Miracle-dz/UniFAR-FTUs huggingface dataset:
Management of Pregnancy-Associated Thrombotic Thrombocytopenia Purpura

Abstract Thrombotic thrombocytopenia purpura (TTP) is an infrequent but serious disease. Pregnancy is a known risk factor for presentation or relapse of TTP. Difficulties in differentiating TTP from preeclampsia/HELLP (hemolysis, elevated liver enzymes and low platelets) syndrome, and current treatment recommendations are discussed in this case report. A woman with previously treated and stable TTP had a relapse at 36 weeks' gestation. Careful surveillance led to an early diagnosis. Severe disease in the peripartum period was treated successfully with cryosupernatant plasma-based plasmapheresis and platelet transfusion, with good maternal and neonatal outcomes. Cryosupernatant plasma is a viable alternative to fresh frozen plasma for plasmapheresis for TTP and may offer some therapeutic and logistical advantages. Platelet transfusion can be undertaken safely if needed to prevent or treat significant hemorrhage.
The Power of Words: Why Communication fosters Cooperation and Efficiency

We present a game-theoretical model that accounts for abundant experimental evidence from games with non-binding communication (‘cheap talk’). It is based on two key ideas: People are conditionally averse to break norms of honesty and fairness (i.e., the emotional cost of breaking a norm is low if few people comply), and heterogeneous with regard to their concern for norms. The model explains (a) why cooperation in social dilemmas rises if players can previously announce their intended play, (b) why details of the communication protocol like the number of message senders and the order in which players communicate affect cooperation, (c) why players in sender-receiver games tend to transmit more information than a standard analysis would predict, and (d) why senders of false messages are often sanctioned if punishment is available.
Feature-Based Subjectivity Classification of Filipino Text

Subjectivity classification classifies whether a text expresses an opinion or not. Though there are already existing works in this field especially for the English Language, no reports have been made if these approaches are indeed effective when adapted to the Filipino language. This research reports a feature-based approach for subjectivity classification using existing classifiers such as Naïve Bayes, Bagging, Multilayer perceptron and Random Forest Tree. Result shows that the Bagging classifier gave the best results with 64.7% accuracy.
Mutations in the Salmonella enterica serovar Choleraesuis cAMP-receptor protein gene lead to functional defects in the SPI-1 Type III secretion system

Salmonella enterica serovar Choleraesuis (Salmonella Choleraesuis) causes a lethal systemic infection (salmonellosis) in swine. Live attenuated Salmonella Choleraesuis vaccines are effective in preventing the disease, and isolates of Salmonella Choleraesuis with mutations in the cAMP-receptor protein (CRP) gene (Salmonella Choleraesuis ∆crp) are the most widely used, although the basis of the attenuation remains unclear. The objective of this study was to determine if the attenuated phenotype of Salmonella Choleraesuis ∆crp was due to alterations in susceptibility to gastrointestinal factors such as pH and bile salts, ability to colonize or invade the intestine, or cytotoxicity for macrophages. Compared with the parental strain, the survival rate of Salmonella Choleraesuis ∆crp at low pH or in the presence of bile salts was higher, while the ability of the mutant to invade intestinal epithelia was significantly decreased. In examining the role of CRP on the secretory function of the Salmonella pathogenicity island 1 (SPI-1) encoded type III secretion system (T3SS), it was shown that Salmonella Choleraesuis ∆crp was unable to secrete the SPI-1 T3SS effector proteins, SopB and SipB, which play a role in Salmonella intestinal invasiveness and macrophage cytotoxicity, respectively. In addition, caspase-1 dependent cytotoxicity for macrophages was significantly reduced in Salmonella Choleraesuis ∆crp. Collectively, this study demonstrates that the CRP affects the secretory function of SPI-1 T3SS and the resulting ability to invade the host intestinal epithelium, which is a critical element in the pathogenesis of Salmonella Choleraesuis.
Real-time satellite signature suppression shield attitude planning based on improved adaptive genetic algorithm

