Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement and Segmentation of Scar Color Images after a Scoliosis Surgery

In this paper, a color-based segmentation scheme applied to dermatoscopic scar images is proposed. The image is first preprocessed via a partial differential equations (PDE)-based filter which combines in the RGB color space coherent anisotropic diffusion and shock fil- ters. Then the scar image is segmented using a segmentation algorithm based on color distributions. A post-processing algorithm was further- more developed to extract the scar contour, based on spline interpola- tions, smoothed with the generalized-cross validation method. Exper- iments on real patient images show the performance of the proposed method.
The Effect of Homeownership on Geographic Mobility and Labor Market Outcomes

This paper examines the effect of homeownership on mobility and labor income and provides new evidence that owning a home makes workers less likely to move in response to labor market shocks. To identify this effect, I develop and estimate a structural dynamic model of housing choices, migration decisions and labor market outcomes. I find that owning a home has a large negative effect on the probability of moving in response to a labor market shock and a small negative effect on labor income. Owners suffering from a decrease in home equity are 40 percent less mobile. I conduct two policy experiments. The first shows that the home mortgage deduction has a positive effect on homeownership, affects mobility and creates an incentive to buy larger houses. Second, I find that if the down payment requirement for buying a home is eliminated, homeownership exhibits a large increase, while the mobility and labor income of households experiencing negative labor market shocks decrease.
Key obstacles to EDI success: from the US small manufacturing companies' perspective

This study first empirically investigated key EDI obstacles experienced by US small manufacturing firms and, then, examined the relationships between the identified obstacles and their realized EDI benefits. This research revealed the following important findings. First, four dimensions of obstacles were derived: lack of managerial leadership/organizational readiness, lack of integration of EDI with internal/external computer systems, potential technical concerns, and security/legal concerns. Second, the lack of integration of EDI with internal/external computer systems turned out to be the most significant barrier to achieving the overall EDI success and three key EDI benefits, such as reduced administrative/transaction costs, improved information accuracy, and enhanced competitiveness in the marketplace. Third, the “lack of managerial leadership/organizational readiness” and “technical concerns” dimensions were found to be the second and third most significant obstacles to the reduction of administrative/transaction costs, respectively. Finally, the security/legal concerns dimension is considered a significant barrier to the overall EDI success.
Learning New Words from Spontaneous Speech: A Project Summary

This research develops methods that enable spoken language systems to detect and correct their own errors, automatically extending themselves to incorporate new words. The occurrence of unknown or out-of-vocabulary words is one of the major problems frustrating the use of automatic speech understanding systems in real world tasks. Novel words cause recognition errors and often result in recognition and understanding failures. Yet, they are common. Real system users speak in a spontaneous and relatively unconstrained fashion. They do not want words the system can recognize and thereby are likely to exceed the system''s coverage. Even if speakers constrained their speech, there would still be a need for self-extending systems as certain tasks inherently require dynamic vocabulary expansion (e.g. new company names, new flight destinations, etc.). Further, it is costly and labor intensive to collect enough training data to develop a representative vocabulary (lexicon) and language model for a spoken interface application. Unlike transcription tasks where it is often possible to find large amounts of on-line data from which a lexicon and language model can be developed, for many tasks this is not feasible. Developers of applications and database interfaces will probably not have the resources to gather a large corpus of examples to train a system to their specific task. Yet, most current speech and language model research is oriented toward training from large corpora. This research enables systems to be developed from small amounts of data and then "bootstrapped. A simple version of a system is initially created using only a small set of examples. Its coverage is then automatically extended as the system encounters new input through interaction with users. The system learns new words from spontaneous speech and incrementally augments its grammar and lexicon. There are two important aspects of our approach: Conjoined use of many knowledge sources to detect recognition errors and determine whether an out-of-vocabulary word is present. The system uses its higher-level knowledge sources to determine the meaning of new words to determine how to add the new words to the system grammar, language models and lexicon.
Blind matching versus matchmaking: Comparison group selection for highly creative researchers

This research examines approaches for constructing a comparison group relative to highly creative researchers in nanotechnology and human genetics in the US and Europe. Such a comparison group would be useful in identifying factors that contribute to scientific creativity in these emerging fields. Two comparison group development approaches are investigated. The first approach is based on propensity score analysis and the second is based on knowledge from the literature on scientific creativity and early career patterns. In the first approach, the log of citations over the years of activity in the domains under analysis produces a significant result, but the distribution of matches is not adequate at the middle and high ends of the scale. The second approach matches highly creative researchers in nanotechnology and human genetics with a comparison group of researchers that have the same or similar early career characteristics were considered: (1) same first year of publication (2) same subject category of the first publication, (3) similar publication volume for the first six years in the specified emerging domain. High levels of diversity among the highly creative researchers, especially those in human genetics, underscore the difficulties of constructing a comparison group to understand factors that have brought about their level of performance.
N-(6-Chloro-pyridin-3-yl)-3,4-difluoro-benzamide (ICA-27243): A Novel, Selective KCNQ2/Q3 Potassium Channel Activator

