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Diverse somatic mutations have been reported to serve as cancer drivers. Recently, it has also been reported that epigenetic regulation is closely related to cancer development. However, the effect of epigenetic changes on cancer is still elusive. In this study, we analyzed DNA methylation data on colon cancer taken from The Caner Genome Atlas. We found that several promoters were significantly hypermethylated in colon cancer patients. Through clustering analysis of differentially methylated DNA regions, we were able to define subgroups of patients and observed clinical features associated with each subgroup. In addition, we analyzed the functional ontology of aberrantly methylated genes and identified the G-protein-coupled receptor signaling pathway as one of the major pathways affected epigenetically. In conclusion, our analysis shows the possibility of characterizing the clinical features of colon cancer subgroups based on DNA methylation patterns and provides lists of important genes and pathways possibly involved in colon cancer development.

Recently, 3D concrete printing has progressed rapidly in the construction industry. However, this technique still contains several factors that influence the buildability and mechanical properties of the printed concrete. Therefore, this study investigated the effects of the nozzle speed, the interlayer interval time, the rotations per minute (RPMs) of the screw in the 3D printing device, and the presence of lateral supports on the buildability of 3D concrete printing. In addition, this paper presents the results of the mechanical properties, including the compressive, splitting tensile, and flexural tensile strengths of 3D printed concrete. The buildability of 3D printed structures was improved with an extended interlayer interval time of up to 300 s. The printing processes were interrupted because of tearing of concrete filaments, which was related to excessive RPMs of the mixing screw. The test results also showed that a lateral support with a wide contact surface could improve the resistance to buckling failure for 3D printed structures. The test results of the mechanical properties of the 3D printed concrete specimens indicated that the compressive, splitting tensile, and flexural tensile strengths significantly depended on the bonding behavior at the interlayers of the printed specimens. In addition, although metal laths were expected to improve the tensile strength of the printed specimens, they adversely affected the tensile performance due to weak bonding between the reinforcements and concrete filaments.

We developed a three-dimensional unstructured grid coastal and estuarine circulation model, named the General Ocean Model (GOM). Combining the finite volume and finite difference methods, GOM achieved both the exact conservation and computational efficiency. The propagation term was implemented by a semi-implicit numerical scheme, the so-called θ scheme, and the time-explicit Eulerian–Lagrangian method was used to discretize the nonlinear advection term to remove the major limitation of the time step, which appears when solving shallow water equations, by the Courant–Friedrichs–Lewy stability condition. Because the GOM uses orthogonal unstructured computational grids, allowing both triangular and quadrilateral grids, considerable flexibility to resolve complex coastal boundaries is allowed without any transformation of governing equations. The GOM was successfully verified with five analytical solutions, and it was also validated when applied to the Texas coast, showing an overall skill value of 0.951. The verification results showed that the algorithm used in GOM was correctly coded, and it is efficient and robust.

We recently proposed a numerical model using equivalent circuit models to analyze the resonance characteristics of Langevin transducers and design them in a systematic manner. However, no pre-load torque biased by a metal bolt was considered in the model. Here, a parametric study is, therefore, carried out to reveal how model parameters are adapted to incorporate the pre-compression effect into our existing model. Analytical results are compared with corresponding experimental data, particularly regarding the input electrical impedance and effective electromechanical coupling coefficient for the transducer at resonance modes. The frequency response of input impedance is presented as a function of torque, both theoretically and experimentally. For 10.0 N·m bias, for instance, both resonance and anti-resonance frequencies are calculated as 38.64 kHz and 39.78 kHz, while these are measured as 38.62 kHz and 39.77 kHz by the impedance analyzer. The impedance difference between these cases is 14 Ω at resonance and 9 kΩ at anti-resonance, while the coupling coefficients in both cases become 0.238 and 0.239, respectively. Hence, these test results are closely matched with their theoretical values. Consequently, this study provides a quantitative guideline that specifies the pre-loading condition of bolt clamps with proper parameter settings to predict the intended resonance characteristics of Langevin transducers.

