A simple ZVI-Fenton pre-oxidation using steel-nails for NOM degradation in water treatment

The feasibility of a heterogeneous Fenton Process (ZVI/H2O2) using commercial low-carbon-steel nails as the Zero-Valent Iron (ZVI) source was evaluated for the first time for the removal of natural organic matter (NOM) from natural surface waters with distinct physico-chemical characteristics. The synergistic effect of ZVI nails and H2O2 on the process was confirmed. Results showed similar removal efficiencies of NOM in water samples from Thames river and Regent's Park lake (both in London, UK) (under initial pH 3.5 and 100% excess of H2O2 dosage), reaching dissolved organic carbon (DOC) removals of 61.6% ± 3.0 and 59.6% ± 4.7, and UV254 removals of 79.9% ± 0.6 and 77.3 ± 6.2, respectively with 60 min of batch reaction time. ZVI nail surface characterization by scanning electron microscopy (SEM), X-ray energy-dispersive spectroscopy (EDS), and X-ray photoemission spectroscopy (XPS) revealed the formation of a passivating oxide-hydroxide layer on the nail during the reaction, which reduces its surface activity with 20% in continuous use. Results indicate that ZVI/H2O2 process using commercial iron nails is a promising pre-oxidation step for drinking water treatment. The low cost of commercial nails together with the facility of separating them from the water are the main advantages for the application of this process in remote regions with limitations in infrastructure and/or finance

Plasma level of LDL-cholesterol at diagnosis is a predictor factor of breast tumor progression

BACKGROUND: Among women, breast cancer (BC) is the leading cancer and the most common cause of cancer-related death between 30 and 69 years. Although lifestyle and diet are considered to have a role in global BC incidence pattern, the specific influence of dyslipidemia in BC onset and progression is not yet completely understood. METHODS: Fasting lipid profile (total cholesterol, LDL-C, HDL-C, and triglycerides) was prospectively assessed in 244 women with BC who were enrolled according to pre-set inclusion criteria: diagnosis of non-hereditary invasive ductal carcinoma; selection for surgery as first treatment, and no history of treatment with lipid-lowering or anti-diabetic drugs in the previous year. Pathological and clinical follow-up data were recorded for further inclusion in the statistical analysis. RESULTS: Univariate associations show that BC patients with higher levels of LDL-C at diagnosis have tumors that are larger, with higher differentiation grade, higher proliferative rate (assessed by Ki67 immunostaining), are more frequently Her2-neu positive and are diagnosed in more advanced stages. Cox regression model for disease-free survival (DFS), adjusted to tumor T and N stages of TNM classification, and immunohistochemical subtypes, revealed that high LDL-C at diagnosis is associated with poor DFS. At 25 months of follow up, DFS is 12% higher in BC patients within the third LDL-C tertile compared to those in the first tertile. CONCLUSIONS: This is a prospective study where LDL-C levels, at diagnosis, emerge as a prognostic factor; and this parameter can be useful in the identification and follow-up of high-risk groups. Our results further support a possible role for systemic cholesterol in BC progression and show that cholesterol metabolism may be an important therapeutic target in BC patients

Photo-electromotive-force from volume speckle pattern vibration with large amplitude

We report an accurate mathematical model describing the photo-electromotive-force signal produced by a speckle pattern of light vibrating in the volume of a photorefractive crystal with a large transverse amplitude. Our model shows that, for vibrations much faster than the material response time, the first harmonic term of the photo-electromotive-force signal exhibits a maximum at a characteristic value of the vibration-amplitude-to-speckle-size ratio that depends on the dark-to-photoconductivity ratio in the material. The theoretical results are in good agreement with experimental data from a vanadium-doped photorefractive CdTe (CdTe:V) crystal under 1064 nm wavelength illumination. (C) 2008 American Institute of Physics.1031

Interplay of quantum and classical fluctuations near quantum critical points

For a system near a quantum critical point (QCP), above its lower critical dimension $d_L$, there is in general a critical line of second order phase transitions that separates the broken symmetry phase at finite temperatures from the disordered phase. The phase transitions along this line are governed by thermal critical exponents that are different from those associated with the quantum critical point. We point out that, if the effective dimension of the QCP, $d_{eff}=d+z$ ($d$ is the Euclidean dimension of the system and $z$ the dynamic quantum critical exponent) is above its upper critical dimension $d_C$, there is an intermingle of classical (thermal) and quantum critical fluctuations near the QCP. This is due to the breakdown of the generalized scaling relation $\psi=\nu z$ between the shift exponent $\psi$ of the critical line and the crossover exponent $\nu z$, for $d+z>d_C$ by a \textit{dangerous irrelevant interaction}. This phenomenon has clear experimental consequences, like the suppression of the amplitude of classical critical fluctuations near the line of finite temperature phase transitions as the critical temperature is reduced approaching the QCP.Comment: 10 pages, 6 figures, to be published in Brazilian Journal of Physic

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. / Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. / Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. / Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics