RNF166 promotes colorectal cancer progression by recognizing and destabilizing poly-ADP-ribosylated angiomotins.

Activation of the Hippo pathway by angiomotins to limit colorectal cancer progression is prevalent, whereas the regulation of angiomotins remains elusive. In this study, we uncover the involvement of an upregulated E3 ubiquitin ligase called RNF166, which destabilizes angiomotins, activates YAP, and is associated with a poor prognosis in colorectal cancer patients. Mechanistically, RNF166 specifically recognizes PARsylated angiomotin, a modification mediated by tankyrase at specific amino acid residues (D506, E513, E516, and E528). The tankyrase inhibitor XAV939, effectively prevents RNF166-dependent destabilization of angiomotins and subsequent activation of YAP. Additionally, YAP-5SA, a constitutively active form of YAP, rescues colorectal cancer progression following knockdown of RNF166. Importantly, the C-terminus of RNF66, particularly the Di19-ZF domain, is the crucial region responsible for recognizing ADP-ribosylated angiomotins. Together, this work not only sheds light on the regulation of the Hippo pathway in colorectal cancer but also uncovers a novel poly(ADP-ribose)-binding domain, which may serve as a potential therapeutic target for intervention.

FLOT1 promotes gastric cancer progression and metastasis through BCAR1/ERK signaling.

Flotillin-1 (FLOT1) is a member of the flotillin family and serves as a hallmark of lipid rafts involved in the process of signaling transduction and vesicular trafficking. Here, we find FLOT1 promotes gastric cancer cell progression and metastasis by interacting with BCAR1, through ERK signaling. FLOT1 regulates BCAR1 phosphorylation and translocation. Overexpression of FLOT1 increases, while knockdown of FLOT1 decreases gastric cancer cell proliferation, migration and invasion. BCAR1 knockdown could block FLOT1 induced gastric cancer cell proliferation, migration and invasion. Re-expression of wildtype rather than mutant BCAR1 (Y410F) could partially restore FLOT1 knockdown induced gastric cancer cell migration and invasion, while the restore could be inhibited by ERK inhibitor. Furthermore, FLOT1 and BCAR1 expression is closely related to gastric cancer patients' poor outcome. Thus, our findings confirm that BCAR1 mediates FLOT1 induced gastric cancer progression and metastasis through ERK signaling, which may provide a novel pathway for gastric cancer treatment.

Caprini risk assessment model combined with D-dimer to predict the occurrence of deep vein thrombosis and guide intervention after laparoscopic radical resection of colorectal cancer.

BACKGROUND: To explore the diagnostic value of Caprini risk assessment model (2005) combined with D-dimer for deep vein thrombosis, and to exclude patients with low incidence of thrombosis who might not need anticoagulation after surgery. METHODS: A total of 171 colorectal cancer patients who underwent surgery from January 2022 to August 2022 were enrolled in this study. Caprini risk assessment model was used to evaluate patients the day before surgery, and full-length venous ultrasonography of lower extremity was used to assess whether patients had thrombosis one day before surgery and the sixth day after surgery. The value of D-dimer was measured by enzyme-linked immunosorbent assays on the first day after surgery, and clinical data of patients were collected during hospitalization. RESULTS: A total of 171 patients were divided into IPC Group and IPC + LMWH Group according to whether low molecular weight heparin (LMWH) were used to prevent thrombus after surgery. Eventually, 17.6% (15/85) patients in IPC Group and 7% (6/86) patients in IPC + LMWH Group developed DVT. Through separate analysis of IPC Group, it is found that Caprini score and D-dimer were independent risk factors for DVT (Caprini OR 3.39 [95% CI 1.38-8.32]; P = 0.008, D-Dimer OR 6.142 [95% CI 1.209-31.187]; P = 0.029). The area under ROC curve of Caprini risk assessment model is 0.792 (95% CI 0.69-0.945, P < 0.01), the cut-off value is 9.5, and the area under ROC curve of D-dimer is 0.738 (95%CI 0.555-0.921, P < 0.01), the cut-off value is 0.835 µg/mL, and the area under the ROC curve was 0.865 (95% CI 0.754-0.976, P < 0.01) when both of them were combined. Based on decision curve analysis, it is found that Caprini risk assessment model combined with D-dimer can benefit patients more. All patients are divided into four groups. When Caprini score < 10 and D-dimer < 0.835 µg/mL, only 1.23% (1/81) of patients have thrombosis and LMWH has little significance. When Caprini score > 10 and D-dimer > 0.835 µg/mL, the incidence of DVT is 38.7% (12/31) and LMWH should be considered. CONCLUSIONS: The Caprini risk assessment model and D-dimer can provide more accurate risk stratification for patients after laparoscopic radical resection of colorectal cancer.

Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering.

Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (R match) of tree tops is up to 0.92, the coefficient of determination (R 2) of tree height estimation is up to .94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping.

Flotillin-1 is a prognostic biomarker for glioblastoma and promotes cancer development through enhancing invasion and altering tumour microenvironment.

Flotillin-1(FLOT1) has long been recognized as a tumour-promoting gene in several types of cancer. However, the expression and function of FLOT1 in glioblastomas (GBM) has not been elucidated. Here, in this study, we find that the expression level of FLOT1 in GBM tissue was much higher than that in normal brain, and the expression was even higher in the more aggressive subtypes and IDH status of glioma. Kaplan-Meier survival revealed that high FLOT1 expression is closely associated with poor outcome in GBM patients. FLOT1 knockdown markedly reduced the proliferation, migration and invasiveness of GBM cells, while FLOT1 overexpression significantly increases GBM cell proliferation, migration and invasiveness. Mechanistically, FLOT1 expression may play a potential role in the microenvironment of GBM. Therefore, FLOT1 promotes GBM proliferation and invasion in vitro and in vivo and may serve as a biomarker of prognosis and therapeutic potential in the fight against GBM.

A Six-microRNA Signature Nomogram for Preoperative Prediction of Tumor Deposits in Colorectal Cancer.

PURPOSE: Tumor deposits (TDs) are acknowledged negative prognostic factors in colorectal cancer (CRC), and their pathogenesis remains a puzzle. This study aimed to construct and validate a nomogram available for preoperative TDs prediction in CRC patients. PATIENTS AND METHODS: Patients from the Surveillance, Epidemiology, and End Results (SEER) and the cancer genome atlas (TCGA) databases were randomly divided into training and validation sets according to the sample size ratio of 7:3. Univariate logistic regression was performed for identifying differentially expressed microRNAs between TDs and non-TDs. Nomograms for TDs prediction were developed from the multivariate logistic regression model with least absolute shrinkage and selection operator and were validated internally in terms of accuracy, calibration, and clinical utility. Based on the target genes, pathways tightly associated with TDs were selected using enrichment analysis. RESULTS: Six clinicopathologic factors and expressions of six microRNAs (miR-614, miR-1197, miR-4770, miR-3136, miR-3173, and miR-4636) differed significantly between TDs and non-TDs CRC patients from the SEER and TCGA training sets. We compared potential prediction discrimination between two nomograms: a clinicopathologic nomogram and a six-microRNA signature nomogram. The six-microRNA signature nomogram revealed better accuracy than the clinicopathologic one for TDs prediction (AUC values of 0.96 and 0.93 in the validation cohort). The calibration plots and decision curve analysis demonstrated that the six-microRNA signature nomogram had better validity and a greater prognostic benefit versus the clinicopathologic one for TDs prediction. Calcium signaling pathways were closely associated with roles of the six microRNAs in TDs of CRC patients. CONCLUSION: The six-microRNA signature nomogram can be used as an efficient tool for preoperative TDs prediction in CRC patients.

Case report: Neurofibromatosis type 1 gastrointestinal stromal tumor and small bowel adenocarcinoma with a novel germline NF1 frameshift mutation.

