The accurate detection of high-frequency transient fault currents in overhead transmission lines is the basis of malfunction detection and diagnosis. This paper proposes a novel differential winding printed circuit board (PCB) Rogowski coil for the detection of transient fault currents in overhead transmission lines. The interference mechanism of the sensor surrounding the overhead transmission line is analyzed and the guideline for the interference elimination is obtained, and then a differential winding printed circuit board (PCB) Rogowski coil is proposed, where the branch and return line of the PCB coil were designed to be strictly symmetrical by using a joining structure of two semi-rings and collinear twisted pair differential windings in each semi-ring. A serial test is conducted, including the frequency response, linearity, and anti-interference performance as well as a comparison with commercial sensors. Results show that a PCB Rogowski coil has good linearity and resistance to various external magnetic field interferences, thus enabling it to be widely applied in fault-current-collecting devices. The overhead transmission line is an important part of the power supply network. However, long-distance overhead transmission lines are exposed to complicated environments and are easily damaged by lightning strikes and foreign matter. This issue can cause a certain amount of transient current, which contains considerable fault information and is the basis of fault location and diagnosis .etc., are more popular than ever [Analyses based on high-frequency components such as high-frequency protection, fault diagnosis, travelling wave fault location, han ever ,2. Therehan ever . Existinhan ever , Hall cuhan ever , opticalhan ever , and RogTraditional electromagnetic current sensors cannot accurately measure high Ampere currents because of the saturation problem of the magnetic core. A high-frequency transient fault current is also difficult to measure because of the working frequency limit of the magnetic core. The Hall current sensor is significantly influenced by the environmental temperature and has poor stability, so it is difficult to capture the fault transient current. The newest optical current sensor based on the Faraday rotation effect can measure both transient and direct currents. Although this new sensor is anticipated to be the direction of current measurements in the future, it is difficult to use in power systems in the short run because of its poor anti-interference properties and high cost.A Rogowski coil is the optimum measuring method for high-frequency transient currents at the moment. However, parameter consistency is hard to guarantee because it is impossible for two random Rogowski coils to have uniform windings and the same cross-section between each turn, so as a result, a Rogowski coil can only be used after calibrations by an integrator . What’s The printed circuit board (PCB) Rogowski coil has attracted increasing attention recently because of its characteristics of high accuracy, small volume, low cost, and easy mass production ,11,12,130). The bobbin has a uniform texture, and its cross section is generally rectangular or round. The other part is wound up on the frame tightly and evenly. The Rogowski coil induces a voltage signal that represents the testing current when flux changes.The Rogowski coil consists of two parts a. One issR, the equivalent circuit of a Rogowski coil can be shown as If the terminal resistance is cC is the distributed capacitance, cR is the resistance inside the coil, cL is the self-inductance of coil, and M is the mutual inductance of coil. In most cases, cC is very little and can be omitted, so the output of Rogowski coil can be:Combining Equations (1) and (2) yields:So when As the result the relationship between output voltage and input current is:Output is proportional to the input, and the Rogowski coil is operating in self-integration model, and when At this time, the input current is equal to the integration of the output voltage, and the Rogowski coil is operating on an external-integration model.The electromagnetic environment surrounding the overhead transmission line is complicated. Thunder, lightning, discharges, and fault transit currents, except for the power frequency magnetic fields (PFMFs) around overhead transmission lines, will all generate electromagnetic interferences which can influence the measurement of a PCB Rogowski coil.In order to estimate the magnetic field around the overhead transmission lines,  calculatWhen the Rogowski coil is installed on the overhead transmission line (Phase A), the magnetic fields generated by the adjacent line (Phases B and C) will also couple in the coil and produce an induced electromotive force as shown in ABd as shown in ith turn of Rogowski coil, which is r away from the circle center, and the distance between Phase-B conductor and point Q is ABd, the angle θi is the included angle between r and ABd.The distance between Phase-A and Phase-B conductors is Q, and only the magnetic induction intensity perpendicular to the coil cross section, i.e., the tangential direction of point Q, has a contribution to the induced electromotive force. The magnetic induction intensity component in the tangential direction of point Q is expressed as follows:According to Ampere’s circuit law, the magnetic induction intensity generated by the Phase-B conductor on point Q is on the vertical line to line BN. If a Rogowski coil is wound evenly and tightly, the induced electromotive force can be expressed as the following continuous integral form:The total turns of a Rogowski coil is ABd > (R+d), e(t)=0. This result shows that as long as the adjacent conductor is beyond the PCB Rogowski coil and the winding of the PCB Rogowski coil is uniform, the interference from adjacent conductors can be omitted.On the basis of numerical calculations with MATLAB, when XB, YB parallel to the plane xOy where the bobbin is located, and ZB perpendicular to the plane xOy, as shown in XB and YB on the Rogowski coil is same as that of an adjacent conductor, so only the impact of ZB needs to be considered.There are various interfering magnetic fields around the sensor, which can be decomposed into zB is parallel to the cross-section where the Rogowski coil was located, thus, no induced electromotive force will be generated in the winding. However, the winding attaches on the coil bobbin spirally, thereby forming a structure similar to an annular solenoid. After the coil winds around the bobbin for one circle, an equivalent large turn is formed as shown in zB is perpendicular to the plane where large turns are located. When zB changes with time, the magnetic flux (ΨZ) will produce an induced electromotive force (ze) in the Rogowski coil. Although the large turn has only one turn, its area is often significantly larger than the sectional area of windings. As a result, it will cause a significant error in the measured result. In practice, a return line [zB as shown in xOy. If the radius of the circle formed by the return line is reR and the induced electromotive force generated by zB on the return line is ree, the final output induced electromotive force ze’ is equal to the sum of ree and ze. If reR is determined equal to the equivalent radius of the large turn (eqR), ree is equal to ze in numerical value but has an opposite direction. This opposite direction can be attributed to the opposite current in the return line to that of the equivalent large turn. Therefore, the effect of the vertical magnetic field on Rogowski coil performance can be effectively eliminated by adding return lines.urn line  shall beFor the fault current sensors installed on the overhead transmission line, the work temperature ranges from −40 °C to 85 °C, so the shape of the bobbin could change with the temperature and cause measurement errors. Meanwhile, the overhead transmission line requires the sensor to be installed without power interruption, so an open sensor structure is indispensable, so the main task to develop a transient fault current sensor based on the PCB Rogowski coil is how to eliminate the errors caused by temperature and interference. The coefficient of thermal expansion was considered when selecting the materials for the Rogowski coil bobbin. The coefficients of thermal expansion of some common materials are listed in Rubber and polyethylene have significantly high thermal expansion coefficients, which will cause large temperature errors in environments where the working temperature changes greatly. Although ceramics have a significantly low coefficient of thermal expansion, they break easily and have a low rate of finished products. Long-term vibrations on overhead transmission lines and sudden electrodynamic forces on short circuits also occur; these phenomena can destroy the ceramic bobbins easily. As a result, epoxy resin was selected as the base material of our PCB.As stated before, if the Rogowski coil only has a signal line, the equivalent large turn is vulnerable to vertical magnetic field interference. A return line is indispensable to eliminate this interference. However, the sensor should be an open structure, so how to design the return line on an open structure sensor is a significant problem for fault current detection. Differential winding, which can achieve the same effect as return lines  is choseNR, MR, CR, BR and AR. One end of the semi-ring comprised the starting via X and end via Y of the turn. External Rows N and M had 58 vias, which were uniformly distributed at an equivalent radian interval of π/58. Internal Rows C, B, and A had 39 vias, which were uniformly distributed at an equivalent radian interval of π/39. The copper foil connections