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Concept: Cluster analysis

280

Humour processing is a complex information-processing task that is dependent on cognitive and emotional aspects which presumably influence frame-shifting and conceptual blending, mental operations that underlie humour processing. The aim of the current study was to find distinctive groups of subjects with respect to black humour processing, intellectual capacities, mood disturbance and aggressiveness. A total of 156 adults rated black humour cartoons and conducted measurements of verbal and nonverbal intelligence, mood disturbance and aggressiveness. Cluster analysis yields three groups comprising following properties: (1) moderate black humour preference and moderate comprehension; average nonverbal and verbal intelligence; low mood disturbance and moderate aggressiveness; (2) low black humour preference and moderate comprehension; average nonverbal and verbal intelligence, high mood disturbance and high aggressiveness; and (3) high black humour preference and high comprehension; high nonverbal and verbal intelligence; no mood disturbance and low aggressiveness. Age and gender do not differ significantly, differences in education level can be found. Black humour preference and comprehension are positively associated with higher verbal and nonverbal intelligence as well as higher levels of education. Emotional instability and higher aggressiveness apparently lead to decreased levels of pleasure when dealing with black humour. These results support the hypothesis that humour processing involves cognitive as well as affective components and suggest that these variables influence the execution of frame-shifting and conceptual blending in the course of humour processing.

Concepts: Psychology, Cluster analysis, Educational psychology, Emotion, Mood disorder, Higher education, Mood, Conceptual blending

168

BACKGROUND: Students enter the medical study with internally generated motives like genuine interest (intrinsic motivation) and/or externally generated motives like parental pressure or desire for status or prestige (controlled motivation). According to Self-determination theory (SDT), students could differ in their study effort, academic performance and adjustment to the study depending on the endorsement of intrinsic motivation versus controlled motivation. The objectives of this study were to generate motivational profiles of medical students using combinations of high or low intrinsic and controlled motivation and test whether different motivational profiles are associated with different study outcomes. METHODS: Participating students (N = 844) from University Medical Center Utrecht, the Netherlands, were classified to different subgroups through K-means cluster analysis using intrinsic and controlled motivation scores. Cluster membership was used as an independent variable to assess differences in study strategies, self-study hours, academic performance and exhaustion from study. RESULTS: Four clusters were obtained: High Intrinsic High Controlled (HIHC), Low Intrinsic High Controlled (LIHC), High Intrinsic Low Controlled (HILC), and Low Intrinsic Low Controlled (LILC). HIHC profile, including the students who are interest + status motivated, constituted 25.2% of the population (N = 213). HILC profile, including interest-motivated students, constituted 26.1% of the population (N = 220). LIHC profile, including status-motivated students, constituted 31.8% of the population (N = 268). LILC profile, including students who have a low-motivation and are neither interest nor status motivated, constituted 16.9% of the population (N = 143). Interest-motivated students (HILC) had significantly more deep study strategy (p < 0.001) and self-study hours (p < 0.05), higher GPAs (p < 0.001) and lower exhaustion (p < 0.001) than status-motivated (LIHC) and low-motivation (LILC) students. CONCLUSIONS: The interest-motivated profile of medical students (HILC) is associated with good study hours, deep study strategy, good academic performance and low exhaustion from study. The interest + status motivated profile (HIHC) was also found to be associated with a good learning profile, except that students with this profile showed higher surface strategy. Low-motivation (LILC) and status-motivated profiles (LIHC) were associated with the least desirable learning behaviours.

Concepts: Cluster analysis, Educational psychology, Behavior, Motivation, Human behavior, Self-determination theory, K-means clustering, Profiles

168

Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in d-dimensional space R(d) and an integer k. The problem is to determine a set of k points in R(d), called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this work, we develop a novel k-means algorithm, which is simple but more efficient than the traditional k-means and the recent enhanced k-means. Our new algorithm is based on the recently established relationship between principal component analysis and the k-means clustering. We provided the correctness proof for this algorithm. Results obtained from testing the algorithm on three biological data and six non-biological data (three of these data are real, while the other three are simulated) also indicate that our algorithm is empirically faster than other known k-means algorithms. We assessed the quality of our algorithm clusters against the clusters of a known structure using the Hubert-Arabie Adjusted Rand index (ARI(HA)). We found that when k is close to d, the quality is good (ARI(HA)>0.8) and when k is not close to d, the quality of our new k-means algorithm is excellent (ARI(HA)>0.9). In this paper, emphases are on the reduction of the time requirement of the k-means algorithm and its application to microarray data due to the desire to create a tool for clustering and malaria research. However, the new clustering algorithm can be used for other clustering needs as long as an appropriate measure of distance between the centroids and the members is used. This has been demonstrated in this work on six non-biological data.

