Miracle-dz/UniFAR-FTUs huggingface dataset:
TGF-β Inhibitor SB431542 Promotes the Differentiation of Induced Pluripotent Stem Cells and Embryonic Stem Cells into Mesenchymal-Like Cells

Due to their potential for tissue engineering applications and ability to modulate the immune system and reduce inflammation, mesenchymal stem cells (MSCs) have been explored as a promising option for the treatment of chronic diseases and injuries. However, there are problems associated with the use of this type of cell that limit their applications. Several studies have been exploring the possibility to produce mesenchymal stem cells from pluripotent stem cells (PSCs). The aim of these studies is to generate MSCs with advantageous characteristics of both PSCs and MSCs. However, there are still some questions concerning the characteristics of MSCs derived from the differentiation of PSCs that must be answered before they can be used to treat diseases and injuries. The objective of this study was, therefore, to determine if PSCs exposed to SB431542, a TGF-β inhibitor, are able to differentiate to MSCs, judging by morphology, expression of mesenchymal and pluripotent stem cell markers, expression of pluripotency-related genes, and ability to differentiate to osteocytes and adipocytes. The results obtained demonstrated that it is possible to induce the differentiation of both embryonic stem cells and induce pluripotent stem cells into cells with characteristics that highly resemble those from MSCs through the inhibition of the TGF-β pathway.
Chapter 9 Some Unitary , Binary , and Ternary Non-TiO 2 Photocatalysts

Among all kinds of green earth and renewable energy projects underway, semiconduc‐ tor photocatalysis has received wide interest because it provides an easy way to directly utilize the energy of either natural sunlight or artificial indoor illumination. TiO2, the most widely used photocatalyst, due to its wide band gap, can only be activated under UV irradiation, and thus, the development of novel semiconductor photocatalysts makes a significant advancement in photocatalytic functional materials. One of the effective strategies to overcome this shortcoming is photosensitizing these wide band gap semiconductors with narrow band gap semiconductors which have proper energy levels. This method can not only improve the photocatalytic activity, due to increasing visiblelight-harvesting efficiency, but also can decrease the recombination of the charge carriers, because the formation of n–n or n–p heterojunctions between the combined semiconduc‐ tors can induce internal electric fields between them. In this regard, this review presents some unitary, binary, and ternary non-TiO2 photocatalysts used for the degradation for organic pollutants and for water splitting.
Efficient Algorithm for RSA Text Encryption Using CUDA-C

Modern-day computer security relies heavily on cryptography as a means to protect the data that we have become increasingly reliant on. The main research in computer security domain is how to enhance the speed of RSA algorithm. The computing capability of Graphic Processing Unit as a co-processor of the CPU can leverage massive-parallelism. This paper presents a novel algorithm for calculating modulo value that can process large power of numbers which otherwise are not supported by built-in data types. First the traditional algorithm is studied. Secondly, the parallelized RSA algorithm is designed using CUDA framework. Thirdly, the designed algorithm is realized for small prime numbers and large prime number . As a result the main fundamental problem of RSA algorithm such as speed and use of poor or small prime numbers that has led to significant security holes, despite the RSA algorithm's mathematical soundness can be alleviated by this algorithm.
Grid-Enabled Quantitative Analysis of Breast Cancer

Abstract : The tong-term goat of this research is to improve breast cancer diagnosis, risk assessment, response assessment, ana patient care via the use of large-scale, multi-modality computerized image analysis. The central hypothesis of this research is that large-scale image analysis for breast cancer research will yield improved accuracy and reliability when optimized over multiple features and large multi-modality databases. In the first year of research, we designed a pilot study utilizing large scale parallel Grid computing harnessing nationwide infrastructure for medical image analysis. Also, using a 256-CPU high-throughput computing cluster, dimension reduction techniques were applied to ultrasound, full-field digital mammography, and DCE-MRI breast CADx feature spaces. Results indicated the ability to rival or exceed traditional breast CA Ox performance. Building on this success, during the second year, we investigated methods for using unlabeled (truth-unknown) data. Often, there are practical difficulties in assembling large, labeled (histo-pathology) breast image data sets, while unlabeled data may be abundant This is problematic for conventional CADx schemes reliant on supervised classifiers trained using labeled data only. We proposed using unlabeled breast image data to enhance breast CADx. We hypothesize that unlabeled data information call act as a 'regularizing factor aiding classifier robustness. After conducting experiments using previously collected data sets. encouraging na results were found indicating unlabeled data can improve CA Dx classifier performance.
On the Di erential Security of Multivariate Public Key Cryptosystems

