720 research outputs found

    Post-Transcriptional Regulation of BCL2 mRNA by the RNA-Binding Protein ZFP36L1 in Malignant B Cells

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    The human ZFP36 zinc finger protein family consists of ZFP36, ZFP36L1, and ZFP36L2. These proteins regulate various cellular processes, including cell apoptosis, by binding to adenine uridine rich elements in the 3â€Č untranslated regions of sets of target mRNAs to promote their degradation. The pro-apoptotic and other functions of ZFP36 family members have been implicated in the pathogenesis of lymphoid malignancies. To identify candidate mRNAs that are targeted in the pro-apoptotic response by ZFP36L1, we reverse-engineered a gene regulatory network for all three ZFP36 family members using the ‘maximum information coefficient’ (MIC) for target gene inference on a large microarray gene expression dataset representing cells of diverse histological origin. Of the three inferred ZFP36L1 mRNA targets that were identified, we focussed on experimental validation of mRNA for the pro-survival protein, BCL2, as a target for ZFP36L1. RNA electrophoretic mobility shift assay experiments revealed that ZFP36L1 interacted with the BCL2 adenine uridine rich element. In murine BCL1 leukemia cells stably transduced with a ZFP36L1 ShRNA lentiviral construct, BCL2 mRNA degradation was significantly delayed compared to control lentiviral expressing cells and ZFP36L1 knockdown in different cell types (BCL1, ACHN, Ramos), resulted in increased levels of BCL2 mRNA levels compared to control cells. 3â€Č untranslated region luciferase reporter assays in HEK293T cells showed that wild type but not zinc finger mutant ZFP36L1 protein was able to downregulate a BCL2 construct containing the BCL2 adenine uridine rich element and removal of the adenine uridine rich core from the BCL2 3â€Č untranslated region in the reporter construct significantly reduced the ability of ZFP36L1 to mediate this effect. Taken together, our data are consistent with ZFP36L1 interacting with and mediating degradation of BCL2 mRNA as an important target through which ZFP36L1 mediates its pro-apoptotic effects in malignant B-cells

    The relevance of application domains in empirical findings

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    The term 'software ecosystem' refers to a collection of software systems that are related in some way. Researchers have been using different levels of aggregation to define an ecosystem: grouping them by a common named project (e.g., the Apache ecosystem); or considering all the projects contained in online repositories (e.g., the GoogleCode ecosystem). In this paper we propose a definition of ecosystem based on application domains: software systems are in the same ecosystem if they share the same application domain, as described by a similar technological scope, context or objective. As an example, all projects implementing networking capabilities to trade Bitcoin and other virtual currencies can be considered as part of the same "cryp-tocurrency" ecosystem. Utilising a sample of 100 Java software systems, we derive their application domains using the Latent Dirichlet Allocation (LDA) approach. We then evaluate a suite of object-oriented metrics per ecosystem, and test a null hypothesis: 'the OO metrics of all ecosystems come from the same population'. Our results show that the null hypothesis is rejected for most of the metrics chosen: the ecosystems that we extracted, based on application domains, show different structural properties. From the point of view of the interested stakeholders, this could mean that the health of a software system depends on domain-dependent factors, that could be common to the projects in the same domain-based ecosystem

    The Origins of Mutual Forbearance: Learning to Trust to Mutually Forbear

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    Multi-market contact can either escalate or deescalate rivalry. Recent empirical work has revealed an inverted U-shaped relationship between multi-market contact and rivalry. These findings have lead many to suggest that mutual forbearance (MF), a switch from competition to cooperation across markets, is a natural outcome of increasing multi-market contact between two firms. Despite the relatively widespread acceptance of this suggestion, we do not have a theoretically grounded explanation for how this switch from rivalry to mutual forbearance occurs. This dissertation takes up this task. Theories of learning and trust are used as the grounding for the development of a theoretical model of the process by which multi-market rivals switch from competition to cooperation across markets. The model is tested using data from the U.S. Scheduled Passenger Airline Industry. Results support the general theoretical foundations of the model and provide new insights into the genesis of mutual forbearance

    Competitive Intelligence Behaviour and Attitude Antecedents in French Small and Medium Sized Enterprises in a Funded Intervention Environment

