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Abstract Bacterial mRNAs have short life cycles, in which transcription is rapidly followed by translation and degradation within seconds to minutes. The resulting diversity of mRNA molecules across different life-cycle stages impacts their functionality but has remained unresolved. Here we quantitatively map the 3’ status of cellular RNAs in Escherichia coli during steady-state growth and report a large fraction of molecules (median>60%) that are fragments of canonical full-length mRNAs. The majority of RNA fragments are decay intermediates, whereas nascent RNAs contribute to a smaller fraction. Despite the prevalence of decay intermediates in total cellular RNA, these intermediates are underrepresented in the pool of ribosome-associated transcripts and can thus distort quantifications and differential expression analyses for the abundance of full-length, functional mRNAs. The large heterogeneity within mRNA molecules in vivo highlights the importance in discerning functional transcripts and provides a lens for studying the dynamic life cycle of mRNAs.

The aim of this study was to determine the prevalence of gingivitis associated with dental biofilm in students with intellectual disabilities from “Unidad Educativa Especial del Azuay” and “Escuela de Educación Básica Fiscomisional San José de Calasanz” in the city of Cuenca - Ecuador during the period March 2018 - March 2019. It consists of a descriptive cross-sectional study, which analyzed 133 students with mental retardation, Down syndrome and autism between 3 and 20 years old. The quality of hygiene (O'Leary Index) and gingival status (Löe and Sillness Gingival Index and periodontogram data based on the classification of biofilm-induced gum diseases) were evaluated to identify the presence of gingival conditions associated with biofilm. The study included 65 women (48.87%) and 68 men (51.13%), of whom 51.88% were located within a mild cognitive disability. According to the O'Leary index, it was evidenced that 100% of the participants had poor hygiene, and when relating it to the Löe and Sillnes gingival index, a prevalence of moderate gingivitis was demonstrated at 58.65%. Only one person with healthy gums was found (0.75%). Regarding the Armitage classification of gingival diseases, a frequency of 68 participants (51.13%) presented localized gingivitis. In conclusion a high prevalence of gum disease is observed in the population evaluated, demonstrating an inadequate oral hygiene state where 99.25% of people with intellectual disabilities analyzed in the study presented indicators of gingivitis, with no differences in gingival health between the two groups of students.

Understanding genome organization requires integration of DNA sequence and three-dimensional spatial context; however, existing genome-wide methods lack either base pair sequence resolution or direct spatial localization. Here, we describe in situ genome sequencing (IGS), a method for simultaneously sequencing and imaging genomes within intact biological samples. We applied IGS to human fibroblasts and early mouse embryos, spatially localizing thousands of genomic loci in individual nuclei. Using these data, we characterized parent-specific changes in genome structure across embryonic stages, revealed single-cell chromatin domains in zygotes, and uncovered epigenetic memory of global chromosome positioning within individual embryos. These results demonstrate how IGS can directly connect sequence and structure across length scales from single base pairs to whole organisms.

Methods for highly multiplexed RNA imaging are limited in spatial resolution and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to the mouse brain, which yielded the readout of thousands of genes, including splice variants. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in the neurons of the mouse hippocampus, revealing patterns across multiple cell types, layer-specific cell types across the mouse visual cortex, and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus, ExSeq enables highly multiplexed mapping of RNAs from nanoscale to system scale.

The aim of this study was to determine the prevalence of gingivitis associated with dental biofilm in students with intellectual disabilities from “Unidad Educativa Especial del Azuay” and “Escuela de Educación Básica Fiscomisional San José de Calasanz” in the city of Cuenca - Ecuador during the period March 2018 - March 2019. It consists of a descriptive cross-sectional study, which analyzed 133 students with mental retardation, Down syndrome and autism between 3 and 20 years old. The quality of hygiene (O'Leary Index) and gingival status (Löe and Sillness Gingival Index and periodontogram data based on the classification of biofilm-induced gum diseases) were evaluated to identify the presence of gingival conditions associated with biofilm. The study included 65 women (48.87%) and 68 men (51.13%), of whom 51.88% were located within a mild cognitive disability. According to the O'Leary index, it was evidenced that 100% of the participants had poor hygiene, and when relating it to the Löe and Sillnes gingival index, a prevalence of moderate gingivitis was demonstrated at 58.65%. Only one person with healthy gums was found (0.75%). Regarding the Armitage classification of gingival diseases, a frequency of 68 participants (51.13%) presented localized gingivitis. In conclusion a high prevalence of gum disease is observed in the population evaluated, demonstrating an inadequate oral hygiene state where 99.25% of people with intellectual disabilities analyzed in the study presented indicators of gingivitis, with no differences in gingival health between the two groups of students.

