is most effective; lack of attention to the multiple dimensions, stakeholders and systems involved in health transitions; and lack of consideration of the developmental appropriateness of transition interventions and the services/organisations/systems where such interventions are delivered. Successful transition has major implications to both the young people with juvenile-onset rheumatic disease and their families. Future research in this area will need to reflect both the multidimensional (biopsychosocial) and the multisystemic (multiple systems and stakeholders across personal/social/family support networks and health/social care/education systems). Only then will we be able to determine which aspects of transition readiness and service components influence which dimension. It is therefore imperative we continue to research and develop this area, involving both paediatric and adult rheumatology clinicians and researchers, remembering to look beyond both the condition and our discipline. Neither should we forget to tap into the exciting potential associated with digital technology to ensure further advances in transitional care are brought about in and beyond rheumatology

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas

Geochronological and geochemical data from fracture-filling calcites from the Lower Pedraforca thrust sheet (SE Pyrenees)

U-Pb dating using laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS), δ13C, δ18O, clumped isotopes and 87Sr/86Sr analysis, and electron microprobe have been applied to fracture-filling calcites and host carbonates from the Lower Pedraforca thrust sheet, in the SE Pyrenees. These data are used to determine the type and origin of migrating fluids, the evolution of the palaeohydrological system and timing of fracturing during the emplacement of this thrust sheet, as described in the article “From hydroplastic to brittle deformation: controls on fluid flow in fold and thrust belts. Insights from the Lower Pedraforca thrust sheet (SE Pyrenees)” – Marine and Petroleum Geology (2020). The integration of these data is also used to compare the fluid flow evolution of the Southern Pyrenees with that of other orogens worldwide and to generate a fluid flow model in fold and thrust belts. At a more local scale, the U-Pb dataset provides new absolute ages recording the deformation in the Lower Pedraforca thrust sheet, which was previously dated by means of indirect methods such as biostratigraphy of marine sediments and magnetostratigraphy of continental deposits

The Alpha Linolenic Acid Content of Flaxseed is Associated with an Induction of Adipose Leptin Expression

Dietary flaxseed has cardioprotective effects that may be achieved through its rich content of the omega-3 fatty acid, alpha linolenic acid (ALA). Because ALA can be stored in adipose tissue, it is possible that some of its beneficial actions may be due to effects it has on the adipose tissue. We investigated the effects of dietary flaxseed both with and without an atherogenic cholesterol-enriched diet to determine the effects of dietary flaxseed on the expression of the adipose cytokines leptin and adiponectin. Rabbits were fed one of four diets: a regular (RG) diet, or a regular diet with added 0.5% cholesterol (CH), or 10% ground flaxseed (FX), or both (CF) for 8 weeks. Levels of leptin and adiponectin expression were assessed by RT-PCR in visceral adipose tissue. Consumption of flaxseed significantly increased plasma and adipose levels of ALA. Leptin protein and mRNA expression were lower in CH animals and were elevated in CF animals. Changes in leptin expression were strongly and positively correlated with adipose ALA levels and inversely correlated with levels of en face atherosclerosis. Adiponectin expression was not significantly affected by any of the dietary interventions. Our data demonstrate that the type of fat in the diet as well as its caloric content can specifically influence leptin expression. The findings support the hypothesis that the beneficial cardiovascular effects associated with flaxseed consumption may be related to a change in leptin expression

Hierarchy measure for complex networks

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.Comment: 29 pages, 9 figures, 4 table