A synchronous case of small bowel adenocarcinoma(SAB) is reported, accompanied with gastrointestinal stromal tumor(GIST),and gangliocytomain in an elderly woman with neurofibromatosis type 1 (NF-1). A 67-year-old female was hospitalized with the chief complaint of abdominal pain, the computed tomography scan indicated a large bowel mass. Multiple tumors were found in the small intestine, through which two larger tumors (7 cm and 1.5 cm) were resected. A novel germline NF1 mutation and a PMS2 mutation were identified after genetic testing, followed by the exploration of possible relationship between them in promoting tumorigenesis. Our results suggest multiple gastrointestinal tumors emerging in NF1 patients, and genetic testing can better guide postoperative treatment in a more efficient way.

Improving Shadow Suppression for Illumination Robust Face Recognition.

2D face analysis techniques, such as face landmarking, face recognition and face verification, are reasonably dependent on illumination conditions which are usually uncontrolled and unpredictable in the real world. The current massive data-driven approach, e.g., deep learning-based face recognition, requires a huge amount of labeled training face data that hardly cover the infinite lighting variations that can be encountered in real-life applications. An illumination robust preprocessing method thus remains a very interesting but also a significant challenge in reliable face analysis. In this paper we propose a novel model driven approach to improve lighting normalization of face images. Specifically, we propose to build the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborate the formation of face color appearance. The proposed illumination processing pipeline enables generation of the Chromaticity Intrinsic Image (CII) in a log chromaticity space which is robust to illumination variations. Moreover, as an advantage over most prevailing methods, a photo-realistic color face image is subsequently reconstructed, which eliminates a wide variety of shadows whilst retaining the color information and identity details. Experimental results under different scenarios and using various face databases show the effectiveness of the proposed approach in dealing with lighting variations, including both soft and hard shadows, in face recognition.

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification.

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

Registration of optical imagery and LiDAR data using an inherent geometrical constraint.

A novel method for registering imagery with Light Detection And Ranging (LiDAR) data is proposed. It is based on the phenomenon that the back-projection of LiDAR point cloud of an object should be located within the object boundary in the image. Using this inherent geometrical constraint, the registration parameters computation of both data sets only requires LiDAR point clouds of several objects and their corresponding boundaries in the image. The proposed registration method comprises of four steps: point clouds extraction, boundary extraction, back-projection computation and registration parameters computation. There are not any limitations on the geometrical and spectral properties of the object. So it is suitable not only for structured scenes with man-made objects but also for natural scenes. Moreover, the proposed method based on the inherent geometrical constraint can register two data sets derived from different parts of an object. It can be used to co-register TLS (Terrestrial Laser Scanning) LiDAR point cloud and UAV (Unmanned aerial vehicle) image, which are obtaining more attention in the forest survey application. Using initial registration parameters comparable to POS (position and orientation system) accuracy, the performed experiments validated the feasibility of the proposed registration method.

Study of a QCM dimethyl methylphosphonate sensor based on a ZnO-modified nanowire-structured manganese dioxide film.

Sensitive, selective and fast detection of chemical warfare agents is necessary for anti-terrorism purposes. In our search for functional materials sensitive to dimethyl methylphosphonate (DMMP), a simulant of sarin and other toxic organophosphorus compounds, we found that zinc oxide (ZnO) modification potentially enhances the absorption of DMMP on a manganese dioxide (MnO(2)) surface. The adsorption behavior of DMMP was evaluated through the detection of tiny organophosphonate compounds with quartz crystal microbalance (QCM) sensors coated with ZnO-modified MnO(2) nanofibers and pure MnO(2) nanofibers. Experimental results indicated that the QCM sensor coated with ZnO-modified nanostructured MnO(2) film exhibited much higher sensitivity and better selectivity in comparison with the one coated with pure MnO(2) nanofiber film. Therefore, the DMMP sensor developed with this composite nanostructured material should possess excellent selectivity and reasonable sensitivity towards the tiny gaseous DMMP species.

