Miracle-dz/UniFAR-FTUs huggingface dataset:
Subspecific Status and Population Genetic Structure of Least Terns (Sternula antillarum) Inferred by Mitochondrial DNA Control-Region Sequences and Microsatellite DNA

ABSTRACT. The taxonomic identity of endangered populations of the Least Tern (Sternula antillarum) has long been debated. Their current conservation status provides even more impetus to examine the taxonomic distinctness of these groups. We used rapidly evolving mitochondrial DNA control-region sequences (840 base pairs; n = 188) and microsatellite DNA data (7 loci; n = 417) to examine genetic structure within and among three subspecies that occur within the United States: California Least Tern (S. a. browni), Interior Least Tern (S. a. athalassos), and Eastern Least Tern (S. a. antillarum). Although significant genetic structure was observed among breeding populations from across the species' range, our data indicated little evidence of genetic structure within traditional subspecific groups. Isolation-by-distance analyses, however, identified subtle patterns that may reflect sex-specific differences in dispersal behavior. Our analyses likewise demonstrated little population subdivision among subspecific groups, which raises questions regarding the taxonomic status of traditionally defined subspecies. Our findings can therefore be used to consider a reevaluation of Least Tern subspecies by the American Ornithologists' Union's Committee on Taxonomy and Nomenclature. We further emphasize the need for studies of range-wide breeding-site fidelity and natal philopatry to better understand interpopulation movements of individuals throughout the annual cycle.
Case gender and severity in cerebral palsy varies with intrauterine growth

Background: There is an unexplained excess of cerebral palsy among male babies. There is also variation in the proportion of more severe cases by birth weight. It has recently been shown that the rate of cerebral palsy increases as intrauterine size deviates up or down from an optimum about one standard deviation heavier than population mean weight-for-gestation. Aims: To determine whether the gender ratio or the severity of cases also varies with intrauterine size. Methods: A total of 3454 cases of cerebral palsy among single births between 1976 and 1990 with sufficient data to assign case severity (based on intellectual impairment and walking ability) and to compare weight-for-gestation at birth to sex specific fetal growth standards, were aggregated from nine separate registers in five European countries. Results: The greater the degree to which growth deviates either up or down from optimal weight-for-gestation at birth, the higher is the rate of cerebral palsy, the larger is the proportion of male cases, and the more severe is the functional disability. Compared to those with optimum growth the risk of more severe cerebral palsy in male babies is 16 times higher for those with a birth weight below the 3rd centile and four times higher when birth weight is above the 97th centile. In contrast, for mild cerebral palsy in female babies the excess risks at these growth extremes are about half these magnitudes. Conclusions: Among singleton children with cerebral palsy, abnormal intrauterine size, either small or large, is associated with more severe disability and male sex.
Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localization

MOTIVATION Determining locations of protein expression is essential to understand protein function. Advances in green fluorescence protein (GFP) fusion proteins and automated fluorescence microscopy allow for rapid acquisition of large collections of protein localization images. Recognition of these cell images requires an automated image analysis system. Approaches taken by previous work concentrated on designing a set of optimal features and then applying standard machine-learning algorithms. In fact, trends of recent advances in machine learning and computer vision can be applied to improve the performance. One trend is the advances in multiclass learning with error-correcting output codes (ECOC). Another trend is the use of a large number of weak detectors with boosting for detecting objects in images of real-world scenes. RESULTS We take advantage of these advances to propose a new learning algorithm, AdaBoost.ERC, coupled with weak and strong detectors, to improve the performance of automatic recognition of protein subcellular locations in cell images. We prepared two image data sets of CHO and Vero cells and downloaded a HeLa cell image data set in the public domain to evaluate our new method. We show that AdaBoost.ERC outperforms other AdaBoost extensions. We demonstrate the benefit of weak detectors by showing significant performance improvements over classifiers using only strong detectors. We also empirically test our method's capability of generalizing to heterogeneous image collections. Compared with previous work, our method performs reasonably well for the HeLa cell images. AVAILABILITY CHO and Vero cell images, their corresponding feature sets (SSLF and WSLF), our new learning algorithm, AdaBoost.ERC, and Supplementary Material are available at http://aiia.iis. sinica.edu. tw/
Evaluation of levels of knowledge about etiology and symptoms of oral cancer in southern Iran.