Since the discovery of an algorithm for factoring and computing discrete logarithms in polynomial time on a quantum computer, the cryptographic community has been searching for an alternative for security in the approaching post-quantum world. One excellent candidate is multivariate public key cryptography. Though the speed and parameterizable nature of such schemes is desirable, a standard metric for determining the security of a multivariate cryptosystem has been lacking. We present a reasonable measure for security against the common di erential attacks and derive this measurement for several modern multivariate public key cryptosystems.
PII: S0167-2681(01)00165-2

Second-price auctions are designed to induce people to reveal their private preferences for a good. Laboratory evidence suggests that while these auctions do a reasonable job on aggregate, they fall short at the individual level, especially for bidders who are off-margin of the market-clearing price. Herein we introduce and explore whether a random nth-price auction can engage all bidders to bid sincerely. Our results first show that the random nth-price auction can induce sincere bidding in theory and practice. We then compare the random nth-price to the second-price auction. We find that the second-price auction works better on-margin, and the random nth-price auction works better off-margin. © 2001 Elsevier Science B.V. All rights reserved. JEL classification: C7; C9; Q0
Quantum entanglement shared in hydrogen bonds and its usage as a resource in molecular recognition

Quantum tunneling events occurring through biochemical bonds are capable of generating quantum correlations between bonded systems, which in turn makes the conventional second law of thermodynamics approach insufficient to investigate these systems. This means that the utilization of these correlations in their biological functions could give an evolutionary advantage to biomolecules to an extent beyond the predictions of molecular biology that are generally based on the second law in its standard form. To explore this possibility, we first compare the tunneling assisted quantum entanglement shared in the ground states of covalent and hydrogen bonds. Only the latter appears to be useful from a quantum information point of view. Also, significant amounts of quantum entanglement can be found in the thermal state of hydrogen bond. Then, we focus on an illustrative example of ligand binding in which a receptor protein or an enzyme is restricted to recognize its ligands using the same set of proton-acceptors and donors residing on its binding site. In particular, we show that such a biomolecule can discriminate between [Formula: see text] agonist ligands if it uses the entanglement shared in [Formula: see text] intermolecular hydrogen bonds as a resource in molecular recognition. Finally, we consider the molecular recognition events encountered in both the contemporary genetic machinery and its hypothetical primordial ancestor in pre-DNA world, and discuss whether there may have been a place for the utilization of quantum entanglement in the evolutionary history of this system.
Annual routines and life history of the amphipod Onisimus litoralis: seasonal growth, body composition and energy budget

The scheduling of life history events over the year is a central characteristic of organisms in seasonal environments. Here, we report on seasonality in growth rate and cellular energy alloca- tion, as well as timing of maturation, egg production and brood release, through a full annual cycle of Onisimus litoralis, a lysianassoid amphipod dominating soft bottoms in the intertidal zone in the high Arctic. We observed that O. litoralis follows a semelparous 2 yr life cycle, with growth and devel- opment mainly taking place in summer, but with slow growth also in winter, which with the lack of energy reserves suggests that it feeds year-round. The importance of the spring bloom of phytoplank- ton in fueling growth and development is reflected in carbohydrate and protein content. Relatively stable lipid content and no build-up of energy stores prior to egg production indicate income breed- ing. Seasonal differences in energy consumption are apparent, with higher consumption in summer, but as a result of increased energy content during summer, the energy budget remains relatively sta- ble during the whole year. The life history observed is similar to that of previous studies, but our observations do not support iteroparity, which has been suggested by others. Parental care, through a 'start pack' of energy to the offspring and the ability to time the brood release to favorable condi- tions, add flexibility to the life history strategy. Easy access to the species and a good understanding of its biology support further use of O. litoralis as an indicator species for environmental change in the Arctic.
Efficient Network Structure via Ensemble Deep Evolutionary Synthesis

