Miracle-dz/UniFAR-FTUs huggingface dataset:
The microfracture technique for the treatment of articular cartilage lesions in the knee. A prospective cohort study.

BACKGROUND Microfracture is a frequently used technique for the repair of articular cartilage lesions of the knee. Despite the popularity of the technique, prospective information about the clinical results after microfracture is still limited. The purpose of our study was to identify the factors that affect the clinical outcome from this cartilage repair technique. METHODS Forty-eight symptomatic patients with isolated full-thickness articular cartilage defects of the femur in a stable knee were treated with the microfracture technique. Prospective evaluation of patient outcome was performed for a minimum follow-up of twenty-four months with a combination of validated outcome scores, subjective clinical rating, and cartilage-sensitive magnetic resonance imaging. RESULTS At the time of the latest follow-up, knee function was rated good to excellent for thirty-two patients (67%), fair for twelve patients (25%), and poor for four (8%). Significant increases in the activities of daily living scores, International Knee Documentation Committee scores, and the physical component score of the Short Form-36 were demonstrated after microfracture (p < 0.05). A lower body-mass index correlated with higher scores for the activities of daily living and SF-36 physical component, with the worst results for patients with a body-mass index of >30 kg/m(2). Significant improvement in the activities of daily living score was more frequent with a preoperative duration of symptoms of less than twelve months (p < 0.05). Magnetic resonance imaging in twenty-four knees demonstrated good repair-tissue fill in the defect in thirteen patients (54%), moderate fill in seven (29%), and poor fill in four patients (17%). The fill grade correlated with the knee function scores. All knees with good fill demonstrated improved knee function, whereas poor fill grade was associated with limited improvement and decreasing functional scores after twenty-four months. CONCLUSIONS Microfracture repair of articular cartilage lesions in the knee results in significant functional improvement at a minimum follow-up of two years. The best short-term results are observed with good fill grade, low body-mass index, and a short duration of preoperative symptoms. A high body-mass index adversely affects short-term outcome, and a poor fill grade is associated with limited short-term durability.
Recognizing subjective sentences: a computational investigation of narrative text

Part of understanding third-person fictional narrative text is determining for each sentence whether it takes some character's psychological point of view (is subjective) and, if it does, identifying the character whose point of view is taken (the subjective character). This dissertation is a computational investigation of how a reader can perform these tasks. We identify regularities, found during extensive examinations of naturally occurring texts, in the ways that texts initiate, continue, and resume a character's psychological point of view, and present an algorithm that exploits these regularities in order to recognize subjective sentences and their subjective characters. Linguistic elements (syntactic, lexical, and morphological) are an important source of information for recognizing subjective sentences. While there are some that always indicate that a sentence is subjective, we show that there are many that only potentially do so. In deciding if an element is subjective, we show that it is useful to consider the kinds of things that have appeared since the last subjective sentence (which determine the current text situation). Elements with weak potential to be subjective should usually be interpreted to be subjective only in the most continuous of situations, while those with strong potential to be subjective can be subjective in less continuous situations. The subjective character may be identifiable from the sentence itself. One case we identify is a sentence that reports a character's private state, such as wanting or seeing. We specify a way to distinguish reports from other uses of private-state sentences, so that it can be determined if the subjective character is identifiable from the sentence. We also identify characters who are likely to become subjective characters (called expected subjective characters); if the subjective character is not identifiable from the sentence, it should usually be identified to be one of these. One is the last subjective character and the other is a recent actor; we specify conditions under which these are expected subjective characters and give a heuristic for resolving competition between them. We show that references are understood differently in conversation, subjective sentences, and non-subjective sentences; namely, they are understood with respect to different sets of beliefs. We describe an implementation of the algorithm and demonstrate it in the body of the dissertation.
Supporting predictive change impact analysis: a control call graph based technique

Change impact analysis plays an important role in software maintenance. It allows developers assessing the possible effects of a change. We present, in this paper, a new static technique supporting software change impact analysis. The technique uses a new model based on control call graphs. It captures the control related to components calls and generates the different control flow paths in a program. The generated paths, in a compacted form, are used to identify the potential set of components that may be affected by a given change. Furthermore, the tool developed can be used to perform predictive impact analysis. It can also be used to support regression testing. We performed an experimental study on several Java programs. The reported results show that the proposed technique can predict impact sets that are more accurate than those obtained using traditional approaches based on call graphs.
Modeling a personal profile by Fuzzy Ontology Map and its applications

