Sway differences in the ML direction were lowest in the presence of the visual system. It was concluded that the effect of the visual system on the balance can be examined mostly by the ML sway while the proprioceptive and vestibular systems can be examined mostly by the AP direction sway.Kohonen neural network (KNN) was used to investigate the effects of the visual, proprioceptive and vestibular systems using the sway information in the mediolateral (ML) and anterior-posterior (AP) directions, obtained from an inertial measurement unit, placed at the lower backs of 23 healthy adult subjects  age: 24.5 (4.0) years, height: 173.6 (6.8) centimeter, weight: 72.7 (9.9) kg). The measurements were based on the modified Clinical Test of Sensory Interaction and Balance (mCTSIB). KNN clustered the subjects’ time-domain sway measures by processing their sway’s root mean square position, velocity, and acceleration. Clustering effectiveness was established using external performance indicators such as purity, precision-recall, and F-measure. Differences in these measures, from the clustering of each mCTSIB condition with its condition, were used to extract information about the balance-related sensory systems, where smaller values indicated reduced sway differences. The results for the parameters of purity, precision, recall, and F-measure were higher in the AP direction as compared to the ML direction by 7.12%, 11.64%, 7.12%, and 9.50% respectively, with their differences statistically significant ( The maintenance of balance is mainly a combined process of the interaction of the central nervous system (CNS) and the balance-related sensory systems. In addition to other activities, in order to maintain balance, the CNS must correctly integrate information from the relevant sensory systems, i.e., the visual, proprioceptive, and vestibular systems . These sThe operations of the balance-related sensory systems could be assessed using the modified Clinical Test of Sensory Interaction and Balance (mCTSIB) ,6,7,8. TThe sway directions (ML and/or AP) carry information that is crucial in understanding the behavioral patterns of the sensory systems which are necessary for diagnostic and monitoring purposes. These directions have been used in several studies to carry out postural sway analysis on various cohorts of subjects using varying analytical techniques. For example, utilizing the statistical technique of two way mixed design model and employing the root mean square (RMS) variables of COP displacement and velocity in the ML and AP directions, a comparison of postural strategies used to regain balance between older adults of ages 60 to 90 years, and younger adults of ages 20 to 40 years, undergoing a balance task i.e., standing quietly for 30 seconds, walking in place and then taking a lateral step and stand quietly (30 seconds) was carried out . The worCluster analysis allows for the examining of structural similarities or dis-similarities . By combThe purpose of this study was to use the Kohonen neural network to investigate and demonstrate the effects of sensory systems to balance using their respective time-domain directional sway information. The subsequent sections are organized as follows: a brief introduction of the Kohonen neural network is provided, followed by the methodological section which is divided into data collection and analysis parts, and finally the results are explained.Developed by the Finnish researcher Tuevo Kohonen in the 1980s . The Kohi.InitializationThe operation of the network as described in ,25,26,27mnw is associated with the connection from thm feature of the input vector (representing an input example) to thn neuron. The number of weights for each neuron is the same as the number of features in the input vector. The weights can be initialized based on prior information or small randomly selected values close to zero. The learning rate and neighborhood size are also initialized (these are defined in step iii). Kohonen network requires the learning termination point to be initialized. The manners this can be achieved are explained in step (iii).The learning process starts with initializing the weights associated with each neuron. A weight ii.Competitionthj training input vector (Xj). Each neuron determines the closeness (or distance) of the current input vector to the weights associated with its connections. The neuron with the closest connection weights to the input vector  is selected as the winner. When the Euclidean distance is used to determine closeness, the distance (nd) for neuron n isKohonen network is presented with the iii.AdaptationThis step can be divided into two tasks. Firstly, the neighbors of the winning neuron are identified and secondly, the weights of the winning neuron and those of its identified neighbors are updated. The neighboring neurons are located in the neighborhood region shown in  that surW-(j_current) and W-(j_updated) represent the current and updated weight vectors for the winning neuron respectively. The learning rate (0 < μ ≤ 1) controls the amount of weights changes taking place during each learning iteration. Larger values of μ lead to faster learning but they may reduce adaptation effectiveness. Its initial value is typically chosen heuristically.Adaptation involves updating the weight vectors of the winning neuron and its neighbors. This is carried out using the Kohonen learning rule,In order to improve learning, the weights associated with the neighborhood neurons are also updated to a lesser extent than the winning neuron. Typically, a Gaussian function is used to influence the extent of this update, with relatively (as compared to the winning neuron) smaller weights changes as neurons become further from the winning neuron. The initial size of the neighborhood region and learning rate can be reduced as learning progresses. This is to move from a coarse adjustment of the weights to finer adjustments.iv.Termination of iterationsSteps (ii) and (iii) are repeated until either the required number of iterations is completed, or the magnitude of weight changes reaches a predefined value.The Kohonen network can be implemented using sequential or batch processing. The difference between the sequential and the batch processing is that in the former, the neurons’ weights are updated after each input vector is presented to the network while for the batch processing the weights are updated after an epoch, i.e., a single pass over the entire input data set . The advThe analysis of postural sway involved the projection of the body movements at the center of mass (COM) to the ground surface. This could be achieved by considering the principle of the inverted pendulum  as shownL represents the distance of the COM position to the ground which is assumed to be constant for each individual. The resultant acceleration is shown as R. The angles made by each directional acceleration xa, y,a and za are shown as α, β, and γ respectively. They are obtained from their directional cosines cos(α), cos(β), and cos(γ) respectively. The angles φ1, φ2, φ3, φ4, and φ5 are used for mathematical justifications. The ground displacements from the origin in the x and y directions are xd and yd respectively and H represents the ground projection of the COM height. Equations (3)–(5) describe the algorithm’s operation. The units for distance, acceleration and angle were chosen as centimeters (cm), centimeters per second square (cm/s2), and degrees (°) respectively.x, y, and z. Each signal indicated the movement in one of the 3 orthogonal directions. Communication between the wireless transceiver and the microcontroller was via the serial peripheral interface (SPI) protocol. The signals were wirelessly sent to the receiver unit by the transceiver.The transmitter unit consisted of a microcontroller (type: Arduino Nano), an inertia measurement unit , and a wireless transceiver (type: nRF24L01). It was responsible for measuring the subject’s postural sway. Communication between the IMU and the microcontroller was facilitated using the inter-integrated circuit (I2C) protocol. The IMU signals utilized and processed in the study were the IMU’s accelerometer signals, represented by The receiver unit was responsible for receiving the signal from the transmitter unit and transferring the information to a laptop computer for storage. It consisted of a microcontroller (type: Arduino Uno) and a wireless transceiver (type: nRF24L01). It was interfaced with the laptop computer using a USB cable.Twenty-three healthy adult subjects , mean (standard deviation) age: 24.5 (4.0) years, mean height (standard deviation): 173.6 (6.8) cm, mean weight (standard deviation): 72.7 (9.9) kg, with no previous history of balance dysfunction, participated in the study. During recording, the subject stood relaxed while looking at a wall at a distance of 1 m. The transmitter unit was strapped to the subject’s lower back, at approximately the iliac crest, as shown in The analysis had three stages: (i) processing the accelerometry raw data into sway information, (ii) clustering the sway information, and (iii) interpreting the clustering results to determine the information provided by ML and AP sway directions.x, y and z) of the tri-axial accelerometer. The raw accelerometer data were converted into accelerations in units of gravity (g) by dividing the digital output into units of least significant bits (LSB) of the tri-axial accelerometer by the accelerometer’s sensitivity scale factor in units of LSB/g. The accelerometer’s sensitivity scale factor for a full-scale range of ±2 g was 16,384 (LSB/g). Furthermore, the acceleration outputs  were lowpass filtered using a second order lowpass Butterworth filter with a cut of frequency of 4 Hz. The resultant acceleration (R), the directional cosines: cos(α) cos(β) and cos(γ) and the displacement in the x and y axes from the origin (position), xd and yd were obtained using Equations (3)–(5). The subject’s body position, velocity, and acceleration were obtained using Equations (6) and (7), where k corresponds to the direction of interest, i.e., ML and AP, The raw accelerometry data used for carrying out the analysis consisted of the body’s acceleration in the three axes Dkn=Correlation analysis was carried out among the variables of the ML and AP directions, i.e., the RMS measures of position, velocity, and acceleration, to identify the variables that had strong correlations with each other. Highly correlated variables were defined by the coefficient of correlation being equal to, or greater than, 0.85 . Prior t©  batch algorithm of the Kohonen network was used for training with a Kohonen map size of 10 by 10 neurons . A large map was used to explore the relationships between the four conditions of mCTSIB. The default value of the initial neighborhood size  was used for the training and the number of training iterations was set to 1000. The entire dataset was used as the training and test set because the interest was to explore the interactions between the conditions of the mCTSIB. Each mCTSIB condition consisted of the data from the 23 subjects and the clustering of the data set was conducted by pairing each condition with condition 1, taking condition 1 as the reference . Therefore, three examination combinations could be carried out, i.e., conditions: 1 and 2, 1 and 3, and 1 and 4. The clustering of the input data was carried out in two abstraction levels. The two-level approach has been shown to be computationally more efficient than using only the K-means clustering  as only ®, Massachusetts, USA) batch was used for the implementation of the K-means clustering with the default distance metric, i.e. Euclidean. The processes involved were representing the porotype vectors formed during the first abstraction level by their centroids and then clustering the resulting centroids. The prototype vectors represented the neurons associated with at least one input data. The main issue that needed considering when performing the K-means algorithm, was determining the number of clusters. To this effect, the number of clusters was determined by performing K-means clustering on the resulting centroids of the prototypes formed during the first abstraction level for the different number of clusters (K) varying from 2 to 30 and evaluating the resulting cluster separation, using the Davies-Bouldin (DB) index as the measure of the clustering separation. The DB index is an internal evaluation measure based