Early post-cleavage stages and abnormalities identified in the embryonic development of chokka squid eggs Loligo vulgaris reynaudii

Six early, post-cleavage embryonic stages for chokka squid Loligo vulgaris reynaudii eggs that were developed in an aquarium are identified and described, expanding the embryonic stages for this species from 14 to 20. The influence of water temperature on embryonic development is described. At temperatures 15&#176C, high percentages of morphological abnormalities were observed in embryonic development. Gross forms are described and illustrated.Keywords: abnormalities, aquarium, embryonic development, chokka squidAfrican Journal of Marine Science 2002, 24: 379–38

The structure of the PapD-PapGII pilin complex reveals an open and flexible P5 pocket

P pili are hairlike polymeric structures that mediate binding of uropathogenic Escherichia coli to the surface of the kidney via the PapG adhesin at their tips. PapG is composed of two domains: a lectin domain at the tip of the pilus followed by a pilin domain that comprises the initial polymerizing subunit of the 1,000-plus-subunit heteropolymeric pilus fiber. Prior to assembly, periplasmic pilin domains bind to a chaperone, PapD. PapD mediates donor strand complementation, in which a beta strand of PapD temporarily completes the pilin domain's fold, preventing premature, nonproductive interactions with other pilin subunits and facilitating subunit folding. Chaperone-subunit complexes are delivered to the outer membrane usher where donor strand exchange (DSE) replaces PapD's donated beta strand with an amino-terminal extension on the next incoming pilin subunit. This occurs via a zip-in-zip-out mechanism that initiates at a relatively accessible hydrophobic space termed the P5 pocket on the terminally incorporated pilus subunit. Here, we solve the structure of PapD in complex with the pilin domain of isoform II of PapG (PapGIIp). Our data revealed that PapGIIp adopts an immunoglobulin fold with a missing seventh strand, complemented in parallel by the G1 PapD strand, typical of pilin subunits. Comparisons with other chaperone-pilin complexes indicated that the interactive surfaces are highly conserved. Interestingly, the PapGIIp P5 pocket was in an open conformation, which, as molecular dynamics simulations revealed, switches between an open and a closed conformation due to the flexibility of the surrounding loops. Our study reveals the structural details of the DSE mechanism

Structural and functional characterization of Pseudomonas aeruginosa CupB chaperones

Pseudomonas aeruginosa, an important human pathogen, is estimated to be responsible for,10% of nosocomial infections worldwide. The pathogenesis of P. aeruginosa starts from its colonization in the damaged tissue or medical devices (e. g. catheters, prothesis and implanted heart valve etc.) facilitated by several extracellular adhesive factors including fimbrial pili. Several clusters containing fimbrial genes have been previously identified on the P. aeruginosa chromosome and named cup [1]. The assembly of the CupB pili is thought to be coordinated by two chaperones, CupB2 and CupB4. However, due to the lack of structural and biochemical data, their chaperone activities remain speculative. In this study, we report the 2.5 A crystal structure of P. aeruginosa CupB2. Based on the structure, we further tested the binding specificity of CupB2 and CupB4 towards CupB1 (the presumed major pilus subunit) and CupB6 (the putative adhesin) using limited trypsin digestion and strep-tactin pull-down assay. The structural and biochemical data suggest that CupB2 and CupB4 might play different, but not redundant, roles in CupB secretion. CupB2 is likely to be the chaperone of CupB1, and CupB4 could be the chaperone of CupB4:CupB5:CupB6, in which the interaction of CupB4 and CupB6 might be mediated via CupB5

Spatial patterns in the biology of the chokka squid, Loligo reynaudii on the Agulhas Bank, South Africa

Although migration patterns for various life history stages of the chokka squid (Loligo reynaudii) have been previously presented, there has been limited comparison of spatial variation in biological parameters. Based on data from research surveys; size ranges of juveniles, subadults and adults on the Agulhas Bank were estimated and presented spatially. The bulk of the results appear to largely support the current acceptance of the life cycle with an annual pattern of squid hatching in the east, migrating westwards to offshore feeding grounds on the Central and Western Agulhas Bank and the west coast and subsequent return migration to the eastern inshore areas to spawn. The number of adult animals in deeper water, particularly in autumn in the central study area probably represents squid spawning in deeper waters and over a greater area than is currently targeted by the fishery. The distribution of life history stages and different feeding areas does not rule out the possibility that discrete populations of L. reynaudii with different biological characteristics inhabit the western and eastern regions of the Agulhas Bank. In this hypothesis, some mixing of the populations does occur but generally squid from the western Agulhas Bank may occur in smaller numbers, grow more slowly and mature at a larger size. Spawning occurs on the western portion of the Agulhas Bank, and juveniles grow and mature on the west coast and the central Agulhas Bank. Future research requirements include the elucidation of the age structure of chokka squid both spatially and temporally, and a comparison of the statolith chemistry and genetic characterization between adults from different spawning areas across the Agulhas Bank

