Miracle-dz/UniFAR-FTUs huggingface dataset:
5-Methyl-1-(3-nitrobenzyl)-1H-1,2,3-triazole-4-carboxylic acid monohydrate

The title compound, C11H10N4O4·H2O, was synthesized from 1-azidomethyl-3-nitrobenzene and ethyl acetylacetate. Single-crystal X-ray analysis reveals that the dihedral angle between the triazole and benzene ring planes is 84.80 (2)°. The packing of the molecules is stabilized by strong O—H⋯O hydrogen bonds involving the solvent water molecules as donors and acceptors. The resulting layers are arranged into a three-dimensional framework via weak C—H⋯O interactions.
Automatic Multi-Layer Corpus Annotation for Evaluation Question Answering Methods: CBC4Kids

Reading comprehension tests are receiving increased attention within the NLP community as a controlled test-bed for developing, evaluating and comparing robust question answering (NLQA) methods. To support this, we have enriched the MITRE CBC4Kids corpus with multiple XML annotation layers recording the output of various tokenizers, lemmatizers, a stemmer, a semantic tagger, POS taggers and syntactic parsers. Using this resource, we have built a baseline NLQA system for wordoverlap based answer retrieval.
Clustering analyses in peptidomics revealed that peptide profiles of infant formulae are descriptive

Prompted by the accumulating evidence on bioactive moieties of milk-derived peptides, novel methods were applied to compare the peptide composition among commercially available hydrolysate formulations and to determine batch-to-batch variations of protein hydrolysate products. Despite the availability of general methods to measure, for example, the degree of hydrolysis and peptide mass distribution at a high level, the objective of this study was to more qualitatively compare peptide sequences and composition. By a comprehensive approach combining peptidomics technologies and multivariate clustering analyses, the peptide profiles of different hydrolyzed milk protein formulations were compared. Moreover, peptide profiles of various hydrolysate batches that had been produced over a period of 5 years were included. Coupling of identified peptide sequences to the position in their corresponding milk proteins produced numerical datasets that subsequently were utilized for multivariate data analyses. These analyses revealed that batch-to-batch variation in the peptide profiles of a specific extensively hydrolyzed casein preparation was low. Moreover, extensive multivariate evaluations revealed that the peptide profiles of different commercially available hydrolyzed milk protein formulations provided a descriptive and distinct signature. Overall, the described methodology may contribute to the field of peptide research as observed dissimilarities in peptide profiles of similar products may be related to differences in their overall functionality.
Estimation of DSGE Models When the Data are Persistent

Dynamic Stochastic General Equilibrium (DSGE) models are often solved and estimated under specific assumptions as to whether the exogenous variables are difference or trend stationary. However, even mild departures of the data generating process from these assumptions can severely bias the estimates of the model parameters. This paper proposes new estimators that do not require researchers to take a stand on whether shocks have permanent or transitory effects. These procedures have two key features. First, the same filter is applied to both the data and the model variables. Second, the filtered variables are stationary when evaluated at the true parameter vector. The estimators are approximately normally distributed not only when the shocks are mildly persistent, but also when they have near or exact unit roots. Simulations show that these robust estimators perform well especially when the shocks are highly persistent yet stationary. In such cases, linear detrending and first differencing are shown to yield biased or imprecise estimates.
An Efficient Compromised Node Revocation Scheme in Fog-Assisted Vehicular Crowdsensing

In this paper, we propose an efficient compromised node revocation scheme for enhancing security in road surface condition monitoring system (RSCMS) using fog computing. On the basis of certificateless aggregate signcryption scheme (CLASC), a data transmission protocol for monitoring road surface conditions is designed with security properties including reports confidentiality, integrity, mutual authenticity, privacy, revocation functionality and key escrow resilience. Extensive simulations are conducted to validate the proposed protocol. It is demonstrated that the proposed protocol outperfroms counterparts in terms of scalability, user revocation and signature verification efficiency.
Controlled drug delivery from biomaterials used for cardiac repair

