Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Genomic Typing in Taxonomy, Evolutionary Genetics, and Microbial Epidemiology

SUMMARY Currently, genetic typing of microorganisms is widely used in several major fields of microbiological research. Taxonomy, research aimed at elucidation of evolutionary dynamics or phylogenetic relationships, population genetics of microorganisms, and microbial epidemiology all rely on genetic typing data for discrimination between genotypes. Apart from being an essential component of these fundamental sciences, microbial typing clearly affects several areas of applied microbiogical research. The epidemiological investigation of outbreaks of infectious diseases and the measurement of genetic diversity in relation to relevant biological properties such as pathogenicity, drug resistance, and biodegradation capacities are obvious examples. The diversity among nucleic acid molecules provides the basic information for all fields described above. However, researchers in various disciplines tend to use different vocabularies, a wide variety of different experimental methods to monitor genetic variation, and sometimes widely differing modes of data processing and interpretation. The aim of the present review is to summarize the technological and fundamental concepts used in microbial taxonomy, evolutionary genetics, and epidemiology. Information on the nomenclature used in the different fields of research is provided, descriptions of the diverse genetic typing procedures are presented, and examples of both conceptual and technological research developments for Escherichia coli are included. Recommendations for unification of the different fields through standardization of laboratory techniques are made.
A Method for Evidence-Based Architecture Discovery

We describe an evidence-based architecture discovery approach that organizes architectural decisions in a semi-formal reasoning framework that is self-reinforced by the evidence of accommodating future design decisions. In this approach, the taxonomy of these design decisions follows the SOLID design principles [5]. We apply these principles of software design to the topics that represent the architecture itself. Decisions, once made, serve as a starting point and context for future decisions. Architecture is, therefore, discovered within the confines of decisions already made, and it is managed by ensuring that future design decisions conform to the constraints set forth by the current decisions. When evidence emerges that conformance is not possible, early design decisions must be challenged and adjusted. In this sense, future design decisions serve as a test for, and provide evidence on the validity and robustness of earlier design decisions. By creating a taxonomy of architecture topics and managing constant tension and systematic interaction between early and future design decisions, we have found that this evidence-based feedback mechanism leads to intermediate forms of an architecture that are more stable, more easily reviewed, and evolve more systematically and gracefully over time.
Positron emission tomography of the vulnerable atherosclerotic plaque in man – a contemporary review

Atherosclerosis is the primary underlying cause of cardiovascular disease (CVD). It is the leading cause of morbidity and mortality in the Western world today and is set to become the prevailing disease and major cause of death worldwide by 2020. In the 1950s surgical intervention was introduced to treat symptomatic patients with high‐grade carotid artery stenosis due to atherosclerosis – a procedure known as carotid endarterectomy (CEA). By removing the atherosclerotic plaque from the affected carotid artery of these patients, CEA is beneficial by preventing subsequent ipsilateral ischemic stroke. However, it is known that patients with low to intermediate artery stenosis may still experience ischemic events, leading clinicians to consider plaque composition as an important feature of atherosclerosis. Today molecular imaging can be used for characterization, visualization and quantification of cellular and subcellular physiological processes as they take place in vivo; using this technology we can obtain valuable information on atherosclerostic plaque composition. Applying molecular imaging clinically to atherosclerotic disease therefore has the potential to identify atherosclerotic plaques vulnerable to rupture. This could prove to be an important tool for the selection of patients for CEA surgery in a health system increasingly focused on individualized treatment. This review focuses on current advances and future developments of in vivo atherosclerosis PET imaging in man.
Exome sequencing identifies an MYH3 mutation in a family with distal arthrogryposis type 1.

BACKGROUND Few genes responsible for distal arthrogryposis type 1 are known, although genes coding for the proteins in the sarcomere have been implicated in other types of distal arthrogryposis. Cost-effective sequencing methods are now available to examine all genes in the human genome for the purpose of establishing the genetic basis of musculoskeletal disorders. METHODS A multigenerational family with distal arthrogryposis type 1 characterized by clubfoot and mild hand contractures was identified, and exome sequencing was performed on DNA from one of the affected family members. Linkage analysis was used to confirm whether a genetic variant segregated with distal arthrogryposis. RESULTS Exome sequencing identified 573 novel variants that were not present in control databases. A missense mutation in MYH3 (a gene coding for the heavy chain of myosin), causing an F437I amino acid substitution, was identified that segregated with distal arthrogryposis in this family. Linkage analysis confirmed that this MYH3 mutation was the only exome variant common to all six affected individuals. CONCLUSIONS Identification of an MYH3 mutation in this family with distal arthrogryposis type 1 broadens the phenotype associated with MYH3 mutations to include distal arthrogryposis types 1, 2A (Freeman-Sheldon syndrome), and 2B (Sheldon-Hall syndrome). Exome sequencing is a useful and cost-effective method to discover causative genetic mutations, although data from extended families may be needed to confirm the importance of the hundreds of identified variants.
You’re damned if you do and damned if you don’t: the tension-filled relationships between Japanese beginning and senior teachers

