HSPA4 Is a Biomarker of Placenta Accreta and Enhances the Angiogenesis Ability of Vessel Endothelial Cells

Placenta accreta spectrum (PAS) accounts for 7% of maternal mortality and is associated with intraoperative and postoperative morbidity caused by massive blood loss, infection, and adjacent organ damage. The aims of this study were to identify the protein biomarkers of PAS and to further explore their pathogenetic roles in PAS. For this purpose, we collected five placentas from pregnant subjects with PAS complications and another five placentas from normal pregnancy (NP) cases. Then, we enriched protein samples by specifically isolating the trophoblast villous, deeply invading into the uterine muscle layer in the PAS patients. Next, fluorescence-based two-dimensional difference gel electrophoresis (2D-DIGE) and MALDI-TOF/MS were used to identify the proteins differentially abundant between PAS and NP placenta tissues. As a result, nineteen spots were determined as differentially abundant proteins, ten and nine of which were more abundant in PAS and NP placenta tissues, respectively. Then, specific validation with western blot assay and immunohisto/cytochemistry (IHC) assay confirmed that heat shock 70 kDa protein 4 (HSPA4) and chorionic somatomammotropin hormone (CSH) were PAS protein biomarkers. Further tube formation assays demonstrated that HSPA4 promoted the in vitro angiogenesis ability of vessel endothelial cells, which is consistent with the in vivo scenario of PAS complications. In this study, we not only identified PAS protein biomarkers but also connected the promoted angiogenesis with placenta invasion, investigating the pathogenetic mechanism of PAS.     

Effect of under-layer treatment of Ta/TaN barrier film on corrosion between Cu seed and Ta in chemical-mechanical-polishing slurry

Tantalum/tantalum nitride (Ta/TaNx) composite film is widely used as a copper (Cu) diffusion barrier layer. In order to reduce via-resistance, an additional argon (Ar) re-sputtering process is used to thin the barrier thickness at the feature bottom. In this study, the effect of Ar re-sputtering of the under-layer of TaNx barrier films on the corrosion between Cu seeds and upper Ta films in chemical-mechanical-polishing (CMP) slurries was investigated. The results show that Ar re-sputtering of the under-layer of the TaNx barrier has a strong influence on the corrosion of Cu seeds and Ta films. The equivalent circuit, simulated using data from electrochemical analysis, reveals changes in resistance and capacitance elements of the Cu-Ta electrochemical system, proving that the phase transformation of upper Ta films under different TaNx conditions leads to different degrees corrosion of Cu seeds and the Ta films.     

SURFACE ROUGHNESS OF SPUTTERED SILICON. II. MODEL VERIFICATION

Experimental verification of the mathematical surface roughness model for sputtered silicon was performed. The beam shape and its significant level of intensity were determined first by measuring the topography of craters sputtered by focused ion beam (FIB). Then the beam function was generated for various combinations of beam parameters. The material function was developed both by theoretical and experimental analysis. These two functions were then used in the model to calculate the theoretical surface roughness. Microsurface analysis was formed by FIB sputtering of a (100) silicon wafer. The surface roughness at the bottom of the sputtered features was then measured using an atomic force microscope. The theoretical surface roughness was found to be within ±1 and ±5 nm of the measured surface roughness with the measurement uncertainty (standard deviation) of about ±0.36 and ±0.85 nm for R_a and R_t, respectively.     

A multi-dimensional scheduling scheme in a Grid computing environment

In this paper, we propose a novel distributed resource-scheduling algorithm capable of handling multiple resource requirements for jobs that arrive in a Grid computing environment. In our proposed algorithm, referred to as multiple resource scheduling (MRS) algorithm, we take into account both the site capabilities and the resource requirements of jobs. The main objective of the algorithm is to obtain a minimal execution schedule through efficient management of available Grid resources. We first propose a model in which the job and site resource characteristics can be captured together and used in the scheduling algorithm. To do so, we introduce the concept of a n-dimensional virtual map and resource potential. Based on the proposed model, we conduct rigorous simulation experiments with real-life workload traces reported in the literature to quantify the performance. We compare our strategy with most of the commonly used algorithms in place on performance metrics such as job wait times, queue completion times, and average resource utilization. Our combined consideration of job and resource characteristics is shown to render high-performance with respect to above-mentioned metrics in the environment. Our study also reveals the fact that MRS scheme has a capability to adapt to both serial and parallel job requirements, especially when job fragmentation occurs. Our experimental results clearly show that MRS outperforms other strategies and we highlight the impact and importance of our strategy.     

Complete Boolean Satisfiability Solving Algorithms Based on Local Search

Boolean satisfiability (SAT) is a well-known problem in computer science, artificial intelligence, and operations research. This paper focuses on the satisfiability problem of Model RB structure that is similar to graph coloring problems and others. We propose a translation method and three effective complete SAT solving algorithms based on the characterization of Model RB structure. We translate clauses into a graph with exclusive sets and relative sets. In order to reduce search depth, we determine search order using vertex weights and clique in the graph. The results show that our algorithms are much more effective than the best SAT solvers in numerous Model RB benchmarks, especially in those large benchmark instances.     

A parallel genetic/neural network learning algorithm for MIMDshared memory machines

A new algorithm is presented for training of multilayer feedforward neural networks by integrating a genetic algorithm with an adaptive conjugate gradient neural network learning algorithm. The parallel hybrid learning algorithm has been implemented in C on an MIMD shared memory machine (Cray Y-MP8/864 supercomputer). It has been applied to two different domains, engineering design and image recognition. The performance of the algorithm has been evaluated by applying it to three examples. The superior convergence property of the parallel hybrid neural network learning algorithm presented in this paper is demonstrated.     

Investigation of creep behavior of 316L stainless steel produced by selective laser melting with various processing parameters

Journal of Mechanical Science and Technology - Creep behavior of 316L stainless steel was experimentally investigated using small punch (SP) specimens produced by the selective laser melting (SLM)...     

Effects of process parameters on the high temperature strength of 17-4PH stainless steel produced by selective laser melting

Journal of Mechanical Science and Technology - In this study, the effects of process parameters on the high temperature strength of 17-4PH stainless steel manufactured by selective laser melting...     

Temperature-Aware Task Allocation and Scheduling for Embedded Multiprocessor Systems-on-Chip (MPSoC) Design

Temperature affects not only the performance but also the power, reliability, and cost of the embedded system. This paper proposes a temperature-aware task allocation and scheduling algorithm for MPSoC embedded systems. Thermal-aware heuristics are developed, and a temperature-aware floorplanning tool is used to reduce the peak temperature and achieve a thermally even distribution while meeting real time constraints. The paper investigates both power-aware and thermal-aware approaches to the task allocation and scheduling. The experimental results show that the thermal-aware approach outperforms the power-aware schemes in terms of maximal and average temperature reductions. To the best of our knowledge, this is the first MPSoC task allocation and scheduling algorithm that takes temperature into consideration.

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