LLukas22/scidocs huggingface dataset:
Renal recovery after conversion to a calcineurin inhibitor-free immunosuppression in late cardiac transplant recipients.

OBJECTIVE
Calcineurin inhibitor (CNI)-related renal failure is a common problem after cardiac transplantation (HTx). The aim of this prospective study was to evaluate the safety and efficacy of a completely CNI-free immunosuppressive regimen [mycophenolate mofetil (MMF) and sirolimus (Sir)] in HTx-recipients with late post-transplant renal impairment.


METHODS
Since 2001, 30 HTx-patients (25 men, 6 women; 0.2-14.2 years after transplantation) with CNI-based immunosuppression and a serum creatinine >1.9 mg/dl were included in the study. Creatinine and cystatin levels were monitored to detect renal function. Conversion was started with 6 mg Sir or 500 mg MMF according to the pre-existing regimen and was continued with the dose adjusted to achieve target trough levels between 8 and 14 ng/ml (Sir) or 1.5 and 4 microg/ml (mycophenolate). Subsequently, the CNIs were tapered down and stopped. Clinical follow-up included endomyocardial biopsies, echocardiography and laboratory studies. Additionally, every HTx-patient treated at our centre between 1996 and 2001 due to chronic renal failure without immunosuppressive conversion and fulfilling the inclusion criteria were retrospectively analysed and acted as control group.


RESULTS
Patient demographics and 1-year survival [93 (conversion) vs 90% (control)] were compared. No acute rejection episode was detected in either group. Renal function improved significantly in the conversion group (creatinine: 3.18+/-0.71 vs 2.22+/-0.79 mg/dl, P=0.001; cystatin pre- vs post-conversion: 2.95+/-1.06 vs 2.02+/-1.1 mg/l, P=0.01). In three patients haemodialysis therapy was stopped completely after conversion. In the control group renal impairment was deteriorating, creatinine increased from 2.44+/-0.8 to 3.28+/-1 mg/dl (P=0.01). In 10 out of 33 patients chronic haemodialysis had to be initiated within 1 year. Although side effects of CNI-free immunosuppression were common (76%), no patient had to be excluded due to adverse effects.


CONCLUSIONS
Conversion from CNI-based immunosuppression to MMF and Sir in HTx-patients with chronic renal failure was safe, preserved graft function and improved renal function.
Iyengar Yoga for Distressed Women: A 3-Armed Randomized Controlled Trial

Distress is an increasing public health problem. We aimed to investigate the effects of an Iyengar yoga program on perceived stress and psychological outcomes in distressed women and evaluated a potential dose-effect relationship. Seventy-two female distressed subjects were included into a 3-armed randomized controlled trial and allocated to yoga group 1 (n = 24) with twelve 90 min sessions over 3 months, yoga group 2 (n = 24) with 24 sessions over 3 months, or a waiting list control group (n = 24). The primary outcome was stress perception, measured by Cohen Stress Scale; secondary outcomes included state trait anxiety, depression, psychological and physical quality of life (QOL), profile of Mood States, well being, and bodily complaints. After three months, women in the yoga groups showed significant improvements in perceived stress (P = 0.003), state trait anxiety (P = 0.021 and P = 0.003), depression (P = 0.008), psychological QOL (P = 0.012), mood states being (P = 0.007), and bodily complaints well(P = 0.012) when compared to controls. Both yoga programs were similarly effective for these outcomes; however, compliance was better in the group with fewer sessions (yoga group 1). Dose effects were seen only in the analysis of group-independent effects for back pain, anxiety, and depression. These findings suggest that Iyengar yoga effectively reduces distress and improves related psychological and physical outcomes. Furthermore, attending twice-weekly yoga classes was not superior to once-weekly classes, as a result of limited compliance in the twice-weekly group.
Exploratory use of cardiac magnetic resonance imaging in liver transplantation; a one-stop shop for preoperative cardio-hepatic evaluation

Introduction Preoperative cardiovascular (CV) risk stratification in liver transplant (LvTx) candidates has proven challenging due to limitations of current noninvasive modalities. Additionally, the evaluation can be cumbersome and expensive given the need for separate cardiac, vascular and abdominal imaging. Over the last decade cardiovascular MRI (CMR) has emerged as the ‘gold standard’ for many important CV metrics used to define such risk due to its unparalleled spatial resolution, lack of ionizing radiation and intrinsic 3D capabilities.
Telephone interviews can be used to collect follow-up data subsequent to no response to postal questionnaires in clinical trials.

