Impacts of the feedback loop between sense-antisense RNAs in regulating circadian rhythms.

Antisense transcripts are a unique group of non-coding RNAs that are transcribed from the opposite strand of a sense coding gene in an antisense orientation. Even though they do not encode a protein, these transcripts play a regulatory role in a variety of biological processes, including circadian rhythms. We and others found an antisense transcript, Per2AS , that is transcribed from the strand opposite the sense transcript Period2 ( Per2 ) and exhibits a rhythmic and antiphasic expression pattern compared to Per2 in mouse. By assuming that Per2AS and Per2 mutually repress each other, our previous mathematical model predicted that Per2AS regulates the robustness and the amplitude of circadian rhythms. In this study, we revised our previous model and developed a new mathematical model that mechanistically described the mutually repressive relationship between Per2 and Per2AS via transcriptional interference. We found that the simulation results are largely consistent with experimental observations including the counterintuitive ones that could not be fully explained by our previous model. These results indicate that our revised model serves as a foundation to build more detailed models in the future to better understand the impact of Per2AS-Per2 interaction in the mammalian circadian clock. Our mechanistic description of Per2AS-Per2 interaction can also be extended to other mathematical models that involve sense-antisense RNA pairs that mutually repress each other.

Analysis of Vibration Signals Based on Machine Learning for Crack Detection in a Low-Power Wind Turbine.

Currently, renewable energies, including wind energy, have been experiencing significant growth. Wind energy is transformed into electric energy through the use of wind turbines (WTs), which are located outdoors, making them susceptible to harsh weather conditions. These conditions can cause different types of damage to WTs, degrading their lifetime and efficiency, and, consequently, raising their operating costs. Therefore, condition monitoring and the detection of early damages are crucial. One of the failures that can occur in WTs is the occurrence of cracks in their blades. These cracks can lead to the further deterioration of the blade if they are not detected in time, resulting in increased repair costs. To effectively schedule maintenance, it is necessary not only to detect the presence of a crack, but also to assess its level of severity. This work studies the vibration signals caused by cracks in a WT blade, for which four conditions (healthy, light, intermediate, and severe cracks) are analyzed under three wind velocities. In general, as the proposed method is based on machine learning, the vibration signal analysis consists of three stages. Firstly, for feature extraction, statistical and harmonic indices are obtained; then, the one-way analysis of variance (ANOVA) is used for the feature selection stage; and, finally, the k-nearest neighbors algorithm is used for automatic classification. Neural networks, decision trees, and support vector machines are also used for comparison purposes. Promising results are obtained with an accuracy higher than 99.5%.

Supervised Machine-Learning Methodology for Industrial Robot Positional Health Using Artificial Neural Networks, Discrete Wavelet Transform, and Nonlinear Indicators.

Robotic systems are a fundamental part of modern industrial development. In this regard, they are required for long periods, in repetitive processes that must comply with strict tolerance ranges. Hence, the positional accuracy of the robots is critical, since degradation of this can represent a considerable loss of resources. In recent years, prognosis and health management (PHM) methodologies, based on machine and deep learning, have been applied to robots, in order to diagnose and detect faults and identify the degradation of robot positional accuracy, using external measurement systems, such as lasers and cameras; however, their implementation is complex in industrial environments. In this respect, this paper proposes a method based on discrete wavelet transform, nonlinear indices, principal component analysis, and artificial neural networks, in order to detect a positional deviation in robot joints, by analyzing the currents of the actuators. The results show that the proposed methodology allows classification of the robot positional degradation with an accuracy of 100%, using its current signals. The early detection of robot positional degradation, allows the implementation of PHM strategies on time, and prevents losses in manufacturing processes.

Prevalence of adiposity-based chronic disease and its association with anthropometric and clinical indices: a cross-sectional study.

