Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-term Results of Balloon Pulmonary Valvuloplasty in Children with Congenital Pulmonary Valve Stenosis

Objective Immediate, short and midterm outcome of balloon pulmonary valvuloplasty are well known, but there is limited information on long term results. We report long term results of 2–13. 5 years follow up of balloon pulmonary valvuloplasty in children. Methods From June 1998 to January 2012 sixty consecutive patients (33 females, 27 males) with moderate to severe valvar pulmonary stenosis (right ventricular to pulmonary artery pressure gradient greater than 50 mmHg) were considered for balloon valvuloplasty. The gradient was measured pre and immediately post–valvuloplasty at catheterization, and then by echocardiography at follow up. Follow up studies were performed 2–13.5 years (mean±SD; 7.1±2.5 years, median: 5.5 years) after procedure, by Doppler echocardiography in all patients and catheterization and angiography in two patients. Findings Balloon pulmonary valvuloplasty BPV was successful in 53 of 60 (88.3%) patients whereas surgical valvotomy was necessary in 6 to 60 (10%). There was one immediate death due to perforation of the right ventricular outflow tract. Pulmonary valve systolic pressure gradient decreased from 83.3±32.1 to 19.3±14.2 mmHg immediately after BPV and to 12.3±6.6 mmHg at late follow up (P<0.001). Pulmonary insufficiency was noted in 20 (38%) patient at short–term, but it was demonstrated in 17 (32%) at late follow up. A second valvuloplasty was performed in two (3.8%) patients presenting with re-stenosis. Conclusion The short, intermediate and long-term outcomes of pulmonary balloon valvuloplasty in children are excellent. Therefore it can be considered as the treatment of choice for children with pulmonary valve stenosis.
The Inventory of Altered Self-Capacities (IASC)

This article describes the Inventory of Altered Self-Capacities (IASC), a 63-item standardized measure of disturbed functioning in relation to self and others. The seven scales of the IASC are Interpersonal Conflicts, Idealization-Disillusionment, Abandonment Concerns, Identity Impairment, Susceptibility to Influence, Affect Dysregulation, and Tension Reduction Activities. The psychometric properties of the IASC were examined in general population, clinical, and university samples. The IASC was found to have internal consistency/reliability and validity in all three samples. Generally as predicted, IASC scales were associated with existing measures tapping borderline and antisocial personality features, depression, suicidality, substance abuse, somatization, and dysfunctional sexual behavior.
Evaluation of cognition enhancing activity of pioglitazone in albino mice

Background: Worldwide, 47.5 million people have dementia, with just over half (58%) living in lowand middle-income countries. Every year, there are 7.7 million new cases. The estimated proportion of the general population aged 60 and over with dementia at a given time is between 5 to 8 per 100 people. The total number of people with dementia is projected to be 75.6 million in 2030 and almost triple by 2050 to 135.5 million. Methods: All animals were allowed to acclimatize with laboratory conditions at least two weeks before starting the experiment and they were maintained under the same condition throughout the experiment. They were given food and water ad libitum. The experiments were performed as per the Committee for the Purpose of Control and Supervision on Experiments on Animals (CPCSEA) guidelines. The animals were subjected to experimentation between 0900-1600 hours in noise free atmosphere with ambient temperature 23-300oC. Results: The Pioglitazone treated groups showed statistically significant results in the SDL when compared to the control group (p<0.01 on both days) and the scopolamine group (p<0.01 on both days) on both acquisition day and retention day. Conclusions: There was no significant difference in SDL in standard and Pioglitazone treated groups on both acquisition day and retention day.
Artificial neural network versus linear regression for predicting Grid-Connected Photovoltaic system output

This paper presents a classically trained Multi-Layer Feedforward Neural Network (MLFNN) technique for predicting the output from a Grid-Connected Photovoltaic (GCPV) system. In the proposed MLFNN, the selection of the training parameters was conducted using a series of prescribed steps. The MLFNN utilized solar irradiance (SI) and module temperature (MT) as its inputs and AC kWh energy as its output. When compared with the linear regression method, the MLFNN offered superior performance by producing lower prediction error.
The validity and reliability of the GymAware linear position transducer for measuring counter-movement jump performance in female athletes

ABSTRACT The current study aimed to assess the validity and test–retest reliability of a linear position transducer when compared to a force plate through a counter-movement jump in female participants. Twenty-seven female recreational athletes (19 ± 2 years) performed three counter-movement jumps simultaneously using the linear position transducer and force plate for validity. In addition, 11 elite female athletes (23 ± 6 years) performed 3 counter-movement jumps with the linear position transducer on three separate days for test–retest reliability. Pearson correlations for jump height between the devices were at a high level (r = .90), with the linear position transducer overestimating jump height by 7.0 ± 2.8 cm. The reliability measured by the linear position transducer resulted in a mean intraclass correlation of . 70 for jump height, . 90 for peak velocity, and . 91 for mean velocity. The linear position transducer was reliable for measuring counter-movement jumps in elite female athletes; however, caution should be taken for one-off jump measures as it may over-estimate jump height.
A Cross-Firm Analysis of the Impact of Corporate Governance on the East Asian Financial Crisis

In a sample of 399 firms from Indonesia, Korea, Malaysia, the Philippines, and Thailand, firm-level differences in variables related to corporate governance had a significant impact on firm performance during the East Asian financial crisis of 1997-1998. First, indicators of higher disclosure quality (having ADRs and auditors from Big Six accounting firms) are associated with significantly better stock price performance during the crisis. Second, higher outside (but not managerial) ownership concentration led to better performance during the crisis. Finally, diversified firms, particularly those with high variation in investment opportunities across divisions, performed worse than single-segment firms during the crisis.
The capacity to innovate: a meta-analysis of absorptive capacity

Abstract For nearly 30 years, ACAP has been the bedrock of theories of innovation. A meta-analysis is timely to glean insights from the rich empirical evidence to date and guide future work on the topic. Our meta-analysis of 241 studies reveals that ACAP is a strong predictor of innovation and knowledge transfer, and that its effects on financial performance are fully mediated by these two outcomes. As different from most theoretical discourse, we also find that the firm size-ACAP relationship is positive for small firms but negative for larger firms and that the firm age-ACAP relationship is negative for mature firms and not significant for young firms. Our findings present a clearer picture of the performance implications of ACAP and also suggest the need to revisit traditional theoretical arguments on innovation, especially regarding the causal arguments underlying age and size. These results provoke scholars to revisit traditional assumptions of organizations and their patterns of innovation. Finally, we also take this opportunity to investigate factors that have been commonly considered to be relevant for the ACAP-innovation relationship, as we detail in our additional analysis.
Minimizing weighted flow time

We consider the problem of minimizing weighted flow time on a single machine in the preemptive setting. Our main result is an O(log W) competitive online algorithm where the maximum to the minimum ratio of weights is W. More generally our algorithm achieves a competitive ratio of k if there are k weight classes. This gives the first O(1)-competitive algorithm for constant k. No O(1) competitive algorithm was known previously even for the special case of k = 2. These results settle a question posed by Chekuri et al [5] about the existence of a "truly" online algorithm with a non-trivial competitive ratio. We also give a "semi-online" algorithm with competitive ratio O(log n + log P), where P is ratio of the maximum to minimum job size. Our second result deals with the non-clairvoyant setting where the job sizes are unknown (but the weight of the jobs are known). We consider the resource augmentation model, and give a non-clairvoyant online algorithm, which if allowed a (1 + ε) speed-up, is (1 + l/ε) competitive against an optimal offline, clairvoyant algorithm.
Running-specific prostheses limit ground-force during sprinting

Running-specific prostheses (RSP) emulate the spring-like behaviour of biological limbs during human running, but little research has examined the mechanical means by which amputees achieve top speeds. To better understand the biomechanical effects of RSP during sprinting, we measured ground reaction forces (GRF) and stride kinematics of elite unilateral trans-tibial amputee sprinters across a range of speeds including top speed. Unilateral amputees are ideal subjects because each amputee's affected leg (AL) can be compared with their unaffected leg (UL). We found that stance average vertical GRF were approximately 9 per cent less for the AL compared with the UL across a range of speeds including top speed (p < 0.0001). In contrast, leg swing times were not significantly different between legs at any speed (p = 0.32). Additionally, AL and UL leg swing times were similar to those reported for non-amputee sprinters. We infer that RSP impair force generation and thus probably limit top speed. Some elite unilateral trans-tibial amputee sprinters appear to have learned or trained to compensate for AL force impairment by swinging both legs rapidly.
Self-Understanding, Confidence, and Career Decision-Making In Traditional-Age College Students

SELF-UNDERSTANDING, CONFIDENCE, AND CAREER DECISION-MAKING IN TRADITIONAL-AGE COLLEGE STUDENTS Marrisa Thornton, M.A. George Mason University 2016 Thesis Director, Dr. Amy Swan This study examined undeclared undergraduate students’ understanding of themselves, how that understanding affected their confidence in career decision-making, and whether a career education course had any impact on those factors in the career decision-making process. Individual student experiences and the theoretical foundation in social constructionist theory, social cognitive career theory, and Super and Gottfredson’s career theories provided findings that illustrated a relationship between self-understanding and confidence in undergraduate students’ career decision-making. Those findings were closely related to the presence of career education, personal and societal influences, and the timing in which a student has received enough experience to make an accurate career decision. The subjective experiences provided by the students resulted in the expansion of the first phase of the career development model. This expansion illustrated the way in which innate and external factors influence students’ success within the first phase of the career development process and led to the proposal of potential expansions within other phases in the career development model.
Latent factor models for statistical relational learning

To simplify modeling procedures, traditional statistical machine learning methods always assume that the instances are independent and identically distributed (i.i.d.). However, it is not uncommon for some real-world data, such as web pages and research papers, to contain relationships (links) between the instances. Different instances in such data are correlated (linked) with each other, which implies that the common i.i. d. assumption is unreasonable for such relational data. Hence, naively applying traditional statistical learning methods to relational data may lead to misleading conclusions about the data. Statistical relational learning (SRL), which attempts to perform learning and inference in domains with complex relational structures, has become an emerging research area because relational data widely exist in a large variety of application areas, such as web mining, social network analysis, bioinformatics, economics and marketing. The existing mainstream SRL models extend traditional graphical models, such as Bayesian networks and Markov networks, by eliminating their underlying i.i. d. assumption. Some typical examples of such SRL models include relational Bayesian networks, relational Markov networks, and Markov logic networks. Because the dependency structure in relational data is typically very complex, structure learning for these relational graphical models is often very time-consuming. Hence, it might be impractical to apply these models to large-scale relational data sets. In this thesis, we propose a series of novel SRL models, called relational factor models (RFMs), by extending traditional latent factor models from i.i. d. domains to relational domains. These proposed RFMs provide a toolbox for different learning settings: some of them are well suited for transductive inference while others can be used for inductive inference; some of them are parametric while others are nonparametric; some of them can be used to model data with undirected relationships while others can be used for data with directed relationships. One promising advantage of our RFMs is that there is no need for time-consuming structure learning and the time complexity of most of them is linear to the number of observed links in the data. This implies that our RFMs can be used to model large-scale data sets. Experimental results show that our models can achieve state-of-the-art performance in many real-world applications such as linked-document classification and social network analysis.
Effects of Social Housing Condition on Chemotherapeutic Efficacy in a Shionogi Carcinoma (SC115) Mouse Tumor Model: Influences of Temporal Factors, Tumor Size, and Tumor Growth Rate

Objective The objective of this study was to investigate 1) whether social housing condition, tumor size, and tumor growth rate alter responses to chemotherapy and 2) whether the timing of tumor cell injection or chemotherapy initiation (relative to housing condition formation) influences tumor growth rate or the efficacy of chemotherapy. Methods Mice were reared individually (I) or in groups (G). In experiment 1, mice were rehoused (IG or GI) or left in group housing (GG) immediately after tumor cell injection. In experiment 2, housing conditions (II, IG, GG, or GI) were formed when tumors weighed 1 g. Chemotherapy (adriamycin 4 mg/kg and cyclophosphamide 61.5 mg/kg IP) and exposure to acute novelty stress (15 min/d, 5 d/wk) were initiated 1 day after housing condition formation. Results If chemotherapy was initiated when the tumor burden was undetectable (experiment 1), housing condition did not alter tumor response to chemotherapy, although IG mice lost the most weight and overall had the lowest probability of survival. If chemotherapy was initiated when tumors weighed 1 g (experiment 2), both tumor and host responses to chemotherapy were poorest for IG mice. Timing of tumor cell injection relative to housing condition formation also differentially influenced the rate of tumor growth in mice treated with the drug vehicle; in experiment 1, tumor growth rate was faster in GI and GG mice than in IG mice, whereas in experiment 2, the rate of tumor growth was faster in II mice than in GG and IG mice. Conclusions Altering the temporal relationships among social housing condition formation, tumor cell injection, and chemotherapy initiation differentially influences the rate of tumor growth and the efficacy of chemotherapy. Effects of housing condition are independent of tumor growth rate at chemotherapy initiation and, in terms of host responses, independent of tumor burden.
Complexity of flow analysis, inductive assertion synthesis and a language due to Dijkstra

Two different methods of flow analysis are discussed, one a significant generalization of the other. It is shown that the two methods have significantly different intrinsic computational complexities. As an outgrowth of our observations it is shown that a feature of the programming language used by Dijkstra in A Discipline of Programming makes it unsuitable for compile-time type checking, thus suggesting that flow analysis is applicable to the design of programming languages, as well as to their implementation. It is also shown that program verification by the method of inductive assertions is very likely to lead to assertions whose lengths and proofs are not polynomially bounded in the size of the program being verified, even for very simple programs. This last observation casts further doubt on the practicality and relevance of mechanized verification of arbitrary programs.
The effect of inquiry-based learning experiences on adolescents’ science-related career aspiration in the Finnish context

ABSTRACT Much research has been conducted to investigate the effects of inquiry-based learning on students’ attitude towards science and future involvement in the science field, but few of them conducted in-depth studies including young learners’ socio-cognitive background to explore mechanisms which explain how inquiry experiences influence on career choices. Hence, the aim of this study was to investigate in what ways and to what extent the inquiry learning experiences in school science affect students’ future career orientation in the context of socio-cognitive mechanisms based on socio-cognitive career theory(SCCT). For the purpose, Programme for International Student Assessment (PISA) 2015 data were used focusing on science literacy, and the sample of Finnish 15-year-old students (N = 5782) was analysed by structural equation modelling with the hypothesised Inquiry-SCCT model. The results of the study showed that inquiry learning experiences were indicated as a positive predictor for the students’ career aspiration, and most of its effects were mediated by outcome expectations. Indeed, although self-efficacy and interest in learning science indicated positive correlations with future aspiration, outcome expectation presented the highest correlation with the science-related career. Gender differences were found in the model, but girls indicated higher outcome expectation and career aspiration than boys in Finland.
Mutations in the HFE gene and sporadic amyotrophic lateral sclerosis risk: a meta-analysis of observational studies

Iron homeostasis dysregulation has been regarded as an important mechanism in neurodegenerative diseases. The H63D and C282Y polymorphisms in the HFE gene may be involved in the development of sporadic amyotrophic lateral sclerosis (ALS) through the disruption of iron homeostasis. However, studies investigating the relationship between ALS and these two polymorphisms have yielded contradictory outcomes. We performed a meta-analysis to assess the roles of the H63D and C282Y polymorphisms of HFE in ALS susceptibility. PubMed, MEDLINE, EMBASE, and Cochrane Library databases were systematically searched to identify relevant studies. Strict selection criteria and exclusion criteria were applied. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of associations. A fixed- or random-effect model was selected, depending on the results of the heterogeneity test. Fourteen studies were included in the meta-analysis (six studies with 1692 cases and 8359 controls for C282Y; 14 studies with 5849 cases and 13,710 controls for H63D). For the C282Y polymorphism, significant associations were observed in the allele model (Y vs C: OR=0.76, 95%CI=0.62-0.92, P=0.005) and the dominant model (YY+CY vs CC: OR=0.75, 95%CI=0.61-0.92, P=0.006). No associations were found for any genetic model for the H63D polymorphism. The C282Y polymorphism in HFE could be a potential protective factor for ALS in Caucasians. However, the H63D polymorphism does not appear to be associated with ALS.
Efficient, distributed data placement strategies for storage area networks (extended abstract)

In the last couple of years a dramatic growth of enterprise data storage capacity can be observed. As a result, new strategies have been sought that allow servers and storage being centralized to better manage the explosion of data and the overall cost of ownership. Nowadays, a common approach is to combine storage devices into a dedicated network that is connected to LANs and/or servers. Such networks are usually called storage area networks (SAN). A very important aspect for these networks is scalability. If a SAN undergoes changes (for instance, due to insertions or removals of disks), it may be necessary to replace data in order to allow an efficient use of the system. To keep the influence of data replacements on the performance of the SAN small, this should be done as efficiently as possible. In this paper, we investigate the problem of evenly distributing and efficiently locating data in dynamically changing SANs. We consider two scenarios: (1) all disks have the same capacity, and (2) the capacities of the disks are allowed to be arbitrary. For both scenarios, we present placement strategies capable of locating blocks efficiently and that are able to quickly adjust the data placement to insertions or removals of disks or data blocks. Furthermore, we study how the performance of our placement strategies changes if we allow to waste a certain amount of capacity of the disks.
Comparing Metacognition Assessments of Mathematics in Academically Talented Students

Two studies were conducted to examine and compare the construct validity of scores on the Junior Metacognition Awareness Inventory (JMAI) and problem-solving interview protocols. Participants consisted of 183 middle and high school students attending a university summer program for academically talented youth. Study 1 results indicated that JMAI scores were internally consistent and yielded an interpretable two-factor structure after the elimination of several items; however, the scores were not significantly or meaningfully related to GPA or current and future mathematics achievement. In Study 2 (n = 30), JMAI scores did not predict students’ metacognitive behaviors during mathematics problem-solving tasks. In contrast, students’ metacognitive behaviors observed during problem solving were meaningfully related to mathematics achievement with medium to high effect sizes. Findings support the predictive validity of metacognition with regard to academic achievement when operationalized with problem-solving interviews, but call into question the criterion-related validity of JMAI scores.