Pharmaceutical companies and drug distributors are intensely scrutinized in numerous lawsuits for their role in instigating the opioid epidemic. Many individual physicians have also been held accountable for activities related to prescribing opioid medications. The purpose of this study was to examine the epidemiologic patterns of criminal cases against physicians charged with opioid-related offenses reported in the US news media.We searched the Nexis Uni® database for news media reports on physicians who had been arrested, indicted or criminally charged for illegally prescribing opioids between January 1995 and December 2019. Data collected from the news media reports include defendant’s age, sex, clinical specialty, type of crime and legal consequences.The annual number of criminal cases against physicians charged with opioid-related offenses reported in the US news media increased from 0 in 1995 to 42 in 2019. Of the 372 physician defendants in these criminal cases, 90.1% were male, 27.4% were 65 years and older, and 23.4% were charged in Florida. Of the 358 physician defendants with known clinical specialty, 245 (68.4%) practiced in internal medicine, family medicine, or pain management. Drug trafficking was the most commonly convicted crime , followed by fraud (19.1%), money laundering (11.0%) and manslaughter (5.6%). Of the 244 convicted physicians with known sentences, 85.0% were sentenced to prison with an average prison term of 127.3 ± 120.3 months.The US news media has reported on an increasing number of opioid-related criminal cases against physicians from a wide variety of clinical specialties. The most commonly convicted crime in these cases is drug trafficking, followed by fraud, money laundering, and manslaughter. Misuse and overdose of opioids is a public health crisis in the United States and many other countries. From 1999 to 2018, nearly 450,000 people in the United States died from overdoses involving prescription opioids (Centers for Disease Control and Prevention (CDC), Reducing opioid prescriptions, particularly illegal opioid prescriptions, could help limit the overall quantity of opiates being distributed to the communities and thereby decrease the availability and consumption of these addictive drugs. In recent years, pharmaceutical companies and drug distributors have been intensely scrutinized in numerous lawsuits for their role in instigating the opioid epidemic. Many individual physicians have also been held accountable for activities related to prescribing opioid medications. The purpose of this study was to examine the epidemiologic patterns of criminal cases against physicians charged with opioid-related offenses reported in the US news media.Nexis Uni® is an electronic database that houses an archive of public record documents, such as full-text newspapers, business and legal publications, and journals. An initial search was conducted on the database with search terms “overprescribing opioids”, “overprescribe opioids”, “overprescribed opioids”, and “pill mill.” The search was refined with terms (“doctor” or “dr.” or “MD”) AND (“sentenced” or “charged” or “convicted” or “sentence” or “charge”) AND (“years” or “fined” or “months” or “prison”) AND (“pill mill”) AND (“opioid” or “narcotic” or “drugs”) and limited to publications released after January 1st, 1995. The database was queried with these terms so as to identify news media articles that reported incidents of physicians overprescribing opioids since 1995. The search yielded over 2000 results of full-text newspapers and court reports, which were manually examined to extract a list of physicians, who, according to the news media reports, had been arrested, criminally charged, or indicted for illegally prescribing opioids. Duplicate physicians were manually excluded by reviewing names.Next, individual searches for each physician were executed on Nexis Uni®; the database was queried using only the physician name, in order to produce narrower, more focused results. From these searches, the following information was ascertained: name, state in which the incident occurred, type of medical facility in which the physician worked , medical specialty, age at the time of incident, sex, type of criminal charge, and outcome of the legal proceeding. Multiple news media reports were examined for each physician. Further, these articles uncovered other physicians who fit the search criteria but had not come up in the initial search; these physicians were added to the list. Searches on Google News were carried out for physicians with incomplete information. Data collected from news media reports for each physician involved in these incidents were analyzed using descriptive statistics such as frequencies, percentages, means, and standard deviations.During January 1995 through December 2019, the US news media reported on a total of 372 physicians who were involved in opioid-related criminal cases, exclusive of reports on 12 physicians involved in civil lawsuits for negligent opioid prescribing behaviors. There were no opioid-related criminal cases against physicians reported in the US news media between 1995 and 1998. Of the 372 criminal cases covered by the US news media, 231 (86.3%) occurred between January, 2010 and December, 2019  of physicians involved in the criminal cases were male. Ages of the physicians ranged from 33 to 87 (mean = 58.6 ± 10.7 years), with 27.4% being 65 years and older. Information on clinical specialty was available for 358 physicians. These physicians practiced in a variety of clinical specialties, with 25.7% in family medicine, 24.9% in internal medicine, and 17.9% in pain management  worked in private practices. Of the 250 cases with known outcomes, representing 67.2% of the 372 cases, 246 physicians (98.4%) were convicted and 4 (1.6%) were acquitted. Drug trafficking accounted for 54.2% of the crimes convicted, followed by fraud (19.1%), money laundering (11.0%), manslaughter (5.6%), and other or unknown (10.1%). Of the 244 convicted physicians with known sentences, 85.0% served time in prison, with an average prison term of 127.3 (± 120.3) months and an average probation term of 65.5 (± 61.0) months.Results of this study indicate that criminal cases against physicians charged with opioid-related offenses increased over time between 1995 and 2019, with the majority of the cases occurring between 2010 and 2019. This temporal pattern is generally consistent with the time trend of the opioid epidemic in the United States , followed by fraud, money laundering, and manslaughter.


In the Internet of Things (IoT), numerous devices can interact with each other over the Internet. A wide range of IoT applications have already been deployed, such as transportation systems, healthcare systems, smart buildings, smart factories, and smart cities. Wireless sensor networks (WSNs) play crucial roles in these IoT applications. Researchers have published effective (but not entirely secure) approaches for merging WSNs into IoT environments. In IoT environments, the security effectiveness of remote user authentication is crucial for information transmission. Computational efficiency and energy consumption are crucial because the energy available to any WSN is limited. This paper proposes a notably efficient and secure authentication scheme based on temporal credential and dynamic ID for WSNs in IoT environments. The Burrows–Abadi–Needham (BAN) logic method was used to validate our scheme. Cryptanalysis revealed that our scheme can overcome the security weaknesses of previously published schemes. The security functionalities and performance efficiency of our scheme are compared with those of previous related schemes. The result demonstrates that our scheme’s security functionalities are quantitatively and qualitatively superior to those of comparable schemes. Our scheme can improve the effectiveness of authentication in IoT environments. Notably, our scheme has superior performance efficiency, low computational cost, frugal energy consumption, and low communication cost. Internet of Things (IoT) is an emerging technology, which is the extension of Internet connectivity into various devices such as sensors, vehicles, and mobile phones. These devices can interact with each other over the Internet . A wide Mutual authentication. Users and sensor nodes must mutually authenticate each other. After they have authenticated each other, they must arrange a session key for information transmission.Masquerade attack resistance. An adversary cannot impersonate a legal user to log in to WSNs. In addition, the adversary cannot masquerade as a sensor node to spoof the user.Replay attack resistance. The adversary cannot attempt to replay previously intercepted messages to spoof the GWN.Guessing attack resistance. The adversary cannot obtain useful information to devise an offline check of the correctness of guessed passwords.In this subsection, we introduce some preliminaries and the principal technologies that our scheme is based on, such as temporal credential  and dynaA temporal credential is an impermanent attestation of authority issued by a third party. The GWN can issue a temporal credential to each user and sensor node . The expi contains a dynamic ID, called DIDi. The login message is dynamic for each login. For all i, the parameter DIDi is associated with nonce Ni and changed dynamically for each login. The use of a dynamic ID in each login message can avoid the risk of ID-theft . Ha. HaUi), Ui undertakes to register, he or she selects the identification IDi and password PWi. Subsequently, Ui generates a random number ri and sends IDi and h(ri⊕PWi) to the GWN for registration through a secure channel. After receiving the messages from Ui, the GWN selects the expiration time TEi of the temporal credential of Ui. The GWN computes the temporal credential TCi and verification information Ri for Ui. The GWN then issues a smart card with the temporal credential TCi, expiration time TEi, and verification information Ri to Ui through a secure channel. The steps are detailed as follows .Step U3.Ui ⇒ GWN: {h(ri⊕PWi), IDi}.Ui transmits h(ri⊕PWi) and IDi to the GWN through a secure channel.Step U4.GWN ⇒ Ui: {IDGWN, PTCi, TEi, Bi, Ri, h(.)