Column Row Convolutional Neural Network: Reducing Parameters for Efficient Image Processing.

Recent advancements in deep learning have achieved significant progress by increasing the number of parameters in a given model. However, this comes at the cost of computing resources, prompting researchers to explore model compression techniques that reduce the number of parameters while maintaining or even improving performance. Convolutional neural networks (CNN) have been recognized as more efficient and effective than fully connected (FC) networks. We propose a column row convolutional neural network (CRCNN) in this letter that applies 1D convolution to image data, significantly reducing the number of learning parameters and operational steps. The CRCNN uses column and row local receptive fields to perform data abstraction, concatenating each direction's feature before connecting it to an FC layer. Experimental results demonstrate that the CRCNN maintains comparable accuracy while reducing the number of parameters and compared to prior work. Moreover, the CRCNN is employed for one-class anomaly detection, demonstrating its feasibility for various applications.

Four-Bits-Per-Cell Operation in an HfO2 -Based Resistive Switching Device.

The quadruple-level cell technology is demonstrated in an Au/Al2 O3 /HfO2 /TiN resistance switching memory device using the industry-standard incremental step pulse programming (ISPP) and error checking/correction (ECC) methods. With the highly optimistic properties of the tested device, such as self-compliance and gradual set-switching behaviors, the device shows 6σ reliability up to 16 states with a state current gap value of 400 nA for the total allowable programmed current range from 2 to 11 µA. It is demonstrated that the conventional ISPP/ECC can be applied to such resistance switching memory, which may greatly contribute to the commercialization of the device, especially competitively with NAND flash. A relatively minor improvement in the material and circuitry may enable even a five-bits-per-cell technology, which can hardly be imagined in NAND flash, whose state-of-the-art multiple-cell technology is only at three-level (eight states) to this day.

Riboflavin as an independent and accurate biomarker for adherence in a randomized double-blind and placebo-controlled clinical trial.

BACKGROUND: Medication adherence is critical for success of clinical trials. OBJECTIVE: To assess oral riboflavin is an adherence marker. METHODS: Riboflavin was incorporated into active treatment and placebo pills for a clinical trial lasting for 2 years. RESULTS: The accuracy (area under the receiver operating curve) of urinary riboflavin was 0.91 as a binary classifier of adherence, and was similar or better than for two active study ingredients daidzein (0.92) and genistein (0.87) (all p < 0.0001). Decreased adherence over time was similar in the two study groups. CONCLUSION: Riboflavin is an accurate and useful biomarker for study pill ingestion.

Peripheral Venous Pressure as an Indicator of Preload Responsiveness During Volume Resuscitation from Hemorrhage.

BACKGROUND: Fluid resuscitation of hypovolemia presumes that peripheral venous pressure (PVP) increases more than right atrial pressure (RAP), so the net pressure gradient for venous return (PVP-RAP) rises. However, the heart and peripheral venous system function under different compliances that could affect their respective pressures during fluid infusion. In a porcine model of hemorrhage resuscitation, we examined whether RAP increases more than PVP, thereby reducing the venous return pressure gradient and blood flow. METHODS: Anesthetized pigs (n = 8) were bled to a mean arterial blood pressure of 40 mm Hg and resuscitated with stored blood and albumin for pulmonary artery occlusion pressures (PAOPs) of 5, 10, 15, and 20 mm Hg. Venous pressures, inferior vena cava blood flow (ultrasonic flowprobe), and left ventricular diastolic compliance (Doppler echocardiography) were measured. Stroke volume variability was calculated. RESULTS: With volume resuscitation, the slope of RAP exceeded PVP (P ≤ 0.0001) when PAOP is 10 to 20 mm Hg, causing the pressure gradient for venous return to progressively decrease. Inferior vena cava blood flow did not further increase after PAOP > 10 mm Hg. The E/e' ratio increased (P = 0.001) during resuscitation indicating reduced diastolic compliance. A significant curvilinear relationship was found between PVP and stroke volume variability (R = 0.62; P < 0.001), where fluid responders had PVP < 15 mm Hg. CONCLUSIONS: Fluid resuscitation above a PAOP 10 mm Hg reduces myocardial compliance and reduces the venous return pressure gradient. The hemodynamic response to fluid resuscitation becomes limited by diastolic properties of the heart. PVP measurement during hemorrhage resuscitation may predict fluid responsiveness and nonresponsiveness.

Statistical Approaches to Candidate Biomarker Panel Selection.

The statistical analysis of robust biomarker candidates is a complex process, and is involved in several key steps in the overall biomarker development pipeline (see Fig. 22.1, Chap. 19 ). Initially, data visualization (Sect. 22.1, below) is important to determine outliers and to get a feel for the nature of the data and whether there appear to be any differences among the groups being examined. From there, the data must be pre-processed (Sect. 22.2) so that outliers are handled, missing values are dealt with, and normality is assessed. Once the processed data has been cleaned and is ready for downstream analysis, hypothesis tests (Sect. 22.3) are performed, and proteins that are differentially expressed are identified. Since the number of differentially expressed proteins is usually larger than warrants further investigation (50+ proteins versus just a handful that will be considered for a biomarker panel), some sort of feature reduction (Sect. 22.4) should be performed to narrow the list of candidate biomarkers down to a more reasonable number. Once the list of proteins has been reduced to those that are likely most useful for downstream classification purposes, unsupervised or supervised learning is performed (Sects. 22.5 and 22.6, respectively).

Biomarker discovery for early detection of hepatocellular carcinoma in hepatitis C-infected patients.

Chronic hepatic disease damages the liver, and the resulting wound-healing process leads to liver fibrosis and the subsequent development of cirrhosis. The leading cause of hepatic fibrosis and cirrhosis is infection with hepatitis C virus (HCV), and of the patients with HCV-induced cirrhosis, 2% to 5% develop hepatocellular carcinoma (HCC), with a survival rate of 7%. HCC is one of the leading causes of cancer-related death worldwide, and the poor survival rate is largely due to late-stage diagnosis, which makes successful intervention difficult, if not impossible. The lack of sensitive and specific diagnostic tools and the urgent need for early-stage diagnosis prompted us to discover new candidate biomarkers for HCV and HCC. We used aptamer-based fractionation technology to reduce serum complexity, differentially labeled samples (six HCV and six HCC) with fluorescent dyes, and resolved proteins in pairwise two-dimensional difference gel electrophoresis. DeCyder software was used to identify differentially expressed proteins and spots picked, and MALDI-MS/MS was used to determine that ApoA1 was down-regulated by 22% (p < 0.004) in HCC relative to HCV. Differential expression quantified via two-dimensional difference gel electrophoresis was confirmed by means of (18)O/(16)O stable isotope differential labeling with LC-MS/MS zoom scans. Technically independent confirmation was demonstrated by triple quadrupole LC-MS/MS selected reaction monitoring (SRM) assays with three peptides specific to human ApoA1 (DLATVYVDVLK, WQEEMELYR, and VSFLSALEEYTK) using (18)O/(16)O-labeled samples and further verified with AQUA peptides as internal standards for quantification. In 50 patient samples (24 HCV and 26 HCC), all three SRM assays yielded highly similar differential expression of ApoA1 in HCC and HCV patients. These results validated the SRM assays, which were independently confirmed by Western blotting. Thus, ApoA1 is a candidate member of an SRM biomarker panel for early diagnosis, prognosis, and monitoring of HCC. Future multiplexing of SRM assays for other candidate biomarkers is envisioned to develop a biomarker panel for subsequent verification and validation studies.

Evaluation of INK4A promoter methylation using pyrosequencing and circulating cell-free DNA from patients with hepatocellular carcinoma.

