Unfortunately, we found that Moon et al.’s scheme still has weaknesses. In this paper, we show that Moon et al.’s scheme is vulnerable to insider attack, server spoofing attack, user impersonation attack and guessing attack. Furthermore, we propose a robust anonymous multi-server authentication scheme using public key encryption to remove the aforementioned problems. From the subsequent formal and informal security analysis, we demonstrate that our proposed scheme provides strong mutual authentication and satisfies the desirable security requirements. The functional and performance analysis shows that the improved scheme has the best secure functionality and is computational efficient.In order to improve the security in remote authentication systems, numerous biometric-based authentication schemes using smart cards have been proposed. Recently, Moon  Nowadays security has becoming an urgent issue for the distributed networks. The remote user authentication scheme allows the transmission of secret data via public channels, thus is an important cryptographic tool for distributed networks. In 1981, Lamport , 3. Howeet al. [et al.’s scheme cannot resist impersonation attack and designed a protocol without verification table to fix the flaws of Lee et al.’s scheme. In 2007, Khang-Zhang [et al. [et al.’s scheme is vulnerable to forgery attack and stolen smart card attack, and put forward a three-factor remote user authentication scheme. After that, Chaturvedi et al. [et al.’s scheme doesn’t resist known session specific temporary information attack and doesn’t protect user’s privacy. They also proposed a novel authentication and key agreement protocol to overcome the weaknesses of Li et al.’s scheme.The biometric keys, such as fingerprint and iris, are considered to be a unique identifier of a user, thus have many advantages. For example, the biometric keys cannot be forgotten or lost, are difficult to copy or share, and are not easy to forge or guess. Additionally, one can carry biometric keys at anytime and from anywhere. With the security requirements of the distributed networks and the good security performance and advantages of the biological characteristic, biometrics authentication protocols come to be more crucial and widely deployed –36. In 2et al.  designedet al.  demonstrng-Zhang  pointed ng-Zhang  demonstrng-Zhang  exhibite [et al.  pointed i et al.  demonstret al. [et al. [et al.’s scheme is insecure against server spoofing attack and impersonation attack, and can not provide forward secrecy. They introduced two independent three-factor authentication schemes [et al. [et al.’s scheme [et al.’s scheme.In 2014, Chuang-Chen  proposedet al.  showed t [et al.  illustra schemes , 31 for  [et al.  showed ts scheme  is vulneet al.’s biometric-based remote user authentication scheme still has some flaws. In this paper, we firstly showed that Moon et al.’s scheme is vulnerable to insider attack, server spoofing attack, user impersonation attack and guessing attack. Moreover, we exhibited that their scheme is not anonymous for the user. Then we proposed an improved authentication scheme for multi-server environment to fix their design flaws. After that, we show that our scheme is robust against all known attacks through the formal and informal security analysis. Finally we demonstrate that the improved scheme has the best secure functionality and is computational efficient.Unfortunately, we found that Moon et al.’s biometric-based remote user authentication scheme. Section 4 shows the design flaws in Moon et al.’s scheme. In order to eliminate the shortcomings discussed in section 4, we propose an enhancement authentication protocol in section 5. Section 6 analyzes the security of the proposed scheme, and Section 7 compares the performance of the enhanced scheme with other related schemes. Finally, we conclude in section 8.The rest of the paper is organized as follows. In section 2, we introduce some preliminary knowledge. Section 3 briefly reviews Moon This section elaborates the definitions of one-way hash function and BioHashing, and the security model.One-way hash function. A one-way hash function h: {0, 1}* → {0, 1}n takes an arbitrary-length input x ∈ {0, 1}*, and produces a fixed-length output h(x) ∈ {0, 1}n, called the message digest. The hash function has the following attributes:y = h(x) if x and h(⋅) are specified.Computationally, it is easy to compute t to know two inputs x1 and x2, such that h(x1) = h(x2).It is almost impossible through polynomial time BioHashing. BioHashing technique [x ∈ Rn is reduced down to a bit vector b ∈ {0, 1}l with l the length of the bit string (l ≤ n) through BioHashing.echnique  is desigIn this paper, we adopt the security model proposed by Abdalla et al.  to proveParticipants. An oracle t of a party Sj, u of Ui, and v of RS.Partnering. The partner of an instance Ui is the instance Sj and conversely. The partial transcript of all exchanged messages between Ui and Sj is unique, and is said as a session ID Freshness.SK is not leaked to Adversary. In the ROR model, Execute: The Execute query helps Send: The Send query corresponds to an active attack. πt executes the protocol and responds with an outgoing message after receiving a message x from Reveal(πt): The Reveal query to reveal of session keys. If the session has been accepted, πt returns the session key SK as its response that is computed between πt and its partner, otherwise returns a null value.CorruptSC(πt): It is about modeling smart card loss attack and outputs the information stored in SCi.Test(πt): At some point, the adversary t. Πt flips an unbiased coin b and responds with the real agreed session key SK if SK is established and fresh, if b = 1; otherwise it returns a random sample generated according to the distribution of the session key. Otherwise, it returns ⊥.Semantic security of the session key. In an experiment, the adversary SK and a random key. Test queries to either the server instance or the user instance. The outcome of Test query must be consistent with the random bit b. Eventually, b′ for b. we say b.E denotes the event that Let Random oracle. To prove the security of the proposed protocol, the one-way hash function h(⋅) is treated as a random oracle(say Hash oracle), and is provided to the adversary u, v) table of binary strings. When a hash query h(u) is made, the Hash oracle returns v if u is found in the table; otherwise, it returns a uniformly random string v and stores the pair  in the table.et al.’s scheme, which consists of four phases: registration phase, login phase, authentication phase and password change phase. In this section, we briefly review Moon Ui firstly selects an identity IDi and password PWi and inputs biometrics BIOi.The registration and authentication phases are shown in PWDi = h(PWi||H(BIOi)) and sends < IDi, PWDi > to the registration server through a secure channel.Using the password and the biometrics, the smart card computes IDi, PWDi >, the registration server computes Vi = h(IDi||PWDi), Wi = h(yi||PSK) ⊕ IDi, Xi = h(IDi||x), Yi = yi⊕ h(PSK). Then RS stores < Vi, Wi, Xi, Yi, h(⋅), H(⋅) > onto a smart card and sends the smart card to Ui.Upon receiving the message < Ui inserts his smart card into the smart card reader, inputs his identity IDi and password PWi, and imprints biometric information BIOi. Upon receiving an input, the smart card uses the following steps to perform a login session:During the login phase, the user PWDi = h(PWi||H(BIOi)) and verifies Vi? = h(IDi|| PWDi). If succeeds, it executes the next step. Otherwise the session aborts.The smart card computes n1 and computes K = h((Wi ⊕ IDi)|| SIDj), M1 = IDi ⊕ K, M2 = n1 ⊕ K, M3 = PWDi ⊕ K, Zi = h(Xi||n1|| PWDi||Ti).The smart card generates a random number Yi, Zi, M1, M2, M3, T1 > to the server Sj through a public channel, where T1 is the current timestamp.The smart card transmits the login request message < Yi, Zi, M1, M2, M3, T1 > from the user Ui, the server Sj executes the following steps to authenticate each other.After receiving the authentication request < Sj firstly checks whether |Tc − T1| < ΔT, then uses its pre-shared key PSK and achieves yi = Yi ⊕ h(PSK). The server also retrieves K = h(h(yi||PSK)||SIDj), n1 = M2 ⊕ K, IDi = M1 ⊕ K, PWDi = M3 ⊕ K, Xi = h(IDi||x) and verifies Zi? = h(Xi||n1||PWDi||T1). If they are not equal, Sj rejects the login request and terminates the session. Otherwise, the server generates a random number n2 and computes M4 = n2 ⊕ h(n1||PWDi||Xi), M5 = h(IDi||n1||n2||K||T2), SKij = h(n1||n2||K||T2) and then responds with the message < M4, M5, T2 > to the smart card (user Ui) over a public channel.The server M4, M5, T2 > and checking the freshness of T2, the smart card retrieves the value n2 = M4 ⊕ h(n1||PWDi||Xi). Then it verifies M5? = h(IDi||n1||n2||K||T2). If the verification holds, it computes the session key SKij = h(n1||n2||K||Xi), which would be shared between Ui and Sj. Finally, the smart card computes M6 = h(SKij||IDi||n2||T3) and sends the message < M6, T3 > to Sj via a public channel.Upon receiving the message < M6, T3 >, Sj checks the freshness of T3 and verifies h(SKij||IDi||n2||T3)? = M6. If the equation holds, the server ensures the identity of Ui. Otherwise, the server aborts the session.Upon receiving the message < Ui can change his password any time when he wants. In order to change password, the user performs the following steps:In this phase, Ui inserts his smart card into the smart card reader and then inputs IDi and PWi and biometrics BIOi.SCi computes PWDi = h(PWi||H(BIOi)), then checks if Vi′ = h(IDi||PWDi) is the same as the stored Vi. If they are the same, SCi accepts Ui to enter a new password The smart card SCi computes Vi with et al. claimed that their scheme satisfies the required security requirements, we found that their scheme still has some weakness, i.e., fail to resist the insider attack, server spoofing attack, guessing attack and impersonation attack. Moreover, their scheme is not anonymous for users.Although Moon et al.’s scheme, during authentication phase, Ui sends < Yi, Zi, M1, M2, M3, T1 > as authentication request message to Sj. Note that all the information transmitted in public channel can be intercepted by the adversary. The parameter M1 = K ⊕ IDi where K = h((Wi ⊕ IDi)||SIDj)) in the message < Yi, Zi, M1, M2, M3, T1 >, is unique and static for each user during all logins to the same server. Thus anyone