Event Mining Research Articles

Gene network analyses unveil possible molecular basis underlying drug-induced glaucoma

BackgroundDrug-induced glaucoma (DIG) is a kind of serious adverse drug reaction that can cause irreversible blindness. Up-to-date, the molecular mechanism of DIG largely remains unclear yet due to the medical complexity of glaucoma onset.MethodsIn this study, we conducted data mining of tremendous historical adverse drug events and genome-wide drug-regulated gene signatures to identify glaucoma-associated drugs. Upon these drugs, we carried out serial network analyses, including the weighted gene co-expression network analysis (WGCNA), to illustrate the gene interaction network underlying DIG. Furthermore, we applied pathogenic risk assessment to discover potential biomarker genes for DIG.ResultsAs the results, we discovered 13 highly glaucoma-associated drugs, a glaucoma-related gene network, and 55 glaucoma-susceptible genes. These genes likely played central roles in triggering DIGs via an integrative mechanism of phototransduction dysfunction, intracellular calcium homeostasis disruption, and retinal ganglion cell death. Further pathogenic risk analysis manifested that a panel of nine genes, particularly OTOF gene, could serve as potential biomarkers for early-onset DIG prognosis.ConclusionsThis study elucidates the possible molecular basis underlying DIGs systematically for the first time. It also provides prognosis clues for early-onset glaucoma and thus assists in designing better therapeutic regimens.

A data science approach to drug safety: Semantic and visual mining of adverse drug events from clinical trials of pain treatments

Clinical trials are the basis of Evidence-Based Medicine. Trial results are reviewed by experts and consensus panels for producing meta-analyses and clinical practice guidelines. However, reviewing these results is a long and tedious task, hence the meta-analyses and guidelines are not updated each time a new trial is published. Moreover, the independence of experts may be difficult to appraise. On the contrary, in many other domains, including medical risk analysis, the advent of data science, big data and visual analytics allowed moving from expert-based to fact-based knowledge. Since 12 years, many trial results are publicly available online in trial registries. Nevertheless, data science methods have not yet been applied widely to trial data.In this paper, we present a platform for analyzing the safety events reported during clinical trials and published in trial registries. This platform is based on an ontological model including 582 trials on pain treatments, and uses semantic web technologies for querying this dataset at various levels of granularity. It also relies on a 26-dimensional flower glyph for the visualization of the Adverse Drug Events (ADE) rates in 13 categories and 2 levels of seriousness. We illustrate the interest of this platform through several use cases and we were able to find back conclusions that were initially found during meta-analyses. The platform was presented to four experts in drug safety, and is publicly available online, with the ontology of pain treatment ADE.

Induced Seismic Events—Distribution of Ground Surface Displacements Based on InSAR Methods and Mogi and Yang Models

In this article, we present a possible approach to use satellite radar data for a complete description of the formation process of a subsidence trough resulting from an induced seismic event—a mining tremor. Our main goal was to verify whether SAR data allow for the calculation of the basic indicators for the trough (w—subsidence, T—trough slope, K—curvature, u—horizontal displacements, ε—horizontal deformations). We verified the extent to which the Mogi and Yang models can be fitted to match the actual displacements recorded after an induced seismic tremor. The calculations were performed for the Legnica-Glogow Copper Belt (LGCB) area in southwest Poland. Due to intensive mining operations and specific geological and tectonic conditions, the area shows a high level of induced seismic activity. Our detailed analysis focused on four powerful mining tremors: the first tremor occurred on 29 November 2016 (MW3.4), the second on 7 December 2017 (MW3.3), the next on 26 December 2017 (MW3.6) and the last tremor on 29 January 2019 (MW3.7). For each analyzed event, we determined the displacements based on the Differential Interferometric Synthetic Aperture Radar (DInSAR) method and Sentinel 1 synthetic aperture radar (SAR) data from two paths (22 and 73). Additionally, for the period from November 2014 to October 2020, we calculated the displacements using the Small Baseline Subset method (SBAS) time series method. In all cases, the tremor was followed by the development of long-lasting surface deformations. The obtained results allowed us to conclude that it is possible to calculate indicators that result from a specific induced mining event. Considering the full moment tensor and nature of the tremor source, we demonstrated that the Mogi and Yang models can be employed to describe the influence of an induced tremor on the surface in an area of mining activity. We also confirmed the global character of the influence of the reduced troposphere on SAR data calculations. Our conclusions indicate that accounting for the tropospheric correction does not distort horizontal and vertical displacement values in regions influenced by mining activity/tremors.

