Recently, Li et al. proposed a novel smart card and dynamic ID-based remote user authentication scheme for multi-server environments. They claimed that their scheme can resist several types of attacks. However, through careful analysis, we find that Li et al.’s scheme is vulnerable to stolen smart card and off-line dictionary attacks, replay attacks, impersonation attacks and server spoofing attacks. By analyzing other similar schemes, we find that a certain type of dynamic ID-based multi-server authentication scheme in which only hash functions are used and whereby no registration center participates in the authentication and session key agreement phase faces difficulties in providing perfectly efficient and secure authentication. To compensate for these shortcomings, we propose a novel dynamic ID-based remote user authentication scheme for multi-server environments based on pairing and self-certified public keys. Security and performance analyses show that the proposed scheme is secure against various attacks and has many excellent features. With the rapid development of network technologies, increasingly more people are beginning to use networks to acquire various services such as on-line financial information, on-line medical information, on-line shopping, on-line bill payment, and on-line documentation and data exchange. In addition, the architecture of servers providing services to be accessed over a network often consists of many different servers around the world instead of just one. Although they currently enjoy the comfort and convenience of the internet, people are facing emerging challenges with regard to network security.Identity authentication is the key security issue facing various types of on-line applications and service systems. Before a user accesses services provided by a service provider server, mutual identity authentication between the user and server is needed to prevent unauthorized personnel from accessing services provided by the server and avoiding an illegal system defrauding the user by masquerading as a legitimate server. In a single-server environment, password-based authentication schemes  and enhaBased on the utilized basic cryptographic algorithms, multi-server authentication schemes can be divided into two types: hash-based authentication schemes and public-key-based authentication schemes. Simultaneously, among existing multi-server authentication schemes, some of them need a registration center (RC) to participate in the authentication and session key agreement phase, whereas others do not have this requirement. Therefore, based on whether the RC participates in the authentication and session key agreement phase, we divide the multi-server authentication schemes into RC-dependent authentication schemes and non-RC-dependent authentication schemes.In this paper, we analyze a novel multi-server authentication scheme, Li et al.’s scheme , which iA large number of authentication schemes have been proposed for multi-server environments. Hash functions are a key technology in the construction of multi-server authentication schemes. In 2004, Juang et al.  proposedPublic-key cryptography is another useful technique that is widely used in the construction of multi-server authentication schemes. In 2000, Lee and Chang  proposedIn this section, we introduce the concepts of bilinear pairings, self-certified public keys, as well as some related mathematical assumptions.G1 be an additive cyclic group with a large prime order q, and let G2 be a multiplicative cyclic group with the same order q. In particular, G1 is a subgroup of the group of points on an elliptic curve over a finite field E(Fp), and G2 is a subgroup of the multiplicative group over a finite field. P is a generator of G1.Let e: G1 × G1 → G2 and satisfies the following properties:A bilinear pairing is a map e = eab for all P, Q ∈ G1 and (1) Bilinear: P, Q ∈ G1 such that e≠1.(2) Non-degenerate: There exists e for all P, Q ∈ G1.(3) Computability: There is an efficient algorithm to compute In , Liao etE(Fp) defined over a finite field, which is used with a point-based generator P of prime order q. Then, the TTP freely chooses his/her secret key sT and computes his/her public key pubT = sT ⋅ P. The related parameters and pubT are publicly and authentically available.(1) Initialization: The trusted third party (TTP) first generates all the needed parameters of the scheme. The TTP chooses a non-singular high elliptic curve A chooses a random number kA, computes KA = kA ⋅ P and sends his/her identity IDA and KA to the TTP. The TTP chooses a random number rA, computes WA = KA + rA ⋅ P and WA and A. Then, A obtains his/her secret key by calculating (2) Private key generation: A user A’s public key pubA = h(IDA ∥ WA)pubT + WA given WA.(3) Public key extraction: Anyone can calculate To prove the security of our proposed protocol, we present some important mathematical problems and assumptions for bilinear pairings defined on elliptic curves. The related concrete description can be found in , 41.R = x ⋅ P, where P, R ∈ G1, it is easy to calculate R given x and P, but it is hard to determine x given P and R.(1) Computational discrete logarithm (CDL) problem: Given P and R = x ⋅ P + y ⋅ P for x ⋅ P and y ⋅ P.(2) Elliptic curve factorization (ECF) problem: Given two points P, xP, yP ∈ G1, it is hard to compute xyP ∈ G1.(3) Computational Diffie-Hellman (CDH) problem: Given RC, the server Sj, and the user Ui. RC generates the master secret key x and a secret number y to construct h(x‖y) and h(SIDj‖h(y)), in which SIDj is the identity of server Sj; then, it delivers them to the server Sj through a secure channel. Li et al.’s scheme contains four phases:the registration phase, the login phase, the verification phase and the password change phase.There are three participants in Li et al.’s scheme: the registration center Ui and RC:When the remote user authentication scheme starts, the registration process should be first performed by the user Ui generates a random number b and freely chooses his/her identity IDi and the password PWi. Then, Ui calculates Ai = h(b ⊕ PWi). After that, Ui transmits IDi and Ai to RC for registration through a secure channel.(1) RC computes Bi = h(IDi‖x), Ci = h(IDi‖h(y)‖Ai), Di = h(Bi‖h(x‖y)) and Ei = Bi ⊕ h(x‖y). Then, RC stores {Ci, Di, Ei, h(⋅), h(y)} on the smart card of Ui and sends it to Ui by a secure channel.(2) Ui adds the random number b into the smart card, which ultimately possesses the information {Ci, Di, Ei, b, h(⋅), h(y)}.(3) Ui wants to log into the server Sj, the following procedures should be performed:When user IDi and PWi. After that, the smart card calculates Ai = h(b ⊕ PWi), Ci. If Ci, the Login process continues. Otherwise, the session will be aborted.(1) After the smart card is inserted into the card reader, the user is prompted to enter his/her Ni randomly and calculates Pij = Ei ⊕ h(h(SIDj‖h(y))‖Ni), CIDi = Ai ⊕ h(Di‖SIDj‖Ni), M1 = h(Pij‖CIDi‖Di‖Ni) and M2 = h(SIDj‖h(y)) ⊕ Ni.(2) The smart card produces a number Pij, CIDi, M1, M2} to Sj.(3) The smart card transmits the login request message {Sj receives the login request message, the mutual authentication and session key agreement between Sj and Ui will be performed in accordance with the following steps.When Sj calculates Ni = M2 ⊕ h(SIDj‖h(y)), Ei = Pij ⊕ h(h(SIDj‖h(y))‖Ni), Bi = Ei ⊕ h(x‖y), Di = h(Bi‖h(x‖y)), and Ai = CIDi ⊕ h(Di‖SIDj‖Ni).(1) The server Sj calculates h(Pij‖CIDi‖Di‖Ni); if the calculated result is not equal to M1, Sj rejects the login request and aborts this session. Otherwise, Sj accepts the login request message. Then, Sj chooses a random number Nj and calculates M3 = h(Di‖Ai‖Nj‖SIDj), M4 = Ai ⊕ Ni ⊕ Nj. Finally, Sj sends {M3, M4} to Ui.(2) The server M3, M4}, Ui calculates Nj = Ai ⊕ Ni ⊕ M4, M3. If they are not equal, Ui rejects these messages and terminates this session. Otherwise, Ui successfully authenticates Sj. In addition, Ui calculates M5 = h(Di‖Ai‖Ni‖SIDj) and sends it to Sj.(3) According to the received message {Sj computes h(Di‖Ai‖Ni‖SIDj) and compares it with the received {M5} sent from Ui. If they are equal, Ui is successfully authenticated by Sj, and the mutual authentication is completed. After the mutual authentication phase, the user Ui and the server Sj calculate SK = h(Di‖Ai‖Ni‖Nj‖SIDj) as their session key in future secure communication.(4) The server Ui wants to replace the old password PWi with a new password For security, the password of the user should be changed frequently. The password change phase is performed when user Ui inserts his/her smart card into the card reader and inputs his/her IDi and PWi.(1) The user Ai = h(b ⊕ PWi), Ci. If they are not equal, the password change request will be rejected. Otherwise, the user Ui provides a new random number bnew and a new password (2) The smart card calculates (3) The smart card calculates bnew to replace Ci and b. The password change phase is completed.(4) The smart card uses A is an adversary who has broken a user Um and a server Sn or a combination of a malicious user Um and a dishonest server Sn, then A can obtain the secret number h(x‖y) and h(y) and perform stolen smart card and offline dictionary attacks, replay attacks, impersonation attacks and server spoofing attacks on Li et al.’s scheme. The concrete cryptanalysis of the Li et al.’s scheme is shown as follows.Li et al. claimed that their scheme can resist many types of attacks and satisfy all the essential requirements for multi-server architecture authentication. However, if we assume that Ui’s smart card is stolen by an adversary A, A can extract the information {Ci, Di, Ei, b, h(⋅), h(y)} from the memory of the stolen smart card. Furthermore, if A intercepts a valid login request message {Pij, CIDi, M1, M2} sent from user Ui to server Sj in the public communication channel, A can compute Ni = h(SIDj‖h(y)) ⊕ M2, Ei = Pij ⊕ h(h(SIDj‖h(y))‖Ni), Bi = Ei ⊕ h(x‖y), Di = h(Bi‖h(x‖y)) and Ai = CIDi ⊕ h(Di‖SIDj‖Ni) using h(y) and h(x‖y). Then, A can launch an offline dictionary attack on Ci = h(IDi‖h(y)‖Ai) to determine the identity IDi of user Ui because A knows the values of Ai and h(y) corresponding to the user Ui. In addition, A can launch offline dictionary attacks on Ai = h(b ⊕ PWi) to determine the password PWi of Ui because A knows the value of b from the stolen smart card of the user Ui. Now, A possesses the valid smart card of user Ui, knows the identity IDi and password PWi corresponding to user Ui and hence can login to any service provider server.If a user A has intercepted a valid login request message {Pij, CIDi, M1, M2} sent from user Ui to server Sj in the public communication channel, then A can compute Ni = h(SIDj‖h(y)) ⊕ M2, Ei = Pij ⊕ h(h(SIDj‖h(y))‖Ni), Bi = Ei ⊕ h(x‖y), Di = h(Bi‖h(x‖y)) and Ai = CIDi ⊕ h(Di‖SIDj‖Ni) using h(y) and h(x‖y). Then, adversary A can replay this login request message {Pij, CIDi, M1, M2} to Sj by masquerading as the user Ui at some later time. After verification of the login request message, Sj computes M3 = h(Di‖Ai‖Nj‖SIDj) and M4 = Ai ⊕ Ni ⊕ Nj and sends the message {M3, M4} to A, who is masquerading as the user Ui. The adversary A can verify the received value of {M3, M4} and compute Ni, Ei, Bi, Di and Ai. Then, A sends Sj. The server Sj computes h(Di‖Ai‖Ni‖SIDj) and checks it with the received message Ui and the service provider server Sj, and the login request is accepted. Finally, after