Miracle-dz/UniFAR-FTUs huggingface dataset:
ARTIST: An activated method in internal coordinate space for sampling protein energy landscapes

We present the first applications of an activated method in internal coordinate space for sampling all‐atom protein conformations, the activation–relaxation technique for internal coordinate space trajectories (ARTIST). This method differs from all previous internal coordinate‐based studies aimed at folding or refining protein structures in that conformational changes result from identifying and crossing well‐defined saddle points connecting energy minima. Our simulations of four model proteins containing between 4 and 47 amino acids indicate that this method is efficient for exploring conformational space in both sparsely and densely packed environments, and offers new perspectives for applications ranging from computer‐aided drug design to supramolecular assembly. Proteins 2006. © 2006 Wiley‐Liss, Inc.
DrugComboRanker: drug combination discovery based on target network analysis

Motivation: Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients’ outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities. Results: In this study, we propose a novel systematic computational tool DrugComboRanker to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients’ genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations. Availability and implementation: The program is available on request. Contact: stwong@tmhs.org
Applying a customer centric development approach for web 2.0 applications

In this work we demonstrate our experiences on applying a customer centric approach to the development of an electronic food ordering Web 2.0 application. Placing customers at the core, interacting with them in a reiterative way and gaining insights from their feedback can be leveraged to create a serviceable product. Combining this approach with the use of existing Web 2.0 APIs simplifies and shortens the development procedure. To scrutinize consumers' viewpoint of electronic food ordering applications we conducted a survey composed of rapid customer-focused iterations. This survey constituted a guideline on developing our application based on customers feedback.
Low-Density Lipoproteins Oxidation and Endometriosis

The etiopathogenesis of endometriosis still remains unknown. Recent data provide new valuable information concerning the role of oxidative stress in the pathophysiology of the disease. It has been proved that levels of different lipid peroxidation end products are increased in both peritoneal fluid (PF) and serum of endometriotic patients. We assessed the concentration of oxidized low-density lipoproteins (oxLDL) in PF of 110 women with different stages of endometriosis and 119 women with serous (n = 78) or dermoid (n = 41) ovarian cysts, as the reference groups. PF oxLDL levels were evaluated by ELISA. We found that concentrations of oxLDL in PF of endometriotic women were significantly higher compared to women with serous but not dermoid ovarian cysts. Interestingly, by analyzing concentrations of oxLDL in women with different stages of the disease, it was noted that they are significantly higher only in the subgroup of patients with stage IV endometriosis as compared to women with ovarian serous cysts. In case of minimal, mild, and moderate disease, PF oxLDL levels were similar to those noted in reference groups. Our results indicate that disrupted oxidative status in the peritoneal cavity of women with endometriosis may play a role in the pathogenesis of advanced stages of the disease.
Hybridized Level Set Based Image Segmentation in UAV Images for Surveillance Applications

Image segmentation is one of the most difficult, dynamic and challenging problems in the image processing domain. It denotes a process by which a raw input image is partitioned into non-overlapping regions such that each region is homogeneous and the union of two adjacent regions is heterogeneous. This paper proposes a novel level set based method for image segmentation, which is able to deal with intensity inhomogeneities in the segmentation for UAV captured images. Level set algorithm is often used for region-based homogeneous image segmentation. When noisy, inhomogeneous image segmentation is required, Level set algorithm should be modified such that it can be less sensitive to noise and inhomogeneity in an image. In that context, our proposed method for noisy images, by hybridizing level set algorithm with superseding, is proposed. Experiments show that our method is more robust to initialization, faster and more accurate than the well-known other image segmentation model.
The Perks and Perils of Interdisciplinary Research

This paper synthesizes findings from two studies the author conducted that examine how engagement in interdisciplinary research (IDR) influences scholars’ careers. Results from these two studies, one large-scale and quantitative and the other small-scale and qualitative, provide a much needed empirical assessment of IDR’s effects on individual careers. In essence, they provide a nice antidote (and some caution) to the rhetoric and enthusiasm surrounding IDR. My co-authors of these studies and I find that engaging in interdisciplinary research increases a scholar’s visibility in terms of citations, but also presents challenges, including reduced productivity, cognitive challenges, lack of support, extra time and commitment, and framing of one’s work. This paper concludes by discussing the policy implications of this research.
Exploring the Barriers in the Commercial Use of Open Government Data

