Non-gestational choriocarcinoma (NGC) is a rare subtype of choriocarcinoma differing in origin and phenotypic characteristics compared to gestational choriocarcinoma (GC). This study aimed to analyze ...the molecular biology of GC and NGC and evaluate genetic anomalies of choriocarcinoma subtypes. DNA was extracted and paired from tumor-normal tissue of one NGC and one GC (control) patient for whole-exome sequencing. To further understand the role of
, a
regulator mutated in the NGC tumor, on
upregulation in choriocarcinoma, CRISPR/Cas9 was used to induce
site-specific mutations in choriocarcinoma cells JEG-3. We hypothesized that
dysfunction would result in
overexpression. Sequencing revealed the GC tumor contained > 7 times more somatic mutations than the NGC tumor. Missense (98.86% vs. 94.97%), stop-gain (0.57% vs. 0.93%), and frameshift mutations (0.57% vs. 4.10%) were observed in the GC and NGC samples, respectively (
= 24.63,
< 0.00001). The transition substitution rate was 67.54% and 55.71% in the GC and NGC samples, while the transversion substitution rate was 32.46% and 44.29% in the GC and NGC samples, respectively (
= 11.56,
< 0.000673). Pathway enrichment analysis revealed ECM-receptor interaction and graft-versus-host disease were most enriched in the GC and NGC tumors, respectively.
investigations showed that
mRNA and protein levels were downregulated in Cas9-
-sgRNA transfected cells compared to the control (
< 0.001), while
protein levels were upregulated. Our findings display the genetic distinctness of choriocarcinoma subtypes, especially NGC, and further highlight the relationship between
and
in choriocarcinoma cells, laying the foundation for further investigations.
An enhanced cathodic electrochemiluminescence (ECL) assay for prostate-specific antigen (PSA) is developed based on the in situ activation of a semiconductor nanomaterial. An excellent ECL emitter ...(CdS/TiO
nanotubes) was fabricated by the combination of TiO
nanotubes (NTs) and thioglycolic acid-capped CdS nanocrystals (NCs). After the activation of the hydrogen peroxide-citric acid solution, the ECL signal was enhanced 265 times compared with that of the original TiO
NT with H
O
as co-reactant. For the ECL assay, activated CdS/TiO
NTs were assembled with complementary DNA, PSA aptamer and probe DNA-functionalized SiO
@Pt nanoparticles (NPs) via DNA hybridization to form the detection platform. The SiO
@Pt NPs acted as ECL quencher of CdS/TiO
NTs. In the presence of PSA, ECL increased after the release of pDNA-SiO
@Pt NPs because of the binding of PSA to the aptamer. An "off-on" ECL phenomenon appeared. The enhanced ECL signals were used for sensitive determination of PSA. The dynamic range was 0.001 to 50 ng mL
with a detection limit of 0.4 pg mL
(S/N = 3). This new approach conceivably paves the way for fabricating various other enhanced ECL emitter systems, with good application prospects in clinical practice. Graphical abstract The activated CdS/TiO
nanotubes and SiO
@Pt nanoparticles were synthesized and used to develop an energy-transfer electrochemiluminescence analysis method with high sensitivity and anti-interference performance.
Hunpu is a wastewater-irrigated area southwest of Shenyang. To evaluate petroleum contamination and identify its sources at the area, the aliphatic hydrocarbons and compound-specific carbon stable ...isotopes of n-alkanes in the soil, irrigation water, and atmospheric deposition were analyzed. The analyses of hydrocarbon concentrations and geochemical characteristics reveal that the water is moderately contaminated by degraded heavy oil. According to the isotope analysis, inputs of modern C3 plants and degraded petroleum are present in the water, air, and soil. The similarities and dissimilarities among the water, air, and soil samples were determined by concentration, isotope, and multivariate statistical analyses. Hydrocarbons from various sources, as well as the water/atmospheric deposition samples, are more effectively differentiated through principal component analysis of carbon stable isotope ratios (δ
13
C) relative to hydrocarbon concentrations. Redundancy analysis indicates that 57.1 % of the variance in the δ
13
C of the soil can be explained by the δ
13
C of both the water and air, and 35.5 % of the variance in the hydrocarbon concentrations of the soil can be explained by hydrocarbon concentrations of both the water and the air. The δ
13
C in the atmospheric deposition accounts for 28.2 % of the δ
13
C variance in the soil, which is considerably higher than the variance in hydrocarbon concentrations of the soil explained by hydrocarbon concentrations of the atmospheric deposition (7.7 %). In contrast to δ
13
C analysis, the analysis of hydrocarbon concentrations underestimates the effect of petroleum contamination in the irrigated water and air on the surface soil. Overall, the irrigated water exerts a larger effect on the surface soil than does the atmospheric deposition.
