Development of a nanomechanical biosensor for analysis of endocrine disrupting chemicals

A nanomechanical transducer is developed to detect and screen endocrine disrupting chemicals (EDCs) combining fluidic sample injection and delivery with bioreceptor protein functionalized microcantilevers (MCs). The adverse affects of EDCs on the endocrine system of humans, livestock, and wildlife provides strong motivation for advances in analytical detection and monitoring techniques. The combination of protein receptors, which include estrogen receptor alpha (ER-alpha) and estrogen receptor beta (ER-beta), as well as monoclonal antibodies (Ab), with MC systems employing modified nanostructured surfaces provides for excellent nanomechanical response sensitivity and the inherent selectivity of biospecific receptor-EDC interactions. The observed ranking of binding interaction of the tested EDCs with ER-beta is diethylstilbestrol (DES) > 17-beta-estradiol > 17-alpha-estradiol > 2-OH-estrone > bisphenol A > p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) with measurements exhibiting intra-day RSDs of about 3%. A comparison of responses of three EDCs, which include 17-beta-estradiol, 17-alpha-estradiol, and 2-OH-estrone, with ER-beta and ER-alpha illustrates which estrogen receptor subtype provides the greatest sensitivity. Antibodies specific to a particular EDC can also be used for analyte specific screening. Calibration plots for a MC functionalized with anti-17-beta-estradiol Ab show responses in the range of 1 x 10(-11) through 1 x 10(-7) M for 17-beta-estradiol with a linear portion extending over two orders of magnitude in concentration.     

Kinetic study of the reactions of Cl atoms with CF3CH2CH2OH, CF3CF2CH2OH, CHF2CF2CH2OH, and CF3CHFCF2CH2OH

The reaction kinetics of chlorine atoms with a series of partially fluorinated straight-chain alcohols, CF(3)CH(2)CH(2)OH (1), CF(3)CF(2)CH(2)OH (2), CHF(2)CF(2)CH(2)OH (3), and CF(3)CHFCF(2)CH(2)OH (4), were studied in the gas phase over the temperature range of 273-363 K by using very low-pressure reactor mass spectrometry. The absolute rate coefficients were given by the expressions (in cm(3) molecule(-1) s(-1)): k(1) = (4.42 +/- 0.48) x 10(-11) exp(-255 +/- 20/T); k(1)(303) = (1.90 +/- 0.17) x 10(-11), k(2) = (2.23 +/- 0.31) x 10(-11) exp(-1065 +/- 106/ T); k(2)(303) = (6.78 +/- 0.63) x 10(-13), k(3) = (8.51 +/- 0.62) x 10(-12) exp(-681 +/- 72/T); k(3)(303) = (9.00 +/- 0.82) x 10(-13) and k(4) = (6.18 +/- 0.84) x 10(-12) exp(-736 +/- 42/T); k(4)(303) = (5.36 +/- 0.51) x 10(-13). The quoted 2sigma uncertainties include the systematic errors. All title reactions proceed via a hydrogen atom metathesis mechanism leading to HCl. Moreover, the oxidation of the primarily produced radicals was investigated, and the end products were the corresponding aldehydes (R(F)-CHO; R(F) = -CH(2)CF(3), -CF(2)CF(3), -CF(2)CHF(2), and -CF(2)CHFCF(3)), providing a strong experimental indication that the primary reactions proceed mainly via the abstraction of a methylenic hydrogen adjacent to a hydroxyl group. Finally, the bond strengths and ionization potentials for the title compounds were determined by density functional theory calculations, which also suggest that the alpha-methylenic hydrogen is mainly under abstraction by Cl atoms. The correlation of room-temperature rate coefficients with ionization potentials for a set of 27 molecules, comprising fluorinated C2-C5 ethers and C2-C4 alcohols, is good with an average deviation of a factor of 2, and is given by the expression log(k) (in cm(3) molecule(-1) s(-1)) = (5.8 +/- 1.4) - (1.56 +/- 0.13) x (ionization potential (in eV)).     

Information Bottleneck Classification in Extremely Distributed Systems

We present a new decentralized classification system based on a distributed architecture. This system consists of distributed nodes, each possessing their own datasets and computing modules, along with a centralized server, which provides probes to classification and aggregates the responses of nodes for a final decision. Each node, with access to its own training dataset of a given class, is trained based on an auto-encoder system consisting of a fixed data-independent encoder, a pre-trained quantizer and a class-dependent decoder. Hence, these auto-encoders are highly dependent on the class probability distribution for which the reconstruction distortion is minimized. Alternatively, when an encoding–quantizing–decoding node observes data from different distributions, unseen at training, there is a mismatch, and such a decoding is not optimal, leading to a significant increase of the reconstruction distortion. The final classification is performed at the centralized classifier that votes for the class with the minimum reconstruction distortion. In addition to the system applicability for applications facing big-data communication problems and or requiring private classification, the above distributed scheme creates a theoretical bridge to the information bottleneck principle. The proposed system demonstrates a very promising performance on basic datasets such as MNIST and FasionMNIST.     

