Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterned Films of Nanotubes Using Microcontact Printing of Catalysts

change in the vibrational structure of the PL spectrum were effected by MWNTs. The reduction of the PL efficiency can be a result of energy transfer and partial hole transfer from PPV chains to MWNTs, together with scattering and absorption by MWNTs. Using the composite, photovoltaic devices have been fabricated by employing MWNT as a hole-collecting electrode. We obtained good quantum efficiency (1.8 % at 2.9±3.2 eV), about twice that of the standard ITO device. It is considered that the high efficiency arises from a complex interpenetrating network of PPV chains with MWNTs and the relatively high work function of the MWNT film. The present results suggest the possible application of carbon nanotubes as a new interesting electrode material in macroscale devices.
Soma-to-Germline Transmission of RNA in Mice Xenografted with Human Tumour Cells: Possible Transport by Exosomes

Mendelian laws provide the universal founding paradigm for the mechanism of genetic inheritance through which characters are segregated and assorted. In recent years, however, parallel with the rapid growth of epigenetic studies, cases of inheritance deviating from Mendelian patterns have emerged. Growing studies underscore phenotypic variations and increased risk of pathologies that are transgenerationally inherited in a non-Mendelian fashion in the absence of any classically identifiable mutation or predisposing genetic lesion in the genome of individuals who develop the disease. Non-Mendelian inheritance is most often transmitted through the germline in consequence of primary events occurring in somatic cells, implying soma-to-germline transmission of information. While studies of sperm cells suggest that epigenetic variations can potentially underlie phenotypic alterations across generations, no instance of transmission of DNA- or RNA-mediated information from somatic to germ cells has been reported as yet. To address these issues, we have now generated a mouse model xenografted with human melanoma cells stably expressing EGFP-encoding plasmid. We find that EGFP RNA is released from the xenografted human cells into the bloodstream and eventually in spermatozoa of the mice. Tumor-released EGFP RNA is associated with an extracellular fraction processed for exosome purification and expressing exosomal markers, in all steps of the process, from the xenografted cancer cells to the spermatozoa of the recipient animals, strongly suggesting that exosomes are the carriers of a flow of information from somatic cells to gametes. Together, these results indicate that somatic RNA is transferred to sperm cells, which can therefore act as the final recipients of somatic cell-derived information.
A conceptual model for text summarisation based on reader requirements

This research establishes a document summarisation model to generate summaries on the basis of reader requirements. To establish this summarisation model, the document summarisation problem is transformed into a mathematical problem by the analysis of the quality factors for summary and calculation of summary quality indices and constraints of quality factors. The genetic algorithm can be applied to solve the optimisation problem for text summarisation, and the text summary can be generated based on the optimal solution derived via the genetic algorithm.
Epilepsy due to a temporal ganglioglioma and its subsequent malignant transformation into a primitive neuroectodermal tumor

Background: Gangliogliomas (GGs) are rare brain tumors, and their malignant changes are still less frequent. In these cases, almost always the malignant component is the glial one. However, there are few cases in which the neuronal component exhibits malignant transformation. Case Description: We described a case of a 14-year-old male patient who started with seizures and 5 years later, its frequency was almost daily despite being treated with several antiepileptic drugs. Magnetic resonance imaging showed a nonenhancing lesion located at the right inferior temporal gyri. He underwent surgery, and the tumor was completely removed. Histological diagnosis was GG. He had no seizures anymore, but 3 years later, the tumor recurred as a giant heterogeneously enhancing space-occupying mass within the right temporal lobe. A second surgical resection was performed. Histological diagnosis was a primitive neuroectodermal tumor (PNET). One month and a half later, the tumor has recurred again. He and his family decided not to undergo another operation, so he was referred to the radiotherapy department. Conclusion: As in this patient, GGs may recur as a primitive neuroectodermal tumor, likely because both types of neoplasm form part of the same heterogeneous group of brain tumors which contains neuronal components, but on the other side of the malignancy spectrum.
Ship attitude prediction based on Input Delay Neural Network and measurements of gyroscopes

Due to the uncertainty and random nature of ocean waves, the accurate prediction of ship attitude is hard to be achieved, especially in high sea states. A ship attitude prediction method using Input Delay Neural Network (IDNN) is proposed in this paper. One of the advantages of this method is that it takes the measurements of Microelectromechanical Systems (MEMS) gyrosocpes, besides ship Euler angles, as the inputs of IDNN, which can greatly increase the prediction precision of ship attitude with little increase in system cost. The effectiveness of proposed method is validated through a data set sampled in a ship simulation hardware system. Moreover, the factors that affect the prediction performance are also explored through a set of experiments. The prediction method proposed can achieve high precision, that is, the root-mean-square prediction errors for roll, pitch and yaw, are 0.26 deg, 0.12 deg and 0.26 deg, respectively, when the prediction time is 2 sec. This precision is high enough for most attitude stabilization control systems.
nfluenza virus hemagglutinin spike neck architectures and interaction ith model enzymes evaluated by MALDI-TOF mass spectrometry and ioinformatics tools

