Notes

Biomaterials along with osteoradionecrosis in the jaw: Review of your books based on the Go swimming methodology.
Near itinerant cubic bulk CoV2O4is at variance with other spinel vanadates by not showing orbital ordering down to low temperature, albeit it displays fragile anomalies related to spin, and lattice structure, signaling a spin/orbital glass transition around 95 K. We investigate tetragonal-like epitaxial CoV2O4films on SrTiO3and (La0.3Sr0.7)(Al0.65Ta0.35)O3substrates that exhibit pronounced signature of spin reorientation transition from toa/bplane around 90 K unlike its bulk counterpart. Using in-plane and out-of-plane magnetic measurements, we demonstrate the intricate link between Co2+and V3+sublattice magnetizations that give rise to anisotropic magnetic switching. read more In-plane magnetic measurements reveal a wasp-waist shapedM(H) loop below reorientation transition temperature, while the out-of-plane follows antiferromagnet-likeM(H) response. The wasp-waist shaped feature could be linked to in-plane spin-canted (anti)ferromagnetism induced by canting away of V-spins away from antiferromagnetically coupled Co-spin direction below reorientation transition temperature. Further, we uncover the evidence for slow relaxation over a period of ∼104 s at 20 K and memory effect that indicates the possible existence for magnetic glassy phase in the low temperature regime. Using epitaxial strain as a control knob, our results inspire future study to manipulate orbital states, spin texture and itinerant electron character in tailored CoV2O4films away from cubic lattice symmetry.A simple and green approach is studied for the preparation of a high-activity metal-free N,O-codoped porous carbon (NOPC) electrocatalyst by one-step pyrolysis of pristine zinc-based zeolite-like metal-organic framework (Zn-ZMOF) synthesized by hydrothermal method from Zn2+and 4,5-imidazoledicarboxylic acid (H3IDC) in H2O solvent. It is found that the structure and electroactivity of Zn-ZMOF and NOPC vary with the molar ratio of H3IDC to zinc acetate. NOPC shows pH-universal electrocatalytic property for oxygen reduction reaction and its electrocatalytic performance is similar to that of Pt/C in alkaline and neutral electrolytes, and is close to that of Pt/C in acidic electrolyte, which is a relatively rare case for metal-free porous carbon derived from pristine MOF. Meanwhile, NOPC displays higher long-term stability and better tolerance to methanol and carbon monoxide poisoning than that of commercial Pt/C. The excellent performance of NOPC is mainly due to the special structure of the precursor Zn-ZMOF, and the synergism of abundant active sites, micro/mesoporous structure, large specific surface area, and high degree of graphitization.
Electrical impedance tomography (EIT) for lung perfusion imaging is attracting considerable interest in intensive care, as it might open up entirely new ways to adjust ventilation therapy. A promising technique is bolus injection of a conductive indicator to the central venous catheter, which yields the indicator-based signal (IBS). Lung perfusion images are then typically obtained from the IBS using the maximum slope technique. However, the low spatial resolution of EIT results in a partial volume effect (PVE), which requires further processing to avoid regional bias.

In this work, we repose the extraction of lung perfusion images from the IBS as a source separation problem to account for the PVE. We then propose a model-based algorithm, called gamma decomposition (GD), to derive an efficient solution. The GD algorithm uses a signal model to transform the IBS into a parameter space where the source signals of heart and lung are separable by clustering in space and time. Subsequently, it reconstructs lung model signals from which lung perfusion images are unambiguously extracted.

We evaluate the GD algorithm on EIT data of a prospective animal trial with eight pigs. The results show that it enables lung perfusion imaging using EIT at different stages of regional impairment. Furthermore, parameters of the source signals seem to represent physiological properties of the cardio-pulmonary system.

This work represents an important advance in IBS processing that will likely reduce bias of EIT perfusion images and thus eventually enable imaging of regional ventilation/perfusion (V/Q) ratio.
This work represents an important advance in IBS processing that will likely reduce bias of EIT perfusion images and thus eventually enable imaging of regional ventilation/perfusion (V/Q) ratio.Alzheimer's disease is a multifactorial neurodegenerative disorder preceded by a prodromal stage called mild cognitive impairment (MCI). Early diagnosis of MCI is crucial for delaying the progression and optimizing the treatment. In this study we propose a random forest (RF) classifier to distinguish between MCI and healthy control subjects (HC), identifying the most relevant features computed from structural T1-weighted and diffusion-weighted magnetic resonance images (sMRI and DWI), combined with neuro-psychological scores. To train the RF we used a set of 60 subjects (HC = 30, MCI = 30) drawn from the Alzheimer's disease neuroimaging initiative database, while testing with unseen data was carried out on a 23-subjects Mexican cohort (HC = 12, MCI = 11). Features from hippocampus, thalamus and amygdala, for left and right hemispheres were fed to the RF, with the most relevant being previously selected by applying extra trees classifier and the mean decrease in impurity index. All the analyzed brain structures presented changes in sMRI and DWI features for MCI, but those computed from sMRI contribute the most to distinguish from HC. However, sMRI+DWI improves classification performance in training area under the receiver operating characteristic curve (AUROC = 93.5 ± 8%, accuracy = 88.8 ± 9%) and testing with unseen data (AUROC = 93.79%, accuracy = 91.3%), having a better performance when neuro-psychological scores were included. Compared to other classifiers the proposed RF provide the best performance for HC/MCI discrimination and the application of a feature selection step improves its performance. These findings imply that multimodal analysis gives better results than unimodal analysis and hence may be a useful tool to assist in early MCI diagnosis.
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