Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Bureaucracy Benefit Organizational Women?

A significant body of evidence suggests that bureaucratized organizations provide greater career rewards to women than do less bureaucratized organizations. Beyond career rewards, are there ways in which bureaucracy can benefit organizational women? This study explores potential answers to this question by examining perceptions of bureaucracy held by public employees. Analyzing qualitative and quantitative data collected from the employees of four cities in a Midwestern state, the study detects pronounced gender differences in perceptions of bureaucracy, particularly with regard to legitimacy, efficiency, equity, and control. These results suggest ways in which bureaucracy can empower the participation of women in organizations.
A survey of speech and language therapists’ views on electropalatography therapy outcomes in Scotland

Questionnaires elicited information from speech and language therapists (SLTs) working in Scotland about individuals they had treated with Electropalatography (EPG) between 1993 and 2003. The results showed that the majority of the group (n = 60) who had received EPG therapy during this period were school-age children with either functional articulation disorders or cleft palate. The sounds most frequently targeted in EPG therapy were /s/, /s/, /t/ and /d/. The (SLTs) judged that the majority of the group had improved their articulation to some extent and almost all had increased awareness of their own articulation difficulties following EPG therapy. Despite these gains, most experienced difficulties generalizing new patterns into everyday speaking situations. The results suggest that when using EPG, SLTs need to adopt specific strategies to promote generalization and maintenance.
Trauma in Transgender Populations: Risk, Resilience, and Clinical Care

ABSTRACT The transgender community is disproportionately affected by violence (Wilchins, Lombardi, Priesing, & Malouf, 1997). Transphobia—prejudice, discrimination, and gender-related violence due to negative attitudes toward transgender identity—may pose a risk factor for experiencing trauma in transgender individuals. Other risk and resiliency factors associated with experiencing traumatic violence among transgender populations are addressed in this article. Finally, culturally competent treatment suggestions are provided for clinicians working with transgender survivors of trauma in a clinical setting.
Factors of Addiction

Most state inmates incarcerated under the jurisdiction of the New Jersey Department of Corrections are driven to crimes by drug abuse. Understanding the factors contributing to addiction is the first step in developing strategies for successful inmate reintegration. This study presents an analysis of inmate addiction and factor association using multivariate analysis procedures on 2003 New Jersey male inmate addiction data to identify significant demographic and criminal justice factors and to quantify the relative proportion of risk of addiction likelihood via the odds ratio measure. Significant factors identified include age, race, county of offense, controlled dangerous substances possession, burglary, robbery, and other nonviolent offenses. Odds ratios are presented for significant single factors and among subclass groups of multilevel variables. A statistically interesting and socially important multifactor analysis is performed for insight into particular subgrouping factors. Black offenders in their 20s are further studied as an extension of the analysis.
The Magdalena Ridge Observatory Interferometer: a fully optimized aperture synthesis array for imaging

The Magdalena Ridge Observatory Interferometer will be the first facility-class optical interferometer optimized strictly for an imaging science mission. The array in its final form is envisaged to comprise ten 1.4 m aperture movable telescopes in a Y configuration, baselines extending from 8 to 400 meters, delay lines capable of tracking well-placed sources for 6 continuous hours, fringe-tracking in the near-infrared , and undertake science observations at both near-infrared and optical wavelengths. The science reference mission includes studies of young stellar objects, a full range of stellar astrophysics, and imaging studies of the nearest and brightest 100 active galactic nuclei. We will be staffing up to our full complement of personnel in New Mexico over the next year. Our goal for first fringes on the first baseline is 2007.
A complementary matching pursuit algorithm for sparse approximation

This paper introduces the concept of a complementary matching pursuit for sparse approximation. The algorithm is analogous to the classical matching pursuit but done in the row-space of the dictionary matrix. A deeper analysis of the algorithm shows that the residual error at any iteration may not be orthogonal to the immediately selected atom, however, this brings about the possibility of increasing the convergence speed and improving the sparsity of the solution vector. This is validated through simulations with a random dictionary created using the K-SVD algorithm.
Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model

The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.
The impact of different diagnostic criteria on PTSD prevalence

The diagnostic criteria for PTSD have undergone several changes in the last two decades. This may in part explain the great variance in PTSD prevalence found in existing research. The objective of this study is to investigate the influence of different diagnostic criteria and different combinations of criteria on PTSD prevalence. A sample of 242 Danish social work students (M =29.2 years) completed a list of potentially traumatizing events, major life events and the Harvard Trauma Questionnaire. A considerable difference in PTSD prevalence as a result of different diagnostic criteria of PTSD was found. Future meta-analyses and reviews of PTSD prevalence must take into account the impact of changing criteria on prevalence. Clinicians also need to address this issue when assessing PTSD.
Malthus and Climate Change: Betting on a Stable Population