A novel malaria vaccine candidate antigen expressed in Tetrahymena thermophila

Development of effective malaria vaccines is hampered by the problem of producing correctly folded Plasmodium proteins for use as vaccine components. We have investigated the use of a novel ciliate expression system, Tetrahymena thermophila, as a P. falciparum vaccine antigen platform. A synthetic vaccine antigen composed of N-terminal and C-terminal regions of merozoite surface protein-1 (MSP-1) was expressed in Tetrahymena thermophila. The recombinant antigen was secreted into the culture medium and purified by monoclonal antibody (mAb) affinity chromatography. The vaccine was immunogenic in MF1 mice, eliciting high antibody titers against both N- and C-terminal components. Sera from immunized animals reacted strongly with P. falciparum parasites from three antigenically different strains by immunofluorescence assays, confirming that the antibodies produced are able to recognize parasite antigens in their native form. Epitope mapping of serum reactivity with a peptide library derived from all three MSP-1 Block 2 serotypes confirmed that the MSP-1 Block 2 hybrid component of the vaccine had effectively targeted all three serotypes of this polymorphic region of MSP-1. This study has successfully demonstrated the use of Tetrahymena thermophila as a recombinant protein expression platform for the production of malaria vaccine antigens

Guidelines for the deployment and implementation of manufacturing scheduling systems

