A review on electronic bio-sensing approaches based on non-antibody recognition elements

In this review, recent advances in the development of electronic detection methodologies based on non-antibody recognition elements such as functional liposomes, aptamers and synthetic peptides are discussed. Particularly, we highlight the progress of field effect transistor (FET) sensing platforms where possible as the number of publications on FET-based platforms has increased rapidly. Biosensors involving antibody-antigen interactions have been widely applied in diagnostics and healthcare in virtue of their superior selectivity and sensitivity, which can be attributed to their high binding affinity and extraordinary specificity, respectively. However, antibodies typically suffer from fragile and complicated functional structures, large molecular size and sophisticated preparation approaches (resource-intensive and time-consuming), resulting in limitations such as short shelf-life, insufficient stability and poor reproducibility. Recently, bio-sensing approaches based on synthetic elements have been intensively explored. In contrast to existing reports, this review provides a comprehensive overview of recent advances in the development of biosensors utilizing synthetic recognition elements and a detailed comparison of their assay performances. Therefore, this review would serve as a good summary of the efforts for the development of electronic bio-sensing approaches involving synthetic recognition elements

Learning-based Nonlinear Model Predictive Control

© 2017 This paper presents stabilizing Model Predictive Controllers (MPC) in which prediction models are inferred from experimental data of the inputs and outputs of the plant. Using a nonparametric machine learning technique called LACKI, the estimated (possibly nonlinear) model function together with an estimation of Holder constant is provided. Based on these, a number of predictive controllers with stability guaranteed by design are proposed. Firstly, the case when the prediction model is estimated offline is considered and robust stability and recursive feasibility is ensured by using tightened constraints in the optimisation problem. This controller has been extended to the more interesting and complex case: the online learning of the model, where the new data collected from feedback is added to enhance the prediction model. An on-line learning MPC based on a double sequence of predictions is proposed.Spanish MINECO Grant PRX15-00300 and projects DPI2013-48243-C2-2-R and DPI2016-76493-C3-1-R. UK Engineering and Physical Research Council, grant no.EP/J012300/1

Penyelesaian Tindak Pidana Perjudian yang Dilakukan oleh Anak Menurut UU No.11 Tahun 2012

The title of this legal writing is "The Completion of the Crime of Gambling Carried Out by minors based on the law Number 11 of 2012 on the Juvenile Justice system". This type of research is normative legal research. Normative legal research is a research conducted or focusing on norm of positive law in the form of legislation. Legal issues raised is whether the completion of the crime of gambling by children is in conformity with the law Number 11 of 2012 about the juvenile justice system. The purpose of this research is to determine and analyze the completion of the crime of gambling by children under the law of the juvenile justice system. The result showed that the efforts made to prevent criminal acts of a child is an attempt preventive and repressive efforts. Juvenile justice system is closely related to restorative justice. Regarding the obligation to make a diversion conducted by law enforcement officials, in particular under Article 7 and 96 of the law number 11 of 2012 on the Juvenile Justice System

Table1_nPCA: a linear dimensionality reduction method using a multilayer perceptron.DOCX

Background: Linear dimensionality reduction techniques are widely used in many applications. The goal of dimensionality reduction is to eliminate the noise of data and extract the main features of data. Several dimension reduction methods have been developed, such as linear-based principal component analysis (PCA), nonlinear-based t-distributed stochastic neighbor embedding (t-SNE), and deep-learning-based autoencoder (AE). However, PCA only determines the projection direction with the highest variance, t-SNE is sometimes only suitable for visualization, and AE and nonlinear methods discard the linear projection.Results: To retain the linear projection of raw data and generate a better result of dimension reduction either for visualization or downstream analysis, we present neural principal component analysis (nPCA), an unsupervised deep learning approach capable of retaining richer information of raw data as a promising improvement to PCA. To evaluate the performance of the nPCA algorithm, we compare the performance of 10 public datasets and 6 single-cell RNA sequencing (scRNA-seq) datasets of the pancreas, benchmarking our method with other classic linear dimensionality reduction methods.Conclusion: We concluded that the nPCA method is a competitive alternative method for dimensionality reduction tasks.</p

Rare-Earth Metal Substitutions in Ca_9–<i>x</i><i>RE</i>_<i>x</i>Mn₄Sb₉ (<i>RE</i> = La–Nd, Sm; <i>x</i> ≈ 1). Synthesis and Characterization of a New Series of Narrow-Gap Semiconductors

