KPCA-WRF-prediction of heart rate using deep feature fusion and machine learning classification with tuned weighted hyper-parameter.

The rapid advancement of technology such as stream processing technologies, deep-learning approaches, and artificial intelligence plays a prominent and vital role, to detect heart rate using a prediction model. However, the existing methods could not handle high -dimensional datasets, and deep feature learning to improvise the performance. Therefore, this work proposed a real-time heart rate prediction model, using K-nearest neighbour (KNN) adhered to the principle component analysis algorithm (PCA) and weighted random forest algorithm for feature fusion (KPCA-WRF) approach and deep CNN feature learning framework. The feature selection, from the fused features, was optimized by ant colony optimization (ACO) and particle swarm optimization (PSO) algorithm to enhance the selected fused features from deep CNN. The optimized features were reduced to low dimensions using the PCA algorithm. The significant straight heart rate features are plotted by capturing out nearest similar data point values using the algorithm. The fused features were then classified for aiding the training process. The weighted values are assigned to those tuned hyper parameters (feature matrix forms). The optimal path and continuity of the weighted feature representations are moved using the random forest algorithm, in K-fold validation iterations.

Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space.

Previous research has demonstrated the potential to reconstruct human facial skin spectra based on the responses of RGB cameras to achieve high-fidelity color reproduction of human facial skin in various industrial applications. Nonetheless, the level of precision is still expected to improve. Inspired by the asymmetricity of human facial skin color in the CIELab* color space, we propose a practical framework, HPCAPR, for skin facial reflectance reconstruction based on calibrated datasets which reconstruct the facial spectra in subsets derived from clustering techniques in several spectrometric and colorimetric spaces, i.e., the spectral reflectance space, Principal Component (PC) space, CIELab*, and its three 2D subordinate color spaces, La*, Lb*, and ab*. The spectra reconstruction algorithm is optimized by combining state-of-art algorithms and thoroughly scanning the parameters. The results show that the hybrid of PCA and RGB polynomial regression algorithm with 3PCs plus 1st-order polynomial extension gives the best results. The performance can be improved substantially by operating the spectral reconstruction framework within the subset classified in the La* color subspace. Comparing with not conducting the clustering technique, it attains values of 25.2% and 57.1% for the median and maximum errors for the best cluster, respectively; for the worst, the maximum error was reduced by 42.2%.

Dimension Reduction of Digital Image Descriptors in Neural Identification of Damaged Malting Barley Grains.

The paper covers the problem of determination of defects and contamination in malting barley grains. The analysis of the problem indicated that although several attempts have been made, there are still no effective methods of identification of the quality of barley grains, such as the use of information technology, including intelligent sensors (currently, quality assessment of grain is performed manually). The aim of the study was the construction of a reduced set of the most important graphic descriptors from machine-collected digital images, important in the process of neural evaluation of the quality of BOJOS variety malting barley. Grains were sorted into three size fractions and seed images were collected. As a large number of graphic descriptors implied difficulties in the development and operation of neural classifiers, a PCA (Principal Component Analysis) statistical method of reducing empirical data contained in the analyzed set was applied. The grain quality expressed by an optimal set of transformed descriptors was modelled using artificial neural networks (ANN). The input layer consisted of eight neurons with a linear Postsynaptic Function (PSP) and a linear activation function. The one hidden layer was composed of sigmoid neurons having a linear PSP function and a logistic activation function. One sigmoid neuron was the output of the network. The results obtained show that neural identification of digital images with application of Principal Component Analysis (PCA) combined with neural classification is an effective tool supporting the process of rapid and reliable quality assessment of BOJOS malting barley grains.

Robust principal component analysis-based prediction of protein-protein interaction hot spots.

Proteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein-protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help design protein-protein interaction inhibitors for therapy. Unfortunately, current machine learning methods to predict hot spots, suffer from limitations caused by gross errors in the data matrices. Here, we present a novel data pre-processing pipeline that overcomes this problem by recovering a low rank matrix with reduced noise using Robust Principal Component Analysis. Application to existing databases shows the predictive power of the method.

