Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel surface targets and serum biomarkers from the ovarian cancer vasculature

The molecular phenotype of tumor vasculature is different from normal vasculature, offering new opportunities for diagnosis and therapy of cancer, but the identification of tumor-restricted targets remains a challenge. We investigated 13 tumor vascular markers (TVMs) from 50 candidates identified through expression profiling of ovarian cancer vascular cells and selected to be either transmembrane or secreted, and to be either absent or expressed at low levels in normal tissues while overexpressed in tumors, based on analysis of 1,110 normal and tumor tissues from publicly available Affymetrix microarray data. Tumor-specific expression of each TVM was confirmed at the protein level in tumor tissue and/or in serum. Among the 13 TVMs, 11 were expressed on tumor vascular endothelium; the remaining 2 TVMs were expressed by tumor leukocytes. Our results demonstrate that certain transmembrane TVMs such as ADAM12 and CDCP1 are selectively expressed in tumor vasculature and represent promising targets for vascular imaging or anti-vascular therapy of epithelial ovarian cancer, while secreted or shed molecules such as TNFRSF21/DR6 can function as serum biomarkers. We have identified novel tumor-specific vasculature markers which appear promising for cancer serum diagnostics, molecular imaging and/or therapeutic targeting applications and warrant further clinical development.
The New Zealand workforce survey II: occupational risk factors for asthma.

INTRODUCTION We conducted a cross-sectional population-based survey in New Zealand that collected information on work history, current workplace exposures, and selected health outcomes. We report here the findings on occupational risk factors for asthma symptoms. METHODS A random sample of men and women aged 20-64 years were selected from the New Zealand Electoral Roll and invited to take part in a telephone survey. Current asthma was defined as: (i) woken up by shortness of breath in the past 12 months; or (ii) an attack of asthma in the past 12 months; or (iii) currently taking asthma medication. Adult-onset asthma was defined as first attack of asthma at age 18 or over. Prevalence odds ratios (ORs) for all occupations were calculated using logistic regression adjusting for sex, age, smoking, and deprivation. RESULTS Totally, 2903 participants were included in the analyses. The prevalence of current asthma was 17% and the prevalence of adult-onset asthma was 9%. Prevalence ORs for current asthma were elevated for ever working as a printer [OR = 2.26; 95% confidence interval (CI) = 1.09-4.66], baker (OR = 1.98; 95% CI = 1.02-3.85), sawmill labourer (OR = 3.26; 95% CI = 1.05-10.16), metal processing plant operator (OR = 2.48; 95% CI = 1.22-5.05), and cleaner (OR = 1.60; 95% CI = 1.09-2.35). Excess risks of adult-onset asthma were also found for ever working as a printer, baker, and sawmill labourer as well as ever-working as a market-oriented animal producer (OR = 1.66; 95% CI = 1.14-2.41), and other agricultural worker (OR = 2.08; 95% CI = 1.03-4.20). A number of occupations not previously considered at high risk for asthma were also identified, including teachers and certain sales professionals. CONCLUSION This population-based study has confirmed findings of previous international studies showing elevated risks in a number of high-risk occupations. The strongest risks were consistently observed for printers, bakers, and sawmill labourers. Several occupations were also identified that have not been previously associated with asthma, suggesting that the risk of occupational asthma may be more widely spread across the workforce than previously assumed.
Fast motion compensated temporal interpolation for video

Recently, MPEG-4 is being formed to study very-low-bit-rate (VLBR) video coding for applications in videotelephony. In this paper, we propose a possible postprocessing technique for VLBR coding. In videophone applications, temporal subsampling is a simple technique which can be combined with other compression schemes to achieve very large compression ratio, so as to satisfy the VLBR requirement. As a result, object motions tend to be jerky and disturbing to the human eyes. To smooth out object motions, we propose a postprocessing technique, motion compensated temporal interpolation (MCTI), to increase the instantaneous decoder frame rate. In MCTI, block-based exhaustive motion search is used to establish temporal association between two reconstructed frames. Both forward and backward searches are used to account for uncovered and newly covered areas properly. With MCTI, we show that one or more frames can be interpolated with acceptable visual quality. After showing the feasibility of MCTI, we propose a fast algorithm FMCTI with reduced computation requirement and negligible performance degradation.
Optimism, pessimism and the unforeseen: Modelling an endogenous business cycle driven by strong beliefs

