Computer Simulation Study on Adsorption and Conformation of Polymer Chains Driven by External Force

In this work, Monte Carlo simulations are used to study the critical adsorption behaviors of flexible polymer chains under the action of an external driving force F parallel to an attractive flat surface. The critical adsorption temperature T_c decreases linearly with increasing F,indicating that the driving force suppresses the adsorption of polymer. The conformation of polymer is also affected by the driving force.However, the effect of F is dependent on the competition between the driving force and temperature. Under strong force or at low temperature,the polymer is stretched along the direction of the force, while under weak force or at high temperature, the polymer is not stretched. When the force is comparable to the temperature, the polymer may be stretched perpendicular to the driving force, and below T_c, we observe conformational transitions from parallel to perpendicular and again to parallel by decreasing the temperature. We found that the perpendicular stretched conformation leads the polymer chain to synchronously move along the direction of the driving force. Moreover, the conformational transitions are attributed to the competition and cooperation between the driving force and the temperature.     

Contradictory effect of gold nanoparticle-decorated molybdenum sulfide nanocomposites on amyloid-β-40 aggregation

The effect of gold nanoparticle-decorated molybdenum sulfide (AuNP-MoS₂) nanocomposites on amyloid-β-40 (Aβ₄₀) aggregation was investigated. The interesting discovery was that the effect of AuNP-MoS₂ nanocomposites on Aβ₄₀ aggregation was contradictory. Low concentration of AuNP-MoS₂ nanocomposites could enhance the nucleus formation of Aβ₄₀ peptides and accelerate Aβ₄₀ fibrils aggregation. However, although high concentration of AuNP-MoS₂ nanocomposites could enhance the nucleus formation of Aβ₄₀ peptides, it eventually inhibited Aβ₄₀ aggregation process. It might be attributed to the interaction between AuNP-MoS₂ nanocomposites and Aβ₄₀ peptides. For low concentration of AuNP-MoS₂ nanocomposites, it was acted as nuclei, resulting in the acceleration of the nucleation process. However, the structural flexibility of Aβ₄₀ peptides was limited as the concentration of AuNP-MoS₂ nanocomposites was increased, resulting in the inhibition of Aβ₄₀ aggregation. These findings suggested that AuNP-MoS₂ nanocomposites might have a great potential to design new multifunctional material for future treatment of amyloid-related diseases.     

Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.     

Rapid and label-free classification of pathogens based on light scattering,reduced power spectral features and support vector machine

The rapid identification of pathogens is crucial in controlling the food quality and safety. The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes. The clinical prototype consists of three parts: the laser beam, photodetectors, and the data acquisition system. The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber. When the bacterial microbes pass by the laser beam, the scattering of light occurs due to variation in size, shape, and morphology. Due to this reason, different types of pathogens show their unique light scattering patterns. The photo-detectors were arranged at the surroundings of the sample at different angles to collect the scattered light. The photodetectors convert the scattered light intensity into a voltage waveform. The waveform features were acquired by using the power spectral characteristics, and the dimensionality of extracted features was reduced by applying minimal-redundancy-maximal-relevance criterion (mRMR). A support vector machine (SVM) classifier was developed by training the selected power spectral features for the classification of three different bacterial microbes. The resulting average identification accuracies of E. faecalis,E. coli and S. aureus were 99%, 87%, and 94%, respectively. The overall experimental results yield a higher accuracy of 93.6%, indicating that the proposed device has the potential for label-free identification of pathogens with simplicity, rapidity, and cost-effectiveness.     

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data

To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of supervised machine learning methods is limited by lacking of experimentally-validated uncorrelated miRNA-disease pairs. Without these negative samples, training a highly accurate model is much more difficult. Different from traditional miRNA-disease prediction models using randomly selected unknown samples as negative training samples, we propose an ensemble learning framework to solve this positive-unlabeled (PU) learning problem. The framework incorporates two steps, i.e., a novel semi-supervised Kmeans (SS-Kmeans) to extract reliable negative samples from unknown miRNA-disease pairs and subagging method to generate diverse training sample sets to make full use of those reliable negative samples for ensemble learning. Combined with effective random vector functional link (RVFL) network as prediction model, the proposed framework showed superior prediction accuracy comparing with other popular approaches. A case study on lung and gastric neoplasms further confirms the framework’s efficacy at identifying miRNA disease associations.     

Driving Organic Nanocrystals Dissolution Through Electrochemistry

We have recently discussed how organic nanocrystal dissolution appears in different morphologies and the role of the solution pH in the crystal detriment process. We also highlighted the role of the local molecular chemistry in porphyrin nanocrystals having comparable structures: in water-based acid solutions, protonation of free-base porphyrin molecules is the driving force for crystal dissolution, whereas metal (Zn^II) porphyrin nanocrystals remain unperturbed. However, all porphyrin types, having an electron rich π-structure, can be electrochemically oxidized. In this scenario, a key question is: does electrochemistry represent a viable strategy to drive the dissolution of both free-base and metal porphyrin nanocrystals? In this work, by exploiting electrochemical atomic force microscopy (EC-AFM), we monitor in situ and in real time the dissolution of both free-base and metal porphyrin nanocrystals, as soon as molecules reach the oxidation potential, showing different regimes according to the applied EC potential.     

