Relationship between four tumor-associated bio-markers and prognosis of gastric cancer

Purpose: To investigate the relationship between prognosis of gastric cancer (GC) and the expression of P53, Epidermal growth factor receptor (EGFR), Human epidermal growth factor receptor-2 (HER-2), and Vascular endothelial growth factor (VEGF).Methods: One hundred and forty-seven patients admitted to People's Liberation Army General Hospital (Beijing, China) with diagnosis of locally advanced GC were enrolled in the study. Follow-up data were obtained by outpatient review or telephone follow-up. Expressions of P53, EGFR, HER-2 and VEGF were determined by immunohistochemical staining. The relationship between protein expression, clinico-pathological factors, disease-free survival time (DFS) and overall survival (OS) were analyzed.Results: The expressions of EGER, HER-2, P53 and VEGF in GC were 17.7, 17.0, 41.0 and 55.9%, respectively. The expressions of EGFR and P53 were positively correlated (r = 0.306, p < 0.05), while the expressions of VEGF and HER-2 were negatively correlated (r = -0.2, p < 0.05). The expressions of EGFR, HER-2 and VEGF were not related to the clinico-pathological factors (p > 0.05) while expression of P53 was related only to histological grade (p < 0.05). Univariate analysis showed that OS and DFS were longer (p < 0.05) when P53 was lowly expressed. Multiple-factor analysis revealed that histological grade, infiltration depth and P53 expression were independent factors that influenced DFS.Conclusion: These results indicate that the expression of P53, EGFR, HER2 and VEGF can be used to predict prognosis of GC and screening of patients’ benefits from adjuvant chemotherapy.Keywords: Gastric cancer, Prognosis, Biomarkers, Adjuvant chemotherap

Pathologic complete response of hepatoid adenocarcinoma of the stomach after chemo-immunotherapy: A rare case report and literature review

BackgroundHepatoid adenocarcinoma of the stomach (HAS) is a highly malignant subtype of gastric carcinoma with specific clinicopathological features and extremely poor prognosis. We present an exceedingly rare case of complete response after chemo-immunotherapy.Case DescriptionA 48-year-old woman with highly elevated serum alpha-fetoprotein (AFP) level was found to have HAS verified by pathological examination based on gastroscopy. Computed tomography scan was done and TNM staging of the tumor was T4aN3aMx. Programmed cell death ligand-1 (PD-L1) immunohistochemistry was performed, revealing a negative PD-L1 expression. Chemo-immunotherapy including oxaliplatin plus S-1 and PD-1 inhibitor terelizumab was given to this patient for 2 months until the serum AFP level decreased from 748.5 to 12.9 ng/mL and the tumor shrank. D2 radical gastrectomy was then performed and histopathology of the resected specimen revealed that the cancerous cells had disappeared. Pathologic complete response (pCR) was achieved and no evidence of recurrence has been found after 1 year of follow-up.ConclusionsWe, for the first time, reported an HAS patient with negative PD-L1 expression who achieved pCR from the combined chemotherapy and immunotherapy. Although no consensus has been reached regarding the therapy, it might provide a potential effective management strategy for HAS patient

Enhanced protein isoform characterization through long-read proteogenomics

[Background] The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g., PacBio or Oxford Nanopore) provides full-length transcripts which can be used to predict full-length protein isoforms.[Results] We describe here a long-read proteogenomics approach for integrating sample-matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data to enable detection of protein isoforms previously intractable to MS-based detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.[Conclusions] Our work suggests that the incorporation of long-read sequencing and proteomic data can facilitate improved characterization of human protein isoform diversity. Our first-generation pipeline provides a strong foundation for future development of long-read proteogenomics and its adoption for both basic and translational research.This work was supported by a National Institutes of Health (NIH) grant R35GM142647 (G.M.S.), NIH grant R35GM126914 (L.M.S.), and Jackson Laboratory (A.D.M.). The codeathon which initiated the project was supported by the NIH STRIDES Initiative at the NIH.Peer reviewe

