Études sur la stabilité des machines locomotives en mouvement,

Imprint covered by label: Paris, E. Lacroix.

Bibliography of electric arc welding--Supplement I. Bibliography, welding electrodes and arc welding machines.

On cover: Compiler: Mattie L. Houghten.

A study of the effect of automatic sequence clothes washing machines on individual sewage disposal systems, conducted for the Federal Housing Administration under contract no. Ha-fh-646 (amendment no. 1) March 22, 1956.

Mode of access: Internet.

The law relating to weights, measures, and weighing machines.

Appendix (p. [135]-199): 1. The Weights and measures act, 1878, 41 & 42 Vict. c. 49.--2. The Annoyance jurors (Westminster) act, 1861, 24 & 25 Vict. c. 78.--3. Forms.--4. List of offenses.--5. Orders in Council.

Recherches expérimentales et analytiques sur les machines à vapeur. Vérification d'une série d'essais sur une machine de Woolf.

Mode of access: Internet.

Alternating-current machines.

Mode of access: Internet.

Electric circuits and machines,

The 1941 edition has title: Electrical circuits and machines.

Reducing voter waiting time : how to allocate voting machines to the polls.

Mode of access: Internet.

Milking machines and mastitis.

Mode of access: Internet.

Far travelers : the exploring machines /

No longer available for sale by the Supt. of Docs., U.S. G.P.O.

Safety code for mechanical power-transmission apparatus : rules for guarding prime movers, intermediate equipment, and driven machines : approved by American Standards Association as an American standard, 1927 /

"ASA B15-1927"--Cover.

Bubble load measurement in the pulp zone of industrial flotation machines - a new device for determining the froth recovery of attached particles

A new device has been developed to directly measure the bubble loading of particle-bubble aggregates in industrial flotation machines, both mechanical flotation cells as well as flotation column cells. The bubble loading of aggregates allows for in-depth analysis of the operating performance of a flotation machine in terms of both pulp/collection zone and froth zone performance. This paper presents the methodology along with an example showing the excellent reproducibility of the device and an analysis of different operating conditions of the device itself. (C) 2004 Elsevier B.V All rights reserved.

SVMtm: Support vector machines to predict transmembrane segments

A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is given to show the strength of transmembrane signal and the prediction reliability. In particular, this method can distinguish transmembrane proteins from soluble proteins with an accuracy of similar to99%. This method can be used to complement current transmembrane helix prediction methods and can be Used for consensus analysis of entire proteomes . The predictor is located at http://genet.imb.uq.edu.au/predictors/ SVMtm. (C) 2004 Wiley Periodicals, Inc.

Poly(beta-hydroxybutyrate-co-beta-hydroxyvalerate) supports in vitro osteogenesis

Studies have demonstrated that polymeric biomaterials have the potential to support osteoblast growth and development for bone tissue repair. Poly( beta- hydroxybutyrate- co- beta- hydroxyvalerate) ( PHBV), a bioabsorbable, biocompatible polyhydroxy acid polymer, is an excellent candidate that, as yet, has not been extensively investigated for this purpose. As such, we examined the attachment characteristics, self- renewal capacity, and osteogenic potential of osteoblast- like cells ( MC3T3- E1 S14) when cultured on PHBV films compared with tissue culture polystyrene ( TCP). Cells were assayed over 2 weeks and examined for changes in morphology, attachment, number and proliferation status, alkaline phosphatase ( ALP) activity, calcium accumulation, nodule formation, and the expression of osteogenic genes. We found that these spindle- shaped MC3T3- E1 S14 cells made cell - cell and cell - substrate contact. Time- dependent cell attachment was shown to be accelerated on PHBV compared with collagen and laminin, but delayed compared with TCP and fibronectin. Cell number and the expression of ALP, osteopontin, and pro- collagen alpha 1( I) mRNA were comparable for cells grown on PHBV and TCP, with all these markers increasing over time. This demonstrates the ability of PHBV to support osteoblast cell function. However, a lag was observed for cells on PHBV in comparison with those on TCP for proliferation, ALP activity, and cbfa- 1 mRNA expression. In addition, we observed a reduction in total calcium accumulation, nodule formation, and osteocalcin mRNA expression. It is possible that this cellular response is a consequence of the contrasting surface properties of PHBV and TCP. The PHBV substrate used was rougher and more hydrophobic than TCP. Although further substrate analysis is required, we conclude that this polymer is a suitable candidate for the continued development as a biomaterial for bone tissue engineering.

Detecting and sorting targeting peptides wtih neural networks and support vector machines

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).