The present study aimed to determine the prevalence of adiposity-based chronic disease (ABCD) and its association with anthropometric indices in the Mexican population. A cross-sectional study was conducted in 514 adults seen at a clinical research unit. The American Association of Clinical Endocrinology/AACE/ACE criteria were used to diagnose ABCD by first identifying subjects with BMI ≥ 25 kg/m2 and those with BMI of 23-24·9 kg/m2 and waist circumference ≥ 80 cm in women or ≥ 90 cm in men. The presence of metabolic and clinical complications associated with adiposity, such as factors related to metabolic syndrome, prediabetes, type 2 diabetes, dyslipidaemia and arterial hypertension, were subsequently evaluated. Anthropometric indices related to cardiometabolic risk factors were then determined. The results showed the prevalence of ABCD was 87·4 % in total, 91·5 % in men and 86 % in women. The prevalence of ABCD stage 0 was 2·4 %, stage 1 was 33·7 % and stage 2 was 51·3 %. The prevalence of obesity according to BMI was 57·6 %. The waist/hip circumference index (prevalence ratio (PR) = 7·57; 95 % CI 1·52, 37·5) and the conicity index (PR = 3·46; 95 % CI 1·34, 8·93) were better predictors of ABCD, while appendicular skeletal mass % and skeletal muscle mass % decreased the risk of developing ABCD (PR = 0·93; 95 % CI 0·90, 0·96; and PR = 0·95; 95 % CI 0·93, 0·98). In conclusion, the prevalence of ABCD in our study was 87·4 %. This prevalence increased with age. It is important to emphasise that one out of two subjects had severe obesity-related complications (ABCD stage 2).

Synthesis, Optical Characterization in Solution and Solid-State, and DFT Calculations of 3-Acetyl and 3-(1'-(2'-Phenylhydrazono)ethyl)-coumarin-(7)-substituted Derivatives.

Intramolecular charge transfer (ICT) effects are responsible for the photoluminescent properties of coumarins. Hence, optical properties with different applications can be obtained by ICT modulation. Herein, four 3-acetyl-2H-chromen-2-ones (1a-d) and their corresponding fluorescent hybrids 3- (phenylhydrazone)-chromen-2-ones (2a-d) were synthesized in 74-65% yields. The UV-Vis data were in the 295-428 nm range. The emission depends on the substituent in position C-7 bearing electron-donating groups. Compounds 1b-d showed good optical properties due to the D-π-A structural arrangement. In compounds 2a-d, there is a quenching effect of fluorescence in solution. However, in the solid, an increase is shown due to an aggregation-induced emission (AIE) effect given by the rotational restraints and stacking in the crystal. Computational calculations of the HOMO-LUMO orbitals indicate high absorbance and emission values of the molecules, and gap values represent the bathochromic effect and the electronic efficiency of the compounds. Compounds 1a-d and 2a-d are good candidates for optical applications, such as OLEDs, organic solar cells, or fluorescence markers.

ECG-Based Identification of Sudden Cardiac Death through Sparse Representations.

Sudden Cardiac Death (SCD) is an unexpected sudden death due to a loss of heart function and represents more than 50% of the deaths from cardiovascular diseases. Since cardiovascular problems change the features in the electrical signal of the heart, if significant changes are found with respect to a reference signal (healthy), then it is possible to indicate in advance a possible SCD occurrence. This work proposes SCD identification using Electrocardiogram (ECG) signals and a sparse representation technique. Moreover, the use of fixed feature ranking is avoided by considering a dictionary as a flexible set of features where each sparse representation could be seen as a dynamic feature extraction process. In this way, the involved features may differ within the dictionary's margin of similarity, which is better-suited to the large number of variations that an ECG signal contains. The experiments were carried out using the ECG signals from the MIT/BIH-SCDH and the MIT/BIH-NSR databases. The results show that it is possible to achieve a detection 30 min before the SCD event occurs, reaching an an accuracy of 95.3% under the common scheme, and 80.5% under the proposed multi-class scheme, thus being suitable for detecting a SCD episode in advance.

The value of gut microbiota to predict feed efficiency and growth of rabbits under different feeding regimes.