}. After receiving the message from Ui, the GWN selects the expiration time TEi of the temporal credential of Ui and computes the following equations to issue the temporal credential TCi for Ui.Pi= h(IDi∥IDGWN∥TEi), TCi= h(Pi∥KGWN-U∥TEi), PTCi= TCi⊕h(ri⊕PWi),Qi= h(IDi∥KGWN-U), Bi= Qi⊕h(IDi∥h(ri⊕PWi)), and Ri= h(Qi).GWN then issues a smart card with the secret parameters {IDGWN, PTCi, TEi, Bi, Ri, h(.)} to Ui through a secure channel.The Step U5.Ui stores ri in the smart card, after which the smart card holds the parameters {IDGWN, PTCi, TEi, Bi, Ri, ri, h(.)}.Sj is pre-configured with SIDj. After deployment, the sensor node Sj generates a random number rj and then sends SIDj and h(rj⊕SIDj) to the GWN for registration through a secure channel. After receiving the messages from Sj, the GWN issues a temporal credential TCj to Sj through a secure channel. The steps are detailed as follows .Step S3.Sj ⇒ GWN: {SIDj, h(rj⊕SIDj)}.Sj sends SIDj and h(rj⊕SIDj) to the GWN through a secure channel.Step S4.GWN ⇒ Sj: {RTCj}. After receiving the message from Sj, the GWN computes TCj = h(KGWN-S∥SIDj) to issue the temporal credential TCj for Sj and then calculates RTCj = TCj⊕h(h(rj⊕SIDj)∥SIDj). The GWN sends RTCj to Sj through a secure channel.Step S5. After receiving the message from the GWN, Sj computes TCj = RTCj⊕h(h(rj⊕SIDj)∥SIDj) to find its temporal credential TCj and then stores it.Ui first inserts a smart card into the smart card reader to log in to the system. Ui then gives  that correspond to the smart card. The smart card of Ui computes verification information Ri in the smart card. After passing verification, the legitimacy of Ui is ensured. Afterward, Ui can read the information stored in the smart card and find its temporal credential TCi. The steps are detailed as follows . The smart card of user Ui then computes Qi = Bi⊕h(IDi∥h(ri⊕PWi)) and h(Qi). The smart card validates whether Ri in the smart card are equal. If the values are unequal, the smart card rejects the login request. Otherwise, the legitimacy of Ui is ensured, and Ui can read the information stored in the smart card.Step L2.Ui computes TCi = PTCi⊕h(ri⊕PWi) to find its temporal credential TCi.Ui and finding the temporal credential TCi, the system must complete mutual authentication among Ui, the GWN, and Sj. The first step of the mutual authentication phase involves identity verification for Ui, which is conducted by the GWN. Afterward, the second step entails identity verification of the GWN, which is conducted by Sj. The third step involves identity verification for Sj, which is conducted by Ui as well as the GWN. Finally, a session key KEYij is negotiated between Ui and Sj to conduct encryption during data transmission later on. The steps are detailed as follows , DIDi = IDi ⊕h(TCi∥IDGWN∥Ni), and q1 = h(IDi∥TCi∥Ni). Afterward, Ui randomly chooses a secret sharing key Ki and computes PKSi = Ki⊕h(TCi∥Ni). After computation, Ui sends the login request message m1 = {DIDi, q1,PKSi,TEi, Pi, Ni} to the GWN.Step V2.GWN→ Sj: {DIDi, DIDGWN, q2, PKSGWN, IDGWN, Ni, NGWN}. After obtaining message m1, the GWN computes TCi = h(Pi∥KGWN-U∥TEi), IDi = DIDi ⊕h(TCi∥IDGWN∥Ni), and h(IDi∥TCi∥Ni). The GWN then verifies whether q1 are equal. If q1, then the GWN terminates the request and sends a reject message to Ui. Otherwise, the legitimacy of Ui is ensured, and the GWN accepts the login request. The GWN then records the login status of Ui to indicate that Ui is logging in to the system. The GWN computes Ki = PKSi ⊕h(TCi∥Ni). At this point, the GWN selects a proper sensor node Sj with identification SIDj and calculates its temporal credential TCj = h(KGWN-S∥SIDj). The GWN then generates a nonce NGWN and computes DIDGWN = IDi ⊕h(TCj∥DIDi∥NGWN), q2 = h(IDi∥TCj∥NGWN), and PKSGWN = Ki ⊕h(TCj∥NGWN). After computation, the GWN sends the message m2 = {DIDi, DIDGWN, q2, PKSGWN, IDGWN, Ni, NGWN}to Sj.Step V3.Sj →Ui, GWN: {SIDj, q3, PKSj, Ni, NGWN}. After receiving message m2, Sj assesses IDGWN to verify whether the GWN is a participant. If verification is true, Sj computes IDi = DIDGWN ⊕h(TCj∥DIDi∥NGWN) and h(IDi∥TCj∥NGWN). Sj then verifies whether q2 are equal. If q2, then Sj terminates the request and returns a reject message. Otherwise, the legitimacy of the GWN is ensured, and Sj accepts the request. Sj computes Ki = PKSGWN ⊕h(TCj∥NGWN). Afterward, Sj randomly selects a secret sharing key Kj. Sj computes q3 = h(IDi∥SIDj∥Ki∥Ni∥NGWN) and PKSj = Kj⊕h(Ki∥Ni∥NGWN). After computation, Sj sends the message m3 = {SIDj, q3, PKSj, Ni, NGWN}to Ui and the GWN.Step V4. After receiving the message m3, Ui and the GWN separately compute h(IDi∥SIDj∥Ki∥Ni∥NGWN). After computation, the GWN verifies whether q3 are equal. If q3, then the GWN can verify the legitimacy of Sj. User Ui also verifies whether q3 are equal. If q3, then Ui can verify the legitimacy of Sj and the GWN. Afterward, Ui and the GWN separately compute Kj = PKSj⊕h(Ki∥Ni∥NGWN). Finally, after ending the mutual authentication phase, Ui, the GWN, and Sj separately generate the shared session key KEYij by computing KEYij = h(Ki∥Kj∥Ni∥NGWN∥SIDj).Ui must insert his/her smart card into the smart card reader. Afterward, Ui gives IDi and PWi, which correspond to the smart card. In the first step of the password change phase, the smart card of Ui computes verification information Ri in the smart card. After passing verification, the legitimacy of Ui is ensured. Ui can then read the information stored in the smart card. The second step involves finding the updated value of the parameters {PTCi, Bi, ri} in the smart card with the updated value of the parameters {To update or change the password, a user  follows :Step P1. A user Ui inserts a smart card into the smart card reader and gives . The smart card of Ui calculates Qi = Bi⊕h(IDi∥h(ri⊕PWi)) and h(Qi) and then verifies whether Ri in the smart card are equal. If the values are unequal, the smart card rejects the login request. Otherwise, the legitimacy of Ui is ensured, and Ui can read the information stored in the smart card.Step P2. The user Ui selects a new password Ui generates a random number Qi⊕h(IDi∥h (PTCi⊕h(ri⊕PWi)⊕h}.This section presents the security analysis of the proposed scheme and proves its security strength. Our scheme can overcome the weaknesses of previous schemes. Our proposed scheme has the following main security features.GWN, Ui, and Sj in WSNs. Because encryption and a message authentication code (MAC) are required to protect data transmission between Ui and Sj, a session key must be negotiated in advance between these two participants [GWN and Sj is accomplished by calculating verification information q2 and q3. In Step V3, Sj can verify the legitimacy of the GWN after determining whether q2 and q2 = h(IDi∥TCj∥NGWN). Temporal credential TCj is included in verification information q2. This shows that the sensor node Sj can authenticate the validity of the GWN. In Step V4, the GWN can verify the legitimacy of Sj after confirming whether q3 and q3 = h(IDi∥SIDj∥Ki∥Ni∥NGWN). A secret sharing key Ki is included in verification information q3. This shows that the GWN can authenticate Sj. By contrast, mutual authentication between Ui and the GWN is accomplished by calculating verification information q1 and q3. In Step V2, the GWN can verify the legitimacy of Ui after determining whether q1 are equal, where q1 = h(IDi∥TCi∥Ni). Temporal credential TCi is included in verification information q1. This shows that the GWN can authenticate the user Ui. In Step V4, Ui can verify the legitimacy of Sj after confirming whether q3 and q3 = h(IDi∥SIDj∥Ki∥Ni∥NGWN). A secret sharing key Ki is included in verification information q3. This shows that the user Ui can authenticate the sensor node Sj. In addition, because Sj has authenticated the validity of the GWN, the user Ui further authenticates the validity of the GWN as well. Therefore, on the basis of temporal credential signing and the secret sharing key, Ui, Sj, and the GWN can mutually authenticate each other in the proposed protocol. In Step V4, after completing the mutual authentication phase, Ui, the GWN, and Sj can separately generate the shared session key KEYij by computing KEYij = h(Ki∥Kj∥Ni∥NGWN∥SIDj), where secret sharing key Ki and Kj are selected randomly. This shows that Ui, Sj, and the GWN can share a common session key after finishing the mutual authentication phase. The common session key is validated by Ui, the GWN, and Sj. This illustration indicates that our scheme provides session key agreement and mutual authentication. The formal proofs are given in the following lemmas and Proposition 1. We use the BAN logic method [Q and P are principals [Mutual authentication is a critical feature for verifying mutual validity among the icipants . In thisc method –24 to foc method –24. The incipals –22, 42.The essential logical postulates for the BAN logic are listed as follows –22, 42:Freshness-propagation rule: P is entitled to believe that one part of a formula  is fresh, then he also is entitled to believe that the entire formula  must also be fresh.Receiving rule: P can receive and read a formula  or formula 〈X〉Y, then he also can receive and read its components X.Nonce-verification rule: P is entitled to believes that X is a fresh statement and that Q once said X, then P believes that Q believes X.Jurisdiction rule: P believes that Q has jurisdiction over X and P believes that Q believes X, then P believes X.Message-meaning rule: P is entitled to believe that the key Y is shared with Q, and P sees X encrypted under Y, then P is entitled to believe that Q once said X.Session-key rule: X is an element of the combination session key K [P is entitled to believe that K is a fresh statement and that Q believes X, then P believes that P and Q share a common key K.on key K , 44. ThaTo validate the proposed protocol, we first summarize our scheme in the generic form , 21, 42:m1. Ui → GWN: {DIDi, q1, PKSi, TEi, Pi, Ni}Message IDi⊕h(TCi∥IDGWN∥Ni), h(IDi∥TCi∥Ni), Ki⊕h(TCi∥Ni), TEi,= {h(IDi∥IDGWN∥TEi), Ni}.m2. GWN→ Sj: {DIDi, DIDGWN, q2, PKSGWN, IDGWN, Ni, NGWN}Message IDi ⊕h(TCi∥IDGWN∥Ni), IDi ⊕h(TCj∥DIDi∥NGWN),= {h(IDi∥TCj∥NGWN), Ki⊕h(TCj∥NGWN), IDGWN, Ni, NGWN}.m3. Sj →GWN: {SIDj, q3, PKSj, Ni, NGWN}Message SIDj, h(IDi∥SIDj∥Ki∥Ni∥NGWN), Kj⊕h(Ki∥Ni∥NGWN),Ni, NGWN}.