Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of recycling on the properties of wood fibre — Plastic composites

The manufacture of composites offers the greatest flexibility to convert and utilize waste plastics, paper and wood into high-value products. This paper reports on two studies regarding the recycling of these materials. In one study, recycling was simulated by regrinding and injection-moulding composites of polypropylene (PP), polyethylene (PE) and old newsprint (ONP) eight times. The results indicated that reprocessing had only minor deleterious effect on the mechanical properties (tensile and flexural). The viscosity of neat PP decreased with repeated recycling indicating some thermo-oxidative degradation. The overall rheological character of PP composites did not change much. PE composites, on the other hand, showed increasing melt flow with reprocessing. The second study examined the effects of various coupling agents on the properties of multicomponent PP composites. Maleated polypropylene (MAPP) was the most effective coupling agent.
Advanced Spectral Imaging for Microanalysis of Cultural Heritage

The Preservation Research and Testing Division at the Library of Congress established an advanced spectral imaging program for preservation research and analysis of the diverse range of cultural heritage objects in its collection. Adaptations to the spectral imaging system have enabled the acquisition of detailed high resolution images of up to 6000 ppi, in effect facilitating hyperspectral microscopy (figure 1). This capacity has led to the capture and discovery of previously unknown information about important cultural and historic objects.
Inhibition of Transforming Growth Factor β (TGF-β) Signaling can Substitute for Oct4 Protein in Reprogramming and Maintain Pluripotency*

Background: Pluripotent stem cells (PSCs) have been used widely to study molecular mechanisms involved in early embryo development. Results: The TGF-β inhibitor SB431542 substitutes for Oct4 in reprogramming and maintains pluripotency. Conclusion: TGF-β signaling inhibition promotes generation and maintenance of PSCs. Significance: Our study reveals a novel function of TGF-β signaling in the generation of PSCs. Mouse pluripotent stem cells (PSCs), such as ES cells and induced PSCs (iPSCs), are an excellent system to investigate the molecular and cellular mechanisms involved in early embryonic development. The signaling pathways orchestrated by leukemia inhibitor factor/STAT3, Wnt/β-catenin, and FGF/MEK/ERK play key roles in the generation of pluripotency. However, the function of TGF-β signaling in this process remains elusive. Here we show that inhibiting TGF-β signaling with its inhibitor SB431542 can substitute for Oct4 during reprogramming. Moreover, inhibiting TGF-β signaling can sustain the pluripotency of iPSCs and ES cells through modulating FGF/MEK/ERK signaling. Therefore, this study reveals a novel function of TGF-β signaling inhibition in the generation and maintenance of PSCs.
Characterization of RyDEN (C19orf66) as an Interferon-Stimulated Cellular Inhibitor against Dengue Virus Replication

Dengue virus (DENV) is one of the most important arthropod-borne pathogens that cause life-threatening diseases in humans. However, no vaccine or specific antiviral is available for dengue. As seen in other RNA viruses, the innate immune system plays a key role in controlling DENV infection and disease outcome. Although the interferon (IFN) response, which is central to host protective immunity, has been reported to limit DENV replication, the molecular details of how DENV infection is modulated by IFN treatment are elusive. In this study, by employing a gain-of-function screen using a type I IFN-treated cell-derived cDNA library, we identified a previously uncharacterized gene, C19orf66, as an IFN-stimulated gene (ISG) that inhibits DENV replication, which we named Repressor of yield of DENV (RyDEN). Overexpression and gene knockdown experiments revealed that expression of RyDEN confers resistance to all serotypes of DENV in human cells. RyDEN expression also limited the replication of hepatitis C virus, Kunjin virus, Chikungunya virus, herpes simplex virus type 1, and human adenovirus. Importantly, RyDEN was considered to be a crucial effector molecule in the IFN-mediated anti-DENV response. When affinity purification-mass spectrometry analysis was performed, RyDEN was revealed to form a complex with cellular mRNA-binding proteins, poly(A)-binding protein cytoplasmic 1 (PABPC1), and La motif-related protein 1 (LARP1). Interestingly, PABPC1 and LARP1 were found to be positive modulators of DENV replication. Since RyDEN influenced intracellular events on DENV replication and, suppression of protein synthesis from DENV-based reporter construct RNA was also observed in RyDEN-expressing cells, our data suggest that RyDEN is likely to interfere with the translation of DENV via interaction with viral RNA and cellular mRNA-binding proteins, resulting in the inhibition of virus replication in infected cells.
Low-Rank and Sparse Matrix Decomposition for Genetic Interaction Data

