Interplay of size and Landau quantizations in the de Haas-van Alphen oscillations of metallic nanowires

We examine the interplay between size quantization and Landau quantization in the De Haas-Van Alphen oscillations of clean, metallic nanowires in a longitudinal magnetic field for `hard' boundary conditions, i.e. those of an infinite round well, as opposed to the `soft' parabolically confined boundary conditions previously treated in Alexandrov and Kabanov (Phys. Rev. Lett. {\bf 95}, 076601 (2005) (AK)). We find that there exist {\em two} fundamental frequencies as opposed to the one found in bulk systems and the three frequencies found by AK with soft boundary counditions. In addition, we find that the additional `magic resonances' of AK may be also observed in the infinite well case, though they are now damped. We also compare the numerically generated energy spectrum of the infinite well potential with that of our analytic approximation, and compare calculations of the oscillatory portions of the thermodynamic quantities for both models.Comment: Title changed, paper streamlined on suggestion of referrees, typos corrected, numerical error in figs 2 and 3 corrected and final result simplified -- two not three frequencies (as in the previous version) are observed. Abstract altered accordingly. Submitted to Physical Review

Doppler Ultrasound of Vascular Complications After Pediatric Liver Transplantation:Incidence, Time of Detection, and Positive Predictive Value

Purpose Doppler ultrasound (DUS) is widely used to detect vascular complications after pediatric liver transplantation (LT). This study aimed to assess the moment of first detection of vascular complications with DUS, and to determine the positive predictive value (PPV) of DUS. Materials and Methods Patients aged 0–18 years who underwent LT between 2015 and 2019 were retrospectively included. 92 LTs in 83 patients were included (median age: 3.9 years, interquartile range: 0.7–10.5). Patients underwent perioperative (intra-operative and immediately postoperative) and daily DUS surveillance during the first postoperative week, and at 1, 3, and 12 months. Vascular complications were categorized for the hepatic artery, portal vein, and hepatic veins. DUS findings were compared to surgical or radiological findings during the 1-year follow-up. Results 52 vascular complications were diagnosed by DUS in 35/92 LTs (38%). 15 out of 52 (28.8%) were diagnosed perioperatively, 29/52 (55.8%) were diagnosed on postoperative days 1–7, and 8/52 (15.4%) after day 7. The PPV for all vascular complications diagnosed with DUS was 92.3%. During the 1-year follow-up, 18/19 (94.7%) hepatic artery complications, 19/26 (73.1%) portal vein complications, and 7/7 (100%) hepatic vein complications were diagnosed perioperatively or during the first week. Conclusion The majority of vascular complications during the first year after pediatric LT were diagnosed by DUS perioperatively or during the first week, with a high PPV. Our findings provide important information regarding when to expect different types of vascular complications on DUS, which might improve DUS post-LT surveillance protocols

Liver fibrosis staging by deep learning:a visual-based explanation of diagnostic decisions of the model

OBJECTIVES: Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. METHODS: The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. RESULTS: The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2-F4), advanced fibrosis (F3-F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). CONCLUSIONS: Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning-based liver fibrosis staging algorithms. KEY POINTS: • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage

Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging

Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning

The K2 Mission: Characterization and Early results

The K2 mission will make use of the Kepler spacecraft and its assets to expand upon Kepler's groundbreaking discoveries in the fields of exoplanets and astrophysics through new and exciting observations. K2 will use an innovative way of operating the spacecraft to observe target fields along the ecliptic for the next 2-3 years. Early science commissioning observations have shown an estimated photometric precision near 400 ppm in a single 30 minute observation, and a 6-hour photometric precision of 80 ppm (both at V=12). The K2 mission offers long-term, simultaneous optical observation of thousands of objects at a precision far better than is achievable from ground-based telescopes. Ecliptic fields will be observed for approximately 75-days enabling a unique exoplanet survey which fills the gaps in duration and sensitivity between the Kepler and TESS missions, and offers pre-launch exoplanet target identification for JWST transit spectroscopy. Astrophysics observations with K2 will include studies of young open clusters, bright stars, galaxies, supernovae, and asteroseismology.Comment: 25 pages, 11 figures, Accepted to PAS

Precision asteroseismology of the pulsating white dwarf GD 1212 using a two-wheel-controlled Kepler spacecraft

