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We study the quantum version of a decision tree classifier to fill the gap between quantum computation and machine learning. The quantum entropy impurity criterion which is used to determine which node should be split is presented in the paper. By using the quantum fidelity measure between two quantum states, we cluster the training data into subclasses so that the quantum decision tree can manipulate quantum states. We also propose algorithms constructing the quantum decision tree and searching for a target class over the tree for a new quantum object.

We consider the problem of finding one or more desired items out of an unsorted database. Patel has shown that if the database permits quantum queries, then mere digitization is sufficient for efficient search for one desired item. The algorithm, called factorized quantum search algorithm, presented by him can locate the desired item in an unsorted database using O( $$log_4N$$ ) queries to factorized oracles. But the algorithm requires that all the attribute values must be distinct from each other. In this paper, we discuss how to make a database satisfy the requirements, and present a quantum search engine based on the algorithm. Our goal is achieved by introducing auxiliary files for the attribute values that are not distinct, and converting every complex query request into a sequence of calls to factorized quantum search algorithm. The query complexity of our algorithm is O( $$log_4N$$ ) for most cases.

Bacterial cell division occurs in conjunction with the formation of a cytokinetic Z-ring structure comprised of FtsZ subunits. Agents that can disrupt Z-ring formation have the potential, through this unique mechanism, to be effective against several of the newly emerging multi-drug resistant strains of infectious bacteria. 1- and 12-Aryl substituted benzo[c]phenanthridines have been identified as antibacterial agents that could exert their activity by disruption of Z-ring formation. Substituted 4- and 5-amino-1-phenylnaphthalenes represent substructures within the pharmacophore of these benzo[c]phenanthridines. Several 4- and 5-substituted 1-phenylnaphthalenes were synthesized and evaluated for antibacterial activity against Staphylococcus aureus and Enterococcus faecalis. The impact of select compounds on the polymerization dynamics of S. aureus FtsZ was also assessed.

Power consumption of routing protocols is one of the main issues that wireless sensor networks (WSNs) encounter in their lifetime. Low-Energy Adaptive Clustering Hierarchy (LEACH) clustering protocol was introduced to reduce power consumption. However, in LEACH, power consumption increases massively as the distance between sink node and cluster heads (CHs) increases. This drawback introduces distance as one major issue in LEACH since it does not contain routing. In this work, a LEACH based protocol consists of three layers (LEACH-T) is proposed. Each layer has its own CHs. The layers attempt to reduce the distance between sink node and CHs. The third layer is utilized if the distances between CHs and sink node exceed a threshold value. To measure the performance of LEACH-T, simulation is utilized to compare its consumption to pure LEACH. Results show that the proposed protocol extends network lifetime and reduces power consumption.

We present a fully secure ciphertext-policy attribute-based encryption (CP-ABE) scheme in prime order group by using a tool which can convert composite order paring-based encryption scheme into prime order one. Although, a fully secure CP-ABE scheme in prime order group has been proposed recently, our scheme needs less master secret key size and can be seen as a different version of it. Besides, the sketch of our scheme's security proof is also given.

Document retrieval is the basic task of search engines, and seize amount of attention by the pattern matching community. In this paper, we focused on the dynamic version of this problem, in which the text insertion and deletion is allowable. By using the generalized suffix array and other data structure, we proposed a new index structure. Our scheme achieved better time complexity than the existing ones, and a bit more space overhead is needed as return.

In this context, we study three different strategies to improve the time complexity of the widely used adiabatic evolution algorithms when solving a particular class of quantum search problems where both the initial and final Hamiltonians are one-dimensional projector Hamiltonians on the corresponding ground state. After some simple analysis, we find the time complexity improvement is always accompanied by the increase of some other “complexities” that should be considered. But this just gives the implication that more feasibilities can be achieved in adiabatic evolution based quantum algorithms over the circuit model, even though the equivalence between the two has been shown. In addition, we also give a rough comparison between these different models for the speedup of the problem.

In this paper, three different approaches for implementing a quantum search algorithm by adiabatic evolution are shown. As expected, either one of them can provide a quadratic speed up as opposed to the classical search algorithm. This implies that adiabatic evolution based quantum computation gives more feasibilities than the quantum circuit model, although the equivalence between them has already been proven in the corresponding literature.