Simultaneous fMRI and fast-scan cyclic voltammetry bridges evoked oxygen and neurotransmitter dynamics across spatiotemporal scales.

The vascular contributions of neurotransmitters to the hemodynamic response are gaining more attention in neuroimaging studies, as many neurotransmitters are vasomodulatory. To date, well-established electrochemical techniques that detect neurotransmission in high magnetic field environments are limited. Here, we propose an experimental setting enabling simultaneous fast-scan cyclic voltammetry (FSCV) and blood oxygenation level-dependent functional magnetic imaging (BOLD fMRI) to measure both local tissue oxygen and dopamine responses, and global BOLD changes, respectively. By using MR-compatible materials and the proposed data acquisition schemes, FSCV detected physiological analyte concentrations with high temporal resolution and spatial specificity inside of a 9.4 T MRI bore. We found that tissue oxygen and BOLD correlate strongly, and brain regions that encode dopamine amplitude differences can be identified via modeling simultaneously acquired dopamine FSCV and BOLD fMRI time-courses. This technique provides complementary neurochemical and hemodynamic information and expands the scope of studying the influence of local neurotransmitter release over the entire brain.

Applications of an electronic nose in the prediction of oxidative stability of stored biodiesel derived from soybean and waste cooking oil.

Waste cooking oil (WCO) is a valuable feedstock for the synthesis of biodiesel but the product exhibits poor oxidative stability. Techniques available for assessing this parameter are generally expensive and time-consuming, hence the purpose of this study was to develop and validate a rapid and reliable predictive system based on signals from the sensors of a commercial hand-held e-nose instrument. Biodiesels were synthesized from soybean oil and six samples of WCO, and their physicochemical characteristics and oxidative stabilities determined before and after storage in different types of containers for 30 or 60 days at room temperature or 43 °C. Linear regression models were constructed based on principal component analysis of the signals generated by all 32 e-nose sensors and stochastic modeling of signal profiles from individual sensors. The regression model with principal components as predictors was unable to explain the oxidative stability of biodiesels, while the regression model with stochastic parameters (combining signals from 11 sensors) as predictors showed an excellent goodness of fit (R2 = 0.91) with a 45-sample training set and a good quality of prediction (R2 = 0.84) with a 18-sample validation set. The proposed e-nose system was shown to be accurate and efficient and could be used to advantage by producers/distributors of biodiesel in the assessment fuel quality.

Industrial application of QbD and NIR chemometric models in quality improvement of immediate release tablets.

Quality by Design (QbD) and chemometric models are different sides of the same coin. While QbD models utilize experimentally designed settings for optimization of some quality attributes, these settings can also be utilized for chemometric prediction of the same attributes. We aimed to synchronize optimization of comparative dissolution results of carvedilol immediate release tablets with chemometric prediction of dissolution profile and content uniformity of the product. As an industrial application, selection of variables for optimization was done by performing risk assessment utilizing the archived product records at the pharmaceutical site. Experimental tablets were produced with 20 different settings with the variables being contents of sucrose, sodium starch glycolate, lactose monohydrate, and avicel Ph 101. Contents of the excipients were modelled with F1 dissimilarity factor and F2 similarity factor in HCL, acetate, and USP dissolution media to determine the design space. We initiatively utilized Partial Least Square based Structural Equation Modelling (PLS-SEM) to explore how the excipients and their NIR records explained dissolution of the product. Finally, the optimized formula was utilized with varied content of carvedilol for chemometric prediction of the content uniformity.

Application of Tamarind Waste Extracts to Improve the Antioxidant Properties of Tamarind Nectars.