Woody-to-total area ratio determination with a multispectral canopy imager.

Leaf area index (LAI) - defined as one half of the total green leaf area per unit ground surface area - can be determined by direct or indirect methods. Three major sources of errors exist in indirect LAI measurements: within-shoot clumping, beyond-shoot clumping and non-photosynthetic components. The effect of non-photosynthetic components on LAI measurements can be described by the woody-to-total area ratio, alpha; however, no convenient and efficient indirect methods have been developed to estimate alpha, especially the variations in alpha with zenith angle , alpha(theta). We describe the development and use of a multispectral canopy imager (MCI) to estimate alpha and alpha(theta) by considering the effects of non-random distributions of canopy elements and woody components and the overestimation of needle-to-shoot area ratio on woody components. The MCI, which mainly comprises a near-infrared band camera (Fujifilm IS-1), two visible band cameras (Canon 40D), filters and a pan tilt, was developed to measure clumping index, woody-to-total area ratio and geometric parameters of isolated trees. Two typical sampling plots (Plots 1 and 5) chosen from among 16 permanent forest experiment plots were selected for the estimation of alpha and alpha(theta). The non-random distributions of canopy elements and woody components were estimated separately at eight zenith angles (from 0 degrees to 70 degrees in increments of 10 degrees) using MCI images based on the gap size distribution theory. The visible/near-infrared image pairs captured by the MCI were able to discriminate among sky, leaves, cloud and woody components. Based on three methods of estimation, we obtained woody-to-total area ratios of 0.24, 0.19, 0.19 for Plot 1 and 0.23, 0.18, 0.17 for Plot 5. If clumping effects were ignored, alpha values were overestimated by as much as 21% and 24% at Plots 1 and 5, respectively. We demonstrated that alpha(theta) varied with the zenith angle, with variations in the range of 3-33% at Plot 1 and 2-65% at Plot 5. A new formula for the precise determination of LAI is proposed.

Improving the efficiency of evolutionary de novo peptide design: strategies for probing configuration and parameter settings.

Evolutionary molecular design based on genetic algorithms (GAs) has been demonstrated to be a flexible and efficient optimization approach with potential for locating global optima. Its efficacy and efficiency are largely dependent on the operations and control parameters of the GAs. Accordingly, we have explored new operations and probed good parameter setting through simulations. The findings have been evaluated in a helical peptide design according to "Parameter setting by analogy" strategy; highly helical peptides have been successfully obtained with a population of only 16 peptides and 5 iterative cycles. The results indicate that new operations such as multi-step crossover-mutation are able to improve the explorative efficiency and to reduce the sensitivity to crossover and mutation rates (CR-MR). The efficiency of the peptide design has been furthermore improved by setting the GAs at the good CR-MR setting determined through simulation. These results suggest that probing the operations and parameter settings through simulation in combination with "Parameter setting by analogy" strategy provides an effective framework for improving the efficiency of the approach. Consequently, we conclude that this framework will be useful for contributing to practical peptide design, and gaining a better understanding of evolutionary molecular design.

Exploration of structural features of monomeric helical peptides designed with a genetic algorithm.

A genetic algorithm (GA)-based strategy to dissect the determinants of peptide folding into alpha-helix was developed. The structural information of helical peptides was obtained with respect to patterns of sequence variability. In many previously reported studies the intrinsic alpha-helical propensities of amino acids although sequence-dependent are apparently independent of the amino acid position. In this research, monomeric helical peptides selected from possible sequences produced by a GA-chemical synthesis were analyzed to identify possible influential structural features. These hexadeca-peptides were obtained after four successive generations. A total of 128 synthetic peptides were evaluated via circular dichroism (CD) measurements in aqueous solution, while the mean ellipticity at 222 nm confirmed the monomeric state of the peptides. The results presented here show that our GA-based strategy may be useful in the design of proteins with increased alpha-helix content.