Interactions between model enzymes and the influenza virus hemagglutinin (HA) homotrimeric spike were addressed. We digested influenza virions (naturally occurring strains and laboratory reassortants) with bromelain or subtilisin Carlsberg and analyzed by MALDI-TOF mass spectrometry the resulting HA2 C-terminal segments. All cleavage sites, together with (minor) sites detected in undigested HAs, were situated in the linker region that connects the transmembrane domain to the ectodomain. In addition to cleavage at highly favorable amino acids, various alternative enzyme preferences were found that strongly depended on the HA subtype/type. We also evaluated the surface electrostatic potentials, binding cleft
Classroom-Based Science Research at the Introductory Level: Changes in Career Choices and Attitude

Our study, focused on classroom-based research at the introductory level and using the Phage Genomics course as the model, shows evidence that first-year students doing research learn the process of science as well as how scientists practice science. A preliminary but notable outcome of our work, which is based on a small sample, is the change in student interest in considering different career choices such as graduate education and science in general. This is particularly notable, as previous research has described research internships as clarifying or confirming rather than changing undergraduates’ decisions to pursue graduate education. We hypothesize that our results differ from previous studies of the impact of engaging in research because the students in our study are still in the early stages of their undergraduate careers. Our work builds upon the classroom-based research movement and should be viewed as encouraging to the Vision and Change in Undergraduate Biology Education movement advocated by the American Association for the Advancement of Science, the National Science Foundation, and other undergraduate education stakeholders.
A review of bone cell and substratum interactions: An illustration of the role of scanning electron microscopy

The first experimental studies concerning observations of changes in bone cell functional morphology were made using the SEM, and SEM has remained paramount in this field. Bone forming and resorbing cells only exist on surfaces – which are available for study after removal of adjacent tissue layers: The underlying matrix surface can then be studied after removal of the cells, and the mineral front examined after removing the matrix (with an appropriate solvent or by plasma ashing). In this review, we analyse the main findings which we have made in this laboratory concerning the biological activities of osteoblasts (bone forming cells) and osteoclasts (bone resorbing cells). The technical problems of specimen preparation of cells which shrink more than the substrate to which they are attached have been convered previously (Boyde et al. 1977). Such problems obviously affect the lateral, cell to cell inter-relationships more than the cell to substrate effects which we cover here. At present, we can conclude that SEM has made a major contribution to bone biology by permitting observation of normal cells and natural and surrogate substrates. We confidently predict that it will continue to play a pivotal role in the closer observation of cell-cell-substrate interactions particularly in respect of local hormonal effects, as well as in bone pathology and implantology.
Quick-VDR: interactive view-dependent rendering of massive models

We present a novel approach for interactive view-dependent rendering of massive models. Our algorithm combines view-dependent simplification, occlusion culling, and out-of-core rendering. We represent the model as a clustered hierarchy of progressive meshes (CHPM). We use the cluster hierarchy for coarse-grained selective refinement and progressive meshes for fine-grained local refinement. We present an out-of-core algorithm for computation of a CHPM that includes cluster decomposition, hierarchy generation, and simplification. We make use of novel cluster dependencies in the preprocess to generate crack-free, drastic simplifications at runtime. The clusters are used for occlusion culling and out-of-core rendering. We add a frame of latency to the rendering pipeline to fetch newly visible clusters from the disk and to avoid stalls. The CHPM reduces the refinement cost for view-dependent rendering by more than an order of magnitude as compared to a vertex hierarchy. We have implemented our algorithm on a desktop PC. We can render massive CAD, isosurface, and scanned models, consisting of tens or a few hundreds of millions of triangles at 10−35 frames per second with little loss in image quality.
The CoGenT project: co-generating compilers and simulators for dynamically compiled languages

To understand the performance of modern Java systems one must observe execution in the context of specific architectures. It is also important that we make these observations using a compiler that is capable of producing optimizations that are specialized to the target machine. Current architectural simulators, however, provide little or no support for dynamically compiled languages and their environments. At the same time, high-quality optimizing compilers lack the flexibility needed to be quickly retargeted to architectural variations. Experimenting with innovative architectural ideas requires modifications not only to the simulator, but to the compiler backend as well. Currently, it is difficult if not impossible to accomplish exploration of this sort. This paper proposes a system for the coordinated effort of generating simulators and matching compiler backends automatically from machine descriptions. Machine descriptions are processed by tools to produce efficient compiler and simulator components and these components "plug in" to an existing framework. This system provides an exploratory environment for compiler writers, computer architects, and students, while maintaining the performance and flexibility required for evaluating real systems.
In situ and on-demand lubrication by tribo-coating for space applications