has ability to track the activities of a legal user, if he captures the value of M1.User anonymity means that the adversary cannot obtain or track the identity of the user according to the message transmitted via the public channel, which is an important property to protect the privacy of users. In Moon RS. In Moon et al.’s scheme, during user registration phase, Ui submits his identity IDi and PWDi to RS. In order to prevent duplicate user registration, RS has to store the user’s ID. If an adversary obtains the list of ID, it would cause great devastation. The adversary can impersonate himself as Ui as described in the following user impersonation attack.Insider attack means that an insider can get the sensitive credentials from the information stored in et al.’s protocol, RS shares the same secret information  with all the application severs. The compromised sever can impersonate as another legitimate server to deceive any legal user. Now we show the reason why Moon et al.’s scheme cannot withstand this kind of server spoofing attack.In Moon Ui submits his login request message < Yi, Zi, M1, M2, M3, T1 > to Sj, the legal but malicious server Sk can intercept this message and compute yi = Yi ⊕ h(PSK), K = h(h(yi||PSK)||SIDj), n1 = M2 ⊕ K, IDi = M1 ⊕ K, PWDi = M3 ⊕ K, Xi = h(IDi||x) and to check Z? = h(Xi||n1||PWDi||T1).When Sk generates a random number n2 and computes M4 = n2 ⊕ h(n1||PWDi||Xi), M5 = h(IDi||n1||n2||K||T2), SKij = h(n1||n2||K||T2), then sends < M4, M5, T2 > to Ui.Ui computes n2 = M4 ⊕ h(n1||PWDi||Xi), M5 = h(IDi||n1||n2||K||T2) and compares it with M5. It is obvious that the values are the same, thus Ui responds with the message M6 = h(SKij||IDi||n2||T3).Ui computes the session key SKij = h(n1||n2||K||T2) and believes that he is communicating with Sj.Sk can masquerade as another server Sj to fool any legal user and Moon et al.’s scheme is vulnerable to server spoofing attack.Therefore, a legal but malicious server et al.’s scheme is vulnerable to identity guessing attack, which is a critical concern in their scheme. If the adversary can extract the secret value Wi from the legal user’s smart card by some means and get the value of M1 from public channel, the adversary can easily find out IDi can be verified as the following steps.Moon The adversary chooses The adversary verifies the correctness of The adversary repeats the above steps until a correct et al.’s protocol, an adversary can impersonate a valid user as described below.In a remote user communication scheme, anyone should be considered as a legal user if a user has valid authentication credentials or could be capable of constructing an effective authentication request message. In Moon Ui’s personal identifiable information IDi. He also extracts the secret values Wi and Xi from the legal user’s smart card by some means.As enlightened in insider attack and guessing attack mentioned above, an adversary obtains Yi, Zi, M1, M2, M3, T1 > which is sent from IDi via the public channel, then the adversary computes K = IDi ⊕ M1, PWDi = K ⊕ M3, chooses random number n1, and calculates Mm1 = IDi ⊕ K, Mm2 = n1 ⊕ K, Mm3 = PWDi ⊕ K, Zim = h, Mm5 = h(IDi||n1 ||n2||K||T2) and responds with the message < Mm4, Mm6, T2 > to the adversary who is masquerading as Ui.After the authentication of the login request message, the server Mm4 with the values of n1 and K. Then the masquerading user Ui computes n2 = Mm4 ⊕ h(n1||PWDi||Xi), SKij = h(n1||n2||K||T2), Mm6 = h(SKij||IDi||n2||T3), and sends the message < Mm6, T3 > back to the server Sj.The masquerading adversary verifies the correctness of Sj computes Mm6 = h(SKij||IDi||n2||T3) and verifies it with the received value of Mm6. It is obvious that they are equal, so the sever authenticates successfully the legitimacy of the user Ui and the login request message information is accepted.The server Sj and the malicious adversary who masquerades as the user Ui agree on the common session key as SKij = h(n1|| n2||K||Xi).After mutual authentication, the server et al.’s scheme. Our proposed protocol consists of four phases: registration, login, mutual authentication with key-agreement and password change. In this section, we propose an improved remote user authentication scheme to fix the drawbacks in Moon Ui and the server Sj need to perform the following steps to register with the registration server(RS).When the remote user authentication scheme starts, the user Sj submits his identity SIDj and his public key Pubj which can be obtained by all the users. Then Sj sends his identity SIDj and his public key Pubj to RS. Upon reception, RS shares the secret key PSK with Sj and publishes Sj’s public key Pubj.To register with the system, a server Ui freely selects his identity IDi which uniquely identifies the user’s identity, password PWi and scans his biometrics BIOi. Then Ui computes IDBi = h(IDi ||H(BIOi)), PWDi = h(PWi||H(BIOi)) and sends < h(IDi), IDBi, PWDi > to RS on a secure channel.RS computes Vi = h(h(IDi)||PWDi), Wi = h(h(IDi)||PSK) ⊕ IDBi and stores < Vi, Wi, h(⋅), H(⋅) > in the smart card SC.Upon reception, RS sends SC to Ui over a secure channel.Ui sends the login request by inserting smart card (SC), and inputting IDi, PWi and BIOi.SC computes PWDi = h(PWi||H(BIOi)) and then checks whether the condition Vi? = h(h(IDi)||PWDi). If the result is negative, the login session can be aborted. Otherwise, SC generates a random number n1 and computes K = h((Wi ⊕ IDBi) ⊕ h(IDi||n1)), Zi = h(n1||IDi||K||T1) and sends < M1, Zi, T1 > to the server Sj as the login request message.Sj checks freshness of T1. Sj computes (IDi||n1) = K = h(h(h(IDi)||PSK) ⊕ h(IDi||n1)) and verifies if Zi? = h(n1|| IDi||K||T1). If they are same, Sj authenticates Ui. Otherwise the session is terminated.On getting login message, Sj further generates a random number n2, and computes M2 = n2 ⊕ K, M3 = h(IDi||n1||n2||K||T2), SKij = h(n1||n2||K||IDi). Sj sends < M2, M3, T2 > to SC.T2, SC computes n2 = M2 ⊕ K and verifies the condition M3? = h(IDi||n1||n2||K||T2). If the condition holds, Ui authenticates Sj. Otherwise the process is terminated. Then, SC computes SKij = h(n1||n2 ||K||IDi) and M4 = h(SKij||IDi||n2||T3), then sends < M4, T3 > to Sj.On checking the freshness of Sj checks the freshness of T3. Sj verifies M4? = h(SKij||IDi||n2||T3) and reconfirms the authenticity of Ui. Now, Ui and Sj share with the computed session key SKij = h(n1||n2||K||IDi) for further communication.Ui) wants to update his password with a new password RS.This procedure is invoked whenever a user ) and then verifies the condition Vi? = h(IDi||PWDi). If the condition doesn’t hold, the request can be dropped.Ui chooses a new password In this section, we use Burrows-Abadi-Needham logic (BAN-logic)  to verifet al. is a formal method of analyzing the security features of the information exchange protocol. It helps determine whether the exchanged information is credible, whether it can prevent eavesdropping or both. In this paper, we use BAN logic to prove that a user and a server share a session key after successfully running the protocol. We first introduce the BAN logic notations used in this paper in The BAN logic introduced by Burrows BAN logical postulatesP believes that K is the shared key of P and Q, and P receives the message X encrypted with K, then P believe that Q has sent message X.Message-meaning rule: P believes that Q has the right to control X and P believes that Q also trusts X, then P trusts X.Jurisdiction rule: P believes that X is fresh and P believes that Q has sent X, then P believes that Q believes X.Nonce-verification rule: P believes that X is new, then the information of  is also fresh.Freshness-conjuncatenation rule: P believes X and Y, then P believes .Belief rule: Establishment of security goalsg1: g2: g3: g4: Initiative premisesUi| ≡ #n1. p2. Ui| ≡ Sj ⇒ #n2.p1. Sj| ≡ #n1. p4. Sj| ≡ #n2.p3. p5. Ui| ≡ IDi. p8. Sj| ≡ Ui ⇒ IDi.p7. p9. Scheme analysisa0. Sj can get the value of IDi and n1. One can get the value of K unless he has the true Prij and PSK at the same time.Since a1. Sj ⊲ K, T1p5 and a1 to drive:We employ Message-meaning rule according to a2. Sj| ≡ Ui| ∼ a2 and p3, we apply the Freshness-conjuncatenation rule and Nonce-verification rule to get the following information:According to a3. Sj| ≡ Ui| ≡ a3 and p8, we employ Jurisdiction rule and belief rule to obtain:According to a4. Sj| ≡ IDia4 and According to a5. a5 and p4, we apply Nonce-verification rule and Freshness- conjuncatenation rule to obtain:According to a6. Finally, we employ The belief rule to obtain:g1. g1 and p9, we utilize Jurisdiction rule to obtain:According to g2. p6 and Ui ⊲ K, we employ Message-meaning rule to obtain:According to a7. Ui| ≡ Sj| ∼ a7 and p1 we apply Nonce-verification rule and Freshness- conjuncatenation rule to derive:According to a8. Ui| ≡ Sj| ≡ a8 and p1, p3, p4, p6 and SKij = h(n1||n2||K||IDi), we apply Freshness-conjuncatenation rule and Nonce-verification rule to derive:According to g3. g3 and p10 we utilize Jurisdiction rule to obtain:According to g4. We use provable security to prove the security of our scheme. The security proof is based on the model of RSA-based password authentication.Theorem 1. Let t against our protocal D be a uniformly distributed password dictionary and l denotes the number of bits in the biometric key BIOi, |Hash| and|D| denotes the range space of hash function and the size of D, respectively. If an attacker qhHash queries, qsendSend queries, then, the advantage of AdvRSA(t) is the advantage that an adversary Proof. The proof is finished by executing a sequence of hybrid games Gi. For each game Gi, let Ei denote the event that the adversary succeeds in guessing the bit b in game Gi.GameG0: This game corresponds to the real attack in the random oracle model. Thus, we can writeGameG1: By querying Execute oracle, this game simulates Test oracle, and decides whether the outcome of the Test oracle is the real session key SK or a random number, where SKij is computed from SKij = h(n1||n2||K||IDi). Note that PSK and IDBi are secret to Sj and Ui. The adversary has no knowledge about PSK, IDBi and IDi, thus eavesdropping of message can not increase the chance of winning for the adversary in G1. So we haveGameG2: The difference between G2 and G1 is that we add the simulations of the Send and the Hash oracles. G2 models an active attack where Hash queries to find the collisions. Note that the messages {M1, Zi, T1} and {M2, M3, T2} are associated with timestamp T1, T2, random numbers n1 and n2, and IDi of Ui, hence there is no collision when querying the Send oracle. According to the birthday paradox, we haveGameG3: In this game, G3 simulates the CorruptSC oracle which models the smart card lost attack. Since the chosen password has low entropy, Bi from information collected from the smart card SCi. Our protocol l nearly random bits, therefore the probability of guessing biometric key Bi ∈ {0, 1}l by GameG4: This game models an attack wherein SKij = h(n1||n2||K||IDi) using K, IDi from the eavesdropping messages {M1, Zi, T1} and {M2, M3, T2}. K = h((Wi ⊕ IDBi) ⊕ h(IDi||n1)) and IDi, Prij and IDBi are unknown. n1 and n2 from M1 and M2, respectively. We then havec is revealed to Additionally, since all session keys are random and independent and no information about the value of From Eqs This subsection describes the security analysis of our scheme. To evaluate the security of the improved scheme, we assume that the adversary might access the smart card of legal user and extract the information stored in the smart card and intercept information transmitted over the public channel.Ui, Sj checks if Zi? = h(n1||IDi ||K||T1) holds or not. The adversary who masquerades as the legal user cannot forge Zi without knowing IDi and the biometrics BIOi of Ui. Likewise, upon receiving the message M3, Ui checks M3? = h(IDi||n1||n2||K||T2), where K = h(h(h(IDi)|| PSK) ⊕ h(IDi||n1)), which requires the computation of Ui’s identity IDi, the random number n1 and PSK. Only the server who has the private key Prij can compute IDi and n1 so as to get the value of K. Hence only legal user can share the session key with corresponding server. Therefore, our proposed scheme can provide proper mutual authentication.After receiving the login request information from M1, Zi, T1 > is dynamic for every login and does not disclose any information about Ui, since it is associated with random number n1. The identity is protected by the encrypted message Pubj. The adversary cannot obtain IDi without having the knowledge of Prij. In addition, the unauthorized server cannot decrypt the user’s authentication message successfully since it does not own the private key Prij. As a result, the user’s real identity cannot be retrieved. Thus our protocol can achieve the anonymity property of users as well as protect the privacy of users.In the proposed scheme, the login request message < PWi from the extracted smart card stored parameters < Vi, Wi, h(⋅), H(⋅) >. The stored parameter contains the password PWi in the form Vi = h(h(IDi)||PWDi) where PWDi = h(PWi||H(BIOi)). An adversary attempts to verify the condition Vi? = h(h(IDi)||h(PWi||H(BIOi)) while constantly guessing PWi. Adversary needs the value of IDi and BIOi of Ui in order to achieve the password guessing attack. However, the value of BIOi is nowhere stored and an adversary cannot get the value of IDi without knowing the private key Prij. As a result, the adversary cannot guess the correct password PWi. Therefore, our proposed improved protocol can withstand this kind of attack.An adversary may try to guess the password Ui does not send his IDi, password PWi or his biometrics BIOi in plain text during user registration phase. Ui submits only h(IDi), IDBi and PWDi to RS instead of original credentials, where PWDi = h(PWi||H(BIOi)), IDBi = h(IDi||H(BIOi)). Hence, an insider cannot obtain the original sensitive information of any user. On the other hand, the authentication of entities is being done by verifying message like Zi? = h(n1||IDi||K||T1) in which IDi is necessary. Moreover, RS doesn’t participate in the authentication process. Therefore, the proposed protocol attains resistance to insider attack.In our proposed protocol, Vi, Wi, h(⋅), H(⋅) > stored in the smart card by means of power analysis. Assume a legal user’s smart card is stolen by an adversary and the stored information < Vi, Wi, h(⋅), H(⋅) > on it are extracted. Then, the adversary may try to get IDi, PWi, BIOi from the extracted information. However, adversary cannot obtain any valuable information from these values, where Vi = h(h(IDi)||PWDi) and Wi = h(h(IDi)||PSK) ⊕ IDBi, since all the important parameters such as IDi and PWi are protected by a one-way hash function. The adversary cannot obtain any login information using the smart card stored parameters Vi and Wi. At the same time guessing the real identity IDi and password PWi is impractical. Therefore, the proposed protocol is secure against smart card stolen attack.The adversary can extract the information < M1, Zi, T1 > and < M2, M3, T2 >, he tries to replay them to Ui or Sj to masquerade as a legal user. However, once the message is replayed, the server can immediately detect the attack and reject the request due to the apply of timestamp. Hence, our scheme is secure against replay attack.If an adversary has intercepted all the communication message < RS, he still cannot get IDi, PWi, BIOi or other valid information of users. Sj does not store the password or the biometrics table of users as well. Therefore, even if an adversary steals the database from RS, he still cannot obtain user’s sensitive information of users.In the proposed scheme, the registration server and application servers do not store the password and the biometrics database of the user. Therefore, even if an adversary steals the information stored in Ui, he needs to build a login request message < M1, Zi, T1 > firstly, where Zi = h(n1||IDi||K||T1). Conversely, the adversary cannot compute the messages M1 and Zi without user’s private information IDi and H(BIOi). At the same time, the adversary has to go through login phase before sending login request information. During login phase, SC computes PWDi = h(PWi||H(BIOi)) and then verifies if Vi? = h(IDi||PWDi) is correct. Unless the adversary enters the correct credentials, the process will be terminated. Therefore, the adversary certainly requires IDi, PWi and BIOi for any furthermore computations. However, the probability of obtaining correct IDi, PWi and BIOi is negligible.Assume an adversary steals a smart card from a legal user and wants to get service by perpetrating user impersonation attack. If an adversary forges messages so as to impersonate as et al.’s protocol, the server Sj not only keeps unique long-term key PSK, but also contains the key pair < Pubj, Prij >. Note that the key pair of each server is distinctive, and Prij is known to only server Sj. Consider a scenario where an adversary captures < M1, Zi, T1 > and tries to impersonate valid server by responding with message < M2, M3, T2 >. The values of IDi, K and n1 are prerequisite. However, adversary cannot yield either of the values without having the knowledge of Prij. Though, the adversary cannot get the right values of IDi, K and n1, if the adversary forges the massage < M2, M3, T2 >. Upon receiving the response message < M2, M3, T2 >, Ui can identify it as a malicious attempt due to the non-equivalence of message Unlike Moon SKij = h(n1||n2||K||IDi), and the values of the long term private key of the servers vary from server to server and are not shared with any registered Ui. Assume that the adversary has obtained the long term key PSK, he still cannot compute a valid session key without the secret parameters IDi and n1, which are protected by Pubj and are decryptable only with Prij. Moreover, the parameters n1 and n2 are random for each session. Therefore, the session key is considered to be safe even though the long term private key of the server is compromised.In our improved protocol, the session key is et al.’s scheme, Mishra et al.’s scheme and Lu et al.’s scheme.In this section, we compare our proposed scheme with the other related schemes in term of the functionality, including Chuang We perform a comparative analysis of previous schemes, which is illustrated in Th, TRe, TRd, Tsym and Tepm refer to the execution time of one-way hash, RSA encryption, RSA decryption, symmetric key encryption/decryption operation and complexity of executing an elliptic curve point multiplication operation. According to Kilinc et al.’s [Th is about 0.0023ms, TRe is 3.8500ms, TRd is 0.1925ms, Tsym is 0.1303 ms and Tepm is 2.229ms. Now we compare the computational costs and execution time between the proposed scheme and the other related schemes. For the evaluation of the computational costs, let et al.’s  estimatiThe time consumption of our proposed scheme and of the other related schemes is listed in et al’s scheme, and demonstrated that their scheme is vulnerable to the known internal attack, guess attack and impersonation attack. Moreover, their scheme is found not anonymous for the user. To withstand these drawbacks, we proposed an improved biometric-based authentication scheme for multi-server environment and proved that the improved scheme provides secure authentication through the formal security analysis using Burrows-Abadi-Needham logic (BAN-logic) and random oracle model. Moreover, we have shown that our scheme is robust against all known attacks through the informal security analysis. The functional and performance analysis shows that the improved scheme has the best secure functionality and is computational efficient.In this paper, we firstly analyzed the security of Moon


