Strong regional heterogeneity and generally sub-optimal rates of measles vaccination in Italy have, to date, hampered attainment of WHO targets for measles elimination, and have generated the need for the new Italian National Measles Elimination Plan. Crucial to success of the plan is the identification of intervention priorities based upon a clear picture of the regional epidemiology of measles derived from the use of data to estimate basic parameters. Previous estimates of measles force of infection for Italy have appeared anomalously low. It has been argued elsewhere that this results from Italian selective under-reporting by age of cases and that the true measles force of infection in Italy is probably similar to that of other European countries. A deeper examination of the evidence for this conjecture is undertaken in the present paper.R0) and critical vaccination coverage (pc)) are calculated for each region and for each of 5 plausible contact patterns. An analysis of the spectra of incidence profiles is also carried out. Finally a transmission dynamics model is employed to explore the correspondence between projections using different estimates of force of infection and data on seroprevalence in Italy.Using monthly regional case notifications data from 1949 to the start of vaccination in 1976 and notifications by age from 1971–76, summary equilibrium parameters (force of infection (FOI), basic reproductive ratio (FOI estimates are lower than comparable European FOIs and there is substantial regional heterogeneity in basic reproductive ratios; certain patterns of contact matrices are demonstrated to be unfeasible. Most regions show evidence of 3-year epidemic cycles or longer, and compared with England & Wales there appears to be little synchronisation between regions. Modelling results suggest that the lower FOI estimated from corrected aggregate national data matches serological data more closely than that estimated from typical European data.Results suggest forces of infection in Italy, though everywhere remaining below the typical European level, are historically higher in the South where currently vaccination coverage is lowest. There appears to be little evidence to support the suggestion that a higher true force of infection is masked by age bias in reporting. The WHO target of eliminating indigenous measles in Europe by 2007 represents a challenge for public health systems. The requirements for success in this battle are summarised by Gay . Italy, In designing an optimal elimination strategy, it is crucial that planners have at their disposal a clear picture of the regional "geography" of the intensity of effort required for measles elimination as a preliminary step for ranking intervention priorities. This is especially in view of the claim that there could be spatial heterogeneity in transmission rates due to the large socio-economical differences existing in the country . HoweverR0, the mean number of secondary infections which would arise from the introduction of a primary infection in a wholly susceptible population). As primarily shown by Anderson & May ) = 1.0 contacts) [From the pre-vaccination age distribution of cases we computed, for each region, summary equilibrium parameters from the SEIR model: i) forces of infection, FOI; ii) mixing or contact  matrices; iii) basic reproduction numbers, ontacts) .et al. [For comparison purposes we used the same age groups as in Edmunds et al. , i.e. 0–et al.  were comβij summarises the risk of acquiring infection for a susceptible individual in age group i due to contacts with infective individuals aged j. When estimating from age-structured data (e.g. serology or case reports), a mixing matrix with m age groups can have at most m distinct elements [βij entries are independent of age . Other types of mixing matrix are considered in the paper and are listed below. However not all forms of mixing are compatible with a given force of infection. As noted by Anderson & May [non feasible for the given FOI  and in such circumstances "the chosen matrix is inappropriate to the observed age dependence in the FOI". Hethcote [Mixing matrices Table  are usedelements . In the on & May , a mixinHethcote  further R0 to achieve its upper bound [Individuals of a given age are assumed to mix only with individuals of the same age, yielding a diagonal matrix. Given a specific FOI it is the form of mixing allowing er bound .et al. [β5) as that between adults [This matrix  has then adults .This matrix emphasises transmission between school age groups .