Agile Software Development Ecosystems
This book describes---in depth---the most important principles of Agile development: delivering value to the customer, focusing on individual developers and their skills, collaboration, an emphasis on producing working software, the critical contribution of technical excellence, and a willingness to change course when demands shift. All major Agile methods are presented: * Scrum, * Dynamic Systems Development Method, *CrystalMethods , * Feature-Driven Development, * Lean Development, * Extreme Programming, and* Adaptive Software Development. Throughout the book, case stories are used to illustrate how Agile practices empower success around the world in today's chaotic software development industry. Agile Software Development Ecosystems also examines how to determine your organization's Agile readiness, how to design a custom Agile methodology, and how to transform your company into a truly Agile organization.
Severe organ involvement in systemic sclerosis with diffuse scleroderma.
OBJECTIVE To determine the natural history and timing of severe involvement of the kidney, heart, lung, gastrointestinal (GI) tract, and skin in patients with systemic sclerosis (SSc) and diffuse cutaneous involvement. METHODS This study used the Pittsburgh Scleroderma Databank and included patients with diffuse scleroderma who were seen between January 1, 1972 and December 31, 1995. Patients had frequent follow-ups, and a 95% accountability for these patients was maintained. Severe organ involvement was defined as the presence of any of the following: 1) in the kidney, scleroderma "renal crisis"; 2) in the heart, cardiomyopathy, symptomatic pericarditis, or an arrhythmia requiring treatment; 3) in the lung, pulmonary fibrosis on chest radiograph and a forced vital capacity of <55% of predicted; 4) in the GI tract, malabsorption, repeated episodes of pseudoobstruction, or severe problems requiring hyperalimentation; and 5) in the skin, a modified Rodnan skin score >40. The timing from disease onset to survival for each case of severe organ involvement was determined. RESULTS Of the 953 patients with diffuse scleroderma, kidney involvement developed in 177 (19%), heart involvement in 143 (15%), lung involvement in 151 (16%), GI tract involvement in 74 (8%), and skin involvement in 233 (24%). Severe skin and kidney involvement occurred during the first 3 years in 70% of those who ever developed these problems throughout a mean of 10 years of followup. Severe heart, lung, and GI tract involvement developed during the first 3 years in 45-55% of those who were ever affected. The survival of patients with severe organ involvement was poor. The 9-year cumulative survival rate of all patients with severe organ involvement was 38%, compared with 72% in patients without such involvement (P < 0.0001). CONCLUSION This study demonstrates that severe organ involvement in SSc patients with diffuse scleroderma most often occurs early in the course of the disease. Survival for patients with severe organ involvement is markedly reduced. Patients should therefore be monitored very closely during the first 3 years of disease for signs and symptoms that may signal the subsequent development of severe organ damage. Potential disease-modifying therapies must be initiated early to modify the natural history of SSc and to improve survival. Patients who survive the first few years without developing severe organ involvement are less likely to develop such life-threatening involvement later in the disease course.
Tisagenlecleucel in Adult Relapsed or Refractory Diffuse Large B-Cell Lymphoma
Background Patients with diffuse large B-cell lymphoma that is refractory to primary and second-line therapies or that has relapsed after stem-cell transplantation have a poor prognosis. The chimeric antigen receptor (CAR) T-cell therapy tisagenlecleucel targets and eliminates CD19-expressing B cells and showed efficacy against B-cell lymphomas in a single-center, phase 2a study. Methods We conducted an international, phase 2, pivotal study of centrally manufactured tisagenlecleucel involving adult patients with relapsed or refractory diffuse large B-cell lymphoma who were ineligible for or had disease progression after autologous hematopoietic stem-cell transplantation. The primary end point was the best overall response rate (i.e., the percentage of patients who had a complete or partial response), as judged by an independent review committee. Results A total of 93 patients received an infusion and were included in the evaluation of efficacy. The median time from infusion to data cutoff was 14 ...