Tamarind fruits are consumed worldwide and their seeds have an underexploited potential. We assessed the effect of the addition of a freeze-dried aqueous of extract tamarind seed (FAE) at three concentration levels (0.3, 1.15 and 2%) on the antioxidant capacity (DPPH, ABTS, FRAP and ORAC) and concentrations of total phenolic compounds in tamarind pulp. Conditions used to prepare the aqueous extracts were established using multivariate optimization. Moreover, nectars prepared from pulps combined with FAE were subjected to sensory tests. Tamarind fruits from three geographic regions in Brazil (Minas Gerais, São Paulo and Bahia) that were harvested in 2013 and 2014 were used in the study. Generally, the freeze-dried aqueous extracts increased the concentrations of antioxidants in the pulp. The results revealed a positive correlation between the FAE concentration in the pulp and the antioxidant capacity of all samples, particularly samples from Bahia and Minas Gerais, which presented an increase of up to 1,942% in the ABTS method when 2% FAE was incorporated into the pulp, from approximately 40.1 to 209.1 mMTrolox/gdw and 13.4 to 143.4 mMTrolox/gdw, respectively. Sensory tests indicated the satisfactory acceptance and non-distinction between nectar samples to which FAE was or was not added when the FAE concentration was less than 2.3gFAE/L, regardless of the geographic origin of the samples.

A comparison of the effect of a Growing Up Milk - Lite (GUMLi) v. cows' milk on longitudinal dietary patterns and nutrient intakes in children aged 12-23 months: the GUMLi randomised controlled trial.

The second year of life is a period of nutritional vulnerability. We aimed to investigate the dietary patterns and nutrient intakes from 1 to 2 years of age during the 12-month follow-up period of the Growing Up Milk - Lite (GUMLi) trial. The GUMLi trial was a multi-centre, double-blinded, randomised controlled trial of 160 healthy 1-year-old children in Auckland, New Zealand and Brisbane, Australia. Dietary intakes were collected at baseline, 3, 6, 9 and 12 months post-randomisation, using a validated FFQ. Dietary patterns were identified using principal component analysis of the frequency of food item consumption per d. The effect of the intervention on dietary patterns and intake of eleven nutrients over the duration of the trial were investigated using random effects mixed models. A total of three dietary patterns were identified at baseline: 'junk/snack foods', 'healthy/guideline foods' and 'breast milk/formula'. A significant group difference was observed in 'breast milk/formula' dietary pattern z scores at 12 months post-randomisation, where those in the GUMLi group loaded more positively on this pattern, suggesting more frequent consumption of breast milk. No difference was seen in the other two dietary patterns. Significant intervention effects were seen on nutrient intake between the GUMLi (intervention) and cows' milk (control) groups, with lower protein and vitamin B12, and higher Fe, vitamin D, vitamin C and Zn intake in the GUMLi (intervention) group. The consumption of GUMLi did not affect dietary patterns, however, GUMLi participants had lower protein intake and higher Fe, vitamins D and C and Zn intake at 2 years of age.

Dietary patterns and hearing loss in older men enrolled in the Caerphilly Study.

The association between dietary patterns (DP) and prevalence of hearing loss in men enrolled in the Caerphilly Prospective Study was investigated. During 1979-1983, the study recruited 2512 men aged 45-59 years. At baseline, dietary data were collected using a semi-quantitative FFQ, and a 7-d weighed food intake (WI) in a 30 % subsample. Five years later, pure-tone unaided audiometric threshold was assessed at 0·5, 1, 2 and 4 kHz. Principal component analysis (PCA) identified three DP and multiple logistic and ordinal logistic regression models examined the association with hearing loss (defined as pure-tone average of frequencies 0·5, 1, 2 and 4 kHz >25 dB). Traditional, healthy and high-sugar/low-alcohol DP were found with both FFQ and WI data. With the FFQ data, fully adjusted models demonstrated significant inverse association between the healthy DP and hearing loss both as a dichotomous variable (OR=0·83; 95 % CI 0·77, 0·90; P<0·001) and as an ordinal variable (OR=0·87; 95 % CI 0·81, 0·94; P<0·001). With the WI data, fully adjusted models showed a significant and inverse association between the healthy DP and hearing loss (OR=0·85; 95 % CI 0·73, 0·99; P<0·03), and a significant association between the traditional DP (per fifth increase) and hearing loss both as a dichotomous variable (OR=1·18; 95 % CI 1·02, 1·35; P=0·02) and as an ordinal variable (OR=1·17; 95 % CI 1·03, 1·33; P=0·02). A healthy DP was significantly and inversely associated with hearing loss in older men. The role of diet in age-related hearing loss warrants further investigation.

Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface.

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications.

Dietary patterns within educational groups and their association with CHD and stroke in the European Prospective Investigation into Cancer and Nutrition-Netherlands cohort.

Higher-educated people often have healthier diets, but it is unclear whether specific dietary patterns exist within educational groups. We therefore aimed to derive dietary patterns in the total population and by educational level and to investigate whether these patterns differed in their composition and associations with the incidence of fatal and non-fatal CHD and stroke. Patterns were derived using principal components analysis in 36 418 participants of the European Prospective Investigation into Cancer and Nutrition-Netherlands cohort. Self-reported educational level was used to create three educational groups. Dietary intake was estimated using a validated semi-quantitative FFQ. Hazard ratios were estimated using Cox Proportional Hazard analysis after a mean follow-up of 16 years. In the three educational groups, similar 'Western', 'prudent' and 'traditional' patterns were derived as in the total population. However, with higher educational level a lower population-derived score for the 'Western' and 'traditional' patterns and a higher score on the 'prudent' pattern were observed. These differences in distribution of the factor scores illustrate the association between education and food consumption. After adjustments, no differences in associations between population-derived dietary patterns and the incidence of CHD or stroke were found between the educational groups (P interaction between 0·21 and 0·98). In conclusion, although in general population and educational groups-derived dietary patterns did not differ, small differences between educational groups existed in the consumption of food groups in participants considered adherent to the population-derived patterns (Q4). This did not result in different associations with incident CHD or stroke between educational groups.

Dietary intake of cod protein beneficially affects concentrations of urinary markers of kidney function and results in lower urinary loss of amino acids in obese Zucker fa/fa rats.

Obesity increases the risk for developing kidney disease, and protection of kidneys through changes in diet should be investigated. Fish intake has been associated with reduced risk of developing kidney disease; therefore, we wanted to investigate whether cod protein intake could prevent or delay the development of kidney damage in an obese rat model that spontaneously develops proteinuria and focal segmental glomerulosclerosis. The aim of the study was to investigate any effects of cod protein intake on established markers of kidney function, amino acid composition, protein utilisation and growth in obese Zucker fa/fa rats in the early stage of decreased renal function. Male obese Zucker fa/fa rats (HsdOla:Zucker-Lepr) were fed cod muscle proteins in an amount corresponding to 25 % of dietary protein, with the remaining protein from a casein/whey mixture (COD diet). A control group was fed a diet with a casein/whey mixture as the only protein source (CAS diet). The intervention started when rats were 9-10 weeks old, and the rats were fed these diets for 4 weeks. At the end of the study, rats fed the COD diet had lower urine concentration of cystatin C, T-cell immunoglobulin mucin-1 (TIM-1), amino acids, carbamide, uric acid and ammonium and higher concentrations of creatine, trimethylamine N-oxide, 1-methylhistidine and 3-methylhistidine, lower kidney concentration of TIM-1 and showed better growth when compared with the CAS group. To conclude, cod protein may have the potential to delay the development of kidney damage in young obese Zucker rats and to improve protein utilisation and growth.

Exploratory dietary patterns: a systematic review of methods applied in pan-European studies and of validation studies.