The International Bottom Trawl Survey Working Group (IBTSWG) coordinates fishery-inde-pendent multispecies bottom-trawl surveys within the ICES area. These long-term monitoring surveys provide data for stock assessments and facilitate examination of changes in fish distri-bution and abundance. The group also promotes the standardization of fishing gears and meth-ods and survey coordination. This report summarizes the national contributions in 2020–2021 and plans for the 2021–2022 surveys coordinated by IBTSWG. In the North Sea, the surveys are performed in quarters (Q) Q1 and Q3 while in the Northeast Atlantic the surveys are conducted in Q1, Q3, and Q4 with a suite of 14 national surveys covering a large area of continental shelf that ranges from North of Scotland to the Gulf of Cádiz. Despite the COVID-measures and bad weather, most surveys were able to complete the majority of the planned hauls. The Portuguese survey (PT-GFS-Q4) was cancelled in 2020 due to issues associated with the new vessel and a COVID-outbreak. A COVID-related delay in submitting the cruise application form for the French CGFS20 survey resulted in no authorisation to trawl in UK waters and only 70% of the core stations were completed. Issues with the UK permits, were also experienced in the North Sea surveys, only being resolved at the last moment, expected to be a returning issue. Therefore, IBTSWG addressed the permit issue in further detail in order to better evaluate the impact and propose possible solutions. All surveys, except for the Spanish GCGF-Q1 21 which is cancelled due to a vessel refit, are planned to take place according to the manuals in the next year. The SCOROC Q3 20 survey recorded second highest recruitment of zero group haddock on the Rockall Bank since the start of the new survey series in 2011. The North Sea Q1 21 survey rec-orded good recruitment of haddock as well, and high recruitment of mackerel, while overall herring recruitment seemed low except for three excepti

© 2020 The Author(s) In order to analyze how a signal transduction network converts cellular inputs into cellular outputs, ideally one would measure the dynamics of many signals within the network simultaneously. We found that, by fusing a fluorescent reporter to a pair of self-assembling peptides, it could be stably clustered within cells at random points, distant enough to be resolved by a microscope but close enough to spatially sample the relevant biology. Because such clusters, which we call signaling reporter islands (SiRIs), can be modularly designed, they permit a set of fluorescent reporters to be efficiently adapted for simultaneous measurement of multiple nodes of a signal transduction network within single cells. We created SiRIs for indicators of second messengers and kinases and used them, in hippocampal neurons in culture and intact brain slices, to discover relationships between the speed of calcium signaling, and the amplitude of PKA signaling, upon receiving a cAMP-driving stimulus.

Understanding genome organization requires integration of DNA sequence and three-dimensional spatial context; however, existing genome-wide methods lack either base pair sequence resolution or direct spatial localization. Here, we describe in situ genome sequencing (IGS), a method for simultaneously sequencing and imaging genomes within intact biological samples. We applied IGS to human fibroblasts and early mouse embryos, spatially localizing thousands of genomic loci in individual nuclei. Using these data, we characterized parent-specific changes in genome structure across embryonic stages, revealed single-cell chromatin domains in zygotes, and uncovered epigenetic memory of global chromosome positioning within individual embryos. These results demonstrate how IGS can directly connect sequence and structure across length scales from single base pairs to whole organisms.

The International Bottom Trawl Survey Working Group (IBTSWG) coordinates fishery-independent multispecies bottom-trawl surveys within the ICES area. These long-term monitoring surveys provide data for stock assessments and facilitate examination of changes in fish distribution and abundance. The group also promotes the standardization of fishing gears and methods and survey coordination. This report summarizes the national contributions in 2020–2021 and plans for the 2021–2022 surveys coordinated by IBTSWG. In the North Sea, the surveys are performed in quarters (Q) Q1 and Q3 while in the Northeast Atlantic the surveys are conducted in Q1, Q3, and Q4 with a suite of 14 national surveys covering a large area of continental shelf that ranges from North of Scotland to the Gulf of Cádiz. Despite the COVID-measures and bad weather, most surveys were able to complete the majority of the planned hauls. The Portuguese survey (PT-GFS-Q4) was cancelled in 2020 due to issues associated with the new vessel and a COVID-outbreak. A COVID-related delay in submitting the cruise application form for the French CGFS20 survey resulted in no authorisation to trawl in UK waters and only 70% of the core stations were completed. Issues with the UK permits, were also experienced in the North Sea surveys, only being resolved at the last moment, expected to be a returning issue. Therefore, IBTSWG addressed the permit issue in further detail in order to better evaluate the impact and propose possible solutions. All surveys, except for the Spanish GCGF-Q1 21 which is cancelled due to a vessel refit, are planned to take place according to the manuals in the next year. The SCOROC Q3 20 survey recorded second highest recruitment of zero group haddock on the Rockall Bank since the start of the new survey series in 2011. The North Sea Q1 21 survey recorded good recruitment of haddock as well, and high recruitment of mackerel, while overall herring recruitment seemed low except for

