This work introduces the concept of time–frequency map of the phase difference between the cantilever response signal and the driving signal, calculated with a wavelet cross-correlation technique. The wavelet cross-correlation quantifies the common power and the relative phase between the response of the cantilever and the exciting driver, yielding “instantaneous” information on the driver-response phase delay as a function of frequency. These concepts are introduced through the calculation of the response of a free cantilever subjected to continuous and impulsive excitation over a frequency band. Atomic force microscopy (AFM) has made important progresses towards the characterization of material properties at the nanoscale  by means of dynamic techniques that extended the microscope capabilities well beyond simple topographic measurements –2. Amongphase information when complex functions are used as a wavelet basis.Thermal noise analysis has been performed, with the aid of wavelet transforms, to characterize the time–frequency response of a thermally excited cantilever in dynamic force spectroscopy –12. In tThe scope of this work is to introduce the idea that a time–frequency map of the phase difference between the cantilever response signal and the driving signal can be extracted with a wavelet cross-correlation (WCC) technique, based on the inherent phase information residing within the complex Gabor transform. This analysis has been exploited principally in the field of meteorology, oceanography and geophysical studies –15. SincThe wavelet transform has shown great potential in various scientific disciplines, but it is not widespread in the context of noncontact AFM. This may be due to the absence of discussions of the practical and technical aspects of wavelet analysis relating to noncontact AFM. This article shows the use of wavelet cross-correlation by means of two simple but paradigmatic examples: The continuous and the impulsive band excitation of a free cantilever.f(t) , where t is the time index, onto a set of continuous functions Ψs,d(t) derived from the translations and dilations of a mother wavelet Ψ(t), whereBefore introducing the cross-correlation concept, we give a brief introduction to wavelet transform theory . Wavelets and d are real parameters and s > 0. Any set of functions constructed as in t) is an oscillating function, and rapid decay at infinity  must be continuous and have a compact support; this is called the admissibility condition), are called wavelets.f(t) with Ψs,d(t), at the scale s and delay d, is the wavelet transform (WT) of the signal Wf:The convolution of s and d vary continuously. The translation parameter d corresponds to time and the dilation parameter s corresponds to temporal period . f(t) into a bidimensional parameter space  and gives a local measure of the relative resemblance of the wavelet to the signal.This is a continuous wavelet transform, because the parameters The complex mother wavelet  used in this work, as described in , is reprt in the expression of the Gabor wavelet can be regarded as a (dimensionless) discrete index and likewise σ and η are dimensionless wavelet parameters defining the wavelet shape over the discrete sampling string. The Gabor wavelet (dimensionless) center frequency at scale s is given by f = η/(2πs). It is possible to associate a pseudofrequency F (in Hz) at a scale s by considering that f is sampled with a time interval T, such that F = f/T. Therefore, the wavelet dilations set by the scale parameter s are inversely proportional to the frequency F. In the following analysis, the dimensionless wavelet parameters are chosen as σ = 1 and η = 6. This choice of parameter gives an adequate balance between time and frequency localization, which in wavelet analysis are subjected to a classical Heisenberg-like principle of indetermination .f(t) and g(t), with wavelet transforms Wf and Wg, the cross-wavelet spectrum is defined as:Given two time series Wf = |Wf|exp). |Wf| represents the wavelet amplitude, Φf is the absolute phase. Both amplitude and phase are relative to the “point”  in the frequency–time plane. The cross-wavelet power, |Wfg|, shows regions in time–frequency space where the time series have a high common power. The relative phase difference between the two time series  = phase of f; Φg = phase of g), can be calculated as:where * denotes the complex conjugate. Since the cross-correlation coefficients are complex numbers, they can be represented as where < > represents a smoothing operator. It must be noted that this definition depends essentially on the action of the smoothing operator on the various wavelet spectra. The same situation in found in the definition of optical coherence, see . For a dz(t) that obeys the classical mass–spring equationAs an example to highlight the characteristics of wavelet cross-correlation, consider the case of a damped cantilever with a displacement f(t) is the driving force per unit mass.where f0 = ω0/(2π) = 1 MHz, a quality factor Q = 4 and an excitation driving frequency that linearly sweeps the frequency interval 0.1f0 < Δf < 0.9f0 in 50 μs (chirped driver). The driving function is f(t) = zdcos(νd(t)t), where zd is the driving amplitude and νd(t) the driving frequency that is linearly chirped: νd(t) = A + Bt, A = 0.1 MHz and B = 0.016 MHz/μs. Note that the actual instantaneous driver frequency as a function of time is the time derivative of the total driver phase, i.e., A + 2Bt. As a consequence, the resonance at f0 is excited when the instantaneous driving frequency sweeps through f0, which does not coincide with the frequency νd(t). In The above equation of motion is integrated numerically with a free resonant frequency of Wzf = WzWf* and the relative phase (arrows). Phase arrows indicate the phase relationship of the oscillator to the driving sinusoid . The edge effects are delimited by continuous lines. We note that the representation in terms of the cross-correlation between the damped harmonic oscillator and the chirped driver allows us to capture more intuitively the evolution of the spectral content of the cantilever oscillations, in a way that is not possible with a traditional Fourier transform. However, the utility of this technique is even more relevant when we deal with impulsive excitation.The wavelet cross-correlation analysis in An excitation signal that can be used in AFM band excitation is the sinc function. It is defined asThis function is the continuous inverse Fourier transform of the rectangular pulse of width 2π and height 1. It is used as a simultaneous excitation over a limited frequency range. The time response of the damped cantilever to a properly scaled sinc function is shown in Although the above description of the spectral phase appears intuitive, it would not be possible to obtain it by means of a classical Fourier analysis. If the signal is not stationary, as is the case in band excitation, the squared magnitude of the Fourier coefficients measure the average energy contained in a spectral interval without tracing its effective time evolution. In this case the phase relative to each spectral component is not “local” in time, preventing its interpretation in terms of a causality relationship with a specific perturbing agent.Q-factor, Q = 40. The time evolution is shown in It is interesting to note that the cross-correlation analysis allows us to separate those spectral components that are directly influenced by the driver and those relative to the subsequent evolution of the oscillator response, when the impulsive driver action has died down. We consider the same excitation as in artificial signal can be used as a reference. The phase of the oscillator can be tracked by correlating it with a reference harmonic signal at the resonant frequency, as we demonstrate in Since the oscillator signal extending beyond the driver pulse can carry useful information but is not visible in the cross correlation, an not engaged in interaction. Successive excitation of the interacting cantilever provides the interaction “phase carpet”. Subtracting the interacting phase carpet from its reference, allows us to retrieve the local phase rotation, that is a function of the tip–surface interaction and the chosen excitation/driving signal. With this approach the phase rotation is measured at each frequency that resides within the excited band around a cantilever resonance and it is possible to follow its time evolution. The advantage with respect to single-frequency techniques is a more robust all-frequency characterization of the phase rotation and the possibility of connecting this information with the amplitude variation at each point in the time–frequency plane. With respect to traditional analysis, in which the spectral information is extracted with a Fourier transform, the wavelet representation disentangles the interaction spectra in the time domain. The spectral components acquire an interaction causality that is absent in the Fourier spectrum, revealing the time succession in which the phase or the amplitude at a specified frequency has been altered by the interaction. In certain cases this information may be of great utility, for example to enable correlation of phase-jumps with the interaction processes, that usually have time-scales that are a fraction of the oscillation period. It is foreseen that in similar cases the wavelet analysis could track dynamics otherwise not visible in a Fourier spectrum because of the superposition of spectral contributions generated at different times.In this section we would like to comment on how to exploit WCC and wavelet phase analysis in a practical AFM experiment, discussing the implications for the real cantilever dynamics as opposed to modeling a harmonic oscillator. A fundamental feature of wavelet phase analysis consists of measuring the phase response of the cantilever with respect to complex excitation signals , and displaying the results in the time–frequency plane, with a resolution set by the Heisenberg principle, as shown in the simulations reported in In amplitude-modulation AFM (tapping mode) wavelet analysis is useful to track the time evolution of the nonlinearities in tip–surface dynamics. The wavelet analysis allows one to follow more than a single flexural mode simultaneously –11 and tsingle approach curve under standard operating conditions. Regarding the thermal noise, the excitation signals must have amplitudes exceeding that of the thermal noise, because averaging is limited or absent. In this case, the choice of the excitation amplitude depends on the type of cantilever, on its quality factor and on the parameters to be measured. We anticipated that only extremely low amplitude excitations should have portions of the time–frequency map rendered useless below the noise floor. Further (ongoing) studies will be necessary to gain insights into the limitations of wavelet analysis.A final remark is due concerning the effect of noise  on wavelet analysis while processing data collected under normal AFM operating conditions. One might expect noise to be a limiting factor when performing wavelet analysis, due to the fact that the wavelet analyzes the signals for a shorter time and therefore loses the averaging effect present in traditional Fourier spectra. Regarding environmental noise, it has been demonstrated that by using only thermal excitation it is possible to retrieve useful information from force spectroscopy  with a sThe application of wavelet analysis to interacting cantilevers is a promising route to the characterization of material properties on the nanoscale. The wavelet correlation technique allows one to measure the phase relationship between driver force and cantilever response in complex excitation schemes. The complete time–frequency picture of the phase evolution can be exploited as an important tool to characterize material response and tip–sample interactions. The wavelet correlation analysis sets into a different perspective the AFM techniques, which have been analyzed so far only in terms of Fourier transform.


