Effects of pretreatment on morphology, chemical composition and enzymatic digestibility of eucalyptus bark: a potentially valuable source of fermentable sugars for biofuel production – part 1

Abstract Background In recent years, the growing demand for biofuels has encouraged the search for different sources of underutilized lignocellulosic feedstocks that are available in sufficient abundance to be used for sustainable biofuel production. Much attention has been focused on biomass from grass. However, large amounts of timber residues such as eucalyptus bark are available and represent a potential source for conversion to bioethanol. In the present paper, we investigate the effects of a delignification process with increasing sodium hydroxide concentrations, preceded or not by diluted acid, on the bark of two eucalyptus clones: Eucalyptus grandis (EG) and the hybrid, E. grandis x urophylla (HGU). The enzymatic digestibility and total cellulose conversion were measured, along with the effect on the composition of the solid and the liquor fractions. Barks were also assessed using Fourier-transform infrared spectroscopy (FTIR), solid-state nuclear magnetic resonance (NMR), X-Ray diffraction, and scanning electron microscopy (SEM). Results Compositional analysis revealed an increase in the cellulose content, reaching around 81% and 76% of glucose for HGU and EG, respectively, using a two-step treatment with HCl 1%, followed by 4% NaOH. Lignin removal was 84% (HGU) and 79% (EG), while the hemicellulose removal was 95% and 97% for HGU and EG, respectively. However, when we applied a one-step treatment, with 4% NaOH, higher hydrolysis efficiencies were found after 48 h for both clones, reaching almost 100% for HGU and 80% for EG, in spite of the lower lignin and hemicellulose removal. Total cellulose conversion increased from 5% and 7% to around 65% for HGU and 59% for EG. NMR and FTIR provided important insight into the lignin and hemicellulose removal and SEM studies shed light on the cell-wall unstructuring after pretreatment and lignin migration and precipitation on the fibers surface, which explain the different hydrolysis rates found for the clones. Conclusion Our results show that the single step alkaline pretreatment improves the enzymatic digestibility of Eucalyptus bark. Furthermore, the chemical and physical methods combined in this study provide a better comprehension of the pretreatment effects on cell-wall and the factors that influence enzymatic digestibility of this forest residue.

Exercise training in childhood-onset systemic lupus erythematosus: a controlled randomized trial

Abstract Introduction Exercise training has emerged as a promising therapeutic strategy to counteract physical dysfunction in adult systemic lupus erythematosus. However, no longitudinal studies have evaluated the effects of an exercise training program in childhood-onset systemic lupus erythematosus (C-SLE) patients. The objective was to evaluate the safety and the efficacy of a supervised aerobic training program in improving the cardiorespiratory capacity in C-SLE patients. Methods Nineteen physically inactive C-SLE patients were randomly assigned into two groups: trained (TR, n = 10, supervised moderate-intensity aerobic exercise program) and non-trained (NT, n = 9). Gender-, body mass index (BMI)- and age-matched healthy children were recruited as controls (C, n = 10) for baseline (PRE) measurements only. C-SLE patients were assessed at PRE and after 12 weeks of training (POST). Main measurements included exercise tolerance and cardiorespiratory measurements in response to a maximal exercise (that is, peak VO2, chronotropic reserve (CR), and the heart rate recovery (ΔHRR) (that is, the difference between HR at peak exercise and at both the first (ΔHRR1) and second (ΔHRR2) minutes of recovery after exercise). Results The C-SLE NT patients did not present changes in any of the cardiorespiratory parameters at POST (P > 0.05). In contrast, the exercise training program was effective in promoting significant increases in time-to-exhaustion (P = 0.01; ES = 1.07), peak speed (P = 0.01; ES = 1.08), peak VO2 (P = 0.04; ES = 0.86), CR (P = 0.06; ES = 0.83), and in ΔHRR1 and ΔHRR2 (P = 0.003; ES = 1.29 and P = 0.0008; ES = 1.36, respectively) in the C-SLE TR when compared with the NT group. Moreover, cardiorespiratory parameters were comparable between C-SLE TR patients and C subjects after the exercise training intervention, as evidenced by the ANOVA analysis (P > 0.05, TR vs. C). SLEDAI-2K scores remained stable throughout the study. Conclusion A 3-month aerobic exercise training was safe and capable of ameliorating the cardiorespiratory capacity and the autonomic function in C-SLE patients. Trial registration NCT01515163.