mutual authentication, adversary A masquerading as the user Ui and the server Sj agree on the common session key as SK = h(Di‖Ai‖Ni‖Nj‖SIDj). Therefore, the adversary A can masquerade as user Ui to login to server Sj by replaying the same login request message that had been sent from Ui to Sj.A replay attack is when an adversary replays the same message of a receiver or sender again. If adversary A who possesses h(y) and h(x‖y) can masquerade as any user Ui to login to any server Sj as follows.In this subsection, we show that an adversary A chooses two random numbers ai and bi and computes Ai = h(ai) and Bi = h(bi). Then, A can compute Di = h(Bi‖h(x‖y)), Ei = Bi ⊕ h(x‖y), Pij = Ei ⊕ h(h(SIDj‖h(y))‖Ni), CIDi = Ai ⊕ h(Di‖SIDj‖Ni), M1 = h(Pij‖CIDi‖Di‖Ni) and M2 = h(SIDj‖h(y)) ⊕ Ni using h(y) and h(x‖y). Now, A sends the login request message {Pij, CIDi, M1, M2} by masquerading as the user Ui to server Sj. After receiving the login request message, Sj computes Ni = h(SIDj‖h(y)) ⊕ M2, Ei = Pij ⊕ h(h(SIDj‖h(y))‖Ni), Bi = Ei ⊕ h(x‖y), Di = h(Bi‖h(x‖y)) and Ai = CIDi ⊕ h(Di‖SIDj‖Ni) using {Pij, CIDi, M1, M2}, h(x‖y) and h(SIDj‖h(y)). Then, Sj computes M3 = h(Di‖Ai‖Nj‖SIDj) and M4 = Ai ⊕ Ni ⊕ Nj and sends the message {M3, M4} to A, who is masquerading as the user Ui. Then, adversary A computes Nj = Ai ⊕ Ni ⊕ M4 and verifies M3 by computing h(Di‖Ai‖Nj‖SIDj). Then, A computes M5 = h(Di‖Ai‖Ni‖SIDj) and sends {M5} back to the server Sj. The server Sj computes h(Di‖Ai‖Ni‖SIDj) and checks it against the received message {M5}. This equivalency authenticates the legitimacy of the user Ui and the service provider server Sj, and the login request is accepted. Finally, after mutual authentication, adversary A masquerading as the user Ui and the server Sj agree on the common session key as SK = h(Di‖Ai‖Ni‖Nj‖SIDj).Adversary A who possesses h(y) and h(x‖y) can masquerade as the server Sj to spoof user Ui if A has intercepted a valid login request message {Pij, CIDi, M1, M2} sent from user Ui to server Sj over a public communication channel.In this subsection, we show that an adversary Pij, CIDi, M1, M2} sent from user Ui to server Sj over a public communication channel, A can compute Ni = h(SIDj‖h(y)) ⊕ M2, Ei = Pij ⊕ h(h(SIDj‖h(y))‖Ni), Bi = Ei ⊕ h(x‖y), Di = h(Bi‖h(x‖y)) and Ai = CIDi ⊕ h(Di‖SIDj‖Ni) corresponding to Ui. Then, A can choose a random number A then sends the message {M3, M4} by masquerading as the server Sj to the user Ui. After receiving the message {M3, M4}, Ui computes M3 by computing Ui computes M5 = h(Di‖Ai‖Ni‖SIDj) and sends it to the server Sj, who is masquerading as the adversary A. Then, A computes h(Di‖Ai‖Ni‖SIDj) and checks it against the received message {M5}. Finally, after mutual authentication, the adversary A masquerading as the server Sj and the user Ui agree on the common session key as After intercepting a valid login request message {Except for Li et al.’s scheme, we also analyzed four other dynamic ID-based authentication schemes for multi-server environments , 17–19. Ui, the service provider server Sj, and the registration center RC. A legitimate user Ui can easily login to the service provider server using his smart card, identity and password. There are six phases in the proposed scheme: the system initialization phase, the user registration phase, the server registration phase, the login phase, the authentication and session key agreement phase, and the password change phase. The notations used in our proposed scheme are summarized in In this section, we propose a novel dynamic ID-based and non-RC-dependent remote user authentication scheme for multi-server environments using pairing and self-certified public keys. Our scheme contains three participants: the user RC is assumed to be a TTP. In the system initialization phase, RC generates all the needed parameters of the scheme.In the proposed scheme, the registration center RC selects a cyclic additive group G1 of prime order q, a cyclic multiplicative group G2 of the same order q, a generator P of G1, and a bilinear map e: G1 × G1 → G2.(1) The RC freely chooses a number pubRC = sRC ⋅ P as the system public key.(2) The RC selects two cryptographic hash functions H(⋅) and h(⋅).(3) The e, G1, G2, q, P, PubRC, H(⋅), h(⋅)} are publicly and authentically available.Finally, all the related parameters {Ui wants to access the services, he/she has to submit some of his/her related information to the registration center RC for registration. The steps of the user registration phase are as follows:When the user Ui freely generates his/her identity IDi and password pwi and chooses a random number bi. Then, Ui computes HPWi = h(IDi ∥ pwi ∥ bi) ⋅ P and submits IDi and HPWi to RC for registration through a secure channel.(1) IDi and HPWi, RC computes QIDi = H(IDi), CIDi = sRC ⋅ QIDi, Hi = h(QIDi ∥ CIDi). Then, RC stores the message Ui’s smart card and submits the smart card to Ui through a secure channel.(2) When receiving the message Ui enters bi into the smart card. Finally, the smart card contains the parameters (3) After receiving the smart card, Sj wants to provide services to the users, he/she must perform the registration to the registration center RC to become a legal service provider server. The process of the server registration phase of the proposed scheme is based on SCPK.If a service provider server Sj chooses a random number vj and computes Vj = vj ⋅ P. Then, Sj submits SIDj and Vj to RC for registration via a secure channel.(1) SIDj, Vj}, RC chooses a random number wj and computes Wj = wj ⋅ P + Vj and q. Then, RC submits the message Sj through a secure channel.(2) After receiving the message {Sj computes their private key q and checks the validity of the values issued to them by checking the following equation: pubj = sj ⋅ P = h(SIDj ∥ Wj) ⋅ pubRC + Wj. Finally, Sj’s personal information contains {SIDj, pubj, sj, Wj}(3) After receiving The details of the user registration phase and server registration phase are shown in Ui wants to access the services provided by server Sj, Ui needs to login to Sj, where the process of the login phase are as follows:If user Ui inserts their smart card into the smart card reader and inputs their identity IDi and password pwi. The smart card then calculates QIDi = H(IDi), Hi. If they are equal, it is verified that Ui has the correct user identity and password. Thus, Ui is a legitimate user. Otherwise, the smart card aborts the session.(1) The user ui and ri, and it computes DIDi = ui ⋅ QIDi and Ri = ri ⋅ P. Then, the smart card sends the login request message {DIDi, Ri} to server Sj over a public channel.(2) The smart card chooses two random numbers DIDi, Ri} sent from the user Ui, the server Sj chooses a random number rj and computes Rj = rj ⋅ P, Tji = rj ⋅ Ri, Kji = sj ⋅ Ri and Authji = h(DIDi ∥ SIDj ∥ Kji ∥ Rj). Then, Sj sends the message {Wj, Rj, Authji} to Ui.(1) Based on the received login request message {Wj, Rj, Authji}, Ui computes Tij = ri ⋅ Rj, pubj = h(SIDj ∥ Wj) ⋅ pubRC + Wj, Kij = ri ⋅ pubj and Authij = h(DIDi ∥ SIDj ∥ Kij ∥ Rj). Then, Ui checks Authij with the received Authji. If they are not equal, Ui terminates this session. Otherwise, Sj is proven to have the correct private key sj, and thus, Sj is authenticated. Ui continues to compute Mi = ri ⋅ DIDi, Ni = ui ⋅ CIDi, dij = h(DIDi ∥ SIDj ∥ Kij ∥ Mi) and Bi = (ri + dij) ⋅ Ni. Finally, Ui sends the message {Mi, Bi} to Sj.(2) When receiving {Mi, Bi} sent from Ui, Sj computes dji = h(DIDi ∥ SIDj ∥ Kji ∥ Mi) and checks whether e = e. If they are not equal, Sj terminates this session. Otherwise, Ui is authenticated.(3) After receiving the message {Ui and the server Sj agree on a common session key as Ui: SK = h(DIDi ∥ SIDj ∥ Kij ∥ Tij), Sj: SK = h(DIDi ∥ SIDj ∥ Kji ∥ Tji).Finally, the user Sections 5.4 and 5.5 give the detailed procedures of the login phase and authentication and session key agreement phase, which are also depicted in Ui.For security purposes, users need to change their passwords frequently. The following steps show the password change phase process for a user Ui inserts his/her smart card into the smart card reader and inputs their identity IDi and password pwi. Then, the smart card computes QIDi = H(IDi), Ui is verified as a legitimate user; otherwise, the smart card rejects the password change request.(1) The user zi and computes Zi = zi ⋅ P and AIDi = CIDi ⊕ zi ⋅ pubRC. Then, the smart card sends the message {IDi, AIDi, Zi} to the registration center RC.(2) The smart card generates a random number IDi, AIDi, Zi}, RC computes CIDi = AIDi ⊕ sRC ⋅ Zi, QIDi = H(IDi), and checks whether e = e. If they are equal, user Ui is authenticated. Then, RC computes V1 = h(CIDi ∥ sRC ⋅ Zi) and sends {V1} to Ui.(3) After receiving the message {V1}, the user computes V1. If they are equal, the registration center RC is authenticated. Then, Ui chooses his/her new password Ui submits {V2, V3} to RC.(4) When receiving {V2, V3}, the registration server RC computes RC compares V3. If they are equal, RC continues to compute RC sends {V4, V5} to Ui.(5) Upon receiving the response {V4, V5}, Ui computes Ui checks whether Ui replaces the original bi with (6) After receiving {In addition to the descriptions listed above, the procedures of the password change phase of the proposed scheme are also given in A can access a large dictionary of words that likely includes the user’s password and intercept the communications between the user and server.In the proposed scheme, we assume that if a smart card is stolen, physical protection methods cannot prevent malicious attackers for obtaining the stored secure elements. Simultaneously, an adversary Ui’s smart card is stolen by an adversary A, the latter can extract A has intercepted a previous full session of messages {DIDi, Ri, Wj, Rj, Authji, Mi, Bi} between the user Ui and server Sj. However, the adversary still cannot obtain Ui’s identity IDi and password pwi except by guessing IDi and pwi simultaneously. Therefore, it is impossible to obtain Ui’s identity IDi and password pwi from a stolen smart card and using offline dictionary attacks in our proposed scheme.In the proposed scheme, if a user ri and rj, and the user’s identity is different in each new session. These factors make all messages dynamic and valid for that session only. If we assume that an adversary A replies with an intercepted previous login request {DIDi, Ri} to Sj, after receiving the response message {Wj, Rj, Authji} sent from Sj, A cannot compute the correct response message {Mi, Bi} to pass Sj’s authentication since they do not know the values of IDi, pwi, ui and ri. Therefore, the proposed scheme is robust to replay attacks.Replaying a message of a previous session into a new session is useless in our proposed scheme because the user’s smart card and the server choose different rand numbers A wants to masquerade as a legitimate user Ui to pass the authentication of a server Sj, the user must have the values of both QIDi and CIDi. However, QIDi and CIDi are protected by Ui’s smart card, IDi and pwi since QIDi = H(IDi) and A can obtain the user Ui’s smart card, IDi and pwi simultaneously, the proposed scheme is secure to impersonation attacks.If an adversary A wants to masquerade as a legal server Sj to cheat a user Ui, the adversary must calculate a valid Authji that is embedded with the shared secret key Kji = sj ⋅ Ri to pass the authentication of Ui. However, the adversary A cannot