Localization and motion planning of mobile robots

Robot navigation system is a key functional module of autonomous robots. The development of the navigation system largely depends on the working environment and tasks. Key components including localization and motion planning are usually customized according to the specific application. Therefore, how to design the application-oriented robot navigation system and integrate all the sub-modules properly is a worthy research topic. This thesis studies robot navigation in two application scenarios. The first part of the thesis introduces a navigation system intending to fulfill an autonomous route repeating and fleet maintenance problem for multiple mobile robots in the open area. An Ultra-Wideband-based robot localization method is firstly implemented to provide the pose estimation of every robot in the troop. UWB sensors are selected due to their robustness in featureless open area environments compared to other localization algorithms using Lidar or visual cameras. The localization system relies on the Extended Kalman Filter(EKF) as the backbone while is initialized by non-linear trilateration. Afterward, a record-and-repeat pipeline is designed for the single robot's route repeating tasks. The waypoints of the desired route are sampled in the record loop, and a feasible trajectory is generated accordingly in an offline algorithm and serves as the reference for the single robot route following. Finally, a leader-follower controlling mechanism is proposed to solve the multi-agent formation maintenance part of the system. The whole system as well as every sub-modules are all validated to be effective in the open area testing sites. The localization can achieve an error less than $0.15\mathrm{m}$ on average in the testing site with $50\mathrm{m}\times50\mathrm{m}$ size. Meanwhile, the offline reference trajectory generation mechanism and a low-level motion controller for both single and multiple robots are tested to work successfully under different route settings, traveling speed, and fleet configurations, which demonstrates the robustness of the motion planning sub-system. The proposed system can be applied to various cases such as unmanned inspection or autonomous transportation in seaports or industrial parks, facilitating the working efficiency and making humans free from arduous tasks. In the second part, another typical application case for autonomous driving in urban road environments is investigated. The unmanned road sweeper has been one of the first industrialized unmanned vehicle products in recent years, which usually needs to travel along the urban road curb for environmental cleaning. To handle this problem, an urban road curb following strategy is proposed to serve as part of the navigation module of the unmanned sweeper. A curb detection method is firstly applied to extract candidate curb points in a single frame, followed by a multi-frame fusion further generating representative points from the curb detection results in the single frame. The pipeline is designed in this way as a result of balancing the accuracy and real-time performance. Afterward, a novel reference path segment generation and switching mechanism are proposed to bridge the perception with the low-level controller. The well-designed mechanism also guarantees the smoothness of reference trajectories and the feasibility of robot motion under the limited perception of the road curb ahead. Finally, a path-following controller is adopted in the system to drive the robot traveling along the reference path generated in the previous step. The proposed strategy is tested, in both simulation and real-world experiments, to be adaptable to various types of urban roads, including the straight, arc-shaped, and concave-shaped roads. Both qualitative and quantitative results are presented for curb detection and the following part, respectively.Master of Engineerin

Lateral Performance Simulation of Conventional CLT Shear Wall and Structure by Equivalent Decomposed Wall Model

The study aims to simulate the lateral responses of the conventional cross laminated timber (CLT) shear wall by establishing the equivalent decomposed wall models and providing lateral performance evaluation of walls with varied connections and walls in mass timber structures. First, the connection hysteresis model was built by calibrating the corresponding test results from references. Then, a detailed wall model, assembled with the calibrated connection models and shell elements, was calibrated with the wall experiments available in the literature. Based on the detailed wall model and the referred connection test results, an improved decomposed model with equivalent springs and shell elements was developed. The case studies indicate that both models captured the behavior of different connection configurations; however, the equivalent decomposed model provided a better prediction of the equivalent viscous damping ratio. The equivalent decomposed model was then applied in a full-scale three-story building time-history seismic analysis. The building simulation results indicate that the developed models can accurately estimate the wall hysteresis behavior, which can be a reference for CLT shear wall design

Reliability Assessment of Electrical Grids Subjected to Wind Hazards and Ice Accretion with Concurrent Wind

The supportive structures of power grids are vital but susceptible to weather-related events, such as extreme wind and icing rain with concurrent wind. The objective of this paper is to assess the reliability of the power grids subjected to high wind and ice accretion. The material and geometry uncertainty and the strength deterioration of poles due to decay are included in probabilistic models considering the bending failure of the poles and tensile failure of the wires. The extreme wind speed and icing accretion thickness are modeled with the Weibull distribution and generalized Pareto distribution, respectively. The fragility and reliability are analyzed with a Monte Carlo simulation for the comparison of two example locations with different hazard conditions. Case studies are implemented with a notional system and the system of Centerville, a fictitious study domain. The results illustrated that the fragility of wires is noteworthy in icing hazard reliability assessment. During icing rain, the concurrent wind speed significantly impacts the reliability of the power grids. The system reliability subjected to wind hazards is more sensitive to pole strength deterioration than in icing scenarios. The presented analysis framework will be beneficial for the design and maintenance of power grids subjected to both wind and icing hazards

Integrated experimental and numerical study on flexural properties of cross laminated timber made of low-value sugar maple lumber

