A lipid gating mechanism for the channel-forming O antigen ABC transporter

Extracellular glycan biosynthesis is a widespread microbial protection mechanism. In Gram-negative bacteria, the O antigen polysaccharide represents the variable region of outer membrane lipopolysaccharides. Fully assembled lipid-linked O antigens are translocated across the inner membrane by the WzmWzt ABC transporter for ligation to the lipopolysaccharide core, with the transporter forming a continuous transmembrane channel in a nucleotide-free state. Here, we report its structure in an ATP-bound conformation. Large structural changes within the nucleotide-binding and transmembrane regions push conserved hydrophobic residues at the substrate entry site towards the periplasm and provide a model for polysaccharide translocation. With ATP bound, the transporter forms a large transmembrane channel with openings toward the membrane and periplasm. The channel’s periplasmic exit is sealed by detergent molecules that block solvent permeation. Molecular dynamics simulation data suggest that, in a biological membrane, lipid molecules occupy this periplasmic exit and prevent water flux in the transporter’s resting state

Assessing and Selecting Sustainable and Resilient Suppliers in Agri-Food Supply Chains Using Artificial Intelligence: A Short Review

[EN] The supplier evaluation and selection process is critical to increase the sustainability and resilience of the agri-food supply chain. Therefore, in this sector, it is necessary to consider sustainability and resilience criteria in the supplier evaluation and selection process. The use of arti¿cial intelligence techniques allows managing of a lot of information and the reduction of uncertainty for decision making. The objective of this article is to analyze articles that address the selection of suppliers in agrifood supply chains that pursue to increase their sustainability and resilience by using arti¿cial intelligence techniques to analyze the techniques and criteria used and draw conclusions.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.Zavala-Alcívar, A.; Verdecho Sáez, MJ.; Alfaro Saiz, JJ. (2020). Assessing and Selecting Sustainable and Resilient Suppliers in Agri-Food Supply Chains Using Artificial Intelligence: A Short Review. IFIP Advances in Information and Communication Technology. 598:501-510. https://doi.org/10.1007/978-3-030-62412-5_41S501510598Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S.: Quantitative models for sustainable supply chain management: developments and directions. Eur. J. Oper. Res. 233, 299–312 (2014)Ocampo, L.A., Abad, G.K.M., Cabusas, K.G.L., Padon, M.L.A., Sevilla, N.C.: Recent approaches to supplier selection: a review of literature within 2006–2016. Int. J. Integr. Supply Manage. 12, 22–68 (2018)Valipour, S., Safaei, A.: A resilience approach for supplier selection: using Fuzzy analytic network process and grey VIKOR techniques. J. Clean. Prod. 161, 431–451 (2017)Amindoust, A.: A resilient-sustainable based supplier selection model using a hybrid intelligent method. Comput. Ind. Eng. 126, 122–135 (2018)Zavala-Alcívar, A., Verdecho, M.-J., Alfaro-Saiz, J.-J.: A conceptual framework to manage resilience and increase sustainability in the supply chain. Sustainability 12(16), 6300 (2020)Villalobos, J.R., Soto-Silva, W.E., González-Araya, M.C., González-Ramirez, R.G.: Research directions in technology development to support real-time decisions of fresh produce logistics: A review and research agenda. Comput. Electron. Agric. 167, 105092 (2019)Ristono, A., Santoso, P.B., Tama, I.P.: A literature review of design of criteria for supplier selection. J. Ind. Eng. Manage. 11, 680–696 (2018)Torres-Ruiz, A., Ravindran, A.R.: Multiple criteria framework for the sustainability risk assessment of a supplier portfolio. J. Clean. Prod. 172, 4478–4493 (2018)Setak, M., Sharifi, S., Alimohammadian, A.: Supplier selection and order allocation models in supply chain management: a review. World Appl. Sci. J. 18, 55–72 (2012)Ravindran, A.R., Warsing, D.P.: Supplier selection models and methods. In: Supply Chain Engineering: Models and Applications. Taylor and Francis Group, Boca Raton, Florida (2013)De Boer, L., Labro, E., Morlacchi, P.: A review of methods supporting supplier selection. Eur. J. Purch. Supply Manage. 7, 75–89 (2011)De Felice, F., Deldoost, M.H., Faizollahi, M., Petrillo, A.: Performance measurement model for the supplier selection based on AHP. Int. J. Eng. Bus. Manag. 7, 1–13 (2015)Zimmer, K., Fröhling, M., Schultmann, F.: Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. Int. J. Prod. Res. 54, 1412–1442 (2016)Christopher, M., Peck, H.: Building the resilient supply chain. Int. J. Logist. Manag. 