OBJECTIVE
Follow-up data were collected using postal questionnaires and if participants did not respond, then data was collected using telephone interviews. The objectives of this study were to examine, for the two methods, how respondents differed in characteristics and whether the observed treatment difference varied.


STUDY DESIGN AND SETTING
A large clinical trial of lower back pain.


RESULTS
About 60% (98/163) of the nonresponders to postal questionnaire provided data by telephone, which increased the overall response rate by 14% (from 71% to 85%). A consistent treatment difference was found across the methods for the outcome measures at 12 months, implying that the observed treatment effect had not been modified. There were some differences between the participants: responders of postal questionnaire were older, likely to be female, white (ethnic origin), not working, with less disability of back pain, compared with those who responded by a telephone interview. At 12 months, there was greater improvement in back pain, disability, and general health for those who responded by postal questionnaires.


CONCLUSION
Researchers should consider the use of more than one method of collecting data as this increases response rate, participant representativeness, and enhances precision of effect estimates.
Artificial Intelligence and Economic Growth

This paper examines the potential impact of artificial intelligence (A.I.) on economic growth. We model A.I. as the latest form of automation, a broader process dating back more than 200 years. Electricity, internal combustion engines, and semiconductors facilitated automation in the last century, but A.I. now seems poised to automate many tasks once thought to be out of reach, from driving cars to making medical recommendations and beyond. How will this affect economic growth and the division of income between labor and capital? What about the potential emergence of “singularities” and “superintelligence,” concepts that animate many discussions in the machine intelligence community? How will the linkages between A.I. and growth be mediated by firm-level considerations, including organization and market structure? The goal throughout is to refine a set of critical questions about A.I. and economic growth and to contribute to shaping an agenda for the field. One theme that emerges is based on Baumol’s “cost disease” insight: growth may be constrained not by what we are good at but rather by what is essential and yet hard to improve. ∗We are grateful to Ajay Agrawal, Mohammad Ahmadpoor, Adrien Auclert, Sebastian Di Tella, Patrick Francois, Joshua Gans, Avi Goldfarb, Pete Klenow, Hannes Mahlmberg, Pascual Restrepo, Chris Tonetti, Michael Webb, and participants at the NBER Conference on Artificial Intelligence for helpful discussion and comments. 2 P. AGHION, B. JONES, AND C. JONES
WalkSafe: a pedestrian safety app for mobile phone users who walk and talk while crossing roads

Research in social science has shown that mobile phone conversations distract users, presenting a significant impact to pedestrian safety; for example, a mobile phone user deep in conversation while crossing a street is generally more at risk than other pedestrians not engaged in such behavior. We propose WalkSafe, an Android smartphone application that aids people that walk and talk, improving the safety of pedestrian mobile phone users. WalkSafe uses the back camera of the mobile phone to detect vehicles approaching the user, alerting the user of a potentially unsafe situation; more specifically WalkSafe i) uses machine learning algorithms implemented on the phone to detect the front views and back views of moving vehicles and ii) exploits phone APIs to save energy by running the vehicle detection algorithm only during active calls. We present our initial design, implementation and evaluation of the WalkSafe App that is capable of real-time detection of the front and back views of cars, indicating cars are approaching or moving away from the user, respectively. WalkSafe is implemented on Android phones and alerts the user of unsafe conditions using sound and vibration from the phone. WalkSafe is available on Android Market.
RObOTiC fiNGERSPELLiNG HAND fOR THE AiD Of THE DEAf AND bLiND