Open government data is currently used by private sector organizations to create new products and services, and/or to achieve competitive advantage. However, little is known about which barriers are encountered by companies when using the data. The aim of our study is to further extend our understanding of the barriers encountered by firms in the use of open government data, and what is the impact of these barriers on the business. Drawing on interviews with 12 private companies we investigate which barriers are predominant in the commercial use of open government data. Barriers were found mostly in the dimensions of data accessibility, data usability, and interaction. We briefly explore the impact that these barrier can have on the firm, and finally provide suggestions on aspects that can be explored by both governments and firms to improve use of open government data. Our work aims to extend the existing understanding of the commercial use of open government data, as well as to provide insights to both firm managers and government officials in the implementation of open government data strategies.
Trend of Narratives in the Age of Misinformation

Social media enabled a direct path from producer to consumer of contents changing the way users get informed, debate, and shape their worldviews. Such a disintermediation might weaken consensus on social relevant issues in favor of rumors, mistrust, or conspiracy thinking—e.g., chem-trails inducing global warming, the link between vaccines and autism, or the New World Order conspiracy. Previous studies pointed out that consumers of conspiracy-like content are likely to aggregate in homophile clusters—i.e., echo-chambers. Along this path we study, by means of a thorough quantitative analysis, how different topics are consumed inside the conspiracy echo-chamber in the Italian Facebook. Through a semi-automatic topic extraction strategy, we show that the most consumed contents semantically refer to four specific categories: environment, diet, health, and geopolitics. We find similar consumption patterns by comparing users activity (likes and comments) on posts belonging to these different semantic categories. Finally, we model users mobility across the distinct topics finding that the more a user is active, the more he is likely to span on all categories. Once inside a conspiracy narrative users tend to embrace the overall corpus.
A Novel and Successful Credit Card Fraud Detection System Implemented in a Turkish Bank

We developed a credit card fraud detection solution for a major bank in Turkey. The study was completed in about three years and the developed system has been in use since February 2013. It had a great impact in the rule based fraud detection process used by the bank. Indeed, while eighty percent of the rules have been eliminated and the number of alerts has been reduced to half, a significant increase in fraud detection has been recorded. As of now the system can catch ninety seven percent of fraud attempts online or, nearly online. The study is interesting in both the formulation of the problem and the algorithms implemented. In fact, we noticed that the standard classification algorithms are not fully suitable for the fraud detection problem (as the cost of every individual false negative can be different from the others), and we looked for alternative methods, especially the meta-heuristics. Among them the newly introduced migrating birds optimization algorithm (MBO) turned out to be superior and was implemented. In addition, during the study a cost sensitive decision tree algorithm was developed and introduced to the literature.
Modulation of irinotecan-induced diarrhea by cotreatment with neomycin in cancer patients.

This study was designed to evaluate irinotecan (CPT-11) disposition and pharmacodynamics in the presence and absence of the broad-spectrum antibiotic neomycin. Seven evaluable cancer patients experiencing diarrhea graded > or =2 after receiving CPT-11 alone (350 mg/m(2) i.v. once every 3 weeks) received the same dose combined with oral neomycin at 1000 mg three times per day (days -2 to 5) in the second course. Neomycin had no effect on the systemic exposure of CPT-11 and its major metabolites (P > or = 0.22). However, it changed fecal beta-glucuronidase activity from 7.03 +/- 1.76 microg/h/mg (phenolphthalein assay) to undetectable levels and decreased fecal concentrations of the pharmacologically active metabolite SN-38. Although neomycin had no significant effect on hematological toxicity (P > 0.05), diarrhea ameliorated in six of seven patients (P = 0.033). Our findings indicate that bacterial beta-glucuronidase plays a crucial role in CPT-11-induced diarrhea without affecting enterocycling and systemic SN-38 levels.
Indoor Location System Using ZigBee Technology

This paper describes a location system for persons and objects in an indoor environment, where wireless nodes can include sensors and provide unique identifiers. The system nodes, using ZigBee technology, can function as RFID tags, having each one a unique EPC identification number. Sensors can be associated with the wireless nodes Zigbee to create applications for home, health and traffic control. Location systems are analysed with emphasis on indoor location systems.The implemented location algorithm includes a propagation model based on the wall attenuation factor propagation model together with triangulation. A variety of tests were carried out in an indoor environment. Results demonstrate that the location system is viable, showing itself to be effective, flexible and easily adaptable to various locations.
Multiple Reassortment Events in the Evolutionary History of H1N1 Influenza A Virus Since 1918