Title compound (X) is synthesized using the cyclization of protected nitroalcohol (VII) as the key step.

Galaxy classification has an important role in understanding the formation of galaxies and the evaluation of our universe. Most of the machine learning methods were used to improve galaxy image classification. However, these methods suffer from some limitations, such as getting stuck in local point and slow convergence. Therefore, an alternative method to enhance the performance of galaxy images classification and avoid the limitations of other methods is proposed. The proposed method for galaxy classification (called BSOMFOG) is based on an improvement in the brain storm optimization (BSO) through combining it with the moth flame optimization (MFO). In this modified version of BSO (called BSOMFO), the MFO algorithm works as a local search operator to enhance the exploitation ability of BSO. The performance of the BSOMFO algorithm is compared against other algorithms through two experiments. In the first one, a set of 15 global optimization problems is used to evaluate the ability of the BSOMFO algorithm to find the solution for these problems. Meanwhile, in the second experiment, the BSOMFO is included in the BSOMFOG framework to improve the classification of the galaxy images into three classes, namely, spiral, lenticular, and elliptical. BSOMFOG consists of three phases: the first phase is to extract the shape, color, and texture features from the galaxy images, while the second phase used the BSOMFO algorithm to select the relevant features from the extracted features. The last phase is to evaluate the selected features through classification using the k -nearest neighbor classifier. The experimental results show that the BSOMFO algorithm provides better results than the traditional BSO algorithm and other metaheuristic algorithms to solve the optimization problem. Moreover, it makes the proposed BSOMFOG framework improves the classification accuracy (∼97 % ) for galaxy images, and its general purpose makes it suitable for automatic classification of galaxies.

This paper presents an improved quantum-behaved particle swarm optimization (IQPSO) for short-term combined economic emission hydrothermal scheduling, which is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing emission cost. In this paper, quantum-behaved particle swarm optimization is improved employing heuristic strategies in order to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also devised to handle the reservoir storage volumes constraints. To show feasibility and effectiveness of the proposed method, different case studies, such as economic load scheduling (ELS), economic emission scheduling (EES) and combined economic emission scheduling (CEES) in hydrothermal scheduling, are carried out and the test results are compared with those of other methods reported in the literature. It is also very important to note that the proposed method is capable of yielding higher-quality solutions while strictly satisfying all constraints of the test system.

This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.

This paper presents a novel approach based on differential evolution for short-term combined economic emission hydrothermal scheduling, which is formulated as a bi-objective problem: 1) minimizing fuel cost and 2) minimizing emission cost. A penalty factor approach is employed to convert the bi-objective problem into a single objective one. In the proposed approach, heuristic rules are proposed to handle water dynamic balance constraints and heuristic strategies based on priority list are employed to repair active power balance constraints violations. A feasibility-based selection technique is also devised to handle the reservoir storage volumes constraints. The feasibility and effectiveness of the proposed approach are demonstrated and the test results are compared with those of other methods reported in the literature. Numerical experiments show that the proposed method can obtain better-quality solutions with higher precision than any other optimization methods. Hence, the proposed method can well be extended for solving the large-scale hydrothermal sched-uling.

High-throughput screening of a subset of the JJ compound library containing the carboxylic acid functional group uncovered a bromophenyl derivative as a moderate potent GPR40 agonist. Chemical elaboration of this bromophenyl led to the discovery of a novel series of GPR40 agonists with submicromolar potency. Among them, 22 and 24 behaved as full agonists when compared to the endogenous GPR40 ligand linolenic acid in a functional Ca+2 flux assay in HEK cells expressing GPR40 receptor. Several GPR40 agonists have also demonstrated the ability to induce glucose-mediated insulin secretion in the mouse MIN6 pancreatic beta-cell line. Our data supports the hypothesis that GPR40 may play an important role in fatty acid-mediated glucose-dependent insulin secretion. Compound 22 exhibited good pharmacokinetic profile in rat and may serve as a good candidate for in vivo study and may help to determine if GPR40 agonists would be beneficial in the treatment of type II diabetes.