Besides a priori approaches, using previous knowledge about food characteristics, exploratory dietary pattern (DP) methods, using data at hand, are commonly applied. This systematic literature review aimed to identify exploratory methods on DP in pan-European studies and to inform the development of the DEterminants of DIet and Physical ACtivity (DEDIPAC) toolbox of methods suitable for use in future European studies. The search was conducted in three databases on prospective studies in healthy, free-living people across the whole life span. To identify validated DP methods, an additional search without regional restrictions was conducted. Studies including at least two European countries were retained. The search resulted in six pan-European studies applying principal component/factor analysis (PC/FA) (n 5) or cluster analysis (n 2). The criteria to retain PC/factors ranged from the application of the eigenvalue>1 criterion, the scree plot and/or the interpretability criterion. Furthermore, seven validation studies were identified: DP, derived by PC/FA (n 6) or reduced rank regression (RRR) (n 1) were compared using dietary information from FFQ (n 6) or dietary history (n 1) as study instrument and dietary records (n 6) or 24-h dietary recalls (n 1) as reference. The correlation coefficients for the derived DP ranged from modest to high. To conclude, PC/FA was predominantly applied using the eigenvalue criterion and scree plot to retain DP, but a better description of the applied criteria is highly recommended to enable a standardised application of the method. Research gaps were identified for the methods cluster analysis and RRR, as well as for validation studies on DP.

The 1975 Japanese diet has a stress reduction effect in mice: search for physiological effects using metabolome analysis.

We aimed to find new physiological effects of the Japanese diet. First, to determine the key components in serum from mice fed the 1975 diet, serum from mice fed the 1960, 1975, 1990 or 2005 Japanese diet was analyzed using CE-TOFMS and LC-TOFMS. Based on these results, the key components were determined by principal component analysis. Among the identified compounds, GABA was included. Therefore, a stress reduction effect was inferred as a novel physiological effect of this diet. Next, we tested whether the 1975 diet had an actual stress reduction effect in mice. Mice were given the 1975 diet or a control diet for 4 weeks, after which they were divided into restraint stress and non-stress groups. Mice fed the 1975 diet had significantly decreased stress parameters compared with those fed the control diet. These results provide the first evidence that the 1975 Japanese diet has a stress reduction effect.

Anti-biofilm effect of a butenolide/polymer coating and metatranscriptomic analyses.

Butenolide is an environmentally friendly antifouling natural product, but its efficiency and mechanism in preventing biofilm formation have not been examined. Furthermore, controlling the release of butenolide from paints into seawater is technically challenging. A coating was developed by mixing butenolide with a biodegradable polymer, poly (ε-caprolactone)-based polyurethane, and a one-month in situ anti-biofilm test was conducted in a subtidal area. The constant release of butenolide from the surface suggested that its release was well controlled. Direct observation and confocal microscope investigation indicated that the coating was effective against both biofilm formation and attachment of large fouling organisms. Metatranscriptomic analysis of biofilm samples implied that the coating selectively inhibited the adhesion of microbes from a variety of phyla and targeted particular functional pathways including energy metabolism, drug transport and toxin release. These integrated analyses demonstrated the potential application of this butenolide/polymer coating as an anti-biofilm material.

The occurrences of heavy metals in farmland soils and their propagation into paddy plants.

In this research, heavy metal accumulation pattern was investigated using the data measured from the soil, paddy plants, and irrigation water samples in Jessore district in Bangladesh with the aid of principal component analysis. A total of 28 samples representing farmland soil and irrigation water along with paddy plant were collected from 28 locations in the Jessore district in November 2016. In agricultural soil, arsenic (As) and nickel (Ni) were found 2.78 and 1.11 times more concentrated than their background values. In addition, 89% of the sample sites exhibited enhanced As concentrations relative to the background value. Principal component analysis (PCA) of soil data showed strong homogeneity in many species (e.g., Ni, Cu, Fe, and As) to reflect intense agricultural activities. In contrast, Pb showed no such homogeneity in soil accumulation pattern. In plant samples, Cu, Fe, and As were strongly correlated and homologous. This homology of pollution was in agreement with the pollution homology in the agricultural soil in which the plants were grown. In irrigation water, Cu and Ni were homologous. Observation of spatial distribution and other variables indicated that the accumulation of any particular metal in paddy plants was correlated with its content in soil and irrigation water, which was influenced by the soil organic matter, soil/water pH, and other metals present in that environment.