In this paper, we seek solutions for reducing the computation complexity of transformer-based models for speech representation learning. We evaluate 10 attention algorithms; then, we pre-train the transformer-based model with those attention algorithms in a self-supervised fashion and treat them as feature extractors on downstream tasks, including phoneme classification and speaker classification. With the assistance of t-SNE, PCA and some observation, the attention weights in self-supervised audio transformers can be categorized into four general cases. Based on these cases and some analyses, we are able to use a specific set of attention weights to initialize the model. Our approach shows comparable performance to the typical self-attention yet requires 20% less time in both training and inference.

CT image denoising can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain $X$ (noisy images) and a target domain $Y$ (clean images). Recently, cycle-consistent adversarial denoising network (CCADN) has achieved state-of-the-art results by enforcing cycle-consistent loss without the need of paired training data. Our detailed analysis of CCADN raises a number of interesting questions. For example, if the noise is large leading to significant difference between domain $X$ and domain $Y$, can we bridge $X$ and $Y$ with an intermediate domain $Z$ such that both the denoising process between $X$ and $Z$ and that between $Z$ and $Y$ are easier to learn? As such intermediate domains lead to multiple cycles, how do we best enforce cycle-consistency? Driven by these questions, we propose a multi-cycle-consistent adversarial network (MCCAN) that builds intermediate domains and enforces both local and global cycle-consistency. The global cycle-consistency couples all generators together to model the whole denoising process, while the local cycle-consistency imposes effective supervision on the process between adjacent domains. Experiments show that both local and global cycle-consistency are important for the success of MCCAN, which outperforms the state-of-the-art., Comment: Accepted in ISBI 2020. 5 pages, 4 figures

Recent advances in deep neural network demand more than millions of parameters to handle and mandate the high-performance computing resources with improved efficiency. The cross-bar array architecture has been considered as one of the promising deep learning architectures that shows a significant computing gain over the conventional processors. To investigate the feasibility of the architecture, we examine non-idealities and their impact on the performance. Specifically, we study the impact of failed cells due to the initialization process of the resistive memory based cross-bar array. Unlike the conventional memory array, individual memory elements cannot be rerouted and, thus, may have a critical impact on model accuracy. We categorize the possible failures and propose hardware implementation that minimizes catastrophic failures. Such hardware optimization bounds the possible logical value of the failed cells and gives us opportunities to compensate for the loss of accuracy via off-line training. By introducing the random weight defects during the training, we show that the model becomes more resilient on the device initialization failures, therefore, less prone to degrade the inference performance due to the failed devices. Our study sheds light on the hardware and software co-optimization procedure to cope with potentially catastrophic failures in the cross-bar array., Comment: 17 pages, 7 figures

With the coming era of Big Data, hardware implementation of machine learning has become attractive for many applications, such as real-time object recognition and face recognition. Performance of this kind of applications is determined by the robustness of memory access and monitoring of the circuit uncertainty. As current nanotechnology semiconductor device scaling down dramatically with additional strain engineering for device enhancement, the overall device characteristic is no longer dominated by the device size but also circuit layout and interconnect. Hence to accurately measure and model interconnect parasitic in order to predict interconnect performance on silicon, a set of test structures that can be used to study the timing performance (i.e. propagation delay and crosstalk) of various interconnect configurations was developed. To improve the validation of the higher order layout effects (WPE, OSE and PSE), Design for Manufacturability (DFM) impacts of two analog layout structures, guard ring and dummy fills impact were monitored with current mirror test circuit using TSMC 28nm HPM process. Finally, a new design of high-speed SRAM with fast access time (cycle time: 650 ps, access time: 350 ps), low sensitivity to temperature variation and high reconfigurability (less than 10% performance difference between 125_rcw_tt vs 0_rcw_tt) was developed. These results described in this thesis provide comprehensive solutions to the requirement of the circuit uncertainty monitoring and rapid memory access of machine learning hardware implementations.