To evaluated the female–male health–survival paradox by estimating the contribution of women’s mortality advantage versus women’s disability disadvantage.Disability prevalence was measured from the 2006 Survey on Income and Living Conditions in 25 European countries. Disability prevalence was applied to life tables to estimate healthy life years (HLY) at age 15. Gender differences in HLY were split into two parts: that due to gender inequality in mortality and that due to gender inequality in disability. The relationship between women’s mortality advantage or disability disadvantage and the level of population health between countries was analysed using random-effects meta-regression.Women’s mortality advantage contributes to more HLY in women; women’s higher prevalence of disability reduces the difference in HLY. In populations with high life expectancy women’s advantage in HLY was small or even a men’s advantage was found. In populations with lower life expectancy, the hardship among men is already evident at young ages.The results suggest that the health–survival paradox is a function of the level of population health, dependent on modifiable factors. Life expectancy in women is higher than in men. Although women live longer in nearly all countries of the world  the difference due to inequality in age-specific mortality rates ; and (2) difference due to gender inequality in age-specific prevalence of activity limitations .Does the mortality advantage and/or disability disadvantage of women vary between countries with high versus low life expectancy? We expect that in populations with high life expectancy, the gender gap in HLY is smaller because of a combination of a smaller mortality advantage and a larger disability disadvantage in females.Is there a shift in the concentration of the mortality and disability effects on the gender gap in HLY towards older age groups (50 years and above) when indicators of population health  are improving? We hypothesize that in countries with better scores on the population health indicators, the effects of both mortality and disability on the gender gap in HLY is more concentrated in older age groups.The current paper aims to better understand the health–survival paradox within the EU by examining the contribution of women’s mortality advantage versus the disability disadvantage, and their differences between countries with better and worse population health. More specifically, we explore the following questions:The added value of the paper is to study the health–survival paradox through HLY, which combines both health and survival.http://www.ehemu.eu): age and sex-specific data on (1) number of deaths (2006); (2) population ; and (3) prevalence of activity limitations  from the 2006 Statistics of Living and Income Survey (SILC). The SILC is an EU-wide survey, initiated in 2005. A description of the survey can be found in “http://circa.europa.eu/public/irc/dsis/eusilc.library”. The SILC survey population consists of nationally representative probabilistic samples from community dwelling populations. The 2006 SILC survey covers a total of 375,243 participants of age 16 years and above. Overall response rate averaged over countries is about 20 % but with large between countries variations (from 95 % in Cyprus to 60 % in Denmark and Belgium)  in HLY and ULY, a decomposition methodology was used  each assessing the relationship of the mortality effect of the gender difference in HLY (dependent variable) with an independent variable representing the overall length of life . Model Type 2 is multivariable, adjusting the gender difference in LE for women’s LE to account for the association between gender difference in LE and longer life  is less than that for LE (5.6 years). The variation in the gender difference in HLY  is also smaller than that for LE . In 7 countries , men at age 15 can expect to live more years without activity limitations than women, the gender difference in HLY reaching statistical significance in 3 of these countries . In all countries, women live more years with activity limitations than men, the median gender difference in ULY being 5.4 years .The gender difference is HLY is the sum of two opposing forces. Women’s mortality advantage increases the HLY gender difference, while women’s disability disadvantage reduces the HLY gender difference. In all countries, the value of the mortality effect is positive Table  meaning women’s LE: −0.25, Table women’s LE: −0.20, Table Figure LE_gender_gap: 0.30), and a smaller women’s disability disadvantage (coefficientLE_gender_gap: 0.21). For every increase of 1 year in the gender gap in LE, the HLY gender gap increases by 0.51 years [(0.30) + (0.21)]. Only the mortality component of the HLY gender difference is significantly associated with the gender difference in LE and the association remains significant after adjustment for women’s LE.The wider the gender gap in LE, the larger the gender difference in HLY due to a larger mortality women advantage , gender differences in mortality at older ages (50 years and above) contribute more proportionally to the mortality effect of the gender difference in HLY. For instance, for every increase of 1 year in women’s LE, the gender differences in the age group 50+ years contribute almost 3 % more to the mortality effect of the gender difference in HLY -health. Mortality and disability tend to play in opposite ways on the magnitude of the gender differentials in HLY. While women’s longer life and higher disability prevalence translate into more years to be lived with disability by women, in all but two countries the disability effect compensates the mortality effect reducing the gender difference in HLY. Even more, in some countries, the disability effect overpasses the mortality effect and women live fewer years without disability. The health–survival paradox appears to be a function of the level of population health indicators and their gender difference. We observed that in populations with a high LE, the gender difference in HLY is small or even negative. Current cross-sectional analysis does not recognize that the health trajectories and the evolution of the LE during the last decades of the twentieth century were substantially different in Western European countries compared to countries of Central and Eastern Europe or the Baltic States Leon . TherefoOur analysis has several strengths. The country data were not pooled and the substantial heterogeneity in HLY and ULY among the EU member states is used (Jagger et al. Limitations of the study that should be considered are related to the cross-sectional design and the Sullivan method. The latter produces health expectancy indicators which are not period indicators and which may introduce bias in the absence of a steady state (Barendregt et al. Multiple causes have been considered to explain the mechanisms which drive the health–survival paradox between the genders. The impact of methodological issues such as gender differences in survey participation or in reporting health problems have been minimized (Oksuzyan et al. The novelty of this paper studying the health–survival paradox is the use of HLY and the exploration of the two components of the gender difference in HLY: difference due to inequality in survival and difference due to inequality in disability. We observed large inequalities in the gender difference in health between European countries which corroborate our hypotheses. In populations with a high LE the gender difference in HLY is smaller because of the additive effect of a reduced mortality effect and a larger disability effect. In countries with a lower level of population health as indicated by a low LE, a low HLY and a large ULY, men are in the worst position having not only a higher mortality compared to women but also a high prevalence of activity limitations. Additionally, in contrast to men in populations with a better health profile, the ill-health of these men begins early in life.


