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The bioavailability of metals in soil is only partially explained by their partition among the solid and aqueous phase and is more related to the characterization of their speciation in the soil solution. The organic ligands in solution that largely determine metal speciation involve complex mixtures and the characterization of fluorescence components of dissolved organic matter (DOM) can identify pools of molecules that participate in metal speciation, this being essential for risk assessment. The bioavailability of Cd, Cu, Pb and Zn in three agricultural soils was examined in the laboratory to recreate irrigation with greywater enriched in anionic surfactants (Aerosol 22 and Biopower). Field capacity and saturation regimes were considered for this study. Irrigation with aqueous solutions of the anionic surfactants increased total DOM concentrations and metals in the soil solution (Pb > Cu > Zn > Cd). Significant correlation (p < 0.05) between the readily available pool of metals with the concentration of DOM was determined for Cu (r = 0.67), Pb (r = 0.82) and Zn (r = 0.68). However, speciation analysis performed with the software WHAM indicated that mobilisation of DOM and metals into the soluble phase resulted in a low concentration of free ion activities and promoted the formation of metal-organo complexes. The characterization of fluorescence components revealed that DOM in soil solution from soils irrigated with Aerosol 22 was enriched in a reduced quinone-like and a humic-like component. Besides, fluorescence quenching provided further evidence of metal complexation with organic ligands in solution. Hence, metal mobilization in soil irrigated with surfactant enriched greywater occurs with solubilisation of high affinity organic ligands, which substantially decreases the potential risk of metal toxicity. [ABSTRACT FROM AUTHOR]

Cefiderocol is a new siderophore cephalosporin with potent in vitro activity against gram-negative bacilli including Enterobacterales that produce all kinds of carbapenemases and non-fermenting Gram-negative with difficult-to-treat resistance. As a β-lactam, its efficacy is optimized in extended-perfusion and requires dose adjustment in renal dysfunction and hyperclearance. Its efficacy has been validated in three clinical trials, one of them in the treatment of hospital-acquired pneumonia and ventilator-associated pneumonia. The clinical trial aimed at difficult-to-treat gram-negatives achieved the clinical and microbiological target, but the increase in mortality observed in the cefiderocol arm makes it necessary to demonstrate efficacy in real clinical practice. Cefiderocol is a good option among the new β-lactams for the treatment of pneumonia caused by Gram-negative bacilli carbapenem-resistant.

Background: Patient adherence to treatment in chronic obstructive pulmonary disease (COPD) is essential to optimize disease management. We aimed to assess the impact of patients' perception of their treatment and disease on adherence and Health-Related Quality of Life (HRQL) in patients attending a community pharmacy, where usually subjects have a better condition than those in clinical settings., Methods: We performed a cross-sectional study of 318 patients with COPD in treatment with inhalers in the last 3 months from 53 community pharmacies. We assessed HRQL with St George's Respiratory Questionnaire (SGRQ). Persistence was assessed from the three previous refills and adherence through the Test of Adherence to Inhalers test., Results: Persistence was achieved by 78.6% of the patients and 58.5% had good adherence. Patients having a multidose DPI and those with MDI showed a 2.8-fold and 4.1-fold increased association, respectively, with intermediate/poor adherence in comparison with those having a single dose DPI. Those patients who did not have knowledge about COPD (aOR 2.106, p = 0.006) and those who thought that the inhaler effectiveness was fair/poor (aOR 2.361, p = 0.006) were more likely to have intermediate/poor adherence. Overall SGRQ score was significantly worse in patients with intermediate/poor adherence (p = 0.036) and in those who thought the inhaler's effectiveness was fair/poor (p < 0.001)., Conclusions: The type of inhaler and patients' knowledge and perceptions of their disease and treatment were associated with good adherence and higher HRQL. Clinicians should promote shared-decision making in the choice of inhaler depending on patients' individual abilities and beliefs., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

In real-world dynamical systems, technical limitations may prevent complete access to their dynamical variables. Such a lack of information may cause significant problems, especially when monitoring or controlling the dynamics of the system is required or when decisions need to be taken based on the dynamical state of the system. Cross-predicting the missing data is, therefore, of considerable interest. Here, we use a machine learning algorithm based on reservoir computing to perform cross-prediction of unknown variables of a chaotic dynamical laser system. In particular, we chose a realistic model of an optically injected single-mode semiconductor laser. While the intensity of the laser can often be acquired easily, measuring the phase of the electric field and the carriers in real time, although possible, requires a more demanding experimental scheme. We demonstrate that the dynamics of two of the three dynamical variables describing the state of the laser can be reconstructed accurately from the knowledge of only one variable, if our algorithm has been trained beforehand with all three variables for a limited period of time. We analyze the accuracy of the method depending on the parameters of the laser system and the reservoir. Finally, we test the robustness of the cross-prediction method when adding noise to the time series. The suggested reservoir computing state observer might be used in many applications, including reconstructing time series, recovering lost time series data and testing data encryption security in cryptography based on chaotic synchronization of lasers.

We investigate the dynamics of semiconductor lasers subject to time-delayed optical feedback from the perspective of dynamical self-injection locking. Based on the Lang-Kobayashi model, we perform an analysis of the well-known Low Frequency Fluctuations (LFFs) in the frequency-intensity plane. Moreover, we investigate a recently found dynamical regime of fragmented LFFs by means of a locking-range analysis, spectral comparison and precursor pulse identification. We show that LFF dynamics can be explained by dynamical optical injection locking due to the delayed optical feedback. Moreover, the fragmented LFFs occur due to a re-injection locking induced by a particular optical pulse structure in the chaotic feedback dynamics. This is corroborated by experiments with a semiconductor laser experiencing delayed feedback from an optical fiber loop. The dynamical nature of the feedback injection results in an eventual loss, but also possible regaining, of the locking, explaining the recently observed phenomenon of fragmented LFFs.