With the coming era of Big Data, hardware implementation of machine learning has become attractive for many applications, such as real-time object recognition and face recognition. Performance of this kind of applications is determined by the robustness of memory access and monitoring of the circuit uncertainty. As current nanotechnology semiconductor device scaling down dramatically with additional strain engineering for device enhancement, the overall device characteristic is no longer dominated by the device size but also circuit layout and interconnect. Hence to accurately measure and model interconnect parasitic in order to predict interconnect performance on silicon, a set of test structures that can be used to study the timing performance (i.e. propagation delay and crosstalk) of various interconnect configurations was developed. To improve the validation of the higher order layout effects (WPE, OSE and PSE), Design for Manufacturability (DFM) impacts of two analog layout structures, guard ring and dummy fills impact were monitored with current mirror test circuit using TSMC 28nm HPM process. Finally, a new design of high-speed SRAM with fast access time (cycle time: 650 ps, access time: 350 ps), low sensitivity to temperature variation and high reconfigurability (less than 10% performance difference between 125_rcw_tt vs 0_rcw_tt) was developed. These results described in this thesis provide comprehensive solutions to the requirement of the circuit uncertainty monitoring and rapid memory access of machine learning hardware implementations.

With Yu-Fang Chen, violin, Joana Genova, violin, Mei-Chun Chen, viola, Peter Opie, cello. ; Includes biographical information about Pei-Chun Liao., Series LXXIII, Number 117., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about

This study examined the impact of research and development (R&D) investment behaviour on the corporate performance of the Taiwanese semiconductor industry, which faced the economic downturn caused by the global financial crisis of 2008, for the period 2005–2016. A dynamic panel data model was used to empirically analyse the impact of R&D intensity on business performance. A generalised method of moments estimator was adopted to avoid endogeneity problems caused by adding dynamics to the model. Further, the model was used to explore the impact of the lag effect of R&D investments on business performance. It was found that significant R&D investments in a given period may reduce business performance in the same period and continue to influence it in the next few periods, thus indicating the presence of a positive and lagged effect of R&D investments in the high-tech industry. Firm size was also found to be positively correlated with business performance, that is, the larger the firm size, the greater is the use of resources for R&D, which, in turn, leads to more sophisticated technologies and profitable outcomes, forming a positive cycle. This indicates that R&D expenditures affect firms’sustainable management.

With Yu-Fang Chen, violin, Joana Genova, violin, Mei-Chun Chen, viola, Peter Opie, cello. ; Includes biographical information about Pei-Chun Liao., Series LXXIII, Number 117., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about

With Misha Vayman, Annette Jakovcic, Aaron Tan, Trio 826, Mei-Chun Chen, Yu-Fang Chen, Aether Eos, Pei-Chun Liao, Mary Bircher, Elizabeth Richter, and Gabriel Bianco.; Trio 826 : Susanna Klein, violin, Julia Bullard, viola, Hannah Holman, cello. ; Aether Eos : Leah Asher, violin, Chris Goddard, piano., This archival material has been provided for educational purposes. Ball State University Libraries recognizes that some historic items may include offensive content. Our statement regarding objectionable content is available at: https://dmr.bsu.edu/digital/about

This study examined the impact of research and development (R&D) investment behaviour on the corporate performance of the Taiwanese semiconductor industry, which faced the economic downturn caused by the global financial crisis of 2008, for the period 2005–2016. A dynamic panel data model was used to empirically analyse the impact of R&D intensity on business performance. A generalised method of moments estimator was adopted to avoid endogeneity problems caused by adding dynamics to the model. Further, the model was used to explore the impact of the lag effect of R&D investments on business performance. It was found that significant R&D investments in a given period may reduce business performance in the same period and continue to influence it in the next few periods, thus indicating the presence of a positive and lagged effect of R&D investments in the high-tech industry. Firm size was also found to be positively correlated with business performance, that is, the larger the firm size, the greater is the use of resources for R&D, which, in turn, leads to more sophisticated technologies and profitable outcomes, forming a positive cycle. This indicates that R&D expenditures affect firms’sustainable management.

As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become a hot research topic, both at the global and local level. Locally, most explanation methods have focused on identifying relevance of features, limiting the types of explanations possible. In this paper, we investigate a new direction by leveraging latent features to generate contrastive explanations; predictions are explained not only by highlighting aspects that are in themselves sufficient to justify the classification, but also by new aspects which if added will change the classification. The key contribution of this paper lies in how we add features to rich data in a formal yet humanly interpretable way that leads to meaningful results. Our new definition of "addition" uses latent features to move beyond the limitations of previous explanations and resolve an open question laid out in Dhurandhar, et. al. (2018), which creates local contrastive explanations but is limited to simple datasets such as grayscale images. The strength of our approach in creating intuitive explanations that are also quantitatively superior to other methods is demonstrated on three diverse image datasets (skin lesions, faces, and fashion apparel). A user study with 200 participants further exemplifies the benefits of contrastive information, which can be viewed as complementary to other state-of-the-art interpretability methods., Comment: Accepted to KDD 2021