derive the shared secret key Kji without knowing the private key sj of the server Sj. Therefore, our scheme is secure against server spoofing attacks.If an adversary RC cannot obtain Ui’s password pwi. Since in the registration phase Ui chooses a random number bi and sends IDi and HPWi = h(IDi ∥ pwi ∥ bi) ⋅ P to RC, RC cannot derive pwi from HPWi based on the CDL problem. Therefore, the proposed scheme is robust to insider attacks.In the proposed scheme, the registration center A updates the identity and password verification information on the smart card to some arbitrary value, and hence, legitimate users cannot login successfully in subsequent login requests to the server. In the proposed scheme, the smart card checks the validity of user Ui’s identity IDi and password pwi before the password update procedure. An adversary can insert the stolen smart card of the user Ui into the smart card reader and must guess the identity IDi and password pwi corresponding to the user Ui correctly. The smart card computes Hi in its memory to verify the legitimacy of the user Ui before the smart card accepts the password update request. It is not possible to guess the identity IDi and password pwi correctly simultaneously in real polynomial time even after obtaining the smart card of the user Ui. Therefore, the proposed scheme is secure against denial of service attacks.In denial of service attacks, an adversary SK = h(DIDi ∥ SIDj ∥ Kij ∥ Tij) SK = h(DIDi ∥ SIDj ∥ Kij ∥ Tji) is generated by three single-use random numbers ui, ri and rj in each session. These single-use random numbers are only held by the user Ui and the server Sj and cannot be retrieved from SK based on the security of the CDH problem. Thus, even if an adversary obtains previous session keys, it cannot compromise other session keys. Hence, the proposed scheme achieves perfect forwarding secrecy.Perfect forwarding secrecy means that even if an adversary compromises all the passwords of the users, it still cannot compromise the session key. In the proposed scheme, the session key Ui’s login message is different in each login phase. For each login message, DIDi = ui ⋅ H(IDi) is associated with a random number ui, which is known by Ui alone. Therefore, no adversary can identity the real identity of the logged on user, and our scheme can ensure the user’s anonymity.In our proposed scheme, the user In our proposed scheme, it is obvious that the user, server and registration center do not maintain a verification table.Ui’s identity IDi and password pwi before logging into server Sj. Since the adversary cannot compute the correct CIDi without knowledge of IDi and pwi to satisfy the verification equation In the proposed scheme, the smart card checks the validity of user Ui sends the message {DIDi, Ri} to the server Sj to establish a connection. After receiving the response message {Wj, Rj, Authji} sent from Sj, Ui computes Tij, pubj, Kij, and Authij and checks whether Authij = Authji. If they are equal, Sj is authenticated by Ui. Otherwise, Ui stops to login to this server. Since Authji = h(DIDi ∥ SIDj ∥ Kji ∥ Rj) and Kji = sj ⋅ Ri, an adversary A cannot compute the correct Kji without knowledge of the value of sj. Any fabricated message Ui computes Mi, Ni, dij, and Bi and sends the message {Mi, Bi} to Sj. After receiving the message {Mi, Bi} sent from Ui, Sj computes dji and checks whether e = e. If they are not equal, Sj terminates this session; otherwise, Ui is authenticated. Since Bi = (ri + dij) ⋅ Ni, an adversary A cannot compute the correct Bi without knowledge of the values of ui, ri etc. Any fabricated message In our scheme, the user first authenticates the server. In this section, we compare the performance and functionality of our proposed scheme with some previous schemes. To analyze the computation cost, some notations are defined as follows.TGe: The time for executing a bilinear map operation, e: G1 × G1 → G2.TGmul: The time for executing point scalar multiplication on the group G1.TGH: The time for executing a map-to-point hash function H(.).TGadd: The time for executing point addition on the group G1.Th: The time for executing a one-way hash function h(.).Since the XOR operation and the modular multiplication operation require very few computations, it is usually negligible considering their computation costs.TGmul+TGH+TGadd+5Th. However, similar to Liao et al.’s scheme, the user Ui can pre-compute Ri = ri ⋅ P in the client, and then, the computational cost of the user (C2) requires 8TGmul+TGH+TGadd+5Th on-line computations. It can be found that our proposed scheme has a slightly higher computational cost than Liao et al.’s scheme in C2, and the others are almost equal. However, Liao et al.’s scheme is vulnerable to stolen smart card and offline dictionary attacks and denial of service attacks and cannot provide user anonymity and local password verification.In this paper, we note that Li et al.’s scheme is vulnerable to stolen smart card and offline dictionary attacks, replay attacks, impersonation attacks and server spoofing attacks. Furthermore, by analyzing some other similar schemes, we find that certain types of dynamic ID-based and non-RC-dependent multi-server authentication schemes in which only hash functions are used face difficulties in providing perfectly efficient and secure authentication. To compensate for these shortcomings, we propose a novel dynamic ID-based and non-RC-dependent remote user authentication scheme for multi-server environments using pairing and self-certified public keys. The security and performance analyses show that the proposed scheme is secure against various attacks and has many excellent features. In the future, the use of authentication for high-tech industries, such as cloud computing –44 and b


Snake venoms are attractive natural sources for drug discovery and development, with a number of substances either in clinical use or in research and development. These drugs were developed based on RGD-containing snake venom disintegrins, which efficiently antagonize fibrinogen activation of αIIbβ3 integrin (glycoprotein GP IIb/IIIa). Typical examples of anti-platelet drugs found in clinics are Integrilin (Eptifibatide), a heptapeptide derived from Barbourin, a protein found in the venom of the American Southeastern pygmy rattlesnake and Aggrastat (Tirofiban), a small molecule based on the structure of Echistatin, and a protein found in the venom of the saw-scaled viper. Using a similar drug discovery approach, linear and cyclic peptides containing the sequence K(R)TS derived from VP12, a C-type lectin protein found in the venom of Israeli viper venom, were used as a template to synthesize Vipegitide, a novel peptidomimetic antagonist of α2β1 integrin, with anti-platelet activity. This review focus on drug discovery of these anti-platelet agents, their indications for clinical use in acute coronary syndromes and percutaneous coronary intervention based on several clinical trials, as well as their adverse effects. Integrins are a large group of receptors composed of different combinations of the α and β chains. These receptors mediate cell adhesion to extracellular matrix (ECM) proteins and are involved in cell-cell interactions. ECM protein ligands bind integrins through the Arg-Gly-Asp (RGD) sequence motif, surrounded by disulphide bonds and neighbored by charged amino acids. This motif is present in ECM proteins such as vitronectin, fibronectin, osteopontin, and fibrinogen. Sequences neighboring the RGD motif are important for integrin recognition specificity. Integrin receptors activation upon binding the respective ECM ligand promotes intracellular signaling. As a consequence, they have diverse roles in cell migration and wound healing, cell differentiation, and apoptosis and can also regulate the metastatic and invasiveness of tumor cells. The regulation of integrin activity is complex. They are activated by extracellular ligands that initiate intracellular signals which convert (‘inside-out’) the extracellular domains to a high-affinity phase by a conformational change. Binding of ECM ligands to the integrin receptor propagates signaling across the membrane (‘outside-in’) to activate cytoplasmic protein kinases and cytoskeletal-signaling cascades. These biochemical activities control fundamental cell physiology processes such as survival adherence, movement, growth, and differentiation ,2,3. In Disintegrins are a group of low molecular weight, polypeptides folded by cysteine bridges that modulate cell adhesion, migration, apoptosis, platelet aggregation, and angiogenesis. They are naturally derived by proteolytic processing from metalloproteinase precursors and carrying the integrins’ recognition motifs RGD, KGD, WGD, VGD, MGD, RTS, KTS ,5,6. CurPlatelets express several β1 and β3 containing integrins: α2β1 (collagen receptor), α5β1 (fibronectin receptor), α6β1 (laminin receptor), αIIbβ3 , and αVβ3 (vitronectin receptor). The dominant integrin on the platelet membrane is αIIbβ3, with a supplementary cytoplasmic pool that is exposed on the platelet surface upon activation. It binds RGD containing ECM ligands such as fibrinogen, fibrin, von Willebrand factor (vWF), fibronectin, thrombospondin, and vitronectin . αIIbβ3 2 (TxA2) and adenosine diphosphate (ADP) from platelets, along with thrombin activation by the blood vessel wall’s tissue factor  such as unstable angina pectoris, non–ST elevation myocardial infarction (NSTEMI) and ST-elevation myocardial infarction (STEMI), cardiovascular diseases challenging the health care system of many countries. This process is also responsible for abrupt blood vessel closure in patients undergoing percutaneous coronary intervention (PCI). Blood vessel rupture exposes the sub-endothelium to thrombogenic substances and initiates platelet activation and aggregation. Glycoprotein IIb/IIIa receptors (αIIbβ3 integrin) are the most prevalent integrins in the platelets and therefore, their activation is considered the final major pathway for platelet aggregation. Platelet activation promotes conformational changes in the αIIbβ3, permitting fibrinogen to bind with this integrin. Each fibrinogen molecule has two αIIbβ3 binding sites enabling binding to integrin receptors on two neighboring platelets, property contributing to the formation of platelet-rich arterial thrombi. During healthy conditions, the circulating platelets do not bind to blood vessels’ endothelium. Upon vessel injury, exposure of the sub-endothelial ECM proteins promotes adhesion of platelets surrounding the injury. Of particular importance to platelet adhesion are the glycoproteins GP Ib/V/IX, GP VI, and GP Ia/IIa receptors (α2β1 integrin). von Willebrand factor (vWF), which is inactive, is deposited constitutively from endothelial cells as part of the ECM proteins and is also secreted from activated endothelial cells. Following blood vessel injury, vWF is deposited on collagen fibers exposed at the injury location. As the vWF-GP Ib complex is relatively weak, supplementary adhesion mediated by platelet integrins is also provided for strong platelet attachment to the injury location. Platelet adhesion is a process characterized by several steps. First, shear forces rapidly generated as blood circulates over endothelial injury location initiate temporary platelet-endothelium interactions. The key element of this critical step is the weak binding of GP Ib to vWF