= {m3. Sj →Ui: {SIDj, q3, PKSj, Ni, NGWN}Message SIDj, h(IDi∥SIDj∥Ki∥Ni∥NGWN), Kj⊕h(Ki∥Ni∥NGWN), Ni, NGWN}.= {Subsequently, we transform the generic form into the idealized form:1. Ui → GWN: I2. GWN→ Sj: I3. Sj → GWN: I4. Sj →Ui:ITo analyze our scheme, we use the following assumptions:1. 2. 3. 4. 5. GWN|≡#(Ni)                       A6. Sj|≡#(NGWN)A7. Ui|≡#              A8. GWN|≡#A9. GWN|≡Ui|⇒Ni              A10. Sj|≡GWN|⇒NGWNA11.GWN|≡Sj|⇒              A12. Ui|≡Sj|⇒ALemma 1. The GWN in our scheme can authenticate Ui; Sjcan authenticate the GWN.Proof: In our scheme, Ui produces a nonce Ni. Then, Ui transmits Ni to the GWN. After obtaining Ni, the GWN generates a nonce NGWN and then sends nonces  to Sj.GWN can authenticate Ui, the following belief must be demonstrated:To prove that the 1. GWN|≡NiBSj can authenticate the GWN, the following belief must be demonstrated:To prove that 2. Sj|≡NGWNB1:1. GWNseesReceiving rule and I1)S2. GWNbelievesUisaidNi. S3. GWNbelievesUibelievesNi. S4. GWNbelievesNi. That is, GWN|≡Ni SThe steps for proving BGWN authenticates Ui.Consequently, the 2:5. Sjsees2 and Receiving rule)S6. SjbelievesGWNsaidNGWN. S7. SjbelievesGWNbelievesNGWN. S8. SjbelievesNGWN. That is, Sj|≡NGWN .SSimilarly, the steps of the proof for BLemma 2. The GWN in our scheme can authenticate Sj; Uican also authenticate Sj.Proof: In our scheme, after receiving nonces , the Sj returns  to the GWN and Ui.GWN can authenticate Sj, the following belief must be demonstrated:To prove that the 3. GWN|≡BUi can authenticate Sj, the following belief must be demonstrated:To prove that the 4. Ui|≡B3:9. GWNseesReceiving rule and I3)S10. GWNbelievesSjsaid . S11. GWNbelievesSjbelieves .S12. GWNbelieves . That is, GWN|≡ .SThe steps of the proof for BGWN can authenticate Sj.Consequently, the 4:13. UiseesReceiving rule and I4)S14. UibelievesSjsaid . S15. UibelievesSjbelieves . S16. Uibelieves . That is, Ui|≡. SSimilarly, the steps of the proof for BLemma 3.In our scheme, the GWN, Ui, and Sjcan coordinate the common session key KEYij.Proof: To prove that Ui, the GWN, and Sj in our scheme can share a session key KEYij = h(Ki∥Kj∥Ni∥NGWN∥SIDj), the following beliefs must be demonstrated:5. B6. B7. B8. 5 are:17. UibelievesSjbelieves . (Apply S15)S18. SjbelievesGWNbelievesNGWN. (Apply S7)S19. UibelievesGWNbelievesNGWN. S20. Uibelieves fresh . (Apply A7)S21. Uibelieves fresh (KEYij). (Apply S20 and Freshness-propagation rule)S22. UibelievesSession-key rule, S19, and S21)SBUi believes that Ui shares the session key KEYij with the GWN.Consequently, 6:23. GWNbelievesUibelievesNi. (Apply S3)S24. GWNbelieves fresh (Ni). (Apply A5)S25. GWNbelieves fresh (KEYij). (Apply S24 and Freshness-propagation rule)S26. GWNbelieves23, S25, and Session-key rule)SSimilarly, the steps of the proof for BGWN believes that GWN shares the session key KEYij with Ui.Consequently, the 7 are:27. SjbelievesGWNbelievesNGWN. (Apply S7)S28. Sjbelieves fresh (NGWN). (Apply A6)S29. Sjbelieves fresh (KEYij). (Apply the Freshness-propagation rule and S28)S30. SjbelievesSession-key rule, S27, S29)SThe steps of the proof for BSj believes that Sj shares the session key KEYij with the GWN.Consequently, 8 are:31. GWNbelievesSjbelieves . (Apply S11)S32. GWNbelieves fresh (Ni). (Apply A5)S33. GWNbelieves fresh (KEYij). (Apply S32 and Freshness-propagation rule)S34. GWNbelieves31, S33, and Session-key rule)SSimilarly, the steps of the proof for BGWN believes that GWN shares the session key KEYij with Sj.Consequently, the Proposition 1. Ui, the GWN, and Sjin our scheme can mutually authenticate each other; they can share a common session key.Proof: From Lemma 2, Ui in our scheme can authenticate Sj. In addition, Sj can authenticate the GWN (Lemma 1). Thus, Ui can further authenticate the GWN as well. Conversely, the GWN can authenticate Ui (Lemma 1). Consequently, the GWN and Ui in our scheme can mutually authenticate each other. The GWN can authenticate Sj (Lemma 2). Conversely, Sj can authenticate the GWN (Lemma 1). Consequently, the GWN and Sj in our scheme can mutually authenticate each other. Mutual authentication can be provided in our scheme. After finishing the mutual authentication, Ui, the GWN, and Sj can share a session key KEYij = h(Ki∥Kj∥Ni∥NGWN∥SIDj) (Lemma 3). Session key agreement can also be provided in our scheme.When a user’s smart card is stolen or lost in a stolen smart card attack, an adversary can acquire information from the smart card. Then, the adversary masquerades as an authorized user to access to the GWN. However, password protection functionality can prevent the leakage of password information, such that the adversary cannot obtain useful information to perform an off-line password guessing attack.Proposition 2. The proposed scheme can provide password protection, guessing attack resistance, and stolen smart card attack resistance.Proof: In our scheme, the password presents with the h(ri ⊕PWi) form, in which PWi and ri are hidden. h(ri⊕PWi) is not stored in the smart card, the GWN, or any other device. Thus, the adversary cannot directly obtain PWi by performing an off-line password guessing attack on h(ri⊕PWi) [GWN when the adversary has obtained a legitimate user's smart card. Suppose that when the smart card of user Ui is stolen or lost, the adversary obtains that the smart card. The adversary can obtain the secret parameters {IDGWN, PTCi, TEi, Bi, Ri, ri, h(.)} from the smart card. To impersonate a legitimate user, the adversary must produce a new TEi, Pi, GWN. The imitative parameters {Pi} are obtained using the following equations:(ri⊕PWi) . Therefo(ri⊕PWi) , 42, 46.i ⊕h(TCi∥IDGWN∥i∥TCi∥i∥i = h(IDi∥IDGWN∥TEi).PPi}, the adversary must first obtain TCi and IDi by using the following equations:Therefore, to obtain the imitative parameters {i = h(Pi∥KGWN-U∥TEi),TCTCi= PTCi⊕h(ri⊕PWi),IDi= DIDi⊕h(TCi∥IDGWN∥Ni).TCi and IDi because he/she does not possess KGWN-U and PWi. Only the GWN knows the private key KGWN-U in our scheme. As previously discussed, the proposed scheme can provide password protection, and that the adversary cannot acquire PWi by executing an off-line password guessing attack. Therefore, the imitative parameter set {Pi}of a login request message is not acquired. The adversary cannot masquerade as an authorized user by only using a smart card.Nevertheless, the adversary cannot acquire By involving a smart card and a password in the login phase, two-factor security in our scheme can be achieved , 47, 48.Proposition 3. Two-factor security can be provided in our scheme.Proof: First, assume that the adversary only has the smart card of Ui. Let us even assume that the adversary can intercept login request message m1 = {DIDi, q1, PKSi, TEi, Pi, Ni}. As mentioned in Proposition 2, the adversary can obtain the secret parameters {IDGWN, PTCi, TEi, Bi, Ri, ri, h(.)} from the smart card. To impersonate a legitimate user, the adversary must produce a new i, Pi, i ⊕h(TCi∥IDGWN∥i∥TCi∥i∥i and IDi by applying the following equations: TCi = h(Pi∥KGWN-U∥TEi), TCi = PTCi ⊕h(ri⊕PWi), and IDi = DIDi⊕h(TCi∥IDGWN∥Ni). Nevertheless, the adversary cannot acquire TCi and IDi because he/she does not possess KGWN-U and PWi. Only the GWN knows the private key KGWN-U in our scheme, and we have proven that the proposed scheme can provide password protection to prevent the leakage of PWi information (Section 4.2). Therefore, the parameter set {i and identification IDi of Ui. Under this condition, the adversary also cannot acquire TCi to calculate the parameters {GWN-U and PTCi (which are not stored in the smart card). Therefore, the adversary cannot impersonate an authorized user when he/she either acquires information from the smart card or knows {IDi, PWi}. Our scheme can withstand this type of masquerade attack and provide two-factor security.GWN.Protection against masquerade attacks is a principal security feature for any remote user authentication scheme. Replay attack resistance means that the adversary cannot attempt to replay any previously intercepted message to spoof the Proposition 4. Our scheme can provide masquerade attack resistance and replay attack resistance.Proof: Proposition 3 has demonstrated that our scheme can protect against masquerade attacks caused by either the loss of a smart card or the revelation of sensitive identification and password details {IDi, PWi}. The reliability of our scheme against other masquerade attacks must be demonstrated. We can even assume that the adversary is a legitimate user L and undertakes to impersonate a user Ui. Adversary L may intercept the login request message m1 = {DIDi, q1, PKSi, TEi, Pi, Ni}. Adversary L can have {IDl, PWl} and acquire {IDGWN, PTCl, TEl, Bl, Rl, rl, h(.)