Indicators of trust, confidence, optimism or sentiment among consumers and/or investors, are published continuously in the mass media. More importantly, these indices seem not only to reflect how the state of the real economy is perceived by private agents, but can also help predict the future course of the business cycle. Moreover, in econometric analyses they have even been found to cause business activity. In this paper, we first make an attempt to clear all of the above mentioned notions and to interpret their economic content. We thus intend to provide a theoretical foundation for how pessimism and optimism, in conjunction with estimation errors committed by private agents, can drive the real economy. Furthermore, the model presented is capable of incorporating the revision of expectations of private agents through Bayesian updating, to create a fully endogenized business cycle. The results achieved in simulation experiments confirm the possibility of constant, rising and declining oscillations in the growth rate of consumption and income.
Image recognition of plant diseases based on backpropagation networks

To achieve automatic diagnosis of plant diseases and improve the image recognition accuracy of plant diseases, two kinds of grape diseases (grape downy mildew and grape powdery mildew) and two kinds of wheat diseases (wheat stripe rust and wheat leaf rust) were selected as research objects, and the image recognition of the diseases was conducted based on image processing and pattern recognition. After image preprocessing including image compression, image cropping and image denoising, K_means clustering algorithm was used to segment the disease images, and then 21 color features, 4 shape features and 25 texture features were extracted from the images. Backpropagation (BP) networks were used as the classifiers to identify grape diseases and wheat diseases, respectively. The results showed that identification of the diseases could be effectively achieved using BP networks. While the dimensions of the feature data were not reduced by using principal component analysis (PCA), the optimal recognition results for grape diseases were obtained as the fitting accuracy and the prediction accuracy were both 100%, and that for wheat diseases were obtained as the fitting accuracy and the prediction accuracy were both 100%. While the dimensions of the feature data were reduced by using PCA, the optimal recognition result for grape diseases was obtained as the fitting accuracy was 100% and the prediction accuracy was 97.14%, and that for wheat diseases was obtained as the fitting accuracy and the prediction accuracy were both 100%.
Shape Description of Curved Surfaces from Contact Sensing Using Surface Normals

We describe a spline-based framework for incorporating both position and surface-normal information into estimates of curvaturebased shape parameters. Such position and normal data can be obtained with different types of contact sensors, for example, tactile sensors, force-moment sensors, etc. The heart of our framework is an extended B-spline formulation that incorporates the surfacenormal information into a B-spline surface fit of the position data. The surface-normal information provides additional constraints for the surface fit, and can also significantly improve the approximation of the surface. Curvature-based shape parameters applied to this B-spline surface are then used to characterize the local shape of the object surface. Preliminary experiments with simple primitives—spherical, cylindrical, and planar shapes—and an off theshelf force/moment sensor to obtain the position and surfacenormal data show that despite coarse resolution of the sensor, this approach succeeds in qualitative shape recognition.
Alterations of Epigenetic Regulators in Pancreatic Cancer and Their Clinical Implications

Pancreatic cancer is one of the most aggressive human cancer types with a five-year survival less than 7%. Emerging evidence revealed that many genetic alterations in pancreatic cancer target epigenetic regulators. Some of these mutations are driver mutations in cancer development. Several most important mechanisms of epigenetic regulations include DNA methylation, histone modifications (methylation, acetylation, and ubiquitination), chromatin remodeling, and non-coding ribonucleic acids (RNAs). These modifications can alter chromatin structure and promoter accessibility, and thus lead to aberrant gene expression. However, exactly how these alterations affect epigenetic reprogramming in pancreatic cancer cells and in different stages of tumor development is still not clear. This mini-review summarizes the current knowledge of epigenetic alterations in pancreatic cancer development and progression, and discusses the clinical applications of epigenetic regulators as diagnostic biomarkers and therapeutic targets in pancreatic cancer.
A Novel SNR-Based Packet Scheduling Algorithm and Compensation Model for Wireless Networks

We present improved SNR-based packet scheduling (I-SPS) algorithm as an extension of SPS algorithm. In I-SPS, the connection with better channel condition will be assigned higher weight in scheduling and the connection suffered burst error will be compensated by increasing its weight after its channel condition becomes good. Theoretical analysis and simulation results demonstrate that I-SPS algorithm can achieve QoS guarantee for synchronous connections.
Alternative approaches to the assessment of mild head injury in athletes.