The objective of this study is to examine the mechanical performance of cross laminated timber (CLT) panels made of low-value sugar maple under out of plane loads through mechanical tests and numerical simulation. The laminations were sorted into High and Low classes based on the measured modulus of elasticity (MOE). Two 3-layer sugar maple CLT layups as High-Low-High and Low-High-Low glued with resorcinol-based adhesive and one CLT layup of High-Low-High glued with melamine-based adhesive were prepared. Block shear, long-span bending (span-to-depth ratio of 33:1) and short-span bending (5.5:1) tests were conducted to evaluate the bonding, flexural and shear behavior of these low-value sugar maple CLTs. With a limited sample size, the lab-manufactured low-value sugar maple CLT provided a 50% to 80% higher MOE and at least two times higher MOR than CLT type E1 from APA/PRG 320. Similar MOE and MOR improvements were found by comparing CLT made with other species from literatures. The finite element simulation of bending tests was conducted with the orthogonal constitutive law and the progressive damage model based on the calibrated material properties parameters from lumber rating and references. The simulation results on each CLT panel type have a reasonable comparison with experimental test data. Therefore, these integrated experiment and simulation methods can provide detailed mechanical behaviors of the low-value sugar maple CLT, which can also be applied to other CLT species and layup

Surface layer modulus prediction of asphalt pavement based on LTPP database and machine learning for Mechanical-Empirical rehabilitation design applications

Evaluating the modulus of the existing asphalt concrete (AC) layer is a critical procedure in Mechanical-Empirical (ME) rehabilitation analysis. Generally, the modulus could be back-calculated by the Falling Weight Deflectometer (FWD) test. However, the raw FWD data of each pavement section is not always readily prepared for local highway agencies. To address this issue, the main objective of this study is to establish a reliable model by machine learning (ML) methods to predict AC layer modulus for the existing flexible pavement with data readily available from the local pavement management system, which could be an auxiliary tool for network-level sections with no FWD tests. The long-term pavement performance (LTPP) database was used to collect the original data for model training and testing, including pavement structures, service age, climate records, and pavement distresses. After preliminary data processing, matrix correlation analysis, and feature selection, the prepared dataset (total data points = 6477) with 14 predictors was fed into three regression models, including Ordinary Least-Squares regression (OLS), Random Forest regression (RF), and Gradient Boosting regression Method (GBM). The related key hyperparameters were optimized by grid search and 5-folds cross-validation. By comparison, the GBM model was finally selected due to its considerably higher prediction accuracy (R2 = 0.7921) than RF model (R2 = 0.7525) and OLS model (R2 = 0.4371) in the test set. According to the variable importance given by GBM model, surface temperature and AC layer thickness are more dominant variables in modulus estimation. In addition, a case study with predicted AC layer moduli in ME rehabilitation design was provided to verify the model application. In summary, the trained GBM model can be utilized to predict AC layer modulus for pavement evaluation and then ME rehabilitation when FWD data is not available

Fresh and mechanical performance and freeze-thaw durability of steel fiber-reinforced rubber self-compacting concrete (SRSCC)

The self-compacting concrete (SCC) with the replacement of recycled rubber aggregates is limited for the field application due to high-performance requirements. The steel fiber was introduced to the rubberized SCC (RSCC) to enhance its performance and promote its application. The fresh and mechanical properties and durability performance of steel fiber-reinforced rubber self-compacting concrete (SRSCC) were evaluated. The SRSCC samples were prepared with replaced rubber aggregate based on fine aggregate volume percentages of 10%, 15%, and 25% and a consistent steel fiber ratio of 0.2%. The plain SCC and rubberized SCC samples were also produced for comparison. The fresh performance was evaluated with slump flow, J-ring flow, V-funnel, and U-box tests. The results showed that both filling and passing ability could be affected by the added steel fiber and rubber aggregate. However, the SRSCC could still meet most of the recommended criteria for passing and filling abilities when the rubber content is lower than 25%. Regarding the hardened properties, the compressive strength was reduced in rubber SCC samples with increased rubber contents by comparing with the control SCC samples. Nevertheless, SRSCC samples with 10% rubbers have higher splitting tensile strength than RSCC and plain SCC. Also, the SRSCC specimens showed excellent freeze-thaw resistance after 600 F-T cycles. The relative dynamic modulus of elasticity slightly increased without any dimensional expansion in SRSCC samples. In summary, the proposed SRSCC can meet required flowability, filling and passing abilities along with good mechanical and freeze-thaw performance. This study will provide lab test data for the applications of recycling waste tire aggregates in steel fiber-reinforced SCC