Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptions of Part-Time Faculty by Chairpersons of Undergraduate Health Education Programs

In recent years, it has become commonplace for universities to hire part-time and non–tenure track faculty to save money. This study examined how commonly part-time faculty are used in health education and how they are used to meet program needs. The American Association of Health Education’s 2009 “Directory of Institutions Offering Undergraduate and Graduate Degree Programs in Health Education” was used to send a three-wave mailing to programs that were not schools of public health (n = 215). Of the 125 departments (58%) that responded, those that used part-time faculty averaged 7.5 part-time faculty in the previous academic year, teaching on average a total of 10 classes per year. A plurality of departments (38%) were currently using more part-time faculty than 10 years ago and 33% perceived that the number of part-time faculty has resulted in decreases in the number of full-time positions. Although 77% of department chairs claimed they would prefer to replace all of their part-time faculty with one full-time tenure track faculty member. As colleges downsize, many health education programs are using more part-time faculty. Those faculty members who take part-time positions will likely be less involved in academic activities than their full-time peers. Thus, further research is needed on the effects of these changes on the quality of health education training and department productivity.
Order Estimation of Japanese Paragraphs by Supervised Machine Learning and Various Textual Features

Abstract In this paper, we propose a method to estimate the order of paragraphs by supervised machine learning. We use a support vector machine (SVM) for supervised machine learning. The estimation of paragraph order is useful for sentence generation and sentence correction. The proposed method obtained a high accuracy (0.84) in the order estimation experiments of the first two paragraphs of an article. In addition, it obtained a higher accuracy than the baseline method in the experiments using two paragraphs of an article. We performed feature analysis and we found that adnominals, conjunctions, and dates were effective for the order estimation of the first two paragraphs, and the ratio of new words and the similarity between the preceding paragraphs and an estimated paragraph were effective for the order estimation of all pairs of paragraphs.
Frequent Gain and Loss of Introns in Fungal Cytochrome b Genes

In this study, all available cytochrome b (Cyt b) genes from the GOBASE database were compiled and the evolutionary dynamics of the Cyt b gene introns was assessed. Cyt b gene introns were frequently present in the fungal kingdom and some lower plants, but generally absent or rare in Chromista, Protozoa, and Animalia. Fungal Cyt b introns were found at 35 positions in Cyt b genes and the number of introns varied at individual positions from a single representative to 32 different introns at position 131, showing a wide and patchy distribution. Many homologous introns were present at the same position in distantly related species but absent in closely related species, suggesting that introns of the Cyt b genes were frequently lost. On the other hand, highly similar intron sequences were observed in some distantly related species rather than in closely related species, suggesting that these introns were gained independently, likely through lateral transfers. The intron loss-and-gain events could be mediated by transpositions that might have occurred between nuclear and mitochondria. Southern hybridization analysis confirmed that some introns contained repetitive sequences and might be transposable elements. An intron gain in Botryotinia fuckeliana prevented the development of QoI fungicide resistance, suggesting that intron loss-and-gain events were not necessarily beneficial to their host organisms.
Do patients with schizophrenia exhibit aberrant salience?

Background It has been suggested that some psychotic symptoms reflect ‘aberrant salience’, related to dysfunctional reward learning. To test this hypothesis we investigated whether patients with schizophrenia showed impaired learning of task-relevant stimulus–reinforcement associations in the presence of distracting task-irrelevant cues. Method We tested 20 medicated patients with schizophrenia and 17 controls on a reaction time game, the Salience Attribution Test. In this game, participants made a speeded response to earn money in the presence of conditioned stimuli (CSs). Each CS comprised two visual dimensions, colour and form. Probability of reinforcement varied over one of these dimensions (task-relevant), but not the other (task-irrelevant). Measures of adaptive and aberrant motivational salience were calculated on the basis of latency and subjective reinforcement probability rating differences over the task-relevant and task-irrelevant dimensions respectively. Results Participants rated reinforcement significantly more likely and responded significantly faster on high-probability-reinforced relative to low-probability-reinforced trials, representing adaptive motivational salience. Patients exhibited reduced adaptive salience relative to controls, but the two groups did not differ in terms of aberrant salience. Patients with delusions exhibited significantly greater aberrant salience than those without delusions, and aberrant salience also correlated with negative symptoms. In the controls, aberrant salience correlated significantly with ‘introvertive anhedonia’ schizotypy. Conclusions These data support the hypothesis that aberrant salience is related to the presence of delusions in medicated patients with schizophrenia, but are also suggestive of a link with negative symptoms. The relationship between aberrant salience and psychotic symptoms warrants further investigation in unmedicated patients.
Color Image Edge Detection using Pseudo-Complement and Matrix Operations