Recent studies have shown that adversarial examples in state-of-the-art image classifiers trained by deep neural networks (DNN) can be easily generated when the target model is transparent to an attacker, known as the white-box setting. However, when attacking a deployed machine learning service, one can only acquire the input-output correspondences of the target model; this is the so-called black-box attack setting. The major drawback of existing black-box attacks is the need for excessive model queries, which may give a false sense of model robustness due to inefficient query designs. To bridge this gap, we propose a generic framework for query-efficient black-box attacks. Our framework, AutoZOOM, which is short for Autoencoder-based Zeroth Order Optimization Method, has two novel building blocks towards efficient black-box attacks: (i) an adaptive random gradient estimation strategy to balance query counts and distortion, and (ii) an autoencoder that is either trained offline with unlabeled data or a bilinear resizing operation for attack acceleration. Experimental results suggest that, by applying AutoZOOM to a state-of-the-art black-box attack (ZOO), a significant reduction in model queries can be achieved without sacrificing the attack success rate and the visual quality of the resulting adversarial examples. In particular, when compared to the standard ZOO method, AutoZOOM can consistently reduce the mean query counts in finding successful adversarial examples (or reaching the same distortion level) by at least 93% on MNIST, CIFAR-10 and ImageNet datasets, leading to novel insights on adversarial robustness., Comment: Chun-Chen Tu, Paishun Ting and Pin-Yu Chen contribute equally to this work; Paper accepted to AAAI 2019; updated model information in Table S2

In this paper we propose a novel method that provides contrastive explanations justifying the classification of an input by a black box classifier such as a deep neural network. Given an input we find what should be %necessarily and minimally and sufficiently present (viz. important object pixels in an image) to justify its classification and analogously what should be minimally and necessarily \emph{absent} (viz. certain background pixels). We argue that such explanations are natural for humans and are used commonly in domains such as health care and criminology. What is minimally but critically \emph{absent} is an important part of an explanation, which to the best of our knowledge, has not been explicitly identified by current explanation methods that explain predictions of neural networks. We validate our approach on three real datasets obtained from diverse domains; namely, a handwritten digits dataset MNIST, a large procurement fraud dataset and a brain activity strength dataset. In all three cases, we witness the power of our approach in generating precise explanations that are also easy for human experts to understand and evaluate.

The commercial computer aided engineering (CAE) software is used to analyze the linear-static construction, stress and deformation for the secondary shaft systems in great five-axis turning-milling complex computer numerical control (CNC) machine. It is convenient and always only three dimensional (3D) graphic parts needed firstly prepared and further more detail used for the commercial CAE. It is desirable to predict a deformed position for the cut tool under external pressure loads in the working process of CNC machine. The linear results for static analysis of stresses, displacements in corresponding to the screw shaft locates at top, medium and bottom positions of the secondary shaft systems are obtained by using the simulation module of SOLIDWORKS®.

The commercial computer aided engineering (CAE) software is used to analyze the linear-static construction, stress and deformation for the secondary shaft systems in great five-axis turning-milling complex computer numerical control (CNC) machine. It is convenient and always only three dimensional (3D) graphic parts needed firstly prepared and further more detail used for the commercial CAE. It is desirable to predict a deformed position for the cut tool under external pressure loads in the working process of CNC machine. The linear results for static analysis of stresses, displacements in corresponding to the screw shaft locates at top, medium and bottom positions of the secondary shaft systems are obtained by using the simulation module of SOLIDWORKS®.

For nanotechnology nodes, the feature size is shrunk rapidly, the wire becomes narrow and thin, it leads to high RC parasitic, especially for resistance. The overall system performance are dominated by interconnect rather than device. As such, it is imperative to accurately measure and model interconnect parasitic in order to predict interconnect performance on silicon. Despite many test structures developed in the past to characterize device models and layout effects, only few of them are available for interconnects. Nevertheless, they are either not suitable for real chip implementation or too complicated to be embedded. A compact yet comprehensive test structure to capture all interconnect parasitic in a real chip is needed. To address this problem, this paper describes a set of test structures that can be used to study the timing performance (i.e. propagation delay and crosstalk) of various interconnect configurations. Moreover, an empirical model is developed to estimate the actual RC parasitic. Compared with the state-of-the-art interconnect test structures, the new structure is compact in size and can be easily embedded on die as a parasitic variation monitor. We have validated the proposed structure on a test chip in TSMC 28nm HPM process. Recently, the test structure is further modified to identify the serious interconnect process issues for critical path design using TSMC 7nm FF process.

Equally sloped tomography series of tip of a tungsten needle. Series covers full 180 degree range. Note: Raw data is not aligned.

Reconstruction of tiltser_W.tif produced using an equally sloped tomography iterative algorithm.