The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely nonlinear Hebbian learning. When nonlinear Hebbian learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities. The question of how the brain self-organizes to develop precisely tuned neurons has puzzled neuroscientists at least since the discoveries of Hubel and Wiesel. In the past decades, a variety of theories and models have been proposed to describe receptive field formation, notably V1 simple cells, from natural inputs. We cut through the jungle of candidate explanations by demonstrating that in fact a single principle is sufficient to explain receptive field development. Our results follow from two major insights. First, we show that many representative models of sensory development are in fact implementing variations of a common principle: nonlinear Hebbian learning. Second, we reveal that nonlinear Hebbian learning is sufficient for receptive field formation through sensory inputs. The surprising result is that our findings are robust of specific details of a model, and allows for robust predictions on the learned receptive fields. Nonlinear Hebbian learning is therefore general in two senses: it applies to many models developed by theoreticians, and to many sensory modalities studied by experimental neuroscientists. Neurons in sensory areas of the cortex are optimally driven by stimuli with characteristic features that define the receptive field of the cell. While receptive fields of simple cells in primary visual cortex V1) are localized in visual space and sensitive to the orientation of light contrast  are loca. The conThe characteristic receptive fields of simple cells in V1 have been related to statistical properties of natural images . These fSince disparate models appear to achieve similar results, the question arises whether there exists a general underlying concept in unsupervised learning models , 17. HerHere we unify and broaden the above concepts and show that plastic neural networks, sparse coding models and independent component analysis can all be reformulated as nonlinear Hebbian learning. For natural images as sensory input, we find that a broad class of nonlinear Hebbian rules lead to orientation selective receptive fields, and explain how seemingly disparate approaches may lead to similar receptive fields. The theory predicts the diversity of receptive field shapes obtained in simulations for several different families of nonlinearities. The robustness to model assumptions also applies to alternative sensory modalities, implying that the statistical properties of the input strongly constrain the type of receptive fields that can be learned. Since the conclusions are robust to specific properties of neurons and plasticity mechanisms, our results support the idea that synaptic plasticity can be interpreted as nonlinear Hebbian learning, implementing a statistical optimization of the neuron’s receptive field properties.x, is connected to a downstream neuron with activity y, through synaptic connections with weights w (y = g(wTx), where g is the frequency-current (f-I) curve. In most models of Hebbian plasticity , and y =  is the vector of neuronal activities, with yj ≥ 0 for 1 ≤ j ≤ K. The total error E combines a sparsity constraint S with weight λ and the reconstruction error, E has to be minimal, averaged across all input samples, under the constraint yj ≥ 0 for all j.A sparse coding model, with E with respect to all hidden units yjwj of synapses projecting onto unit yj by ||wj||2 = 1, defined the activation function g(.) = T−1(.), the inverse of T(y) = (y+λS′(y)), and defined recurrent synaptic weights x, this equation shall be iterated until convergence. The equation can be interpreted as a recurrent neural network, where each neuron has an activation function g, and the input is given by the sum of the feedforward drive k ≠ jvjkyk. To avoid instability, we implement a smooth membrane potential uj, which has the same convergence point [uj(t) = 0.The minimization problem is solved by a two-step procedure. In the first step, for each input sample, one minimizes ce point τuddtuj are treated as constants, so that In the second step, we optimize the weights ng study , which iwj|| = 1 at each step. For a single unit y, the model simplifies to a nonlinear Hebbian formulation, The decay term cov = 0. As an aside we note that the choice vjk ∝ (yj−〈yj〉) ⋅ yk, where 〈yj〉 is a moving average (we use τ = 1000 input samples). Thus by substituting fixed recurrent connections by anti-Hebbian plasticity, convergence Δvjk = 0 implies cov = 0. While this implementation does not guarantee cov = 0, it does lead to optimal decorrelation, which is the basis of the normative assumption. Additionally we constrain vjk ≥ 0 to satisfy Dale’s law. Although some weights would converge to negative values otherwise, most neuron pairs have correlated receptive fields, and thus positive recurrent weights.There are two non-local terms that need to be implemented by local mechanisms so as to be biologically plausible. First, the recurrent weights depend on the overlap between receptive fields, ian rule , 12, Δvjk ≠ jwkykyj in the update rule. Although this approximation is not theoretically justified, we observed in simulations that receptive fields do not qualitatively differ when this term is removed.Second, we ignore the non-local term ∑The resulting Hebbian formulation can be summarized asThis derivation unifies previous results on the biological implementation of sparse coding: the relation of the sparseness constraint to a specific activation function , the derf, synaptic weights w, and input samples x, is given by R = 〈F(wTx)〉, where x. Relative optimization values in Figs w, R* = (R − Rmin)/(Rmax − Rmin). The selectivity index of a nonlinearity f is defined as SI = (〈F(l)〉 − 〈F(g)〉)/σF, where l and g are Laplacian and Gaussian variables respectively, normalized to unit variance. f is not altered by multiplicative scaling, L0 sparse coding nonlinearity f = T − 1, where f(u) = 1 − 2/(1 + eu − 2), a polynomial function f(u) = u3, trigonometric functions sin(u) and cos(u), a symmetric piece-wise linear function f(u) = u.The optimization value for a given effective nonlinearity 6 patches). Patches were randomly rotated by ±90° degrees to avoid biases in orientation. The dataset was whitened by mean subtraction and a standard linear transformation x* = Mx, where M = RD − 1/2RT and 〈xxT〉 = RDRT is the eigenvalue decomposition of the input correlation matrix. In f0 = 200 cycles per image. The linear factor f was designed to whiten the images by canceling the approximately 1/f power law spatial correlation observed in natural images [Natural image patches (16 by 16 pixel windows) were sampled from a standard dataset [l images . But sinf, evolved through xk, with a small learning rate η. We enforced normalized weights at each time step, ||w||2 = 1, through multiplicative normalization, implicitly assuming rapid homeostatic mechanisms [Synaptic weights were initialized randomly  and, for an effective nonlinearity chanisms , 29. ForTx = Ty = 8 pixels), difference of Gaussians filter , low-frequency Fourier filter , and centered localized Gabor filter . Fourier filters were modeled as wab = sin(2πa/Tx) ⋅ cos(2πb/Ty); difference of Gaussians filters as the difference between two centered 2D Gaussians with same amplitude and standard deviations σ1 and σ2; and we considered standard Gabor filters, with center , spatial frequency f, width σx, length σy, phase ϕ and angle θ. In Figs σx and σy. In s had parameters σx = 0.3 ⋅ s, σy = 0.6 ⋅ s, f = 1/s and θ = π/3. In σx = 1.2, σy = 2.4, f = 0.25. All receptive fields were normalized to ||w||2 = 1. In Figs ϕ = 0 and ϕ = π/2. For each width and length, the maximum value among frequencies and phases was plotted.In er Tx = 1, Ty = 32er Tx = 1, Ty = 32σx = 1.2, σy = 2.4, f = 0.3), with centers placed on a 8 by 8 grid (3.1 pixels spacing), with 8 different orientations at each position . For the non-orientation selective receptive field in the V2 statistical analysis (Tt = Tf = 10). For all datasets, the input ensemble was whitened after the preprocessing steps, by the same linear transformation described above for natural images, and all receptive fields were normalized to ||w||2 = 1.For the strabismus model, two independent natural image patches were concatenated, representing non-overlapping left and right eye inputs, forming a dataset with 16 by 32 patches . For theanalysis , the orianalysis , a Fouri


Candidatus Carsonella ruddii” strain BC, a nutritional endosymbiont of the tomato psyllid Bactericera cockerelli. The 173,802-bp genome contains 198 protein-coding genes, with a G+C content of 14.8%.Here, we report the genome of “ Candidatus Carsonella ruddii” as a source of essential amino acids   4–, making Ca. Carsonella,” from 10 tomato psyllids were dissected and pooled, and DNA was extracted. The extracted DNA was prepared using the Nextera DNA library preparation kit  and sequenced within a 1/4 of a lane on an Illumina HiSeq 2500 (Illumina) using TruSeq SBS sequencing chemistry (Illumina). Fastq files were generated with the software Casava 1.8.2 (Illumina). The initial assembly and quality trimming were performed using a customized A5ud pipeline, which was modified from the A5 genome assembly pipeline (Bacteriocytes, specialized insect cells that harbor “pipeline . Furtherpipeline . Due to pipeline  and the pipeline .Ca. Carsonella ruddii” BC is 173,802 bp long, with 198 protein-coding genes and a G+C content of 14.8%. Genes involved in essential amino acid biosynthesis that are present and absent in all other “Ca. Carsonella” strains sequenced to date have the same pattern of presence and absence in “Ca. Carsonella ruddii” BC (pheA, a gene conserved in all other sequenced “Ca. Carsonella” genomes to date and that is involved in phenylalanine biosynthesis, is missing from “Ca. Carsonella ruddii” BC’s genome. Potentially, a homolog of pheA that is microbe in origin has been horizontally transferred to Bactericera cockerelli’s genome to complement “Ca. Carsonella ruddii” BC’s essential amino acid pathways, analogous to mealybug, whitefly, and psyllid obligate symbiont symbioses (13Ca. Carsonella ruddii” BC for the biosynthesis of essential amino acids for its insect host, Bactericera cockerelli.The complete circular genome of “ddii” BC , 13. Onebioses 1315. In suCa. Carsonella ruddii” BC and its annotation are deposited at GenBank, NCBI, under accession number CP019943.The complete genome sequence of “


Scientific Reports 10.1038/s41598-017-09128-x, published online 17 August 2017Correction to: et al. (Ref 18).In this Article, the authors misinterpreted the data previously presented by Karami In the Introduction section,et al.18 concluded that the amount of MPs in salt of different origins is nil, in the range 0-10 MPs/kg, without a comparison with previous studies. Surprisingly, the authors of the paper18 are trying to retain microfibers of a size of between 10 and 200 μm, approximately, in a filter whose pore size is 150 μm.”“On the contrary, an study recently published in Scientific Reports by Karami should read:et al.18 concluded that the amount of MPs in salt of different origins is nil, in the range 0-10 MPs/kg, but only MPs of >149 μm are considered.”“On the contrary, a study recently published in Scientific Reports by Karami In the Results and Discussion section under the subheading ‘Microfiber characterization’,et al.18 found amount of MPs in salts in the range 0-10 MPs/kg, but an experimental error made this result unessential.”“As mentioned before, the work published by Karami should read:et al.18 found amount of MPs in salts in the range 0-10 MPs/kg, but experimental differences make the comparison difficult.”“As mentioned before, the work published by Karami


