Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903–1.000) for local recurrence; 0.864 (0.726–0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or “radiomic risk score”, increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.
Ta C x N y films were grown by a plasma-enhanced atomic layer deposition using Ta(N-t-C5H11)[N(CH3)2]3 as the precursor and H2 or Ar∕H2 plasma as the reducing agent. The Ar∕H2 plasma appeared to efficiently break the Ta–N bonds in the Ta precursor and formed more TaCx, which significantly decreased the resistivity of the films (∼255μΩcm) compared with the case of the H2 plasma (∼1570μΩcm). The Ar∕H2 plasma also made the films denser and efficiently eliminated the oxygen from the films. This improved the resistance against the elemental diffusion as well as the aging characteristics of the films after exposure to air.
This study examined the interfacial reaction of plasma-enhanced atomic layer deposited TaCxNy films with underlying SiO2 and HfO2 layers, as well as their effective work functions (EWFs). The adoption of Ar∕H2 plasma as a reducing agent suppressed the interfacial reactions resulting in a lower electrical thickness. However, it increased the interface state density due to the massive Ar+ plasma damage on the dielectric films. The interfacial reactions were suppressed in TaCxNy on HfO2 compared with that on SiO2. The EWF of TaCxNy with the H2 plasma and Ar∕H2 plasma on HfO2 was ∼4.9–5.2 and ∼4.6eV, respectively.
Guest
molecular diffusion in porous crystalline materials is pivotal
in their functionality, stability, and reactivity. Understanding the
diffusion behavior of guest molecules in clathrate frameworks has
been hindered, however, by the lack of experimental data and theoretical
investigations over long time scales. We report here extremely slow
diffusion of argon atoms in hydroquinone clathrate, an exemplary host–guest
material. The diffusion coefficient of argon in one-dimensional cage
channels of hydroquinone clathrate is estimated as 4.9 × 10–19 m2 s–1 at 298 K with
an activation energy of 79.1 kJ mol–1. This value
is 4 orders of magnitude slower than the diffusivities of all clathrate
materials reported to date. Coupled with the umbrella sampling method,
molecular dynamics simulations reveal that no spontaneous hopping
events of atoms across the neighboring cages occur during one microsecond
as the hydrogen-bonded hexagonal entrance of the cages sets a high
energy barrier for diffusion. Our results shed light on the long-term
stability of clathrate compounds as well as on tailoring guest–host
materials for gas storage.
N2O has 300 times more global warming potential than
CO2 and is also one of the main stratospheric ozone-depleting
substances emitted by human activities such as agriculture, industry,
and the combustion of fossil fuels and solid waste. We present here
an energy-efficient clathrate-based greenhouse gas-separation (CBGS)
technology that can operate at room temperature for selectively recovering
N2O from gas mixtures. Clathrate formation between α-form/β-form
hydroquinone (α-HQ/β-HQ) and gas mixtures reveals guest-specific
and structure-driven selectivity, revealing the preferential capture
of N2O in β-HQ and the molecular sieving characteristics
of α-HQ. With a maximum gas storage capacity and cage occupancy
of 54.1 cm3 g–1 and 0.86, respectively,
HQ clathrate compounds including N2O are stable at room
temperature and atmospheric pressure and thus can be easily synthesized,
treated, and recycled via commercial CBGS processes. High selectivity
for N2O recovery was observed during β-HQ clathrate
formation from N2O/N2 gas mixtures with N2O concentrations exceeding 20%, whereas α-HQ traps only
N2 molecules from gas mixtures. Full characterization using
X-ray diffraction, scanning electron microscopy, Raman spectroscopy,
solid-state nuclear magnetic resonance, and compositional analysis
and the formation kinetics of HQ clathrates was conducted to verify
the peculiar selectivity behavior and to design the conceptual CBGS
process. These results provide a new playground on which to tailor
host–guest materials and develop commercial processes for the
recovery and/or sequestration of greenhouse gases.
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