Machine Learning Based PCB/Package Stack-up Optimization For Signal Integrity

PCB/package stack-up design optimization is time-consuming and requiring a great deal of experience. Although some iterative optimization algorithms are applied to implement automatic stack-up design, evaluating the results of each iteration is still time-intensive. This paper proposes a combined Bayesian optimization-artificial neural network (BO-ANN) algorithm, utilizing a trained ANN-based surrogate model to replace a 2D cross-section analysis tool for fast PCB/package stack-up design optimization. With the acceleration of ANN, the proposed BO-ANN algorithm can finish 100 iterations in 40 seconds while achieving the target characteristic impedance. To better generalize the BO-ANN algorithm, a strategy of effective dielectric calculation is applied to multiple-dielectric stack-up optimization. the BO-ANN algorithm will be able to output optimized stack-up designs with dielectric layers chosen from the pre-defined library and the obtained designs are verified by 2D solver

Breast Cancer Diagnosis Using a Microfluidic Multiplexed Immunohistochemistry Platform

BACKGROUND: Biomarkers play a key role in risk assessment, assessing treatment response, and detecting recurrence and the investigation of multiple biomarkers may also prove useful in accurate prediction and prognosis of cancers. Immunohistochemistry (IHC) has been a major diagnostic tool to identify therapeutic biomarkers and to subclassify breast cancer patients. However, there is no suitable IHC platform for multiplex assay toward personalized cancer therapy. Here, we report a microfluidics-based multiplexed IHC (MMIHC) platform that significantly improves IHC performance in reduction of time and tissue consumption, quantification, consistency, sensitivity, specificity and cost-effectiveness. METHODOLOGY/PRINCIPAL FINDINGS: By creating a simple and robust interface between the device and human breast tissue samples, we not only applied conventional thin-section tissues into on-chip without any additional modification process, but also attained perfect fluid control for various solutions, without any leakage, bubble formation, or cross-contamination. Four biomarkers, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), progesterone receptor (PR) and Ki-67, were examined simultaneously on breast cancer cells and human breast cancer tissues. The MMIHC method improved immunoreaction, reducing time and reagent consumption. Moreover, it showed the availability of semi-quantitative analysis by comparing Western blot. Concordance study proved strong consensus between conventional whole-section analysis and MMIHC (n = 105, lowest Kendall's coefficient of concordance, 0.90). To demonstrate the suitability of MMIHC for scarce samples, it was also applied successfully to tissues from needle biopsies. CONCLUSIONS/SIGNIFICANCE: The microfluidic system, for the first time, was successfully applied to human clinical tissue samples and histopathological diagnosis was realized for breast cancers. Our results showing substantial agreement indicate that several cancer-related proteins can be simultaneously investigated on a single tumor section, giving clear advantages and technical advances over standard immunohistochemical method. This novel concept will enable histopathological diagnosis using numerous specific biomarkers at a time even for small-sized specimens, thus facilitating the individualization of cancer therapy

Dynamic relocalization of NHERF1 mediates chemotactic migration of ovarian cancer cells toward lysophosphatidic acid stimulation

NHERF1/EBP50 (Na+/H+ exchanger regulating factor 1; Ezrin-binding phosphoprotein of 50 kDa) organizes stable protein complexes beneath the apical membrane of polar epithelial cells. By contrast, in cancer cells without any fixed polarity, NHERF1 often localizes in the cytoplasm. The regulation of cytoplasmic NHERF1 and its role in cancer progression remain unclear. In this study, we found that, upon lysophosphatidic acid (LPA) stimulation, cytoplasmic NHERF1 rapidly translocated to the plasma membrane, and subsequently to cortical protrusion structures, of ovarian cancer cells. This movement depended on direct binding of NHERF1 to C-terminally phosphorylated ERM proteins (cpERMs). Moreover, NHERF1 depletion downregulated cpERMs and further impaired cpERM-dependent remodeling of the cell cortex, suggesting reciprocal regulation between these proteins. The LPA-induced protein complex was highly enriched in migratory pseudopodia, whose formation was impaired by overexpression of NHERF1 truncation mutants. Consistent with this, NHERF1 depletion in various types of cancer cells abolished chemotactic cell migration toward a LPA gradient. Taken together, our findings suggest that the high dynamics of cytosolic NHERF1 provide cancer cells with a means of controlling chemotactic migration. This capacity is likely to be essential for ovarian cancer progression in tumor microenvironments containing LPA

Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions

While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)—present in some but not all cells—remains unknown. We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders. Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e−4), with recurrent somatic deletions of exons 1–5 of the NRXN1 gene in five SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5′ deletions in NRXN1. We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in five treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections. Our results indicate potential roles of sCNVs in SCZ risk

Fast SPT-Term Allocation and Efficient FPGA Implementation of FIR Filters for Software Defined Radio Applications