KCNQ2 (Kv7.2) and KCNQ3 (Kv7.3) are voltage-gated K+ channel subunits that underlie the neuronal M current. In humans, mutations in these genes lead to a rare form of neonatal epilepsy (Biervert et al., 1998; Singh et al., 1998), suggesting that KCNQ2/Q3 channels may be attractive targets for novel antiepileptic drugs. In the present study, we have identified the compound N-(6-chloro-pyridin-3-yl)-3,4-difluoro-benzamide (ICA-27243) as a selective activator of the neuronal M current and KCNQ2/Q3 channels. In SH-SY5Y human neuroblastoma cells, ICA-27243 produced membrane potential hyperpolarization that could be prevented by coadministration with the M-current inhibitors 10,10-bis(4-pyridinylmethyl)-9(10H)-anthracenone dihydrochloride (XE-991) and linopirdine. ICA-27243 enhanced both 86Rb+ efflux (EC50 = 0.2 μM) and whole-cell currents in Chinese hamster ovary cells stably expressing heteromultimeric KCNQ2/Q3 channels (EC50 = 0.4 μM). Activation of KCNQ2/Q3 channels was associated with a hyperpolarizing shift of the voltage dependence of channel activation (V½ shift of -19 mV at 10 μM). In contrast, ICA-27243 was less effective at activating KCNQ4 and KCNQ3/Q5 and was selective over a wide range of neurotransmitter receptors and ion channels such as voltage-dependent sodium channels and GABA-gated chloride channels. ICA-27243 (1-10 μM) was found to reversibly suppress seizure-like activity in an ex vivo hippocampal slice model of epilepsy and demonstrated in vivo anticonvulsant activity (ED50 = 8.4 mg/kg) in the mouse maximal electroshock epilepsy model. In conclusion, ICA-27243 represents the first member of a novel chemical class of selective KCNQ2/Q3 activators with anticonvulsant-like activity in experimental models of epilepsy.
Interaction Terms in NonlinearModels

Objectives. To explain the use of interaction terms in nonlinear models. Study Design. We discuss the motivation for including interaction terms in multivariate analyses. We then explain how the straightforward interpretation of interaction terms in linear models changes in nonlinear models, using graphs and equations. We extend the basic results from logit and probit to difference‐in‐differences models, models with higher powers of explanatory variables, other nonlinear models (including log transformation and ordered models), and panel data models. Empirical Application. We show how to calculate and interpret interaction effects using a publicly available Stata data set with a binary outcome. Stata 11 has added several features whichmake those calculations easier. LIMDEP code also is provided. Conclusions. It is important to understand why interaction terms are included in nonlinear models in order to be clear about their substantive interpretation.
Multi-disciplinary Modality Classification for Medical Images

Modality is a key facet in medical image retrieval, as a user is likely interested in only one of e.g. radiology images, ﬂowcharts, and pathology photos. While assessing image modality is trivial for humans, reliable automatic methods are required to deal with large un-annotated image bases, such as ﬁgures taken from the millions of scientiﬁc publications. We present a multi-disciplinary approach to tackle the classi-ﬁcation problem by combining image features, meta-data, textual and referential information. Our system achieved an accuracy of 96.86 % in cross-validation on the ImageCLEF 2011 training dataset having 18 imbalanced modality classes, and an accuracy of 90.2 % on the Image-CLEF 2010 dataset having 8 well-balanced modality classes. We evaluate the importance of the individual feature sets in detail, and provide an error analysis pointing at weaknesses of our method and obstacles in the classiﬁcation task. For the beneﬁt of the image classiﬁcation community, we make the results of our feature extraction methods publicly available at http://categorizer.tmit. bme.hu/~illes/imageclef2011modality .
Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates

Abstract Next generation sequencing technology provides a powerful tool for measuring gene expression (mRNA) levels in the form of RNA-sequence data. Method development for identifying differentially expressed (DE) genes from RNA-seq data, which frequently includes many low-count integers and can exhibit severe overdispersion relative to Poisson or binomial distributions, is a popular area of ongoing research. Here we present quasi-likelihood methods with shrunken dispersion estimates based on an adaptation of Smyth's (2004) approach to estimating gene-specific error variances for microarray data. Our suggested methods are computationally simple, analogous to ANOVA and compare favorably versus competing methods in detecting DE genes and estimating false discovery rates across a variety of simulations based on real data.
Schizophrenia and sex differences in emotional processing