This paper explores the satellite shield attitude planning for low observable satellites equipped with signature suppression shields. The on-orbit satellite's operational effectiveness and survivability are coupled with its radar cross section (RCS) and laser cross section (LCS) in threat directions, which are determined by the shield's attitude. This paper presents a real-time satellite shield attitude planning algorithm (RSSAPA), which aims to reduce the satellite's RCS and LCS in threat directions by limited satellite shield attitude control in real-time. The algorithm establishes a new mathematical model based on satellite orbit, threat distribution and earth curvature directly, and develops an improved adaptive genetic algorithm (IAGA) with novel crossover and mutation strategies, to reduce the computational complexity and ensure the real-time performance. Also, curved surface pixel method (CSPM) is introduced in RSSAPA to calculate satellite's RCS, and a refined CSPM is developed to complete the satellite's LCS calculation at the same time. According to the simulation results, the algorithm is proved to be efficient.
Regulators and characteristic classes of flat bundles

This is a substantial revision of the older version of this paper. The main result of the old version (the equality, up to a factor of 2 of the Beilinson and Borel regulators) is now a conjecture. The main results give equality of Beilinson chern classes and Cheeger-Simons-Chern classes in various situations such as for flat bundles over quasi projective varieties. We also prove the equality (up to a factor of two) of the Borel regulator element and the universal Cheeger-Simons Chern class.
Evaluation of the Gramotr on Parser for German

Thepaperdescribesanexperimentin inside-outsideestimationof alexicalizedprobabilisticcontext freegrammarfor German.Grammar andformalismfeatureswhich make theexperimentfeasiblearedescribed.Successi ve modelsareevaluatedon precisionandrecallof phrasemarkupconsistingof labelsfor nounchunksandsubcategorizationframes.Our approachto parsingis a blendof symbolicand stochasticmethodswherewe useevaluationresultsin both incrementalgrammardevelopmentandvalidationof selectedoutputto be usedin lexical semanticclustering.Our resultsarethat (i) scrambling-stylefreephraseorder, casemorphology, subcategorization,and NP-internalgender , numberandcaseagreementcanbe dealtwithin a lexicalizedprobabilisticcontext-free grammarformalism,and (ii) inside-outsideestimationappearsto bebeneficial,however relieson a carefullybuilt grammarandanevaluationbasedon carefully selectedlinguistic criteria. Additionally, we reportexperimentson overtrainingwith inside-outsideestimation,especiallyfocusingon comparisonof theresultsof mathematical andlinguistic evaluations.
Incremental Bayesian classification for Chinese question sentences based on fuzzy feedback

Aiming at problems such as fixed training set and lacking of completed information in traditional Bayesian classification, incremental learning mechanism is introduced. Combining with the characteristics of question sentences in Chinese question answering system, Semi-Naive Bayesian model is used to construct classifier. In order to make prior distribution of samples lean to even distribution, samples whose posterior probability approach 1/n (n: number of classes) were selected and appended into training set. The approaching extent of samples is described by fuzzy set, the fuzzy distinguish result is returned to classifier, therefore a fuzzy feedback mechanism is formed. Incremental Semi-Naive Bayesian classifier based on fuzzy feedback is proposed in this paper. The results of experiments show that this Bayesian classification can improve the accuracy of classifier effectively.
Optical variability of the BL Lacertae object GC 0109+224. Multiband behaviour and time scales from