A standard assumption in integrated assessment models of climate change is that population and productivity are growing, but at a decreasing rate. We explore the signifcance of the assumption of population and productivity growth for greenhouse gas abatement. After all, there has been no long run slow down in the growth of productivity over the past few centuries, and the rate of population growth has actually been increasing for the past 19 centuries. Even if either of these growth rates were expected to slow, by how much is subject to great uncertainty. We show computationally that such continued growth greatly increases the severity of climate change. Indeed we nd that climate change is a problem in large part caused by exogenous population and productivity growth. Rapid reductions in growth make climate change a small problem; smaller reductions in growth imply climate change is a very serious problem indeed. Analogously, reductions in the growth rate of population can be effective in controlling climate change.
Election of Diagnosis Codes Words as Responsible Citizens

Providing computer-aided support for the assignment of diagnosis codes has been approached in numerous ways, often by exploiting free-text fields in patient records. Modeling the 'meaning' of diagnosis codes through statistical data on co-occurrences of words and assigned codes - using a method known as Random Indexing - has only recently been explored as an interesting, alternative solution. It involves words in a clinician's notes 'voting' for semantically associated diagnosis codes, the election results yielding a single list of recommendations. This approach is here applied and evaluated on a corpus of over 250,000 coded patient records. The evaluation is performed by comparing the recommended codes generated by the model with those assigned by the clinicians. Applying the tf-idf weighting scheme somewhat improves results for general models (23% recall for exact matches) but has little effect on domain-specific models (32% and 59% recall for exact matches). These results confirm the potential of Random Indexing for diagnosis code assignment support, and merits further attention.
Medical Temporal-Knowledge Discovery via Temporal Abstraction

Medical knowledge includes frequently occurring temporal patterns in longitudinal patient records. These patterns are not easily detectable by human clinicians. Current knowledge could be extended by automated temporal data mining. However, multivariate time-oriented data are often present at various levels of abstraction and at multiple temporal granularities, requiring a transformation into a more abstract, yet uniform dimension suitable for mining. Temporal abstraction (of both the time and value dimensions) can transform multiple types of point-based data into a meaningful, time-interval-based data representation, in which significant, interval-based temporal patterns can be discovered. We introduce a modular, fast time-interval mining method, KarmaLego, which exploits the transitivity inherent in temporal relations. We demonstrate the usefulness of KarmaLego in finding meaningful temporal patterns within a set of records of diabetic patients; several patterns seem to have a different frequency depending on gender. We also suggest additional uses of the discovered patterns for temporal clustering of the mined population and for classifying multivariate time series.
Compiling Irregular Accesses for the Cell Broadband Engine ∗

A class of scientific problems represents a physical system in the form of sparse and irregular kernels. Parallelizing scientific applications that comprise of sparse data structures on the Cell Broadband Engine (Cell BE) is a challenging problem as the memory access pattern is irregular and cannot be determined at compile time. In this paper we present a compiler framework for the Cell BE that provides automatic run-time support for memory communication and parallelization to indirectly indexed applications. The memory communication scheme generates DMA communication schedule after performing data flow analysis and facilitates the gather and scatter operations. The run-time parallelization technique judiciously partitions the data and computational work taking into account any coherence issues that may arise due to irregular accesses. We evaluate the performance of our compiled code on a3.2 GHz Cell processor and demonstrate a parallel speedup of up to factor of 4.7 when using eight threads.
Transcription Factor NF-κB Is Transported to the Nucleus via Cytoplasmic Dynein/Dynactin Motor Complex in Hippocampal Neurons

Background Long-term changes in synaptic plasticity require gene transcription, indicating that signals generated at the synapse must be transported to the nucleus. Synaptic activation of hippocampal neurons is known to trigger retrograde transport of transcription factor NF-κB. Transcription factors of the NF-κB family are widely expressed in the nervous system and regulate expression of several genes involved in neuroplasticity, cell survival, learning and memory. Principal Findings In this study, we examine the role of the dynein/dynactin motor complex in the cellular mechanism targeting and transporting activated NF-κB to the nucleus in response to synaptic stimulation. We demonstrate that overexpression of dynamitin, which is known to dissociate dynein from microtubules, and treatment with microtubule-disrupting drugs inhibits nuclear accumulation of NF-κB p65 and reduces NF-κB-dependent transcription activity. In this line, we show that p65 is associated with components of the dynein/dynactin complex in vivo and in vitro and that the nuclear localization sequence (NLS) within NF-κB p65 is essential for this binding. Conclusion This study shows the molecular mechanism for the retrograde transport of activated NF-κB from distant synaptic sites towards the nucleus.
Real-time cartoon-like stylization of AR video streams on the GPU