The continued risk of death due to heart failure has spurred the advancement of biomaterials-based treatments for cardiac repair, two of which are the application of a cardiac patch or intramyocardial material injection. The inclusion of signaling molecules in these materials can improve the treatment results; however, appropriate molecule delivery is not trivial. This research presents the broad application of therapeutics from both a patch and injectable system to demonstrate how system properties influence drug delivery and how these systems may be engineered to provide added benefit in the desired application. We demonstrated the ability to release a gene-inducing molecule, Rheoswitch ligand 1 (RSL1), from a biodegradable elastomer that has been used as a cardiac patch. Not only was bioactive RSL1 released over an extended period, the ability to differentially load scaffolds with RSL1 allowed for spatial patterning of gene expression in cells cultured on the scaffold surface, which has implications for creating complex 3D tissues in vitro. This same elastomeric material was able to release bioactive insulin-like growth factor 1 (IGF1) and hepatocyte growth factor (HGF) in vitro. The complex release behavior of IGF1 into saline was replaced by a much simpler release profile during simulated in vivo degradation, demonstrating that the implant environment must be considered when studying drug delivery behavior. A strong, thermoresponsive and degradable hydrogel was developed for intramyocardial injection. We showed the release rates of a model protein from this gel, or from protein-loaded microparticles inside the gel, could be controlled by changing the material composition. The combination of hydrogel with microparticles delivered two proteins in a sequential manner. We further used this system to release bioactive basic fibroblast growth factor (bFGF) followed by IGF1 in vitro. Injection of the unloaded polymer into infarcted rat hearts was able to improve the remodeling process for at least 16 weeks. While increased tissue bFGF and IGF1 were demonstrated following injection of growth factor-loaded gels, there were no clear benefits seen from protein inclusion. This points to the primary ability of the hydrogel alone to benefit cardiac function and cellular environment and warrants further investigation.
Face Image Analysis using AAM, Gabor, LBP and WD features for Gender, Age, Expression and Ethnicity Classification

The growth in electronic transactions and human machine interactions rely on the information such as gender, age, expression and ethnicity provided by the face image. In order to obtain these information, feature extraction plays a major role. In this paper, retrieval of age, gender, expression and race information from an individual face image is analysed using different feature extraction methods. The performance of four major feature extraction methods such as Active Appearance Model (AAM), Gabor wavelets, Local Binary Pattern (LBP) and Wavelet Decomposition (WD) are analyzed for gender recognition, age estimation, expression recognition and racial recognition in terms of accuracy (recognition rate), time for feature extraction, neural training and time to test an image. Each of this recognition system is compared with four feature extractors on same dataset (training and validation set) to get a better understanding in its performance. Experiments carried out on FG-NET, Cohn-Kanade, PAL face database shows that each method has its own merits and demerits. Hence it is practically impossible to define a method which is best at all circumstances with less computational complexity. Further, a detailed comparison of age estimation and age estimation using gender information is provided along with a solution to overcome aging effect in case of gender recognition. An attempt has been made in obtaining all (i.e. gender, age range, expression and ethnicity) information from a test image in a single go.
Trehalose‐6‐phosphate synthase from the cat flea Ctenocephalides felis and Drosophila melanogaster: gene identification, cloning, heterologous functional expression and identification of inhibitors by high throughput screening

Trehalose phosphate synthase (EC 2.4.1.15; TPS) is the crucial enzyme for the biosynthesis of trehalose, the main haemolymph sugar of insects, and therefore a potential insecticidal molecular target. In this study, we report the functional heterologous expression of Drosophila melanogaster TPS, the gene identification, full length cDNA cloning and functional expression of cat flea (Ctenocephalides felis) TPS, and the Michaelis–Menten constants for their specific substrates glucose‐6‐phosphate and uridinediphosphate‐glucose. A novel high throughput screening‐compatible TPS assay and its use for the identification of the first potent insect TPS inhibitors from a large synthetic compound collection (>115 000 compounds) is described. One compound class that emerged in this screening, the 4‐substituted 2,6‐diamino‐3,5‐dicyano‐4H‐thiopyrans, was further investigated by analysing preliminary structure–activity relationships. Here, compounds were identified that show low µM to high nM half maximal inhibitory concentrations on insect TPS and that may serve as lead compounds for the development of insecticides with a novel mode of action.
Machine‐Assisted Organic Synthesis

