Anticancer effects of 7,8-dihydromethysticin in human leukemia cells are mediated via cell-cycle dysregulation, inhibition of cell migration and invasion and targeting JAK/STAT pathway

The main focus of this research work was to study the anticancer properties of 7,8-dihydromethysticin against HL-60 leukemia cells. Investigations were also performed to check its impact on the phases of the cell cycle, cell migration and invasion, JAK/STAT signalling pathway and intracellular mitochondrial membrane potential (MMP) and reactive oxygen species (ROS). Cell proliferation was assessed through 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay and effects on colony formation were examined via clonogenic assay. Flow cytometry and western blott analysis were performed to investigate the distribution of cell cycle phases. Flow cytometric analysis was performed for the examination of MMP and ROS production. The effect on JAK/STAT signalling pathway was examined through western blot analysis. Results depicted that 7,8-dihydromethysticin induced concentration- as well as time-dependent inhibition of cell proliferation in leukemia HL-60 cells. Clonogenic assay indicated potential suppression in leukemia HL-60 cell colonies. The 7,8-dihydromethysticin molecule also caused cell cycle arrest at G2/M-phase along with concentration-dependent inhibition of cyclin B1, D1 and E. ROS and MMP measurements indicated significant ROS enhancement and MMP suppression with increasing 7,8-dihydromethysticin concentrations. Additionally, 7,8-dihydromethysticin led to remarkable dose-reliant inhibition of cell invasion as well as cell migration. Therefore, 7,8-dihydromethysticin should be considered a valuable candidate for leukemia research and chemoprevention

A Novel Multiinstance Learning Approach for Liver Cancer Recognition on Abdominal CT Images Based on CPSO-SVM and IO

A novel multi-instance learning (MIL) method is proposed to recognize liver cancer with abdominal CT images based on instance optimization (IO) and support vector machine with parameters optimized by a combination algorithm of particle swarm optimization and local optimization (CPSO-SVM). Introducing MIL into liver cancer recognition can solve the problem of multiple regions of interest classification. The images we use in the experiments are liver CT images extracted from abdominal CT images. The proposed method consists of two main steps: (1) obtaining the key instances through IO by texture features and a classification threshold in classification of instances with CPSO-SVM and (2) predicting unknown samples with the key instances and the classification threshold. By extracting the instances equally based on the entire image, the proposed method can ignore the procedure of tumor region segmentation and lower the demand of segmentation accuracy of liver region. The normal SVM method and two MIL algorithms, Citation-kNN algorithm and WEMISVM algorithm, have been chosen as comparing algorithms. The experimental results show that the proposed method can effectively recognize liver cancer images from two kinds of cancer CT images and greatly improve the recognition accuracy

Autonomous Electron Tomography Reconstruction with Machine Learning

Modern electron tomography has progressed to higher resolution at lower doses by leveraging compressed sensing methods that minimize total variation (TV). However, these sparsity-emphasized reconstruction algorithms introduce tunable parameters that greatly influence the reconstruction quality. Here, Pareto front analysis shows that high-quality tomograms are reproducibly achieved when TV minimization is heavily weighted. However, in excess, compressed sensing tomography creates overly smoothed 3D reconstructions. Adding momentum into the gradient descent during reconstruction reduces the risk of over-smoothing and better ensures that compressed sensing is well behaved. For simulated data, the tedious process of tomography parameter selection is efficiently solved using Bayesian optimization with Gaussian processes. In combination, Bayesian optimization with momentum-based compressed sensing greatly reduces the required compute time$-$an 80% reduction was observed for the 3D reconstruction of SrTiO$_3$ nanocubes. Automated parameter selection is necessary for large scale tomographic simulations that enable the 3D characterization of a wider range of inorganic and biological materials.Comment: 8 pages, 4 figure

Urban Building Energy Planning With Space Distribution and Time Dynamic Simulation

It is important to deal with energy saving in buildings of one city level, and plan the energy system from one building to one city level. We strongly suggest conducting urban building energy planning (UBEP

Nanomechanical-resonator-assisted induced transparency in a Cooper-pair-box system

We propose a scheme to demonstrate the electromagnetically induced transparency (EIT) in a system of a superconducting Cooper-pair box coupled to a nanomechanical resonator. In this scheme, the nanomechanical resonator plays an important role to contribute additional auxiliary energy levels to the Cooper-pair box so that the EIT phenomenon could be realized in such a system. We call it here resonator-assisted induced transparency (RAIT). This RAIT technique provides a detection scheme in a real experiment to measure physical properties, such as the vibration frequency and the decay rate, of the coupled nanomechanical resonator.Comment: To appear in New Journal of Physics: Special Issue "Mechanical Systems at the Quantum Limit

Charge qubit dynamics in a double quantum dot coupled to phonons

The dynamics of charge qubit in a double quantum dot coupled to phonons is investigated theoretically in terms of a perturbation treatment based on a unitary transformation. The dynamical tunneling current is obtained explicitly. The result is compared with the standard perturbation theory at Born-Markov approximation. The decoherence induced by acoustic phonons is analyzed at length. It is shown that the contribution from deformation potential coupling is comparable to that from piezoelectric coupling in small dot size and large tunneling rate case. A possible decoupling mechanism is predicted.Comment: 8 pages, 6 figure