} from his/her smart card because he/she is an admissible user. Adversary L generates a new nonce TEi, Pi, GWN, where IDi ⊕h. In addition, adversary L cannot compute the shared session key KEYij = h(Ki∥Kj∥Ni∥NGWN∥SIDj) because he or she does not know Ki and Kj in KEYij. Thus, adversary L cannot impersonate any other legitimate user. Consequently, our scheme can protect against masquerade attacks when an adversary impersonates any other legitimate user. Adversary L can undertake to replay the intercepted message {DIDi, q1, PKSi, TEi, Pi, Ni} to the GWN. However, after receiving message m3 = {SIDj, q3, PKSj, Ni, NGWN}, adversary L cannot compute the shared session key KEYij = h(Ki∥Kj∥Ni∥NGWN∥SIDj) because he or she cannot obtain Ki and Kj in KEYij. Consequently, resistance to replay attacks is guaranteed as well. Next, we prove that an adversary cannot masquerade as a sensor node to spoof the user. Suppose adversary L has intercepted message m2 when the GWN attempts to send it to Sj; that is, the message {DIDi, DIDGWN, q2, PKSGWN, IDGWN, Ni, NGWN}. To masquerade as a sensor node to spoof the user, the adversary must randomly choose an imitative secret sharing key SIDj, q3, Ni, NGWN} to the GWN, where q3 = h(IDi∥SIDj∥Ki∥Ni∥NGWN) and h(Ki∥Ni∥NGWN). To obtain the parameters {q3, Ki. Moreover, Ki can be obtained by using the equation Ki = PKSGWN⊕h(TCj∥NGWN). Nevertheless, the adversary cannot acquire Ki because he/she does not possess the temporal credential TCj. Therefore, the parameters {q3, SIDj, q3, Ni, NGWN} to the GWN. Consequently, our scheme can protect against masquerade attacks when an adversary masquerades as a sensor node to spoof the user.GWN or Sj. By contrast, an insider attack involves any privileged insider of the GWN purposely obtaining a user password, which leads to security defects in the remote user authentication scheme [The stolen verifier attack means that the adversary steals the verification table from the n scheme , 49.Proposition 5. Our scheme can protect against stolen verifier attacks and insider attacks.Proof: The GWN and Sj in our scheme do not retain any verification table for verifying the legitimacy of registered users or sensor nodes. Therefore, the adversary cannot find any verifiable information in the GWN or Sj to impersonate a legitimate user. Consequently, our scheme can protect against stolen verifier attacks [Ui presents h(ri⊕PWi) to register with the GWN. ri and PWi are hidden from the GWN. In addition, the GWN does not store any verifier h(ri⊕PWi). The privileged insider of the GWN cannot acquire PWi by executing any off-line password guessing attack [ attacks , 40. Morg attack . Consequg attack .GWN before the registration. During the registration process, a new user Ui can freely choose some identification string IDi and password PWi as favorite strings without requiring assistance from the GWN. Any new legitimate user can be freely added to the system after the registration. Therefore, the proposed scheme provides a convenient functionality for adding new users. Moreover, as mentioned, it is strongly recommended that for security policy, users update or change their passwords frequently to protect against compromise [Ui can freely choose his/her new password to update or change the password without requiring extra communication message overhead to exchange messages with the GWN  means that the identity of any user is disclosed only to service providers .Proposition 6. Our scheme can provide user anonymity to protect user identity.Proof: The adversary can intercept message m1 = {DIDi, q1, PKSi, TEi, Pi, Ni} to acquire the identification string of Ui,. The parameters DIDi, q1, PKSi, and Pi are obtained using the following equations:DIDi= IDi ⊕h(TCi∥IDGWN∥Ni),q1= h(IDi∥TCi∥Ni),PKSi= Ki⊕h(TCi∥Ni),Pi= h(IDi∥IDGWN∥TEi).IDi and TCi because he or she does not know KGWN-U and PWi. The identification string IDi also cannot be derived from the equations above. Therefore, an adversary cannot acquire IDi to identify the user Ui, and our scheme can provide user anonymity to protect user identity.However, in Proposition 2, we show that the adversary cannot obtain GWN bypassing attack occurs when an adversary can bypass the GWN to forge a verification message straight to the sensor node Sj without passing the GWN login [GWN spoofing attack occurs when an adversary may impersonate the GWN to obtain private login information of Ui.A WN login . By contProposition 7. Our scheme can protect against GWN bypassing attacks and GWN spoofing attacks.Proof: To bypass the GWN, an adversary must send an imitative verification message m2 = {DIDi, DIDGWN, q2, PKSGWN, IDGWN, Ni, NGWN} straight to Sj, where q2 = h(IDi∥TCj∥NGWN). However, the adversary cannot obtain q2 to create an imitative message m2 because he or she does not know the temporal credential TCj; thus, the adversary cannot bypass the GWN to forge m2 to Sj. Without m2, Sj cannot respond with any other messages. Consequently, our scheme can prevent GWN bypassing attacks. By contrast, the adversary may attempt to impersonate the GWN to acquire the secret login information of Ui. To pose as the GWN, the adversary can intercept some login request message m1 = {DIDi, q1, PKSi, TEi, Pi, Ni} and respond with an imitative message m3 = {SIDj, q3, PKSj, Ni, NGWN} to Ui, where q3 = h(IDi∥SIDj∥Ki∥Ni∥NGWN). Verification information q3 includes a secret sharing key Ki. However, as mentioned in Proposition 4, the adversary cannot acquire Ki because he/she does not know temporal credential TCj. Therefore, the adversary cannot obtain q3; thus, the adversary cannot send an imitative message m3 = {SIDj, q3, PKSj, Ni, NGWN} to respond to Ui. The adversary cannot convince Ui that he/she is a legitimate GWN. Consequently, our scheme can protect against GWN spoofing attacks.Performance and functionality evaluations are critical to establish validity for practical deployment. In this section, the performance and functionality of our scheme are evaluated. The performance efficiency and functional effectiveness of our authentication scheme are demonstrated.Yes denotes the scheme has a security feature; No denotes the contrary. The weaknesses of the previous related schemes for WSNs are mentioned in Section 2 and summarized in Eve, undertakes to damage our scheme by executing the following attacks: guessing attack, stolen smart card attack, masquerade attack, replay attack, stolen verifier attack, insider attack, user anonymity attack, or GWN bypassing attack. Section 4 has shown that our scheme has the following abilities. Eve cannot directly obtain a user’s password by executing a password guessing attack. When Eve steals a user’s smart card, she cannot impersonate an authorized user to access the system. When Eve is even a legitimate user who pretends to be a different legitimate user, our scheme can protect against this masquerade attack. Moreover, Eve may undertake to replay some intercepted message to the GWN. Our scheme can provide resistance to replay attacks. Eve cannot breach the system by stealing the verification table. Even as an insider, Eve cannot acquire a password by executing any password guessing attack. Eve may intercept a login request message from the user to acquire the identification information, but the identification information of a user cannot be derived. Finally, Eve cannot forge an imitative message and send it straight to the sensor node to bypass the GWN. Moreover, our scheme has other security functionalities, which include updating passwords, choosing passwords freely, adding new users, and time synchronization avoidance. Our scheme provides a secure common session key and mutual authentication. Our scheme can thus protect against all listed attacks from Eve.Th is defined as the time complexity of the hash function; Tecc is the time complexity of the encryption/decryption operation in elliptic curve cryptography (ECC) algorithm [The proposed scheme comprises four phases: registration phase, login phase, authentication and key agreement phase, and password change phase. In a WSN environment, the performance of the authentication scheme is affected mainly by the authentication and key agreement phase , 34, 35.lgorithm . The comlgorithm , 34, 35.Th to compute the parameters of the login request message and the response message. The GWN must spend 14Th to compute the parameters in a response message for the user and a request message for the sensor node. The sensor node must expend 3Th to confirm whether the verification equations hold. In addition, the user, GWN, and sensor node must expend 2Th, 3Th, and 2Th separately to negotiate the shared session key in the key agreement phase. Accordingly, the total computational costs for the user, GWN, and sensor node are 12Th, 17Th, and 5Th, respectively [In the authentication phase of the Ostad-Sharif et al. scheme , the useTh to compute the parameters of the login request message and the response message. The GWN must spend 14Th to compute the parameters in a request message for the sensor node and a response message for the user. The sensor node must expend 2Th to confirm whether the verification equations hold. In addition, the user, GWN, and sensor node must expend 1Th, 3Th, and 2Th separately to negotiate the shared session key in the key agreement phase. Accordingly, the total computational costs for the user, GWN, and sensor node are 14Th, 17Th, and 4Th, respectively [In the authentication phase of the Amin et al. scheme , the useectively .Th to compute the parameters of