Abstract This article contributes to theoretical discussions about beginning teachers’ work being both relational and emotional. Specifically, we have examined the tensions that frequently characterise the relationships between beginning and senior teachers. Our research material consisted of narrative interviews with seven beginning teachers and seven senior teachers working at the same junior high school in Japan. Through thematic analysis, we have identified three categories of tensions: (1) tension between dependence and independence, (2) tension between obedience and assertiveness and (3) tension between loyalty to one’s students and loyalty to one’s colleagues. These tensions are meaningful for both beginning and senior teachers, but they view them in different ways and connect them to different expectations regarding appropriate actions and attitudes. These tensions are also related to wider cultural expectations and general principles that are often concretely realised through different practices within the micropolitical environment of a school.
Path-based scheduling for synthesis

A path-based scheduling algorithm for synchronous digital systems is presented. It yields solutions with the minimum number of control steps, taking into account arbitrary constraints that limit the amount of operations in each control step. The result is a finite-state machine that implements the control. Although the complexity of the algorithm is proportional to the number of paths in the control-flow graph, it is shown to be practical for large examples with thousands of nodes. <<ETX>>
Elastic algorithms for guaranteeing quality monotonicity in big data mining

When mining large data volumes in big data applications users are typically willing to use algorithms that produce acceptable approximate results satisfying the given resource and time constraints. Two key challenges arise when designing such algorithms. The first relates to reasoning about tradeoffs between the quality of data mining output, e.g. prediction accuracy for classification tasks and available resource and time budgets. The second is organizing the computation of the algorithm to guarantee producing better quality of results as more budget is used. Little work has addressed these two challenges together in a generic way. In this paper, we propose a novel framework for developing elastic big data mining algorithms. Based on Shannon's entropy, an information-theoretic approach is introduced to reason about how result quality is affected by the allocated budget. This is then used to guide the development of algorithms that adapt to the available time budgets while guaranteeing producing better quality results as more budgets are used. We demonstrate the application of the framework by developing elastic k-Nearest Neighbour (kNN) classification and collaborative filtering (CF) recommendation algorithms as two examples. The core of both elastic algorithms is to use a naïve kNN classification or CF algorithm over R-tree data structures that successively approximate the entire datasets. Experimental evaluation was performed using prediction accuracy as quality metric on real datasets. The results show that elastic mining algorithms indeed produce results with consistent increase in observable qualities, i.e., prediction accuracy, in practice.
An Assessment of Real Convergence of Less Developed EU Members: Lessons for the CEEC Candidates

The main purpose of this study is to provide an assessment of the different experiences of convergence of the four less developed EU members (Spain, Portugal, Ireland and Greece) after joining the EU and their main determining factors. Here special emphasis is placed on the assessment of their respective capital stocks, both physical and intangible, given their essential influence on growth and, consequently, on economic welfare. In addition, it aims to draw conclusions for these experiences that may help in the elaboration of suitable strategies of accession for the CEEC candidates.
Wave Field Reconstruction Filtering in Cylindrical Harmonic Domain for With-Height Recording and Reproduction

For sound field reproduction that includes height (with-height reproduction), it is more efficient to record and reproduce the sound field with lower resolution in elevation than in azimuth due to the spatial abilities of human auditory perception. We propose a sound field reproduction method using horizontally arranged cylindrical arrays of microphones and loudspeakers, which is based on the wave field reconstruction (WFR) filter. With the use of cylindrical array configurations, it is possible to reproduce sound waves arriving from upper and lower directions with a smaller number of array elements at angular positions. The WFR filter is analytically derived in the cylindrical harmonic domain and allows direct transformation from the received signals of the microphones into the driving signals of the loudspeakers. A model in which microphones are mounted on a rigid cylindrical baffle is introduced to stabilize the WFR filter. Numerical simulation results indicated that the reproduction accuracy in the neighboring region along the central axis of the cylindrical array was better preserved when using the proposed method than when the method with planar arrays was used.
Phase I Pharmacokinetic, Food Effect, and Pharmacogenetic Study of Oral Irinotecan Given as Semisolid Matrix Capsules in Patients with Solid Tumors