We present a preliminary analysis of the cool pulsating white dwarf GD 1212, enabled by more than 11.5 days of space-based photometry obtained during an engineering test of the two-reaction-wheel-controlled Kepler spacecraft. We detect at least 19 independent pulsation modes, ranging from 828.2-1220.8 s, and at least 17 nonlinear combination frequencies of those independent pulsations. Our longest uninterrupted light curve, 9.0 days in length, evidences coherent difference frequencies at periods inaccessible from the ground, up to 14.5 hr, the longest-period signals ever detected in a pulsating white dwarf. These results mark some of the first science to come from a two-wheel-controlled Kepler spacecraft, proving the capability for unprecedented discoveries afforded by extending Kepler observations to the ecliptic.Comment: 8 pages, 4 figures, accepted for publication in The Astrophysical Journa

Assessment of hepatic artery anatomy in pediatric liver transplant recipients:MR angiography versus CT angiography

During LT screening, children undergo CTA to determine hepatic artery anatomy. However, CTA imparts radiation, unlike MRA. The aim was to compare MRA to CTA in assessing hepatic artery anatomy in pediatric LT recipients. Twenty-one children (median age 8.9 years) who underwent both CTA and fl3D-ce MRA before LT were retrospectively included. Interreader variability between 2 radiologists, image quality, movement artifacts, and confidence scores, were used to compare MRA to CTA. Subgroup analyses for ages <6 years and ≥6 years were performed. Interreader variability for MRA and CTA in children <6 years was comparable (k = 0.839 and k = 0.757, respectively), while in children ≥6 years CTA was superior to MRA (k 1.000 and k 0.000, respectively). Overall image quality and confidence scores of CTA were significantly higher compared to MRA at all ages (2.8/3 vs. 2.3/3, p = .001; and 2.9/3 vs. 2.5/3, p = .003, respectively). Movement artifacts were significantly lower in CTA compared to MRA in children ≥6 years (1.0/3 vs. 1.7/3, p = .010, respectively). CTA is preferred over fl3D-ce MRA for the preoperative assessment of hepatic artery anatomy in children receiving LT, both at ages <6 years and ≥6 years

Doppler-ultrasound reference values after pediatric liver transplantation:a consecutive cohort study

OBJECTIVES: Doppler ultrasound (DUS) is the main imaging modality to evaluate vascular complications of pediatric liver transplants (LT). The current study aimed to determine reference values and their change over time.METHODS: A consecutive cohort of pediatric patients undergoing an LT were retrospectively included between 2015 and 2020. Timepoints for standardized DUS were intra-operative and postoperative (day 0), days 1-7, months 1 and 3, and years 1 and 2. DUS measurements of the hepatic artery (HA), portal vein (PV), and hepatic vein(s) (HV) were included if there were no complications during 2 years follow-up. Measurements consisted of: peak systolic velocity (PSV) and resistive index (RI) for the HA, PSV for the PV, and venous pulsatility index (VPI) for the HV. Generalized estimating equations were used to analyze change over time.RESULTS: One hundred twelve pediatric patients with 123 LTs were included (median age 3.3 years, interquartile range 0.7-10.1). Ninety-five HAs, 100 PVs, and 115 HVs without complications were included. Reference values for HA PSV and RI, PV PSV, and HV VPI were obtained for all timepoints (4043 included data points in total) and presented using 5th-95th percentiles and threshold values. All reference values changed significantly over time (p = 0.032 to p &lt; 0.001).CONCLUSIONS: DUS reference values of hepatic vessels in children after LT are presented, reference values change over time with specific vessel-dependent patterns. Timepoint-specific reference values improve the interpretation of DUS values and may help to better weigh their clinical significance.KEY POINTS: • Doppler ultrasound reference values of pediatric liver transplantations are not static but change over time. Applying the correct reference values for the specific timepoint may further improve the interpretation of the measurements. • The pattern of change over time of Doppler ultrasound measurements differs between the hepatic vessel and measurement; knowledge of these patterns may help radiologists to better understand normal postoperative hemodynamic changes.</p

Editorial: Advances in Computational Neuroscience

© 2022 Nowotny, van Albada, Fellous, Haas, Jolivet, Metzner and Sharpee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/Peer reviewedFinal Published versio