Current ontology markup language, such as resource description framework (RDF) and web ontology language (OWL), supports modeling information with hard semantics. However, it is not possible for us to represent some uncertain information by using these standard modeling languages. We proposed an extension called fuzzy ontology map (FOM) which can be used to extend the current crisp ontology representations to model uncertain information. FOM was developed with strong mathematical basis. It is based on fuzzy theory and graph theory. In this paper, we presented the details about this fuzzy extension. In addition, we also showed how to adopt FOM for storing the personal profile information. This profile can provide enhanced functionalities for applications such as information filtering and personalization. At the end of this paper, we also presented an example application, CompBook, to show the idea how FOM profile works.
Listen-and-Talk: Protocol Design and Analysis for Full-Duplex Cognitive Radio Networks

In traditional cognitive radio networks (CRNs), secondary users (SUs) typically access the spectrum of primary users (PUs) by a two-stage “listen-before-talk” (LBT) protocol, i.e., SUs sense the spectrum holes in the first stage before transmitting in the second. However, there exist two major problems: transmission time reduction due to sensing and sensing accuracy impairment due to data transmission. In this paper, we propose a “listen-and-talk” (LAT) protocol with the help of full-duplex (FD) technique that allows SUs to simultaneously sense and access the vacant spectrum. Spectrum utilization performance is carefully analyzed, with the closed-form spectrum waste ratio and collision ratio with the PU provided. In addition, with regard to the secondary throughput, we report the existence of a tradeoff between the secondary transmit power and throughput. Based on the power–throughput tradeoff, we derive the analytical local optimal transmit power for SUs to achieve both high throughput and satisfying sensing accuracy. Numerical results are given to verify the proposed protocol and the theoretical results.
Home Detention: Issues, Dilemmas and Impacts for Detainees' Co-Residing Family Members

Brief Historical Context Home detention, 1 as a means of confinement and control is not a recent invention (Gibbs & King 2003a: 1 ). It has an extensive and prominent history where it was mainly used as a means of silencing political dissidents and radical thinkers (Ball et al 1988:34; Morris & Tonry 1990:213; Whitfield 1997:31). Even in biblical times St Paul the Apostle was detained under house arrest with a Roman soldier in front of his house guarding him at all times (Gibbs & King 2003a:l). Contemporary use of home detention, which utilises electronic monitoring technology, can be traced back to the early 1980s in the United States when this sanction became an actual sentence of the court (Enos et all999:71; Whitfield 1997:31). Viewed as a modem solution to the increasingly unsustainable cost and prison overcrowding crisis, the implementation of home detention programs expanded rapidly across the United States (Doherty 1995:129; Renzema 1992:47). This sanction has been pioneered as a 'stand-alone' alternative to imprisonment (Blomberg et all993:187; Gibbs & King 2003a:2; Maxfield & Baumer 1990:521 ).
An expectation maximisation algorithm for behaviour analysis in video

Surveillance systems require advanced algorithms able to make decisions without a human operator or with minimal assistance from human operators. In this paper we propose a novel approach for dynamic topic modeling to detect abnormal behaviour in video sequences. The topic model describes activities and behaviours in the scene assuming behaviour temporal dynamics. The new inference scheme based on an Expectation-Maximisation algorithm is implemented without an approximation at intermediate stages. The proposed approach for behaviour analysis is compared with a Gibbs sampling inference scheme. The experiments both on synthetic and real data show that the model, based on Expectation-Maximisation approach, outperforms the one, based on Gibbs sampling scheme.
Investigating the Anomalous States of Knowledge Hypothesis in a Real-Life Problem Situation : A Study of History and Psychology Undergraduates Seeking Information for a Course Essay

needs of 59 McGill University undergraduates researching essay topics for either a history or psychology course, interviewed just after they had selected their essay topic. The interview’s purpose was to transform the undergraduate’s query from general topic terms, based on vague conceptions of their essay topic, to an information need-based query. To chart the transformation, the authors investigate N. J. Belkin, R. N. Oddy, and H. M. Brooks’ Anomalous States of Knowledge (ASK) hypothesis (1982a, 1982b), which links the user’s ASK to a relevant document set via a common code based on structural facets. In the present study an interoperable structural code based on eight essay styles is created, then notions of structural facets compatible with a highimpact essay structure are presented. The important findings of the study are: (a) the undergraduates’ topic statements and terms derived from it do not constitute an effective information need statement because for most of the subjects in the study the topic terms conformed to a low-impact essay style; (b) essay style is an effective interoperable structural code for charting the evolution of the undergraduate’s knowledge state from ASK to partial resolution of the ASK in an information need statement.
Genetic Variations in Human Glutathione Transferase Enzymes: Significance for Pharmacology and Toxicology

