LLukas22/scidocs huggingface dataset:
Diagnostic accuracy of endometrial thickness for the detection of intra-uterine pathologies and appropriateness of performed hysteroscopies among asymptomatic postmenopausal women.

OBJECTIVE
To measure the diagnostic accuracy of endometrial thickness for the detection of intra-uterine pathologies among asymptomatic postmenopausal women, and to test the diagnostic accuracy and appropriateness of performed hysteroscopies.


STUDY DESIGN
Prospective study of 268 asymptomatic postmenopausal women with endometrial thickness ≥4 mm referred to diagnostic hysteroscopy. The diagnostic accuracy of various endometrial thickness cut-off values was tested. Histological and hysteroscopic results were compared to measure the diagnostic accuracy of outpatient hysteroscopies.


RESULTS
No endometrial thickness cut-off values had optimal diagnostic accuracy [positive likelihood ratio (LR+) >10 and negative likelihood ratio (LR-) <0.1]. The best endometrial thickness cut-off value for the detection of all intra-uterine pathologies was ≥8 mm (LR+ 10.05 and LR- 0.22). An endometrial thickness cut-off value ≥10 mm did not miss any cases of endometrial cancer. The success rate of diagnostic hysteroscopy was 89%, but 97% of these revealed a benign intra-uterine pathology. The diagnostic accuracy of hysteroscopy was optimal for all intra-uterine pathologies, except endometrial hyperplasia (LR- 0.52).


CONCLUSION
Using an endometrial thickness cut-off value ≥4 mm, only 3% of performed hysteroscopies were useful for the detection of pre-malignant or malignant lesions. Despite the finding that endometrial thickness did not show optimal diagnostic accuracy, using the best cut-off value (≥8 mm) may be helpful to decrease the number of false-positive results. No cases of endometrial cancer were diagnosed in asymptomatic postmenopausal women with endometrial thickness <10mm.
Clinical significance of CTNNB1 mutation and Wnt pathway activation in endometrioid endometrial carcinoma.

BACKGROUND
Endometrioid endometrial carcinoma (EEC) is the most common form of endometrial carcinoma. The heterogeneous clinical course of EEC is an obstacle to individualized patient care.


METHODS
We performed an integrated analysis on the multiple-dimensional data types including whole-exome and RNA sequencing, RPPA profiling, and clinical data from 271 EEC cases in The Cancer Genome Atlas (TCGA) to identify molecular fingerprints that may account for this clinical heterogeneity. Significance analysis of microarray was used to identify marker genes of each subtype that were subject to pathway analysis. Association of molecular subtypes with clinical features and mutation data was analyzed with the Mann Whitney, Chi-square, Fisher's exact, and Kruskal-Wallis tests. Survival analysis was evaluated with log-rank test. All statistical tests were two-sided.


RESULTS
Four transcriptome subtypes with distinct clinicopathologic characteristics and mutation spectra were identified from the TCGA dataset and validated in an independent sample cohort of 184 EEC cases. Cluster II consisted of younger, obese patients with low-grade EEC but diminished survival. CTNNB1 exon 3 mutations were present in 87.0% (47/54) of Cluster II (P < .001) that exhibited a low overall mutation rate; this was statistically significantly associated with Wnt/β-catenin signaling activation (P < .001). High expression levels of CTNNB1 (P = .001), MYC (P = .01), and CCND1 (P = .01) were associated with poorer overall survival in low-grade EEC tumors.


CONCLUSIONS
CTNNB1 exon 3 mutations are likely a driver that characterize an aggressive subset of low-grade and low-stage EEC occurring in younger women.
Machine Learning for Bioelectromagnetics: Prediction Model using Data of Weak Radiofrequency Radiation Effect on Plants