on the ratio of within to between cluster separations [ic, jc, ic, jc, ijDB is an array of DB indices for cluster i with respect to the thj cluster, where i ≠ j, j = i + 1:k, iDB is DB index for the thi cluster. The number of clusters with the minimum DB was used for external evaluation. The second abstraction level consisted of the clustering of the formed Kohonen network prototype vectors. For this purpose, the clustering was performed using the K-means algorithm. The K-means algorithm divides the data set into K clusters such that the within cluster’s sum of the square is minimized . The buiarations . UsuallyK-means clustering, selected based on the highest F-measure. The DB index selected from the clustering between conditions 1 and 4 was used for the clustering between conditions 1 and 2, and conditions 1 and 3 as the greatest differences were expected to be between conditions 1 and 4.The minimum DB may not be optimal , thus fuijn is the number of elements of class j in the thi cluster, in is the total number of elements in the thi cluster, n is the total number of elements of the dataset, ipurity is the thi purity of the clustering. thi cluster, iprec is the thi precision. thj class of the thi cluster, irecall is the recall of the thi cluster. The F-measure of the clustering was obtained by taking the average over all the clusters asAfter clustering the prototype centroids into K clusters, the centroids were replaced by their actual constituent data points that were obtained from the first abstraction level. The class labels corresponding to the four conditions of the mCTSIB were assigned to the data points of the formed clusters. For evaluation purposes, each cluster was labeled by most of the categories present in the cluster and also a labeled category was assigned to more than one cluster if there was a dominant grouping in that cluster. External evaluation measures such as purity, precision, recall, and F-measure were utilized to measure the differences between the four conditions of the mCTSIB. The purity measure evaluated the coherence of a cluster, i.e., the degree to which a cluster contained data entries from a single category . The greiF is the F-measure of the thi cluster. More details about these measures are available [vailable .©  batch while the statistical test was carried out using SPSS® .The clustering process was repeated thirty times, with the data randomized at each repetition and its corresponding external measures recorded and utilized for further analysis. The repetition of the clustering 30 times was conducted to guarantee the reliability of the values. A statistical test was carried out on the recorded external measures between the ML and AP directions to determine whether a significant difference existed between their results. The choice of statistical test for the significant difference was subject to the outcome of the test of normality from the external measures. In cases the outcome indicated data were from a normal distribution, the independent sample t-test was used otherwise the Mann-Whitney U-test was used. The software program for the external evaluation of the clustering performance was writing in Matlabp < 0.05). Thus, Kendall’s tau correlation was conducted between the variables and the result suggested that there was a strong positive correlation between the RMS velocity and RMS acceleration (rτ (21) = 0.929, p < 0.01) while a significant weak positive correlation existed between the RMS position and RMS velocity rτ (21) = 0.344, p = 0.022), and the RMS position and RMS acceleration (rτ (21) = 0.32, p = 0.032) with the level of significance (α) equal to 5%. Thus, RMS position and velocity were used for the subsequent analysis.The correlations between the variables, i.e., the RMS values of position, velocity, and acceleration as examined using the weights of the Kohonen network’s neurons (weights planes) are shown in The visualization of the Kohonen map and its structure using the test set for conditions 1 and 4 for both the AP and ML directions is shown in The results of the DB index for clusters K = 2 to 30 are plotted in The mean F-measures obtained for the AP direction between conditions 1 and 4 for 30 repetitions for the number of clusters (K) equal to 2, 4, and 9 were 0.717, 0.448, and 0.249 respectively. Similarly, the mean F-measure obtained for the ML direction between conditions 1 and 4 for K = 2, 4, and 9 clusters were 0.532, 0.356, and 0.246 respectively. Since the values of the F-measure for two clusters were higher than those of four and 9 clusters, two clusters were used for further analysis.Using two clusters for the K means clustering, the averages of the respective clustering performance measures between conditions 2, 3, and 4 of the mCTSIB and condition 1 are shown in p < 0.05) existed between the corresponding results of the external measures obtained from each direction. The results of these measures are representative of healthy adult subjects and thus the results may differ across subjects with balance problems.Similar values of the external measures would have been observed if the sway in the ML and AP directions were to be the same. On average, over all the four mCTSIB conditions, the purity, precision, recall, and F-measure for the AP direction were respectively 7.12%, 11.64%, 7.12%, and 9.5% higher than the respective values obtained from the ML direction. The Mann-Whitney U-test showed that significant differences  with a mean (standard deviation): ages 24.5 (4.0) years, height: 173.6 (6.8) centimeter, and weight: 72.7 (9.9) kg, by examining the clustering characteristics of their sway in the mediolateral (ML) and anterior-posterior (AP) directions.Poor clustering was observed using the sway from the ML and AP directions across the four conditions of the mCTSIB as the sway was from a cohort of healthy adult subjects. However, disparities existed in clustering results from the respective directions with a reduced similarity in the AP direction as compared to the ML direction. This was an indication of sway differences and highlights that as the sensory systems were reduced/altered, i.e., from condition 1 to 4 of the mCTSIB, an increase in the sway occurred with more sway in the AP direction than the ML direction. Therefore, the AP direction was more sensitive to the responses of the sensory systems. Dependencies on the sensory systems to balance for the subjects using the AP direction indicated that the subjects depended more on the proprioceptive system than the visual system. Contrarily, considering the ML direction, the reverse was the case. A combination of the ML and AP sway reduced the differences between sensory systems, although the result still suggested that more reliance was placed on the proprioceptive system than the visual system. A similar result was reported in , that inThe presence or absence of the proprioceptive system as indicated by the comparison between the clustering results of conditions 1 and 2 and conditions 1 and 4, affected the AP directional sway but had only a minor effect on the ML sway as indicated by their clustering results with a value of 0.5 being the lowest attainable value for the external measures. This indicated that the proprioceptive system had only a minor effect on the ML sway. In contrast, the presence or absence of the visual system as indicated by the comparison between the clustering results of conditions 1 and 3 and conditions 1 and 4, affected both the AP and ML directional sway as indicated by their clustering results with a value of 0.5 being the lowest attainable value for the external measures. This indicated that the visual system in addition to reducing the AP directional sway, had a significant effect on the ML directional sway as it completely removed any sway differences. A similar result was reported in  with a dIn general, the clustering results revealed that in healthy adult subjects, the sensory systems sway is highly related. Thus, emphasis should be placed on training programs that ensure the full operation of all the systems. In the case where full operation cannot be guaranteed, the interaction of the visual or the proprioceptive with the vestibular can help to achieve functional balance. The importance of the vestibular system has been reported in several studies. In , patienti.The clustering of the postural sway, based on the RMS measures of the body’s position and velocity of the AP direction, showed larger values of external measures of the clustering performances as compared to similar variables from the ML direction. As a result, it may be inferred that the AP direction was more sensitive to the effect of the information of the sensory system as compared to the ML direction.ii.Hindrance in the operation of the visual system leads to an increase in the external performance measures of the clustering in the ML direction. In clustering between the eyes open conditions (conditions 1 and 3), the clustering evaluation measures, i.e. purity, precision, recall, and F-measure were 0.5, which was equal to the minimum value that could occur from the clustering. Thus, postural sway in the ML direction is a characteristic of the contribution of the visual system.iii.Using separate directions resulted in differing order of similarities across the four conditions of the mCTSIB. When the clustering measures of the AP direction were used for analysis, the order of similarities of the conditions were conditions 1, 2, 3, and 4. However, when the clustering measures of the ML direction were used, the order of similarities were conditions 1, 3, 2, and 4 and the results showed less disparity across the conditions. When the measures from the AP and ML directions were combined, the external clustering performance measures were reduced. This indicated that combining the results of ML and AP directions results in closer similarities between the conditions.iv.There was not a large variation between the maximum and the minimum (0.5) values across all external measures of the clustering performance between the conditions of the mCTSIB using the RMS values of the position and velocity of the respective ML and AP directions. The maximum value of the external measures corresponded to the value of the precision measure (0.795) for the clustering between the conditions 1 and 4. In the case of clustering with two clusters, the minimum and maximum values of the external measures that can occur for two groups with an equal number of samples are 0.5 and 1 respectively. Thus, the difference of 0.295 between the precision value and the minimum i.e. 0.795 minus 0.5 is considered small. Therefore, it was concluded that for healthy young adult subjects, there is a strong interrelationship between the mCTSIB conditions and their postural sway results cannot be clustered well into two distinct groups.The study’s findings can be summarized as: For diagnostic purposes, the effect of the sensory systems in balance obtained from the mediolateral (ML) and anterior-posterior (AP) directions of 23 healthy young adults  with closely related age, height, and weight were investigated. The subjects engaged in the four conditions associated with the modified Clinical Test of Sensory Interaction and Balance (mCTSIB). The measurements of the root mean square sway values of body position and velocity obtained from an inertial measurement unit (IMU) placed at the lower back of the subjects at approximately the position of the iliac crest.Their effects were investigated using Kohonen neural network clustering performances which were evaluated using external measures such as purity, precision, recall, and F-measure. The results indicated that the information provided by ML and AP directions in balance analysis differ and that the direction of sway plays an important role in correctly assessing a subject’s balance. In particular, it was observed that the AP direction performed better than the ML direction in clustering the data from the four conditions associated with the mCTSIB. The ML sway was characterized by the hindrance of the visual system. The combination of the sway variables from the ML and AP directions resulted in smaller differences between the mCTSIB conditions. We suggest with addition to a reduction in AP sway, a reduction in ML sway should particularly be used to assess the functionality of the visual system while a reduction in AP sway should be used in particular for the proprioceptive and vestibular systems.


