Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings.

Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuators. Therefore, most of the diagnosis approaches for bearing fault detection are highly dependent of the bearing technology, commonly focused on the metallic bearings. Although the mechanical principles remain as the basis to analyze the characteristic patterns and effects related to the fault appearance, the quantitative response of the vibration pattern considering different bearing technology varies. In this regard, in this work a novel data-driven diagnosis methodology is proposed based on deep feature learning applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology consists of three main stages: first, a deep learning-based model, supported by stacked autoencoder structures, is designed with the ability of self-adapting to the extraction of characteristic fault-related features from different signals that are processed in different domains. Second, in a feature fusion stage, information from different domains is integrated to increase the posterior discrimination capabilities during the condition assessment. Third, the bearing assessment is achieved by a simple softmax layer to compute the final classification results. The achieved results show that the proposed diagnosis methodology based on deep feature learning can be effectively applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology is validated in front of two different electromechanical systems and the obtained results validate the adaptability and performance of the proposed approach to be considered as a part of the condition-monitoring strategies where different bearing technologies are involved.

Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems.

The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.

Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems.

Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be applied over modern production systems. In fact, the integration of multiple mechanical components, the consideration of multiple operating conditions, and the appearance of combined fault patterns due to eventual multi-fault scenarios lead to complex electromechanical systems requiring advanced monitoring strategies. In this regard, data fusion schemes supported with advanced deep learning technology represent a promising approach towards a big data paradigm using cloud-based software services. However, the deep learning models' structure and hyper-parameters selection represent the main limitation when applied. Thus, in this paper, a novel deep-learning-based methodology for fault diagnosis in electromechanical systems is presented. The main benefits of the proposed methodology are the easiness of application and high adaptability to available data. The methodology is supported by an unsupervised stacked auto-encoders and a supervised discriminant analysis.

The RhoJ-BAD signaling network: An Achilles' heel for BRAF mutant melanomas.

Genes and pathways that allow cells to cope with oncogene-induced stress represent selective cancer therapeutic targets that remain largely undiscovered. In this study, we identify a RhoJ signaling pathway that is a selective therapeutic target for BRAF mutant cells. RhoJ deletion in BRAF mutant melanocytes modulates the expression of the pro-apoptotic protein BAD as well as genes involved in cellular metabolism, impairing nevus formation, cellular transformation, and metastasis. Short-term treatment of nascent melanoma tumors with PAK inhibitors that block RhoJ signaling halts the growth of BRAF mutant melanoma tumors in vivo and induces apoptosis in melanoma cells in vitro via a BAD-dependent mechanism. As up to 50% of BRAF mutant human melanomas express high levels of RhoJ, these studies nominate the RhoJ-BAD signaling network as a therapeutic vulnerability for fledgling BRAF mutant human tumors.

ATR Mutations Promote the Growth of Melanoma Tumors by Modulating the Immune Microenvironment.

Melanomas accumulate a high burden of mutations that could potentially generate neoantigens, yet somehow suppress the immune response to facilitate continued growth. In this study, we identify a subset of human melanomas that have loss-of-function mutations in ATR, a kinase that recognizes and repairs UV-induced DNA damage and is required for cellular proliferation. ATR mutant tumors exhibit both the accumulation of multiple mutations and the altered expression of inflammatory genes, resulting in decreased T cell recruitment and increased recruitment of macrophages known to spur tumor invasion. Taken together, these studies identify a mechanism by which melanoma cells modulate the immune microenvironment to promote continued growth.

GRHL3/GET1 and trithorax group members collaborate to activate the epidermal progenitor differentiation program.

The antagonistic actions of Polycomb and Trithorax are responsible for proper cell fate determination in mammalian tissues. In the epidermis, a self-renewing epithelium, previous work has shown that release from Polycomb repression only partially explains differentiation gene activation. We now show that Trithorax is also a key regulator of epidermal differentiation, not only through activation of genes repressed by Polycomb in progenitor cells, but also through activation of genes independent of regulation by Polycomb. The differentiation associated transcription factor GRHL3/GET1 recruits the ubiquitously expressed Trithorax complex to a subset of differentiation genes.

Brain and muscle Arnt-like protein-1 (BMAL1) controls circadian cell proliferation and susceptibility to UVB-induced DNA damage in the epidermis.