Patients with schizophrenia are known to be impaired in several domains of emotional processing. These deficits have been associated with impaired social functioning. Since female patients show better social skills than male patients and healthy women outperform men in emotion recognition and empathic capacity, studying sex differences in emotional processing might contribute to a better understanding of the differences between men and women in the clinical manifestation of the illness. To this end, sex differences were studied in facial emotion recognition, emotion recognition in prosody and semantics of spoken language; empathy and perception of social interaction; working memory in simultaneous and sequential processing of language and emotion; long-term memory for visual information with positive, negative and neutral content; sensitivity to threat (inhibition) and reward (activation) in relationship to heartmeasures. The female advantage in emotion recognition, empathy and social perception was found to be preserved in schizophrenia. In addition, men but not women with schizophrenia were significantly impaired in performing a simultaneous language/emotion working memory task. These findings may explain in part the better course and outcome of female patients with schizophrenia. However, in more complex sequential or long-term processing of (emotional) stimuli, no female advantage was observed. Regarding inhibition, both male and female patients showed enhanced sensitivity to threat. Given the impaired performance in patients on all tasks involving emotional stimuli, our results confirm the accumulating evidence that schizophrenia is pre-eminently an emotional disorder.
Literature mining in support of drug discovery

The drug discovery enterprise provides strong drivers for data integration. While attention in this arena has tended to focus on integration of primary data from omics and other large platform technologies contributing to drug discovery and development, the scientific literature remains a major source of information valuable to pharmaceutical enterprises, and therefore tools for mining such data and integrating it with other sources are of vital interest and economic impact. This review provides a brief overview of approaches to literature mining as they relate to drug discovery, and offers an illustrative case study of a 'lightweight' approach we have implemented within an industrial context.
Serum osmolarity in rats exposed to hypoxia and / or nutritional deprivation

Sakuma, Tsutomu, Mieko Hida, Yoshihiro Nambu, Kazuhiro Osanai, Hirohisa Toga, Keiji Takahashi, Nobuo Ohya, Masao Inoue, and Yoh Watanabe. Effects of hypoxia on alveolar fluid transport capacity in rat lungs. J Appl Physiol 91: 1766–1774, 2001. —There is little information regarding the effect of hypoxia on alveolar fluid clearance capacity. We measured alveolar fluid clearance, lung water volume, plasma catecholamine concentrations, and serum osmolality in rats exposed to 10% oxygen for up to 120 h and explored the mechanisms responsible for the increase in alveolar fluid clearance. The principal results were 1) alveolar fluid clearance did not change for 48 h and then increased between 72 and 120 h of exposure to hypoxia; 2) although nutritional impairment during hypoxia decreased basal alveolar fluid clearance, endogenous norepinephrine increased net alveolar fluid clearance; 3) the changes of lung water volume and serum osmolality were not associated with those of alveolar fluid clearance; 4) an administration of b-adrenergic agonists further increased alveolar fluid clearance; and 5) alveolar fluid clearance returned to normal within 24 h of reoxygenation after hypoxia. In conclusion, alveolar epithelial fluid transport capacity increases in rats exposed to hypoxia. It is likely that a combination of endogenous norepinephrine and nutritional impairment regulates alveolar fluid clearance under hypoxic conditions.
Finite-state speech-to-speech translation

A fully integrated approach to speech input language translation in limited domain applications is presented. The mapping from the input to the output language is modeled in terms of a finite state translation model which is learned from examples of input output sentences of the task considered. This model is tightly integrated with standard acoustic phonetic models of the input language and the resulting global model directly supplies, through Viterbi search, an optimal output language sentence for each input language utterance. Several extensions to this framework, recently developed to cope with the increasing difficulty of translation tasks, are reviewed. Finally, results for a task in the framework of hotel front desk communication, with a vocabulary of about 700 words, are reported.
Adolescents' proxy reports of parents' socioeconomic status: How valid are they?

STUDY OBJECTIVE Children's proxy reports on indicators of their parents' socioeconomic status (SES) have either been used uncritically or dismissed as invalid. This paper examines the validity of young adolescents' reports of parental SES by comparing adolescent reports with parents' own reports of SES. DESIGN, SETTING AND PARTICIPANTS In 1990, 924 13 year olds, along with 648 of their fathers and 735 of their mothers, participated in the baseline survey of The Norwegian Longitudinal Health Behaviour Study. Data on parental occupation were collected from both adolescents and parents at baseline, while data on parental education were collected at follow up from the adolescents at age 15 and from their parents when the adolescents were 19 years old. MAIN RESULTS Three different ways of grouping the SES categories based on occupational data were investigated, and the strength of agreement was good for all three groups, with κ statistics ranging from 0.65 to 0.86. There were no significant improvements of agreement when comparing adolescent data from age 15 to adolescent data from age 13. The strength of agreements between the adolescents' and parents' reports of parental education were fair; κ statistics were 0.30 and 0.38 for fathers' and mothers' education, respectively. The proportions of unclassified answers or no responses from the adolescents were similar for questions on occupation and education, and ranged from 11% to 16%. CONCLUSIONS The agreement between adolescents' and parents' reports of SES based on occupation was judged to be good, but adding a few specific questions may cue the adolescent to provide more detailed information, thereby reducing the numbers of unclassified answers or non-responders.
Microarray Analysis of Gene Expression by Skeletal Muscle of Three Mouse Models of Kennedy Disease/Spinal Bulbar Muscular Atrophy