We present the most continuous data base of optical BVR c I c observations ever published on the BL Lacertae object GC 0109+224, collected mainly by the robotic telescope of the Perugia University Observatory in the periodNovember 1994-February 2002. These observations have been complemented by data from the Torino Observatory, collected in the period July 1995-January 1999, and Mt. Maidanak Observatory (December 2000). GC 0109+224 showed rapid optical variations and six major outbursts were observed at the beginning and end of 1996, in fall 1998, at the beginning and at the end of 2000, and at the beginning of 2002. Fast and large-amplitude drops characterized its flux behaviour. The R c magnitude ranged from 13.3 (16.16 mJy) to 16.46 (0.8 mJy), with a mean value of 14.9 (3.38 mJy). In the periods where we collected multi-filter observations, we analyzed colour and spectral indexes, and the variability patterns during some flares. The long-term behaviour seems approximatively achromatic, but during some isolated outbursts we found evidence of the typical loop-like hysteresis behaviour, suggesting that rapid optical variability is dominated by non-thermal cooling of a single emitting particle population. We performed also a statistical analysis of the data, through the discrete correlation function (DCF), the structure function (SF), and the Lomb-Scargle periodogram, to identify characteristic times scales, from days to months, in the light curves, and to quantify the mode of variability. We also include the reconstruction of the historical light curve and a photometric calibration of comparison stars, to favour further extensive optical monitoring of this interesting blazar.
YOUR MEMBERS ARE ALSO YOUR CUSTOMERS : MARKETING FOR INTERNET SOCIAL NETWORKS

Perhaps the fastest growing arena in the World Wide Web is the space of social networking sites (e.g., Friendster, Facebook, MySpace). The success of these sites directly depends on the number and activity level of their users. What attracts users to the site is a continually changing digital content (e.g., messages, pictures, photos, music, videos, blogs) generated by other users. In contrast, e-commerce sites (e.g., Amazon) or portals (e.g., Yahoo! ) can support their well being by manipulating content (adding products, adjusting prices, updating news, etc.). We propose a model which allows social networking site managers to identify community members whose network influence makes them important for the business. We link individual user activity to the online behavior of other users in the network. Using Bayesian methods and a variable selection approach, we infer which users in the community are “influential” in affecting the site usage of other members. The developed method is specifically tailored to the variable selection task with a very large number of predictors. We show that in a user-generated content environment, network effects are critical in determining a customer’s value to the firm. We find significant heterogeneity among users on two dimensions: susceptibility to influence from other users and the extent of influence on others in the network. Our findings indicate that social influence in an online community is similar to what is experienced offline. The average user is influenced by very few other network members and, in turn, influences very few others. Finally, a small proportion of people (25% of community members) participates in a major share (75%) of all influential ties identified in the social network.
Fast and accurate cooperative localization in wireless sensor networks

Cooperative localization capability is a highly desirable characteristic of wireless sensor networks. It has attracted considerable research attention in academia and industry. The sum-product algorithm over a wireless sensor network (SPAWN) is a powerful method to cooperatively estimate the positions of many sensors (agents) using knowledge of the absolute positions of a few sensors (anchors). Drawbacks of the SPAWN, however, are its high computational complexity and communication load. In this paper we address the complexity issue, reformulate it as convolution problem and utilize the fast Fourier transform (FFT), culminating in a fast and accurate localization algorithm, which we named SPAWN-FFT. Our simulation results show SPAWN-FFT's superiority over SPAWN regarding the computational effort, while maintaining its full flexibility and localization performance.
Formal verification of crossover operator in Genetic Algorithms using Prototype Verification System (PVS)

Genetic Algorithms (GA) are commonly used for solving search problems. Modeling of evolutionary systems is another interesting area for GA research. Using Prototype Verification System (PVS), this article presents a method of formal specification and verification for the most rudimentary properties of genetic algorithms. This research formally proves a generalized version of the crossover operator of GA. Starting with a single-point crossover operator and its properties using PVS specification language, it specifies a multi-point crossover operator (along with properties). Operators used in both single-point and multi-point crossover operation are specified and their properties are proved. The exact relation between these operators has also been established. The research also proves the correctness of both the operators using proof assistant PVS.
Classifying matrices with a spectral regularization