High throughput screening identified 2-acetamido-thiazolylthio acetic ester 1 as an inhibitor of cyclin-dependent kinase 2 (CDK2). Because this compound is inactive in cells and unstable in plasma, we have stabilized it to metabolic hydrolysis by replacing the ester moiety with a 5-ethyl-substituted oxazole as in compound 14. Combinatorial and parallel synthesis provided a rapid analysis of the structure-activity relationship (SAR) for these inhibitors of CDK2, and over 100 analogues with IC(50) values in the 1-10 nM range were rapidly prepared. The X-ray crystallographic data of the inhibitors bound to the active site of CDK2 protein provided insight into the binding modes of these inhibitors, and the SAR of this series of analogues was rationalized. Many of these analogues displayed potent and broad spectrum antiproliferative activity across a panel of tumor cell lines in vitro. In addition, A2780 ovarian carcinoma cells undergo rapid apoptosis following exposure to CDK2 inhibitors of this class. Mechanism of action studies have confirmed that the phosphorylation of CDK2 substrates such as RB, histone H1, and DNA polymerase alpha (p70 subunit) is reduced in the presence of compound 14. Further optimization led to compounds such as water soluble 45, which possesses a favorable pharmacokinetic profile in mice and demonstrates significant antitumor activity in vivo in several murine and human models, including an engineered murine mammary tumor that overexpresses cyclin E, the coactivator of CDK2.

Mining association rules is one of the most well studied problems in data mining. Current algorithms for finding association rules require several passes over the databases, and obviously the role of I/O overhead is significant for very large databases. In this paper, we present MARC (Mining Association Rules using Clustering), a new algorithm that makes only one full pass over the database. Firstly, we partition the collection of transactions so that similar transactions fall into the same cluster. Then we mine association rules on the summaries of clusters instead of the entire data set. Consequently, a proper method for summarizing a cluster of transactions is proposed. The results of experiments show that the proposed algorithm can learn association rules efficiently in single database pass, and also show that MARC algorithm does not affect too much the accuracy of the association rules learned.

Association rules are useful for determining correlations between items and have applications in marketing, financial and retail sectors. Lots of algorithms have been proposed for finding the association rules in databases. Most of these algorithms treat each item as uniformity. However, in real applications, the user may have more interest in the rules that contain those fashionable items that occur frequently. Usually too many outdated items exist in databases, but they seldom occur recently. Those outdated items hamper us to find the interesting rules efficiently and effectively. Another case is the user sometimes may want to mine the association rules with more emphasis on some items. To solve these problems, in this paper, we propose the vertical and mixed weighted association rules. We can divide the database into several time intervals, and assign a weight for each interval. Furthermore, we also assign a weight for each item to identify the important items. We present an algorithm MWAR (Mixed Weighted Association Rules) to handle the problem of mining mixed weighted association rules. The experiments show that the rules from our methods have much better predictive ability on future data. We also demonstrate the efficiency of our methods on real data and synthetic datasets.

In this paper we focus on beacon-based routing optimization of data-gathering wireless sensor networks. Such a network consists of a sink node and a number of scattered sensor nodes which send data packets back to the sink in a multihop fashion. Usually the routing messages, denoted as “beacon”, are initiated by the sink and spread out the whole network by flooding or gossiping. We believe that the mechanism of selecting upstream node as relay has great impact to the packet delivery performance. In this work several beacon rebroadcasting mechanism are inspected and the data packet delivery performance of them are compared by simulations. The simulation result shows that current Zigbee tree-based routing has great flaw in large scale networks. Based on the comparison of the results we propose an optimal link-state beacon-based routing (OLSBR) which shows that parent selection by link state can greatly improve the packet delivery performance.

