Nuclear proteins from liver and kidney bind a 37 bp sequence in the 5' upstream region of the mGAT1 gene

The mouse GABA transporter (mGAT1) gene has been shown to be exclusively expressed in brain by Northern and Western blot analyses. The interactions between the 5' flanking region of the mGAT1 gene and nuclear proteins from different mouse tissues were studied by means of gel-shift assay. Our results show that nuclear protein factors from non-nervous tissues can specifically recognize a 37 bp sequence that is conserved in the 5' flanking region between the human and mouse GAT1 genes. Similar nuclear protein factors were also found to exist in rat, rabbit and pig.     

Proadrenomedullin N-terminal 20 peptide: minimal active region to regulate nicotinic receptors

Proadrenomedullin N-terminal 20 peptide (PAMP-[1-20]; ARLDVASEFRKKWNKWALSR-amide) is a potent hypotensive and catecholamine release-inhibitory peptide released from chromaffin cells. We studied the mechanism of PAMP action and how its function is linked to structure. We tested human PAMP-[1-20] on catecholamine secretion in PC12 pheochromocytoma cells and found it to be a potent, dose-dependent (IC50 approximately 350 nmol/L) secretory inhibitor. Inhibition was specific for nicotinic cholinergic stimulation since PAMP-[1-20] failed to inhibit release by agents that bypass the nicotinic receptor. Nicotinic cationic (22Na+,45Ca2+) signal transduction was disrupted by this peptide, and potencies for inhibition of 22Na+ uptake and catecholamine secretion were comparable. Even high-dose nicotine failed to overcome the inhibition, suggesting noncompetitive nicotinic antagonism. N- and C-terminal PAMP truncation peptides indicated a role for the C-terminal amide and refined the minimal active region to the C-terminal 8 amino acids (WNKWALSR-amide), a region likely to be alpha-helical. PAMP also blocked (EC50 approximately 270 nmol/L) nicotinic cholinergic agonist desensitization of catecholamine release, as well as desensitization of nicotinic signal transduction (22Na+ uptake). Thus, PAMP may exert both inhibitory and facilitatory effects on nicotinic signaling, depending on the prior state of nicotinic stimulation. PAMP may therefore contribute to a novel, autocrine, homeostatic (negative-feedback) mechanism controlling catecholamine release.     

Fractionation studies of palm oil by density gradient

The paper describes a method of fractionating vegetable, animal and fish oils, and in particular palm oil. The method involves addition of a medium comprising two common solvents to the semisolid oils. On centrifugation, the olein and stearin are separated by the medium in the middle. Thirteen media made up from binary combinations of nine solvents, viz. water, propylene glycol, glycerine, methanol, ethanol,n-propanol, isopropanol (IPA), acetone and butanone, are found to be effective in olein-stearin separation. However, only the water/IPA and water/methanol systems have been studied in detail. The aqueous IPA provides a higher yield of olein than water/ methanol but intersolubility between oil and medium is also greater. The fractionation process can be carried out at any suitable temperature. Fractionation of the special prime bleached (SPB) palm oil at 16 C yields an olein with a cloud point of 4.8 C. Some hybrid palm oils produce a large quantity of low cloud point olein which can be bleached readily. The process can be extended to include degumming and neutralization by using an alkaline medium for centrifugation. The olein fractions obtained have been found to be free of phosphatides and the free fatty acids reduced to as low as 0.02%. Metal-scavenging agents have also been added to the medium in an attempt to remove copper and iron. The development of this process into a continuous one has been demonstrated on the AlfaLaval LAPX 202 Separator. Fractionation of crude palm oil using a density gradient provides seven fractions of different characteristics. The iodine values vary from 37.5 to 57.4 and the unsaturated fatty acids range from 32.7% to 51.2%. Triglyceride analysis by carbon numbers shows great differences in the C₄₈ and C₅₂ constituents of the fractions. aThe volume ratio of oil to medium in each case was 1:1. The separation involved the oil and wax.     

Automatic learners with feedback queries

Automatic classes are classes of languages for which a finite automaton can decide whether a given element is in a set given by its index. The present work studies the learnability of automatic families by automatic learners which, in each round, output a hypothesis and update a long-term memory, depending on the input datum, via an automatic function. Many variants of automatic learners are investigated: where the long-term memory is restricted to be the current hypothesis whenever this exists, cannot be of length larger than the length of the longest datum seen, or has to consist of a constant number of examples seen so far. Learnability is also studied with respect to queries which reveal information about past data or past computation history; the number of queries per round is bounded by a constant.     