We propose a method for the classification of matrices. We use a linear classifier with a novel regularization scheme based on the spectral l1-norm of its coefficient matrix. The spectral regularization not only provides a principled way of complexity control but also enables automatic determination of the rank of the coefficient matrix. Using the Linear Matrix Inequality technique, we formulate the inference task as a single convex optimization problem. We apply our method to the motor-imagery EEG classification problem. The method not only improves upon conventional methods in the classification performance but also determines a subspace in the signal that concentrates discriminative information without any additional feature extraction step. The method can be easily generalized to regression problems by changing the loss function. Connections to other methods are also discussed.
Extraction of fuzzy rules from trained neural network using evolutionary algorithm

This paper presents our approach to the rule extraction problem from trained neural network. A method called REX is briefly described. REX acquires a set of fuzzy rules using an evolutionary algorithm. Evolutionary algorithm searches not only fuzzy rules, but also a description of fuzzy sets. The way of coding and evaluation process of an individual is presented. The method was tested using the following benchmark data sets: IRIS, WINE and Wisconsin Breast Cancer Diagnosis. On the basis of the experimental studies shown in this paper, we can conclude that rules obtained by REX can be easily understood by human - they include small number of premises, and their fidelity is very high. Obtained results are compared to other rule extraction methods.
Functional Roles of E6 and E7 Oncoproteins in HPV-Induced Malignancies at Diverse Anatomical Sites

Approximately 200 human papillomaviruses (HPVs) infect human epithelial cells, of which the alpha and beta types have been the most extensively studied. Alpha HPV types mainly infect mucosal epithelia and a small group of these causes over 600,000 cancers per year worldwide at various anatomical sites, especially anogenital and head-and-neck cancers. Of these the most important is cervical cancer, which is the leading cause of cancer-related death in women in many parts of the world. Beta HPV types infect cutaneous epithelia and may contribute towards the initiation of non-melanoma skin cancers. HPVs encode two oncoproteins, E6 and E7, which are directly responsible for the development of HPV-induced carcinogenesis. They do this cooperatively by targeting diverse cellular pathways involved in the regulation of cell cycle control, of apoptosis and of cell polarity control networks. In this review, the biological consequences of papillomavirus targeting of various cellular substrates at diverse anatomical sites in the development of HPV-induced malignancies are highlighted.
Low Rates of Acute Recanalization With Intravenous Recombinant Tissue Plasminogen Activator in Ischemic Stroke: Real-World Experience and a Call for Action

Background and Purpose— Acute rates of recanalization after intravenous (IV) recombinant tissue plasminogen activator (rt-PA) in proximal vessel occlusion have been estimated sparingly, typically using transcranial Doppler (TCD). We aimed to study acute recanalization rates of IV rt-PA in CT angiogram-proven proximal (internal carotid artery [ICA], M1 middle cerebral artery [MCA], M2-MCA, and basilar artery) occlusions and their effects on outcome. Materials and Methods— The CT angiogram database of the Calgary stroke program was reviewed for the period 2002 to 2009. All patients with proximal vessel occlusions receiving IV rt-PA who were assessed for recanalization by TCD or angiogram (for acute endovascular treatment) were included for analysis. Rates of acute recanalization as observed on TCD/first run of angiogram and postendovascular therapy recanalization rates were noted. Modified Rankin Scale score ≤2 at 3 months was used as a good outcome. Results— Among 1341 patients in the CT angiogram database, 388 patients with proximal occlusion were identified. Of these, 216 patients had received IV rt-PA; 127 patients underwent further imaging to assess recanalization. Among the patients undergoing TCD (n=46) and cerebral angiogram (n=103), only 27 (21.25%) patients had acute recanalization. By occlusion subtype, the rates of recanalization were: distal ICA (with or without ICA neck occlusion or stenotic disease) 1 of 24 (4.4%); M1-MCA (with or without ICA neck occlusion or stenotic disease) 21 of 65 (32.3%); M2-MCA 4 of 13 (30.8%); and basilar artery 1 of 25 (4%). Onset to rt-PA time was comparable in patients with and without recanalization. Recanalization (P<0.0001; risk ratio, 2.7; 95% confidence interval, 1.5–4.6) was the strongest predictor of outcome (adjusted for age and National Institutes of Health Stroke Scale score). Conclusions— A low rate of acute recanalization was observed with IV rt-PA in proximal vessel occlusions identified by baseline CT angiogram. Recanalization was the strongest predictor of good outcome.
Molecular dynamics and free energy analysis of neuraminidase–ligand interactions