Plant sensitivity and its bio-effects on non-thermal weak radio-frequency electromagnetic fields (RF-EMF) identifying key parameters that affect plant sensitivity that can change/unchange by using big data analytics and machine learning concepts are quite significant. Despite its benefits, there is no single study that adequately covers machine learning concept in Bioelectromagnetics domain yet. This study aims to demonstrate the usefulness of Machine Learning algorithms for predicting the possible damages of electromagnetic radiations from mobile phones and base station on plants and consequently, develops a prediction model of plant sensitivity to RF-EMF. We used rawdata of plant exposure from our previous review study (extracted data from 45 peer-reviewed scientific publications published between 1996-2016 with 169 experimental case studies carried out in the scientific literature) that predicts the potential effects of RF-EMF on plants. We also used values of six different attributes or parameters for this study: frequency, specific absorption rate (SAR), power flux density, electric field strength, exposure time and plant type (species). The results demonstrated that the adaptation of machine learning algorithms (classification and clustering) to predict 1) what conditions will RF-EMF exposure to a plant of a given species may not produce an effect; 2) what frequency and electric field strength values are safer; and 3) which plant species are affected by RF-EMF. Moreover, this paper also illustrates the development of optimal attribute selection protocol to identify key parameters that are highly significant when designing the in-vitro practical standardized experimental protocols. Our analysis also illustrates that Random Forest classification algorithm outperforms with highest classification accuracy by 95.26% (0.084 error) with only 4% of fluctuation among algorithm measured. The results clearly show that using K-Means clustering algorithm, demonstrated that the Pea, Mungbean and Duckweeds plants are more sensitive to RF-EMF (p ≤ 0.0001). The sample size of reported 169 experimental case studies, perhaps low significant in a statistical sense, nonetheless, this analysis still provides useful insight of exploiting Machine Learning in Bioelectromagnetics domain. As a direct outcome of this research, more efficient RF-EMF exposure prediction tools can be developed to improve the quality of epidemiological studies and the long-term experiments using whole organisms. Keywords—Machine learning; plants; prediction; mobile phones; base station; radiofrequency electromagnetic fields; RFEMF; plant sensitivity; classification; clustering
The New England Air Quality Forecasting Pilot Program: development of an evaluation protocol and performance benchmark.

The National Oceanic and Atmospheric Administration recently sponsored the New England Forecasting Pilot Program to serve as a "test bed" for chemical forecasting by providing all of the elements of a National Air Quality Forecasting System, including the development and implementation of an evaluation protocol. This Pilot Program enlisted three regional-scale air quality models, serving as prototypes, to forecast ozone (O3) concentrations across the northeastern United States during the summer of 2002. A suite of statistical metrics was identified as part of the protocol that facilitated evaluation of both discrete forecasts (observed versus modeled concentrations) and categorical forecasts (observed versus modeled exceedances/nonexceedances) for both the maximum 1-hr (125 ppb) and 8-hr (85 ppb) forecasts produced by each of the models. Implementation of the evaluation protocol took place during a 25-day period (August 5-29), utilizing hourly O3 concentration data obtained from over 450 monitors from the U.S. Environment Protection Agency's Air Quality System network.
A Novel Solution of Distributed Memory NoSQL Database for Cloud Computing

The traditional relational database to online storage are becoming increasingly problematic: the low performance do not gracefully meet the needs of mass data, the storage approaches of massive data are still not perfect presents. NoSQL and distributed memory database technologies have the potential to simplify or eliminate many of these challenges. NoSQL database technologies can provide Key-value style of data storage and largely ensure high performance. Distributed memory database technologies provide a means for easily store mass data in cloud in a dynamic and scalable manner. This paper argues for a new architecture called CDSA, which is a distributed memory NoSQL database architecture for Cloud computing to improve the performance of querying data and ensure mass data storage in cloud by using rational strategy. Furthermore, add or deleted any node from the distributed database cluster, the others node can work without stop service. We believe that CDSA can provide durable storage with high throughput and lower access latency.
Enhanced creativity in bipolar disorder patients: a controlled study.

OBJECTIVE
Associations between eminent creativity and bipolar disorders have been reported, but there are few data relating non-eminent creativity to bipolar disorders in clinical samples. We assessed non-eminent creativity in euthymic bipolar (BP) and unipolar major depressive disorder (MDD) patients, creative discipline controls (CC), and healthy controls (HC).