While deep neural networks have shown promising results in a wide range of applications on highly powerful computational devices, one challenging task is to deploy a deep neural network on embedded devices for the widespread use. Deep neural networks and specially convolutional neural networks are usually over-parameterized and one possible solution is to remodel the network architecture with a smaller network architecture with a trade-off on modeling accuracy and performance. Here we take advantage of meta-learning algorithms to synthesize a more efficient model while it boosts the modeling performance. To this end, we propose an ensemble of deep evolutionary intelligence frameworks where it synthesizes several very efficient models with less than 3% drop on modeling accuracy and then aggregates them to boost the modeling performance. Experimental results demonstrates that the proposed ensemble of Deep Evolutionary Synthesis approach synthesizes an ensemble model which is 1.5X smaller than the original network architecture while performing more accurate (83.30% compared to 83.18%) than the original network in terms of modeling accuracy for binary object segmentation.
Parsing Arabic Nominal Sentences Using Context Free Grammar and Fundamental Rules of Classical Grammar

This work falls within the framework of the Arabic natural language processing. We are interested in parsing Arabic texts. Existing parsers generate parse trees that give an idea about the structure of the sentence without considering the syntactic functions specific to the Arabic language. Thus, the results are still insufficient in terms of syntactic information. The system we have developed in this article takes into consideration all these syntactic functions. This system begins with a morphological analysis in the context. Then, it uses a CFG grammar to extract the phrases and ends by exploiting the formalism of unification grammar and traditional grammar to combine these phrases and generate the final sentence structure.
FIRST-based survey of compact Steep Spectrum sources. I. MERLIN images of arc-second scale objects

Compact Steep Spectrum (CSS) sources are powerful extragalactic radio sources with angular dimensions of the order of a few arcseconds or less. Such a compactness is apparently linked to the youth of these objects. The majority of CSSs investigated so far have been known since the early 1980s. This paper is the first in a series where we report the results of an observational campaign targeted on a completely new sample of CSSs which are significantly weaker than those investigated before. The ultimate goal of that campaign is to find out how "weak" CSSs compare to "strong", classical ones, especially with regard to the morphologies. Here we present an analysis of morphological and physical properties of five relatively large sources based on MERLIN observations at 1.6 and 5 GHz.
Precision Target Guide Strategy for Applying SERS into Environmental Monitoring

Surface enhanced Raman spectroscopy (SERS) is a promising analytical technique that exhibits various applications in trace detection and identification. When it is applied into environmental monitoring, we should concern several key points to improve detection sensitivity and selectivity for the detection in complex matrix. In this tutorial review, we mainly focus on the strategies for improving the use of SERS into environ‐ mental application. The strategies are summarized for enhancing the ability of the substrate to selectively capture specific targets, and for achieving separation and concentration of the analytes from the matrix and the assembly structures for multiple phase detection. We have also introduced several newly developed detection systems using portable instruments and miniaturized devices that are more suitable for infield applications. In addition, we discuss the present challenges that hide it from wide real application and give the outlook for the future development in applying SERS in environmental monitoring. For water‐soluble ionic pollutant NO 2− , it was emulsified and reacted with the CD‐ captured SERS‐inactive OPD to form SERS‐active benzotriazole, and then, the indirect quantification of its concentration was realized with the LOD down to 1 μmol L −1 . For the oil‐ soluble OPD, its trace detection was also achieved by using the surface catalyzed oxidation to form a SERS‐active 2,3‐diaminophenazine (DAP) under acidic conditions. The detection limit was as low as 1 nmol L −1 . From the in situ SERS monitoring, the kinetic data of the reaction(s) were obtained and the reaction mechanism was also proposed.
A REVIEWON SEMANTICRELATIONSHIP BASED APPLICATIONS