Molecular dynamics (MD) simulation, a standard technique used to study the dynamical properties of biomolecules, is very useful in collecting the trajectories, a series of snapshots – the coordinates of the system - of larger systems for longer simulation times. These MD generated trajectories are huge in size (many gigabytes) and the data analysis may take much longer time than the data generation. Managing the large amount of data and presenting them in a flexible and comprehensible manner are the major challenges. Analyzing these trajectories with standard parameter like root-mean square deviation (RMSD) may not reveal the most interesting properties of the dynamics.α torsion angles [α atoms - are highly valuable as similarity measure on a substructure scale and to find major events - the information on the time at which a transition occurs  and the local structural changes  of it is combinedly called as “event”- occurring in the course of the MD simulation. By calculating the time series of the Cα torsion angles and their clustering it is possible to determine the mechanistic details on a residual length scale and find major events occurring in the simulation of large proteins or protein complexes. The main advantage of the Cα torsion angle criterion is that it does not depend on a previous alignment of the structures, and that the direction of the change is also defined. Heat maps of Cα torsion angle give nice graphical representations of processes described by the MD simulations. Clustering of snapshots according to the specific Cα torsion angles is used to automatically find the spatial domains of the structural changes. If all the snapshots are assigned to a single cluster, then those residues are considered as rigid core and the remaining residues are considered as flexible parts. The temporal domain can be characterized in more detail by finding continuous time intervals assigned to a single cluster as (meta) stable structures and time intervals where the assignment jumps between two clusters as transitional periods. Since the outliers can be removed from the fuzzy clusters, starts and ends of time patches now qualify as important events for the underlying substructure and structural changes of larger regions are caused by an accumulation of such substructure events.To overcome these challenges, Cn angles  - torsioα torsion angle based analysis, shows a hand-like arrangement, including a thumb, a palm and a finger domain [α torsion angles with the differences to the crystal structure of the open form were plotted as heat map. The rigid and the flexible parts were clearly seen with no or a large number of significant changes, respectively, from the heat map. Once the Cα torsion angles corresponding to the rigid parts are removed, the remaining regions change only in a specific time interval of the simulation. The spatial and temporal domains of the structural changes were identified automatically by clustering of snapshots (using KNIME [DNA polymerase I – the open ternary complex of the large fragment of Thermus aquaticus DNA polymerase I (Klentaq1), which is used here as a practical application for Cr domain . The catr domain . To get ng KNIME ) and fin


Recent advances in sequencing technology have created large data sets upon which phylogenetic inference can be performed. Current research is limited by the prohibitive time necessary to perform tree search on a reasonable number of individuals. This research develops new phylogenetic algorithms that can operate on tens of thousands of species in a reasonable amount of time through several innovative search techniques.http://dna.cs.byu.edu/psodaWhen compared to popular phylogenetic search algorithms, better trees are found much more quickly for large data sets. These algorithms are incorporated in the PSODA application available at The use of Partial Tree Mixing in a partition based tree space allows the algorithm to quickly converge on near optimal tree regions. These regions can then be searched in a methodical way to determine the overall optimal phylogenetic solution. Phylogenetic search is an NP-Hard  problem.A phylogenetic search begins by using a greedy heuristic to build an initial tree. This initial tree is then improved by the full search. Unfortunately, the greedy nature of the starting trees limits the effectiveness of the full search. For this reason multiple starting trees are often used, with the hope that at least one will allow the overall search to find the global minimum.Partial Tree Mixing (PTM) addresses this issue through the use of a global representation of partition based tree space . Using tThe most common heuristic method for phylogenetic search is a form of hill climbing. A given possible solution is permuted into several new solutions. The best of these solutions is in turn permuted until no better solutions are found.O(n2) algorithms (where n is the number of taxa).The most common permutation operation is Tree Bisection and Reconnection (TBR) . Common Distance methods begin by computing an all-to-all distance matrix between the taxa. This is typically the hamming distance between the DNA character sequences for each taxa though some other metrics have been used . The neaStepwise maximum parsimony begins by shuffling the taxa into a random order. The first three taxa are joined together into the only possible three taxon tree. In turn each taxon is inserted along every branch in the current tree. It is left in the most parsimonious position. This process continues until all the taxa have been added, resulting in a complete tree.O(n3) algorithm and produces O(n3) trees to be examined. The first step is to select a branch in the tree and remove it, producing two subtrees. A branch is then selected in each of the two subtrees. A new tree is produced by reconnecting the two subtrees at the selected branches. An iteration of TBR ends when the original tree has been split along every branch and each of those splits has been rejoined in all possible ways. If one of the new trees is better, then the search continues by performing a TBR iteration on the improved tree. If no better tree is found the search ends.Tree Bisection and Reconnection (TBR) is a common means of generating new solutions during a phylogenetic search. Each iteration of TBR is an Trees can be considered as collections of bipartitions of taxa. Every branch in a tree divides the taxa into two sets. Some of these bipartitions, those arising from branches connected to the leaves, are common to all trees. These trivial bipartitions are ignored. All other possible partitions are assigned a dimension in tree space. The position of a tree is a vector whose components all have the value 1 or 0. These values respectively represent the presence or absence of the associated bipartition.In this space there is a close relationship between the Euclidean distance between two trees and the Robinson-Foulds (RF)  distancen taxa have 2n – 3 branches. n of these branches are trivial, and are therefore ignored. The position of any resolved tree will therefore have exactly n – 3 elements with the value of 1, all others will have the value of 0. It is easy to see that the distance from this point, the position of an arbitrary fully resolved tree, and the origin is n – 3 non-trivial branches. Consider a tree lacking m branches, by the same argument as used for resolved trees, the distance between this tree and the origin must be It is well known  that alln – 3 concentric hyperspheres. At the origin lies the fully unresolved tree, which possesses no branches. The next sphere out, with a radius of 1, contains all trees with 1 branch. Each succeeding sphere contains trees with one more branch in them than the last sphere, until the final sphere of radius n – 3 is reached.The set of all trees, both resolved and unresolved lie upon the surfaces of a set of O(n!!) n• Resolved trees are projected to the sum of the projections of their component branches, a point in ℝn, also referred to as global tree space• All trees lie in ℝSee  for detaDefinition 6.6. Image of an unresolved or partial tree: The image of an unresolved or partial tree is defined as a volume which contains the image of all resolutions of this tree.Theorem 6.7. The image of an unresolved tree is a hypersphere.Proof. Consider an unresolved tree of n taxa which has n – m – 3 branches. The location of the image of any resolution of this tree contains two components. The first is the sum of the images of the n – m – 3 branches from the unresolved tree. This will be the same for all resolutions, and lies at the center of the hypersphere. The second is the sum of the images of the m branches constructed during resolution. The magnitude of the components of these vectors is at most 1. If the projection is into d dimensions then the maximal magnitude of such a vector is m such vectors, the magnitude of their sum can not exceed Theorem 6.8. If two unresolved trees can he constructed from the same partial tree then the centers of their images are not separated by more than.Proof Consider a partial tree with n – m taxa. This tree has n – m – 3 branches. The branches added when resolving a partial tree to an unresolved tree are identical. Thus, two such resolutions can at most differ by n – m – 3 branches. If two such resolutions differed by every branch possible, and the vectors associated with the differences were all of the maximal magnitude and in opposite directions, the centers of the two images could not be separated by more than Theorem 6.9. The image of a partial tree is bounded by a hypersphere.Proof. Consider a partial tree with n – m taxa. Unresolved trees which are resolutions of this tree will have n – m – 3 branches. The hyperspheres which contain the images of these unresolved trees will all be of radius d is the dimensionality of the image space. By Theorem 6.8 the most distant unresolved trees are not separated by more than KS wrote the majority of the code to implement the algorithm and ran the experimental data to get the results. MC and QS collaborated in algorithmic development and wrote supporting code for the implementation. KC and MW assisted in developing biologically relevant data sets and in providing insights into related algorithms and approaches that were investigated in determining a successful solution. DV provided feedback on how to frame the arguments in a sound manner and provided invaluable feedback on the written document.The author(s) declare that they have no competing interests.


