In a murine model of early systemic inflammation, we have explored the association between the sinusoidal HbsO2 detected with a remission spectroscopy system and 1.) the NAD(P)H autofluorescence  and 2.) the markers of hepatocellular injury.During the early stages of systemic inflammation, the liver integrity is compromised by microcirculatory disturbances and subsequent hepatocellular injury. Little is known about the relationship between the hemoglobin oxygen saturation  and the continuous infusion of endothelin-1 (ET-1) further aggravated the hypoxia in HbsO2. The detected NAD(P)H autofluorescence correlated with the detected HbsO2 values and showed the same developing. Three hours I/R resulted in elevated NAD(P)H autofluorescence compared with sham animals. Animals after 6.0 h I/R and continuous infusion of ET-1 revealed higher NAD(P)H autofluorescence compared with 3.0 h I/R animals. Overall the analysed HbsO2 values correlated with all markers of hepatocellular injury.Animals submitted to 1 hour bilateral hindlimb ischemia (I) and 3 hours of reperfusion (R) (3.0 h I/R) exhibited lower HbsO2. In parallel, we detected an increasing NAD(P)H autofluorescence representing an intracellular inadequate oxygen supply. Both changes are accompanied by increasing markers of liver cell injury. Therefore, remission spectroscopy in combination with NAD(P)H autofluorescence provides information on the oxygen distribution, the metabolic state and the mitochondrial redox potential, within the mouse liver.During the early stages of systemic inflammation, there is a significant decrease in hepatic sinusoidal HbsO Hepatic microcirculatory failure is a major prerequisite for the development of hepatocellular dysfunction in a number of conditions like trauma/hemorrhage, liver transplantation and systemic inflammation. In various inflammatory states, the degree of lethal hepatocyte necrosis can be predicted from the extent of hepatic microcirculatory failure , possibl2 at specific points; but the technique is invasive and consumes oxygen. Electron paramagnetic resonance oximetry techniques or nuclear MRI approaches allow the detection of changes in tissue pO2; however, their resolution is too low [et al. [in vivo visualization of the tissue pO2. This technique allows the visualization of oxygen distribution on tissue surfaces, but this method comprised some technical limitations. The oxygen-sensitive membrane has to be used under gastight and watertight conditions during microscopy and the fluorescent membrane shows a photobleaching effect. Paxian et al. [2. A disadvantage of this method, especially when used in small rodents like mice, is that it requires changing the continuous intravenous infusion rates of the dye to provide stable plasma concentrations. With mice  there is a growing need for a method able to reliably detect tissue oxygenation or, at least, hemoglobin oxygen saturation (HbsO2) in capillaries of small animals.Several methods have been reported in the past couple of years directly quantifying the oxygen distribution in tissues; however, their applicability in tissues, especially in small rodents like mice, is limited due to technical reasons. For instance, microelectrodes measure tissue pO too low . A fluor [et al.  on the bn et al.  recentlyin vivo detection of liver sinusoidal HbsO2. In a mouse model of early systemic inflammation, we examined whether the detected changes in hepatic HbsO2 correlated with the established method of NAD(P)H autofluorescence and hepatocellular injury.The aim of the present study was to investigate whether the utility of a new and simple remission spectroscopy system allows reliable Consistent with previous reports ,11, mean2 for all groups ) when compared with sham (68.4 (14.1); p < 0.01). No statistically significant differences were observed between 3.0 h I/R and 6.0 h I/R treated animals. However, an obvious shift of hepatic HbsO2 towards a lower oxygenation was observed when compared with 3.0 h I/R treated animals. Animals treated with 6.0 h I/R and a continuous infusion of endothelin-1 (ET-1) showed significant reduced HbsO2 values (44.8 (14.7)) when compared with 3.0 h I/R treated animals (56.2 (13.2); p < 0.006). More than half of the measured data from these animals revealed HbsO2 values lower than 50%. There was no apparent difference in the local tissue hemoglobin (Hb) content detected (data not shown).Hepatic sinusoidal HbsO2 detected in the same animal , as depicted in Figure Animals subjected to 3.0 h I/R revealed significantly higher NAD(P)H autofluorescence (141.6 12.8)); therefore, a significant decline in hepatic tissue oxygenation was observed when compared with sham (100.0 (6.7)) ; ther2 revealed a significant correlation , as shown in Figure Serum alanine aminotransferase (ALT) and serum aspartate aminotransferase (AST) levels are summarized in Table in vivo measurement of murine hepatic sinusoidal HbsO2 that showed a significant correlation with the established method of NAD(P)H autofluorescence, as well as with the extent of hepatic tissue injury.In the present study, we demonstrate the utility of a remission spectroscopy system for the In vitro bench analysis capabilities have spurred the desire to accomplish accurate in vivo measurement through various techniques. The 1930's and 1940's were a particularly active period for oximetry advances culminating in the development of pulse oximeters in the 1970's [2 in the tissue. Previously, it has been shown that bilateral hindlimb I/R results in the deterioration of liver microcirculation [2.Oximetry relies on the detection of the spectral properties of oxygenated and reduced Hb. e 1970's . Remissiculation . Since t+, NAD(P)H is highly fluorescent [2 obtained by the remission spectroscopy system under pathophysiological conditions. Whether induced by I/R or by the combination of I/R and infusion of ET-1, both analytical methods showed a decrease in hepatic oxygen supply, either as an elevation in NAD(P)H autofluorescence or as a diminution in hepatic HbsO2. The significant correlation between remission spectroscopy and NAD(P)H fluorescence indicates that after 3.0 h I/R, 6.0 h I/R and 6.0 h I/R+ET-1, hepatic oxygen supply was compromised. This is further emphasized by the statistical relationship found between hepatic HbsO2 and the extent of subsequent hepatocyte death.In the past, we have shown that bilateral hindlimb I/R results in a systemic inflammation with hepatic microcirculatory disturbances, in terms of reduced sinusoidal diameters and sinusoidal volumetric blood flow accompanied by elevated levels of sinusoidal leukocytes ,5. Theseorescent . Therefo2 in the sinusoids, whereas NAD(P)H autofluorescence depends upon the mitochondrial redox state and the activity of the mitochondrial electron transport chain. It was previously proposed that during systemic inflammation the NADH/NAD+ redox potential may increase, and oxygen utilization may be altered [2. Obviously, the observed hypoxia did not occur through altered oxygen utilization, but rather through a reduced oxygen supply induced by sinusoidal microcirculatory disturbances. This corroborates our previous contention that the simultaneous use of remission spectroscopy, and that of NAD(P)H autofluorescence, provides additional information regarding the underlying pathophysiological mechanisms. That technical approach allows the correlation between disturbances in oxygen supply and those of oxygen utilization.Both remission spectroscopy and NAD(P)H autofluorescence provide information on the metabolic state of the murine liver. Remission spectroscopy is directly dependent on the HbsO altered . The pre2 during the early stages of systemic inflammation. In parallel, we detected an increasing NAD(P)H autofluorescence representing an intracellular inadequate oxygen supply. Both changes are accompanied by increasing markers of liver cell injury. Future therapeutic interventions should focus on the amelioration of sinusoidal HbsO2 followed by an improvement in mitochondrial redox state. Remission spectroscopy represents a simple and reliable method for hepatic sinusoidal HbsO2 determination in small rodents. In combination with NAD(P)H autofluorescence, it provides information on the oxygen distribution, the metabolic state and the mitochondrial redox potential within the hepatic tissue.There is a significant reduction in hepatic sinusoidal HbsOMale C57/BL6 mice (eight to ten weeks old, weighing 23.7 (11.1) g) were used for all experiments. The experimental protocols were in compliance with the guidelines of the Committee on the Care and Use of Laboratory Animals of the Institute of Laboratory Animal Resources, National Research Council as well as those of Germany. Animals were maintained under controlled conditions (22°C, 55% humidity and 12-hour day/night cycle) with free access to tap water and a standard laboratory chow.Mice (n = 7, for each group) were randomly assigned to either a Sham or a hindlimb ischemia/reperfusion (I/R) group. Animals of the I/R groups were treated with 60 minutes bilateral hindlimb ischemia induced by tightening a tourniquet above the greater trochanter of each leg while under anaesthesia. Sham animals were not subjected to ischemia, but remained anaesthetized for the same period of time. Tourniquets were removed just prior to recovery from anaesthesia. The animals were awake during the 3 hours (3.0 h I/R) or the 6 hours (6.0 h I/R) reperfusion periods, and re-anaesthetized for the intravital microscopy procedure.To further induce liver microcirculatory disturbances and contribute towards a reduction in liver oxygen supply 6.0 h I/R, mice were further randomized to a group treated with a continuous infusion of ET-1 (70 pmol/min., i.v.) starting 15 minutes prior to microscopy. This dose of ET-1 was chosen because it produced alteration in the oxygen distribution, along with derangements in the hepatic tissue perfusion .Animals received anaesthesia, by inhalation, for all procedures. As previously described , anaesthin vivo with an i.v. bolus of the vital dye PI (0.05 mg/100 g). As previously stated [-1 mm3.Details of this technique have been described elsewhere ,21. For 2 was measured using the remission spectroscopy system Oxygen-to-See (O2C-ATS) supplied with the micro probe VM-3 (Lea Medizintechnik GmbH, Gießen, Germany). White light was continuously emitted via one channel of the micro probe light-guide and was continuously detected via another channel (channel diameter 70 μm). The backscattered light was analyzed in steps of 1 nm (500–650 nm). Each HbsO2 value was defined by specific Hb spectra. The local tissue light absorbance depends on the total local tissue content of Hb. The local content of Hb was calculated from the local light absorbance and emission. The flexible VM-3 micro probe allowed the detection of oxygen saturation of the left liver lobe placed on the glass slide of the inverted microscope. A special clamping system fixed the micro probe close to the surface of the glass slide and permitted contact-free systematic scanning of the liver lobe .p < 0.05.Data in text and Tables is given as: Mean (SD). Statistical differences between groups and from baseline within each group were determined by ANOVA, followed by the Tukey post-hoc test. The Kolmogorov-Smirnov test was previously used to confirm the normal distribution of data. For checking the nature and extend of the relationship between two variables linear regression analysis was performed. All figures were generated with Sigma Plot (Ver. 8.0) and statistical analyses were performed using Sigma Stat software . Differences were considered significant for CW conceived the design of the study and conducted the laboratory experiments; RB drafted the manuscript and coordinated the study; AK assisted in technical questions. NR participated in design and coordination and OE participated in animal procedures and in drafting the paper. All authors approved and read the final manuscript.


