PSVII-20 Optimization of intestinal permeability assays to study the degree of fasting in gut permeability of unweaned Angus-Holstein bull calves

In-vivo assessments of intestinal permeability can be expensive and time consuming. Additionally, the correct choice of test molecules to use and the optimum sampling time under fasting situations needs be optimized. Fifteen unweaned Angus-Holstein bull calves (44.1 ± 2.0 kg and 14.7 ± 0.63 d) were randomly assigned to 1 of 3 treatments: Control (CT; n = 5): no fasting; fasted during 9 h (FAS9; n = 5); and fasted during 19 h (FAS19; n = 5). All calves were fed 2.5 L of MR and treatments were applied on d -1. Chromium-EDTA (Cr-EDTA), lactulose and D-mannitol were administered orally before blood collection. Samples were taken on d -4 and -1 before fasting and on d 0 and 2, at 60, 120, 180, and 240 minutes. To choose the best test molecule, correlations between serum concentration of the test molecules were used. To decide the optimum sampling time, data from d 0 were used to calculate area under the curve, then data were analyzed with mixed models with fasting degree and sampling time as fixed effects. Correlation between Cr-EDTA and D-mannitol was r= 0.92 (R2=84%), and correlation between Cr-EDTA and lactulose was r= 0.86 (R2=75%). Differences in AUC of Cr-EDTA between CT and t fasting treatments were observed up to 120 min. Differences in AUC of Cr-EDTA between FAS9 and FAS19 were observed later at 240 min. To optimize the intestinal permeability test, the use of only one test molecule might be sufficient. The Cr-EDTA was proposed to optimize the methodology due to price and simplicity of the analysis. The optimum sampling time after Cr-EDTA administration was 120 min when comparing fasting and control, and 240 min when comparing different fasting degrees.

Cellulose Beads as Novel Support of Copper Active Site for the Catalytic Abatement of Water Emerging Contaminant

Different regenerated cellulose (RC) beads were synthesized as supports of copper as the active site for catalytic degradation of emerging contaminants (ECs) in water. Starting from a commercial dissolving pulp, RC beads were prepared from the direct dissolution and from both cellulose carbamate and viscose solutions. Copper was added to the supports by a simple green method. The material characterization by FTIR, TGA, SEM and XPS confirmed the successful incorporation of copper in all the prepared supports. Phenol was adopted as EC test molecule, and catalytic wet peroxide oxidation (CWPO) at 70°C was used to analyse the Cu-cellulosic beads catalytic performance. The novelty and importance of preparing bead-shape catalysts with cellulose reside in the use of an economic, renewable and biodegradable matrix, and the simple separation of the structured catalyst from the heterogeneous solid/liquid reaction media.

Palladium Nanoparticles on Modified Cellulose as a Novel Catalyst for Low Temperature Gas Reactions

Palladium was incorporated into carboxymethylated cellulose fibers as a support, thereby becoming an efficient and stable catalyst for low temperature gas phase reaction. Thus, NO was used as test molecule of Greenhouse Gas to be catalytically reduced with hydrogen on an eco-friendly sustainable material containing palladium as the active site. Prior to the catalytic test, the catalysts were reduced with glucose as an eco-friendly reagent. The material characterization was performed by SEM-EDS, XRD, LRS, TGA and FTIR.The catalytic results showed that at 170°C, NO conversion was 100% with a selectivity of 70% to nitrogen. While NOX species were completely converted into N2 at temperatures higher than 180°C. The starting commercial material Solucell® was also studied, but its performance resulted lower than the ones of functionalized fibers.The use of this strategy, i.e., the functionalization of cellulose fibers followed by in-situ formation of metallic nanoparticles, can be further applied for the design of a wide range of materials with interesting applications for gas and liquid phase reactions under mild conditions.

Advanced Fiber-Coupled Diode Laser Sensor for Calibration-Free 1f-WMS Determination of an Absorption Line Intensity

A new scheme for a calibration-free diode laser absorption spectroscopy (DLAS) sensor for measuring the parameters of harsh zones is proposed. The key element of the scheme is a micro-prism retroreflector (MPRR). The MPRR facilitates an increase in the mechanical stability of the sensor and a decrease in the background thermal radiation in the hot areas of a tested zone. Reduction in the broadband thermal emission allowed the application of a differential logarithmic conversion (LC) technique for elimination of the residual amplitude modulation and other sources of non-selective attenuation of the probing laser beam. LC allows the use of a 1f-wavelength modulation spectroscopy (WMS) detection scheme. Combination of LC and a 1f-WMS algorithm provided a new modification of calibration-free DLAS, which could be particularly useful for probing harsh zones with pronounced strong turbulence and high levels of acoustic and electrical noise. The influence of the experimental parameters and characteristics of the main electronic components of the recording and processing system on the accuracy of the integral line intensity determination is investigated theoretically and experimentally. The proposed optical scheme of a DLAS sensor and algorithm for the data processing allowed the integral intensity of an absorption line to be obtained. The potential for the scheme was exemplified with a single water vapor absorption line at 7185.6 cm−1. Simultaneous detection of several absorption lines and data processing using the developed algorithm provides the final goal of a DLAS sensor—determination of temperature and partial pressure of a test molecule in a probed gas volume. The developed scheme allows the spatial multiplexing of the radiation of different diode lasers (DLs), which can be used if various test molecules are to be detected, or absorption lines of a test molecule are detected over different wavelength intervals.