The paper presents the methodology and the algorithm developed to analyze sonar images focused on fish detection in small water bodies and measurement of their parameters: volume, depth and the GPS location. The final results are stored in a table and can be exported to any numerical environment for further analysis.The measurement method for estimating the number of fish using the automatic robot is based on a sequential calculation of the number of occurrences of fish on the set trajectory. The data analysis from the sonar concerned automatic recognition of fish using the methods of image analysis and processing.Image analysis algorithm, a mobile robot together with its control in the 2.4 GHz band and full cryptographic communication with the data archiving station was developed as part of this study. For the three model fish ponds where verification of fish catches was carried out , the measurement error for the described method was not exceeded 8%.Created robot together with the developed software has features for remote work also in the variety of harsh weather and environmental conditions, is fully automated and can be remotely controlled using Internet. Designed system enables fish spatial location (GPS coordinates and the depth). The purpose of the robot is a non-invasive measurement of the number of fish in water reservoirs and a measurement of the quality of drinking water consumed by humans, especially in situations where local sources of pollution could have a significant impact on the quality of water collected for water treatment for people and when getting to these places is difficult. The systematically used robot equipped with the appropriate sensors, can be part of early warning system against the pollution of water used by humans  which can be dangerous for their health. Distribution of wild fish is rarely homogeneous since it is determined by such parameters as e.g.: physical and chemical conditions of the water and food availability . TemperaSonars as the tools in quantitative assessment have been for many years successfully applied in the sea fisheries and in monitoring large inland water bodies. They are used in tracking the movement of large shoals of fish in the sea fisheries and also in recording the fishes migrating to their spawning grounds. Most commercially available sonars enable identification and determination of fishes. Many methods of the image analysis for sonars -8 or texAt present, a reliable, reproducible and inexpensive method of estimation the number and density of freely swimming fishes in stillwater reservoirs is not available. By now, some drawbacks rather discourage the use of sonars to carry out such analysis. These include variability of fish species, their shape, behavior or age related differences and effects of climatic conditions. Net fishing or electrofishing may operate only in small areas. Moreover, net fishing contribute to high mortality of some fish species, and further problems with utilization of the carcasses.In the aquaculture the initial number of the fish in the breeding ponds and their approximate age are generally known. During the season their number is difficult to estimate. Mewchanistic techniques used for their estimation might also affect fish behavior (Heisenberg uncertainty principle). The placement of any measuring device, irrespective of its size, and even appearance of its shade, may result in a variety of unpredictable swimming responses of fish. The acoustic analysis showed that 76% of the fish in the trawling area were captured and 24% swam out of it. The shadow of the boat was the primary reason for this situation while, other factors have a minor impact .In summary, the problem to be solved is a reliable estimation of the fish number in reservoirs, using efficient, fast, cheap and readily available tools for this purpose. A key objective of this study was to develop an effective quantitative method to assess the number and density of the fish population with simulteneaously recorded various physicochemical environmental factors. The low-cost, remote-controlled boat model (robot) was developed, equipped with the sonar and/or other probes measuring physicochemical parameters in the water body. Through scanning the entire reservoir, the data necessary to estimate the number of fish were tested and verified in various environmental conditions.Variability of many parameters such as changes of the depth of the analyzed reservoir, the angle of the sonar head cone, the differences between fish species or the depth of their foraging were taken into account. The tool constructed for estimation of the fish number is based on the data collected from a cheap sonar module mounted on a sailing robot. The sonar covering the set trajectory by the robot is supported with the module recording the time-space location of sonar images and physicochemical parameters of the water. This paper is given a detailed description of the developed functionality of the robot and the types of designing modules used in the pilot studies.The method of estimating the number of fish was based on three elements:• a mobile sailing robot,• the software for the wireless communication between the robot and the station,• the calibration of the measurement technique.The mobile sailing robot Figure  meets th• control and data transmission in full duplex mode with encrypted transmission - communication module Aerocomm AC 4490LR-1000M-01,• a catamaran type, due to greater stability of sailing in comparison with a classic boat,• a wireless transmission of the GPS data, water temperature, compass coordinates, the number of available satellites, battery charging level, date retrieved from the satellite time, sailing speed,• local data archiving from the Lowrance sonar unit directly in the boat (22 GB memory) and the possibility of their remote transmission,• the range up to 10 km in an open water reservoir,• 2.4 GHz wireless bandwidth, data transmission rate in full duplex mode at 57 kB/s,• sounding an alarm (105 dB) in case of a sudden loss of communication or undue displacement,• the robot control using a 51 microprocessor family.The software meets the following assumptions:• full communication with the robot: setting of all the parameters and visualization of the received data,• archiving of data received from the robot,• remote control in manual and automatic mode according to the set trajectory,• automatic estimation of the number of fish in the reservoir,• working in the Windows operating system.The robot with particular emphasis on the control systems and the deployment of the sonar head is shown in Figure The drive and control systems of the robot are powered separately. This made possible to send location information even if the mobility is completely blocked. When the movement of the robot is correct and the battery voltage drops from 12 V to 9 V, the appropriate information is sent to the station. If the operator does not take any controlling action the robot turns back to the station on the autopilot.The measurement method for estimating the number of fish using the described robot is based on a sequential calculation of the number of occurrences of fish on the set trajectory. The trajectory, set with the use of the developed software, takes the form of parallel lines arranged horizontally or vertically in the analyzed area Figure . Figure • one constant depth of the fish location,• fixed width of the sonar ultrasonic beam,• each measurement is a mean value of 5000 simulations,• rectangular trajectory of robot’s movement,• random distribution of the fish with variable number of clusters – from 1 to 60.The following variables were analyzed:s ranging from 1 to 60,• variable number of fish clusters Tm/Ts,• variable frequency of robot’s crossings comprising 10% to 100% of the analyzed reservoir surface ps covering 10% to 100% of the reservoir surface.• variable cluster areas Sample trajectories for the robot movements were shown in Figure Lo with a resolution M×N containing the value "1" in places where the fish occur and "0" in the other ones, the measurement error of the total number of fish in the reservoir is:Assuming the surface of the reservoir as the image matrix where:m,n) – are the coordinates of the point  of the image matrix Lf,. A discrete image of the reservoir area is not limiting in any way the scope of discussion since the resolution of the images Lf,Lo and Ls can be freely expanded. According to this image, the measurements of the impact of changes in the surface percentage measured by sonar pf and in the number of fish clusters s on the value δf were carried out. The value pf was defined as:Therefore, the error value δf as a function of changes in the surface percentage measured by sonar pf were shown in Figure Examples of changes in the value δf as a function of percentage changes in the surface covered by sonar enables to estimate the number of fish based only on the known pf value. The closer the course marked with the red line gets to the ideal diagonal  in the range from 1% to 70% of the whole area, the results obtained are as shown in Figure δp below 0.5%. The total area of clusters below 10% of the total reservoir surface causes a measurement error exceeding 10%. Therefore, analysis of combined simulation results presented in Figures δp is 11% and is linked to the pf value equal to 40% and the degree of covering the reservoir with the fish clusters not higher than 10%. The pf value below 40% should be selected if the reservoir was considerably covered with fish clusters. For other cases, the error δp is equal to a few percent.In the case of a random distribution of fish at a depth of 5 meters covered by the sonar beam with a cone angle of 30°, the simulation results were shown in Figure m,n,k) with 10 formed clusters. The area covered by the cone of the sonar ultrasonic beam (an example of discrete location of one of the estimation layer) was also marked. The measurement error δps, due to the shape of the ultrasound beam and its incomplete covering of the reservoir, is:Using simulations for the studied area estimated only for the bottom of the reservoir, considerations were extended to three-dimensional reservoir models and the conical shape of the ultrasound beam. Extending the discussion of the real three-dimensional reservoir and spatially distributed fishes, a discrete model was built and a series of simulations were carried out. Figure where:k – discrete depth coordinate,K – maximum depth in pixels.M×N×K where K is the total number of levels of depth distinguished by sonar. This is a value strictly dependent on setting the depth scale of the sonar and the resolution of its image. For the used sonar module, it is K = 1970 pixels (ping) for each measurement, which, e.g., for a range of 10 meters, means that for one meter there are 197 pixels. As the influence of the number of robot’s crossings on the obtained accuracy δps in the estimation of the number of fish is important, the following assumptions were adopted:For the error, defined in this way, a simulation for a random location of fish containing 10 clusters was carried out. The reservoir is magnified with a three-dimensional matrix sized M×N×K = 200×200×50 pixels,• discrete size of the reservoir – • the number of fish clusters – 10,o.• the angle of ultrasonic beam cone of the sonar – 30δpa defined as:For such assumptions, a simulation was carried out of changes in the error pfs values defined as:for a variable number of crossings and thus different δpa increase for reduced capacities pfs analyzed by sonar. This relationship is not linear. As it is apparent from the graph, the value pfs equal to 10% or 15% is the best choice, taking into account a compromise between the accuracy and coverage of the reservoir volume. Assuming that the pfs value is to be 10%, relations between the number of crossings, the cone angle α and the depth (number of pixels) k of the reservoir were calculated. It can be easily deduced from Figure The results are shown in Figure Tm value (expressed in meters), for the cone angle of the sonar ultrasonic beam changes (from 2° to 50°) and three different depths of 0.1, 1 and 3 meters. The results were shown in Figure α = 30° are indicated in the graph. For such a value of the angle, the maximum distance of neighboring crossings (Tm) was 0.24, 2.4 and 7.2 meters for the maximum depths of the reservoir 0.1, 1 and 3 m, respectively. This is very practical information related to the maximum possible distance between adjacent crossings of the robot for a given type of sonar ultrasound head and a given depth. In the case of undulated bottom, the mean value of the depth should be taken as the mean value determined on the basis of the data on the bottom depth taken from the sonar.Using this equation, simulations were carried out of changes in the maximum The necessary information about the location, depth and physicochemical parameters of the reservoir were transferred wirelessly from the robot to the station. The information about the trajectory sent to the robot from the station was constantly corrected. The software (home tab) for controlling the robot is shown in Figure The analyses identified many factors which might have an effect on the error related to measurements of the number of fish. However, they can be easily reduced and their impact on the result can be estimated. The presented methodology involves the use of images acquired from the sonar as one of the main (besides location) sources of the information on the number of fish. The authors used here already available and own new methods of image (from sonar) analysis and processing.Lwe with a resolution of M×N = 1970×200 underwent subsequent stages of analysis [The data analysis from the sonar concerned automatic recognition of fish using the methods of image analysis and processing. Input image analysis -16, i.e.