Tuberculosis in otherwise healthy adults with inherited TNF deficiency.

Severe defects in human IFNγ immunity predispose individuals to both Bacillus Calmette-Guérin disease and tuberculosis, whereas milder defects predispose only to tuberculosis1. Here we report two adults with recurrent pulmonary tuberculosis who are homozygous for a private loss-of-function TNF variant. Neither has any other clinical phenotype and both mount normal clinical and biological inflammatory responses. Their leukocytes, including monocytes and monocyte-derived macrophages (MDMs) do not produce TNF, even after stimulation with IFNγ. Blood leukocyte subset development is normal in these patients. However, an impairment in the respiratory burst was observed in granulocyte-macrophage colony-stimulating factor (GM-CSF)-matured MDMs and alveolar macrophage-like (AML) cells2 from both patients with TNF deficiency, TNF- or TNFR1-deficient induced pluripotent stem (iPS)-cell-derived GM-CSF-matured macrophages, and healthy control MDMs and AML cells differentiated with TNF blockers in vitro, and in lung macrophages treated with TNF blockers ex vivo. The stimulation of TNF-deficient iPS-cell-derived macrophages with TNF rescued the respiratory burst. These findings contrast with those for patients with inherited complete deficiency of the respiratory burst across all phagocytes, who are prone to multiple infections, including both Bacillus Calmette-Guérin disease and tuberculosis3. Human TNF is required for respiratory-burst-dependent immunity to Mycobacterium tuberculosis in macrophages but is surprisingly redundant otherwise, including for inflammation and immunity to weakly virulent mycobacteria and many other infectious agents.

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).

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.

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%.

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.

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.

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.

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.

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.

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.

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.

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 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.

Genetic parameters and expected responses to selection for components of feed efficiency in a Duroc pig line.

BACKGROUND: Improving feed efficiency ([Formula: see text]) is a key factor for any pig breeding company. Although this can be achieved by selection on an index of multi-trait best linear unbiased prediction of breeding values with optimal economic weights, considering deviations of feed intake from actual needs ([Formula: see text]) should be of value for further research on biological aspects of [Formula: see text]. Here, we present a random regression model that extends the classical definition of [Formula: see text] by including animal-specific needs in the model. Using this model, we explore the genetic determinism of several [Formula: see text] components: use of feed for growth ([Formula: see text]), use of feed for backfat deposition ([Formula: see text]), use of feed for maintenance ([Formula: see text]), and unspecific efficiency in the use of feed ([Formula: see text]). Expected response to alternative selection indexes involving different components is also studied. RESULTS: Based on goodness-of-fit to the available feed intake ([Formula: see text]) data, the model that assumes individual (genetic and permanent) variation in the use of feed for maintenance, [Formula: see text] and [Formula: see text] showed the best performance. Joint individual variation in feed allocation to maintenance, growth and backfat deposition comprised 37% of the individual variation of [Formula: see text]. The estimated heritabilities of [Formula: see text] using the model that accounts for animal-specific needs and the traditional [Formula: see text] model were 0.12 and 0.18, respectively. The estimated heritabilities for the regression coefficients were 0.44, 0.39 and 0.55 for [Formula: see text], [Formula: see text] and [Formula: see text], respectively. Estimates of genetic correlations of [Formula: see text] were positive with amount of feed used for [Formula: see text] and [Formula: see text] but negative for [Formula: see text]. Expected response in overall efficiency, reducing [Formula: see text] without altering performance, was 2.5% higher when the model assumed animal-specific needs than when the traditional definition of [Formula: see text] was considered. CONCLUSIONS: Expected response in overall efficiency, by reducing [Formula: see text] without altering performance, is slightly better with a model that assumes animal-specific needs instead of batch-specific needs to correct [Formula: see text]. The relatively small difference between the traditional [Formula: see text] model and our model is due to random intercepts (unspecific use of feed) accounting for the majority of variability in [Formula: see text]. Overall, a model that accounts for animal-specific needs for [Formula: see text], [Formula: see text] and [Formula: see text] is statistically superior and allows for the possibility to act differentially on [Formula: see text] components.

Interaction of direct and social genetic effects with feeding regime in growing rabbits.