15, 1–14 (2014)Ali, A., Mahfouz, A., Arisha, A.: Analysing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review. Supply Chain Manage. 22, 16–39 (2017)Verdecho, M., Alarcón-Valero, F., Pérez-Perales, D., et al.: A methodology to select suppliers to increase sustainability within supply chains. Cent. Eur. J. Oper. Res. (2020). https://doi.org/10.1007/s10100-019-00668-3Rabelo, L., Bhide, S., Gutierrez, E.: Artificial Intelligence: Advances in Research and Applications. Nova Science Publishers, Inc., Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States (2017)Denyer, D., Tranfield, D.: Producing a systematic review. In: The Sage Handbook of Organizational Research Methods. SAGE Publications Ltd., pp. 671–689 (2019)Chen, Y.-J.: Structured methodology for supplier selection and evaluation in a supply chain. Inf. Sci. (Ny) 181, 1651–1670 (2011)Hamdi, F., Ghorbel, A., Masmoudi, F., Dupont, L.: Optimization of a supply portfolio in the context of supply chain risk management: literature review. J. Intell. Manuf. 29(4), 763–788 (2015). https://doi.org/10.1007/s10845-015-1128-3Kumar, V., Srinivasan, S., Das, S.: Optimal solution for supplier selection based on SMART fuzzy case base approach. In: 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems. SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems. ISIS 2014, Institute of Electrical and Electronics Engineers Inc., Department of Computer Science, IISJ Yokohama, Tokai Chiba, Japan, pp. 386–391 (2014)Jahani, A., Murad, M.A.A., bin Sulaiman, M.N., Selamat, M.H.: An agent-based supplier selection framework: Fuzzy case-based reasoning perspective. Strateg. Outsourcing 8, 180–205 (2015)Wang, Q.: Hybrid knowledge-based flexible supplier selection. In: 8th International Conference on Management of e-Commerce and e-Government. ICMeCG 2014. Institute of Electrical and Electronics Engineers Inc., Department of Information Management, Shanghai Finance University, Shanghai, China, pp. 235–239 (2014)Bai, C., Sarkis, J.: Green supplier development: analytical evaluation using rough set theory. J. Clean. Prod. 18, 1200–1210 (2010)Bai, C., Sarkis, J.: Integrating sustainability into supplier selection with grey system and rough set methodologies. Int. J. Prod. Econ. 124, 252–264 (2010)Guo, F., Lu, Q.: Partner selection optimization model of agricultural enterprises in supply chain. Adv. J. Food Sci. Technol. 5, 1285–1291 (2013)Azadnia, A.H., Saman, M.Z.M., Wong, K.Y.: Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process. Int. J. Prod. Res. 53, 383–408 (2015)Miranda-Ackerman, M.A., Azzaro-Pantel, C., Aguilar-Lasserre, A.A.: A green supply chain network design framework for the processed food industry: application to the orange juice agrofood cluster. Comput. Ind. Eng. 109, 369–389 (2017)Hajikhani, A., Khalilzadeh, M., Sadjadi, S.J.: A fuzzy multi-objective multi-product supplier selection and order-allocation problem in supply chain under coverage and price considerations: an urban agricultural case study. Sci. Iran. 25, 431–449 (2018)Zhang, H., Cui, Y.: A model combining a Bayesian network with a modified genetic algorithm for green supplier selection. Simulation 95, 1165–1183 (2019)Yadav, S., Garg, D., Luthra, S.: Selection of third-party logistics services for internet of things-based agriculture supply chain management. Int. J. Logist. Syst. Manage. 35, 204–230 (2020)Yazdani, M., Wang, Z.X., Chan, F.T.S.: A decision support model based on the combined structure of DEMATEL, QFD and fuzzy values. Soft. Comput. 24(16), 12449–12468 (2020). https://doi.org/10.1007/s00500-020-04685-2Zhang, H., Feng, H., Cui, Y., Wang, Y.: A fuzzy Bayesian network model for quality control in O2O e-commerce. Int. J. Comput. Commun. Control 15(1), (2020). article number 1003. https://doi.org/10.15837/ijccc.2020.1.3783Amiri, S.A.H.S., Zahedi, A., Kazemi, M., Soroor, J., Hajiaghaei-Keshteli, M.: Determination of the optimal sales level of perishable goods in a two-echelon supply chain network. Comput. Ind. Eng. 139, 106156 (2020)Roy, S., et al.: A framework for sustainable supplier selection with transportation criteria. Int. J. Sustain. Eng. 13(2), 77–92 (2020)Parkouhi, S.V., Ghadikolaei, A.S., Lajimi, H.F.: Resilient supplier selection and segmentation in grey environment. J. Clean. Prod. 207, 1123–1137 (2019)Camarinha-Matos, L.M., Afsarmanesh, H., Galeano, N., Molina, A.: Collaborative networked organizations – concepts and practice in manufacturing enterprises. Comput. Ind. Eng. 57, 46–60 (2009)Lezoche, M., Panetto, H., Kacprzyk, J., Hernandez, J., Díaz, M.A.: Agri-food 4.0: a survey of the supply chains and technologies for the future agriculture. Comput. Ind. 117, 103187 (2020)Alikhani, R., Torabi, S., Altay, N.: Strategic supplier selection under sustainability and risk criteria. Int. J. Prod. Econ. 208, 69–82 (2019