• filtration with a median filter whose mask size is 3×3 which enables to eliminate small artifacts and noise,• morphological opening for a large structural element (40×40 pixels) which enables to remove uneven brightness of particular pixels,• binarization of the resulting image with the threshold being 20% of the total average brightness in the analyzed image,• labeling of individual fish which enables to count them and calculate what area they occupy.All these parameters were adjusted once for a given type of sonar module during the production stage. The user and the operating person introduce no parameters for both, the software and robot settings. Figure Verification of the effectiveness of the robot and the method of measuring the number of fishes was based on the created software tested in Matlab and then rewritten to the C++. The main window of the program consists of two parts: The results of automatic recognition and indexing of fish are seen on the left window. The right side contains the table with the information on the location, volume and the number of the recognized object (fish) in successive rows. Buttons at the top of the window enable to navigate the saved image coming from the sonar and change the visible area of analysis. Moreover, it is possible to hide or expose the selected fragment of analysis. The belt at the bottom of the window shows in green and white the studied areas and inform whether the GPS coordinates were sent. In case of very bad weather, a correct image from the sonar can be archived but the GPS coordinates will not be saved. Then it is possible to identify individual fish, but this information cannot be applied to their location in the reservoir Figure .The relationships shown in Figure A, 56 m × 26 m × 1.5 m (length × width × depth),• reservoir B, 55 m × 26 m×1.5 m,• reservoir C, 130 m × 22 m × 1.3 m.• reservoir Sonar measurements were carried out for each reservoir, and then all the fish from the ponds were caught and underwent a direct quantitative and qualitative analysis. A list of fish species, their mean body mass and body length was given in Table The robot automatically followed the set trajectory. The automatic trajectory correction was possible to a limited extent because of the wind. Keeping the trajectory for allowing only slight variations needs more energy for the driving motors and reduce the time available for continuous robot operation.A, the measurement error was of 8% and for the reservoirs B and C, it was 7% and 8%, respectively. These values are within the acceptable error range of the percentage volume analyzed by the sonar in relation to the total volume of the reservoir. This confirms the usefulness of the robot for estimating the number of fish in the reservoir.The results of direct counting were used as the reference values for the determination of the error of the described measurement method. Using the collected data, the total number of fish in the reservoirs were calculated Table . A compaA direct analysis of the number of fish is performed using sonar attached to the boat. Commercial software for most types of sonar enables automatic marking of fish and their location. However, it is not possible to approximate the results obtained for the whole water reservoir. Various types of robots, mainly for monitoring the water bodies, are also used. These include Robo-fish developed by the LiveScience in partnership with the National Science Foundation . The invOur model differs from the above mentioned robots in terms of details related to the wireless transmission transmission of the data. The device presented here, allows encrypted transmission of the data and automatic calculaton of the number of fishes. The operation with this model is autonomic – our robot automatically returns to the station when a voltage drop is detected.Various types of the wired robots are also available. For example, Hayes et al. , describThe estimation of the number of fish in the reservoirs based on the data derived from the sonar was presented in a series of publications -28. TakiQuantitative analyses using split-beam are more convenient in in the assessment of large rivers Matveev  obtainedIn most of the analyzed studies, the authors recommend calibration of the adopted method to specific conditions of the measurement. Then it is possible to obtain quite accurate measurements with an error as low as 3% . In the • low impact resistance – despite the hull made of 5 layers of the glass fiber, it is little resistant to shocks during transport,• low resistance to high waves – despite the proper balance of the robot, larger waves can cause swinging of the boat, which results in the appearance of artifacts in sonar images,• selection of batteries, that enable evaluation of the capacity in case of their partial wear or damage,• cylindrical arrangement of solar panels, in order to reduce compliance to dirt ,• sending the GPS data for each sonar ping – used GPS module sent data every 10–15 sonar pings,• lack of automatic calibration of complex probes,• relatively high price of the production . The authors intend to modify the used modules and to develop a more cost-effective model.The developed software works correctly in the continuous operation of the robot for up to several hundred hours. Each loss of the wireless contact starts a self-test procedure. The robot can work continuously (without charging and return to the base) for 3 hours. Depending on the weather conditions, it can perform measurements over a distance of several kilometers.The created robot together with the developed software meets all the necessary assumptions. The listed above disadvantages, can be eliminated in further work of the project. The robot works properly in a variety of harsh weather and environmental conditions, is fully automated and can be remotely controlled using Internet. The main advantage of the robot is the ability to monitor the reservoirs that supply drinking water. This is very important for human health. It enables to obtain the said parameters from any selected area of a given reservoir and in any period of time. Moreover, the robot does not require any participation of the operating personnel. All the actions (control of the robot) and transfer of the results can be done remotely from different places in the world. Alternatively, the practical usefulness for the protection of human health is the analysis of the areas of water bodies used as public swimming pools. The advantage of the robot is the possibility of its use for the quantitative analysis of fish in small, shallow inland water bodies.GPS: Global Positioning System; MAC: Medium Access Control; UTM: Universal Time Clock; NMEA: National Marine Electronics Association.The authors declare that they have no competing interests.RK and ZW suggested the algorithm for image analysis and processing, implemented it and analysed the images. AW and BŁ formulated the concept of connections low-cost sonar with remote mini-boat to estimate fish populations in small reservoirs. SW, AK, AW, BŁ, MP, JK, PM performed the acquisition of the sonar images and consulted the obtained results. All authors have read and approved the final manuscript.


MrBUMP and BALBES are reviewed, with an emphasis on understanding their output. Conclusions are drawn from their performance in extensive trials.The automated pipelines for molecular replacement  MrBUMP and BALBES are two recent developments from CCP4 that have been designed to automate and speed up the process of determining and preparing the best search models and putting them through molecular replacement. Their intention is to provide the user with a broad set of results using many search models and to highlight the best of these for further processing. An overview of both programs is presented along with a description of how best to use them, citing case studies and the results of large-scale testing of the software.Molecular replacement is one of the key methods used to solve the problem of determining the phases of structure factors in protein structure solution from X-ray image diffraction data. Its success rate has been steadily improving with the development of improved software methods and the increasing number of structures available in the PDB for use as search models. Despite this, in cases where there is low sequence identity between the target-structure sequence and that of its set of possible homologues it can be a difficult and time-consuming chore to isolate and prepare the best search model for molecular replacement.  Performing this can be very time-consuming and tedious. Two developments by CCP4 . Recent years have seen rapid advances in MR techniques that have helped to improve the success rate of the method. In spite of this, in limiting cases  at York University in the UK since 2007.            2.1.BALBES consists of three components: (i) a database of protein structures specifically designed for MR, (ii) a pipeline manager written in Python and (iii) a group of executable programs such as MOLREP  between corresponding CPISA  and sequence (FASTA format) information. Users can also define their own library of PDB files in the command line. BALBES can also be used to find similar structures in the database. These methods are shown on the York software website (http://www.ysbl.york.ac.uk/YSBLPrograms/index.jsp).(i) A simple command line; the most typical one is ccp4i interface  The Via the York Structural Biology Laboratory web server available at http://www.ysbl.york.ac.uk/YSBLPrograms/index.jsp.(iii) The best solution found by BALBES can be sent directly to the ARP/wARP ’ and ‘model No.’