BACKGROUND: Hyper-methylation of CpG dinucleotides in the promoter region of inhibitor of cyclin-dependent kinase 4A (INK4A) has been reported in 60%-80% of hepatocellular carcinoma (HCC). As INK4A promoter hypermethylation event occurs early in HCC progression, the quantification of INK4A promoter methylation in blood sample may represent a useful biomarker for non-invasive diagnosis and prediction of response to therapy. METHODS: We examined INK4A promoter methylation using circulating cell-free DNA (ccfDNA) in a total of 109 serum specimens, including 66 HCC and 43 benign chronic liver diseases. Methylation of the individual seven CpG sites was examined using pyrosequencing. RESULTS: Our results showed that there were significantly higher levels of methylated INK4A in HCC specimens than controls and that the seven CpG sites had different levels of methylation and might exist in different PCR amplicons. The area under receiver operating characteristic (ROC) curve was 0.82, with 65.3% sensitivity and 87.2% specificity at 5% (LOD), 39.0% sensitivity and 96.5% specificity at 7% LOD, and 20.3% sensitivity and 98.8% specificity at 10% LOD, respectively. CONCLUSIONS: Our results support additional studies incorporating INK4A methylation testing of ccfDNA to further validate the diagnostic, predictive, and prognostic characteristics of this biomarker in HCC patients. The knowledge of the existence of epi-alleles should help improve assay design to maximize detection.

A structured approach to predictive modeling of a two-class problem using multidimensional data sets.

Biological experiments in the post-genome era can generate a staggering amount of complex data that challenges experimentalists to extract meaningful information. Increasingly, the success of an appropriately controlled experiment relies on a robust data analysis pipeline. In this paper, we present a structured approach to the analysis of multidimensional data that relies on a close, two-way communication between the bioinformatician and experimentalist. A sequential approach employing data exploration (visualization, graphical and analytical study), pre-processing, feature reduction and supervised classification using machine learning is presented. This standardized approach is illustrated by an example from a proteomic data analysis that has been used to predict the risk of infectious disease outcome. Strategies for model selection and post hoc model diagnostics are presented and applied to the case illustration. We discuss some of the practical lessons we have learned applying supervised classification to multidimensional data sets, one of which is the importance of feature reduction in achieving optimal modeling performance.

Analysis and predictive modeling of asthma phenotypes.

Molecular classification using robust biochemical measurements provides a level of diagnostic precision that is unattainable using indirect phenotypic measurements. Multidimensional measurements of proteins, genes, or metabolites (analytes) can identify subtle differences in the pathophysiology of patients with asthma in a way that is not otherwise possible using physiological or clinical assessments. We overview a method for relating biochemical analyte measurements to generate predictive models of discrete (categorical) clinical outcomes, a process referred to as "supervised classification." We consider problems inherent in wide (small n and large p) high-dimensional data, including the curse of dimensionality, collinearity and lack of information content. We suggest methods for reducing the data to the most informative features. We describe different approaches for phenotypic modeling, using logistic regression, classification and regression trees, random forest and nonparametric regression spline modeling. We provide guidance on post hoc model evaluation and methods to evaluate model performance using ROC curves and generalized additive models. The application of validated predictive models for outcome prediction will significantly impact the clinical management of asthma.

Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators.

Memristor-integrated passive crossbar arrays (CAs) could potentially accelerate neural network (NN) computations, but studies on these devices are limited to software-based simulations owing to their poor reliability. Herein, we propose a self-rectifying memristor-based 1 kb CA as a hardware accelerator for NN computations. We conducted fully hardware-based single-layer NN classification tasks involving the Modified National Institute of Standards and Technology database using the developed passive CA, and achieved 100% classification accuracy for 1500 test sets. We also investigated the influences of the defect-tolerance capability of the CA, impact of the conductance range of the integrated memristors, and presence or absence of selection functionality in the integrated memristors on the image classification tasks. We offer valuable insights into the behavior and performance of CA devices under various conditions and provide evidence of the practicality of memristor-integrated passive CAs as hardware accelerators for NN applications.

Fibriform Organic Electrochemical Diodes with Rectifying, Complementary Logic and Transient Voltage Suppression Functions for Wearable E-Textile Embedded Circuits.

