Possible attenuation of the G2 DNA damage cell cycle checkpoint in HeLa cells by extremely low frequency (ELF) electromagnetic fields

BACKGROUND: The issue remains unresolved as to whether low frequency magnetic fields can affect cell behaviour, with the possibility that they may be in part responsible for the increased incidence of leukaemia in parts of the population exposed to them. METHODS: Combined treatment of HeLa cells with gamma-irradiation (1, 3 and 5 Grays) and extra low frequency magnetic fields of ~50 Hz was carried out under rigorously controlled conditions. RESULTS: Synchronised cells progressing from S-phase arrived at mitosis on average marginally ahead of irradiation controls not exposed to ELF. In no instance out of a total of twenty separate experiments did this "double-insult" further delay entry of cells into mitosis, as had been anticipated. CONCLUSION: This apparently "non-genotoxic" agent (ELF) appears to be capable of affecting cells that would normally arrest for longer in G2, suggesting a weakening of the stringency of the late cycle (G2) checkpoint

The John Psathas Percussion Project

Professor Omar Carmenates, along with some select percussion students, worked in collaboration with New Zealand’s most famous composer, resulting in new works for percussion ensemble. Will perform some of the music that was recorded as part of the project. Will also discuss the process of preparation for the project

Decomposition and its effects on mechanical properties in Al-Zn-Mg-Cu alloys

The effects of variations in composition on the decomposition process in Al-Zn-Mg-Cu alloys (i.e. – 7xxx-series aluminum alloy) were studied emphasizing their effect on mechanical properties. Several experimental quaternary alloys were studied to compare their behavior with commercial 7xxx-series alloys. The investigation included studies on the effects of natural aging, artificial aging, quench sensitivity, precipitate free zone formation, and homogenization. Additionally, “true aging” curves (i.e. – hardness/strength vs. conductivity) were presented in order to visualize and quantify the entire precipitation process. It is obvious that fluctuations in the main alloying elements/processing parameters can alter the precipitation process, but the purpose of this work was to quantify those changes using standard industrial techniques. It was found that natural aging was detrimental for strength in the T6 temper for alloys containing more than 1.0 wt.% Cu, and was shown to alter the coarsening kinetics in the over-aged condition (T7). Conversely, for alloys with Cu contents less than 0.5% natural aging was shown to be beneficial for strength. Altering the Zn:Mg ratio was also shown to effect natural aging response of an alloy in addition to introducing additional precipitation processes (T-phase). Therefore, this work is a blueprint for advanced alloy manufacturing that allows for the rapid production of new alloys and tempers by narrowing the research focus depending on an alloy’s composition.Ph.D

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

Transient Reactivation of a Deep-Seated Landslide by Undrained Loading Captured With Repeat Airborne and Terrestrial Lidar

Landslides reactivate due to external environmental forcing or internal mass redistribution, but the process is rarely documented quantitatively. We capture the three-dimensional, 1-m resolution surface deformation field of a transiently reactivated landslide with image correlation of repeat airborne lidar. Undrained loading by two debris flows in the landslide’s head, rather than external forcing, triggered reactivation. After that loading, the lower 2 km of the landslide advanced by up to 14 m in 2 years before completely stopping. The displacement field over those 2 years implies that the slip surface gained 1 kPa of shear strength, which was likely accomplished by a negative dilatancy-pore pressure feedback as material deformed around basal roughness elements. Thus, landslide motion can be decoupled from external environmental forcing in cases, motivating the need to better understand internal perturbations to the stress field to predict hazards and sediment fluxes as landscapes evolve

Transient Reactivation of a Deep-Seated Landslide by Undrained Loading Captured With Repeat Airborne and Terrestrial Lidar

Landslides reactivate due to external environmental forcing or internal mass redistribution, but the process is rarely documented quantitatively. We capture the three-dimensional, 1-m resolution surface deformation field of a transiently reactivated landslide with image correlation of repeat airborne lidar. Undrained loading by two debris flows in the landslide’s head, rather than external forcing, triggered reactivation. After that loading, the lower 2 km of the landslide advanced by up to 14 m in 2 years before completely stopping. The displacement field over those 2 years implies that the slip surface gained 1 kPa of shear strength, which was likely accomplished by a negative dilatancy-pore pressure feedback as material deformed around basal roughness elements. Thus, landslide motion can be decoupled from external environmental forcing in cases, motivating the need to better understand internal perturbations to the stress field to predict hazards and sediment fluxes as landscapes evolve

Transient Reactivation of a Deep-Seated Landslide by Undrained Loading Captured With Repeat Airborne and Terrestrial Lidar

Landslides reactivate due to external environmental forcing or internal mass redistribution, but the process is rarely documented quantitatively. We capture the three-dimensional, 1-m resolution surface deformation field of a transiently reactivated landslide with image correlation of repeat airborne lidar. Undrained loading by two debris flows in the landslide’s head, rather than external forcing, triggered reactivation. After that loading, the lower 2 km of the landslide advanced by up to 14 m in 2 years before completely stopping. The displacement field over those 2 years implies that the slip surface gained 1 kPa of shear strength, which was likely accomplished by a negative dilatancy-pore pressure feedback as material deformed around basal roughness elements. Thus, landslide motion can be decoupled from external environmental forcing in cases, motivating the need to better understand internal perturbations to the stress field to predict hazards and sediment fluxes as landscapes evolve

A method for high-throughput gene expression signature analysis

Genome-wide transcriptional profiling has shown that different biologic states (for instance, disease and response to pharmacologic manipulation) can be recognized by the expression pattern of relatively small numbers of genes. However, the lack of a practical and cost-effective technology for detection of these gene expression 'signatures' in large numbers of samples has severely limited their exploitation in important medical and pharmaceutical discovery applications. Here, we describe a solution based on the combination of ligation-mediated amplification with an optically addressed microsphere and flow cytometric detection system