BACKGROUND Knowledge regarding risk factors and primary signs of oral cancer in the general population can help them to avoid risky behaviour and seek timely advice from a physician. The aim of this study was to survey adult knowledge about risk factors and signs of oral cancer in Shiraz Dental University. MATERIALS AND METHODS In this descriptive cross-sectional study using an investigator-made questionnaire, 783 adults who were referred to Shiraz Dental University participated. Questionnaire data were analyzed by ANOVA and T-test. RESULTS The results showed that 32.3% of participants were aware of oral cancer and gained their knowledge from the media. The risk factors mentioned by these participants was as follows: sunlight 30.8%, hot and peppery food 40.7%, alcoholic drinks 47.4% and smoking 73.6% (there was significant relationship for drinking alcohol and gender, education and times of using dental service, p<0.05). They also mentioned the primary signs of oral cancer as follows: red patch without pain 27.8%, white patch without pain 13.5% and prolong scarring without pain 56.7% (there was significant relationship between these three signs and education and times of using dental service, p<0.05). The mean knowledge about the risk factors and signs of oral cancer were 1.94 from 5 with 1.14 standard deviation and 0.96 from 3 with 0.93 standard deviation. CONCLUSIONS The observed low level of knowledge of people regarding both risk factors and signs of oral cancer emphasizes the need for more efforts to be made about the above mentioned issues by the media.
Vision for a UAV helicopter

This paper presents and overview of the basic and applied research carried out by the Computer Vision Laboratory, Linkoping University, in the WITAS UAV Project. This work includes customizing and redesigning vision methods to fit the particular needs and restrictions imposed by the UAV platform, e.g., for low-level vision, motion estimation, navigation, and tracking. It also includes a new learning structure for association of perception-action activations, and a runtime system for implementation and execution of vision algorithms. The paper contains also a brief introduction to the WITAS UAV Project.
Intensive Insulin Therapy in the Intensive Care Unit

OBJECTIVE—Hyperglycemia occurs in most critically ill patients. Using continuous glucose monitoring (CGM), we investigated whether intensive insulin therapy based on discontinuous glucose monitoring can achieve normoglycemia (80–110 mg/dl) in a medical intensive care unit (MICU). RESEARCH DESIGN AND METHODS—Fifty adults (men/women 31/19, age 62 ± 16 years, nondiabetic/diabetic 30/20, intravenous/subcutaneous insulin 22/28, and Acute Physiology and Chronic Health Evaluation II score 22 ± 7) were prospectively recruited. Forty-eight–hour CGM was performed using a subcutaneous glucose sensor (GlucoDay) and compared with arterial glycemia. Main outcome measures were percent of time in normoglycemia and accuracy/applicability of CGM. RESULTS—During 48-h CGM, glycemia reached target (80–110 mg/dl) in only 22 ± 18%, was >140 mg/dl in 39 ± 27%, and was <60 mg/dl in 5 ± 10% of the time. Patients on subcutaneous versus intravenous insulin had more glycemia readings >110 mg/dl (P = 0.016). Glycemia was higher in diabetic patients (170 ± 77 vs. 129 ± 35 mg/dl, P = 0.013). BMI was an independent determinant for bad glycemic control (β = 0.73, P < 0.0001). Diabetic state (β = 0.47, P < 0.0001), septic shock (β = 0.22, P = 0.045), sequential organ failure assessment score (β = 0.40, P = 0.001), and use of corticoids (β = 0.28, P = 0.014) and inotropics (β = −0.24, P = 0.035) were independent determinants of insulin dose. GlucoDay values and arterial glycemia correlated well (r = 0.85, P < 0.0001, n = 555 after six-point calibration), with 97% of data falling in regions A and B of error grid analysis. There were no adverse events using GlucoDay. CONCLUSIONS—GlucoDay, a well-tolerated 48-h CGM system, revealed that normoglycemia was only achieved 22% of the time in MICU patients. Further studies should investigate whether application of CGM to titrate insulin therapy can improve patient outcome.
Paying More Attention to Saliency