A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1.The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases.Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides. Thus, an accurate depiction of an RNA's secondary and tertiary structure is fundamental for our understanding of the mechanisms and consequences of its function, and an accurate prediction of an RNA folding into its secondary and tertiary structure from its primary structure will have a significant effect on our study of molecular biology. This RNA folding problem is usually divided into two components: the first is the determination of an RNA's folding pathway, and the second is the accurate and reliable prediction of an RNA's secondary and tertiary structure from its primary structure. In this paper, we focus on the second aspect and in particular RNA secondary structure prediction, which is a difficult problem. It has been estimated that the number of secondary structures models is greater than 1.8default setting) of the minimum energy structure. The set of suboptimal foldings exists and covers such a large energy range due to uncertainties in the thermodynamic data[The most popular method for predicting RNA secondary structure from a single sequence is free energy minimization using a dynamic programming approach,3, basedamic data. Mfold aamic data. Attemptamic data.Comparative analysis is another method for predicting RNA secondary and some tertiary structure-25. Compsingle RNA sequence. The most energetically stable RNA secondary structure(s) that are composed of canonical G:C, A:U, and G:U base-pairs and organized into standard helices are predicted. Non-canonical base-pairs and base-pairs not in standard helices cannot be predicted at this time. In contrast, RNA comparative sequence analysis methods predict a structure by searching an alignment for base-pairings that are common to all sequences in the dataset. This latter method can accurately predict canonical and non-canonical base-pairs that occur in secondary and tertiary structures. However, RNA comparative analysis is an iterative process requiring substantial sequence data, accurate sequence alignments, and the analysis of a structure that is common to all of the sequences in the dataset. In order to create an initial alignment, sequences must have significant identity to be aligned accurately while having sufficient variation (and covariation) to identify potential base-pairs and posit a structural hypothesis. The structural hypothesis is subsequently tested, refined, and expanded by the addition of more sequences to the alignment. Much of this process is still done manually, as computational tools to automate the process do not currently exist. The most recent comparative structure model for SSU rRNA is based on an alignment of 7,000 sequences[The superior performance of the comparative method may lead one to incorrectly assume that the other methods for RNA secondary structure prediction are no longer necessary. Different methods for predicting RNA secondary structure are utilized in different situations and can have different objectives. Free energy minimization based RNA structure prediction methods are usually applied to a sequences. The datIn 1995 and 1996, the Gutell Lab conducted two comprehensive studies that: 1) determined how well the optimal secondary structure model predicted with the program Mfold (version 2.3) matched the structure model determined with comparative analysis for a set of 16S and 23S rRNAs, and 2) examined other aspects of the folding, such as the prediction accuracy of "short-range" base-pairs , or the prediction accuracy for base-pairs in different loop environments, to learn more about differences between the thermodynamic and comparative models,30. The Since those studies were completed in 1995, four new developments have directly affected RNA structure prediction. 1) A new version of Mfold (3.1) was released with energy parameters revised to include sequence dependence and different secondary structure motifs. 2) The For more than 20 years, the basic paradigm for RNA secondary structure prediction, from a single sequence, has essentially remained the same: global free energy minimization with refinements to the nearest-neighbor energy parameters and minimization algorithms in an attempt to improve prediction accuracy. Refinements to the energy parameters, summarized in multiple sources-33, inclTo begin to address this question, we analyzed the ability of Mfold 3.1 to predict RNA secondary structure models determined with comparative analysis. In addition, we compared the predictions and accuracies of Mfold 3.1 with its predecessor, Mfold 2.3, for a large set of phylogenetically diverse 16S and 23S rRNA sequences. All metrics considered in previous studies to evaluate the accuracy of Mfold 2.3,30 were et al. with Mfold 3.1 analyzed a total of 151,503 nt and 43,519 comparatively predicted canonical base-pairs (i.e., G:C, A:U and G:U) from 955 sequences, which included 22 16S, 5 23S, and 309 5S rRNA sequences, 484 tRNA sequences 23 Group I and three Group II intron sequences, 91 SRP sequences, and 16 RNase P sequences[The dataset assembled for this study was significantly larger than previous studies comparing RNA structure models predicted by comparative analysis and the Mfold folding program-31,35. IHaloferax volcanii 16S rRNA secondary structure model, 30 base-pairs were added, 17 base-pairs were removed, and 427 base-pairs remained unchanged, resulting in a net difference of approximately 3% in the total number of base-pairs in the model. Similar numbers were observed for the other comparatively predicted structures evaluated. The comparative structure database used by Mathews et al.(1999) utilized known modified nucleotide information in tRNA to limit the base-pairing potential for those nucleotides that are modified[While the size of the comparative structure databases increased significantly between this study and previous Gutell Lab studies, only minor differences exist between the 1995 and 2004 versions of the 16S and 23S rRNA comparative structure models. For the 2004 version of the  modified. In thisOur dataset was extremely diverse in sequence and structure and included sequences from each of the three major phylogenetic domains, the Archaea, Bacteria, and Eucarya. The eukFor our 16S rRNA dataset, 75% of the Archaeal, 86% of the Bacteria, 71% of the Chloroplast, 99% of the Mitochondrial, and 94% of the Eukaryotic Nuclear sequence pairs had less than 80% sequence identity, while only 4% or fewer of the pairs in a given phylogenetic classification had 95% or more sequence identity , percent suboptimality (P), and the inclusion or exclusion of additional energy calculations based on coaxial stacking (efn2). The percent suboptimality variable establishes the energy range for computed foldings. The range is ΔGgs et al. and Fiell.[et al., with Mfl.[et al. used MfoNote: some sequences did not yield 749 suboptimal structure predictions under the folding conditions used in this study.) We configured the folding of our RNA sequences to: 1. maximize the number of structures predicted for any given sequence, 2. densely sample the suboptimal population close to the minimum free energy structure, since the structure with the lowest free energy (based on nearest-neighbor energetics) is expected to be most similar to the structure observed in nature for the free energy minimization techniques, and 3. to include coaxial stacking in the energy calculations with the efn2 option in Mfold 3.1.Each of the 1,411 sequences in our dataset was folded with Mfold 3.1, using a window size (W) of 1, percent suboptimality (P) of 5%, and maximum number of suboptimal foldings (MAX) of 750. The optimal, or minimum, free energy prediction and 749 suboptimal predictions were determined after re-ordering the structure predictions by the efn2 re-computed energetics.  but upon efn2 re-ordering becomes the minimum energy structure. If this predicted structure was more accurate than any other prediction in the population, the Mathews et al. study would reflect a higher prediction accuracy score for that given sequence.The study by Mathews Prediction Accuracy Calculations in Methods). This method for calculating accuracy was the same as the previous Gutell Lab studies that utilized Mfold 2.3[Given that 97–98% of the RNA secondary structure base-pairs predicted with comparative analysis were verified with the high resolution crystal structures, we scorMfold 2.3,30, withet al.[et al. study included a measure of the percentage of comparative base-pairs considered pseudoknotted, we were unsure if those base-pairs were specifically excluded from their accuracy calculations. Based on these differences in the accuracy calculations, the Mathews et al. study is reporting higher accuracies than our study.In contrast, base-pairs predicted with Mfold 3.1 in the Mathews et al. study weet al. study are impacted by differences in the folding parameters and the scoring criteria.Direct comparisons between the current study and the two previous Gutell Lab studies are meaningful due to the use of similar methodologies to calculate prediction accuracy. The only difference is the scoring method between the studies is the exclusion of pseudoknotted base-pairs from previous Gutell Lab studies. However, direct comparison of results between the current study and the Mathews et al. reported average accuracy per sequence of 78% for 5S rRNA and 83% for tRNA[et al. reported average accuracies (for folding complete RNA sequences) of 51% for a dataset of 22 16S rRNAs and 57% for a dataset of 5 23S rRNAs[The compilation of the accuracies for each sequence and the accuracy ranges for each RNA type and phylogenetic grouping were summarized in Table  for tRNA. Accurac for tRNA,30. The 23S rRNAs. When coi.e., at least one of the predicted tRNA structures had 100% of the base-pairs in the comparative model), 98% for 5S rRNA, 77% for 16S rRNA, and 74% for 23S rRNA. In contrast, at least one of the optimal folds for 5S rRNA or tRNA had a score of 0 (i.e., none of the base-pairs in the comparative structure model were predicted with Mfold). The lowest accuracy score was 5% for 16S rRNA and 1% for 23S rRNA.To gauge the variation in accuracy score for the optimal structures predicted with Mfold, the percentages of scores greater than 60% and less than 20%, the median accuracy score, and the highest and lowest accuracy scores were identified for the four RNA types (Table The median accuracy score observed for each RNA type was 70% for tRNA, 81% for 5S rRNA, 41% for 16S rRNA and 41% for 23S rRNA. For 16S and 23S rRNA the overwhelming majority (86% for 16S rRNA and 89% for 23S rRNA) of optimal structures predicted with Mfold had an accuracy score greater than 20% and less than 60%, a trend also observed in our previous studies . This relationship between the comparative structure model for G. intestinalis 16S and 23S rRNA and the poor prediction of this structure with both versions of Mfold  The accuracy scores for the majority of the 16S and 23S rRNA secondary structure models predicted with Mfold 2.3 and 3.1 were between 20% and 60%, while the accuracy scores for the majority of 5S secondary structure models predicted with Mfold were greater than 60%. 4) Some secondary structure models predicted with Mfold 2.3 and 3.1 have accuracy scores less than 20%. 5) The folding accuracy for Archaeal rRNAs was the highest, followed by Bacterial, Eukaryotic Chloroplast, Nuclear, and Mitochondrial rRNAs. 