Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine structure mapping of five temperature-sensitive mutants in the 22- and 147-kilodalton subunits of vaccinia virus DNA-dependent RNA polymerase

We have mapped the temperature-sensitive (ts) lesions of three mutants, ts51, ts53, and ts65, and two other mutants, ts7 and ts20, to regions on the vaccinia virus genome that encode the 147- and 22-kilodalton subunits of the viral DNA-dependent RNA polymerase, respectively. Plasmid and bacteriophage clones from the HindIII J region and the region spanning the HindIII J-H junction were used in marker rescue experiments to map the mutations. Sequence analysis of the region encoding the 22-kilodalton subunit in the wild-type, ts7, and ts20 viruses revealed a single base change in the mutants compared with that in the wild-type virus. The identification of these RNA polymerase mutants provides us with tools to understand transcription and its regulation in vaccinia virus.
GrouseFlocks: Steerable Exploration of Graph Hierarchy Space

Several previous systems allow users to interactively explore a large input graph through cuts of a superimposed hierarchy. This hierarchy is often created using clustering algorithms or topological features present in the graph. However, many graphs have domain-specific attributes associated with the nodes and edges, which could be used to create many possible hierarchies providing unique views of the input graph. GrouseFlocks is a system for the exploration of this graph hierarchy space. By allowing users to see several different possible hierarchies on the same graph, the system helps users investigate graph hierarchy space instead of a single fixed hierarchy. GrouseFlocks provides a simple set of operations so that users can create and modify their graph hierarchies based on selections. These selections can be made manually or based on patterns in the attribute data provided with the graph. It provides feedback to the user within seconds, allowing interactive exploration of this space.
Macrophages require Skap2 and Sirp&agr; for integrin-stimulated cytoskeletal rearrangement

Summary Macrophages migrate to sites of insult during normal inflammatory responses. Integrins guide such migration, but the transmission of signals from integrins into the requisite cytoskeletal changes is poorly understood. We have discovered that the hematopoietic adaptor protein Skap2 is necessary for macrophage migration, chemotaxis, global actin reorganization and local actin reorganization upon integrin engagement. Binding of phosphatidylinositol [3,4,5]-triphosphate to the Skap2 pleckstrin-homology (PH) domain, which relieves its conformational auto-inhibition, is critical for this integrin-driven cytoskeletal response. Skap2 enables integrin-induced tyrosyl phosphorylation of Src-family kinases (SFKs), Adap, and Sirp&agr;, establishing their roles as signaling partners in this process. Furthermore, macrophages lacking functional Sirp&agr; unexpectedly have impaired local integrin-induced responses identical to those of Skap2−/− macrophages, and Skap2 requires Sirp&agr; for its recruitment to engaged integrins and for coordinating downstream actin rearrangement. By revealing the positive-regulatory role of Sirp&agr; in a Skap2-mediated mechanism connecting integrin engagement with cytoskeletal rearrangement, these data demonstrate that Sirp&agr; is not exclusively immunoinhibitory, and illuminate previously unexplained observations implicating Skap2 and Sirp&agr; in mouse models of inflammatory disease.
Automatic Evaluation of Translation Quality for Distant Language Pairs

Automatic evaluation of Machine Translation (MT) quality is essential to developing high-quality MT systems. Various evaluation metrics have been proposed, and BLEU is now used as the de facto standard metric. However, when we consider translation between distant language pairs such as Japanese and English, most popular metrics (e.g., BLEU, NIST, PER, and TER) do not work well. It is well known that Japanese and English have completely different word orders, and special care must be paid to word order in translation. Otherwise, translations with wrong word order often lead to misunderstanding and incomprehensibility. For instance, SMT-based Japanese-to-English translators tend to translate 'A because B' as 'B because A. ' Thus, word order is the most important problem for distant language translation. However, conventional evaluation metrics do not significantly penalize such word order mistakes. Therefore, locally optimizing these metrics leads to inadequate translations. In this paper, we propose an automatic evaluation metric based on rank correlation coefficients modified with precision. Our meta-evaluation of the NTCIR-7 PATMT JE task data shows that this metric outperforms conventional metrics.
Real Time and Offline Network Intrusion Detection using Improved Decision Tree Algorithm