Image captioning has been recently gaining a lot of attention thanks to the impressive achievements shown by deep captioning architectures, which combine Convolutional Neural Networks to extract image representations and Recurrent Neural Networks to generate the corresponding captions. At the same time, a significant research effort has been dedicated to the development of saliency prediction models, which can predict human eye fixations. Even though saliency information could be useful to condition an image captioning architecture, by providing an indication of what is salient and what is not, research is still struggling to incorporate these two techniques. In this work, we propose an image captioning approach in which a generative recurrent neural network can focus on different parts of the input image during the generation of the caption, by exploiting the conditioning given by a saliency prediction model on which parts of the image are salient and which are contextual. We show, through extensive quantitative and qualitative experiments on large-scale datasets, that our model achieves superior performance with respect to captioning baselines with and without saliency and to different state-of-the-art approaches combining saliency and captioning.
Geon Theory as an Account of Shape Recognition in Mind, Brain and Machine

In a fraction of a second humans are able to comprehend novel images of objects and scenes. Indeed, the human represents the only existence proof that a general shape recognizer is even possible. Geon theory offers an account of this phenomenon characterized by four general assumptions: a) Objects are represented as an arrangement of simple convex or singly concave parts (geons), b) The geons can be distinguished by binary contrasts (differences) in viewpoint invariant properties, such as straight vs. curved, rather than metric properties such as degree of curvature, c) The relations among the geons are explicit, such as PERPENDICULAR-TO or TOP-OF, as part of a structural description, rather than implicit in a coordinate space, and d) A relatively small number of geons is sufficient. Recent research evaluating these assumptions is reviewed.
Eurographics Symposium on Point-based Graphics (2006) Template Deformation for Point Cloud Fitting

The reconstruction of high-quality surface meshes from measured data is a vital stage in digital shape processing. We present a new approach to this problem that deforms a template surface to fit a given point cloud. Our method takes a template mesh and a point cloud as input, the latter typically shows missing parts and measurement noise. The deformation process is initially guided by user specified correspondences between template and data, then during iterative fitting new correspondences are established. This approach is based on a Laplacian setting for the template without need of any additional meshing of the data or cross-parameterization. The reconstructed surface fits to the point cloud while it inherits shape properties and topology of the template. We demonstrate the effectiveness of the approach for several point data sets from different sources.
The role of mortality awareness in hero identification

Abstract Three studies examine hypotheses derived from terror management theory to investigate the relationship between mortality concerns and hero identification. Study 1 found reminders of death, followed by a distraction task and a self-prime, led to greater inclusion of heroes in the self. Study 2 found that writing about a personal hero, but not other’s heroes or acquaintances, led to lower death-thought accessibility after being reminded of mortality. Finally, Study 3 found that after death reminders, participants led to identify with a hero exemplifying traits of legacy and/or sacrifice showed lower death thought accessibility. Findings are discussed as generative for heroism research, informing a previously overlooked motivation underlying hero identification and the existential function of such identification.
Gender Difference in the Credibility Perception of Mobile Websites: A Mixed Method Approach