the login request message. The sensor node must expend 1Th to compute the parameters in a message for the GWN. The GWN must spend 5Th to verify the login request. In addition, the user, GWN, and sensor node must expend 3Th, 3Th, and 4Th separately to negotiate the shared session key in the key agreement phase. Accordingly, the total computational costs for the user, GWN, and sensor node are 6Th, 8Th, and 5Th, respectively [In the authentication phase of the Chang et al. scheme , the useectively .Th to compute the parameters of the login request message. The GWN must spend 11Th to verify the login request message and compute the parameters of the request message for the sensor node. The sensor node must expend 3Th to confirm whether the verification equations hold. Moreover, the user, GWN, and sensor node must expend 3Th, 3Th, and 3Th separately to negotiate the shared session key in the key agreement phase. Accordingly, the total computational costs for the user, GWN, and sensor node are 8Th, 14Th, and 6Th, respectively [In the authentication phase of the Xue et al. scheme , the useectively .Th to generate a login request message. The GWN must expend 5Th to confirm whether the verification equations hold and to calculate the parameters of the request message for the sensor node. The sensor node requires 2Th to confirm whether the verification equations hold and to generate a response message for the GWN. However, the Khan et al. scheme does not provide the key agreement phase for the session key agreement.In the Khan et al. scheme , the useTh to produce a login request message and to validate a response message. The GWN requires 5Th to validate a login request message and to respond to a user’s request. The sensor node must expend 2Th to verify the request message from the GWN and to generate a response message for the user. However, the Chen et al. scheme also does not provide any key agreement phase.In the Chen et al. scheme , the useTh to generate the login request message. The GWN must expend 4Th to confirm whether the verification equations hold and to calculate the parameters of the request message for the sensor node. The sensor node requires 1Th to confirm whether the verification equations hold and to generate a response message for the user. The Das scheme [In the Das scheme , the uses scheme  does notTecc + 1Th, 4Tecc + 3Th, and 2Tecc + 2Th separately to complete the authentication phase [Th, 1Th, and 1Th separately to compute a shared session key in the key agreement phase [Tecc + 2Th, 4Tecc + 4Th, and 2Tecc + 3Th, respectively [The Yeh et al. scheme  uses ellon phase . Moreovent phase . Accordiectively .Th to calculate the parameters of a login request message. The GWN expends only 8Th to verify the login request and to calculate the parameters of the request message for the sensor node. The sensor node requires only 3Th to confirm whether the verification equations hold. In the key agreement phase, the user, GWN, and sensor node expend only 3Th, 3Th, and 3Th, respectively, to negotiate the shared session key. Accordingly, the total computational costs for the user, GWN, and sensor node are 7Th, 11Th, and 6Th, respectively.Our proposed scheme provides both the authentication phase and key agreement phase. In the authentication phase of our scheme, the user requires only 4Our proposed scheme uses only the hash function and XOR operations to design a simple authentication and key agreement scheme. However, the Yeh et al. scheme  providesTecc + 2Th. The Amin et al. scheme requires 0.28 ms for 14Th. The Ostad-Sharif et al. scheme requires 0.24 ms for 12Th. By contrast, our scheme can perform the run in only 0.14 ms for 7Th. Therefore, the computational load of the user in the proposed scheme is reduced to 14.89% compared with the Yeh et al. scheme and to 58.33% compared with the Ostad-Sharif et al. scheme.For the user in each scheme run, the Yeh et al. scheme requires 0.94 ms for 2Tecc + 4Th. The Amin et al. scheme requires 0.34 ms for 17Th. The Ostad-Sharif et al. scheme requires 0.34 ms for 17Th. By contrast, our scheme can perform the run in only 0.22 ms for 11Th. Therefore, the computational load of the GWN in the proposed scheme is reduced to 11.7% compared with the Yeh et al. scheme and to 64.7% compared with the Ostad-Sharif et al. scheme.For the GWN in each scheme run, the Yeh et al. scheme requires 1.88 ms for 4Tecc + 3Th. The Amin et al. scheme requires 0.08 ms for 4Th. The Ostad-Sharif et al. scheme requires 0.1 ms for 5Th. By contrast, our scheme can perform the run in 0.12 ms for 6Th. Therefore, the computational load of the sensor node in the proposed scheme is reduced to 12.5% compared with the Yeh et al. scheme.For the sensor node in each scheme run, the Yeh et al. scheme requires 0.96 ms for 2Tecc+9Th, 28Th, 19Th, 35Th, 34Th, and 24Th, respectively. Therefore, the total running time of the schemes of Yeh et al., Xue et al., Chang et al., Amin et al., Ostad-Sharif et al., and ours are 3.78, 0.56, 0.38, 0.7, 0.68, and 0.48 ms, respectively , low energy consumption (18.2 μJ for the authentication and key agreement phase), and low communication cost .This paper analyzes the security weaknesses of related authentication schemes and proposes a more efficient and secure authentication scheme for WSNs in IoT environments. The BAN logic method is used to prove our scheme. Finally, we compare the functional effectiveness and performance efficiency of our scheme with those of previously published schemes. Cryptanalysis revealed that our scheme overcomes the security weaknesses of the previously published schemes. Our scheme satisfies the requirement of basic design criteria for the authentication scheme as well. Consequently, our scheme can enhance security effectiveness in real-world IoT environments and provide additional security functionalities compared with the other discussed schemes. Moreover, performance analysis revealed that our scheme demonstrates high efficiency and superior performance.Our future work and challenges include attempting to find security risks in heterogeneous IoT environments. Various heterogeneous IoT applications can cause serious challenges in securing networks. Future studies will further evaluate the reliability and scalability of the proposed scheme in heterogeneous IoT environments. Moreover, we also study highly secure machine learning-based authentication schemes for WSNs in intelligent IoT environments. The integration of Big Data with intelligent IoT networks will be challenging due to the limited resources of WSNs.


The authors were not aware of some errors and imprecise descriptions made in the proofreading phase, therefore, we wish to make the following corrections to this paper . These cIn page 1, Section 1, paragraph 1, the word ”re-cquisition” should be read as “re-acquisition”. In page 4, Section 2.2, paragraph 1, the sentence “The adaptive over-complete KSVD dictionary method is designed as follows” should be read as “The adaptive over-complete KSVD dictionary method is designed as follows ”. In page 5, Section 3, the corrected second paragraph reads “To overcome the above issue, inspired by the ideas of the unconstrained low-rank matrix recovery in refs. [40–42] that have been implemented in the compressed sensing field [31–33], a novel smoothing penalty sparse representation (SPSR) method is introduced, which is different from the ones studied in refs. [40–42] where a uniform random matrix  was used. In this work, the matrix is obtained via the mutual coherence technique [37] and over-complete KSVD dictionary that satisfies the RIP criterion. The objective function is as follows,”.40.ql minimization with 0 < q ≤ 1 for sparse solution of underdetermined linear systems. SIAM J. Optim. 2011, 21, 82–101.Lai, M.J.; Wang, J.Y. An unconstrained 41.SIAM J. Numer. Anal. 2013, 51, 927–957.Lai, M.J.; Xu, Y.Y.; Yin, W.T. Improved iteratively reweighted least squares for unconstrained smoothed lq minimization. 42.2l/ql (0 < q ≤ 1) norm minimization. EURASIP J. Adv. Signal Process.2013, 76.Wang, Y.; Wang, J.; Xu, Z. On recovery of block-sparse signals via mixed In page 6, the corrected Equation (16) is “In page 6, the sentence “To prove the theorem 1, the following two lemmas are required” should be read as: “To prove the theorem 1, the following two lemmas   are required”. The titles “Lemma 1 and its proof” and “Lemma 2 and its proof” should be read as “Lemma 1 ”, “proof ” and “Lemma 2 ”, “proof ”. In pages 8, the corrected Equation (23) is ”www.ebsd.cn)”. In page 11, Section 4.1, final paragraph, the sentence “The first damaged image is contaminated, due to the charged particles and dust that exists in the electron back-scattered diffraction (EBSD) system. The second damaged image is a low-pixel (or low-resolution) image that is contaminated due to the thicker contamination membrane residues on the AA7075 sample surface. The orientation images were acquired by the Oxford Instruments AZtecHKL EBSD system” should be read as “The first damaged image is contaminated, due to the charged particles and dust that exists in the electron back-scattered diffraction (EBSD) system, which is artificially contaminated via adding Gaussian and impulse noises. The second damaged image is a low-pixel (or low-resolution) image that is contaminated due to the thicker contamination membrane residues on the sample surface, which is artificially contaminated via pixel masking operation. The orientation images were acquired by the Oxford Instruments AZtecHKL EBSD system (see open website Due to the addition of new references [40–42], the order of the original citations [40–45] should be changed with [43–48], correspondingly.