The role of the circadian clock in skin and the identity of genes participating in its chronobiology remain largely unknown, leading us to define the circadian transcriptome of mouse skin at two different stages of the hair cycle, telogen and anagen. The circadian transcriptomes of telogen and anagen skin are largely distinct, with the former dominated by genes involved in cell proliferation and metabolism. The expression of many metabolic genes is antiphasic to cell cycle-related genes, the former peaking during the day and the latter at night. Consistently, accumulation of reactive oxygen species, a byproduct of oxidative phosphorylation, and S-phase are antiphasic to each other in telogen skin. Furthermore, the circadian variation in S-phase is controlled by BMAL1 intrinsic to keratinocytes, because keratinocyte-specific deletion of Bmal1 obliterates time-of-day-dependent synchronicity of cell division in the epidermis leading to a constitutively elevated cell proliferation. In agreement with higher cellular susceptibility to UV-induced DNA damage during S-phase, we found that mice are most sensitive to UVB-induced DNA damage in the epidermis at night. Because in the human epidermis maximum numbers of keratinocytes go through S-phase in the late afternoon, we speculate that in humans the circadian clock imposes regulation of epidermal cell proliferation so that skin is at a particularly vulnerable stage during times of maximum UV exposure, thus contributing to the high incidence of human skin cancers.

Vanek's polyp or inflammatory fibroid polyp. Case report and review of the literature.

BACKGROUND: Intussusception in an adult must make us suspect the presence of a tumor (benign or potentially dangerous) as the most frequent cause. Accurate diagnosis is of great importance in order to provide appropriate treatment and improve patient prognosis. CLINICAL CASE: We report the case of a 42-year-old male with abdominal pain. We performed a CT and found a small bowel intussusception. Definitive diagnosis according to the surgical specimen was inflammatory fibroid polyp (Vanek's polyp). CONCLUSIONS: Vanek's polyp is a benign lesion that occurs most frequently in the stomach and secondarily in the small bowel. Generally, it is uncommon, and its etiology is not completely known. Accurate diagnosis is done with immunohistochemistry. Because of the consequences that depend on the size and location of the lesion, it may be considered a malignant lesion. Treatment is resection.

Manejo de la primera alimentación de dorada Brycon sinuensis ofreciendo larvas de bocachico Prochilodus magdalenae / First feeding of Dorada (Brycon sinuensis) with Bocachico (Prochilodus magdalenae) larvae / Manejo da primeira alimentação de dorada Brycon sinuensis forneciendo larvas de bocachico Prochilodus magdalenae

El objetivo del estudio fue evaluar el efecto de la densidad de presas en la larvicultura de dorada Brycon sinuensis, para lo cual fueron ofrecidas, durante 24 horas, cuatro proporciones presa-predador 0:1 (T1), 1:1 (T2), 2:1 (T3) y 4:1 (T4) al inicio de la alimentación exógena. Como presa fueron utilizadas larvas recién eclosionadas de bocachico Prochilodus magdalenae, suministradas por una sola vez. Las larvas de dorada se pesaron y se midieron al inicio y al final del experimento para estimar ganancia en peso (Gp), ganancia en longitud (Gl) y tasa de crecimiento específico (G); también se calculó la sobrevivencia (S), resistencia al estrés (Re), mortalidad natural (Mn) y por canibalismo (Mc). Los mayores valores de Gp (3.9 ± 0.4 mg) y G se registraron en T4 (5.8 ± 0.4%/h), presentando diferencia significativa con los demás tratamientos (p<0.05); mientras que los mayores registros de Gl se presentaron en T2, T3 y T4 (2.0-2.4 mm), sin presentarse diferencia significativa entre estos tratamientos (p>0.05); pero si con relación a T1. Cuando se alimentó con dos (T3=84.2%) y cuatro larvas forrajeras por cada larva de dorada (T4=86.7%) se observaron las mayores sobrevivencias; mientras que la menor se obtuvo cuando no se suministraron larvas forrajeras (T1=51.8%); encontrándose en ese tratamiento la mayor mortalidad por canibalismo (46.3%). Asimismo, las menores mortalidades por canibalismo se presentaron cuando se ofrecieron entre dos (T3=12.7%) y cuatro larvas forrajeras por cada larva de dorada (T4=12.0%). La mortalidad natural fue baja y osciló entre 1.3% (T4) y 3.2% (T3) sin observarse diferencia significativa (p>0.05). Los valores de la prueba de Re oscilaron entre 86.7 ± 10.3% (T1) y 95.0 ± 5.5% (T3) sin observarse diferencia significativa (p>0.05). Estos resultados indican que suministrar entre dos y cuatro larvas forrajeras como primera alimentación en dorada reduce el canibalismo y ofrece mejores resultados de sobrevivencia y crecimiento durante la larvicultura.

