1,272 research outputs found
Prediction of protein β-residue contacts by Markov logic networks with grounding-specific weights
No full textMotivation: Accurate prediction of contacts between β-strand residues can significantly contribute towards ab initio prediction of the 3D structure of many proteins. Contacts in the same protein are highly interdependent. Therefore, significant improvements can be expected by applying statistical relational learners that overcome the usual machine learning assumption that examples are independent and identically distributed. Furthermore, the dependencies among β-residue contacts are subject to strong regularities, many of which are known a priori. In this article, we take advantage of Markov logic, a statistical relational learning framework that is able to capture dependencies between contacts, and constrain the solution according to domain knowledge expressed by means of weighted rules in a logical language. Results: We introduce a novel hybrid architecture based on neural and Markov logic networks with grounding-specific weights. On a non-redundant dataset, our method achieves 44.9% F1 measure, with 47.3% precision and 42.7% recall, which is significantly better (P < 0.01) than previously reported performance obtained by 2D recursive neural networks. Our approach also significantly improves the number of chains for which β-strands are nearly perfectly paired (36% of the chains are predicted with F1 ≥ 70% on coarse map). It also outperforms more general contact predictors on recent CASP 2008 targets. © The Author 2009. Published by Oxford University Press. All rights reserved
MetalDetector v2.0: Predicting the geometry of metal binding sites from protein sequence
No full textMetalDetector identifies CYS and HIS involved in transition metal protein binding sites, starting from sequence alone. A major new feature of release 2.0 is the ability to predict which residues are jointly involved in the coordination of the same metal ion. The server is available at http://metaldetector.dsi.unifi.it/v2.0/. © 2011 The Author(s)
Ultrathin organic membranes: Can they sustain the quest for mechanically robust device applications?
No full textSummary: Ultrathin polymeric films have recently attracted tremendous interest as functional components of coatings, separation membranes, and sensors, with applications spanning from environment-related processes to soft robotics and wearable devices. In order to support the development of robust devices with advanced performances, it is necessary to achieve a deep comprehension of the mechanical properties of ultrathin polymeric films, which can be significantly affected by confinement effects at the nanoscale. In this review paper, we collect the most recent advances in the development of ultrathin organic membranes with emphasis on the relationship between their structure and mechanical properties. We provide the reader with a critical overview of the main approaches for the preparation of ultrathin polymeric films, the methodologies for the investigation of their mechanical properties, and models to understand the primary effects that impact their mechanical response, followed by a discussion on the current trends for designing mechanically robust organic membranes
Transient Stiffness Patterning in Hydrogels Driven by Dissipative Mechanochemical Coupling
No full textLiving systems adapt to mechanical forces through a series of biochemical feedback loops and dissipative signal transduction mechanisms across multiple length scales. By contrast, synthetic materials are static, closed systems with minimal interaction with their surroundings and lack the ability to adapt to mechanical deformations. Here, a strategy that enables a hydrogel to adapt to mechanical forces through the temporal modulation of its stiffness properties is reported. It is demonstrated that force-induced bond rupture at the disulfide linkages of the hydrogel, coupled with their chemical reoxidation leads to dissipative, transient stiffness functions. The electrochemical generation of the oxidant as the output of a feedback loop triggered by an externally applied force provides high spatiotemporal control over the dissipative process, enabling the engineering of hydrogels with out-of-equilibrium stiffness patterns. Additionally, dose-controlled, spatiotemporal transient release of model protein payloads from the hydrogel is demonstrated. The proposed concept has the potential to enhance the autonomous and interactive functionalities of hydrogels, advancing their applications in the biomedical field and soft robotics
Electrochemical and surface plasmon resonance characterization of beta-cyclodextrin-based self-assembled monolayers and evaluation of their inclusion complexes with glucocorticoids
No full textThis paper describes the characterization of a self-assembled beta-cyclodextrin (beta-CD)-derivative monolayer (beta-CD-SAM) on a gold surface and the study of their inclusion complexes with glucocorticoids. To this aim the arrangement of a self-assembled beta-cyclodextrin-derivative monolayer on a gold surface was monitored in situ by means of surface plasmon resonance (SPR) spectroscopy and double-layer capacitance measurements. Film thickness and dielectric constant were evaluated for a monolayer of beta-CD using one-color-approach SPR. The selectivity of the beta-CD host surface was verified by using electroactive species permeable and impermeable in the beta-CD cavity. The redox probe was selected according to its capacity to permeate the beta-CD monolayer and its electrochemical behavior. In order to evaluate the feasibility of an inclusion complex between beta-CD-SAM with some steroids such as cortisol and cortisone, voltammetric experiments in the presence of the redox probes as molecules competitive with the steroids have been performed. The formation constant of the surface host-guest by beta-CD-SAM and the steroids under study was calculated
Fingerprint Classification by Combination of Flat and Structural Approaches
