Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeting glioblastoma multiforme with an IL-13/diphtheria toxin fusion protein in vitro and in vivo in nude mice.

Fusion proteins composed of tumor binding agents and potent catalytic toxins show promise for intracranial therapy of brain cancer and an advantage over systemic therapy. Glioblastoma multiforme (GBM) is the most common form of brain cancer and overexpresses IL-13R. Thus, we developed an interleukin-13 receptor targeting fusion protein, DT(390)IL13, composed of human interleukin-13 and the first 389 amino acids of diphtheria toxin. To measure its ability to inhibit GBM, DT(390)IL13 was tested in vitro and found to inhibit selectively the U373 MG GBM cell line with an IC(50) around 12 pmol/l. Cytotoxicity was neutralized by anti-human-interleukin-13 antibody, but not by control antibodies. In vivo, small U373 MG glioblastoma xenografts in nude mice completely regressed in most animals after five intratumoral injections of 1 microg of DT(390)IL13 q.o. d. , but not by the control fusion protein DT(390)IL-2. DT(390)IL13 was also tested against primary explant GBM cells of a patient's excised tumor and the IC(50) was similar to that measured for U373 MG. Further studies showed a therapeutic window for DT(390)IL13 of 1-30 microg/injection and histology studies and enzyme measurements showed that the maximum tolerated dose of DT(390)IL13 had little effect on kidney, liver, spleen, lung and heart in non-tumor-bearing immunocompetent mice. Together, these data suggest that DT(390)IL13 may provide an important, alternative therapy for brain cancer.
GIS / Simulation Framework for Assessing Change in Water Yield over Large Spatial Scales ( Short title : Assessing Change in Water Yield )

Recent legislation to,initiate vegetation management in the Central Sierra hydrologic region of California includes a focus on corresponding changes in water yield. This served as the impetus for developing a combined geographic information system (GIS) and simulation assessment framework. Using the existing vegetation density condition, together with proposed rules for thinning to reduce fire risk, a set of simulation model inputs were generated for examining the impact of the thinning scenario on water yield. The approach allows results to be expressed as the mean and standard deviation of change in water yield for each 1 km2 map cell that is treated. Values for groups of cells are aggregated for typical watershed units using * Author to whom correspondence should be addressed.
Incorporating Knowledge of Developmental Needs and Critical Thinking into a Parenting Curriculum Module for Undergraduate Nursing Students

This non-traditional dissertation incorporated two previously published articles with a review of critical thinking literature. These led to the development of an Adolescent Parenting Module designed to help students improve delivery of nursing care to adolescent mothers. The first article, reviewing theory and parenting research, led to identification of the needs and problems of adolescent mothers. Since undergraduate nursing students provide some care to these young women during a brief maternity hospitalization, their perceptions of adolescent mothers were explored in a pilot study, the second publication. Critical thinking research and theory were reviewed as a basis for meeting selected needs of both adolescent mothers and nursing students. This led to development of the Adolescent Parenting Module, based on Ralph Tyler's curriculum principles. Module-influenced changes may enhance personal and professional growth in the nursing student, initial parenting in the adolescent mother, and subsequently improve initial newborn trust. Suggestions for further research include expanded comparative studies of parenting in adolescents and older mothers, the use of critical thinking in fostering clinical judgments of nursing students in varied settings, and replication of the pilot study with a larger sample of novice nursing students, advanced beginners, or practicing nurses.
Exploiting Hierarchical Domain Structure to Compute Similarity 1

The notion of similarity between objects nds use in many contexts, e.g., in search engines, collaborative ltering, and clustering. Objects being compared often are modeled as sets, with their similarity traditionally determined based on set intersection. Intersection-based measures do not accurately capture similarity in certain domains, such as when the data is sparse or when there are known relationships between items within sets. We propose new measures that exploit a hierarchical domain structure in order to produce more intuitive similarity scores. We also extend our similarity measures to provide appropriate results in the presence of multisets (also handled unsatisfactorily by traditional measures), e.g., to correctly compute the similarity between customers who buy several instances of the same product (say milk), or who buy several products in the same category (say dairy products). We also provide an experimental comparison of our measures against traditional similarity measures, and describe an informal user study that evaluated how well our measures match human intuition.
A Negative D-Dimer Assay Does Not Rule Out Cerebral Venous Thrombosis: A Series of Seventy-Three Patients