METHODS
49 BP, 25 MDD, 32 CC, and 47 HC (all euthymic) completed four creativity measures yielding six parameters: the Barron-Welsh Art Scale (BWAS-Total, and two subscales, BWAS-Dislike and BWAS-Like), the Adjective Check List Creative Personality Scale (ACL-CPS), and the Torrance Tests of Creative Thinking--Figural (TTCT-F) and Verbal (TTCT-V) versions. Mean scores on these instruments were compared across groups.


RESULTS
BP and CC (but not MDD) compared to HC scored significantly higher on BWAS-Total (45% and 48% higher, respectively) and BWAS-Dislike (90% and 88% higher, respectively), but not on BWAS-Like. CC compared to MDD scored significantly higher (12% higher) on TTCT-F. For all other comparisons, creativity scores did not differ significantly between groups.


CONCLUSIONS
We found BP and CC (but not MDD) had similarly enhanced creativity on the BWAS-Total (driven by an increase on the BWAS-Dislike) compared to HC. Further studies are needed to determine the mechanisms of enhanced creativity and how it relates to clinical (e.g. temperament, mood, and medication status) and preclinical (e.g. visual and affective processing substrates) parameters.
Commonsense Conceptions of Emergent Processes: Why Some Misconceptions Are Robust.

This article offers a plausible domain-general explanation for why some concepts of processes are resistant to instructional remediation although other, apparently similar concepts are more easily understood. The explanation assumes that processes may differ in ontological ways: that some processes (such as the apparent flow in diffusion of dye in water) are emergent and other processes (such as the flow of blood in human circulation) are direct. Although precise definition of the two kinds of processes are probably impossible, attributes of direct and emergent processes are described that distinguish them in a domain-general way. Circulation and diffusion, which are used as examples of direct and emergent processes, are associated with different kinds of misconceptions. The claim is that students’ misconceptions for direct kinds of processes, such as blood circulation, are of the same ontological kind as the correct conception, suggesting that misconceptions of direct processes may be nonrobust. However, students’ misconceptions of emergent processes are robust because they misinterpret emergent processes as a kind of commonsense direct processes. To correct such a misconception requires a re-representation or a conceptual shift across ontological kinds. Therefore, misconceptions of emergent processes are robust because such a shift requires that students know about the emergent kind and can overcome their (perhaps even innate) predisposition to conceive of all processes as a direct kind. Such a domain-general explanation suggests that teaching students the causal structure underlying emergent processes may enable them to recognize and understand a variety of emergent processes for which they have robust misconceptions, such as concepts of electricity, heat and temperature, and evolution. THE JOURNAL OF THE LEARNING SCIENCES, 14(2), 161–199 Copyright © 2005, Lawrence Erlbaum Associates, Inc.
Stair Climber Smart Mobile Robot (MSRox)

MSRox is a wheeled mobile robot with two actuated degrees of freedom which enables it to have smooth motion on flat surfaces. It has the capability of climbing stairs and traversing obstacles, and adaptability toward uphill, downhill and slope surfaces. MSRox with 82 cm in length, 54 cm in width and 29 cm in height has been designed to climb stairs of 10 cm in height and 15 cm in width; nevertheless, it has the capability of climbing stairs up to about 17 cm in height and unlimited widt. In this paper, the motion systems and the capabilities of MSRox are described. Furthermore, experimental results of stair climbing and a comparison of the results with others are presented.
Statistical Parsing of Morphologically Rich Languages (SPMRL) What, How and Whither

The term Morphologically Rich Languages (MRLs) refers to languages in which significant information concerning syntactic units and relations is expressed at word-level. There is ample evidence that the application of readily available statistical parsing models to such languages is susceptible to serious performance degradation. The first workshop on statistical parsing of MRLs hosts a variety of contributions which show that despite languagespecific idiosyncrasies, the problems associated with parsing MRLs cut across languages and parsing frameworks. In this paper we review the current state-of-affairs with respect to parsing MRLs and point out central challenges. We synthesize the contributions of researchers working on parsing Arabic, Basque, French, German, Hebrew, Hindi and Korean to point out shared solutions across languages. The overarching analysis suggests itself as a source of directions for future investigations.
The pH-dependent decomposition of .beta.-hydroxy-substituted organophosphorus complexants

Substituted ethane-1,1-diphosphonic acids containing a {beta}-hydroxyl group undergo facile pH-dependent decomposition to phosphoric acid and acetylphosphonate. The decomposition is observed in acidic medium, while the system is stable in basic solutions.
Exercise testing in hypertensive patients taking different angiotensin-converting enzyme inhibitors.