Indication for the disappearance of reactor electron antineutrinos in the Double Chooz experiment

The Double Chooz Experiment presents an indication of reactor electron antineutrino disappearance consistent with neutrino oscillations. A ratio of 0.944 $\pm$ 0.016 (stat) $\pm$ 0.040 (syst) observed to predicted events was obtained in 101 days of running at the Chooz Nuclear Power Plant in France, with two 4.25 GW$_{th}$ reactors. The results were obtained from a single 10 m$^3$ fiducial volume detector located 1050 m from the two reactor cores. The reactor antineutrino flux prediction used the Bugey4 measurement as an anchor point. The deficit can be interpreted as an indication of a non-zero value of the still unmeasured neutrino mixing parameter \sang. Analyzing both the rate of the prompt positrons and their energy spectrum we find \sang = 0.086 $\pm$ 0.041 (stat) $\pm$ 0.030 (syst), or, at 90% CL, 0.015 $<$ \sang $\ <$ 0.16.Comment: 7 pages, 4 figures, (new version after PRL referee's comments

Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events

[EN] Agri-food supply chains (AFSCs) are very vulnerable to high risks such as pandemics, causing economic and social impacts mainly on the most vulnerable population. Thus, it is a priority to implement resilient strategies that enable AFSCs to resist, respond and adapt to new market challenges. At the same time, implementing resilient strategies impact on the social, economic and environmental dimensions of sustainability. The objective of this paper is twofold: analyze resilient strategies on AFSCs in the literature and identify how these resilient strategies applied in the face of high risks affect the achievement of sustainability dimensions. The analysis of the articles is carried out in three points: consequences faced by agri-food supply chains due to high risks, strategies applicable in AFSCs, and relationship between resilient strategies and the achievement of sustainability dimensions.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.Zavala-Alcívar, A.; Verdecho Sáez, MJ.; Alfaro Saiz, JJ. (2020). Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events. IFIP Advances in Information and Communication Technology. 598:560-570. https://doi.org/10.1007/978-3-030-62412-5_46S560570598Gray, R.: Agriculture, transportation, and the COVID-19 crisis. Can. J. Agric. Econ. 68, 239–243 (2020)Queiroz, M.M., Ivanov, D., Dolgui, A., Fosso Wamba, S.: Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-020-03685-7Hobbs, J.: Food supply chains during the COVID-19 pandemic. Can. J. Agric. Econ. 68, 171–176 (2020)Shashi, P., Centobelli, P., Cerchione, R., Ertz, M.: Managing supply chain resilience to pursue business and environmental strategies. Bus. Strateg. Environ. 29(3), 1215–1246 (2019)Ivanov, D.: Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp. Res. Part E Logist. Transp. Rev. 136, 101922 (2020)Mamani, H., Chick, S.E., Simchi-Levi, D.: A game-theoretic model of international influenza vaccination coordination. Manage. Sci. 59(7), 1650–1670 (2013)Liu, M., Zhang, D.: A dynamic logistics model for medical resources allocation in an epidemic control with demand forecast updating. J. Oper. Res. Soc. 67, 841–852 (2016)Hessel, L.: Pandemic influenza vaccines: meeting the supply, distribution and deployment challenges. Influenza Other Respir. Viruses 3, 165–170 (2009)Orenstein, W., Schaffner, W.: Lessons learned: role of influenza vaccine production, distribution, supply, and demand—what it means for the provider. Am. J. Med. 121, S22–S27 (2008)Büyüktahtakın, I., Des-Bordes, E., Kıbış, E.: A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa. Eur. J. Oper. Res. 26, 1046–1063 (2018)Anparasan, A., Lejeune, M.: Analyzing the response to epidemics: concept of evidence-based Haddon matrix. J. Humanit. Logist. Supply Chain Manag. 