In addition, by taking full advantage of the robustnessof the built-in algorithm of the simulator, the convergence of themodel is improved; meanwhile, simplification of the process and reductionof the optimization time are accessible with the proposed design specificationsand assumptions. For a convergence-difficult column, a stepwise convergenceadjustment was used to ensure their convergence. Based on this, therobustness and effectiveness of the method is proven through a casestudy, and it can also provide guidance for model selection, processsimplification, and optimization of the same type of absorption process.For an energy-intensivesweetening process, it is common that sourgases from different sources are sent to a single sweetening plantin industries. In our previous work, a multiple gas feed sweeteningprocess was proposed, which can simultaneously improve the purityof H Althoughthe sweetening process is well-matured, the energy consumption ofthe plant is very high. As far as we know, steam consumption of theplant accounts for more than 10% of that of the entire refinery, sothe research and improvement of the process have been going on.Inindustry, sweetening is an indispensable process to protectthe environment, prevent pipeline corrosion and catalyst deactivation,etc. Several methods have been applied in sweetening plants, suchas absorption through a column or membrane contactor,11 and the development of new solutions.14 Among them, the improvement of the process configuration can bedirectly applied to the optimization of the plant. Ahn et al.15 evaluated 10 different configurations of sweeteningprocesses and the most representative ones are the absorber intercoolingconfiguration and split-solvent configuration. The absorber intercoolingconfiguration can remove the absorption heat through the intercoolerand effectively promote the absorption of acid components. The split-solventconfiguration obtains a semilean solvent through partial separationor flash, which significantly reduces the amount of the lean solvent.All of the abovementioned configurations can ultimately reduce theenergy consumption of the plant. Antonini et al.16 proposed a modified version based on the split-solventconfiguration. The semilean solvent is split twice to obtain a coldsemilean solvent and enters the top of the desorber, which can partiallyreplace the reflux of the desorber, resulting in lower consumptionof the plant. In addition, there are various schemes18 to increase the H2S/CO2 ratio in the feedgas by recycling part of the acid gas back to the raw materials sothat the content of H2S in the acid gas will be increased,thereby reducing the energy consumption of the downstream sulfur recoveryplant.The improvement of the process is mainly concentrated in threeaspects, namely, the optimization of the process configuration, theindustrialization of other technologies mentioned above or the developmentof hybrid technologies,19 kinetically controlled reactions(2)equilibrium-controlledreactionsSince MDEA, asa tertiary alkanolamine, cannot directly react with COFor the calculation of chemical reactions,the equilibrium-controlled eq 41 and Pinsentet al.42 The rate constants of each kineticallycontrolled reaction are presented in 8For the kinetically controlled 3.1.243 was employed to establish the rate-based model. The schematic diagramof the two-film theory is shown in 45 for the model development are summarized below:(1)It is assumed that there is a phaseinterface where the phase equilibrium is reached.(2)The resistances of mass transfer andheat transfer are located in the boundary layer on the gas and liquidside, respectively.(3)The two films are stagnating, so onlydiffusive mass transfer is considered.(4)The bulk phases are ideally mixedwith uniform concentrations and temperatures.(5)The interfacial area of mass transferand heat transfer is the same.(6)Flow is one-dimensional and the variationof concentration and temperature in the radial direction is negligible.(7)The column is under adiabaticmodeof operation.In this work, the two-film theory methodThe equations of the entire model can be dividedinto three parts:governing equations, transfer equations, and auxiliary equations.Similar to the equilibrium-basedmodel, the governing equations of the rate-based model also containmaterial balance, energy balance, phase equilibrium, and summations,and the reaction process is considered in the material and energybalance. However, the difference is that the amount of mass transferand heat transfer must be taken into consideration for the rate-basedmodel.The calculations of material balance for bulk liquid,bulk vapor, liquid film, and vapor film are displayed in The calculations of energy balance for bulk liquid, bulk vapor,liquid film, and vapor film are displayed in The calculation of phase equilibrium at theinterface is displayedin The summations of the components in bulk liquid, bulk vapor, liquidfilm, and vapor film are displayed in 47 to calculate the chemical absorption process. However, in the sweeteningprocess, there are complex parallel and consecutive reactions in theliquid phase, which makes it difficult to accurately describe theprocess only by the enhancement factors. Therefore, the characteristicsof the ions are considered in the model to directly describe the absorptionprocess instead of enhancement factors.48(1)Mass transferequation of liquid phase:49 with binary mass transfer coefficients, wasused to calculate multicomponent mass transfer coefficients and masstransfer rates of each component between the vapor and liquid phases.contrary to the equilibrium-basedmodel, the calculation of interfacialflux in the rigorous rate-based model is required. The Maxwell–Stefantheory, a rigorous multicomponent mass transfer theoryThe introduction of the electrical potential requires an additionalequation to assure that the liquid phase is electroneutral everywhere(2)Mass transfer equationof gas phase:the mass transfer equation of the gas phase issimilar to thatof the liquid phase, the only difference is that there are no ionsin the gas phase and ionic characteristics need not be considered.(3)Heattransfer equation of liquid phase:the heat flux through theliquid film comprises the conductiveand convective terms:(4)Heat transfer equation of gas phase:For the absorption processinvolving chemical reactions, the conventional methods introduce theconcept of the enhancement factor based on several simplificationsThe heat transfer equation of the gas phaseis the sameas that of the liquid phase.50 the Scheffeand Weiland,51 the Chilton and Colburn,43 and the Bennett, Agrawal, and Cook52 correlations are used to calculate binary masstransfer coefficients, interfacial area, heat transfer coefficients,and liquid holdup, respectively.In this work, the well-knownand accepted correlations, which have been fitted to experimentalmeasurements from absorption and distillation columns of laboratoryand pilot plant, are used. According to the applicable conditionsof each correlation, the AICHE,3.22S and CO2, which are further applied to solve the columnbased on the conventional equilibrium-based method. The simplificationsin efficiency mode are that it does not account for resistance toheat transfer and assumes the liquid phase is in chemical equilibrium.Allof the above equations are built-in equations of rate-based distillationin Aspen HYSYS, and the sweetening process model is also simulatedthrough Aspen HYSYS v.11 with the Acid Gas property package. The equationsof the rate-based model are more than that of the equilibrium modeland containing a lot of differential and high power equations, whichmakes calculations more difficult. Aspen HYSYS uses an efficient,Newton-based simultaneous correction approach in which the full setof equations is solved using Newton’s method, and the equilibrium-basedmodel is used to obtain the initial guess. In addition, there aretwo rate-based modes for the simulation of the absorber and stripper:efficiency and advanced mode. The advanced mode uses the above equationsand through the Maxwell–Stefan theory to rigorously calculatethe heat and mass transfer rates without thermal or chemical equilibriumassumption between the vapor and liquid phases for each stage. Theefficiency mode uses the rigorous rate-based calculations with somesimplifications to calculate the Murphree efficiencies for HSection S1).Due to the relatively high operating temperature of the stripper,the heat transfer and reactions are fast, which conforms to the assumptionsof the efficiency mode. Therefore, the efficiency mode is suitablefor simulating the stripper and its built-in algorithm can ensuregood convergence even if various types of design specifications aredefined. Since the mass and heat transfer have significant impactson the absorber, the use of advanced mode can ensure the accuracyof the simulation results. Detailed comparison of the two simulationmodes and the accuracy of the simulation results are provided in theSupporting Information  and operating cost (Cop).Due to the negligiblechanges in equipmentcost for different configurations, the economic benefit is consideredto be the only difference between the product value (P2SH) and flow rate (m2SHacid) of H2S in acid gas. The price of H2S is toreflect the impact of H2S with different purity on thefixed investment and operating costs of the downstream sulfur recoveryunit. Thus, it was estimated based on that of the basic purity (y2SHbase) with known price (P2SHbase) and the additionalvalue (ΔP2SH). The y2SHbase, P2SHbase, and ΔP2SH are all derived from the literature,38 which are 70%, 142.86 $/t, and 0.72 $/t, respectively.The product value is calculated based on the price (Qu) of the condenser and reboiler and the price (Pu) of the utilities used. For cooler and pumps,their operating cost change is trivial and negligible compared tothat of the condenser and reboiler. The information of utilities introducedis shown in The operating cost is calculated by the duties ((1)Split ratio of eachsour gas to differentfeed stages (SR)(2)Fs)Circulatingflow rate of the leansolvent 2S in acidgas (m2SHacid)Flow rate of H(2)2S in acid gas(y2SHacid)Purity of H(3)Qcon and Qreb)Duties of the condenser and reboiler((4)2S in sweetgas (y2SHsweet)Content of HThe process design parameters (output from HYSYS) included in theobjective function are:4.2Constraints include explicitconstraints and implicit constraints. Explicit constraints are toensure that the content of certain components in the sweet gas iswithin the specified range, which appears in the objective functionin the form of a penalty function see 35 to con30 These implicitconstraints are as below.(1)30 Inthis way, the content of water in the acid gas can be reduced as muchas possible to meet the transportation and process requirements ofthe downstream plant.53Specify the condensation temperatureat the top of the stripper: since the temperature of cooling wateris 30 °C, the condensation temperature is 40 °C to keepthe minimum temperature approach to 10 °C, which is often usedin industry and literature studies.(2)2S in the leansolvent at the bottom of the stripper is used as an indicator of theseparation effect. Through this design specification, the contentof H2S in the lean solvent can be maintained unchangedduring circulation so that the process is better converged.Specify the separation effect of thestripper: in this work, the content of HThe implicit constraints, which wereapplied based on surrounding environmental conditions, process designguarantee, specifications, and common standard practice in the field,were imposed on the Aspen HYSYS model through a large list of “designspecifications”.Through the above design specifications,the freedom degree ofthe stripper is zero. Aspen HYSYS can automatically adjust the refluxratio and overhead vapor rate of the stripper to meet the constraints,and its built-in algorithm can effectively ensure the column convergence,which can avoid the reflux ratio and overhead vapor rate as decisionvariables, reducing the complexity of optimization.4.354 especially when there are loops in the process, where convergenceis time-consuming. The information flow diagram of the process isshown in In eachiteration of the optimization, almost all of the computational time(>99.0%) is spent on running the simulation,(1)At the design stage,it is appropriateto replace the cross heat exchanger with a heater and a cooler, sothe rich solvent enters a heater and finally to the top of the stripper.Because the temperature of the lean solvent at the bottom of the stripperis generally between 110 and 120 °C, to ensure the minimum heattransfer temperature difference, the temperature of the rich solventsent to the stripper is specified as 100 °C.