Gut microbiota plays an important role in nutrient absorption and could impact rabbit feed efficiency. This study aims at investigating such impact by evaluating the value added by microbial information for predicting individual growth and cage phenotypes related to feed efficiency. The dataset comprised individual average daily gain and cage-average daily feed intake from 425 meat rabbits, in which cecal microbiota was assessed, and their cage mates. Despite microbiota was not measured in all animals, consideration of pedigree relationships with mixed models allowed the study of cage-average traits. The inclusion of microbial information into certain mixed models increased their predictive ability up to 20% and 46% for cage-average feed efficiency and individual growth traits, respectively. These gains were associated with large microbiability estimates and with reductions in the heritability estimates. However, large microbiabililty estimates were also obtained with certain models but without any improvement in their predictive ability. A large proportion of OTUs seems to be responsible for the prediction improvement in growth and feed efficiency traits, although specific OTUs taxonomically assigned to 5 different phyla have a higher weight. Rabbit growth and feed efficiency are influenced by host cecal microbiota, thus considering microbial information in models improves the prediction of these complex phenotypes.

Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA.

One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals.

The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development.

Natural Frequencies Identification by FEM Applied to a 2-DOF Planar Robot and Its Validation Using MUSIC Algorithm.

In this paper, the natural frequencies (NFs) identification by finite element method (FEM) is applied to a two degrees-of-freedom (2-DOF) planar robot, and its validation through a novel experimental methodology, the Multiple Signal Classification (MUSIC) algorithm, is presented. The experimental platforms are two different 2-DOF planar robots with different materials for the links and different types of actuators. The FEM is carried out using ANSYS™ software for the experiments, with vibration signal analysis by MUSIC algorithm. The advantages of the MUSIC algorithm against the commonly used fast Fourier transform (FFT) method are also presented for a synthetic signal contaminated by three different noise levels. The analytical and experimental results show that the proposed methodology identifies the NFs of a high-resolution robot even when they are very closed and when the signal is embedded in high-level noise. Furthermore, the results show that the proposed methodology can obtain a high-frequency resolution with a short sample data set. Identifying the NFs of robots is useful for avoiding such frequencies in the path planning and in the selection of controller gains that establish the bandwidth.

A New dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms.

A new EEG-based methodology is presented for differential diagnosis of the Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and healthy subjects employing the discrete wavelet transform (DWT), dispersion entropy index (DEI), a recently-proposed nonlinear measurement, and a fuzzy logic-based classification algorithm. The effectiveness and usefulness of the proposed methodology are evaluated by employing a database of measured EEG data acquired from 135 subjects, 45 MCI, 45 AD and 45 healthy subjects. The proposed methodology differentiates MCI and AD patients from HC subjects with an accuracy of 82.6-86.9%, sensitivity of 91 %, and specificity of 87 %.

Fatal consequences of decreased sensitivity to pain and temperature in a frontotemporal dementia patient.

Frontotemporal dementia (FTD) is a heterogeneous syndrome characterized by the progressive damage of frontal and temporal brain regions. These networks largely overlap with those involved in pain and temperature processing. Although the impaired perception of pain and temperature has been previously described to be relatively common in patients with FTD, these symptoms are often not consistently assessed by Neurologists. We present the case of a patient with a probable behavioral variant FTD who died due to scalding with hot water in the shower. Impairments in the perception of pain and temperature might have played a fundamental role in this accident.

Networks of inbreeding coefficients in a selected population of rabbits.

The correlation between pedigree and genomic-based inbreeding coefficients is usually discussed in the literature. However, some of these correlations could be spurious. Using partial correlations and information theory, it is possible to distinguish a significant association between two variables which is independent from associations with a third variable. The objective of this study is to implement partial correlations and information theory to assess the relationship between different inbreeding coefficients using a selected population of rabbits. Data from pedigree and genomic information from a 200K SNP chip were available. After applying filtering criteria, the data set comprised 437 animals genotyped for 114,604 autosomal SNP. Fifteen pedigree- and genome-based inbreeding coefficients were estimated and used to build a network. Recent inbreeding coefficient based on runs of homozygosity had 9 edges linking it with different inbreeding coefficients. Partial correlations and information theory approach allowed to infer meaningful associations between inbreeding coefficients and highlighted the importance of the recent inbreeding based on runs of homozygosity, but a good proxy of it could be those pedigree-based definitions reflecting recent inbreeding.