In this study, a fibriform electrochemical diode capable of performing rectifying, complementary logic and device protection functions for future e-textile circuit systems is fabricated. The diode was fabricated using a simple twisted assembly of metal/polymer semiconductor/ion gel coaxial microfibers and conducting microfiber electrodes. The fibriform diode exhibited a prominent asymmetrical current flow with a rectification ratio of over 102, and its performance was retained after repeated bending deformations and washings. Fundamental studies on the electrochemical interactions of polymer semiconductors with ions reveal that the Faradaic current generated in polymer semiconductors by electrochemical reactions results in an abrupt current increase under a forward bias, in which the threshold voltages of the device are determined by the oxidation or reduction potential of the polymer semiconductor. Textile-embedded full-wave rectifiers and logic gate circuits were implemented by simply integrating the fibriform diodes, exhibiting AC-to-DC signal conversion and logic operation functions, respectively. It was also confirmed that the proposed fibriform diode can suppress transient voltages and thus protect a low-voltage operational wearable e-textile circuit.

Bio-inspired electronic fingerprint PUF device with single-walled carbon nanotube network surface mediated by M13 bacteriophage template.

Human fingerprints are randomly created during fetal activity in the womb, resulting in unique and physically irreproducible fingerprint patterns that are applicable as a biological cryptographic primitive. Similarly, stochastically knitted single-walled carbon nanotube (SWNT) network surfaces exhibit inherently random and unique electrical characteristics that can be exploited as a physical unclonable function (PUF) in the authentication. In this study, filamentous M13 bacteriophages are used as a biological gluing template to create a random SWNT network surface with mechanical flexibility, with electrical properties determined by random variation during fabrication. The resistance profile between two adjacent electrodes was mapped for these M13-mediated SWNT network surfaces, with the results demonstrating a unique resistance profile for each M13-SWNT device, similar to that of human fingerprints. Randomness and uniqueness measures were evaluated as respectively 50.5% and 50% using generated challenge-response pairs. Min-entropy for unpredictability evaluation of the M13-SWNT based PUFs resulted in 0.98. Our results showed that M13-SWNT random network exhibits cryptographic characteristics when used in a bio-inspired PUF device.

How cytokines co-occur across asthma patients: from bipartite network analysis to a molecular-based classification.

Asthmatic patients are currently classified as either severe or non-severe based primarily on their response to glucocorticoids. However, because this classification is based on a post-hoc assessment of treatment response, it does not inform the rational staging of disease or therapy. Recent studies in other diseases suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. We therefore measured cytokine values in bronchoalveolar lavage (BAL) samples of the lower respiratory tract obtained from 83 asthma patients, and used bipartite network visualizations with associated quantitative measures to conduct an exploratory analysis of the co-occurrence of cytokines across patients. The analysis helped to identify three clusters of patients which had a complex but understandable interaction with three clusters of cytokines, leading to insights for a state-based classification of asthma patients. Furthermore, while the patient clusters were significantly different based on key pulmonary functions, they appeared to have no significant relationship to the current classification of asthma patients. These results suggest the need to define a molecular-based classification of asthma patients, which could improve the diagnosis and treatment of this disease.

Dendritic Network Implementable Organic Neurofiber Transistors with Enhanced Memory Cyclic Endurance for Spatiotemporal Iterative Learning.

