Clinical Significance of Tumor Infiltrating Lymphocytes in Association with Hormone Receptor Expression Patterns in Epithelial Ovarian Cancer

Hormone receptor expression patterns often correlate with infiltration of specific lymphocytes in tumors. Specifically, the presence of specific tumor-infiltrating lymphocytes (TILs) with particular hormone receptor expression is reportedly associated with breast cancer, however, this has not been revealed in epithelial ovarian cancer (EOC). Therefore, we investigated the association between hormone receptor expression and TILs in EOC. Here we found that ERα, AR, and GR expression increased in EOC, while PR was significantly reduced and ERβ expression showed a reduced trend compared to normal epithelium. Cluster analysis indicated poor disease-free survival (DFS) in AR+/GR+/PR+ subgroup (triple dominant group); while the Cox proportional-hazards model highlighted the triple dominant group as an independent prognostic factor for DFS. In addition, significant upregulation of FoxP3+ TILs, PD-1, and PD-L1 was observed in the triple dominant group compared to other groups. NanoString analyses further suggested that tumor necrosis factor (TNF) and/or NF-κB signaling pathways were activated with significant upregulation of RELA, MAP3K5, TNFAIP3, BCL2L1, RIPK1, TRAF2, PARP1, and AKT1 in the triple dominant EOC group. The triple dominant subgroup correlates with poor prognosis in EOC. Moreover, the TNF and/or NF-κB signaling pathways may be responsible for hormone-mediated inhibition of the immune microenvironment.     

Characterizing the deformation of cold-rolled copper sheet

An advanced x-ray diffraction technique allows the development of texture components to be monitored and provides supplementary information about the cold work and residual stresses in cold-rolled copper sheet. The new technique confirms the results of an ultrasonic test, except for the ultrasonic test’s sensitivity to grain size. The new x-ray diffraction technique could be adapted to provide in-process, nondestructive evaluation of thin copper sheet, even for material moving at high speeds.     

Robust M-estimation of multivariate GARCH models

The Gaussian quasi-maximum likelihood estimator of Multivariate GARCH models is shown to be very sensitive to outliers in the data. A class of robust M-estimators for MGARCH models is developed. To increase the robustness of the estimators, the use of volatility models with the property of bounded innovation propagation is recommended. The Monte Carlo study and an empirical application to stock returns document the good robustness properties of the M-estimator with a fat-tailed Student t loss function.     

A generalized multiple layer data envelopment analysis model for hierarchical structure assessment: A case study in road safety performance evaluation

Data envelopment analysis (DEA) is a powerful analytical research tool for measuring the relative efficiency of a homogeneous set of decision making units (DMUs) by obtaining empirical estimates of relations between multiple inputs and multiple outputs related to the DMUs. To further embody multilayer hierarchical structures of these inputs and outputs in the DEA framework, which are prevalent in today’s performance evaluation activities, we propose a generalized multiple layer DEA (MLDEA) model. Starting from the input-oriented CCR model, we elaborate the mathematical deduction process of the MLDEA model, formulate the weights in each layer of the hierarchy, and indicate different types of possible weight restrictions. Meanwhile, its linear transformation is realized and further extended to the BCC form. To demonstrate the proposed MLDEA model, a case study in evaluating the road safety performance of a set of 19 European countries is carried out. By using 13 hierarchical safety performance indicators in terms of road user behavior (e.g., inappropriate or excessive speed) as the model’s input and 4 layered road safety final outcomes (e.g., road fatalities) as the output, we compute the most optimal road safety efficiency score for the set of European countries, and further analyze the weights assigned to each layer of the hierarchy. A comparison of the results with the ones from the one layer DEA model clearly indicates the usefulness and effectiveness of this improvement in dealing with a great number of performance evaluation activities with hierarchical structures.     

