Andrea Petta

SambVca 2. A Web Tool for Analyzing Catalytic Pockets with Topographic Steric Maps

Developing more efficient catalysts remains one of the primary targets of organometallic chemists. To accelerate reaching this goal, effective molecular descriptors and visualization tools can represent a remarkable aid. Here, we present a Web application for analyzing the catalytic pocket of metal complexes using topographic steric maps as a general and unbiased descriptor that is suitable for every class of catalysts. To show the broad applicability of our approach, we first compared the steric map of a series of transition metal complexes presenting popular mono-, di-, and tetracoordinated ligands and three classic zirconocenes. This comparative analysis highlighted similarities and differences between totally unrelated ligands. Then, we focused on a recently developed Fe(II) catalyst that is active in the asymmetric transfer hydrogenation of ketones and imines. Finally, we expand the scope of these tools to rationalize the inversion of enantioselectivity in enzymatic catalysis, achieved by point mutat...

Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers’ performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked models in native-like solutions. The best performing clustering approaches we tested indeed lead to more than double the number of cases for which at least one correct solution can be included within the top ten ranked models.

Engaging Citizens with a Social Platform for Open Data

Open Data are valuable initiatives in favour of transparency. Public administrations are increasing the availability of datasets for citizens, associations, innovators and other stakeholders, by releasing their data with open licenses. Open initiatives are achieving less success than expected, mainly due to the lack of engagement. There is a growing demand for approaches to actively engage citizens in exploiting Open Data. This paper introduces SPOD, a Social Platform for Open Data, which aims to engage citizens, local associations and organizations in forming communities of interests, stimulating the interpretation of Open Data and exploiting their use in Data-driven discussions, something not well-supported on traditional social networks. Social collaboration is the key aspect to increase the public value, where citizens participate in the discussions, co-create knowledge and data. The paper describes the engagement of four communities of citizens, which contributed to the public value by discussing topics in the context of Cultural Heritage, generating information from existing and co-created open datasets, by using SPOD.

Datalet-Ecosystem Provider (DEEP): Scalable Architecture for Reusable, Portable and User-Friendly Visualizations of Open Data

This paper presents the DatalEt-Ecosystem Provider (DEEP), an extensible, and scalable Edge-centric architecture to visualize Open Data, retrieved in real time from institutional open data portals. The aim is to engage citizens and stakeholders through reusable, portable and interactive visualizations, named datalets. The DEEP architecture exploits the increasing computing power and capacity of end-users devices, moving the computation to process and visualize data, from the central server, directly to the client-side ensuring data trustiness, privacy, scalability and dynamic data loading. DEEP and its datalets have been fully exploited, in the ROUTE-TO-PA, HORIZON 2020 funded project, by five public administrations across Europe as pilot projects. The project engages and involves citizens in creating, sharing and commenting existing visualizations of Open Data. DEEP is open source, its source code is fully available on GitHub, thus every single component can be reused by other projects.

An infrastructure for remote virtual exploration on PDAs

In this paper we present a prototyped system to enable the virtual exploration of a complex virtual environment. Our approach exploits Quest3D as main rendering engine, its output is conveyed toward users PDAs to allow them to explore using the PDA as a (mobile) interface to the virtual environment. An important aspect of the system is that it relies on an off-the-shelf PC and low end wireless network. Some early results showed that the prototype is able to easily manage 5 PDAs. Suggested fields of use of our system are virtual cultural heritage, educational virtual environments, videogames.

Increasing Public Value through Co-Creation of Open Knowledge

The aim of our research is to study how to increase Public Value through the collective participation, involving Public Administrations, stakeholders and citizens together. The Public Value for citizens is in the available and gained Knowledge. The paper models this concept by introducing a variant of the classic Data-Information-Knowledge pyramid, considering everything published as open and public. The paper introduces a social and iterative process designed for user appropriation, that includes the Knowledge and Data Co-Creation with the aim to generate public Open Knowledge. Users with process and technology appropriation can creatively follow the process in different ways. The paper concludes by introducing a brief preliminary scenario that exploits the process, platform and technology in the context of Cultural Heritage.

Multiparallel decompression simultaneously using multicore central processing unit and graphic processing unit

Abstract The discrete wavelet transform (DWT)-based compression algorithm is widely used in many image compression systems. The time-consuming computation of the 9 / 7 discrete wavelet decomposition and the bit-plane decoding is usually the bottleneck of these systems. In order to perform real-time decompression on a massive bit stream of compressed images continuously down-linked from the satellite, we propose a different graphic processing unit (GPU)-accelerated decoding system. In this system, the GPU and multiple central processing unit (CPU) threads are run in parallel. To obtain the maximum throughput via a different pipeline structure for processing continuous satellite images, an additional balancing algorithm workload has been implemented to distribute the jobs to both CPU and GPU parts to have approximately the same processing speed. Through the pipelined CPU and GPU heterogeneous computing, the entire decoding system approaches a speedup of 15 × as compared to its single-threaded CPU counterpart. The proposed channel and source decoding system is able to decompress 1024 × 1024 satellite images at a speed of 20     frames / s .