Local dependency in networks

Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network's nodes from the structure in local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effectiveness due to the locality of our approach and also high quality node ranking comparable to PageRank.Web of Science25229328

Inflammation time-axis in aseptic loosening of total knee arthroplasty: A preliminary study

Objective Aseptic loosening (AL) is the most frequent long-term reason for revision of total knee arthroplasty (TKA) affecting about 15–20% patients within 20 years after the surgery. Although there is a solid body of evidence about the crucial role of inflammation in the AL pathogenesis, scared information on inflammation signature and its time-axis in tissues around TKA exists. Design The inflammation protein signatures in pseudosynovial tissues collected at revision surgery from patients with AL (AL, n = 12) and those with no clinical/radiographic signs of AL (non-AL, n = 9) were investigated by Proximity Extension Assay (PEA)-Immunoassay and immunohistochemistry. Results AL tissues had elevated levels of TNF-family members sTNFR2, TNFSF14, sFasL, sBAFF, cytokines/chemokines IL8, CCL2, IL1RA/IL36, sIL6R, and growth factors sAREG, CSF1, comparing to non-AL. High interindividual variability in protein levels was evident particularly in non-AL. Levels of sTNFR2, sBAFF, IL8, sIL6R, and MPO discriminated between AL and non-AL and were associated with the time from index surgery, suggesting the cumulative character of inflammatory osteolytic response to prosthetic byproducts. The source of elevated inflammatory molecules was macrophages and multinucleated osteoclast-like cells in AL and histiocytes and osteoclast-like cells in non-AL tissues, respectively. All proteins were present in higher levels in osteoclast-like cells than in macrophages. Conclusions Our study revealed a differential inflammation signature between AL and non-AL stages of TKA. It also highlighted the unique patient’s response to TKA in non-AL stages. Further confirmation of our preliminary results on a larger cohort is needed. Analysis of the time-axis of processes ongoing around TKA implantation may help to understand the mechanisms driving periprosthetic bone resorption needed for diagnostic/preventative strategies.Web of Science148art. no. E022105

Lokální vlastnosti sociálních sítí

Due to the development of information technology, we have a large amount of data available in recent years, perhaps from all areas of research. Many real world data and processes have a network structure and can usefully be represented as graphs. The network representation of complex systems provides a useful model for studying many processes, including biological, technological and social networks. Network analysis focuses on the relations among the nodes exploring the properties of each network. Owing to enormous and sustained growth of real world networks, the current trend in analyzing networks is to focus on local methods. This thesis is focused on investigating local characteristics of social networks and application of local methods on large datasets. Two novel local measures for node importance in the network are presented. The first one can be used for ranking of nodes or as an approach to transforming an unweighted network to weighted one, or to assist community detection. The second one can be utilized for network sampling or graph construction. Another topic of this thesis is also the analysis of large co-authorship datasets in order to develop a model capable of generating realistic graphs.Vzhledem k rozvoji informačních technologií máme v posledních letech k dispozici velké množství dat, možná ze všech oblastí výzkumu. Mnoho dat a procesů v reálném světě má síťovou strukturu a může být užitečně reprezentováno jako graf. Síťová reprezentace komplexních systémů poskytuje užitečný model pro studium mnoha procesů, včetně biologických, technologických a sociálních sítí. Analýza sítě se zaměřuje na vztahy mezi uzly a zkoumaní vlastností dané sítě. Vzhledem k trvalému růstu reálných sítí je současný trend v analýze sítí zaměřen na lokální metody. Tato práce se zabývá zkoumáním lokálních charakteristik sociálních sítí a aplikací lokálních metod na velké datové sady. Jsou zde představeny dvě nové lokální metriky pro důležitost uzlů v síti. První může být použita pro klasifikaci uzlů nebo k převodu nevážené sítě na váženou, a také při detekci komunit. Druhá může být použita pro vzorkování síťových dat nebo konstrukci grafů. Dalším tématem práce je analýza rozsáhlých spoluautorských sítí s cílem vytvořit model schopný generovat realistické grafy.460 - Katedra informatikyvyhově

Local dependency in networks

Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network’s nodes from the structure in local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effectiveness due to the locality of our approach and also high quality node ranking comparable to PageRank

Modulatory effect of the Euro-Lupus low-dose intravenous cyclophosphamide regimen on circulating immune cells in systemic lupus erythematosus

