A fingerprint of a heterogeneous data set

Advances in Data Analysis and Classification - In this paper, we describe the fingerprint method, a technique to classify bags of mixed-type measurements. The method was designed to solve a...     

Identification of Protein Functional Regions

Protein sequence stores the information relative to both functionality and stability, thus making it difficult to disentangle the two contributions. However, the identification of critical residues for function and stability has important implications for the mapping of the proteome interactions, as well as for many pharmaceutical applications, e. g. the identification of ligand binding regions for targeted pharmaceutical protein design. In this work, we propose a computational method to identify critical residues for protein functionality and stability and to further categorise them in strictly functional, structural and intermediate. We evaluate single site conservation and use Direct Coupling Analysis (DCA) to identify co-evolved residues both in natural and artificial evolution processes. We reproduce artificial evolution using protein design and base our approach on the hypothesis that artificial evolution in the absence of any functional constraint would exclusively lead to site conservation and co-evolution events of the structural type. Conversely, natural evolution intrinsically embeds both functional and structural information. By comparing the lists of conserved and co-evolved residues, outcomes of the analysis on natural and artificial evolution, we identify the functional residues without the need of any a priori knowledge of the biological role of the analysed protein.     

Models and algorithms for the 2-dimensional cell suppression problem in statistical disclosure control

Lagrangean dualization and subgradient optimization techniques are frequently used within the field of computational optimization for finding approximate solutions to large, structured optimization problems. The dual subgradient scheme does not automatically produce primal feasible solutions; there is an abundance of techniques for computing such solutions (via penalty functions, tangential approximation schemes, or the solution of auxiliary primal programs), all of which require a fair amount of computational effort. We consider a subgradient optimization scheme applied to a Lagrangean dual formulation of a convex program, and construct, at minor cost, an ergodic sequence of subproblem solutions which converges to the primal solution set. Numerical experiments performed on a traffic equilibrium assignment problem under road pricing show that the computation of the ergodic sequence results in a considerable improvement in the quality of the primal solutions obtained, compared to those generated in the basic subgradient scheme. Received February 11, 1997 / Revised version received June 19, 1998?Published online June 28, 1999     

In Vivo Anti-Inflammatory Effect,Antioxidant Activity,and Polyphenolic Content of Extracts from Capsicum chinense By-Products

By-products of Capsicum chinense Jacq., var Jaguar could be a source of bioactive compounds. Therefore, we evaluated the anti-inflammatory effect, antioxidant activity, and their relationship with the polyphenol content of extracts of habanero pepper by-products obtained from plants grown on black or red soils of Yucatán, Mexico. Moreover, the impact of the type of extraction on their activities was evaluated. The dry by-product extracts were obtained by maceration (ME), Soxhlet (SOX), and supercritical fluid extraction (SFE). Afterward, the in vivo anti-inflammatory effect (TPA-induced ear inflammation) and the in vitro antioxidant activity (ABTS) were evaluated. Finally, the polyphenolic content was quantified by Ultra-Performance Liquid Chromatography (UPLC), and its correlation with both bioactivities was analyzed. The results showed that the SFE extract of stems of plants grown on red soil yielded the highest anti-inflammatory effect (66.1 ± 3.1%), while the extracts obtained by ME and SOX had the highest antioxidant activity (2.80 ± 0.0052 mM Trolox equivalent) and polyphenol content (3280 ± 15.59 mg·100 g⁻¹ dry basis), respectively. A negative correlation between the anti-inflammatory effect, the antioxidant activity, and the polyphenolic content was found. Overall, the present study proposed C. chinense by-products as a valuable source of compounds with anti-inflammatory effect and antioxidant activity.     

Water-polysaccharides interactions during apples drying process

In this work the state of water in samples of a food (apple) during the drying process was analyzed. Low resolution NMR, differential scanning calorimetry (DSC) and sorption measurements were performed on differently dried samples. It was possible to correlate the NMR proton relaxation time to the water activity aw=P/P₀, in order to have an independent method for measuring this parameter. The calorimetric curves showed the thermal transitions of different water populations and the sorption measurements on dry apple gave the fraction of water tightly bond to the structure, which resulted about 30%.     

Hydration Dynamics and the Future of Small-Amplitude AFM Imaging in Air

Here, we discuss the effects that the dynamics of the hydration layer and other variables, such as the tip radius, have on the availability of imaging regimes in dynamic AFM—including multifrequency AFM. Since small amplitudes are required for high-resolution imaging, we focus on these cases. It is possible to fully immerse a sharp tip under the hydration layer and image with amplitudes similar to or smaller than the height of the hydration layer, i.e., ~1 nm. When mica or HOPG surfaces are only cleaved, molecules adhere to their surfaces, and reaching a thermodynamically stable state for imaging might take hours. During these first hours, different possibilities for imaging emerge and change, implying that these conditions must be considered and reported when imaging.     

Golem: an algorithm for robust experiment and process optimization

Numerous challenges in science and engineering can be framed as optimization tasks, including the maximization of reaction yields, the optimization of molecular and materials properties, and the fine-tuning of automated hardware protocols. Design of experiment and optimization algorithms are often adopted to solve these tasks efficiently. Increasingly, these experiment planning strategies are coupled with automated hardware to enable autonomous experimental platforms. The vast majority of the strategies used, however, do not consider robustness against the variability of experiment and process conditions. In fact, it is generally assumed that these parameters are exact and reproducible. Yet some experiments may have considerable noise associated with some of their conditions, and process parameters optimized under precise control may be applied in the future under variable operating conditions. In either scenario, the optimal solutions found might not be robust against input variability, affecting the reproducibility of results and returning suboptimal performance in practice. Here, we introduce Golem, an algorithm that is agnostic to the choice of experiment planning strategy and that enables robust experiment and process optimization. Golem identifies optimal solutions that are robust to input uncertainty, thus ensuring the reproducible performance of optimized experimental protocols and processes. It can be used to analyze the robustness of past experiments, or to guide experiment planning algorithms toward robust solutions on the fly. We assess the performance and domain of applicability of Golem through extensive benchmark studies and demonstrate its practical relevance by optimizing an analytical chemistry protocol under the presence of significant noise in its experimental conditions.

Numerous challenges in science and engineering can be framed as optimization tasks. Golem is an uncertain-input algorithm that ensures the reproducible performance of optimized experimental protocols and processes.