It has frequently been stated that there exists a gap between production scheduling theory and practice. In order to put theoretical findings into practice, advances in scheduling models and solution procedures should be embedded into a piece of software - a scheduling system - in companies. This results in a process that entails (1) determining its functional features, and (2) adopting a successful strategy for its development and deployment. In this paper we address the latter question and review the related literature in order to identify descriptions and recommendations of the main aspects to be taken into account when developing such systems. These issues are then discussed and classified, resulting in a set of guidelines that can help practitioners during the process of developing and deploying a scheduling system. In addition, identification of these issues can provide some insights to drive theoretical scheduling research towards those topics more in demand by practitioners, and thus help to close the aforementioned gap.Framiñan Torres, JM.; Ruiz García, R. (2012). Guidelines for the deployment and implementation of manufacturing scheduling systems. International Journal of Production Research. 50(7):1799-1812. doi:10.1080/00207543.2011.564670S17991812507Baek, D. H. (1999). A visualized human-computer interactive approach to job shop scheduling. International Journal of Computer Integrated Manufacturing, 12(1), 75-83. doi:10.1080/095119299130489Comesaña Benavides, J. A., & Carlos Prado, J. (2002). Creating an expert system for detailed scheduling. International Journal of Operations & Production Management, 22(7), 806-819. doi:10.1108/01443570210433562Bensana, E. 1986. An expert-system approach to industrial job-shop scheduling. In: Proceedings of the 1986 IEEE international conference on robotics and automation. 1986. Vol. 3, pp.1645–1650.Berglund, M., & Karltun, J. (2007). Human, technological and organizational aspects influencing the production scheduling process. International Journal of Production Economics, 110(1-2), 160-174. doi:10.1016/j.ijpe.2007.02.024Besbes, W., Teghem, J., & Loukil, T. (2010). Scheduling hybrid flow shop problem with non-fixed availability constraints. European J. of Industrial Engineering, 4(4), 413. doi:10.1504/ejie.2010.035652Bhattacharyya, S., & Koehler, G. J. (1998). Learning by Objectives for Adaptive Shop-Floor Scheduling. Decision Sciences, 29(2), 347-375. doi:10.1111/j.1540-5915.1998.tb01580.xBitran, G. R., & Tirupati, D. (1988). OR Practice—Development and Implementation of a Scheduling System for a Wafer Fabrication Facility. Operations Research, 36(3), 377-395. doi:10.1287/opre.36.3.377Buxey, G. (1989). Production scheduling: Practice and theory. European Journal of Operational Research, 39(1), 17-31. doi:10.1016/0377-2217(89)90349-4Chen, J.-F. (2004). Unrelated parallel machine scheduling with secondary resource constraints. The International Journal of Advanced Manufacturing Technology, 26(3), 285-292. doi:10.1007/s00170-003-1622-1Collinot, A., Le Pape, C., & Pinoteau, G. (1988). SONIA: A knowledge-based scheduling system. Artificial Intelligence in Engineering, 3(2), 86-94. doi:10.1016/0954-1810(88)90024-6Cowling, P. (2003). A flexible decision support system for steel hot rolling mill scheduling. Computers & Industrial Engineering, 45(2), 307-321. doi:10.1016/s0360-8352(03)00038-xDudek, R. A., Panwalkar, S. S., & Smith, M. L. (1992). The Lessons of Flowshop Scheduling Research. Operations Research, 40(1), 7-13. doi:10.1287/opre.40.1.7Dumond, E. J. (2005). Understanding and using the capabilities of finite scheduling. Industrial Management & Data Systems, 105(4), 506-526. doi:10.1108/02635570510592398Fox, M. S., & Smith, S. F. (1984). ISIS?a knowledge-based system for factory scheduling. Expert Systems, 1(1), 25-49. doi:10.1111/j.1468-0394.1984.tb00424.xFraminan, J. M., & Ruiz, R. (2010). Architecture of manufacturing scheduling systems: Literature review and an integrated proposal. European Journal of Operational Research, 205(2), 237-246. doi:10.1016/j.ejor.2009.09.026Freed, T., Doerr, K. H., & Chang, T. (2007). In-house development of scheduling decision support systems: case study for scheduling semiconductor device test operations. International Journal of Production Research, 45(21), 5075-5093. doi:10.1080/00207540600818351Gao, C and Tang, L. 2008. A decision support system for color-coating line in steel industry. In: Proceedings of the IEEE international conference on automation and logistics, ICAL 2008. 