Electron tomography in materials science has flourished with the demand to characterize nanoscale materials in three dimensions (3D). Access to experimental data is vital for developing and validating reconstruction methods that improve resolution and reduce radiation dose requirements. This work presents five high-quality scanning transmission electron microscope (STEM) tomography datasets in order to address the critical need for open access data in this field. The datasets represent the current limits of experimental technique, are of high quality, and contain materials with structural complexity. Included are tomographic series of a hyperbranched Co2P nanocrystal, platinum nanoparticles on a carbon nanofibre imaged over the complete 180o tilt range, a platinum nanoparticle and a tungsten needle both imaged at atomic resolution by equal slope tomography, and a through-focal tilt series of PtCu nanoparticles. A volumetric reconstruction from every dataset is provided for comparison and development of post-processing and visualization techniques. Researchers interested in creating novel data processing and reconstruction algorithms will now have access to state of the art experimental test data.

Reconstruction of tiltser_PtNP.tif produced using an equally sloped tomography iterative algorithm.

For nanotechnology, the semiconductor device is scaled down dramatically with additional strain engineering for device enhancement, the overall device characteristic is no longer dominated by the device size but also circuit layout. The higher order layout effects, such as well proximity effect (WPE), oxide spacing effect (OSE) and poly spacing effect (PSE), play an important role for the device performance, it is critical to understand Design for Manufacturability (DFM) impacts with various layout topology toward the overall circuit performance. Currently, the layout effects (WPE, OSE and PSE) are validated through digital standard cell and analog differential pair test structure. However, two analog layout structures: the guard ring and dummy fill impact are not well studied yet, then, this paper describes the current mirror test circuit to examine the guard ring and dummy fills DFM impacts using TSMC 28nm HPM process., Comment: 4 pages, 5 figures

The success of the application of machine-learning techniques to compilation tasks can be largely attributed to the recent development and advancement of program characterization, a process that numerically or structurally quantifies a target program. While great achievements have been made in identifying key features to characterize programs, choosing a correct set of features for a specific compiler task remains an ad hoc procedure. In order to guarantee a comprehensive coverage of features, compiler engineers usually need to select excessive number of features. This, unfortunately, would potentially lead to a selection of multiple similar features, which in turn could create a new problem of bias that emphasizes certain aspects of a program's characteristics, hence reducing the accuracy and performance of the target compiler task. In this paper, we propose FEAture Selection for compilation Tasks (FEAST), an efficient and automated framework for determining the most relevant and representative features from a feature pool. Specifically, FEAST utilizes widely used statistics and machine-learning tools, including LASSO, sequential forward and backward selection, for automatic feature selection, and can in general be applied to any numerical feature set. This paper further proposes an automated approach to compiler parameter assignment for assessing the performance of FEAST. Intensive experimental results demonstrate that, under the compiler parameter assignment task, FEAST can achieve comparable results with about 18% of features that are automatically selected from the entire feature pool. We also inspect these selected features and discuss their roles in program execution.

Patterns of event propagation in online social networks provide novel insights on the modeling and analysis of information dissemination over networks and physical systems. This paper studies the importance of follower links for event propagation on Twitter. Three recent event propagation traces are collected with the Twitter user language field being used to identify the Network of Networks (NoN) structure embedded in the Twitter follower networks. We first formulate event propagation on Twitter as an iterative state equation, and then propose an effective score function on follower links accounting for the containment of event propagation via link removals. Furthermore, we find that utilizing the NoN model can successfully identify influential follower links such that their removals lead to a remarkable reduction in event propagation on Twitter follower networks. Experimental results find that the between-network follower links, though only account for a small portion of the total follower links, are crucial to event propagation on Twitter., Comment: Published at IEEE Transactions on Signal and Information Processing over Networks

International Computer Music Conference Proceedings: vol. 2004, (dlps) bbp2372.2004.174, http://hdl.handle.net/2027/spo.bbp2372.2004.174, This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. Please contact mpub-help@umich.edu to use this work in a way not covered by the license.

International Computer Music Conference Proceedings: vol. 2004, (dlps) bbp2372.2004.174, http://hdl.handle.net/2027/spo.bbp2372.2004.174, This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. Please contact mpub-help@umich.edu to use this work in a way not covered by the license.

We present a method for synthesizing compositions of mixins using type inhabitation in intersection types. First, recursively defined classes and mixins, which are functions over classes, are expressed as terms in a lambda calculus with records. Intersection types with records and record-merge are used to assign meaningful types to these terms without resorting to recursive types. Second, typed terms are translated to a repository of typed combinators. We show a relation between record types with record-merge and intersection types with constructors. This relation is used to prove soundness and partial completeness of the translation with respect to mixin composition synthesis. Furthermore, we demonstrate how a translated repository and goal type can be used as input to an existing framework for composition synthesis in bounded combinatory logic via type inhabitation. The computed result corresponds to a mixin composition typed by the goal type.