Background Emerging evidence implicates altered gene expression within skeletal muscle in the pathogenesis of Kennedy disease/spinal bulbar muscular atrophy (KD/SBMA). We therefore broadly characterized gene expression in skeletal muscle of three independently generated mouse models of this disease. The mouse models included a polyglutamine expanded (polyQ) AR knock-in model (AR113Q), a polyQ AR transgenic model (AR97Q), and a transgenic mouse that overexpresses wild type AR solely in skeletal muscle (HSA-AR). HSA-AR mice were included because they substantially reproduce the KD/SBMA phenotype despite the absence of polyQ AR. Methodology/Principal Findings We performed microarray analysis of lower hindlimb muscles taken from these three models relative to wild type controls using high density oligonucleotide arrays. All microarray comparisons were made with at least 3 animals in each condition, and only those genes having at least 2-fold difference and whose coefficient of variance was less than 100% were considered to be differentially expressed. When considered globally, there was a similar overlap in gene changes between the 3 models: 19% between HSA-AR and AR97Q, 21% between AR97Q and AR113Q, and 17% between HSA-AR and AR113Q, with 8% shared by all models. Several patterns of gene expression relevant to the disease process were observed. Notably, patterns of gene expression typical of loss of AR function were observed in all three models, as were alterations in genes involved in cell adhesion, energy balance, muscle atrophy and myogenesis. We additionally measured changes similar to those observed in skeletal muscle of a mouse model of Huntington's Disease, and to those common to muscle atrophy from diverse causes. Conclusions/Significance By comparing patterns of gene expression in three independent models of KD/SBMA, we have been able to identify candidate genes that might mediate the core myogenic features of KD/SBMA.
Anthropometric approaches and their uncertainties to assigning computational phantoms to individual patients in pediatric dosimetry studies

Current efforts to reconstruct organ doses in children undergoing diagnostic imaging or therapeutic interventions using ionizing radiation typically rely upon the use of reference anthropomorphic computational phantoms coupled to Monte Carlo radiation transport codes. These phantoms are generally matched to individual patients based upon nearest age or sometimes total body mass. In this study, we explore alternative methods of phantom-to-patient matching with the goal of identifying those methods which yield the lowest residual errors in internal organ volumes. Various thoracic and abdominal organs were segmented and organ volumes obtained from chest-abdominal-pelvic (CAP) computed tomography (CT) image sets from 38 pediatric patients ranging in age from 2 months to 15 years. The organs segmented included the skeleton, heart, kidneys, liver, lungs and spleen. For each organ, least-squared regression lines, 95th percentile confidence intervals and 95th percentile prediction intervals were established as a function of patient age, trunk volume, estimated trunk mass, trunk height, and three estimates of the ventral body cavity volume based on trunk height alone, or in combination with circumferential, width and/or breadth measurements in the mid-chest of the patient. When matching phantom to patient based upon age, residual uncertainties in organ volumes ranged from 53% (lungs) to 33% (kidneys), and when trunk mass was used (surrogate for total body mass as we did not have images of patient head, arms or legs), these uncertainties ranged from 56% (spleen) to 32% (liver). When trunk height is used as the matching parameter, residual uncertainties in organ volumes were reduced to between 21 and 29% for all organs except the spleen (40%). In the case of the lungs and skeleton, the two-fold reduction in organ volume uncertainties was seen in moving from patient age to trunk height—a parameter easily measured in the clinic. When ventral body cavity volumes were used, residual uncertainties were lowered even further to a range of between 14 and 20% for all organs except the spleen, which continued to remain at around 40%. The results of this study suggest that a more anthropometric pairing of computational phantom to individual patient based on simple measurements of trunk height and possibly mid-chest circumference or thickness (where influences of subcutaneous fat are minimized) can lead to significant reductions in organ volume uncertainties: ranges of 40–50% (based on patient age) to between 15 and 20% (based on body cavity volumes tied to trunk height). An expanded series of non-uniform rational B-spine (NURBS) pediatric phantoms are being created at the University of Florida to allow the full application of this new approach in pediatric medical imaging studies.