of turns in this design are shown in Two rows of vias were set on one side of the external ring of the semicircular printed board, and three rows of vias were set on one side of the internal ring. They were marked from external to internal as Rows N, M, C, B and A. The distances between these rows of vias and the circle center were recorded as reR and R strictly equal. However, the signal phase were completely opposite. The induced electromotive forces produced by the vertical disturbing magnetic field in the equivalent large turn of clockwise winding and the equivalent large turn of counterclockwise winding had the same absolute value but opposite direction. They offset each other, thus, the disturbance of the vertical disturbing magnetic field was eliminated.In Three rows of internal vias of the PCB coil were successively connected to two rows of external vias to form six specifications of small turns .i.e., the turn formed by NB. The total mutual inductance [iM is the mutual inductance of the ith type of small turn; iN is the number of turns of the ith type of small turn; OiR and IiR are the outer radius and inner radius of the ith type of small turn, respectively. The structure of multiple rows of vias realized counterclockwise winding of coil and made effective use of the space on the PCB. The self-inductance can be expressed as follows:The connection between Y and Y′ was identified as turn type 5, ductance  of the cThe main frequency component of the fault current ranges from 50 Hz to several hundred kHz, so the work frequency band of the sensor should cover most of the frequency band. A T-integrating circuit was designed for the PCB Rogowski coil as shown in The transfer function of Rogowski coil can be obtained from fR, sR, R, C and C0 are selected, the corresponding performance can be obtained. If the work frequency bandwidth of the sensor was 30 Hz to 1 MHz, the parameters of the integrator are listed in If the appropriate parameters of In order to verify the performance of the proposed sensor, a serial test, including the frequency response, linearity, anti-interference performance as well as a comparison with commercial sensors were conducted. An experimental platform was established to test the high-frequency transient current measurement accuracy of the proposed PCB Rogowski coil a. The imThe output line of the impulse current generator was extended to simulate the effect of adjacent wires on the PCB Rogowski coil. The plane where the PCB Rogowski coil was located was perpendicular to the wire a. The amIn The impulse current generator was used to produce an 8/20 interference current waveform and to test the effect of vertical wire on the PCB Rogowski coil. A wire that transmitted the interference current was wound on a cylinder to form a solenoid. This solenoid would generate a uniform magnetic field perpendicular to the horizontal plane, as shown in According to In order to test the frequency response of the proposed sensor, 50 Hz, 8/20 μs and 30/80 μs currents were selected as the current source, so the frequency could cover 50 Hz to 43.75 kHz. The parameters of the current source were listed in In order to compare the performance among the newly designed sensor and commercial ones, two commercial sensors, a Pearson 4997 and a FCT 200 were selected. Their main specifications are shown in After setting the input current range from 1000 A to 5000 A, and recording the output of different sensors, The newly designed sensor has been implemented in an overhead transmission line online monitoring device named distribution fault locator as shown in An open structure PCB Rogowski coil has been presented. The mechanism of interference around the overhead transmission line and the guidelines of anti-interference were established. The proposed PCB Rogowski coil according to the guidelines was designed and tested. For transient fault current detection, the proposed PCB Rogowski coil has almost the same excellent performance in work frequency bandwidth and linearity as commercial ones. Furthermore, the strictly symmetrical structure of the PCB Rogowski coil could eliminate almost all the magnetic field interference from the radial direction, so it is immune from the interference of adjacent conductors, while, the differential winding of the PCB Rogowski coil could offset the axial magnetic field interference, and as a result, the proposed sensor has perfect anti-interference performance, which was the key to measuring transient fault currents. Through the tests, the proposed PCB Rogowski coil was proved to be more machinable, repeatable, and of commercial value.


In May 2012, the health authority (RHA) of the Central Denmark Region decided to transfer inpatient rehabilitation for stroke patients to community-based rehabilitation, as part of a major reform of regional stroke care. It was implemented top-down in three months. Patients were promised a more integrated care pathway with early discharge stroke teams bridging the two sectors. In this decentralized system, hospital care is the responsibility of the Regional health authority, and rehabilitation at home that of municipalities. The implementation of this reform therefore poses some challenges. The HA’s reform required written cooperation agreements to be made by hospitals and municipalities. The implementation of these agreements and their aim of establishing a coordinated rehabilitation pathway was analysed, with a focus on (inter) organisational aspects, as the change was similar throughout the region.A multiple case study was designed, which compared cases of municipalities (n=7), and cases of stroke teams (n=5). Municipalities were selected based on two criteria from Greenhalgh’s conceptual model for determinants of implementation of innovations: size  and the existence of a boundary spanner in the implementation process. We hypothesized that these factors influence the implementation of the cooperation agreements. Data was gathered by means of semi-structured interviews (n=12), and document analysis.Both sectors accepted the change, as they believe the reform will benefit the patient. The study does not show any influence of size of the municipality, and having a boundary spanner in the process, on the implementation of coordination of stroke rehabilitation care between stroke teams and municipalities. Stroke teams experience opposition of municipalities to their existence, as municipalities state they do not need the Region’s stroke teams, who they are obliged to reimburse for their services. Stroke teams feel they coordinate care with municipalities. Municipalities do not experience this. Care is therefore not optimally coordinated around the patient. Ease-of-access to contact-persons is the main challenge for coordination at practice level.The cooperation agreements might have been implemented at the administrative level, but at the practice level the coordination of care is lacking. Facilitating regular face-to-face contact between both sectors seems crucial for further implementation at practice level. In the future, financial consequences need to be communicated before implementation, and top-down implementation needs to be complemented with local implementation plans at lower levels of health care, with a clearer role for the boundary spanners.


Therefore, we performed a meta-analysis of the literature of all the proteomic investigations of neuronal alterations due to MPP+ treatment and compared it with our results obtained with a mitochondrial proteomic analysis of SH-SY5Y cells treated with MPP+. By using open-source bioinformatics tools, we identified the biochemical pathways and the molecular functions mostly affected by MPP+, i.e., ATP production, the mitochondrial unfolded stress response, apoptosis, autophagy, and, most importantly, the synapse funcionality. Eventually, we generated protein networks, based on physical or functional interactions, to highlight the relationships among the molecular actors involved. In particular, we identified the mitochondrial protein HSP60 as the central hub in the protein-protein interaction network. As a whole, this analysis clarified the cellular responses to MPP+, the specific mitochondrial proteome alterations induced and how this toxic model can recapitulate some pathogenetic events of PD.Parkinson's disease (PD) is a complex neurodegenerative disease whose etiology has not been completely characterized. Many cellular processes have been proposed to play a role in the neuronal damage and loss: defects in the proteosomal activity, altered protein processing, increased reactive oxygen species burden. Among them, the involvement of a decreased activity and an altered disposal of mitochondria is becoming more and more evident. The mitochondrial toxin 1-methyl-4-phenylpyridinium (MPP It is pathologically characterized by the loss of dopaminergic neurons in the midbrain +), which primarily targets nigrostriatal dopaminergic neurons because it is transported into cells via the dopamine transporter (DAT)  was originally observed to reproduce Parkinson's like symptoms in heroin addicts ingesting a synthetic narcotic containing the toxic substance  and, at the same time, the most severe and stable form of SNpc damage Tieu, . MonkeysCurrent research on the molecular bases of PD is exploiting high-throughput screening techniques to detect altered neuronal gene expression. However, changes in mRNA levels do not always correlate with similar changes in protein levels or activity  compared to control cells. Proteomics data were obtained both by 2-DE and shotgun LC-MS/MS , Reactome (http://www.reactome.org/), and Webgestalt online tools  against Kyoto Encyclopedia of Genes and Genomes (KEGG), WikiPathway and Pathway