(2)2Sin the lean solvent at the bottom of the stripper is fixed, the contentof CO2 changes little during the optimization process sothat its influence on the process is negligible. Therefore, the contentof CO2 in the lean solvent is specified equal to that ofthe conventional process.When the content of HTo simplify the simulation difficulty and reflect the realisticsof the process, the following assumptions are considered in the simulation:Through replacementof the cross heat exchanger with a heater anda cooler, the entire system contains only one loop; the informationflow diagram is shown in 4.437 These problems can be avoided through metaheuristicalgorithms for black-box optimization, where optimal solutions canbe obtained through population-based search techniques without requiringany derivative information.55Optimizationof the multiple gas feed sweetening process is a strongly nonlinearproblem due to rigorous rate-based equations and thermodynamic propertieswith ionic characteristics as well as considerable amount of decisionvariables and built-in design specifications. In Aspen HYSYS, thebuilt-in algorithm of the column relaxes the accuracy of the solutionto ensure the convergence of the model as much as possible, usuallyresulting in small numerical noise. This numerical noise can be largeenough to prevent the calculation of accurate derivatives and makethe derivative-based optimization algorithms unreliable.56 the relativeease of implementation, and requiring only a few tuning parameters.57 The PSO is a new swarm intelligence algorithmproposed by Kennedy and Eberhart.58 Inthis model, each particle’s own state is composed of a setof position vectors and a velocity vector, which, respectively, representthe feasible solution of the problem and its direction of motion inthe search space. The particle constantly learns the group optimalsolutions and individual optimal solution, updates its own velocityand position, and realizes the global optimal search. Therefore, theparticle position and velocity update equation is the core of thePSO algorithm  algorithm was adoptedbecause of its robust convergence in the optimization of complicatedand nonlinear problems,ithm see 37 and 38ed for w 3939To combine the simulator with the PSO algorithm, a hybrid simulationplatform was used. Aspen HYSYS v11 is automated by MATLAB R2018a asthe external solver, which programmatically runs Aspen HYSYS as afrontend. On the one hand, Aspen HYSYS can strictly calculate theabsorption and desorption process, and the built-in algorithm hasgood convergence for a single column. On the other hand, MATLAB programmaticallycontrolled black-box functions inside Aspen HYSYS and takes all relevantdecisions to attain the optimal design with the PSO algorithm.SRij) and the flow rate of solution (Fs),where the split ratio of each raw gas is multiplied by its flow rate(Fi) to obtain the flowrate of each raw gas to each feed stage, and then these flow ratesand the flow rate of solution are transferred from MATLAB to HYSYSfor simulation. After simulation, the flow rate of H2Sin acid gas (m2SHacid), purity of H2S in acidgas (y2SHacid), duties of the condenser and reboiler(Qcon and Qreb), and content of H2S in sweet gas (y2SHsweet) by HYSYS are sent back to MATLAB for the calculation of the PSOfitness function, which performs the optimization. In addition, MATLABcan also control the active and reset status of unit operation andeffectively diagnose fault. Through the connection between MATLABand Aspen HYSYS, it can not only perform high-fidelity model calculationsbut also obtain optimal solutions by the metaheuristic algorithm tomeet the needs of conceptual design. All simulation runs and executedalgorithms are performed on a PC with an i5-1035G1 CPU and 8 GB RAM.A connection between MATLAB and Aspen HYSYS can be establishedthrough a Component Objective Model (COM) in ActiveX, which allowsdirect two-way communication between Aspen HYSYS and MATLAB. As shownin 4.5A simulation-basedoptimization framework is demonstrated in For the simulation-based optimization, the PSO algorithm firstinitializes all particles and obtains the information of each particle,which is the split ratio of each sour gas and the amount of the solvent.Since the information data is continuous variable generated underunconstrained conditions, the value of split ratio is normalized andthen the information data is sent to Aspen HYSYS. To ensure the continuityand improve the convergence of the simulation, a sequential modulesimulation method is used to control the simulation sequence of eachcolumn through MATLAB. This method can control the simulation sequenceby switching the operation status of the absorber and stripper. Onlywhen the absorber converges, the simulation of the stripper starts.When a column does not converge, a convergence adjustment is performed.The most effective adjustment method is to give the column a reasonableinitial value to promote its convergence. In this work, a stepwiseapproach is used to promote convergence. The convergence adjustmentprocess is shown in 52S content in the sweet gas is less than20 ppmv. The information of each dry gas and absorber andstripper is shown in A refinery in Shandong Province,China, where two sulfur-containingdry gases are produced by two catalytic cracking plants, is takenfor demonstration. The two dry gases are sent to the same plant forsweetening treatment with 30 wt % MDEA solution, and it is finallyensured that the H5.1m,n) ranging from 0 to 1 and correspondingto five feed stages. The subscript m can be 1 or2, respectively, representing dry gas-1 or dry gas-2, while subscript n can be 1–5, respectively, representing the 6thto 10th tray. In addition, the amount of solvent (Fs) also needs to be optimized to ensure the content ofH2S in sweet gas is less than 20 ppmv. The upperbound of the amount of solvent is determined by the worst case whereall of the feed gases are sent to the 6th tray of absorber. Sincethe proposed process can reduce the amount of the solvent, the lowerbound is set to be 0.9 times that of the conventional process.In the conventionalprocess, the two dry gases are mixed first and then sent to the bottomof the absorber. In the optimization process, feed gas is allowedto enter from the 6th tray to the bottom of the absorber. When thefeed stage of the feed gas is above the 6th tray, the number of traysused for absorption will be too small, which will lead to a significantincrease in the amount of solvents and energy consumption. Hence,each dry gas contains five split ratios .For PSO, the larger the number of particles, the larger the searchingspace of the algorithm, which means it is more likely to find a bettersolution, but this in turn would require more running time. A guidancerule is that the number of particles generally needs to be a bit morethan the number of decision variables in the optimization. In thecase study, the number of decision variables in the optimization is11, and 15 particles are selected according to the tradeoff betweenthe performance and the complexity of the PSO algorithm. The maximumnumber of iterations of the PSO algorithm is set to 200. By multipletest experiments, it is found that after 200 times, the optimizationresults tend to be stable for our cases. When the number of iterationsis exceeded or the deviation of the results of 100 consecutive iterationsdoes not exceed 102 and the MDEA solutionis slow, the absorption of CO2 is similar to physical absorption,while the absorption of H2S is obviously promoted by chemicalreactions, and the temperature and concentration distribution in theabsorber also have a significant impact on it. Dry gas-1 with higherCO2/H2S ratio is sent to the 6th–8thtrays; this can not only reduce the temperature in the absorber improvingthe absorption of H2S but also effectively reduce the absorptionamount of CO2 and ultimately increase the purity of H2S in acid gas. Due to the competitive relationship in absorptionbetween CO2 and H2S, the reduction of the CO2 absorption amount can decrease the concentration of CO2 in the liquid phase in the absorber, thereby further improvingthe absorption of H2S and reducing the amount of the leansolvent and energy consumption.The optimized results of decision variables are shown in Section S3). Compared with the conventional process,the proposed process has achieved the effect of improving the purityof H2S and reducing energy consumption. The purity of H2S is increased from 69.28 to 72.32%, and the product valueis increased accordingly. In addition, the duty of the reboiler isreduced from 2750.38 to 2558.89 kW. Eventually, the operating costis decreased by 7.07%, and the overall economic benefit is increasedby 73.36%.The optimizationresults of conventional and proposed processesare shown in 5.2P2SH is increased from 0.72 $/t to 4.5 $/t.Theeconomic benefits of the sweetening process are mainly determinedby product prices and utility costs. For the conventional process,there is a lack of adjustment means to respond to market fluctuations.The advantage of the new process is that it can flexibly adjust theproduct purity and plant energy consumption to respond to market fluctuations.Therefore, to study the effect of product price on the process, thecost of utilities is fixed, and ΔP2SH increases from 0.72 $/t to 4.5 $/t, the product purityincreases from 72.32% to 73.62%, and the duty of the reboiler increasesfrom 2559 kW to 2700 kW, which is still lower than that of the conventionalprocess, and the economic benefits of the entire plant increase. Optimizationflowsheets of the new process for ΔP2SH being 2.5 $/t and 4.5 $/t, respectively, are displayedin 2S at the expense of increasedenergy consumption.The optimization results of eachdecision variable and processare shown in 5.359 are shown in 2S and CO2 in sour gases from different sources vary greatly.Therefore, to explore the impact of the composition of the sour gason the proposed process, a comparison group is set up, the informationis shown in 60 and Abdulrahman.61 The difference in the ratio of CO2/H2S between the two sour gases is smaller. Since theseparation of the stripper in the comparison group is more difficult,the amount of the gas phase in the column is larger, so the innerdiameter of the stripper is adjusted to 1 m.Thequality of sour gases is determined by their composition, andit plays an important role in the influence of energy consumptionand product purity. The compositions of sour gases from differentsources2/H2S ratio of the two sour gases,the adjustment space for the concentration distribution in the absorberis reduced, so the reduction in energy consumption and the increasein the purity of H2S are correspondingly reduced, and theeconomic benefit is only increased by 24.06% compared with the conventionalprocess. Through the above analysis, the greater the difference inthe composition of sour gases, the more obvious the advantages obtainedby the proposed process.Theoptimization results of each decision variable and processare shown in 6In this study, a simulation-based optimization method was presentedto maximize the economic benefits of the multiple gas feed sweeteningprocess. A series of design specifications are imposed on the AspenHYSYS model based on surrounding environmental conditions, processdesign guarantee, and common standard practice. Moreover, a seriesof reasonable assumptions were introduced to simplify the process,eliminate the loop structure, and greatly reduce the optimizationtime. In addition, due to the complexity of the mass transfer modeland the thermodynamic model considering the ion characteristics, astrategy of controlling the simulation sequence along with a stepwiseadjustment method to adjust the nonconvergent column by MATLAB wasadopted to ensure the convergence of the simulation during the optimizationprocess.2S from 69.28% to 72.32%, while the energy consumptionis reduced by 7.07%, and the overall economic benefit is increasedby 73.36%. Through the analysis of the proposed process, it is foundthat the proposed process can flexibly adjust the process parametersto respond to market fluctuations. Furthermore, for the larger differencein the composition of sour gases, the proposed process is more economical.A case study was carried out to demonstrate the proposedapproach.With the above method, the times of nonconvergence can be reducedto zero. The results show that the optimal process can increase thepurity of H7(1)The current optimization method willgenerate too complicated configurations for industrial implementation.The complete preallocations will lead to a complex pipeline networkand control system in actual applications. Therefore, to introducethe complexity index to characterize the complexity of the sour gaspreallocation and using multiobjective optimization to tradeoff theprocess complexity and economic benefits is one of the priority works.