We aimed to determine whether linked color imaging (LCI), a new image-enhanced endoscopy that enhances subtle differences in mucosal colors, can distinguish the border of endoscopic mucosal atrophy. ∗, a∗, b∗) color space. Color differences at the atrophic border, defined as Euclidean distances of color values between the atrophic and nonatrophic mucosae, were compared between WLI and LCI for the overall cohort and separately for patients with Helicobacter pylori infection status.  This study included 30 patients with atrophic gastritis. In endoscopy, we continuously took images in the same composition with both LCI and white light imaging (WLI). In each image, the color values of atrophic and nonatrophic mucosae were quantified using the International Commission on Illumination 1976 (LP < 0.00001). LCI was also more effective in both of the Helicobacter pylori-infected group (P = 0.00003) and the Helicobacter pylori-eradicated group (P = 0.00002).  We found that the color difference became significantly higher with LCI than with WLI in the overall samples of 90 points in 30 patients. LCI was 14.79 ± 6.68, and WLI was 11.06 ± 5.44 (Helicobacter pylori infection status. LCI allows clear endoscopic visualization of the atrophic border under various conditions of gastritis, regardless of  Helicobacter pylori (H. pylori) ..14].H. pylori infection status and progression of endoscopic atrophy was collected and analyzed. This study was conducted according to the principles of the Declaration of Helsinki and with the approval of the ethics committee of Oita University.This study targeted patients aged ≥20 years who underwent esophagogastroduodenography (EGD) at Oita University Hospital . The disease cases causing extensive changes in the gastric mucosa, such as liver cirrhosis, were excluded. During EGD, the atrophic border was identified. Following identical compositions, images were continuously captured via WLI and LCI. In each patient, information regarding H. pylori infection status was established based on biopsies collected during EGD and sampled from two sites, namely from the greater curvature side of antrum and from the greater curvature side of the upper corpus. The biopsy specimens for histopathology were fixed in 10% neutral formalin for 24 hours and subsequently embedded in paraffin. Sections were obtained and underwent hematoxylin and eosin staining and May-Giemsa staining. Active H. pylori infection was established in patients found positive on any one of the following tests, rapid urease test, culture, and histopathology, and these patients were included in the H. pylori-infected group. Those who were found negative on all tests, presented with endoscopic atrophy, and had a clear history of H. pylori eradication were included in the H. pylori-eradicated group.Endoscopic findings of gastric mucosal atrophy were assessed according to the Kimura-Takemoto classification , which dColor differences between atrophic and nonatrophic mucosal areas were measured on WLI- and LCI-acquired images. The color differences at the atrophic border were compared between WLI and LCI.WLI and LCI were captured with the same light composition. In each image, sample areas of gastric regions containing the atrophic border (circles in E) were calculated using the International Commission on Illumination (CIE) 1976  color space [∗a∗b∗) color space is a 3-dimensional model composed of a black-white axis (L∗), a red-green axis (a∗), and a yellow-blue axis (b∗)  color space, which were previously shown to correspond to color differences in endoscopic images [Color differences (Δor space . The CIExis (b∗) . L∗ defic images , 17.E was calculated via the following procedure: (1) The L∗a∗b∗ values were calculated from the red-green-blue values for each pixel in each of the atrophic and three nonatrophic sample areas in each image [∗, a∗, and b∗ were used as the L∗a∗b∗ values representative of the atrophic and nonatrophic mucosae in each image. (3) For each image, ΔE  was calculated using the following formula: {(ΔL∗)2 + (Δa∗)2 + (Δb∗)2}1/2, where ΔL∗, Δa∗, and Δb∗ are, respectively, the differences in the L∗, a∗, and b∗ values between the atrophic and nonatrophic mucosae. Pixels affected by halation were excluded.Δch image . (2) TheE between WLI and LCI images collected in all patients . The secondary endpoint was a comparison of ΔE between WLI and LCI images in patients stratified according to H. pylori infection status . In addition, the visibilities of the atrophic border on LCI were assessed at points where the borders were difficult to determine on WLI. These borders were identified as the points showing ΔE < 5 on WLI, based on the CIE 1976  color space.The primary endpoint was a comparison of ΔE values between LCI and WLI. A P value of less than 0.05 was considered to indicate statistical significance. Statistical analyses were performed using R, version 3.1.1.The Wilcoxon signed-rank test was used to compare ΔH. pylori infection status, 9 patients had active infection, whereas H. pylori had been eradicated in 21 patients. The period following eradication was 6.13 ± 6.7 years (range 0–21 years) on average .This study enrolled 30 patients . According to the Kimura-Takemoto classification, mucosal atrophy was mild  in 4 patients, moderate  in 17, and severe (O2) in 9. Regarding E was 11.06 ± 5.44 on WLI and 14.79 ± 6.68 on LCI, which was significantly higher  make up the primary endpoint of this study; ΔH. pylori-infected group  represent secondary endpoints of this study, ΔE was 10.8 ± 4.41 on WLI and 15.1 ± 5.4 on LCI, which was significantly higher , ΔE was 11.2 ± 5.58 on WLI and 14.7 ± 7.19 on LCI, which was significantly higher  on WLI. As for the other assessments, we found that, although ΔE was 4.2 ± 0.53 on WLI, the visibility of the border was significantly improved on LCI, which showed ΔE = 8.3 ± 3.05  where the atrophic border was difficult to distinguish , the color difference between atrophic and nonatrophic areas presumably increases (Moreover, our study demonstrated that LCI improved the visibility of the atrophic border regardless of e mucosa . In the ncreases .H. pylori-eradicated group also showed significant differences in ΔE between WLI and LCI. Specifically, on LCI images of H. pylori-eradicated patients, nonatrophic mucosa appeared bluish (H. pylori eradication, endoscopic findings reveal resolution of both inflammation and diffuse redness [In this study, the d bluish . In our  redness . However redness , 22. By H. pylori-eradicated group. We divided H. pylori-eradicated cases into two groups: One was eradicated within less than 5 years, and the other was eradicated more than 5 years ago. We investigated the visibility of atrophic border. However, in this study, we could not see a statistical significant difference on ΔE score of WCI. In general, after eradication, mucosal atrophy may resolve with the recovery of the function of gastric glands, which occurs over a long period of time [H. pylori eradication have become the focus of intense research [H. pylori eradication and the severity or resolution of atrophic gastritis has also been reported [In our sample, we found that 11 points determined have low visibility on WLI, which led to difficulty in identifying the atrophic border; of these, 9 points were found in the  of time , 23. Sinreported –27, it iAlthough conventional image-enhanced endoscopies such as narrow band imaging or blue laser imaging have been proven useful for qualitative diagnosis in combination with magnifying endoscopy , 29, theThere are some limitations in this study. First, the present study is that the sample size was small. But this study sufficiently proved a statistical significant difference and showed its availability, so we think that it is a valuable pilot study. Second, we were unable to find histological support for the color differences highlighted on LCI. Future studies are expected awaited to investigate the histological significance of endoscopic findings enabled by LCI.H. pylori infection or duration after eradication. Comparison with histology is necessary to validate these findings.Our study found significant evidence that LCI allows clear endoscopic visualization of the atrophic border under various conditions of gastritis, regardless of the presence or absence of


Youth violence is an important public health challenge around the world, yet the literature on this problem in low- and middle-income countries (LMICs) has been limited. The present study aims to examine the prevalence of adolescent physical fighting  in selected LMICs, and its relations with potential risk factors. We included 6377 school-going adolescents aged 13–15 years from six Western Pacific (WP) countries that had recently conducted a Global School-based Student Health Survey. Information was gathered through a self-administered anonymous closed-ended questionnaire. The prevalence of adolescent physical fighting varied across countries, ranging from 34.5% in Kiribati to 63.3% in Samoa. The prevalence was higher in boys than in girls, and lower at age 15 than 13–14 years. Physical fighting was significantly associated (pooled odds ratios (ORs), 95% confidence intervals (CIs)) with smoking , drinking , drug use , and missing school . The association with physical fighting increased with increasing number of joint adverse behaviors (increased from 1.99 (1.73–2.29) for one risk behavior to 4.95 (4.03–6.07) for at least 3 risk behaviors, versus having none of the 4 risk behaviors). The high prevalence of physical fighting and the associations with risk behaviors emphasize the need for comprehensive prevention programs to reduce youth violence and associated risk behaviors. Youth violence is an important public health issue worldwide, with physical fighting being a common manifestation ,2,3. In Physical violence is a major cause of youth morbidity and mortality, and was ranked the fourth leading cause of death in 2013 among adolescents aged 15–19 years old in the world (5.5%) . It has Considering the magnitude and severity of this issue, the World Health Organization (WHO) has called for