Background and Purpose— Cerebral venous thrombosis (CVT) is an infrequent variety of cerebrovascular disease with a wide spectrum of clinical presentations and a notoriously difficult diagnosis. Previous reports have emphasized the potential clinical utility of D-dimer assay in CVT diagnosis. Methods— A rapid sensitive D-dimer assay was performed at entry in 73 patients with CVT <30 days duration, examined in our institution between 1999 and 2003. Results— The mean value of D-dimer levels was 1521 ng/mL; 7 patients (10% of all patients and 26% of those presenting with isolated headache) had values <500 ng/mL. In a multivariate analysis, isolated headache was the only variable associated with a negative D-dimer assay. Conclusion— A negative D-Dimer assay does not confidently rule out CVT, particularly in the setting of recent isolated headache.
Direct Algorithms for Computing the Transitive Closure of Database Relations

We present new algorithms for computing the transitive closure of large database relations. Unlike iterative algorithms, such as the semi-naive and the logarithmic algorithms, the termination of our algorithms does not depend on the length of paths in the underlying graph (hence, the name direct algorithms). We also present simulation results that show that these direct algorithms perform uniformly better than the best of the iterative algorithms. A side benefit of this work is that we have proposed a new methodology for evaluating the performance of recursive queries.
Sub-1 V Input Single-Inductor Dual-Output (SIDO) DC–DC Converter With Adaptive Load-Tracking Control (ALTC) for Single-Cell-Powered Systems

In this paper, a sub-1 V input single-inductor dual-output (SIDO) dc-dc converter with an adaptive load-tracking control (ALTC) technology is proposed for single-cell-powered portable devices. Having a minimal number of switches and an optimum current sequence, the proposed ALTC technique adaptively and accurately adjusts storage energy in the form of inductor current according to the actual load condition, without wasting surplus charge and without increasing cross regulation. Moreover, a current-mode ring oscillator with a self-bias current source circuit, in place of the conventional start-up ring oscillator, is proposed to produce a nearly constant system clock for the requirement of sub-1 V start-up procedure. Because the proposed current-mode ring oscillator operates between the start-up process and steady state of the SIDO dc-dc converter, its simplified design efficiently addresses the high switching frequency losses at sub-1 V start-up procedure, reducing chip area and power consumption. The proposed sub-1 V input SIDO dc-dc converter was fabricated via Taiwan Semiconductor Manufacturing Company 0.25 μm 2.5 V/5 V Bipolar-CMOS-DMOS process, and the experimental results show high efficiency of 92% with a good cross regulation smaller than 10 mV.
Semantic combination of textual and visual information in multimedia retrieval

The goal of this paper is to introduce a set of techniques we call semantic combination in order to efficiently fuse text and image retrieval systems in the context of multimedia information access. These techniques emerge from the observation that image and textual queries are expressed at different semantic levels and that a single image query is often ambiguous. Overall, the semantic combination techniques overcome a conceptual barrier rather than a technical one: these methods can be seen as a combination of late fusion and image reranking. Albeit simple, this approach has not been used yet. We assess the proposed techniques against late and cross-media fusion using 4 different ImageCLEF datasets. Compared to late fusion, performances significantly increase on two datasets and remain similar on the two other ones.
The Pseudomonas aeruginosa AlgZR two-component system coordinates multiple phenotypes