7, 266–283 (2017)Anparasan, A.A., Lejeune, M.A.: Data laboratory for supply chain response models during epidemic outbreaks. Ann. Oper. Res. 270, 53–64 (2018). https://doi.org/10.1007/s10479-017-2462-yAnparasan, A., Lejeune, M.: Resource deployment and donation allocation for epidemic outbreaks. Ann. Oper. Res. 283, 9–32 (2019). https://doi.org/10.1007/s10479-016-2392-0Ivanov, D., Dolgui, A.: Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. Int. J. Prod. Res. 58, 2904–2915 (2020)Ivanov, D.: Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-020-03640-6Ekici, A., Keskinocak, P., Swann, J.: Modeling influenza pandemic and planning food distribution. Manuf. Serv. Oper. Manag. 16, 11–27 (2014)Miranda, R., Schaffner, D.: Virus risk in the food supply chain. Curr. Op. Food Sci. 30, 43–48 (2019)Magalhães, A., Rossi, A., Zattar, I., Marques, M., Seleme, R.: Food traceability technologies and foodborne outbreak occurrences. Br. Food J. 121, 3362–3379 (2019)Denyer, D., Tranfield, D.: Producing a systematic review. In: Buchanan, D., Bryman, A. (eds.) The Sage Handbook of Organizational Research Methods, pp. 671–689. SAGE Publications Ltd., London (2009)Christopher, M., Peck, H.: Building the resilient supply chain. Int. J. Logist. Manag. 15, 1–14 (2004)Dolgui, A., Ivanov, D., Sokolov, B.: Ripple effect in the supply chain: an analysis and recent literature. Int. J. Prod. Res. 56, 414–430 (2018)Jüttner, U., Peck, H., Christopher, M.: Supply chain risk management: outlining an agenda for future research. Int. J. Logist. Res. 6, 197–210 (2003)Behzadi, G., O’Sullivan, M., Olsen, T., Zhang, A.: Agribusiness supply chain risk management: a review of quantitative decision models. Omega (United Kingdom) 79, 21–42 (2018)Kleindorfer, P., Saad, G.: Managing disruption risks in supply chains. Pr. Op. Man. 14, 53–68 (2005)Vishnu, C., Sridharan, R., Gunasekaran, A., Ram Kumar, P.: Strategic capabilities for managing risks in supply chains: current state and research futurities. J. Adv. Manag. Res. 17(2), 173–211 (2019)Deaton, B., Deaton, B.: Food security and Canada’s agricultural system challenged by COVID-19. Can. J. Agric. Econ. 68(2), 143–149 (2020)Richards, T., Rickard, B.: COVID-19 impact on fruit and vegetable markets. C. J. Ag. Ec. 68(2), 189–194 (2020)Larue, B.: Labor issues and COVID-19. Can. J. Agric. Econ. Can. d’agroeconomie (2020). https://doi.org/10.1111/cjag.12233Hollnagel, E.: Epilogue: RAG: the resilience analysis grid. In: Hollnagel, E., Paries, J., Woods, D., Wreathall, J. (eds.) Resilience Engineering in Practice: A Guidebook. Ashgate Pr., pp. 275–296 (2011)Ponomarov, S., Holcomb, M.: Understanding the concept of supply chain resilience. Int. J. Logist. Manag. 20, 124–143 (2009)Wu, T., Huang, S., Blackhurst, J., Zhang, X., Wang, S.: Supply chain risk management: an agent-based simulation to study the impact of retail stockouts. IEEE Trans. Eng. Manag. 60, 676–686 (2013)Schmitt, A., Singh, M.: A quantitative analysis of disruption risk in a multi-echelon supply chain. Int. J. Prod. Econ. 139, 22–32 (2012)Vroegindewey, R., Hodbod, J.: Resilience of agricultural value chains in developing country contexts: a framework and assessment approach. Sustainability 10, 916 (2018)Behzadi, G., O’Sullivan, M., Olsen, T., Scrimgeour, F., Zhang, A.: Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain. Int. J. Prod. Econ. 191, 207–220 (2017)Bottani, E., Murino, T., Schiavo, M., Akkerman, R.: Resilient food supply chain