The GAUGAA Motif Is Responsible for the Binding between circSMARCA5 and SRSF1 and Related Downstream Effects on Glioblastoma Multiforme Cell Migration and Angiogenic Potential

Circular RNAs (circRNAs) are a large class of RNAs with regulatory functions within cells. We recently showed that circSMARCA5 is a tumor suppressor in glioblastoma multiforme (GBM) and acts as a decoy for Serine and Arginine Rich Splicing Factor 1 (SRSF1) through six predicted binding sites (BSs). Here we characterized RNA motifs functionally involved in the interaction between circSMARCA5 and SRSF1. Three different circSMARCA5 molecules (Mut1, Mut2, Mut3), each mutated in two predicted SRSF1 BSs at once, were obtained through PCR-based replacement of wild-type (WT) BS sequences and cloned in three independent pcDNA3 vectors. Mut1 significantly decreased its capability to interact with SRSF1 as compared to WT, based on the RNA immunoprecipitation assay. In silico analysis through the “Find Individual Motif Occurrences” (FIMO) algorithm showed GAUGAA as an experimentally validated SRSF1 binding motif significantly overrepresented within both predicted SRSF1 BSs mutated in Mut1 (q-value = 0.0011). U87MG and CAS-1, transfected with Mut1, significantly increased their migration with respect to controls transfected with WT, as revealed by the cell exclusion zone assay. Immortalized human brain microvascular endothelial cells (IM-HBMEC) exposed to conditioned medium (CM) harvested from U87MG and CAS-1 transfected with Mut1 significantly sprouted more than those treated with CM harvested from U87MG and CAS-1 transfected with WT, as shown by the tube formation assay. qRT-PCR showed that the intracellular pro- to anti-angiogenic Vascular Endothelial Growth Factor A (VEGFA) mRNA isoform ratio and the amount of total VEGFA mRNA secreted in CM significantly increased in Mut1-transfected CAS-1 as compared to controls transfected with WT. Our data suggest that GAUGAA is the RNA motif responsible for the interaction between circSMARCA5 and SRSF1 as well as for the circSMARCA5-mediated control of GBM cell migration and angiogenic potential.     

Vandetanib versus Cabozantinib in Medullary Thyroid Carcinoma: A Focus on Anti-Angiogenic Effects in Zebrafish Model

Medullary thyroid carcinoma (MTC) is a tumor deriving from the thyroid C cells. Vandetanib (VAN) and cabozantinib (CAB) are two tyrosine kinase inhibitors targeting REarranged during Transfection (RET) and other kinase receptors and are approved for the treatment of advanced MTC. We aim to compare the in vitro and in vivo anti-tumor activity of VAN and CAB in MTC. The effects of VAN and CAB on viability, cell cycle, and apoptosis of TT and MZ-CRC-1 cells are evaluated in vitro using an MTT assay, DNA flow cytometry with propidium iodide, and Annexin V-FITC/propidium iodide staining, respectively. In vivo, the anti-angiogenic potential of VAN and CAB is evaluated in Tg(fli1a:EGFP)^y1 transgenic fluorescent zebrafish embryos by analyzing the effects on the physiological development of the sub-intestinal vein plexus and the tumor-induced angiogenesis after TT and MZ-CRC-1 xenotransplantation. VAN and CAB exert comparable effects on TT and MZ-CRC-1 viability inhibition and cell cycle perturbation, and stimulated apoptosis with a prominent effect by VAN in MZ-CRC-1 and CAB in TT cells. Regarding zebrafish, both drugs inhibit angiogenesis in a dose-dependent manner, in particular CAB shows a more potent anti-angiogenic activity than VAN. To conclude, although VAN and CAB show comparable antiproliferative effects in MTC, the anti-angiogenic activity of CAB appears to be more relevant.     