2008. pp.1463–1468.Grant, T. J. (1986). Lessons for O.R. from A.I.: A Scheduling Case Study. Journal of the Operational Research Society, 37(1), 41-57. doi:10.1057/jors.1986.7Graves, S. C. (1981). A Review of Production Scheduling. Operations Research, 29(4), 646-675. doi:10.1287/opre.29.4.646HALSALL, D. N., MUHLEMANN, A. P., & PRICE, D. H. R. (1994). A review of production planning and scheduling in smaller manufacturing companies in the UK. Production Planning & Control, 5(5), 485-493. doi:10.1080/09537289408919520Higgins, P. G. (1996). Interaction in hybrid intelligent scheduling. International Journal of Human Factors in Manufacturing, 6(3), 185-203. doi:10.1002/(sici)1522-7111(199622)6:33.0.co;2-6Kanet, J. J., & Adelsberger, H. H. (1987). Expert systems in production scheduling. European Journal of Operational Research, 29(1), 51-59. doi:10.1016/0377-2217(87)90192-5Kathawala, Y., & Allen, W. R. (1993). Expert Systems and Job Shop Scheduling. International Journal of Operations & Production Management, 13(2), 23-35. doi:10.1108/01443579310025286Kerr, R. M. (1992). Expert systems in production scheduling: Lessons from a failed implementation. Journal of Systems and Software, 19(2), 123-130. doi:10.1016/0164-1212(92)90063-pKnolmayer, G., Mertens, P., & Zeier, A. (2002). Supply Chain Management Based on SAP Systems. doi:10.1007/978-3-540-24816-3Leachman, R. C., Benson, R. F., Liu, C., & Raar, D. J. (1996). IMPReSS: An Automated Production-Planning and Delivery-Quotation System at Harris Corporation—Semiconductor Sector. Interfaces, 26(1), 6-37. doi:10.1287/inte.26.1.6MACCARTHY, B. L., & LIU, J. (1993). Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. International Journal of Production Research, 31(1), 59-79. doi:10.1080/00207549308956713McKay, K. N., & Black, G. W. (2007). The evolution of a production planning system: A 10-year case study. Computers in Industry, 58(8-9), 756-771. doi:10.1016/j.compind.2007.02.002McKay, K. N., Safayeni, F. R., & Buzacott, J. A. (1988). Job-Shop Scheduling Theory: What Is Relevant? Interfaces, 18(4), 84-90. doi:10.1287/inte.18.4.84McKay, K. N., Morton, T. E., Ramnath, P., & Wang, J. (2000). ?Aversion dynamics? scheduling when the system changes. Journal of Scheduling, 3(2), 71-88. doi:10.1002/(sici)1099-1425(200003/04)3:23.0.co;2-0MCKAY, K., PINEDO, M., & WEBSTER, S. (2009). PRACTICE-FOCUSED RESEARCH ISSUES FOR SCHEDULING SYSTEMS*. Production and Operations Management, 11(2), 249-258. doi:10.1111/j.1937-5956.2002.tb00494.xMissbauer, H., Hauber, W., & Stadler, W. (2009). A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry. International Journal of Production Research, 47(15), 4147-4172. doi:10.1080/00207540801950136Numao, M and Morishita, S. 1989. A scheduling environment for steel-making processes. In: Proceedings of the 5th conference on artificial intelligence applications. 1989. pp.279–286.Olhager, J., & Rapp, B. (1995). Operations Research Techniques in Manufacturing Planning and Control Systems. International Transactions in Operational Research, 2(1), 29-43. doi:10.1111/j.1475-3995.1995.tb00003.xPerez-Gonzalez, P., & Framinan, J. M. (2009). Scheduling permutation flowshops with initial availability constraint: Analysis of solutions and constructive heuristics. Computers & Operations Research, 36(10), 2866-2876. doi:10.1016/j.cor.2008.12.018Pinedo, M., & Yen, B. P.-C. (1997). Annals of Operations Research, 70, 359-378. doi:10.1023/a:1018986524234Portougal, V., & Robb, D. J. (2000). Production Scheduling Theory: Just Where Is It Applicable? Interfaces, 30(6), 64-76. doi:10.1287/inte.30.6.64.11623Reisman, A., Kumar, A., & Motwani, J. (1997). Flowshop scheduling/sequencing research: a statistical review of the literature, 1952-1994. IEEE Transactions on Engineering Management, 44(3), 316-329. doi:10.1109/17.618173Steffen, MS. 1986. A survey of artificial intelligence-based scheduling systems. In: Proceedings of the fall industrial engineering conference. 1986.Storer, R. H., Wu, S. D., & Vaccari, R. (1992). New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling. Management Science, 38(10), 1495-1509. doi:10.1287/mnsc.38.10.1495Tang, L., & Wang, G. (2008). Decision support system for the batching problems of steelmaking and continuous-casting production. Omega, 36(6), 976-991. doi:10.1016/j.omega.2007.11.002T’kindt, V., Billaut, J.-C., Bouquard, J.-L., Lenté, C., Martineau, P., Néron, E., … Tacquard, C. (2005). The e-OCEA project: towards an Internet decision system for scheduling problems. Decision Support Systems, 40(2), 329-337. doi:10.1016/j.dss.2004.04.001Wiers, VCS. 1997. Human–computer interaction in production scheduling: Analysis and design of decision support systems for production scheduling tasks. Ph.D. Thesis, Technische Universiteit Eindhoven, NetherlandsWiers, V. C. S. (2002). A case study on the integration of APS and ERP in a steel processing plant. Production Planning & Control, 13(6), 552-560. doi:10.1080/09537280210160321Wiers, V. C. S., & Van Der Schaaf, T. W. (1997). A framework for decision support in production scheduling tasks. Production Planning & Control, 8(6), 533-544. doi:10.1080/095372897234876Zhang, L., Krishnamurthy, A., Malmborg, C. J., & Heragu, S. S. (2009). Variance-based approximations of transaction waiting times in autonomous vehicle storage and retrieval systems. European J. of Industrial Engineering, 3(2), 146. doi:10.1504/ejie.2009.02360

