Counting Causal Paths in Big Times Series Data on Networks

Graph or network representations are an important foundation for data mining and machine learning tasks in relational data. Many tools of network analysis, like centrality measures, information ranking, or cluster detection rest on the assumption that links capture direct influence, and that paths represent possible indirect influence. This assumption is invalidated in time-stamped network data capturing, e.g., dynamic social networks, biological sequences or financial transactions. In such data, for two time-stamped links (A,B) and (B,C) the chronological ordering and timing determines whether a causal path from node A via B to C exists. A number of works has shown that for that reason network analysis cannot be directly applied to time-stamped network data. Existing methods to address this issue require statistics on causal paths, which is computationally challenging for big data sets. Addressing this problem, we develop an efficient algorithm to count causal paths in time-stamped network data. Applying it to empirical data, we show that our method is more efficient than a baseline method implemented in an OpenSource data analytics package. Our method works efficiently for different values of the maximum time difference between consecutive links of a causal path and supports streaming scenarios. With it, we are closing a gap that hinders an efficient analysis of big time series data on complex networks.Comment: 10 pages, 2 figure

Emerging trends in the epidemiology of West Nile and Usutu virus infections in Southern Europe

The epidemiology of West Nile (WNV) and Usutu virus (USUV) has changed dramatically over the past two decades. Since 1999, there have been regular reports of WNV outbreaks and the virus has expanded its area of circulation in many Southern European countries. After emerging in Italy in 1996, USUV has spread to other countries causing mortality in several bird species. In 2009, USUV seroconversion in horses was reported in Italy. Co-circulation of both viruses was detected in humans, horses and birds. The main vector of WNV and USUV in Europe is Culex pipiens, however, both viruses were found in native Culex mosquito species (Cx. modestus, Cx. perexiguus). Experimental competence to transmit the WNV was also proven for native and invasive mosquitoes of Aedes and Culex genera (Ae. albopictus, Ae. detritus, Cx. torrentium). Recently, Ae. albopictus and Ae. japonicus naturally-infected with USUV were reported. While neuroinvasive human WNV infections are well-documented, USUV infections are sporadically detected. However, there is increasing evidence of a role of USUV in human disease. Seroepidemiological studies showed that USUV circulation is more common than WNV in some endemic regions. Recent data showed that WNV strains detected in humans, horses, birds, and mosquitoes mainly belong to lineage 2. In addition to European USUV lineages, some reports indicate the presence of African USUV lineages as well. The trends in WNV/USUV range and vector expansion are likely to continue in future years. This mini-review provides an update on the epidemiology of WNV and USUV infections in Southern Europe within a multidisciplinary "One Health" context

Analysis of cardiac manifestation and treatment of multisystem inflammatory syndrome in children related to SARS-CoV-2

Cardiovascular manifestations are common (35–100%) in the multisystem inflammatory syndrome in children. Our study aimed to analyze treatment impact and cardiovascular involvement in patients with multisystem inflammatory syndrome in children. The retrospective cohort included 81 patients treated between April 2020 and December 2021 (9.3±4.6 years). Elevated cardiac troponin I and pro-B-type natriuretic peptide were observed in 34.2% and 88.5% of patients, respectively. Myocardial dysfunction was observed in 50.6%. Children older than 10 years had a 4-fold increased risk of myocardial dysfunction (odds ratio [OR] 3.6, 95% confidence interval [CI] 1.4-8.9; p=0.006). A moderate negative correlation was proved between left ventricle ejection fraction and C-reactive protein (rr = - 0.48; p < 0.001). More than one-fifth of the patients presented with shock. Coronary artery dilatation was observed in 6.2% of patients. Mild pericardial effusion was detected in 27.1% of children. On standard electrocardiogram, 52.6% of children had negative T waves in the inferior and/or precordial leads; transient QTc prolongation was registered in 43% of patients. Treatment failure was observed in 19 patients. Patients initially treated with intravenous immunoglobulins had 10-fold higher chances for treatment failure than patients treated with corticosteroids (OR 10.6, 95% CI 3,18 – 35.35; p < 0.001). Cardiovascular manifestations were observed in more than half of the patients, with acute myocardial dysfunction being the most common, especially in children older than 10 years. We established a negative association between the degree of elevation of inflammatory markers and left ventricular ejection fraction. Patients treated with intravenous immunoglobulins who had cardiovascular manifestations had treatment failures more frequently than patients treated with corticosteroids

Counting Causal Paths in Big Time Series Data on Networks

Graph or network representations are an important foundation for data mining and machine learning tasks in relational data. Many tools of network analysis, like centrality measures, information ranking, or cluster detection rest on the assumption that links capture direct influence, and that paths represent possible indirect influence. This assumption is invalidated in time-stamped network data capturing, e.g., dynamic social networks, biological sequences or financial transactions. In such data, for two time-stamped links (A,B) and (B,C) the chronological ordering and timing determines whether a causal path from node A via B to C exists. A number of works has shown that for that reason network analysis cannot be directly applied to time-stamped network data. Existing methods to address this issue require statistics on causal paths, which is computationally challenging for big data sets. Addressing this problem, we develop an efficient algorithm to count causal paths in time-stamped network data. Applying it to empirical data, we show that our method is more efficient than a baseline method implemented in an OpenSource data analytics package. Our method works efficiently for different values of the maximum time difference between consecutive links of a causal path and supports streaming scenarios. With it, we are closing a gap that hinders an efficient analysis of big time series data on complex networks