A color image edge detection algorithm is proposed in this paper using Pseudo-complement and matrix rotation operations. First, pseudo-complement method is applied on the image for each channel. Then, matrix operations are applied on the output image of the first stage. Dominant pixels are obtained by image differencing between the pseudo-complement image and the matrix operated image. Median filtering is carried out to smoothen the image thereby removing the isolated pixels. Finally, the dominant or core pixels occurring in at least two channels are selected. On plotting the selected edge pixels, the final edge map of the given color image is obtained. The algorithm is also tested in HSV and YCbCr color spaces. Experimental results on both synthetic and real world images show that the accuracy of the proposed method is comparable to other color edge detectors. All the proposed procedures can be applied to any image domain and runs in polynomial time. Keywords—Color edge detection, dominant pixels, matrix rotation/shift operations, pseudo-complement. complement acts like a low pass filter and highlights the weak intensity pixels. The nature of the pseudo-complement process is that it reduces the gray intensity distribution to 50% i.e., a gray level image having the intensities in the range 0-255 will be reduced to the intensities of the range 0-128 thereby the weak intensity pixels will be highlighted. In (13), a new method to extract dominant pixels has been introduced. These dominant pixels will represent the core pixels of any image. To achieve this, they used various matrix rotation operations. In the proposed method, we have used the pseudo- complement approach as well as dominant pixel method for extracting the edge pixels of the image. Initially, the algorithm is tested on RGB images, where in each channel is separately processed by the pseudo-complement operation followed by matrix rotation operations. Then, any dominant pixel occurring in at least any two channels are taken and applied median filtering to extract the edge of the given color image. The proposed algorithm is further tested on HSV and YUV color spaces also. In addition, the algorithm is tested on both synthetic and real images and the results are compared with the results of the other methods. All the procedures used in the algorithm runs in polynomial time. The rest of the paper is organized as follows. In Section II, various color spaces and their inter conversion is introduced. In sections III and IV, Pseudo-complement approach and dominant pixel identification using various matrix rotation operations are discussed respectively. The proposed methodology is presented in Section V and the experimental results are presented in Section VI. Section VII concludes the work.
Fuzzy and Neural Controllers for a Pneumatic Actuator

There is a great diversity of ways to use fuzzy inference in robot control systems, either in the place where it is applied in the control scheme or in the form or type of inference algorithms used. On the other hand, artificial neural networks ability to simulate nonlinear systems is used in different researches in order to develop automated control systems of industrial processes. In these applications of neural networks, there are two important steps: system identification (development of neural process model) and development of control (definition of neural control structure). In this paper we present some modelling applications, which uses fuzzy and neural controllers, developed on a pneumatic actuator containing a force and a position sensor, which can be used for robotic grinding operations. Following the simulation one of the algorithms was tested on an experimental setup. The paper also presents the development of a NARMA-L2 neural controller for a pneumatic actuator using position feedback. The structure had been trained and validated, obtaining good results.
New Estimators and Guidelines for Better Use of Fetal Heart Rate Estimators with Doppler Ultrasound Devices

Characterizing fetal wellbeing with a Doppler ultrasound device requires computation of a score based on fetal parameters. In order to analyze the parameters derived from the fetal heart rate correctly, an accuracy of 0.25 beats per minute is needed. Simultaneously with the lowest false negative rate and the highest sensitivity, we investigated whether various Doppler techniques ensure this accuracy. We found that the accuracy was ensured if directional Doppler signals and autocorrelation estimation were used. Our best estimator provided sensitivity of 95.5%, corresponding to an improvement of 14% compared to the standard estimator.
Quinacrine promotes replication and conformational mutation of chronic wasting disease prions