Commons databases.The over-representation analysis (ORA) of protein lists (EXP and META) was carried out using the GO Consortium , refers to the probability to get a model of the same quality for a random gene list of the same size  and physical protein-protein interactions (database: IntAct) Antonov, . Accordip < 0.01, were further considered to interpret and discuss proteomics results. The enriched networks were exported as a. xgmml file and visualized and modified by Cytoscape 3.1.0 (http://www.cytoscape.org/)  and D2 , http://apps.cytoscape.org/apps/jepetto), which interacts simultaneously with three servers, i.e., EnrichNet, PathExpand, and TopoGSA. Significantly over-represented GO terms were selected if their Xd score was greater than that calculated as the intercept of the linear regression of Xd scores vs. the Fisher's exact test p-value after Benjamini-Hochberg correction   and network topology analysis of the enriched networks, we used the Jepetto Cytoscape application  was employed to fragment the META list, PPI D2 model  and those found in the literature (META list) were analyzed in terms of ORA by the GO enrichment tool of the GO consortium, by Webgestalt and by Reactome. Webgestalt was used to find over-represented pathways in KEGG, WikiPathways and Pathway Commons databases. All the results coming from the different analyses are detailed in Supplementary Tables In order to understand which cellular pathways are mainly altered by MPP+ directly acts on the complex I of the ETC  and protein folding, inducing a chaperone-mediated response. Both analyses pointed out the involvement of the ubiquitin proteasomal system and the role of parkin in responding to mitochondrial stress. Additionally, the analysis of the META list highlighted the influence of the treatment on the functionality of the synapse, both at the structural and at the molecular level. Eventually, several signaling pathways probably involved in the cellular response to the toxin were shown to be over-represented.The “ATP production” pathway served as an internal control, as MPP+, we performed a network-based analysis of the two lists, using as reference knowledge a global gene network constructed by combining signaling and metabolic pathways from Reactome and KEGG (Rspider). Both D1 model, where all nodes belong to the input list, and D2 model, the enriched network where the insertion of one node between two existing nodes is allowed, generated by the EXP list were superimposable and included only proteins belonging to the ETC .+ suggests that the unfolded protein response, which is clearly activated, is probably guided by HSP60.Figure Figure The list of proteins coming from the D2 network of the EXP list and the D1 network of the META list were analyzed by Jepetto, a Cytoscape app querying the EnrichNet and the PathExpand servers. Results for the analysis of the EXP list, PPI D2 model are summarized in Table On the other side, the analysis of the PPI D1 model of the META list (non-enriched model) allowed us to obtain a statistical representation of information present in the network. Results are shown in Table Since the PPI D2 model for the META list was very complex, we firstly segmented it into subnetworks using the GLay community detection algorithms. Secondly, we performed ORA on the proteins of each cluster, using the same tools as in the previous analyses , in order to recognize the univocal biological meaning of each topological cluster.Figure + on ATP production by mitochondria; the alteration of VDACs and the consequent dysregulation of the transport to and from the mitochondria; the activation of the mitochondrial unfolded stress response (mt-UPR), together with the pivotal role of HSP60 in directing it; the alteration of the synapse functionality, in particular of the SNARE complex and proteins involved in the endocytosis; the possible signaling cascades activated by the toxin, e.g., EGF, MAPK; the triggering of apoptosis and the autophagy induction.In summary, our analysis revealed the direct effect of MPP+ model has been widely used to reproduce a Parkinson-like syndrome in animal models as well as mitochondrial impairment in cellular models. Notwithstanding this, very few is known about which specific pathways are altered by the toxin and how MPP+ treatment is able to recapitulate PD pathogenesis at the cellular level. The MPTP model is particularly suitable to study PD neurodegeneration for two main reasons: it specifically affects dopaminergic neurons since its active metabolite, MPP+, enters via the DAT transporter. Secondly, it acts as a complex I inhibitor and several lines of evidence have demonstrated an involvement of the complex I dysfunction in PD pathogenesis. Complex I deficiency and oxidative damage have been frequently observed in affected neurons and in peripheral tissues of PD patients . Adding nodes to the network may reveal these proteins and the ORA of the enriched networks may highlight previously hidden molecular pathways. As networks generated with Rspider are concerned, the only significant network generated with the EXP list was a four-nodes graph, which stressed the alteration of the ATP production at the mitochondrial level and transport of ATP in the cytosol through carriers (SLC family members). By taking into account several reports on proteomics investigations on cellular and animal models, though, it was possible to obtain two extended networks. The D1 model was particularly interesting because, by simply merging the literature data without adding any node, it evidenced the action of the mitochondrial toxin on the synapse functionality. Two clusters were enriched in proteins involved in the synaptic transmission: members of the SNARE complex and associated regulatory proteins or adaptor proteins for the vesicles endocytosis, responsible for the neurotransmitter re-uptake and release. The D2 model further enlarged the view, integrating those proteins with others involved in the signaling pathways, in the triggering of apoptosis and in ATP production. The proteins of the EXP list were evidenced by a hexagonal shape to show how our experimental findings contributed to the state-of-the-art. Apoptosis induction was frequently observed after MPP+ treatment by our mitochondrial proteomics analysis. Accumulating unfolded proteins, unassisted by chaperone HSP60 in stressed mitochondria, are committed to be digested by proteases . In PPI networks hubs may represent higher-order communication points between protein complexes  appeared to be over-represented. This observation is in agreement with previous reports on the autophagy induction by MPP+. However, it is possible that several proteins playing an important role in some of these processes were not detected by any of the considered papers. The reasons might be various. For example, all the proteomics studies included in the present work dealt with quantitative changes, whereas the contribution of a protein can be determined by other factors, e.g., post-translational modifications or cellular localization. To this purpose, we generated the D2 model of the META list in order to further enrich the scenario. Nevertheless, it was necessary to use a different strategy to analyze this extended network. Using a network clustering algorithm, we were able to obtain subnetworks that may represent highly interconnected proteins in protein complexes or functional modules (Stelzl et al., +, e.g., the Wnt signaling pathway, already described to be involved in the MPTP-induced loss and repair of nigrostriatal dopaminergic neurons (L'Episcopo et al., The simultaneous consideration of several proteome alterations by the meta-analysis allowed us to define which pathways are mainly influenced by MPP+ alters the mitochondrial proteome, overcoming the issues of every single methodology or model. In some cases, the information obtained putting all the results together is more than simply their sum. For example, the dysregulation at the synapse level may be one of the leading event in describing MPP+ toxicity. As a whole, this analysis clarified the cellular pathways altered by MPP+ and how this toxic model can recapitulate some pathogenetic events of PD. This may serve at one side to guide future research on PD using these models and on the other side to envisage new neuroprotective agents to revert the degenerative process.The bioinformatics analysis presented here may have some shortcomings. First of all, we decided to include only papers based on unbiased proteomics experiments, thus ignoring information coming from other kinds of methodological approaches. This effort was done to complete the interpretation of data of each single paper and to suggest a possible workflow to interpret the long list of proteins normally resulting from proteomics studies. Nevertheless, the neglected information was implicitly taken into account when missing proteins were added by the enrichment procedures. Another shortcoming of PPI graphs is their static nature. On the contrary, interacting proteins are highly dynamic structures, changing in time and space. Eventually, all the false positive or negative data present in the databases are mirrored in the networks built on those information. Being aware of the limits of this approach, we succeeded to describe how the mitochondrial toxin MPPTiziana Alberio and Mauro Fasano conceived and designed the studies. Chiara Monti and Tiziana Alberio performed the analyses. Tiziana Alberio and Chiara Monti wrote the manuscript. Andrea Urbani and Heather Bondi critically revised the data. All authors contributed to the editing and preparation of the manuscript. All authors approved the final version of 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.


Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using  With the increasing availability of huge databases for financial systems, financial study becomes a hot research topic. Scientists attempted to understand the statistical mechanics of financial systems, e.g., analyzing long-term trend and fluctuation of stock indices –3, modelExisting methods for stock price prediction generally fall into three main paradigms. The first kind of methods predicts stock price by exploiting autocorrelation of price dynamics –18. Sun Stock price is actually the outcome of a game among investors. For a particular stock, each investor has his/her own estimation to the fundamental value of the stock. Investors submit ask price or bid price to stock exchange, with these prices reflecting their estimation to the value of stock. Next, stock exchange executes ask orders and bid orders according to predefined rules, and stock price is a result of the execution of orders. In this process, investors’ estimation to the value of stock is the determinant of stock price. The estimated value of a stock is a full reflection of investors’ judgment according to all relevant information they could get about the stock. To buy or to sell a certain stock reflects a trader’s expectation of the movement trend of price. The decision of an individual investor may be based on incomplete information, but the collective behavior of all investors could remedy the lack of information and finally determines the price of a stock. Therefore, stock price dynamics reflects investors’ estimation to the trend of the value of stock. Investors’ confidence to their estimation offers us a natural predictive indicator for stock price.In this paper, we study the problem of stock price prediction from the perspective of market confidence. Different from existing methods that attempt to capture market confidence by their behavior in contexts that are exogenous to stock market, we propose to extract market confidence directly from stock transaction records. Our study is conducted on transaction data of 50 stocks in two Chinese Stock Exchanges. The data contains the identifier of traders in each transaction, allowing us to identify how active each trader is. To characterize market confidence, we check the fraction of traders who also participate trades in the immediately previous trading day. Using Granger causality test, we find that stock price is strongly correlated with an index of market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. By combining the market confidence index together with time series of stock price, we propose a stock price prediction model based on feed forward neural network. Results demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. Furthermore, we investigate four types of trading patterns for traders that participate trades in two successive trading days: buy-buy, buy-sell, sell-buy, and sell-sell. We find a negative correlation between trading pattern buy-buy and price return, and sell-sell is the most relevant pattern with stock price. For manipulated stock, buy-sell is the most relevant trading pattern to stock price.t, we consider three quantities, i.e., the number of sellers n (1 ≤ n ≤ 7) days are useful at predicting stock price in the current day.Price is determined by supply and demand. In stock market, supply and demand is reflected by the trading activity of investors, i.e., sell and buy. Stock price is correlated with the number of sellers and the number of buyers . Here wep-value < 0.01 between the time series of stock price and the time series of the number of buyers, while significant Granger causality is observed in 39 stocks when the number of sellers is considered. Second, Granger causality between the change of stock price and the supply and demand of stock is generally less significant for manipulated stocks than non-manipulated stocks. Finally, we can see that the significance of Granger causality varies remarkably from stock to stock, indicating that the supply and demand of stock is not a robust indicator for stock price prediction.t with t − 1 and trading day t. We define a market confidence index asThe unreliability of supply and demand at predicting stock price motivates us to find alternative indicators for stock price prediction from investors’ trading behavior. From this perspective, our data possesses a unique advantage, i.e., it contains trader identifier in each executed transaction. With trader identifier, we could investigate the trading behavior of the same trader across trading days. Based on cross-day trading behavior, we propose an index to characterize market confidence of investors. Specifically, for a given trading day t, we extract the ratio of sellers pt  with the market confidence index included besides the above two inputs. These two groups of inputs are formally written asWe now validate whether the proposed market confidence index is effective at predicting the change of stock price. Given a stock, for each trading day  Δpt See , resultin = 3. When incorporating market confidence index, the prediction accuracy significantly outperforms the method that only uses the ratio of sellers and historical stock price, increasing from 60% to 76.9%. Meanwhile, the MAPE is less than 0.12, with the error being smaller than the baseline in most stocks. However, when only using the market confidence index for stock price prediction, the prediction performance is not remarkable. To offer some intuition about the prediction performance, we use one example to show the predicted change of stock price and the real change of stock price  into four categories according to their trading patterns in two successive trading days, being B-B, B-S, S-B, and S-S respectively. The first letter denotes sell or buy in the first day and the second letter denotes sell or buy in the second day. We denote the number of traders in each category as NB−B and the change of stock price for most stocks. Compared with non-manipulated stocks, manipulated stocks behave differently. We find that the correlation between NB−B and the change of stock price is not significant. This has two implications. First, price determines traders’ behavior for non-manipulated stocks. If some people buy in the first day and still buy in the second day, the stock price falls. For manipulated stocks, this phenomenon diminishes. Second, compared to other trading pattern, S − S is the most relevant with the change of stock price. In contrast, the pattern B − S is positively correlated with stock price for manipulated stocks. This means that there are more short-term investment in manipulated stocks. This phenomenon is attributed to some malpractices involving a group of traders trading with large and frequent trades. Manipulators trade frequently to artificially increase the price and volume of a stock for the purpose of attracting other investors to buy the stock.Active traders provide critical indictors for understanding trading behavior. As an illustration, we now show that the distribution of active traders over four categories could differentiate manipulated stocks from non-manipulated stocks. We investigated the dynamic behavior of traders in stock markets. Our study is based on transaction data, i.e., the executed transaction generated by electronic trading system in stock exchange. This kind of data provides us an effective way to grasp the trading relationship among investors and provides us a potential way to learn the trading behavior of investors. Based on transaction data, we consider the supply-demand relationship for each stock. We study whether trading behavior could predict the change of stock price.Using transaction record of stocks with identifier of traders, we introduce an index to characterize investors’ confidence to the estimated value of stock. Strong Granger causality is found between stock price and the index of market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. We further deployed a feed forward neural network to predict stock price, with the input being historical stock price, trading activity, and market confidence. Results showed that the inclusion of market confidence could significantly improve the prediction accuracy of stock price. Then we refine the active traders’ behavior into four trading pattern: buy-buy, buy-sell, sell-buy, and sell-sell. We find a negative correlation between trading pattern buy-buy and the change of stock price, and sell-sell is the most relevant pattern to the change of stock price. For the manipulated stock, buy-sell is most relevant to the change of stock price. This phenomenon means manipulators affect stock price by frequent short-term trade shares.The data used in this paper are transaction data of stocks listed on Shanghai Stock Exchange and Shenzhen Stock Exchange in 2004. This data is also used in our previous studies . TransacX exhibits a statistically significant correlation with a signal Y, then the past values of X should contain information that helps predict Y better than only leveraging the information contained in past values of Y.Following the method used in our previous works , we use t is defined aspt is the opening price at day t. Trading activity is characterized by the ratio of sellers in the traders at each trading day. For each trading day, market confidence is characterized by the fraction of traders who sell or buy stocks in previous trading day. The null-hypothesis for our Granger causality test is that trading activity or market confidence Granger-causes the change of stock price. With X denoting the time series of trading activity or market confidence, the test is conducted by comparing the following two regression modelsα, β, γ are model parameters, and ξ denotes prediction error. In the first linear regression model, n lagged values of the change Δp of stock price are used to predict its future value. In the second linear regression model, both the lagged values of Δp and X are employed for prediction. The Granger causality analysis is conducted on all the 50 stocks in our data. Before Granger causality test, we use Augmented Dickey-Fuller (ADF) test to test our time series data, and find that the non-stationarity hypothesis is rejected at the significant level of 0.01 for all the stocks.In this paper, we use Granger causality test to judge whether trading activity and market confidence are useful at forecasting the change of stock price. The change of stock price at day Granger causality test could provide some insight about which trading activity is potential at predicting stock price. However, Granger causality test is based on linear regression model and thus cannot uncover the relevant factors which are non-linearly predictive for stock price. To address this problem, we develop a three-layered feed forward neural network model which is non-linear model and could fully exploit the potential prediction power of its input.To train the neural network, we divide all the data into two equal-sized parts: the training set and the test set. For the stocks in training set, the future stock price is used to train the neural network. For the stocks in test set, only the past time series of stock price, trading activity, and market confidence are known. To assess the role of trading activity and market confidence, we compare the performance of neural networks with two different sets of inputs: (1) the time series of stock price and the time series of trading activity; (2) the time series of stock price, the time series of trading activity, and the time series of market confidence. The output of neural networks is the change of stock price.At the real change of stock price and Ft the predicted change of stock price, MAPE is defined as:The effectiveness of prediction method is measured in terms of the Mean Absolute Percentage Error (MAPE) and the accuracy at predicting the rise or fall of stock price. MAPE is a measure to evaluate the accuracy of the predicted time series relative to the real time series. Denoting with


