Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of Electronically Modified Ru-Based Neutral 16 VE Allenylidene Olefin Metathesis Precatalysts

Electronic modifications within Ru-based olefin metathesis precatalysts have provided a number of new complexes with significant differences in reactivity profiles. So far, this aspect has not been studied for neutral 16 VE allenylidenes. The first synthesis of electronically altered complexes of this type is reported. Following the classical dehydration approach (vide infra) modified propargyl alcohols were transformed to the targeted allenylidene systems in the presence of PCy3. The catalytic performance was investigated in RCM reaction (ring closing metathesis) of benchmark substrates such as diallyltosylamide (6) and diethyl diallylmalonate (7).
How to Perform and Interpret Upper Esophageal Sphincter Manometry

Manometry of the pharynx and the upper esophageal sphincter (UES) provides important information on the swallowing mechanism, especially about details on the pharyngeal contraction and relaxation of the UES. However, UES manometry is challenging because of the radial asymmetry of the sphincter, and upward movement of the UES during swallowing. In addition, the rapidity of contraction of the pharyngoesophageal segment requires high frequency recording for capturing these changes in pressure; this is best done with the use of solid state transducers and high-resolution manometry. UES manometry is not required for routine patient care, when esophageal manometry is being performed. The major usefulness of UES manometry in clinical practice is in the evaluation of patients with oropharyngeal dysphagia.
Preprocessing using SVD towards illumination invariant face recognition

Uncontrolled lighting Conditions poses obstacle to face recognition. To deal with this problem, this paper proposes a preprocessing scheme using Singular Value Decomposition and Histogram Equalization to enhance and facilitate illumination invariant face recognition. The proposed method first generates synthetic image using Histogram equalization. Original and synthetic images are singular value decomposed; from the estimates of singular values enhanced image is reconstructed. Enhanced image is discrete wavelet decomposed (Haar & Db4) in to different frequency sub bands (LL, LH, HL, HH). The LL sub band is the best approximation of original image with lower-dimensional space and is used as biometric template. Pose Invariant Feature vectors are extracted from this template using Kernel Principal Component Analysis (KPCA). To show the performance, the proposed method is tested on YaleB, ORL benchmarking Databases. The results obtained show the impact of the method and is compared with PCA, KPCA without any preprocessing.
Principles for Engineering IoT Cloud Systems

Engineering Internet of Things (IoT) and cloud services to provide a coherent software layer for continuous deployment, provision, and execution of applications for various domains is complex. The authors consider whether IoT cloud systems could provide a uniform layer to enable continuous execution of complex applications consisting of diverse types of software components, although these systems are built by integrating and blending IoT infrastructures with cloud-based datacenters. The authors analyze requirements and perspectives for engineering such IoT cloud systems and discuss the main engineering principles that should be supported. They then highlight seven main principles, covering different development and operation phases of IoT cloud systems. To show the importance and feasibility of these principles, they present some of their recent work in providing concepts and tools for IoT cloud systems.
1 Base Noun Phrase Chunking with Support Vector Machines

We apply Support Vector Machines (SVMs) to identify base noun phrases in sentences. SVMs are known to achieve high generalization performance even in high dimensional feature space. We explore two different chunk representations (IOB and open/close brackets) and use a two-layer system approach for the classification task. Experiments show that despite using a dynamic programming approach to find the most probable pairing in the open/close brackets representation, the IOB representation performs significantly better. In addition, using higher degree polynomial kernel functions lead to slightly better results. We conclude that SVMs are extremely powerful machine learning approach for many natural language processing tasks and outperforms other learning systems because of SVMs’ ability to generalize in high dimension.
Different Residues in the GABAA Receptor α1T60-α1K70 Region Mediate GABA and SR-95531 Actions*