generating multiple platelet membrane extensions. In the next step, the low-affinity interaction between collagen and GP VI platelets causes platelet activation and aggregation, a process enhanced by α2β1 integrin. The process of platelet activation and aggregation is further enhanced by paracrine and autocrine-mediated signaling through the release of thromboxane Ae factor . Furthere factor . Thrombie factor , Table 1®) has FDA labeled support for both of these clinical indications. The αIIbβ3 integrin antagonists have similar mechanisms of action to inhibit the platelet aggregation process. Abciximab, the chimeric monoclonal antibody blocks binding of not only αIIbβ3 integrin important for platelet aggregation, but also other integrins such as αVβ3 vitronectin receptor also involved in platelet, and therefore, may result in more bleeding adverse effects. Tirofiban and Eptifibatide are small, synthetic molecules developed from the snake venom RGD-disintegrins, which have high affinity and relative selectivity for the αIIbβ3 integrin. These compounds compete with fibrinogen for the αIIbβ3 integrin receptor in a dose-dependent manner and prevent platelet aggregation. The clinical therapeutic goal of using these drugs is to achieve 80% inhibition of platelet aggregation with minimal bleeding adverse effects.Clinical trials have demonstrated the efficacy of αIIbβ3 integrin antagonists in patients with acute coronary syndrome (ACS) based on risk stratification (the process of separating patient population groups according to the degree of risk). High-risk patients with unstable angina/NSTEMI myocardial infarction derive particular benefit from αIIbβ3 integrin antagonists and an early invasive strategy . The 200The first antiplatelet drug derived from a snake venom protein was Tirofiban. Tirofiban is a non-peptide molecule that was developed based on the RGD motif present in the parent disintegrin molecule, Echistatin .50-values: αvβ3 (0.46 nM), α5β1 (0.57 nM), and αIIbβ3 (0.9 nM)]. Considering that the distance between the positively charged argininyl and the negatively charged aspartyl centers of RGD is a key determinant of potency, a tyrosine analog was developed by Merck Co. Through optimization of the RGD-motif lead peptide using, at the NH2-terminal, a 4-(4-piperidinyl) butyl group and, at the COOH-terminal, an (S)-butylsulfonylamino group, a peptidomimetic was developed with a 3000-fold increase in potency for inhibition of platelet aggregation, with preservation of selectivity for αIIbβ3 over other integrins. This compound, initially designated MK-0383, was very effective in inhibiting αIIbβ3 function in animal models [Echistatin was first isolated in 1988 from the venom of the saw-scaled viper and found as an effective antagonist of fibrinogen-induced platelet aggregation . This dil models  and final models . Tirofibl models . The clip = 0.160) in RESTORE and 36%  in PRISM and 27%  in PRISM-PLUS. In the PRISM trial, there was no difference in bleeding times between the Tirofiban and placebo groups, and bleeding increased only modestly in the PRISM-PLUS  Tirofiban together with Heparin compared to Heparin-alone groups and in RESTORE  Tirofiban compared to placebo groups  and IV (non-fibrillary collagen) are the most reactive on platelet adhesion, activation, and aggregation. Collagen types I and III are largely exposed to platelets aggregation when the blood vessel wall injury extends to the deeper layers of media and adventitia. Collagens are strong inducers of platelets aggregation. GP Ia/IIa and GP VI are the two well-known collagen receptors present on platelets  playing Vipera palestinae [1KTSRTSHY9 were used as a template to prepare a novel peptidomimetic antagonist of α2β1 integrin, with platelet aggregation-inhibiting activity, named Vipegitide [10 M and 1.5 × 1010 M, respectively) and intermediate efficacy  as well as selectivity toward α2 integrin. Binding of these peptidomimetics with a recombinant extracellular α2 integrin A domain was confirmed in a cell-free receptor binding assay. Vipegitide and Vipegitide-PEG2 inhibited α2β1 integrin-mediated adhesion and aggregation of human and murine platelets under static and flow conditions by 50%. They potently inhibited collagen I-induced platelet aggregation in platelet-rich plasma and whole human blood. Higher potency of Vipegitide compared to Vipegitide-PEG2 was correspondent with results of computer modeling in water. These peptidomimetics were acutely tolerated in mice upon intravenous bolus injection of 50 mg/kg [Despite recent progress in the development of antithrombotic drugs, there is an unmet clinical need for effective new drugs to prevent and treat cardiovascular diseases, while minimally impairing physiological hemostasis. In particular, drugs that target platelets involved in the formation of thrombi would specifically affect atherosclerotic lesions but with a reduced risk of bleeding side effects. Therefore, α2β1 integrin is a good target for novel antithrombotic therapy because it is associated with high cardiovascular risk of stroke and myocardial infarction  and sinclestinae ,57,58,59The research and development of integrin antagonists is a good example of a translational medicine approach whereby the discovery of the naturally occurring disintegrins and C-type lectin proteins in snake venoms has inspired much research into the molecular interaction of RGD/KGD/RTS/KTS disintegrins with integrin αIIbβ3 andα2β1, leading to drug development of small-molecule that were successful in clinical trials. In turn, the results and adverse effects observed in these clinical trials have enriched our understanding of the pathophysiology of different cardiovascular diseases. Antiplatelet therapy  is a cor


Escherichia coli transketolase as a model, we explore the epistatic interactions between a set of stabilizing mutations from across two different domains within the protein structure. Surprisingly, not all pairwise effects between distant mutations from the surface and core regions of different domains were additive. Additionally, the epistatic behaviors observed were dependent on the type of stability measured. We found single mutations that altered local protein dynamics, which then induced correlated changes in the dynamics of a second domain of the same monomer. This mechanism mediated epistasis between distant mutations.Incomplete understanding of the mechanisms of epistasis between two or more substitutions in a protein can hamper protein-engineering strategies. With  Escherichia coli transketolase (TK) as a model, we explored the epistatic interactions between two single variants H192P and A282P, and also between the double-mutant H192P/A282P and two single variants, I365L or G506A. Epistasis was determined for several measures of protein stability, including the following: the free-energy barrier to kinetic inactivation, ∆∆G‡; thermal transition midpoint temperatures, Tm; and aggregation onset temperatures, Tagg. Nonadditive epistasis was observed between neighboring mutations as expected, but also for distant mutations located in the surface and core regions of different domains. Surprisingly, the epistatic behaviors for each measure of stability were often different for any given pairwise recombination, highlighting that kinetic and thermodynamic stabilities do not always depend on the same structural features. Molecular-dynamics simulations and a pairwise cross-correlation analysis revealed that mutations influence the dynamics of their local environment, but also in some cases the dynamics of regions distant in the structure. This effect was found to mediate epistatic interactions between distant mutations and could therefore be exploited in future protein-engineering strategies.Multiple mutations are typically required to significantly improve protein stability or aggregation kinetics. However, when several substitutions are made in a single protein, the mutations can potentially interact in a nonadditive manner, resulting in epistatic effects, which can hamper protein-engineering strategies to improve thermostability or aggregation kinetics. Here, we have examined the role of protein dynamics in mediating epistasis between pairs of mutations. With  Proteins and enzymes are increasingly used as therapeutics, as diagnostics, and for industrial biocatalysis. These applications often require function at elevated temperatures or after long-term storage, and so the development of efficient strategies to enhance their stability remains a major goal in protein engineering. High thermal stability is also strongly correlated with expression yield, in vitro half-life, and in vivo serum survival time –3. AdditDirected evolution is a powerful strategy for engineering protein stability through the accumulation of beneficial substitutions. The desired property is obtained by screening or selection of a library of randomly mutated variants. However, it is often necessary to screen large numbers of mutants in several rounds of evolution to obtain a desired level of change . Where tAB for A and B) are found to be mathematically additive, where ΔAB = ΔA + ΔB. The contributions of some are affected by mutations made at other sites in the protein, in a phenomenon known as intragenic epistasis (A > 0) are combined into a single protein, two or more mutations often interact in a nonadditive manner. This epistatic behavior can be measured via its effect on various protein properties and can have either positive epistasis, where ΔAB = ΔA + ΔB + X; negative epistasis , where (ΔA and ΔB) < ΔAB < ΔA + ΔB; negative sign epistasis, where ΔAB < (ΔA or ΔB); or reciprocal sign epistasis, where ΔAB < 0  as a model, we investigated how the combination of stabilizing mutations influenced various measures of protein stability, including thermal transition midpoint (Tm), aggregation onset temperature (Tagg), rates of irreversible thermal inactivation at elevated temperature, and the fraction unfolded at that temperature (fT). For each, we explored the additivity for pairs of mutations, compared their structural locations, and investigated their impact on protein flexibility to determine the role of dynamics in epistasis. TK, a thiamine diphosphate-dependent (ThDP) enzyme, catalyses the reversible transfer of a C2-ketol unit from d-xylulose-5-phosphate to either d-ribose-5-phosphate or d-erythrose-4-phosphate in living cells  simulations were analyzed using a dynamics cross-correlation matrix to reveal the role of dynamics in mediating the observed epistasis between mutations.Here, we created a set of variants along different evolutionary pathways from WT to H192P/A282P/I365L/G506A, based on four individually thermostabilizing single mutations identified previously. H192P and A282P were located 33 Å apart on the surface of the PP-binding domain, whereas I365L and G506A were located 12 Å apart in the hydrophobic core of the Pyr-binding domain, and at least 25 Å from H192P or A282P. The PP and Pyr domains from opposite chains interact strongly with each other in the homodimer, and so mutations in each domain might be expected to influence those in the other through either an interchain or intrachain mechanism. We generated four new variants, I365L/G506A, H192P/A282P/I365L, H192P/A282P/G506A, and H192P/A282P/I365L/G506A, and then investigated the epistatic interactions among the four mutation sites by analyzing the kinetics