The current research examined dog's ability to dsicriminate between different amounts of food. Using a two alternative forced choice procedure dogs werre required to discriminate between a constant amount of 4 pieces of food and another amount that varied across a range from 1 to 7 pieces. The dogs reliably selected the larger of two alternatives. Discrimination was better when there were fewer rather than more than 4 pieces of food available on the varying alternative. Specifically, 1 was discriminated from 4 more easily than 4 was discriminated from 7 pieces of food. These results confirmed the ability of dogs to discriminate food amount on a psychophyical choice procedure.

Illusory line motion (ILM) refers to the perception of motion in a line that is, in fact, presented in full at one time. One form of this illusion (flashILM) occurs when the line is presented between two objects following a brief luminance change in one of them and flashILM is thought to result from exogenous attention being captured by the flash. Exogenous attention fades with increasing delays, which predicts that flashILM should show a similar temporal pattern. Exogenous attention appears to follow flashILM to become more or less equally distributed along the line.The current study examines flashILM in order to test these predictions derived from the attentional explanation for flashILM and the results were consistent with them. The discussion then concludes with an exploratory analysis approach concerning states of consciousness and decision making and suggests a possible role for attention.

Tetracycline antibiotics are used extensively in veterinary medicine, but the majority of the administrated dose is eliminated unmodified from the animal through various excretion routes including urine, faeces and milk. In dairy animals, limits on residues secreted in milk are strictly controlled by legislation. Tetracyclines (TCs) have metal chelation properties and form strong complexes with iron ions under acidic conditions. In this study, we exploit this property as a strategy for low cost, rapid electrochemical detection of TC residues. TC-Fe(III) complexes in a ratio of 2:1 were created in acidic conditions (pH 2.0) and electrochemically measured on plasma-treated gold electrodes modified with electrodeposited gold nanostructures. DPV measurements showed a reduction peak for the TC-Fe(III) complex that was observed at 50 mV (vs. Ag/AgCl QRE). The limit of detection in buffer media was calculated to be 345 nM and was responsive to increasing TC concentrations up to 2 mM, added to 1 mM FeCl3. Whole milk samples were processed to remove proteins and then spiked with tetracycline and Fe(III) to explore the specificity and sensitivity in a complex matrix with minimal sample preparation, under these conditions the LoD was 931 nM. These results demonstrate a route towards an easy-to-use sensor system for identification of TC in milk samples taking advantage of the metal chelating properties of this antibiotic class.

To achieve the UN Sustainable Development Goal of universal access to clean water and sanitation, we need to rethink centralized water systems with global net-zero carbon and sustainability in mind. One approach is to develop scalable off-grid systems that are reliable and easy to use and maintain. A major challenge for such systems is translating the standard laboratory-based monitoring of centralized systems to a more sustainable and scalable model for regularly and routinely monitoring system outputs, which consist of complex mixtures with varying concentrations of molecules and ions in water. Here, we demonstrate a preliminary sensor that, once fully developed, could allow for point-of-use measurements with a single output to monitor. Rather than developing multiple sensors to monitor the levels of each individual component in the water, our label-free, array-based design mimics the biological system of taste. The sensor is comprised of an array of nano-tastebuds made of tailored plasmonic metasurfaces. The combination of different signals from each nano-tastebud to the same sample yields a unique fingerprint for that sample. Through training, these fingerprints build an identification model. By integrating a fully developed sensor into decentralized water systems, we seek to provide non-expert end-users with an easy-to-read output capable of warning of imminent system failures.

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