A Euro-Lupus regimen of low-dose intravenous cyclophosphamide (CFA) is commonly used to treat severe organ manifestations of systemic lupus erythematosus (SLE), particularly lupus nephritis (LN). There are no data on the distributions and dynamics of immune cell populations in patients with various treatment outcomes. The circulating immune cells of 11 female SLE patients were assessed before and after Euro-Lupus regimen (cumulative dose of 3000 mg CFA) by flow cytometry together with those of 16 healthy women. A subanalysis was performed in LN patients who achieved complete remission (CR; n = 3), partial remission (PR; n = 4), and no response (NR; n = 2). In SLE, the Euro-Lupus regimen decreased the percentage and absolute count of B cells; increased the percentage of CD8(+) T cells, T regulatory cells, neutrophils, and monocyte subsets; and activated T and NK cells compared to healthy controls (P < 0.050). Patients with LN achieving CR had significantly lower proportions of CD27(+) B memory cells compared to poor responders (PR/NR, P = 0.035). The post-treatment percentages and absolute numbers of B cells, T cells, NK cells, monocytes, and neutrophils showed high inter-individual variability with no association with treatment outcome. Our pilot study revealed the dynamics of changes in immune cell populations in SLE patients during a Euro-Lupus regimen, mainly the lowering of B cells. In LN patients who achieved CR, a lower proportion of CD27(+) B memory cells was evident compared to poor responders (PR/NR). Further studies on usefulness of monitoring immune cells for treatment response prediction on larger cohorts are needed.Web of Science67642541

Quantitative assessment of informative immunophenotypic markers increases the diagnostic value of immunophenotyping in mature CD5-positive B-cell neoplasms

Background The data on the clinical utility of the quantitative assessment of immunophenotypes in distinguishing mature CD5-positive B-cell neoplasms is limited. The study aim was to assess the diagnostic value of the quantitative assessment of a panel of 18 markers and to identify the most informative ones. Methods The immunophenotype of the neoplastic population was determined in diagnostic specimens from 188 patients. BD FACSCanto II flow cytometer and FACSDiva software were used to analyze the positivity/negativity and mean fluorescence intensity (MFI) of the surface expression of 18 markers. Advanced data mining methods were used to define the key differential diagnostic features of CLL/SLL (chronic lymphocytic leukemia/small lymphocytic lymphoma), MCL (mantle cell lymphoma), and CD5+ MZL (marginal zone lymphoma). Results The most informative markers for the distinction of CLL/SLL, MCL, CD5+ MZL, including atypical cases, were the MFI values of CD79b, CD20, CD23, CD43, CD38, CD11c, FMC7, CD200, kappa light chain, and their combinations. CD23 and CD200 were the most discriminant between CLL/SLL and MCL and CD23 plus CD79b between CLL/SLL and CD5+ MZL. The quantitative analysis of the most informative markers failed to accurately distinguish MCL and CD5+ MZL. The study highlights the data mining methods for the analysis and selection of the most informative immunophenotypic markers and for the design of a predictive model (diagnostic classifier), minimizing the subjectivity of expert-based assessment. Conclusions Our data confirmed that the quantification of the expression of informative markers increases the diagnostic value of immunophenotyping in mature CD5+ B-cell neoplasms.Web of Science94458757

Dynamic changes in HLA-DR expression during short-term and long-term ibrutinib treatment in patients with chronic lymphocytic leukemia

There is the first evidence of changes in the kinetics of B cell antigen receptor (BCR) internalisation of neoplastic cells in chronic lymphocytic leukemia (CLL) after the short-term and long-term administration of ibrutinib. We aimed to assess the influence of short-term and long-term ibrutinib treatment on the HLA-DR expression on CLL cells, T cells and monocytes. The immunophenotyping of CLL and immune cells in peripheral blood was performed on 16 high-risk CLL patients treated with ibrutinib. After early ibrutinib administration, the HLA-DR expression on CLL cells reduced (P = 0.032), accompanied by an increase in CLL cell counts in peripheral blood (P = 0.001). In vitro culturing of CLL cells with ibrutinib also revealed the reduction in the HLA-DR expression at protein and mRNA levels (P < 0.01). The decrease in HLA-DR on CLL cells after the first month was followed by the gradual increase of its expression by the 12th month (P = 0.001). A one-month follow-up resulted in elevated absolute counts of CD4(+) (P = 0.002) and CD8(+) (P < 0.001) T cells as well as CD4(+) and CD8(+) cells bearing HLA-DR (P < 0.01). The long-term administration of ibrutinib was associated with the increased numbers of CD4(+) bearing HLA-DR (P = 0.006) and elevation of HLA-DR expression on all monocyte subsets (P <= 0.004). Our results provide the first evidence of the time-dependent immunomodulatory effect of ibrutinib on CLL and T cells and monocytes. The clinical consequences of time-dependent changes in HLA-DR expression in ibrutinib treated patients deserve further investigation.Web of Science7211911