(2)2Sprocessing unit, such as the sulfur recovery unit, NaHSO4 unit, and thiourea unit. This will avoid misleading caused by theinappropriate price of H2S.The other important workis to simultaneouslyoptimize the sweetening process and the downstream HIn the future work, we mainly focuson the following two aspects:


The problem of environmental pollution in today's world is not just a problem for the country or a specific territory. This research was conducted with the aim of investigating the environmental behavior of Iranian citizens. The present study was carried out using meta-analysis method on the performed studies (1998–2018) on pro-environmental behavior Papers related to the subject were gathered with the keywords for environmental behavior and environmental support behavior through searching on Magiran, SID, Scopus, Medline (PubMed) and Science-direct databases. Heterogeneity of studies was obtained using the I2 index, and data were analyzed in the comprehensive meta-analysis software. In 21 papers submitted to the meta-analysis process, 10 papers in the score range 20–100 (A) and 11 papers in the score range 1–5 (B), the total number of samples entered into the study was 3670 in the age range of 15–87 year scores (A), 4413 in the age range of 10–70 year scores (B). The overall mean and standard deviation of the environmental behavior (A) based on a meta-analysis of 50.4 ± 3.1 (44.56%–2.5%: 95% confidence limit) and in the score range 1–5 (B), 3.4 ± 0.18 , were obtained. The effect of sample size and the publication issue of studies based on meta-regression in order to investigate the effect of heterogeneity in studies. The mean increases in each interval from 1 to 5, with an increase in sample size; in both intervals with an increase in the publication issue, the mean scores increase (P < 0.05). According to Mata-analysis results, the environmental behavior of Iranian citizens in both study intervals, is about average the global mean index of environmental performance. Environmental support behavior, Iran, Meta-analysis, Systematic review, Pro-environmental behavior. EnvironThe effects of population growth in the world have prompted farmers to excessively use agricultural land to produce the required food. Hence, human activities have been endangering and destroying the environment . Savari In the 2018 ranking of the global environmental performance index, Iran ranked 80th among 180 countries , and has2This study was conducted in a structured, systematic and meta-analytic review based on performed studies on the environmental behavior, including the papers published in Persian and English in national and international journals and searching on the Science direct, Medline (PubMed), Scopus, Magiran, and Scientific Information Database (SID) databases from March 1998 to December 2018. The search process in these databases was done using Persian keywords and their equivalent English words, including environmental behavior and pro-environmental behavior. Then all the papers related to the subject, which were conducted in different regions of Iran in Persian and English based on descriptive  and interventional studies, and papers addressed to the other factors, were excluded from the list. The search studies were classified according to the environmental behavior scores in two scores range from 20 to 100 (A) and 1 to 5 (B) and were placed in the original list of papers. Then a checklist of the selected papers information, including the name of the researcher, the title of the article, the year of the study, the age range, the sample size, and the mean and standard deviation examined, and the quadruple stages of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2.1Conceptually, the environmental behavior refers to the clear and visible actions that are performed by the individual and in response to the environment . Operati2 test. In general, heterogeneity is classified into three categories, the heterogeneity less than 25% (low heterogeneity), between 25% and 75% (medium heterogeneity) and above 75% (high heterogeneity), which, according to the results in the range of 20–100 (I2 = 99%) (A), and in the range of 1–5 (I2 = 99%) (B), and high heterogeneity in the studies, the random effects model was used to combine the results of the studies. Data were analyzed using Comprehensive Meta-analysis software  and Magiran, 3 studies in Science Direct, 6 studies in Scopus, and 50 studies from Google Scholar search engines. Then among the papers with initial condition for entry into the study, 42 studies were selected based on preliminary studies, and with the elimination of 55 repetitive papers. Eventually, with eliminating 18 unrelated papers and removing 14 studies over the secondary studies due to the lack of abstract and the main paper, as well as the poor quality of the papers, 21 papers were obtained. According to the classification of the range of environmental scores, 10 studies in the scores range from 20 to 100 (A) and 11 studies in the range of environmental scores from 1 to 5, were separately introduced into the meta-analysis process . The tot4One of the most important issues of the twenty-first century is the environmental issues. Solving Iran's environmental problems requires planning and providing solutions. The aim of this study was to evaluate the environmental behavior in Iran that based on the meta-analysis of the existing studies, the average overall of environmental behavior was obtained at the global mean average level. In a study done by Also in China, the environmental behavior of citizens was reported 0.74 ± 3.50 . In CanaObviously, many factors and variables are involved in the process of shaping behavior; various factors such as situational factors, cultural and social context, socialist methods, knowledge, attitude and approach of individuals to the topic, all affect the formation of particular behavior. For this reason, moving towards a greater understanding of the complexities of values, attitudes, areas, and personality factors that affect the particular behavior, is necessary . SituatiFinally, it can be said that reducing individuality and increasing the participation of individuals and strengthening their sense of social responsibility, makes people feel belonging to the environment and behave more appropriately to the environment.5According to the results of this study, Iranian people are at the average of the global mean average in terms of environmental behaviors in both areas of the examined scores. Since the environmental issues have become one of the most important issues and challenges of the 21st century, and reducing its consequences requires the serious determination, cooperation, mutual understanding and awareness of it, applying some strategies to increase citizens' environmental behavior and consequently reducing the environmental impacts, can be very helpful.The constraints of the present research were the lack of access to the full text of some papers and the poor quality of some papers under review. Thus, further results based on this methodology should be conducted to corroborate the present results in these and in other countries all over the world.Jahangir Karami: Conceived and designed the study; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Fateme Dehghan: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.Masoud Mohammadi: Contributed reagents, materials, analysis tools or data; Wrote the paper.10.13039/501100006766Iran University of Science and Technology (97013613).This work was supported by Data included in article/supplementary material/referenced in article.The authors declare no conflict of interest.No additional information is available for this paper.


Information on 16,193 infants delivered in Great Britain in one week of April, 1970 was collected by midwives at the birth and during the first 7 days of life. Using multiple sources, 33 children developing cancer by 1980 were identified from this cohort, giving an incidence of 2.04 per 1,000 total births by the age of 10. Comparisons of these 33 children were made with 99 controls, three for each index case, matched on maternal age, parity and social class. Statistically significant associations were initially found with maternal X-rays and smoking during pregnancy, and the use of analgesics such as pethidine during labour, confirming the findings of retrospective case-control studies. Unexpected statistically significant associations were found with delivery of the child outside term, and drug administration in the first week of life. The latter was found in the absence of an association with neonatal abnormalities in the child and relates mostly to the administration of prophylactic drugs such as vitamin K. Logistic regression involving the whole cohort showed independent statistical associations with maternal smoking (OR 2.5), and drugs to the infant (OR 2.6). After adjusting for these factors no other statistically significant associations were found.


The association between coffee consumption and the incidence rate of breast cancer has been analysed in 152 incident cases of breast cancer that developed among 14,593 Norwegian women during a mean follow-up of 12 years. At the time of inquiry they were between 35 and 51 years of age, and at the end of follow-up between 46 and 63. There was an overall weak negative association between daily intake of coffee and risk of breast cancer, which was not statistically significant. However, the association with coffee varied, depending on the body mass index (BMI) of the women. In the lean  there was an inverse relation between coffee intake and risk of breast cancer . In this group, women who reported drinking 5 cups or more per day had an age-adjusted IRR of 0.5  compared to women who had 2 cups or less. In women with Quetelet's index equal to or greater than 24 there was a positive relation between coffee intake and breast cancer risk , where the corresponding age-adjusted IRR was 2.1 . This interaction effect between coffee intake and BMI was statistically significant . In summary, the results of this study suggest that coffee consumption reduces the risk of breast cancer in lean women, whereas coffee might have the opposite effect in relatively obese women.


