Single photon imaging with multi-scale time resolution.

To avoid echo photons to be submerged in noise in rough terrain or dynamic applications, a single photon imaging mechanism with multi-scale time resolution is proposed in this paper. Combining with adaptively thresholding technique, multiple histograms with different time resolutions are produced to cluster the echo photons into a time bin and then separate them from the noise. With microsecond-scale resolution, uncertainty in the position of an object can be reduced from several kilometers to 300 meters, and therefore the computational overheads are saved by only investigating depths with picosecond-scale resolution where an object is present. Reconstructed results of the two near surfaces show that the depth accuracy is less than 0.15 m in the conditions of 8 echo photons and 1 Mcps background count rate, even though the pulse width of laser source reaches 3.5 ns (equivalent to an uncertainty of 0.525 m). In addition, the echo can be distinguished from the noise clearly when the background count rate varies from 200 kcps to 1 Mcps. The proposed method is suitable for implementation in digital signal processor (DSP) due to low data volumes and computational overheads.

Single photon imaging based on a photon driven sparse sampling.

Single photon three-dimensional (3D) imager can capture 3D profile details and see through obscuring objects with high sensitivity, making it promising in sensing and imaging applications. The key capabilities of such 3D imager lie on its depth resolution and multi-return discrimination. For conventional pulsed single photon lidar, these capabilities are limited by transmitter bandwidth and receiver bandwidth simultaneously. A single photon imager is proposed and experimentally demonstrated to implement time-resolved and multi-return imaging. Time-to-frequency conversion is performed to achieve millimetric depth resolution. Experimental results show that the depth resolution is better than 4.5 mm, even though time jitter of the SPAD reaches 1 ns and time resolution of the TCSPC module reaches 10 ns. Furthermore, photon driven sparse sampling mechanism allows us to discriminate multiple near surfaces, no longer limited by the receiver bandwidth. The simplicity of the system hardware enables low-cost and compact 3D imaging.

microRNA-454 promotes liver tumor-initiating cell expansion by regulating SOCS6.

Tumor-initiating cells (T-ICs) are involved in the tumorigenesis, progression, drug resistance and recurrence of hepatocellular carcinoma (HCC). However, the underlying mechanism for the propagation of liver T-ICs remains unclear. Herein, we find that miR-454 is upregulated in liver T-ICs and has an important function in liver T-ICs. Functional studies have revealed that knockdown of miR-454 inhibits liver T-IC self-renewal and tumorigenesis. Conversely, forced miR-454 expression promotes liver T-IC self-renewal and tumorigenesis. Mechanistically, we found that miR-454 downregulates SOCS6 expression in liver T-ICs. The correlation between miR-454 and SOCS6 is validated in human HCC tissues. Furthermore, HCC cells that overexpress miR-454 are resistant to sorafenib treatment. Analysis of patient-derived xenografts (PDXs) further demonstrates that miR-454 may predict sorafenib benefits in HCC patients. In conclusion, our findings reveal the crucial role of miR-454 in liver T-IC expansion and sorafenib response.

Adaptive single photon detection under fluctuating background noise.

To enhance the performance of single photon lidar in remote sensing applications, an adaptive single photon detection method under fluctuating background noise is proposed in this work. Given a desired false alarm probability, an adaptive threshold can be determined according to the background noise in real time. As a result, the single photon lidar operating under fluctuating background noise can implement high sensitivity and low false alarm rate simultaneously. Experiments are performed to verify the proposed method, and the experimental results are in accordance well within the theoretical values. In addition, the mean number of echo photons can be estimated in a fluctuating background noise environment, and the estimation accuracy is less than 1 photon.

Exosome-transmitted p120-catenin suppresses hepatocellular carcinoma progression via STAT3 pathways.