Significance Searching for drugs to prevent conversion of host-encoded prion protein (PrPC) to its infectious conformation (PrPSc) is a key strategy in the pursuit of therapies for prion disorders: fatal, transmissible epidemic diseases of unpredictable occurrence and uncertain zoonotic potential. Despite quinacrine’s ability to reduce mouse PrPSc in cell models, its use to treat patients has been unsuccessful. Here, we show that quinacrine augments PrPSc and intensifies replication of prions, causing chronic wasting disease of deer, elk, and moose, and that the resulting prions have altered conformational and transmission properties. Our finding, that a drug capable of restraining PrPSc in one species acts to improve replicative ability and induce mutation in another, forces reexamination of current strategies to combat these diseases. Quinacrine’s ability to reduce levels of pathogenic prion protein (PrPSc) in mouse cells infected with experimentally adapted prions led to several unsuccessful clinical studies in patients with prion diseases, a 10-y investment to understand its mechanism of action, and the production of related compounds with expectations of greater efficacy. We show here, in stark contrast to this reported inhibitory effect, that quinacrine enhances deer and elk PrPSc accumulation and promotes propagation of prions causing chronic wasting disease (CWD), a fatal, transmissible, neurodegenerative disorder of cervids of uncertain zoonotic potential. Surprisingly, despite increased prion titers in quinacrine-treated cells, transmission of the resulting prions produced prolonged incubation times and altered PrPSc deposition patterns in the brains of diseased transgenic mice. This unexpected outcome is consistent with quinacrine affecting the intrinsic properties of the CWD prion. Accordingly, quinacrine-treated CWD prions were comprised of an altered PrPSc conformation. Our findings provide convincing evidence for drug-induced conformational mutation of prions without the prerequisite of generating drug-resistant variants of the original strain. More specifically, they show that a drug capable of restraining prions in one species/strain setting, and consequently used to treat human prion diseases, improves replicative ability in another and therefore force reconsideration of current strategies to screen antiprion compounds.
Phase Transitions in Machine Learning

Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.
Prognostic Value of Renal Dysfunction Indicators in Normotensive Patients With Acute Pulmonary Embolism

Introduction: Glomerular filtration rate (GFR) and blood urea nitrogen (BUN) are important prognostic indicators for cardiovascular disease. However, data on the relationship between renal dysfunction (RD) and prognosis in patients with acute pulmonary embolism (APE) are limited. The estimated-GFR (eGFR), based on the Modification of Diet in Renal Disease (MDRD) equation, has been suggested as a possible prognostic marker in patients with APE; however, at present, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation is thought to be more accurate than the MDRD equation for the estimation of RD. Objective: We investigated whether eGFRCKD-EPI or BUN could predict adverse outcomes (AOs) better than eGFRMDRD in normotensive patients with APE. Methods: Ninety-nine normotensive patients with APE (aged 22-96, 56% male) were enrolled in the study retrospectively. Adverse outcomes were defined as the occurrence of any of the following: death, cardiopulmonary resuscitation, use of vasopressors, thrombolysis, or mechanical ventilation. Results: In univariate analyses, age, gender (male), heart rate (>110 bpm), serum creatinine, BUN, cardiac troponin (cTn) positivity, right ventricle–left ventricle ratio, eGFRMDRD, and eGFRCKD-EPI were found to be significantly different between those with and without AOs. Comparing area under the curves for AO, we found statistically significant differences between eGFRCKD-EPI and eGFRMDRD (P = .01) but not between BUN and eGFRCKD-EPI or BUN and eGFRMDRD. Furthermore, 30-day mortality was 36% versus 11% in cTn-positive patients with an eGFRCKD-EPI < and ≥ 60 mL/min, respectively. Conclusion: There is a close relationship between RD and APE prognosis. We conclude eGFRCKD-EPI is a potential prognostic marker for risk stratification in normotensive patients with APE.
New insights into the covariation of stomatal, mesophyll and hydraulic conductances from optimization models incorporating nonstomatal limitations to photosynthesis.

Optimization models of stomatal conductance (gs ) attempt to explain observed stomatal behaviour in terms of cost--benefit tradeoffs. While the benefit of stomatal opening through increased CO2 uptake is clear, currently the nature of the associated cost(s) remains unclear. We explored the hypothesis that gs maximizes leaf photosynthesis, where the cost of stomatal opening arises from nonstomatal reductions in photosynthesis induced by leaf water stress. We analytically solved two cases, CAP and MES, in which reduced leaf water potential leads to reductions in carboxylation capacity (CAP) and mesophyll conductance (gm ) (MES). Both CAP and MES predict the same one-parameter relationship between the intercellular : atmospheric CO2 concentration ratio (ci /ca ) and vapour pressure deficit (VPD, D), viz. ci /ca ≈ ξ/(ξ + √D), as that obtained from previous optimization models, with the novel feature that the parameter ξ is determined unambiguously as a function of a small number of photosynthetic and hydraulic variables. These include soil-to-leaf hydraulic conductance, implying a stomatal closure response to drought. MES also predicts that gs /gm is closely related to ci /ca and is similarly conservative. These results are consistent with observations, give rise to new testable predictions, and offer new insights into the covariation of stomatal, mesophyll and hydraulic conductances.
Quantifying the Economic and Cultural Biases of Social Media through Trending Topics