In this paper, we study two different nonlinear interpolating paths in adiabatic evolution algorithms for solving a particular class of quantum search problems where both the initial and final Hamiltonian are one-dimensional projector Hamiltonians on the corresponding ground state. If the overlap between the initial state and final state of the quantum system is not equal to zero, both of these models can provide a constant time speedup over the usual adiabatic algorithms by increasing some another corresponding "complexity". But when the initial state has a zero overlap with the solution state in the problem, the second model leads to an infinite time complexity of the algorithm for whatever interpolating functions being applied while the first one can still provide a constant running time. However, inspired by a related reference, a variant of the first model can be constructed which also fails for the problem when the overlap is exactly equal to zero if we want to make up the "intrinsic" fault of the second model-an increase in energy. Two concrete theorems are given to serve as explanations why neither of these two models can improve the usual adiabatic evolution algorithms for the phenomenon above. These just tell us what should be noted when using certain nonlinear evolution paths in adiabatic quantum algorithms for some special kind of problems., Comment: 11 pages

The general class of models of adiabatic evolution was proposed to speed up the usual adiabatic computation in the case of quantum search problem. It was shown [8] that, by temporarily increasing the ground state energy of a time-dependent Hamiltonian to a suitable quantity, the quantum computation can perform the calculation in time complexity O(1). But it is also known that if the overlap between the initial and final states of the system is zero, then the computation based on the generalized models of adiabatic evolution can break down completely. In this paper, we find another severe limitation for this class of adiabatic evolution-based algorithms, which should be taken into account in applications. That is, it is still possible that this kind of evolution designed to deal with the quantum search problem fails completely if the interpolating paths in the system Hamiltonian are chosen inappropriately, while the usual adiabatic evolutions can do the same job relatively effectively. This implies that it is not always recommendable to use nonlinear paths in adiabatic computation. On the contrary, the usual simple adiabatic evolution may be sufficient for effective use.

Authentication takes its place to grant authorized user a remote access to certain online resources. As well, prevent unauthorized user from accessing that resources. Unforyunatly most of authentication schemes consider only security factors without taking in consideration the communication resources required. Recently, Li et. al. proposed an enhanced smart card based remote user password authentication scheme. We analyzed their scheme and we pointed out that, their scheme required high communication overhead. Furthermore, their scheme suffer from forgery, user impersonation and server impersonation attacks. Through this paper to address aforesaid weaknesses, we propose a Lightweight communication overhead authentication scheme using smart card. The security and performance analysis shows that, our proposed scheme is lightweight communication and computation cost as well secure and can withstand wide spectrum of malicious attacks, like forgery, insider, replay and stolen smart card attack. Besides, our scheme encompasses desired security attributes. Therefore, it is suitable for practical use compared to other related scheme.

Berberine is a substituted dibenzo[a,g]quinolizin-7-ium derivative whose modest antibiotic activity is derived from its disruptive impact on the function of the essential bacterial cell division protein FtsZ. The present study reveals that the presence of a biphenyl substituent at either the 2- or 12-position of structurally-related dibenzo[a,g]quinolizin-7-ium derivatives significantly enhances antibacterial potency versus Staphylococcus aureus and Enterococcus faecalis. Studies with purified S. aureus FtsZ demonstrate that both 2- and 12-biphenyl dibenzo[a,g]quinolizin-7-ium derivatives act as enhancers of FtsZ self-polymerization.

Short-term combined economic emission hydrothermal scheduling (CEES) is a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. In this paper, quadratic approximation based differential evolution with valuable trade off approach (QADEVT) has been developed to solve the bi-objective hydrothermal scheduling problem. The practical hydrothermal system possesses various constraints which make the problem of finding global optimum difficult. In this paper, heuristic rules are proposed to handle the water dynamic balance constraints and heuristic strategies based on priority list are employed to handle active power balance constraints. A feasibility-based selection technique is also introduced to satisfy the reservoir storage volumes constraints. To demonstrate the superiority of the proposed approach, simulation results have been compared with those obtained by differential evolution (DE) and particle swarm optimization (PSO) with same heuristic strategies and the earlier reported methods available in literature. The simulation results reveal that the proposed approach is capable of efficiently providing superior solutions.

Title compound (X) is synthesized using the cyclization of protected nitroalcohol (VII) as the key step.