BACKGROUND: Most rabbit production farms apply feed restriction at fattening because of its protective effect against digestive diseases that affect growing rabbits. However, it leads to competitive behaviour between cage mates, which is not observed when animals are fed ad libitum. Our aim was to estimate the contribution of direct ([Formula: see text]) and social ([Formula: see text]) genetic effects (also known as indirect genetic effects) to total heritable variance of average daily gain ([Formula: see text]) in rabbits on different feeding regimens (FR), and the magnitude of the interaction between genotype and FR (G × FR). METHODS: A total of 6264 contemporary kits were housed in cages of eight individuals and raised on full ([Formula: see text]) or restricted ([Formula: see text]) feeding to 75% of the ad libitum intake. A Bayesian analysis of weekly records of [Formula: see text] (from 32 to 60 days of age) in rabbits on [Formula: see text] and [Formula: see text] was performed with a two-trait model including [Formula: see text] and [Formula: see text]. RESULTS: The ratio between total heritable variance and phenotypic variance ([Formula: see text]) was low (<0.10) and did not differ significantly between FR. However, the ratio between [Formula: see text] (i.e. variance of [Formula: see text] relative to phenotypic variance) and [Formula: see text] was ~0.52 and 0.86 for animals on [Formula: see text] and [Formula: see text], respectively, thus [Formula: see text] contributed more to the heritable variance of animals on [Formula: see text] than on [Formula: see text]. Feeding regimen also affected the sign and magnitude of the correlation between [Formula: see text] and [Formula: see text], i.e. -0.5 and ~0 for animals on [Formula: see text] and [Formula: see text], respectively. The posterior mean (posterior sd) of the correlation between estimated total breeding values (ETBV) of animals on [Formula: see text] and [Formula: see text] was 0.26 (0.20), indicating very strong G × FR interactions. The correlations between [Formula: see text] and [Formula: see text] in rabbits on [Formula: see text] and [Formula: see text] ranged from -0.47 ([Formula: see text] on [Formula: see text] and [Formula: see text] on [Formula: see text]) to 0.64. CONCLUSIONS: Our results suggest that selection of rabbits for [Formula: see text] under [Formula: see text] may completely fail to improve [Formula: see text] in rabbits on [Formula: see text]. Social genetic effects contribute substantially to ETBV of rabbits on [Formula: see text] but not on [Formula: see text]. Selection for [Formula: see text] should be performed under production conditions regarding the FR, by accounting for [Formula: see text] if the amount of food is limited.

Structure, Ionization, and Fragmentation of Neutral and Positively Charged Hydrogenated Carbon Clusters: C(n)H(m)(q+) (n = 1-5, m = 1-4, q = 0-3).

In this work we present a systematic theoretical study of neutral and positively charged hydrogenated carbon clusters (C(n)H(m)(q+) with n = 1­5, m = 1­4, and q = 0­3). A large number of isomers and spin states (1490 in total) was investigated. For all of them, we optimized the geometry and computed the vibrational frequencies at the B3LYP/6-311++G(3df,2dp) level of theory; more accurate values of the electronic energy were obtained at the CCSD(T)/6-311++G(3df,2dp) level over the geometry previously obtained. From these simulations we evaluated several properties such as relative energies between isomers, adiabatic and vertical ionization potentials, and dissociation energies of several fragmentation channels. A new analysis technique is proposed to evaluate a large number of fragmentation channels in a wide energy range.

A proposal of new diagnostic pathway for fatal familial insomnia.

BACKGROUND: In absence of a positive family history, the diagnosis of fatal familial insomnia (FFI) might be difficult because of atypical clinical features and low sensitivity of diagnostic tests. FFI patients usually do not fulfil the established classification criteria for Creutzfeldt-Jakob disease (CJD); therefore, a prion disease is not always suspected. OBJECTIVE: To propose an update of diagnostic pathway for the identification of patients for the analysis of D178-M129 mutation. DESIGN AND METHODS: Data on 41 German FFI patients were analysed. Clinical symptoms and signs, MRI, PET, SPECT, polysomnography, EEG and cerebrospinal fluid biomarkers were studied. RESULTS: An algorithm was developed which correctly identified at least 81% of patients with the FFI diagnosis during early disease stages. It is based on the detection of organic sleep disturbances, either verified clinically or by a polysomnography, and a combination of vegetative and focal neurological signs and symptoms. Specificity of the approach was tested on three cohorts of patients (MM1 sporadic CJD patients, non-selected sporadic CJD and other neurodegenerative diseases). CONCLUSIONS: The proposed scheme may help to improve the clinical diagnosis of FFI. As the sensitivity of all diagnostic tests investigated but polysomnography is low in FFI, detailed clinical investigation is of special importance.

EEMD-MUSIC-based analysis for natural frequencies identification of structures using artificial and natural excitations.

This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals.

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

Antisense transcripts are a unique group of non-coding RNAs and play regulatory roles in a variety of biological processes, including circadian rhythms. Per2AS is an antisense transcript to the sense core clock gene Period2 (Per2) in mouse and its expression is rhythmic and antiphasic to Per2. To understand the impact of Per2AS-Per2 interaction, we developed a new mathematical model that mechanistically described the mutually repressive relationship between Per2 and Per2AS. This mutual repression can regulate both amplitude and period of circadian oscillation by affecting a negative feedback regulation of Per2. Simulations from this model also fit with experimental observations that could not be fully explained by our previous model. Our revised model can not only serve as a foundation to build more detailed models to better understand the impact of Per2AS-Per2 interaction in the future, but also be used to analyze other sense-antisense RNA pairs that mutually repress each other.