EXECUTiVE SUMMARY This quarter, work continued on the design and construction of a robotic fingerspelling hand. The hand is being designed to aid in communication for individuals who are both deaf and blind. In the winter quarter, research was centered on determining an effective method of actuation for the robotic hand. This spring 2008 quarter, time was spent designing the mechanisms needed to mimic the size and motions of a human hand. Several methods were used to determine a proper size for the robotic hand, including using the ManneQuinPro human modeling system to approximate the size of an average male human hand and using the golden ratio to approximate the length of bone sections within the hand. After a proper average hand size was determined, a finger mechanism was designed in the SolidWorks design program that could be built and used in the robotic hand.
Semiotics and the Historical Sciences

Lotman says that the science of history has been caught in an unfortunate position between the description of Hegel’s idealism of the logical movement of history through great events on the one hand and the new school’s picture of immobile history rooted in the practice and mentality of the collective on the other.
Center for the Study of Reading Cognitive Apprenticeship: Teaching the Craft of Reading, Writing, and Mathematics Cognitive Apprenticeship: Teaching the Craft of Reading, Writing, and Mathematics 1. Schooling and Apprenticeship 2. Three Success Models for Cognitive Apprenticeship

Noting that skills and knowledge taught in schools have become abstracted from their uses in the world, this paper clarifies some of the implications for the nature of the knowledge that students acquire through a i;roposal for the retooling of apprenticeship methods for the teaching and learning of cognitive skills. The paper specifically proposes the development of a new cognitive apprenticeship to teach students the thinking and problem-solving skills involved in school subjects such as reading, writing, and mathematics. The first section of the paper, after discussing key shortcomings in current curricular and pedagogical practices, presents some of the structural features of traditional apprenticeship, detailing what would be required to adapt these characteristics to the teaching and learning of cognitive skills. The central section of the paper considers three recently developed pedagogical models that exemplify aspects of apprenticeship methods in teaching thinking and reasoning skills. The section notes that these methods--A. S. Palincsar and A. L. Brown's reciprocal reading teaching, M. Scardamalia and C. Bereiter's procedural facilitation of writing, and A. H. Schoenfeld's method for teaching mathematical problem solving--appear to develop successfully not only the cognitive, but also the metacognitive, skills required for true expertise. The final section organizes ideas on the purposes and characteristics of successful teaching into a general framework for the design of learning "environments," including the content being taught, pedagogical methods employed, sequencing of learning activities, and the sociology of learning--emphasizing how cognitive apprenticeship goes beyond the techniques of traditional apprenticeship. Tables of data are included, and references are appended. (Author/NKA) CENTER FOR THE STUDY OF READING Technical Report No. 403 COGNITIVE APPRENTICESHIP: TEACHING THE CRAFT OF READING, WRITING, AND MATHEMATICS Allan Collins BBN Laboratories John Seely Brown Susan E. Newman Xerox Palo Alto Research Center
Towards Multilevel Human Body Modeling and Tracking in 3 D : Investigation in Laplacian Eigenspace ( LE ) Based Initialization and Kinematically Constrained Gaussian Mixture Modeling ( KC-GMM )

Vision-based automatic human body pose estimation has many potential applications and it is also a challenging task. Together, these two factors have made vision-based human body pose estimation an attractive research area with closely related research areas including body pose, hand pose, and head pose estimation. Up to now, these research works however only deal with each task of estimating body pose, hand pose or head pose separately. In this paper, we bring out the issue of multilevel human body pose estimation and focus on model based methods for articulated human body pose estimation using volumetric data (voxel data). Important steps in this kind of methods will be described and several recent techniques will be analyzed and compared. Based on this analysis, we propose a fairly general method combining the discovered properties of LE transformation in [23] and KC-GMM method [3] for automatic initialization and tracking of both body model and hand model using voxel data. We also propose a framework for human body pose estimation at multilevel (i.e. body pose, hand pose, head pose) in an integrated way. The proposed method and framework will be presented along with experimental support and other possible avenues for future work in the area will be discussed.
Environmental and sow-related factors affecting the duration of farrowing.

