The influence of protein free calf blood extract eye gel on dry eye after pterygium surgery

AIM: To investigate the influence of protein free calf blood extract eye gel on dry eye after pterygium surgery. <p>METHODS: Thirty six patients(40 eyes)with primary nasal pterygium were enrolled in this study, which were divided into study group and control group randomly, with 20 eyes in each group. All patients received pterygium excision and limbal stem cell autograft surgery and tobramicin dexamethasone eye drops after surgery. Patients of the study group received protein free calf blood extract eye gel while those of the control group received 0.1% sodium hyaluronate eye drops furthermore. Ocular surface disease index(OSDI)questionnaire, tear film break-up time(BUT)and Schirmer's Ⅰ test Ⅰ(SⅠt)were carried before and 3 months after surgery to evaluate the dry eye degree of the patients. <p>RESULTS: There was no statistical difference between the age, gender and size of the pterygium of the study and control groups preoperatively. There was no statistical difference between the OSDI(2.33±1.02 <i>vs</i> 2.32±0.93), BUT(8.80±2.48 <i>vs</i> 8.35±2.28)seconds and SⅠt(4.30±2.30 <i>vs</i> 4.40±2.44)of the two groups preoperatively. There was statistical difference between the OSDI(1.45±0.47 <i>vs</i> 1.81±0.60), BUT(11.20±2.07 <i>vs</i> 9.50±2.40)seconds and SⅠt(8.35±3.13 <i>vs</i> 6.35±2.18)of the two groups 3 months postoperatively, which was also different from that of the preoperative data correspondingly. <p>CONCLUSION: Protein free calf blood extract eye gel could reduce the dry eye after pterygium surgery

Comparison between anterolateral thigh perforator free flaps and pectoralis major pedicled flap for reconstruction in oral cancer patients-A quality of life analysis

The aim of this study was to compare the differences between anterolateral thigh perforator free flaps (ALTFF) and pectoralis major myocutaneous flap (PMMF) for reconstruction in oral cancer patients. Method and Patients: who received free flap or PMMF reconstruction after ablation surgeries were eligible for the current study. The patients' demographic data, medical history, and quality of life scores(Medical Outcomes Study-Short Form-36 (MOS SF-36) and the University of Washington Quality of Life (UW-QOL) questionnaires were collected. Results: 81 of 118 questionnaires were returned (68.64%). There was signi.cant differences between two groups in the gender (P<0.005). Patients reconstructed with ALTFF had better appearance domains and better shoulders domains, in addition to better role emotion domains. Conclusions: Using either PMMF or ALTFF for reconstruction of oral defects after cancer resection signi.cantly in.uences a patient's quality of life. Data from this study provide useful information for physicians and patients during their discussion of reconstruction modalities for oral cancers

Extracting double-quantum coherence in two-dimensional electronic spectroscopy under pump-probe geometry

Two-dimensional electronic spectroscopy (2DES) can be implemented with different geometries, e.g., BOXCARS, collinear and pump-probe geometries. The pump-probe geometry has its advantage of overlapping only two beams and reducing phase cycling steps. However, its applications are typically limited to observe the dynamics with single-quantum coherence and population, leaving the challenge to measure the dynamics of the double-quantum (2Q) coherence, which reflects the many-body interactions. We propose an experimental technique in 2DES under pump-probe geometry with a designed pulse sequence and the signal processing method to extract 2Q coherence. In the designed pulse sequence with the probe pulse arriving earlier than pump pulses, our measured signal includes the 2Q signal as well as the zero-quantum (0Q) signal. With phase cycling and the data processing using causality enforcement, we extract the 2Q signal. The proposal is demonstrated with the rubidium atoms. And we observe the collective resonances of two-body dipole-dipole interactions of both $D_{1}$ and $D_{2}$ lines.Comment: 7 pages, 5 figure

A study of photoluminescence properties and performance improvement of Cd-doped ZnO quantum dots prepared by the sol–gel method

In the present work, ZnO quantum dots (QDs) have been prepared by the sol–gel method, and the performance of the QDs has been improved. The effect of Cd concentration on the structural and luminescent properties of the QDs, as well as the effect of the mass ratio of trioctylphosphine oxide (TOPO)/octadecylamine (ODA), has been investigated. The ZnO and Cd-doped ZnO QDs have hexagonal wurtzite structures and are 3 to 6 nm in diameter. When the Cd content was increased, the QD particle size was reduced; this effect was confirmed in the corresponding ultraviolet–visible spectra. The fluorescence intensity was simultaneously enhanced significantly. Both the UV and fluorescence spectra were blue-shifted. The luminous intensity was further enhanced when the QDs were modified with TOPO/ODA. Fourier transform infrared and X-ray diffraction techniques proved that the polymer successfully coated the surfaces of the QDs. A TOPO/ODA mass ratio of 1:2 was determined to result in the best optical performance among the different ratios examined. The results showed that the described synthetic method is appropriate for the preparation of doped QDs with high-fluorescence quantum efficiency

Bis[2-(2-fur­yl)-1-(2-furylmeth­yl)-1H-benzimidazole-κN 3]diiodidocadmium

In the title complex, [CdI2(C16H12N2O2)2], the CdII atom is located on a twofold rotation axis and is four-coordinated by two N atoms from symmetry-related 2-(2-fur­yl)-1-(2-furyl­meth­yl)-1H-benzimidazole ligands and two I atoms in a distorted tetra­hedral configuration. The benzimidazole rings in adjacent mol­ecules are parallel, with an average inter­planar distance of 3.486 Å. The I atom is disordered over two sites in a 0.85 (5):0.15 (5) ratio

Leveraging junk information to enhance the quantum error mitigation

Noise in quantum information processing poses a significant obstacle to achieving precise results. Quantum error mitigation techniques are crucial for improving the accuracy of experimental expectation values in this process. In the experiments, it is commonly observed that some measured events violate certain principles, such as symmetry constraints. These events can be considered junk information and should be discarded in a post-selection process. In this work, we introduce a quantum error mitigation method named Self-Trained Quantum Noise Filter (SQNF), which leverages the junk information to differentiate errors from the experimental population distributions, thereby aiming to approximate the error-free distribution. Our numerical results demonstrate that the proposed method can significantly reduce the infidelity of population distributions compared to the traditional post-selection method. Notably, the infidelity reduction is achieved without additional experimental resource consumption. Our method is scalable and applicable to multi-qubit computing systems

Lidar Point Cloud Guided Monocular 3D Object Detection

Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box labels, where the annotating process is expensive. In this paper, we find that the precisely and carefully annotated labels may be unnecessary in monocular 3D detection, which is an interesting and counterintuitive finding. Using rough labels that are randomly disturbed, the detector can achieve very close accuracy compared to the one using the ground-truth labels. We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels. Motivated by the conclusions above and considering the precise LiDAR 3D measurement, we propose a simple and effective framework, dubbed LiDAR point cloud guided monocular 3D object detection (LPCG). This framework is capable of either reducing the annotation costs or considerably boosting the detection accuracy without introducing extra annotation costs. Specifically, It generates pseudo labels from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measurements in 3D space, such pseudo labels can replace manually annotated labels in the training of monocular 3D detectors, since their 3D location information is precise. LPCG can be applied into any monocular 3D detector to fully use massive unlabeled data in a self-driving system. As a result, in KITTI benchmark, we take the first place on both monocular 3D and BEV (bird's-eye-view) detection with a significant margin. In Waymo benchmark, our method using 10% labeled data achieves comparable accuracy to the baseline detector using 100% labeled data. The codes are released at https://github.com/SPengLiang/LPCG.Comment: ECCV 202