No full textThis paper investigates the advantages of the combination of flat and structural approaches for fingerprint classification. A novel structural classification method is described and compared with the “multichannel” flat method recently proposed by Jain et al. [1]. Performances and complementarity of the two methods are evaluated using NIST-4 Database. A simple approach based on the concept of “metaclassification” is proposed for the combination of the two fingerprint classification methods. Reported results point out the potential advantages of the combination of flat and structural fingerprint-classification approaches. In particular, such results show that the exploitation of structural information allows increasing classification performances
Relational random forests based on random relational rules
No full textRandom Forests have been shown to perform very well in propositional learning. FORF is an upgrade of Random Forests for relational data. In this paper we investigate shortcomings of FORF and propose an alternative algorithm, R⁴F, for generating Random Forests over relational data. R⁴F employs randomly generated relational rules as fully self-contained Boolean tests inside each node in a tree and thus can be viewed as an instance of dynamic propositionalization. The implementation of R⁴F allows for the simultaneous or parallel growth of all the branches of all the trees in the ensemble in an efficient shared, but still single-threaded way. Experiments favorably compare R⁴F to both FORF and the combination of static propositionalization together with standard Random Forests. Various strategies for tree initialization and splitting of nodes, as well as resulting ensemble size, diversity, and computational complexity of R⁴F are also investigated
Photo-responsive graphene and carbon nanotubes to control and tackle biological systems
No full textPhoto-responsive multifunctional nanomaterials are receiving considerable attention for biological applications because of their unique properties. The functionalization of the surface of carbon nanotubes (CNTs) and graphene, among other carbon based nanomaterials, with molecular switches that exhibit reversible transformations between two or more isomers in response to different kind of external stimuli, such as electromagnetic radiation, temperature and pH, has allowed the control of the optical and electrical properties of the nanomaterial. Light-controlled molecular switches, such as azobenzene and spiropyran, have attracted a lot of attention for nanomaterial's functionalization because of the remote modulation of their physicochemical properties using light stimulus. The enhanced properties of the hybrid materials obtained from the coupling of carbon based nanomaterials with light-responsive switches has enabled the fabrication of smart devices for various biological applications, including drug delivery, bioimaging and nanobiosensors. In this review, we highlight the properties of photo-responsive carbon nanomaterials obtained by the conjugation of CNTs and graphene with azobenzenes and spiropyrans molecules to investigate biological systems, devising possible future directions in the field
A semiparametric generative model for efficient structured-output supervised learning
No full textWe present a semiparametric generative model for supervised learning with structured outputs. The main algorithmic idea is to replace the parameters of an underlying generative model (such as a stochastic grammars) with input-dependent predictions obtained by (kernel) logistic regression. This method avoids the computational burden associated with the comparison between target and predicted structure during the training phase, but requires as an additional input a vector of sufficient statistics for each training example. The resulting training algorithm is asymptotically more efficient than structured output SVM as the size of the output structure grows. At the same time, by computing parameters of a joint distribution as a function of the full input structure, typical expressiveness limitations of related conditional models (such as maximum entropy Markov models) can be potentially avoided. Empirical results on artificial and real data (in the domains of natural language parsing and RNA secondary structure prediction) show that the method works well in practice and scales up with the size of the output structures. © Springer Science+Business Media B.V. 2009
Experiences with Physical Weed Control on Hard Surfaces in Central Italy
No full textIn Italy weed control in urban areas is mainly performed by means of mowing cutting and
herbicide distribution. While trimmers are not effective in reducing weed density and they are also
potentially injurious for hard surfaces and the safety of citizens and operators, chemical control
induces resistance to active compounds in spontaneous plants and it is surely a source of
environmental pollution and a risk factor for the health of human beings and animals. For this
reason the use of herbicides in urban areas is strictly regulated by laws. As an alternative to ordinary
weed control devices, thermal equipments can be used successfully for weed control on hard
surfaces. Flaming machines are the most efficient among thermal devices and they are suitable for
treatments in many urban contexts.
The aim of this research was to evaluate the effects of different weed managements (flaming,
mowing, herbicide application, and flaming+herbicide application) on weed dynamics in two cities
of Tuscany (Central Italy) and to compare the total working time and costs of operations in order to
define a proper strategy for the control of weed flora growing on hard surfaces in a typical
mediterranean environment.
A hand knapsack device and three motorized flaming prototypes were also projected, built and
tested at the University of Pisa. One of the self-propelled versions of the flaming machines was also
used for a trial of weed management in the city of Livorno between October 2006 and June 2007 in
order to evaluate its technical performances and effectiveness
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