Book review: Piotr Rygus, Aleksandra Goniewicz "Black memory - mining sites of memory", Institute of Polish Thought Wojciech Korfantego, Katowice 2020, p. 166.

The article contains a review of a book published by the Institute of Polish Thought. Wojciecha Korfanty, touching upon the subject of mining memorials. In the three mining regions, the landscape is complemented by monuments, statues, monuments and plaques that serve to remind us of mining catastrophes, events and people whose often German-sounding names have been erased by history from memorial and even grave plaques. The book deals with legal and social aspects of the issue related to mining work memorials. The publication is the result of an educational and research project and constitutes a guidebook to these special places of memory.

Программа для интерактивной обработки сейсмических и инфразвуковых записей LOS

For many years, the Kola Division of the Geophysical Survey of the Russian Academy of Sciences carries out work on testing and implementation of modern techniques and algorithms for seismic and infrasonic data processing and event location. The KoD staff has developed several original algorithms that appeared to be useful for seismic and infrasonic event location and discrimination. In 2020, the LOS program was created. The most efficient tools for data processing and analysis, event location algorithms have been united in the program. The program also contains a modern mapping system and database. The following tools have been implemented: bandpass and adaptive filtration, polarization analysis and backazimuth computation for 3C stations, computation of backazimuths, and apparent velocities for seismic and infrasonic arrays (beamforming). To analyze records of infrasonic arrays the program has a cross-correlation tool, which enables to observe changes of signal’s backazimuths and apparent velocities in time. For seismic event location, the program uses two basic algorithms: minimization of origin time estimation residual and grid search based on generalized beamforming approach. These algorithms can be used in different combinations depending on the location scenario selected by a user. In addition, a new location algorithm based on a presentation of the seismic medium in a form of random graph where vertices correspond to points in the medium and edges are wave paths between the points, has been implemented. It can be useful for locating events in a substantially heterogeneous media, possibly with voids and cavities, as well as for taking into account the relief. This algorithm can be used, in particular, when locating events in mines using local mine seismic networks. The LOS program has been put into the practice of the Kola Division.

Local-Distance Seismic Event Relocation and Relative Magnitude Estimation, Applications to Mining Related Seismicity in the Powder River Basin, Wyoming

ABSTRACTRecent efforts to characterize small (Mw<3) seismic events at local distances have become more important because of the increased observation of human-triggered and induced seismicity and the need to advance nuclear explosion monitoring capabilities. The signals generated by low-magnitude seismic sources necessitate the use of nearby short-period observations, which are sensitive to local geological heterogeneity. Local to near-regional distance (<300 km) surface and shear waves can dominate short-period observations from small, shallow seismic sources. In this work, we utilize these observations to estimate precise, relative locations and magnitudes of ∼700 industrial mining events in Wyoming, using nearly 360,000 observations. The precise, relative location estimates (with formal location uncertainty estimates of less than 1 km) collapse a diffuse collection of mining events into discrete clusters associated with individual blasting operations. We also invert the cross-correlation amplitudes to estimate precise, relative moment magnitude estimates, which help validate and identify disparities in the event sizes reported by regional network catalogs. Joint use of multiple phases allows for the inclusion of more seismic events due to the increase in the number of observations. In some cases, using a single phase allowed us to relocate only 50% of the original reported seismic events within a cluster. Combining shear- and surface-wave phases increased the number of events to above 90% of the original events, allowing us to characterize a broader range of event sizes, source to station distances, and event distributions. This analysis takes a step toward making a fuller characterization of small industrial seismic events observed at local distances.

Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study.

BackgroundIndividuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation.ObjectiveIn our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health.MethodsWe expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months.ResultsOur study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19–related stress assessments.ConclusionsOur study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health.International Registered Report Identifier (IRRID)DERR1-10.2196/25775

Highway Event Detection Algorithm Based on Improved Fast Peak Clustering

Aiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data. The highway toll data are first analyzed, and a data cleaning method based on the sum of similar coefficients is proposed to process the original data. Next, to avoid the shortcomings of the excessive subjectivity of the original algorithm, an improved fast peak clustering algorithm is proposed. Finally, the improved algorithm is applied to highway traffic condition analysis and abnormal event mining to obtain more accurate and intuitive clustering results. Compared with two classical algorithms, namely, the k-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms, as well as the unimproved original fast peak clustering algorithm, the proposed algorithm is faster and more accurate and can reveal the complex relationships among massive data more efficiently. During the process of reforming the toll system, the algorithm can automatically and more efficiently analyze massive toll data and detect abnormal events, thereby providing a theoretical basis and data support for the operation monitoring and maintenance of highways.

On Public Sentiment and Topic Mining during the COVID-19 Pandemic Based on Sina Weibo Comment Data

In the wake of the COVID-19 outbreak, the public resorted to Sina Weibo as a major platform for the trend of the pandemic. Research on public sentiment and topic mining of major public sentiment events based on Sina Weibo’s comment data is important for understanding the trend of public opinions during major epidemic outbreaks. Based on classification of the Chinese language into emotion categories in psychology, we use open source tools to build naive Bayesian models to classify Weibo comments. Visualization of comment topics is achieved with word co-occurrence network methods. Commented topics are mined with the help of the latent Dirichlet distribution model. The results show that the psychological sentiment classification combined with the naive Bayesian model can reflect the evolvement of public sentiment during the epidemic, and that the latent Dirichlet distribution model and word co-occurrence network can effectively mine the topics of public concerns.

The assessment of the optimal time window for prediction of seismic hazard for longwall coal mining: the case study

The dynamic nature of rock mass damage during mining activity generates seismic events. This article shows, how the time window for the database influences on the actual status of seismic hazard for the longwall mining area in one of Polish coal mines using Gutenberg–Richter law. A time window of 10–90 days was assumed with similar or shorter prediction times forecast on its basis. Additionally, for each seismic database the hazard prediction accuracy was determined. The analysis shows that the 10- and 20-day base periods are too short for prediction purposes. The higher-energy seismic events sometimes do not occur within such a short period of time, preventing regression analysis and parameter b determination. The best time window for the seismic hazard prognosis in given geological and mining conditions seems to be 30–50 days. The shorter periods cause the underestimation of the seismic hazard prognosis. Low range of tremor energies and the relatively low number of seismic events with high energy cause the low probability of prediction of the seismic mining events (10–40%) of the energy of min. 106 J, even for longer day base periods. The accuracy of hazard prediction, obtained from each seismic database period, was determined, using the developed coefficient of hazard autoregression CN. The analysis of the Gutenberg–Richter distribution should serve as complementary tool of seismic hazard prediction only.