Although γ-aminobutyric acid type A receptor agonists and antagonists bind to a common site, they produce different conformational changes within the site because agonists cause channel opening and antagonists do not. We used the substituted cysteine accessibility method and two-electrode voltage clamping to identify residues within the binding pocket that are important for mediating these different actions. Each residue from α1T60 to α1K70 was mutated to cysteine and expressed with wild-type β2 subunits in Xenopus oocytes. Methanethiosulfonate reagents reacted with α1T60C, α1D62C, α1F64C, α1R66C, α1S68C, and α1K70C. γ-Aminobutyric acid (GABA) slowed methanethiosulfonate modification of α1F64C, α1R66C, and α1S68C, whereas SR-95531 slowed modification of α1D62C, α1F64C, and α1R66C, demonstrating that different residues are important for mediating GABA and SR-95531 actions. In addition, methanethiosulfonate reaction rates were fastest for α1F64C and α1R66C, indicating that these residues are located in an open, aqueous environment lining the core of the binding pocket. Positively charged methanethiosulfonate reagents derivatized α1F64C and α1R66C significantly faster than a negatively charged reagent, suggesting that a negative subsite important for interacting with the ammonium group of GABA exists within the binding pocket. Pentobarbital activation of the receptor increased the rate of methanethiosulfonate modification of α1D62C and α1S68C, demonstrating that parts of the binding site undergo structural rearrangements during channel gating.
EBV-encoded RNA via TLR3 induces inflammation in nasopharyngeal carcinoma

Pathogen-induced inflammation has been one of the intensive research areas in carcinogenesis. EBV encoded RNAs (EBERs) have been suggested to play roles in anti-apoptosis and growth-promotion in lymphoid and immune disorders. However, pathological roles of EBERs in solid tumors of epithelia origin remain to be elucidated. Given their characteristic dsRNA structures, recent studies provided evidences for the activation of some pattern recognition receptors (PRR) by EBERs, which is fundamental in the process of pathogenesis. Here, we show that EBERs induce inflammatory response in nasopharyngeal carcinoma (NPC) cells through Toll-like receptor 3 (TLR3), mainly featured by high level of TNFα production. Interestingly, EBERs and EBV latent membrane protein 1 (LMP1) form a positive regulatory loop with NF-κB as a key node that amplifies the inflammatory signals in EBV infected epithelial cells. We demonstrate in vivo that EBERs can interact with TLR3 and induce tumor cells to produce cytokines in B16 synergetic tumor and human NPC xenograft models, in which macrophages are recruited and activated, leading to a favorable microenvironment for solid tumor growth. Lastly, we verify a positive association between EBER and TNFα levels in NPC clinical samples and the combination of EBER and TNFα expressions provides a predictor of poor survival of NPC patients. In conclusion, EBERs play a pivotal role in inflammation-to-oncogenesis transition in NPC development.
The Formation of Cataclysmic Variables with Brown Dwarf Secondaries

The present-day formation of cataclysmic variables (CVs) with brown dwarf (BD) secondaries (0.013 M☉ ≤ Ms ≤ 0.075 M☉) is investigated using a population synthesis technique. Results from the latest, detailed models for BDs have been incorporated into the population synthesis code. The present-day orbital period distribution of zero-age CVs (ZACVs) that form with BD secondaries is calculated. For our models, we find that ZACVs with BD secondaries have orbital periods in the range 46 minutes to 2.5 hr. We also find that ZACVs with BD secondaries comprise 18% of the total, present-day ZACV population. In addition, we find that 80% of ZACVs with BD secondaries have orbital periods shorter than 78 minutes. This implies that 15% of the present-day ZACV population should have orbital periods shorter than the observed orbital period minimum for CVs. We also investigate the dependence of the present-day formation rate of CVs with BD secondaries on the assumed value of the common envelope efficiency parameter, αCE, for three different assumed mass ratio distributions in zero-age main-sequence (ZAMS) binaries. Surprisingly, we find that the common envelope process must be extremely inefficient (αCE < 0.1) in order for CVs with BD secondaries not to be formed. Finally, we find that the progenitor binaries of ZACVs with BD secondaries have ZAMS orbital separations of smaller than 3 AU and ZAMS primary masses between ~1 and 10 M☉, with ~75% of the primary masses of smaller than ~1.6 M☉. Interestingly, these ranges in orbital separation and primary mass place the majority of the progenitor binaries within the so-called brown dwarf desert. The implications of these results are discussed in the context of V485 Cen and 1RXS J232953.9+062814, the only known CVs with orbital periods shorter than the period minimum, CVs above the period minimum that are strongly suspected of containing BD secondaries (such as WZ Sge), common envelope evolution involving very low mass secondaries, and the BD mass function in ZAMS binaries.
Hoarding of International Reserves: Mrs Machlup's Wardrobe and the Joneses