Increasing productivity and protein content using early-maturing rices and efficient nitrogen management

Germplasm with shorter duration than that of the currently grown varieties is being generated to maximize productivity of irrigated rice. However, short-duration varieties often produce yields lower than the medium- and long-duration varieties. Experiments were conducted during the 1980–82 dry and wet seasons to increase productivity through the use of very early-maturing rices and the improved management of nitrogen (N) fertilizers.Results over three years showed that IR58 and IR9729-67-3 (growth duration 100 ± 5 days) yield as well as or higher than IR36 although earlier maturing. They generally had a higher productivity (kg ha^–1 day^–1) than IR36 (110 ± 5 days).Three years' data suggest that the improved timing of broadcast applications of urea in split doses increased grain yield comparable with the basal incorporation of slow-release sulfur-coated urea (SCU) or deep point-placement of urea supergranules (USG).Results on elite breeding lines showed that the early-maturing IR9729-67-3 produced higher protein yield ha^–1 than longer duration varieties such as IR8 and IR42 in the dry season. Furthermore, contrary to earlier results, single basal incorporation of slow-release SCU increased the protein yield of rice by 53 kg ha^–1 and deep point-placement of USG by 43 kg ha^–1 over split application of prilled urea.     

Cellulose-binding domains: potential for purification of complex proteins

The endoglucanase CenA and the exoglucanase Cex from Cellulomonasfimi each contain a discrete cellulose-binding domain (CBD),at the amino-terminus or carboxyl-terminus respectively. Thegene fragment encoding the CBD can be fused to the gene of aprotein of interest. Using this approach hybrid proteins canbe engineered which bind reversibly to cellulose and exhibitthe biological activity of the protein partner. Alkaline phosphatase(PhoA) from Escherichia coli, and a ß-glucosidase(Abg) from an Agrobacterium sp. are dimeric proteins. The fusionpolypeptides CenA-PhoA and Abg-CBC_cex are sensitive to proteolysisat the junctions between the fusion partners. Proteolysis resultsin a mixture of homo- and heterodimers; these bind to celluloseif one or both of the monomers carry a CBD, e.g. CenA-PhoA/CenA-PhoAand CenA-PhoA/PhoA. CBD fusion polypeptides could be used inthis way to purify polypeptides which associate with the fusionpartner.     

Fourier transform infrared (FTIR) authentication and batch-to-batch consistency for different types of paints using benchtop and handheld FTIR spectrophotometers for oil and gas industry

Standardization of Fourier transform infrared (FTIR) fingerprint region for paints and assessment on the reproducibility using different spectrophotometers were investigated. While selective fingerprint regions may be confusing for technicians/analysts who are non-chemists, we attempt to generalize these regions (e.g., 1300–1000 cm⁻¹ for Epoxy part A and 1400–1000 cm⁻¹ for Epoxy part B) by choosing a universal region (2000–900 cm⁻¹) that works for different paints. Comparison result using a paired student t-test shows that the degree of similarity (r) values from the studied regions are not statistically different. The paint fails the screening analysis occasionally on-site when analyzed using handheld FTIR due to the higher level of noise that gives low r values (r < 0.900 ± 0.002). The same samples were analyzed using a benchtop FTIR and the r values are above 0.900 ± 0.002. While the screening may lead to a false rejection of the sample on-site, there could be occurrence of false acceptance. The on-site screening of EPZ part A with different formulations, for instance, shows that the r values over the entire IR spectrum are above 0.900 ± 0.002 when analyzed using handheld FTIR. After the samples were analyzed using the benchtop, the r values fall below 0.900 ± 0.002.     

Analytic center cutting plane method for multiple kernel learning

Multiple Kernel Learning (MKL) is a popular generalization of kernel methods which allows the practitioner to optimize over convex combinations of kernels. We observe that many recent MKL solutions can be cast in the framework of oracle based optimization, and show that they vary in terms of query point generation. The popularity of such methods is because the oracle can fortuitously be implemented as a support vector machine. Motivated by the success of centering approaches in interior point methods, we propose a new approach to optimize the MKL objective based on the analytic center cutting plane method (accpm). Our experimental results show that accpm outperforms state of the art in terms of rate of convergence and robustness. Further analysis sheds some light as to why MKL may not always improve classification accuracy over naive solutions.     

Feature selection via sensitivity analysis of SVM probabilistic outputs

Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) learning. Like most feature-selection methods, the proposed method ranks all features in decreasing order of importance so that more relevant features can be identified. It uses a novel criterion based on the probabilistic outputs of SVM. This criterion, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates the importance of a specific feature by computing the aggregate value, over the feature space, of the absolute difference of the probabilistic outputs of SVM with and without the feature. The exact form of this criterion is not easily computable and approximation is needed. Four approximations, FSPP1-FSPP4, are proposed for this purpose. The first two approximations evaluate the criterion by randomly permuting the values of the feature among samples of the training data. They differ in their choices of the mapping function from standard SVM output to its probabilistic output: FSPP1 uses a simple threshold function while FSPP2 uses a sigmoid function. The second two directly approximate the criterion but differ in the smoothness assumptions of criterion with respect to the features. The performance of these approximations, used in an overall feature-selection scheme, is then evaluated on various artificial problems and real-world problems, including datasets from the recent Neural Information Processing Systems (NIPS) feature selection competition. FSPP1-3 show good performance consistently with FSPP2 being the best overall by a slight margin. The performance of FSPP2 is competitive with some of the best performing feature-selection methods in the literature on the datasets that we have tested. Its associated computations are modest and hence it is suitable as a feature-selection method for SVM applications. Editor: Risto Miikkulainen.