A short duration of farrowing is important for piglet survival as a delay can increase the number of stillborn. Many factors may affect the duration of farrowing, including breed, age of the sow, length of gestation, number of piglets born, housing (CRATE vs. PEN), body condition of the sow and state of constipation. The aim of the present study was to investigate these factors and how they interact with each other and thus increasing the risk of prolonged farrowing. The total duration of farrowing and average piglet birth interval were recorded in 172 sows from two herds (HERD-1, n = 76; HERD-2, n = 96). Back-fat measurements and intestinal activity (based on the mean of a constipation index) were measured in all 172 sows. The total duration of farrowing was 272 +/- 152 min (mean +/- SD, n = 172): 301 +/- 165 min (n = 115) in the CRATE group and 212 +/- 95 min (n = 57; P < 0.05) in the PEN group. The average piglet birth interval was 26 +/- 25 min (mean +/- SD, n = 172): 29 +/- 29 min (n = 115) in the CRATE group and 19 +/- 10 min (n = 57; P < 0.05) in the PEN group. Housing (P < 0.05), stillborn (P < 0.001), back-fat average (P < 0.001) and constipation index (P < 0.05) significantly correlated with the duration of farrowing. In conclusion, allowing the sow to move freely before farrowing, reducing the constipation state and avoid excessive fattening during late pregnancy all appear to be key factors in shortening farrowing time and reducing perinatal mortality.
Sematch: Semantic similarity framework for Knowledge Graphs

Sematch is an integrated framework for the development, evaluation and application of semantic similarity for Knowledge Graphs. The framework provides a number of similarity tools and datasets, and allows users to compute semantic similarity scores of concepts, words, and entities, as well as to interact with Knowledge Graphs through SPARQL queries. Sematch focuses on knowledge-based semantic similarity that relies on structural knowledge in a given taxonomy (e.g. depth, path length, least common subsumer), and statistical information contents. Researchers can use Sematch to develop and evaluate semantic similarity metrics and exploit these metrics in applications. © 2017 Elsevier B.V. All rights reserved.
The use of the area under the ROC curve in the evaluation of machine learning algorithms

-In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Nearest Neighbours, and a Quadratic Discriminant Function) on six "real world" medical diagnostics data sets. We compare and discuss the use of AUC to the more conventional overall accuracy and find that AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invafiant to a priori class probabilities. The paper concludes with the recommendation that AUC be used in preference to overall accuracy for "single number" evaluation of machine learning algorithms. © 1997 Pattern Recognition Society. Published by Elsevier Science Ltd. The ROC curve Cross-validation The area under the ROC curve (AUC) Wilcoxon statistic Standard error Accuracy measures
Applications of irradiance tensors to the simulation of non-Lambertian phenomena

We present new techniques for computing illumination from non-diffuse luminaires and scattering from non-diffuse surfaces. The methods are based on new closed-form expressions derived using a generalization of irradiance known as irradiance tensors. The elements of these tensors are angular moments, weighted integrals of the radiation field that are useful in simulating a variety of non-diffuse phenomena. Applications include the computation of irradiance due to directionally-varying area light sources, reflections from glossy surfaces, and transmission through glossy surfaces. The principles apply to any emission, reflection, or transmission distribution expressed as a polynomial over the unit sphere. We derive expressions for a simple but versatile subclass of these functions, called axial moments, and present complete algorithms their exact evaluation in polyhedral environments. The algorithms are demonstrated by simulating Phong-like emission and scattering effects. CR
Features Based IUGR Diagnosis Using Variational Level Set Method and Classification Using Artificial Neural Networks