Dendritic network implementable organic neurofiber transistors with enhanced memory cyclic endurance for spatiotemporal iterative learning are proposed. The architecture of the fibrous organic electrochemical transistors consisting of a double-stranded assembly of electrode microfibers and an iongel gate insulator enables the highly sensitive multiple implementation of synaptic junctions via simple physical contact of gate-electrode microfibers, similar to the dendritic connections of a biological neuron fiber. In particular, carboxylic-acid-functionalized polythiophene as a semiconductor channel material provides stable gate-field-dependent multilevel memory characteristics with long-term stability and cyclic endurance, unlike the conventional poly(alkylthiophene)-based neuromorphic electrochemical transistors, which exhibit short retention and unstable endurance. The dissociation of the carboxylic acid of the polythiophene enables reversible doping and dedoping of the polythiophene channel by effectively stabilizing the ions that penetrate the channel during potentiation and depression cycles, leading to the reliable cyclic endurance of the device. The synaptic weight of the neurofiber transistors with a dendritic network maintains the state levels stably and is independently updated with each synapse connected with the presynaptic neuron to a specific state level. Finally, the neurofiber transistor demonstrates successful speech recognition based on iterative spiking neural network learning in the time domain, showing a substantial recognition accuracy of 88.9%.

Acculturation and serum nutrients thought to be involved with cancer prevention among Mexican American men in the United States.

BACKGROUND: Mexican American men living in the United States who are more acculturated exhibit higher rates of cancer compared to those less acculturated. This study explored the association between acculturation and serum levels of nutrients thought to be involved with cancer prevention among Mexican American men. METHODS: Our sample included 2,479 Mexican American men from the Third National Health and Nutrition Examination Survey (1988-1994). Outcomes were serum levels of micronutrients. Acculturation in Mexican American men was assessed by a combined measure including country of origin, language of interview, and years of residence in the United States and was categorized as follows: (1) foreign-born, 0-5 years in the United States (lowest acculturation), (2) foreign-born, 6-15 years in the United States, (3) foreign-born, > 15 years in the United States, (4) US-born Spanish-speaking, and (5) US-born English-speaking (highest acculturation). RESULTS: Adjusted analyses showed that acculturation decreased the serum levels for vitamin E, vitamin C, and folate and also for some carotenoids (alpha and beta carotenes, beta cryptoxanthin, and lutein-zeaxanthin). By contrast, acculturation increased the serum levels for selenium and lycopene. CONCLUSIONS: With the exception of selenium and lycopene, acculturation among Mexican American men decreased the serum levels for most carotenoids and for vitamin E, vitamin C, and folate. These changes in nutrient profiles, reflecting altered patterns in food consumption or other behaviors, may explain in part why Mexican American men who are more acculturated have an increased risk for diet-related cancer

Neighbourhood ethnic composition and diet among Mexican-Americans.

OBJECTIVES: We explore the association between a neighbourhood's ethnic composition and the foods and nutrients consumed by Mexican-Americans. DESIGN: Cross-sectional survey of a large national sample, from the Third National Health and Nutrition Examination Survey (1988-94), was linked to the 1990 Census. The outcomes were food frequencies and serum levels of micronutrients. The variable of interest was percentage of Mexican-Americans at the census tract level. SETTING: United States. SUBJECTS: A total of 5306 Mexican-American men and women aged 17-90 years. RESULTS: Increased percentage of Mexican-Americans at the census tract level was associated with less consumption of fruits, carrots, spinach/greens and broccoli and with lower serum levels of Se, lycopene, alpha-carotene, vitamin C and folate. By contrast, increased percentage of Mexican-Americans at the census tract level was associated with more consumption of corn, tomatoes, hot red chilli peppers and legumes such as beans, lentils or chickpeas. CONCLUSIONS: An increased percentage of Mexican-Americans at the census tract level was associated with less consumption of selective foods (e.g. some fruits, broccoli) and low levels of serum Se or vitamin C, but it was associated with more consumption of other foods (e.g. legumes, tomatoes, corn products) that may have positive effects on health in this population.

Molecular phenotyping of severe asthma using pattern recognition of bronchoalveolar lavage-derived cytokines.

