QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br

Probable Person-to-Person Transmission of Legionnaires’ Disease

Correspondence to the Editor.Legionnaires’ disease is an often severe form of pneumonia that is typically acquired by susceptible persons (e.g., elderly persons and smokers) through inhalation of aerosols that contain legionella species.1-4 A cluster of cases of this disease occurred in Vila Franca de Xira, Portugal, in 2014

QSAR modeling: a new open source computational package to generate and validate QSAR models

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br.554560Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Evolution during three ripening stages of Évora cheese

ALT20-03-0145-FEDER-023356 UIDP/04035/2020 UIDB/05064/2020The variability and heterogeneity found in Évora cheeses, Protected Designation of Origin (PDO), can affect consumers’ choices. Assessing the ripening conditions and their effect can be helpful. To study the effect of ripening duration in Évora cheese PDO, sensory and chemical analyses were performed in cheese samples subjected to 30, 60, and 120 days of ripening under controlled conditions (temperature 14 to 15 ◦C and humidity 65 to 70%). Sensory analysis was conducted with a homogenous panel previously familiarized with the product after a short training period, and chemical analyses including pH, moisture, NaCl content, aw, and salt-in-moisture were determined. Panelists were able to distinguish the differences in the organoleptic characteristics of the three cheese stages, and chemical determinations showed significant differences between stages. Interrater agreement was higher in the sensory evaluation of cheeses with a longer maturation period. As expected, cheeses in the 120 days ripening period presented lower pH, moisture, and water activity and had higher salt-in-moisture content. This stage received the highest scores in hardness and color of the crust, intensity, pungency of the aroma, intensity of taste and piquancy, and firmness and granular characteristics of texture. Overall acceptance of cheese samples was positive, regardless of the ripening stage, which probably reflects both the homogeneity of taster profiles and the previous knowledge of this particular product. The degree of ripeness influences the physical, chemical, and sensory characteristics but does not affect the acceptance of this product by the consumer.publishersversionpublishe

In silico scrutiny of genes revealing phylogenetic congruence with clinical prevalence or tropism properties of Chlamydia trachomatis strains

Microbes possess a multiplicity of virulence factors that confer them the ability to specifically infect distinct biological niches. Contrary to what is known for other bacteria, for the obligate intracellular human pathogen Chlamydia trachomatis, the knowledge of the molecular basis underlying serovars’ tissue specificity is scarce. We examined all ~900 genes to evaluate the association between individual phylogenies and cell-appetence or ecological success of C. trachomatis strains. Only ~1% of the genes presented a tree topology showing the segregation of all three disease groups (ocular, urogenital, and lymphatic) into three wellsupported clades. Approximately 28% of the genes, which include the majority of the genes encoding putative type III secretion system effectors and Inc proteins, present a phylogenetic tree where only lymphogranuloma venereum strains form a clade. Similarly, an exclusive phylogenetic segregation of the most prevalent genital serovars was observed for 61 proteins. Curiously, these serovars are phylogenetically cosegregated with the lymphogranuloma venereum serovars for ~20% of the genes. Some clade-specific pseudogenes were identified (novel findings include the conserved hypothetical protein CT037 and the predicted a-hemolysin CT473), suggesting their putative expendability for the infection of particular niches. Approximately 3.5% of the genes revealed a significant overrepresentation of nonsynonymous mutations, and the majority encode proteins that directly interact with the host. Overall, this in silico scrutiny of genes whose phylogeny is congruent with clinical prevalence or tissue specificity of C. trachomatis strains may constitute an important database of putative targets for future functional studies to evaluate their biological role in chlamydial infections.This work was supported by a grant, ERA-PTG/0004/2010, from Fundação para a Ciência e a Tecnologia (FCT) (to J.P.G.), in the frame of ERA-NET PathoGenoMics. A.N. is recipient of a FCT post-doctoral fellowship (SFRH/BPD/75295/2010), V.B. and R.F. are recipients of Ph.D. fellowships (SFRH/BD/68527/2010 and SFRH/BD/68532/2010, respectively) from FCT, and V.D. is a recipient of fellowship on behalf of the grant ERA-PTG/0004/2010

Parameterisation effect on the behaviour of a head-dependent hydro chain using a nonlinear model

This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning a head-dependent hydro chain. We use a method based on nonlinear programming (NLP), namely quadratic programming, to consider hydroelectric power generation a function of water discharge and of the head. The method has been applied successfully to solve a test case based on a realistic cascaded hydro system with a negligible computational time requirement and is also applied to show that the role played by reservoirs in the hydro chain do not depend only on their relative position. As a new contribution to earlier studies, which presented reservoir operation rules mainly for medium and long-term planning procedures, we show that the physical data defining hydro chain parameters used in the nonlinear model have an effect on the STHS, implying different optimal storage trajectories for the reservoirs accordingly not only with their position in the hydro chain but also with the new parameterisation defining the data for the hydro system. Moreover, considering head dependency in the hydroelectric power generation, usually neglected for hydro plants with a large storage capacity, provides a better short-term management of the conversion of the potential energy available in the reservoirs into electric energy, which represents a major advantage for the hydroelectric utilities in a competitive electricity market

Short-term electricity prices forecasting in a competitive market: A neural network approach

This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California

Creation of a vehicular delay-tolerant network prototype

Vehicular Delay-Tolerant Network (VDTN) is a new disruptive network architecture where vehicles act as the communication infrastructure. VDTN follows a layered architecture based on control and data planes separation, and positioning the bundle layer under the network layer. VDTN furnishes low-cost asynchronous communications coping with intermittent and sparse connectivity, variable delays and even no end-to-end connection. This paper presents a VDTN prototype (testbed) proposal, which implements and validates the VDTN layered architecture considering the proposed out-of-band signaling. The main goals of the prototype are emulation, demonstration, performance evaluation, and diagnose of protocol stacks and services, proving the applicability of VDTNs over a wide range of environments.Part of this work has been supported by the Instituto de Telecomunicações, Next Generation Networks and Applications Group (NetGNA), Covilhã Delegation, Portugal in the framework of the VDTN@Lab Project, and by the Euro-NF Network of Excellence from the Seventh Framework Programme of EU

Biocompatible and biodegradable functional coatings with natural occurring materials for the corrosion protection of Mg alloys

Magnesium alloys are amidst the most innovative materials for biomedical applications, as they show a set of unique properties, namely appropriate mechanical properties and biodegradability, when compared to other alloys. Although these properties make them suitable for medical implants, the main challenge is the uncontrolled corrosion. Mg degradation is fast, inhomogeneous, localized and often accompanied by hydrogen formation which can lead to complications in vivo. Here, we propose the development of a functional coating, containing natural-based capsules for the controlled release of biocompatible corrosion inhibitors and well known pharmaceutical agents. Empty and loaded capsules toxicity tests were performed as a first step for materials selection. Subsequently, they were incorporated into polyetherimide (PEI) coatings and tested using electrochemical impedance spectroscopy (EIS) under aggressive conditions. The obtained results showed a successful synthesis of natural-based microcapsules, constituting a fast, simple and environmentally friendly method. Additionally, the high cell proliferation observed in the presence of the aforementioned materials demonstrates their low toxicity. Preliminary results carried out with capsule-modified coatings show that the incorporation of Ca2+-loaded gelatin capsules in PEI coatings leads to barrier and active corrosion protection properties improvement and that anti-inflammatory agent ibuprofen may have a role in active corrosion protection as well.publishe