P<0.0001), more frequent decontamination of transmission points and disinfection of healthcare workers (P<0.0001) and better compliance of healthcare workers with hygiene practices (P<0.0001). More frequent decontamination of heavily trafficked transmission points was especially effective at reducing transmission of the model pathogen.Veterinary nosocomial infections caused by antibiotic resistant bacteria cause increased morbidity, higher cost and length of treatment and increased zoonotic risk because of the difficulty in treating them. In this study, an individual-based model was developed to investigate the effects of movements of canine patients among ten areas (transmission points) within a veterinary teaching hospital, and the effects of these movements on transmission of antibiotic susceptible and resistant pathogens. The model simulates contamination of transmission points, healthcare workers, and patients as well as the effects of decontamination of transmission points, disinfection of healthcare workers, and antibiotic treatments of canine patients. The model was parameterized using data obtained from hospital records, information obtained by interviews with hospital staff, and the published literature. The model suggested that transmission resulting from contact with healthcare workers was common, and that certain transmission points  presented higher risk for transmission than others (lobby and surgery). Sensitivity analyses using a range of parameter values demonstrated that the risk of acquisition of colonization by resistant pathogens decreased with shorter patient hospital stays ( Escherichia coli and Klebsiella spp. in particular have been strongly associated with urinary tract infections among human patients E. coli and Klebsiella pneumoniae are commonly diagnosed in veterinary settings and increasing numbers of antibiotic resistance cases in these bacterial species have made effective treatment more difficult Antimicrobial resistance is a growing concern in modern health care settings as it increases morbidity, cost of treatment and mortality E. coliE. coli while those patients who had been hospitalized previously and/or had been treated with fluoroquinolones previously had higher probability of carrying MDR E. coli on arrival to the hospital. Veterinary hospitals may be the major source of resistant and MDR E. coli in horses Risk-based case control studies have shown that hospitalization is a serious risk factor for dogs becoming rectal carriers of multi-drug resistant (MDR) Mathematical epidemic models have been applied to human hospital settings to analyze the risk factors associated with transmission of antibiotic resistant pathogens, to study associated molecular mechanisms and to evaluate control measures A model for veterinary settings has to account for the more frequent movement of the patients that is characteristic of these settings as compared to human hospitals. This movement is due, in part, to patient needs . Also, in veterinary settings there is reduced control over animal contacts with healthcare workers and surfaces due to petting, hand carriage of smaller animals, more proximity to the floor and defecation in cages. There are also important differences in housing and intensive care unit (ICU) arrangements. More canine patients can be accommodated in a much smaller veterinary hospital ICU by stacking their cages on top of each other as compared to the more spacious accommodation usually provided to human patients.Published mathematical models for veterinary settings have been limited to deterministic approaches or regression analyses E. coli, K. pneumoniae, or Enterococcus spp. P patients in the hospital at any time. During their visits, each patient visits a sequence of transmission points , which represent locations within the hospital where colonization can occur . The patients are attended to by H human healthcare workers, each of whom is assigned to a single patient at any time. In the model, patients may be colonized by the pathogen by either contact with a contaminated transmission point or a contaminated health care worker. The model also incorporates the bacterial loads of colonized and infected patients, as well as the effects of antibiotic treatment of the infections. Specific components of the model discussed below include: 1) the temporal resolution and scope of the model, 2) intake of patients, 3) movement and care of patients in the hospital, 4) colonization and contamination, and 5) treatment.We developed a stochastic IBM that tracks colonization of individual patients with resistant and non-resistant strains of a single bacterial pathogen as the individuals move through a veterinary hospital. For this model, we assumed that the pathogen could be carried asymptomatically in the gastrointestinal tract (colonization) and in some patients cause systemic infections such as wound, bloodstream or urinary tract infections, similar to known veterinary and human nosocomial pathogens such as The model tracks colonization of patients by the pathogen over a long time horizon (months to years), with model dynamics resolved across several time scales. In particular, the intake of patients into the hospital and treatment with antibiotics is captured at a daily scale. Further, the day is subdivided into several multi-hour shifts , after which health-care workers are replaced and treatment efforts re-initiated. Finally, colonization of patients and contamination of healthcare workers and transmission points is modeled at a fine resolution . A smaller time step size allows use of an exponential distribution to select values for various duration parameters in the model. We will refer to these different time resolutions in describing different aspects of the model.PD, with patients taken in up to the capacity of the hospital. Each patient taken into the hospital is in one of Q classes , which reflect their treatment needs . Each of these classes would either require hospitalization or not. Specifically, each patient is modeled as being in class q ε 1, … , Q with probability qp, independently of the other patients. The duration that each hospitalized patient in a class q remains in the hospital (or the time of visit of the patient) is modeled as an independent exponential random variable, with mean qv. This duration is specified according to the stochastic model at the time of intake. Each incoming patient may be pre-colonized with resistant, non-resistant, or both strains at small probabilities, independently of the other patients. As soon as the patient is admitted to the hospital a healthcare worker is assigned to that patient.New patients are taken into the hospital on a daily basis. The number of new patients is modeled as a Poisson random variable with a mean Q classes are governed by distinct stochastic-sequence models: for instance, a regular-checkup patient may only transition among the hospital lobby, exam room, and diagnostics facility, while a surgery patient visits a larger number of transmission points . The route sequence can have both complex pre-determined transitions (to account for restricted movement during certain times of day) or simpler randomized transitions. As each patient follows its route, the patient remains at each transmission point for a stochastically-determined time-duration. Specifically, the patient remains in the transmission point for an exponentially-distributed time with average stay duration of tTAV depending on the transmission point t, or until the patient's hospital-visit duration is exceeded. Once the healthcare worker's assignment to the patient is completed, he/she is immediately re-assigned to any unassigned patient with equal probability (or is re-assigned as soon as a patient becomes available); the health-care worker continues to transition among patients in this fashion. The healthcare worker remains with each patient for an exponentially distributed duration with mean duration given by AV, unless the assigned patient leaves the hospital.Each patient is modeled as following a route through the hospital, i.e. transitioning through a sequence of transmission points during its time at the hospital. Specifically, the routes followed by the patients in each of the PI. Similarly, each transmission point and each health care worker may be classified into four contamination categories: uncontaminated, contaminated with the non-resistant strain, contaminated with the resistant strain, or contaminated with both strains. Broadly, patients may become colonized due to contact with either contaminated healthcare workers or contaminated transmission points. Further, colonized patients may contaminate both healthcare workers and transmission points, and there may also be direct cross-contamination between health-care-workers and transmission points.Each patient is in one of four colonization states at each time-step during its visit to the hospital: uncolonized (U), colonized with a non-resistant strain of pathogen (NR), colonized with a resistant strain of pathogen (R) or colonized with both resistant and non-resistant strains of pathogen (NR+R). For each colonized patient, the model also captures the patient's bacterial loads for the non-resistant and resistant strains, and determines infection status. A subset of colonized patients become infected with the colonizing strain; in the model each colonized patient develops an infection with probability PC with two provisos. First, the probability of contamination of the transmission point and/or the healthcare worker per patient visit, with the strain from a colonized patient, is directly proportional to the patients' bacterial load and inversely proportional to the surface area of the transmission point. Healthcare worker and transmission point can also cross-contaminate with a probability of PC. Contaminated healthcare workers are disinfected and contaminated transmission points are decontaminated with a probability DE after time intervals that are exponentially distributed with an average decontamination time AC. Secondly, the bacterial load of the colonized patient increases in absence of antibiotic therapy and is updated at the end of every shift. The simultaneous evolution of resistant and non-resistant bacterial loads is derived from a model previously described as equation 2 by Webb et al.Within this system of movement, a patient can be colonized with an initial arbitrary bacterial load due to a contaminated healthcare worker and/or transmission point per visit with a probability of PI and the probability that an infection is detected at the end of shift is DR (detection rate). Patients detected with an infection are given a primary antibiotic treatment immediately, which initiates a decrease in the load of non-resistant strains every shift. After antibiotic susceptibility information is available, the treatment is suitably modified to reduce any resistant strain load carried by the patient as well. When the bacterial load of colonized patients goes below an arbitrary recovery threshold, they become “uncolonized.”The probability that a colonized patient becomes infected  is We modelled the transmission of antibiotic resistant enteric bacteria among canine patients at the WSU-VTH. Data for parameterization of the model was drawn in part from hospital infection control surveillance activities. This surveillance involved collection of individual rectal swab samples from canine patients in three small animal services  between September, 2009 and April, 2013. Data including antibiotic treatments, which services the animal visited, and the number of days in the hospital were recorded at the time of sample collection. Fecal swabs were plated directly onto MacConkey agar supplemented with ampicillin (16 ug/ml) and nalidixic acid (32 ug/ml) to select for Gram-negative bacteria that were resistant to both of these antibiotics. Any growth was noted and isolated