6) Sequences that were well-predicted with Mfold 2.3 tend to be well-predicted with Mfold 3.1, and sequences that were poorly-predicted using Mfold 2.3 tend to be poorly-predicted using Mfold 3.1.Six significant results came from this section. 1) Direct comparison of previous Gutell Lab studies with the current study (in light of the significantly larger size and richness in sequence variation of the comparative structure database for the current study and the inclusion of comparative base-pairs in pseudoknots) suggests that refinements in the energy parameters and folding algorithm have not improved the accuracy of the Mfold program between versions 2.3 and 3.1 for the set of 16S and 23S rRNAs analyzed. 2) The discrepancies between the results of the Mathews For a given protein, the average sequence separation between pairs of amino acids involved in non-covalent interactions is defined as the "Contact Order". Two simPer Sequence Averages in Methods for a discussion on how per sequence averages are computed). By comparison, in the 1995 study, an average accuracy of approximately 55% per sequence was observed for short-range base-pairs[The 496 16S rRNA comparative structure models in this study were comprised of 191,994 canonical base-pairs. A total of 145,058 (76%) of these base-pairs were short-range, and 75,763 (52%) of these base-pairs were predicted correctly by Mfold 3.1  of the 23S rRNA comparative, canonical base-pairs were short-range, and 67,130 (50%) of those base-pairs were predicted correctly by Mfold 3.1  were correct.Similar results were observed for our 23S rRNA dataset Table . A totalLogarithmic Binning of Base-pairs by Contact Distance for 16S rRNA in Methods).These results prompted a more sophisticated analysis to quantify the relationship between the accuracy of base-pairs predicted with Mfold 3.1 and RNA contact distance. Figure The Mfold prediction accuracy for each of these bins was determined. The prediction accuracy was 61% for base-pairs in the smallest contact distance bin, 3–8, 57% for base-pairs in the 9–19 contact distance bin, 47% for base-pairs in the 20–47 bin, 46% for the 48–117 bin, 15% for the 118–293 bin, 7% for the 294–733 bin, and 0% for the 734–1833 bin Figure . The appFive significant results were observed from the analysis in this section. 1) The accuracy of the predictions for short-range base-pairings was similar for Mfold 3.1 and Mfold 2.3. 2) More base-pairs were predicted with Mfold for any given sequence than in the corresponding comparative structure model. 3) Significantly more long-range base-pairs were predicted with Mfold 3.1 than in the comparative structure models. 4) The number of base-pairs in the comparative structure models decreases exponentially as the contact distance increases. 5) Base-pairs with a contact distance between 3 and 8 were predicted with the highest accuracy (61%) by Mfold, and accuracy values decreased exponentially as the RNA contact distance increases. The complete set of results for the prediction of 16S and 23S rRNA short-, mid-, and long-range base-pairs with Mfold 3.1 and comparisons with the comparative structure models are provided at our website.et al. included metrics which consider how suboptimal population may impact the prediction accuracy[et al. study. Rather, our metrics provide a different perspective from which to excogitate the importance of the suboptimal structure predictions. In particular, we considered: 1) the amount of structural variation and the ΔΔG difference  for pairs of structure predictions in the suboptimal population, 2) how many additional unique, canonical base-pairs in the comparative models were found in the suboptimal population, and how many incorrect base-pairs were predicted, and 3) which comparative base-pairs were predicted correctly in all, an intermediate number, or no structure predictions, in the set of suboptimal foldings.One of the features of the dynamic programming algorithm for free energy minimization employed by Mfold was the ability to provide a set of suboptimal structure predictions in addition to the minimum free energy or optimal structure prediction,12. Math accuracy. In thisSuboptimal Structural Variation Score in Methods) for pairs of structure predictions within a suboptimal population. Higher structural variation scores indicate that two structures compared were more different from one another, while lower structural variation scores indicate that two structures were more similar. We analyzed the two 16S rRNA sequences with the highest and lowest optimal accuracy before efn2 re-evaluation and re-ordering in the Archaea dataset, Haloferax volcanii and Methanospirillum hungatei. The accuracy for H. volcanii based on the pre-efn2 minimum free energy structure prediction was 80%, while M. hungatei was predicted at 46% accuracy  of structure predictions had a structural variation score of 501 or higher, while 134,44 pairs (48%) had a score of 100 or less  of structure predictions had a structural variation score of 501 or higher, while only 43,376 pairs (16%) had a score of 100 or less . In other words, free energy alone was not sufficient to adequately distinguish between different structure predictions. While the results suggest that suboptimal structural variation score could potentially be used as an indicator of the reliability of the structure prediction by Mfold, further investigation is required to evaluate the extent of this correlation.The most important observation from this section was that the ΔΔG between two structure predictions appeared to be independent of the similarity between the structure predictions. For example, Per Sequence Average in Methods). However, when considering the entire suboptimal population of structure predictions for each sequence, a total of 137,000 comparative canonical base-pairs were predicted correctly by Mfold, an average accuracy of 71% per sequence  When all of the base-pairs in the suboptimal population were included in the accuracy computation, we observed a 30% increase in average accuracy per sequence. 2) This same collection of suboptimal structures contained a 1,664% overall increase in the number of base-pairs that were not in the comparative model compared to the optimal structure prediction. In other words, a large decrease in selectivity was observed.Counts of Suboptimal Predictions More or Less Accurate than the Optimal Structure Prediction for Different 16S rRNA Sequences under Additional Information at our website[i.e., optimal + suboptimal population) predicted with Mfold 3.1 and 2) the relationship between this frequency and the RNA contact distance.In the previous section, we considered the unique base-pairs predicted with Mfold 3.1. As mentioned earlier, the number of unique base-pairs is very small compared to the total number of base-pairs predicted within the top 750 predicted structures. A total of 166,690,139 base-pairs were predicted in the top 750 structure predictions for all 496 16S rRNA sequences in our dataset (some of the sequences did not yield 750 structure predictions with the Mfold folding parameters used in this study, see r website). Of theH. volcanii and M hungatei for the panels in Figure H. volcanii 16S rRNA  For a given sequence, some of the comparative base-pairs were predicted correctly in all 750 structures . 2) A sequence with higher optimal accuracy contained a larger percentage of the comparative base-pairs predicted correctly in more of the structure predictions within the suboptimal population, compared to a sequence with lower optimal accuracy. 3) Base-pairs predicted correctly in more suboptimal structure predictions tend to have a smaller RNA contact distance.In this paper, we evaluated how well the computer program Mfold 3.1, with thThe comparative structure models for most sequences are predicted with similar accuracy by Mfold 2.3 and Mfold 3.1  are considered. The average folding accuracy for our current study is 41% for complete 16S rRNA sequences and 41% for complete 23S rRNA sequences, which is slightly less than in our earlier studies (Table 1) 1 Figure  when theBase-pairs with smaller RNA contact distances are both abundant in comparatively predicted structures and predicted more accurately by both versions of Mfold, and base-pairs with large RNA contact distances which are abundant in Mfold predicted structures only, are frequently incorrect. The prediction accuracy for individual base-pairs decreases exponentially as RNA contact distance increases. Figure 2) While uncertainties in the energy parameters may play a small role, free energy  alone is insufficient to distinguish between different structural possibilities for the same sequence. The variation between any two structure predictions within the suboptimal population is not correlated with the ΔΔG between those two structure predictions (Table 3) Without prior knowledge of the correct structure model, analysis of the suboptimal structure models can not improve our ability to both predict correctly the base-pairs in the secondary structure and assemble them into a single secondary structure model. Our analysis of the accuracy of base-pairs predicted in the suboptimal population for our 16S rRNA dataset reveals that the entire population contained a higher percentage of base-pairs present in the comparative model than the optimal structure prediction alone (Table 4) ne Table . When cone Table  vs. onlyne Table . In otheThe frequency of correctly predicted base-pairs in the suboptimal population is extremely variable, and base-pairs with a smaller RNA contact distance are more likely to be observed at a higher frequency. A qualitative analysis of Figure 5) et al. have started to address this issue using experimental studies[From our previous analysis with version 2.3 of Mfold, we had determined that free energy minimization does not consistently identify the correct base-pairs in the 16S and 23S rRNA comparative secondary structure models,30. We al studies,40 and kl studies to generWe believe that another potential reason for the inaccurate structures predicted with Mfold is that kinetics plays a role in RNA folding. Here, we suspect that nucleotide interactions with smaller contact distances will form more quickly, as suggested by Higgs, and wilKnowledge-based approaches that incorporate comparative analysis and high-resolution crystal structure data have been successful in the prediction of protein structures-44. Withet al.