The compositional and metabolic responses of gilthead seabream (Sparus aurata) to a gradient of dietary fish oil and associated n-3 long-chain PUFA content.

The replacement of fish oil (FO) with vegetable oil (VO) in feed formulations reduces the availability of n-3 long-chain PUFA (LC-PUFA) to marine fish such as gilthead seabream. The aim of this study was to examine compositional and physiological responses to a dietary gradient of n-3 LC-PUFA. Six iso-energetic and iso-nitrogenous diets (D1-D6) were fed to seabream, with the added oil being a blend of FO and VO to achieve a dietary gradient of n-3 LC-PUFA. Fish were sampled after 4 months feeding, to determine biochemical composition, tissue fatty acid concentrations and lipid metabolic gene expression. The results indicated a disturbance to lipid metabolism, with fat in the liver increased and fat deposits in the viscera reduced. Tissue fatty acid profiles were altered towards the fatty acid compositions of the diets. There was evidence of endogenous modification of dietary PUFA in the liver which correlated with the expression of fatty acid desaturase 2 (fads2). Expression of sterol regulatory element binding protein 1 (srebp1), fads2 and fatty acid synthase increased in the liver, whereas PPARα1 pathways appeared to be supressed by dietary VO in a concentration-dependent manner. The effects in lipogenic genes appear to become measurable in D1-D3, which agrees with the weight gain data suggesting that disturbances to energy metabolism and lipogenesis may be related to performance differences. These findings suggested that suppression of ß-oxidation and stimulation of srebp1-mediated lipogenesis may play a role in contributing toward steatosis in fish fed n-3 LC-PUFA deficient diets.

Chemometric evaluation of the efficacy of locally administered chlorhexidine in patients with periodontal disease.

The process of assessment of drug efficacy produces multivariate data which are difficult to interpret. The interpretation and extraction of relevant data requires application of chemometric algorithms for multivariate data analysis. The aim of our study was evaluation of the efficacy of local treatment with chlorhexidine (CHX) in patients suffering from periodontal disease by chemometric algorithms for multivariate data analysis. Several algorithms were used: principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA). The PCA models identified the examined variables as suitable for monitoring the periodontal disease progression at the same time revealing mutual relationship among them. The developed PLS-DA model successfully distinguished patients treated with CHX from non-treated patients. The OPLS-DA model revealed differences in the mechanism of action of the two widely applied treatments in periodontal disease, local administration of CHX and local administration of doxycycline (DOX). The approach presented in this study opens the possibility of application of chemometric algorithms for multivariate data analysis for assessment of treatment efficacy.

Dietary patterns in India: a systematic review.

Dietary patterns analysis is an emerging area of research. Identifying distinct patterns within a large dietary survey can give a more accurate representation of what people are eating. Furthermore, it allows researchers to analyse relationships between non-communicable diseases (NCD) and complete diets rather than individual food items or nutrients. However, few such studies have been conducted in developing countries including India, where the population has a high burden of diabetes and CVD. We undertook a systematic review of published and grey literature exploring dietary patterns and relationships with diet-related NCD in India. We identified eight studies, including eleven separate models of dietary patterns. Most dietary patterns were vegetarian with a predominance of fruit, vegetables and pulses, as well as cereals; dietary patterns based on high-fat, high-sugar foods and more meat were also identified. There was large variability between regions in dietary patterns, and there was some evidence of change in diets over time, although no evidence of different diets by sex or age was found. Consumers of high-fat dietary patterns were more likely to have greater BMI, and a dietary pattern high in sweets and snacks was associated with greater risk of diabetes compared with a traditional diet high in rice and pulses, but other relationships with NCD risk factors were less clear. This review shows that dietary pattern analyses can be highly valuable in assessing variability in national diets and diet-disease relationships. However, to date, most studies in India are limited by data and methodological shortcomings.