Microstructure and Doping/Temperature-Dependent Photoluminescence of ZnO Nanospears Array Prepared by Hydrothermal Method

Abstract Al-doped ZnO nanospears were prepared by a hydrothermal method. The crystalline structure and photoluminescence properties of ZnO nanospears were characterized for investigating the effect of Al doping on the properties of ZnO nanospears. ZnO nanospears grow preferentially along the c-axis and have a fine tip. Al doping reduces the length of ZnO nanospears. In room temperature, photoluminescence spectra of Al-doped ZnO nanospears, a near band edge emission (~3.16 eV), and a violet emission (~2.91 eV) exhibit a strong doping-dependent characteristic and a temperature-independent characteristic, while deep level emission peak shows a temperature-dependent characteristic. In variable temperature, photoluminescence spectra near band edge emission (~3.31 eV) and its fine structures were observed when the measurement temperature is less than 57 K, and it shows an obvious temperature-dependent characteristic. The thermal quenching of this near band edge emission should be attributed to exciton scattering by defects and the presence of a high concentration of defects in Al-doped ZnO nanospears

Mesh-reinforced pancreaticojejunostomy versus conventional pancreaticojejunostomy after pancreaticoduodenectomy: a retrospective study of 126 patients

Abstract Background Pancreatic fistula is a major cause of morbidity and mortality after pancreaticoduodenectomy. The aim of this study is to compare the safety and efficacy of a newly developed technique, namely mesh-reinforced pancreaticojejunostomy, in comparison with the conventional use of pancreaticojejunostomy after undergoing a pancreaticoduodenectomy. Methods Data was collected from regarding 126 consecutive patients, who underwent the mesh-reinforced pancreaticojejunostomy or conventional pancreaticojejunostomy, after standard pancreaticoduodenectomy by one group of surgeons, between the time period of 2005 and 2016. This data was collected retrospectively. Surgical parameters and perioperative outcomes were compared between these two groups. Results A total of 65 patients received mesh-reinforced pancreaticojejunostomy and 61 underwent conventional pancreaticojejunostomy. There were no substantial differences in surgical parameters, mortality, biliary leakage, delayed gastric emptying, gastrojejunostomy leakage, intra-abdominal fluid collection, postpancreatectomy hemorrhage, reoperation, and the total hospital costs between the two groups. Pancreatic fistula rate (15 versus 34%; p = 0.013), overall surgical morbidity (25 versus 43%; p = 0.032), and length of hospital stay (18 ± 9 versus 23 ± 12 days; p = 0.016) were significantly reduced after mesh-reinforced pancreaticojejunostomy. Multivariate analysis of the postoperative pancreatic fistula revealed that the independent factors that were highly associated with pancreatic fistula were a soft pancreatic texture and the type of conventional pancreaticojejunostomy. Conclusions This retrospective single-center study showed that mesh-reinforced pancreaticojejunostomy appears to be a safe technique for pancreaticojejunostomy. It may reduce pancreatic fistula rate and surgical complications after pancreaticoduodenectomy. Trial registration This research is waivered from trial registration because it is a retrospective analysis of medical records

Stroboscope Based Synchronization of Full Frame CCD Sensors

The key obstacle to the use of consumer cameras in computer vision and computer graphics applications is the lack of synchronization hardware. We present a stroboscope based synchronization approach for the charge-coupled device (CCD) consumer cameras. The synchronization is realized by first aligning the frames from different video sequences based on the smear dots of the stroboscope, and then matching the sequences using a hidden Markov model. Compared with current synchronized capture equipment, the proposed approach greatly reduces the cost by using inexpensive CCD cameras and one stroboscope. The results show that our method could reach a high accuracy much better than the frame-level synchronization of traditional software methods

OCCS Classification and Treatment Algorithm for Comminuted Mandibular Fractures Based on 109 Patients and 11 Years Experiences: A Retrospective Study