How can we find patterns and anomalies in a tensor, i.e., multi-dimensional array, in an efficient and directly interpretable way? How can we do this in an online environment, where a new tensor arrives at each time step? Finding patterns and anomalies in multi-dimensional data have many important applications, including building safety monitoring, health monitoring, cyber security, terrorist detection, and fake user detection in social networks. Standard tensor decomposition results are not directly interpretable and few methods that propose to increase interpretability need to be made faster, more memory efficient, and more accurate for large and quickly generated data in the online environment. We propose two versions of a fast, accurate, and directly interpretable tensor decomposition method we call CTD that is based on efficient sampling method. First is the static version of CTD, i.e., CTD-S, that provably guarantees up to 11× higher accuracy than that of the state-of-the-art method. Also, CTD-S is made up to 2.3× faster and up to 24× more memory-efficient than the state-of-the-art method by removing redundancy. Second is the dynamic version of CTD, i.e. CTD-D, which is the first interpretable dynamic tensor decomposition method ever proposed. It is also made up to 82× faster than the already fast CTD-S by exploiting factors at previous time step and by reordering operations. With CTD, we demonstrate how the results can be effectively interpreted in online distributed denial of service (DDoS) attack detection and online troll detection.

Robust PCA is a modification of PCA, which works well on corrupted observations. Existing robust PCA algorithms are typically based on batch optimization, and have to load all the samples into memory. Therefore, those algorithms have large computational complexity as the size of data increases, and have difficulty with real time processing. In this paper, we propose a projection based Robust Principal Component Analysis (RPCA) in order to use RPCA as an online algorithm for real time processing. The proposed online algorithm in this paper reduces computational complexity significantly, although the proposed algorithm has negligible performance degradation compared to conventional schemes. The proposed technique can be applied to various applications, which need real time processing of RPCA.

Science and technology is critical for execution and operations of modern businesses. As business operations consists of purposeful execution of technologies with appropriate social cooperation and coordination, knowledge in use is actually cross-disciplinary irrespective of domain or discipline of business. As information technology infiltrates today's businesses, people would need to work smarter using technologies including information technologies. This study explores executives’ perceptions of cross-disciplinary knowledge in coming era of smart businesses. An instrument was developed, asking questions about relative weights of different cross-disciplinary knowledge, frequencies and necessity of cross-disciplinary training, decision criteria in recruiting new employees and in promoting existing employees, and preferences in cross-disciplinary curriculum and sequence of relevant training courses. Results indicate that executives maintain high opinions concerning the value of science and technology as a critical contributor to the successful cross-disciplinary operation of their businesses. They seem to understand clearly that science and technology may not benefit their businesses unless it is applied in a cross-disciplinary manner. Executives prioritize cross-disciplinary knowledge domains as follows in order of importance: (1) Science Technology Enterprise (STE); (2) Science Technology Society (STS); and (3) Science Technology Humanities (STH). Implications are discussed with further research issues.

Supercooling preservation holds the potential to drastically extend the preservation time of organs, tissues and engineered tissue products, and fragile cell types that do not lend themselves well to cryopreservation or vitrification. Here, we investigate the effects of supercooling preservation (SCP at -4(o)C) on primary rat hepatocytes stored in cryovials and compare its success (high viability and good functional characteristics) to that of static cold storage (CS at +4(o)C) and cryopreservation. We consider two prominent preservation solutions a) Hypothermosol (HTS-FRS) and b) University of Wisconsin solution (UW) and a range of preservation temperatures (-4 to -10 (o)C). We find that there exists an optimum temperature (-4(o)C) for SCP of rat hepatocytes which yields the highest viability; at this temperature HTS-FRS significantly outperforms UW solution in terms of viability and functional characteristics (secretions and enzymatic activity in suspension and plate culture). With the HTS-FRS solution we show that the cells can be stored for up to a week with high viability (~56%); moreover we also show that the preservation can be performed in large batches (50 million cells) with equal or better viability and no loss of functionality as compared to smaller batches (1.5 million cells) performed in cryovials.

Acute kidney injury is a devastating syndrome that afflicts over 2,000,000 people in the US per year, with an associated mortality of greater than 70% in severe cases. Unfortunately, standard-of-care treatments are not sufficient for modifying the course of disease. Many groups have explored the use of bone marrow stromal cells (BMSCs) for the treatment of AKI because BMSCs have been shown to possess unique anti-inflammatory, cytoprotective, and regenerative properties in vitro and in vivo. It is yet unresolved whether the primary mechanisms controlling BMSC therapy in AKI depend on direct cell infusion, or whether BMSC-secreted factors alone are sufficient for mitigating the injury. Here we show that BMSC-secreted factors are capable of providing a survival benefit to rats subjected to cisplatin-induced AKI. We observed that when BMSC-conditioned medium (BMSC-CM) is administered intravenously, it prevents tubular apoptosis and necrosis and ameliorates AKI. In addition, we observed that BMSC-CM causes IL-10 upregulation in treated animals, which is important to animal survival and protection of the kidney. In all, these results demonstrate that BMSC-secreted factors are capable of providing support without cell transplantation, and the IL-10 increase seen in BMSC-CM-treated animals correlates with attenuation of severe AKI.