Synergetic effects were observed in the hybrid composite, as the addition of SnO nanoparticles improves the stability and electrical conductivity of the polymer, while the polymeric matrix in which the nanoparticles are embedded hinders formation of SnO2. Finally, the Si passivation behavior of the hybrid composites was studied.In this work, layered hybrid composites formed by tin oxide (SnO) nanoparticles synthesized by hydrolysis and poly-poly(styrenesulfonate) (PEDOT:PSS) have been analyzed. Prior to the composite study, both SnO and PEDOT:PSS counterparts were characterized by diverse techniques, such as X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), photoluminescence (PL), atomic force microscopy (AFM), optical absorption and Hall effect measurements. Special attention was given to the study of the stability of the polymer under laser illumination, as well as the analysis of the SnO to SnO Hence, the applicability of SnO is commonly hindered, regardless of its potential interest as a p-type material in optoelectronics and energy-related applications, based on its good electrical conductivity due to native Sn vacancies and its wide bandgap (2.7–3.4 eV). In order to overcome this limitation, in most cases SnO is encapsulated or combined with SnO2 [SnO is a p-type semiconducting oxide which has demonstrated promising applicability as an anode for Li-ion batteries , coatingith SnO2 ,6 or othandgap 2.–3.4 eV. ith SnO2 ,9. Fabriith SnO2 , and in ith SnO2 , as addiith SnO2  of technith SnO2 . However−1, which can be improved by adding appropriate additives such as sorbitol, glycerol, or polar solvents including Dimethyl sulfoxide (DMSO) [Poly doped with polystyrenesulfonate (PEDOT:PSS) has been extensively used in organic electro-optical devices manly due to its transparency in the visible range and good p-type electrical conductivity. This polymer has demonstrated potential applicability in thermoelectric devices , Li-ion e (DMSO) , Ethylene (DMSO) . Such ade (DMSO) . The come (DMSO) . As an ee (DMSO) . In thisIn the present work, hybrid composites formed by SnO nanoparticles combined in a controlled ratio with a conductive polymer PEDOT:PSS were synthesized. The hybrid layers were spin coated onto Si or glass substrates for their study. SnO nanoparticles, PEDOT:PSS, and hybrid composites were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), selected area electron diffraction (SAED), Raman spectroscopy, photoluminescence (PL), atomic force microscopy (AFM), optical absorption, Hall effect measurements, and quasi steady state photoconductance (QSS-PC).SnO (nanoparticles), PEDOT:PSS (polymer with DMSO and Triton X-100), and Composite .Hereafter, the samples are named as X-ray diffraction results confirmed that samples can be indexed based on the romarchite SnO structure (ICDS 01-072-1012) with tetragonal structure, as shown in 2 during the analysis. Two main peaks centered at 110.5 cm−1 and 208.6 cm−1 can be clearly distinguished, which can be assigned to the characteristic B1g and A1g vibrational modes from SnO, respectively [2 are not detected in the Raman spectra, within the resolution of the technique, in agreement with XRD measurements. The Raman analysis confirms not only high SnO purity but also that transition to SnO2 is avoided when using the He-Ne (633 nm) laser. On the other hand, by irradiation with the UV laser (325 nm), peaks from SnO2 are clearly observed in the Raman spectra at 460 cm−1 (Eg) and 640 cm−1 (A1g)   due to tr oxides , hence t2, as confirmed by Raman analysis, hence neutral filters were used during the PL analysis in order to reduce the laser power density. All the PL spectra in 2 also shows characteristic emissions at 1.94 eV and 2.25 eV associated with oxygen vacancies-related defects, 2.58 eV due to surface defect states [2 can be considered. However, considering that high, intense UV illumination promotes the formation of SnO2, the emissions in the high energy range, of which relative intensity is enhanced by using high laser power density, could be attributed to the increased presence of SnO2.As SnO exhibits a direct optical bandgap of 2.7 eV , togethet states , and ~3 t states . All theBare PEDOT:PSS films deposited by spin coating onto Si or glass substrate have also been studied. AFM measurements indicate that the spin-coated PEDOT:PSS films onto Si substrates show smooth surfaces, with an average surface roughness of 3.5 ± 0.5 nm, formed by grains tens of nm in length a. The thPhotoluminescence from bare PEDOT:PSS was studied using a UV laser λ = 325 nm) as the excitation source b. In thi5 nm as t−1 related to C-C interring stretching [−1 due to single Cβ-Cβ stretching [−1 associated with Cα=Cβ, 1429 cm−1 due to Cα=Cβ symmetric stretching vibrations, and 1500 and 1561 cm−1 due to Cα=Cβ asymmetric stretching vibration related to the carbons on the thiophone ring in PEDOT in the middle and the end of the chains, respectively [−1 arises from the splitting of the Cα=Cβ asymmetric vibrations [−1 exhibit the higher relative intensity in all the Raman spectra, apart from the peak from the Si substrate centered at 520 cm−1. In this case, bare PEDOT:PSS shows good stability under UV laser illumination, however slight changes are observed in the Raman signal when high laser power density was used, as observed in −1 and to a lower extent at 1429 cm−1. Some authors proposed changes in this region are due to laser-induced variation from benzoid to quinoid form [α=Cβ stretching vibrations in the polymer [−1, decreases for high power density irradiation. These effects indicate small, structural changes in PEDOT:PSS promoted under high power UV illumination. It should be remarked that these structural changes under UV laser irradiation are enhanced in thinner PEDOT:PSS films, as expected (not shown here).Raman spectroscopy was employed not only for the PEDOT:PSS characterization but also to study its stability under laser irradiation. retching , 1366 cmretching ,35, 1400 polymer . The relThe hybrid composite shows good transparency in the visible range and slight improvement of the absorption in the range 350–450 nm, as compared with bare PEDOT:PSS, due to the addition of SnO nanoparticles a. The wi2 were not observed after UV illumination, which can be related to the fact that the PEDOT:PSS matrix prevents SnO oxidation to SnO2. The addition of SnO nanoparticles, even in a low concentration (1% wt.), induces slight variations in the Raman signal from the hybrid composite in comparison with bare PEDOT:PSS, as shown in −1, 1500 cm−1, and 1540 cm−1 can be distinguished, which can be due to structural changes in the polymer related to interactions between the polymer and the SnO nanoparticles. Actually, some authors propose conformational changes in the polymer coils are due to electrostatic interactions with embedded nanoparticles showing charged surfaces [Raman spectra from the hybrid composite acquired with the UV laser and variable laser intensity are shown in surfaces . These c2 Ω−1 cm−1, similar to other reported values [2 Ω−1 cm−1 and high charge carrier concentration values around 1.4 1022 cm−3, even when the conductivity of SnO nanoparticles shows lower values around 1.8 10-2 Ω−1 cm−1. Actually, conductivity values of about 5·10-6 (Ω−1 cm−1) have been reported for bulk SnO at room temperature [Electrical characterization of the samples was performed by Hall effect measurements. Averaged values of charge carrier concentration, resistivity, and conductivity are shown in d values  due to tperature ,39. Someperature . Rearranperature . Hence, 15 cm−3 are included as 2 nanoparticles (1% wt.) for comparison, leading to lower lifetime values of 137 μs  was initially dissolved in water. After dissolution, and with continuous stirring at low temperature, NH4OH was added until pH = 8 was obtained and hydrolysis occurred. Then, the temperature was increased up to 100 °C for 2 h. The final product was centrifuged and washed several times until reaching neutral pH and dried at 50 °C for 12 h.SnO nanoparticles were synthesized by a soft chemistry route based on hydrolysis of the starting compound. The selected precursor SnClFor the preparation of the hybrid composite, PEDOT:PSS  in solution with the SnO nanoparticles in a controlled concentration (1% wt.) was deposited by spin coating onto silicon n-type float-zone substrates  or glass substrates previously treated with isopropanol and boiling water. In order to avoid nanoparticle agglomeration as well as to enhance dispersion and conductivity in the composite, dimethyl sulfoxide  was added in 5% wt. ,40. TritBare PEDOT:PSS films were deposited by spin coating onto Si or glass substrates following different steps. Firstly, an initial step using 2000 rpm for 50 s was used, followed by a second step using 6000 rpm for 20 s, and finally waiting for 10 s for a complete stop. Hybrid composites were deposited following the same procedure as before until the deposition step, in which 1% wt. of the SnO nanoparticles were added to the mixture and mixed in an ultrasonic bath for 15 min and then 30 s with an ultrasonic probe.Cu = 1.5404 Å. Transmission electron microscopy (TEM) and selected area electron diffraction (SAED) were performed in a Jeol JEM 1400 TEM microscope . Raman spectroscopy measurements were carried out on a Horiba Jobin Yvon LabRam HR 800 using a He-Ne UV laser (λ = 633 nm) and a He-Cd laser (λ = 325 nm). Photoluminescence (PL) was studied at room temperature using the same confocal microscope using a He-Cd UV laser (λ = 325 nm) as the excitation source and charge coupled device (CCD). Different neutral filters were used in order to attenuate the total laser intensity, when necessary. In this configuration, nominal excitation light can be attenuated by using neutral filters to reduce the total laser intensity by a factor of 0.5, 0.25, and 0.1. AFM study was carried out in a Nanotec AFM controlled by Dulcinea electronics, using a silicon tip in contact mode . Optical absorption was measured with a UV-vis-NIR light source DH-200 ocean optics with a Deuterium and Halogen lamp. Hall effect measurements were performed at room temperature using a Hall Ecopia AMP55T HMS-7000 with 4 gold probes. Gold contacts around 20 nm were fabricated via Au evaporation on a Quorum Q150T ES using a mask in order to obtain a square for the Van der Pauw configuration . On top of the Au contacts, silver paint was added. Finally, the effective charge carrier lifetime values were calculated from the PL intensity based on the quasi steady state photoconductance (QSS-PC) measurements using an LIS-R1 PL imaging setup from BT Imaging with an excitation wavelength of 808 nm and a constant illumination intensity of 4.2 × 10−2 W cm−2 .Characterization of the samples was carried out by diverse experimental techniques. X-ray diffraction (XRD) in PANanalytical X’Pert Powder equipment , using the Cu-Kα line where λ2 can be induced by UV irradiation, as demonstrated by Raman spectroscopy and PL. PEDOT:PSS layers were successfully fabricated via spin coating onto Si or glass substrates, obtaining smooth surfaces. Raman spectroscopy indicates changes in the vibrational modes around 1400 cm−1 during UV illumination, probably due to variation from benzoid to quinoid form, involving reduced stability of the polymer under irradiation. By adding SnO nanoparticles (1% wt.) to the PEDOT:PSS, enhanced electrical conductivity was achieved (6.3 102 Ω−1 cm−1), also leading to improved stability of the composite under UV irradiation, and a weakly enhanced optical absorption. Conformational changes in the polymer chains induced by the presence of the embedded nanoparticles can lead to variations in the electrical conductivity. Enhanced conductivity and improved stability is a first-step factor for possible photovoltaic applications.SnO nanoparticles were synthesized via hydrolysis, showing high purity and crystallinity. TEM measurements reveal SnO nanoparticles with dimensions around 10 nm, together with a low amount of SnO nanoplates 20–50 nm wide. Rapid transition from SnO to SnO2 nanoparticles are embedded in the composite.Finally, Si passivation behavior was evaluated and charge carrier lifetime values of τ~308 µs were obtained for the SnO-based polymer, which is worsened when SnO