and free energy of thermal inactivation, E. coli TK, respectively. Here, we created variants along a set of evolutionary pathways but divided into two steps. The first step combined H192P (A) and A282P (B) into a double mutant, H192P/A282P (AB), as previously, to explore the epistasis between A and B. The second step combined three variants I365L (C), G506A (D), and H192P/A282P (AB) to generate one double, I365L/G506A (CD); two triple, H192P/A282P/I365L (ABC) and H192P/A282P/G506A (ABD); and a quadruple mutant, H192P/A282P/I365L/G506A (ABCD). This enabled us to measure the additive and nonadditive effects from across the eight (=23) possible variants, combined in six (=3!) possible forward pathways from WT: P1, C-CD-ABCD; P2, C-ABC-ABCD; P3, D-CD-ABCD; P4, D-ABD-ABCD; P5, AB-ABC-ABCD; P6, AB-ABD-ABCD . Whereas, in previous work, inactivation profiles were fit with less accuracy to single-exponential decays .For industrial enzymes, kinetic stability is critical for retaining activity during the time course of the bioconversion at a given operating temperature. The kinetic stability of TK at elevated temperatures relates to the rate of inactivation due to irreversible aggregation, promoted by increased partial unfolding of the native protein upon heating . The kinl decays , 43, givl decays . All thrl decays . The quaT5015), for WT was around 58.5 °C, which was 2.5 °C lower than that of the quadruple variant (61.0 °C)  . At 50 °fter 1 h , while hfter 1 h . The morTm, a measure of thermodynamic conformational stability obtained from intrinsic fluorescence measurements, for all variants. Their aggregation onset temperatures, Tagg, were simultaneously determined from static light scattering measurements  was determined for the WT and mutant TKs, to evaluate the extent to which global protein unfolding influenced the inactivation rates at 60 °C. Surprisingly, f60 did not show any clear correlation with the Tm values . During the thermal unfolding process, the holo-TK homodimer undergoes unfolding of all three domains in each monomer, cofactor release, and also dimer dissociation, apparently at the same time  molecular reorganization after interaction of native states; (ii) partial local unfolding of native states before interaction; or (iii) global unfolding before interaction. Thermodynamic stability, as measured by Tm, and more specifically by f60, can reveal the extent to which global unfolding is important in inactivation by aggregation (kd), are shown in Tm gave a good linear correlation to the kinetics of inactivation, with an R2 value of 0.93 , indicating a clear link between kinetic and thermodynamic stability. By contrast, the correlation between f60 and ln(kd) was poor , indicating that global unfolding was not the only factor to influence inactivation by aggregation, and likely also involved the decoupling of local unfolding events that manifested as changes in ∆Svh. In particular, the inactivation rate at 60 °C for H192P/A282P/I365L and H192P/A282P/I365L/G506A decreased significantly, while their fraction unfolded remained at 11–13%, similar to those of H192P/A282P and WT. These two variants had the lowest ∆Svh values and also the highest Tm values of those tested kinetically  based on the activities retained after heat treatment at 60 °C for 1 h, and also for changes in thermodynamic stabilities, ΔTm and ΔTagg. Fitness landscapes containing the two mutagenic pathways from WT to H192P/A282P (AB), and the six further mutagenic pathways that formed the quadruple-mutant H192P/A282P/I365L/G506A (ABCD), were constructed for all three properties. All pathways were favorable in all three properties, with no local minima due to sign epistasis or reciprocal sign epistasis . The epistatic interactions between mutations were quantified using G‡, ΔTm, and ΔTagg in SI Appendix, Fig. S5). Despite this long distance, they showed a strong negative epistasis for ΔΔG‡, a moderately positive epistasis for ΔTm, and an additive effect for ΔTagg. The two single variants, I365L (C) and G506A (D), were located in the Pyr domain (323–539 aa). The ∆∆G‡ of the double mutant, CD (4.88 kJ⋅mol−1), was higher than the 4.09 kJ⋅mol−1 expected from additivity between I365L (C) and G506A (D), indicating a moderately positive epistasis  and H192P/A282P (AB). The final variant ABCD was formed via three combinations, ABC+D, CD+AB, and ABD+C. The observed ∆∆G‡ for ABCD was 9.30 kJ⋅mol−1, which was as expected for ABC+D (9.01 ± 0.3 kJ⋅mol−1), but lower than the expected values for CD+AB (11.34 kJ⋅mol−1) and ABD+C (10.19 kJ⋅mol−1), consistent with the negative epistasis found above for AB+C  and G506A (D) were both negatively epistatic, in contrast to the moderately positive epistasis observed for ΔΔG‡ in G‡) and thermodynamic (Tm and Tagg) stabilities of C+D fits with the observation that kinetic and thermodynamic stabilities were correlated, but that global unfolding was not the only factor to influence kinetic inactivation. I365L and G506A were only 12 Å apart within the same hydrophobic core of the Pyr domain  and packed onto opposite faces of the indole ring of W503 . This indirect structural interaction could readily mediate their negative epistasis in ΔTm and ΔTagg, and also their positive epistasis in ΔΔG‡. The combination of D+AB showed additive effects for ΔTm and ΔTagg, consistent with ΔΔG‡ . The combination of C+AB showed additive effects for ΔTm and ΔTagg, again in contrast to ΔΔG‡, which was negatively epistatic.Improvements in ith ΔΔG‡ . This waG‡, ΔTm, and ΔTagg. As described above, for ΔΔG‡, the combination of I365L (C) and H192P/A282P (AB) in any context  was negatively epistatic. By contrast, for ΔTm, negative epistasis was observed, but now for the combination of I365L (C) with G506A (D) in any context . Finally, for ΔTagg, while C+D was negatively epistatic, and C+AB was additive, the combination of C and AB at the final step was positively epistatic, particularly for CD+AB. For the pathway to the quadruple mutant, ABCD, all epistatic effects involved I365L (C) and either AB or D, but never occurred specifically between AB and D. As discussed above, the I365L mutation, in the presence of H192P/A282P, also appeared to have a particularly strong stabilizing influence on the inactivation rate, and this contributed to the epistatic effect found between AB+C, AB+CD, and ABD+C. For example, the selective stabilization by I365L of an aggregation-prone motif in the quadruple mutant (ABCD) could lead to the positive epistasis in Tagg, while remaining additive for Tm.The three possible final combination steps leading to ABCD showed distinctly different epistatic effects for ΔΔTo understand how the mutations interacted to produce both short- and long-range epistasis, we investigated and compared the flexibilities of the WT, H192P, A282P, H192P/A282P, I365L, H192P/A282P/G506A, H192P/A282P/I365L, and the quadruple mutant, H192P/A282P/I365L/G506A, using MD simulations at 370 K. Root-mean-square fluctuation (RMSF) values for each residue indicated a complex interdependence between the dynamics around each mutation, whereby mutations changed their local dynamics, but also sometimes altered the dynamics of certain other, often distant regions . Previouij) indicate the extent to which the fluctuation of an atom is correlated (or anticorrelated) with one other atom, and dynamics cross-correlation maps show the correlation coefficients (Cij) between all Cα atom pairs. Most cross-correlations were between structural neighbors , in agreement with the previous observation that cross-correlations decreased with distance  was also found between 270–300 and 190–210 aa. Apart from at 0–100 aa, these regions coincided with the four mutation sites, and so the cross-correlated dynamics linked several pairs of mutations . Specifiij against the ΔRMSF in all residues resulting from each mutation showed that (i) most residues do not have dynamics that are strongly correlated with the mutation sites, (ii) significant ΔRMSF (>0.2 or less than −0.2) mostly occurs in regions with a strong dynamics correlation coefficient (>0.2) with the mutation site, and (iii) the absolute strength and direction of ΔRMSF were not predictable from the pairwise correlation coefficients for the dynamics . This demonstrates that the correlated dynamics between two regions manifest via specific networks of interactions that induce changes in RMSF within the paired second region, upon mutation within the first region. The link between dynamics correlation and epistasis for several pairs of mutations is described in detail below. By way of example, we examined the impact of each mutation on the local RMSF at only the four mutation sites. This articulates how coupled changes in local RMSF can mediate epistasis between the mutations. Although changes in flexibility in these local regions are expected to contribute to changes in ΔTm, ΔTagg, and ΔΔG‡, these stability parameters would also depend on the RMSF from other regions of the protein, in a complex manner. Also, the underlying epistasis on local RMSF may manifest in different ways for ΔTm, ΔTagg, and ΔΔG‡. Therefore, we did not attempt to determine any precise relationship between these and RMSF.The mutations tended to cause significant changes in RMSF (ΔRMSF) only within regions that were strongly correlated to the mutation site via dynamics. A plot of CSI Appendix, Fig. S5). By contrast, the A282P mutation decreased its local flexibility and yet also increased that around H192P . This short network of interactions would readily mediate the correlated dynamics and the epistasis observed between I365L and G506A for all measures of stability, except the Tm within H192P/A282P. The relationship between dynamics correlation and epistasis for local RMSF is outlined schematically in Tm, negative epistasis for ΔΔG, and positive epistasis for ΔTagg. This reemphasizes that the type of epistasis observed for different properties from a given mutational pair are not necessarily the same.A strong cross-correlation was found between the regions containing Ile365 and Gly506, with a correlation coefficient higher than 0.3 for the WT and all variants . They arSI Appendix, Fig. S5). In this case, only the coupling of dynamics through a long network of interactions could be expected to cause the observed negative epistasis for ΔΔG and ΔTm, and positive epistasis for ΔTagg, between I365L and H192P/A282P. The relationship between dynamics correlation and epistasis for local RMSF is outlined schematically in Ile365 is located 25 and 46 Å from His192 and Ala282, respectively . The DCCM analysis revealed a strong correlation between this fragment and the region around the A282P mutation, which provided a long-range mechanism through which the mutation could have stabilized the fragment . Stabilization of this region could therefore decrease the propensity of H192P/A282P/I365L/G506A to aggregate, and explain the unusually high Tagg.A positive epistatic behavior in fragment . This frIn this work, we explored the epistatic interaction between the mutations H192P and A282P, located on the surface of the PP domain, and then between the double-mutant H192P/A282P and two single mutations, I365L and G506A, located distantly in the core region of the Pyr domain. Surprisingly, not all pairwise effects between distant mutations from the surface and core regions of different domains were additive. This study has identified