Comminuted mandibular fractures (CMFs) pose significant challenges to surgeons for their serious complications and poor outcomes. We aimed at proposing a classification with treatment algorithm of each category for CMFs. Patients with CMFs were retrospectively reviewed and classified into five categories: Type I: relatively good occlusion, no or slightly displaced fragments, no continuity destruction or bone defect; Type II: relatively good occlusion, damaged morphology, low comminution degree but intact continuity without bone defect; Type III: damaged morphology and higher comminution degree with intact continuity and relatively good occlusion; Type IV: high comminution, impaired continuity and poor occlusion without segmental bone defect; Type V: segmental bone defect. Conservative treatment, open reduction and internal fixation or microvascular osteocutaneous free flap transplantation was performed, accordingly. Demographics, perioperative data, complications and reasons for reoperations were recorded. The chi-square test was used for statistical analysis. In total, 109 patients were included in the study. After surgery, in the following group, 5 manifested infections, 1 manifested bone non-union, and 2 experienced reoperations, while in the unfollowing group, 10 manifested infections, 5 manifested bone non-union and 8 experienced reoperations. The OCCS classification and algorithm for CMFs achieve better outcomes and with lower complication rate

The Preoperative Prognostic Nutritional Index in Hepatocellular Carcinoma After Curative Hepatectomy: A Retrospective Cohort Study and Meta-Analysis

Objective Conflicting results existed about the role of prognostic nutritional index (PNI) for hepatocellular carcinoma (HCC) patients who received curative hepatectomy. The aim of this study is to identify the predictive capacity of PNI for survival after hepatectomy. Methods Preoperative PNI, neutrophil-to-lymphocyte ratio (NLR), tumor feature and clinical information of 187 patients with HCC from Sir Run Run Shaw hospital were evaluated. We also conducted a meta-analysis of seven cohort studies. Results Our study showed that HCC patients with a low PNI of <45 had a poor recurrence-free survival (RFS) rate (hazard ratio [HR] 1.762, 95% confidence interval [CI] 1.066–2.911, p = 0.027, respectively). The 5-year OS and RFS rates of the high PNI (≥45) vs low PNI (<45) were 76.7% vs 50.1% (p = 0.001) and 47.0% vs 28.9% (p = 0.001), respectively. In HCC TNM I patients (n = 144), a low PNI remained an independent prognostic factor of OS and RFS (HR 2.305, 95% CI 1.008–5.268, p = 0.048; HR 2.122, 95% CI 1.149–3.920, p = 0.016). The 5-year OS and RFS rates of the high PNI vs low PNI were 81.3% vs 62.4% (p = 0.041) and 53.4% vs 45.6% (p = 0.013), respectively. In the pooled analysis, the data showed that a low PNI was significantly associated with poor OS and RFS (HR 2.27, 95% CI 1.03–4.07, p < 0.001 and HR 1.68, 95% CI 1.45–1.94, p < 0.001, respectively). Conclusions The preoperative PNI was an independent prognostic factor for OS and RFS rates in HCC patients who received hepatectomy

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems. Generally speaking, however, a good loss function can take on much more flexible forms, and should be tailored for different tasks and datasets. Motivated by how functions can be approximated via Taylor expansion, we propose a simple framework, named PolyLoss, to view and design loss functions as a linear combination of polynomial functions. Our PolyLoss allows the importance of different polynomial bases to be easily adjusted depending on the targeting tasks and datasets, while naturally subsuming the aforementioned cross-entropy loss and focal loss as special cases. Extensive experimental results show that the optimal choice within the PolyLoss is indeed dependent on the task and dataset. Simply by introducing one extra hyperparameter and adding one line of code, our Poly-1 formulation outperforms the cross-entropy loss and focal loss on 2D image classification, instance segmentation, object detection, and 3D object detection tasks, sometimes by a large margin.Comment: Add ablation studies on COCO detection using RetinaNet (Section 8