Data mining has been used extensively and broadly by several network organizations. Classification based algorithms provide a significant advantage in order to detect attacks in the training data. Network applications usage is being increased every day as the internet usage is exponentially increasing. In the same way, Network attacks detection is gradually decreased as data source is increasing. There is a need to develop some robust decision tree in order to produce effective decision rules from the attacked data. In this paper improved, decision tree is implemented in order to detect network attacks like TCP SYN , Ping of Death, ARP Spoof attacks. This improved tree is also tested on famous network intrusion dataset Kddcup 99 dataset. Experimental result shows this improved decision tree classifier gives effective decision rules compare to existing decision tree techniques like ID3 and C45 algorithms. Finally, this robust decision tree evaluates less false positive and true negative alarm rates compare to existing algorithms.
LANDSCAPE LEVEL VEGETATION CHARACTERIZATION OF LIDDER VALLEY USING GEOINFORMATICS

The present study was carried out to understand t he vegetation structure and dynamics of the Lidder Valley. The datasets used in the study include IRS-LISS III biseasonal satellite data, dated October 2005 and May 2006 having a spatial resolution of 23.5m. During the study, land use/ land cover w as mapped u sing o n s creen digitization. Moreover, phyto-sociological sampling was done for the forest, pasture and scrub classes using Nested-Quadrat approach to characterize the vegetation. A total of 9 phyto-sociologic al parameters were analyzed which include the Frequency, Density, Abundance, Basal Area, Relative Dominance, Relative Frequency, Relative Density and Importance Value Index (IVI) of t he respect ive species. A tot al of 13 categories of LU/LC were delineated from the given satellite data which include
Incentivizing Verifiable Privacy-Protection Mechanisms for Offline Crowdsensing Applications

Incentive mechanisms of crowdsensing have recently been intensively explored. Most of these mechanisms mainly focus on the standard economical goals like truthfulness and utility maximization. However, enormous privacy and security challenges need to be faced directly in real-life environments, such as cost privacies. In this paper, we investigate offline verifiable privacy-protection crowdsensing issues. We firstly present a general verifiable privacy-protection incentive mechanism for the offline homogeneous and heterogeneous sensing job model. In addition, we also propose a more complex verifiable privacy-protection incentive mechanism for the offline submodular sensing job model. The two mechanisms not only explore the private protection issues of users and platform, but also ensure the verifiable correctness of payments between platform and users. Finally, we demonstrate that the two mechanisms satisfy privacy-protection, verifiable correctness of payments and the same revenue as the generic one without privacy protection. Our experiments also validate that the two mechanisms are both scalable and efficient, and applicable for mobile devices in crowdsensing applications based on auctions, where the main incentive for the user is the remuneration.
Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions

Background Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence. Objective This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention. Methods We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence. Results We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p = .004), setup (p < .001), updates (p < .001), frequency of interaction with a counselor (p < .001), the system (p = .003) and peers (p = .017), duration (F = 6.068, p = .004), adherence (F = 4.833, p = .010) and the number of primary task support elements (F = 5.631, p = .005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence. Conclusions Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adhere.
Generating T E X from mathematical content with respect to notational settings

We describe how to obtain client-preferred notations in TEX generated from the output of mathematical software environments. Our approach is based on the fact that most packages can produce MathML or other XML-based formats for mathematical content. Generating TEX from these allows notational choices to be applied during the translation process. The particular choices of notation can be made either at the time TEX is generated or later, by the use of TEX macros. We show how this approach may be applied to the generation of TEX from both presentationally- and conceptually-oriented mathematical content and how MathML may be used in the process. Our implementation conserves the implicit high-level semantics of macro use in both TEX and MathML. Since the expressions generated by mathematical software may be quite lengthy, we also discuss issues that arise in line-breaking.
Fuzzy Logic Based Method for Improving Text Summarization

Text summarization can be classified into two approaches: extraction and abstraction. This paper focuses on extraction approach. The goal of text summarization based on extraction approach is sentence selection. One of the methods to obtain the suitable sentences is to assign some numerical measure of a sentence for the summary called sentence weighting and then select the best ones. The first step in summarization by extraction is the identification of important features. In our experiment, we used 125 test documents in DUC2002 data set. Each document is prepared by preprocessing process: sentence segmentation, tokenization, removing stop word, and word stemming. Then, we used 8 important features and calculate their score for each sentence. We proposed text summarization based on fuzzy logic to improve the quality of the summary created by the general statistic method. We compared our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that the best average precision, recall, and fmeasure for the summaries were obtained by fuzzy method.
Developing Interfield Nomological Nets

