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Dielectrophoretic functionalization of nanoelectrode arrays for the detection of influenza viruses
(2017)
Our Conclusions
(2018)
Charges dropped
(2015)
Cold regulated protein 15A (COR15A) is a nuclear encoded, intrinsically disordered protein that is found in Arabidopsis thaliana. It belongs to the Late Embryogenesis Abundant (LEA) family of proteins and is responsible for increased freezing tolerance in plants. COR15A is intrinsically disordered in dilute solutions and adopts a helical structure upon dehydration or in the presence of co-solutes such as TFE and ethylene glycol. This helical structure is thought to be important for protecting plants from dehydration induced by freezing. Multiple protein sequence alignments revealed the presence of several conserved glycine residues that we hypothesize keeps COR15A from becoming helical in dilute solutions. Using AGADIR, the change in helical content of COR15A when these conserved glycine residues were mutated to alanine residues was predicted. Based on the predictions, glycine to alanine mutants were made at position 68, and 54,68,81, and 84. Labeled samples of wildtype COR15A and mutant proteins were purified and NMR experiments were performed to examine any structural changes induced by the mutations. To test the effects of dehydration on the structure of COR15A, trifluoroethanol, an alcohol based co solvent that is proposed to induce/stabilize helical structure in peptides was added to the NMR samples, and the results of the experiment showed an increase in helical content, compared to the samples without TFE. To test the functional differences between wild type and the mutants, liposome leakage assays were performed. The results from these assays suggest the more helical mutants may augment membrane stability.
High Mountain Asia provides water for more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow - the vast majority of which is not monitored by sparse weather networks. We leverage passive microwave data from the SSMI series of satellites (SSMI, SSMI/S, 1987-2016), reprocessed to 3.125 km resolution, to examine trends in the volume and spatial distribution of snow-water equivalent (SWE) in the Indus Basin. We find that the majority of the Indus has seen an increase in snow-water storage. There exists a strong elevation-trend relationship, where high-elevation zones have more positive SWE trends. Negative trends are confined to the Himalayan foreland and deeply-incised valleys which run into the Upper Indus. This implies a temperature-dependent cutoff below which precipitation increases are not translated into increased SWE. Earlier snowmelt or a higher percentage of liquid precipitation could both explain this cutoff.(1) Earlier work 2 found a negative snow-water storage trend for the entire Indus catchment over the time period 1987-2009 (-4 x 10(-3) mm/yr). In this study based on an additional seven years of data, the average trend reverses to 1.4 x 10(-3). This implies that the decade since the mid-2000s was likely wetter, and positively impacted long-term SWE trends. This conclusion is supported by an analysis of snowmelt onset and end dates which found that while long-term trends are negative, more recent (since 2005) trends are positive (moving later in the year).(3)
Capsella
(2018)
In cloud computing, users are able to use their own operating system (OS) image to run a virtual machine (VM) on a remote host. The virtual machine OS is started by the user using some interfaces provided by a cloud provider in public or private cloud. In peer to peer cloud, the VM is started by the host admin. After the VM is running, the user could get a remote access to the VM to install, configure, and run services. For the security reasons, the user needs to verify the integrity of the running VM, because a malicious host admin could modify the image or even replace the image with a similar image, to be able to get sensitive data from the VM. We propose an approach to verify the integrity of a running VM on a remote host, without using any specific hardware such as Trusted Platform Module (TPM). Our approach is implemented on a Linux platform where the kernel files (vmlinuz and initrd) could be replaced with new files, while the VM is running. kexec is used to reboot the VM with the new kernel files. The new kernel has secret codes that will be used to verify whether the VM was started using the new kernel files. The new kernel is used to further measuring the integrity of the running VM.
The emergence of cloud computing allows users to easily host their Virtual Machines with no up-front investment and the guarantee of always available anytime anywhere. But with the Virtual Machine (VM) is hosted outside of user's premise, the user loses the physical control of the VM as it could be running on untrusted host machines in the cloud. Malicious host administrator could launch live memory dumping, Spectre, or Meltdown attacks in order to extract sensitive information from the VM's memory, e.g. passwords or cryptographic keys of applications running in the VM. In this paper, inspired by the moving target defense (MTD) scheme, we propose a novel approach to increase the security of application's sensitive data in the VM by continuously moving the sensitive data among several memory allocations (blocks) in Random Access Memory (RAM). A movement function is added into the application source code in order for the function to be running concurrently with the application's main function. Our approach could reduce the possibility of VM's sensitive data in the memory to be leaked into memory dump file by 2 5% and secure the sensitive data from Spectre and Meltdown attacks. Our approach's overhead depends on the number and the size of the sensitive data.
Participants of the 2017 European Space Weather Week in Ostend, Belgium, discussed the stakeholder requirements for space weather-related models. It was emphasized that stakeholders show an increased interest in space weather-related models. Participants of the meeting discussed particular prediction indicators that can provide first-order estimates of the impact of space weather on engineering systems.