colonies were submitted to the Washington State Animal Disease Diagnostic Laboratory (WADDL) for bacterial species identification. The average number of different categories of patients visiting each day and their duration of stay was calculated using this surveillance data and computerized hospital medical records. Each new patient was classified into one of the three categories: (i) surgery including elective, non-elective and emergency surgery; (ii) non-surgical disease including infectious disease, inflammatory disease, metabolic and other chronic diseases; or (iii) regular check-up involving routine visits for physical exams and vaccinations.Based on the category assigned in the model, the patients will have different average lengths of stay  and follow different routes in the hospital . We consWe assumed that 10% of the daily new arrivals are colonized with a non-resistant strain and an additional 3% are colonized with the resistant strain at the time of entry into the hospital. The number of patients visiting daily averages 24.32. Baseline parameters for average time spent at each transmission point, average number of healthcare workers present in the hospital at any given time, and the maximum hospitalized patient load were based on information from hospital staff. The time required by WADDL to report antibiotic resistance profiles for hospitalized patients was most often 2 days , and this was used as the time before treatment modification in the model. Baseline parameter values are given in Model code was developed using MATLAB vR2012a . Simulations begin with an empty hospital and continue over a period of one year. Five hundred simulations using baseline parameter values discussed above  were runEnterococcus spp or antibiotic resistant coliforms. Five samples were taken from each area at two hour intervals  for 12 sampling sessions over three weeks. Samples were collected using standard 10 inch2 sponges  soaked in 30 ml LB broth . LB broth (30 ml) was added to each sample sponge and samples were enriched by incubating overnight at 37°C. After incubation 1 ml of each enriched sample was spread on mEnterococcus agar  plates and incubated for 48 hrs, at the end of which presence or absence of colonies was recorded. Positive samples on mEnterococcus agar were confirmed to be Enterococcus sp. using the PYR-salt tolerance tests To compare the model results with the actual contamination prevalence inside the hospital, we conducted environmental sampling at four locations  in the hospital to estimate the fraction of time these areas were contaminated with Following simulations with different parameter sets the mean fraction of patients in the hospital that were colonized with a resistant strain and/or a non-resistant strain was evaluated using multivariate linear regression analysis. Pair-wise comparisons of individual parameters were used to determine the trend of increase or decrease in the mean fraction of patients in the hospital found to be colonized with a resistant and/or non-resistant strain due to an increase or decrease in the parameter value, in effect a sensitivity analysis to determine effects of parameters. Unpaired t-tests were used to compare the percent of times each transmission point remained contaminated according to the model and according to the environmental survey. Results from the environmental survey were also subjected to two-way ANOVA to identify associations between the level of contamination and the transmission points sampled. All statistical analyses were done in SAS analytics software We start the simulation for a year with a clean and empty hospital and as inpatients accumulate, the mean population of patients inside the hospital at any time reaches a stable level. At the baseline values for all parameters, the fraction of the hospital patient population colonized with any strain stabilizes at approximately 32%. Approximately 30% of the patients are colonized with the non-resistant strain, 7% are colonized with the resistant strain, and 5% are colonized with both strains .P<0.0001, unpaired t-test) throughout the year , the transmission points are contaminated with both resistant and non-resistant strains for longer durations overall , although there were more positive samples for both types of bacteria in the housing wards, the ICU and the diagnostics room than in the exam rooms  were frequently contaminated with am rooms . The preoliforms .P<0.001) with an R2 = 0.957. The Type III sums of squares indicated that the average length of stay parameter explained the bulk of the variance in the model with probability of colonization given contact, average time to disinfection of healthcare workers and decontamination of transmission points, and number of healthcare workers in the hospital also being significant in descending order of importance. The starting day of effective antibiotic therapy, efficiency of disinfection of healthcare workers and decontamination of transmission points, and infection detection rate were not significant  with an R2 = 0.965. The average length of stay parameter again explained most of the variance in the model with probability of colonization given contact, average time of disinfection of healthcare worker and decontamination of transmission point, and number of healthcare workers in the hospital also significant in descending order of importance. The rate of infection detection was also a significant contributor to the mean fraction of the patient population that was colonized with a non-resistant stain  and a consistent decrease in the fraction of population colonized with the non-resistant strain with increases in the detection rate . There was a significant increase in the mean fraction of the patient population colonized with increasing duration of TP or HCW contamination . Analysis of maximum likelihood parameter estimates for the interaction between increasing the mean time of disinfection/decontamination with a change in transmission point showed that the lobby, exam rooms and diagnostic area have significantly greater increases in proportions of visiting patients that are colonized with resistant and/or non-resistant strains as compared to the housing wards and ICU. Increasing the probability of colonization of a patient given contact  caused a significant increase in the mean fraction of the patient population colonized with . There was a significant increase in the mean fraction of patient population colonized given an increasing number of healthcare workers from 15 to 30, but increasing beyond this level did not alter the outcome (P>0.05)  .Enterococcus spp. the reverse was true  require more frequent disinfection/decontamination.The bacterial species considered in this model are enteric bacterial pathogens like This model provides a significant contribution to the field of hospital modeling because it accounts for individual patient movements through the hospital rather than assuming a strictly compartmental structure of patient movements. The primary purpose of this effort was to generate a conceptual framework for predicting changes in antimicrobial resistant bacterial transmission in response to changes in the chosen parameters. The determination of baseline parameter values was limited because of a lack of empirical data; for example the true probability of initial colonization and infection given a previous colonization is not known. Nonetheless the relative effect of changing a parameter, for example changing the average length of hospital stay, is unlikely to be biased by the choice of a baseline. While intensive sampling for more empirically based parameterization would strengthen the model, such sampling was beyond the scope of the current effort. This will be included in future work to refine and expand on this model. Regardless of specific parameter values, the sensitivity analysis provides information about which variables will have the most impact and therefore where interventions should be targeted.In summary, this model suggests that reducing the average length of stay of patients and more frequent disinfection of healthcare workers and decontamination of transmission points are the most important control measures to minimize nosocomial transmission and frequency of colonization or infection with resistant strains inside the hospital. Extensions of this model, such as considering multiple patient and pathogen species, variable healthcare worker population and using empirical data as a basis for the transmission probability estimates used here may give further insight into the risk factors associated with the spread of antibiotic resistance in veterinary hospitals with availability of extensive hospital data.Table S1Results of least square means comparisons between the average fractions of the population colonized with the resistant strain for different parameter values.(PDF)Click here for additional data file.Table S2Results of least square means comparisons between the average fractions of the population colonized with the non-resistant strain for different parameter values.(PDF)Click here for additional data file.


The prediction of the structures of proteins without detectable sequence similarity to any protein of known structure remains an outstanding scientific challenge. Here we report significant progress in this area. We first describe de novo blind structure predictions of unprecendented accuracy we made for two proteins in large families in the recent CASP11 blind test of protein structure prediction methods by incorporating residue–residue co-evolution information in the Rosetta structure prediction program. We then describe the use of this method to generate structure models for 58 of the 121 large protein families in prokaryotes for which three-dimensional structures are not available. These models, which are posted online for public access, provide structural information for the over 400,000 proteins belonging to the 58 families and suggest hypotheses about mechanism for the subset for which the function is known, and hypotheses about function for the remainder.DOI:http://dx.doi.org/10.7554/eLife.09248.001 Proteins are long chains made up of small building blocks called amino acids. These chains fold up in various ways to form the three-dimensional structures that proteins need to be able work properly. Therefore, to understand how a protein works it is important to determine its structure, but this is very challenging. It is possible to predict the structure of a protein with high accuracy if previous experiments have revealed the structure of a similar protein. However, for almost half of all known families of proteins, there are currently no members whose structures have been solved.The three-dimensional shape of a protein is determined by interactions between various amino acids. During evolution, the structure and activity of proteins often remain the same across species, even if the amino acid sequences have changed. This is because pairs of amino acids that interact with each other tend to ‘co-evolve’; that is, if one amino acid changes, then the second amino acid also changes in order to accommodate it. By identifying these pairs of co-evolving amino acids, it is possible to work out which amino acids are close to each other in the three-dimensional structure of the protein. This information can be used to generate a structural model of a protein using computational methods.Now, Ovchinnikov et al. developed a new method to predict the structures of proteins that combines data on the co-evolution of amino acids, as identified by GREMLIN with the structural prediction software called Rosetta. A community-wide experiment called CASP—which tests different methods of protein prediction—showed that, in two cases, this new method works much better than anything previously used to predict the structures of proteins. Ovchinnikov et al. then used this method to make models for proteins belonging to 58 different protein