and characterized MD that mediated long-range epistatic interactions between mutations for various measures of protein stability. We found that the protein dynamics between the four mutations sites were correlated via a network of interactions that then mediated the observed long-range epistasis. These effects have the potential to be exploited for developing improved protein-engineering strategies. For example, the strategy of rigidifying flexible sites has been proven to be a powerful method to improve the stability of enzymes. When combining mutations that independently improve protein stability, an absence of epistatic interactions might be preferred as this would lead to predictable increases in performance. As mutations with correlated dynamics could potentially interact with each other, protein-engineering strategies could consider combining only those mutations in regions that have no cross-correlated dynamics to maximize the likelihood of additive improvements. Alternatively, experimental and computational protein-engineering approaches may also benefit from deliberately identifying cross-correlated sites. Targeting mutagenic libraries to two or more of these sites simultaneously has the potential to exploit positive epistasis and to rapidly evolve stability via a small number of residues that form a critical network.https://www.agilent.com/genomics/qcpd). Site-directed mutagenesis of tktA within plasmid pQR791  at 60 °C was calculated as t1/2 = 1/(100*kd). Retained activity (RA) after time t of heating was calculated as At)(/A(0):Thermal inactivation was measured as in ref. Kinetic parameters were obtained at saturating 50 mM Li-HPA and 4–80 mM glycolaldehyde as in ref. Tagg was determined from SLS counts at 266 nm using the instrument software. Fluorescence intensity ratio at 350 nm: 330 nm vs. temperature was fitted to a two-state transition model using Intrinsic protein fluorescence  and static light scattering (SLS) at 266 and 473 nm, were measured simultaneously with a UNit (Unchained Laboratories) at every 1 °C for 30–90 °C after 30-s equilibration at each temperature. Microcuvettes were loaded with 9 µL of 0.1 mg/mL samples in triplicate. IT is the observed signal; IN and ID, the native and denatured baseline intercepts; a and b, the native and denatured baseline slopes; T, the temperature; ∆Hvh, the van’t Hoff enthalpy; R, the gas constant ; and Tm, the thermal transition midpoint. The van’t Hoff entropy was calculated using fT, of unfolded protein at any temperature T was calculated from where G(X), ∆∆G(Y), and ∆∆G are changes in free energy relative to WT for single mutations X and Y, and double mutant XY, respectively. ∆GI is the coupling energy for interaction between X and Y:Epistasis was quantified using variant and RAWT are the activities retained for the variant and WT after the same heat treatment, and A0 = 100%:The change in free energy of inactivation relative to WT was calculated from Tm and Tagg using T(X), ΔT(Y), and ΔT for single mutants X and Y, and double mutant, respectively. ΔTI is the epistatic interaction between X and Y:Epistatic interactions between mutations X and Y were quantified for MD simulations of WT TK (Protein Data Bank ID code 1QGD) and variants constructed with the Pymol Mutagenesis Wizard (Schrödinger) were carried out in triplicate at 370 K using Gromacs, version 5.0, exactly as in ref. α atoms selected for calculating the correlation coefficients. Dynamics correlation matrices were averaged from triplicate trajectories and visualized using OriginPro9.0.DCCMs were computed using Bio3D , 55. TheSupplementary File


Platelets have a vital role in antimicrobial host defenses. The objective of this study was to evaluate if increased mean platelet volume to platelet count (MPV/PC) ratio in acute ischemic stroke patients complicated with pneumonia was associated with increased mortality risk.The current study was conducted at Zagazig University Hospitals. It included 500 acute ischemic stroke patients classified as group 1 that included 51 patients complicated with pneumonia after admission and group 2 that included the remaining 449 patients. Clinical assessment was carried out to exclude comorbid medical illnesses likely to interfere with platelet function or morphology. Laboratory investigations including MPV/PC ratio and brain imaging were carried out for all patients.p = 0.02, 0.03, 0.01). There was a significant difference between survivors and non-survivors of group 1 regarding to pneumonia severity index (PSI) classes IV and V . Also, there was a significant difference regarding confusion, urea ≥ 7 mmol/L, respiratory rater ≥ 30 breaths/min, systolic blood pressure ≤ 90 mmHg or diastolic blood pressure ≤ 60 mmHg, and age ≥ 65 years at pneumonia occurrence (CURB-65) scores 3, 4, and 5 . Moreover, there was a significant difference regarding decreased GCS score at pneumonia occurrence, higher NIHSS scores, PSI, and higher MPV/PC ratio . Age > 65 years, need for mechanical ventilation, GCS score of > 9, PSI class ≥ IV, CURB-65 scores ≥ 3, and increased MPV/PC ratio were all significantly associated with 30-day mortality in group 1 . The predictors of 30-day mortality risk factors were GCS less than 9, increased MPV/PC ratio, and CURB-65 scores ≥ 3 .There was a significant difference between both groups regarding age, National Institutes of Health Stroke Scale (NIHSS) score, and mortality within 30 days (Once pneumonia develops, MPV/PC ratio could be considered a significant laboratory indicator of 30-day mortality. Stroke is a major cause of disability and death worldwide  ratio have been established in patients with different subtypes of stroke than in controls. Furthermore, mean platelet volume (MPV) has been shown to be predictive of stroke, in patients with previous cerebrovascular events. These findings lead to the hypothesis that the increase of MPV/PC might have a critical role in the outcome of stroke (Elsayed and Mohamed The aim of the work is to investigate the association of increased mean platelet volume to platelet count (MPV/PC) ratio with increased mortality in hospitalized acute ischemic stroke patients complicated with pneumonia.This is a cross-sectional study that was done in the period from April 2015 to March 2017. All patients meeting the inclusion criteria of this study and admitted in the stroke units of Neurology, and Internal Medicine departments in Zagazig University Hospitals, Sharkia Governorate, Egypt, during this period were included in the study. Five patients died within 3 days after admission out of 505 patients, so only 500 were included in the study, and the patient’s age (mean ± SD/years) was 67.3 ± 10.2. Fifty-one out of 500 complicated with pneumonia, those patients were classified as group 1 , and 449 were not complicated with pneumonia, those patients were classified as group 2 .A written consent was taken from all of the participants or their relatives after explaining the details of the study to them. The study was approved by the Institutional Ethical Committee of Faculty of Medicine, Zagazig University, Egypt (ZU-IRB#4728\24-3-2017).Acute ischemic stroke. Stroke was diagnosed clinically  and proved via brain imaging (computed tomography with or without magnetic resonance imaging) , positive microbiologic findings , and new onset of pulmonary infiltrates on chest radiography , using automated cell counter “model XS 500i ,” together with examination of Leishman stained peripheral blood smears for differential leukocytic count. Mean platelet volume to platelet count ratio (MPV/PC) was calculated. MPV/PC ratio was considered increased if it is more than 0.031 according to Elsayed and Mohamed, 2016 -Liver and kidney functions using automated analyzer “Cobas 501” .-C-reactive protein (CRP) and arterial blood gas analysis.2 < 60 mmHg or O2 saturation < 90%  and chest plain X-ray.5-Scales for assessment of patients included in the study:Neurological scales:A-. It can be elicited via assessment of eye opening, motor response, and verbal response. Total score ranged from 3 to 15.Neurologic status was assessed by the Glasgow Coma Scale (GCS) on admission and on pneumonia occurrence B-The clinical severity of stroke was assessed on the day of admission using the National Institutes of Health stroke scale (NIHSS)  score is based on age; coexisting disease, abnormal physical finding, and abnormal laboratory finding upon presentation patients were assigned to the appropriate PSI risk classes (I–V). Outpatient therapy should be considered, especially for patients in classes I and II, while patient in classes IV and V should be hospitalized . A single point is given for the presence of confusion, a blood urea nitrogen value ≥ 20, a respiratory rate ≥ 30, a systolic blood pressure < 90 mmHg, or a diastolic blood pressure ≤ 60, and an age ≥ 65 years. If the score is 0 or 1, then most likely this patient could be safely treated as an outpatient. A score of 2 might suggest closely supervised outpatient treatment or inpatient observation admission. A score of 3, 4, or 5 would usually indicate inpatient treatment  . For comparisons with the continuous variables, Student’s Levesque .The patients were classified into two groups; their age (mean ± SD/years) was 67.3 ± 10.2 and 59.2 ± 25.3 years, respectively. Twenty-four (53%) patients died out of group 1, and 67 (15%) patients out of group 2 died within 30 days of admission.p = 0.02), National Institutes of Health Stroke Scale score, and number of cases that died within 30 days of admission  . Also, there was a significant difference regarding to CURB-65 classes III, IV, and V  (Table Group 1 included 51 10.2%) patients (those who were complicated with pneumonia). Twenty-four (53%) patients out of them died within 30 days of admission. There was a significant difference between survivor patients and non-survivors of group 1 regarding PSI classes IV and V  , according to risk factors of 30 days mortality, there was significant difference regarding lower GCS score at pneumonia occurrence, higher National Institutes of Health Stroke Scale scores, PSI, and higher MPV/PC ratio   , and number of cases that died within 30 days of admission. This is in agreement with a previous study of Mirsaeidi and colleagues (Mirsaeidi et al. Thirty day mortality was significantly associated with PSI risk classes ≥ IV and CURB-65 scores ≥ 3 in our ischemic stroke patients with pneumonia, and these results are consistent with Mirsaeidi and colleagues (Mirsaeidi et al. Moreover, in the present study, patients who survived from pneumonia had statistically significant better scores in GCS score at pneumonia occurrence, NIHSS scores, PSI score, and MPV/PC ratio than non-survivors. This is in agreement with the findings of Chen and colleagues (Chen et al. The need for mechanical ventilation and the presence of thrombocytosis were significantly associated with 30-day mortality in patients with acquired pneumonia according to the findings of Mirsaeidi and colleagues (Mirsaeidi et al. Authors of the present study found that the most significant 30-day mortality risk factors were GCS < 9 at pneumonia occurrence, increased MPV/PC ratio, and CURB-65 scores ≥ 3. These observations were in agreement with Elsayed and Mohamed (Elsayed and Mohamed Once pneumonia develops in acute ischemic stroke patients, MPV/PC ratio could be considered as a significant laboratory marker.