The H1N1 subtype of influenza A virus has caused substantial morbidity and mortality in humans, first documented in the global pandemic of 1918 and continuing to the present day. Despite this disease burden, the evolutionary history of the A/H1N1 virus is not well understood, particularly whether there is a virological basis for several notable epidemics of unusual severity in the 1940s and 1950s. Using a data set of 71 representative complete genome sequences sampled between 1918 and 2006, we show that segmental reassortment has played an important role in the genomic evolution of A/H1N1 since 1918. Specifically, we demonstrate that an A/H1N1 isolate from the 1947 epidemic acquired novel PB2 and HA genes through intra-subtype reassortment, which may explain the abrupt antigenic evolution of this virus. Similarly, the 1951 influenza epidemic may also have been associated with reassortant A/H1N1 viruses. Intra-subtype reassortment therefore appears to be a more important process in the evolution and epidemiology of H1N1 influenza A virus than previously realized.
Tax Clientele Eeects in the German Bond Market Credits

This paper presents an analysis of tax clientele eeects in the Ger-man government bond market from the viewpoint of private investors. The methods developed here allow the identiication of bonds that are over-valued from the viewpoint of a certain tax class, the estimation of tax-speciic term structures, and the identiication of representative investors. Regression and no-arbitrage approaches are uniied. The empirical results presented have important implications for the estimation of the term structure from coupon bond prices and the valuation of interest rate derivatives.
Communication In The Presence Of Noise

A method is developed for representing any communication system geometrically. Messages and the corresponding signals are points in two "function spaces," and the modulation process is a mapping of one space into the other. Using this representation, a number of results in communication theory are deduced concerning expansion and compression of bandwidth and the threshold effect. Formulas are found for the maximum rate of transmission of binary digits over a system when the signal is perturbed by various types of noise. Some of the properties of "ideal" systems which transmit at this maximum rate are discussed The equivalent number of binary digits per second for certain information sources is calculated.
Depth free entropy based ghost removal on synthetic aperture images

The depth resolution of synthetic aperture photography (SAP) is significantly improved than traditional imaging system. However, as one side effect, ghost artifact is brought in defocused regions of SAP images. In this paper, we propose an entropy based ghost removal algorithm on SAP images, which is free of depth estimation. First, we analyze the essence of ghost artifact from light field. Then we present three entropy based assumptions for predicting the focus trend and estimating the kernel of low pass filter for each pixel. Based on the neighborhood pixel similarity and adjacent frame continuity, a low pass filter is employed to reduce the unexpected ghosting in the spatial domain. Experimental results have verified the validity of proposed algorithm on both virtual and real data. The ghosting areas are greatly smoothed while focused regions are kept as same as original image.
Two Salt Bridges Differentially Contribute to the Maintenance of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) Channel Function*

Background: Two salt bridges, Arg347–Asp924 and Arg352–Asp993, have been identified in CFTR, but the timing of their interaction remains unknown. Results: Arg347-Asp924-Asp993 form a triangular salt bridge and work together with the Arg352-Asp993 salt bridge to maintain the open pore architecture of CFTR. Conclusion: These salt bridge residues interact and contribute differently in stabilizing the open pore during gating cycle. Significance: Understanding the CFTR pore dynamic open-closed transition is crucial for rational drug design. Previous studies have identified two salt bridges in human CFTR chloride ion channels, Arg352-Asp993 and Arg347-Asp924, that are required for normal channel function. In the present study, we determined how the two salt bridges cooperate to maintain the open pore architecture of CFTR. Our data suggest that Arg347 not only interacts with Asp924 but also interacts with Asp993. The tripartite interaction Arg347-Asp924-Asp993 mainly contributes to maintaining a stable s2 open subconductance state. The Arg352-Asp993 salt bridge, in contrast, is involved in stabilizing both the s2 and full (f) open conductance states, with the main contribution being to the f state. The s1 subconductance state does not require either salt bridge. In confirmation of the role of Arg352 and Asp993, channels bearing cysteines at these sites could be latched into a full open state using the bifunctional cross-linker 1,2-ethanediyl bismethanethiosulfonate, but only when applied in the open state. Channels remained latched open even after washout of ATP. The results suggest that these interacting residues contribute differently to stabilizing the open pore in different phases of the gating cycle.
Peak detection algorithm for ECG wave