design: modelling framework and metaheuristic solution approach. Comput. Ind. Eng. 135, 177–198 (2019)Meuwissen, M., et al.: A framework to assess the resilience of farming systems. Agric. Syst. 176, 102656 (2019)Dutta, P., Shrivastava, H.: The design and planning of an integrated supply chain for perishable products under uncertainties: a case study in milk industry. J. Model. Manag. (2020). https://doi.org/10.1108/JM2-03-2019-0071Aboah, J., Wilson, M., Rich, M., Lyne, M.: Operationalising resilience in tropical agricultural value chains. Supply Chain Manag. 24, 271–300 (2019)Ravulakollu, A., Urciuoli, L., Rukanova, B., Tan, Y., Hakvoort, R.: Risk based framework for assessing resilience in a complex multi-actor supply chain domain. Supply Chain Forum 19, 266–281 (2018)Das, K.: Integrating lean, green, and resilience criteria in designing a sustainable food supply chain. Proc. Int. Conf. Ind. Eng. Oper. Manag. 2018, 462–473 (2018)Zhu, Q., Krikke, H.: Managing a sustainable and resilient perishable food supply chain (PFSC) after an outbreak. Sustainability 12, 5004 (2020)Rozhkov, M., Ivanov, D.: Contingency production-inventory control policy for capacity disruptions in the retail supply chain with perishable products. IFAC-PapersOnLine 51, 1448–1452 (2018)Yavari, M., Zaker, H.: Designing a resilient-green closed loop supply chain network for perishable products by considering disruption in both supply chain and power networks. Comput. Chem. Eng. 134, 106680 (2020)Ye, F., Hou, G., Li, Y., Fu, S.: Managing bioethanol supply chain resiliency: a risk-sharing model to mitigate yield uncertainty risk. Ind. Manag. Data Syst. 118, 1510–1527 (2018)Jabbarzadeh, A., Fahimnia, B., Sheu, J., Moghadam, H.: Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transp. Res. Part B Methodol. 94, 121–149 (2016)O’Leary, D.: Evolving information systems and technology research issues for COVID-19 and other pandemics. J. Organ. Comput. Electron. Commer. 30, 1–8 (2020)Zavala-Alcívar, A., Verdecho, M.-J., Alfaro-Saiz, J.-J.: A conceptual framework to manage resilience and increase sustainability in the supply chain. Sustainability 12(16), 6300 (2020)Fahimni, B., Jabbarzadeh, A.: Marrying supply chain sustainability and resilience: a match made in heaven. Transp. Res. Part E Logist. Transp. Rev. 91, 306–324 (2016)Verdecho, M.-J., Alarcón-Valero, F., Pérez-Perales, D., Alfaro-Saiz, J.-J., Rodríguez-Rodríguez, R.: A methodology to select suppliers to increase sustainability within supply chains. CEJOR (2020). https://doi.org/10.1007/s10100-019-00668-3Bai, C., Sarkis, J.: Integrating sustainability into supplier selection with grey system and rough set methodologies. Int. J. Prod. Econ. 124(1), 252–264 (2010)Bai, C., Sarkis, J.: Green supplier development: analytical evaluation using rough set theory. J. Clean. Prod. 18, 1200–1210 (2010)Valipour, S., Safaei, A., Fallah, H.: Resilient supplier selection and segmentation in grey environment. J. Clean. Prod. 207, 1123–1137 (2019)Zimmer, K., Fröhling, M., Schultmann, F.: Sustainable supplier management – a review of models supporting sustainable supplier selection, monitoring and development. Int. J. Prod. Res. 54, 1412–1442 (2016)Yang, S., Xiao, Y., Kuo, Y.: The supply chain design for perishable food with stochastic demand. Sustainability 9, 1195 (2017)Zahiri, B., Zhuang, J., Mohammadi, M.: Toward an integrated sustainable-resilient supply chain: a pharmaceutical case study. Transp. Res. Part E Logist. Transp. Rev. 103, 109–142 (2017)Duong, L., Chong, J.: Supply chain collaboration in the presence of disruptions: a literature review. Int. J. Prod. Res. 58, 3488–3507 (2020