[All 1,411 sequences in our dataset were folded using Mfold 3.1. The optet al. study usArchaeoglobus fulgidus, the Mfold 3.1 optimal structure prediction contained 256 of the 448 comparatively predicted canonical base pairings in the A. fulgidus comparative structure model; thus, the accuracy was 65%. Although the comparatively-derived models included non-canonical pairing predictions (e.g., U:U), these were not considered in the accuracy measure, since Mfold 3.1 does not predict non-canonical pairings.The accuracy of an Mfold prediction for a sequence was determined by: 1) counting the number of canonical base-pairs (excluding any base-pairs with IUPAC symbols other than G,C,A, or U) in the comparatively-derived secondary structure model that appeared in the Mfold 3.1 prediction, and 2) dividing that value by the total number of canonical base-pairs in the comparatively-derived model. Any canonical, pseudoknotted base-pairs in a comparatively derived secondary structure model were included in the total number of comparatively predicted base-pairs for the given model. For example, in the 16S rRNA from A total of 1,411 secondary structure models were determined with comparative analysis  to log10 (1833). This range was divided into equal increments to define our contact distance bins. After evaluating many increment sets, with the requirement that the sizes of the bins be within one order of magnitude of one another, seven distance bins were established (Figure Figure d Figure .The suboptimal structural variation score measures the agreement between two different secondary structure models for the same RNA sequence. We compute the score by comparing the paired or unpaired state of each nucleotide in the two structure models. We increment the score when either a given position is unpaired in one structure model and paired in the other or when the position is paired to different positions in the respective structure models. We do not increment the score when a given position is paired to the same position in both structure models or is unpaired in both structure models. The higher the structural variation score, the lower the level of agreement between the two secondary structure models. The structural variation score is zero for two identical structure models. For two structure models that are different at every position the structural variation score equals the number of nucleotides in the sequence.KJD developed the workflow based management system for folding the sequences, constructed the database for analyzing the folding results, tabulated the results and drafted the manuscript. JJC developed the secondary structure models for the tRNA dataset, contributed ideas to the analysis and the figure and table generation, and edited the manuscript. CWC assembled the tRNA alignment, tabulated results, and edited the manuscript. RRG provided vision and direction and rewrote portions of the manuscript. All authors read and approved the final manuscript.Nucleotide – nt


E. coli developed in the outer cell wall a number of sophisticated channels called porins. One of them, maltoporin, is a passive specific channel for the maltodextrin uptake. This channel was also named LamB as the bacterial virus phage Lambda mis-uses this channel to recognise the bacteria. The first step is a reversible binding followed after a lag phase by DNA injection. To date little is known about the binding capacity and less on the DNA injection mechanism. To elucidate the mechanism and to show the sensitivity of our method we reconstituted maltoporin in planar lipid membranes. Application of an external transmembrane electric field causes an ion current across the channel. Maltoporin channel diameter is around a few Angstroem. At this size the ion current is extremely sensitive to any modification of the channels surface. Protein conformational changes, substrate binding etc will cause fluctuations reflecting the molecular interactions with the channel wall. The recent improvement in ion current fluctuation analysis allows now studying the interaction of solutes with the channel on a single molecular level.To harvest nutrition from the outside bacteria e.g. We could demonstrate the asymmetry of the bacterial phage Lambda binding to its natural receptor maltoporin.We suggest that this type of measurement can be used as a new type of biosensors. Nature created and optimized proteins for specific tasks which makes them often interesting in material science. For example, membrane transporters could control the permeability of artificial nanometer sized container. A typical application could be to control the enzymatic activity in a liposome . AnotherE. coli with high yields [Porins are attractive candidates for applications because they are very stable. Moreover, recombinant technology permits production of porins in h yields . A thirdE. coli is fairly permeable to smaller solutes below a molecular weight of about 400 Da [E. coli [E. coli consists of an 18 stranded β-barrel with short turns at the periplasmic side and large irregular loops at the outside of the cell [The outer cell wall of Gram-negative bacteria from t 400 Da . Such su[E. coli ,8. Malto[E. coli ,10. The the cell .The bacteriophage Lambda is a virus recognizing Maltoporin at the outer cell surface . In abse2-105 times in the buffer containing 1% OPOE. From our own experience in our laboratory the insertion was optimal if smallest amounts (less than 1 μl) were injected. In a second measurement we used painted membranes as described previously [A typical set-up for conductance measurements is shown in figure eviously . Here thMembrane current was measured via homemade Ag/AgCl electrodes. One electrode was used as ground and the other connected to the headstage of an Axopatch 200B amplifier (Axon Instruments, USA), allowing the application of adjustable potentials  across the membrane. A similar set-up was used in the second measurement.We recently investigated the sugar penetration on a single molecular level . We wereIn a second series of experiments we were interested to probe for Lambda phage binding. In principle this should be possible despite the enormous size (about 100 nm size in comparison to 4 nm sized channels). However in a preliminary step we have produced larger quantities of the phage endterminal protein gpJ fused to Maltose Binding Protein (MBP). We reconstituted a larger number of maltoporin in solvent containing membranes and titrated small quantities of the fusion construct MBP-gpJ. We know from the experiments described above that most of the channels are oriented the same direction during the reconstitution. In fig. Sensing with membrane channel is a new way in screening for solute molecules and several promising examples are already shown ,16,19,20


The ability to detect disease outbreaks early is important in order to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Many national, state, and local health departments are launching disease surveillance systems with daily analyses of hospital emergency department visits, ambulance dispatch calls, or pharmacy sales for which population-at-risk information is unavailable or irrelevant.We propose a prospective space–time permutation scan statistic for the early detection of disease outbreaks that uses only case numbers, with no need for population-at-risk data. It makes minimal assumptions about the time, geographical location, or size of the outbreak, and it adjusts for natural purely spatial and purely temporal variation. The new method was evaluated using daily analyses of hospital emergency department visits in New York City. Four of the five strongest signals were likely local precursors to citywide outbreaks due to rotavirus, norovirus, and influenza. The number of false signals was at most modest.If such results hold up over longer study times and in other locations, the space–time permutation scan statistic will be an important tool for local and national health departments that are setting up early disease detection surveillance systems. A new, flexible method for disease outbreak surveillance and its application to emergency department data from New York City The World Trade Center and anthrax terrorist attacks in 2001, as well as the recent West Nile virus and SARS outbreaks, have motivated many public health departments to develop early disease outbreak detection systems using non-diagnostic information, often derived from electronic data collected for other purposes (“syndromic surveillance”) ,15,16,17Most analytical methods in use for the early detection of disease outbreaks are purely temporal in nature ,20,21,22First studied by Naus , the scaTo date, all scan statistics require either a uniform population at risk, a control group, or other data that provide information about the geographical and temporal distribution of the underlying population at risk. Census population numbers are useful as a denominator for cancer, birth defects, and other registry data, where the expected number of cases can be accurately estimated based on the underlying population. They are less relevant for surveillance data such as emergency department visits and pharmacy sales, since the catchment area for each hospital/pharmacy is undefined. Even if it were available, the catchment area population would not be a good denominator since there can be significant natural geographical variation in health-care utilization data, due to disparities in disease prevalence, access to health care, and consumer behavior . One optIn this paper we present a prospective space–time permutation scan statistic that does not require population-at-risk data, and which can be used for the early detection of disease outbreaks when only the number of cases is available. The method can be used prospectively to regularly scan a geographical region for outbreaks of any location and any size. For each location and size, it looks at potential one-day as well as multi-day outbreaks, in order to quickly detect a rapidly rising outbreak and still have power to detect a slowly emerging outbreak by combining information from multiple days.The space–time permutation scan statistic was gradually developed as part of the New York City Department of Health and Mental Hygiene (DOHMH) surveillance initiatives, in parallel with the adaptation of population-at-risk-based scan statistics for dead bird reports (for West Nile virus) , emergenThe New York City Emergency Department syndromic surveillance system is described in detail elsewhere . In brieData are verified for completeness and accuracy, concatenated into a single dataset, and appended to a master archive using SAS . To cateThe goal of data analysis, which is carried out seven days per week, is to detect unusual increases in key syndrome categories. To run the space–time permutation scan statistic we have written a SAS program that generates the necessary case and parameter files, invokes the SaTScan software , and reaTwo sets of analyses are performed, one based on assigning each individual to the coordinates of their residential zip code and the other based on their hospital address. With 183 zip codes versus 38 hospitals, the former utilizes more detailed geographical information, while the latter may be able to pick up outbreaks not only related to place of residence but also to place of work or other outside activities . Residential zip code is not recorded by the hospital for about 3% of patients, and for the analyses described here, these individuals are only included in the hospital-based analyses.The performance of the prospective space–time permutation scan statistic was evaluated using both hospital and residential analyses. We used historical diarrhea data to mimic a prospective surveillance system with daily analyses from 15 November 2001 to 14 November 2002. For each of these days, the analysis only used data prior to and including the day in question, ignoring all data from subsequent days. This corresponds to the actual data available at the DOHMH 8–12 h after the end of that day, when that analysis would have been conducted if the system has been in place at that time. We also present one week of daily prospective analyses conducted in November 2003, where the daily analysis was run about 12 h after the conclusion of each day, as part of the regular syndromic surveillance activities at the DOHMH.As with the Poisson- and Bernoulli-based prospective space–time scan statistics , the spazdc is the observed number of cases in zip-code area z during day d. The total number of observed cases (C) isWhat is new with the space–time permutation scan statistic is the probability model. Since we do not have population-at-risk