Concepts: Cluster analysis, Algorithm, Principal component analysis, Machine learning, Computational complexity theory, K-means clustering, Rand index

168

Near infrared spectroscopy (NIRS) has been successfully used for non-invasive diagnosis of diseases and abnormalities where water spectral patterns are found to play an important role. The present study investigates water absorbance patterns indicative of estrus in the female giant panda. NIR spectra of urine samples were acquired from the same animal on a daily basis over three consecutive putative estrus periods. Characteristic water absorbance patterns based on 12 specific water absorbance bands were discovered, which displayed high urine spectral variation, suggesting that hydrogen-bonded water structures increase with estrus. Regression analysis of urine spectra and spectra of estrone-3-glucuronide standard concentrations at these water bands showed high correlation with estrogen levels. Cluster analysis of urine spectra grouped together estrus samples from different years. These results open a new avenue for using water structure as a molecular mirror for fast estrus detection.

Concepts: Spectroscopy, Regression analysis, Cluster analysis, Water, Infrared spectroscopy, Infrared, Near infrared spectroscopy, Giant Panda

166

SUMMARY: This application note describes a new scalable semi-automatic approach, the Dual Point Decision Process (DP2), for segmentation of 3D structures contained in 3D microscopy. The segmentation problem is distributed to many individual workers such that each receives only simple questions regarding whether or not two points in an image are placed on the same object. A large pool of micro-labor workers available through Amazon’s Mechanical Turk system provide the labor in a scalable manner.Availability and Implementation: Python based code for non-commercial use and test data are available in the source archive at http://cytoseg.googlecode.com/files/imageprocessing.tar.gz. CONTACT: rgiuly@ucsd.edu SUPPLEMENTARY INFORMATION: Test result information, discussion, and a DP2 process flowchart are available at Bioinformatics online.

Concepts: Cluster analysis, Decision theory, Computer graphics, Computer program, Amazon Web Services, Amazon.com, Flowchart, Amazon Mechanical Turk

145

With many benefits and applications, immunochromatographic (ICG) assay detection systems have been reported on a great deal. However, the existing research mainly focuses on increasing the dynamic detection range or application fields. Calibration of the detection system, which has a great influence on the detection accuracy, has not been addressed properly. In this context, this work develops a calibration strip for ICG assay photoelectric detection systems. An image of the test strip is captured by an image acquisition device, followed by performing a fuzzy c-means (FCM) clustering algorithm and maximin-distance algorithm for image segmentation. Additionally, experiments are conducted to find the best characteristic quantity. By analyzing the linear coefficient, an average value of hue (H) at 14 min is chosen as the characteristic quantity and the empirical formula between H and optical density (OD) value is established. Therefore, H, saturation (S), and value (V) are calculated by a number of selected OD values. Then, H, S, and V values are transferred to the RGB color space and a high-resolution printer is used to print the strip images on cellulose nitrate membranes. Finally, verification of the printed calibration strips is conducted by analyzing the linear correlation between OD and the spectral reflectance, which shows a good linear correlation (R² = 98.78%).

Concepts: Cluster analysis, Function, Color, RGB color model, Color space, Segmentation, RGB color space, Adobe RGB color space

32

Cluster analysis is aimed at classifying elements into categories on the basis of their similarity. Its applications range from astronomy to bioinformatics, bibliometrics, and pattern recognition. We propose an approach based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. This idea forms the basis of a clustering procedure in which the number of clusters arises intuitively, outliers are automatically spotted and excluded from the analysis, and clusters are recognized regardless of their shape and of the dimensionality of the space in which they are embedded. We demonstrate the power of the algorithm on several test cases.