OBJECTIVE
To compare blood pressure response to dynamic exercise in hypertensive patients taking trandolapril or captopril.


METHODS
We carried out a prospective, randomized, blinded study with 40 patients with primary hypertension and no other associated disease. The patients were divided into 2 groups (n=20), paired by age, sex, race, and body mass index, and underwent 2 symptom-limited exercise tests on a treadmill before and after 30 days of treatment with captopril (75 to 150 mg/day) or trandolapril (2 to 4 mg/day).


RESULTS
The groups were similar prior to treatment (p<0.05), and both drugs reduced blood pressure at rest (p<0.001). During treatment, trandolapril caused a greater increase in functional capacity (+31%) than captopril (+17%; p=0.01) did, and provided better blood pressure control during exercise, observed as a reduction in the variation of systolic blood pressure/MET (trandolapril: 10.7 1.9 mmHg/U vs 7.4 1.2 mmHg/U, p=0.02; captopril: 9.1 1.4 mmHg/U vs 11.4 2.5 mmHg/U, p=0.35), a reduction in peak diastolic blood pressure (trandolapril: 116.8 3.1 mmHg vs 108.1 2.5 mmHg, p=0.003; captopril: 118.2 3.1 mmHg vs 115.8 3.3 mmHg, p=0.35), and a reduction in the interruption of the tests due to excessive elevation in blood pressure (trandolapril: 50% vs 15%, p=0.009; captopril: 50% vs 45%, p=0.32).


CONCLUSION
Monotherapy with trandolapril is more effective than that with captopril to control blood pressure during exercise in hypertensive patients.
Intergroup relations and health disparities: a social psychological perspective.

OBJECTIVE
This article considers how the social psychology of intergroup processes helps to explain the presence and persistence of health disparities between members of socially advantaged and disadvantaged groups.


METHOD
Social psychological theory and research on intergroup relations, including prejudice, discrimination, stereotyping, stigma, prejudice concerns, social identity threat, and the dynamics of intergroup interactions, is reviewed and applied to understand group disparities in health and health care. Potential directions for future research are considered.


RESULTS
Key features of group relations and dynamics, including social categorization, social hierarchy, and the structural positions of groups along dimensions of perceived warmth and competence, influence how members of high status groups perceive, feel about, and behave toward members of low status groups, how members of low status groups construe and cope with their situation, and how members of high and low status groups interact with each other. These intergroup processes, in turn, contribute to health disparities by leading to differential exposure to and experiences of chronic and acute stress, different health behaviors, and different quality of health care experienced by members of advantaged and disadvantaged groups. Within each of these pathways, social psychological theory and research identifies mediating mechanisms, moderating factors, and individual differences that can affect health.


CONCLUSIONS
A social psychological perspective illuminates the intergroup, interpersonal, and intrapersonal processes by which structural circumstances which differ between groups for historical, political, and economic reasons can lead to group differences in health.
Remote FPGA lab platform for computer system curriculum

This paper presents a MOOC-ready online FPGA laboratory platform which targets computer system experiments. Goal of design is to provide user with highly approximate experience and results as offline experiments. Rich functions are implemented by utilizing SoC FPGA as the controller of lab board. The design details and effects are discussed in this paper.
Automatic Parallelization and Scheduling of Programs on Multiprocessors using CASCH