Analysis of the Coding Genome of Diffuse Large B-Cell Lymphoma
Diffuse large B-cell lymphoma (DLBCL) is the most common form of human lymphoma. Although a number of structural alterations have been associated with the pathogenesis of this malignancy, the full spectrum of genetic lesions that are present in the DLBCL genome, and therefore the identity of dysregulated cellular pathways, remains unknown. By combining next-generation sequencing and copy number analysis, we show that the DLBCL coding genome contains, on average, more than 30 clonally represented gene alterations per case. This analysis also revealed mutations in genes not previously implicated in DLBCL pathogenesis, including those regulating chromatin methylation (MLL2; 24% of samples) and immune recognition by T cells. These results provide initial data on the complexity of the DLBCL coding genome and identify novel dysregulated pathways underlying its pathogenesis.
Complex Networks: Structure, Robustness and Function
1. Introduction Part I. Random Network Models: 2. The Erdos-Renyi models 3. Observations in real-world networks 4. Models for complex networks 5. Growing network models Part II. Structure and Robustness of Complex Networks: 6. Distances in scale-free networks - the ultra small world 7. Self-similarity in complex networks 8. Distances in geographically embedded networks 9. The network's structure - the generating function method 10. Percolation on complex networks 11. Structure of random directed networks - the bow tie 12. Introducing weights - bandwidth allocation and multimedia broadcasting Part III. Network Function - Dynamics and Applications: 13. Optimization of the network structure 14. Epidemiological models 15. Immunization 16. Thermodynamic models on networks 17. Spectral properties, transport, diffusion and dynamics 18. Searching in networks 19. Biological networks and network motifs Part IV. Appendices References Index.
Oncogenic CARD11 Mutations in Human Diffuse Large B Cell Lymphoma
Diffuse large B cell lymphoma (DLBCL) is the most common form of non-Hodgkin's lymphoma. In the least curable (ABC) subtype of DLBCL, survival of the malignant cells is dependent on constitutive activation of the nuclear factor–κB (NF-κB) signaling pathway. In normal B cells, antigen receptor–induced NF-κB activation requires CARD11, a cytoplasmic scaffolding protein. To determine whether CARD11 contributes to tumorigenesis, we sequenced the CARD11 gene in human DLBCL tumors. We detected missense mutations in 7 of 73 ABC DLBCL biopsies (9.6%), all within exons encoding the coiled-coil domain. Experimental introduction of CARD11 coiled-coil domain mutants into lymphoma cell lines resulted in constitutive NF-κB activation and enhanced NF-κB activity upon antigen receptor stimulation. These results demonstrate that CARD11 is a bona fide oncogenein DLBCL, providing a genetic rationale for the development of pharmacological inhibitors of the CARD11 pathway for DLBCL therapy.
Made to Fit: How Practices Vary as They Diffuse
We extend research on the diffusion of corporate practices by providing a framework for studying practice variation during diffusion processes. Specifically, we theorize about how population-level mechanisms of diffusion link with organization-level mechanisms of implementation that lead to the adaptation of practices. We also identify technical, cultural, and political elements of fit (or misfit) between diffusing practices and adopters and analyze how the process of attaining fit across these elements can trigger different patterns of adaptation.
The complex network of global cargo ship movements
Transportation networks play a crucial role in human mobility, the exchange of goods and the spread of invasive species. With 90 per cent of world trade carried by sea, the global network of merchant ships provides one of the most important modes of transportation. Here, we use information about the itineraries of 16 363 cargo ships during the year 2007 to construct a network of links between ports. We show that the network has several features that set it apart from other transportation networks. In particular, most ships can be classified into three categories: bulk dry carriers, container ships and oil tankers. These three categories do not only differ in the ships' physical characteristics, but also in their mobility patterns and networks. Container ships follow regularly repeating paths whereas bulk dry carriers and oil tankers move less predictably between ports. The network of all ship movements possesses a heavy-tailed distribution for the connectivity of ports and for the loads transported on the links with systematic differences between ship types. The data analysed in this paper improve current assumptions based on gravity models of ship movements, an important step towards understanding patterns of global trade and bioinvasion.
