Stress Analysis Based on Simultaneous Heart Rate Variability and EEG Monitoring.

OBJECTIVE: Stress is a significant risk factor for various diseases such as hypertension, heart attack, stroke, and even sudden death. Stress can also lead to psychological and behavioral disorders. Heart rate variability (HRV) can reflect changes in stress levels while other physiological factors, like blood pressure, are within acceptable ranges. Electroencephalogram (EEG) is a vital technique for studying brain activities and provides useful data regarding changes in mental status. This study incorporates EEG and a detailed HRV analysis to have a better understanding and analysis of stress. Investigating the correlation between EEG and HRV under stress conditions is valuable since they provide complementary information regarding stress. METHODS: Simultaneous electrocardiogram (ECG) and EEG recordings were obtained from fifteen subjects. HRV /EEG features were analyzed and compared in rest, stress, and meditation conditions. A one-way ANOVA and correlation coefficient were used for statistical analysis to explore the correlation between HRV features and features extracted from EEG. RESULTS: The HRV features LF (low frequency), HF (high frequency), LF/HF, and rMSSD (root mean square of the successive differences) correlated with EEG features, including alpha power band in the left hemisphere and alpha band power asymmetry. CONCLUSION: This study demonstrated five significant relationships between EEG and HRV features associated with stress. The ability to use stress-related EEG features in combination with correlated HRV features could help improve detecting stress and monitoring the progress of stress treatments/therapies. The outcomes of this study could enhance the efficiency of stress management technologies such as meditation studies and bio-feedback training.

Identification of Prognostic Biomarker Candidates Associated With Melanoma Using High-Dimensional Genomic Data.

Survival of patients with metastatic melanoma varies widely. Melanoma is a highly proliferative, chemo-resistant disease. With the recent availability of immunotherapies such as checkpoint inhibitors, durable response rates have improved but are often still limited to 2-3 years. Response rates to treatment range from 30 to 45% with combination therapy however no improvement in overall survival is frequently observed. Of the available therapies, many have targeted the BRAFV600E mutation that results in abnormal MAPK pathway activation which is important for regulating cell proliferation. Immune checkpoint inhibitors such as anti-PD-1 and anti-PD-L1 offer better success but response rates are still low. Identifying biomarkers to better target those who will respond and identify the right combination of treatment is the best approach. In this study, we utilize data from the Cancer Cell Line Encyclopedia (CCLE), including 62 samples, to examine features of gene expression (19K+) and copy number (20K+) in the melanoma cell lines. We perform a clustering analysis on the feature set to assess genetically similarity among the cell lines. We then discover which specific genes and combinations thereof maximize cluster density. We design a feature selection approach for high-dimensional datasets that integrates multiple disparate machine learning techniques into one cohesive pipeline. Our approach provides a small subset of genes that can accurately distinguish between the clusters of melanoma cell lines across multiple types of classifiers. In particular, we find only the 15 highest ranked genes among the original 19 K are necessary to achieve perfect or near-perfect test split classification performance. Of these 15 genes, some are known to be linked to melanoma or other cancer progressions, while others have not previously been linked to melanoma and are of interest for further examination.

Identifying Clinical and Genomic Features Associated With Chronic Kidney Disease.

We apply a pattern-based classification method to identify clinical and genomic features associated with the progression of Chronic Kidney disease (CKD). We analyze the African-American Study of Chronic Kidney disease with Hypertension dataset and construct a decision-tree classification model, consisting 15 combinatorial patterns of clinical features and single nucleotide polymorphisms (SNPs), seven of which are associated with slow progression and eight with rapid progression of renal disease among African-American Study of Chronic Kidney patients. We identify four clinical features and two SNPs that can accurately predict CKD progression. Clinical and genomic features identified in our experiments may be used in a future study to develop new therapeutic interventions for CKD patients.

PEALD of HfO2 Thin Films: Precursor Tuning and a New Near-Ambient-Pressure XPS Approach to in Situ Examination of Thin-Film Surfaces Exposed to Reactive Gases.