The lack of a versatile software tool for parallel program development has been one of the major obstacles for exploiting the potential of high-performance architectures. In this paper, we describe an experimental software tool called CASCH (Computer Aided SCHeduling) for parallelizing and scheduling applications to parallel processors. CASCH transforms a sequential program to a parallel program with automatic scheduling, mapping, communication, and synchronization. The major strength of CASCH is its extensive library of scheduling and mapping algorithms representing a broad range of state-of-the-art work reported in the recent literature. These algorithms are applied for allocating a parallelized program to the processors, and thus the algorithms can be interactively analyzed, tested and compared using real data on a common platform with various performance objectives. CASCH is useful for both novice and expert programmers of parallel machines, and can serve as a teaching and learning aid for understanding scheduling and mapping algorithms.
H∞ control of an irrigation canal pool with a mixed control politics

This paper presents a method to design efficient automatic controllers for an irrigation canal pool, that realize a compromise between the water resource management and the performance in terms of rejecting unmeasured perturbations. This mixed controller design is casted into theH∞ optimization framework, and experimentally tested on a real canal located in Portugal. The experimental results show the effectiveness of the method. We also interpret classical control politics for an irrigation canal (local upstream and distant downstream control) using automatic control tools, and show that our method enables to combine both classical politics, keeping the distant downstream control water management while recovering the local upstream control real-time performance with respect to the user.
Comparison of aligned Friedman rank and parametric methods for testing interactions in split-plot designs

Parametric methods are commonly used despite evidence that model assumptions are often violated. Various statistical procedures have been suggested for analyzing data from multiple-group repeated measures (i.e., split-plot) designs when parametric model assumptions are violated (e.g., Akritas and Arnold (J. Amer. Statist. Assoc. 89 (1994) 336); Brunner and Langer (Biometrical J. 42 (2000) 663)), including the use of Friedman ranks. The e8ects of Friedman ranking on data and the resultant test statistics for single sample repeated measures designs have been examined (e.g., Harwell and Serlin (Comput. Statist. Data Anal. 17 (1994) 35; Comm. Statist. Simulation Comput. 26 (1997) 605); Zimmerman and Zumbo (J. Experiment. Educ. 62 (1993) 75)). However, there have been fewer investigations concerning Friedman ranks applied to multiple groups of repeated measures data (e.g., Beasley (J. Educ. Behav. Statist. 25 (2000) 20); Rasmussen (British J. Math. Statist. Psych. 42 (1989) 91)). We investigate the use of Friedman ranks for testing the interaction in a split-plot design as a robust alternative to parametric procedures. We demonstrated that the presence of a repeated measures main e8ect may reduce the power of interaction tests performed on Friedman ranks. Aligning the data before applying Friedman ranks was shown to produce more statistical power than simply analyzing Friedman ranks. Results from a simulation study showed that aligning the data (i.e., removing main e8ects) before applying Friedman ranks and then performing either a univariate or multivariate test can provide more statistical power than parametric tests if the error distributions are skewed. c © 2002 Elsevier Science B.V. All rights reserved.
Can mobile usage predict illiteracy in a developing country?

The present study provides the first evidence that illiteracy can be reliably predicted from standard mobile phone logs. By deriving a broad set of mobile phone indicators reflecting users’ financial, social and mobility patterns we show how supervised machine learning can be used to predict individual illiteracy in an Asian developing country, externally validated against a large-scale survey. On average the model performs 10 times better than random guessing with a 70% accuracy. Further we show how individual illiteracy can be aggregated and mapped geographically at cell tower resolution. Geographical mapping of illiteracy is crucial to know where the illiterate people are, and where to put in resources. In underdeveloped countries such mappings are often based on out-dated household surveys with low spatial and temporal resolution. One in five people worldwide struggle with illiteracy, and it is estimated that illiteracy costs the global economy more than $1 trillion dollars each year [1]. These results potentially enable costeffective, questionnaire-free investigation of illiteracy-related questions on an unprecedented scale.
Gender and the Politics of Possibilities: Rethinking Globalization

Chapter 1 Introduction Chapter 2 Cross Border Traders Chapter 3 Transnational Feminists Chapter 4 Modemmujers Chapter 5 Conlcusion