Glossina) can be examined through the construction of a dynamic mass-energy budget specific to these flies but such a budget depends on efficiencies of metabolic conversion which are unknown. These efficiencies of conversion determine the overall yields when food or storage tissue is converted into body tissue or into metabolic energy. A biochemical approach to the estimation of these efficiencies uses stoichiometry and a simplified description of tsetse metabolism to derive estimates of the yields, for a given amount of each substrate, of conversion product, by-products, and exchanged gases. This biochemical approach improves on estimates obtained through calorimetry because the stoichiometric calculations explicitly include the inefficiencies and costs of the reactions of conversion. However, the biochemical approach still overestimates the actual conversion efficiency because the approach ignores all the biological inefficiencies and costs such as the inefficiencies of leaky membranes and the costs of molecular transport, enzyme production, and cell growth.The time varying flows of biomass and energy in tsetse (This paper presents estimates of the net amounts of ATP, fat, or protein obtained by tsetse from a starting milligram of blood, and provides estimates of the net amounts of ATP formed from the catabolism of a milligram of fat along two separate pathways, one used for resting metabolism and one for flight. These estimates are derived from stoichiometric calculations constructed based on a detailed quantification of the composition of food and body tissue and on a description of the major metabolic pathways in tsetse simplified to single reaction sequences between substrates and products. The estimates include the expected amounts of uric acid formed, oxygen required, and carbon dioxide released during each conversion. The calculated estimates of uric acid egestion and of oxygen use compare favorably to published experimental measurements.This biochemical analysis provides reasonable first estimates of the conversion efficiencies for the major pathways used by tsetse metabolism. These results now enable a deeper analysis of tsetse ecology based on the construction of a dynamic mass-energy budget for tsetse and their populations. Glossina Wiedemann), the time-varying rates of biomass ingestion and use, influence most aspects of the ecology of these flies including the nutrition and growth of individuals, the intrinsic growth rate of tsetse populations, and the transmission dynamics of the tsetse vectored trypanosomiases. A particularly effective approach to the examination of these dynamics involves the assembly and evaluation of a dynamic mass-energy budget [The dynamics of mass and energy flows in tsetse  and including adenosine triphosphate (ATP). These each have other closely related molecules with essentially identical roles in metabolism. In this paper, all these energetic molecules are treated for simplicity as their equivalent amount of ATP.Metabolic energy is provided by a number of molecules including the reducing molecules nicotinamide-adenine dinucleotide (hereinafter NADH) and flavin-adenine dinucleotide  ,27. The ic acids . Glycogeic acids  so that in utero. This secretion comprises the major gain in body mass of females during pregnancy. This milk consists entirely, by dry weight, of fat and protein [In pregnant female tsetse a significant portion of the bloodmeals are used to produce the milky secretion (milk) of the milk gland which nourishes the offspring  protein -31 and iTsetse fat acts as an energy reserve which is catabolized for metabolic ATP following two pathways: one pathway, common to all organisms, produces energy for general metabolism and another pathway, specific to tsetse, powers flight by converting alanine to proline and then back ,32-37.The major metabolic pathways of tsetse therefore consist of the conversion of amino acids from blood, into the the energy of the outer bi-phosphate bond in ATP, into body or milk fat, or into body or milk protein, and the conversion of fat into ATP along two seperate pathways, one for resting metabolism and one for flight. These pathways are presented schematically as the five arrows of figure × 10-4 and 5.465 × 10-4 moles of ATP from adenosine diphosphate (ADP), between 0.33 and 0.44 milligrams of Fat, or 1 milligram body protein, since the change in free energy during protein combustion in oxygen is between -3 and -4 kcal/g, the change from the release of the third phosphate of ATP is -7.32 kcal/mol, and the change due to fat combustion is around -9 kcal/g. Each milligram of fat could produce at most 1.230 × 10-3 moles of ATP from ADP. These estimates are high since they do not include the energetic costs and inefficiencies of the conversion process, the costs of disposal of toxic by-products, or the biological costs of digestion and growth. These crude estimates do not provide the amount of respiratory gases consumed or produced in the conversion process and do not distinguish between the two pathways tsetse use to catabolize fat. More detailed estimates require the use of a stoichiometric approach.Crude estimates of the conversion efficiencies of these pathways can be derived from calculations based on the chemical energy content of the end products. Each milligram of blood protein could produce at most between 4.098 The efficiency of conversion along each of these pathways depends, first, on the detailed chemical composition of the reactants, both substrates and products, in these pathways and, second, on the actual sequences of reactions used in each pathway. These two issues are discussed next.The exact chemical composition of the reactants involved in these conversions define the end points of the metabolic reaction pathways. This composition constrains the overall yield of the pathway, determines the mass balance of the reactions, and determines the amounts of toxic by-products produced by conversion.Water, despite forming the major component of the blood food source and of tsetse body tissue, is ignored throughout this analysis because it has no influence on the calculated efficiencies of conversion. Water participates in the biochemical reactions of energy production only as the aqueous reaction medium and as the end product of full catabolism. Since biochemical reactions occur in an aqueous medium, water is never a limiting factor for the reactions. This analysis of the net efficiencies of biochemical conversion can therefore focus entirely on the non-aqueous, 'dry,' components of the tissues.The biochemical composition of bloodmeal protein is presented in Bursell  based onThe composition of tsetse body fat is estimated in Langley and Pimley . The fatThe composition of tsetse muscle protein is presented in Bursell  and can The composition of the fat produced in the milky secretion of females and incorporated into the developing embryo was analyzed by Langley and Pimley . They foet al. [The composition of the protein in tsetse milk is presented in an article by Cmelik et al.  and repeet al. . This anTsetse dispose of the excess nitrogen in the bloodmeal by egesting haematin, by excreting arginine and histidine, and by forming uric acid ,40. TherIn this paper, the five major metabolic pathways presented in figure The sequence of reactions used by tsetse for the conversions described by figure The metabolic pathways used by tsetse combine standard pathways used by most organisms with pathways specific to tsetse. The formation, from amino acids, of ATP and of fat proceed through standard pathways, respectively, the Krebs cycle followed by the electron transport chain and the triglyceride synthesis pathways. The pathway used by tsetse to form uric acid proceeds via glycine ,24. TsetBiochemical Pathways by Michal [The specific reactions involved in each step of the conversion pathways are similar for most organisms and have been established by biochemists and presented in the biochemical literature. The book y Michal  providesBiologically realistic calculations of the proportions of conversion between substrates (blood or fat) and products  in the five pathways presented in figure An overview of the biochemical conversions considered here is presented in figure The results section presents the estimated yield, uric acid production, and gas exchange in each of the five major metabolic pathways. First, the analysis examines the pathway of conversion of blood into the energy in the outer bi-phosphate bond of ATP, requiring an estimate of the concomitant cost of uric acid formation. Next, conversion estimate of blood to protein is calculated as a bulk transfer of blood amino acids to protein coupled with the cost of protein formation. Third, the conversion of blood to fat is analyzed. Finally, the last two sections consider the catabolic breakdown of fat for the formation of ATP from ADP, first, following the pathway used for resting metabolism, and then, following the pathway used for flight.The discussion section evaluates the yield estimates by comparing them to empirical measurements described in the literature, then assesses the results, examines the assumptions, and considers the validity of the approach.The conversion efficiency of each of the five pathways of figure The first major metabolic pathway involves the conversion of the amino acids obtained from the protein in a bloodmeal into the high energy bonds in ATP used to power cellular processes and muscular motion. All adult tsetse use part of their bloodmeals for this purpose. The net estimate of the yield of this conversion pathway must incorporate the costs of uric acid formation required to excrete all of the nitrogen in the bloodmeal.The proportion of conversion of blood to ATP is calculated starting, for convenience, with a pool of blood which consists of 100 moles of amino acids in the molar proportions of human blood presented in  and repeWhen bloodmeal amino acids are converted to ATP, all of the absorbed nitrogen is excreted as uric acid . Uric acThe overall costs of uric acid formation are calculated as conversions of alanine and cysteine to serine, serine to glycine and glycine to uric acid based on the reaction sequences in Michal , with th2 + 1 O2 + 23 ATP → 2 Uric Acid1 Alanine + 7 CO2 + 1 O2 + 23 ATP → 2 Uric Acid1 Cysteine + 7 CO2 + 0.5 O2 + 11.5 ATP → 1 Uric Acid1 Glycine + 3 CO2 + 0.5 O2 + 25.5 ATP → 2 Uric Acid1 Serine + 7 CO10-Formyltetrahydrofolate [The numbers of ATP molecules required are higher than the results presented for avian biochemistry by Stevens  and pressure (1 atmosphere), 1.0 mg of dry blood requires 782 mm3 oxygen and yields 615 mm3 of carbon dioxide when degraded entirely to create ATP.These numbers become more meaningful when considered in terms of mass. For convenience we can start with 1.0 mg of dry blood. Dry blood is approximately 20% of the wet weight of blood ,44 so 1.The second major metabolic pathway involves the conversion of blood into protein. Adult flies which have just emerged from the puparial stage create protein to increase their flight musculature ,46. Matuth of the uric acid and carbon dioxide from the previous section and require 1/5th of the oxygen.Starting again with 100 moles of original amino acids, the conversion of this entire pool of amino acids to protein requires 100 moles of peptide bonds or 400 moles of ATP. By expanding the original pool with an additional 25 moles of amino acids, the 400 moles of ATP required can be obtained since the previous section showed that 100 moles of amino acids yield 1600 moles of ATP. Therefore the pool of 100 moles of amino acids which are used for the protein biomass must be combined with the 25 moles of amino acids used to generate energy thereby requiring a total of 125 moles of amino acids from the blood to create protein containing 100 moles of amino acids. Equivalently, 100 moles of blood amino acids can be partitioned into 80 moles to make protein while burning the remaining 