Dynamic swarm class rebalancing for the process mining of rare events

Process mining is becoming an indispensable method in workflow model reconstructions, offering insights into mission critical systems. The efficacy of process mining depends on whether the underlying data mining algorithms can accurately classify or predict future events from process logs. However, exceptional events are scarce in most operational processes. Hence, the process logs generated from these processes are highly imbalanced. It is quite often that any model learned from imbalanced data tends to be overly generalized toward the normal classes but under-trained to recognize the rare classes. In this paper, we propose 3 methods to rectify this class imbalance problem. They are founded upon a meta-heuristic–swarm intelligence algorithm. The first method, and also the base of the remaining 2 methods, is Dynamic Multi-objective Rebalancing Algorithm, which considers both high accuracy and high confidence level of classification in its objective function, and it is draw upon the particle swarm optimization algorithm. The other two algorithms are hybrid methods by combining the first base method with over-sampling and under-sampling techniques. Experiments are conducted using the three above-mentioned methods to process rebalanced dataset, as well as using other classic resampling methods for comparison. According to the results, our proposed methods show satisfactory performance over other comparison methods, and we extracted meaningful decision rules from a rebalanced dataset in process mining.

On the aftershock productivity in mining-induced seismicity—insight into seismicity of Rudna copper ore mine, Poland

SUMMARYMining-induced seismic events can be followed by aftershocks that increase the risk associated with the exploitation. The understanding of the aftershock generation process in induced seismicity may improve post-earthquake safety procedures applied in mines. Rudna copper ore mine in southwestern Poland commonly experiences intense and strong seismic activity accompanying the room-and-pillar exploitation of copper ore. Some strong (magnitude >2) mining events are followed by numerous aftershocks and some are not followed by any. In this study, we seek to find whether there is any geological, technological or seismological cause of this diversity. We study 46 strong mining events and focus on their aftershock productivity. We analyse the geological and mining setting of the studied events, their signal similarity, stress drops and the ground motion effect using data from three different seismic networks. Our results show that seismic events producing large aftershock sequences may share similar focal mechanisms and have larger ground effects than events with no aftershocks. The results also indicate the potential differences in stress drops. This interesting observation may help to better evaluate the aftershock hazard in mines. It also indicates the need for a more detailed analysis of the focal mechanisms of strong events and their relationship to the exploitation technique.

Evaluation of changes in groundwater quality caused by a water inrush event in Taoyuan coal mine, China

The effects of a water inrush event on the chemical composition of groundwater in a coal measures sandstone aquifer that is important for water supply was investigated at the Taoyuan coal mine in the Huaibei region in China. Changes in groundwater quality caused by the inrush event were investigated using statistical techniques, a Piper trilinear diagram, saturation index, ion combination ratios and a fuzzy comprehensive evaluation method. These investigations indicated that Na+ + K+ and SO42− were the main cation and anion, and the average contents were 272 mg/L and 732 mg/L before water inrush, and 394 mg/L and 779 mg/L after water inrush, respectively. The concentrations of anions in groundwater before the inrush event were in the order SO42− > HCO3− > Cl− and the cation concentrations were in the order Na+ + K+ > Ca2+ > Mg2+. The chemical composition of groundwater was mainly SO4·Cl–Ca·Mg before water inrush event, and SO4·Cl–Na and Na–HCO3–Cl after this event. According to the saturation index method, gypsum, calcite, magnesite, and aragonite controlling the chemical composition of groundwater were reactive minerals in groundwater system. The oxidation of pyrite, dissolution of sulfate and carbonate minerals were main geochemical process before and after the event; while the oxidation of pyrite and cation exchange were enhanced after the water inrush event. The fuzzy comprehensive evaluation results showed that the type V water accounted for a large proportion of poor-quality groundwater. Groundwater quality in the aquifer near the mine generally deteriorated after the water inrush event.

Mining events with declassified diplomatic documents

Since 1973, the U.S. State Department has been using electronic record systems to preserve classified communications. Recently, approximately 1.9 million of these records from 1973–77 have been made available by the U.S. National Archives. While some of these communication streams have periods witnessing an acceleration in the rate of transmission, others do not show any notable patterns in communication intensity. Given the sheer volume of these communications, far greater than what had been available until now, scholars need automated statistical techniques to identify the communications that warrant closer study. We develop a statistical framework that can identify from a large corpus of documents a handful that historians would consider more interesting. Our approach brings together techniques from nonparametric signal estimation, statistical hypothesis testing and modern optimization methods—leading to a set of tools that help us identify and analyze various geometrical aspects of the communication streams. Dominant periods of heightened activities, as identified through these methods, correspond well with historical events recognized by standard reference works on the 1970s.