Concepts: Cluster analysis, Bioinformatics, Mathematics, Data analysis, Uranium, Machine learning, Pattern recognition, Space exploration

31

Text messaging may be excessive and young people may be dependent on it. We distributed the Self-perception of Text-message Dependency Scale (STDS), Hospital Anxiety and Depression Scale (HADS), Temperament and Character Inventory (TCI), and Relationship Questionnaire (RQ) to 223 Japanese university students in a two-wave study, separated by a 5-month interval. The STDS yielded a three-factor structure. The STDS scores across the two measurement occasions were stable across time (except for the Relationship Maintenance subscale). A hierarchical cluster analysis suggested a three-class structure interpreted as Normal Users, Excessive Users, and Dependent Users. Excessive Users and Dependent Users were characterized by a young age at initial mobile phone use, more frequent use of text messaging, higher Novelty Seeking, and better Other-Model patterns of adult attachment. Unlike Excessive Users, Dependent Users were characterized by lower Self-directedness, poorer Self-Model of adult attachment, and higher anxiety and depression. The Excessive Users, but not the Dependent Users, were characterized by high Reward Dependence and Co-operativeness. The present study demonstrated that the STDS has a robust factor structure, good construct validity, and temporal stability (except for Relationship Maintenance subscale); students could be classified into normal, excessive, and Dependent Users of the text messaging; and Dependent Users were characterized by Excessive Use and personality immaturity.

Concepts: Cluster analysis, Psychometrics, Mobile phone, Dependency, Text messaging, SMS, Instant messaging, Tridimensional Personality Questionnaire

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A comprehensive understanding of the phenomenology of auditory hallucinations (AHs) is essential for developing accurate models of their causes. Yet, only 1 detailed study of the phenomenology of AHs with a sample size of N ≥ 100 has been published. The potential for overreliance on these findings, coupled with a lack of phenomenological research into many aspects of AHs relevant to contemporary neurocognitive models and the proposed (but largely untested) existence of AH subtypes, necessitates further research in this area. We undertook the most comprehensive phenomenological study of AHs to date in a psychiatric population (N = 199; 81% people diagnosed with schizophrenia), using a structured interview schedule. Previous phenomenological findings were only partially replicated. New findings included that 39% of participants reported that their voices seemed in some way to be replays of memories of previous conversations they had experienced; 45% reported that the general theme or content of what the voices said was always the same; and 55% said new voices had the same content/theme as previous voices. Cluster analysis, by variable, suggested the existence of 4 AH subtypes. We propose that there are likely to be different neurocognitive processes underpinning these experiences, necessitating revised AH models.

Concepts: Sample, Cluster analysis, Philosophy of science, Schizophrenia, Hallucination, Psychosis, Proposal, Phenomenology

28

EDS-HT is a connective tissue disorder characterized by large inter-individual differences in the clinical presentation, complicating diagnosis and treatment. We aim to describe the clinical heterogeneity and to investigate whether differences in the symptom profile are also reflected as disparity in functional impairment and pain experience. In this study, 78 patients were asked to describe their symptoms due to EDS-HT. Next, a hierarchical cluster analysis was performed using the Jaccard measure of similarity to assess whether subgroups could be distinguished based on the symptoms reported. This analysis yielded 3 clusters of participants with distinct complaint profiles. The key differences were found in the domain of non-musculoskeletal complaints, which was significantly larger in cluster 2. Furthermore, cluster 2 was characterized by a worse physical and psychosocial health, a higher pain severity and a larger pain interference in daily life. The results emphasize that non-musculoskeletal symptoms are an important complication of EDS-HT, as the number of these complaints was found to be a significant predictor for both functional health status (SIP) and pain experience (MPI). In conclusion, this study confirms that EDS-HT is a heterogeneous entity and encourages the clinician to be more aware of the large variety of EDS-HT symptoms, in order to improve disease recognition and to establish more tailored treatment strategies.

Concepts: Cluster analysis, Disease, Collagen, Heterogeneity, Marfan syndrome, Connective tissue, Ehlers-Danlos syndrome, Hypermobility