This study evaluated several predator-prey ratios on Dorada (Brycon sinuensis) larviculture. The onset of exogenous feeding using four predator-prey ratios 0:1 (T1), 1:1 (T2), 2:1 (T3) and 4:1 (T4) was studied. The preys used were newly hatched Bocachico (Prochilodus magdalenae) larvae, which were fed just once. The Dorada larvae were weighed and measured at the beginning and the end of the experiment. The variables measured were weight gain (Wg), length gain (Gl), specific growth rate (G), survival (S), stress resistance (Rs), natural mortality (Nm) and cannibalism mortality (Mc). The T4 had the highest Wg (3.9 ± 0.4 mg) and G (5.8 ± 0.4%/h). Survivability was increased when feeding two (T3 = 84.2%) and four forage larvae for each Dorada larvae (T4 = 86.7%). The lowest survivability was observed when no forage larvae were fed (T1 = 51.8%), coinciding with the greatest mortality from cannibalism (46.3%). The lowest mortality from cannibalism occurred when two (T3 = 12.7%) to four forage larvae per Dorada larvae (T4 = 12.0%) were offered. Natural mortality was low, ranging between 1.3% (T4) and 3.2% (T3) (p> 0.05). The test values of Rs ranged from 86.7 ± 10.3% (T1) and 95.0 ± 5.5% (T3) (p>0.05). These results indicate that offering between two and four forage larvae as first feeding to Dorada larvae reduces cannibalism and offers the best results for survival and growth during larviculture.

O objetivo do estudo foi avaliar o efeito da densidade de presas na larvicultura de Dorada Brycon sinuensis, para o qual foram fornecidas, durante 24 horas, quatro diferente proporção presa-predador 0:1 (T1), 1:1 (T2), 2:1 (T3) e 4:1 (T4) ao inicio da alimentação exógena. Como presa foram utilizadas larvas recém eclodidas de Bocachico Prochilodus magdalenae, as cuais foram fornecidas por uma única vez. As larvas de Dorada foram pesadas e medidas ao inicio e final do experimento para calcular ganho de peso (Gp), ganho de comprimento (Gl) e taxa de crescimento específico (G); também foi calculada a supervivência (S), resistência ao estresse (Re), mortalidade natural (Mn) y mortalidade pelo canibalismo (Mc). Os maiores valores de Gp (3.9 ± 0.4 mg) e G (5.8 ± 0.4%/h) foram registradas no T4, apresentando diferença com os demais tratamentos (p<0.05); entanto que os maiores registros de Gl (2.0-2.4 mm) apresentaram se entre os tratamentos T2, T3 e T4 sem apresentar se diferença significativa entre estes (p>0.05); mas sim com relação a T1. Quando foi alimentado com duas (T3 = 84.2%) e quatro larvas forrageiras por cada larva de dorada (T4 = 86.7%) foram observadas as maiores supervivências; ainda que a menor obteve se quando não foram fornecidas larvas forrageiras (T1 = 51.8%); foi encontrado nesse tratamento a maior mortalidade pelo canibalismo (46.3%). Assim mesmo, as menores mortalidades por canibalismo apresentaram-se quando foram fornecidas entre duas (T3 = 12.7%) e quatro larvas forrageiras por cada larva de Dorada (T4 = 12.0%). A mortalidade natural foi baixa e oscilou entre 1.3% (T4) e 3.2% (T3) sem diferença significativa (p>0.05). Os valores do teste de Re estiveram entre 86.7 ± 10.3% (T1) e 95.0 ± 5.5% (T3) sem diferença significativa (p>0.05). Estes resultados indicam que fornecer entre duas e quatro larvas forrajeiras como primeira alimentação em larvas de Dorada diminui o canibalismo e oferece melhores resultados de supervivência e crescimento durante a larvicultura.