Discrimination of Genetic Biomarkers of Disease through Machine-Learning-Based Hypothesis Testing of Direct SERS Spectra of DNA and RNA.

Cancer is globally a leading cause of death that would benefit from diagnostic approaches detecting it in its early stages. However, despite much research and investment, cancer early diagnosis is still underdeveloped. Owing to its high sensitivity, surface-enhanced Raman spectroscopy (SERS)-based detection of biomarkers has attracted growing interest in this area. Oligonucleotides are an important type of genetic biomarkers as their alterations can be linked to the disease prior to symptom onset. We propose a machine-learning (ML)-enabled framework to analyze complex direct SERS spectra of short, single-stranded DNA and RNA targets to identify relevant mutations occurring in genetic biomarkers, which are key disease indicators. First, by employing ad hoc-synthesized colloidal silver nanoparticles as SERS substrates, we analyze single-base mutations in ssDNA and RNA sequences using a direct SERS-sensing approach. Then, an ML-based hypothesis test is proposed to identify these changes and differentiate the mutated sequences from the corresponding native ones. Rooted in "functional data analysis," this ML approach fully leverages the rich information and dependencies within SERS spectral data for improved modeling and detection capability. Tested on a large set of DNA and RNA SERS data, including from miR-21 (a known cancer miRNA biomarker), our approach is shown to accurately differentiate SERS spectra obtained from different oligonucleotides, outperforming various data-driven methods across several performance metrics, including accuracy, sensitivity, specificity, and F1-scores. Hence, this work represents a step forward in the development of the combined use of SERS and ML as effective methods for disease diagnosis with real applicability in the clinic.

SPATIO-TEMPORAL SHORT-TERM WIND FORECAST: A CALIBRATED REGIME-SWITCHING METHOD.

Accurate short-term forecasts are indispensable for the integration of wind energy in power grids. On a wind farm, local wind conditions exhibit sizeable variations at a fine temporal resolution. Existing statistical models may capture the in-sample variations in wind behavior, but are often shortsighted to those occurring in the near future, that is, in the forecast horizon. The calibrated regime-switching method proposed in this paper introduces an action of regime dependent calibration on the predictand (here the wind speed variable), which helps correct the bias resulting from out-of-sample variations in wind behavior. This is achieved by modeling the calibration as a function of two elements: the wind regime at the time of the forecast (and the calibration is therefore regime dependent), and the runlength, which is the time elapsed since the last observed regime change. In addition to regime-switching dynamics, the proposed model also accounts for other features of wind fields: spatio-temporal dependencies, transport effect of wind and nonstationarity. Using one year of turbine-specific wind data, we show that the calibrated regime-switching method can offer a wide margin of improvement over existing forecasting methods in terms of both wind speed and power.

Spatio-temporal asymmetry of local wind fields and its impact on short-term wind forecasting.

The massive amounts of spatio-temporal data collected in today's wind farms have created a necessity for accurate spatio-temporal models. Despite the growing recognition for non-separable spatio-temporal models, a significant reliance on separable, symmetric models is still the norm in today's renewable industry. We discover that the broad use of separable models is due to the handling of wind data in a setting that does not reveal their fine-scale spatio-temporal structure. The contribution of this research is two-fold. First, we devise a special pair of spatio-temporal "lens" that allows us to see the fine-scale spatio-temporal variations and interactions, and subsequently, we conclude that local wind fields exhibit strong signs of non-separability and asymmetry. Using one year of turbine-specific wind measurements, we show that asymmetry can in fact be detected in more than 93% of the time. Second, making use of the spatio-temporal lens, we propose an enhanced procedure for short-term wind speed forecast. Substantial improvements in forecast accuracy in both wind speed and wind power were observed. When combined with certain intelligent methods such as support vector machine, additional improvements are possible.