The ultimate goal of many applications of augmented reality is to immerse the user into the augmented scene, which is enriched with virtual models. In order to achieve this immersion, it is necessary to create the visual impression that the graphical objects are a natural part of the user’s environment. Producing this effect with conventional computer graphics algorithms is a complex task. Various rendering artifacts in the three-dimensional graphics create a noticeable visual discrepancy between the real background image and virtual objects. We have recently proposed a novel approach to generating an augmented video stream. With this new method, the output images are a non-photorealistic reproduction of the augmented environment. Special stylization methods are applied to both the background camera image and the virtual objects. This way the visual realism of both the graphical foreground and the real background image is reduced, so that they are less distinguishable from each other. Here, we present a new method for the cartoon-like stylization of augmented reality images, which uses a novel post-processing filter for cartoon-like color segmentation and high-contrast silhouettes. In order to make a fast postprocessing of rendered images possible, the programmability of modern graphics hardware is exploited. We describe an implementation of the algorithm using the OpenGL Shading Language. The system is capable of generating a stylized augmented video stream of high visual quality at real-time frame rates. As an example application, we demonstrate the visualization of dinosaur bone datasets in stylized augmented reality.
Robustness Improvement of Predictive Current Control Using Prediction Error Correction for Permanent-Magnet Synchronous Machines

The predictive control method deals with the prediction of the motor behavior based on a mathematical model of the motor. This model is dependent on the motor parameters. However, these parameters may not match with their actual values due to the measurement error or they may change during the operation of the motor. All these uncertainties and model inexactitude lead to inaccurate prediction of the motor behavior and deteriorate the performance of the predictive algorithm. This paper presents an extension of the predictive current control (PCC) method to improve the prediction accuracy for a permanent-magnet synchronous motor (PMSM). The proposed strategy not only reduces the current ripple but also improves the robustness of the system against parameter uncertainties. Simulation and experimental results that confirm the good performance of the proposed method are presented.
Integrating scale out and fault tolerance in stream processing using operator state management

As users of "big data" applications expect fresh results, we witness a new breed of stream processing systems (SPS) that are designed to scale to large numbers of cloud-hosted machines. Such systems face new challenges: (i) to benefit from the "pay-as-you-go" model of cloud computing, they must scale out on demand, acquiring additional virtual machines (VMs) and parallelising operators when the workload increases; (ii) failures are common with deployments on hundreds of VMs-systems must be fault-tolerant with fast recovery times, yet low per-machine overheads. An open question is how to achieve these two goals when stream queries include stateful operators, which must be scaled out and recovered without affecting query results. Our key idea is to expose internal operator state explicitly to the SPS through a set of state management primitives. Based on them, we describe an integrated approach for dynamic scale out and recovery of stateful operators. Externalised operator state is checkpointed periodically by the SPS and backed up to upstream VMs. The SPS identifies individual operator bottlenecks and automatically scales them out by allocating new VMs and partitioning the checkpointed state. At any point, failed operators are recovered by restoring checkpointed state on a new VM and replaying unprocessed tuples. We evaluate this approach with the Linear Road Benchmark on the Amazon EC2 cloud platform and show that it can scale automatically to a load factor of L=350 with 50 VMs, while recovering quickly from failures.
North American (Panax quinquefolius) and Asian Ginseng (Panax ginseng) Preparations for Prevention of the Common Cold in Healthy Adults: A Systematic Review

Background: Standardized ginseng extract has become the best-selling cold and flu remedy in Canada, yet much controversy regarding the efficacy of ginseng in preventing common colds remains. Objective: To assess the efficacy of ginseng preparations for the prevention of common colds in healthy adults. Methods: Comprehensive bibliographic database, trial registry and grey literature searches were conducted up to December 2007. Randomized controlled trials or controlled clinical trials comparing North American (Panax quinquefolius) or Asian ginseng (Panax ginseng) root extract to placebo or no treatment in healthy adults were included. Two reviewers independently applied the study selection criteria and assessed methodological quality. Results: Five trials involving 747 participants were included. All five trials examined North American ginseng. The methodological quality of the trials varied widely. Ginseng preparations significantly reduced the total number of common colds by 25% compared to placebo (one trial; 95% CI: 5–45). There was a tendency toward a lower incidence of having at least one common cold or other acute respiratory infection (ARI) in the ginseng group compared to the placebo group (five trials; relative risk: 0.70; 95% CI: 0.48–1.02). Compared to placebo, ginseng significantly shortened the duration of colds or ARIs by 6.2 days (two trials; 95% CI: 3.4–9.0). Conclusions: There is insufficient evidence to conclude that ginseng reduces the incidence or severity of common colds. North American ginseng appears to be effective in shortening the duration of colds or ARIs in healthy adults when taken preventatively for durations of 8–16 weeks.
The cholesterol-binding protein NPC2 restrains recruitment of stromal macrophage-lineage cells to early-stage lung tumours