.At the beginning of a structural solution process,  al. 2008. These mBALBES for a case where three target sequences have been provided. BALBES tries to search the database for the assembly models using all three as well as any combination of two sequences. Once such models are found, BALBES outputs details of these models, as shown in Table 1a). It can be seen that the assembly model given in Table 1a) is obtained by using sequences 1, 2 and 3. The model comes from PDB file 2a74 and contains 1109 residues. The overall sequence similarity is 54%. It is expected to have four copies of such an assembly molecule in the unit cell.Fig. 1BALBES also tries to find models associated with each sequence. As shown in Fig. 1b). It can be seen that BALBES finds one dimer, one monomer and two domains, all of which have a sequence similarity of 1.0, from PDB file 1n9w. If ‘(ENS)’ is shown in the table it means that the chains or domains of that structure are used to generate ensembles. The details of ensemble generation will be published elsewhere. BALBES uses the ensembles first and if a solution is not found it then switches to single-chain models.In addition to these assemblies, BALBES is shown in Table 1c). Unlike the models in Table 1b), these models are all domains from different structures (PDB files).Another set of search models provided by BALBES uses the search models generated on the fly during the later stages of the structure-solution process. For example, when there are several sequences and no assembly models of multiple chains are found or the assembly models found did not give good solutions, BALBES works on all of the search models generated using the single sequences. It keeps the best solution for each sequence. It then fixes the best solution of one sequence and uses the best solution of another sequence as the search model in turn until all of the best solutions are used. In this case, the search models are actually the solution generated in the previous step. Those solution structures could be a combination of multiple domains which are different from the original models. A detailed example is shown in §Finally, 2.3.2.BALBES to find a structure solution are briefly described in the flow chart shown in Fig. 2BALBES tries the assembly models associated with multiple sequences first. If this is not successful it then tries all models associated with each chain and stores the best model for each sequence. It then combines all of the best models to give the final solution.The protocols for Q is used to define the direction of the solution process and the final decision for the best solution. The Q factor is calculated after refinement using the following empirical formula, w = 0.75 and R and R                  free are the R and free R factors. Once the Q is calculated, the solutions are assigned a probability using the formulaQ > 0.75 BALBES has found a solution that is definitely correct. If Q < 0.25 the solution found is very unlikely to be correct. When Q falls between 0.25 and 0.75 the probability given by P                  solution shows how likely the solution is to be correct. However, the direct link between a partial solution and Q has not been established. Furthermore, it does not take into account crystal-growth peculiarities such as twinning and pseudo-translation and therefore the scores may be misleading in these cases. A better criterion could be established via theoretical modelling and statistical data-analysis techniques based on the data from our routine test cases.As we can see from the flow chart Fig. 2, a qualiQ factor and its associated probability are presented. Table 2Table 22.4.BALBES with the existing database. Solutions found by BALBES are compared with the deposited PDB file. We have continually carried out these tests for more than three years. Our test results show that BALBES is able to solve about 80% of deposited structures automatically. Table 3Every half a month we take the newly released data from the PDB. Before putting the relevant entries into the database, we use the structure-factor amplitudes and sequences to test BALBES finds three assemblies, with each containing two sequences. However, these models do not result in high-quality solutions. BALBES then works on the models generated from each sequence. 35 models have been tried, which come from 15 different PDB files with similarity ranging from 0.26 to 1.0. The best solutions are generated by the monomer models from 1nld, 1ozn and 1nlb. BALBES then takes these three best solutions and tries using one solution as the starting fixed model and the others in turn as the search models to perform MR and refinement. The final solution is indexed as sq2st2m1_sq3st1m1_sq1st4m1, which means it is obtained by first fixing the best solution from sequence 2 and using that from sequence 3 as the search model and then fixing the new solution from that and using the best solution from sequence 1 as the search model. Fig. 3Fig. 3 shows an example of a complex structure solution from the test cases. The input file contains three sequences of 219, 219 and 283 residues, respectively. BALBES are also able to handle models with low sequence similarity in some cases. Table 5The algorithms in BALBES varies significantly from case to case depending on many factors, e.g. the number and sizes of the search models found. For a case of moderate difficulty, such as the case shown in Table 1BALBES completed all of the model searching, MR and refinement on all search models in about 2 h on a laptop with an Intel Core 2 Duo 2.0 GHz CPU.The time spent to solve a structure using 3.MrBUMP (Molecular replacement with BUlk Model Preparation) was originally developed as part of the e-HTPX project for high-throughput protein crystallography in the UK , MrBUMP should identify pertinent search models, prepare them and produce a molecular-replacement solution that in its refinement clearly shows the potential for a solution. In less clear-cut cases the exhaustive nature of MrBUMP provides a means for a user to explore a large parameter space. It has been found that this approach has successfully produced a solution in several cases . The ccp4i interface requires the specification of input and output files and provides access to many of the underlying keywords with sensible defaults for each of them.               FASTA search . These are all added to the list of template search models. They may provide solutions in molecular replacement where the parent chain fails. Phaser  is created called ‘search_JobID’, where JobID is the job number assigned by the ccp4i interface if run through ccp4i or the value assigned to the JOBID keyword variable if run from the command line. Below this, several directories are created. The most important of these is the ‘data’ directory, which stores all of the data-processing information for the template search models. This includes the model-preparation data, MR data and refinement data. Fig. 4b) shows the ‘data’ directory tree for an individual template search model.One of the key benefits of using 3.1.3.MrBUMP is that it allows the farming out of molecular-replacement jobs to a compute cluster where available. It currently supports the Sun Grid Engine (SGE) and Portable Batch System (PBS) queuing systems. When running from the command line, enabling the ‘CLUSTER’ keyword will cause the program to submit each of the MR jobs to the job queue. In the ccp4i interface the option to submit jobs to the queue system is presented automatically if either of the systems mentioned above are detected. The master process will then monitor the queue to determine when jobs have completed. This can help to speed up the processing of the template search models and can also allow the testing of a larger set of search models in molecular replacement.A useful feature of 3.1.4.Lactobacillus lactis. This bacterium is used in the dairy industry to make cheese and buttermilk. The bacteriophages can cause significant product-quality problems owing to their ability to stop the bacteria converting lactose to lactic acid , we have expressed proteins from certain genes taken from some bacteriophages that infect the bacterium Escherichia coli and then crystallized at the Collaborative Crystallization Centre (C3) facility. The resulting crystals are exposed to X-rays at the Australian Synchrotron and a set of diffraction images are collected. These diffraction images are processed into an MTZ file using MOSFLM .Once we have the MTZ file and the 3.2.MrBUMP for a set of 552 randomly chosen PDB structures deposited in 2009. The processing was carried out using the depositions in the PDB up to 2009 as the source for search models. One constraint we have used is that the data are for structures containing a single molecule only. As such we will not discuss the benefits of the multimer search option in MrBUMP. A more comprehensive survey will be carried out using all of the 2009 depositions and will be the subject of a future article, but for the purposes of illustrating the advantages of MrBUMP here the chosen set will suffice.To illustrate the usefulness of the program, we have carried out a large-scale survey of the results of MrBUMP performs, in each example we have enabled many of its features and instructed it to try a wide selection of models. Depending on the results of the FASTA search, up to 15 whole-chain search models can be generated as well as the potential for several SCOP-based domain search models and an ensemble model of the best-scoring individual models for use in Phaser. Of the set we have chosen, 342 produced at least a ‘good’ solution, 98 produced at best a ‘marginal’ solution, 66 produced at best a ‘poor’ solution and the remaining 46 had no suitable search models available in the PDB. 312 of the targets had homologues of at least 80% sequence identity, while 92 of the structures that had suitable search models had homologues of sequence identity 40% or less. Originally, 432 of the set were solved by molecular replacement.In order to gain an in-depth appreciation of how MrBUMP, it is possible to farm out the processing of each of the search models in MR using a cluster-queuing system . This is very useful when running a single MrBUMP job; however, given the vast number of jobs carried out in this work we opted not to use this facility in these tests. On the older CCP4 cluster the average run time for a MrBUMP job was 72 min. On the more up-to-date Diamond machine the average run time was considerably faster at about 39 min. It should be noted that in some cases jobs were found to take as much as 24 h to complete (on the CCP4 cluster). As with BALBES, the length of time it takes to run MrBUMP depends on many factors such as the number of search models tested, the size of the target structure and the sequence identity of the target with its homologues.The tests were carried out on the compute cluster facilities of the CCP4 Group and the Diamond synchrotron facility in the UK. The CCP4 cluster is made up of eight nodes, each with two 2.0 GHz dual-core AMD processors and 4 GB of RAM. The Diamond system has a total of 77 compute nodes. The bulk of its nodes are 2.5 GHz dual-processor quad-core Intel Xeon chips with 16 GB of RAM. For a single run of 3.2.1.MrBUMP has completed, the list of search models that will go forward to the MR stage are ranked in accordance with a score derived from the alignment of the template sequence with that of the target. Making the choice of best search model based on sequence identity is similar to what a user might do if they were performing a blind search for potential models manually. At the end of a MrBUMP job each of the models are again ranked according to how well they refined in REFMAC. Fig. 5MrBUMP jobs (post-refinement) lies in terms of how that specific model was ranked before MR was initiated in the pipeline. Only ‘good’ or ‘marginal’ solutions have been considered here. The histogram clearly shows how the best solution quite often lies outside the first five molecules tested and can even be found beyond the 30th ranked search model. MrBUMP provides the user with the ability to test a large set of search models and identify the one that is likely to provide the best possible starting point for model building. As demonstrated here, this best solution quite often does not stem from what the user might anticipate as the best search model. Also, in cases where a solution is hard to find the exhaustive nature of MrBUMP provides a means of finding that solution.When the model search and preparation stage of 3.2.2.MrBUMP pipeline allows the creation of several different model types for each template search model. In Fig. 6MrBUMP jobs in the test set what type of model was used for the best solution after all models have been processed in MR. The figure plots the sequence identity of the search model with the target sequence against the final R                  free for each of the best solutions. Again, we only consider solutions that are marginal or better. As is commonly accepted, where the sequence identity is lower than 30% it is difficult to find a solution. There are a few cases below this threshold where marginal solutions are indicated and we will discuss one of these (example 2zzt) in more detail later.As outlined above, the model-preparation step in the MOLREP (green crosses) works best on many occasions. However, the mixed models generated by CHAINSAW (red crosses) feature more prominently where the sequence identity is low. This can be explained by the use of a multiple sequence alignment to generate the alignments used by CHAINSAW to prepare the template search models. MOLREP performs its own pairwise alignment of the search-model sequence and the target sequence, making use of some structural information from the template model. At lower sequence identities (<30%) the accuracy of the multiple alignment is better than the pairwise alignment and potentially produces better search models for molecular replacement. In all of the examples in the test set we have used ClustalW2  used to generate the ensemble search model for 2wfb. The signal from the nonconserved regions of the alignment such as the flexible loops and the N-terminal residues are down-weighted by Phaser in molecular replacement. In example 3fyq the individual search models (chain A and chain B of 3dyj) have a high sequence identity with the target (66%) but they both fail to give an acceptable solution in MR. Again, the signal from the nonconserved regions in their structure alignment is downweighted in the ensemble search model by Phaser, allowing a solution to be identified. 