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To ensure the long-term reliability of tidal stream turbines, waves and currents must be considered during the design phase. In this paper, a three-bladed horizontal axis turbine with a diameter of 0.9 m was used as the baseline model. OpenFOAM, an open-source computational fluid dynamics (CFD) library platform, was used to predict the performance of a horizontal axis tidal stream turbine (HATST) under waves and currents. A mesh dependency test was carried out to select the optimal mesh to capture the flow’s features. As a validation study, the power of the turbine under only the current was predicted and was found to be consistent with the experimental results. The generated wave profile under a current was compared with the results obtained using the third-order Stokes wave theory. The performance of the HATST was predicted for various wave frequencies and heights and compared with experimental data. The effect of the wave height on the power performance was greater than the wave frequency. Vortical flow structures behind the turbine were investigated for various wave conditions. The generated tip vortices propagated upward and downward at wave crest and trough conditions, respectively.

The tumor mutational burden (TMB) is a genomic biomarker, which can help in identifying patients most likely to benefit from immunotherapy across a wide range of tumor types including bladder cancer. DNA sequencing, such as whole exome sequencing (WES) is typically used to determine the number of acquired mutations in the tumor. However, WES is expensive, time consuming and not applicable to all patients, and hence it is difficult to be incorporated into clinical practice. This study investigates the feasibility to predict bladder cancer patients TMB by using histological image features. We design an automated whole slide image analysis pipeline that predicts bladder cancer patient TMB via histological features extracted by using transfer learning on deep convolutional networks. The designed pipeline is evaluated to publicly available large histopathology image dataset for a cohort of 253 patients with bladder cancer obtained from The Cancer Genome Atlas (TCGA) project. Experimental results show that our technique provides over 73% classification accuracy, and an area under the receiver operating characteristic curve of 0.75 in distinguishing low and high TMB patients. In addition, it is found that the predicted low and high TMB patients have statistically different survivals, with the p value of 0.047. Our results suggest that bladder cancer patient TMB is predictable by using histological image features derived from digitized H&E slides. Our method is extensible to histopathology images of other organs for predicting patient clinical outcomes.

Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, 3rd Generation Partnership Project (3GPP) sets an aggressive requirement that a packet should be delivered with 10^-5 packet error rate within 1 ms transmission period. Since the current wireless transmission scheme designed to maximize the coding gain by transmitting capacity achieving long codeblock is not relevant for this purpose, a new transmission scheme to support URLLC is required. In this paper, we propose a new approach to support the short packet transmission, called sparse vector coding (SVC). Key idea behind the proposed SVC technique is to transmit the information after the sparse vector transformation. By mapping the information into the position of nonzero elements and then transmitting it after the random spreading, we obtain an underdetermined sparse system for which the principle of compressed sensing can be applied. From the numerical evaluations and performance analysis, we demonstrate that the proposed SVC technique is very effective in URLLC transmission and outperforms the 4G LTE and LTE-Advanced scheme., To appear in IEEE Transactions on Wireless Communications. Copyright 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses

Identifying altered pathways that are associated with specific cancer types can potentially bring a significant impact on cancer patient treatment. Accurate identification of such key altered pathways information can be used to develop novel therapeutic agents as well as to understand the molecular mechanisms of various types of cancers better. Tri-matrix factorization is an efficient tool to learn associations between two different entities (e.g., cancer types and pathways in our case) from data. To successfully apply tri-matrix factorization methods to biomedical problems, biological prior knowledge such as pathway databases or protein-protein interaction (PPI) networks, should be taken into account in the factorization model. However, it is not straightforward in the Bayesian setting even though Bayesian methods are more appealing than point estimate methods, such as a maximum likelihood or a maximum posterior method, in the sense that they calculate distributions over variables and are robust against overfitting. We propose a Bayesian (semi-)nonnegative matrix factorization model for human cancer genomic data, where the biological prior knowledge represented by a pathway database and a PPI network is taken into account in the factorization model through a finite dependent Beta-Bernoulli prior. We tested our method on The Cancer Genome Atlas (TCGA) dataset and found that the pathways identified by our method can be used as a prognostic biomarkers for patient subgroup identification., Comment: This work has been accepted to "ML4H: Machine Learning for Health" workshop at NIPS 2017 (https://ml4health.github.io/2017/)

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response., Author Summary Acute Myeloid Leukemia (AML) is a hematological cancer with a very low 5-year survival rate. It is a very heterogeneous disease, meaning that the molecular underpinnings that cause AML vary greatly among patients, necessitating the use of precision medicine for treatment. While this personalized approach could be greatly improved by the incorporation of high-throughput proteomics data into AML patient prognosis, the quantitative methods to do so are lacking. We held the DREAM 9 AML Outcome Prediction Challenge to foster support, collaboration, and participation from multiple scientific communities in order to solve this problem. The outcome of the challenge yielded several accurate methods (AUROC >0.78, BAC > 0.69) capable of predicting whether a patient would respond to therapy. Moreover, this study also determined aspects of the methods which enabled accurate predictions, as well as key signaling proteins that were informative to the most accurate models.

Nanotechnology has held great promise for revolutionizing biology. The biological behavior of nanomaterials depends primarily on how they interface to biomolecules and their surroundings. Unfortunately, interface issues like non-specific adsorption are still the biggest obstacles to the success of nanobiotechnology and nanomedicine, and have held back widespread practical use of nanotechnology in biology. Not only does the biological interface of nanoparticles (NPs) need to be understood and controlled, but also NPs must be treated as biological entities rather than inorganic ones. Furthermore, one can adopt an engineering perspective of the NP–biological interface, realizing that it has unique, exploitable properties., National Institutes of Health (U.S.) (R21 EB008156-01), National Science Foundation (U.S.) (DMR 0906838)