βij = bibj j = 1,...m.Under PM  contactsh) weighted average of proportionate (PM) and fully assortative (DIAG) mixing: PREF = (1 - h) * DIAG + h* PM, 0<h<1 [PREF matrix has (m+1) distinct entries: the extra parameter h is usually estimated "ad hoc".The PREF matrix is a single parameter , and hence PREF is a weighted average between two limit cases. There is thus no clear relationship between, for instance, RDIAG & DEF on the one hand and PREF on the other. For this reason, we explored all forms that have been reported in the paper.The matrices RDIAG & DEF are defined "In contrast to the spatially well synchronised biennial England & Wales oscillation, visual inspection of Italian regional data did not suggest clear common patterns of oscillation or hence of the inter-epidemic period. Thus investigation of periodicities in regional measles incidence became necessary to provide satisfactory characterisation of the inter-epidemic period. Though incidence data might be seriously affected by under-reporting, recent work  suggestset al. [The cyclical behaviour in the monthly incidence time series of measles for each Italian region in the pre-vaccination period 1949–1976 was analysed in the frequency domain  with sp2π/T (T = series length); this is the basis of "harmonic" analysis. Such analysis may not identify exactly cyclical components where true frequency falls between two "harmonic" frequencies, e.g. it may suggest periodicity of either 3 or 4 observations when true periodicity lies between these values and hence is not identifiable exactly with this procedure. This problem may be overcome with "non-harmonic frequency domain" analysis where dependence of periodicities of cyclical components on series length is relaxed. Here the following procedure, as proposed in [λ, the quantityA drawback of periodogram analysis is the assumption that cyclical components of frequencies depend on the length of the observed series, i.e. that they are integer multiples of posed in , is usedposed in , by minixt denotes the detrended series .where K of the expected duration of the latent and infectious states is short compared to the life of the host (as is the case with measles), disease incidence will have a long-term oscillation around its endemic equilibrium with the period given, to an excellent approximation, by [A is the average age at infection and K can be taken to be 14 days in the case of measles.The results from the time series analysis were also compared with the prediction from the homogeneous mixing SEIR model that where the sum tion, by , where AAnalyses described above were carried out for all Italian regions .In addition to published official Italian data on births, and birth and death rates, monthly measles case reports were provided by the Istituto Nazionale di Statistica (ISTAT) for the period from the first available year, 1949, to 1976, together with regional age structured measles case reports from the first available year, 1971. Pre-vaccination FOIs at the regional level were estimated from data for the time window 1971–76, which encompassed about two full three-years long epidemic cycles .The spectral densities of the pre-vaccination (1949–76) Italian regional time series of measles incidence indicate, besides a well-pronounced annual cycle, the presence of a less pronounced longer term cycle of varying length, which is in contrast with the sharp biennial oscillation in England & Wales. Given the importance of the long term oscillation which is taken as representing the true "inter-epidemic period", the non-harmonic estimator was calculated .A); ii) the non-harmonic estimate TO of the period of the long-term oscillation; iii) the length of the inter-epidemic period from the homogeneous SEIR model via formula for T given in the Methods section.Table TO in table T predicted by the SEIR model range from a minimum of 2.8 (Puglia) to 3.3 years (Tuscany). The agreement between T and TO in some cases is not very good: North-Eastern regions show a higher average age at infection, A, compared to Southern regions and yet the shorter inter-epidemic periods in the former compared with the latter, as suggested by the time series analysis, would imply the reverse.Values of Compared to England & Wales there is also a surprising lack of synchronisation between regional cycles in the pre-vaccination period. A cross-spectral analysis suggests limited correlation between pre-vaccination long term cycles; coherencies greater than 0.7 were observed, as expected, only for a few neighbouring regions such as Emilia-Romagna and Marche, Marche and Umbria, Friuli and Trentino, Lombardia and Piemonte, Liguria and PiemontePre-vaccination age-distributed forces of infections in the Italian regions (we report a sample of results) can be well summarised by 3 clusters, North, Centre, South. These are shown in Fig. A , that coarsely summarises the force of infection, shows similar spatial patterns. For instance A is systematically smaller in the South (little above 5 years), around 7 years in the Centre, and takes intermediate values in the North  is, with the exception of Southern regions, higher than in the "young" (0–1 yr.). A verifiable consequence of this fact, is that "realistic assortative" mixing matrices (RDIAG) can be non-feasible (they can yield negative coefficients), and this indeed happens in all Central and several Northern regions. In other words, such mixing is not compatible, Southern Regions