Responsive glyco-poly(2-oxazoline)s: synthesis, cloud point tuning, and lectin binding

A new sugar-substituted 2-oxazoline monomer was prepared using the copper-catalyzed alkyne-azide cycloaddition (CuAAC) reaction. Its copolymerization with 2-ethyl-2-oxazoline as well as 2-(dec-9-enyl)-2-oxazoline, yielding well-defined copolymers with the possibility to tune the properties by thiol-ene "click" reactions, is described. Extensive solubility studies on the corresponding glycocopolymers demonstrated that the lower critical solution temperature behavior and pH-responsiveness of these copolymers can be adjusted in water and phosphate-buffered saline (PBS) depending on the choice of the thiol. By conjugation of 2,3,4,6-tetra-O-acetyl-1-thio-beta-D-glucopyranose and subsequent deprotection of the sugar moieties, the hydrophilicity of the copolymer could be increased significantly, allowing a cloud-point tuning in the physiological range. Furthermore, the binding capability of the glycosylated copoly(2-oxazoline) to concanavalin A was investigated

A flexible framework for mobile device forensics based on cold boot attacks

Mobile devices, like tablets and smartphones, are common place in everyday life. Thus, the degree of security these devices can provide against digital forensics is of particular interest. A common method to access arbitrary data in main memory is the cold boot attack. The cold boot attack exploits the remanence effect that causes data in DRAM modules not to lose the content immediately in case of a power cut-off. This makes it possible to restart a device and extract the data in main memory. In this paper, we present a novel framework for cold boot-based data acquisition with a minimal bare metal application on a mobile device. In contrast to other cold boot approaches, our forensics tool overwrites only a minimal amount of data in main memory. This tool requires no more than three kilobytes of constant data in the kernel code section. We hence sustain all of the data relevant for the analysis of the previously running system. This makes it possible to analyze the memory with data acquisition tools. For this purpose, we extend the memory forensics tool Volatility in order to request parts of the main memory dynamically from our bare metal application. We show the feasibility of our approach on the Samsung Galaxy S4 and Nexus 5 mobile devices along with an extensive evaluation. First, we compare our framework to a traditional memory dump-based analysis. In the next step, we show the potential of our framework by acquiring sensitive user data

A lightweight framework for cold boot based forensics on mobile devices

Mobile devices, like tablets and smartphones, are common place in everyday life. Thus, the degree of security these devices can provide against digital forensics is of particular interest. A common method to access arbitrary data in main memory is the cold boot attack. The cold boot attack exploits theremanence effect that causes data in DRAM modules not to lose the content immediately in case of a power cut-off. This makes it possible to restart a device and extract the data in main memory. In this paper, we present a novel framework for cold boot based data acquisition with a minimal bare metal application on a mobile device. In contrast to other cold boot approaches, our forensics tool overwrites only a minimal amount of data in main memory. This tool requires no more than five kilobytes of constant data in the kernel code section. We hence sustain all of the data relevant for the analysis of the previously running system. This makes it possible to analyze the memory with data acquisition tools. For this purpose, we extend the memory forensics tool Volatility in order to request parts of the main memory dynamically from our bare metal application. We show the feasibility of our approach by comparing it to a traditional memory dump based analysis using the Samsung Galaxy S4 mobile device

Detection of Ischemic Infarct Core in Non-contrast Computed Tomography

Fast diagnosis is of critical importance for stroke treatment. In clinical routine, a non-contrast computed tomography scan (NCCT) is typically acquired immediately to determine whether the stroke is ischemic or hemorrhagic and plan therapy accordingly. In case of ischemia, early signs of infarction may appear due to increased water uptake. These signs may be subtle, especially if observed only shortly after symptom onset, but hold the potential to provide a crucial first assessment of the location and extent of the infarction. In this paper, we train a deep neural network to predict the infarct core from NCCT in an image-to-image fashion. To facilitate exploitation of anatomic correspondences, learning is carried out in the standardized coordinate system of a brain atlas to which all images are deformably registered. Apart from binary infarct core masks, perfusion maps such as cerebral blood volume and flow are employed as additional training targets to enrich the physiologic information available to the model. This extension is demonstrated to substantially improve the predictions of our model, which is trained on a data set consisting of 141 cases. It achieves a higher volumetric overlap (statistically significant,) of the predicted core with the reference mask as well as a better localization, although significance could not be shown for the latter. Agreement with human and automatic assessment of affected ASPECTS regions is likewise improved, measured as an increase of the area under the receiver operating characteristic curve from 72.7% to 75.1% and 71.9% to 83.5%, respectively.</p