Beaked whales respond to simulated and actual navy sonar

This article is distributed under the terms of the Creative Commons Public Domain declaration. The definitive version was published in PLoS One 6 (2011): e17009, doi:10.1371/journal.pone.0017009.Beaked whales have mass stranded during some naval sonar exercises, but the cause is unknown. They are difficult to sight but can reliably be detected by listening for echolocation clicks produced during deep foraging dives. Listening for these clicks, we documented Blainville's beaked whales, Mesoplodon densirostris, in a naval underwater range where sonars are in regular use near Andros Island, Bahamas. An array of bottom-mounted hydrophones can detect beaked whales when they click anywhere within the range. We used two complementary methods to investigate behavioral responses of beaked whales to sonar: an opportunistic approach that monitored whale responses to multi-day naval exercises involving tactical mid-frequency sonars, and an experimental approach using playbacks of simulated sonar and control sounds to whales tagged with a device that records sound, movement, and orientation. Here we show that in both exposure conditions beaked whales stopped echolocating during deep foraging dives and moved away. During actual sonar exercises, beaked whales were primarily detected near the periphery of the range, on average 16 km away from the sonar transmissions. Once the exercise stopped, beaked whales gradually filled in the center of the range over 2–3 days. A satellite tagged whale moved outside the range during an exercise, returning over 2–3 days post-exercise. The experimental approach used tags to measure acoustic exposure and behavioral reactions of beaked whales to one controlled exposure each of simulated military sonar, killer whale calls, and band-limited noise. The beaked whales reacted to these three sound playbacks at sound pressure levels below 142 dB re 1 µPa by stopping echolocation followed by unusually long and slow ascents from their foraging dives. The combined results indicate similar disruption of foraging behavior and avoidance by beaked whales in the two different contexts, at exposures well below those used by regulators to define disturbance.The research reported here was financially supported by the United States (U.S.) Office of Naval Research (www.onr.navy.mil) Grants N00014-07-10988, N00014-07-11023, N00014-08-10990; the U.S. Strategic Environmental Research and Development Program (www.serdp.org) Grant SI-1539, the Environmental Readiness Division of the U.S. Navy (http://www.navy.mil/local/n45/), the U.S. Chief of Naval Operations Submarine Warfare Division (Undersea Surveillance), the U.S. National Oceanic and Atmospheric Administration (National Marine Fisheries Service, Office of Science and Technology) (http://www.st.nmfs.noaa.gov/), U.S. National Oceanic and Atmospheric Administration Ocean Acoustics Program (http://www.nmfs.noaa.gov/pr/acoustics/), and the Joint Industry Program on Sound and Marine Life of the International Association of Oil and Gas Producers (www.soundandmarinelife.org)

Clinical Pattern of Tolvaptan-Associated Liver Injury in Subjects with Autosomal Dominant Polycystic Kidney Disease: Analysis of Clinical Trials Database

IntroductionSubjects with autosomal dominant polycystic kidney disease (ADPKD) who were taking tolvaptan experienced aminotransferase elevations more frequently than those on placebo in the TEMPO 3:4 (Tolvaptan Efficacy and Safety in Management of Autosomal Dominant Polycystic Kidney Disease and its Outcomes) clinical trial.MethodsAn independent, blinded, expert Hepatic Adjudication Committee re-examined data from TEMPO 3:4 and its open-label extension TEMPO 4:4, as well as from long-term (>14months) non-ADPKD tolvaptan trials, using the 5-point Drug-Induced Liver Injury Network classification.ResultsIn TEMPO 3:4, 1445 subjects were randomized 2:1 (tolvaptan vs. placebo) and 1441 had post-baseline assessments of hepatic injury. Sixteen patients on tolvaptan and one on placebo had significant aminotransferase elevations judged to be at least probably related to study drug. No association with dose or systemic exposure was found. Two of 957 subjects taking tolvaptan (0.2%) and zero of 484 taking placebo met the definition of a Hy’s Law case. One additional Hy’s Law case was identified in a TEMPO 4:4 subject who had received placebo in the lead study. The onset of a hepatocellular injury occurred between 3 and 18months after starting tolvaptan, with gradual resolution over the subsequent 1–4months. None of the events were associated with liver failure or chronic liver injury/dysfunction. No imbalance in hepatic events was observed between tolvaptan and placebo in lower-dose clinical trials of patients with hyponatremia, heart failure, or cirrhosis.ConclusionsAlthough hepatocellular injury following long-term tolvaptan treatment in ADPKD subjects was infrequent and reversible, the potential for serious irreversible injury exists. Regular monitoring of transaminase levels is warranted in this patient population