Purpose: To characterize the maximum-tolerated dose, recommended dose, dose-limiting toxicities (DLT), pharmacokinetic profile, and food effect of orally administered irinotecan formulated as new semisolid matrix capsules. Experimental Design: Irinotecan was given orally in fasted patients once daily for 5 consecutive days and repeated every 3 weeks. Patients were randomly assigned to take the drug along with a high-fat, high-calorie breakfast for the administration at day 1 of the first or second cycle. Dosages tested were 70 and 80 mg/m2/day. Results: Twenty-five patients received 101 cycles of therapy (median two cycles, range 1-15). During the first cycle, grade 3 delayed diarrhea and grade 3 fever were the DLTs at the dosage of 80 mg/m2/day in three out of five patients. Hematologic and nonhematologic toxicities were mild to moderate. Exposure to the active metabolite SN-38 was relatively high compared with i.v. infusion, but no relevant accumulation was observed. Food had no significant effect on irinotecan pharmacokinetics. One confirmed partial remission and 10 disease stabilizations were observed in previously treated patients. No association was found between the UGT1A1*28 genotype and the risk of severe irinotecan-induced toxicity. Conclusions: For oral irinotecan, a dose of 70 mg/m2/day for 5 consecutive days every 3 weeks is recommended for further studies. Delayed diarrhea was the main DLT, similar to that observed with intravenously administered irinotecan. This study confirms that oral administration of irinotecan is feasible and may have favorable pharmacokinetic characteristics.
Interaction Analysis in Aspect-Oriented Models

Aspect-oriented concepts are currently introduced in all phases of the software development life cycle. However, the complexity of interactions among different aspects and between aspects and base entities may reduce the value of aspect-oriented separation of cross-cutting concerns. Some interactions may be intended or may be emerging behavior, while others are the source of unexpected inconsistencies. It is therefore desirable to detect inconsistencies as early as possible, preferably at the modeling level. We propose an approach for analyzing interactions and potential inconsistencies at the level of requirements modeling. We use a variant of UML to model requirements in a use case driven approach. Activities that are used to refine use cases are the join points to compose crosscutting concerns. The activities and their composition are formalized using the theory of graph transformation systems, which provides analysis support for detecting potential conflicts and dependencies between rule-based transformations. This theory is used to effectively reason about potential interactions and inconsistencies caused by aspect-oriented composition. The analysis is performed with the graph transformation tool AGG. The automatically analyzed conflicts and dependencies also serve as an additional view that helps in better understanding the potential behavior of the composed system
Process Mining-based Understanding and Analysis of Volvo IT's Incident and Problem Management Processes

Recently, there has been a strong interest in the application of innovative process mining techniques to promote evidence-based understanding and analysis of organizations' business processes. Following the trend, we analyze real life event logs of incident and problem management processes supported by Volvo IT's VINST system by using process mining and other analytical techniques. The incident and problem management logs contain 7554/2306 cases and 65533/9011 events respectively. To create relevant datasets for answering the given questions, we preprocess the logs with the help of PL-SQL and Java. The datasets are analyzed using ProM and Disco's state- of-the-art process mining capabilities, SQL, and traditional spreadsheet-based techniques. We provide evidence-based answers to the questions and demonstrate the potential benefits of process mining-based understanding and analysis of business processes. Finally, concluding remarks and limitations are discussed.
Sampling the repairs of functional dependency violations under hard constraints

Violations of functional dependencies (FDs) are common in practice, often arising in the context of data integration or Web data extraction. Resolving these violations is known to be challenging for a variety of reasons, one of them being the exponential number of possible "repairs". Previous work has tackled this problem either by producing a single repair that is (nearly) optimal with respect to some metric, or by computing consistent answers to selected classes of queries without explicitly generating the repairs. In this paper, we propose a novel data cleaning approach that is not limited to finding a single repair or to a particular class of queries, namely, sampling from the space of possible repairs. We give several motivating scenarios where sampling from the space of FD repairs is desirable, propose a new class of useful repairs, and present an algorithm that randomly samples from this space. We also show how to restrict the space of generated repairs based on user-defined hard constraints that define an immutable trusted subset of the input relation, and we experimentally evaluate our algorithm against previous approaches. While this paper focuses on repairing FDs, we envision the proposed sampling approach to be applicable to other integrity constraints with large repair spaces.
Accurate Self-Localization in RFID Tag Information Grids Using FIR Filtering