concerted efforts to prevent physical violence in both youth and adults worldwide and to build global commitments to violence prevention ,2,3. HowIn order to better inform youth violence prevention strategies, a growing body of literature has focused on risk or protective factors associated with physical fighting among youth ,13,17. Hhttp://www.cdc.gov/gshs/.The GSHS, an ongoing surveillance project, has been developed by the WHO and the US Centers for Disease Control and Prevention. Its goal is to provide data on health and risk behaviors among school-going adolescents aged 13–15 years in LMICs in order to assist these countries in establishing programs and policies accordingly. Details on methods and main results (country fact sheets) of the GSHS are available at In each GSHS-participating country, a two-stage probability sampling strategy was used to generate a representative sample of all students aged 13–15 years. In the first stage, schools were selected with a probability proportional to their enrollment size. In the second stage, classes in the selected schools were randomly chosen. All students in these classes were eligible to participate in the survey, irrespective of their actual age. They were invited to complete, in class, a self-administered questionnaire. They provided answers on a computer-scannable answer sheet, which was subsequently added to a database using optical character recognition at the US CDC. The survey was anonymous. The GSHS questionnaire was made of several modules , of which countries are free to select when they conduct the survey. However, countries are not allowed to change the wording of the questions, so that results can be compared directly between countries and over time. Two years after a survey is completed, data files are freely available to the public from the web site of the US CDC.Six countries from WP  had gathered information on physical fighting  and selected variables of interest . After excluding subjects aged younger than 13 and older than 15, as well as those with missing data on the variables of interest, a total of 6377 (boys: 47.5%) young adolescents were included in the final data analyses.All subjects and their parents/guardians gave the informed consent for inclusion before they participated in the study. The GSHS database is publically open for use.A question on physical fighting appears in the module “violence and unintentional injury”. The question reads: “During the past 12 months, how many times were you involved in a physical fight?” with eight given possible answers, ranging from “0 time”, “1 time”, “2 or 3 times”, “4 or 5 times”, “6 or 7 times”, “8 or 9 times”, “10 or 11 times” to “12 or more times”. Questions on smoking appear in the module “tobacco use” and questions considered in this study read as follows: “During the past 30 days, on how many days did you smoke cigarettes?” and “During the past 30 days, on how many days did you use any tobacco products other than cigarettes, such as tobacco roll, snuff or chew tobacco?” and possible answers were: “0 day”, “1 or 2 days”, “3 to 5 days”, “6 to 9 days”, “10 to 19 days”, “20 to 29 days” and “all 30 days”.The question on alcohol use considered in this study came from the module “alcohol use” and reads “During the past 30 days, how many days did you have at least one drink containing alcohol?” and possible responses were: “0 day”, “1 or 2 days”, “3 to 5 days”, “6 to 9 days”, “10 to 19 days”, “20 to 29 days” and “all 30 days”.The questions on drug use came from the module “drug use” and read: “During your life, how many times have you used marijuana )?” and “During your life, how many times have you used amphetamines or methamphetamines )?” Responses were: “0 time”, “1 or 2 times”, “3 to 9 times”, “10 to 19 times” and “20 or more times”.Missing school without permission was assessed with the question, “During the past 30 days, on how many days did you miss classes or school without permission?” with possible responses being: “0 day”, “1 or 2 days”, “3 to 5 days”, “6 to 9 days” and “10 or more days”.All these variables were recoded as negative (coded 0) for 0 times/days and positive (coded 1) for one or more times or days. Additionally, for the Pearson partial correlation analysis, a continuous measure of every variable was derived by taking the midpoints or values of the responses, e.g., for physical fights, the highest option, “12 or more times”, was top-coded at 12.5 times .p values less than 0.05 indicate statistical significance. The Bonferroni correction method was applied for multiple comparisons.Data analysis was performed using SAS software V. 9.3 . Analyses were performed using the weighting variables from GSHS corresponding to the two-stage sampling strategy of the GSHS. Pearson partial correlation analyses and logistic regression models were used to estimate the relationship between physical fight and the selected risk factors, i.e., age, sex, smoking, drinking, drug use, and missing school. In the multivariate logistic analysis, each independent variable was regarded as the main exposure, controlling for the other independent variables (including the other risk factors). We also evaluated the joint influence of risk behaviors  on the risk of physical fighting using multivariate logistic analysis after adjustment for age and sex. The results were presented as odds ratios (OR) and 95% confidence intervals (CIs). Pooled estimates in the WP region were calculated by meta-analysis using Stata 11.0 . Characteristics of the country samples are presented in The pooled prevalence of adolescents engaged in one or more physical fights during the past 12 months was 47.2%  in the WP region. However, the prevalence varied across countries. The highest prevalence was 63.3% in Samoa, and the lowest was 34.5% in Kiribati. In general, physical fight was more prevalent in boys than girls. The pooled prevalence of smoking, drinking, drug use, and missing school were 29.5%, 25.5%, 15.3% and 43.7%, respectively, in boys; the corresponding values were 20.5%, 17.5%, 8.5% and 37.6% in girls see .In the partial correlation analyses, smoking, drinking, drug use and missing school were all positively associated with physical fighting in both genders after controlling for age see .After controlling for the other covariates, the multivariate logistic regression analysis showed that male adolescents and those who reported to have experienced smoking, drinking, drug use or missing school in the past 30 days were more likely to engage in physical fighting than those who did not. The pooled ORs (95% CIs) of smoking, drinking, drug use and missing school were 1.78 (1.53–2.06), 1.57 (1.33–1.85), 1.72 (1.33–2.23), 1.72 (1.51–1.95), respectively. Compared with those aged 13–14 years, adolescents aged 15 years were less likely to engage in fights in pooled analysis, although the difference was not significant at the country level see . The staThe likelihood for physical fighting increased with increasing number of risk behaviors , after adjusting for age and sex. Compared to subjects without risk behaviors, those with 1, 2 and ≥3 risk factors were more likely to engage in physical fighting. The pooled ORs (95% CIs) were 1.99 (1.73–2.29), 3.52 (2.94–4.21), 4.95 (4.03–6.07) see . The staThe present study shows that adolescent physical fighting is a common problem in the WP region, with a pooled prevalence of 47.2%, which is similar to or higher than in developed and other developing countries ,11,13,17In line with previous studies ,8,11,19,Some previous studies have shown that the prevalence of physical fighting declined with age among adolescents ,25. As fConsistent with other studies ,11,13,26Our study also showed that the physical fighting increased greatly with increasing clustering of risk behaviors. This result supports previous research results suggesting that being engaged in physical fighting could be a useful indicator of multiple-risk-behavior syndrome . Hence, The present study adds to the limited existing literature on adolescent physical fighting in LMICs. Furthermore, population-based sampling and the use of standardized methods and same questionnaire in the GSHS project allow direct comparison of findings across countries. However, there are several limitations to this study. First, the questionnaire was self-administered, and answers may be misreported in unpredictable ways. Since the physical violence and risk behaviors considered are generally not well accepted, socially, especially among girls, responses might be biased towards socially desirable forms. This may result in regression of estimates to the null values, and underestimate the magnitude of the association estimates between physical violence and risk behaviors. Furthermore, participants were required to recall information related to event occurring 12 months prior, which have led to inaccuracy in the responses. Second, the study only included in-school adolescents, who tend to have fewer risk behaviors than students not in school ,33. ThirIn summary, physical fighting among school-going young adolescents aged 13–15 years old was fairly frequent in WP countries, and was associated with several risk behaviors. These findings may be useful to advocate and guide policy makers with regards to implementation of strategies to prevent both unhealthy behaviors and physical violence among adolescents. Schools constitute a particularly suitable setting to address physical fighting and associated risk factors among adolescents, by providing counseling and skills to cope and resolve conflicts, through programs involving teachers, peers and parents. The clustering of physical violence with other risk behaviors emphasizes the need for integrated prevention programs address multiple risk behaviors and physical violence. More generally, a broad range of stakeholders in the society  needs to join forces to effectively fight against all forms of violence, including verbal violence, bullying, sexual harassment, and victimization. This includes developing policy and regulations, for schools and other settings, to define what is not acceptable; training teachers, sport trainers, and other key personnel in other sectors to respond adequately to violence cases and to be able to run prevention programs; consistently enforcing strong sanctions against offenders; and, perhaps most importantly, building social norms that do not tolerate violence.