Pseudomonas aeruginosa is an opportunistic pathogen that causes a multitude of infections. These infections can occur at almost any site in the body and are usually associated with a breach of the innate immune system. One of the prominent sites where P. aeruginosa causes chronic infections is within the lungs of cystic fibrosis patients. P. aeruginosa uses two-component systems that sense environmental changes to differentially express virulence factors that cause both acute and chronic infections. The P. aeruginosa AlgZR two component system is one of its global regulatory systems that affects the organism's fitness in a broad manner. This two-component system is absolutely required for two P. aeruginosa phenotypes: twitching motility and alginate production, indicating its importance in both chronic and acute infections. Additionally, global transcriptome analyses indicate that it regulates the expression of many different genes, including those associated with quorum sensing, type IV pili, type III secretion system, anaerobic metabolism, cyanide and rhamnolipid production. This review examines the complex AlgZR regulatory network, what is known about the structure and function of each protein, and how it relates to the organism's ability to cause infections.
CLOUD-BASED E-LEARNING TOOLS FOR DATA ANALYSIS

This paper describes the most important characteristics of cloud computing paradigm in order to be used as powerful arrows towards efficient approaches in teaching data analysis. Also High Performance Computing aspects are considered for solving large complexity data analysis problems. The usage of both Cloud Computing and High Performance Computing will assure not only an excellent framework for research but also a powerful and flexible environment for e-Learning. The presentation is structured in three sections, being closed with the list of references. The first section considers the resources of cloud computing: SaaS (Software as a Service), IaaS (Infrastructure as a Service), PaaS (Platform as a Service - predefined integrated platform), and Managed Services. For every mentioned resource the most important features which are valuable for e-learning are outlined. High Performance Computing for data analysis is described in the second section. Also, software tools useful to analyse collections of data having small, medium or large size are presented and compared in order to identify the best tool to be used in a virtual learning framework. The third section describes both data manipulation techniques and results of experiments. Finally, concluding remarks are presented related to an online course on data analysis based on cloud computing paradigm. Selected references 1. Abadi D.J., Data Management in the Cloud: Limitations and Opportunities, Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 2009. 2. Grossman R. , Gu Y. , Data Mining Using High Performance Data Clouds: Experimental Studies Using Sector and Sphere, http://sector.sourceforge. net/pub/grossman-gu-ncdm-tr-08-04.pdf 3.G Albeanu, L. Serbanescu, Fl. Popentiu-Vladicescu. On teaching data analysis and optimisation using software tools. In Grigore Albanu, Dorin Mircea Popovici , Marin Vlada (eds.), Proceedings of the 2nd International Conference on Virtual Learning, Constata, 26-28 October, Bucharest, ISBN: 973-737-218-2 978-973-737-380-9, Romania. , I, pp. 255-260. 2007.
A rainfall forecasting using fuzzy system based on genetic algorithm

Weather forecasting information is very crucial in decision making process regarding to activities and works, such as in the field of agriculture to determine initial growing season. Recently, climate change causes trouble in weather forecasting. In this paper, rainfall forecasting system using fuzzy system based on genetic algorithm (GA) is made. The data used within this research is taken from Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) for Kemayoran area, Jakarta. Those data include temperature, air pressure, rainfall, solar radiation, relative humidity, and wind speed. Based on experiment result, it can be concluded that the combination of GA and Fuzzy for Kemayoran weather data can produce prediction model with higher than 90% accuracy with several population size and crossover probability. This condition is highly affected by input data and conducted classification. Obtained model can be used in predicting rainfall class for the next day that divided into 4 class of no-rain or mild rain (<;=20 mm), moderate rain (<;=50 mm), heavy rain (<;=100 mm), and very heavy rain (>100 mm).
EFFECT OF NANOSCALE ZINC OXIDE PARTICLES ON THE GERMINATION, GROWTH AND YIELD OF PEANUT