Analysis and Visualisation of Time Series Data on Networks with Pathpy

The Open Source software package pathpy, available at https://www.pathpy.net, implements statistical techniques to learn optimal graphical models for the causal topology generated by paths in time-series data. Operationalizing Occam's razor, these models balance model complexity with explanatory power for empirically observed paths in relational time series. Standard network analysis is justified if the inferred optimal model is a first-order network model. Optimal models with orders larger than one indicate higher-order dependencies and can be used to improve the analysis of dynamical processes, node centralities and clusters

Decomposition aided attention-based recurrent neural networks for multistep ahead time-series forecasting of renewable power generation

Renewable energy plays an increasingly important role in our future. As fossil fuels become more difficult to extract and effectively process, renewables offer a solution to the ever-increasing energy demands of the world. However, the shift toward renewable energy is not without challenges. While fossil fuels offer a more reliable means of energy storage that can be converted into usable energy, renewables are more dependent on external factors used for generation. Efficient storage of renewables is more difficult often relying on batteries that have a limited number of charge cycles. A robust and efficient system for forecasting power generation from renewable sources can help alleviate some of the difficulties associated with the transition toward renewable energy. Therefore, this study proposes an attention-based recurrent neural network approach for forecasting power generated from renewable sources. To help networks make more accurate forecasts, decomposition techniques utilized applied the time series, and a modified metaheuristic is introduced to optimized hyperparameter values of the utilized networks. This approach has been tested on two real-world renewable energy datasets covering both solar and wind farms. The models generated by the introduced metaheuristics were compared with those produced by other state-of-the-art optimizers in terms of standard regression metrics and statistical analysis. Finally, the best-performing model was interpreted using SHapley Additive exPlanations

Marine Vessel Classification and Multivariate Trajectories Forecasting Using Metaheuristics-Optimized eXtreme Gradient Boosting and Recurrent Neural Networks

Maritime vessels provide a wealth of data concerning location, trajectories, and speed. However, while these data are meticulously monitored and logged to maintain course, they can also provide a wealth of meta information. This work explored the potential of data-driven techniques and applied artificial intelligence (AI) to tackle two challenges. First, vessel classification was explored through the use of extreme gradient boosting (XGboost). Second, vessel trajectory time series forecasting was tackled through the use of long-short-term memory (LSTM) networks. Finally, due to the strong dependence of AI model performance on proper hyperparameter selection, a boosted version of the well-known particle swarm optimization (PSO) algorithm was introduced specifically for tuning the hyperparameters of the models used in this study. The introduced methodology was applied to real-world automatic identification system (AIS) data for both marine vessel classification and trajectory forecasting. The performance of the introduced Boosted PSO (BPSO) was compared to contemporary optimizers and showed promising outcomes. The XGBoost model tuned using boosted PSO attained an overall accuracy of 99.72% for the vessel classification problem, while the LSTM model attained a mean square error (MSE) of 0.000098 for the marine trajectory prediction challenge. A rigid statistical analysis of the classification model was performed to validate outcomes, and explainable AI principles were applied to the determined best-performing models, to gain a better understanding of the feature impacts on model decisions

The Relative Preservation of the Central Retinal Layers in Leber Hereditary Optic Neuropathy

(1) Background: The purpose of this study was to evaluate the thickness of retinal layers in Leber hereditary optic neuropathy (LHON) in the atrophic stage compared with presumably inherited bilateral optic neuropathy of unknown cause with the aim of seeing if any LHON-specific patterns exist. (2) Methods: 14 patients (24 eyes) with genetically confirmed LHON (LHON group) were compared with 13 patients (23 eyes) with negative genetic testing results (mtDNA + WES) and without identified etiology of bilateral optic atrophy (nonLHON group). Segmentation analysis of retinal layers in the macula and peripapillary RNFL (pRNFL) measurements was performed using Heidelberg Engineering Spectralis SD-OCT. (3) Results: In the LHON group, the thickness of ganglion cell complex (GCC) (retinal nerve fiber layer (RNFL)&mdash;ganglion cell layer (GCL)&mdash;inner plexiform layer (IPL)) in the central ETDRS (Early Treatment Diabetic Retinopathy Study) circle was significantly higher than in the nonLHON group (p &lt; 0.001). In all other ETDRS fields, GCC was thinner in the LHON group. The peripapillary RNFL (pRNFL) was significantly thinner in the LHON group in the temporal superior region (p = 0.001). Longitudinal analysis of our cohort during the follow-up time showed a tendency of thickening of the RNFL, GCL, and IPL in the LHON group in the central circle, as well as a small recovery of the pRNFL in the temporal region, which corresponds to the observed central macular thickening. (4) Conclusions: In LHON, the retinal ganglion cell complex thickness (RNFL-GCL-IPL) appears to be relatively preserved in the central ETDRS circle compared to nonLHON optic neuropathies in the chronic phase. Our findings may represent novel biomarkers as well as a structural basis for possible recovery in some patients with LHON