Background. Epistatic miniarray profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. One approach to analyze EMAP data is to identify gene modules with densely interacting genes. In addition, genetic interaction score (S score) reflects the degree of synergizing or mitigating effect of two mutants, which is also informative. Statistical approaches that exploit both modularity and the pairwise interactions may provide more insight into the underlying biology. However, the high missing rate in EMAP data hinders the development of such approaches. To address the above problem, we adopted the matrix decomposition methodology “low-rank and sparse decomposition” (LRSDec) to decompose EMAP data matrix into low-rank part and sparse part. Results. LRSDec has been demonstrated as an effective technique for analyzing EMAP data. We applied a synthetic dataset and an EMAP dataset studying RNA-related processes in Saccharomyces cerevisiae. Global views of the genetic cross talk between different RNA-related protein complexes and processes have been structured, and novel functions of genes have been predicted.
A three-dimensional analysis of marine radar images for the determination of ocean wave directionality and surface currents

A series of spatial wave images recorded by a conventional marine radar is analyzed to determine the three-dimensional E(kx, ky, ω) spectrum. In the absence of a surface current the spectral energy in this three-dimensional wave number frequency space will lie on a shell defined by the dispersion relationship. Any deviation from the expected dispersion relationship can be interpreted as being due to a current induced Doppler shift of the wave frequency. A least squares curve fitting technique is used to determine the surface current required to account for the observed Doppler shift. A comparison of the radar determined spectra and surface currents with ground truth data indicates that the radar system and analysis technique produces results consistent with conventional instrumentation.
Modeling of a carbon nanotube ultracapacitor

The modeling of carbon nanotube ultracapacitor (CNU) performance based on the simulation of electrolyte ion motion between the cathode and the anode is described. Using a molecular dynamics (MD) approach, the equilibrium positions of the electrode charges interacting through the Coulomb potential are determined, which in turn yield the equipotential surface and electric field associated with the capacitor. With an applied ac voltage, the current is computed based on the nanotube and electrolyte particle distribution and interaction, resulting in the frequency-dependent impedance Z(ω). From the current and impedance profiles, the Nyquist and cyclic voltammetry (CV) plots are then extracted. The results of these calculations compare well with existing experimental data. A lumped-element equivalent circuit for the CNU is proposed and the impedance computed from this circuit correlates well with the simulated and measured impedances.
Extending a network-of-elaborations representation to polyphonic music: Schenker and species counterpoint.

A system of representing melodies as a network of elaborations has been developed, and used as the basis for software which generates melodies in response to the movements of a dancer. This paper examines the issues of extending this representation system to polyphonic music, and of deriving a structural representation of this kind from a musical score. The theories of Heinrich Schenker and of Species Counterpoint are proposed as potentially fruitful bases.
Preparation and properties of silver‐containing nylon 6 nanofibers formed by electrospinning