Removal of sulfamethoxazole, sulfadiazine, and sulfamethazine by UV radiation and HO• and SO4•- radicals using a response surface model and DFT calculations.

In this work, the degradation of sulfamethazine (SMT), sulfadiazine (SMD), and sulfamethoxazole (SMX) by using UV light, UV/H2O2, and UV/S2O8-2 was analyzed. Direct photolysis was studied by varying the lamp power and the solution pH. DFT calculations were carried out to corroborate the efficiency of the degradation as a function of the solution pH. The variation of the apparent rate constant, kap, was determined in the indirect photolysis by employing an experimental Box-Behnken-type response surface design. The results evidenced that SMX can be efficiently degraded by applying UV radiation independent of the operating conditions. Nevertheless, the quantum yields for SMT and SMD were close to zero, indicating a low energy efficiency for their photochemical transformation. The effect of the solution pH showed that the photodegradation of sulfonamides depends both on the amount of radiation absorbed as the electronic density. Calculations based on density functional theory and supported by the quantum theory of atoms in molecules allowed to describe fragmentation patterns in the systems under study, proving the lability of S14-C2, N17-C18, and N22-O22 bonds, for SMT, SMD, and SMX, respectively. From response surface methodology, four statistically reliable equations were obtained to determine the kap value as a function of the system operating conditions. Finally, SO4•- radicals proved to have a higher reactivity to degrade SMT and SMD compared with HO• radicals regardless of the operating conditions of the system.

Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors.

Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time-frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.

Breeding farm, level of feeding and presence of antibiotics in the feed influence rabbit cecal microbiota.

BACKGROUND: The effect of the production environment and different management practices in rabbit cecal microbiota remains poorly understood. While previous studies have proved the impact of the age or the feed composition, research in the breeding farm and other animal management aspects, such as the presence of antibiotics in the feed or the level of feeding, is still needed. Characterization of microbial diversity and composition of growing rabbits raised under different conditions could help better understand the role these practices play in cecal microbial communities and how it may result in different animal performance. RESULTS: Four hundred twenty-five meat rabbits raised in two different facilities, fed under two feeding regimes (ad libitum or restricted) with feed supplemented or free of antibiotics, were selected for this study. A 16S rRNA gene-based assessment through the MiSeq Illumina sequencing platform was performed on cecal samples collected from these individuals at slaughter. Different univariate and multivariate approaches were conducted to unravel the influence of the different factors on microbial alpha diversity and composition at phylum, genus and OTU taxonomic levels. The animals raised in the facility harboring the most stable environmental conditions had greater, and less variable, microbial richness and diversity. Bootstrap univariate analyses of variance and sparse partial least squares-discriminant analyses endorsed that farm conditions exerted an important influence on rabbit microbiota since the relative abundances of many taxa were found differentially represented between both facilities at all taxonomic levels characterized. Furthermore, only five OTUs were needed to achieve a perfect classification of samples according to the facility where animals were raised. The level of feeding and the presence of antibiotics did not modify the global alpha diversity but had an impact on some bacteria relative abundances, albeit in a small number of taxa compared with farm, which is consistent with the lower sample classification power according to these factors achieved using microbial information. CONCLUSIONS: This study reveals that factors associated with the farm effect and other management factors, such as the presence of antibiotics in the diet or the feeding level, modify cecal microbial communities. It highlights the importance of offering a controlled breeding environment that reduces differences in microbial cecal composition that could be responsible for different animal performance.

A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection.

Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works.

A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer's disease using EEG signals.

