Myo-differentiation reporter screen reveals NF-Y as an activator of PAX3-FOXO1 in rhabdomyosarcoma.

Recurrent chromosomal rearrangements found in rhabdomyosarcoma (RMS) produce the PAX3-FOXO1 fusion protein, which is an oncogenic driver and a dependency in this disease. One important function of PAX3-FOXO1 is to arrest myogenic differentiation, which is linked to the ability of RMS cells to gain an unlimited proliferation potential. Here, we developed a phenotypic screening strategy for identifying factors that collaborate with PAX3-FOXO1 to block myo-differentiation in RMS. Unlike most genes evaluated in our screen, we found that loss of any of the three subunits of the Nuclear Factor Y (NF-Y) complex leads to a myo-differentiation phenotype that resembles the effect of inactivating PAX3-FOXO1. While the transcriptomes of NF-Y- and PAX3-FOXO1-deficient RMS cells bear remarkable similarity to one another, we found that these two transcription factors occupy nonoverlapping sites along the genome: NF-Y preferentially occupies promoters, whereas PAX3-FOXO1 primarily binds to distal enhancers. By integrating multiple functional approaches, we map the PAX3 promoter as the point of intersection between these two regulators. We show that NF-Y occupies CCAAT motifs present upstream of PAX3 to function as a transcriptional activator of PAX3-FOXO1 expression in RMS. These findings reveal a critical upstream role of NF-Y in the oncogenic PAX3-FOXO1 pathway, highlighting how a broadly essential transcription factor can perform tumor-specific roles in governing cellular state.

An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines.

The collapse of overhead power line guyed towers is one of the leading causes of power grid failures, subjecting electricity companies to pay considerable, high-value fines. In this way, the current work proposes a novel and complete framework for the remote monitoring of mechanical stresses in guyed towers. The framework method comprises a mesh network for data forwarding and neural networks to improve the performance of Low-Power and Lossy Networks. The method also considers the use of multiple sensors in the sensor fusion technique. As a result, the risk of collapse of guyed cable towers reduces, due to the remote monitoring and preventive actions promoted by the framework. Furthermore, the proposed method uses multiple input variable fusions, such as accelerometers and tension sensors, to estimate the tower's displacement. These estimations help address the structural health of the tower against failures (i.e., loosening of the stay cables, displacement, and vibrations) that can cause catastrophic events, such as tower collapse or even cable rupture.

Evaluation of Machine Learning Methods for Monitoring the Health of Guyed Towers.

This paper presents the development of a methodology to detect and evaluate faults in cable-stayed towers, which are part of the infrastructure of Brazil's interconnected electrical system. The proposed method increases system reliability and minimizes the risk of service failure and tower collapse through the introduction of predictive maintenance methods based on artificial intelligence, which will ultimately benefit the end consumer. The proposed signal processing and interpretation methods are based on a machine learning approach, where the tower vibration is acquired from accelerometers that measure the dynamic response caused by the effects of the environment on the towers through wind and weather conditions. Data-based models were developed to obtain a representation of health degradation, which is primarily based on the finite element model of the tower, subjected to wind excitation. This representation is also based on measurements using a mockup tower with different types of provoked degradation that was subjected to ambient changes in the laboratory. The sensor signals are preprocessed and submitted to an autoencoder neural network to minimize the dimensionality of the resources involved, being analyzed by a classifier, based on a Softmax configuration. The results of the proposed configuration indicate the possibility of early failure detection and evolution evaluation, providing an effective failure detection and monitoring system.

ZBED2 is an antagonist of interferon regulatory factor 1 and modifies cell identity in pancreatic cancer.

Lineage plasticity is a prominent feature of pancreatic ductal adenocarcinoma (PDA) cells, which can occur via deregulation of lineage-specifying transcription factors. Here, we show that the zinc finger protein ZBED2 is aberrantly expressed in PDA and alters tumor cell identity in this disease. Unexpectedly, our epigenomic experiments reveal that ZBED2 is a sequence-specific transcriptional repressor of IFN-stimulated genes, which occurs through antagonism of IFN regulatory factor 1 (IRF1)-mediated transcriptional activation at cooccupied promoter elements. Consequently, ZBED2 attenuates the transcriptional output and growth arrest phenotypes downstream of IFN signaling in multiple PDA cell line models. We also found that ZBED2 is preferentially expressed in the squamous molecular subtype of human PDA, in association with inferior patient survival outcomes. Consistent with this observation, we show that ZBED2 can repress the pancreatic progenitor transcriptional program, enhance motility, and promote invasion in PDA cells. Collectively, our findings suggest that high ZBED2 expression is acquired during PDA progression to suppress the IFN response pathway and to promote lineage plasticity in this disease.