Nylon 6 nanofibers containing silver nanoparticles (nylon 6/silver) were successfully prepared by electrospinning. The structure and properties of the electrospun fibers were studied with the aid of scanning electron microscopy, transmission electron microscopy, energy-dispersive spectroscopy, and X-ray diffraction. The structural analysis indicated that the fibers electrospun at maximum conditions were straight and that silver nanoparticles were distributed in the fibers. Finally, the antibacterial activities of the nylon 6/silver nanofiber mats were investigated in a broth dilution test against Staphylococcus aureus (Gram-positive) and Klebsiella pneumoniae (Gram-negative) bacteria. It was revealed that nylon 6/silver possessed excellent antibacterial properties and an inhibitory effect on the growth of S. aureus and K. pneumoniae. On the contrary, nylon 6 fibers without silver nanoparticles did not show any such antibacterial activity. Therefore, electrospun nylon 6/silver nanocomposites could be used in water filters, wound dressings, or antiadhesion membranes. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009
Same or Different? A Neural Circuit Mechanism of Similarity-Based Pattern Match Decision Making

The ability to judge whether sensory stimuli match an internally represented pattern is central to many brain functions. To elucidate the underlying mechanism, we developed a neural circuit model for match/nonmatch decision making. At the core of this model is a “comparison circuit” consisting of two distinct neural populations: match enhancement cells show higher firing response for a match than a nonmatch to the target pattern, and match suppression cells exhibit the opposite trend. We propose that these two neural pools emerge from inhibition-dominated recurrent dynamics and heterogeneous top–down excitation from a working memory circuit. A downstream system learns, through plastic synapses, to extract the necessary information to make match/nonmatch decisions. The model accounts for key physiological observations from behaving monkeys in delayed match-to-sample experiments, including tasks that require more than simple feature match (e.g., when BB in ABBA sequence must be ignored). A testable prediction is that magnitudes of match enhancement and suppression neural signals are parametrically tuned to the similarity between compared patterns. Furthermore, the same neural signals from the comparison circuit can be used differently in the decision process for different stimulus statistics or tasks; reward-dependent synaptic plasticity enables decision neurons to flexibly adjust the readout scheme to task demands, whereby the most informative neural signals have the highest impact on the decision.
A genetic algorithms approach to modeling the performance of memory-bound computations

Benchmarks that measure memory bandwidth, such as STREAM, Apex-MAPS and MultiMAPS, are increasingly popular due to the "Von Neumann" bottleneck of modern processors which causes many calculations to be memory-bound. We present a scheme for predicting the performance of HPC applications based on the results of such benchmarks. A Genetic Algorithm approach is used to "learn" bandwidth as a function of cache hit rates per machine with MultiMAPS as the fitness test. The specific results are 56 individual performance predictions including 3 full-scale parallel applications run on 5 different modern HPC architectures, with various CPU counts and inputs, predicted within 10% average difference with respect to independently verified runtimes.
Non-Photorealistic Rendering of Neural Cells from their Morphological Description

Gaining a better understanding of the human brain continues to be one of the great- est and most elusive of challenges. Its extreme complexity can only be addressed through the coordinated and collaborative work of researchers from a range of disciplines. 3D visualization has proven to be a useful tool for simplifying the analysis of complex systems, where gaining meaningful understanding from unstructured raw data is almost impossible, such as in the case of the brain. This paper presents a novel approach for visualizing neurons directly from the mor- phological descriptions extracted by neuroscience laboratories, pursuing two goals: improving the readability of complex neuronal scenarios and avoiding the need to store 3D models of the intricate geometry of neurons, since such models are demanding of computer resources. The proposed rendering method involves illustration techniques that facilitate the visual analysis of dense neural scenes. The work presented here brings the field of neuroscience and the benefits of 3D visualization environments closer together, increasing the interpretability of massive neural scenarios through visual inspection. A preliminary user study has proven the utility of the pro- posed rendering techniques for the visual exploration of dense neuronal scenes. The feasibility of parallelizing the implemented algorithms has also been assessed, representing a further step towards interactive illustrative visualization of neuronal forests.
Investigating the Value of Privacy in Online Social Networks: Conjoint Analysis

