Ohio school laws, from revised statutes passed June 20th, 1879, to take effect January 1st, 1880, as amended up to April 19th, 1883, accompanied by general statues, Supreme court decisions, and explanatory notes, bearing on the management of schools and school property: to which are added parliamentary rules of order and a complete set of blank forms.

D. F. De Wolf, State Commissioner of Common Schools.

An abridgement of Murray's grammar ; to which is added a set of lessons, containing examples, explanations, rules, and questions, suited to the several parts of speech and forms of the English language /

Shaw & Shoemaker

Rules and Regulations of the St. Catharines Club

The booklet is a printed set of rules and regulations for the St. Catharines Club. It is pocket size and has several blank pages in the back. The blank pages have handwritten names including H.K. Woodruff. Also included in the booklet are lists of past officers. R. Woodruff is listed as president in 1883, and H.K. Woodruff is listed as a committee member in 1885 and 1886.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.

A new data set of soil mineralogy for dust-cycle modeling

The mineralogy of airborne dust affects the impact of dust particles on direct and indirect radiative forcing, on atmospheric chemistry and on biogeochemical cycling. It is determined partly by the mineralogy of the dust-source regions and partly by size-dependent fractionation during erosion and transport. Here we present a data set that characterizes the clay and silt-sized fractions of global soil units in terms of the abundance of 12 minerals that are important for dust–climate interactions: quartz, feldspars, illite, smectite, kaolinite, chlorite, vermiculite, mica, calcite, gypsum, hematite and goethite. The basic mineralogical information is derived from the literature, and is then expanded following explicit rules, in order to characterize as many soil units as possible. We present three alternative realizations of the mineralogical maps, taking the uncertainties in the mineralogical data into account. We examine the implications of the new database for calculations of the single scattering albedo of airborne dust and thus for dust radiative forcing.

Rules of origin and trade facilitation in preferential trade agreements in Latin America

Includes bibliography

Classification of Satellite Images by means of Fuzzy Rules generated by a Genetic Algorithm

The data acquired by Remote Sensing systems allow obtaining thematic maps of the earth's surface, by means of the registered image classification. This implies the identification and categorization of all pixels into land cover classes. Traditionally, methods based on statistical parameters have been widely used, although they show some disadvantages. Nevertheless, some authors indicate that those methods based on artificial intelligence, may be a good alternative. Thus, fuzzy classifiers, which are based on Fuzzy Logic, include additional information in the classification process through based-rule systems. In this work, we propose the use of a genetic algorithm (GA) to select the optimal and minimum set of fuzzy rules to classify remotely sensed images. Input information of GA has been obtained through the training space determined by two uncorrelated spectral bands (2D scatter diagrams), which has been irregularly divided by five linguistic terms defined in each band. The proposed methodology has been applied to Landsat-TM images and it has showed that this set of rules provides a higher accuracy level in the classification process

DEVELOPMENT OF A NOVEL SET OF MICROSATELLITE MARKERS FOR CASTOR BEAN, RICINUS COMMUNIS (EUPHORBIACEAE)

Premise of study: Microsatellite primers were developed for castor bean (Ricinus communis L.) to investigate genetic diversity and population structure, and to provide support to germplasm management. Methods and Results: Eleven microsatellite loci were isolated using an enrichment cloning protocol and used to characterize castor bean germplasm from the collection at the Instituto Agronomico de Campinas (IAC). In a survey of 76 castor bean accessions, the investigated loci displayed polymorphism ranging from two to five alleles. Conclusions: The information derived from microsatellite markers led to significant gains in conserved allelic richness and provides support to the implementation of several molecular breeding strategies for castor bean.

Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence

We examine the representation of judgements of stochastic independence in probabilistic logics. We focus on a relational logic where (i) judgements of stochastic independence are encoded by directed acyclic graphs, and (ii) probabilistic assessments are flexible in the sense that they are not required to specify a single probability measure. We discuss issues of knowledge representation and inference that arise from our particular combination of graphs, stochastic independence, logical formulas and probabilistic assessments. (C) 2007 Elsevier B.V. All rights reserved.