A self-restoring lubrication system with an in situ and on-demand controllable lubrication method is required to improve reliability and to overcome unexpected tribological problems in space. A new type of lubrication method called ‘tribo-coating’, which forms a thin lubricious film (tribo-coating film) in nanometre-scale by vacuum deposition during sliding, is promising as a required lubrication method for future space systems. This method gives the unique advantage of excellent tribological performance of friction coefficient of<0.02 in sliding contacts and 0.002 in ball bearings. Its possibility as a self-restored lubrication system with in situ and on-demand control of lubrication for ball bearings in vacuum systems is clarified.
Identification of a protein in several Borrelia species which is related to OspC of the Lyme disease spirochetes

Using oligonucleotide probes which have previously been shown to be specific for the ospC gene found in the Lyme disease spirochete species Borrelia burgdorferi, B. garinii, and group VS461, we detected an ospC homolog in other Borrelia species including B. coriaceae, B. hermsii, B. anserina, B. turicatae, and B. parkeri. In contrast to the Lyme disease spirochetes, which carry the ospC gene on a 26-kb circular plasmid, we mapped the gene in other Borrelia species to linear plasmids which varied in size among the isolates tested. Some isolates carry multiple copies of the gene residing on linear plasmids of different sizes. The analyses conducted here also demonstrate that these Borrelia species contain a linear chromosome. Northern (RNA) blot analyses demonstrated that the gene is transcriptionally expressed in all species examined. High levels of transcriptional expression were observed in some B. hermsii isolates. Transcriptional start site analyses revealed that the length of the untranslated leader sequence was identical to that observed in the Lyme disease spirochete species. By Western blotting (immunoblotting) with antiserum (polyclonal) raised against the OspC protein of B. burgdorferi, we detected an immunoreactive protein of the same molecular weight as the OspC found in Lyme disease spirochete species. The results presented here demonstrate the presence of a protein that is genetically and antigenically related to OspC which is expressed in all species of the genus Borrelia tested.
Deriving Rhetorical Complexity Data from the RST-DT Corpus

This paper describes a study of the levels at which different rhetorical relations occur in rhetorical structure trees. In a previous empirical study (Williams and Reiter, 2003) of the RST-DT (Rhetorical Structure Theory Discourse Treebank) Corpus (Carlson et al., 2003), we noticed that certain rhetorical relations tended to occur more frequently at higher levels in a rhetorical structure tree, whereas others seemed to occur more often at lower levels. The present study takes a closer look at the data, partly to test this observation, and partly to investigate related issues such as the relative complexity of satellite and nucleus for each type of relation. One practical application of this investigation would be to guide discourse planning in Natural Language Generation (NLG), so that it reflects more accurately the structures found in documents written by human authors. We present our preliminary findings and discuss their relevance for discourse planning.
Social network analysis: Evolving Twitter mining

The growth of techniques of social network analysis is fast at present. These techniques are of interest to many researchers in different areas such as sociology, communication and computer science, social psychologist and so on. Nowadays, by analyzing how the members of network interact, share information or establish relationships, useful knowledge about them and their relations can be extracted. However, information related to how these members are presented to the world (by their users profiles) could give use also very useful knowledge. In this paper, we present an approach to automatically analyze the Twitter user profiles of a specific community of users. The locations of these users can also be selected by the user. The proposed analysis is done by extracting some characteristics of the collected profiles (of that given community). This analysis includes the detection of outliers, the clustering of profiles and their classification. The most important characteristic of the presented approach is that it can cope with the data of thousands of twitter profiles in real-time. Thus, this work is related to big data in the area of big data analytics. The approach presented in this paper is based on evolving fuzzy systems, which makes possible not only that we can cope with thousands of data in real-time, but also that the knowledge that we obtain from the social networks can be constantly updated (evolving).
Exploiting domain and task regularities for robust named entity recognition