CD40L (CD154), a member of the tumor necrosis factor superfamily, is a co-stimulatory molecule that was first discovered on activated T cells. Beyond its fundamental role in adaptive immunity—ligation of CD40L to its receptor CD40 is a prerequisite for B cell activation and antibody production—evidence from more than two decades has expanded our understanding of CD40L as a powerful modulator of inflammatory pathways. Although inhibition of CD40L with neutralizing antibodies has induced life-threatening side effects in clinical trials, the discovery of cell-specific effects and novel receptors with distinct functional consequences has opened a new path for therapies that specifically target detrimental properties of CD40L. Here, we carefully evaluate the signaling network of CD40L by gene enrichment analysis and its cell-specific expression, and thoroughly discuss its role in cardiovascular pathologies with a specific emphasis on atherosclerotic and thrombotic disease. Cardiovascular disease is the major cause of mortality worldwide and is predominantly caused by atherosclerosis, a chronic narrowing of middle sized and large arteries by the buildup of atherosclerotic plaques . SubclinCd40lg) were identified as the cause of the human X-linked immunodeficiency hyper IgM-syndrome (XHIM), a condition characterized by a loss of T cell-dependent humoral immunity and specific IgG antibodies  is a member of the TNF superfamily that was first identified on activated T cells, where it interacts with CD40 receptor on B cells to induce B cell activation, proliferation, and IgG-class switching during co-stimulation –10. Mutatibodies . Apart ftibodies –14 , αIIbβ3, and α5β1, and the “classical” receptor CD40. These are distinguishable by their cellular expression patterns, signaling events, and functional consequences , an interaction that occurs on a separate site from the CD40L binding epitope , althougItgam) is confined to neutrophils and macrophages , which is distinct from other ligand-binding sites and, thus, is highly selective for CD40L (Mac-1 (CD11b/CD18) is a member of the β2-integrin family and a heterodimer of the αM (CD11b) and β2 (CD18) integrin subunit. Mac-1 is predominantly expressed on myeloid cells, such as monocytes, macrophages, and neutrophils, but is also present on NK cells, and, to a smaller extent, on some B cell subsets (B1); however, the highest mRNA abundance of the CD11b αM subunit  is most abundant in ECs  is a heterodimer of the integrin subunits α5 (CD49e) and β1 CD29). It serves as a primary receptor for fibronectin and vitronectin through an RGD sequence in both molecules , 79. It 9. It sers Figure  have beepression . ECs reqpression . VLA-5 apression , angiopopression , and endpression , which mpression . Leveillpression , similarroblasts  and to iroblasts . Bindingroblasts , 80. SpeItga2b, which encodes for the αIIb (CD61) subunit of the integrin, are restricted to the megakaryocyte lineage  can form the integrin αVβ3 on platelets and myeloid cells. Platelets express αIIbβ3 on the cell surface, but additional integrin heterodimers are stored in platelet α- and dense granules. These are translocated to the cell surface during platelet activation and degranulation  and the transcript nulation . Bindingnulation . Ligand nulation . Clinicanulation . The mainulation . Ligand nulation , 93. Andnulation . Bindingnulation , 55. sCDnulation , 54, 94.Traditionally, CD40 was believed to be the only receptor for CD40L. Newer evidence, however, has identified at least three alternative receptors of the integrin family: Mac-1, αIIbβ3, and α5β1 Figure . CD40L/MAtherosclerosis is a chronic inflammatory disease of medium to large arteries that is characterized by the buildup of fibro-fatty plaques in the intimal layer of the vessel, which limits blood flow into the distant vascular bed or results in total thrombotic occlusion during myocardial infarction or stroke . AtherosMoreover, plasma levels of soluble CD40L have been proposed as a biomarker for cardiovascular risk; in particular, several studies have established a positive correlation of sCD40L with cardio-metabolic risk factors, such as dyslipidemia, diabetes, and obesity –114. TheData from genome-wide association studies have suggested that certain polymorphisms in the CD40 gene  may be associated with traits of cardiovascular disease, although studies have yielded inconsistent results. rs1535045 and rs3765459 correlated with the calcification score of coronary arteries from diabetic  and CVD −/−Lldr mice , and diet regimens (high-cholesterol diet vs. standard chow diet) tested. In addition, it remains unclear as to whether the blocking antibodies tested would be able to equally target all cell types, as cells within atherosclerotic plaques are less likely to be exposed to the same concentration of antibodies as circulating cells are. However, the common finding that CD40L blockade attenuates lesional accumulation of leukocytes, along with the predominant expression of CD40L on cells of the hematopoietic lineage, suggests that CD40L therapies would block leukocyte migration into the plaque. Yet, in bone marrow transplantation studies where CD40L-deficient bone marrow was transplanted into −/−Ldlr mice, neither lesion size nor the cellular composition in the plaque were changed CD40L has been shown to be platelet agonist itself, promoting activation and aggregation of platelets, thereby driving thrombus formation, growth, and stability , 140–142h sCD40L . CD40L din vitro . In addiin vitro  or afterin vitro , 145 shovia binding to αIIβ3 or α5β1, but not via CD40.The integrins αIIβ3, α5β1 , and its−/−Cd40 mice  on circulating, rolling, or arrested leukocytes, which activates pro-inflammatory signaling cascades in these  failed to induce thromboembolism in Cynomolgus monkeys, while dose-dependently inhibiting CD40L-dependent immune function  . ExpressP-value of 0.2 was considered significant. The expression of the enrichment-driving genes was blotted on a heatmap and colored by a row score.GenePattern 2.0  was usedDW, NM, and AZ analyzed data and designed and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To avoid habituation, the intensity was adjusted every 2min. Soleus H-reflex was recorded before, during, and 10min after TENS by an observer blinded to the stimulus applied. Despite the instruction to apply TENS at a “strong comfortable” level, a significant difference in current density was identified: Therapist 1  applied more than Therapist 2  at the onset of the intervention. Maximal peak-to-peak H-reflex amplitude was inhibited significantly more 10min following TENS applied by Therapist 1  compared with Therapist 2 . Furthermore, current density significantly correlated with the inhibitory effect on peak-to-peak Soleus H-reflex amplitude 10 min after stimulation . TENS intensity dosage by the therapist based on the subjective perception of the participants alone is unreliable and requires objective standardization. In addition, higher current density TENS produced greater inhibition of the Soleus H-reflex.The intensity used during transcutaneous electrical nerve stimulation (TENS) in both, clinical practice and research studies, is often based on subjective commands such as “strong but comfortable sensation”. There is no consensus regarding the effectiveness dose of TENS. The objective was to determine the difference in the effect of spinal TENS on soleus H-reflex modulation when applied by two therapists instructed to apply the stimulation at a “strong but comfortable” intensity. Twenty healthy volunteers divided into two groups: Therapist 1 (n = 10) and Therapist 2 (n = 10). Both therapist applied  Transcutaneous electric nerve stimulation (TENS) is usually applied in the treatment of pain , and for2, whereas Vassal et al. [2, and Claydon et al. [2. To date, no study has attempted a direct comparison of the current intensity applied and the subjective sensation of the stimulus nor the influence of different therapists on adherence to subjective instructions to define TENS intensity.Parameters of the current that influence the dosing of TENS application, such as frequency, pulse width, or intervention time, are expressed using objective values. However, the intensity is usually reported by subjective parameters, such as the intensity that produces a “strong but comfortable” sensation. , 7. In bl et al.  applied n et al.  used 0.4Grading TENS intensity based on subjective sensation is also complicated by other phenomena such as habituation, where the perceived intensity of the stimulus decreases over time, which requires periodic increases in the intensity of the stimulus during the course of the TENS session . StimuluSoleus H-reflex activity has been used as an objective neurophysiological measure of alpha motoneuron excitability . H-refleIn the present study, we hypothesize that, given the same instruction for TENS intensity for evoked sensation, two independent therapists would interpret the same instruction based on a “strong but comfortable” sensation differently. The main objective of this study was, therefore, to quantify TENS intensity applied by two independent therapists who were instructed to apply the same intensity by evaluating the subjective sensation perceived by the participant. The secondary objective was to determine the effect of TENS stimulation applied at the T10–12 spinal level in healthy subjects on Soleus H-reflex amplitude and to assess the effect due to different therapists with the stimulus applied at the same pulse-width and frequency.www.randomizer.org). Therapist 1 and Therapist 2 were two experienced physiotherapists who were instructed to apply both TENS and sham stimulation to their own group of subjects. Both therapists followed the same guidelines for the TENS intervention, with the intensity defined as the stimulus necessary to evoke an innocuous sensation described as “strong but comfortable, just below the motor threshold”. The duration of the intervention was 40min [The procedures and design of the trial were approved by the local ethics committee "Complejo Hospitalario de Toledo" . The trial was registered in ClinicalTrials.gov Protocol Registration System (Number: NCT02718690), it was not registered before participant recruitment began because this phase started in December 2014. A randomized, double-blind, placebo-controlled crossover trial was designed as a specific trial of an ongoing wider project to investigate the effects of two spinal stimulation currents (TENS vs high frequency stimulation) on neurophysiological reflex measures of excitability. Subjects were allocated to two different therapists  or Therapist 2 (n = 10), were informed of the experimental protocol, and gave written informed consent. All experimental sessions were performed in a clinical laboratory at a stable room temperature in the range of 22–26°C.Participants were examined in a prone position with their right lower limb knee joint flexed at 120°. Two surface self-adhesive electrodes (9x5cm)  were fixed on the muscle belly, over the Soleus metamere (S1–S2), corresponding to the vertebral level T10–12 .TENS stimulation: Each therapist used the same electrotherapy device , applying a constant voltage (CV) and a symmetric biphasic current of 200-μs pulse-width at a frequency of 100 Hz, which was calibrated using a digital oscilloscope before the stimulation session, to their assigned group of participants. The