data, the expected must be calculated using only the cases. Suppose we have daily case counts for zip-code areas, where zd conditioning on the observed marginals:For each zip code and day, we calculate the expected number of cases μz times the total number of cases during day d. The expected number of cases μA in a particular cylinder A is the summation of these expectations over all the zip-code-days within that cylinder:In words, this is the proportion of all cases that occurred in zip-code area z, given that it was observed on day d, is the same for all days d.The underlying assumption when calculating these expected numbers is that the probability of a case being in zip-code area Ac be the observed number of cases in the cylinder. Conditioned on the marginals, and when there is no space–time interaction, Ac is distributed according to the hypergeometric distribution with mean μA and probability functionLet zAεzdc and ΣdAεzdc are small compared to AC, c is approximately Poisson distributed with mean μA [A contains an outbreak:When both Σ mean μA . Based oIn words, this is the observed divided by the expected to the power of the observed inside the cylinder, multiplied by the observed divided by the expected to the power of the observed outside the cylinder. Among the many cylinders evaluated, the one with the maximum GLR constitutes the space–time cluster of cases that is least likely to be a chance occurrence and, hence, is the primary candidate for a true outbreak. One reason for using the Poisson approximation is that it is much easier to work with this distribution than the hypergeometric when adjusting for space by day-of-week interaction (see below), as the sum of Poisson distributions is still a Poisson distribution.p-value is p = R/(S + 1) where R is the rank of the maximum GLR from the real dataset and S is the number of simulated datasets [p-values, we also report null occurrence rates [/p d. For example, under the null hypothesis we would at the 0.05 level on average expect one false alarm every 20 d for each syndrome under surveillance.Since we are evaluating a huge number of outbreak locations, sizes, and time lengths, there is serious multiple testing that we need to adjust for. Since we do not have population-at-risk data, this cannot be done in any of the usual ways for scan statistics. Instead, it is done by creating a large number of random permutations of the spatial and temporal attributes of each case in the dataset. That is, we shuffle the dates/times and assign them to the original set of case locations, ensuring that both the spatial and temporal marginals are unchanged. After that, the most likely cluster is calculated for each simulated dataset in exactly the same way as for the real data. Statistical significance is evaluated using Monte Carlo hypothesis testing . If, fordatasets . In addice rates , such asBecause of the Monte Carlo hypothesis testing, the method is computer intensive. To facilitate the use of the methods by local, state, and federal health departments, the space–time permutation scan statistic has been implemented as a feature in the free and public domain SaTScan software .Depending on the application, the method may be used with different parameter settings. For the syndromic surveillance analyses we set the upper limit on the geographical size of the outbreak to be a circle with a 5-km radius, and the maximum temporal length to be 7 d. This means that we are evaluating outbreaks with a circle radius size anywhere between 0 km (one zip code only) and 5 km, and a time length (cylinder height) of 1 to 7 d. The latter restriction is a reflection of the belief that the main purpose of this syndromic surveillance system is early disease outbreak detection, and if the outbreak has existed for over 1 wk, it is more likely to be picked up by reporting of specific disease diagnoses by clinicians or laboratories.Another practical choice is the total number of days to include in the analysis. One option is to include all previous days for which data are available. We chose instead to analyze the last 30 d of data, adding one day and removing another for each daily analysis. We believe this time frame provides enough baseline beyond the 1- to 7-d scanning window to establish the usual pattern of visits while avoiding inclusion of data that may no longer be relevant to the current period.To reduce the computational load, we limited the centers of the circular cylinder bases to be a collection of 446 zip-code area centroids and hospital locations in New York City and adjacent areas. This ensures, among other things, that each zip-code area may constitute an outbreak on its own.p-value we could get was 0.001, so that the smallest null occurrence rate possible for a signal was once every 2.7 y. In our system, signals of that strength clearly merit investigation. For the historical evaluation, in order to obtain more precise null occurrence rates, we set the number of replications to 9,999.The last parameter that we need to set is the number of Monte Carlo replications used for each analysis. For the daily prospective analyses we chose 999, which meant that the smallest The space–time permutation scan statistic automatically adjusts for any purely spatial and purely temporal variation. For many syndromic surveillance data sources, there is also natural space by day-of-week interaction in the data that is not due to a disease outbreak but to consumer behavior, store hours, etc. For example, if a particular pharmacy has an exceptionally high number of sales on Sundays because neighboring pharmacies are closed, we might get a signal for this pharmacy every Sunday unless we adjust for this space by day-of-week interaction. This can be done through a stratified random permutation procedure.z, given that it was observed on a Monday, is the same for all Mondays, etc.The first step is to stratify the data by day of week: Monday, Tuesday,…, Sunday. The space–time permutation randomization step is then done separately for each day of the week. For each zip code and day combination, the expected is then calculated using only data from that day of the week. For each cylinder, both the observed and expected number of cases is summed over all day-of-week strata, zip code, and day combinations within that cylinder. The same technique can be used to adjust for other types of space–time interaction. The underlying assumption when calculating these expected numbers is now that the probability of a case being in zip-code area All our analyses were adjusted for space by day-of-week interaction.Daily disease surveillance systems require rapid transmission of data, and it may not be possible to get complete data from each provider every single day. When we first tried the new method in New York City, a number of highly significant outbreak signals were generated that were artifacts of previously unrecognized missing or incomplete data from one or more hospitals. This is a good reflection on the method, since it should be able to detect abnormalities in the data no matter what their cause, but it also illustrates the importance of accounting for missing data in order to create an early detection system that is useful on a practical level.Depending on the exact nature of the missing data, there are different ways to handle it. We used a combination of three different approaches. (1) If a hospital had missing data for all of the past 7 d , we completely removed that hospital from the analysis, including all previous 23 d. (2) If a hospital had no missing data during the last 7 d, but one or more missing days during the previous 23 baseline days, then we completely removed the baseline days with some missing data, for all of the hospitals. (3) If a hospital had missing data for at least one but not all of the last 7 d, then we removed those missing days together with all previous days for the same hospital and the same day of week. That is, if Monday was missing during the last week, then we removed all Mondays for that hospital. This removal introduces artificial space by day-of-week interaction, so this approach only works if it is implemented in conjunction with the stratified adjustment for space by day-of-week interaction.For some analyses, more than one of these approaches were used simultaneously. Note that, since the missing data depend on the hospital, the solution is to remove specific hospitals and days rather than zip codes and days, even when we are doing the zip-code-based residential analyses. If there are many hospitals with missing data, then the second approach could potentially remove all or almost all of the baseline days. To avoid this, one could sometimes go further back in time and add the same number of earlier days to compensate. Another option is to impute into the cells with missing data a Poisson random number of cases generated under the null hypothesis. Given the completeness of our data, neither of these methods were employed .p ≤ 0.0027 are listed in We first tested the new method by mimicking daily prospective analyses of hospital emergency department data from 15 Nov 2001 to 14 Nov 2002, looking at diarrhea visits. Signals with For the residential zip-code analyses, there were two such signals. For the hospital analyses, there were six, two of which occurred in the same place on consecutive days. It is worth noting that at the false alarm rate chosen, none of the residential signals correspond to any of the hospital signals.For the residential analysis, the strongest signal was on 9 February 2002, covering 17 zip-code areas in southern Bronx and northern Manhattan. This signal had 63 cases observed over 2 d when 34.7 were expected (relative risk = 1.82). With a null occurrence rate of once every 5.5 y, a spike in cases of this magnitude is unlikely to be due to random variation. The signal immediately preceded a sharp increase in citywide diarrheal visits from 10 February to 20 March . In bothThe two hospital signals on 1 November and 2 November 2002, were at the same three hospitals in southern Bronx and northern Manhattan, with null occurrence rates of 1.6 and 3.4 y, respectively. These signals immediately preceded another sharp increase in citywide diarrheal activity, this time among individuals of all ages . This ciFor the hospital analyses, the strongest signal was a 1-d cluster at a single hospital in Queens on 11 January 2002, with ten diarrhea cases when only 2.3 were expected, which one would only expect to happen once every 3.9 y. Being very local in both time and space, it is different from the previously described signals preceding citywide outbreaks. While examination of individual-level data revealed a predominance of infants under the age of two, this cluster could not be associated with any known outbreak, and retrospective investigation was not feasible.p = 0.0027 threshold there were six and two signals for the hospital and residential analyses, respectively, compared to one expected in each. p-value of the most likely cluster was within a given range. Had the null hypothesis been true on all 365 d analyzed, the p-values would have been uniformly distributed between zero and one. The fact that in our data there were more days with low rather than high p-values is an indication that there may be additional true “outbreaks” that are indistinguishable from random noise. These could be very small disease outbreaks, for example, due to spoiled food eaten by only a few people, or they could be artifacts caused by, for example, changes in the hours of operation