Abstract In this Review we describe how the advent of machines is impacting on organic synthesis programs, with particular emphasis on the practical issues associated with the design of chemical reactors. In the rapidly changing, multivariant environment of the research laboratory, equipment needs to be modular to accommodate high and low temperatures and pressures, enzymes, multiphase systems, slurries, gases, and organometallic compounds. Additional technologies have been developed to facilitate more specialized reaction techniques such as electrochemical and photochemical methods. All of these areas create both opportunities and challenges during adoption as enabling technologies.
TACI expression is associated with a mature bone marrow plasma cell signature and C-MAF overexpression in human myeloma cell lines.

BACKGROUND AND OBJECTIVES BAFF and APRIL stimulate the growth of multiple myeloma (MM) cells. BAFF and APRIL share two receptors--TACI and BCMA--and BAFF binds to a third receptor, BAFF-R. We previously reported that TACI gene expression is bimodal in 18 human MM cell lines (HMCL), being either present or absent, unlike BCMA that is expressed on all HMCL. BAFF-R is lacking. TACI expression is a good indicator of a BAFF-binding receptor in HMCL. In primary MM cells, the level of TACI expression correlates with a characteristic phenotypic pattern: TACIhighMM cells resemble bone marrow plasma cells and TACIlow resemble plasmablasts. The aim of this study was to further characterize the role of TACI expression in MM DESIGN AND METHODS: Using gene expression profiling, we investigated whether these patterns are kept in TACI+ or TACI- HMCL. RESULTS Eighty genes/EST interrogated by Affymetrix microarrays were differentially expressed between TACI+ and TACI- HMCL, particularly c-maf, cyclin D2, and integrin beta7. Triggered by the finding that TACI and c-maf expressions correlate in TACI+ HMCL, we demonstrated that TACI activation influences c-maf expression: (i) activation of TACI by BAFF or APRIL increases c-maf, cyclin D2, and integrin beta7 gene expressions in TACI+ HMCL, (ii) blocking of autocrine BAFF/APRIL stimulation in some TACI+ HMCL by the TACI-Fc fusion protein reduces c-maf, cyclin D2, and integrin beta7 gene expression, (iii) nucleofection of siRNA to c-maf decreases c-maf mRNA levels and reduces the expression of cyclin D2 and integrin beta7 gene expressions, without affecting TACI expression INTERPRETATION AND CONCLUSIONS We conclude that TACI activation can upregulate c-maf expression which, in turn, controls cyclin D2, and integrin beta7 gene expression.
Online Scheduling with Lookahead: Multipass Assembly Lines

This article describes our use of competitive analysis and the on-line model of computation in a product development setting; specifically, we use competitive analysis to evaluate on-line scheduling strategies for controlling a new generation of networked reprographic machines (combination printer-copier-fax machines servicing a network) currently being developed by companies such as Xerox Corporation. We construct an abstract machine model, the multipass assembly line, which not only models networked reprographic machines but also models several common manufacturing environments such as a robotic assembly line or a mixed product assembly line. We consider on-line algorithms with finite lookahead because these machines typically have limited knowledge of the future. We first prove some lower bounds on the performance of any online algorithm with finite lookahead. We then show that simple greedy algorithms achieve competitive ratios that are close to these general lower bounds. In particular, we show that lookahead improves the competitive ratio of these simple greedy algorithms from approximately 2 (with no lookahead) to being arbitrarily close to 1 (for large lookahead). This implies these simple greedy algorithms are realistic candidates for field use in future reprographic products.
Modification of Protein Structure and Functionality Using High-Intensity Ultrasound