Doping Titanium Dioxide with Palladiun for Enhancing thePhotocatalytic Decontamination and Mineralization of a Refractory Water Pollutant

The photocatalytic performance of a commercial TiO2 doped by incipient wet impregnation with palladium (sample named TiO2-Pd), was evaluated in the heterogeneous degradation process using as test molecule the clofibric acid (CA), a metabolism product of a drug used as blood lipid regulator. The catalyst was characterised by XRD, SEM and UV-VIS reflectance spectroscopy. The photocatalytic potential of this new catalyst was evaluated at laboratory scale under UV-A irradiation conditions. The influence of a few key parameters was studied in order to optimize the process conditions. The results shown that TiO2-Pd can completely remove the target pollutant under the optimal conditions (0.2 g/L catalyst, 5 mg/L CA, 6.2 mW/cm2 irradiation flux). Moreover, the CA mineralization was evaluated by TOC and a 65% mineralization yield was obtained, confirming the good photocatalytic activity of TiO2-Pd.

Learning Machine Reasoning for Bioactivity Prediction of Chemicals

<p>We describe a method for learning higher-level vector representations of interactions between molecular features and biology. We named the representations as the <i>reason vectors</i>. In contrast to the high-dimensional chemical fingerprints, reason vectors are much simpler with only about 5 dimensions. They allow abstract reasoning for bioactivity of chemicals or absence thereof, uncover causal factors in interactions between chemical features and generalize beyond specific chemical classes or bioactivity. These qualities enable us to perform powerful similarity searches that are vague and conceptual in nature. The methodology can handle novel combinations of features in query molecules and can evaluate chemical classes that are entirely absent in training data. The method consists of similarity-based near neighbor search on a reference database of biologically tested chemicals by a series of substructures obtained from stepwise reconstruction of the test molecule. A data-driven continuous representation of molecular fragments was used for molecular similarity computations. The technique was inspired by the ability of humans to learn and generalize complex concepts by interacting with the physical world. We also show that activity prediction of chemicals using the abstract reason vectors is very easy and straightforward, as compared to modeling in the raw chemistry space, and can be applied to both binary and continuous activity outcomes. Except for utilizing an unsupervised training to construct continuous molecular fingerprints, the methodology is devoid of gradient optimization or statistical fitting.</p>

Learning Machine Reasoning for Bioactivity Prediction of Chemicals

<p>We describe a method for learning higher-level vector representations of interactions between molecular features and biology. We named the representations as the <i>reason vectors</i>. In contrast to the high-dimensional chemical fingerprints, reason vectors are much simpler with only about 5 dimensions. They allow abstract reasoning for bioactivity of chemicals or absence thereof, uncover causal factors in interactions between chemical features and generalize beyond specific chemical classes or bioactivity. These qualities enable us to perform powerful similarity searches that are vague and conceptual in nature. The methodology can handle novel combinations of features in query molecules and can evaluate chemical classes that are entirely absent in training data. The method consists of similarity-based near neighbor search on a reference database of biologically tested chemicals by a series of substructures obtained from stepwise reconstruction of the test molecule. A data-driven continuous representation of molecular fragments was used for molecular similarity computations. The technique was inspired by the ability of humans to learn and generalize complex concepts by interacting with the physical world. We also show that activity prediction of chemicals using the abstract reason vectors is very easy and straightforward, as compared to modeling in the raw chemistry space, and can be applied to both binary and continuous activity outcomes. Except for utilizing an unsupervised training to construct continuous molecular fingerprints, the methodology is devoid of gradient optimization or statistical fitting.</p>

SERS-Active Substrate with Collective Amplification Design for Trace Analysis of Pesticides

Health risks posed by the exposure to trace amounts of pesticide residue in agricultural products have gained a lot of concerns, due to their neurotoxic nature. The applications of surface-enhanced Raman Scattering (SERS) as a detection technique have consistently shown its potential as a rapid and sensitive means with minimal sample preparation. In this study, gold nanoparticles (Au NPs) in elliptical shapes were collected into a layer of ordered zirconia concave pores. The porous zirconia layer (pZrO2) was then deposited with Au NPs, denoted as Au NPs (x)/pZrO2, where x indicates the deposition thickness of Au NPs in nm. In the concave structure of pZrO2, Au-ZrO2 and Au-Au interactions provide a synergistic and physical mechanism of SERS, which is anticipated to collect and amplify SERS signals and thereafter improve the enhancement factor (EF) of Au NPs/pZrO2. By taking Rhodamine 6G (R6G) as the test molecule, EF of Au NPs/pZrO2 might reach to 7.0 × 107. Au NPs (3.0)/pZrO2 was then optimized and competent to detect pesticides, e.g., phosmet and carbaryl at very low concentrations, corresponding to the maximum residue limits of each, i.e., 0.3 ppm and 0.2 ppm, respectively. Au NPs (3.0)/pZrO2 also showed the effectiveness of distinguishing between phosmet and carbaryl under mixed conditions. Due to the strong affinities of the phosphoric groups and sulfur in phosmet to the Au NPs (3.0)/pZrO2, the substrate exhibited selective detection to this particular pesticide. In this study, Au NPs (3.0)/pZrO2 has thus demonstrated trace detection of residual pesticides, due to the substrate design that intended to provide collective amplification of SERS.

Analysis of the Photocatalytic Potential of the C/TiO2 Composite

The control of environmental pollution has led to an intensive search for innovative and efficient technologies for wastewater treatment, especially those with toxic or non-biodegradable compounds. In this sense, this work involved the preparation of a hybrid composite of TiO2 with amorphous carbon by partial pyrolysis method and the analysis of their photocatalytic potential using phenol red dye as a test molecule. The composite was characterized by X-ray diffraction (XRD), infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The evaluation of morphology and the structural characterization of the powder confirmed the formation of the hybrid composite of TiO2 dispersed in a carbon matrix with turbostratic structure, organized in the shape of overlapping plates. The composite presented a discoloration rate of 67% after 4 hours of irradiation. The photocatalytic reaction follows a kinetics of first order type.