Optogenetics and deep brain stimulation neurotechnologies

Brain neural network is composed of densely packed, intricately wired neurons whose activity patterns ultimately give rise to every behavior, thought, or emotion that we experience. Over the past decade, a novel neurotechnique, optogenetics that combines light and genetic methods to control or monitor neural activity patterns, has proven to be revolutionary in understanding the functional role of specific neural circuits. We here briefly describe recent advance in optogenetics and compare optogenetics with deep brain stimulation technology that holds the promise for treating many neurological and psychiatric disorders

Bright ligand-activatable fluorescent protein for high-quality multicolor live-cell super-resolution microscopy

We introduce UnaG as a green-to-dark photoswitching fluorescent protein capable of high-quality super-resolution imaging with photon numbers equivalent to the brightest photoswitchable red protein. UnaG only fluoresces upon binding of a fluorogenic metabolite, bilirubin, enabling UV-free reversible photoswitching with easily controllable kinetics and low background under Epi illumination. The on- and off-switching rates are controlled by the concentration of the ligand and the excitation light intensity, respectively, where the dissolved oxygen also promotes the off-switching. The photo-oxidation reaction mechanism of bilirubin in UnaG suggests that the lack of ligand-protein covalent bond allows the oxidized ligand to detach from the protein, emptying the binding cavity for rebinding to a fresh ligand molecule. We demonstrate super-resolution single-molecule localization imaging of various subcellular structures genetically encoded with UnaG, which enables facile labeling and simultaneous multicolor imaging of live cells. UnaG has the promise of becoming a default protein for high-performance super-resolution imaging. Photoconvertible proteins occupy two color channels thereby limiting multicolour localisation microscopy applications. Here the authors present UnaG, a new green-to-dark photoswitching fluorescent protein for super-resolution imaging, whose activation is based on a noncovalent binding with bilirubin

Quail Genomics: a knowledgebase for Northern bobwhite

<p>Abstract</p> <p>Background</p> <p>The Quail Genomics knowledgebase (<url>http://www.quailgenomics.info</url>) has been initiated to share and develop functional genomic data for Northern bobwhite (<it>Colinus virginianus</it>). This web-based platform has been designed to allow researchers to perform analysis and curate genomic information for this non-model species that has little supporting information in GenBank.</p> <p>Description</p> <p>A multi-tissue, normalized cDNA library generated for Northern bobwhite was sequenced using 454 Life Sciences next generation sequencing. The Quail Genomics knowledgebase represents the 478,142 raw ESTs generated from the sequencing effort in addition to assembled nucleotide and protein sequences including 21,980 unigenes annotated with meta-data. A normalized MySQL relational database was established to provide comprehensive search parameters where meta-data can be retrieved using functional and structural information annotation such as gene name, pathways and protein domain. Additionally, blast hit cutoff levels and microarray expression data are available for batch searches. A Gene Ontology (GO) browser from Amigo is locally hosted providing 8,825 unigenes that are putative orthologs to chicken genes. In an effort to address over abundance of Northern bobwhite unigenes (71,384) caused by non-overlapping contigs and singletons, we have built a pipeline that generates scaffolds/supercontigs by aligning partial sequence fragments against the indexed protein database of chicken to build longer sequences that can be visualized in a web browser. </p> <p>Conclusion</p> <p>Our effort provides a central repository for storage and a platform for functional interrogation of the Northern bobwhite sequences providing comprehensive GO annotations, meta-data and a scaffold building pipeline. The Quail Genomics knowledgebase will be integrated with Japanese quail (<it>Coturnix coturnix</it>) data in future builds and incorporate a broader platform for these avian species. </p

Signal transduction pathways involved in proteolysis-inducing factor induced proteasome expression in murine myotubes

The proteolysis-inducing factor (PIF) is produced by cachexia-inducing tumours and initiates protein catabolism in skeletal muscle. The potential signalling pathways linking the release of arachidonic acid (AA) from membrane phospholipids with increased expression of the ubiquitin-proteasome proteolytic pathway by PIF has been studied using C2C12 murine myotubes as a surrogate model of skeletal muscle. The induction of proteasome activity and protein degradation by PIF was blocked by quinacrine, a nonspecific phospholipase A2 (PLA2) inhibitor and trifluroacetyl AA, an inhibitor of cytosolic PLA2. PIF was shown to increase the expression of calcium-independent cytosolic PLA2, determined by Western blotting, at the same concentrations as those inducing maximal expression of 20S proteasome α-subunits and protein degradation. In addition, both U-73122, which inhibits agonist-induced phospholipase C (PLC) activation and D609, a specific inhibitor of phosphatidylcholine-specific PLC also inhibited PIF-induced proteasome activity. This suggests that both PLA 2 and PLC are involved in the release of AA in response to PIF, and that this is important in the induction of proteasome expression. The two tyrosine kinase inhibitors genistein and tryphostin A23 also attenuated PIF-induced proteasome expression, implicating tyrosine kinase in this process. PIF induced phosphorylation of p44/42 mitogen-activated protein kinase (MAPK) at the same concentrations as that inducing proteasome expression, and the effect was blocked by PD98059, an inhibitor of MAPK kinase, as was also the induction of proteasome expression, suggesting a role for MAPK activation in PIF-induced proteasome expression. © 2003 Cancer Research UK