Chrysanthemum indicum collected from Huangshan, in which they accounted for only 37.45% of total volatiles. The major volatile compounds were camphor, α-pinene, chrysanthenone, safranal, myrcene, eucalyptol, 2,4,5,6,7,7ab-hexahydro-1H-indene, verbenone, β-phellandrene and camphene. In a hierarchical cluster analysis, 39 accessions of Chrysanthemum and its relatives formed six clusters based on their floral volatile compounds. In a principal component analysis, only spider type flowers were located closely on the score plot. The results of this study provide a basis for breeding chrysanthemum cultivars which desirable floral scents.The objective of this study was to identify the major volatile compounds and their relative concentrations in flowers of different chrysanthemum cultivars and their wild relatives. The volatile organic components of fresh flowers were analyzed using a headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry. In total, 193 volatile organic components were detected; the major scent components were monoterpenoids and oxygenated monoterpenoids, which accounted for 68.59%–99.93% of the total volatiles in all tested materials except for  In many angiosperms, floral volatiles are an important factor in attracting pollinating insects . Floral Chrysanthemum  is one of 10 well-known traditional Chinese flowers hept-2-en-4-ol, 2,6,6-trimethyl-acetate, β-caryophyllene and β-farnesene were present in at least 20 accessions.α-Pinene was present in 36 accessions but absent from “Nan nong fen feng che”, “Nan nong ju yu”, and “Zhe jiang 12”. Its concentration ranged from 0.08 µg gChrysanthemum cultivars clustered together in the PCA based on their scent, while the scents of other flower types and Chrysanthemum wild relatives showed wide diversity. We created a 2D loading plot ; verbenone ; β-phellandrene ; and camphene .To simplify the multidimensional dataset (193 volatile compounds), we subjected the peak area data to a principle components analysis (PCA) . PC1, PCing plot  to furthing plot . As follChrysanthemum are used as TCM for a long time, and its active compounds are eucalyptol and camphor [C. indicum essential oils showed that those obtained from processed flowers had a higher percentage of camphor and greater bacteriostatic activity than those obtained from air-dried flowers [To compare volatile compounds compositions among the 39 accessions, we performed a hierarchical cluster analysis based on the relative contents of the 193 aromatic volatile compounds. We used Ward’s method for between-groups linkage, and the squared Euclidean distance between clusters as a proximity measurement. The 39 accessions formed six clusters in the dendrogram . Five ac camphor . An anal flowers . Our resChrysanthemum cultivar “Zhe jiang 12” formed its own cluster, with thujone (65.51%) and α-thujone (19.31%) as the major compounds. Because the compound identification was based solely on mass spectrometric data comparison, stereochemical structure of these major compound were not clearly identified yet. It is necessary to making the stereochemical structure clearly in our ongoing study. These major compounds may also help us to choosing promising cultivars of elegant floral scent parents for chrysanthemum breeding.C. morifolium cultivars, three Ajania, and five wild relatives of Chrysanthemum were used in this study  were cut from the stem with a sharp scalpel blade, and ca. 2.0 g fresh weight of intact inflorescences  was weighed and placed in a 40 mL brown glass vial capped with a polytetrafluoroethylene septum with an aluminum cap . The experiment was conducted in triplicate.16 (44) to optimize these four parameters  attached to a manual SPME holder (Supelco Inc.) was used to extract floral volatiles. The sample weight, fiber exposure time, handle scale, and desorption temperature can affect the extraction efficiency. We designed an orthogonal test Lrameters  for “Nan−1 (no split injection). The injection temperature was set to the optimum conditions established in the orthogonal test. The column oven temperature program was as follows:40 °C for 1 min, increasing to 80 °C at a rate of 20 °C·min−1, then to 160 °C at 5 °C·min−1, then to 270 °C at 20 °C·min−1; hold for 1 min. The ion source and quadrupole temperatures were 230 °C and 150 °C, respectively. The ionizing energy was 70 eV and the mass range scanned was 40–500 amu in the full-scan acquisition mode.Flower volatiles were analyzed using a GC-MS system  equipped with a HP-5MS capillary column . Helium (99.999%) was used as the carrier gas at a flow-rate of 1 mL·min−1) = peak area of each component/peak area of internal standard × concentration of internal standard (ng μL−1) × volume of internal standard (µL)/fresh weight of sample (g).Peaks in the chromatogram were tentative identified based on comparisons with those in the NIST08  mass spectral library. Each volatile compound was quantified based on comparison of its peak area with that of the internal standard, which was added at a known concentration. The following formula was used to calculate the concentration of each component: content of aroma component . Ward’s method was used to obtain consistent cluster results, and the squared Euclidean distance between clusters was selected as the proximity measurement . The datChrysanthemum flower aroma compounds. In addition, we optimized the HS-SPME parameters for extraction and analysis of floral aroma volatiles. The 39 accessions showed substantial differences in the total amount of volatiles. The chrysanthemum cultivars generally produced larger amount of volatile compounds than did their wild relatives. Among the five flower morphotypes, only the pompon type cultivars had a characteristic scent. The hierarchical cluster analysis grouped the 39 accessions into six different clusters.We determined that the collection of fresh flower volatile compounds by HS-SPME coupled with GC-MS represents a sensitive, rapid and effective method for analyzing


Figure 1 as published. The descriptions of the figure labels (A) and (B) were switched. The correct legend appears below.In our original article, there was a mistake in the legend for Viola pubescens var. scabriuscula bearing (A) cleistogamous and (B) chasmogamous flowers. Photographs were taken over native populations located in Sells Park, Athens County, Ohio, 45701 (39°20′40.6″N 82°04′31.9″W).”“Additionally, there was a mistake in The authors apologize for these errors and state that they do not change the scientific conclusions of the article in any way. The original article has been updated.