Grid navigation spaces nested with the radio-frequency identification (RFID) tags are promising for industrial and other needs, because each tag can deliver information about a local two-dimensional or three-dimensional surrounding. The approach, however, requires high accuracy in vehicle self-localization. Otherwise, errors may lead to collisions; possibly even fatal. We propose a new extended finite impulse response (EFIR) filtering algorithm and show that it meets this need. The EFIR filter requires an optimal averaging interval, but does not involve the noise statistics which are often not well known to the engineer. It is more accurate than the extended Kalman filter (EKF) under real operation conditions and its iterative algorithm has the Kalman form. Better performance of the proposed EFIR filter is demonstrated based on extensive simulations in a comparison to EKF, which is widely used in RFID tag grids. We also show that errors in noise covariances may provoke divergence in EKF, whereas the EFIR filter remains stable and is thus more robust.
Detecting Deceptive Groups Using Conversations and Network Analysis

Deception detection has been formulated as a supervised binary classification problem on single documents. However, in daily life, millions of fraud cases involve detailed conversations between deceivers and victims. Deceivers may dynamically adjust their deceptive statements according to the reactions of victims. In addition, people may form groups and collaborate to deceive others. In this paper, we seek to identify deceptive groups from their conversations. We propose a novel subgroup detection method that combines linguistic signals and signed network analysis for dynamic clustering. A social-elimination game called Killer Game is introduced as a case study1. Experimental results demonstrate that our approach significantly outperforms human voting and state-of-theart subgroup detection methods at dynamically differentiating the deceptive groups from truth-tellers.
Transformation dependency analysis - a comparison of two approaches

Transformation dependency analysis is crucial to provide better tool support for current-day software development techniques – two prominent examples are program refactoring and model transformation. Unfortunately, it is unclear how existing tools that provide generic support for these techniques relate to each other, due to their difference in terminology, concepts and formal foundations (graphs versus logic). This article reports on the results of an experimental comparison between two tools: AGG and Condor. Among others, we noticed a performance advantage of several orders of magnitude for the logic-based approach. RESUME. L’analyse de dependance de transformations est essentiel pour ameliorer le support de techniques actuelles de developpement logiciel – deux exemples sont la restructuration de programmes et la transformation de modeles. Or, il est peu clair comment les outils de support actuels peuvent etre compares, a cause d’une difference de terminologie, de concepts utilises, et de fondations formelles (graphes versus logique). Dans cet article nous discutons d’une comparaison experimentale de deux outils : AGG et Condor. Entre autres, nous avons trouve un gain d’efficacite de plusieurs ordres de magnitude en utilisant l’approche basee sur la logique.
Influence of Gender-based Family Roles on Gender Transition for Transgender Women

ABSTRACT The purpose of this study was to describe the influence of gender-based family roles on the process of gender transition for transgender women in the United States. Secondary analysis was undertaken on a subset of data from a larger phenomenological study; this dataset comprised interviews from 10 women ranging in age from 46 to 65. Findings indicated that participants were challenged to imagine alternative ways of inhabiting family roles that were defined in terms of gender and thus feared the loss of those roles with disclosure of their authentic gender identity. Coupled with the anticipation of familial rejection, 3 family roles that participants perceived to be bounded by gender were identified as particularly influential on their decisions to delay gender transition: (a) son, (b) husband, and (c) father. Findings underscore that barriers to transition are greatly influenced by the relationship boundaries of the binary gender system and call attention to the need for a more flexible understanding of gender within the family system.
CLUC: a natural clustering algorithm for categorical datasets based on cohesion

We propose a clustering algorithm for categorical datasets, called CLUC (CLUstering with Cohesion), which uses a novel similarity measure, called cohesion, to determine the degree with which items/objects stick to clusters. We have implemented CLUC and carried out extensive experiments on real-life and synthetic datasets. The results of experiments and their analyses indicate that CLUC generates high quality clusters in that they conform to expert's opinion. Our experiments on large synthetic data confirm that CLUC is scalable when the dataset grows in the number of objects and/or dimensions. We also repeated the experiments with different orders of the items in the datasets. The results show that the proposed algorithm is order insensitive