An investigation was initiated to examine the effects of nanoscale zinc oxide particles on plant growth and development. In view of the widespread cultivation of peanut in India and in other parts of the globe and in view of the potential influence of zinc on its growth, this plant was chosen as the model system. Peanut seeds were separately treated with different concentrations of nanoscale zinc oxide (ZnO) and chelated bulk zinc sulfate (ZnSO4) suspensions (a common zinc supplement), respectively and the effect this treatment had on seed germination, seedling vigor, plant growth, flowering, chlorophyll content, pod yield and root growth were studied. Treatment of nanoscale ZnO (25 nm mean particle size) at 1000 ppm concentration promoted both seed germination and seedling vigor and in turn showed early establishment in soil manifested by early flowering and higher leaf chlorophyll content. These particles proved effective in increasing stem and root growth. Pod yield per plant was 34% higher compared to chelated bulk ZnSO4. Consequently, a field experiment was conducted during Rabi seasons of 2008–2009 and 2009–2010 with the foliar application of nanoscale ZnO particles at 15 times lower dose compared to the chelated ZnSO4 recommended and we recorded 29.5% and 26.3% higher pod yield, respectively, compared to chelated ZnSO4. The inhibitory effect with higher nanoparticle concentration (2000 ppm) reveals the need for judicious usage of these particles in such applications. This is the first report on the effect of nanoscale particles on peanut growth and yield.
R&D, Subsidies and Productivity

Estimating the change in privately financed R&D expenditures induced by government subsidization of R&D activities—the “additionality effect”—is an important item in the current empirical research agenda in the economics of technical change. However, there are very few papers that formulate a model of firm decision-making that explains the mechanism by which this additionality effect can arise. In this paper, we take up this task. Our model extends the traditional theoretical literature on the optimal management of an R&D project and provides a theoretical underpinning for the additionality effect. The model also provides a framework for interpreting anew the empirical association between R&D expenditures and productivity growth found in many micro-level productivity regressions. The theoretical framework illustrates that the sign and size of the additionality effect depends on the model’s parameters and assumptions on the distribution of development costs. Thus, it is not at all surprising that empirical studies of the additionality effect have not come up with a clear-cut answer as to whether R&D subsidies stimulate or crowd out privately financed R&D.
A novel self-organising clustering model for time-event documents

Purpose – The purpose of this paper is to examine neural document clustering techniques, e.g. self‐organising map (SOM) or growing neural gas (GNG), usually assume that textual information is stationary on the quantity.Design/methodology/approach – The authors propose a novel dynamic adaptive self‐organising hybrid (DASH) model, which adapts to time‐event news collections not only to the neural topological structure but also to its main parameters in a non‐stationary environment. Based on features of a time‐event news collection in a non‐stationary environment, they review the main current neural clustering models. The main deficiency is a need of pre‐definition of the thresholds of unit‐growing and unit‐pruning. Thus, the dynamic adaptive self‐organising hybrid (DASH) model is designed for a non‐stationary environment.Findings – The paper compares DASH with SOM and GNG based on an artificial jumping corner data set and a real world Reuters news collection. According to the experimental results, the DASH .
A language-independent and fully unsupervised approach to lexicon induction and part-of-speech tagging for closely related languages

In this paper, we describe our generic approach for transferring part-of-speech annotations from a resourced language towards an etymologically closely related non-resourced language, without using any bilingual (i.e., parallel) data. We first induce a translation lexicon from monolingual corpora, based on cognate detection followed by cross-lingual contextual similarity. Second, POS information is transferred from the resourced language along translation pairs to the non-resourced language and used for tagging the corpus. We evaluate our methods on three language families, consisting of five Romance languages, three Germanic languages and five Slavic languages. We obtain tagging accuracies of up to 91.6%.
Binary Particle Swarm Optimization for TDMA Broadcast Scheduling Problem

This paper presents binary particle swarm optimization (BPSO) for finding an optimum conflict-free transmission schedule for a broadcast radio network. This is known as Broadcast Scheduling Problem (BSP) and shown as an NP-complete problem in earlier studies. Because of this NP-complete nature, earlier studies used genetic algorithms, mean field annealing, neural networks, factor graph and sum product algorithm, and sequential vertex coloring algorithm to obtain the solution. We are able to achieve better results using our proposed BPSO. The results are obtained using our methodology is compared with CPLEX and all the other earlier solution methods.
The Typology of Templates