Development and validation of an LC–MS/MS method for the quantitative analysis of the adenosine A2a receptor antagonist NIR178 and its monohydroxy metabolite in human plasma: Application to clinical pharmacokinetics

We report a selective LC–MS/MS method for the simultaneous quantitative determinations of the adenosine A2a receptor antagonist NIR178 (NIR178) and its major metabolite NJI765 in human plasma. Sample preparation steps involved protein precipitation, sample evaporation and reconstitution using a plasma sample volume of 0.1 ml plasma. Separation was achieved in 10 min on an Acquity UPLC BEH C₁₈ 1.7 μm, 2.1 × 50 mm column heated at 60°C with a gradient elution at 0.6 ml/min mobile phase made of water and acetonitrile both acidified with 0.1% formic acid. The detection was performed in positive ion mode and quantification based on multiple reaction monitoring. The linear response range was 1.00–1,000 ng/ml using a 1/x² weighting factor. The intra- and inter-day accuracies (bias %) and intra- and inter-day precisions (CV, %) obtained for NIR178 and NJI765 were within the acceptance criteria. The normalized NIR178 and NJI765 matrix factor calculated from six lots of normal, lipemic and hemolyzed plasmas ranged from 0.97 to 1.05. The normalized recoveries of both NIR178 and NJI765 compared with their internal standards were consistent and reproducible with a CV ≤8.0. This method was successfully applied to support pharmacokinetic studies in adult patients with cancer.     

On the Hamming distance in combinatorial optimization problems on hypergraph matchings

In this note we consider the properties of the Hamming distance in combinatorial optimization problems on hypergraph matchings, also known as multidimensional assignment problems. It is shown that the Hamming distance between feasible solutions of hypergraph matching problems can be computed as an optimal value of linear assignment problem. For random hypergraph matching problems, an upper bound on the expected Hamming distance to the optimal solution is derived, and an exact expression is obtained in the special case of multidimensional assignment problems with 2 elements in each dimension.     

Uniform parallel machine scheduling problems with fixed machine cost

This paper considers two uniform parallel machine scheduling problems with fixed machine cost under the background of cloud manufacturing. The goal is to minimize the makespan with a given budget of total cost, \(\hat{U}\). All the jobs are homogeneous, i.e., the processing times of the jobs are identical. Non-preemptive and preemptive problems are studied. For the non-preemptive problem, we give a \(2[1+1{/}(h-1)]\)-approximation algorithm, where h is the number of the machine which can not be selected the first time. For the preemptive problem, we give an algorithm whose worst-case bound equals to \(1+1{/}(h-1)\). Preliminary experimental results indicate that the proposed algorithms are reasonably accurate compared with the lower bounds.     

Parallel-batching scheduling of deteriorating jobs with non-identical sizes and rejection on a single machine

This paper studies the bounded parallel-batching scheduling problem considering job rejection, deteriorating jobs, setup time, and non-identical job sizes.     

Molecular Simulation of Fibronectin Adsorption onto Polyurethane Surfaces

Poly(ethylene glycol)-based polyurethanes have been widely used in biomedical applications; however, they are prone to swelling. A natural polyol, castor oil, can be incorporated into these polyurethanes to control the degree of the swelling, which alters mechanical properties and protein adsorption characteristic of the polymers. In this work, we modeled poly(ethylene glycol) and castor oil copolymers of hexamethylene diisocyanate-based polyurethanes (PEG-HDI and CO-HDI, respectively) and compared their mechanisms for fibronectin adsorption using molecular mechanics and molecular dynamics simulations. Results showed that the interplay between the hydrophobic residues concentrated at the N-terminal end of the protein, the surface roughness, and the hydrophilicity of the polymer surface determine the overall protein adsorption affinity. Incorporating explicit water molecules in the simulations results in higher affinity for fibronectin adsorption to more hydrophobic surface of CO-HDI surfaces, emphasizing the role that water molecules play during adsorption. We also observed that the strain energies that are indicative of flexibility and consequently entropy are significantly affected by the changes in the patterns of β-sheet formation/breaking. Our study lends supports to the view that while castor oil controls the degree of swelling, it increases the adsorption of fibronectin to a limited extent due to the interplay between its hydrophobicity and its surface roughness, which needs to be taken into account during the design of polyurethane-based biomaterials.     

Robust portfolio optimization: a conic programming approach

The Markowitz Mean Variance model (MMV) and its variants are widely used for portfolio selection. The mean and covariance matrix used in the model originate from probability distributions that need to be determined empirically. It is well known that these parameters are notoriously difficult to estimate. In addition, the model is very sensitive to these parameter estimates. As a result, the performance and composition of MMV portfolios can vary significantly with the specification of the mean and covariance matrix. In order to address this issue we propose a one-period mean-variance model, where the mean and covariance matrix are only assumed to belong to an exogenously specified uncertainty set. The robust mean-variance portfolio selection problem is then written as a conic program that can be solved efficiently with standard solvers. Both second order cone program (SOCP) and semidefinite program (SDP) formulations are discussed. Using numerical experiments with real data we show that the portfolios generated by the proposed robust mean-variance model can be computed efficiently and are not as sensitive to input errors as the classical MMV??s portfolios.     

A GMDH-based fuzzy modeling approach for constructing TS model

In this paper, a new learning algorithm based on group method of data handling (GMDH) is proposed for the identification of Takagi-Sugeno fuzzy model. Different from existing methods, the new approach, called TS-GMDH, starts from simple elementary TS fuzzy models, and then uses the mechanism of GMDH to produce candidate fuzzy models of growing complexity until the TS model of optimal complexity has been created. The main characteristic of the new approach is its ability to identify the structure of TS model automatically. Experiments on Box-Jenkins gas furnace data and UCI datasets have shown that the proposed method can achieve satisfactory results and is more robust to noise in comparison with other TS modeling techniques such as ANFIS.