Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers ofsurvival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time

Nonlinear System Modeling, Optimal Cam Design, and Advanced System Control for an Electromechanical Engine Valve Drive

A cam-based shear force-actuated electromechanical valve drive system offering variable valve timing in internal combustion engines was previously proposed and demonstrated. To transform this concept into a competitive commercial product, several major challenges need to addressed, including the reduction of power consumption, transition time, and size. As shown in this paper, by using nonlinear system modeling, optimizing cam design, and exploring different control strategies, the power consumption has been reduced from 140 to 49 W (65%), the transition time has been decreased from 3.3 to 2.7 ms (18%), and the actuator torque requirement has been cut from 1.33 to 0.30 N·m (77%).Sheila and Emanuel Landsman Foundatio

Data-driven discovery and extrapolation of parameterized pattern-forming dynamics

Pattern-forming systems can exhibit a diverse array of complex behaviors as external parameters are varied, enabling a variety of useful functions in biological and engineered systems. First-principles derivations of the underlying transitions can be characterized using bifurcation theory on model systems whose governing equations are known. In contrast, data-driven methods for more complicated and realistic systems whose governing evolution dynamics are unknown have only recently been developed. Here we develop a data-driven approach, the {\em sparse identification for nonlinear dynamics with control parameters} (SINDyCP), to discover dynamics for systems with adjustable control parameters, such as an external driving strength. We demonstrate the method on systems of varying complexity, ranging from discrete maps to systems of partial differential equations. To mitigate the impact of measurement noise, we also develop a weak formulation of SINDyCP and assess its performance on noisy data. We demonstrate applications including the discovery of universal pattern-formation equations, and their bifurcation dependencies, directly from data accessible from experiments and the extrapolation of predictions beyond the weakly nonlinear regime near the onset of an instability.Comment: 6 pages, 4 figures, plus supplemen

Strong Anharmonicity at the Origin of Anomalous Thermal Conductivity in Double Perovskite Cs2 NaYbCl6

Anomalous thermal transport of Cs2 NaYbCl6 double-halide perovskite above room temperature is reported and rationalized. Calculations of phonon dispersion relations and scattering rates up to the fourth order in lattice anharmonicity have been conducted to determine their effective dependence on temperature. These findings show that specific phonon group velocities and lifetimes increase if the temperature is raised above 500 K. This, in combination with anharmonicity, provides the microscopic mechanism responsible for the increase in lattice thermal conductivity at high temperatures, contrary to the predictions of phonon transport theories based on solely cubic anharmonicity. The model accurately and quantitatively reproduces the experimental thermal conductivity data as a function of temperature

Distinctive South and East Asian Monsoon circulation responses to global warming

The Asian summer monsoon (ASM) is the most energetic circulation system. Projecting its future change is critical for the mitigation and adaptation of billions of people living in the region. There are two important components within the ASM: South Asian summer monsoon (SASM) and East Asian summer monsoon (EASM). Although current state-of-the-art climate models projected increased precipitation in both SASM and EASM due to the increase of atmospheric moisture, their circulation changes differ markedly—A robust strengthening (weakening) of EASM (SASM) circulation was projected. By separating fast and slow processes in response to increased CO2 radiative forcing, we demonstrate that EASM circulation strengthening is attributed to the fast land warming and associated Tibetan Plateau thermal forcing. In contrast, SASM circulation weakening is primarily attributed to an El Niño-like oceanic warming pattern in the tropical Pacific and associated suppressed precipitation over the Maritime Continent

Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices

Sound event detection (SED) is a hot topic in consumer and smart city applications. Existing approaches based on Deep Neural Networks are very effective, but highly demanding in terms of memory, power, and throughput when targeting ultra-low power always-on devices. Latency, availability, cost, and privacy requirements are pushing recent IoT systems to process the data on the node, close to the sensor, with a very limited energy supply, and tight constraints on the memory size and processing capabilities precluding to run state-of-the-art DNNs. In this paper, we explore the combination of extreme quantization to a small-footprint binary neural network (BNN) with the highly energy-efficient, RISC-V-based (8+1)-core GAP8 microcontroller. Starting from an existing CNN for SED whose footprint (815 kB) exceeds the 512 kB of memory available on our platform, we retrain the network using binary filters and activations to match these memory constraints. (Fully) binary neural networks come with a natural drop in accuracy of 12-18% on the challenging ImageNet object recognition challenge compared to their equivalent full-precision baselines. This BNN reaches a 77.9% accuracy, just 7% lower than the full-precision version, with 58 kB (7.2 times less) for the weights and 262 kB (2.4 times less) memory in total. With our BNN implementation, we reach a peak throughput of 4.6 GMAC/s and 1.5 GMAC/s over the full network, including preprocessing with Mel bins, which corresponds to an efficiency of 67.1 GMAC/s/W and 31.3 GMAC/s/W, respectively. Compared to the performance of an ARM Cortex-M4 implementation, our system has a 10.3 times faster execution time and a 51.1 times higher energy-efficiency.Comment: 6 pages conferenc

The metal-responsive transcription factor-1 protein is elevated in human tumors

We previously identified metal-responsive transcription factor-1 (MTF-1) as a positive contributor to mouse fibrosarcoma growth through effects on cell survival, proliferation, tumor angiogenesis and extracellular matrix remodeling. In the present study, we investigated MTF-1 protein expression in human tissues by specific immunostaining of both normal and tumor tissue samples. Immunohistochemical (IHC) staining of a human tissue microarray (TMA), using a unique anti-human MTF-1 antibody, indicated constitutive MTF-1 expression in most normal tissues, with liver and testis displaying comparatively high levels of expression. Nevertheless, MTF-1 protein levels were found to be significantly elevated in diverse human tumor types, including breast, lung and cervical carcinomas. IHC analysis of a separate panel of full-size tissue sections of human breast cancers, including tumor and normal adjacent, surrounding tissue, confirmed and extended the results of the TMA analysis. Taken with our previous findings, this new study suggests a role for MTF-1 in human tumor development, growth or spread. Moreover, the study suggests that MTF-1 could be a novel therapeutic target that offers the opportunity to manipulate metal or redox homeostasis in tumor cells

GeneCAI: Genetic Evolution for Acquiring Compact AI

In the contemporary big data realm, Deep Neural Networks (DNNs) are evolving towards more complex architectures to achieve higher inference accuracy. Model compression techniques can be leveraged to efficiently deploy such compute-intensive architectures on resource-limited mobile devices. Such methods comprise various hyper-parameters that require per-layer customization to ensure high accuracy. Choosing such hyper-parameters is cumbersome as the pertinent search space grows exponentially with model layers. This paper introduces GeneCAI, a novel optimization method that automatically learns how to tune per-layer compression hyper-parameters. We devise a bijective translation scheme that encodes compressed DNNs to the genotype space. The optimality of each genotype is measured using a multi-objective score based on accuracy and number of floating point operations. We develop customized genetic operations to iteratively evolve the non-dominated solutions towards the optimal Pareto front, thus, capturing the optimal trade-off between model accuracy and complexity. GeneCAI optimization method is highly scalable and can achieve a near-linear performance boost on distributed multi-GPU platforms. Our extensive evaluations demonstrate that GeneCAI outperforms existing rule-based and reinforcement learning methods in DNN compression by finding models that lie on a better accuracy-complexity Pareto curve