Red Blood Cell Stiffness and Adhesion Are Species-Specific Properties Strongly Affected by Temperature and Medium Changes in Single Cell Force Spectroscopy

Besides human red blood cells (RBC), a standard model used in AFM-single cell force spectroscopy (SCFS), little is known about apparent Young’s modulus (Ea) or adhesion of animal RBCs displaying distinct cellular features. To close this knowledge gap, we probed chicken, horse, camel, and human fetal RBCs and compared data with human adults serving as a repository for future studies. Additionally, we assessed how measurements are affected under physiological conditions (species-specific temperature in autologous plasma vs. 25 °C in aqueous NaCl solution). In all RBC types, Ea decreased with increasing temperature irrespective of the suspension medium. In mammalian RBCs, adhesion increased with elevated temperatures and scaled with reported membrane sialic acid concentrations. In chicken only adhesion decreased with higher temperature, which we attribute to the lower AE-1 concentration allowing more membrane undulations. Ea decreased further in plasma at every test temperature, and adhesion was completely abolished, pointing to functional cell enlargement by adsorption of plasma components. This halo elevated RBC size by several hundreds of nanometers, blunted the thermal input, and will affect the coupling of RBCs with the flowing plasma. The study evidences the presence of a RBC surface layer and discusses the tremendous effects when RBCs are probed at physiological conditions.     

Interactions of DNA with graphene and sensing applications of graphene field-effect transistor devices: A review

Graphene field-effect transistors (GFET) have emerged as powerful detection platforms enabled by the advent of chemical vapor deposition (CVD) production of the unique atomically thin 2D material on a large scale. DNA aptamers, short target-specific oligonucleotides, are excellent sensor moieties for GFETs due to their strong affinity to graphene, relatively short chain-length, selectivity, and a high degree of analyte variability. However, the interaction between DNA and graphene is not fully understood, leading to questions about the structure of surface-bound DNA, including the morphology of DNA nanostructures and the nature of the electronic response seen from analyte binding. This review critically evaluates recent insights into the nature of the DNA graphene interaction and its affect on sensor viability for DNA, small molecules, and proteins with respect to previously established sensing methods. We first discuss the sorption of DNA to graphene to introduce the interactions and forces acting in DNA based GFET devices and how these forces can potentially affect the performance of increasingly popular DNA aptamers and even future DNA nanostructures as sensor substrates. Next, we discuss the novel use of GFETs to detect DNA and the underlying electronic phenomena that are typically used as benchmarks for characterizing the analyte response of these devices. Finally, we address the use of DNA aptamers to increase the selectivity of GFET sensors for small molecules and proteins and compare them with other, state of the art, detection methods.     

Measurement and modeling on hydrodynamic forces and deformation of an air bubble approaching a solid sphere in liquids

The interaction between bubbles and solid surfaces is central to a broad range of industrial and biological processes. Various experimental techniques have been developed to measure the interactions of bubbles approaching solids in a liquid. A main challenge is to accurately and reliably control the relative motion over a wide range of hydrodynamic conditions and at the same time to determine the interaction forces, bubble–solid separation and bubble deformation. Existing experimental methods are able to focus only on one of the aspects of this problem, mostly for bubbles and particles with characteristic dimensions either below 100 μm or above 1 cm. As a result, either the interfacial deformations are measured directly with the forces being inferred from a model, or the forces are measured directly with the deformations to be deduced from the theory. The recently developed integrated thin film drainage apparatus (ITFDA) filled the gap of intermediate bubble/particle size ranges that are commonly encountered in mineral and oil recovery applications. Equipped with side-view digital cameras along with a bimorph cantilever as force sensor and speaker diaphragm as the driver for bubble to approach a solid sphere, the ITFDA has the capacity to measure simultaneously and independently the forces and interfacial deformations as a bubble approaches a solid sphere in a liquid. Coupled with the thin liquid film drainage modeling, the ITFDA measurement allows the critical role of surface tension, fluid viscosity and bubble approach speed in determining bubble deformation (profile) and hydrodynamic forces to be elucidated. Here we compare the available methods of studying bubble–solid interactions and demonstrate unique features and advantages of the ITFDA for measuring both forces and bubble deformations in systems of Reynolds numbers as high as 10. The consistency and accuracy of such measurement are tested against the well established Stokes–Reynolds–Young–Laplace model. The potential to use the design principles of the ITFDA for fundamental and developmental research is demonstrated.     

Hydrophilic interaction liquid chromatography method for measuring the composition of aquatic humic substances

A hydrophilic interaction liquid chromatography (HILIC) method was developed to measure the composition of humic substances from river, reservoir, and treated wastewater based on their physicochemical properties. The current method fractionates the humic substances into four well-defined groups based on parallel analyses with a neutral and a cationic HILIC column, using mobile phases of varied compositions and pH. The results indicate that: (i) the proportion of carboxylic acids in the humic substances from terrestrial origins is less than half of that from treated wastewater (Jeddah, KSA), (ii) a higher content of basic compounds was observed in the humic substances from treated wastewater and Ribou Reservoir (Cholet, France) than in the sample from Loire River (France), (iii) a higher percentage of hydrophobic macromolecules were found in the humic substances from Loire River than in the other samples, and (iv) humic substances of treated wastewater contained less ionic neutral compounds (i.e., pK_a 5–9) than the waters from terrestrial origins. The physicochemical property disparity amongst the compounds in each humic substances sample was also evaluated. The humic substances from the lightly humic Loire river displayed the highest disparity, whereas the highly humic Suwannee river (Georgia, USA) showed the most homogeneous humic substances.