families with currently unknown structures.These predictions were found to be highly accurate and the protein families each have thousands of members, so Ovchinnikov et al.'s findings are expected to be useful to researchers in a wide variety of research areas. A future challenge is to extend these methods to the many protein families that have hundreds rather than thousands of members.DOI:http://dx.doi.org/10.7554/eLife.09248.002 Despite substantial efforts over decades, high-resolution structure prediction is currently limited to proteins that have homologs of known structure, or small proteins where thorough sampling of the conformational space is possible . For roughly 41% of protein families, there is currently no member with known structure . While hRecent work has shown that residue–residue contacts can be accurately inferred from co-evolution patterns in sequences of related proteins . While eRosetta@Home distributed computing project, and the lowest energy  models were recombined and further optimized using a new iterative version  of the RosettaCM hybridization protocol  blind test of protein structure prediction methods, we predicted the structures of proteins from large families with no representatives of known structure by integrating co-evolution derived contact information from GREMLIN  into theprotocol . The fivWhen several months later the actual structures of these proteins were revealed, the predictions were found to be considerably more accurate than any previous predictions made in the 20 years of CASP experiments for proteins over 100 amino acids that lack homologs of known structure. Two particularly striking examples are shown in Having found that protein structures can accurately be modeled using co-evolution information, we set out to build models for representatives of large protein families in bacteria with no detectable structural homologs. To facilitate evaluation of such models, we developed a length-independent measure of the fit between a set of predicted contacts and a model: the ratio of the total GREMLIN score of the model to the score expected if it were the native structure .. Families with detectable structure homologs were excluded using a sensitive sequence search method , and an additional 22, 5, and 4 families in Bacillus subtilis , Halobacterium salinarum (Euryarchaeota), and Sulfolobus solfataricus (Crenarchaeota), respectively (We chose to focus on families with at least 4× (protein length) sequences to ensure that the predicted contacts have high accuracy (HHsearch ). For coectively . For eacectively  and the ectively . We elimAn alternative approach to structure modeling using predicted contacts is to search for weak fold recognition matches to known protein structures and determine if any of the hits fit the predicted contacts. This approach is not very effective for the families identified as described above; for only 4 of the 122 families with HHsearch E-values greater than 1E-04 did one of the top ten hits from HHsearch or SPARKS-X match the predicted contacts .Many of the families we identified with no homologs of known structure are transmembrane (TM) proteins. To evaluate the accuracy of our co-evolution-based structure prediction method on TM proteins, we tested it on a benchmark of 13 TM proteins with recently determined structures. Rather than evaluating the lowest energy five models as in the case of the CASP experiment, we instead selected the most central  low-energy model and eliminated positions not converged within the lowest energy models or not constrained by contact information . As shown inWe built models for representatives of the 121 families with unknown structures using the Rosetta co-evolution-guided structure prediction protocol, eliminating from the lowest energy structures the non-converged and non-constrained residues. The calculations converged for 58 of the 121 proteins . Four taBased on the benchmark, we expect that our monomeric protein models should be within 4.0 Å RMSD of the actual structure. Provided there are not large conformational changes upon docking, protein–protein complexes can be accurately assembled from crystal structures or comparative models of the constituent monomers using GREMLIN contact predictions . Thus, thttp://gremlin.bakerlab.org/structures/). The biological implications of all of these structures cannot be explored in a single paper; here, we describe functional insights obtained from a subset of the models. These insights derive in part from the distribution of evolutionarily conserved residues in the models, as conserved sequence motifs tend to mark functional sites in structures  synthase when oxygen is limited. The enzyme has two integral membrane subunits (CydA and CydB) with three hemes  that mediate transfer of electrons from quinol to oxygen. Using the co-evolution-guided structure prediction protocol described above, we generated models for the structures of CydA and CydB, and then docked the subunits together using inter-protein predicted contacts as described in E. coli under anaerobic conditions to convert L-tartrate (carbon source) to oxaloacetate. The enzyme is a hetero-tetramer, with two copies of TtdA and two copies of TtdB (L-tartrate dehydratase is used by  of TtdB . TtdA is of TtdB . The Ttd of TtdB . In our  of TtdB . This poE. coli coupled to proton symport (SatP (Succinate-acetate/proton symporter) mediates the uptake of succinate and acetate in  symport . Our pre symport  is very  symport . The ure symport . Residue symport  line the symport . Most of symport . The simE. coli O-antigen ligase RfaL, and our predicted structure for RfaL is similar in structure  is an inner membrane protein that transfers the diacylglyceryl moiety from phosphatidylglycerol to an N-terminal cysteine residue that follows the signal peptide of prolipoproteins. Our predicted structure of Lgt has a novel seven trans-membrane helix (TMH) fold, with many of the conserved residues  clustering near the proposed periplasmic surface to form a putative active site (ive site . The topive site .10.7554/UppP (undecaprenyl pyrophosphate phosphatase), an integral membrane protein with unknown structure, catalyzes the dephosphorylation of undecaprenyl pyrophosphate to form undecaprenyl phosphate, an essential carrier lipid for bacterial peptidoglycan cell wall synthesis . In our B. subtilis is an intramembrane protease that cleaves site-1 anti-σ factor RsiW, a crucial step in the resistance to antimicrobial peptides . Remarkably, although our structural model of YeiH  superimpE. coli chloramphenical resistance protein RarD is an apparent duplication of the homodimeric E. coli EmrE drug transporter, and our predicted structure of RarD indeed adopts an internal pseudo-symmetric fold. Our RarD model is structurally superimposable on the EmrE homodimer, but the first helices of the domains corresponding to the EmrE monomer are swapped . The YqfA sequence belongs to a large family of integral membrane proteins, with members in all three kingdoms of life. The eukaryotic members are seven-TM pass receptors for ligands such as prostaglandin and adipoQ (the progesterone-adiponectin receptor (PAQR) was predicted to be bacteriorhodopsin-like by Our models of Our model of YfiP, from a family of unknown function, contains a non-trivial trefoil knot topology characteristic of the alpha/beta knot methyltransferase (SPOUT) superfamily . SPOUT sOur model of YitE, from a protein family of unknown function, has an arrangement of secondary structures nearly identical to the aquaporin water channel fold , includiE. coli YgdD protein belongs to a family of unknown function (Pfam: DUF423) with members widely distributed in both bacteria and eukaryotes, including proteins from plants, fungi, and metazoans. The predicted contacts of YgdD are best accommodated in a homotrimer model , with the initial goal ‘to make the three-dimensional, atomic-level structures of most proteins easily obtainable from knowledge of their corresponding DNA sequences ’. It is E. coli (AUP000000625), B. subtilis (AUP000001570), H. salinarum (AUP000000554), S. solfataricus (AUP000001974) reference genomes in the UniProt proteome database /(length of representative protein) were greater than four were selected for modeling as described below. If the GREMLIN-predicted contacts (see below) were sparse and primarily between residues close along the linear sequence, the alignment was regenerated at an e-value 1E-40 cutoff, and the GREMLIN calculation repeated. If this resulted in too few sequences, the family was discarded (this eliminated six families).Protein-coding genes were extracted from the database . Each prdatabase  from Jandatabase , a measuTM protein domains that had a hit  in 2015, but no hit  in 2012 were selected for the TM benchmark. Alignments were created using the UniProt sequence associated with the PDB, and trimmed at the N and C termini to match the crystal structure. We also include aquaporin (PDB: 1SOR_A) to test our protocol in modeling reentrant helices.. The 3392 pdb chains with more than 10 sequences per length were subsampled to create MSAs with varying number of sequences, which were used to estimate probability of contact (GREMLIN (v2.01) was used to learn a global statistical model of the sequences in large families using pseudolikelihood optimization . We prev contact . CCMPRED contact , was use contact . The top contact , and theTo evaluate the significance of a match between predicted contacts and a model, we determined the expected total GREMLIN score over all contacts with sequence separation of 6 or greater using P(contact). To evaluate the fit of a particular model to a predicted contact set, we take the ratio of the actual total GREMLIN score of the model to the expected total score computed as above; we refer to this ratio of observed and expected contact scores as ‘Rc’ throughout the text. As shown in Residue-pair-specific distance restraints for use in the Rosetta structure prediction calculations were generated based on the normalized GREMLIN scores. Distance restraints were implemented as sigmoidal functions of the form: restraint(d) = weight(1 + exp(−slope(d − cutoff)) + intercept), where d is the distance between the constrained Cβ atoms (Cα in the case of glycine), the distance cutoffs and slopes are amino acid pair specific , Proteins over 400 amino acids for which there was little convergence of the lowest energy generated models were parsed into multiple domains (<200 residues) guided by the predicted contact information keeping overlaps of at least 50 residues between each domain, and Rosetta ab initio was used to generate models for each domain separately. If the overlapping regions in each domain converged during modeling, these were used to assemble the full model, otherwise the domains were trimmed to converged residues and docked using RosettaDock .If models converged overall in the Rosetta ab initio calculations but specific sets of contact restraints were consistently violated, we explored the possibility that the violations correspond to interactions between monomers in a homo-oligomer. To test for oligomeric contacts, docking was performed between two copies of the model using RosettaDock guided by the co-evolution-derived constraints.2 were trimmed. We also eliminated residues in regions in which there were either very few contact restraints, or the majority of the restraints were violated.We developed a simple measure of convergence and contact violation after the hybridization protocol to trim regions with higher chance of being in error. The top five percent of the models were selected based on the sum of the Rosetta all atom energy and the contact restraint score and superimposed using THESEUS v3.1 . The meaFor the benchmark set, the model closest to the average of the lowest energy 5% models was selected, and the RMSD to the native structure was computed over (1) the full length of the protein, (2) the converged and constrained