Motivated by the observed international reserve hoarding behavior in the post-1997 crisis period, we explore the Mrs Machlup's Wardrobe hypothesis and the related keeping up with the Joneses argument. It is conceived that, in addition to psychological reasons, holding a relatively high level of international reserves reduces the vulnerability to speculative attacks and promotes growth. A stylized model is constructed to illustrate this type of hoarding behavior. The relevance of the keeping up with the Joneses effect is examined using a few plausible empirical specifications and data from 10 East Asian economies. Panel-based regression results are suggestive of the presence of the Joneses effect, especially in the post-1997 crisis period.
Optical Number Count Estimation of IRIS Far-Infrared Survey of Galaxies

Infrared Imaging Surveyor (IRIS) is a satellite which will be launched at the beginning of 2003. One of the main purposes of the IRIS mission is an all-sky survey in the far-infrared region with a flux limit much deeper than that of IRAS. Detection of a large number of galaxies ($\sim several \times 10^6$ in the whole sky) is expected in this survey. We investigated the expected optical and near-infrared (NIR) number counts of galaxies detected by the far-infrared scanner (FIS) of IRIS (hereafter, IRIS galaxies) and possibility of optical and NIR follow-up of them. The spectral energy distribution and the luminosity function of the IRIS galaxies are modeled based on the properties of galaxies observed by IRAS. The IRIS galaxies are divided into two populations according to their infrared luminosities ($L_{IR}$); normal spirals ($L_{IR} 10^{10}\LO$). The expected number counts of IRIS galaxies for both of the populations are calculated in $B$ and $H$ bands. We show that about 60 normal galaxies and about 80 starburst galaxies are detected per square degree in both of the two bands, when galaxy evolution is not taken into account. All of the normal population of IRIS galaxies are located at the redshift $z\ltsim 0.1$. As for the starburst population, we also calculated number of galaxies with a simple model of evolution. The total number of starburst population predicted by the evolution model is larger by 20% than that expected from the non-evolution model. In the evolution model, the numbers of low-z ($z 3$) galaxies are 100, 20, and 0.2 per square degree, respectively.
Supervised image segmentation using learning and merging

The segmentation problem can be viewed as a learning and merging problem based on superpixels (image segments), which can incorporate a group of cues to guide the segmentation. So the proposed multi-label segmentation algorithm mainly consists of two stages: the learning stage and the merging stage. In the learning stage, Gaussian Mixture Models (GMMs) firstly learn color models for different components of objects. Based on the likelihood, we execute the alpha-expansion algorithm only once in order to alleviate the shrinking bias. The initial labels help determine whether a superpixel is too noisy, and the contour responses between superpixels can distinguish spurious boundaries. Those superpixels containing too much noisy pixels and spurious boundaries will be unlabeled. In the merging stage, unlabeled superpixels may have similar color information while differing in texture information. Therefore, they can be correctly classified by a novel region merging algorithm based on maximal similarity. In this way the advantages of features in different levels are enhanced by uniting them in different stages. Finally, the proposed method is evaluated on the Berkeley segmentation benchmark, the Graz benchmark and the Grabcut benchmark. Experimental results show that our method obtains the highest accuracy on the Graz benchmark, and the performance on other benchmarks can also be comparable or better than current leading algorithms.
Creating Completely Both Male and Female Sterile Plants by Specifically Ablating Microspore and Megaspore Mother Cells