Under stereo settings, the problem of image super-resolution (SR) and disparity estimation are interrelated that the result of each problem could help to solve the other. The effective exploitation ...of correspondence between different views facilitates the SR performance, while the high-resolution (HR) features with richer details benefit the correspondence estimation. According to this motivation, we propose a Stereo Super-Resolution and Disparity Estimation Feedback Network (SSRDE-FNet), which simultaneously handles the stereo image super-resolution and disparity estimation in a unified framework and interact them with each other to further improve their performance. Specifically, the SSRDE-FNet is composed of two dual recursive sub-networks for left and right views. Besides the cross-view information exploitation in the low-resolution (LR) space, HR representations produced by the SR process are utilized to perform HR disparity estimation with higher accuracy, through which the HR features can be aggregated to generate a finer SR result. Afterward, the proposed HR Disparity Information Feedback (HRDIF) mechanism delivers information carried by HR disparity back to previous layers to further refine the SR image reconstruction. Extensive experiments demonstrate the effectiveness and advancement of SSRDE-FNet.
To observe the characteristics of acetabular labrum injury in Pipkin fractures and the effectiveness of repairing the labrum with suture anchor.
Between July 2010 and July 2013, 10 cases of Pipkin ...fractures accompanied by acetabular labrum injury were treated. There were 7 males and 3 females with an average age of 32.5 years (range, 24-56 years). The causes of injury included traffic accident in 8 cases and falling from height in 2 cases. According to the Pipkin classification criteria, there were 6 cases of type II, 2 cases of type III, and 2 cases of type IV. The average interval from injury to operation was 8 days (range, 6-14 days). All the patients underwent open reduction and internal fixation through transtrochanteric approach with trochanteric osteotomy, and repair the labrum with suture anchor. The effectiveness was evaluated according to the Thompson & Epstein scoring scales after operation.
All incisions healed primarily without early complication of deep infection or deep vein thrombosis of lower
As a promising approach to deal with distributed data, Federated Learning (FL) achieves major advancements in recent years. FL enables collaborative model training by exploiting the raw data ...dispersed in multiple edge devices. However, the data is generally non-independent and identically distributed, i.e., statistical heterogeneity, and the edge devices significantly differ in terms of both computation and communication capacity, i.e., system heterogeneity. The statistical heterogeneity leads to severe accuracy degradation while the system heterogeneity significantly prolongs the training process. In order to address the heterogeneity issue, we propose an Asynchronous Staleness-aware Model Update FL framework, i.e., FedASMU, with two novel methods. First, we propose an asynchronous FL system model with a dynamical model aggregation method between updated local models and the global model on the server for superior accuracy and high efficiency. Then, we propose an adaptive local model adjustment method by aggregating the fresh global model with local models on devices to further improve the accuracy. Extensive experimentation with 6 models and 5 public datasets demonstrates that FedASMU significantly outperforms baseline approaches in terms of accuracy (0.60% to 23.90% higher) and efficiency (3.54% to 97.98% faster).
Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine learning jobs because ...of regulations and laws. As a practical approach to handling decentralized data, Federated Learning (FL) enables collaborative global machine learning model training without sharing sensitive raw data. The servers schedule devices to jobs within the training process of FL. In contrast, device scheduling with multiple jobs in FL remains a critical and open problem. In this paper, we propose a novel multi-job FL framework, which enables the training process of multiple jobs in parallel. The multi-job FL framework is composed of a system model and a scheduling method. The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process. We propose a novel intelligent scheduling approach based on multiple scheduling methods, including an original reinforcement learning-based scheduling method and an original Bayesian optimization-based scheduling method, which corresponds to a small cost while scheduling devices to multiple jobs. We conduct extensive experimentation with diverse jobs and datasets. The experimental results reveal that our proposed approaches significantly outperform baseline approaches in terms of training time (up to 12.73 times faster) and accuracy (up to 46.4% higher).
With the development of photoelectric imaging, people's requirements for machine vision are getting more ang more intelligence. In order to facilitate use, automatic focusing has become a popular ...research field. People from different directions, different dimensions, and different methods are used to analyze, obtaining a clarity evaluation function, and then focusing control. When high-temperature objects such as huge fire pits, tail flame of airplanes appear in the scene, based on the traditional SOBEL operator to calculate the grayscale gradient of images for automatic optical focusing, the infrared image response will be very large, and the grayscale will become very high, leading to the failure of the SOBEL operator and the failure of focusing. This article successfully solves the above problem by introducing grayscale average assistance.
Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i.e., limited computational resources and low training efficiency. In this paper, we propose a ...novel FL framework, i.e., FedDUAP, with two original contributions, to exploit the insensitive data on the server and the decentralized data in edge devices to further improve the training efficiency. First, a dynamic server update algorithm is designed to exploit the insensitive data on the server, in order to dynamically determine the optimal steps of the server update for improving the convergence and accuracy of the global model. Second, a layer-adaptive model pruning method is developed to perform unique pruning operations adapted to the different dimensions and importance of multiple layers, to achieve a good balance between efficiency and effectiveness. By integrating the two original techniques together, our proposed FL model, FedDUAP, significantly outperforms baseline approaches in terms of accuracy (up to 4.8% higher), efficiency (up to 2.8 times faster), and computational cost (up to 61.9% smaller).
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results. ...This challenge has 1 track aiming at the stereo image super-resolution problem under a standard bicubic degradation. In total, 238 participants were successfully registered, and 21 teams competed in the final testing phase. Among those participants, 20 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR.
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