residues, and (3) the residues structurally aligned using TM-align. The latter alignments are longer and more accurate, but selection of the subset of residues requires knowledge of the native structure; this is not the case for (2).Our models provide starting templates to model any member of these families using comparative modeling, which requires relatively little computer time. To evaluate the protein space our 58 models cover, we carried out Jackhmmer search  with unTo compare our method to the EvFold method, we submitted the alignments in our membrane protein benchmark to the EvFold web server. We compared the accuracy of the best of the 50 models generated by the server to our single selected model for each family in http://gremlin.bakerlab.org/structures/ the external database provides the models generated and links to the GREMLIN web server output. The web server output provides the predicted contacts, overlay of the predicted contacts on the top 10 HHsearch pdb hits, restraints used in modeling and the alignment used for contact prediction. We also provide contact predictions for all protein coding genes with enough sequences for all of the four model organisms used in this paper. The models are also available at Dryad (at Dryad . review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and 3 reviewers, one of whom is a member of our Board of Reviewing Editors.Thank you for submitting your work entitled “Large scale determination of previously unsolved protein structures using evolutionary information” for peer review at The following individuals responsible for the peer review of your submission have agreed to reveal their identity: Yibing Shan (Reviewing editor) and Nir Ben-Tal (peer reviewer).The reviewers have discussed the reviews with one another and the Reviewing editor has drafted this decision to help you prepare a revised submission.The manuscript by Ovchinnikov et al. represents a bold attempt in predicting structures of proteins without structural templates. The predictions rely on a physical model  guided by residue–residue contacts derived from a large number of protein sequences. The latter has been an important source of progress in the field of protein structure prediction in recent years. Using evolutionary data in conjunction with a well-calibrated physical model is a more recent development. This manuscript reports atomic-details structural models of 58 proteins, each represents a large prokaryotic protein family of no 3D structures available. While the accuracy of these models will be examined against the crystal structures when they are available, there are reasons to feel optimistic given the impressive performance of the program in the CASP11 blind test. Importantly, these models are available online and some interesting lessons will be learned regardless of the outcome.In spite of years of structure determination efforts using X-ray crystallography and other experimental techniques, including the various structural genomics projects, the structure of many important proteins has yet to be determined. To some extent, structure prediction can supplement this. Structures of query proteins can be predicted if suitable templates can be detected. However, this approach is limited by our current ability to assign templates to the queries. This situation is changing now because of two major breakthroughs. First, protein sequence data are becoming available at ever increasing rate. With this data there are now hundreds and thousands of homologues for many proteins. With this, we can think of large protein families, rather than single proteins. The second breakthrough is the emergence of new algorithms for accurate detection of evolutionary couplings between pair of amino acids in large multiple alignment of homologues, and the ability to translate these into distance restraints on the relative locations of the amino acid pair in the 3D protein structure. The premise here is that the detected evolutionary coupling  results from evolutionary pressure, and is therefore indicative of direct contact.A) making the distance restraint depended on the strength of the coevolution signal, (B) applying restrains between pairs of amino acids that are close in sequence first and adding restraints between remote residues later in the simulation to avoid trapping in wrong conformations, and (C) using the ROSETTA framework.Recently the Baker's lab introduced its own variant for the detection of coevolution. The improvements over previous implementations of coevolution are .Essential points to address:1) While the results of CASP11 blind test are important and indicative, they cannot serve as a systematic and direct assessment of the accuracy of Rosetta-GREMLIN. It is necessary to directly compare the accuracy of Rosetta-GREMLIN with other leading methods, particularly EVFOLD. With such comparisons, the extent of progress in terms of prediction accuracy brought by this new method will be more clear and the authors' claims of unprecedented accuracy can be measured in specific metrics (e.g. Ca RMSD or TM score) and calibrated appropriately.2) The angle and the motivation of this paper is reminiscent of the previous paper by Hopf et al. , which reported structure predictions, through exploiting evolutions covariance, for a relative larger number of membrane proteins without experimentally resolved structures. Of course the methodology is different here, but additional discussion of previous work along this line will help place the current work in an historical context. There have been structural predictions for some membrane proteins this paper discussed (e.g. adiponectin receptor and YeiH transporters). Briefly discussing these previous predictions and comparing them with the predictions by Rosetta-GREMLIN is needed.A) making the distance restraint depended on the strength of the coevolution signal, (B) applying restrains between pairs of amino acids that are close in sequence first and adding restraints between remote residues later in the simulation to avoid trapping in wrong conformations, and (C) using the ROSETTA framework. This apt summary could be considered as a framework for a brief description of the method.3) The paragraphs in the Introduction describing the method are too long, too technical, and difficult to read. The Introduction should describe the method at a high level in a language accessible by a general audience and the technical details should be moved to the Methods. One reviewer summed up the new ingredients of this method as (4) As one reviewer pointed out, much of the descriptive text concerning the specific structure models is too detailed with not enough motivation. A substantial portion of such text could go in a supplement so that the main discoveries are more visible. Alternatively, the text should be revised to be better connected to the main concern of the paper. Essential points to address:1) While the results of CASP11 blind test are important and indicative, they cannot serve as a systematic and direct assessment of the accuracy of Rosetta-GREMLIN. It is necessary to directly compare the accuracy of Rosetta-GREMLIN with other leading methods, particularly EVFOLD. With such comparisons, the extent of progress in terms of prediction accuracy brought by this new method will be more clear and the authors' claims of unprecedented accuracy can be measured in specific metrics (e.g. Ca RMSD or TM score) and calibrated appropriately.Sander, because this is not the point of the paper. However, we appreciate the question of reviewer II about how much the increased computer time required for Rosetta structure predictions increases model accuracy, and more generally, the need for a more thorough comparison to existing methods, in particular EVFOLD. We have added to the manuscript a table comparing the Rosetta-GREMLIN method to the other methods for both the CASP targets and the membrane protein benchmark. For the CASP targets, we compare to the results of Jones who made submissions for the same targets for CASP. For EVFOLD, we used the excellent EVFOLD web server since EVFOLD did not officially participate in CASP11. For the membrane protein set, we do not compare to the benchmark/results of the previously published Evfold paper, as it would be unfair, given there were far less available sequences then and a less accurate contact prediction method (mfDCA) was used. For the comparison in the paper, the alignments we used were provided as input to the Evfold-webserver, and a contact prediction method very similar to GREMLIN (PLM) was selected. The EVFOLD web server returns 50 models, we computed the CA RMSD and GDT-TS for each of them, and selected the best one. The table compares this “best out of 50” selection to the single Rosetta-GREMLIN model chosen without knowledge of the correct structure as described in the text. We emphasize in the text that while the Rosetta models are more accurate, the EVFOLD models require orders of magnitude less computing time.We did not argue in the original version of the paper that the Rosetta-GREMLIN method is better than those of Jones and Marks/2) The angle and the motivation of this paper is reminiscent of the previous paper by Hopf et al. , which reported structure predictions, through exploiting evolutions covariance, for a relative larger number of membrane proteins without experimentally resolved structures. Of course the methodology is different here, but additional discussion of previous work along this line will help place the current work in an historical context. There have been structural predictions for some membrane proteins this paper discussed (e.g. adiponectin receptor and YeiH transporters). Briefly discussing these previous predictions and comparing them with the predictions by Rosetta-GREMLIN is needed.We have added a sentence making this excellent point just before the discussion of the models. As described in the submitted manuscript, our adiponectin prediction is similar to the EVfold prediction, while in the other overlap case (EamA) the two models are quite different. We found no match of the YeiH transporter to any of those modelled in 3) The paragraphs in the Introduction describing the method are too long, too technical, and difficult to read. The Introduction should describe the method at a high level in a language accessible by a general audience and the technical details should be moved to the Methods. One reviewer summed up the new ingredients of this method as (A) making the distance restraint depended on the strength of the coevolution signal, (B) applying restrains between pairs of amino acids that are close in sequence first and adding restraints between remote residues later in the simulation to avoid trapping in wrong conformations, and (C) using the ROSETTA framework. This apt summary could be considered as a framework for a brief description of the method.We have considerably simplified the multiple paragraph description of the method for identifying the families modeled in the paper and moved much of the detail to the Methods and table legend. The description of the method for generating the models is already quite brief (less than one paragraph), and it is difficult to shorten it further.4) As one reviewer pointed out, much of the descriptive text concerning the specific structure models is too detailed with not enough motivation. A substantial portion of such text could go in a supplement so that the main discoveries are more visible. Alternatively, the text should be revised to be better connected to the main concern of the paper.We have eliminated much of the descriptive text about specific conserved residues that are likely functional in the sections concerning specific structural model. We have emphasized in each individual case the gain from analyzing the coevolution data in the context of the three dimensional Rosetta-GREMLIN models over what could be gleaned from the co-evolution and conservation data alone.