apart, with observed forces of infection. This may be a problem because "realistic assortative" is the mixing pattern that provides under a EURO-type FOI, the upper bound of "plausible" values of R0  and 8.5–12 in the Centre , with intermediate values in the North.This mixing yields an der EURO . Thanks R0 and critical coverages happen to be systematically higher. Critical coverages range from 80% in the North up to 88% in the South .It happens that this mixing pattern is never admissible for all Central Regions and for some Northern ones, as it can yield negative transmission rates when, as is the case in Central Italy, the FOI is rather low in the youngest age groups. Default mixing is, on the contrary, admissible in all Southern regions where R0. Predicted critical coverages ranged from 72% in the Centre, with intermediate values in the North, up to 83% in the South  are the type of mixing ,17 yieldh starting from h = 1 (proportionate mixing) and then [h until "minimally plausible" contact rates were achieved. Essentially, for all Italian regions , use of a moderate degree (h = 0.9) of assortativeness allowed the "minimal plausibility" threshold to be surpassed. This led, compared to the case of proportionate mixing, to increases of 1% to 3% in the corresponding critical coverages  may be needed to describe Italian pre-vaccination infection patterns.An issue is the degree of assortativeness, Hethcote ). AlthouThis also leads to the nub of the real gain provided by preferred matrices (just because they are "many") compared to "ad hoc" behavioural matrices. One could ask whether Hethcote's  preferenOur analysis of the pre-vaccination force of infection of measles in Italy formed a step toward developing a mathematical model for the transmission dynamics of measles in Italy. The model has the following features:et al. [i) it provides bounds for the uncertainty surrounding the estimate of the FOI Table , by takiet al. ;ii) it incorporates "realistic" demography in order to mirror the broad process of population ageing observed in Italy in the past 20 years (and which would be observed in the future if the present state should continue) as a consequence of the onset of sustained low fertility;iii) it considers two distinct options as regards interaction between the demography and the epidemiology: the first (D1) assumes no effects on transmission arise as a consequence of population ageing; the second (D2) assumes low fertility and population ageing could lead to a significant decline in transmission (e.g. a decline in the FOI), via proxy mechanisms such as a substantial decline in intra-family transmission which might occur as a consequence of contraction in average family size.By crossing the two FOI assumptions, EURO and UR , with the two assumptions D1, D2 we obtain four assumptions: EURO/D1, EURO/D2, UR/D1, UR/D2. The model simulations were carried out by adopting for the post-vaccination era what we considered the "best" approximation for the profile of routine vaccination coverages Fig. , obtaineFig. An attempt has been made to characterise the measles pre-vaccination epidemiological "landscape" in Italy, and also with reference to what we have referred to as the "EURO conjecture" (i.e. that age-selective under-reporting disguises underlying homology with FOIs elsewhere in Europe). There are points open to criticism in this analysis: first, the fact that by looking at "local" forces of infection, e.g. treating spatial sub-areas as autonomous epidemiological units, we disregard spatial correlations between the various local dynamics; this makes the present analysis, at best, a first step. Second, the present work relies only on case reports, the only available pre-vaccination data. Third, the increasing temporal distance between the present time and the pre-vaccination era (ending in 1976) seems to make more and more heroic the assumption that mixing patterns remain unchanged since then. Nevertheless several interesting facts emerge.Clearly, good data on immunisation coverage and reports of outbreaks of infection would provide some broad picture of patterns of susceptibility. However, as the level of vaccine-based immunity increases, the mean interval between outbreaks also increases so that case reports, even if unbiased, become much less useful as a source of timely information on patterns of susceptiblity. Also in Italy, historically, data on immunisation coverage has been very poor or in many instances completely absent; in many regions case reporting has also been very poor, forming an insensitive tool for detecting small pockets of infection, and large outbreaks, almost by definition, cannot be timely indicators of the presence of susceptibles. Hence the first motivation for the paper was to construct a map of the efforts needed for elimination of measles in Italy, which could then constitute a useful tool for prioritising regional intervention priorities in the context of the agreed WHO target for measles elimination and with