The role of electronic pocket dictionaries as an English learning tool among Chinese students

This study addressed the role of electronic pocket dictionaries as a language learning tool among university students in Hong Kong and Taiwan. The target groups included engineering and humanities students at both undergraduate and graduate level. Speed of reference was found to be the main motivator for using an electronic pocket dictionary. Next, the functionality used was found to be connected to the language proficiency of the learner. Finally, multimedia content was ranked least important. The results of this study have implications for the design of electronic dictionaries and for the teaching of second languages with electronic dictionaries. In particular, device developers should focus on improving the accessing speed and pay less attention to multimedia functionality. Educators should ensure that the device functionality matches the language proficiency level of the students.     

A well-based reverse-phase protein array applicable to extracts from formalin-fixed paraffin-embedded tissue

Proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissue offers significant diagnostic utility but is complicated due to the high level of covalently crosslinked proteins arising from formalin fixation. To address these challenges, we developed a reliable protein extraction method for FFPE tissue, based on heat-induced antigen retrieval within a pressure cooker. The protein extraction yield from archival FFPE tissue section is approximately 90% of that recovered from frozen tissue. This method demonstrates preservation of immunoreactivity and recovery of full-length proteins by Western blotting. Additionally, we developed a well-based RP protein array platform utilizing an electrochemiluminescence detection system. Protein samples derived from FFPE tissue by means of laser capture dissection, with as few as 500 shots demonstrate measurable signal differences for different proteins. The lysates coated to the array plate, remain stable over 1?month at room temperature. Theses data suggest that this new protein-profiling platform coupled with the protein extraction method can be used for molecular profiling analysis in FFPE tissue, and contribute to the validation and development of biomarkers in clinical studies.     

Matisse: A System-on-Chip Design Methodology Emphasizing Dynamic Memory Management

MATISSE is a design environment intended for developing systems characterized by a tight interaction between control and data-flow behavior, intensive data storage and transfer, and stringent real-time requirements. Matisse bridges the gap from a system specification, using a concurrent object-oriented language, to an optimized embedded single-chip hardware/software implementation. Matisse supports stepwise exploration and refinement of dynamic memory management, memory architecture exploration, and gradual incorporation of timing constraints before going to traditional tools for hardware synthesis, software compilation, and inter-processor communication synthesis. With this approach, specifications of embedded systems can be written in a high-level programming language using data abstraction. Application of MATISSE on telecom protocol processing systems in the ATM area shows significant improvements in area usage and power consumption.     

Functional networks as a new data mining predictive paradigm to predict permeability in a carbonate reservoir

Permeability prediction has been a challenge to reservoir engineers due to the lack of tools that measure it directly. The most reliable data of permeability obtained from laboratory measurements on cores do not provide a continuous profile along the depth of the formation. Recently, researchers utilized statistical regression, neural networks, and fuzzy logic to estimate both permeability and porosity from well logs. Unfortunately, due to both uncertainty and imprecision, the developed predictive modelings are less accurate compared to laboratory experimental core data. This paper presents functional networks as a novel approach to forecast permeability using well logs in a carbonate reservoir. The new intelligence paradigm helps to overcome the most common limitations of the existing modeling techniques in statistics, data mining, machine learning, and artificial intelligence communities. To demonstrate the usefulness of the functional networks modeling strategy, we briefly describe its learning algorithm through simple distinct examples. Comparative studies were carried out using real-life industry wireline logs to compare the performance of the new framework with the most popular modeling schemes, such as linear/nonlinear regression, neural networks, and fuzzy logic inference systems. The results show that the performance of functional networks (separable and generalized associativity) architecture with polynomial basis is accurate, reliable, and outperforms most of the existing predictive data mining modeling approaches. Future work can be achieved using different structure of functional networks with different basis, interaction terms, ensemble and hybrid strategies, different clustering, and outlier identification techniques within different oil and gas challenge problems, namely, 3D passive seismic, identification of lithofacies types, history matching, rock mechanics, viscosity, risk assessment, and reservoir characterization.