Notes

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Both secondary pollution and the low mechanical strength of adsorbents have severely impeded the practical application of adsorption methods in the dye wastewater treatment. In this work, we innovatively synthesized a composite hydrogel adsorbent by incorporating polydopamine (PDA) and montmorillonite (MMT) into the pullulan hydrogel matrix for dye adsorption. First, the successful formation of the resultant adsorbents was verified by Fourier-transform infrared spectroscopy and scanning electron microscope elemental mapping analysis. Then, several physicochemical properties (such as thermal and swelling properties, microarchitecture, and mechanical strength) of the five prepared adsorbents (PM1-PM5) were investigated. These results demonstrated the adsorbents had tunable properties, which can be achieved by adjusting the PDA/MMT mass ratio. Next, the dye adsorption performance was systematically explored. The resultant adsorbent (PM3) had a maximum adsorption capacity of 112.45 mg/g and its adsorption data was best described by a Langmuir isotherm and pseudo-second-order kinetic. Finally, the adsorption mechanisms and potential commercial practicability of the adsorbent were studied. Altogether, the designed adsorbent could effectively avoid generating the secondary pollution typical of adsorbents and it displayed excellent mechanical strength, thus opening up a new horizon in mitigating environmental pollution from textile industries.Self-regulation is the ability to monitor and modulate emotions, behaviour, and cognition in order to adapt to changing circumstances. Developing adequate self-regulation is associated with better social coping and higher educational achievement later in life; poor self-regulation has been linked to a variety of detrimental developmental outcomes. Here, we focus on the development of neurocognitive processes essential for self-regulation. We outline a conceptual framework emphasizing that this is inherently an integrated, dynamic process involving interactions between brain maturation, child characteristics (genetic makeup, temperament, and pre- and perinatal factors) and environmental factors (family characteristics, parents and siblings, peers, and broader societal influences including media development). We introduce the Consortium of Individual Development (CID), which combines a series of integrated large-scale, multi-modal, longitudinal studies to take essential steps towards the ultimate goal of understanding and supporting this process.The rapid expansion of machine learning is offering a new wave of opportunities for nuclear medicine. This paper reviews applications of machine learning for the study of attenuation correction (AC) and low-count image reconstruction in quantitative positron emission tomography (PET). Specifically, we present the developments of machine learning methodology, ranging from random forest and dictionary learning to the latest convolutional neural network-based architectures. For application in PET attenuation correction, two general strategies are reviewed 1) generating synthetic CT from MR or non-AC PET for the purposes of PET AC, and 2) direct conversion from non-AC PET to AC PET. For low-count PET reconstruction, recent deep learning-based studies and the potential advantages over conventional machine learning-based methods are presented and discussed. In each application, the proposed methods, study designs and performance of published studies are listed and compared with a brief discussion. Finally, the overall contributions and remaining challenges are summarized.
To evaluate the impact on dose distribution to eye organs-at-risk (eOARs) of a computed tomography (CT)-based treatment planning in eye plaque brachytherapy (EPB) treatment.

We analyzed 19 ocular melanoma patients treated with ruthenium-106 plaques to a total dose of 100Gy to tumor apex using conventional central-axis-point dose calculation. Treatments were re-planned using the Plaque Simulator (PS) software implementing two different strategies a personalized CT-eye-model (CT-PS) and a standard-eye-model (SEM-PS) defined by Collaborative Ocular Melanoma Study. Dice coefficient and Hausdorff distance evaluated the concordance between eye-bulb-models. Mean doses (D
) to tumor and eOARs were extracted from Dose-Volume-Histograms and Retinal-Dose-Area-Histogram. Selleck Capivasertib Differences between planning approaches were tested by Wilcoxon signed-rank test.

In the analyzed cohort, 8 patients (42%) had posterior tumor location, 8 (42%) anterior, and 3 (16%) equatorial. The SEM did not accurately described the real CT eye-bulb geometry (median Hausdorff distance 0.8mm, range (0.4-1.3) mm). Significant differences in fovea and macula D
values were found (p=0.04) between CT-PS and SEM-PS schemes. No significant dosimetric differences were found for tumor and other eOARs. The planning scheme particularly affects the OARs closest to the tumor with a general tendency of SEM-PS to overestimate the doses to the OARs closest to the tumor.

The dosimetric accuracy achievable with CT-PS EPB treatment planning may help to identify ocular melanoma patients who could benefit the most from a personalized eye dosimetry for an optimal outcome in terms of tumor coverage and eOARs sparing. Further research and larger studies are underway.
The dosimetric accuracy achievable with CT-PS EPB treatment planning may help to identify ocular melanoma patients who could benefit the most from a personalized eye dosimetry for an optimal outcome in terms of tumor coverage and eOARs sparing. Further research and larger studies are underway.There is an increasing number of radiobiological experiments being conducted with low energy protons (less than 5 MeV) for radiobiological studies due to availability of sub-millimetre focused beam. However, low energy proton has broad microdosimetric spectra which can introduce dosimetric uncertainty. In this work, we quantify the impact of this dosimetric uncertainties on the cell survival curve and how it affects the estimation of the alpha and beta parameters in the LQ formalism. Monte Carlo simulation is used to generate the microdosimetric spectra in a micrometer-sized water sphere under proton irradiation. This is modelled using radiobiological experiment set-up at the Centre of Ion Beam Application (CIBA) in National University of Singapore. Our results show that the microdosimetric spectra can introduce both systematic and random shifts in dose and cell survival; this effect is most pronounced with low energy protons. The alpha and beta uncertainties can be up to 10% and above 30%, respectively for low energy protons passing through thin cell target (about 10 microns).
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