As behavioral research has expanded in Information Systems and other scientific fields, researchers are recognizing that construct proliferation increases the difficulty in identifying the nomological networks of constructs pertaining to any given research question. An Inter-Nomological Network uses semantic analysis to systematically identify, categorize, and predict relationships among the constructs that define the combined cognitive interest of behavioral scientific fields. Researchers can thereby identify concentrations in behavioral research around similar phenomena related to human experiences that transcend field boundaries, and that may in fact have common cognitive underpinnings. Interfield theory development is supported by discovery of nomological relationships between scientific fields. Preliminary results demonstrating confirmatory, exploratory, and interfield research applications are presented.
The “Sunshine Deficit” and Cardiovascular Disease

In past decades, the primary focus on vitamin D was the recognition and treatment of deficiency as it related to metabolic bone disease (rickets, osteomalacia, and secondary hyperparathyroidism). In the last 10 years, however, with the discovery of vitamin D receptors in multiple tissue types has come the recognition that the role of vitamin D extends beyond the musculoskeletal system.1 The presence of abundant vitamin D receptors in myocardial tissue and vasculature and the observation that hypertension may be ameliorated with vitamin D suggest a greater role for vitamin D in the cardiovascular system.2 Presently, large numbers of people are found to have hypovitaminosis D (a term chosen for this review to indicate any concentration below normal under substrate-saturated conditions) resulting in part from more indoor activities and the purposeful avoidance of sunshine. This review first describes why vitamin D, parathyroid hormone (PTH), and the skeleton are important to the heart and vasculature, then outlines why the epidemic of hypovitaminosis D deserves further scrutiny by the cardiovascular community, and finally suggests why treatment options for reducing hypovitaminosis D may favorably affect the morbidity and mortality of common cardiovascular disorders. Vitamin D is both a nutrient and a hormone. It is an essential precursor of calcitriol, 1,25-hydroxyvitamin D3 [1,25(OH)2D], which is necessary for bone development, growth, and mineralization and the maintenance of skeletal integrity. A cascade of steps is needed to cause progression of lesser active nutritionally ingested or synthesized vitamin D to more biologically active forms as depicted in Figure 1.1 The cascade starts with the photolysis of 7 dehydrocholesterol in the epidermis by solar ultraviolet B (UVB) radiation to previtamin D3, which then undergoes thermal isomerization to vitamin D3. Vitamin D3 undergoes primary hydroxylation in the liver to 25-hydroxyvitamin D [25(OH)D] and then …
Performance Optimization of Forward Aware Factor -Energy Balanced Routing Protocol In Wireless Sensor Networks

A wireless sensor network (WSN) consists of a large number of wireless sensor nodes. Since wireless sensor nodes are battery powered devices, they have limited processing and transmission power. In order to transmit sensing data to receiver effectively, it is important to design a routing protocol for WSNs. Since energy conservation is a key issue in WSNs, data aggregation should be exploited in order to save energy. In this case, redundant data can be aggregated at intermediate nodes reducing the size and number of exchanged messages and, thus, decreasing communication costs and energy consumption. This paper propose a Forward Aware Factor-Energy Balanced Routing Method (FAF-EBRM). In the experiments, this method is compared with LEACH. Experimental results show that this method FAF-EBRM outperforms LEACH, which balances the energy consumption, prolongs the network function lifetime and provides the best aggregation quality . Keywords: forward aware factor(FAF), Energy balance, Routing, Wireless sensor networks(WSNs).
Gravitational waveforms for neutron star binaries from binary black hole simulations

Gravitational waves from binary neutron star (BNS) and black-hole/neutron star (BHNS) inspirals are primary sources for detection by the Advanced Laser Interferometer Gravitational-Wave Observatory. The tidal forces acting on the neutron stars induce changes in the phase evolution of the gravitational waveform, and these changes can be used to constrain the nuclear equation of state. Current methods of generating BNS and BHNS waveforms rely on either computationally challenging full 3D hydrodynamical simulations or approximate analytic solutions. We introduce a new method for computing inspiral waveforms for BNS/BHNS systems by adding the post-Newtonian (PN) tidal effects to full numerical simulations of binary black holes (BBHs), effectively replacing the non-tidal terms in the PN expansion with BBH results. Comparing a waveform generated with this method against a full hydrodynamical simulation of a BNS inspiral yields a phase difference of < 1 radian over ~ 15 orbits. The numerical phase accuracy required of BNS simulations to measure the accuracy of the method we present here is estimated as a function of the tidal deformability parameter ⋋.
A Preliminary Analysis of TCP Performance in an Enterprise Network