Glutathione transferase enzymes (GSTs) catalyze reactions in which electrophiles are conjugated to the tripeptide thiol glutathione. While many GST-catalyzed transformations result in the detoxication of xenobiotics, a few substrates, such as dihaloalkanes, undergo bioactivation to reactive intermediates. Many molecular epidemiological studies have tested associations between polymorphisms (especially, deletions) of human GST genes and disease susceptibility or response to therapy. This review presents a discussion of the biochemistry of GSTs, the sources—both genetic and environmental—of interindividual variation in GST activities, and their implications for pharmaco- and toxicogenetics; particular attention is paid to the Theta class GSTs.
Secondary Metabolism and Development Is Mediated by LlmF Control of VeA Subcellular Localization in Aspergillus nidulans

Secondary metabolism and development are linked in Aspergillus through the conserved regulatory velvet complex composed of VeA, VelB, and LaeA. The founding member of the velvet complex, VeA, shuttles between the cytoplasm and nucleus in response to alterations in light. Here we describe a new interaction partner of VeA identified through a reverse genetics screen looking for LaeA-like methyltransferases in Aspergillus nidulans. One of the putative LaeA-like methyltransferases identified, LlmF, is a negative regulator of sterigmatocystin production and sexual development. LlmF interacts directly with VeA and the repressive function of LlmF is mediated by influencing the localization of VeA, as over-expression of llmF decreases the nuclear to cytoplasmic ratio of VeA while deletion of llmF results in an increased nuclear accumulation of VeA. We show that the methyltransferase domain of LlmF is required for function; however, LlmF does not directly methylate VeA in vitro. This study identifies a new interaction partner for VeA and highlights the importance of cellular compartmentalization of VeA for regulation of development and secondary metabolism.
An approach for selective state machine based regression testing

Model-based regression testing is an important activity that ensures the reliability of evolving software. One of the major issues in this type of testing is the optimal selection of test-cases to test the affected portion of the software. In this paper, we present a UML based selective regression testing strategy that uses state machines and class diagrams for change identification. We identify the changes using the UML 2.1 semantics of state machines and class diagram. The changes are classified as Class-driven (obtained from class diagram) and State-driven (obtained from state machine). The Class-driven changes are important as these changes are not reflected on the state machines and they might be helpful in identifying some fault-revealing test cases. With the help of the identified changes, we classify the test cases of the test suite as Obsolete, Reusable, and Retestable. We apply the approach on a case study to demonstrate its validity.
Precise, dynamic information flow for database-backed applications

We present an approach for dynamic information flow control across the application and database. Our approach reduces the amount of policy code required, yields formal guarantees across the application and database, works with existing relational database implementations, and scales for realistic applications. In this paper, we present a programming model that factors out information flow policies from application code and database queries, a dynamic semantics for the underlying $^JDB$ core language, and proofs of termination-insensitive non-interference and policy compliance for the semantics. We implement these ideas in Jacqueline, a Python web framework, and demonstrate feasibility through three application case studies: a course manager, a health record system, and a conference management system used to run an academic workshop. We show that in comparison to traditional applications with hand-coded policy checks, Jacqueline applications have 1) a smaller trusted computing base, 2) fewer lines of policy code, and 2) reasonable, often negligible, additional overheads.
Musical genres classification using Markov models

Some audio files' formats contain metadata such as musical genre. This facilitates the usability of musical devices. However there are still many audio files' formats in which no metadata about the sound can be found. Our goal is to provide to users the same comfort in these conditions by computing the genres automatically. Various techniques have been proposed to determine a song's genre among N known genres. In this paper, we propose a new way of using Markov models as classifiers to perform genres classification. Experiments on 10 genres including 4 cameroonian genres showed an accuracy of 69.4%.
Automatic Temporal Expectancy: A High-Density Event-Related Potential Study

How we compute time is not fully understood. Questions include whether an automatic brain mechanism is engaged in temporally regular environmental structure in order to anticipate events, and whether this can be dissociated from task-related processes, including response preparation, selection and execution. To investigate these issues, a passive temporal oddball task requiring neither time-based motor response nor explicit decision was specifically designed and delivered to participants during high-density, event-related potentials recording. Participants were presented with pairs of audiovisual stimuli (S1 and S2) interspersed with an Inter-Stimulus Interval (ISI) that was manipulated according to an oddball probabilistic distribution. In the standard condition (70% of trials), the ISI lasted 1,500 ms, while in the two alternative, deviant conditions (15% each), it lasted 2,500 and 3,000 ms. The passive over-exposition to the standard ISI drove participants to automatically and progressively create an implicit temporal expectation of S2 onset, reflected by the time course of the Contingent Negative Variation response, which always peaked in correspondence to the point of S2 maximum expectation and afterwards inverted in polarity towards the baseline. Brain source analysis of S1- and ISI-related ERP activity revealed activation of sensorial cortical areas and the supplementary motor area (SMA), respectively. In particular, since the SMA time course synchronised with standard ISI, we suggest that this area is the major cortical generator of the temporal CNV reflecting an automatic, action-independent mechanism underlying temporal expectancy.
Closing the Gap: Increases in Life Expectancy among Treated HIV-Positive Individuals in the United States and Canada