3fyq was originally solved using a MAD experiment  are of particular interest as the search models used had very low sequence identity with the target sequence (<13%). On closer examination 2waf proved to be a negative result, but the 2zzt example solved with a domain-search model with 12.1% sequence identity did prove to be valid and model building could produce a structure for the target. We can see why in Fig. 82zzt structure (pink) with the parent molecule  of the domain that was used to find a solution. It is obvious to the naked eye that the smaller domain in 2qfi matches the structure of 2zzt very closely in its secondary-structure conformation. Given the very low sequence identity, this search model could easily be overlooked. Indeed, the structure of 2zzt was originally determined using a MAD experiment despite the existence of this search model at the time  In cases where only poor solutions can be found, it is still worthwhile investigating these by hand. The pipelines by their nature do not facilitate access to all of the control parameters for the underlying MR engines MrBUMP provides the option to make use of the ACORN program  Where the resolution is good enough (better than 1.7 Å) BALBES facilitates the checking of all space groups related to the input target space group. MrBUMP will allow the checking of enantiomorphic space groups.(iii) Check all space groups. BALBES through its web-service interface it may be combined with the ARP/wARP web-service facility to produce a model for the target.(iv) When utilizing MrBUMP allows the use of different alignment programs to perform the multiple alignment step of the target sequence and the template sequences. Different and possibly better alignments can result in improved search models. The possibilities are MAFFT, PROBCONS, ClustalW and T-Coffee.(v) MrBUMP will exit if a good solution is found, but allowing it to try all models may reveal several potential solutions. For model building, the better the starting electron-density map and/or model is, the easier it will be to complete. It is worth sifting through the various solutions produced by MrBUMP to see which one gives the best starting point.(vi) Use the option to test all generated search models in MR. By default


Human short term memory has a capacity of several items maintained simultaneously. We show how the number of short term memory representations that an attractor network modeling a cortical local network can simultaneously maintain active is increased by using synaptic facilitation of the type found in the prefrontal cortex. We have been able to maintain 9 short term memories active simultaneously in integrate-and-fire simulations where the proportion of neurons in each population, the sparseness, is 0.1, and have confirmed the stability of such a system with mean field analyses. Without synaptic facilitation the system can maintain many fewer memories active in the same network. The system operates because of the effectively increased synaptic strengths formed by the synaptic facilitation just for those pools to which the cue is applied, and then maintenance of this synaptic facilitation in just those pools when the cue is removed by the continuing neuronal firing in those pools. The findings have implications for understanding how several items can be maintained simultaneously in short term memory, how this may be relevant to the implementation of language in the brain, and suggest new approaches to understanding and treating the decline in short term memory that can occur with normal aging. Short term memory can be implemented in for example the prefrontal cortex by the continuing firing of neurons during the short term memory period George Miller published a paper in 1956 entitled “The magic number 7, plus or minus two: some limits on our capacity for processing information” Kohonen and other pioneers made neuronal network models that could maintain their activity during a short term memory period −w as shown below. Nonetheless, by using sparse representations with a = 0.01 (where the sparseness a is the proportion of neurons that is active for any one memory pattern), Amit et al. were able to maintain six memory patterns simultaneously active in short term memory To provide background information, we note that there are currently a number of theories regarding the underlying mechanisms that yield limits to the capacity of short term memory. The main two competing models are “fixed capacity models” (or slot models) The attractor network architecture for short term memory that was simulated is shown in To the standard integrate-and-fire network u (the fraction of resources used) reflecting the calcium level. When a spike reaches the presynaptic terminal, calcium influx in the presynaptic terminal causes an increase of u which increases the release probability of transmitter and thus the strength of that synapse. The time constant of the decay of the synaptic facilitation is regulated by a parameter τF which experimentally is around 1–2 s U (0.15) and for τF (1.5 s) used here are similar to values reported experimentally and used elsewhere j is multiplied by the presynaptic utilization factor ju (t), which is described by the following dynamics:k. The first term shows how the synaptic utilization factor ju decays to the baseline utilization factor U = 0.15 with time constant Fτ = 1500 ms, and the second term shows how ju is increased by each presynaptic action potential k to reach a maximum value of 1 when the neuron is firing fast. The modulation by the presynaptic utilization factor u is implemented by multiplying the synaptic weight by u to produce an effective synaptic weight weff. This models the underlying synaptic processes Synaptic facilitation is common in higher cortical areas such as the prefrontal cortex EN = 800 pyramidal cells (excitatory) and IN = 200 interneurons (inhibitory). There are ten populations or pools of excitatory neurons each with 0.1 EN neurons (i.e. 80 neurons). The network is fully connected. Neurons within the selective population are coupled, by a factor w+ = 2.3 (unless otherwise stated) above the baseline connection synaptic weight w = 1. Connections to inhibitory cells are set to the baseline level, w = 1, and from the inhibitory neurons winh = 0.945 (unless otherwise stated).The integrate-and-fire attractor network, developed from an earlier model Next = 800 synaptic connections to an external source of Poisson-distributed, independent spike trains of rate 3.05 Hz per synapse, so that each neuron received 2440 spikes/s. The presence of cue stimuli to initiate the short term memory was modeled by an increase of λ to 3.3125 spikes/synapse. This value of λ was applied to any of the pools S1 to S10 .To model spontaneous background activity, every neuron in the network is coupled through V of a neuron evolves according tomC is the membrane capacitance  neurons. The subthreshold membrane potential The synaptic current includes glutamatergic excitatory components (mediated by AMPA and NMDA receptors) and inhibitory components (mediated by GABA). External cells contribute to the current only through AMPA receptors. The total current is given byx = AMPA, NMDA, GABA. Synaptic facilitation is implemented through the utilization factor ju which modulates the recurrent excitatory synapses, specifically the synaptic gating variables s as can be seen from Equations 5 and 6. The values for the synaptic conductances and the reversal potentials EV and IV are given in 2+] = 1 mM), with parameters γ = [Mg2+]/(3.57 mM) = 0.280 and β = 0.062 (mV) −1.j, for all receptors, is described by the following differential equations, where s in time:NMDA,riseτ = 2 ms, and α = 0.5 ms−1; rise time constants for AMPA and GABA currents are neglected. Decay time constants for AMPA, NMDA, and GABA synapses are AMPAτ = 2 ms, NMDA,decayτ = 100 ms, and GABAτ = 10 ms. The sums over k represent a sum over spikes emitted by pre-synaptic neuron j at time The fraction of open channels in cell To complement the integrate-and-fire simulations described in the paper, we performed mean field analyses of the network when it is operating with synaptic facilitation. The mean field analysis provides a simplification of the integrate-and-fire dynamics considered in the spiking simulations, which are computationally expensive and therefore not suitable for extensive parameter explorations, though they are important for establishing how the system operates with stochastic dynamics when it is of finite size and has noise, that is randomness, introduced by the almost random spiking times of the neurons for a given mean rate u(t) increases particularly for each excitatory subpopulation that has received external stimulation produced by the cues, and reaches a value close to 1 during the delay period. Only small increases occur in the pools that have not received this external stimulation by a memory cue (and those increases occur because of effects produced through the w− connections between the excitatory pools shown in u(t) converges in both cases to a given value, we can then define a new effective synaptic strength (wj)eff which operates during the steady state, and this opens the possibility to perform a mean field analysis of the network endowed with STP. The effective synaptic strengths are defined as:ju∞ is the asymptotic synaptic facilitation value that is estimated from the average ju(t) observed in the last 500 ms of the delay period of the spiking network simulations. The protocol used in such simulations is described in detail in the next section. A conventional mean field analysis with the effective synaptic strengths can then be performed and can be used to systematically scan the parameter space {+, wwinh} with the aim to reproduce a short term memory regime in which only the stimulated (i.e. cued) subpopulations remain active during the delay period.We extended and modified the standard mean field approach 1−λ7 were applied to excitatory neuron pools S1 to S7 during the period 500–1500 ms. This produced a high firing rate of approximately 70 spikes/s in pools S1–S7 , then the firing in the previously cued pools could be restored by restoring λext within approximately 1 s, before the synaptic facilitation had decayed too far. However, if λext was delayed for longer (e.g. 3 s) so that the synaptic facilitation had decayed, then the firing of the previously cued pools could no longer be selectively restored by restoring λext at its baseline or at any other value. An