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    This thesis examines the Competitive Intelligence behaviour and attitude antecedents of SME decision-makers in a funded environment in France. As a leader in CI national policy programmes, France draws attention to the imbalance between European nations on the tangible support afforded to SME communities. This two stage sequential mixed method study within the pragmatic paradigm evaluates Competitive Intelligence as a public policy to enhance SME sustainable competitiveness. Semi-structured interviews were undertaken with the directors of 15 Competitive Intelligence programmes at French Chambers of Commerce and Industry. Guided by the structure and Theory of Planned Behaviour, the findings from this qualitative phase were then used to develop a research instrument to test research questions that relate to behaviours, attitudes, background factors, choice of CI advisor, terminology, and perceived constraints. In this second stage data was obtained via questionnaire from 176 SME decision makers in the RhĂŽne-Alpes and Ile de France regions, for the two sectors of Automobile and Telecoms. The findings show that tangible results have been achieved despite resistance from small businesses in regard to their Competitive Intelligence practices. The thesis uncovers innovative practices to change SME awareness, attitude, and practices of Competitive Intelligence. Evidence of significant relationships between terminology usage, advisor choice, and SME decision-maker attitudes towards CI practices provides insight for future behaviour intervention programmes and future research. The contribution of this study of SME Competitive Intelligence practices is a five stage Competitive Intelligence typology overlaid by corresponding CI manifestations. The findings will be of interest to future initiatives by public/private partnerships in both CI programme design and implementation. The originality of this study is the investigation of SME CI behaviour and attitude antecedents in a funded environment. The findings from this study will be of interest to SME managers, current and future government CI support programmes, and the academic community

    Leakage Detection with Kolmogorov-Smirnov Test

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    Leakage detection seeking the evidence of sensitive data dependencies in the side-channel traces instead of trying to recover the sensitive data directly under the enormous efforts with numerous leakage models and state-of-the-art distinguishers can provide a fast preliminary security assessment on the cryptographic devices for designers and evaluators. Therefore, it is a popular topic in recent side-channel research of which the Welch\u27s tt-test-based Test Vector Leakage Assessment (TVLA) methodology is the most widely used one. However, the TVLA is not always the best option under all kinds of conditions (as we can see in the latter section of this paper). Kolmogorov-Smirnov test is a well-known nonparametric method for statistical analysis to determine whether the samples are from the same distribution by analyzing the cumulative distribution. It has been proposed into side-channel analysis as a successful distinguisher. This paper proposes---to our knowledge, for the first time---Kolmogorov-Smirnov test as a new method for leakage detection. Besides, we propose two implementations to speed up the KS leakage detection procedure. Experimental results on simulated leakage with various parameters and the practical traces verify that KS is an effective and robust leakage detection tool and the comprehensive comparison with TVLA shows that KS-based leakage detection can be a right-hand supplement to TVLA when performing the side-channel assessment

    Motif dan Kepuasan Mahasiswa dalam Mengapresiasi Jogja-NETPAC Asian Film Festival 2020 melalui Akun Instagram

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    Penyelenggaraan sebuah festival film lazimnya dilakukan secara langsung. Hal tersebut berkaitan erat dengan faktor – faktor pendukung penyelenggaraan kegiatan tersebut. Terutama hal – hal yang berkaitan dengan program yang akan berlangsung didalamnya, seperti duduk bersama di dalam satu ruangan untuk menyaksikan film – film yang dipilih oleh pihak penyelenggara. Namun hal tersebut harus kita kesampingkan saat ini, mengingat krisis pandemi covid-19. Penelitian ini akan melihat motif serta kepuasan dari pengunjung festival film Jogja-NETPAC Asian Film Festival 2020 yang diselenggarakan dalam jaringan. Metode survey dengan pendekatan uses and gratification akan melihat sejauh mana efektifitas pemanfaatan aplikasi Instagram berkaitan dengan pemenuhan kepuasan serta motif dari pengunjung festival film. Penelitian ini melihat pemanfaatan media sosial dapat menjadi salah satu alternatif penyelenggaraan kegiatan seperti festival film dimasa pandemi covid-19

    Estimating Dependency, Monitoring and Knowledge Discovery in High-Dimensional Data Streams