Exploitation of semantic relationship in the represented knowledge is crucial to move the system towards intelligent system. This paper brings a detailed survey about wide range of relations that has been exploited from literature to modern computational theory. The classification of the relations may be content dependent relationship, content independent relationship, foundation relations, spatial relations, temporal relations, participation relations, mereological relations, etc. The role of relations in various domains such as searching, auditing, ranking and reasoning clearly indicates the power of relationships to make the system more human like thinking. It is very complex to reason the relation between the concepts as it differs in context, semantics and properties. This paper provides a novel attempt to explore the significance of relations in reasoning and information retrieval.
Towards Controllable Story Generation

We present a general framework of analyzing existing story corpora to generate controllable and creative new stories. The proposed framework needs little manual annotation to achieve controllable story generation. It creates a new interface for humans to interact with computers to generate personalized stories. We apply the framework to build recurrent neural network (RNN)-based generation models to control story ending valence and storyline. Experiments show that our methods successfully achieve the control and enhance the coherence of stories through introducing storylines. with additional control factors, the generation model gets lower perplexity, and yields more coherent stories that are faithful to the control factors according to human evaluation.
The Contribution of the obscuring torus to the x-ray spectrum of Seyfert galaxies: A Test for the unification model

The presence of an obscuring torus around the nucleus of a Seyfert galaxy, as supposed in the popular unification scheme, can strongly modify the X-ray spectrum for both type 1 and type 2 Seyfert galaxies. We study this problem by means of Monte Carlo simulations, finding that, if the torus is Compton-thick, it can scatter at small angles a significant fraction of the nuclear radiation, and contribute to the continuum of Seyfert 1 galaxies above ∼10 keV, and to the fluorescence iron line at 6.4 keV. At large inclination angles and for large torus column densities, the spectrum is attenuated by photoabsorption and Compton scattering, while the iron fluorescence line produced by the torus can have large equivalent widths
Evidence from MESSENGER for sulfur‐ and carbon‐driven explosive volcanism on Mercury

Targeted MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) X‐Ray Spectrometer measurements of Mercury's largest identified pyroclastic deposit are combined with neutron and reflectance spectroscopy data to constrain the composition of volatiles involved in the eruption that emplaced the pyroclastic material. The deposit, northeast of the Rachmaninoff basin, is depleted in S (relative to Ca and Si) and C, compared with the rest of Mercury's surface. Spectral reflectance measurements of the deposit indicate relatively high overall reflectance and an oxygen‐metal charge transfer (OMCT) absorption band at ultraviolet wavelengths. These results are consistent with oxidation of graphite and sulfides during magma ascent, via reaction with oxides in the magma or assimilated country rock, and the formation of S‐ and C‐bearing volatile species. Consumption of graphite during oxidation could account for the elevated reflectance of the pyroclastic material, and the strength of the OMCT band is consistent with ~0.03–0.1 wt % FeO in the deposit.
Improving Retrievals of Cirrus Cloud Particle Size Coupling Lidar and Three-Channel Radiometric Techniques

This study is intended to illustrate the potential advantage of combining lidar measurements and the split-window technique based on the infrared spectral information contained at the 8.65-, 11.15-, and 12.05-μm bands for inferring the microphysical properties of cirrus clouds. The lidar returns are employed to detect cirrus clouds. The optical properties of nonspherical ice crystals computed from the state-of-the-art scattering computational methods are used for the present forward radiative transfer simulation that fully accounts for both gaseous absorption and multiple scattering processes in the atmosphere. A combination of the radiances at the three infrared (IR) bands with lidar backscatter returns cannot uniquely specify the effective size of ice crystals because of its dependence on the particle aspect ratios. To avoid the shortcoming associated with a potential multivalued retrieval, lidar depolarization observation is used to constrain the specification of the particle aspect ratio in the retrieval implementation based on a precalculated lookup library. The present methodology for inferring the microphysical properties of cirrus clouds is implemented for nine cases by using the measurements from a 532-nm lidar located at the Palaiseau, France, ground-based site and the infrared spectral bands from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra platform. It is shown that the three IR wavelengths are quite complementary in constraining the retrieval of the particle size, leading to a significant advance in comparison with two-channel techniques, whereas the aspect ratio constraint due to lidar depolarization reduces the uncertainty of retrieved particle size by more than 20% for 70% of the cases and more than 65% for 40% of the cloud cases.
Ab initio computational study on the lattice thermal conductivity of Zintl clathrates [Si19 P4]Cl4 and Na4[Al4Si19]