A bottom-up approach starting with the development of new Hf precursors for plasma-enhanced atomic layer deposition (PEALD) processes for HfO2 followed by in situ thin-film surface characterization of HfO2 upon exposure to reactive gases via near-ambient-pressure X-ray photoelectron spectroscopy (NAP-XPS) is reported. The stability of thin films under simulated operational conditions is assessed, and the successful implementation of HfO2 dielectric layers in metal-insulator-semiconductor (MIS) capacitors is demonstrated. Among the series of newly synthesized mono-guanidinato-tris-dialkyl-amido class of Hf precursors, one of them, namely, [Hf{η2-(iPrN)2CNEtMe}(NEtMe)3], was representatively utilized with oxygen plasma, resulting in a highly promising low-temperature PEALD process at 60 °C. The new precursors were synthesized in the multigram scale and thoroughly characterized by thermogravimetric analyses, revealing high and tunable volatility reflected by appreciable vapor pressures and accompanied by thermal stability. Typical ALD growth characteristics in terms of linearity, saturation, and a broad ALD window with constant growth of 1.06 Å cycle-1 in the temperature range of 60-240 °C render this process very promising for fabricating high-purity smooth HfO2 layers. For the first time, NAP-XPS surface studies on selected HfO2 layers are reported upon exposure to reactive H2, O2, and H2O atmospheres at temperatures of up to 500 °C revealing remarkable stability against degradation. This can be attributed to the absence of surface defects and vacancies. On the basis of these promising results, PEALD-grown HfO2 films were used as dielectric layers in the MIS capacitor device fabrication exhibiting leakage current densities less than 10-7 A cm-2 at 2 MV cm-1 and permittivities of up to 13.9 without postannealing.

Low-Temperature Plasma-Enhanced Atomic Layer Deposition of Tin(IV) Oxide from a Functionalized Alkyl Precursor: Fabrication and Evaluation of SnO2-Based Thin-Film Transistor Devices.

A bottom-up process from precursor development for tin to plasma-enhanced atomic layer deposition (PEALD) for tin(IV) oxide and its successful implementation in a working thin-film transistor device is reported. PEALD of tin(IV) oxide thin films at low temperatures down to 60 °C employing tetrakis-(dimethylamino)propyl tin(IV) [Sn(DMP)4] and oxygen plasma is demonstrated. The liquid precursor has been synthesized and thoroughly characterized with thermogravimetric analyses, revealing sufficient volatility and long-term thermal stability. [Sn(DMP)4] demonstrates typical saturation behavior and constant growth rates of 0.27 or 0.42 Å cycle-1 at 150 and 60 °C, respectively, in PEALD experiments. Within the ALD regime, the films are smooth, uniform, and of high purity. On the basis of these promising features, the PEALD process was optimized wherein a 6 nm thick tin oxide channel material layer deposited at 60 °C was applied in bottom-contact bottom-gate thin-film transistors, showing a remarkable on/off ratio of 107 and field-effect mobility of µFE ≈ 12 cm2 V-1 s-1 for the as-deposited thin films deposited at such low temperatures.

Water assisted atomic layer deposition of yttrium oxide using tris(N,N'-diisopropyl-2-dimethylamido-guanidinato) yttrium(iii): process development, film characterization and functional properties.

We report a new atomic layer deposition (ALD) process for yttrium oxide (Y2O3) thin films using tris(N,N'-diisopropyl-2-dimethylamido-guanidinato) yttrium(iii) [Y(DPDMG)3] which possesses an optimal reactivity towards water that enabled the growth of high quality thin films. Saturative behavior of the precursor and a constant growth rate of 1.1 Å per cycle confirm the characteristic self-limiting ALD growth in a temperature range from 175 °C to 250 °C. The polycrystalline films in the cubic phase are uniform and smooth with a root mean squared (RMS) roughness of 0.55 nm, while the O/Y ratio of 2.0 reveal oxygen rich layers with low carbon contaminations of around 2 at%. Optical properties determined via UV/Vis measurements revealed the direct optical band gap of 5.56 eV. The valuable intrinsic properties such as a high dielectric constant make Y2O3 a promising candidate in microelectronic applications. Thus the electrical characteristics of the ALD grown layers embedded in a metal insulator semiconductor (MIS) capacitor structure were determined which resulted in a dielectric permittivity of 11, low leakage current density (≈10-7 A cm-2 at 2 MV cm-1) and high electrical breakdown fields (4.0-7.5 MV cm-1). These promising results demonstrate the potential of the new and simple Y2O3 ALD process for gate oxide applications.

A Classification Model to Predict the Rate of Decline of Kidney Function.

The African American Study of Kidney Disease and Hypertension (AASK), a randomized double-blinded treatment trial, was motivated by the high rate of hypertension-related renal disease in the African-American population and the scarcity of effective therapies. This study describes a pattern-based classification approach to predict the rate of decline of kidney function using surface-enhanced laser desorption ionization/time of flight proteomic data from rapid and slow progressors classified by rate of change in glomerular filtration rate. An accurate classification model consisting of 7 out of 5,751 serum proteomic features is constructed by applying the logical analysis of data (LAD) methodology. On cross-validation by 10-folding, the model was shown to have an accuracy of 80.6 ± 0.11%, sensitivity of 78.4 ± 0.17%, and specificity of 78.5 ± 0.16%. The LAD discriminant is used to identify the patients in different risk groups. The LAD risk scores assigned to 116 AASK patients generated a receiver operating curves curve with AUC 0.899 (CI 0.845-0.953) and outperforms the risk scores assigned by proteinuria, one of the best predictors of chronic kidney disease progression.