Popularity of Online Social Networks has been recently overshadowed by the privacy problems they pose. Users are getting increasingly vigilant concerning information they disclose and are strongly opposing the use of their information for commercial purposes. Nevertheless, as long as the network is offered to users for free, providers have little choice but to generate revenue through personalized advertising to remain financially viable. Our study empirically investigates the ways out of this deadlock. Using conjoint analysis we find that privacy is indeed important for users. We identify three groups of users with different utility patterns: Unconcerned Socializers, Control-conscious Socializers and Privacy-concerned. Our results provide relevant insights into how network providers can capitalize on different user preferences by specifically addressing the needs of distinct groups in the form of various premium accounts. Overall, our study is the first attempt to assess the value of privacy in monetary terms in this context.
Towards a Simpler Account of Modules and Generativity : Abstract Types have Open Existential Types

We present a variant of the explicitly-typed secondorder polymorphic λ-calculus with primitive open existential types, i.e. a collection of more atomic constructs for introduction and elimination of existential types. We equip the language with a call-by-value small-step reduction semantics that enjoys the subject reduction property. Traditional closed existential types can be defined as syntactic sugar. Conversely, programs with no free existential types can be rearranged to only use closed existential types, but the translation is not compositional. We argue that open existential types model abstract types and modules with generativity. We also introduce a new notion of paths at the level of types instead of terms that allows for writing type annotations more concisely and modularly.
HUWE1 interacts with PCNA to alleviate replication stress

Defects in DNA replication, DNA damage response, and DNA repair compromise genomic stability and promote cancer development. In particular, unrepaired DNA lesions can arrest the progression of the DNA replication machinery during S‐phase, causing replication stress, mutations, and DNA breaks. HUWE1 is a HECT‐type ubiquitin ligase that targets proteins involved in cell fate, survival, and differentiation. Here, we report that HUWE1 is essential for genomic stability, by promoting replication of damaged DNA. We show that HUWE1‐knockout cells are unable to mitigate replication stress, resulting in replication defects and DNA breakage. Importantly, we find that this novel role of HUWE1 requires its interaction with the replication factor PCNA, a master regulator of replication fork restart, at stalled replication forks. Finally, we provide evidence that HUWE1 mono‐ubiquitinates H2AX to promote signaling at stalled forks. Altogether, our work identifies HUWE1 as a novel regulator of the replication stress response.
A development of a novel TEC-lensed fiber with working distance of 60μm using a standard single-mode fiber

The paper presented a new thermally expanded core(TEC) lensed fiber using a standard single-mode fiber(SMF), which was fabricated with a lens at fiber tip using mechanical polishing after expanding the core diameter of a single-mode fiber into about 20μm. This TEC lensed fiber shows that it has the working distance of 60μm and high coupling efficiency (>75%).
Attention-Aware Cultural Heritage Applications on Mobile Phones

In this paper we report a set of results extracted from our experience in using PDAs and 3G multimedia cellular phones in Cultural Heritage (CH) through projects covering an activity spanning 10 years. In particular, we focus on a interesting variant of context-awareness, namely attention-awareness, derived from our more recent ICT European project, named Agamemnon. Such concepts are essential to the development of next generation mobile applications in cultural heritage and in other comparable fields. We present an experiment tackling this new issue by exploiting image recognition technology and we deeply analyze advantages and drawbacks of this approach, showing some preliminary results.
Programming with variable functions

What is a good method to specify and derive imperative programs? This paper argues that a new form of functional programming fits the bill: where variable functions can be updated at specified points in their domain.Traditional algebraic specification and functional programming are a powerful pair of tools for specifying and implementing domains of discourse and operations on them. Recent work on evolving algebras has introduced the function update in algebraic specifications, and has applied it with good success in the modelling of reactive systems. We show that similar concepts allow one to derive efficient programs in a systematic way from functional specifications. The final outcome of such a derivation can be made as efficient as a traditional imperative program with pointers, but can still be reasoned about at a high level.Variable functions can also play an important role in the structuring of large systems. They can subsume object-oriented programming languages, without incurring the latter's problems with pointer aliasing and modularity.
Hypertensive Disorders and Severe Obstetric Morbidity in the United States