BRAF Gene and Melanoma: Back to the Future

As widely acknowledged, 40–50% of all melanoma patients harbour an activating BRAF mutation (mostly BRAF V600E). The identification of the RAS–RAF–MEK–ERK (MAP kinase) signalling pathway and its targeting has represented a valuable milestone for the advanced and, more recently, for the completely resected stage III and IV melanoma therapy management. However, despite progress in BRAF-mutant melanoma treatment, the two different approaches approved so far for metastatic disease, immunotherapy and BRAF+MEK inhibitors, allow a 5-year survival of no more than 60%, and most patients relapse during treatment due to acquired mechanisms of resistance. Deep insight into BRAF gene biology is fundamental to describe the acquired resistance mechanisms (primary and secondary) and to understand the molecular pathways that are now being investigated in preclinical and clinical studies with the aim of improving outcomes in BRAF-mutant patients.     

Input design for guaranteed fault diagnosis using zonotopes

An input design method is presented for guaranteeing the diagnosability of faults from the outputs of a system. Faults are modeled by discrete switches between linear models with bounded disturbances and measurement errors. Zonotopes are used to efficiently characterize the set of inputs that are guaranteed to lead to outputs that are consistent with at most one fault scenario. Provided that this set is nonempty, an element is then chosen that is minimally harmful with respect to other control objectives. This approach leads to a nonconvex optimization problem, but is shown to be equivalent to a mixed-integer quadratic program that can be solved efficiently. Methods are given for reducing the complexity of this program, including an observer-based method that drastically reduces the number of binary variables when many sampling times are required for diagnosis.     

Structural optimization strategies to design green products

This paper addresses the need for a structured approach to environmental assessment and improvement. We propose a computer-aided methodology, named Eco-OptiCAD, based on the integration of Structural Optimization and Life Cycle Assessment (LCA) tools. Eco-OptiCAD supports the designer during product development, highlighting when and where the core of the environmental impact lies. Furthermore, it provides effective tools to address such impacts, improving the original product, while ensuring structural and functional requirements. It foresees the synergic use of (1) virtual prototyping tools, such as 3D CAD, Finite Element Analysis (FEA) and Structural optimization, (2) function modeling methodology and (3) Life Cycle Assessment (LCA) tools. The kernel of the methodology is constituted by a set of optimization strategies and a module, named Life Cycle Mapping (LCM). In particular, we have conceived ten optimization strategies converting environmental objectives and constraints into structural and geometrical parameters. They enable the designer to generate alternative green scenarios according to the triad shape–material–production. The LCM tool has been specifically developed to easily trace the growth of environmental impacts throughout the product's life cycle and allow the user to focus his effort on the most relevant aspects. Thanks to the integration of the structural optimizer with an LCA map, the designer becomes aware of the consequences that each change in the geometry, the material or the manufacturing process will produce on the environmental impact of the product throughout its life cycle. With a complete view of the product life cycle, the designer can improve a single phase, while retaining a global perspective; thus avoiding the possibility of gaining a local green improvement at the cost of a global increase in environmental impacts.     

Taxonomy-based relaxation of query answering in relational databases

Traditional information search in which queries are posed against a known and rigid schema over a structured database is shifting toward a Web scenario in which exposed schemas are vague or absent and data come from heterogeneous sources. In this framework, query answering cannot be precise and needs to be relaxed, with the goal of matching user requests with accessible data. In this paper, we propose a logical model and a class of abstract query languages as a foundation for querying relational data sets with vague schemas. Our approach relies on the availability of taxonomies, that is, simple classifications of terms arranged in a hierarchical structure. The model is a natural extension of the relational model in which data domains are organized in hierarchies, according to different levels of generalization between terms. We first propose a conservative extension of the relational algebra for this model in which special operators allow the specification of relaxed queries over vaguely structured information. We also study equivalence and rewriting properties of the algebra that can be used for query optimization. We then illustrate a logic-based query language that can provide a basis for expressing relaxed queries in a declarative way. We finally investigate the expressive power of the proposed query languages and the independence of the taxonomy in this context.     

