Clinical Significance and Regulation of ERK5 Expression and Function in Cancer

Extracellular signal-regulated kinase 5 (ERK5) is a unique kinase among MAPKs family members, given its large structure characterized by the presence of a unique C-terminal domain. Despite increasing data demonstrating the relevance of the ERK5 pathway in the growth, survival, and differentiation of normal cells, ERK5 has recently attracted the attention of several research groups given its relevance in inflammatory disorders and cancer. Accumulating evidence reported its role in tumor initiation and progression. In this review, we explore the gene expression profile of ERK5 among cancers correlated with its clinical impact, as well as the prognostic value of ERK5 and pERK5 expression levels in tumors. We also summarize the importance of ERK5 in the maintenance of a cancer stem-like phenotype and explore the major known contributions of ERK5 in the tumor-associated microenvironment. Moreover, although several questions are still open concerning ERK5 molecular regulation, different ERK5 isoforms derived from the alternative splicing process are also described, highlighting the potential clinical relevance of targeting ERK5 pathways.

Příspěvek k salašnickému hospodaření na Moravském Valašsku. Ovčín na panské salaši jako zapomenutý objekt tradičního stavitelství na Moravě

Carpathian shepherding, i.e., seasonal mountain cattle farming is an inseparable part of life in the highland of eastern Moravia and Silesia, regardless of the debate as to its origins and extent. It is possible to observe not only the blending of what were essentially mountain practices with the domestic peasant tradition, but also the use of this method of farming across social groups. In one lithograph by the well-known Moravian painter and graphic artist František Kalivoda (1824–1859), in the sheep pasture above Rožnov pod Radhoštěm, there is an unusually large structure, which is presumed to be a sheepshed. This has not yet been reflected in the ethnography of these lands. The research, which was based primarily on archival, ethnological and archaeological research, complements the well-known but very fragmentary facts about mountain farming under manorial management and reconstructs the architectural form of the building and its functions.

Multi-Monostatic Interferometric Radar with Radar Link for Bridge Monitoring

In recent years, interferometric radars have been extensively used as sensors for static and dynamic monitoring of bridges. Generally speaking, a radar can only detect the displacement component along its view direction. As the movement of a real bridge or a large structure can be rather complex, this limitation can be a significant drawback in engineering practice. In order toovercome this limitation, in this article, a multi-monostatic interferometric radar with radio link is proposed. This radar is able to detect a second component of displacement using a transponder. The transponder is connected to the radar through a radio link. The radio link allows the installation of the transponder far away from the radar, and even in the opposite direction. The equipment is based on a MIMO radar, two transceivers for the radio link, and a transponder. The transceivers and the transponder are essentially two antennas and an amplifier system. The equipment is experimentally tested in controlled scenarios and in the case study of Indiano Bridge, Florence, Italy.

Ship Blast Analysis

Naval vessels and Submarines structures in their fighting role are susceptible to explosion of torpedoes, mines, TNT etc. The damage inflicted by Contact explosion consists of direct shock wave damage to hull, whipping damage to keel and mechanical damage to onboard equipment and associated systems. The order to design a shock resistant structure, it is important to simulate these structures and loads and then subsequently analyze the same to predict the response (as performing experiments would be expensive). The TNT (Trinitrotoluene) explosion analysis of large structures like ships could be considered as one of the most complicated numerical analysis. Loads can be calculated by using published empirical formulas, which are complicated if calculated the large structure. By using of the FE Software ANSYS, backed up with in the developed software, for Explosion analysis of structures.

Machine Learning Designs Non-Hemolytic Antimicrobial Peptides

<p>Machine learning (ML) consists in the recognition of patterns from training data and offers the opportunity to exploit large structure-activity database sets for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however without addressing the associated toxicity to human red blood cells. Here we trained recurrent neural networks (RNN) with data from DBAASP (Database of Antimicrobial Activity and Structure of Peptides) to design short non-hemolytic AMPs. Synthesis and testing of 28 generated peptides, each at least 5 mutations away from training data, allowed us to identify eight new non-hemolytic AMPs against <i>Pseudomonas aeruginosa</i>, <i>Acinetobacter baumannii</i>, and methicillin resistant<i> Staphylococcus aureus</i> (MRSA). These results show that machine learning (ML) can be used to design new non-hemolytic AMPs.</p>

Machine Learning Designs Non-Hemolytic Antimicrobial Peptides

<p>Machine learning (ML) consists in the recognition of patterns from training data and offers the opportunity to exploit large structure-activity database sets for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however without addressing the associated toxicity to human red blood cells. Here we trained recurrent neural networks (RNN) with data from DBAASP (Database of Antimicrobial Activity and Structure of Peptides) to design short non-hemolytic AMPs. Synthesis and testing of 28 generated peptides, each at least 5 mutations away from training data, allowed us to identify eight new non-hemolytic AMPs against <i>Pseudomonas aeruginosa</i>, <i>Acinetobacter baumannii</i>, and methicillin resistant<i> Staphylococcus aureus</i> (MRSA). These results show that machine learning (ML) can be used to design new non-hemolytic AMPs.</p>