Kuwanon‐L as a New Allosteric HIV‐1 Integrase Inhibitor: Molecular Modeling and Biological Evaluation

HIV‐1 integrase (IN) active site inhibitors are the latest class of drugs approved for HIV treatment. The selection of IN strand‐transfer drug‐resistant HIV strains in patients supports the development of new agents that are active as allosteric IN inhibitors. Here, a docking‐based virtual screening has been applied to a small library of natural ligands to identify new allosteric IN inhibitors that target the sucrose binding pocket. From theoretical studies, kuwanon‐L emerged as the most promising binder and was thus selected for biological studies. Biochemical studies showed that kuwanon‐L is able to inhibit the HIV‐1 IN catalytic activity in the absence and in the presence of LEDGF/p75 protein, the IN dimerization, and the IN/LEDGF binding. Kuwanon‐L also inhibited HIV‐1 replication in cell cultures. Overall, docking and biochemical results suggest that kuwanon‐L binds to an allosteric binding pocket and can be considered an attractive lead for the development of new allosteric IN antiviral agents.     

Identification of IPMC nonlinear model via single and multi-objective optimization algorithms

Ionic Polymer–Metal Composites (IPMCs) are electro-active polymers transforming mechanical forces into electric signals and vice versa. This paper proposes an improved electro-mechanical grey-box model for IPMC membrane working as actuator. In particular the IPMC nonlinearity has been characterized through experimentation and included within the electric model. Moreover identification of the model parameters has been performed via optimization algorithms using both single- and multi-objective formulation. Minimization was attained via the Nelder–Mead simplex and the Genetic Algorithms considering as cost functions the error between the experimental and modeled absorbed current and the error between experimental and modeled displacement. The obtained results for the different formulations have been then compared.     

Local orthogonal bases I: Construction

We present a new method for constructing local orthogonal bases, both in continuous and discrete time. All basis functions are obtained from a single prototype window, and there exists a fast algorithm for implementation. The approach is very general and can handle a large variety of cases interesting for applications, such as audio and image coding.     

CO2 abatement by co-firing of natural gas and biomass-derived gas in a gas turbine

In this work, a possible way for partial CO₂ emissions reduction from gas turbine exhausts by co-firing with biomass is investigated. The basic principle is the recirculation of a fraction of the exhausts (still rich in oxygen) to a gasifier, in order to produce syngas to mix with natural gas fuel. As biomass is a CO₂ neutral fuel, the fraction of replaced natural gas is a measure of CO₂ removal potential of the powerplant.     

Chemically vapour deposited diamond coatings on cemented tungsten carbides: Substrate pretreatments, adhesion and cutting performance

Chemical vapour deposition (CVD) of diamond films onto Co-cemented tungsten carbide (WC-Co) tools and wear parts presents several problems due to interfacial graphitization induced by the binder phase and thermal expansion mismatch of diamond and WC-Co. Methods used to improve diamond film adhesion include substrate-modification processes that create a three-dimensional compositionally graded interface. This paper reviews substrate pretreatments and adhesion issues of chemically vapour deposited diamond films on WC-Co. The combined effect of pretreatments and substrate microstructure on the adhesive toughness and wear rate of CVD diamond in dry machining of highly abrasive materials was analyzed. The role of diamond film surface morphology on chip evacuation in dry milling of ceramics was also investigated by comparing feed forces of coated and uncoated mills. The overall tribological performance of diamond coated mills depended on coating microstructure and smoothness. The use of smother films did allow to reduce cutting forces by facilitating chip evacuation.     

Low Sulfur Amino Acid,High Polyunsaturated Fatty Acid Diet Inhibits Breast Cancer Growth

Cancer cells may acquire resistance to stress signals and reprogram metabolism to meet the energetic demands to support their high proliferation rate and avoid death. Hence, targeting nutrient dependencies of cancer cells has been suggested as a promising anti-cancer strategy. We explored the possibility of killing breast cancer (BC) cells by modifying nutrient availability. We used in vitro models of BC (MCF7 and MDA-MB-231) that were maintained with a low amount of sulfur amino acids (SAAs) and a high amount of oxidizable polyunsatured fatty acids (PUFAs). Treatment with anti-apoptotic, anti-ferroptotic and antioxidant drugs were used to determine the modality of cell death. We reproduced these conditions in vivo by feeding BC-bearing mice with a diet poor in proteins and SAAs and rich in PUFAs (LSAA/HPUFA). Western blot analysis, qPCR and histological analyses were used to assess the anti-cancer effects and the molecular pathways involved. We found that BC cells underwent oxidative damage to DNA and proteins and both apoptosis and ferroptosis were induced. Along with caspases-mediated PARP1 cleavage, we found a lowering of the GSH-GPX4 system and an increase of lipid peroxides. A LSAA/HPUFA diet reduced tumor mass and its vascularization and immune cell infiltration, and induced apoptosis and ferroptotic hallmarks. Furthermore, mitochondrial mass was found to be increased, and the buffering of mitochondrial reactive oxygen species limited GPX4 reduction and DNA damage. Our results suggest that administration of custom diets, targeting the dependency of cancer cells on certain nutrients, can represent a promising complementary option for anti-cancer therapy.     

Detecting similarities in virtual machine behavior for cloud monitoring using smoothed histograms

The growing size and complexity of cloud systems determine scalability issues for resource monitoring and management. While most existing solutions consider each Virtual Machine (VM) as a black box with independent characteristics, we embrace a new perspective where VMs with similar behaviors in terms of resource usage are clustered together. We argue that this new approach has the potential to address scalability issues in cloud monitoring and management. In this paper, we propose a technique to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. This innovative technique models VMs behavior exploiting the probability histogram of their resources usage, and performs smoothing-based noise reduction and selection of the most relevant information to consider for the clustering process. Through extensive evaluation, we show that our proposal achieves high and stable performance in terms of automatic VM clustering, and can reduce the monitoring requirements of cloud systems.