A complete map of the Calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) signaling pathway.

Calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) is a serine/threonine-protein kinase belonging to the Ca2+/calmodulin-dependent protein kinase subfamily. CAMKK2 has an autocatalytic site, which gets exposed when Ca2+/calmodulin (CAM) binds to it. This results in autophosphorylation and complete activation of CAMKK2. The three major known downstream targets of CAMKK2 are 5'-adenosine monophosphate (AMP)-activated protein kinase (AMPKα), calcium/calmodulin-dependent protein kinase 1 (CAMK1) and calcium/calmodulin-dependent protein kinase 4 (CAMK4). Activation of these targets by CAMKK2 is important for the maintenance of different cellular and physiological processes within the cell. CAMKK2 is found to be important in neuronal development, bone remodeling, adipogenesis, and systemic glucose homeostasis, osteoclastgensis and postnatal myogensis. CAMKK2 is reported to be involved in pathologies like Duchenne muscular dystrophy, inflammation, osteoporosis and bone remodeling and is also reported to be overexpressed in prostate cancer, hepatic cancer, ovarian and gastric cancer. CAMKK2 is involved in increased cell proliferation and migration through CAMKK2/AMPK pathway in prostate cancer and activation of AKT in ovarian cancer. Although CAMKK2 is a molecule of great importance, a public resource of the CAMKK2 signaling pathway is currently lacking. Therefore, we carried out detailed data mining and documentation of the signaling events associated with CAMKK2 from published literature and developed an integrated reaction map of CAMKK2 signaling. This resulted in the cataloging of 285 reactions belonging to the CAMKK2 signaling pathway, which includes 33 protein-protein interactions, 74 post-translational modifications, 7 protein translocation events, and 22 activation/inhibition events. Besides, 124 gene regulation events and 25 activator/inhibitors involved in CAMKK2 activation were also cataloged. The CAMKK2 signaling pathway map data is made freely accessible through WikiPathway database ( https://www.wikipathways.org/index.php/Pathway:WP4874 ). We expect that data on a signaling map of CAMKK2 will provide the scientific community with an improved platform to facilitate further molecular as well as biomedical investigations on CAMKK2 and its utility in the development of biomarkers and therapeutic targets.

Isolation and Characterization of Soil Bacteria from an Abandoned Coal Mine in Southeast Kansas

Acid mine drainage (AMD) is found in areas of abandoned coal mines in southeast Kansas as a result of mine waste and tailings from century old mining events. Soil bacterial population acts as a reliable indicator of ecosystem health in these human-perturbed areas. The goals of the present study were to isolate and characterize soil bacteria from an abandoned coal mine land that included an AMD site in southeast Kansas and to isolate acid-tolerant bacterial species for bioremediation purpose. Soil samples were collected seasonally from five topographically diverse locations at the mine land. Soil texture was evaluated and soil chemistry was analyzed using inductively-coupled plasma optical emission spectroscopy. Up to sixty morphologically different colonies were isolated by dilution plating of soil samples on nutrient rich media, characterized using physiological and biochemical tests, checked for their growth on selective acid media, and identified at species-level using 16S rRNA gene sequencing. Data showed that soil pH ranged from 2.4-6.8 and contained varied concentrations of arsenic, manganese, and iron. Total bacterial concentration was 102-106 CFU/g of soil. Biochemical tests revealed a diverse metabolic potential of the bacterial population. A total of 13 acid-tolerant bacterial strains were recovered. Majority of bacterial species belonged to phylum Firmicutes, followed by Actinobacteria, and Proteobacteria. Baseline measurements of culturable bacterial diversity as well as soil chemistry at AMD sites in this region are novel and the findings would have potential use in bioremediation of AMD sites.