We report molecular dynamics calculations of neuraminidase in complex with an inhibitor, 4‐amino‐2‐deoxy‐2,3‐didehydro‐N‐acetylneuraminic acid (N‐DANA), with subsequent free energy analysis of binding by using a combined molecular mechanics/continuum solvent model approach. A dynamical model of the complex containing an ionized Glu119 amino acid residue is found to be consistent with experimental data. Computational analysis indicates a major van der Waals component to the inhibitor‐neuraminidase binding free energy. Based on the N‐DANA/neuraminidase molecular dynamics trajectory, a perturbation methodology was used to predict the binding affinity of related neuraminidase inhibitors by using a force field/Poisson‐Boltzmann potential. This approach, incorporating conformational search/local minimization schemes with distance‐dependent dielectric or generalized Born solvent models, correctly identifies the most potent neuraminidase inhibitor. Mutation of the key ligand four‐substituent to a hydrogen atom indicates no favorable binding free energy contribution of a hydroxyl group; conversely, cationic substituents form favorable electrostatic interactions with neuraminidase. Prospects for further development of the method as an analysis and rational design tool are discussed.
INVARIANCE BREAKPOINT DETECTION

In this paper, the formulation of robust breakpoint detection algorithms that are invariant to the following transformations: translation, scaling, rotation, inversion, and relatively large levels of partial occlusion are presented. The detection of invariant breakpoints is a crucial preprocessing stage in classification problems such as automatic target recognition. The breakpoint detection algorithms formulated in this paper are based on two approaches: global and local. The algorithms formulated not only aim at achieving invariance to the specified transformations but also aim at reducing the number of algorithm parameters and the number of computations. These features are essential for operational flexibility. Test results and the evaluations of the algorithms operating on targets with various noise, occlusion, and combination of noise and occlusion levels are presented. The results indicate that, in general, the local algorithms outperform the global algorithms especially when the targets experience rotation and partial occlusion.
Sense of belonging as an indicator for social capital : a mixed methods analysis of students' sense of belonging to university

Social capital, from the collective social capital theory perspective, is constituted by trust, social network and participation. Social capital is agreed to be crucial for civil society and wellbeing, but there is no general consensus on how to define and measure it. Sense of belonging shares important meanings with social capital, but is more amenable to measurement. Social capital, primarily a metaphor, is elastic, implicative, and versatile, whereas belonging is a more concrete and tangible concept that is suitable for the measurement. This research explores how belonging is related to social capital, and examines whether belonging can be used as an indicator for social capital. A mixed methods, qualitative and quantitative research design was developed to collect data on students’ sense of belonging to Bangor University. A new instrument, the 10 Words Question, was developed to elicit participants’ own thoughts and feelings, while a survey questionnaire was used in parallel, with questions about belonging, social capital, and demographic information. The empirical analysis reveals that there are four main domains of belonging, academic and social engagement, surroundings, and personal spaces. This challenges previous research on the subject in the UK. The findings suggest that students’ sense of belonging is strongly associated with social capital. Further conceptual and statistical analysis shows that there is significant overlap with each of the main components of social capital. One implication of the study is that a one-dimensional approach to students’ sense of belonging to an institution may result in poorly targeted and ineffective policies. The research highlights the complex characteristics of belonging, so if students’ belonging is to be used to promote academic success and retention, more conceptually refined approaches and empirically detailed evidence will be required. This research also demonstrates that belonging data can be used as a simple alternative indicator for social capital.