Prevalência de anomalias congênitas associadas às fissuras labiopalatinas

Objetivo: Verificar a prevalência de anomalias congênitas associadas às fissuras labiopalatinas em crianças de 0 a 3 anos de idade. Métodos: Estudo transversal, observacional, aprovado pelo Comitê de Ética em Pesquisa (Ofício nº 412/2011). Participaram do estudo 325 mulheres, mães biológicas de crianças com fissuras labiopalatinas de 0 a 3 anos de idade, associadas ou não a anomalias congênitas, matriculados no HRAC-USP. A média de idade das mães foi de 29 anos e mediana de 28 anos. O tamanho amostral foi segundo a “Fórmula para cálculo de tamanho de amostra - Populações infinitas”. Os resultados foram tabulados em planilha do programa computacional Microsoft® Excel, apresentados em tabelas apontando a média, mediana, frequência absoluta (fi), frequência absoluta acumulada (Fi), frequência relativa acumulada (Fr). Para a comparação entre a porcentagem do agravo na população e amostra, utilizou-se o teste estatístico “Teste Exato de Fisher”, adotando-se nível de significância de 5%. Resultado: Quanto à prevalência de anomalias congênitas associadas às fissuras labiopalatinas, 209(64,30%) crianças na faixa etária de 0 a 3 anos, apresentaram fissura labiopalatina isolada e 116(35,69%) apresentaram algum tipo de anomalia congênita associada a essas fissuras. A fissura mais prevalente foi a fissura pós-forame, apresentando-se isolada em 63 casos e associadas à anomalias em 42 casos, seguida da fissura trans-forame incisivo unilateral esquerda, sendo 17 casos isolada e 59 casos associada à anomalias. A anomalia congênita associada às fissuras mais encontrada foi a Sequencia de Pierre Robin, seguida das cardiopatias diversas e malformações de pés e mãos. Conclusão: a prevalência de anomalias congênitas associadas às fissuras labiopalatinas foi de 35,69%.

Identifying common and specific microRNAs expressed in peripheral blood mononuclear cell of type 1, type 2, and gestational diabetes mellitus patients

Abstract Background Regardless the regulatory function of microRNAs (miRNA), their differential expression pattern has been used to define miRNA signatures and to disclose disease biomarkers. To address the question of whether patients presenting the different types of diabetes mellitus could be distinguished on the basis of their miRNA and mRNA expression profiling, we obtained peripheral blood mononuclear cell (PBMC) RNAs from 7 type 1 (T1D), 7 type 2 (T2D), and 6 gestational diabetes (GDM) patients, which were hybridized to Agilent miRNA and mRNA microarrays. Data quantification and quality control were obtained using the Feature Extraction software, and data distribution was normalized using quantile function implemented in the Aroma light package. Differentially expressed miRNAs/mRNAs were identified using Rank products, comparing T1DxGDM, T2DxGDM and T1DxT2D. Hierarchical clustering was performed using the average linkage criterion with Pearson uncentered distance as metrics. Results The use of the same microarrays platform permitted the identification of sets of shared or specific miRNAs/mRNA interaction for each type of diabetes. Nine miRNAs (hsa-miR-126, hsa-miR-1307, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-144, hsa-miR-199a-5p, hsa-miR-27a, hsa-miR-29b, and hsa-miR-342-3p) were shared among T1D, T2D and GDM, and additional specific miRNAs were identified for T1D (20 miRNAs), T2D (14) and GDM (19) patients. ROC curves allowed the identification of specific and relevant (greater AUC values) miRNAs for each type of diabetes, including: i) hsa-miR-1274a, hsa-miR-1274b and hsa-let-7f for T1D; ii) hsa-miR-222, hsa-miR-30e and hsa-miR-140-3p for T2D, and iii) hsa-miR-181a and hsa-miR-1268 for GDM. Many of these miRNAs targeted mRNAs associated with diabetes pathogenesis. Conclusions These results indicate that PBMC can be used as reporter cells to characterize the miRNA expression profiling disclosed by the different diabetes mellitus manifestations. Shared miRNAs may characterize diabetes as a metabolic and inflammatory disorder, whereas specific miRNAs may represent biological markers for each type of diabetes, deserving further attention.