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    Data Mining – known as the process of extracting knowledge from massive data sets – leads to phenomenal impacts on our society, and now affects nearly every aspect of our lives: from the layout in our local grocery store, to the ads and product recommendations we receive, the availability of treatments for common diseases, the prevention of crime, or the efficiency of industrial production processes. However, Data Mining remains difficult when (1) data is high-dimensional, i.e., has many attributes, and when (2) data comes as a stream. Extracting knowledge from high-dimensional data streams is impractical because one must cope with two orthogonal sets of challenges. On the one hand, the effects of the so-called "curse of dimensionality" bog down the performance of statistical methods and yield to increasingly complex Data Mining problems. On the other hand, the statistical properties of data streams may evolve in unexpected ways, a phenomenon known in the community as "concept drift". Thus, one needs to update their knowledge about data over time, i.e., to monitor the stream. While previous work addresses high-dimensional data sets and data streams to some extent, the intersection of both has received much less attention. Nevertheless, extracting knowledge in this setting is advantageous for many industrial applications: identifying patterns from high-dimensional data streams in real-time may lead to larger production volumes, or reduce operational costs. The goal of this dissertation is to bridge this gap. We first focus on dependency estimation, a fundamental task of Data Mining. Typically, one estimates dependency by quantifying the strength of statistical relationships. We identify the requirements for dependency estimation in high-dimensional data streams and propose a new estimation framework, Monte Carlo Dependency Estimation (MCDE), that fulfils them all. We show that MCDE leads to efficient dependency monitoring. Then, we generalise the task of monitoring by introducing the Scaling Multi-Armed Bandit (S-MAB) algorithms, extending the Multi-Armed Bandit (MAB) model. We show that our algorithms can efficiently monitor statistics by leveraging user-specific criteria. Finally, we describe applications of our contributions to Knowledge Discovery. We propose an algorithm, Streaming Greedy Maximum Random Deviation (SGMRD), which exploits our new methods to extract patterns, e.g., outliers, in high-dimensional data streams. Also, we present a new approach, that we name kj-Nearest Neighbours (kj-NN), to detect outlying documents within massive text corpora. We support our algorithmic contributions with theoretical guarantees, as well as extensive experiments against both synthetic and real-world data. We demonstrate the benefits of our methods against real-world use cases. Overall, this dissertation establishes fundamental tools for Knowledge Discovery in high-dimensional data streams, which help with many applications in the industry, e.g., anomaly detection, or predictive maintenance. To facilitate the application of our results and future research, we publicly release our implementations, experiments, and benchmark data via open-source platforms

    A Unified Formalism for Physical Attacks

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    Technical reportThe security of cryptographic algorithms can be considered in two contexts. On the one hand, these algorithms can be proven secure mathematically. On the other hand, physical attacks can weaken the implementation of an algorithm yet proven secure. Under the common name of physical attacks, different attacks are regrouped: side channel attacks and fault injection attacks. This paper presents a common formalism for these attacks and highlights their underlying principles. All physical attacks on symmetric algorithms can be described with a 3-step process. Moreover it is possible to compare different physical attacks, by separating the theoretical attack path and the experimental parts of the attacks

    Servitization strategies & firm boundary decisions

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    This PhD thesis focuses on a particular manifestation of the servitization of manufacturing phenomenon, namely the offering of advanced asset management services for mature capital equipment in a business to business context. In contrast to past research in the field, the study approaches the issue from the often neglected point of view of the offerings’ intended customers and assumes a strategic perspective to shed light on the considerations that affect the customers’ propensity to accept or reject them. Upon conceptually analysing what the acceptance of such offerings actually requires of customers at an operational level, the study reveals that the latter are in most cases required to outsource a number of activities that have traditionally been handled in‐house. Thus, the issue of accepting servitized offerings of this nature is treated as a make‐or‐buy, or otherwise a firm boundary decision dilemma on behalf of customers. In adopting this treatment, the study then engages with the firm boundary/outsourcing literature and considers the state‐of‐the–art in four contemporary theoretical frameworks of make‐or‐buy decisions that reflect a customer firm’s efficiency, dependence, competence and identity related strategic considerations. In particular, insights are drawn respectively from Transaction Cost Economics, Resource Dependency Theory, a strand of the Resource‐Based View of the firm as well as the tenet of Identity Coherence. Augmented with a number of novel propositions, the collective body of considerations is then empirically explored through a quasi‐experimental cross‐sectional survey of deep‐sea dry and wet cargo shipping firms (considered as customers of servitization) that focuses on six key maintenance activities related to a ship’s main propulsion engine (considered as the object of servitization). In performing a two tier statistical analysis of the empirical data through logistic and multiple regression techniques, the study finds that alternative considerations affect a customer firm’s decision of whether to outsource an activity or not and the decision of how much of an activity to outsource once the first‐tier dilemma is answered positively. Furthermore, the study finds that combined theoretical perspective approaches offer better explanations of the phenomenon in question. With its conclusion, the thesis offers a number of implications directed at the literature streams involved as well as the practice of outsourcing and pursuing a servitization strategy
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