A new set of microsatellite markers for the genetic characterization of Ceratocystis fimbriata, an economically important plant pathogen

Ceratocystis fimbriata is a fungal pathogen which attacks several economically important plants, but occurs in host-associated, morphologically indistinguishable forms. In Brazil, this fungus seriously attacks mango trees (Mangifera indica), causing severe loss of yield. This work aimed to develop and characterize a novel set of microsatellite markers for this important pathogen, providing researchers with new molecular tools for the characterization of isolates. Twenty polymorphic primer pairs were designed from a microsatellite-enriched library. We tested the usefulness of these markers through genotyping thirteen isolates of the fungus. On average, 6.65 alleles per locus were detected, revealing the ability of this set of markers to characterize C. fimbriata isolates associated to mango and to other plant species.

QSAR Modeling of a Set of Pyrazinoate Esters as Antituberculosis Prodrugs

Tuberculosis is an infection caused mainly by Mycobacterium tuberculosis. A first-line antimycobacterial drug is pyrazinamide (PZA), which acts partially as a prodrug activated by a pyrazinamidase releasing the active agent, pyrazinoic acid (POA). As pyrazinoic acid presents some difficulty to cross the mycobacterial cell wall, and also the pyrazinamide-resistant strains do not express the pyrazinamidase, a set of pyrazinoic acid esters have been evaluated as antimycobacterial agents. In this work, a QSAR approach was applied to a set of forty-three pyrazinoates against M. tuberculosis ATCC 27294, using genetic algorithm function and partial least squares regression (WOLF 5.5 program). The independent variables selected were the Balaban index (I), calculated n-octanol/water partition coefficient (ClogP), van-der-Waals surface area, dipole moment, and stretching-energy contribution. The final QSAR model (N = 32, r(2) = 0.68, q(2) = 0.59, LOF = 0.25, and LSE = 0.19) was fully validated employing leave-N-out cross-validation and y-scrambling techniques. The test set (N = 11) presented an external prediction power of 73%. In conclusion, the QSAR model generated can be used as a valuable tool to optimize the activity of future pyrazinoic acid esters in the designing of new antituberculosis agents.

Molecular modeling and QSAR studies of a set of indole and benzimidazole derivatives as H(4) receptor antagonists

Histamine is an important biogenic amine, which acts with a group of four G-protein coupled receptors (GPCRs), namely H(1) to H(4) (H(1)R - H(4)R) receptors. The actions of histamine at H(4)R are related to immunological and inflammatory processes, particularly in pathophysiology of asthma, and H(4)R ligands having antagonistic properties could be helpful as antiinflammatory agents. In this work, molecular modeling and QSAR studies of a set of 30 compounds, indole and benzimidazole derivatives, as H(4)R antagonists were performed. The QSAR models were built and optimized using a genetic algorithm function and partial least squares regression (WOLF 5.5 program). The best QSAR model constructed with training set (N = 25) presented the following statistical measures: r (2) = 0.76, q (2) = 0.62, LOF = 0.15, and LSE = 0.07, and was validated using the LNO and y-randomization techniques. Four of five compounds of test set were well predicted by the selected QSAR model, which presented an external prediction power of 80%. These findings can be quite useful to aid the designing of new anti-H(4) compounds with improved biological response.

Molecular modeling studies and in vitro bioactivity evaluation of a set of novel 5-nitro-heterocyclic derivatives as anti-T. cruzi agents

In this study, in vitro anti-T. cruzi activity assays of nifuroxazide (NX) analogues, such as 5-nitro-2-furfuryliden and 5-nitro-2-theniliden derivatives, were performed. A molecular modeling approach was also carried out to relate the lipophilicity potential ( LP) property and biological activity data. The majority of the NX derivatives showed increased anti-T. cruzi activity in comparison to the reference drug, benznidazole (BZN). Additionally, the 5-nitro-2-furfuryliden derivatives presented better pharmacological profile than the 5-nitro-2-theniliden analogues. The LP maps and corresponding ClogP values indicate that there is an optimum lipophilicity value, which must be observed in the design of new potential anti-T. cruzi agents. (c) 2009 Elsevier Ltd. All rights reserved.