We have proposed an algorithm for detection of the QRS complexes of ECG signals. There are three peaks i.e. P wave, QRS complex and T wave. Here we are detecting QRS complex. QRS complex wave detection is very important for the HRV analysis of ECG signal in biomedical engineering. We have used derivative of the signal to remove P Wave and T wave from signal. Band pass filter is used to remove noise such as baseline wander, power-line noise, and out of band noise. Squaring of the signal makes all data points positive. Squaring of the signal is taken to emphasize the higher frequencies i.e. predominantly the ECG frequencies.
PCR-Induced Sequence Artifacts and Bias: Insights from Comparison of Two 16S rRNA Clone Libraries Constructed from the Same Sample

ABSTRACT The contribution of PCR artifacts to 16S rRNA gene sequence diversity from a complex bacterioplankton sample was estimated. Taq DNA polymerase errors were found to be the dominant sequence artifact but could be constrained by clustering the sequences into 99% sequence similarity groups. Other artifacts (chimeras and heteroduplex molecules) were significantly reduced by employing modified amplification protocols. Surprisingly, no skew in sequence types was detected in the two libraries constructed from PCR products amplified for different numbers of cycles. Recommendations for modification of amplification protocols and for reporting diversity estimates at 99% sequence similarity as a standard are given.
Central energy equipartition in multimass models of globular clusters

In the construction of multimass King-Michie models of globular clusters, an approximated central energy equipartition between stars of different mass is usually imposed by scaling the velocity parameter of each mass class inversely with the stellar mass, as if the distribution function were isothermal. In this paper, this 'isothermal approximation' has been checked and its consequences on the model parameters studied by a comparison with models including central energy equipartition correctly. It is found that, under the isothermal approximation, the 'temperatures' of a pair of components can differ to a non-negligible amount for low concentration distributions. It is also found that, in general, this approximation leads to a significantly reduced mass segregation in comparison with that given under the exact energy equipartition at the centre. As a representative example, an isotropic three-component model fitting a given projected surface brightness and line-of-sight velocity dispersion profiles is discussed. In this example, the isothermal approximation gives a cluster envelope much more concentrated (central dimensionless potential W = 3.3) than under the true equipartition (W = 5.9 x 10 -2 ), as well as a higher mass function logarithmic slope. As a consequence, the inferred total mass (and then the global mass-to-light ratio) is a factor of 1.4 times lower than the correct value and the amount of mass in heavy dark remnants is 3.3 times smaller. Under energy equipartition, the fate of stars having a mass below a certain limit is to escape from the system. This limit is derived as a function of the mass and W of the component of giant and turn-off stars.
Medical Image Annotation and Retrieval by Using Classification Techniques

Given the rapid increase in the number of medical images, the process of image retrieval is considered an effective solution that can be used in the automatic search and storage of images. Content-based image retrieval is considerably affected by image classification, also called image annotation. The performance of image annotation is significantly affected by two main issues, namely, automatic extraction for image features and the annotation algorithm. This study addresses these issues by constructing a feature vector from the extraction of multi-level features. Two machine learning techniques are used for evaluation. The K-nearest neighbor and support vector machine methods of learning machine are employed to classify images. Image CLEF med2005 is used as the database for the classification approaches. Furthermore, principal component analysis is utilized thrice to decrease the length of the feature vector. Results demonstrate that the accuracy is significantly improved compared with those of similar classification approaches related to the same database.
A Design Framework for Wireless Transmission of Multiple Hi-Fi Audio Streams using DSSS Technique

♠ This work was supported by National Science and Technology Development Agency (NSTDA) and National Electronic and Computer TEchnology Center (NECTEC) through the project “A Feasibility Study of Wireless Hi-Fi Audio Transmission using Direct Sequence Spread Spectrum (DSSS) Technique”, grant number NT−C005−47. ♣ SCORPion stands for Superior COmmunication Research and Prototyping for commercialization Abstract
Adapting the use of attributes to the task environment in joint action: results and a model

Speakers use referring expressions to identify an object in the environment. To generate a referring expression, features of the intended referent have to be selected that distinguish the object from the other potential referents. Current accounts of referring expressions consider a number of factors that influence the choice of features but ignore the influences of the task environment. In particular, they do not address how these influences change the generation of referring expressions over an extended period of time. We present results of how colour terms are used to describe landmarks in a task oriented dialogue (a route communication task) and describe a computational cognitive model of the observed adaptations over time.