Intrauterine growth restriction (IUGR) is the failure of the fetus to achieve his/her intrinsic growth potential. IUGR results in significant perinatal and long-term complications, including the development of insulin resistance/metabolic syndrome in adulthood [5]. Accurate and effective monitoring of fetal growth is one of the key component of prenatal care [3]. Ultrasound evaluation is considered the cornerstone of diagnosis and surveillance of the growth-restricted fetus [2]. Ultrasound measurements play a significant role in obstetrics as an accurate means for the estimation of the fetal age. Several parameters are used as aging parameters, the most important of which are the biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC) and femur length (FL). Serial measurement of these parameters over time is used to determine the fetal condition. Hence, consistency and reproducibility of measurements is an important issue. Consequently the automatic segmentation of anatomical structures in ultrasound imagery is a real challenge due to acoustic interferences (speckle noise) and artifacts which are inherent in these images. In this paper, a novel method is proposed for developing a Computer Aided Diagnosis (CAD) system for diagnosis and classification of IUGR foetuses. Diagnosis is performed by segmenting and extracting the required foetus features from an ultrasound image, using the Re-initialization free level set with Reaction Diffusion (RD) technique. An artificial neural network (ANN) classifier is developed, the features extracted are provided to the designed ANN model. The ANN then classifies normal and abnormal fetuses based on features provided.
Identifying Program Characteristics for Preparing Pre-Service Teachers for Diversity.

Background/Context: Educating pre-service teachers to develop multicultural awareness, knowledge, and skills for teaching diverse students is a major responsibility of teacher education program coordinators and teacher educators. Numerous studies have discussed and explored the characteristics of teacher preparation that improve pre-service teachers’ competency to teach diverse students. However, only a few empirical studies have examined the relationship between preparation characteristics and pre-service teachers’ multicultural awareness, knowledge, and skills. Research Questions: The study answered two research questions: 1) How do the initial level and change in pre-service teachers’ beliefs about diversity in personal and professional contexts differ by their background characteristics? and 2) What characteristics of teacher preparation for diversity reported by pre-service teachers are associated with positive changes in their beliefs about diversity in personal and professional contexts, controlling for their background characteristics? Participants: The participants were 243 pre-service teachers enrolled in eight sections in a diversity course and accompanied field experience component in a teacher education program in a Midwest Research I university. Research Design: Preand post-surveys were conducted to examine: 1) pre-service teachers’ background characteristics (gender, socioeconomic status, class standing, hometown location, and prior exposure to diversity), 2) beliefs about diversity in personal and professional contexts, and 3) four characteristics of teacher preparation for diversity: classroom as a learning community; instructor modeling constructivist and culturally-responsive teaching; field experience for understanding diverse students; and opportunity for reflection. Findings: The study found that three characteristics of teacher preparation for diversity
Qualitative and quantitative cell recovery in umbilical cord blood processed by two automated devices in routine cord blood banking: a comparative study.

BACKGROUND
Volume reduction is a widely used procedure in umbilical cord blood banking. It concentrates progenitor cells by reducing plasma and red blood cells, thereby optimising the use of storage space. Sepax and AXP are automated systems specifically developed for umbilical cord blood processing. These systems basically consist of a bag processing set into which cord blood is transferred and a device that automatically separates the different components during centrifugation.


METHODS
The aim of this study was to analyse and compare cell recovery of umbilical cord blood units processed with Sepax and AXP at Valencia Cord Blood Bank. Cell counts were performed before and after volume reduction with AXP and Sepax.


RESULTS
When analysing all the data (n =1,000 for AXP and n= 670 for Sepax), the percentages of total nucleated cell recovery and red blood cell depletion were 76.76 ± 7.51% and 88.28 ± 5.62%, respectively, for AXP and 78.81 ± 7.25% and 88.32 ± 7.94%, respectively, for Sepax (P <0.005 for both variables). CD34(+) cell recovery and viability in umbilical cord blood units were similar with both devices. Mononuclear cell recovery was significantly higher when the Sepax system was used.


DISCUSSION
Both the Sepax and AXP automated systems achieve acceptable total nucleated cell recovery and good CD34(+) cell recovery after volume reduction of umbilical cord blood units and maintain cell viability. It should be noted that total nucleated cell recovery is significantly better with the Sepax system. Both systems deplete red blood cells efficiently, especially AXP which works without hydroxyethyl starch.