Although TCP behavior is one of the most studied aspects of Internet traffic, little is known about TCP performance within modern enterprise networks. In this paper we analyze aspects of TCP performance observed in packet traces taken over four months from a medium-sized enterprise. We assess the prevalence of broken TCP transactions, applications used, throughput of TCP connections, and phenomena that influence performance, such as retransmissions, out-of-order delivery, and packet corruption. While much remains to explore, this work represents a first step towards understanding TCP performance in the under-studied environment.
Analysis of TTL-Based Consistency in Unstructured Peer-to-Peer Networks

Consistency maintenance is important to the sharing of dynamic contents in peer-to-peer (P2P) networks. The TTL-based mechanism is a natural choice for maintaining freshness in P2P content sharing. This paper investigates TTL-based consistency maintenance in unstructured P2P networks. In this approach, each replica is assigned an expiration time beyond which the replica stops serving new requests unless it is validated. While TTL-based consistency is widely explored in many client-server applications, there has been no study on TTL-based consistency in P2P networks. Our main contribution is an analytical model that studies the search performance and the freshness of P2P content sharing under TTL-based consistency. Due to the random nature of request routing, P2P networks are fundamentally different from most existing TTL-based systems in that every node with a valid replica has the potential to serve any other node. We identify and discuss the factors that affect the performance of P2P content sharing under TTL-based consistency. Our results indicate a tradeoff between search performance and freshness: the search cost decreases sublinearly with decreasing freshness of P2P content sharing. We also compare two types of unstructured P2P networks and find that clustered P2P networks improve the freshness of content sharing over flat P2P networks under TTL-based consistency.
Serum Metabolomics Investigation of Humanized Mouse Model of Dengue Virus Infection

ABSTRACT Dengue is an acute febrile illness caused by dengue virus (DENV) and a major cause of morbidity and mortality in tropical and subtropical regions of the world. The lack of an appropriate small-animal model of dengue infection has greatly hindered the study of dengue pathogenesis and the development of therapeutics. In this study, we conducted mass spectrometry-based serum metabolic profiling from a model using humanized mice (humice) with DENV serotype 2 infection at 0, 3, 7, 14, and 28 days postinfection (dpi). Forty-eight differential metabolites were identified, including fatty acids, purines and pyrimidines, acylcarnitines, acylglycines, phospholipids, sphingolipids, amino acids and derivatives, free fatty acids, and bile acid. These metabolites showed a reversible-change trend—most were significantly perturbed at 3 or 7 dpi and returned to control levels at 14 or 28 dpi, indicating that the metabolites might serve as prognostic markers of the disease in humice. The major perturbed metabolic pathways included purine and pyrimidine metabolism, fatty acid β-oxidation, phospholipid catabolism, arachidonic acid and linoleic acid metabolism, sphingolipid metabolism, tryptophan metabolism, phenylalanine metabolism, lysine biosynthesis and degradation, and bile acid biosynthesis. Most of these disturbed pathways are similar to our previous metabolomics findings in a longitudinal cohort of adult human dengue patients across different infection stages. Our analyses revealed the commonalities of host responses to DENV infection between humice and humans and suggested that humice could be a useful small-animal model for the study of dengue pathogenesis and the development of dengue therapeutics. IMPORTANCE Dengue virus is the most widespread arbovirus, causing an estimated 390 million dengue infections worldwide every year. There is currently no effective treatment for the disease, and the lack of an appropriate small-animal model of dengue infection has greatly increased the challenges in the study of dengue pathogenesis and the development of therapeutics. Metabolomics provides global views of small-molecule metabolites and is a useful tool for finding metabolic pathways related to disease processes. Here, we conducted a serum metabolomics study on a model using humanized mice with dengue infection that had significant levels of human platelets, monocytes/macrophages, and hepatocytes. Forty-eight differential metabolites were identified, and the underlying perturbed metabolic pathways are quite similar to the pathways found to be altered in dengue patients in previous metabolomics studies, indicating that humanized mice could be a highly relevant small-animal model for the study of dengue pathogenesis and the development of dengue therapeutics.