This paper details the synthesis and the structural characterization of the new antimonides with general formulas Ca_9–<i>x</i>­<i>RE</i>_<i>x</i>­Mn₄Sb₉ (<i>RE</i> = La, Ce, Pr, Nd, and Sm; <i>x</i> ≈ 1). The synthesis of these phases was accomplished by both high temperature reactions of the respective elements and by Pb-flux experiments. The structures were determined by single-crystal X-ray diffraction methods. All title compounds are isostructural and crystallize with the orthorhombic space group <i>Pbam</i> (No. 55). The structure is similar, but not isotypic to Ca₉­Mn₄Bi₉ (same space group; Pearson code <i>oP</i>44). On the basis of that, Ca_9–<i>x</i>­<i>RE</i>_<i>x</i>­Mn₄Sb₉ can be considered as new <i>derivatives</i> of this structure type, presenting the first examples of rare-earth metal substitutions within the “9-4-9” family of compounds. Electrical resistivity measurements confirm the successful electron doping, achieved by the aliovalent replacement of Ca²⁺ with <i>RE</i>³⁺ cations in the structure, leading to the emergence of narrow band-gap semiconducting behavior. Temperature-dependent magnetization measurements indicate paramagnetic behavior and complex magnetic interactions

Estimation of Nanodiamond Surface Charge Density from Zeta Potential and Molecular Dynamics Simulations

Molecular dynamics simulations of nanoparticles (NPs) are increasingly used to study their interactions with various biological macromolecules. Such simulations generally require detailed knowledge of the surface composition of the NP under investigation. Even for some well-characterized nanoparticles, however, this knowledge is not always available. An example is nanodiamond, a nanoscale diamond particle with surface dominated by oxygen-containing functional groups. In this work, we explore using the harmonic restraint method developed by Venable et al., to estimate the surface charge density (σ) of nanodiamonds. Based on the Gouy–Chapman theory, we convert the experimentally determined zeta potential of a nanodiamond to an effective charge density (σ_eff), and then use the latter to estimate σ via molecular dynamics simulations. Through scanning a series of nanodiamond models, we show that the above method provides a straightforward protocol to determine the surface charge density of relatively large (> ∼100 nm) NPs. Overall, our results suggest that despite certain limitation, the above protocol can be readily employed to guide the model construction for MD simulations, which is particularly useful when only limited experimental information on the NP surface composition is available to a modeler

Metal-Free Catalytic Approach for Allylic C–H Amination Using <i>N</i>‑Heterocycles via sp³ C–H Bond Activation

A versatile metal-free synthesis of allylic <i>N</i>-heterocycles has been developed using a TBAI/TBHP oxidation system. This general protocol could be applied for the C–N bond formation of electron-deficient phthalimides, imidazoles, triazoles, and sulfonamides with cyclic and acylic olefins. The practical use of the method is demonstrated by the amidation of functionalized biologically active substrates

Time courses of responses of V1 neurons to rapidly changing contrasts.

<p>Responses were plotted as a function of time (left column) and contrast (right column). Panels <i>A</i>–<i>D</i> present two example cells. Panels <i>E</i> and <i>F</i> show the averaged response from a population of neurons (n = 101). <i>A</i>: Cell 1. Post-stimulus time histograms (PSTHs) were plotted for the 9 different contrast levels (different symbols). Each point is the averaged response that occurred within a 10-ms time window moving along the time axis in a step of 1 ms, here plotted every 4 ms for clarity. Each curve represents the responses to a single level of contrast. The responses were only plotted from 35 to 80 ms after stimulus onset for the clarity of viewing the changes that occurred during this time interval. <i>B</i>: The responses shown in <i>A</i> were plotted as a function of contrast at seven time points (different symbols) after the stimulus onset. <i>C</i> and <i>D</i>: Cell 2. <i>E</i> and <i>F</i>: Averaged data for the population of neurons. The conventions used are the same as those as in <i>A</i> and <i>B</i>. The PSTHs of each cell were normalized with their maximal response and aligned to their optimal latency (T_optimal of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0025410#pone-0025410-g003" target="_blank">Fig. 3A</a>; see Methods) before being averaged. The contrast response functions in <i>F</i> were plotted from PSTHs in <i>E</i> at seven time points.</p

Distribution of the parameters of contrast response functions in different ranges of contrast distributions.

<p>Distributions of <i>C₅₀</i>, <i>n</i>, and <i>R_max</i> are shown in the three columns and the Low, Medium, and High contrast ranges are shown in the three rows. The mean ± SD (n = 33) is indicated at the top of each panel. %: % of contrast. i/s: spikes/s.</p

Temporal characteristics of the variance curve of responses to different contrasts.

<p><i>A</i>: The variance curve of a typical cell. Optimal latency (T_optimal) is given by the peak of the curve. Peak width of the curve was defined as the time difference between T_decay and T_develop at which the variance reached half of the peak magnitude. <i>B</i>: The distribution of the optimal latencies of a population of neurons (n = 101). <i>C</i>: The distribution of the peak width. In both histograms, the mean is indicated by an arrow. <i>D</i>: Scatter plot showing the significant correlation between the optimal latency and peak width of the variance curves.</p