Background Combination antiretroviral therapy (ART) has significantly increased survival among HIV-positive adults in the United States (U.S.) and Canada, but gains in life expectancy for this region have not been well characterized. We aim to estimate temporal changes in life expectancy among HIV-positive adults on ART from 2000–2007 in the U.S. and Canada. Methods Participants were from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD), aged ≥20 years and on ART. Mortality rates were calculated using participants' person-time from January 1, 2000 or ART initiation until death, loss to follow-up, or administrative censoring December 31, 2007. Life expectancy at age 20, defined as the average number of additional years that a person of a specific age will live, provided the current age-specific mortality rates remain constant, was estimated using abridged life tables. Results The crude mortality rate was 19.8/1,000 person-years, among 22,937 individuals contributing 82,022 person-years and 1,622 deaths. Life expectancy increased from 36.1 [standard error (SE) 0.5] to 51.4 [SE 0.5] years from 2000–2002 to 2006–2007. Men and women had comparable life expectancies in all periods except the last (2006–2007). Life expectancy was lower for individuals with a history of injection drug use, non-whites, and in patients with baseline CD4 counts <350 cells/mm3. Conclusions A 20-year-old HIV-positive adult on ART in the U.S. or Canada is expected to live into their early 70 s, a life expectancy approaching that of the general population. Differences by sex, race, HIV transmission risk group, and CD4 count remain.
Understanding the diffusion rate of new products through a simple customer behavior model

This paper presents a generic mathematical model for depicting the diffusion of an innovative product on a given market. Our approach relies on a probabilistic modeling of each customer behavior with respect to the commercial process which is used to promote such products. We introduce in particular the concept of coherent market that corresponds to a market which can be analyzed in a uniform way within our model. This last notion allows us to recover the main classical empirical results that were discovered and widely studied by E.M. Rogers and his school. We explain finally how to use our approach as a support for analytic predictive marketing.
Design of Meteorological Data Interoperability Conversion Model

This paper proposed to solve the massive meteorological data from multiple systems integration and sharing between meteorological data interoperability transformation model. The model is based on the characteristics of the meteorological data, data warehouse, metadata and other technical research and design, using the model to standardize formats and effective management of a variety of multi-source heterogeneous meteorological data source, thereby increasing the meteorological operational systems interoperability between efficiency and to better participate in the exchange of information. This paper presents the model of the actual use of the fact table and dimension table, and verify the operability of the model.
A Spectral Graph Theoretical Approach to Oriented Energy Features

In this work, we propose a novel method for determining oriented energy features of an image. Oriented energy features, useful for many machine vision applications like contour detection, texture segmentation and motion analysis, are determined from the filters whose outputs are enhanced at the edges of the image at a given orientation. We use the eigenvectors and eigenvalues of graph Laplacian for determining the oriented energy features of an image. Our method is based on spectral graph theoretical approach in which a graph is assigned complex-valued edge weights whose phases encode orientation information. These edge weights give rise to a complex-valued Hermitian Laplacian whose spectrum enables us to extract oriented energy features of the image. We perform a set of numerical experiments to determine the efficiency and characteristics of the proposed method. In addition, we apply our feature extraction method to texture segmentation problem. We do this in comparison with other known methods, and show that our method performs better for various test textures.
Violence Risk Appraisal of Male and Female Youth, Adults, and Individuals

Data from youth (n = 1,127), adults (n = 1,595), and individuals (N = 2,722, combined youth and adults) were followed 3 to 12 years in records to develop items to predict abuse, violence, and homicide in these 3 relevant groups for risk appraisal or safety scales. Shao's bootstrapped logistic regression yielded 14 predictors for youth (AUC = .91), 11 for adults (AUC = .99), and 13 for individuals (AUC = .96). Three regression equations were cross-validated with in-bag and out-of-bag techniques. Pearson coefficients were computed with intelligence, achievement, adaptive behavior, and perception tests. Test-retest reliability was acceptable. Using case-control quasi-experimental design, this study extends probation-parole decision-making tests to infants and children as young as 3 years, with convergent and divergent validity and reliability with other tests. Sensitivity and specificity were high and minimized over- or under-identification challenges in identifying potentially violent persons in the general population.