the aim of avoiding such outbreaks. During the second half of the 1990's several attempts have been made in Italy to increase routine coverages, which the available data suggested had remained until then disappointingly low . Even though surveillance data suggests incidence has reached a historic minimum during 1999–2001, national coverage was still well below 70% in 2000, with much regional heterogeneity. It is unsurprising therefore that there have been substantial epidemics causing serious concern, especially in Campania (the region with the lowest coverages) during the spring of 2002, with an estimated 20,000 cases between January and May 2002 .From this standpoint the results here (i.e. basic reproductive ratios etc) suggest that the regions which seem to be the most demanding in terms of effort needed to eradicate the infection are systematically the Southern ones. This is rather problematic, as the Southern regions are those presently characterised by the lowest vaccination coverages ,3. The aThe "EURO conjecture" suggests that the measles FOI in Italy is, in fact, homologous to that observed in other European countries but that, in the case notifications data, this fact is camouflaged by a strong under reporting bias with age.Under-reporting of childhood diseases has been a major problem in Italy. It is known ,12 that et al [We therefore made an extensive investigation of forces of infection at the spatial level . Our feeling was that by going more deeply spatially it should have been possible to find some "footprint" of the presence of the presumed underlying EURO pattern . As documented in the Results, we have not been successful in detecting such a "footprint". All "local" forces of infection are systematically substantially smaller compared to the EURO FOI, and surprisingly similar to each other in levels and shape: significant heterogeneity exists only in the youngest age groups. This raises the point, if the EURO conjecture were to be true, of what might be the social processes, if any, underlying the poor age reporting of measles cases, which are capable of so effectively camouflaging the true underlying force of infection. Indeed, in order to appear so stable over space and time, such processes should be profoundly rooted in social responses of families toward diseases of their children and/or medical practitioners' behaviour. In the absence of the relevant data any discussion of the nature of such processes must remain in the realm of speculation, but clearly there is room for bias at many points in the chain leading to reporting of a case -22Fig. : perhapset al  in recena priori , it appears rather unlikely in most circumstances. Indeed, if the "true" percentage age distribution of cases is broadly stationary over time, as we would expect in the pre-vaccination era , then significant (and periodic) time changes in age-specific reporting rates would be needed to effectively mask true incidence patterns.We then investigated the measles incidence time series, encouraged by the fact that  reportinFinally, our modelling work on measles in Italy, shows that the EURO assumption matches rather poorly the serological profile observed for measles in Italy in 1996/7 in the European Sero-epidemiology Network (ESEN) survey ,25.et al. [R0 values estimated for the South suggest that, even with achievement uniformly of WHO targets across the regions, elimination of measles will take longer than in the North.Which then was the true force of infection acting in Italy during the pre-vaccination era: the higher EURO FOI, or the lower FOI emerging from Italian case notifications data, or indeed perhaps something in between? Rather than being essentially homologous, as implicit in the EURO conjecture, the possibility that patterns of infection by measles throughout Europe could be and have been largely different  is a stThe results suggest that critical vaccination coverages for elimination are likely to be higher in the south of Italy, precisely where the existing record of coverage is lowest. Substantial efforts are still required if there is to be a realistic hope of achieving WHO targets for measles elimination in Italy, particularly in the south. Notwithstanding this, the evidence does appear to suggest that the force of infection for measles in Italy is indeed somewhat lower than that applicable to other regions participating in the European Sero-Epidemiology Network. More particularly, the evidence suggests that it is probably unlikely that age biases in reporting (suggested elsewhere) could have led to an underestimate of the measles force of infection in Italy.The author(s) declare that they have no competing interests.PM conceived the work, provided the main theoretical analysis and drafted the manuscript, EMC conducted the time series analysis, JRW undertook the modelling work and SS and MCdA contributed epidemiological insight into the Italian public health context of the work. All authors read and approved the final manuscript.