Hepatocellular carcinoma (HCC) is a fatal disease with increasing morbidity and poor prognosis due to surgical recurrence and metastasis. Moreover, the molecular mechanism of HCC progression remains unclear. Although the role of p120-catenin (p120ctn) in liver cancer is well studied, the effects of secreted p120ctn transported by exosomes are less understood. Here, we show that p120ctn in exosomes secreted from liver cancer cells suppresses HCC cell proliferation and metastasis and expansion of liver cancer stem cells (CSCs). Mechanically, exosome p120ctn inhibits HCC cell progression via the STAT3 pathway, and the STAT3 inhibitor S3I-201 abolishes the observed effects on growth, metastasis, and self-renewal ability between exosome p120ctn-treated HCC cells and control cells. Taken together, we propose that p120ctn-containing exosomes derived from cancer cells inhibit the progression of liver cancer and may offer a new therapeutic strategy.

Modifications of the AJCC 8th edition staging system for intrahepatic cholangiocarcinoma and proposal for a new staging system by incorporating serum tumor markers.

BACKGROUND: Several studies have noted that the discriminatory ability and stratification performance of the AJCC 8th edition staging system is not entirely satisfactory. We aimed to improve the American Joint Committee on Cancer (AJCC) 8th edition staging system for intrahepatic cholangiocarcinoma (ICC). METHODS: A multicentric database from three Chinese mainland centers (n = 1601 patients) was used to modify the 8th edition staging system. This modified TNM (mTNM) staging system was then validated using the SEER database (n = 761 patients). A new TNM staging system, by incorporating serum tumor markers (TNMIS) into the mTNM staging system was then proposed. RESULTS: The 8th edition staging system did not provide an adequate stratification of prognosis in the Chinese multicentric cohort. The mTNM staging system offered a better discriminatory capacity in the multicentric cohort than the original 8th edition. External validation in the SEER cohort showed that the mTNM staging system also had a good stratification performance. After further incorporating a serum marker stage into the mTNM staging, the TNMIS staging system was able to stratify prognosis even better. CONCLUSION: The proposed mTNM staging system resulted in better stratification performance and the TNMIS staging system provided even more accurate prognostic classification than the conventional TNM system.

Highly concentrated, ring-shaped phase conversion laser-induced breakdown spectroscopy technology for liquid sample analysis.

A highly concentrated, ring-shaped phase conversion (RSPC) method was developed for liquid sample analysis using the laser-induced breakdown spectroscopy (LIBS) technique. In this work, test samples were prepared by mixing the metal particles with polyvinyl alcohol (PVA) supporter in liquid phase. With heat, the PVA solution solidified inside a modified glass petri dish, forming a metal-enriched polymer ring film. Distinguished from other traditional liquid-to-solid conversing methods, the proposed new method takes advantage of enhanced homogeneity for the target elements inside the ring film. The modified glass petri dish was used to control the ring-shaped concentration. Due to the specially designed circular groove at the bottom of this dish, where the PVA solution and liquid sample mixture accumulated, the target elements were concentrated in this small ring, which is beneficial for enhancing and stabilizing the plasma signals compared to the direct liquid sample analysis using LIBS. The limits of detection for Ag, Cu, Cr, and Ba obtained with the RSPC-LIBS technology were 0.098 µg·mL-1, 0.18 µg·mL-1, 0.83 µg·mL-1, and 0.046 µg·mL-1, respectively, which provided greater improvement than the direct bulk liquid analysis using LIBS.

A Novel Nomogram for Predicting Postoperative Liver Failure After Major Hepatectomy for Hepatocellular Carcinoma.