What Governs the Spatial and Temporal Distribution of Aftershocks in Mining-Induced Seismicity: Insight into the Influence of Coseismic Static Stress Changes on Seismicity in Kiruna Mine, Sweden

ABSTRACTStrong mining-induced earthquakes are often followed by aftershocks, similar to natural earthquakes. Although the magnitudes of such in-mine aftershocks are not high, they may pose a threat to mining infrastructure, production, and primarily, people working underground. The existing post-earthquake mining procedures usually do not consider any aspects of the physics of the mainshock. This work aims to estimate the rate and distribution of aftershocks following mining-induced seismic events by applying the rate-and-state model of fault friction, which is commonly used in natural earthquake studies. It was found that both the pre-mainshock level of seismicity and the coseismic stress change following the mainshock rupture have strong effects on the aftershock sequence. For mining-induced seismicity, however, we need to additionally account for the constantly changing stress state caused by the ongoing exploitation. Here, we attempt to model the aftershock sequence, its rate, and distribution of two M≈2 events in iron ore Kiruna mine, Sweden. We could appropriately estimate the aftershock sequence for one of the events because both the modeled rate and distribution of aftershocks matched the observed activity; however, the model underestimated the rate of aftershocks for the other event. The results of modeling showed that aftershocks following mining events occur in the areas of pre-mainshock activity influenced by the positive coulomb stress changes, according to the model’s assumptions. However, we also noted that some additional process not incorporated in the rate-and-state model may influence the aftershock sequence. Nevertheless, this type of modeling is a good tool for evaluating the risk areas in mines following a strong seismic event.

Anomaly detection and event mining in cold forming manufacturing processes

Predictive maintenance is one of the main goals within the Industry 4.0 trend. Advances in data-driven techniques offer new opportunities in terms of cost reduction, improved quality control, and increased work safety. This work brings data-driven techniques for two predictive maintenance tasks: anomaly detection and event prediction, applied in the real-world use case of a cold forming manufacturing line for consumer lifestyle products by using acoustic emissions sensors in proximity of the dies of the press module. The proposed models are robust and able to cope with problems such as noise, missing values, and irregular sampling. The detected anomalies are investigated by experts and confirmed to correspond to deviations in the normal operation of the machine. Moreover, we are able to find patterns which are related to the events of interest.

Peak particle velocity of vibration events in underground coal mine and their caused stress increment

Peak particle velocity (PPV) is the most basic and direct parameter of vibration event to connect with stress increment, so they are studied in this paper through statistics and analysis. The vibration events in underground coal mine can be divided into whole mine (WM) event, working face (WF) event and heading face (HF) event, and the WM event normally has the lowest dominant frequency and maximum PPV. Rock burst may occur when PPV causes its corresponding stress increment to reach the triggering value, and PPV should be used as monitoring index, rather than the only adopted post-processing indicators like event energy. The study also discussed about the relationship between event magnitude and PPV and their caused stress increment, which has important reference significance for mechanism study and early warning of rock burst.

Efficient Strategy Mining for Football Social Network

With the growing popularity of social network in sport, it expresses the social relationships between individuals and facilitates realistic applications, e.g., social event mining and discovery. Sport network as a specific social network has been widely studied in research and commercial fields. However, most of the existing works utilize a simplex strategy to improve certain indicators in the team and do not consider the effect of strategy adjustment based on the current situation. In this paper, we study the problem of efficient strategy mining in football social network. To address this problem, we propose a quantitative way to combine the aspects of coordination, adaptability, flexibility, and tempo into a passing network, which notably improves the timeliness and information content of the existing network. On this basis, we design a suppression function to express the impact of strategy. Then, we propose a novel passing network and group cooperation scheme based on quantified team performance to obtain the efficient strategies. At last, the experimental results show that, based on the performance of the same team, our optimized passing network has a higher winning rate in practice.