OBJECTIVE: To examine trends in the rates of hypertensive disorders in pregnancy and compare the rates of severe obstetric complications for delivery hospitalizations with and without hypertensive disorders. METHODS: We performed a cross-sectional study using the 1998–2006 Nationwide Inpatient Sample of the Healthcare Cost and Utilization Project. Logistic regressions and population-attributable fractions were used to examine the effect of hypertensive disorders on severe complications. RESULTS: The overall prevalence of hypertensive disorders among delivery hospitalizations increased significantly from 67.2 per 1,000 deliveries in 1998 to 81.4 per 1,000 deliveries in 2006. Compared with hospitalizations without any hypertensive disorders, the risk of severe obstetric complications ranged from 3.3 to 34.8 for hospitalizations with eclampsia/severe preeclampsia and from 1.4 to 2.2 for gestational hypertension. The prevalence of hospitalizations with eclampsia/severe preeclampsia increased moderately from 9.4 to 12.4 per 1,000 deliveries (P for linear trend <0.001) during the period of study. However, these hospitalizations were associated with 38% of hospitalizations with acute renal failure and 19% or more of hospitalizations with ventilation, disseminated intravascular coagulation syndrome, pulmonary edema, puerperal cerebrovascular disorders, and respiratory distress syndrome. Overall, hospitalizations with hypertensive disorders were associated with 57% of hospitalizations with acute renal failure, 27% of hospitalizations with disseminated intravascular coagulation syndrome, and 30% or more of hospitalizations with ventilation, pulmonary edema, puerperal cerebrovascular disorders, and respiratory distress syndrome. CONCLUSION: The number of delivery hospitalizations in the United States with hypertensive disorders in pregnancy is increasing, and these hospitalizations are associated with a substantial burden of severe obstetric morbidity. LEVEL OF EVIDENCE: III
Bayesian Networks for Fault Diagnosis of Large Power Generating Stations

In this paper, a simplified Bayesian networks with Noisy-OR and Noisy-AND nodes fault diagnosis system is proposed to estimate the faulty item of power generation station that includes; generation units, power transformers, station service transformers, autotransformers and station bus bars. The proposed method utilizes the final information of protective relays and corresponding circuit breakers to construct the Bayesian networks for each section/item. The proposed method can deal with the incomplete information and uncertainties imposed on fault section/item diagnosis. The learning algorithm for network parameters is analogous to the back propagation algorithm of artificial neural networks. Taking the sum of the mean-squared error between the expected values and the computed results of target variables as the minimizing optimization function, it adjusts the network's parameters continuously. By comparing the results believe of possible fault sections/item; the faulty section(s) is candidate. Computer simulation of High Dam power generation station (Hydro Plants Generation Company HPGC) in Egypt shows that type of fault diagnosis has powerful error tolerance ability, rapid reasoning, less storage memory and processing time. It is also flexible and can be applied into control center of the actual large power generation station for online fault diagnosis to make the reasonable decision in critical situations.
Analyticity of Entropy Rate of Hidden Markov Chains

We prove that under mild positivity assumptions the entropy rate of a hidden Markov chain varies analytically as a function of the underlying Markov chain parameters. A general principle to determine the domain of analyticity is stated. An example is given to estimate the radius of convergence for the entropy rate. We then show that the positivity assumptions can be relaxed, and examples are given for the relaxed conditions. We study a special class of hidden Markov chains in more detail: binary hidden Markov chains with an unambiguous symbol, and we give necessary and sufficient conditions for analyticity of the entropy rate for this case. Finally, we show that under the positivity assumptions, the hidden Markov chain itself varies analytically, in a strong sense, as a function of the underlying Markov chain parameters