Background: Post-hepatectomy liver failure (PHLF) is the most common cause of mortality after major hepatectomy in hepatocellular carcinoma (HCC) patients. We aim to develop a nomogram to preoperatively predict grade B/C PHLF defined by the International Study Group on Liver Surgery Grading (ISGLS) in HCC patients undergoing major hepatectomy. Study Design: The consecutive HCC patients who underwent major hepatectomy at the Eastern Hepatobiliary Surgery Hospital between 2008 and 2013 served as a training cohort to develop a preoperative nomogram, and patients from 2 other hospitals comprised an external validation cohort. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify preoperative predictors of grade B/C PHLF. Multivariable logistic regression was utilized to establish a nomogram model. Internal and external validations were used to verify the performance of the nomogram. The accuracy of the nomogram was also compared with the conventional scoring models, including MELD and ALBI score. Results: A total of 880 patients who underwent major hepatectomy (668 in the training cohort and 192 in the validation cohort) were enrolled in this study. The independent risk factors of grade B/C PHLF were age, gender, prothrombin time, total bilirubin, and CSPH, which were incorporated into the nomogram. Good prediction discrimination was achieved in the training (AUROC: 0.73) and validation (AUROC: 0.72) cohorts. The calibration curve also showed good agreement in both training and validation cohorts. The nomogram has a better performance than MELD and ALBI score models. Conclusion: The proposed nomogram showed more accurate ability to individually predict grade B/C PHLF after major hepatectomy in HCC patients than MELD and ALBI scores.

External Validation of Six Liver Functional Reserve Models to predict Posthepatectomy Liver Failure after Major Resection for Hepatocellular Carcinoma.

Objective: To validate and compare the predictive ability of albumin-bilirubin model (ALBI) with other 5 liver functional reserve models (APRI, FIB4, MELD, PALBI, King's score) for posthepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) who underwent major hepatectomy. Methods: Data of patients undergoing major hepatectomy for HCC from 4 hospitals between January 01, 2008 and December 31, 2019 were retrospectively analyzed. PHLF was evaluated according to the definition of the 50-50 criteria. Performances of six liver functional reserve models were determined by the area under the receiver operating characteristic curve (AUC), calibration plot and decision curve analysis. Results: A total of 745 patients with 103 (13.8%) experienced PHLF were finally included in this study. Among six liver functional reserve models, ALBI showed the highest AUC (0.64, 95% CI: 0.58-0.69) for PHLF. All models showed good calibration and greater net benefit than treating all patients at a limit range of threshold probabilities, but the ALBI demonstrated net benefit across the largest range of threshold probabilities. Subgroup analysis also showed ALBI had good predictive performance in cirrhotic (AUC=0.63) or non-cirrhotic (AUC=0.62) patients. Conclusion: Among the six models, the ALBI model shows more accurate predictive ability for PHLF in HCC patients undergoing major hepatectomy, regardless of having cirrhosis or not.

[Landscape stability and its pattern transition in Jinghe watershed].

Based on remote sensing and GIS, this paper analyzed the landscape change patterns in the Jinghe watershed of northwest Loess Plateau, one of the areas most suffered from soil and water erosion. The results indicated that the landscape structure in the watershed did not change very much over the past 15 years, and was dominated by grassland and arable land, which occupied 85% of total watershed area. Forest and other land use accounted for 10% and 5% of total area, respectively. A transition of landscape patterns occurred at large-scale watershed level. During 1986- 2000, the inter-transitions between different landscape types were very complicated, mainly occurred among woodland, grassland and arable land. The self-stableness of dominant landscape patterns was > 89%, and only 11% of them transferred into other types. Among landscape types, the one sharing small percentage of the total had a high speed of vanishing or transferring into other types. A significant transition was found between sub-patterns of a large landscape pattern, e. g., the transition of medium- into low-coverage grassland during 1986 - 1995 and the reverse during 1995 - 2000 was occurred, implying a situation of grassland degradation and improvement over the decades. The spatial-temporal changes of landscape features were observed in the watershed. In terms of spatial change, the dominance index and contagion index of the landscape showed an increasing trend, fractal dimension index showed a decreasing trend, while patch density showed an increasing trend, followed by a decreasing trend from upper to down streams. For temporal change, both landscape dominance and contagion decreased to a certain extent, fractal index remained stable, while patch density increased, showing a fragmentation direction of the landscape in the watershed. The NDVI value changed since the implementation of the policy of conversing farmland into grassland and forest in 2000, which was from 7.4% to 0.8% in low value class, from 29.8% to 25.1% in high value class, from 18.3% to 25.7% in higher value class, and from 2.3% to 5.5% in the highest class, showing an improving trend in landscape quality.