Yet oxygen supplementation remains first line treatment in cardiovascular disease. We hypothesized that hypoxia improves and hyperoxia worsens myocardial performance.Low O2, 79% N2), 2) while inhaling a hypoxic gas mixture , and 3) while inhaling 100% O2. Tissue Doppler recordings were acquired in the apical 4-chamber, 2-chamber, and long-axis views. Strain rate and tissue tracking displacement analyses were carried out in each segment of the 16-segment left ventricular model and in the basal, middle and apical portions of the right ventricle.Seven male volunteers (mean age 38 ± 3 years) were examined with echocardiography at respiratory equilibrium during: 1) normoxia  and decreased with hyperoxia . Hypoxia increased strain rate in four left ventricular segments and global systolic contraction amplitude was increased . Tissue tracking displacement was reduced in the right ventricular segments and tricuspid regurgitation increased with hypoxia . The TEI index and E/E' did not change with hypoxia. Hyperoxia reduced strain rate in 10 left ventricular segments, global systolic contraction amplitude was decreased  while right ventricular function was unchanged. The spectral and tissue Doppler TEI indexes were significantly increased but E/E' did not change with hyperoxia.Hypoxia improves and hyperoxia worsens systolic myocardial performance in healthy male volunteers. Tissue Doppler measures of diastolic function are unaffected by hypoxia/hyperoxia which support that the changes in myocardial performance are secondary to changes in vascular tone. It remains to be settled whether oxygen therapy to patients with heart disease is a consistent rational treatment. Low oxygen tension dilates coronary arteries and high oxygen tension is a coronary vasoconstrictor. Yet oxygen supplementation remains first line treatment in cardiovascular disease states such as myocardial infarction and pulmonary oedema.Endothelium-dependent vasodilation is reduced in patients with ischemic heart disease  and suchHyperoxia, a condition in which the total oxygen content of the body is increased above that normally existing at sea level, is associated with impairment of cardiac relaxation and increased left ventricular filling pressures in patients with and without congestive heart failure .Tissue Doppler imaging (TDI) objectively derives measurements of contraction and relaxation velocities directly from the myocardium and thus yields information not previously accessible by echocardiography . We usedSeven healthy men, (age 25–46 (mean 38) years) completed the study. All subjects were normotensive, non-smokers, on no medication, had a normal left ventricular function by 2-D echocardiography, and had no family history of ischemic heart disease. The local ethical committee approved the study.We used a system consisting of a ventilator, a gas analyser with pulse oximeter, and a computer. Computer programs control the experimental procedure and continuously collect data from the ventilator and gas analyser .Echocardiography was performed from the apical acoustic window. Tissue Doppler recordings were acquired as digital loops in the apical 4-chamber, 2-chamber, and long-axis views . To avoi2 97.9 ± 0.1 %), after 5 minutes of respiratory equilibrium during hypoxia (SpO2 77.6 ± 1.2 %), and after 5 minutes of respiratory equilibrium during hyperoxia (SpO2 99.0 ± 0.2 %). Heart rate was continuously recorded on computer by means of the pulse oximeter. Blood pressure was measured once during each of the three respiratory steady state situations by an automatic blood pressure measuring device based on the oscillometric method.TDI was performed when the subjects had rested for 15 minutes breathing room air  were made with a one-way repeated-measures analysis of variance. The Student-Newman-Keuls test was used post hoc to identify pairwise differences. Differences were considered statistically significant when P < 0.05.All data are presented as mean ± SEM. Comparisons of the responses to changes in F2 and FeO2 decreased with hypoxia and increased with 100% oxygen breathing (table 1). FeCO2 and tidal volume were unchanged in all three test situations, reflecting that the subjects were in respiratory steady state. There was a small, but statistically significant decrease in respiratory rate from normoxia to hypoxia, which might reflect that the subjects were more accustomed to the test situation at this stage.FiOThere was a significant increase in heart rate with hypoxia and a decrease with hyperoxia figure . NeitherA significant increase in strain rate was found in 4 segments figure . GSCA inHyperoxia worsened left ventricular function. Strain rate was reduced in 10 segments figure  with preThe main findings of the present study of healthy volunteers are: 1) hypoxia increased strain rate and tissue tracking displacement. 2) Hypoxia increased tricuspid regurgitation and reduced right ventricular tissue tracking displacement. 3) Hyperoxia reduced strain rate and tissue tracking displacement and increased the TEI index. The novelty of our findings, when compared to the literature, is the demonstration that longitudinal myocardial function, and thus the function of the subendocardium, is sensitive to moderate changes in inspired oxygen.Patients with ischemic heart disease and heart failure frequently encounter hypoxia but the consequences of hypoxia on left ventricular function remain a matter of controversy, in part because of differences in methodology and the measured parameters. In early studies using roentgenkymograms  and dye Hemoglobin saturation in healthy persons increase very little from breathing room air to breathing 100% oxygen. Nevertheless, profound cardiovascular effects were found. This is probably because of an anticipated increase in oxygen dissolved into plasma from 0.32% to 2.09% . HyperoxBecause of the finding of no change  or a disIn conclusion, hypoxia improves and hyperoxia worsens systolic myocardial performance in healthy male volunteers. TDI measures of diastolic function are unaffected by hypoxia/hyperoxia which support that the changes in myocardial performance are secondary to changes in vascular tone. It remains to be settled whether oxygen therapy to patients with heart disease is a rational treatment that may sometimes be harmful or whether supplemental oxygen consistently results in an overall gain in delivered oxygen.