The objective of this study was to determine the effect of high intensity ultrasound on the adsorption kinetics of proteins. Protein solutions (3x10 M ) were treated with ultrasound at power intensity levels ranging from 5 to 35 Wcm and at temperatures ranging from 20 to 85 ÆC with treatment times ranging from 15 to 45 minutes. Also, the effect of pulsing versus continuous application on the ultrasound treatment was evaluated. Surface activity (adsorption and coverage) of proteins was investigated using a drop shape analysis tensiometer. The study consisted of three major parts. The results of the first part of this study showed that application of high intensity ultrasound increased the rate of adsorption of bovine serum albumin (BSA) at the air-water interface. The rate of adsorption increased as the sonication time increased. Ultrasound treatment increased the helical content as measured by CD and surface hydrophobicity measured using fluorescence spectroscopy. In the second part of the study, it was observed that the effect of ultrasound on the adsorption kinetics of BSA was related to the ultrasonic power level and temperature. CD data showed that thermal treatment caused a loss in helical content while ultrasound increased the helical content of BSA. When heat and ultrasound were applied together the loss in helical content of BSA was less pronounced than the loss caused by thermal treatment alone. In the third part of the study, ultrasound susceptibility of the food proteins, BSA, fatty acid free BSA, -lactoglobulin and lysozyme was investigated using drop shape analysis and CD. Lysozyme was the most sensitive protein studied followed by BSA, fatty acid free BSA and -lactoglobulin. The difference in the susceptibility of these proteins was attributed to the differences in their amino acid composition, molecular size and rigidity and secondary structure. The effect of ultrasound on the interfacial properties protein probably is due to the increased flexibility of protein molecules on small length scales. Dimerization of protein molecules with the newly exposed hydrophobic groups is another possible explanation.
Structural and vibrational study of Bi 2 Se 3 under high pressure

The structural and vibrational properties of bismuth selenide (Bi${}_{2}$Se${}_{3}$) have been studied by means of x-ray diffraction and Raman scattering measurements up to 20 and 30 GPa, respectively. The measurements have been complemented with ab initio total-energy and lattice dynamics calculations. Our experimental results evidence a phase transition from the low-pressure rhombohedral ($R$-3$m$) phase (\ensuremath{\alpha}-Bi${}_{2}$Se${}_{3}$) with sixfold coordination for Bi to a monoclinic $C$2/$m$ structure (\ensuremath{\beta}-Bi${}_{2}$Se${}_{3}$) with sevenfold coordination for Bi above 10 GPa. The equation of state and the pressure dependence of the lattice parameters and volume of \ensuremath{\alpha} and \ensuremath{\beta} phases of Bi${}_{2}$Se${}_{3}$ are reported. Furthermore, the presence of a pressure-induced electronic topological phase transition in \ensuremath{\alpha}-Bi${}_{2}$Se${}_{3}$ is discussed. Raman measurements evidence that Bi${}_{2}$Se${}_{3}$ undergoes two additional phase transitions around 20 and 28 GPa, likely toward a monoclinic $C$2/$c$ and a disordered body-centered cubic structure with 8-fold and 9- or 10-fold coordination, respectively. These two high-pressure structures are the same as those recently found at high pressures in Bi${}_{2}$Te${}_{3}$ and Sb${}_{2}$Te${}_{3}$. On pressure release, Bi${}_{2}$Se${}_{3}$ reverts to the original rhombohedral phase after considerable hysteresis. Symmetries, frequencies, and pressure coefficients of the Raman and infrared modes in the different phases are reported and discussed.
A divide-and-conquer approach with adaptive modulation to CDMA systems

A new divide-and-conquer scheme has been proposed with adaptive modulation in CDMA system to improve the spectrum efficiency of the wireless communication system. The proposed method results in increase in average throughput achieved and simultaneously decrease in the outage probability of the system when the user density is high. The proposed scheme is compared with the existing algorithms and the advantage of the scheme is evident from the results obtained.
Automatic Gender Detection Based on Characteristics of Vocal Folds for Mobile Healthcare System