Hyperbolic Geometry of Complex Networks
We develop a geometric framework to study the structure and function of complex networks. We assume that hyperbolic geometry underlies these networks, and we show that with this assumption, heterogeneous degree distributions and strong clustering in complex networks emerge naturally as simple reflections of the negative curvature and metric property of the underlying hyperbolic geometry. Conversely, we show that if a network has some metric structure, and if the network degree distribution is heterogeneous, then the network has an effective hyperbolic geometry underneath. We then establish a mapping between our geometric framework and statistical mechanics of complex networks. This mapping interprets edges in a network as noninteracting fermions whose energies are hyperbolic distances between nodes, while the auxiliary fields coupled to edges are linear functions of these energies or distances. The geometric network ensemble subsumes the standard configuration model and classical random graphs as two limiting cases with degenerate geometric structures. Finally, we show that targeted transport processes without global topology knowledge, made possible by our geometric framework, are maximally efficient, according to all efficiency measures, in networks with strongest heterogeneity and clustering, and that this efficiency is remarkably robust with respect to even catastrophic disturbances and damages to the network structure.
Graph Spectra for Complex Networks
Analyzing the behavior of complex networks is an important element in the design of new man-made structures such as communication systems and biologically engineered molecules. Because any complex network can be represented by a graph, and therefore in turn by a matrix, graph theory has become a powerful tool in the investigation of network performance. This self-contained book provides a concise introduction to the theory of graph spectra and its applications to the study of complex networks. Covering a range of types of graphs and topics important to the analysis of complex systems, this guide provides the mathematical foundation needed to understand and apply spectral insight to real-world systems. In particular, the general properties of both the adjacency and Laplacian spectrum of graphs are derived and applied to complex networks. An ideal resource for researchers and students in communications networking as well as in physics and mathematics.
Pinning a complex dynamical network to its equilibrium
It is now known that the complexity of network topology has a great impact on the stabilization of complex dynamical networks. In this work, we study the control of random networks and scale-free networks. Conditions are investigated for globally or locally stabilizing such networks. Our strategy is to apply local feedback control to a small fraction of network nodes. We propose the concept of virtual control for microscopic dynamics throughout the process with different pinning schemes for both random networks and scale-free networks. We explain the main reason why significantly less local controllers are required by specifically pinning the most highly connected nodes in a scale-free network than those required by the randomly pinning scheme, and why there is no significant difference between specifically and randomly pinning schemes for controlling random dynamical networks. We also study the synchronization phenomenon of controlled dynamical networks in the stabilization process, both analytically and numerically.
Influence maximization in complex networks through optimal percolation
The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Despite the vast use of heuristic strategies to identify influential spreaders, the problem remains unsolved. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. These are topologically tagged as low-degree nodes surrounded by hierarchical coronas of hubs, and are uncovered only through the optimal collective interplay of all the influencers in the network. The present theoretical framework may hold a larger degree of universality, being applicable to other hard optimization problems exhibiting a continuous transition from a known phase.
Vital nodes identification in complex networks
Real networks exhibit heterogeneous nature with nodes playing far different roles in structure and function. To identify vital nodes is thus very significant, allowing us to control the outbreak of epidemics, to conduct advertisements for e-commercial products, to predict popular scientific publications, and so on. The vital nodes identification attracts increasing attentions from both computer science and physical societies, with algorithms ranging from simply counting the immediate neighbors to complicated machine learning and message passing approaches. In this review, we clarify the concepts and metrics, classify the problems and methods, as well as review the important progresses and describe the state of the art. Furthermore, we provide extensive empirical analyses to compare well-known methods on disparate real networks, and highlight the future directions. In despite of the emphasis on physics-rooted approaches, the unification of the language and comparison with cross-domain methods would trigger interdisciplinary solutions in the near future.
A time-varying complex dynamical network model and its controlled synchronization criteria
Today, complex networks have attracted increasing attention from various fields of science and engineering. It has been demonstrated that many complex networks display various synchronization phenomena. In this note, we introduce a time-varying complex dynamical network model. We then further investigate its synchronization phenomenon and prove several network synchronization theorems. Especially, we show that synchronization of such a time-varying dynamical network is completely determined by the inner-coupling matrix, and by the eigenvalues and the corresponding eigenvectors of the coupling configuration matrix of the network.