The notion of a template has been used in a number of linguistic domains to refer to grammatical patterns where the form of some linguistic constituent appears to be well conceptualized as consisting of a fixed linear structure, whether in terms of the arrangement of its subconstituents or its overall length. Most work on templates has restricted the topic of investigation to a single grammatical domain, e.g., morphophonology, rather than looking at templatic phenomena across grammatical domains. Such comparison reveals that a commonality among templatic constructions is that they involve ‘unexpected’ linear stipulation. This leaves open many questions regarding how they might be typologically compared, but the existing literature, nevertheless, indicates some dimensions of variation worthy of further investigation that could form the basis of a comprehensive study of template typology.
The Multi-join Query Optimization for Smart Grid Data

As the worldwide smart grid development, the high volume of data is generated by the smart grid devices. Database system, which stores large amounts of smart grid data is facing the growing amount of data storage and query requirements of increasingly complex. Meanwhile, smart grid has high requirements for data processing speed which makes traditional query strategies show many deficiencies. A new hybrid intelligent algorithm is proposed to optimize the multijoin query problem for smart grid data. The algorithm based on the Genetic Algorithm (GA), involves Guo Tao (GT) algorithm in crossover to maintain population diversity, and prevents premature convergence of the GA, and the mutation operator involves the Particle Swarm Optimization (PSO) to improve convergence speed and solution accuracy. The suggested algorithm ensures rapid processing of data and simulation result shows the query cost of the designed algorithm is lower than the GA, and it meets the smart grid data query requirement.
Water temperature, not fish morph, determines parasite infections of sympatric Icelandic threespine sticklebacks (Gasterosteus aculeatus)

Parasite communities of fishes are known to respond directly to the abiotic environment of the host, for example, to water quality and water temperature. Biotic factors are also important as they affect the exposure profile through heterogeneities in parasite distribution in the environment. Parasites in a particular environment may pose a strong selection on fish. For example, ecological differences in selection by parasites have been hypothesized to facilitate evolutionary differentiation of freshwater fish morphs specializing on different food types. However, as parasites may also respond directly to abiotic environment the parasite risk does not depend only on biotic features of the host environment. It is possible that different morphs experience specific selection gradients by parasites but it is not clear how consistent the selection is when abiotic factors change. We examined parasite pressure in sympatric morphs of threespine stickleback (Gasterosteus aculeatus) across a temperature gradient in two large Icelandic lakes, Myvatn and Thingvallavatn. Habitat-specific temperature gradients in these lakes are opposite. Myvatn lava rock morph lives in a warm environment, while the mud morph lives in the cold. In Thingvallavatn, the lava rock morph lives in a cold environment and the mud morph in a warm habitat. We found more parasites in fish living in higher temperature in both lakes, independent of the fish morph, and this pattern was similar for the two dominating parasite taxa, trematodes and cestodes. However, at the same time, we also found higher parasite abundance in a third morph living in deep cold–water habitat in Thingvallavatn compared to the cold-water lava morph, indicating strong effect of habitat-specific biotic factors. Our results suggest complex interactions between water temperature and biotic factors in determining the parasite community structure, a pattern that may have implications for differentiation of stickleback morphs.
A survey of in vivo WBAN communications and networking: Research issues and challenges

The emerging in vivo communication and networking system is a prospective component in advancing health care delivery and empowering the development of new applications and services. In vivo communications construct wirelessly networked cyber-physical systems of embedded devices to allow rapid, correct and cost-effective responses under various conditions. This paper surveys the existing research which investigates the state of art of the in vivo communication. It also focuses on characterizing and modeling the in vivo wireless channel and contrasting this channel with the other familiar channels. MIMO in vivo is as well regarded in this overview since it significantly enhances the performance gain and data rates. Furthermore, this paper addresses current challenges and future research areas for in vivo communications.