This paper presents a fast SPT-term allocation scheme and an efficient FPGA implementation of FIR filters for Software Defined Radio (SDR) applications. Direct conversion method based on RF direct sampling is nowadays widely used in SDR applications. Fast and accurate digital filters are required for RF direct sampling and processing in direct conversion, however such filters often require large digital circuit area. Signed-Power-of-Two (SPT) terms will be suitable for fast processing and efficient implementation of FIR filters. This paper first aims at developing a fast SPT-term allocation scheme for designing FIR filters, and then tries to efficiently implement FIR filters on FPGA. Performance of SPT-term allocation and FPGA implementation is evaluated through computer simulation and hardware implementation.APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Poster session: Signal Processing and Implementations in Communications (6 October 2009)

Long-Term and Programmable Bacterial Subculture in Completely Automated Microchemostats

A controllable microchemostat can provide an ideal, powerful means to study the growth behavior of microorganisms by improving conventional macroscale chemostat. However, a challenge remains for implementing both continuous growth and active population control of microorganisms at the same time because they keep communicating with nearby culture environments by regulating their metabolism. Here, we present a novel microchemostat that enables reversible bacterial isolation, continuous chemical refreshment, and dynamic physicochemical stimulation. The microchemostat not only controls bacterial growth and subculture conditions in a completely automated and programmed manner but it also makes it possible to manipulate bacterial populations from a single bacterium to an ultrahigh density for long-term subculture periods with ultralow reagent consumption. Moreover, the microchemostat enables in situ measurement and feedback control of bacterial growth and population through various subculture programming modes that are sequentially performed using a single microchemostat over 720 h; to the best of our knowledge, this is the longest microchemostat culture of bacterial cells reported to date. Hence, we ensure that the microchemostat can be further applied to a wide range of microbial studies on a single chip, such as nutrient optimization, genetic induction, environmental selection, high-throughput screening, and evolutionary adaptation

Bayesian network construction from event log for lateness analysis in port logistics

The handling of containers in port logistics consists of several activities, such as discharging, loading, gate-in and gate-out, among others. These activities are carried out using various equipment including quay cranes, yard cranes, trucks, and other related machinery. The high inter-dependency among activities and equipment on various factors often puts successive activities off schedule in real-time, leading to undesirable activity down time and the delay of activities. A late container process, in other words, can negatively affect the scheduling of the following ones. The purpose of the study is to analyze the lateness probability using a Bayesian network by considering various factors in container handling. We propose a method to generate a Bayesian network from a process model which can be discovered from event logs in port information systems. In the network, we can infer the activities' lateness probabilities and, sequentially, provide to port managers recommendations for improving existing activities.close0

A Simple Descriptor to Rapidly Screen CO Oxidation Activity on Rare-Earth Metal-Doped CeO2: From Experiment to First-Principles

Ceria (CeO2) is an attractive catalyst because of its unique properties, such as facile redoxability and high stability. Thus, many researchers have examined a wide range of catalytic reactions on ceria nanoparticles (NPs). Among those contributions are the reports of the dopant-dependent catalytic activity of ceria. On the other hand, there have been few mechanistic studies of the effects of a range of dopants on the chemical reactivity of ceria NPs. In this study, we examined the catalytic activities of pure and Pr, Nd, and Sm-doped CeO2 (PDC, NDC, and SDC, respectively) NPs on carbon monoxide (CO) oxidation. Density functional theory (DFT) calculations were also performed to elucidate the reaction mechanism on rare-earth (RE)-doped CeO2(111). The experimental results showed that the catalytic activities of CO oxidation were in the order of CeO2 > PDC > NDC > SDC. This is consistent with the DFT results, where the reaction is explained by the Mars-van Krevelen mechanism. On the basis of the theoretical interpretation of the experimental results, the ionic radius of the RE dopant can be used as a simple descriptor to predict the energy barrier at the rate-determining step, thereby predicting the entire reaction activity. Using the descriptor, a wide range of RE dopants on CeO2(111) were screened for CO oxidation. These results provide useful insights to unravel the CO oxidation activity on various oxide catalysts.11Nsciescopu

Modeling and formal analysis of virtually synchronous cyber-physical systems in AADL

This paper presents the HybridSynchAADL modeling language and formal analysis tool for virtually synchronous cyber-physical systems with complex control programs, continuous behaviors, and bounded clock skews, network delays, and execution times. We leverage the Hybrid PALS methodology, so that it is sufficient to model and verify the much simpler underlying synchronous designs. We define the HybridSynchAADL language as a sublanguage of the avionics modeling standard AADL for modeling such synchronous designs in AADL. We define the formal semantics of HybridSynchAADL using Maude with SMT solving, which allows us to represent advanced control programs and communication features in Maude, while capturing timing uncertainties and continuous behaviors symbolically with SMT solving. We have developed new general methods for optimizing the performance of such symbolic rewriting, which makes the analysis of HybridSynchAADL models feasible. We have integrated the formal modeling and analysis of HybridSynchAADL models into the OSATE tool environment for AADL. Finally, we demonstrate the effectiveness of the Hybrid PALS methodology and HybridSynchAADL on a number of applications, including autonomous drones that collaborate to achieve common goals, and compare the performance of our tool with other state-of-the-art formal tools for hybrid systems.11Nsci