Although genetically modified (GM) plants have improved commercially important traits, such as biomass and biofuel production, digestibility, bioremediation, ornamental value, and tolerance to biotic and abiotic stresses, there remain economic, political, or social concerns over potential ecological effects of transgene flow from GM plants. The current solution for preventing transgene flow from GM plants is genetically engineering sterility; however, approaches to generating both male and female sterility are limited. In addition, existing strategies for creating sterility lead to loss or modifications of entire flowers or floral organs. Here, we demonstrate that instead of the 1.5-kb promoter, the entire SOLO DANCERS (SDS) gene is required for its meiocyte-specific expression. We then developed an efficient method to specifically ablate microspore and megaspore mother cells using the SDS and BARNASE fusion gene, which resulted in complete sterility in both male and female reproductive organs in Arabidopsis (Arabidopsis thaliana) and tobacco (Nicotiana tabacum), but did not affect plant growth or development, including the formation of all flower organs. Therefore, our research provides a general and effective tool to prevent transgene flow in GM plants.
Average performance of the sparsest approximation in a dictionary

Given data d ∈ RN, we consider its representation u* involving the least number of non-zero elements (denoted by l0(u*)) using a dictionary A (represented by a matrix) under the constraint kAu − dk ≤ τ, for τ > 0 and a norm k.k. This (nonconvex) optimization problem leads to the sparsest approximation of d. We assume that data d are uniformly distributed in θBfd (1) where θ>0 and Bfd (1) is the unit ball for a norm fd. Our main result is to estimate the probability that the data d give rise to a K−sparse solution u*: we prove that P (l0(u*) ≤ K) = CK( τ θ )(N−K) + o(( τ θ )(N−K)), where u* is the sparsest approximation of the data d and CK > 0. The constants CK are an explicit function of k.k, A, fd and K which allows us to analyze the role of these parameters for the obtention of a sparsest K−sparse approximation. Consequently, given fd and θ, we have a tool to build A and k.k in such a way that CK (and hence P (l0(u*) ≤ K)) are as large as possible for K small. In order to obtain the above estimate, we give a precise characterization of the set [\zigma τK] of all data leading to a K−sparse result. The main difficulty is to estimate accurately the Lebesgue measure of the sets {[\zigma τ K] ∩ Bfd (θ)}. We sketch a comparative analysis between our Average Performance in Approximation (APA) methodology and the well known Nonlinear Approximation (NA) which also assess the performance in approximation.
A New-Fangled FES-k-Means Clustering Algorithm for Disease Discovery and Visual Analytics

The central purpose of this study is to further evaluate the quality of the performance of a new algorithm. The study provides additional evidence on this algorithm that was designed to increase the overall efficiency of the original k-means clustering technique—the Fast, Efficient, and Scalable k-means algorithm (FES-k-means). The FES-k-means algorithm uses a hybrid approach that comprises the k-d tree data structure that enhances the nearest neighbor query, the original k-means algorithm, and an adaptation rate proposed by Mashor. This algorithm was tested using two real datasets and one synthetic dataset. It was employed twice on all three datasets: once on data trained by the innovative MIL-SOM method and then on the actual untrained data in order to evaluate its competence. This two-step approach of data training prior to clustering provides a solid foundation for knowledge discovery and data mining, otherwise unclaimed by clustering methods alone. The benefits of this method are that it produces clusters similar to the original k-means method at a much faster rate as shown by runtime comparison data; and it provides efficient analysis of large geospatial data with implications for disease mechanism discovery. From a disease mechanism discovery perspective, it is hypothesized that the linear-like pattern of elevated blood lead levels discovered in the city of Chicago may be spatially linked to the city's water service lines.
Role of Toll-Like Receptor 4 in the Proinflammatory Response to Vibrio cholerae O1 El Tor Strains Deficient in Production of Cholera Toxin and Accessory Toxins

ABSTRACT Following intranasal inoculation, Vibrio cholerae KFV101 (ΔctxAB ΔhapA ΔhlyA ΔrtxA) colonizes and stimulates tumor necrosis factor alpha and interleukin 1β (IL-1β) in mice, similar to what occurs with isogenic strain P4 (ΔctxAB), but is less virulent and stimulates reduced levels of IL-6, demonstrating a role for accessory toxins in pathogenesis. Morbidity is enhanced in C3H/HeJ mice, indicating that Toll-like receptor 4 is important for infection containment.
The Role of Dust in Producing the Cosmic Infrared Background