Update of Neuropathology and Neurological Recovery After Traumatic Brain Injury
This review focuses on the potential for traumatic brain injury to evoke both focal and diffuse changes within the brain parenchyma, while considering the cellular constituents involved and the subcellular perturbations that contribute to their dysfunction. New insight is provided on the pathobiology of traumatically induced cell body injury and diffuse axonal damage. The consequences of axonal damage in terms of subsequent deafferentation and any potential retrograde cell death and atrophy are addressed. The regional and global metabolic sequelae are also considered. This detailed presentation of the neuropathological consequences of traumatic brain injury is used to set the stage for better appreciating the neurological recovery occurring after traumatic injury. Although the pathological and clinical effects of focal and diffuse damage are usually intermingled, the different clinical manifestations of recovery patterns associated with focal versus diffuse injuries are presented. The recognizable patterns of recovery, involving unconsciousness, posttraumatic confusion/amnesia, and postconfusional restoration, that typically occur across the full spectrum of diffuse injury are described, recognizing that the patient's long-term recovery may involve more idiosyncratic combinations of dysfunction. The review highlights the relationship of focal lesions to localizing syndromes that may be embedded in the evolving natural history of diffuse pathology. It is noted that injuries with primarily focal pathology do not necessarily follow a comparable pattern of recovery with distinct phases. Potential linkages of these recovery patterns to the known neuropathological sequelae of injury and various reparative mechanisms are considered and it is proposed that potential biological markers and newer imaging technologies will better define these linkages.
Actin antibody: the specific visualization of actin filaments in non-muscle cells.
Actin purified from mouse fibroblasts by sodium dodecyl sulfate gel electrophoresis was used as antigen to obtain an antibody in rabbits. The elicited antibody was shown to be specific for actin as judged by immunodiffusion and complement fixation against partially purified mouse fibroblast actin and highly purified chicken muscle actin. The antibody was used in indirect immunofluorescence to demonstrate by fluorescence light microscopy the distribution and pattern of actin-containing filaments in a variety of cell types. Actin filaments were shown to span the cell length or to concentrate in "focal points" in patterns characteristic for each individual cell.
Advantages of diffuse radiation for terrestrial ecosystem productivity
[1] Clouds and aerosols alter the proportion of diffuse radiation in global solar radiation reaching the Earth's surface. It is known that diffuse and direct beam radiation differ in the way they transfer through plant canopies and affect the summation of nonlinear processes like photosynthesis differently than what would occur at the leaf scale. We compared the relative efficiencies of canopy photosynthesis to diffuse and direct photosynthetically active radiation (PAR) for a Scots pine forest, an aspen forest, a mixed deciduous forest, a tallgrass prairie and a winter wheat crop. The comparison was based on the seasonal patterns of the parameters that define the canopy photosynthetic responses to diffuse PAR and those that define the responses to direct PAR. These parameters were inferred from half-hourly tower CO2 flux measurements. We found that: (1) diffuse radiation results in higher light use efficiencies by plant canopies; (2) diffuse radiation has much less tendency to cause canopy photosynthetic saturation; (3) the advantages of diffuse radiation over direct radiation increase with radiation level; (4) temperature as well as vapor pressure deficit can cause different responses in diffuse and direct canopy photosynthesis, indicating that their impacts on terrestrial ecosystem carbon assimilation may depend on radiation regimes and thus sky conditions. These findings call for different treatments of diffuse and direct radiation in models of global primary production, and studies of the roles of clouds and aerosols in global carbon cycle.
Adaptive synchronization of an uncertain complex dynamical network
This note further investigates the locally and globally adaptive synchronization of an uncertain complex dynamical network. Several network synchronization criteria are deduced. Especially, our hypotheses and designed adaptive controllers for network synchronization are rather simple in form. It is very useful for future practical engineering design. Moreover, numerical simulations are also given to show the effectiveness of our synchronization approaches.
Consensus clustering in complex networks
The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics.
Graph Spectra for Complex Networks
Analyzing the behavior of complex networks is an important element in the design of new man-made structures such as communication systems and biologically engineered molecules. Because any complex network can be represented by a graph, and therefore in turn by a matrix, graph theory has become a powerful tool in the investigation of network performance. This self-contained book provides a concise introduction to the theory of graph spectra and its applications to the study of complex networks. Covering a range of types of graphs and topics important to the analysis of complex systems, this guide provides the mathematical foundation needed to understand and apply spectral insight to real-world systems. In particular, the general properties of both the adjacency and Laplacian spectrum of graphs are derived and applied to complex networks. An ideal resource for researchers and students in communications networking as well as in physics and mathematics.
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