at an emergency department or coding differences between the emergency department triage nurses.As shown in Since 1 November 2003, the space–time permutation scan statistic has been used daily in parallel with the population-at-risk-based space–time scan statistics  as part For fever/flu there was a strong 7-d hospital signal in southern Bronx and northern Manhattan on 28 November with a null occurrence rate of once every 2.7 y. On each of the following 2 d, there were again strong hospital signals in the same general area as well as residential zip-code signals of lesser magnitude. These signals started 12 d into a gradual citywide increase in fever/flu that continued to grow through the end of December, driven by an unusually early influenza season in New York City.In this paper we have presented a new method for prospective infectious disease outbreak surveillance that uses only case data, handles missing data, and makes minimal assumptions about the spatiotemporal characteristics of an outbreak. When using historical emergency department chief complaint data to mimic a prospective surveillance system with daily analyses, we detected four highly unusual clusters of diarrhea cases, three of which heralded citywide gastrointestinal outbreaks due to rotavirus and norovirus. Three of four weaker signals also occurred immediately preceding or concurrent with these citywide outbreaks. If we assume that all of these clusters were associated with the citywide disease outbreaks, then the method generated at most two false alarms at a signal threshold where we would have expected one by chance alone.For disease outbreak detection, the public-health community has historically relied on the watchful eyes of physicians and other health-care workers. However, the increasing availability of timely electronic surveillance data, both reportable diagnoses and pre-diagnostic syndromic indicators, raises the possibility of earlier outbreak detection and intervention if suitable analytic methods are found. While it is still unclear whether systematic health surveillance using syndromic or reportable disease data will be able to quickly detect a bioterrorism attack ,40, the There are other alternative ways to calculate expected counts from a series of case data. One naive approach is to use the observed count 7 d ago in a zip-code area as the expected count for that same area today, and then apply the regular Poisson-based space–time scan statistic. When applied to the New York City diarrhea data described above, such an approach generated at least one “statistically significant” outbreak signal on each of the 365 d evaluated. The basic problem with this is that there is random variation in the observed counts that are used to calculate the expected, which is not accounted for in the Poisson-based scan statistic. If we based the expected on the average of multiple prior weeks of data, we would get less variability in the expected counts and fewer false signals, but the problem would still persist, and as the number of weeks increase beyond a few months other problems may gradually arise due to, for example, seasonal trends or population size changes.Computing time depends on the size of the dataset and the analysis parameters chosen. With 999 replications, the hospital analyses with 38 data locations take 7 s to run on a 2.5-MHz Pentium 4 computer, while the residential analyses using 183 zip-code area locations take 11 s. The same numbers for 9,999 replications are 27 and 57 s, respectively.There are a number of limitations with the proposed method. The method is highly sensitive to missing or incomplete data. Our first implementation of the method resulted in a number of false alarms, and highlights the need for systematic data quality checks and the analytic adjustments described above. When excellent population-at-risk data are available, we expect the Poisson-based space–time scan statistic that utilizes this extra information to perform better than the space–time permutation scan statistic. If, however, the population-at-risk data are of poor quality or nonexistent, which is often the case, then the space–time permutation scan statistic should be used.Since the space–time permutation scan statistic adjusts for purely temporal clusters, it can only detect citywide outbreaks if they start locally, but not if they occur more or less simultaneously in the whole city. Hence, it does not replace purely temporal surveillance methods, but rather complements them.Finally, it is important to note that the geographical boundary of the detected outbreak is not necessarily the same as the boundary of the true outbreak. Since we used circles as the base for the scanning cylinder, all detected outbreaks are approximately circular. Other shapes of the scanning window are also available , but it The emergency department data used in this study also have some limitations. In addition to the citywide outbreaks, there were several institutional gastrointestinal outbreaks reported to DOHMH during the historical 1-y period but not detected in emergency department data using the space–time permutation scan statistic. One reported outbreak involved school children that went to the emergency department of a nonparticipating hospital. Other outbreaks went undetected because medical care was not sought in emergency departments. Most people with diarrhea do not go to the hospital emergency department. Rather, they call or go to their primary care physician, they visit the pharmacy to buy over-the-counter medication, or they may have symptoms that are so mild that they do not seek medical care. Further studies are needed to evaluate the strengths and weaknesses of different data sources.The geographic units of analysis used were residential zip code and hospital location. It may be hard to detect outbreaks that affect only a small part of a single zip code, especially if the background rate of the syndrome is fairly high. Where available, the exact coordinates of a patient's residence can be used to avoid problems introduced when aggregating data. Furthermore, some outbreaks may not be clustered by place of residence, as in the case of an exposure occurring at the place of work or in a subway. Using the location of the hospital rather than residence may provide higher power to detect workplace-related outbreaks, but the only way to fully address this issue may be to conduct workplace surveillance.In spite of these limitations, we have presented a new method for the early detection of disease outbreaks and illustrated its practical use. The primary advantages of the method are that it is easy to use, it only requires case data, it automatically adjusts for naturally occurring purely spatial and purely temporal variation, it allows adjustment for space by day-of-week interaction, and it is capable of handling missing data.While the method was developed and applied in the context of syndromic surveillance, it may also be used for the early detection of diagnosed disease outbreaks, or for detecting changes in the pattern of chronic diseases, when population census information is unavailable, unreliable, or not available at the fine geographical resolution needed. The ability to perform disease surveillance without population-at-risk data is especially important in developing countries, where these data may be hard to obtain. The space–time permutation scan statistic could also be used for similar early detection problems in other fields, such as criminology, ecology, engineering, social sciences, and veterinary sciences.Detecting disease outbreaks early means that health officials are better able to fight and contain them. Electronic patient records that can be analyzed with statistical methods in computer programs should help with disease surveillance and make it possible to detect outbreaks early without raising too many false alarms.The researchers who did this study have developed and operated real-time disease surveillance systems. In any such system, there will always be more disease cases in some places and time periods than in others, for example, because there are more people living there, or because there are more people of a certain type living there, like older people or children, who are more prone to get sick. The researchers were trying to develop a method that can discover outbreaks without the need to know about the structure of the population under surveillance.They modified an existing method to make it work without data on the structure of the population under surveillance. They also found a way to deal with incomplete data, when, for example, one hospital did not report any data for a particular day.When they applied the method to emergency room data from New York City, they found that it performs well: it seems to be able to detect real outbreaks early and not result in many false alarms.The method can detect only outbreaks that start locally, not those that occur more or less simultaneously in the whole surveillance area. For some outbreaks—for example, those caused by exposure to an infectious agent in the subway—patients will not necessarily live in the same neighborhood or go to the same emergency room. The method will not detect outbreaks with very few cases, such as one case of small pox or three cases of anthrax, such as the anthrax bioterrorism attacks in the fall of 2001. And the method only works for diseases with early symptoms severe enough that people go to the emergency room. Efficient disease surveillance will need the parallel use of different methods, each with their own strengths and weaknesses.The method was developed as part of the New York City Department of Health and Mental Hygiene surveillance initiatives and is now being used every day to analyze emergency department records from 38 hospitals in the city. To facilitate wider use, the method has been integrated into a more diverse software called SaTScan that is freely available.The following websites provide additional information on this and other methods.http://www.satscan.org/Details on SaTScan and software for downloading: http://www.cdc.gov/od/hissb/act_int.htmUnited States Centers of Disease Control and Prevention Web page on electronic disease surveillance: http://www.syndromic.org/index.htmlNational Syndromic Surveillance Conference: http://btsurveillance.org/National Bioterrorism Syndromic Surveillance Demonstration Program: http://openrods.sourceforge.net/The Real-Time Outbreak and Disease Surveillance Open Source Project:


Societies are encouraged to consider their own open-access experiments within the context of the communities they serve This is the second in a series of three editorials that aim to address recurring concerns about the benefits and risks associated with open-access publishing in medicine and the biological sciences.Scientific societies serve their members, their broader scholarly communities, and the different components of their missions in many important ways. Making peer-reviewed literature immediately accessible, searchable, and reusable to anyone in the world with an Internet connection is a uniquely direct means of achieving a number of goals that are common to most scholarly associations and of advancing the diverse interests of their constituencies.Setting aside for the moment the question of how feasible it is for societies to alter their journals' access policies, there is by now a broad consensus that widespread open access to scientific publications is good for scientists and good for science. Society members want to maximize the impact of their work—and articles that are freely available online are cited more frequently than those that are not . Most soIn addition to an interest in exploring new ways to serve their members and their missions, societies have another compelling reason to investigate open access for their journals: the rapidly changing landscape of scholarly publishing. From 1990 to 2000, the average price of an academic journal subscription increased 10% per year . While sThe confluence of forces in favor of open access says nothing about its fiscal implications for scientific societies. As any systemic change in research or publishing would, the movement toward open access has generated concern about its ramifications for the scholarly associations that often serve as the backbones of scientific communities. However, the strength of those societies and their essential role in the communities they serve are precisely what should allay fears about the revenue-eroding effect that some argue would plague societies if they converted their traditional subscription-based journals to open access.Scientific societies perform an array of tremendously valuable functions for their constituents and disciplines. Researchers, educators, and others join societies for the many benefits of membership beyond simply discounted or “free” subscriptions to journals, so the concern that open-access publications would be the death knell of voluntary academic associations is misguided. As Elizabeth Marincola, executive director of the American Society for Cell Biology, recently noted, her society “offers a diverse range of products so that if publications were at risk financially, we wouldn't lose our membership base because there are lots of other reasons why people are members” .While open-access publication can, in fact, be paid for in a number of different ways, there is no question that a transition toward the elimination of online access barriers requires most societies to restructure the business models for their journals. If journal subscriptions generate surplus revenue that supports other society activities, then the business model of the society as a whole may need to be examined. This is not to say that open-access journals cannot generate a surplus or profit—simply that they do not do so by restricting access to their primary research content.Journal of Clinical Investigation (JCI) freely online and recently reaffirmed its commitment to open access: “The financing having been resolved, through author charges and other means,” John Hawley, the executive director of the ASCI writes, “the JCI hopefully can bring the greatest benefit to its authors and readers, regardless of who they might be. It is in this spirit that the JCI has always been free online, and will remain so”  has since 1996 provided the main so” .Physiological Genomics the option to pay a surcharge for their articles to be made freely available online immediately upon publication. A recent survey by the Joint Information Systems Committee (JISC) in the United Kingdom suggests that many authors would use such an option if it were more widely available: 48% of authors who had never published in an open-access journal and 60% of authors who had done so indicated that they would be willing to “pay a publisher of a journal sold according to the traditional subscription model an additional fee for them to make [the author's] particular paper ‘open access’” (In order to experiment cautiously with new access policies, several societies have implemented hybrid models of access-restriction for their publications. The American Physiological Society, for example, offers authors in access’” .JISC is also directly encouraging society and nonprofit publishers to implement hybrid models and other open-access experiments and to launch new open-access journals by providing grants to offset the publication charges for authors during this transitional phase. In the long run, of course, open access will prove sustainable when more funders of research, in addition to interested third parties, designate funds specifically for the costs of publishing articles to be made freely available, searchable, and reusable online.Reaching a “steady-state” system of open-access publishing by scientific societies will require three critical components: recognition that open access serves societies' members and missions; diversified revenue streams not solely dependent on subscription or site-license fees; and society publishers' making use of recent innovations in journal production and dissemination, which can dramatically reduce the costs of publishing. It is, after all, the increased efficiencies born of new technologies—from the Internet itself to electronic journal management systems—that have made the idea of open access possible. And while proponents of open access are confident that publication charges of around $1,500 per article will be sufficient to cover the costs of publishing an efficiently operated society journal, there is no question that many existing journals may need to update their infrastructure in order to make open access financially viable .There is also no question that many societies do not, at present, have a wealth of revenue streams beyond the proceeds from their journals, which they often use to fund valuable activities from education initiatives to annual meetings. As open-access journals become more established, however, and as the benefits of open access to scientific and medical literature become more apparent to society members, the demand for the broadest possible dissemination of research is only likely to grow. Those societies that embrace the developments taking place in scholarly publishing may well see their membership and publications thrive more than societies that cling to the potentially unstable status quo.In any case, a constructive discussion about the pitfalls to be avoided and the benefits to be gained through a transition to open-access publishing would be a worthy first step for any scientific society to take—and PLoS welcomes the questions, comments, and feedback of those who are intrigued by the potential that open access affords and want to learn more.


Can the emerging field of metabolic ecology explain all life's patterns in one unifying theory, from the metabolic rate of a shark to why biodiversity peaks at the equator? Life is complicated. It comes in all sorts of shapes, sizes, places, and combinations, and has evolved a dizzying variety of solutions to the problem of carrying on living. Yet look inside a cell and life takes on, if not simplicity, then at least a certain uniformity—a genetic system based around nucleic acids, for example, and a common set of chemical reactions for turning food into fuel. And looked at in broad swathes, life shows striking generalities and patterns. Every mammal's heart will beat about one billion times in its lifetime. Both within and between species, the density of a population declines in a regular way as the size of individuals increases. And the number of species in all environments declines as you move from the equator towards the poles.Wouldn't it be good if there were a simple theory that used life's shared fundamentals to explain its large-scale regularities, via its diversity of individuals? In the past few years, a team of ecologists and physicists have come up with just such a theory. At its heart is metabolism: the way life uses energy is, they claim, a unifying principle for ecology in the same way that genetics underpins evolutionary biology. They believe that energy use, in the form of metabolic rate, can be understood from the first principles of physics, and that metabolic rate can explain growth, development, population dynamics, molecular evolution, the flux of chemicals through the environment, and patterns of species diversity—to name a few.The work, its originators insist, is not a theory of everything for biology, or even ecology. But it can often seem that way. “We're making advances on a broad range of questions almost on a weekly basis,” says James Gillooly, of the University of New Mexico, Albuquerque. “We've been having an awful lot of fun.”Metabolic ecology, as it has become known, is still controversial. Some think its mathematical foundations are unsound, and that it explains nonexistent trends. It also divides researchers on philosophical lines—those that see life's patterns as fundamental versus those who think that variation is the key, those who think that simple, general ideas can help us understand nature versus those who think that complicated problems require complicated answers. A lot is riding on the debate: “If the theory is right, it's one of the most significant in biology for a long time,” says ecologist David Robinson of the University of Aberdeen. “It would provide a common functional basis for all biodiversity.”2/3. For many years, most biologists thought that it was.Scientists have known for nearly two centuries that larger animals have relatively slower metabolisms than small ones. A mouse must eat about half its body weight every day not to starve; a human gets by on only 2%. The first theories to explain this trend, developed in the late nineteenth century by the German nutritionist Max Rubner and the French physiologist Charles Richet, were based on the ratio between an animal's surface area, which changes with the square of its length, and its volume, which is proportional to its length cubed. So large animals have proportionately less surface area, lose heat more slowly, and, pound for pound, need less food. The square-versus-cube relationship makes the area of a solid proportional to the two-third power of its mass, so metabolic rate should also be proportional to mass0.73—closer to three quarters than two thirds. Kleiber looked at animals ranging in size from a rat to a steer. By the mid-1930s, other workers had put together a “mouse to elephant” curve that supported the three-quarter-power law, and by the 1960s, the plot had been extended for everything from microbes to whales, still seeming to show the same relationship. Quarter-power scaling also began to stretch beyond metabolic rate. Biological times, such as lifespan and heart rate, were found to be proportional to mass1/4, and fractions related to one-quarter show up in other scaling relationships: the diameter of the aorta and tree trunks is proportional to mass3/8, for example.But in 1932, Max Kleiber, an animal physiologist working at the University of California's agricultural station in Davis, re-examined the question, and found that, for mammals and birds, metabolic rate was mass3/4—and more generally, why quarter-power scaling laws should be so prevalent in biology. The impasse meant that by the mid-1980s interest in scaling had waned. But it sparked back into life in 1997, when two ecologists—James Brown of the University of New Mexico, Albuquerque, and his graduate student Brian Enquist, now at the University of Arizona, Tucson—and a physicist, Geoffrey West of the Santa Fe Institute, developed a new explanation of why metabolic rate should equal the three-quarter power of body mass.It was, however, much harder to find a theoretical reason for why metabolic rate should be proportional to massWest, Brown, and Enquist's theory is based on the structure of biological distribution networks, such as blood vessels in vertebrates and xylem in plants. The trio assumed that metabolic rate equals the rate at which these networks deliver resources, and that evolution has minimized the time and energy needed to get materials from where they are taken up—the lungs or roots, for example—to the cells. They also assumed that, although organisms vary greatly in size, the terminal units in their distribution networks, such as blood capillaries or leaf stalks, do not.Bigger plants and animals take longer to transport materials, and so use them more slowly. In West, Brown, and Enquist's model, the maximally efficient network that serves every part of a body has a fractal structure, showing the same geometry at different scales. And the number of uniform terminal units in such a network—and so the rate at which resources are delivered to the cells—is proportional to the three-quarter power of body mass.Whether metabolic rate really varies with the three-quarter power of body mass is still debated—some researchers still favor two-thirds, others think that no one exponent fits all the data—but a majority of biologists favor three-quarters. And whether the fractal theory really explains the relationship of metabolic rate to body size is also still contentious. In the most wide-ranging critique so far, published this April, two Polish researchers, Jan Kozlowski, of Jagiellonian University, Krakow, and Marek Konarzewski of the University of Bialystok, claimed that the theory's maths could not simultaneously contain both uniform terminal units and three-quarter-power scaling, that large animals built along such lines would have more blood than their bodies could contain, that biological scaling laws were not built around quarter powers, and that biological networks were not generally branching fractals.