Infection with dengue virus (DV) causes diseases ranging from self-limited dengue fever to life-threatening dengue hemorrhagic fever and dengue shock syndrome. Vascular leakage, thrombocytopenia and bleeding are the clinical manifestations associated with dengue hemorrhage. We previously showed that anti-DV nonstructural protein 1 (NS1) antibodies (Abs) cross-reacted with endothelial cells. The potential target proteins on endothelial cell surface recognized by anti-DV NS1 Abs showed sequence homology with the C-terminal amino acids (a.a.) 311-352 of DV NS1. In this study, the role of NS1 C-terminal region in dengue autoimmunity was investigated. We deleted the a.a. 277-352 of DV NS1 to prepare truncated NS1 (tNS1) and generated anti-DV tNS1 Abs in mice. The endothelial cell-binding activity of anti-DV tNS1 Abs was lower than that of anti-DV NS1 Abs. In addition, the endothelial cell-binding activity of anti-DV NS1 Abs was inhibited by preabsorption with DV NS1 but not with DV tNS1 proteins. The anti-P311 (a.a. 311-330) and anti-P331 (a.a. 331-350) titers of dengue patient sera were positively correlated with their endothelial cell-binding activity. Dengue patient sera showed lower binding activity to DV tNS1 than to DV NS1 proteins. The endothelial cell-binding activity of dengue patient sera was inhibited by preabsorption with P311 and P331. This study helps to understand the molecular mechanisms of autoimmunity mediated by anti-DV NS1 Abs and to provide the potential implications of tNS1 in dengue vaccine strategies.

Infection with dengue virus (DV) causes diseases ranging from self-limited dengue fever to life-threatening dengue hemorrhagic fever and dengue shock syndrome. Vascular leakage, thrombocytopenia and bleeding are the clinical manifestations associated with dengue hemorrhage. We previously showed that anti-DV nonstructural protein 1 (NS1) antibodies (Abs) cross-reacted with endothelial cells. The potential target proteins on endothelial cell surface recognized by anti-DV NS1 Abs showed sequence homology with the C-terminal amino acids (a.a.) 311-352 of DV NS1. In this study, the role of NS1 C-terminal region in dengue autoimmunity was investigated. We deleted the a.a. 277-352 of DV NS1 to prepare truncated NS1 (tNS1) and generated anti-DV tNS1 Abs in mice. The endothelial cell-binding activity of anti-DV tNS1 Abs was lower than that of anti-DV NS1 Abs. In addition, the endothelial cell-binding activity of anti-DV NS1 Abs was inhibited by preabsorption with DV NS1 but not with DV tNS1 proteins. The anti-P311 (a.a. 311-330) and anti-P331 (a.a. 331-350) titers of dengue patient sera were positively correlated with their endothelial cell-binding activity. Dengue patient sera showed lower binding activity to DV tNS1 than to DV NS1 proteins. The endothelial cell-binding activity of dengue patient sera was inhibited by preabsorption with P311 and P331. This study helps to understand the molecular mechanisms of autoimmunity mediated by anti-DV NS1 Abs and to provide the potential implications of tNS1 in dengue vaccine strategies.

Language issues, often linked with ideas of history, sentiment, identity, ideology, maintenance, revitalization, minority, and indigenous peoples, are raised constantly in Taiwan. This study focused on examining issues related to language planning and bilingual education at the elementary school level in Taiwan. The research purposes were: 1) to examine the current language education practices in the elementary school by employing perspectives of language planning and bilingual education in Taiwan; and 2) to make recommendations about the current language planning and policy of Taiwan with respect to elementary school language education. There were 123 participants involved in this study. They were elementary school language teachers, language professionals, and parents. Data collection began in the winter of 2004 and continued through the summer of 2005. The research context was elementary school language education. Transcripts and questionnaires were the primary sources for data analysis. Five major phenomena with respect to elementary school language education in Taiwan were found. First, there was no systematic language planning for the present elementary school language education in Taiwan. Second, the three language subjects, Mandarin, English, and Dialects, within the Language Arts area were seen as independent courses without any interdisciplinary integration. Third, parental decisions about the prior order of language learning were structured upon a profound process related to language ideology and instrumentalism. Fourth, the elementary school language curriculum was guided by the concept of Han-centrism. Fifth, because of the phenomenon of language shift, Mandarin has become most children's mother tongue rather than the local languages. Based on the five findings, I suggest that first, team teaching is helpful for elementary school language teachers to integrate children's learning of different languages. Second, parents, language professionals, and langu