An automatic gender detection may be useful in some cases of a mobile healthcare system. For example, there are some pathologies, such as vocal fold cyst, which mainly occur in female patients. If there is an automatic method for gender detection embedded into the system, it is easy for a healthcare professional to assess and prescribe appropriate medication to the patient. In human voice production system, contribution of the vocal folds is very vital. The length of the vocal folds is gender dependent; a male speaker has longer vocal folds than a female speaker. Due to longer vocal folds, the voice of a male becomes heavy and, therefore, contains more voice intensity. Based on this idea, a new type of time domain acoustic feature for automatic gender detection system is proposed in this paper. The proposed feature measures the voice intensity by calculating the area under the modified voice contour to make the differentiation between males and females. Two different databases are used to show that the proposed feature is independent of text, spoken language, dialect region, recording system, and environment. The obtained results for clean and noisy speech are 98.27% and 96.55%, respectively.
Nonparametric Estimation of Multi-View Latent Variable Models

Spectral methods have greatly advanced the estimation of latent variable models, generating a sequence of novel and efficient algorithms with strong theoretical guarantees. However, current spectral algorithms are largely restricted to mixtures of discrete or Gaussian distributions. In this paper, we propose a kernel method for learning multi-view latent variable models, allowing each mixture component to be nonparametric. The key idea of the method is to embed the joint distribution of a multi-view latent variable into a reproducing kernel Hilbert space, and then the latent parameters are recovered using a robust tensor power method. We establish that the sample complexity for the proposed method is quadratic in the number of latent components and is a low order polynomial in the other relevant parameters. Thus, our non-parametric tensor approach to learning latent variable models enjoys good sample and computational efficiencies. Moreover, the non-parametric tensor power method compares favorably to EM algorithm and other existing spectral algorithms in our experiments.
Structural and Biochemical Investigation of Bacteriophage N4-Encoded RNA Polymerases

Bacteriophage N4 regulates the temporal expression of its genome through the activity of three distinct RNA polymerases (RNAP). Expression of the early genes is carried out by a phage-encoded, virion-encapsidated RNAP (vRNAP) that is injected into the host at the onset of infection and transcribes the early genes. These encode the components of new transcriptional machinery (N4 RNAPII and cofactors) responsible for the synthesis of middle RNAs. Both N4 RNAPs belong to the T7-like “single-subunit” family of polymerases. Herein, we describe their mechanisms of promoter recognition, regulation, and roles in the phage life cycle.
Least-squares based feature extraction and sensor fusion for explosive detection

The effective and reliable detection of explosive compounds in complex environments is an important problem in many environment and security-related applications. This paper develops an explosive detection approach based on multi-modal sensing and sensor data fusion. A least-squares feature extraction technique is designed to isolate explosive signatures in data collected using electrochemical and polymer nanojunction sensors. The information obtained from the two sensors is then efficiently combined using a Bayesian decision fusion scheme. Results are presented for the detection of the explosive compound TNT showing the merit of the proposed approach.
Coalition Formation Games for Improving Data Delivery in Delay Tolerant Networks

Delay tolerant networks (DTNs) can be composed of multiple heterogeneous groups (i.e., communities) of nodes. The nodes from these communities can cooperate with each other in order to carry and forward data packets so that the performance (e.g., delay) can be improved. However, this cooperation will incur additional cost on the nodes. In this paper, we first develop an analytical model to investigate the performance gain from cooperation of multiple communities in a DTN. Then, we propose a coalitional game model for analyzing the cooperation decisions of multiple rational communities based on the tradeoff between performance gains and associated costs. As a solution to the proposed game, we determine the stable coalitional structure, i.e., the structure where no community can improve its payoff by changing its cooperation decisions. The proposed analytical and game models will be useful for the performance and cost optimization of multi-community DTNs.