TENS intensity was set to evoke a sensation characterized as “strong but comfortable, just below the motor threshold”. The stimulation intensity was gradually increased until a minimal local muscle contraction was observed and subsequently decreased until the muscle contraction disappeared. The TENS stimulus was adjusted so that the defined perceived sensation was maintained throughout the session. To avoid habituation to the stimulus, participants were asked every 2min [ery 2min , 33 to cSham stimulation: Each therapist also applied a sham stimulus intervention. The intensity used for TENS was used for the sham stimulus based on the subject´s sensory perception of the stimulus, which was then decreased to zero where it was fixed until the end of the session. The therapist informed the participant that, in this case, the stimulus intensity could be below the sensory threshold, with the possibility that the participant may or may not feel the current. This method has been validated as an appropriate sham [ate sham , 35 and ate sham .2, which reflects the applied current intensity divided by the electrode area (45cm2), was recorded by Therapist 1 and Therapist 2 at the onset of the TENS session and at the end, including small readjustments for intensity that were made every 2min in order to maintain the same perceived level of stimulation intensity. The soleus (SOL) H-reflex was elicited with 1ms electrical rectangular pulses  applied to the tibial nerve using a bipolar electrode placed in the popliteal fossa. Electromyography activity (EMG) was recorded with surface bipolar silver chloride electrodes  (1000x amplification) and filtered with a built-in 20–450 Hz bandpass filter. EMG electrodes were placed over the Soleus muscle belly at 2/3 of the line between the medial condyle of femur to the medial malleolus following the SENIAM recommendations (http://www.seniam.org/). EMG activity was sampled at 10KHz . Maximal peak-to-peak Soleus H-reflex (Hmax) amplitude was registered after increasing tibial nerve stimulation in steps of 1mA. Maximal M wave peak-to-peak amplitude (Mmax) was also annotated by the same procedure. A normalized H-reflex response (Hnor) was also recorded at a test reflex size adjusted to 15% of Mmax because test reflexes recorded at this amplitude have previously been shown to be the most sensitive to facilitatory and inhibitory neuromodulatory input [Current density, measured in mA/cmry input .t-test was performed to compare participant characteristics  and to compare baseline results. The chi-square test was applied to analyze nominal measures such as gender. Due to the evidenced normal distribution of the data (Kolmogorov-Smirnov test p>0.05), a two-way repeated-measures ANOVA, with the factor “time” (stimulation onset and termination) and independent factors such as “therapist” (Therapist 1 and Therapist 2) was performed to compare differences in the applied current density. The effect of both TENS and Sham interventions was calculated with the difference between the data of “during” or “post-test” minus the data of “pre-test”. To compare the effect of the TENS intervention on SOL H-reflex amplitude, a two-way repeated-measures ANOVA with the factor “intervention” (TENS and Sham) and independent factor “therapist” (Therapist 1 and Therapist 2) was performed. Bonferroni’s post hoc multiple comparison test was used to reveal specific differences between groups. A Pearson’s correlation test was performed to examine the relationship between applied current density and the effect on SOL H-reflex activity. A P value of ≤0.05 was considered to indicate statistical significance. A “posteriori” power analysis was performed to quantify the chance of committing type-II errors. For this, we took into account a bilateral approach, a security level of 95% and a minimum difference to detect of 0.175mV, which correspond to ~20–25% of change.The Kolmogorov-Smirnov test was performed to determine the Gaussian distribution of each group of data. A visual inspection of the data distribution and the Q-Q plots was also performed to support these information. The Student’s 2 (SD 2.5). All raw data regarding this study are available as supporting information in  = 68.1; p<0.001), which reflected the increase in intensity made during the session by both therapists to avoid habituation ; p<0.001). The increase in current density made during the TENS intervention by Therapist 1 was 0.34mA/cm2 (SD 0.30) (p<0.001), and by Therapist 2, it was 0.44mA/cm2 (SD 0.30) (p<0.001). For the “therapist” factor, significant differences were also observed  = 26.29; p<0.001). Therapist 1 applied a higher current density than Therapist 2: at the onset of the stimulation session, Therapist 1 applied a current that was 0.66mA/cm2 (SD 0.54) (p<0.001) higher than that applied by Therapist 2, and at the end of the stimulation, a difference of 0.56mA/cm2 (SD 0.57) (p<0.001) was evidenced  = 1,7 p = 0,206).TENS current density showed differences over time  = 0.07; p = 0.78) neither for the “intervention” factor  = 0.56; p = 0.46), nor the interaction “therapist-intervention”  = 0.00 p = 0,98). Furthermore, no significant differences were observed in Soleus Mmax 10min after the end of the stimulation session for the “therapist” factor  = 1.33; p = 0.26) neither for the “intervention” factor  = 2.92; p = 0.10), nor the interaction “therapist-intervention”  = 0.03 p = 0,86).Soleus Mmax amplitude remained stable in all trials performed by both therapists. No significant differences were observed in any comparison during the intervention, neither considering the “therapist” factor (Fp = 0.35), between TENS applied by Therapist 1 and sham applied by Therapist 1 (p = 0.74), and between TENS applied by Therapist 2 and sham applied by Therapist 2 (p = 0.68). No significant difference was observed during stimulation considering the “therapist” factor  = 2.58, p = 0.1) neither for the “intervention” factor  = 0.23; p = 0.64) nor the interaction “therapist-intervention”  = 0.12; p = 0.74) and power analysis revealed a statistical power between 53% (TENS 1 vs sham 1 comparison) and 98% (TENS 2 vs sham 2 comparison). However, when measured 10min after stimulation, a significant difference for the effect of the “therapist” factor on Hmax was observed  = 9.11; p<0.008), but nor for “intervention” factor  = 1.78; p = 0.2) neither nor the interaction “therapist-intervention”  = 1.27; p = 0.28). For TENS, comparison between therapists showed that Therapist 1, who applied a higher current density, produced greater inhibition of the Hmax amplitude  than Therapist 2. For sham stimulation, no significant difference was observed between therapists. When the effect of TENS was compared with that of sham, Therapist 1 revealed a trend for inhibition of Hmax  while no difference was observed for Therapist 2 .p = 0.47), between TENS applied by Therapist 1 and sham applied by Therapist 1 (p = 0.72), and between TENS applied by Therapist 2 and sham applied by Therapist 2 (p = 0.54). No difference for Hnor amplitude during stimulation was found for the “therapist” factor  = 3.35; p = 0.08) neither nor for the “intervention” factor  = 1.02; p = 0.33), nor the interaction “therapist-intervention”  = 0.02; p = 0.88) and power analysis revealed a statistical power between 31% (TENS 2 vs sham 2 comparison) and 56% (TENS 1 vs TENS 2 comparison). However, Hnor amplitude measured at 10min after the end of the stimulation showed differences for the “therapist” factor  = 4.59; p = 0.047), but not for “intervention” factor  = 3.06; p = 0.09) neither nor the interaction “therapist-intervention”  = 0.66; p = 0.43). When TENS was compared across therapists, Therapist 1 revealed a trend for greater reflex inhibition  compared with Therapist 2. Following sham stimulation, no differences between therapists were identified for Hnor amplitude. When the effect of TENS and sham were compared, a trend for Hnor reflex inhibition was identified for Therapist 1 .p = 0.04, see Correlational analysis between current density applied by both therapists and the effect on normalized SOL H-reflex amplitude revealed a significant relationship 10min after TENS with reflex inhibition  are applied, even though both therapists assumed that they were applying the same current intensity. Different effects of TENS depending on the applied current intensity has been shown in numerous clinical situations. A systematic review of TENS for osteoarthritic knee pain concluded that TENS was more effective for pain relief at higher intensities [2 [2 [Some studies have emphasized the importance of standardizing current intensity for TENS, which shows a direct dose-dependent effect in models of experimental pain, where higher intensity levels produce greater effects , 8–12. Oensities , supportensities . In specities [2  and 1.56es [2 [2 , 24, 41.es [2 [2 . In the es [2 [2 , in whicp = 0.08). However, a slight low statistical power of 65% could mask a real significant difference. Therefore, it is not possible to draw conclusions regarding the efficacy of TENS for H-reflex modulation. Better standardization of TENS current intensity, without relying on the subjective description of the evoked stimulus should help to more clearly demonstrate its neuromodulatory effects. Importantly, our study showed that higher current densities for both therapists correlated with greater inhibition of Soleus H-reflex activity after TENS.Very few studies have applied TENS at the spinal level. Hofstoetter et al.  showed tThis is the first study to address directly the differences between therapists when instructed to apply TENS at an intensity that is corroborated subjectively by the subject. However, several methodological issues should be improved in future studies. Firstly, although the order of the interventions applied by each therapist was randomized, the therapist allocation was performed in a non-randomized, consecutive order. Although no significant differences were found for subject characteristics when compared between both therapists see , the proAnother limitation is that the assessor was not blinded to the therapist allocation, although he was blinded to the application of each intervention (TENS or sham). This was performed because “a priori”, both therapists would apply the same intensity based on the same description of a “strong but comfortable” intensity. Regarding to the blinding of the subjects, although this blinding method was widely used before , 35 the Application of TENS intensity based on the subjective perception of the stimulus made by the participant was differently interpreted by the two therapists. Furthermore, the effectiveness of TENS stimulation on SOL H-reflex amplitude modulation depended on the stimulus intensity applied with the same pulse-width and frequency. Reliable evaluation of TENS intensity should be based on current density calculation and should be routinely documented for TENS sessions to permit comparisons among different studies and to determine the real effectiveness of TENS. Further studies are required to determine the relationship between TENS intensity and differential spinal excitability modulation using accurately determined TENS current density.S1 FileEach label contains the whole information regarding to: subjects characteristics; density current outcome; Mmax outcome; Hmax outcome; Hnor outcome; and the power analysis performed.(XLSX)Click here for additional data file.