To persuade people to buy a product or service online, they must be visually convinced and attracted to use the sales website. Thus, there is need to understand how different user groups perceive various designs of websites for better adaptation. A lot of research has shown that users' judgment of the credibility of a website is critical to its success. However, in the mobile domain, little has been done empirically to 1) investigate users' credibility perception of a website; and 2) how it changes as the user interface (UI) design is systematically altered. This paper bridges this gap by carrying out sentiment and statistical analyses of users' perceptions of four systematically modified mobile websites among 285 subjects from North America, Africa and Asia. The results show that mobile website design affects the perception of its credibility, with 1) females being more critical and sensitive to UI changes than males; and 2) the grid-layout website design preferred to the list-layout website design by both genders. The study contributes to knowledge in three ways. First, it provides a concise model for understanding users' UI perceptions, expectations and gender differences. Second, it presents important findings that will enable a gender-based mobile website adaptation. Third, it provides a set of empirically backed guidelines for mobile web design.
ARTIFICIAL IMMUNE SYSTEM CLASSIFICATION OF MULTIPLE- CLASS PROBLEMS

A new classifier, AIRS, based on the principles of resource-limited artificial immune systems, has been shown recently to consistently rank among the best five to eight known classifiers for a number of well-studied classification problems, including the Iris data, the Cleveland heart disease data, and others. However, each of these previous test problems has involved only a few classes. In this paper, we discuss the general application of AIRS to multi-class problems, and we compare it to a similar well-known classifier, Kohonen's LVQ, on both simulated and real-world data sets.
Genetic Variation and Population Structure in Native Americans

We examined genetic diversity and population structure in the American landmass using 678 autosomal microsatellite markers genotyped in 422 individuals representing 24 Native American populations sampled from North, Central, and South America. These data were analyzed jointly with similar data available in 54 other indigenous populations worldwide, including an additional five Native American groups. The Native American populations have lower genetic diversity and greater differentiation than populations from other continental regions. We observe gradients both of decreasing genetic diversity as a function of geographic distance from the Bering Strait and of decreasing genetic similarity to Siberians—signals of the southward dispersal of human populations from the northwestern tip of the Americas. We also observe evidence of: (1) a higher level of diversity and lower level of population structure in western South America compared to eastern South America, (2) a relative lack of differentiation between Mesoamerican and Andean populations, (3) a scenario in which coastal routes were easier for migrating peoples to traverse in comparison with inland routes, and (4) a partial agreement on a local scale between genetic similarity and the linguistic classification of populations. These findings offer new insights into the process of population dispersal and differentiation during the peopling of the Americas.
Implementation of a BCF mode bio-mimetic robotic-fish underwater vehicle based on Lighthill mathematical model

The objective of this paper is to mimic the propulsion mechanism of the BCF mode carangiform swimming style to show the fish behavior navigating efficiently over large distances at impressive speeds and its exceptional characteristics in the fluid environment. The robofish model (kinematics and dynamics) is integrated with the Lighthill(LH) mathematical model framework. Comparative studies are undertaken between a LH model based and a non-LH based model. A comprehensive propulsion mechanism study of the different parameters namely the tail-beat frequency (TBF), the propulsive wavelength and the caudal amplitude are studied under this framework. The performance factor taken for energy efficient swimming is the overall distance traversed. Inverse kinematics based approach is incorporated for trajectory generation of the robotic fish vehicle motion. Analysis of these critical parameters affecting the kinematics study of the vehicle vis a vis the real fish kinematic study [8] is carried out for a given trajectory. TBF is found to be the effective controlling parameter for the forward speed of the vehicle over a wide operating conditions. Performances and comparative results of propulsive wavelength and amplitude affects are also shown and discussed.
Display Acts in Grounding Negotiations

Display utterances are those utterances, such as verbatim rephrases or cooperative completions, that exhibit the speakers’ construals of their partners’ previous utterances. Drawing on both empirical analyses and theoretical considerations, we study the ways they perform the acts of acknowledgment and repair-request. We show that (1) these grounding acts, when performed by display utterances, are not autonomous acts whose choices are under the control of the speakers, yet (2) they are instantaneous acts whose types are fixed at the time of utterance. Accordingly, the grounding model proposed in this paper reconciles the non-autonomous nature of display utterances (Clark, 1996) with the necessity for on-line classifications of the grounding acts (Traum, 1994).
Evaluation of a Methotrexate Chemiluminescent Microparticle Immunoassay:  Comparison to Fluorescence Polarization Immunoassay and Liquid Chromatography-Tandem Mass Spectrometry.