OBJECTIVES Athletic trainers and team physicians are often faced with decisions concerning the severity and timing of an athletes return to play following mild head injury (MHI). These decisions can be the most difficult ones facing clinicians because of the limited amount of quantitative information indicating injury severity. Several authors have published guidelines for return to play following MHI, however these guidelines are based on limited scientific data. The purpose of this paper was to examine the effects of MHI on two objective measures, postural stability and cognitive function, to determine their usefulness in MHI assessment. The data gathered from these two measures has the potential to establish recovery curves based on objective data. METHODS Eleven Division I collegiate athletes who sustained a MHI and eleven matched control subjects were assessed for postural stability and cognitive function at four intervals following injury. Postural stability was assessed using the Sensory Organization Test on the NeuroCom Smart Balance Master. Cognitive functioning was measured through the use of four neuropsychological tests: Stroop Test, Trail Making Test, Digits Span and Hopkins Verbal Learning Test. Separate mixed model repeated measures ANOVAs were calculated for the composite score and three ratio (vestibular, visual and somato-sensory) scores from the Sensory Organization Test and the scores from the neuropsychological test to reveal significant differences between groups and across days postinjury. RESULTS A significant group by day interaction for overall postural stability (composite score) revealed that MHI athletes displayed increased postural instability for the first few days following MHI (p < .05). Analysis of the ratio scores revealed a significant interaction for the visual ratio. No significant group differences were revealed for any of the neuropsychological tests (p > .05), however significant day differences were revealed (p < .05). CONCLUSIONS The results from this study indicate that athletes demonstrate decreased stability until 3 days postinjury. It appears this deficit is related to a sensory interaction problem, whereby the injured athlete fails to use their visual system effectively. These findings suggest that measures of postural stability may provide clinicians with a useful clinical tool for determining when an athlete may safely return to competition, although these findings need to be confirmed in larger groups of athletes.
Lexical translation model using a deep neural network architecture

In this paper we combine the advantages of a model using global source sentence contexts, the Discriminative Word Lexicon, and neural networks. By using deep neural networks instead of the linear maximum entropy model in the Discriminative Word Lexicon models, we are able to leverage dependencies between different source words due to the non-linearity. Furthermore, the models for different target words can share parameters and therefore data sparsity problems are effectively reduced. By using this approach in a state-of-the-art translation system, we can improve the performance by up to 0.5 BLEU points for three different language pairs on the TED translation task.
A Network-Based Semi-supervised Outlier Detection Technique Using Particle Competition and Cooperation

Outlier detection is a classical topic of data mining acting as an essential task for discovering knowledge. Its aim is to detect patterns that deviate from normal behaviour. Numerous outlier detection techniques have been developed but little work has been done in the context of semi-supervised learning. Semi-supervised outlier detection techniques are relatively new and include some labels of normal instances for improving the accuracy of classifier with respect to outliers. In this paper, we introduced a new outlier score using a stochastic network-based semi-supervised technique. The original algorithm builds a network on the data set and exploits some available normal labels in order to classify the whole network by means of a particle competition and cooperation mechanism. Particles visit vertices in order to conquer as many vertices as possible. Our assumption behind such a score is that an outlier is statistically improbable, and the number of visits received by an outlier vertex is significantly different from the remaining vertices. Experimental results suggest that our technique is capable of detecting many of outliers and outperforms some traditional techniques.
12. My pixels or my friends? Game characters as a lens for understanding user avatars in social networks

: In various interactive digital media, users create representations for themselves – be they images, texts, or interactive characters – that are used mark the user’s identity, function, role, or position in the social landscape. These representations, or avatars, can exist separate from the user who created them. As such, they can be crafted, performed, experimented with, and reflected upon. In this separation, users may experience their own avatars, engaging them more or less as ‘me’ or ‘not-me’ as a function of user and avatar agencies. To better understand these dynamics, in this chapter we draw from current perspectives on a particular type of user-avatar pair – video game players and their graphic in-game characters – to theoretically and empirically contextualize the range of relationships users may have with their digital representations in a variety of social networking platforms, and how those relationships may differently influence social interactions online. Tracing the trajectory of the earliest audience-character scholarship from 1920s scholarship (the parasocial perspective) to emerging findings that gamers sometimes engage their avatars as autonomous social agents, we argue for a relational continuum that demonstrates the full range of non-social, parasocial and fully social relationships that communication technology users can have with their digital avatars.
Response to Hypothetical Social Scenarios in Individuals with Traumatic Brain Injury Who Present Inappropriate Social Behavior: A Preliminary Report

Background: Very little research thus far has examined the decision making that underlies inappropriate social behavior (ISB) post-TBI (traumatic brain injury). Objectives: To verify the usefulness of a new instrument, the Social Responding Task, for investigating whether, in social decision making, individuals with TBI, who present inappropriate social behavior (ISB), have difficulty anticipating their own feelings of embarrassment and others’ angry reactions following an ISB. Methods: Seven subjects with TBI presenting with inappropriate social behavior (TBI-ISB), 10 presenting with appropriate social behavior (TBI-ASB), and 15 healthy controls were given 12 hypothetical scenarios three times, each time ending with a different behavioral response. Subjects were asked to gauge the likelihood of their displaying the behavior in that situation (part A) and of it being followed by an angry reaction from the other or by feelings of embarrassment in themselves (part B). Results: TBI-ISB subjects scored higher than TBI-ASB and healthy controls on a scale of likelihood of displaying an ISB. Results regarding expectations of angry reactions from others and feelings of embarrassment after an ISB were similar among groups. Negative correlations between endorsement of an inappropriate behavior and anticipation of negative emotional consequences were significant for both TBI-ASB and control subjects, but not for TBI-ISB subjects. Conclusions: Results suggest that the TBI-ISB participants were likely to endorse an ISB despite being able to anticipate a negative emotional response in themselves or others, suggesting that there were other explanations for their poor behavior. A self-reported likely response to hypothetical social scenarios can be a useful approach for studying the neurocognitive processes behind the poor choices of individuals with TBI-ISB, but the task needs further validation studies. A comprehensive discussion follows on the underlying mechanisms affecting social behaviors after a TBI.
GSVD-based optimal filtering for single and multimicrophone speech enhancement