20 moles for metabolic energy and uric acid formation. This process would generate 1/53 of oxygen, and the exhalation of 123 mm3 of carbon dioxide.In mass terms, 1 mg of dry blood can be used to create 0.80 mg of muscle or milk protein requiring the formation and excretion of 0.06 mg uric acid, the inhalation of 156 mmThe third major metabolic pathway in tsetse involves the use of the bloodmeal to create triglycerides in the fat body. All adult flies must convert part of the bloodmeal to energetic reserves and pregnant female flies will use part of the blood allocated to reproduction to create the triglycerides in the secreted milk.Starting again with an original pool of 100 moles of blood derived amino acids, a first allocation will be to the formation of uric acid. Fat does not contain nitrogen so all of the nitrogen in the bloodmeal must be formed into uric acid and excreted before fat formation. This process is identical to the excretion of nitrogen involved in the pathway converting blood to ATP so the numbers presented in that section work for this pathway as well.The remaining amino acids provide both the carbon precursors of the fat molecules and the energy required for synthesis. The reactions are taken from Michal  and listThe balanced use of the amino acids in the bloodmeal after uric acid formation comes from the creation of 4.08 moles of fat, necessitating 4.08 moles of pyruvate, 98 moles of acetyl-CoA (4.08 × 24), and 560 moles of ATP (4.08 × 137). The required pyruvate can be obtained by combining 3.88 moles of alanine, the amount remaining after uric acid formation, with 0.2 moles of pyruvate generated from tryptophan degradation. The 98 moles of acetyl-CoA, shown in table The net effect of the conversion of 100 moles of blood amino acids into fat is the creation, as before, of 25 moles of uric acid, the creation of 4.1 moles of fat, the release of 120 moles of carbon dioxide, and the consumption of 210 moles of oxygen.3 carbon dioxide, and requires 391 mm3 oxygen.In mass terms, 1 mg of dry blood yields 0.25 mg fat, 0.32 mg uric acid, 224 mmThe two remaining major metabolic pathways both involve the catabolic degradation of fat to obtain ATP. The first pathway is used by tsetse during all stages of life. This pathway proceeds directly through the Krebs cycle. The second pathway is used by adult tsetse to provide energy to flight muscles and involves the transformation of alanine to proline and back. These two pathways are considered below.The first pathway of triglyceride degradation involves, first, the split of the triester into the glycerol head and the three fatty acid chains , serine (Ser), cysteine (Cys) and alanine (Ala) are assigned to the conversion since they require the fewest biochemical steps. Where alternative intermediate pathways are possible, the most energetically efficient path which could be found was chosen. In all pathways, the costs of reactivation of the enzymes and catalysts are explicitly included in the resulting stoichiometric calculations.2) is assumed equivalent to 1.5 cystolic ATP based on Michal [A number of stoichiometric equivalences are used in this work. Guanosine triphosphate (GTP) is assumed energetically equivalent to adenosine triphosphate (ATP). The conversion of ATP to adenosine monophosphate (AMP) was assumed equivalent to two conversions of ATP to adenosine diphosphate (ADP). Similarly, reduced nicotinamide-adenine dinucleotide phosphate (NADPH) is assumed equivalent to reduced nicotinamide-adenine dinucleotide (NADH). Each of these is assumed to be energetically equivalent to 2.5 cystolic ATP whereas each reduced flavin-adenine dinucleotide  97/2000/XP file format. Since this is a binary, unpublished format, Excel may complain on opening the file but the file should work.Click here for file


The conservation of Mode 2 suggests that it reflects a regulatory mechanism conserved between the two systems. In either dataset, the expression vectors of all the genes form two large clusters that differ in the sign of the contribution of Mode 2, which for the majority of them reflects the difference between down- or up-regulation.We previously demonstrated that gene expression profiles during neuronal differentiation cis-regulation of gene expression in a subspace of a single SVD mode of temporal expression profiles. In the putative upstream regulatory sequences identified by mouse-human homology for all the genes represented in either dataset, we searched for simple features (motifs and pairs of motifs) associated with either sign of the loading of Mode 2. Using a cross-system training-test set approach, we identified E2F binding sites as predictors of down-regulation of gene expression during hippocampal development. NR2F binding sites, for the transcription factors Nr2f/COUP and Hnf4, and also NR2F_SP1 pairs of binding sites, were predictors of down-regulation of expression both during hippocampal development and neuronal differentiation. Analysis of another dataset, from gene profiling of myoblast differentiation in vitro, shows that the conservation of Mode 2 extends to the differentiation of mesenchymal cells. This permitted the identification of two more pairs of motifs, one of which included the CDE/CHR tandem element, as features associated with down-regulation both in the differentiating myoblasts and in the developing hippocampus. Of the features we identified, the E2F and CDE/CHR motifs may be associated with the cycling progenitor cell status, while NR2F may be related to the entry into differentiation along the neuronal pathway.In the current work, we used a novel approach of analyzing Our results constitute the first prediction of an expression pattern from the genomic sequence for the developing mammalian brain, and demonstrate a potential for the analysis of gene regulation in a subspace of a single SVD mode of expression. Transcriptome profiling experiments documented changes in gene expresion in several areas or cellular populations of the developing mammalian brain -4. The acis-regulatory sequences of orthologous genes are often the same, but their precise arrangement may be changed [The motifs present in corresponding  changed ,10. Such changed . Pairs o changed ,13.cis-regulatory modules (CRM), which in turn are built of transcription factor binding sites. Importantly for the computational approach used here, some of these subunits have largely independent and approximately additive  effects on expression. Examples of such subunits include: different enhancers of the same gene [Regulatory regions of genes of metazoans, including mammals, have hierarchical and subunit organization . Promoteame gene , certainame gene ,16, multame gene ,17. Effeame gene  was descame gene ,20.in vivo are highly similar to those in vitro [in silico by experimental work.Hippocampal neuronal culture  is a welin vitro . Apparenin vitro ,24, whicin vitro [in vivo [in vitro and in vivo expression profiles stems from the conservation of a single SVD mode (Mode 2) of temporal expression profiles. In both systems, Mode 2 was the most important among the modes that carried information about the relative changes of expression in time. The contributions (loadings) of Mode 2 were highly conserved across the 453 genes that were common to both datasets, suggesting that this mode reflects an underlying regulatory mechanism conserved between the two systems. A loading of Mode 2 to the expression of a particular gene corresponded to a component of a continous decrease (for the negative loading), or a continuous increase (for the positive loading) to the expression vector of this gene. In both compared datasets, the expression vectors of the genes form two large clusters – differening by the sign of the contribution of Mode 2, which, for the majority of the genes, reflects the difference between down- or up-regulation in the course of neuronal differentiation and hippocampal development.In a recent paper , we applin vitro  and hipp[in vivo . We democis-regulation of gene expression in a subspace of a single conserved SVD mode of temporal expression profiles. Instead of looking for common patterns of expression in the whole space of expression measurements, we considered a common pattern of expression in an orthogonal subspace of the original measurement space with just a single SVD mode as its basis vector. For this analysis we choose the previously characterized Mode 2. In the putative upstream regulatory sequences identified by mouse-human homology, we sought to identify simple features (motifs and pairs of motifs) associated with either sign of the loading of Mode 2 (up- or down-regulation). Conservation of Mode 2 permitted the use of a cross-system training-test set approach, where only the most promising features that were selected in one dataset were tested on the other dataset. This way we identified E2F binding sites as features predictive of down-regulation of gene expression during hippocampal development, and Nr2f/COUP binding sites, and NR2F_SP1 pairs of binding sites as features predictive of down-regulation during both hippocampal development and neuronal differentiation. Addition of another datset to the comparison – from the gene profiling of myoblast differentiation in vitro, demonstrated that the conservation of Mode 2 extends to the differentiation of cells of mesenchymal origin, and led to the identification of two features associated with down-regulation of gene expression during hippocampal development and myoblast differentiation.In the current work, we used a novel approach of analyzing Both the neuronal and the hippocampal dataset can be split (partitioned) into two roughly equal sets of genes by the loading sign of Mode 2 Table . These tH0 hypothesis of no association is equal to the split ratio in the general population of all CNSs, i.e. approximately 1:1 , and their 10878 possible pairs (co-occurrences), in putative upstream regulatory regions, used as the candidate features. Identification of the conserved non-coding sequences (CNSs) used as the putative regulatory sequences, and of conserved non-redundant motifs present in those sequences were performed using established tools, as described in Methods. For each candidate feature, we evaluated its association with the sign of Mode 2, by calculating the probability of the split of the set of CNSs harboring this feature between the sets of CNSs assigned to either sign of the loading of Mode 2. The split ratio (odds) expected for each feature under the We took advantage of the conservation of Mode 2 between the two datasets , and empTo address the issue of multiple testing we used the Bonferroni correction. The p-values obtained on the training set were multiplied by the number of features of a given type scored during the training . The p-values obtained on the test set for the 10 features earlier selected on the training set were multiplied by 10.When the neuronal dataset was used for training and the hippocampal dataset for testing, of the 10 highest-ranking motifs selected on the neuronal dataset, all associated with down-regulation, the NR2F motif scored as significantly associated with down-regulation  during the test on the hippocampal dataset Figure .Notably, the same NR2F motif was identified as significantly associated with down-regulation for the reverse choice of the training and test dataset Figure . The pre-8, Figure During the training on the hippocampal dataset we identified the E2FF motif as being most significantly associated with down-regulation  on the hippocampal dataset Figure .Notably, the pairs NR2F_SP1F and E2FF_NR2F were identified as the only two pairs significantly associated with down-regulation in the cross-system test for the reverse choice of the training and test dataset Figure . These wThe presence of the NR2F_SP1F pair in a CNS was associated with odds of 2.6:1 (52:20) and 2.64:1 (58:22), on the neuronal and hippocampal dataset, that this CNS would be assigned to a gene with