Knockdown of E2F4 suppresses the growth of ovarian cancer cells through the cell cycle pathway.

Ovarian cancer remains one of the major causes of death from gynecologic cancer in developed countries. The E2F family has been shown to have a central role in the control of cell proliferation, differentiation, and cell cycle progression in various types of cancer. Despite advances in cancer research, the carcinogenic role of E2F transcription factor 4 (E2F4) in ovarian cancer remains unclear. In this study, we investigated the underlying molecular mechanism of E2F4 in human ovarian cancer cells (OCC). E2F4 expression was demonstrated by quantitative real time polymerase chain reaction (qRT-PCR) in OCC. The alterations of expression values were determined using 2(-ΔΔCt) method. The effects of suppressing E2F4 on cell proliferation, migration, and differentiation were evaluated by cell proliferation assay, colony formation assay and wound healing assay in vitro. Overexpression of E2F4 was found at both mRNA and protein levels in OCC. Small interfering RNA was used to suppress E2F4 expression. Depletion of E2F4 inhibited cell proliferation and suppressed the cell migration and colony formation ability compared to controls. The expression of cell cycle machinery including cyclin A, cyclin D and cyclin dependent kinase 2 (CDK2) was increased after E2F4 knockdown. E2F4 modulates ovarian cancer cell proliferation and migration through cell cycle components including cyclin A, cyclin D, and CDK2. Our findings indicate that E2F4 may serve as a valuable candidate and therapeutic target for ovarian cancer treatment in regard to cell cycle control.

Two-Stage Latent Dynamics Modeling and Filtering for Characterizing Individual Walking and Running Patterns with Smartphone Sensors.

Recently, data from built-in sensors in smartphones have been readily available, and analyzing data for various types of health information from smartphone users has become a popular health care application area. Among relevant issues in the area, one of the most prominent topics is analyzing the characteristics of human movements. In this paper, we focus on characterizing the human movements of walking and running based on a novel machine learning approach. Since walking and running are human fundamental activities, analyzing their characteristics promptly and automatically during daily smartphone use is particularly valuable. In this paper, we propose a machine learning approach, referred to as 'two-stage latent dynamics modeling and filtering' (TS-LDMF) method, where we combine a latent space modeling stage with a nonlinear filtering stage, for characterizing individual dynamic walking and running patterns by analyzing smartphone sensor data. For the task of characterizing movements, the proposed method makes use of encoding the high-dimensional sequential data from movements into random variables in a low-dimensional latent space. The use of random variables in the latent space, often called latent variables, is particularly useful, because it is capable of conveying compressed information concerning movements and efficiently handling the uncertainty originating from high-dimensional sequential observation. Our experimental results show that the proposed use of two-stage latent dynamics modeling and filtering yields promising results for characterizing individual dynamic walking and running patterns.

LSTM-Guided Coaching Assistant for Table Tennis Practice.

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

Extraction of natural colorant from purple sweet potato and dyeing of fabrics with silver nanoparticles for augmented antibacterial activity against skin pathogens.

The main objective of this study was to extract natural colorant from purple sweet potato powder (PSPP) via a water bath and ultrasound water bath using acidified ethanol (A. EtOH) as the extraction solvent. When optimizing the colorant extraction conditions of the solvents, acidified ethanol with ultrasound yielded a high extraction capacity and color intensity at pH2, temperature of 80°C, 20mL of A. EtOH, 1.5g of PSPP, time of 45min, and ultrasonic output power of 75W. Subsequently, the colorant was extracted using the optimized conditions for dyeing of textiles (leather, silk, and cotton). This natural colorant extraction technique can avoid serious environmental pollution during the extraction and is an alternative to synthetic dyes, using less solvent and simplified abstraction procedures. The extracted purple sweet potato natural colorant (PSPC) was used to dye leather, silk, and cotton fabrics in an eco-friendly approach with augmented antibacterial activity by in situ synthesis of silver nanoparticles (AgNPs) and dyeing. The optimal dyeing conditions for higher color strength (K/S) values were pH2 and 70°C for 45min. The colorimetric parameters L∗, a∗, b∗, C, and H were measured to determine the depth of the color. The Fourier transform infrared spectroscopy (FTIR) spectra of undyed control, dyed with PSPC and dyed with blend of PSPC and AgNPs treated leather, silk and cotton fabric were investigated to study the interaction among fiber type, nanoparticles, and dye. The structural morphology of leather and silk and cotton fabrics and the anchoring of AgNPs with elemental compositions were investigated by scanning electron microscopy-energy-dispersive X-ray spectroscopy (SEM-EDS). The dry and wet rubbing fastness for dye alone and dye with nanoparticles were grade 4-5 and 4, respectively. Thus, the results of the present study clearly suggest that in situ synthesis of AgNPs along with dyeing should be considered in the development of antimicrobial textile finishes.

Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence.

Epipolar resampling is the procedure of eliminating vertical disparity between stereo images. Due to its importance, many methods have been developed in the computer vision and photogrammetry field. However, we argue that epipolar resampling of image sequences, instead of a single pair, has not been studied thoroughly. In this paper, we compare epipolar resampling methods developed in both fields for handling image sequences. Firstly we briefly review the uncalibrated and calibrated epipolar resampling methods developed in computer vision and photogrammetric epipolar resampling methods. While it is well known that epipolar resampling methods developed in computer vision and in photogrammetry are mathematically identical, we also point out differences in parameter estimation between them. Secondly, we tested representative resampling methods in both fields and performed an analysis. We showed that for epipolar resampling of a single image pair all uncalibrated and photogrammetric methods tested could be used. More importantly, we also showed that, for image sequences, all methods tested, except the photogrammetric Bayesian method, showed significant variations in epipolar resampling performance. Our results indicate that the Bayesian method is favorable for epipolar resampling of image sequences.

Large Protein Dynamics Described by Hierarchical-Component Mode Synthesis.

Protein dynamics has played a pivotal role in understanding the biological function of protein. For investigation of such dynamics, normal-mode analysis (NMA) has been broadly employed with atomistic model and/or coarse-grained models such as elastic network model (ENM). For large protein complexes, NMA with even ENM encounters the expensive computational process such as diagonalization of Hessian (stiffness) matrix. Here, we suggest the hierarchical-component mode synthesis (hCMS), which allows the fast computation of low-frequency normal modes related to conformational change. Specifically, a large protein structure is regarded as a combination of several structural units, for which the eigen-value problem is utilized for obtaining the frequencies and their normal modes for each structural unit, and consequently, such frequencies and normal modes are assembled with geometrical constraint for interface between structural units in order to find the low-frequency normal modes of a large protein complex. It is shown that hCMS is able to provide the normal modes with accuracy, quantitatively comparable to those of original NMA. This implies that hCMS may enable the computationally efficient analysis of large protein dynamics.