BACKGROUND: EEG signals obtained from Mild Cognitive Impairment (MCI) and the Alzheimer's disease (AD) patients are visually indistinguishable. NEW METHOD: A new methodology is presented for differential diagnosis of MCI and the AD through adroit integration of a new signal processing technique, the integrated multiple signal classification and empirical wavelet transform (MUSIC-EWT), different nonlinear features such as fractality dimension (FD) from the chaos theory, and a classification algorithm, the enhanced probabilistic neural network model of Ahmadlou and Adeli using the EEG signals. RESULTS: Three different FD measures are investigated: Box dimension (BD), Higuchi's FD (HFD), and Katz's FD (KFD) along with another measure of the self-similarities of the signals known as the Hurst exponent (HE). The accuracy of the proposed method was verified using the monitored EEG signals from 37 MCI and 37 AD patients. COMPARISON WITH EXISTING METHODS: The proposed method is compared with other methodologies presented in the literature recently. CONCLUSIONS: It was demonstrated that the proposed method, MUSIC-EWT algorithm combined with nonlinear features BD and HE, and the EPNN classifier can be employed for differential diagnosis of MCI and AD patients with an accuracy of 90.3%.

Rabbit Microbiota Changes Throughout the Intestinal Tract.

To gain insight into the importance of carefully selecting the sampling area for intestinal microbiota studies, cecal and fecal microbial communities of Caldes meat rabbit were characterized. The animals involved in the study were divided in two groups according to the feed intake level they received during the fattening period; ad libitum (n = 10) or restricted to 75% of ad libitum intake (n = 11). Cecum and internal hard feces were sampled from sacrificed animals. Assessment of bacterial and archaeal populations was performed by means of Illumina sequencing of 16S rRNA gene amplicons in a MiSeq platform. A total of 596 operational taxonomic units (OTUs) were detected using QIIME software. Taxonomic assignment revealed that microbial diversity was dominated by phyla Firmicutes (76.42%), Tenericutes (7.83%), and Bacteroidetes (7.42%); kingdom Archaea was presented at low percentage (0.61%). No significant differences were detected between sampling origins in microbial diversity or richness assessed using two alpha-diversity indexes: Shannon and the observed number of OTUs. However, the analysis of variance at genus level revealed a higher presence of genera Clostridium, Anaerofustis, Blautia, Akkermansia, rc4-4, and Bacteroides in cecal samples. By contrast, genera Oscillospira and Coprococcus were found to be overrepresented in feces, suggesting that bacterial species of these genera would act as fermenters at the end of feed digestion process. At the lowest taxonomic level, 83 and 97 OTUs in feces and cecum, respectively, were differentially represented. Multivariate statistical assessment revealed that sparse partial least squares discriminant analysis (sPLS-DA) was the best approach for this purpose. Interestingly, the majority of the most discriminative OTUs selected by sPLS-DA were found to be differentially represented between sampling origins in univariate analysis. Our study provides evidence that the choice of intestinal sampling area is relevant due to important differences in some taxa's relative abundance that have been revealed between rabbits' cecal and fecal microbiota. An appropriate sampling intestinal area should be chosen in each microbiota assessment.

A Novel Wavelet Transform-Homogeneity Model for Sudden Cardiac Death Prediction Using ECG Signals.

Sudden cardiac death (SCD) is one of the main causes of death among people. A new methodology is presented for predicting the SCD based on ECG signals employing the wavelet packet transform (WPT), a signal processing technique, homogeneity index (HI), a nonlinear measurement for time series signals, and the Enhanced Probabilistic Neural Network classification algorithm. The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 20 SCD and 18 normal patients. The proposed methodology presents the following significant advantages: (1) compared with previous works, the proposed methodology achieves a higher accuracy using a single nonlinear feature, HI, thus requiring low computational resource for predicting an SCD onset in real-time, unlike other methodologies proposed in the literature where a large number of nonlinear features are used to predict an SCD event; (2) it is capable of predicting the risk of developing an SCD event up to 20 min prior to the onset with a high accuracy of 95.8%, superseding the prior 12 min prediction time reported recently, and (3) it uses the ECG signal directly without the need for transforming the signal to a heart rate variability signal, thus saving time in the processing.