To investigate whether (a) lower levels of daily physical activity (PA) and greater sedentary time accounted for contrasting metabolic phenotypes (higher liver fat/presence of metabolic syndrome [METS+] vs lower liver fat/absence of metabolic syndrome [METS−]) in individuals of similar body mass index and (b) the association of sedentary time on metabolic health and liver fat.−2), underwent assessments of PA , cardiorespiratory fitness (V˙O2 peak), body composition (magnetic resonance imaging and magnetic resonance spectroscopy) and multiorgan insulin sensitivity . We undertook a) cross-sectional analysis comparing four groups: nonobese or obese, with and without metabolic syndrome (METS+ vs METS−) and b) univariate and multivariate regression for sedentary time and other levels of PA in relation to liver fat.Ninety-eight habitually active participants  higher liver fat content.Light, moderate, and vigorous PA did not account for differences in metabolic health between individuals, whether nonobese or obese, although METS+ individuals were more sedentary, with a higher number, and prolonged bouts (~1–2 h). Overall, sedentary time, average daily METS and V˙OGreater sedentary time, independent of other levels of PA, is associated with being metabolically unhealthy; even in habitually active people, lesser sedentary time, and higher cardiorespiratory fitness and average daily METS is associated with lower liver fat. Strong epidemiologic evidence suggests an inverse relationship between physical activity (PA) levels and obesity, metabolic syndrome (METS), nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes –5. Incremetabolically unhealthy (METS+)   ; convers (METS−) . Some st (METS−) ,17, but  (METS−) –20. Withbehavior , which obehavior ; no suchbehavior –26. The behavior  and is cbehavior . ObserviThis cross-sectional study will objectively monitor the habitual PA of young to middle-age adults and extensively phenotype these individuals by assessment of metabolic health and magnetic resonance imaging (MRI)-derived body composition. We hypothesize that greater sedentary time and lower levels of PA will be evident in metabolically unhealthy phenotypes (METS+ vs METS−) in BMI-matched individuals; and second, higher MRS-quantified liver fat will be associated with greater sedentary time and lower PA levels.Declaration of Helsinki and was approved by the North West Liverpool Central research ethics committee . All participants were informed of the methods verbally and in writing before providing written informed consent before any assessments. Ninety-eight individuals  with a mean age of 39 ± 13 yr and BMI of 27 ± 5 kg·m−2 were recruited. Before each study visit, participants were required to fast overnight for 12 h (water was permitted ad libitum), abstain from alcohol and caffeine for 24 h and from exercise for 48 h.Habitually active individuals, who engaged in no more than 2 h of exercise per week, were recruited via local advertisements across University of Liverpool campuses and hospital departments. Exclusions included cardiovascular, respiratory, kidney, liver and/or endocrine complications, smoking, and >14 units per week of alcohol consumption. The study conformed to the 2 peak) at University Hospital Aintree and (b) MRI and proton MRS (1H-MRS) at the University of Liverpool MRI Center. Because of the MRI scanner replacement during part of this study, MRI quantification of body fat was conducted in only 72 individuals. Bioimpedance data were collected in all individuals, and V˙O2 peak calculations were based on both total body mass and fat-free mass (FFM).All participants completed measurement of baseline PA and dietary consumption over a period of 4 d (including one weekend day) between January 2016 and February 2017 followed by assessment in the order of (a) anthropometry (including bioimpedance), fasting biochemistry, an oral glucose tolerance test, and assessment of CRF  and the presence or absence of METS according to International Diabetes Federation criteria; we refer to these groups as (i) “nonobese METS−,” (ii) “nonobese METS+,” (iii) “obese METS−” and (iv) “obese METS+.”Individuals were characterized into one of four groups based on BMI .Total energy consumption, carbohydrate, protein, and fat content were determined from 4-d dietary records by a registered nutritionist (K.M.) using Nutritics  and whole-body bioimpedance analysis  was conducted; this provided total body mass, fat percentage, fat mass, FFM, muscle mass, total body water, basal metabolic rate, bone mass, and visceral fat indicator. Waist and hip circumference measurements were taken in duplicate, and blood pressure was determined from an average of three measures .Blood samples were collected and analyzed using the Olympus AU2700 analyzer  with standard proprietary reagents as follows: glucose with hexokinase, total cholesterol and high-density lipoprotein with cholesterol esterase/oxidase and triacylglycerol with glycerol kinase. Low-density lipoprotein was calculated according to the Friedwald formula. Insulin was measured using radio-immunoassay . HOMA-IR was calculated using fasting glucose and insulin concentrations.Following a 12-h fast, blood samples were collected, a 75-g glucose drink was consumed within 5 min and postingestion blood samples were drawn at 30, 60, 90, and 120 min. Matsuda index was calculated to estimate whole-body IS, and indices of hepatic-IR and skeletal muscle IS were determined as previously described .2 peak cardiopulmonary exercise test was performed on a treadmill  in a temperature-controlled room. The cardiopulmonary exercise test provided breath-by-breath monitoring and analysis of expiratory gases and ventilation . The modified Bruce protocol was employed, after an initial 2-min warm-up at 2.2 km·h−1 on a flat gradient, stepwise increments in speed and gradient were employed each minute. V˙O2 peak was determined by exhaustion plus one or more of: respiratory exchange ratio >1.15, heart rate >90% predicted maximum, plateau in V˙O2.A V˙OLiver and skeletal muscle fat were determined using a 1.5T Siemens Symphony MRI scanner as previously described .P < 0.05). Between-group univariate general linear models were conducted for all other variables, with age as a covariate and Bonferroni correction for multiple comparisons. Statistically significant interactions were explored, and nominal P values reported. Univariate and multivariate linear regressions were used to analyze components of PA and fitness associated with liver fat. Decisions were made a priori to include all variables reaching P < 0.1 in univariate regression analysis alongside age and BMI in the multivariate regression model. The statistical cutoff for inclusion in the final model is more stringent than often used to guard against false discovery. The alpha level of statistical significance was set at P < 0.05. Data are presented as mean , unless stated otherwise. Transformed data were back-transformed to original units. P value >1 rounded to 1.000.All data were explored for normality using visual inspection of frequency distribution, and logarithmically transformed where appropriate. Given the small sample size, power achieved on each test was assessed and ranged from 46% to >99%; 20 of 26 achieved at least 80% power. Age was analyzed using a one factor between-groups ANOVA, whereby a significant group effect was observed  but nonobese METS+ BMI was 3 ± 2 kg·m−2 greater than nonobese METS− (P = 0.003). In the general population, MRS defined that liver fat >5.5% corresponds with the prevalence of hepatic steatosis . Mean ± SD macronutrient percentages were 56% ± 16% carbohydrate, 24% ± 9% protein, and 20% ± 7% fat.Total energy consumption, carbohydrate, protein and fat did not differ significantly between groups  but not nonobese METS+  There was no difference between both nonobese groups and obese METS− (P ≥ 0.080)  . There was no group effect for skeletal muscle IS index (P = 0.220). Hepatic-IR index was greater in obese METS+ than nonobese METS−  for HOMA-IR.Nonobese METS− individuals had greater Matsuda index than nonobese METS+ (P ≤ 0.005) ; and liver fat percentage in obese groups was not statistically different .Liver fat was higher in METS+ in both nonobese and obese. Nonobese METS− individuals had 4.6% lower liver fat than obese METS+ (05) Fig. D. Liver There was no group effect for average daily steps Fig. A.P = 1.000; 49 min·d−1·) and obese groups . Nonobese METS− individuals had lower sedentary time than obese METS+ (P = 0.04); there was no difference between obese METS− and both METS+ groups (P ≥ 0.199). There was no group effect for amount of time spent lying down (P = 0.080) or sleeping (P = 0.117).Nonsleep sedentary time Fig. B was notP = 0.711; mean difference, 10 min·d−1) and both obese groups . However, both obese groups had less light activity than both nonobese METS−   Fig. C.P = 0.322) ; for both measures, nonobese METS− had greater values than both obese groups, but were not different to nonobese METS+. Daily average METS in nonobese METS− were 0.3 METS greater than both obese groups (P < 0.0005). The same was observed for PA duration, with nonobese METS− having greater duration that both obese groups . There was no significant difference between obese METS− and both METS+ groups for average daily METS and PA duration .Daily average METS Fig. E and PA duration of sedentary time . During bouts of 81 to 100 min, METS+ obese accumulated 34 min·d−1 more than obese METS− . During bouts of 101 to 120 min, obese METS+ accumulated 28 min·d−1 more than obese METS− .During bouts of 61 to 80 min, nonobese METS+ accumulated 33 min more sedentary time per day than nonobese METS−  than METS−. Obese METS+ had 0.4 more bouts per day  than METS− of 81 to 100 min and 0.3 more bouts per day  of 101 to 120 min.As an average of 4 d, both METS+ groups accumulated one to two more long bouts (between 1 and 2 h) of sedentary behavior, compared with their METS− counterparts. Considering bouts of 61 to 80 min, nonobese METS+ had 0.5 more bouts per day , whereas for a 1-mL·min−1·kg−1 increase in CRF (V˙O2 peak), liver fat reduced by 0.87% .Univariable linear regression analysis revealed that daily average steps, sedentary time, vigorous activity, METS, and V˙Oat Table . Greaterfat Fig. . For a 1The results of this extensive phenotypic analysis of objective measurements of PA and sedentary behavior, metabolic and body composition measurements (including MRS-derived liver fat) in young-middle age adults demonstrate two key messages. First, in this cohort, overall habitual PA was not associated with different metabolic health status in individuals of similar BMI, and the accumulation of sedentary time was weakly associated with the presence of the METS. Second, even in habitually active individuals, there is an association between greater sedentary time and increased liver fat, whereas the amount of MVPA appeared to have little independent association. These data highlight the potential importance of sedentary behavior in determining optimal metabolic health and liver fat.It is recognized that greater sedentary time increases the risk of becoming overweight/obese  and the 2 peak is strongly, inversely, and independently related to the risk of liver fat , the comparatively healthy habitual PA habits of the participants which somewhat limits the external validity of the findings, and the cross-sectional design which cannot determine causality. Noteworthy is the higher BMI in unhealthy nonobese versus healthy nonobese which conforms to the association of a greater BMI with greater metabolic risk. This difference could not be controlled for because it was a component of our grouping analysis but differences in age were statistically controlled for. Although the present results demonstrate that overall sedentary time needs to be considered independently of PA, objective PA monitoring in a larger cohort with a prospective design will be required, and future research should further explore sedentary behavior patterns . The American Diabetes Association has recommended that adults should “decrease the amount of time spent daily in sedentary behavior” and that “prolonged sitting should be interrupted with bouts of light activity every 30 min.” Importantly, these recommendations are in addition to, not a substitute for, a physically active lifestyle. A “cutoff” for harmful sedentary behavior patterns  has not been defined in public health guidelines.In summary, in habitually active adults, the amount of sedentary time is associated in this single-measure observation with metabolic health and the quantity of liver fat. The findings of this study highlight that public health policy designed to optimize the benefits of PA may need to synergistically consider strategies to reduce sedentary behavior.


Scientific Reports 10.1038/s41598-018-28127-0, published online 05 July 2018Correction to: This Article contains an error in the Methods section under subheading ‘Participants and semi-structured interview’,“The interview was delivered by a female psychologist (A.S. or R.B.) to one or both parents/guardians. Interviews were conducted either at the participant’s home, or at a private clinic room at the Brain and Mind Centre, Sydney or the Centre for Children’s Health Research, Brisbane, and lasted approximately 1.5 hours.”should read:“The interview was delivered by a female psychologist  to one or both parents/guardians. Interviews were conducted either at the participant’s home, or at a private clinic room at the Brain and Mind Centre, Sydney or the Centre for Children’s Health Research, Brisbane, and lasted approximately 1.5 hours.”