Inaccurate malaria results can lead to patient mismanagement, misperceptions about malaria resistance patterns and public health misinformation. All laboratories need to be able to demonstrate that their results are accurate. Establishing and maintaining a system for monitoring test accuracy is a complex, expensive and technically demanding process, which very few poor countries have been able to implement. This study described the process and assessed the feasibility of establishing a nation-wide system for improving the accuracy of malaria-related tests in peripheral laboratories in Ghana.A baseline survey of all 693 laboratory staff in 205 sub-regional government and mission health laboratories in Ghana was conducted by a national network of laboratory supervisors. Survey results guided a training programme to improve test accuracy. Outcomes included changes in the quality of laboratory tests and the system was considered to be feasible if >50% of laboratory staff in each region received training and if test accuracy could be documented.74% (mean) of the 693 laboratory staff were assistants with no professional qualifications. There were marked differences between regions in the availability of essential resources for malaria diagnosis (e.g. microscopes). 93% of laboratory staff received training; in six months there were increases of 11% and 7% respectively in the number of laboratories producing haemoglobin and malaria microscopy results of acceptable quality.It is possible to establish a system for improving and monitoring test accuracy in peripheral laboratories on a country-wide basis in a developing country using a model that could be adapted for use in other countries and for other components of health care provision. Clinical laboratory services are a critical component of health systems. They are essential for patient management and for providing accurate public health data including early detection of malaria resistance. In many poorer countries, laboratory services have been neglected due to chronic under-investment. The emergence of drug-resistant malaria and the extra burden of supporting diagnosis and treatment of HIV/AIDS has increased the strain on laboratories. The widespread resistance of malaria to cheap, antimalarial drugs such as chloroquine and the increasing use of relatively expensive combination therapy, means that presumptive treatment of all fevers as malaria may no longer be a sustainable option for some countries . It is, Establishing and maintaining an accurate and reliable laboratory service is a complex, expensive and technically demanding process, which very few poor countries have been able to implement. It depends on good laboratory management to oversee processes such as documentation, audit cycles, quality assurance and external validation, safety practices, and supervisory and accountability structures  and shouIn January 2000, the Ministry of Health in Ghana commenced a two-year programme to determine the feasibility of establishing a nationwide quality assurance system for common tests performed at peripheral (district and sub-district) laboratories. This programme was specifically designed to complement the Ministry's 5-year Programme of Work . In GhanTwo senior technicians from each of Ghana's ten administrative regions were chosen by the Ministry of Health to constitute the national network of laboratory supervisors who would implement the programme. They were selected on the basis of their geographic location, seniority and commitment to improving laboratory services. All the supervisors had technical qualifications (2–3 years certificate or diploma course), but none had any higher technical or educational qualifications.A workshop for the supervisors was held in Ghana at the beginning of the programme to define the objectives and to develop a workplan, timetable and monitoring processes. The agreed programme objective was to establish a system in all peripheral government laboratories in Ghana, for monitoring the accuracy of results of malaria microscopy, haemoglobin estimation and other commonly performed tests. The ability to train over 50% of laboratory staff in each of the ten regions and to monitor changes in the accuracy of results, were used as indicators of programme feasibility. As very little information was available about the state of Ghana's laboratories, supervisors initially carried out a nationwide baseline survey of all Ghana's peripheral laboratory facilities. They personally visited every government and mission laboratory in Ghana and collected first hand information about staff, equipment and tests offered. For each laboratory they also completed a safety checklist based on the World Health Organization's recommendations for laboratory safety [Ideally training should be targeted towards the tests