Saccharomyces cerevisiae is able to respond to metabolic stress (ethanol as nonfermentative carbon source) due to the persistence of duplicated genes. These duplicates respond by transcriptional rewiring, depending on their transcriptional background. Our results shed light on the mechanisms that determine the role of duplicates, and on their evolvability.Gene duplication events have been related with increasing biological complexity through the tree of life, but also with illnesses, including cancer. Early evolutionary theories indicated that duplicated genes could explore alternative functions due to relaxation of selective constraints in one of the copies, as the other remains as ancestral-function backup. In unicellular eukaryotes like yeasts, it has been demonstrated that the fate and persistence of duplicates depend on duplication mechanism , shaping their actual genomes. Although it has been shown that small-scale duplicates tend to innovate and whole-genome duplicates specialize in ancestral functions, the implication of duplicates’ transcriptional plasticity and transcriptional divergence on environmental and metabolic responses remains largely obscure. Here, by experimental adaptive evolution, we show that  Ethanol is the main by-product of yeast sugar fermentation that affects microbial growth parameters, being considered a dual molecule, a nutrient and a stressor. Previous works demonstrated that the budding yeast arose after an ancient hybridization process resulted in a tier of duplicated genes within its genome, many of them with implications in this ethanol “produce-accumulate-consume” strategy. The evolutionary link between ethanol production, consumption, and tolerance versus ploidy and stability of the hybrids is an ongoing debatable issue. The implication of ancestral duplicates in this metabolic rewiring, and how these duplicates differ transcriptionally, remains unsolved. Here, we study the transcriptomic adaptive signatures to ethanol as a nonfermentative carbon source to sustain clonal yeast growth by experimental evolution, emphasizing the role of duplicated genes in the adaptive process. As expected, ethanol was able to sustain growth but at a lower rate than glucose. Our results demonstrate that in asexual populations a complete transcriptomic rewiring was produced, strikingly by downregulation of duplicated genes, mainly whole-genome duplicates, whereas small-scale duplicates exhibited significant transcriptional divergence between copies. Overall, this study contributes to the understanding of evolution after gene duplication, linking transcriptional divergence with duplicates’ fate in a multigene trait as ethanol tolerance.IMPORTANCE Gene duplication events have been related with increasing biological complexity through the tree of life, but also with illnesses, including cancer. Early evolutionary theories indicated that duplicated genes could explore alternative functions due to relaxation of selective constraints in one of the copies, as the other remains as ancestral-function backup. In unicellular eukaryotes like yeasts, it has been demonstrated that the fate and persistence of duplicates depend on duplication mechanism , shaping their actual genomes. Although it has been shown that small-scale duplicates tend to innovate and whole-genome duplicates specialize in ancestral functions, the implication of duplicates’ transcriptional plasticity and transcriptional divergence on environmental and metabolic responses remains largely obscure. Here, by experimental adaptive evolution, we show that Saccharomyces cerevisiae is able to respond to metabolic stress (ethanol as nonfermentative carbon source) due to the persistence of duplicated genes. These duplicates respond by transcriptional rewiring, depending on their transcriptional background. Our results shed light on the mechanisms that determine the role of duplicates, and on their evolvability. Saccharomyces cerevisiae is one of the most biotechnologically important species, being able to tolerate higher ethanol levels during fermentation than any other microbe  faced a new source of carbon (sugars) in a new niche (nectar or fruits from the recently emerged [100 million years ago {MYA}] Angiosperms), a fact that has been postulated at the origin of fermentative metabolism with ethanol as main end product. Behind this biological innovation has been unveiled gene duplication at two scales, whole-genome (WGD) and small-scale (SSD) duplication, and genome shrinkage after it, as evolutionarily driving genomic changes  responses. We reveal the transcriptional reprogramming basis and the interplay of this with gene duplication in the response and adaptation to ethanol.In this study, we undertake the challenge of elucidating the role of transcriptional rewiring to the response and adaptation to ethanol (as sole carbon source) in plicates , 5, 25. 0, t100, and t110, we characterized growth parameters of S. cerevisiae populations in the standard medium (yeast extract-peptone-dextrose [YPD]) and stressful medium (yeast extract-peptone-ethanol [YPE]), using optical density measurements (max) was significantly lower at time t0 in YPE (μmax ± standard deviation of the mean [SDm] = 0.1303 ± 0.0093 h−1) than in YPD . This difference between growth rates was also observed in all the evolved lines at all time points (100) increased the growth rate in YPD for all lines. Furthermore, the populations also increase their carry capacity after diversification  of each population, split out into the different lines, was determined at each control time point  in their evolving medium (YPD or YPE) and in the challenge medium (YPE or YPD). Significant differences of each growth parameter are indicated as *, **, and ***, when the probabilities are P < 0.05, P < 0.005, and P < 10−3, respectively, using a Wilcoxon rank test. Only biologically meaningful comparisons were added to the figure. Download FIG S1, EPS file, 0.1 MB.Maximum growth rate as phenotypic characterization of populations through experimental evolution. Phenotypic characterization was performed by characterization of population growth curves. “Maximum growth rate”  of each population, split out into the different lines, was determined at each control time point  in their evolving medium (YPD or YPE) and in the challenge medium (YPE or YPD). Significant differences of each growth parameter are indicated as *, **, and ***, when the probabilities are P < 0.05, P < 0.005, and P < 10−3, respectively, using a Wilcoxon rank test. Only biologically meaningful comparisons were added to the figure. Download FIG S2, EPS file, 0.1 MB.Evolution of carry capacity of populations under ethanol stress. Phenotypic characterization was performed by characterization of population growth curves. “Carry capacity”   were also upregulated in t100   were also downregulated in t100 (0 and t100.Transcriptome sequencing (RNAseq) was conducted in populations tol (YPE) . The exp in t100 . Adaptatd genes) . The exp in t100 . The ada10.1128/mSystems.00416-20.3FIG S3FIG S3, EPS file, 0.2 MB.Distribution of transcriptomic profiles under ethanol stress. Venn diagrams of up- and downregulated genes in YPE compared to YPD. Download Copyright © 2020 Sabater-Muñoz et al.2020Sabater-Muñoz et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 0 and t100  was high for the t0 population, but t100 showed twice as many upregulated genes as t0, perhaps indicating that experimental evolution in YPD for 100 passages has involved significant polymorphism in the transcriptomic reprogramming of cells in this population. Only 437 genes were core upregulated genes in all three populations. Populations t110, the populations derived from t100 and evolved for 10 days in ethanol, showed an overlap of only 883 upregulated genes with their parental t100 populations despite the low number of passages separating them (The number of upregulated genes among the populations ting them .0 and t100 exhibited enrichment for upregulated genes (P < 0.01) in similar functional categories, affecting mainly the “oxidation-reduction process,” “drug metabolic process,” “aerobic respiration,” “proton transmembrane transport,” “mitochondrion organization,” “small-molecule catabolic process,” “oxidoreductase activity,” “cofactor binding,” and “proton transmembrane transporter activity”  terms of the set of upregulated genes. Populations at tctivity” . The anastances” .0 had enrichment in “cytoplasmic translation and ribosome biogenesis,” which was also enriched at population t110. Furthermore, population t110 had “ncRNA processing and methylation” enriched for downregulated genes. In contrast, population t100 had only a few GO terms enriched, including “carbohydrate transport” and “nucleic acid phosphodiester bond hydrolysis,” with none of them overlapping the other two populations.The GO term analysis for downregulated genes showed even less overlap between the three populations . Populat10.1128/mSystems.00416-20.4FIG S40, t100, and t110), was performed with clusterProfiler. Download FIG S4, EPS file, 0.1 MB.Biological processes enriched for downregulated genes, due to the use of 3% ethanol as the sole carbon source. Enrichment analysis of functional categories  for downregulated genes in YPE compared to YPD, at the three time points  and 524 singleton (11.6%) genes out of the 833 upregulated genes. The proportion of upregulated duplicates was higher than that of singletons  (P = 0.0215) . Likewise, no difference was observed in the expression fold change between WGDs (median FC = 2.92) and SSDs (median FC = 3.13) (Wilcoxon rank test: P = 0.65).Population t 0.0071) . We also 0.0215) . We foun100 showed no difference in the response to ethanol between upregulated duplicates (494) and singletons  . Remarkably, while the proportion of duplicates that were upregulated increased 58% after 100 passages of evolution, the proportion of singletons increased 92% in comparison with the parental population t0 . Interestingly, upregulated duplicates (median FC = 1.346) saw a higher expression fold change than singletons (median FC = 1.298) (Wilcoxon rank test: P = 0.0288). We found significantly more WGDs (319) responding to ethanol than SSDs (259) , but no difference of expression fold change between the two .Population t0 showed a larger proportion of the duplicates (353) being downregulated than singletons (398) . Not only were there more ethanol-responding duplicates, but the response was also higher . Most of this response was coming from WGDs . No difference in the expression fold change was found between the two types of duplicates .The response of duplicates to ethanol was even more apparent when looking at downregulated genes. Population t100 showed similar results as population t0: 343 of the downregulated genes were duplicates and 597 were singletons . The expression fold change was also higher in duplicates (median FC = −1.158) than in singletons (median FC = −1.108) (Wilcoxon rank test: P = 0.0174). More WGDs (199) were responding to ethanol stress than SSDs (144) , but no difference in the expression fold change was observed .Population t110 had no difference in duplicated genes (514) being downregulated compared to singletons (877) . However, duplicated genes had a higher expression fold change than singletons . Interestingly, WGDs (284) were more abundant than SSDs (230) , but SSDs (median FC = −1.421) showed a higher expression fold change than WGDs (median FC = −1.271) (Wilcoxon rank test: P = 0.0266). The core gene of the downregulated genes consisted of more duplicates (4.3%) than singletons (2.84%) , with WGDs as the most affected duplicates .Population tS. cerevisiae to ethanol, then we should expect the transcriptional divergence (TD) between gene copies of a duplicate to be correlated with its transcriptional patterns in ethanol. We identified those duplicated genes that exhibited a fold change expression difference between their gene copies of more than 25%. Of the 1,090 duplicated gene pairs , 867 showed transcriptional divergence between gene copies in YPD. In the populations t0, 274 of the 312 upregulated duplicates in ethanol belonged to duplicates with evidence of TD, a proportion greater than expected by chance  (P = 3.0 × 10−6). The fold change of the gene copy with the highest expression in ethanol divided by that of the least expressed gene copy is also correlated with the expression fold change of the duplicate . Importantly, among the most highly divergent and upregulated duplicates, we identified the plasma membrane H+-ATPase (PMA2), translational elongation factor (HEF3), plasma membrane permeases (GIT1 and SEO1), and a gene involved in the metabolism under respiratory conditions (RGI2), among others (If duplicates were linked to the response and adaptation of  × 10−4) . The mea × 10−4) . The meag others .10.1128/mSystems.00416-20.7TABLE S1Table S1, CSV file, 0.1 MB.Divergent levels and transcriptional difference between YPE and YPD of duplicated genes. Duplicated genes are divided into whole-genome duplicates (Ohn) and small-scale duplicates (par). t0E.t0D is the log fold change of the expression difference between YPE and YPD, where d_t0E.t0D indicates if the gene has been upregulated (1) or downregulated (−1) or if no difference has been observed (0). Download Copyright © 2020 Sabater-Muñoz et al.2020Sabater-Muñoz et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 100 population 426 duplicates of the 492 showed evidence of upregulation and belonged to the set of duplicates with evidence of expression divergence, a proportion greater than expected by chance . Like in the t0 population, upregulated duplicates exhibited greater mean expression divergence between gene copies than expected by chance  .In the t × 10−3) . The phe110 also presented enrichment of upregulated duplicates for duplicates with evidence of expression divergence, with 485 out of the 576 upregulated duplicates exhibiting expression divergence . Interestingly, upregulated duplicated genes in t110 do not show higher transcriptional divergence than expected when calculated from the t0 population  but do show such when calculated from the t110 population , indicating that transcriptional divergence and upregulation are highly dependent on the current transcriptional background. Similar to the t0 and t100 populations, the population of t110 shows correlation between phenotypic plasticity and expression divergence of duplicated genes .Population t0, r = 0.68, P < 2.2e−16; t100, r = 0.67, P < 2.2e−16; t110, r = 0.61, P < 2.2e−16).In contrast to this pattern for the upregulated genes, we see no correlation between TD and downregulated genes, at any of the time points . The onlP = 0.00244). The duplicated genes we are looking at are TD, which means we have one copy with a lower expression than the other (P = 0.0018) and lower TDC in upregulated genes  (P < 10−12), but this group also had the lowest TD. Inspecting the function of these genes, we see that a majority (48 out of the 60 genes) are ribosomal proteins. As would be expected, a lot of overlap of enriched GOs was observed between the different up- and downregulated categories. In the case of both duplicates being upregulated, we saw an enrichment for “carbohydrate metabolic process” and “oxidative phosphorylation.” Furthermore, these categories are the only ones which observe an enrichment of a pathway, namely, “superpathway of TCA cycle and glyoxylate cycle.” For the discordant duplicates, enrichment categories included “glucose 6-phosphate metabolic process,” “NADP metabolic process,” and “oxidoreduction coenzyme metabolic process”  . All gro × 10−9) . Looking × 10−9) . Upregul × 10−9) . Interesprocess” .10.1128/mSystems.00416-20.5FIG S5FIG S5, EPS file, 0.2 MB.Biological processes enriched for transcriptionally divergent duplicated genes. Enrichment analysis of functional categories  for down- and upregulated transcriptionally divergent duplicated genes in YPE compared to YPD was performed with clusterProfiler. Download Copyright © 2020 Sabater-Muñoz et al.2020Sabater-Muñoz et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the CIT1 and CIT2, MDH1 and MDH3, and ACS2 and ACS1). Interestingly, in cases where only one of a duplicated pair was within this pathway, we saw upregulation of just one of the proteins , namely, the one within the TCA cycle.Changing the carbon source from glucose to ethanol implies that the yeast goes from fermentation to aerobic respiration. Combining this with the fact that the tricarboxylic acid (TCA) cycle was enriched for upregulated duplicated pairs, we map the categorized proteins onto the two pathways . At leas0, compared to 824 SSDs, not a significant difference (Fisher’s exact test: P = 0.1389). However, when looking at the magnitude of the TD between duplicates, we observed a significant difference between the two types, with WGDs showing a higher TD than SSDs (Wilcoxon rank sum test: P = 0.01281) (P = 0.1575).If TD of duplicated genes plays the same role in WGDs and SSDs, the same patterns should be observed. It has previously been noted that WGDs are more transcriptionally divergent than SSDs . Using o0.01281) . It is wper se, we had seen in the section above that upregulated genes contained more TD genes than expected , as well as for downregulated genes at t0 and t110 . This is the opposite pattern from what we observed in WGDs, where neither up- nor downregulated genes, at any of the time points, had more TD genes than expected   . In conte points .S. cerevisiae. These changes are observed in a large percentage of the S. cerevisiae genes and encode a wide range of functions. Such genome-wide transcriptional changes have been shown before to take place in the response to numerous stresses, including glucose restriction . We observed transcriptional changes between the evolved and the nonevolved ancestral population. This change is likely due to a genetic change in the population, as the original ancestral population had low variability as it originated from a single clone and the evolved population gained genetic variability through the evolution experiment, as described recently  uncovered the regulatory changes that occur as the population reacts to acute and chronic exposure. It has previously been suggested that reducing the growth rate can lead to increased stress tolerance by redirecting the resources (110) showed an enrichment of upregulation of genes involved in oxidative stress, so overall these populations were upregulating more genes involved in stress response, an indication of higher allocation of resources to stress tolerance. This agrees with the fact that we observed a lower growth rate on ethanol for the evolved population in ethanol than for the population that evolved in YPD.The evolution of the yeast population in the presence of ethanol  was used as described previously (0) was evolved through daily bottlenecks (1%) for 100 days , in 5 ml of YPD medium in 50-ml Corning tubes, at 28°C and 220 rpm. From passage 100 (t100), the population a1 was divided into two sublines, each with three biological replicates. One subline was grown in YPD medium as control (lines Da1), whereas the second subline (lines Ea1) was grown in a medium containing 3% ethanol as the sole carbon source . The populations were evolved for another 10 passages, with a daily bottleneck of 10% of population, in 5 ml of the corresponding medium, as indicated previously. Each 10 passages, a fossil record of each line was established by preserving the entire population in 25% glycerol solution at −80°C  as described in reference 600) of 0.06 to 0.07, distributed in 100-well honeycomb plates, with 6 to 7 technical replicates. The experiment was run for 78 h at 28°C with continuous shaking (high level) and taking OD600 measurements (brown filter) every 15 min. Each run contained at least 3 controls for each medium (uninoculated fresh medium). The data were analyzed with Growthcurver v.0.3.0 under R-studio .The RNA profiling was performed at the thttp://wolfe.gen.tcd.ie//ygob [P value cutoff of <0.01 and with the P value being adjusted with the Benjamini and Hochberg  or small-scale duplicates (SSDs). WGDs (555 pairs) were extracted from the reconciled YGOB list . SSDs .PRJNA321113 (t0 in YPD and YPE), PRJNA610243 (a1t100 in YPD), PRJNA610541 (a1t100 in YPE), PRJNA610474 (Da1t110 in YPD), and PRJNA610515 (Ea1t110 in YPE).Raw reads are available from the Sequence Read Archive (SRA) with accession numbers