General approach for studying first-order phase transitions at low temperatures

By combining different ideas, a general and efficient protocol to deal with discontinuous phase transitions at low temperatures is proposed. For small $T$'s, it is possible to derive a generic analytic expression for appropriate order parameters, whose coefficients are obtained from simple simulations. Once in such regimes simulations by standard algorithms are not reliable, an enhanced tempering method, the parallel tempering -- accurate for small and intermediate system sizes with rather low computational cost -- is used. Finally, from finite size analysis, one can obtain the thermodynamic limit. The procedure is illustrated for four distinct models, demonstrating its power, e.g., to locate coexistence lines and the phases density at the coexistence.Comment: 5 page

Esgotamento sanitário nas áreas de maior concentração da agricultura familiar: situação da Região nordeste.

Este trabalho objetivou analisar as condições de esgotamento sanitário nas áreas de concentração da agricultura familiar na Região Nordeste do Brasil. A distribuição geográfica dos serviços de esgotamento sanitário não ocorre de forma homogênea. As áreas de maior concentração da agricultura familiar apresentam alta percentagem de domicílios rurais com esgotamento sanitário inadequado ou sem esgotamento, estando sujeitas a maiores riscos de incidência de doenças como cólera, diarréia, esquistossomose, dengue, filariose, amebíase, febre tifoide, etc., demandando a destinação de recursos e esforços para a melhoria das condições de esgotamento sanitário e das condições de vida da população rural.GEONORDESTE 2014. Trabalho publicado também no 7º Seminário de Iniciação Científica PIBIC/BIC Júnior, 2014, Sete Lagoas

Eficiência de fungicidas no controle das principais doenças do trigo.

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Purple Passion Fruit, Passiflora edulis Sims f. edulis, Variability for Photosynthetic and Physiological Adaptation in Contrasting Environments

Purple passion fruit (Passiflora edulis Sims f. edulis) is a tropical juice source. The goal of this project was to evaluate photosynthetic and physiological variability for the crop with the hypotheses that landraces contain the diversity to adapt to higher elevation nontraditional growing environments and this is dependent on specific parameters of ecological adaptation. A total of 50 genotypes of this crop were chosen from divergent sources for evaluations of their eco-physiological responses in two equatorial locations at different altitudes in the Andes Mountains, a center of diversity for the species. The germplasm included 34 landraces, 8 commercial cultivars, and 8 genebank accessions. The two locations were contrasting in climates, representing mid and high elevations in Colombia. Mid-elevation valleys are typical regions of production for passion fruit while high elevation sites are not traditional. The location effects and variables that differentiated genotypes were determined. Results showed statistically significant differences between locations and importance of physiological parameters related to photosynthesis and water use efficiency. Some landraces exhibited better water status and gas exchange than commercial types. Parameters like maximum photosynthesis, points of light saturation and compensation, darkness respiratory rate, and apparent quantum yield varied between genotype groups. The landraces, commercial types, and genebank entries also differed in content of carotenoids and chlorophylls a and b. Meanwhile, photosynthesis measurements showed that altitudinal difference had an effect on genotype-specific plant growth and adaptation. An important conclusion was that landraces contained the diversity to adapt to the new growing environment at higher altitudes

Identificação dos principais fungos das sementes de trigo.

Procedimento para identificação dos fungos das sementes de trigo; Descrição diagnóstica dos principais fungos das sementes de trigo; Sclerotium Tode; Rhizoctonia DC; Chaetomium Kunze; Pleospora Rabenh; Sporobolomyces Kluy. & Niel; Rhodotorula Harrison; Phoma Sacc.; Septoria tritici Rob; Stagonospora nodorum (Berk.) Cast. & Germ.; Stagonospora avenae (Frank) Bisset f. sp. triticae; Colletotrichum graminicola (Ces.) Wilson; Fusarium tricinctum (Corda) Sacc; Fusarium moniliforme Sheldon; Fusarium avenaceum (Fr.) Sacc; Fusarium acuminatum Ell. & Kellerm; Fusarium equiseti (Corda) Sacc.; Fusarium graminearum Schw.; Mucor Micheli; Rhizopus Ehrenb; Aspergillus Link.; Penicillium Link.; Alternaria Nees; Epicoccum Link; Cladosporium Link; Nigrospora Zimm; Curvularia Boedijn; Drechslera tritici-repentis (Died.) Drech; Bipolaris sorokiniana (Sacc. in Sorok.) Shoem; Chave sistemática dos principais fungos de sementes de trigo; Ilustrações dos principais fungos encontrados em sementes de trigo.bitstream/item/153957/1/FL-02849.pd

Machine learning in infection management using routine electronic health records:tools, techniques, and reporting of future technologies

Background: Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. Objectives: To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. Sources: A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014–2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. Content: Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. Implications: Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed