Impact of land use on urban mobility patterns, emissions and air quality in a Portuguese medium-sized city

The main objective of this work was to evaluate the impact of urban development trends in mobility patterns of a medium sized Portuguese city and air quality consequences, using a sequential modeling process, comprising i) land use and transportation, TRANUS model; ii) road traffic air pollutants emissions, TREM model and; iii) air quality, TAPM model. This integrated methodology was applied to a medium sized Portuguese city. In order to evaluate the implementation of the methodology, a preliminary study was performed, which consisted on the comparison of modeled mobility patterns and CO and PM(10) concentrations with measured data used in the definition of the current scenario. The comparison between modeled and monitored mobility patterns at the morning peak hour for a weekday showed an RMSE of 31%. Regarding CO concentrations, an underestimation of the modeled results was observed. Nevertheless, the modeled PM(10) concentrations were consistent with the monitored data. Overall, the results showed a reasonable consistency of the modeled data, which allowed the use of the integrated modeling system for the study scenarios. The future scenarios consisted on the definition of different mobility patterns and vehicle technology characteristics, according to two main developing trends: (1) "car pooling" scenario, which imposes a mean occupancy rate of 3 passengers by vehicle and (2) the "Euro 6" scenario, which establishes that all vehicles accomplish at least the Euro 6 standard technology. Reductions of 54% and 83% for CO, 44% and 95% for PM(10), 44% and 87% for VOC and 44% and 79% for NO(x) emissions were observed in scenarios 1 and 2, respectively. Concerning air quality, a reduction of about 100 mug m(-3) of CO annual average concentration was observed in both scenarios. The results of PM(10) annual concentrations showed a reduction of 1.35 mug m(-3) and 2.7 mug m(-3) for scenarios 1 and 2 respectively

Swimming exercise demonstrates advantages over running exercise in reducing proteinuria and glomerulosclerosis in spontaneously hypertensive rats

Experimental studies in animal models have described the benefits of physical exercise (PE) to kidney diseases associated with hypertension. Land- and water-based exercises induce different responses in renal function. Our aim was to evaluate the renal alterations induced by different environments of PE in spontaneously hypertensive rats (SHRs). The SHRs were divided into sedentary (S), swimming exercise (SE), and running exercise (RE) groups, and were trained for 8 weeks under similar intensities (60 min/day). Arterial pressure (AP) and heart rate (HR) were recorded. The renal function was evaluated through urinary volume at each week of training; sodium and potassium excretions, plasma and urinary osmolarities, glomerular filtration rate (GFR), levels of proteinuria, and renal damage were determined. SE and RE rats presented reduced mean AP, systolic blood pressure, and HR in comparison with S group. SE and RE rats showed higher urine osmolarity compared with S. SE rats showed higher free water clearance (P < 0.01), lower urinary density (P < 0.0001), and increased weekly urine volume (P < 0.05) in comparison with RE and S groups. GFR was increased in both SE and RE rats. The proteinuria of SE (7.0 ± 0.8 mg/24 h) rats was decreased at the 8th week of the PE in comparison with RE (9.6 ± 0.8 mg/24 h) and S (9.8 ± 0.5 mg/24 h) groups. The glomerulosclerosis was reduced in SE rats (P < 0.02). SE produced different response in renal function in comparison with RE, in which only swimming-trained rats had better profile for proteinuria and glomerulosclerosis

Indeterminate thyroid cytology: Detecting malignancy using analysis of nuclear images

Background: Thyroid nodules diagnosed as 'atypia of undetermined significance/ follicular lesion of undetermined significance' (AUS/FLUS) or 'follicular neoplasm/ suspected follicular neoplasm' (FN/SFN), according to Bethesda’s classification, represena challenge in clinical practice. Computerized analysis of nuclear images (CANI) could be a useful tool for these cases. Our aim was to evaluate the ability of CANI to correctly classify AUS/FLUS and FN/SFN thyroid nodules for malignancy. Methods: We studied 101 nodules cytologically classified as AUS/FLUS (n = 68) or FN/SFN (n = 33) from 97 thyroidectomy patients. Slides with cytological material were submitted for manual selection and analysis of the follicular cell nuclei for morphometric and texture parameters using ImageJ software. The histologically benign and malignant lesions were compared for such parameters which were then evaluated for the capacity to predict malignancy using the classification and regression trees gini model. The intraclass coefficient of correlation was used to evaluate method reproducibility. Results: In AUS/FLUS nodule analysis, the benign and malignant nodules differed for entropy (P < 0.05), while the FN/SFN nodules differed for fractal analysis, coefficient of variation (CV) of roughness, and CV-entropy (P < 0.05). Considering the AUS/FLUS and FN/SFN nodules separately, it correctly classified 90.0 and 100.0% malignant nodules, with a correct global classification of 94.1 and 97%, respectively. We observed that reproducibility was substantially or nearly complete (0.61–0.93) in 10 of the 12 nuclear parameters evaluated. Conclusion: CANI demonstrated a high capacity for correctly classifying AUS/FLUS and FN/SFN thyroid nodules for malignancy. This could be a useful method to help increase diagnostic accuracy in the indeterminate thyroid cytology.This study received financial support from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP; processes number 2016/14987-0 and number 2016/14988-6). Further funding through 'Fundação para a Ciência e Tecnologi' – FCT and FEDER 'Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020'; by Operacional Programme for Competitiveness and Internationalization 'POCI' (Grant no. POCI-01-0145-FEDER-007274); by the 'Advancing cancer research: from basic knowledge to application' (grant no. NORTE-01-0145-FEDER-000029); and by the 'Projetos Estruturados de I & D & I', funded by Norte 2020 – Programa Operacional Regional do Norte