Online social media has recently irrupted as the last major venue for the propagation of news and cultural content, competing with traditional mass media and allowing citizens to access new sources of information. In this paper, we study collectively filtered news and popular content in Twitter, known as Trending Topics (TTs), to quantify the extent to which they show similar biases known for mass media. We use two datasets collected in 2013 and 2014, including more than 300.000 TTs from 62 countries. The existing patterns of leader-follower relationships among countries reveal systemic biases known for mass media: Countries concentrate their attention to small groups of other countries, generating a pattern of centralization in which TTs follow the gradient of wealth across countries. At the same time, we find subjective biases within language communities linked to the cultural similarity of countries, in which countries with closer cultures and shared languages tend to follow each other’s TTs. Moreover, using a novel methodology based on the Google News service, we study the influence of mass media in TTs for four countries. We find that roughly half of the TTs in Twitter overlap with news reported by mass media, and that the rest of TTs are more likely to spread internationally within Twitter. Our results confirm that online social media have the power to independently spread content beyond mass media, but at the same time social media content follows economic incentives and is subject to cultural factors and language barriers.
Defect Prediction in Software Entities Classified in Terms of Level Dependencies

Unit testing is the core fundamental to ensure code is in accordance with the design specifications. The coding and unit testing standard reflects the stability of project (not to mention the testing effort). Code stability is greatly influenced by the efforts of unit testing, which can be automated to reduce the human efforts. In spite of several tools identified for unit testing, tools need to be able to identify the level dependencies or depth of program entity usage in software fragments. This factor greatly influences unit testing complexity. Higher the level of dependency, the greater the complexity of unit testing the code. Here based on level dependencies we predict defects in any expression. A predicting defect-prone software component is an economically important activity and so has received a good deal of attention. However, making sense of the many, and sometimes seemingly inconsistent, a result is difficult. The main objectives of this paper are unbiased and comprehensive comparison between competing prediction systems. This paper mainly focuses on two learning algorithms OneR, Naive Bayes. By using those two algorithms we calculate the error rate. We can predict defects based on those error rates. KeywordsUnit Testing, Level Dependency, Defect Prediction.
Determination of Benefits of Cochlear Implantation in Children with Auditory Neuropathy

Background Auditory neuropathy (AN) is a recently recognized hearing disorder characterized by intact outer hair cell function, disrupted auditory nerve synchronization and poor speech perception and recognition. Cochlear implants (CIs) are currently the most promising intervention for improving hearing and speech in individuals with AN. Although previous studies have shown optimistic results, there was large variability concerning benefits of CIs among individuals with AN. The data indicate that different criteria are needed to evaluate the benefit of CIs in these children compared to those with sensorineural hearing loss. We hypothesized that a hierarchic assessment would be more appropriate to evaluate the benefits of cochlear implantation in AN individuals. Methods Eight prelingual children with AN who received unilateral CIs were included in this study. Hearing sensitivity and speech recognition were evaluated pre- and postoperatively within each subject. The efficacy of cochlear implantation was assessed using a stepwise hierarchic evaluation for achieving: (1) effective audibility, (2) improved speech recognition, (3) effective speech, and (4) effective communication. Results The postoperative hearing and speech performance varied among the subjects. According to the hierarchic assessment, all eight subjects approached the primary level of effective audibility, with an average implanted hearing threshold of 43.8 ± 10.2 dB HL. Five subjects (62.5%) attained the level of improved speech recognition, one (12.5%) reached the level of effective speech, and none of the subjects (0.0%) achieved effective communication. Conclusion CIs benefit prelingual children with AN to varying extents. A hierarchic evaluation provides a more suitable method to determine the benefits that AN individuals will likely receive from cochlear implantation.
Collecting a heap of shapes

The program heap is fundamentally a simple mathematical concept --- a set of objects and a connectivity relation on them. However, a large gap exists between the set of heap structures that could be constructed and those that programmers actually build. To understand this gap, we empirically study heap structures and sharing relations in large object-oriented programs. To scale and make sense of real world heaps, any analysis must employ abstraction; our abstraction groups sets of objects by role and the aliasing present in pointer sets. We find that the heaps of real-world programs are, in practice, fundamentally simple structures that are largely constructed from a small number of simple structures and sharing idioms, such as the sharing of immutable or unique (e.g., singleton) objects. For instance, we find that, under our abstraction, 53--75% of pointers build tree structures and we classify all but 7--18% of aliasing pointers. These results provide actionable information for rethinking the design of annotation systems, memory allocation/collection, and program analyses.
Characterising Measures of Lexical Distributional Similarity

This work investigates the variation in a word's distributionally nearest neighbours with respect to the similarity measure used. We identify one type of variation as being the relative frequency of the neighbour words with respect to the frequency of the target word. We then demonstrate a three-way connection between relative frequency of similar words, a concept of distributional gnerality and the semantic relation of hyponymy. Finally, we consider the impact that this has on one application of distributional similarity methods (judging the compositionality of collocations).