The extragalactic background light (EBL), exclusive of the cosmic microwave background, consists of the cumulative radiative output from all energy sources in the universe since the epoch of recombination. Most of this energy is released at ultraviolet and optical wavelengths. However, observations show that a significant fraction of the EBL falls in the 10 to 1000 micron wavelength regime. This provides conclusive evidence that we live in a dusty universe, since only dust can efficiently absorbs a significant fraction of the background energy and reemit it at infrared wavelengths. The general role of dust in forming the cosmic infrared background (CIB) is therefore obvious. However, its role in determining the exact spectral shape of the CIB is quite complex. The CIB spectrum depends on the microscopic physical properties of the dust, its composition, abundance, and spatial distribution relative to the emitting sources, and its response to evolutionary processes that can modify all the factors listed above. This paper will present a brief summary of the many ways dust affects the intensity and spectral shape of the cosmic infrared background. In an Appendix we present new limits on the mid-infrared intensity of the CIB using TeV gamma-ray observations of Mrk 501.
Transcriptome analysis of endometrial tissues following GnRH agonist treatment in a mouse adenomyosis model

Purpose Adenomyosis is a common, benign gynecological condition of the female reproductive tract characterized by heavy menstrual bleeding and dysmenorrhea. Gonadotropin-releasing hormone (GnRH) agonists are one of the medications used in adenomyosis treatment; however, their underlying mechanisms are poorly understood. Moreover, it is difficult to obtain endometrial samples from women undergoing such treatment. To overcome this, we generated an adenomyosis mouse model, which we treated with an GnRH agonist to determine its effect on pregnancy outcomes. We also analyzed endometrial gene expression following GnRH agonist treatment to determine the mechanisms that may affect pregnancy outcome in individuals with adenomyosis. Methods Neonatal female mice were divided into a control group, an untreated adenomyosis group, and an adenomyosis group treated with a GnRH agonist (n=6 each). The pregnancy outcome was observed and compared among the groups. Then, three randomly chosen transcriptomes from endometrial tissues from day 4 of pregnancy were analyzed between the adenomyosis group and the GnRH agonist treatment group by RNA sequencing and quantitative reverse transcription polymerase chain reaction (PCR). Results The litter size was significantly smaller in the adenomyosis group than in the control group (7±0.28 vs 11±0.26; P<0.05). However, the average live litter size was increased (10±0.28 vs 7±0.28; P<0.05) after GnRH agonist treatment. Three hundred and fifty-nine genes were differentially expressed in the GnRH agonist-treated group compared with the untreated group (218 were downregulated and 141 were upregulated). Differentially expressed genes were related to diverse biological processes, including estrogen metabolism, cell cycle, and metabolite biosynthesis. Conclusion GnRH agonist treatment appears to improve the pregnancy outcome of adenomyosis in a mouse model. Besides pituitary down-regulation, other possible mechanisms such as the regulation of cell proliferation may play a role in this. These new insights into GnRH agonist mechanisms will be useful for future adenomyosis treatment.
A picture is worth a thousand words: Introducing visual similarity into recommendation

Recent recommender systems work well in terms of prediction accuracy, making use of a variety of features, such as users' personal information, purchasing history, browsing history and comments. However, traditional recommendation models have not made full use of item information and met difficulties with cold-start problems. On the other hands, visual information on item images is one of the most basic and informative features of the item, which has not been well-studied and applied in recommendation yet. In this paper, we introduce “visual similarity” between different items into recommendation, which measures the probability between items that are similar in terms of visual effect or “styles”. Observations on real e-commercial site data show that users tend to buy similar items, or items with similar “style”, indicating that visual information can be considered as a reliable feature in recommending process. Furthermore, a new matrix supplement approach is proposed to integrate item-item similarity matrix and traditional user-item matrix for collaborative filtering. Finally, a novel recommendation model is proposed which leverages visual similarity to collaborative filtering. Experiments on e-commercial website data shows that the proposed approaches result in superior performance compared with traditional recommendation algorithms, including Baseline Predictor, KNN (k-nearest-neighbors) and SVD (Singular Value Decomposition). Results also verifies that visual information does help relieve the “cold-start” problem in recommendation.