Statics Aware Grid Shells

We introduce a framework for the generation of polygonal gridshell architectural structures, whose topology is designed in order to excel in static performances. We start from the analysis of stress on the input surface and we use the resulting tensor field to induce an anisotropic nonEuclidean metric over it. This metric is derived by studying the relation between the stress tensor over a continuous shell and the optimal shape of polygons in a corresponding gridshell. Polygonal meshes with uniform density and isotropic cells under this metric exhibit variable density and anisotropy in Euclidean space, thus achieving a better distribution of the strain energy over their elements. Meshes are further optimized taking into account symmetry and regularity of cells to improve aesthetics. We experiment with quad meshes and hexdominant meshes, demonstrating that our gridshells achieve better static performances than stateoftheart gridshells.     

When simulated environments make the difference: the effectiveness of different types of training of car service procedures

An empirical analysis was performed to compare the effectiveness of different approaches to training a set of procedural skills to a sample of novice trainees. Sixty-five participants were randomly assigned to one of the following three training groups: (1) learning-by-doing in a 3D desktop virtual environment, (2) learning-by-observing a video (show-and-tell) explanation of the procedures, and (3) trial-and-error. In each group, participants were trained on two car service procedures. Participants were recalled to perform a procedure either 2 or 4 weeks after the training. The results showed that: (1) participants trained through the virtual approach of learning-by-doing performed both procedures significantly better (i.e. p < .05 in terms of errors and time) than people of non-virtual groups, (2) the virtual training group, after a period of non-use, were more effective than non-virtual training (i.e. p < .05) in their ability to recover their skills, (3) after a (simulated) long period from the training—i.e. up to 12 weeks—people who experienced 3D environments consistently performed better than people who received other kinds of training. The results also suggested that independently from the training group, trainees’ visuospatial abilities were a predictor of performance, at least for the complex service procedure, adj R ² = .460, and that post-training performances of people trained through virtual learning-by-doing are not affected by learning styles. Finally, a strong relationship (p < .001, R ² = .441) was identified between usability and trust in the use of the virtual training tool—i.e. the more the system was perceived as usable, the more it was perceived as trustable to acquire the competences.     

Maximum density of quantum information in a scalable CMOS implementation of the hybrid qubit architecture

Scalability from single-qubit operations to multi-qubit circuits for quantum information processing requires architecture-specific implementations. Semiconductor hybrid qubit architecture is a suitable candidate to realize large-scale quantum information processing, as it combines a universal set of logic gates with fast and all-electrical manipulation of qubits. We propose an implementation of hybrid qubits, based on Si metal-oxide-semiconductor (MOS) quantum dots, compatible with the CMOS industrial technological standards. We discuss the realization of multi-qubit circuits capable of fault-tolerant computation and quantum error correction, by evaluating the time and space resources needed for their implementation. As a result, the maximum density of quantum information is extracted from a circuit including eight logical qubits encoded by the [[7, 1, 3]] Steane code. The corresponding surface density of logical qubits is 2.6 Mqubit/cm\(^2\).     

Air‐ground Matching: Appearance‐based GPS‐denied Urban Localization of Micro Aerial Vehicles

In this paper, we address the problem of globally localizing and tracking the pose of a camera‐equipped micro aerial vehicle (MAV) flying in urban streets at low altitudes without GPS. An image‐based global positioning system is introduced to localize the MAV with respect to the surrounding buildings. We propose a novel air‐ground image‐matching algorithm to search the airborne image of the MAV within a ground‐level, geotagged image database. Based on the detected matching image features, we infer the global position of the MAV by back‐projecting the corresponding image points onto a cadastral three‐dimensional city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry whenever a good match is detected between the airborne and the ground‐level images. The proposed approach is tested on a 2 km trajectory with a small quadrocopter flying in the streets of Zurich. Our vision‐based global localization can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over‐season variations, thus outperforming conventional visual place‐recognition approaches. The dataset is made publicly available to the research community. To the best of our knowledge, this is the first work that studies and demonstrates global localization and position tracking of a drone in urban streets with a single onboard camera.