example is shown in ext for the excitatory neurons set to 0 for 500 ms. When λext was restored to all excitatory pools  at time t = 2000 ms, there was sufficient synaptic facilitation remaining (ext), the synaptic facilitation had dissipated so much that selective recall of the short term memories became very poor. This helps to show the importance of the synaptic facilitation in the maintenance and even restoration of in this case multiple short term memory representations.Further evidence for the importance of the synaptic facilitation in the process is that if there was a short delay after the end of the cue period in the neuronal firing  in the same network high- (winh)low is larger for the same number of memories (greater than 2) when the system is endowed with synaptic facilitation. Thus the synaptic facilitation mechanism has the advantage that it also makes the system more robust, and thus more biologically plausible, as well as increasing the short term memory capacity. We emphasize that this type of robustness, relative insensitivity to the exact values of the parameters, is likely to be important in biological systems, in which specification of the properties of neurons and networks is not expected to be set with high precision.In order to investigate our hypothesis that synaptic facilitation can not only increase the number of memories that can be maintained simultaneously active, but may also make the system more robust with respect to its sensitivity to small changes in the parameters, we show the result in a = 0.1, one inhibitory pool, and, unless otherwise specified, the same synaptic weights as the integrate-and-fire simulations described eff was obtained from the spiking simulations, and the modified mean field analysis was then performed on the resulting network. w+ for the cued pools, and for the non-cued pools. These values for (w+)eff were obtained from the spiking simulations by multiplying the value of w+ by the value of u∞ that was obtained, and these values of (w+)eff were used in the mean field analyses. winh was 0.945. The other values were the same as those used in the conventional mean field approach and in the spiking simulations shown in In more detail, for each winh within which different operating regimes occur are shown for different numbers of cued short term memory pools. The first regime is with winh <0.94, in which case there is insufficent inhibition in the network, and some of the pools start firing even when no pools have been cued. This regime of unstable firing of the uncued state when the neurons should be firing at the spontaneous rate of approximately 3 Hz is labeled ‘unstable spontaneous activity’ in winh can be anywhere between 0.94 and 1.07. For any 3 of the 10 possible STMs to be stable when cued on, the value for winh can be anywhere between 0.94 and 1.02. For any 7 of the 10 possible STMs to be stable when cued on, the value for winh can be anywhere between 0.94 and 0.96. Thus winh must be set much more accurately when the required capacity is for any 7 of the 10 possible short term memory attractor states to be maintained stably. For any 9 of the 10 possible STMs to be stable when cued on, the value for winh must be 0.94 and nothing else will do. The mean field analysis thus confirms that it is possible to maintain 9/10 possible short term memory states active when the sparse of the representation is 0.1 with binary neurons. The useful operating region for multiple simultaneously active short term memories is thus between the low limit and high limit boundaries shown in The second regime labeled ‘multiple simultaneous STM regime’ in winh is above the solid line, and is so large that only a subset of the cued short term memory pools can maintain their activity. That is, the high firing rates expected of all cued pools cannot be maintained stably. Thus the multiple short term memory capability fails when winh increases to a value above the solid line in winh is so large that no cued pools maintain their high firing rates stably.The third regime shown in u approaches 1.0, and the effective synaptic weights for a cued pool become close to the value of 2.3 that was usually specified for w+. That value for w+ is sufficient to maintain a pool of neurons firing stably. On the other hand, for uncued pools, the value of u remains low, and the effect of this is to reduce (by the multiplicative effect on the synaptic weight) the effective synaptic weights to values that are below the value of approximately 2.1 needed to maintain a pool firing stably with a high rate in an attractor state. It is this that enables the network to remain stable using the mechanism of synaptic facilitation, with very many simultaneously active pools, in the face of the noise  in the system. In a network without synaptic facilitation, all the internal recurrent collateral synaptic weights of the pools (one pool for each stimulus) are of the same strength, so that any noise in the system may cause a jump from an active to an inactive pool as the weights and energy landscape These effects of the synaptic facilitation on the performance of the short term memory network can be understood as follows. The synaptic facilitation has an effect similar to increasing the synaptic connection weights within each neural population that was activated by a cue on a particular trial, relative to the non-cued pools. Thus the effective synaptic weights of just the cued pools are increased for just that trial, and are very different from the synaptic weights of the uncued pools. In more detail, the effective synaptic weights within a pool that result from the synaptic facilitation are shown in a, of the representation of 0.1. The reason for choosing a representation that is not very sparse is that representations in the cerebral cortex are not very sparse, and 0.1 is a biologically realistic value for binary neurons to investigate, as shown by recordings from neurons in different cortical areas a = 0.05. We found that with synaptic facilitation incorporated as described here, it was possible to maintain 20 different short term memories active simultaneously . However, without synaptic facilitation, it was possible to maintain only seven of the 20 memories simultaneously active . These results again indicate that the use of synaptic facilitation can greatly increase the number of short term memories that can be maintained simultaneously active. These results also show that there is no ‘magic’ limit on the number of memories that can be maintained simultaneously active in short term memory. The number is set in part by the sparseness of the representations, with sparse representations allowing more short term memories to be simultaneously active; and by the use of synaptic facilitation, which can increase the number of representations that can be kept simultaneously active, by effectively increasing the synaptic connection weights within each neural population that was activated by a cue on a particular trial, relative to the non-cued pools.The sparseness of the representation, which for binary neurons is the proportion of the excitatory neurons active for any one stimulus, is another factor that we have found to be important in setting the capacity for the number of items that can be maintained simultaneously active in short term memory. So far in this paper, we have considered a network with a sparseness, In this work, we have investigated what parameters appear to be important in setting the limit on the number of short-term memories that can be maintained active simultaneously in a discrete attractor network. A key parameter was found to be the inhibitory synaptic weights, which has led us to suggest that, at least in part, there may be a capacity limit set by how finely in practice the inhibition needs to be tuned in the network, and also by the sparseness of the representation. We have further shown that synaptic facilitation of the type found in the prefrontal cortex boosts such capacity limit by effectively increasing the synaptic strengths just for those pools to which a cue is applied, and then maintaining the synaptic facilitation by the continuing neuronal firing in just these pools when the cue is removed.a = 0.1, memories simultaneously active is very biologically plausible, in that recurrent collateral connections to nearby neurons with associative synaptic plasticity are a feature of the architecture of the neocortex The neural mechanism described here for enabling a single attractor network to maintain multiple, in our case up to 9 with a sparseness a = 0.1 it was possible to maintain only 6 memories simultaneously active without synaptic facilitation, and 9 with synaptic facilitation in the same network. With a = 0.05 it was possible to maintain only 7 memories simultaneously active without synaptic facilitation, and 20 with synaptic facilitation. Thus synaptic facilitation greatly increases the number of memories that can be maintained simultaneously active, and this is one of the new findings described in this research. The use of synaptic facilitation is of conceptual interest in the mechanism described, for it is a non-associative process, which is what enables just the cued pools to remain active when they are cued without any further associative learning. Moreover, as shown here, the effect of the synaptic facilitation is sufficiently large that it can, in combination with the recurrent collateral connections, enable the process including the synaptic facilitation to be regenerated so that the short-term memory of just the cued pools can be maintained for many seconds.The use of synaptic facilitation in the multiple active short-term memory mechanism described is important to the success of the system, for without the synaptic facilitation it is difficult to maintain more than a few representations simultaneously active To be clear, we note that the issue of how many memories can be maintained simultaneously active in a short-term memory is different from the issue of how many memories can be stored in an attractor network and later correctly retrieved one at a time, which is a much larger number that scales with the number of synapses on each neuron, can be increased by a reduction in the sparseness of the representation, is facilitated by diluted connectivity, and is in the order of thousands in a network with 10,000 recurrent collateral synapses onto every neuron a = 0.1), the limit was reached with 9 pools simultaneously active. In this case, 90% of the excitatory neurons were active. Under these circumstances, the constraint is to find a value of winh that is sufficiently small that all these cued pools can be active without quenching each other through the inhibitory neurons; and at the same time for winh to be sufficiently large that when no pools are cued, the spontaneous state is stable, a requirement for a short-term memory system.As shown in a = 0.05, with 20 specific pools in the network. We found that with synaptic facilitation incorporated as described here, it was possible to maintain 20 different short-term memories active simultaneously , the use of saliency, an experimental variable that can be easily manipulated, has also demonstrated its value to contribute remarkable predictions. In particular, the predictions of a discrete attractor network similar to the one proposed in this work  that was presented by the authors Some approaches to short term memory consider continuous attractor networks in which the concept of the precision of the memory is relevant It is also worth noting that in the context of short term memory, the property investigated here is how many such discrete short term memory states can be maintained active simultaneously. While the number of memories that can be stored and correctly recalled is high Click here for additional data file.