LODE: Linking Digital Humanities Content to the Web of Data

Numerous digital humanities projects maintain their data collections in the form of text, images, and metadata. While data may be stored in many formats, from plain text to XML to relational databases, the use of the resource description framework (RDF) as a standardized representation has gained considerable traction during the last five years. Almost every digital humanities meeting has at least one session concerned with the topic of digital humanities, RDF, and linked data. While most existing work in linked data has focused on improving algorithms for entity matching, the aim of the LinkedHumanities project is to build digital humanities tools that work "out of the box," enabling their use by humanities scholars, computer scientists, librarians, and information scientists alike. With this paper, we report on the Linked Open Data Enhancer (LODE) framework developed as part of the LinkedHumanities project. With LODE we support non-technical users to enrich a local RDF repository with high-quality data from the Linked Open Data cloud. LODE links and enhances the local RDF repository without compromising the quality of the data. In particular, LODE supports the user in the enhancement and linking process by providing intuitive user-interfaces and by suggesting high-quality linking candidates using tailored matching algorithms. We hope that the LODE framework will be useful to digital humanities scholars complementing other digital humanities tools

Extracting Secrets from Encrypted Virtual Machines

AMD SEV is a hardware extension for main memory encryption on multi-tenant systems. SEV uses an on-chip coprocessor, the AMD Secure Processor, to transparently encrypt virtual machine memory with individual, ephemeral keys never leaving the coprocessor. The goal is to protect the confidentiality of the tenants' memory from a malicious or compromised hypervisor and from memory attacks, for instance via cold boot or DMA. The SEVered attack has shown that it is nevertheless possible for a hypervisor to extract memory in plaintext from SEV-encrypted virtual machines without access to their encryption keys. However, the encryption impedes traditional virtual machine introspection techniques from locating secrets in memory prior to extraction. This can require the extraction of large amounts of memory to retrieve specific secrets and thus result in a time-consuming, obvious attack. We present an approach that allows a malicious hypervisor quick identification and theft of secrets, such as TLS, SSH or FDE keys, from encrypted virtual machines on current SEV hardware. We first observe activities of a virtual machine from within the hypervisor in order to infer the memory regions most likely to contain the secrets. Then, we systematically extract those memory regions and analyze their contents on-the-fly. This allows for the efficient retrieval of targeted secrets, strongly increasing the chances of a fast, robust and stealthy theft.Comment: Accepted for publication at CODASPY 201

Combining real-time fluorescence spectroscopy and flow cytometry to reveal new insights in DOC and cell characterization of drinking water

Sudden changes in drinking water quality can cause harmful consequences for end users. Thus, real-time monitoring of drinking water quality can allow early warning and provide crucial gains for securing safe water distribution. This study investigated the advantages of simultaneous real-time measuring of flow cytometry and fluorescence spectroscopy. A contamination event was investigated in a laboratory-scale analysis by spiking drinking water samples with organic nutrients. Flow cytometric data were analyzed by creating fingerprints based on differentiation into high and low nucleic acid cells (HNA/LNA). The detailed characterization of these data showed that an increase in HNA cells indicated an increase in the bacterial growth potential even before actual TCC increases. The fluorescence data was decomposed via the PARAFAC method to reveal seven fluorescent components. Three aromatic protein-like components were associated with the microbiological condition of the drinking water cells; namely, Components 4 (λEx = 279 nm, λEm = 351 nm), 6 (λEx = 279 nm, λEm = 332 nm), and 7 (λEx = 276 nm, λEm = 302 nm). Component 6 was identified as a possible organic variable for appropriate monitoring of TCC, whereas Components 4 and 7 were identified as organic compounds representing nutrients for organisms present in drinking water. Overall, combining both methods for real-time monitoring can be a powerful tool to guarantee drinking water quality

Systematic analysis of T7 RNA polymerase based in vitro linear RNA amplification for use in microarray experiments

BACKGROUND: The requirement of a large amount of high-quality RNA is a major limiting factor for microarray experiments using biopsies. An average microarray experiment requires 10–100 μg of RNA. However, due to their small size, most biopsies do not yield this amount. Several different approaches for RNA amplification in vitro have been described and applied for microarray studies. In most of these, systematic analyses of the potential bias introduced by the enzymatic modifications are lacking. RESULTS: We examined the sources of error introduced by the T7 RNA polymerase based RNA amplification method through hybridisation studies on microarrays and performed statistical analysis of the parameters that need to be evaluated prior to routine laboratory use. The results demonstrate that amplification of the RNA has no systematic influence on the outcome of the microarray experiment. Although variations in differential expression between amplified and total RNA hybridisations can be observed, RNA amplification is reproducible, and there is no evidence that it introduces a large systematic bias. CONCLUSIONS: Our results underline the utility of the T7 based RNA amplification for use in microarray experiments provided that all samples under study are equally treated

General method for prediction of thermal conductivity for well-characterized hydrocarbon mixtures and fuels up to extreme conditions using entropy scaling

