Special Interest Groups and the Australia-United States Free Trade Agreement

In the political economy model of Grossman and Helpman (1995), two incumbent governments attempt to negotiate a free trade agreement (FTA), while special interest groups in each country influence negotiations by offering financial contributions to their governments. As a consequence, a set of politically sensitive industries is excluded from the proposed FTA. Using the empirical methodology of Gawande, Sanguinetti, and Bohara (2001), this paper shows that the Grossman-Helpman (1995) model successfully predicts the set of excluded industries for the recently implemented Australia-United States FTA. It is also shown that the set of exclusions favours Australian interest groups, which could indicate that the gains from the FTA are lower for the government of Australia than for the government of the United States.

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.Comment: Minor update

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology

Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data

Recent technological developments in high-dimensional flow cytometry and mass cytometry (CyTOF) have made it possible to detect expression levels of dozens of protein markers in thousands of cells per second, allowing cell populations to be characterized in unprecedented detail. Traditional data analysis by "manual gating" can be inefficient and unreliable in these high-dimensional settings, which has led to the development of a large number of automated analysis methods. Methods designed for unsupervised analysis use specialized clustering algorithms to detect and define cell populations for further downstream analysis. Here, we have performed an up-to-date, extensible performance comparison of clustering methods for high-dimensional flow and mass cytometry data. We evaluated methods using several publicly available data sets from experiments in immunology, containing both major and rare cell populations, with cell population identities from expert manual gating as the reference standard. Several methods performed well, including FlowSOM, X-shift, PhenoGraph, Rclusterpp, and flowMeans. Among these, FlowSOM had extremely fast runtimes, making this method well-suited for interactive, exploratory analysis of large, high-dimensional data sets on a standard laptop or desktop computer. These results extend previously published comparisons by focusing on high-dimensional data and including new methods developed for CyTOF data. R scripts to reproduce all analyses are available from GitHub (https://github.com/lmweber/cytometry-clustering-comparison), and pre-processed data files are available from FlowRepository (FR-FCM-ZZPH), allowing our comparisons to be extended to include new clustering methods and reference data sets

distinct: a novel approach to differential distribution analyses

We present distinct, a general method for differential analysis of full distributions that is well suited to applications on single-cell data, such as single-cell RNA sequencing and high-dimensional flow or mass cytometry data. High-throughput single-cell data reveal an unprecedented view of cell identity and allow complex variations between conditions to be discovered; nonetheless, most methods for differential expression target differences in the mean and struggle to identify changes where the mean is only marginally affected. distinct is based on a hierarchical non-parametric permutation ap- proach and, by comparing empirical cumulative distribution functions, iden- tifies both differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean. We performed extensive bench- marks across both simulated and experimental datasets from single-cell RNA sequencing and mass cytometry data, where distinct shows favourable per- formance, identifies more differential patterns than competitors, and displays good control of false positive and false discovery rates. distinct is available as a Bioconductor R package

Audit Sistem Informasi Pada Perusahaan Dagang Aneka Gemilang Bandar Lampung Menggunakan Framework Cobit 4.1

Aneka Gemilang Trade Enterprises is a trading company that engaged in the trade field, particularly in learning tool needs from primary school to college. Now days, there are many the same type of business, it is certainty tighten the competition, as well as in the product quality side until the quality of services to customers. To measure the quality and service which are provided by the Aneka Gemilang Trade Enterprises can be focused and balanced with the company business objectives, it is needed to determine the alignment level of TI objectives with company objectives. In order to achieve the alignment between TI objectives and company objectives, it is required a measurement of balance level between company objectives with TI objectives using COBIT 4.1. By this audit decision, it is expected there is a significant progress toward the company. The optimum of service and provide of qualified goods are the activities that must be priority to strengthen the available business partner and try to add the business partner in the future, therefore Aneka Gemilang Trade Enterprise be able to open the branches in other potential district

Ultracold neutrons, quantum effects of gravity and the Weak Equivalence Principle

We consider an extension of the recent experiment with ultracold neutrons and the quantization of its vertical motion in order to test the Weak Equivalence Principle. We show that an improvement on the energy resolution of the experiment may allow to establish a modest limit to the Weak Equivalence Principle and on the gravitational screening constant. We also discuss the influence of a possible new interaction of Nature.Comment: Revtex4, 4 pages. Discussion on the equivalence principle altered. Bound is improve

miQC : An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a 'low-quality' cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on certain types of tissues, such as archived tumor tissues, or tissues associated with mitochondrial function, such as kidney tissue [1]. We propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. Our software package is available at https://bioconductor.org/packages/miQC. Author summary We developed the miQC package to predict the low-quality cells in a given scRNA-seq dataset by jointly modeling both the proportion of reads mapping to mitochondrial DNA (mtDNA) genes and the number of detected genes using mixture models in a probabilistic framework. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses.Peer reviewe

Complement downregulation promotes an inflammatory signature that renders colorectal cancer susceptible to immunotherapy

BACKGROUND AND AIMS: The role of inflammatory immune responses in colorectal cancer (CRC) development and response to therapy is a matter of intense debate. While inflammation is a known driver of CRC, inflammatory immune infiltrates are a positive prognostic factor in CRC and predispose to response to immune checkpoint blockade (ICB) therapy. Unfortunately, over 85% of CRC cases are primarily unresponsive to ICB due to the absence of an immune infiltrate, and even the cases that show an initial immune infiltration can become refractory to ICB. The identification of therapy supportive immune responses in the field has been partially hindered by the sparsity of suitable mouse models to recapitulate the human disease. In this study, we aimed to understand how the dysregulation of the complement anaphylatoxin C3a receptor (C3aR), observed in subsets of patients with CRC, affects the immune responses, the development of CRC, and response to ICB therapy. METHODS: We use a comprehensive approach encompassing analysis of publicly available human CRC datasets, inflammation-driven and newly generated spontaneous mouse models of CRC, and multiplatform high-dimensional analysis of immune responses using microbiota sequencing, RNA sequencing, and mass cytometry. RESULTS: We found that patients' regulation of the complement C3aR is associated with epigenetic modifications. Specifically, downregulation of C3ar1 in human CRC promotes a tumor microenvironment characterized by the accumulation of innate and adaptive immune cells that support antitumor immunity. In addition, in vivo studies in our newly generated mouse model revealed that the lack of C3a in the colon activates a microbiota-mediated proinflammatory program which promotes the development of tumors with an immune signature that renders them responsive to the ICB therapy. CONCLUSIONS: Our findings reveal that C3aR may act as a previously unrecognized checkpoint to enhance antitumor immunity in CRC. C3aR can thus be exploited to overcome ICB resistance in a larger group of patients with CRC

State preparation of a fluxonium qubit with feedback from a custom FPGA-based platform

We developed a versatile integrated control and readout instrument for experiments with superconducting quantum bits (qubits), based on a field-programmable gate array (FPGA) platform. Using this platform, we perform measurement-based, closed-loop feedback operations with $428 \, \mathrm{ns}$ platform latency. The feedback capability is instrumental in realizing active reset initialization of the qubit into the ground state in a time much shorter than its energy relaxation time $T_1$. We show experimental results demonstrating reset of a fluxonium qubit with $99.4\,\%$ fidelity, using a readout-and-drive pulse sequence approximately $1.5 \, \mathrm{\mu s}$ long. Compared to passive ground state initialization through thermalization, with the time constant given by $T_1 = ~ 80 \, \mathrm{\mu s}$, the use of the FPGA-based platform allows us to improve both the fidelity and the time of the qubit initialization by an order of magnitude.Comment: 3 pages, 2 figures. The following article has been submitted to the AIP Conference Proceedings of the Fifth International Conference on Quantum Technologies (ICQT-2019