Novel NMDA antagonists: replacement of the pyridinium ring of 6,11-ethanobenzo[b]quinolizinium cations with heteroisoquinolinium cations

Replacement of the pyridinium ring of 6,11-ethanobenzo[b]quinolizinium cations with thiazolium (4a and 4b) and N-methylimidazolium (4c and 4d) resulted in equipotent compounds in the [3H]TCP binding assay. The corresponding N-methyl-1,2,4-triazolium analogs were less potent in this assay. The thiazolium derivative 4b, with a Ki = 2.9 nM, is being evaluated as a possible neuroprotective N-methyl-D-aspartic acid (NMDA) antagonist.     

Simplified method for the measurement of segmental colonic transit time

Segmental colonic transit has been measured in 101 patients. Two MBq of 111Indium absorbed on resin pellets and encapsulated in an enteric coated capsule was given at 7 00 am. Hourly images during the first day, and three images during each subsequent day were acquired for up to three days. Using all scan and patient data the scans were categorised in one of the five patterns of colonic transit: normal, rapid, right delay, left delay, or generalised delay. The geometric centres and per cent activity at each time point was compared between the five groups of colonic transit patients to find the best time for imaging and so to distinguish the five groups. During the first day, early images did not help in diagnosis of patterns of transit, however, in the later images (six hours onwards after the ingestion of the activity) the rapid transit groups could be identified. Images at 27 and 51 hours were both required to distinguish all five groups of patients from each other. Only in the 'normal' transit patients was there some excretion of the activity during the course of the second day, otherwise there was no difference in the images taken in the course of a day (second or third day). A simplified protocol requires a minimum of three images to distinguish all five patterns of colonic transit. The activity should be ingested in the morning (7 00 am) and the first image taken at the end of the working day (8-10 hours after ingestion), the second image on the morning of the second day, and the third image during the course of the third day. This simple protocol would provide all the clinically relevant information necessary for correct classification of the colonic transit.     

Mixed waste ferrite as a novel sorbent for carbon dioxide derived from flue gases

Ferrite is a potential sorbent for flue gases such as CO₂, H₂S and SO₂. This paper discusses the adsorption and decomposition of CO₂ into carbon by hydrogen-activated waste ferrites prepared from Berkeley Pit acid mine water (Butte, MT). The decomposition effectiveness of these waste ferrites was studied at 300 °C and compared with the synthetic magnetite obtained from ferrous sulfate solution in our laboratory. The decomposition was measured by two methods: indirectly by measuring the adsorption rate of CO₂ and directly by analysing the carbon deposited on the samples. The results indicated that the mixed waste ferrite had good affinity for the adsorption and decomposition. The CO₂ decomposition data of both sorbents fitted the first-order reaction kinetics. Even though the surface area of the magnetite was higher than that of waste ferrite, the CO₂ decomposition rate of the waste ferrite was estimated to be 2.5 times higher than that of magnetite under identical conditions. The carbon analysis deposited on the sample indicated that the CO₂ was 100% decomposed into carbon and other carbon/hydrogen compounds by the waste ferrite, whereas the conversion was 43% by the magnetite. In terms of specific adsorption of carbon, ferrite was three to five times more efficient than magnetite.     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.