It is often convenient to make certain assumptions during the learning process. Unfortunately, algorithms built on these assumptions can often break down if the assumptions are not stable between train and test data. Relatedly, we can do better at various tasks (like named entity recognition) by exploiting the richer relationships found in real-world complex systems. By exploiting these kinds of non-conventional regularities we can more easily address problems previously unapproachable, like transfer learning. In the transfer learning setting, the distribution of data is allowed to vary between the training and test domains, that is, the independent and identically distributed (i.i.d.) assumption linking train and test examples is severed. Without this link between the train and test data, traditional learning is difficult. In this thesis we explore learning techniques that can still succeed even in situations where i.i. d. and other common assumptions are allowed to fail. Specifically, we seek out and exploit regularities in the problems we encounter and document which specific assumptions we can drop and under what circumstances and still be able to complete our learning task. We further investigate different methods for dropping, or relaxing, some of these restrictive assumptions so that we may bring more resources (from unlabeled auxiliary data, to known dependencies and other regularities) to bear on the problem, thus producing both better answers to existing problems, and even being able to begin addressing problems previously unanswerable, such as those in the transfer learning setting. In particular, we introduce four techniques for producing robust named entity recognizers, and demonstrate their performance on the problem domain of protein name extraction in biological publications: (1) Feature hierarchies relate distinct, though related, features to one another via a natural linguistically-inspired hierarchy. (2) Structural frequency features exploit a regularity based on the structure of the data itself and the distribution of instances across that structure. (3) Snippets link data not by the distribution of the instances or their features, but by their labels. Thus data that have different attributes, but similar labels, will be joined together, while instances that have similar features, but distinct labels, are segregated to allow for variation between domains. (4) Graph relations represent the entities contained in the data and their relationships to each other as a network which is exploited to help discover robust regularities across domains. Thus we show that learned classifiers and extractors can be made more robust to shifts between the train and test data by using data (both labeled and unlabeled) from related domains and tasks, and by exploiting stable regularities and complex relationships between different aspects of that data.
On Low-Rank Updates to the Singular Value and Tucker Decompositions

The singular value decomposition is widely used in signal processing and data mining. Since the data often arrives in a stream, the problem of updating matrix decompositions under low-rank modification has been widely studied. Brand developed a technique in 2006 that has many advantages. However, the technique does not directly approximate the updated matrix, but rather its previous low-rank approximation added to the new update, which needs justification. Further, the technique is still too slow for large information processing problems. We show that the technique minimizes the change in error per update, so if the error is small initially it remains small. We show that an updating algorithm for large sparse matrices should be sub-linear in the matrix dimension in order to be practical for large problems, and demonstrate a simple modification to the original technique that meets the requirements. We extend Brand’s method to tensors, the multi-way generalization of matrices. The few published comparable techniques either focus on small-magnitude changes, are iterative, or have no published error properties. We show that the technique of Brand for updating the truncated singular value decomposition can be redesigned as a low-rank update scheme for the Tucker decomposition, with analogous run-time and error properties.
Construct Validity of the eHealth Literacy Scale (eHEALS) Among Two Adult Populations: A Rasch Analysis

Background The Internet has become a ubiquitous venue for information seeking, especially for health information. Public health practitioners have noticed the promise and potential of the Internet, however, little is known about individuals' skills of their eHealth literacy. The eHealth Literacy Scale, eHEALS, was designed to measure perceptions of individuals' eHealth literacy skills. Objective The objective of the study was to examine the psychometric validity and reliability of the eHEALS with two adult populations using the Rasch Model. Methods A college-aged sample and an Internet-based sample (Amazon's MTurk) were recruited to complete the eHEALS, demographic questions, and a health literacy scale. Using WINSTEPS and SPSS, unidimensionality, item fit, rating scale, item hierarchy, person ability-item match, and reliability were analyzed, compared, and contrasted against each sample and to other samples found in the literature. Results An exploratory factor analysis supported unidimensionality in both samples. More than 90% of respondents from both samples fit the model. No items were outright misfitting. Both samples separated into three distinct groups. Conclusions Based on the results, the eHEALS is a reliable and consistent measurement tool for a college sample and an Internet-based sample. As these individuals are most likely to use the Internet as a health resource, it is necessary to learn and know their skills versus perceiving that they can critically and successfully navigate the Internet. Further analyses are necessary to ensure that the eHEALS can serve as a standard eHealth literacy measure for public health.
Maze router without a grid map

It is pointed out that maze routers provide powerful and flexible routing algorithms, but require storage of information for every routing grid and layer. The number of these grids is often so large that routing programs either run out of memory or become very slow due to excessive paging. A data organization which, with certain modifications to the algorithm, reduces and possibly eliminates the entire grid map is presented. The router has been coded with a special data structure library. <<ETX>>
Research on Chinese Information Retrieval Based on a Hybrid Language Modeling

For information retrieval, users hope to acquire more relevant information from the top indexing documents. In this paper, a combination of ontology with statistical method is presented to retrieval initial document set and improves the precision of top N ranking documents by re-ranking document set. The experiment with NTCIR-3 Chinese CLIR dataset shows the proposed method improved the precision of information retrieval