The lattice thermal conductivity of silicon clathrate framework ${\mathrm{Si}}_{23}$ and two Zintl clathrates, $[{\mathrm{Si}}_{19}{\mathrm{P}}_{4}]{\mathrm{Cl}}_{4}$ and ${\mathrm{Na}}_{4}[{\mathrm{Al}}_{4}{\mathrm{Si}}_{19}]$, is investigated by using an iterative solution of the linearized Boltzmann transport equation in conjunction with ab initio lattice dynamical techniques. At 300 K, the lattice thermal conductivities for ${\mathrm{Si}}_{23}, [{\mathrm{Si}}_{19}{\mathrm{P}}_{4}]{\mathrm{Cl}}_{4}$, and ${\mathrm{Na}}_{4}[{\mathrm{Al}}_{4}{\mathrm{Si}}_{19}]$ were found to be 43 W/(m K), 25 W/(m K), and 2 W/(m K), respectively. In the case of ${\mathrm{Na}}_{4}[{\mathrm{Al}}_{4}{\mathrm{Si}}_{19}]$, the order-of-magnitude reduction in the lattice thermal conductivity was found to be mostly due to relaxation times and group velocities differing from ${\mathrm{Si}}_{23}$ and $[{\mathrm{Si}}_{19}{\mathrm{P}}_{4}]{\mathrm{Cl}}_{4}$. The difference in the relaxation times and group velocities arises primarily due to the phonon spectrum at low frequencies, resulting eventually from the differences in the second-order interatomic force constants (IFCs). The obtained third-order IFCs were rather similar for all materials considered here. The present findings are similar to those obtained earlier for some skutterudites. The predicted lattice thermal conductivity of ${\mathrm{Na}}_{4}[{\mathrm{Al}}_{4}{\mathrm{Si}}_{19}]$ is in line with the experimentally measured thermal conductivity of recently synthesized type-I Zintl clathrate ${\mathrm{Na}}_{8}[{\mathrm{Al}}_{8}{\mathrm{Si}}_{38}]$ (polycrystalline samples).
Column Generation Algorithms for Constrained POMDPs

In several real-world domains it is required to plan ahead while there are finite resources available for executing the plan. The limited availability of resources imposes constraints on the plans that can be executed, which need to be taken into account while computing a plan. A Constrained Partially Observable Markov Decision Process (Constrained POMDP) can be used to model resource-constrained planning problems which include uncertainty and partial observability. Constrained POMDPs provide a framework for computing policies which maximize expected reward, while respecting constraints on a secondary objective such as cost or resource consumption. Column generation for linear programming can be used to obtain Constrained POMDP solutions. This method incrementally adds columns to a linear program, in which each column corresponds to a POMDP policy obtained by solving an unconstrained subproblem. Column generation requires solving a potentially large number of POMDPs, as well as exact evaluation of the resulting policies, which is computationally difficult. We propose a method to solve subproblems in a two-stage fashion using approximation algorithms. First, we use a tailored point-based POMDP algorithm to obtain an approximate subproblem solution. Next, we convert this approximate solution into a policy graph, which we can evaluate efficiently. The resulting algorithm is a new approximate method for Constrained POMDPs in single-agent settings, but also in settings in which multiple independent agents share a global constraint. Experiments based on several domains show that our method outperforms the current state of the art.