Eg5 is a kinesin whose inhibition leads to cycle arrest during mitosis, making it a potential therapeutic target in cancers. Circular dichroism and isothermal titration calorimetry of our pyrrolotriazine-4-one series of inhibitors with Eg5 motor domain revealed enhanced binding in the presence of adenosine 5′-diphosphate (ADP). Using this information, we studied the interaction of this series with ADP–Eg5 complexes using a thermal shift assay. We measured up to a 7 °C increase in the thermal melting ( T m ) of Eg5 for an inhibitor that produced IC 50 values of 60 and 130 nM in microtubule-dependent adenosine triphosphatase (ATPase) and cell-based cytotoxicity assays, respectively. In general, the inhibitor potency of the pyrrolotriazine-4-one series in in vitro biological assays correlated with the magnitude of the thermal stability enhancement of ADP–Eg5. The thermal shift assay also confirmed direct binding of Eg5 inhibitors identified in a high-throughput screen and demonstrated that the thermal shift assay is applicable to a range of chemotypes and can be useful in evaluating both potent (nM) and relatively weakly binding (μM) leads. Overall, the thermal shift assay was found to be an excellent biophysical method for evaluating direct binding of a large number of compounds to Eg5, and it complemented the catalytic assay screens by providing an alternative determination of inhibitor potency.

Distributed Hash Tables (DHTs) provide a scalable solution for data sharing in P2P systems. To achieve fault tolerance and ensure high data availability, DHTs typically rely on data replication. However, nodes churn, lacking of global view and existence of malicious nodes in P2P environment bring new challenges to the concurrency control for replicated data.Agreement-based algorithms require all-to-all communication among nodes, which does not scale well in P2P environment.Pessimistic approaches perform poorly when clients are malicious. Regards to replicated DHTs, an efficient Quorumbased optimistic concurrency control algorithm is proposed.Hybrid failure model is used to reduce the fault-tolerant cost. The responsibilities of data storage and concurrency control are separated to enable data reliability and data availability to be adjusted independently. Simulation shows the proposed algorithm can obviously improve system scalability and reduce the fault-tolerant cost

Based on the previous introduced quantum-behaved particle swarm optimization (QPSO), in this paper, a revised QPSO with hybrid cooperative and competitive mechanism is proposed. The cooperative and competitive mechanism improves the diversity of the swarm, so as to help the system escape from local optima and converge to global optima. Take full advantages of the cooperative and competitive search among different swarms, cooperative competitive quantum-behaved particle swarm optimization (COQPSO) makes the swarms more efficient in global search. The experimental results on test functions show that COQPSO with cooperative and competitive mechanism outperforms the QPSO and even can search out the minimum value for some test functions.

Opacity of the cornea is one of the important indices for estimating time of death. Now the work is done by forensic medical experts. An unbiased estimation method is needed. This paper proposed a method for finding the relationship between changes of cornea and postmortem intervals by processing and analyzing images. Firstly, a histogram based image segmentation method is proposed to extract corneal regions from pictures of rabbit's eye. Secondly, texture and color features are used to describe the extracted corneal regions. Those features are carefully chosen to represent the changes of cornea in different postmortem intervals. A KNN classifier is used to reveal the association of image features and postmortem intervals. The experimental results show that cornea image features can be used to automatically estimate postmortem interval.

Synthesis and SAR of substituted pyrrolotriazine-4-one analogues as Eg5 inhibitors are described. Many of these analogues displayed potent inhibitory activities in the Eg5 ATPase and A2780 cell proliferation assays. In addition, pyrrolotriazine-4-one analogue 26 demonstrated in vivo efficacy in an iv P388 murine leukemia model. Both NMR and X-ray crystallographic studies revealed that these analogues bind to an allosteric site on the Eg5 protein.

N-Acyl-2-aminothiazoles with nonaromatic acyl side chains containing a basic amine were found to be potent, selective inhibitors of CDK2/cycE which exhibit antitumor activity in mice. In particular, compound 21 [N-[5-[[[5-(1,1-dimethylethyl)-2-oxazolyl]methyl]thio]-2-thiazolyl]-4-piperidinecarboxamide, BMS-387032], has been identified as an ATP-competitive and CDK2-selective inhibitor which has been selected to enter Phase 1 human clinical trials as an antitumor agent. In a cell-free enzyme assay, 21 showed a CDK2/cycE IC(50) = 48 nM and was 10- and 20-fold selective over CDK1/cycB and CDK4/cycD, respectively. It was also highly selective over a panel of 12 unrelated kinases. Antiproliferative activity was established in an A2780 cellular cytotoxicity assay in which 21 showed an IC(50) = 95 nM. Metabolism and pharmacokinetic studies showed that 21 exhibited a plasma half-life of 5-7 h in three species and moderately low protein binding in both mouse (69%) and human (63%) serum. Dosed orally to mouse, rat, and dog, 21 showed 100%, 31%, and 28% bioavailability, respectively. As an antitumor agent in mice, 21 administered at its maximum-tolerated dose exhibited a clearly superior efficacy profile when compared to flavopiridol in both an ip/ip P388 murine tumor model and in a s.c./i.p. A2780 human ovarian carcinoma xenograft model.

