IMPROVING DATA QUALITY AND MANAGEMENT FOR REMOTE SENSING ANALYSIS: USE-CASES AND EMERGING RESEARCH QUESTIONS

During the last decades satellite remote sensing has become an emerging technology producing big data for various application fields every day. However, data quality checking as well as the long-time management of data and models are still issues to be improved. They are indispensable to guarantee smooth data integration and the reproducibility of data analysis such as carried out by machine learning models. In this paper we clarify the emerging need of improving data quality and the management of data and models in a geospatial database management system before and during data analysis. In different use cases various processes of data preparation and quality checking, integration of data across different scales and references systems, efficient data and model management, and advanced data analysis are presented in detail. Motivated by these use cases we then discuss emerging research questions concerning data preparation and data quality checking, data management, model management and data integration. Finally conclusions drawn from the paper are presented and an outlook on future research work is given

Preliminary genetic evidence of two different populations of Opisthorchis viverrini in Lao PDR

Opisthorchis viverrini is a major public health concern in Southeast Asia. Various reports have suggested that this parasite may represent a species complex, with genetic structure in the region perhaps being dictated by geographical factors and different species of intermediate hosts. We used four microsatellite loci to analyze O. viverrini adult worms originating from six species of cyprinid fish in Thailand and Lao PDR. Two distinct O. viverrini populations were observed. In Ban Phai, Thailand, only one subgroup occurred, hosted by two different fish species. Both subgroups occurred in fish from That Luang, Lao PDR, but were represented to very different degrees among the fish hosts there. Our data suggest that, although geographical separation is more important than fish host specificity in influencing genetic structure, it is possible that two species of Opisthorchis, with little interbreeding, are present near Vientiane in Lao PDR

Modeling plasticity and dysplasia of pancreatic ductal organoids derived from human pluripotent stem cells

Personalized in vitro models for dysplasia and carcinogenesis in the pancreas have been constrained by insufficient differentiation of human pluripotent stem cells (hPSCs) into the exocrine pancreatic lineage. Here, we differentiate hPSCs into pancreatic duct-like organoids (PDLOs) with morphological, transcriptional, proteomic, and functional characteristics of human pancreatic ducts, further maturing upon transplantation into mice. PDLOs are generated from hPSCs inducibly expressing oncogenic GNAS, KRAS, or KRAS with genetic covariance of lost CDKN2A and from induced hPSCs derived from a McCune-Albright patient. Each oncogene causes a specific growth, structural, and molecular phenotype in vitro. While transplanted PDLOs with oncogenic KRAS alone form heterogenous dysplastic lesions or cancer, KRAS with CDKN2A loss develop dedifferentiated pancreatic ductal adenocarcinomas. In contrast, transplanted PDLOs with mutant GNAS lead to intraductal papillary mucinous neoplasia-like structures. Conclusively, PDLOs enable in vitro and in vivo studies of pancreatic plasticity, dysplasia, and cancer formation from a genetically defined background

Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults.

New neurons continue to be generated in the subgranular zone of the dentate gyrus of the adult mammalian hippocampus. This process has been linked to learning and memory, stress and exercise, and is thought to be altered in neurological disease. In humans, some studies have suggested that hundreds of new neurons are added to the adult dentate gyrus every day, whereas other studies find many fewer putative new neurons. Despite these discrepancies, it is generally believed that the adult human hippocampus continues to generate new neurons. Here we show that a defined population of progenitor cells does not coalesce in the subgranular zone during human fetal or postnatal development. We also find that the number of proliferating progenitors and young neurons in the dentate gyrus declines sharply during the first year of life and only a few isolated young neurons are observed by 7 and 13 years of age. In adult patients with epilepsy and healthy adults (18-77 years; n = 17 post-mortem samples from controls; n = 12 surgical resection samples from patients with epilepsy), young neurons were not detected in the dentate gyrus. In the monkey (Macaca mulatta) hippocampus, proliferation of neurons in the subgranular zone was found in early postnatal life, but this diminished during juvenile development as neurogenesis decreased. We conclude that recruitment of young neurons to the primate hippocampus decreases rapidly during the first years of life, and that neurogenesis in the dentate gyrus does not continue, or is extremely rare, in adult humans. The early decline in hippocampal neurogenesis raises questions about how the function of the dentate gyrus differs between humans and other species in which adult hippocampal neurogenesis is preserved

Data Stream Clustering for Real-Time Anomaly Detection: An Application to Insider Threats

Insider threat detection is an emergent concern for academia, industries, and governments due to the growing number of insider incidents in recent years. The continuous streaming of unbounded data coming from various sources in an organisation, typically in a high velocity, leads to a typical Big Data computational problem. The malicious insider threat refers to anomalous behaviour(s) (outliers) that deviate from the normal baseline of a data stream. The absence of previously logged activities executed by users shapes the insider threat detection mechanism into an unsupervised anomaly detection approach over a data stream. A common shortcoming in the existing data mining approaches to detect insider threats is the high number of false alarms/positives (FPs). To handle the Big Data issue and to address the shortcoming, we propose a streaming anomaly detection approach, namely Ensemble of Random subspace Anomaly detectors In Data Streams (E-RAIDS), for insider threat detection. E-RAIDS learns an ensemble of p established outlier detection techniques [Micro-cluster-based Continuous Outlier Detection (MCOD) or Anytime Outlier Detection (AnyOut)] which employ clustering over continuous data streams. Each model of the p models learns from a random feature subspace to detect local outliers, which might not be detected over the whole feature space. E-RAIDS introduces an aggregate component that combines the results from the p feature subspaces, in order to confirm whether to generate an alarm at each window iteration. The merit of E-RAIDS is that it defines a survival factor and a vote factor to address the shortcoming of high number of FPs. Experiments on E-RAIDS-MCOD and E-RAIDS-AnyOut are carried out, on synthetic data sets including malicious insider threat scenarios generated at Carnegie Mellon University, to test the effectiveness of voting feature subspaces, and the capability to detect (more than one)-behaviour-all-threat in real-time. The results show that E-RAIDS-MCOD reports the highest F1 measure and less number of false alarm = 0 compared to E-RAIDS-AnyOut, as well as it attains to detect approximately all the insider threats in real-time

Impact analysis of accidents on the traffic flow based on massive floating car data

The wide usage of GPS-equipped devices enables the mass recording of vehicle movement trajectories describing the movement behavior of the traffic participants. An important aspect of the road traffic is the impact of anomalies, like accidents, on traffic flow. Accidents are especially important as they contribute to the the aspects of safety and also influence travel time estimations. In this paper, the impact of accidents is determined based on a massive GPS trajectory and accident dataset. Due to the missing precise date of the accidents in the data set used, first, the date of the accident is estimated based on the speed profile at the accident time. Further, the temporal impact of the accident is estimated using the speed profile of the whole day. The approach is applied in an experiment on a one month subset of the datasets. The results show that more than 72% of the accident dates are identified and the impact on the temporal dimension is approximated. Moreover, it can be seen that accidents during the rush hours and on high frequency road types (e.g. motorways, trunks or primaries) have an increasing effect on the impact duration on the traffic flow