Application and Validation of a Model for Terrain Slope Estimation Using Space-Borne LiDAR Waveform Data

The terrain slope is one of the most important surface characteristics for quantifying the Earth surface processes. Space-borne LiDAR sensors have produced high-accuracy and large-area terrain measurement within the footprint. However, rigorous procedures are required to accurately estimate the terrain slope especially within the large footprint since the estimated slope is likely affected by footprint size, shape, orientation, and terrain aspect. Therefore, based on multiple available datasets, we explored the performance of a proposed terrain slope estimation model over several study sites and various footprint shapes. The terrain slopes were derived from the ICESAT/GLAS waveform data by the proposed method and five other methods in this study. Compared with five other methods, the proposed method considered the influence of footprint shape, orientation, and terrain aspect on the terrain slope estimation. Validation against the airborne LiDAR measurements showed that the proposed method performed better than five other methods (R2 = 0.829, increased by ~0.07, RMSE = 3.596°, reduced by ~0.6°, n = 858). In addition, more statistics indicated that the proposed method significantly improved the terrain slope estimation accuracy in high-relief region (RMSE = 5.180°, reduced by ~1.8°, n = 218) or in the footprint with a great eccentricity (RMSE = 3.421°, reduced by ~1.1°, n = 313). Therefore, from these experiments, we concluded that this terrain slope estimation approach was beneficial for different terrains and various footprint shapes in practice and the improvement of estimated accuracy was distinctly related with the terrain slope and footprint eccentricity

Research on the Properties of Zein, Soy Protein Isolate, and Wheat Gluten Protein-Based Films Containing Cellulose Nanocrystals

Plant protein films are a research hotpot in the current food packaging field for their renewable and bio-compatibility, and further improving the physicochemical properties of plant protein films in combination with biodegradable materials is of great significance. In this study, we selected cellulose nanocrystals (CNC) to modify the protein films with soybean protein isolate (SPI), wheat gluten protein (WGP), and Zein, and the physicochemical properties were studied. The results showed that the hardness and opacity of Zein-based films decreased by 16.61% and 54.12% with the incorporation of CNC, respectively. The SPI-based films performed with lower hardness and higher tensile strength. The thickness and opacity of WGP-based films increased by 39.76% and 214.38% after combination with CNC, respectively. Accordingly, this study showed that CNC could largely modify the physicochemical properties of the plant protein films, which provided a reference for the preparation of modified plant protein films using biodegradable materials

Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors

Development of noninvasive, reliable biomarkers for lung cancer diagnosis has many clinical benefits knowing that most of lung cancer patients are diagnosed at the late stage. For this purpose, we conducted proteomic analyses of 231 human urine samples in healthy individuals (n = 33), benign pulmonary diseases (n = 40), lung cancer (n = 33), bladder cancer (n = 17), cervical cancer (n = 25), colorectal cancer (n = 22), esophageal cancer (n = 14), and gastric cancer (n = 47) patients collected from multiple medical centers. By random forest modeling, we nominated a list of urine proteins that could separate lung cancers from other cases. With a feature selection algorithm, we selected a panel of five urinary biomarkers (FTL: Ferritin light chain; MAPK1IP1L: Mitogen-Activated Protein Kinase 1 Interacting Protein 1 Like; FGB: Fibrinogen Beta Chain; RAB33B: RAB33B, Member RAS Oncogene Family; RAB15: RAB15, Member RAS Oncogene Family) and established a combinatorial model that can correctly classify the majority of lung cancer cases both in the training set (n = 46) and the test sets (n = 14–47 per set) with an AUC ranging from 0.8747 to 0.9853. A combination of five urinary biomarkers not only discriminates lung cancer patients from control groups but also differentiates lung cancer from other common tumors. The biomarker panel and the predictive model, when validated by more samples in a multi-center setting, may be used as an auxiliary diagnostic tool along with imaging technology for lung cancer detection. Keywords: Lung cancer, Machine learning, Urinary biomarker

miR-223 regulates migration and invasion by targeting Artemin in human esophageal carcinoma

Abstract Background Artemin (ARTN) is a neurotrophic factor belonging to the glial cell-derived neurotrophic factor family of ligands. To develop potential therapy targeting ARTN, we studied the roles of miR-223 in the migration and invasion of human esophageal carcinoma. Methods ARTN expression levels were detected in esophageal carcinoma cell lines KYSE-150, KYSE-510, EC-9706, TE13, esophageal cancer tissues and paired non-cancerous tissues by Western blot. Artemin siRNA expression vectors were constructed to knockdown of artemin expression mitigated migration and invasiveness in KYSE150 cells. Monolayer wound healing assay and Transwell invasion assay were applied to observe cancer cell migration and invasion. The relative levels of expression were quantified by real-time quantitative PCR. Results ARTN expression levels were higher in esophageal carcinoma tissue than in the adjacent tissue and was differentially expressed in various esophageal carcinoma cell lines. ARTN mRNA contains a binding site for miR-223 in the 3'UTR. Co-transfection of a mir-223 expression vector with pMIR-ARTN led to the reduced activity of luciferase in a dual-luciferase reporter gene assay, suggesting that ARTN is a target gene of miR-223. Overexpression of miR-223 decreased expression of ARTN in KYSE150 cells while silencing miR-223 increased expression of ARTN in EC9706 cells. Furthermore, overexpression of miR-223 in KYSE150 cells decreased cell migration and invasion. Silencing of miR-223 in EC9706 cells increased cell migration and invasiveness. Conclusions These results reveal that ARTN, a known tumor metastasis-related gene, is a direct target of miR-223 and that miR-223 may have a tumor suppressor function in esophageal carcinoma and could be used in anticancer therapies.</p