N-Aryl aminothiazoles 6-9 were prepared from 2-bromothiazole 5 and found to be CDK inhibitors. In cells they act as potent cytotoxic agents. Selectivity for CDK1, CDK2, and CDK4 was dependent of the nature of the N-aryl group and distinct from the CDK2 selective N-acyl analogues. The N-2-pyridyl analogues 7 and 19 showed pan CDK inhibitory activity. Elaborated analogues 19 and 23 exhibited anticancer activity in mice against P388 murine leukemia. The solid-state structure of 7 bound to CDK2 shows a similar binding mode to the N-acyl analogues.

We present an alternate adiabatic evolution for the Deutsch—Jozsa problem. The biggest difference of our adiabatic evolution constructed here with those appearing before is that an alternate initial Hamiltonian is used for the adiabatic evolution, with which the evolution task can be finished in O(1) time complexity. Our construction mostly resembles the one discussed by Das et al. [Phys. Rev. A 65 (2002) 062310], except for the initial system Hamiltonian of the adiabatic algorithm.

We show that, through the approach of quantum adiabatic evolution, all of the usual quantum gates can be implemented efficiently, yielding running time of order O(1). This may be considered as a useful alternative to the standard quantum computing approach, which involves quantum gates transforming quantum states during the computing process.

A series of pseudopeptides containing alkyl-, cycloalkyl-, aryl-, and aralkyl-substituted 1,3,8-triazaspiro[4.5]decan-4-one-3-acetic acids as amino acid surrogates to replace the Pro 2 -Pro 3 -Gly 4 -Phe 5 section of the peptide bradykinin B2 receptor antagonist [Pro 3 , Phe 5 ]HOE 140 (D-Arg 0 -Arg 1 -Pro 2 -Pro 3 -Gly 4 -Phe 5 -Ser 6 -D-Tic 7 -Oic 8 -Arg 9 ) were prepared. These psuedopeptides were examined in vitro for their B2 receptor affinities as well as for their ability to block bradykinin mediated actions in vivo. Two compounds in particular, NPC 18521 (I) and NPC 18688 (V) were quite potent in these latter assays, indicating that a significant portion of this prototypical second generation decapeptide antagonist can be replaced with a more compact nonpeptide molecule.

In this paper, we discuss the adiabatic evolution of one-dimensional projector Hamiltonians. Three kinds of adiabatic algorithms for this problem are shown, in which two of them provide a quadratic speedup over the other one. But when the ground state of the initial Hamiltonian and that of the final Hamiltonian have a zero overlap, the algorithms above all fail, in the sense of infinite time complexity. A corresponding revised method for this phenomenon through adding a driving Hamiltonian is also shown, from which a constant time complexity can be gained. But by a simple analysis, we find that the original time complexity for the adiabatic evolution is shifted to implement the driving Hamiltonian, which is supported by several early works in the literature.

Five decapeptides were prepared, each having the generic primary sequence D-Arg0-Arg1-Pro2-Hyp3-Gly4-Thi5-Ser6-X7 -Y8-Arg9. A C-terminal beta-turn was anticipated when X was an alkyl ether of D-4-hydroxyproline in either the cis or trans geometric state and Y was either a Tic or Oic residue. Whereas cis ethers have only very weak receptor affinities, the trans ethers are significantly more potent in binding to guinea pig smooth muscle having Ki values as low as 0.16 nM. Notably, these peptides do not contain a D-aromatic amino acid at position 7 of the primary sequence.