“I don't believe there's anything to explain—there's no universal scaling exponent,” says Kozlowski. He is also struck by what is left unexplained when size is accounted for: animals of the same size can still show more than an order of magnitude variation in metabolic rate. “What's striking in nature is the variability,” he says. “There are regularities that call for explanation, but that doesn't mean ignoring the variability is correct.” Kozlowski is the co-author of a theory that relates metabolic rate to cell size and the amount of DNA an organism has, one of several alternative explanations of the scaling of metabolic rate published since West, Brown, and Enquist's model.The criticisms are serious, says Robinson. “The jury is out—questions about the fundamental maths are worrying a few people.” On the other hand, he says, West, Brown, and Enquist's model seems a plausible template for designing an organism, and its predictions fit real-world data remarkably well. Whether this fit truly captures the physical and chemical mechanisms underlying the patterns remains to be seen; Robinson hopes that criticism can strengthen West, Brown, and Enquist's model, perhaps leading to a new, improved theory.The metabolic theory's authors are not budging. “We've yet to see a criticism we feel we can't answer pretty readily,” says Brown. Kozlowski and Konarzewski's arguments are based on a misreading of the work, he says, and criticisms that focus on one aspect, such as the structure of mammalian vascular systems, miss the key point, which is generality: “If we're wrong on quarter powers, why do they keep showing up in everything from life-history processes to evolutionary rates?”3/4 term with the Boltzmann factor. The latter is an expression of the probability that two molecules bumping into each other will spark a chemical reaction. The higher the temperature, the greater the probability, and the faster the reaction.After accounting for size, Brown's group turned its attention to the second most important influence on metabolism: temperature. The effect is exponential, and a 5 °C rise in body temperature equals a roughly 150% rise in metabolic rate. The team built an equation for metabolic rate that combined the massAdding temperature explained much of the variation in metabolic rate that remained after adjusting for size. It also explained some of the metabolic differences between groups. For example, a reptile has a slower metabolic rate than a mammal of the same size. But adjusting for its lower body temperature removes much of the difference, suggesting that the two groups share fundamental metabolic processes. The same even goes for plants and animals. “When you correct for size and temperature, the metabolic rates of a shark, a tomato plant and a tree are remarkably similar,”  says GilThe metabolic theory's third component, resources, is also something of a black box at this stage. Nutrient supply, the team reasons, is the next most important determinant of metabolic rate, and will account for some of the remaining unexplained variation. As with temperature, the overall effect could be a balance of many processes, or it could be due to one limiting element—the growth of lake phytoplankton is often limited by phosphorus, for example, while for marine phytoplankton iron is usually the crucial nutrient. “It's a work in progress,” says Brown. “But our vision for a metabolic theory of life is ultimately going to include material resource limitation.”These three things still do not account for all the variation in metabolic rate, but more detailed knowledge of species can yield more precise predictions. Using body size, altitude, and diet, Brian McNab, of the University of Florida, Gainesville, has explained 99.0% of the variation in metabolic rate for birds of paradise , and 99.“I have serious reservations as to whether there is a single relationship for body size and metabolic rate,” he says. “I think we will be able to find generalizations in ecology, but they're not going to be simple—there will be a bunch of clauses and restrictions, and animals have a lot of ways to bend the rules.”No theory matches data exactly, Brown points out; having a baseline prediction for metabolism lets you identify exceptional cases worthy of further investigation. Viewed from this angle, the metabolic theory is a kind of null hypothesis of how organisms work. “Until you have a theory that makes a prediction, you don't know how to interpret any of the variation,” says Brown. And, he adds, despite this variation, the underlying trends are also meaningful. “There are themes of life that are deep-seated and fundamental.”All the business of life needs energy. So if you know the rate at which an organism burns fuel—or if you know how big and hot it is, and apply the metabolic theory—you can make a suite of predictions about its biology, such as how fast it will grow and reproduce, and how long it will live.By correcting for mass and temperature, Brown, Gillooly, and their colleagues believe they have revealed underlying similarities in all the rates of life. The hatching times for egg-laying animals, including birds, fish, amphibians, insects, and plankton, turn out to follow the same relationship—if a fish egg were the same size and temperature as a bird egg, it would take equally long to hatch . The samIf all organisms work in the same way, understanding individual biology offers an obvious route to explaining nature's patterns—ecological processes become a kind of meta-metabolism. Indeed, the team has used their theory to predict the flux of carbon dioxide through forests—a measure more usually used to determine individual metabolic rate. They have also found that body size and temperature predict the densities and growth rates of populations. So hotter environments should support lower population densities, as each individual consumes resources more quickly, leaving less to go round. One thing that does not scale with a quarter power of body size is the area of animals' home ranges; this increases more or less linearly with body size. But in October, Brown, along with researchers from Princeton University and the Institute of Zoology in London, published a model that brought this, too, into the metabolic theory. They borrowed another trick from physics, using an equation that describes colliding gas molecules to model the interactions between neighboring animals.Temperature could also explain why biodiversity peaks at the equator, the team believes. Organisms with faster metabolisms have faster mutation rates. So the genomes of smaller, hotter animals change more quickly, and they will also get through their generations more rapidly. One would therefore expect to see more new species created in small organisms and warm environments. The large-scale trend in all these rates—hatching time, individual and population growth, ecosystem metabolism, DNA substitution—is closely proportional to a quarter power of body mass.In the future, Brown's group plans to examine the dynamics of colonial organisms and societies through the lens of metabolic ecology; instead of capillaries, the terminal units of the networks would become ants, or people. There are also many applied problems within the theory's scope, including some of the most significant human impacts on the biosphere. Carbon emissions and the consequent global warming are increasing both the temperature and nutrient supply. And exploited populations, such as fisheries often show a decrease in individual size, as larger animals are preferentially killed. Both these would tend to speed up biological processes. Another team of United States and Italian researchers has found that the same model that describes the growth of individuals can also predict the growth of tumors, hinting that metabolic ecology may have medical applications.Brown hopes that metabolic ecology will one day become an uncontroversial part of researchers' toolkits, like the theories population geneticists use to predict changes in the frequencies of genes. Before that happens, both the theory's proponents and its opponents have years of work ahead of them. Adopting the theory may also require a shift in ecologists' worldview. Most ecologists work by carrying out experimental manipulations on small groups of similar organisms: the warblers in a woodland, for example, or the grasses of a meadow. When they build models, they do so from empirical data, not from physical first principles. The philosophy behind metabolic ecology disconcerts many researchers, says Robinson. “A lot of traditional biologists are uncomfortable with thinking about data in these terms.”Kozlowski doubts that simple theories can make precise predictions about the behavior of biological systems on large scales. He believes that metabolic ecology risks leading the discipline up a blind alley: “If I'm right, and the basic model contains an error, correcting the results will be a very long process. If they're not right, they'll have done a disservice to ecology.”But many ecologists are more optimistic that some unifying principles of nature can be found, and that metabolic ecology, and the debate around it, is a step in the right direction. Some think the theory may be part of an even grander idea. Stephen Hubbell, of the University of Georgia, is one of the architects of another idea causing a stir among ecologists. Called neutral ecology, it proposes a general explanation of how competition between individuals produces the dynamics of birth, death, and migration seen in ecosystems, and its predictions match closely the abundance and diversity of species in the wild. He believes that metabolic and neutral ecology can become elements of some larger theoretical framework.“I've never been more excited in my life,” says Hubbell. “Ecology now is like quantum mechanics in the 1930s—we're on the cusp of some major rearrangements and syntheses. I'm having a lot of fun.”