OBJECTIVES For most laboratories, methotrexate (MTX) concentrations are routinely monitored by fluorescence polarization immunoassay (FPIA). In anticipation of an announced withdrawal of the FPIA reagent on the Abbott TDxFLx (Abbott Diagnostics, Abbott Park, IL), we have evaluated a new reagent kit developed by Abbott on the Architect i1000, based on chemiluminescent microparticle immunoassay (CMIA). METHODS Precision, inaccuracy, and selectivity were assessed. Interassay variability was established using 75 plasma patient samples treated with MTX and analyzed by two methods: FPIA and liquid chromatography-tandem mass spectrometry (LC-MS/MS). RESULTS FOR MTX,: the intraday inaccuracy was between -6.37% and +3.52%, while interday performance was between -3.70% and 7.90%. Intraday and interday imprecision was less than 2.65% and less than 2.22%, respectively. The correlation coefficient between CMIA and FPIA or LC-MS/MS was 0.9969 and 0.9985, respectively. CONCLUSIONS These results comparing CMIA vs FPIA and LC-MS/MS indicate that CMIA is a suitable alternative to the FPIA method.
Haemodynamic consequences of etomidate administration in elective cardiac surgery: a randomized double-blinded study.

BACKGROUND The consequences of inhibition of cortisol synthesis by a single dose of etomidate on subsequent vasopressor drug usage and the duration of relative adrenal insufficiency (RAI) after cardiac surgery are not known. METHODS This was a prospective, randomized, double-blinded controlled trial of 100 patients undergoing elective cardiac surgery and receiving either etomidate or propofol at induction of anaesthesia. A short corticotropin test was performed 12, 24, and 48 h after anaesthesia induction. RAI was defined as a response <250 nmol litre(-1). RESULTS The mean (sd) norepinephrine infusion rate during the first 48 postoperative hours was 0.11 (0.01) and 0.11 (0.01) µg kg(-1) min(-1) in the etomidate and propofol groups, respectively (P=0.89). Time to norepinephrine withdrawal was similar between the groups. The incidence of RAI was higher in the etomidate group at 12 h (100% vs 41%, P<0.001) and 24 h (85% vs 25%, P<0.001). CONCLUSIONS A single bolus of etomidate blunts the hypothalamic-pituitary-adrenal axis response for more than 24 h in patients undergoing elective cardiac surgery, but this was not associated with an increase in vasopressor requirements.
Outlier Detection in Heterogeneous Datasets using Automatic Tuple Expansion

Rapidly developing areas of information technology are generating massive amounts of data. Human errors, sensor failures, and other unforeseen circumstances unfortunately tend to undermine the quality and consistency of these datasets by introducing outliers – data points that exhibit surprising behavior when compared to the rest of the data. Characterizing, locating, and in some cases eliminating these outliers offers interesting insight about the data under scrutiny and reinforces the confidence that one may have in conclusions drawn from otherwise noisy datasets. In this paper, we describe a tuple expansion procedure which reconstructs rich information from semantically poor SQL data types such as strings, integers, and floating point numbers. We then use this procedure as the foundation of a new user-guided outlier detection framework, dBoost, which relies on inference and statistical modeling of heterogeneous data to flag suspicious fields in database tuples. We show that this novel approach achieves good classification performance, both in traditional numerical datasets and in highly non-numerical contexts such as mostly textual datasets. Our implementation is publicly available, under version 3 of the GNU General Public License.