The effects of pretreatment with the nucleoside transport inhibitor lidoflazine on repeated ischemia-reperfusion injury induced by normothermic intermittent aortic crossclamping were studied in canine hearts. Eighteen mongrel dogs were allocated to three groups: placebo (n = 6), lidoflazine (1 mg/kg) (n = 6), and lidoflazine (1 mg/kg) plus the adenosine receptor blocker aminophylline (7 mg/kg) (n = 6). Pretreatment was performed intravenously during 15 minutes before extracorporeal circulation. All hearts were subjected to four intervals of 15 minutes of global ischemia each followed by 10 minutes of reperfusion. After weaning from extracorporeal circulation, functional recovery was followed for 1 hour. In the lidoflazine group, myocardial adenosine content (0.25 +/- 0.06 mumol/gm dry weight) was 3.5 times higher than that in the control group (0.07 +/- 0.03 mumol/gm dry weight; p < 0.05) at the end of the last aortic crossclamping. The release of adenosine from the myocardium during each reperfusion period was significantly higher than that in the control group (p < 0.05). Myocardial extraction of lactate was normalized at every reperfusion interval in the lidoflazine group but not in the control group (p < 0.05). In the lidoflazine group functional recovery was significantly better than that in the control group. Positive rate of rise of pressure, negative rate of rise of pressure, and cardiac output recovered to, respectively, 150% +/- 19%, 82% +/- 8%, and 131% +/- 15% in the lidoflazine group versus, respectively, 37% +/- 9%, 23% +/- 7%, and 29% +/- 8% in the control group (p < 0.001) at 1 hour after extracorporeal circulation. When the adenosine receptor blocker aminophylline was administered in association with lidoflazine, protection dropped significantly: positive and negative rate of rise of pressure and cardiac output were, respectively, 58% +/- 8%, 46% +/- 9%, and 67% +/- 16% at 1 hour after extracorporeal circulation (p < 0.05 versus lidof

碩士, 國立清華大學, 化學系, [[abstract]]本論文研究係探討掩飾鄰苯?與電子豐盈性烯類的Diels-Alder反應及雙環[2.2.2]辛二烯酮類化合物64a~e的製備。掩飾鄰苯?50分別與4種具電子豐盈性的?基乙烯基醚、2,3-二氫?喃、3,4-二氫?喃、苯基乙烯基硫醚及具共軛性質的苯乙烯進行分子間的Diels-Alder反應，可得單一產物、具高立體選擇性及位向選擇性的雙環辛烯酮化合物51a~g、54a~g、55a、56a~g、57a~h。由RHF/3-21G的理論計算發現 ，此類Diels-Alder反應，可由掩飾鄰苯?的最低未佔用軌域(LUMO)與?基乙烯基醚、2,3-二氫?喃、苯乙烯的最高佔用軌域(HOMO)之相互作用加以解釋。將所得的Diels-Alder產物56a~e，於-78 ℃下，以 m-CPBA氧化，可得高產率的化合物63a~e，再以1,3,5-三甲基苯為溶劑，於迴流溫度下進行脫去反應，即可得到產物64a~e，以供研究光化學反應或其研究它合成之用。, [[abstract]]The studies on the Diels-Alder reactions of masked o-benzoquinones (MOBs) 50 with electron-rich dienophiles and synthetic applications of Diels-Alder cycloadducts 56a-e are described. This thesis deals with the Diels-Alder reactions of MOBs with electron-rich dienophiles and the synthesis of bicyclo[2.2.2]octadienone derivatives. Diels-Alder reactions of MOBs 50 with four electron-rich dieno- philes namely, benzyl vinyl ether, 2,3-dihydrofuran, 3,4-dihydropyran and phenyl vinyl sulfide and a conjugated dienophile styrene afforded highly functionalized bicyclo[2.2.2]octenones 51a-g, 54a-g, 55a, 56a-g and 57a-h in high regio- and stereoselectivities. Based on RHF/3-21G* calculations, it is suggested that LUMO of MOBs and HOMO of benzyl vinyl ether, 2,3-dihydrofuran and styrene are involved in the inverse electron-demand Diels-Alder reactions. We have carried out the oxidation of Diels-Alder cycloadducts 56a-e derived from MOBs and phenyl vinyl sulfide with m-CPBA at -78 ℃ to produce the corresponding sulfoxides 63a-e in high yields. The sulfo- xides 63a-e at reflux temperature in mesitylene furnished bicyclo[2.2.2]- octa-2,5-dien-1-ones 64a-e in very good yields. The compounds 64a-e are immediate precursors for heteroaromatic-fused barrelenes, which are very useful starting materials for the photochemical rearrangements.