A general and efficient technique is developed to predict the thermal conductivity of well-characterized hydrocarbon mixtures, rocket propellant (RP) fuels, and jet fuels up to high temperatures and high pressures (HTHP). The technique is based upon entropy scaling using the group contribution method coupled with the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state. The mixture number averaged molecular weight and hydrogen to carbon ratio are used to define a single pseudo-component to represent the compounds in a well-characterized hydrocarbon mixture or fuel. With these two input parameters, thermal conductivity predictions are less accurate when the mixture contains significant amounts of iso-alkanes, but the predictions improve when a single thermal conductivity data point at a reference condition is used to fit one model parameter. For eleven binary mixtures and three ternary mixtures at conditions from 288 to 360 K and up to 4,500 bar, thermal conductivities are predicted with mean absolute percent deviations (MAPDs) of 16.0 and 3.0% using the two-parameter and three-parameter models, respectively. Thermal conductivities are predicted for three RP fuels and three jet fuels at conditions from 293 to 598 K and up to 700 bar with MAPDs of 14.3 and 2.0% using the two-parameter and three-parameter models, respectively

Comparison of metabolic and functional parameters using cardiac 18F-FDG-PET in early to mid-adulthood male and female mice

BACKGROUND In this descriptive study of male and female mice at different weeks of age, we use serial non-invasive cardiac 18F-FDG-PET scans to follow up on metabolic alterations, heart function parameters, and the ECG of both sexes in early to mid-adulthood. METHODS ECG-gated 18F-FDG-PET scans were performed in mice on 10, 14, and 18~weeks of age, using a dedicated small-animal PET scanner. The percentage of the injected activity per gram (%IA/g) in the heart, left ventricular metabolic volume (LVMV), myocardial viability and left ventricular function parameters: end-diastolic (EDV), end-systolic (ESV), stroke volume (SV), and the ejection fraction (EF%) were estimated. RESULTS Compared to their age-matched female counterpart, male mice showed a constant increase in LVMV and ventricular volume during the follow-up. In contrast, female mice remain stable after ten weeks of age. Furthermore, male mice showed lower heart rates, positive correlation with cardiac %IA/g, and negative correlation with LVMV. CONCLUSION In this study of serial cardiac PET scans, we provide insight for basic murine research models, showing that mice gender and age show distinct cardiac metabolisms. These physiologic alterations need to be considered when planning in vivo injury models to avoid potential pitfalls

Test Performance Characteristics of Anti-HEV IgG Assays Strongly Influence Hepatitis E Seroprevalence Estimates

Hepatitis E virus (HEV) seroprevalences of 0.3%–53% were reported from industrialized countries. Because these estimates may be influenced by detection assays, this study compares 3 frequently used tests for HEV detection: the MP Diagnostics HEV immunoglobulin G (IgG) enzyme-linked immunosorbent assay (ELISA), the Axiom Diagnostics HEV IgG enzyme immunoassay (EIA), and the Mikrogen recomLine HEV IgG assay. Sera from 200 healthy healthcare workers and 30 individuals with acute HEV infection were analyzed. Among the healthy individuals, HEV IgG was found in 4.5% by the MP Diagnostics assay, in 29.5% by the Axiom Diagnostics assay, and in 18% by the Mikrogen assay. Among individuals with acute HEV infection, positive results were obtained for 83.3%, 100%, and 96.7%, respectively. Thus, the 3 assays show clear differences in diagnostic sensitivity

Preconditioned extracellular vesicles from hypoxic microglia reduce poststroke AQP4 depolarization, disturbed cerebrospinal fluid flow, astrogliosis, and neuroinflammation

Background: Stroke stimulates reactive astrogliosis, aquaporin 4 (AQP4) depolarization and neuroinflammation. Preconditioned extracellular vesicles (EVs) from microglia exposed to hypoxia, in turn, reduce poststroke brain injury. Nevertheless, the underlying mechanisms of such effects are elusive, especially with regards to inflammation, AQP4 polarization, and cerebrospinal fluid (CSF) flow. Methods: Primary microglia and astrocytes were exposed to oxygen-glucose deprivation (OGD) injury. For analyzing the role of AQP4 expression patterns under hypoxic conditions, a co-culture model of astrocytes and microglia was established. Further studies applied a stroke model, where some mice also received an intracisternal tracer infusion of rhodamine B. As such, these in vivo studies involved the analysis of AQP4 polarization, CSF flow, astrogliosis, and neuroinflammation as well as ischemia-induced brain injury. Results: Preconditioned EVs decreased periinfarct AQP4 depolarization, brain edema, astrogliosis, and inflammation in stroke mice. Likewise, EVs promoted postischemic CSF flow and cerebral blood perfusion, and neurological recovery. Under in vitro conditions, hypoxia stimulated M2 microglia polarization, whereas EVs augmented M2 microglia polarization and repressed M1 microglia polarization even further. In line with this, astrocytes displayed upregulated AQP4 clustering and proinflammatory cytokine levels when exposed to OGD, which was reversed by preconditioned EVs. Reduced AQP4 depolarization due to EVs, however, was not a consequence of unspecific inflammatory regulation, since LPS-induced inflammation in co-culture models of astrocytes and microglia did not result in altered AQP4 expression patterns in astrocytes. Conclusions: These findings show that hypoxic microglia may participate in protecting against stroke-induced brain damage by regulating poststroke inflammation, astrogliosis, AQP4 depolarization, and CSF flow due to EV release