Opioid receptors in GtoPdb v.2023.1

Opioid and opioid-like receptors are activated by a variety of endogenous peptides including [Met]enkephalin (met), [Leu]enkephalin (leu), &#946;-endorphin (&#946;-end), &#945;-neodynorphin, dynorphin A (dynA), dynorphin B (dynB), big dynorphin (Big dyn), nociceptin/orphanin FQ (N/OFQ); endomorphin-1 and endomorphin-2 are also potential endogenous peptides. The Greek letter nomenclature for the opioid receptors, &#956;, &#948; and &#954;, is well established, and NC-IUPHAR considers this nomenclature appropriate, along with the symbols spelled out (mu, delta, and kappa), and the acronyms, MOP, DOP, and KOP [124, 101, 92]. However the acronyms MOR, DOR and KOR are still widely used in the literature. The human N/OFQ receptor, NOP, is considered 'opioid-related' rather than opioid because, while it exhibits a high degree of structural homology with the conventional opioid receptors [304], it displays a distinct pharmacology. Currently there are numerous clinically used drugs, such as morphine and many other opioid analgesics, as well as antagonists such as naloxone. The majority of clinically used opiates are relatively selective &#956; agonists or partial agonists, though there are some &#956;/&#954; compounds, such as butorphanol, in clinical use. &#954; opioid agonists, such as the alkaloid nalfurafine and the peripherally acting peptide difelikefalin, are in clinical use for itch

Opioid receptors in GtoPdb v.2021.3

Opioid and opioid-like receptors are activated by a variety of endogenous peptides including [Met]enkephalin (met), [Leu]enkephalin (leu), &#946;-endorphin (&#946;-end), &#945;-neodynorphin, dynorphin A (dynA), dynorphin B (dynB), big dynorphin (Big dyn), nociceptin/orphanin FQ (N/OFQ); endomorphin-1 and endomorphin-2 are also potential endogenous peptides. The Greek letter nomenclature for the opioid receptors, &#956;, &#948; and &#954;, is well established, and NC-IUPHAR considers this nomenclature appropriate, along with the symbols spelled out (mu, delta, and kappa), and the acronyms, MOP, DOP, and KOP. [121, 100, 91]. The human N/OFQ receptor, NOP, is considered 'opioid-related' rather than opioid because, while it exhibits a high degree of structural homology with the conventional opioid receptors [294], it displays a distinct pharmacology. Currently there are numerous clinically used drugs, such as morphine and many other opioid analgesics, as well as antagonists such as naloxone, however only for the &#956; receptor

High-Pressure Synthesis of β-Ir4B5 and Determination of the Compressibility of Various Iridium Borides

"A new iridium boride, beta-Ir4B5, was synthesized under high-pressure/high-temperature conditions of 10.5 GPa and 1500 degrees C in a multianvil press with a Walker-type module. The new modification beta-Ir4B5 crystallizes in a new structure type in the orthorhombic space group Pnma (no. 62) with the lattice parameters a = 10.772(2) angstrom, b = 2.844(1) angstrom, and c = 6.052(2) angstrom with R1 = 0.0286, wR2 = 0.0642 (all data), and Z = 2. The structure was determined by single-crystal X-ray and neutron powder diffraction on samples enriched in B-11. The compound is built up by an alternating stacking of boron and iridium layers with the sequence ABA'B'. Additionally, microcalorimetry, hardness, and compressibility measurements of the binary iridium borides alpha-Ir4B5, beta-Ir4B5, Ir5B4, hexagonal Ir4B3-x and orthorhombic Ir4B3-x were carried out and theoretical investigations based on density function theory (DFT) were employed to complement a comprehensive evaluation of structure-property relations. The incorporation of boron into the structures does not enhance the compressibility but leads to a significant reduction of the bulk moduli and elastic constants in comparison to elemental iridium.