Dickkopf-1 Is Oncogenic and Involved in Invasive Growth in Non Small Cell Lung Cancer

<div><p>Dickkopf-1 (DKK1) is an inhibitor of the Wnt/β-catenin signaling pathway. However, the role of DKK1 in the progression of non small cell lung cancer (NSCLC) is not fully understood. In this study, RT-PCR and Western blot were used to examine the expression of DKK1 in a panel of ten human NSCLC cell lines and NSCLC tissues. DKK1 expression was highly transactivated in the great majority of these cancer lines. The expression of DKK1 was upregulated on both mRNA and protein levels in NSCLC tissues compared with the adjacent normal lung tissues. Immunohistochemistry and immunofluoresence revealed that DKK1 was mainly distributed in the cytoplasm in both carcinoma tissues and cell lines. DKK1 protein expression was also evaluated in paraffin sections from 102 patients with NSCLC by immunohistochemistry, and 65(63.73%)tumors were DKK1 positive. Relative analysis showed a significant relationship between DKK1 positive expression and lymph node metastasis(<i>P</i><0.05). Patients with DKK1-positive tumors had poorer DFS than those with negative ESCC (5-year DFS; 15.4% versus 27%, P = 0.007). To further explore the biological effects of DKK1 in NSCLC cells, we over-expressed DKK1 in NSCLC 95C cell using eukaryotic expression vector pCMV-Tab-2b and performed a knockdown of DKK1 in LTEP-a-2 cell using a short hairpin RNA expression vector pSilencer 5.1. DKK1 did not have any effect on proliferation, but seemed to play a role in migration and invasion capability. Overexpression of DKK1 promotes migratory and invasive activity of 95C, while DKK1 knockdown resulted in the suppression of migration and invasion potentials of LTEP-a-2 cell. Taken together, these results indicate that DKK1 may be a crucial regulator in the progression of NSCLC. DKK1 might be a potential therapeutic target in NSCLC.</p> </div

Discovery of Urinary Proteomic Signature for Differential Diagnosis of Acute Appendicitis

Acute appendicitis is one of the most common acute abdomens, but the confident preoperative diagnosis is still a challenge. In order to profile noninvasive urinary biomarkers that could discriminate acute appendicitis from other acute abdomens, we carried out mass spectrometric experiments on urine samples from patients with different acute abdomens and evaluated diagnostic potential of urinary proteins with various machine-learning models. Firstly, outlier protein pools of acute appendicitis and controls were constructed using the discovery dataset (32 acute appendicitis and 41 control acute abdomens) against a reference set of 495 normal urine samples. Ten outlier proteins were then selected by feature selection algorithm and were applied in construction of machine-learning models using naïve Bayes, support vector machine, and random forest algorithms. The models were assessed in the discovery dataset by leave-one-out cross validation and were verified in the validation dataset (16 acute appendicitis and 45 control acute abdomens). Among the three models, random forest model achieved the best performance: the accuracy was 84.9% in the leave-one-out cross validation of discovery dataset and 83.6% (sensitivity: 81.2%, specificity: 84.4%) in the validation dataset. In conclusion, we developed a 10-protein diagnostic panel by the random forest model that was able to distinguish acute appendicitis from confusable acute abdomens with high specificity, which indicated the clinical application potential of noninvasive urinary markers in disease diagnosis

Expression of DKK1 in NSCLC tissues.

<p>(A) Expression by immunohistochemical staining. DKK1 strong positive expression in lung cancer showing staining mainly in the cytoplasm of tumor Cells (magnification 100×). Representative DKK1 weak positive lung cancer with pale yellow particles in tumor cells (magnification 100×). DKK1 negative lung cancer cell showing almost no appreciable staining of tumor cells (magnification 100×). Normal lung tissue without staining. (B) Expression of DKK1 mRNA in NSCLC tissues and matched normal lung tissues. (C) Expression of DKK1 protein in NSCLC tissues and matched normal lung tissues. N, Normal tissue; T, Tumor tissue. Positive expressions of DKK1 were mainly accompanied with lymph nodes metastasis.</p

Effects of DKK1 overexpression in 95C cell.

<p>(A) RT-PCR and Western blot analysis of the DKK1 expression. DKK1 level in 95C cell transfected with pCMV-Tag-2b-DKK1 is significantly higher than that in pCMV-Tag-2b and blank control group. (B) The wounded and healing 95C cells. (C) Measurement of migration distance (*<i>P</i><0.05). (D) The invasion ability of 95C transfected with pCMV-Tag-2b-DKK1 was significantly enhanced. (E) The number of cells migrating through the Matrigel-coated filters was statistic analyzed. Assays were done in triplicate wells (*<i>P</i><0.01). </p