The tumour microenvironment is known to play an integral role in facilitating cancer progression at advanced stages, but its function in some pre‐cancerous lesions remains elusive. We have used the V600EBRAF‐driven mouse lung model that develop premalignant lesions to understand stroma–tumour interactions during pre‐cancerous development. In this model, we have found that immature macrophage‐lineage cells (IMCs) producing PDGFA, TGFβ and CC chemokines are recruited to the stroma of premalignant lung adenomas through CC chemokine receptor 1 (CCR1)‐dependent mechanisms. Stromal IMCs promote proliferation and transcriptional alterations suggestive of epithelial–mesenchymal transition in isolated premalignant lung tumour cells ex vivo, and are required for the maintenance of early‐stage lung tumours in vivo. Furthermore, we have found that IMC recruitment to the microenvironment is restrained by the cholesterol‐binding protein, Niemann‐Pick type C2 (NPC2). Studies on isolated cells ex vivo confirm that NPC2 is secreted from tumour cells and is taken up by IMCs wherein it suppresses secretion of the CCR1 ligand CC chemokine 6 (CCL6), at least in part by facilitating its lysosomal degradation. Together, these findings show that NPC2 secreted by premalignant lung tumours suppresses IMC recruitment to the microenvironment in a paracrine manner, thus identifying a novel target for the development of chemopreventive strategies in lung cancer.
COPS: Cost Based Object Placement Strategies on Hybrid Storage System for DBaaS Cloud

Solid State Drives (SSD) are integrated together with Hard Disk Drives (HDD) in Hybrid Storage Systems (HSS) for Cloud environment. When it comes to storing data, some placement strategies are used to find the best location (SSD or HDD). These strategies should minimize the cost of data placement while satisfying Service Level Objectives (SLO). This paper presents two Cost based Object Placement Strategies (COPS) for DBaaS objects in HSS: a Genetic based approach (G-COPS) and an ad-hoc Heuristic approach (H-COPS) based on incremental optimization. While G-COPS proved to be closer to the optimal solution in case of small instances, H-COPS showed a better scalability as it approached the exact solution even for large instances (by 10% in average). In addition, H-COPS showed small execution times (few seconds) even for large instances which makes it a good candidate to be used in runtime. Both H-COPS and G-COPS performed better than state-of-the-art solutions as they satisfied SLOs while reducing the overall cost by more than 40% for problems of small and large instances.
Pregnancy-Induced Hypertension and Preeclampsia: Levels of Angiogenic Factors in Malaysian Women

Preeclampsia (PE) is a major contributor to maternal and fetal mortality. The cause of preeclampsia remains unclear, but oxidative stress on the endothelium leading to endothelial dysfunction is said to be the root cause of the disease. The aim of this study was to measure and determine the plasma levels of key angiogenic factors in pregnancy as an indicator for the early onset of preeclampsia in pregnancy. Plasma levels of circulating a soluble fms like tyrosine kinase-1 (sFlt-1), an anti-angiogenic factor, vascular endothelial growth factor (VEGF) and placental growth factor (PIGF), both pro-angiogenic factors were analyzed in normal pregnant Malaysian women (control group, n = 34), women with pregnant induced hypertension (PIH, n = 34) and women with preeclampsia (PE, n = 34) all at three gestational ages, 24–28 weeks (early pregnancy: EP), 32–36 weeks (late pregnancy: LP) and 6 weeks after delivery (postpartum: PN). The plasma levels of angiogenic factors were determined by ELISA. sFlt-1 levels were elevated in PIH and PE patients as compared to controls. PIGF and VEGF were significantly decreased in PIH and PE as compared to the controls. These results suggest that elevated concentration of sFlt-1 and suppressed levels of PIGF and VEGF may contribute to the development of hypertension in pregnancy which precedes preeclampsia.