Effects of Aedes aegypti salivary components on dendritic cell and lymphocyte biology

Abstract Background Saliva is a key element of interaction between hematophagous mosquitoes and their vertebrate hosts. In addition to allowing a successful blood meal by neutralizing or delaying hemostatic responses, the salivary cocktail is also able to modulate the effector mechanisms of host immune responses facilitating, in turn, the transmission of several types of microorganisms. Understanding how the mosquito uses its salivary components to circumvent host immunity might help to clarify the mechanisms of transmission of such pathogens and disease establishment. Methods Flow cytometry was used to evaluate if increasing concentrations of A. aegypti salivary gland extract (SGE) affects bone marrow-derived DC differentiation and maturation. Lymphocyte proliferation in the presence of SGE was estimated by a colorimetric assay. Western blot and Annexin V staining assays were used to assess apoptosis in these cells. Naïve and memory cells from mosquito-bite exposed mice or OVA-immunized mice and their respective controls were analyzed by flow cytometry. Results Concentration-response curves were employed to evaluate A. aegypti SGE effects on DC and lymphocyte biology. DCs differentiation from bone marrow precursors, their maturation and function were not directly affected by A. aegypti SGE (concentrations ranging from 2.5 to 40 μg/mL). On the other hand, lymphocytes were very sensitive to the salivary components and died in the presence of A. aegypti SGE, even at concentrations as low as 0.1 μg/mL. In addition, A. aegypti SGE was shown to induce apoptosis in all lymphocyte populations evaluated (CD4+ and CD8+ T cells, and B cells) through a mechanism involving caspase-3 and caspase-8, but not Bim. By using different approaches to generate memory cells, we were able to verify that these cells are resistant to SGE effects. Conclusion Our results show that lymphocytes, and not DCs, are the primary target of A. aegypti salivary components. In the presence of A. aegypti SGE, naïve lymphocyte populations die by apoptosis in a caspase-3- and caspase-8-dependent pathway, while memory cells are selectively more resistant to its effects. The present work contributes to elucidate the activities of A. aegypti salivary molecules on the antigen presenting cell-lymphocyte axis and in the biology of these cells.

CID: an efficient complexity-invariant distance for time series

The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade. While dozens of classification algorithms have been applied to time series, recent empirical evidence strongly suggests that simple nearest neighbor classification is exceptionally difficult to beat. The choice of distance measure used by the nearest neighbor algorithm is important, and depends on the invariances required by the domain. For example, motion capture data typically requires invariance to warping, and cardiology data requires invariance to the baseline (the mean value). Similarly, recent work suggests that for time series clustering, the choice of clustering algorithm is much less important than the choice of distance measure used.In this work we make a somewhat surprising claim. There is an invariance that the community seems to have missed, complexity invariance. Intuitively, the problem is that in many domains the different classes may have different complexities, and pairs of complex objects, even those which subjectively may seem very similar to the human eye, tend to be further apart under current distance measures than pairs of simple objects. This fact introduces errors in nearest neighbor classification, where some complex objects may be incorrectly assigned to a simpler class. Similarly, for clustering this effect can introduce errors by “suggesting” to the clustering algorithm that subjectively similar, but complex objects belong in a sparser and larger diameter cluster than is truly warranted.We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification and clustering accuracy. We further show that this improvement does not compromise efficiency, since we can lower bound the measure and use a modification of triangular inequality, thus making use of most existing indexing and data mining algorithms. We evaluate our ideas with the largest and most comprehensive set of time series mining experiments ever attempted in a single work, and show that complexity-invariant distance measures can produce improvements in classification and clustering in the vast majority of cases.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.