Automatic classification of iron ore lithologies using petrophysical and geochemical data

We use Fuzzy Inference Systems on a combination of petrophysical and geochemical data to automatically classify iron ore lithologies. Our results show that only two measurements are needed to group the data according to the major rock class and grade. Either Fe and Al, or Fe and Natural Gamma logs may be used, where the Al or gamma log are indicative of shale units. We propose a method to gather all data necessary for iron ore classification in a single down-hole logging run using Spectral Gamma-Gamma to provide a real-Time update of the iron ore resource model

Testing cluster analysis on combined petrophysical and geochemical data for rock mass classification

New drilling, measurement-while-drilling and top-of-hole sensing technologies are being developed to overcome the challenges of exploration for new mineral deposits under deep cover. These methods will provide continuous, near-real time data collection from every drillhole in the future. Consequently, there will be a need for efficient methods of analysing and interpreting this data stream to complement the exploration strategy. We demonstrate the usefulness of cluster analysis for rapid, automated rock mass classification, and the impact of selecting different subsets of the available data on the classification results. Our study shows that only a few measurements are needed to broadly domain the intersected rock mass and highlights the importance of selecting correct input data depending on the purpose of the classification. Our analysis also indicates the potential of identifying textural and rock mechanical properties from petrophysical measurements via cluster analysis

Estimation of P-wave velocity from other borehole data

P-wave velocities are a key parameter for seismic processing and the absence of this parameter reduces the robustness of the images from very expensive seismic surveys. The P-wave velocities in an area are particular to the area, as the P-wave velocity depends on many factors and varies with geological conditions. Hence, using a localized model predicts P-wave velocity better than the application of a generic model for the entire dataset. In this work, we utilized fuzzy c-means (FCM) clustering to build a "fuzzy" relationship that estimates Vp. Our method was tested on a dataset from the Kevitsa Ni-Cu-PGE deposit in northern Finland. The borehole data comprises P-wave velocity, density, natural gamma, magnetic susceptibility, resistivity and assay data of Ni of six boreholes. In this area, there are many boreholes, but very few have P-wave velocity logged or the data is corrupted by tool limitations. Therefore, it is beneficial to predict the velocity from other data to help seismic processing. In order to demonstrate the robustness of our program, we used the data from five holes for training and one hole for Vp testing. The results show that our method can reasonably estimate P-wave velocity from other borehole data

Light-Switchable Peptides with a Hemithioindigo Unit:Peptide Design, Photochromism, and Optical Spectroscopy

This Minireview focuses on the hemithioindigo photoswitch and its use for the reversible control of three-dimensional peptide structure and related biological functions. Both the general design aspects and biophysical properties of various hemithioindigo-based chromopeptides are summarized. Hemithioindigo undergoes reversible ZE photoisomerization after absorption of visible light. The unique ultrafast switching mechanism of hemithioindigo combines picosecond isomerization kinetics with strong double-bond torsion after light absorption, making it the ideal tool for instantaneous modulation of biological structure. Various inhibitors and model peptides based on hemithioindigo are described that can directly regulate biological signaling or allow the fastest events in peptide folding to be studied. Finally, a diverse range of chromopeptides with photoswitchable -hairpin structures based on azobenzenes, stilbenes, and hemithioindigo are compared to emphasize the unique properties of hemithioindigo

Potent pro-apoptotic combination therapy is highly effective in a broad range of cancers

Primary or acquired therapy resistance is a major obstacle to the effective treatment of cancer. Resistance to apoptosis has long been thought to contribute to therapy resistance. We show here that recombinant TRAIL and CDK9 inhibition cooperate in killing cells derived from a broad range of cancers, importantly without inducing detectable adverse events. Remarkably, the combination of TRAIL with CDK9 inhibition was also highly effective on cancers resistant to both, standard-of-care chemotherapy and various targeted therapeutic approaches. Dynamic BH3 profiling revealed that, mechanistically, combining TRAIL with CDK9 inhibition induced a drastic increase in the mitochondrial priming of cancer cells. Intriguingly, this increase occurred irrespective of whether the cancer cells were sensitive or resistant to chemo- or targeted therapy. We conclude that this pro-apoptotic combination therapy has the potential to serve as a highly effective new treatment option for a variety of different cancers. Notably, this includes cancers that are resistant to currently available treatment modalities