BACKGROUND: Asthma is a heterogeneous clinical disorder. Methods for objective identification of disease subtypes will focus on clinical interventions and help identify causative pathways. Few studies have explored phenotypes at a molecular level. OBJECTIVE: We sought to discriminate asthma phenotypes on the basis of cytokine profiles in bronchoalveolar lavage (BAL) samples from patients with mild-moderate and severe asthma. METHODS: Twenty-five cytokines were measured in BAL samples of 84 patients (41 severe, 43 mild-moderate) using bead-based multiplex immunoassays. The normalized data were subjected to statistical and informatics analysis. RESULTS: Four groups of asthmatic profiles could be identified on the basis of unsupervised analysis (hierarchical clustering) that were independent of treatment. One group, enriched in patients with severe asthma, showed differences in BAL cellular content, reductions in baseline pulmonary function, and enhanced response to methacholine provocation. Ten cytokines were identified that accurately predicted this group. Classification methods for predicting methacholine sensitivity were developed. The best model analysis predicted hyperresponders with 88% accuracy in 10 trials by using a 10-fold cross-validation. The cytokines that contributed to this model were IL-2, IL-4, and IL-5. On the basis of this classifier, 3 distinct hyperresponder classes were identified that varied in BAL eosinophil count and PC20 methacholine. CONCLUSION: Cytokine expression patterns in BAL can be used to identify distinct types of asthma and identify distinct subsets of methacholine hyperresponders. Further biomarker discovery in BAL may be informative.

Distribution of petrogenic polycyclic aromatic hydrocarbons (PAHs) in seafood following Deepwater Horizon oil spill.

A community-based participatory research was utilized to address the coastal community's concern regarding Deepwater Horizon oil contamination of seafood. Therefore, we analyzed polycyclic aromatic hydrocarbons (PAHs), major toxic constituents of crude oil, in the seafood collected from gulf coast (Louisiana, Alabama and Mississippi) during December 2011-February 2014. PAHs were extracted from edible part of shrimp, oysters, and crabs by the QuEChERS/dsPE procedure and analyzed by gas chromatography-mass spectrometry. The total PAHs data were further analyzed using the General Linear Mixed Model procedure of the SAS (Version 9.3, SAS Institute, Inc., Cary, NC) statistical software. Brown shrimp showed statistically significant differences in PAHs levels with respect to time and locations while white shrimp showed differences at various time points. PAHs levels in oyster and crab samples were not statistically different at the Type I error of 0.05. Overall, the PAHs levels are far below FDA levels of concern for human consumption.

A New Architecture for Fibrous Organic Transistors Based on a Double-Stranded Assembly of Electrode Microfibers for Electronic Textile Applications.

Herein, a unique device architecture is proposed for fibrous organic transistors based on a double-stranded assembly of electrode microfibers for electronic textile applications. A key feature of this work is that the semiconductor channel of the fiber transistor comprises a twist assembly of the source and drain electrode microfibers that are coated by an organic semiconductor. This architecture not only allows the channel dimension of the device to be readily controlled by varying the thickness of the semiconductor layer and the twisted length of the two electrode microfibers, but also passivates the device without affecting interconnections with other electrical components. It is found that the control of crystalline nanostructure of the semiconductor layer is critical for improving both the production yield of the device and the charge-carrier transport in the device. The resulting fibrous organic transistors show a high output current of over -5 mA at a low operation voltage of -1.3 V and a good on/off current ratio of 105 . The device performance is maintained after repeated bending deformation and washing with a strong detergent solution. Application of the fibrous organic transistors to switch current-driven LED devices and detection of electrocardiography signals from a human body are demonstrated.

How do plants see the world? - UV imaging with a TiO2 nanowire array by artificial photosynthesis.

The concept of plant vision refers to the fact that plants are receptive to their visual environment, although the mechanism involved is quite distinct from the human visual system. The mechanism in plants is not well understood and has yet to be fully investigated. In this work, we have exploited the properties of TiO2 nanowires as a UV sensor to simulate the phenomenon of photosynthesis in order to come one step closer to understanding how plants see the world. To the best of our knowledge, this study is the first approach to emulate and depict plant vision. We have emulated the visual map perceived by plants with a single-pixel imaging system combined with a mechanical scanner. The image acquisition has been demonstrated for several electrolyte environments, in both transmissive and reflective configurations, in order to explore the different conditions in which plants perceive light.