Magnetocaloric effect in GdGeSi compounds measured by the acoustic detection technique: Influence of composition and sample treatment

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)In this paper we explore the acoustic detection method applied to the investigation of the magnetocaloric effect in Gd and Gd(5)(Ge(1-x)Si(x))(4) compounds, in the temperature range from 230 to 360 K and for magnetic fields up to 20 kOe. Measurements were performed in as-cast materials, both for powder and pellet samples, and in tree samples with compositions around Gd(5)Ge(2)Si(2) that underwent different thermal treatments. Small differences were observed when comparing powder and pellet samples of Gd and Gd(5)(Ge(1-x)Si(x))(4) compounds with 0.500<x <= 1.00. For the alloys with composition around Gd(5)Ge(2)Si(2), which exhibit giant magnetostriction and coexistence of distinct phases, expressive changes were observed when comparing powder and pellet samples. Based on these cases, it is easy to see that the acoustic method can distinguish a second-order phase transition from a first-order magnetic-crystallographic one, and that it presents good sensitivity to detect spurious material phase in small quantities. (C) 2010 American Institute of Physics. [doi: 10.1063/1.3357375]1077Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)FAEPEX-UnicampFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

The plant WEE1 kinase is involved in checkpoint control activation in nematode-induced galls

Galls induced by plant‐parasitic nematodes involve a hyperactivation of the plant mitotic and endocycle machinery for their profit. Dedifferentiation of host root cells includes drastic cellular and molecular readjustments. In such background, potential DNA damage in the genome of gall cells is eminent. We questioned if DNA damage checkpoints activation followed by DNA repair occurred, or was eventually circumvented, in nematode‐induced galls. Galls display transcriptional activation of the DNA damage checkpoint kinase WEE1, correlated with its protein localization in the nuclei. The promoter of the stress marker gene SMR7 was evaluated under the WEE1‐knockout background. Drugs inducing DNA damage and a marker for DNA repair, PARP1 were used to understand mechanisms that might cope with DNA damage in galls. Our functional study revealed that gall cells lacking WEE1 conceivably entered mitosis prematurely disturbing the cell cycle despite the loss of genome integrity. The disrupted nuclei phenotype in giant cells hinted to the accumulation of mitotic defects. As well, WEE1‐knockout in Arabidopsis and downregulation in tomato repressed infection and reproduction of root‐knot nematodes. Together with data on DNA damaging drugs, we suggest a conserved function for WEE1 controlling a G1/S cell cycle arrest in response to replication defect in galls

Efficacy of a 7-day course of furazolidone, levofloxacin, and lansoprazole after failed Helicobacter pylori eradication

<p>Abstract</p> <p>Background</p> <p>Increasing resistance to clarithromycin and nitroimidazole is the main cause of failure in the <it>Helicobacter pylori </it>eradication. The ideal retreatment regimen remains unclear, especially in developing countries, where the infection presents high prevalence and resistance to antibiotics. The study aimed at determining the efficacy, compliance and adverse effects of a regimen that included furazolidone, levofloxacin and lansoprazole in patients with persistent <it>Helicobacter pylori </it>infection, who had failed to respond to at least one prior eradication treatment regimen.</p> <p>Methods</p> <p>This study included 48 patients with peptic ulcer disease. <it>Helicobacter pylori </it>infection was confirmed by a rapid urease test and histological examination of samples obtained from the antrum and corpus during endoscopy. The eradication therapy consisted of a 7-day twice daily oral administration of lansoprazole 30 mg, furazolidone 200 mg and levofloxacin 250 mg. Therapeutic success was confirmed by a negative rapid urease test, histological examination and 14C- urea breath test, performed 12 weeks after treatment completion. The Chi-square method was used for comparisons among eradication rates, previous treatments and previous furazolidone use.</p> <p>Results</p> <p>Only one of the 48 patients failed to take all medications, which was due to adverse effects (vomiting). Per-protocol and intention-to-treat eradication rates were 89% (95% CI- 89%–99%) and 88% (88–92%), respectively. Mild and moderate adverse effects were reported by 41 patients (85%). For patients with one previous treatment failure, the eradication rate was 100%. Compared to furazolidone-naïve patients, eradication rates were lower in those who had failed prior furazolidone-containing regimen(s) (74% vs. 100%, p = 0.002).</p> <p>Conclusion</p> <p>An empiric salvage-regimen including levofloxacin, furazolidone and lansoprazole is very effective in the eradication of <it>Helicobacter pylori</it>, particularly in patients that have failed one prior eradication therapy.</p

Tutorial : applying machine learning in behavioral research

Machine-learning algorithms hold promise for revolutionizing how educators and clinicians make decisions. However, researchers in behavior analysis have been slow to adopt this methodology to further develop their understanding of human behavior and improve the application of the science to problems of applied significance. One potential explanation for the scarcity of research is that machine learning is not typically taught as part of training programs in behavior analysis. This tutorial aims to address this barrier by promoting increased research using machine learning in behavior analysis. We present how to apply the random forest, support vector machine, stochastic gradient descent, and k-nearest neighbors algorithms on a small dataset to better identify parents of children with autism who would benefit from a behavior analytic interactive web training. These step-by-step applications should allow researchers to implement machine-learning algorithms with novel research questions and datasets