a negative loading of Mode 2. These odds correspond to odds ratios 2.8:1 and 3.24:1, respectively. For the E2FF_NR2F pair the odds for down-regulation were 2.9:1 (41:14) and 3.7:1 (37:10), corresponding to odds ratios 3.16:1 and 4.5:1, on the neuronal and hippocampal dataset, respectively. The SP1F motif corresponds to a family of 6 matrices describing binding sites for 14 transcription factors, including Sp1–8.Five of the 10 top ranking pairs selected during the training on the hippocampal dataset were significantly associated with the down-regulation on the hippocampal dataset  could be expected, and were indeed observed, with odds ratios on both datasets of about 3. More interesting was the fact that the presence of the ubiquitous SP1F motif in the NR2F_SP1F pair resulted in an increase of the odds for down-regulation, even though SP1F alone was only weakly associated with down-regulation on the hippocampal dataset, and not at all on the neuronal dataset Figure . Two paiH0. This deviation was data-driven, as confirmed by the same plot for a randomized dataset, in which the expression patterns assigned to the CNSs were randomly permuted  were excluded from the analysis, i.e. when the effect of each of the 147 remaining motifs was analyzed only in the CNSs without E2FF  with the loadings of the hippocampal mode 2  of the respective modes 2 (thereafter jointly referred to as Mode 2) between the myoblast and hippocampal dataset, suggested that the cross-system approach may also help to identify cis-regulatory features involved in myoblast differentiation, in particular during the exit from the proliferating progenitor cell stage. As before, we used all the genes with CNSs from either dataset during the feature search , separately for two feature types (motifs and pairs).When the myoblast dataset was used for training and hippocampal for testing, 2 pairs, namely YY1_ZF5F and CHRF_ZBPF that were selected during training on the myoblasts dataset scored significant on the hippocampal dataset Figure . The oddThe identification of mammalian regulatory features by the classical "group by expression approach"  may be lcis-regulation in a subspace of a single SVD mode of expression, chosen because it was biologically interpretable and conserved between hippocampal development and neuronal differentiation in vitro. By employing this approach we identified several features that are associated with down-regulation of gene expression during hippocampal development , or both during hippocampal development and neuronal differentiation , or during myoblast differentiation and hippocampal development .Here we propose a modification of the "group by sequence approach", which is to search for features associated with expression patterns in a subspace of the original measurements space. Particularly, we report the analysis of E2FF was identified through the analysis within a single dataset, for the identification of the remaining features crucial was the conservation of Mode 2 between the datasets, which permitted their combined use – resulting in an increased statistical power of the analysis.The effect size (odds) for most of the features identified by our analysis was moderate, with typical odds for down-regulation of about 3:1. Due to limitations of the microarray technology , some prcis-regulatory regions. Because of a sparse temporal sampling  and only partial synchronization of cells, Mode 2 must reflect many mechanisms of regulation, acting through different cis-regulatory features, and therefore a single feature cannot account for the loading of this mode for the majority of genes. Our identification of the putative cis-regulatory regions was limited only to the CNSs in the upstream -10 kb region, and even in these regions some of the functional motifs were surely missed, and others called incorrectly. Despite all these limitations, our analysis led to identification of features predictive of gene expression during the development of a region of the mouse brain.The identified features account for the sign of Mode 2 of relatively small fractions of all the genes in the analyzed datasets. This was expected, due to the limitations of both the measurement of expression, and of the analysis of The analysis of regulation in subspaces of SVD modes (but without reference to regulatory regions) has been suggested  but not We identified the E2FF motif as associated with the negative sign of Mode 2 during hippocampal development, but not during neuronal or myoblast differentiation in cell culture. The transcription factors from the E2F family have a well-established role in the regulation of gene expression during the cell cycle . Neural cis-regulatory context, in which the E2FF motif is more likely to have a down-regulatory effect on gene expression. A tendency of functional motifs to cluster in cis-regulatory regions is well established [cis-regulatory sequence is interesting, because it seems to be in agreement with a possible role of E2FF in gene silencing during the hippocampal development. The fact that E2FF was not associated with Mode 2 during myoblast differentiation is hard to explain, as a role of E2F in gene silencing of several S-phase genes was demonstrated in this model [The finding that many motifs, when co-present in the same CNS, tend to enhance the down-regulatory effect of E2FF suggests that these motifs provide a ablished . The finis model . A possiis model  that aftin vitro. This suggests that this motif exerts its role in the differentiating neurons. This possibility is supported by several studies. The NR2F motif is predicted to bind several transcription factors, including the orphan nuclear receptor NR2F1 alias COUP-TF. The transcription factor NR2F1/COUP is a marker of neurogenesis from hydra to vertebrates, postulated to regulate the entry of cells into differentiation [We identified the NR2F motif as being associated with down-regulation of gene expression during both the hippocampal development and the neuronal differentiation ntiation . COUP bintiation . COUP-TFntiation , and is ntiation . The signtiation .All the significant features we identified were associated with down-regulation. When we narrowed the feature selection during the training to select only the features that were associated with up-regulation we were not able to identify any feature that was significant (data not shown). This may reflect the importance of the negative regulation of expression during development  and the 1. We identified the E2FF motif as a feature significantly associated with down-regulation of gene expression during hippocampal development.in vitro, suggesting their role in the differentiation of neurons.2. We identified the NR2F motif and the NR2F_SP1F pair of motifs as features significantly associated with down-regulation during hippocampal development and neuronal differentiation in vitro, extends also to the differentiation of myoblasts in vitro.3. We demonstrated that the conservation of Mode 2, previously identified between hippocampal development and neuronal differentiation cis-regulatory regions (such as motifs or pairs of motifs) can be predictive of gene expression in a subspace of a single SVD mode.4. Simple features of in vitro of the C2C12 myoblasts [DH (1855 genes × 5 time-points), neuronal DN (1824 genes × 6 time-points) and myoblast DM (2008 genes × 8 time-points). The matrices DH, DN and DM were each column-normalized (i.e. each column was divided by its vector norm) and then log-transformed, resulting in the matrices AH, AN, and AM.The published datasets from expression profiling of hippocampal development , of neuryoblasts , were maAH, AN, and AM resulting in matrices uH (1855 × 5), mH, vH; uN (1824 × 6), mN, vN; and uM (2008 × 8), mM, vM respectively. The loadings of the respective second mode for every gene in each dataset are given by the entries in the second columns of the matrices uH, uN and uM. Only the signs of the loadings of the respective second mode were used during the feature search.The SVD analysis, and the comparison of loadings between two datasets, were performed as described before . BrieflyuH and uM we selected the gene loadings vectors for the 454 genes common between these two datasets. This resulted in matrices uHM and uMH. Column k of matrix uHM contains the loadings of the k-th hippocampal mode for all the common genes. Column l of matrix uMH contains the loadings of the l-th myoblast mode for all the common genes. We calculated the Pearson correlation coefficient r between each pair of columns of uHM and uMH.For the comparison of loadings between the hippocampal and myoblast dataset, from the matrices We used conserved non-coding sequences (CNSs) between mouse and human as putative regulatory regions. For each mouse-human orthologous gene pair in Ensembl  release Transcription factor binding sites were predicted for all the vertebrate position weight matrices of the Genomatix Matrix Family Library version 6.0 using the program MatInspector ,50. ThisMotifs identified with matrices belonging to the same matrix family were treated as the same non-redundant (n-r) motif. The use of n-r motifs allowed a considerable reduction of the number of features, and increased the number of CNSs containing each feature. A CNS was defined to contain a conserved n-r motif X if both the mouse and the human sequence of this CNS pair contained a nonzero number of instances of X . A pair, designated X_Y, of conserved n-r motifs X and Y was defined as a simultaneous instance of X and Y in the same CNS. The distance of motifs constituting a pair was limited by the typical length of a CNS (see above). Unless indicated otherwise, in the main text we use the words: "motif" and "pair", in the sense of: "conserved non-redundant motif" and of their pair.s of success in a single trial, under the H0 hypothesis of no association, is equal to the ratio of the number of CNSs assigned to the genes with the positive loadings of Mode 2 to the number of all CNSs in a given dataset . From this binomial model we computed two-sided p-values, defined as: the cumulative probability, under H0, of all the numbers of successes equally or less likely than the observed number . Unless indicated otherwise, in the main text we use the word "p-value" in the sense of: "uncorrected single test p-value".In the search for the features associated with the sign of Mode 2, each CNS was assumed to be independent and was assigned the sign of the loading of Mode 2 of its associated gene. In our binomial model, each CNS with an instance of a feature constitutes a binomial trial, in which the positive sign of the loading of Mode 2 for the associated gene is a success, and the negative sign of the loading is a failure. The number of trials for each feature is equal to the number of the CNSs with this feature in a given dataset. The probability MD conceived of the study, carried out the analysis, and drafted the manuscript. SA performed the AVID alignments and revised the manuscript. BK interpreted the results and revised the manuscript. All authors read and approved the final manuscript.supplementary Figure S1. Effect of the E2FF motif on distributions of loadings of all hippocampal SVD modes. For each mode resulting from the SVD on the hippocampal dataset, we compared its distributions of loadings between the CNSs containing the E2FF motif and the general population of all CNSs in the hippocampal dataset. The same comparison was also performed between the genes containing the E2FF motif (in any CNS) and the general population of all the genes with CNSs in the hippocampal dataset. A. Comparison of the distributions of loadings for the CNSs. B. Comparison of the distributions of loadings for the genes.Click here for filesupplementary Figure S2. Effect of the NR2F motif on distributions of loadings of all hippocampal SVD modes. The rest of the description as for Figure S1, but with the NR2F motif used to select CNSs and genes.Click here for file