A generalized singular value decomposition (GSVD) based algorithm is proposed for enhancing multimicrophone speech signals degraded by additive colored noise. This GSVD-based multimicrophone algorithm can be considered to be an extension of the single-microphone signal subspace algorithms for enhancing noisy speech signals and amounts to a specific optimal filtering problem when the desired response signal cannot be observed. The optimal filter can be written as a function of the generalized singular vectors and singular values of a speech and noise data matrix. A number of symmetry properties are derived for the single-microphone and multimicrophone optimal filter, which are valid for the white noise case as well as for the colored noise case. In addition, the averaging step of some single-microphone signal subspace algorithms is examined, leading to the conclusion that this averaging operation is unnecessary and even suboptimal. For simple situations, where we consider localized sources and no multipath propagation, the GSVD-based optimal filtering technique exhibits the spatial directivity pattern of a beamformer. When comparing the noise reduction performance for realistic situations, simulations show that the GSVD-based optimal filtering technique has a better performance than standard fixed and adaptive beamforming techniques for all reverberation times and that it is more robust to deviations from the nominal situation, as, e.g., encountered in uncalibrated microphone arrays.
On importance assessment and expertise in non-routine decisions: an exploratory study on the cognition of the weighting process of capital goods' attributes

How do actors involved in the acquisition of capital goods assess the importance of their attributes? What is the role of expertise? Numerous instruments exist for measuring the importance attached to attributes, but little is known about the importance assessment process that precedes these importance judgments. Expectations concerning the behaviour of actors facing non-routine importance assessment problems are tested, yielding some interesting results. Firstly, the behaviour of these actors is consistent with a newly developed phase model. Even with a non-routine problem, structuring the assessment problem takes less effort than the actual weighting. Surprisingly, weighting attributes in isolation gets much more emphasis than weighting them against each other, despite the latter being the essence of importance judgments. Despite the subjects being laymen, they showed high confidence in their work. Finally, predictions concerning the behaviour of experts are made, based on Van der Heijden's dimensions of expertise.
An Efficient Approach for Flexible Docking Base on Particle Swarm Optimization

Molecular docking is an important tool in identifying potential drug candidates. The molecular dock- ing problem is to find a good conformation for docking ligand to a large receptor molecule. It can be formulated as a parameter optimization problem consisting of a scoring function and a global optimization method. Based on a variant of Particle Swarm Optimization (PSO) named Fully Informed Particle Swarm (FIPS) and the semiempirical free energy force field in AutoDock 4.0, a new approach to flexible docking method called FIPSDock was implemented. The search ability and docking accuracy of FIPSDock were evaluated by multiple redocking experiments, and the results of which demonstrate that FIPS is more suitable than Lamarckian Genetic Algorithm (LGA) for the force field of AutoDock. FIPSDock is superior to AutoDock and SODOCK which was also proposed by improving AutoDock with PSO in term of obtaining a lower binding energy, a better docked conformation, convergence speed and robustness. Compared with the four currently widely used methods-GOLD, DOCK, FlexX and AutoDock, FIPS- Dock is more accurate. Thus, FIPSDock is an efficient and accurate docking method and its promising prospects can be expected in the application to virtual screening.
Similar Term Discovery using Web Search

We present an approach to the discovery of semantically similar terms that utilizes a web search engine as both a source for generating related terms and a tool for estimating the semantic similarity of terms. The system works by associating with each document in the search engines index a weighted term vector comprising those phrases that best describe the documents subject matter. Related terms for a given seed phrase are generated by running the seed as a search query and mining the result vector produced by averaging the weights of terms associated with the top documents of the query result set. The degree of similarity between the seed term and each related term is then computed as the cosine of the angle between their respective result vectors. We test the effectiveness of this approach for building a term recommender system designed to help online advertisers discover additional phrases to describe their product offering. A comparison of its output with that of several alternative methods finds it to be competitive with the best known alternative.