which are performed most poorly. However, as there were no monitoring systems in place in Ghana for laboratory tests and none of Ghana's laboratory staff had had any experience of these systems, it was not possible initially to identify the worst performed tests. The supervisors therefore started by providing training on seven of the most commonly performed tests. They used Ghana's Standard Laboratory Operating Procedures , which dQuality assurance systems used in industrialised countries and published in the literature are not appropriate for rural laboratories in poor countries such as Ghana. They are too complex for a workforce that is primarily made up of laboratory assistants and they are based on assumptions that the methods are generally automated and communication and transport networks are reliable. The supervisors therefore had to devise workable methods for externally monitoring test results from the peripheral laboratories. They distributed samples with known values to peripheral laboratories who processed them under normal working conditions. Once the methods for distributing, preserving and measuring test accuracy had been optimized for local use they were piloted in a small number of laboratories, before being introduced throughout each region. The ways in which the test accuracy was determined varied for each test. For example, for haemoglobin estimations a whole blood sample with known value  was distributed to peripheral laboratories and supervisors decided that laboratories with results outside the target value ±10% would receive priority for training. As the quality of results from peripheral laboratories improved, the supervisors reduced the acceptable range of results to ±5% of the target value. For malaria microscopy, the target malaria result of a whole blood sample or malaria smear was determined by consensus of several technicians from the regional hospital. Results from peripheral laboratories were considered 'accurate' if laboratories reported the presence (or absence) of malaria parasites and quantified them to within one grade  of the target result. For sickle cell tests a blood sample of know sickle status, determined by the regional laboratory, was distributed. Accurate results were those that correctly identified the sample as containing sickle haemoglobin or not. If the majority of results from peripheral laboratories for a single test varied from the target result, the supervisors re-evaluated their test target values. Whole blood samples were distributed in order to check the complete process of testing  rather than just an individual component. Supervisors used results of the quality monitoring to provide constructive feedback to the laboratory staff and to target their training towards tests and laboratories that performed particularly poorly.Initially the programme was funded directly from Ministry of Health headquarters but subsequently, through collaboration with regional Ministry of Health administrators, several supervisors were able to access their own regional funds.There were 205 laboratories in Ghana located in regional and district government hospitals (97), mission hospitals (53) or health centres (54). The total staff complement in these laboratories was 693. The percentage of staff in each of the ten regions with no professional qualifications (assistants or bench-trained with 6 weeks to 1 year training) varied from 60–83% (mean 74%). Even after correcting for differences in regional populations and excluding regions with teaching hospitals, there were wide variations between regions in the number of laboratories and the availability of essential laboratory equipment such as microscopes,  of all Ghana's laboratory staff had been trained in malaria-related and other common tests. During the final six months of the programme the supervisors monitored the quality of haemoglobin estimations, sickle cell test and malaria microscopy results in 48% of all Ghana's 205 laboratories. In 4 regions the quality of up to 11 tests had been monitored. Tests that gave quantitative results were consistently the most poorly performed tests in all regions with only 78%, 78% and 84% of laboratories producing acceptable results for haemoglobin measurements, white blood counts and malaria microscopy respectively. Tests for HIV, hepatitis B, sickle-cell screen and ZN stain were consistently performed well with over 95% of laboratories meeting agreed target results. After a further six months training there were improvements in the accuracy of several tests, particularly in haemoglobin estimation and malaria microscopy with 89%, 83% and 91% of laboratories producing acceptable results for these tests respectively,  rather than simple 'positive' or 'negative' results (such as HIV and hepatitis B screening tests). Further research is needed to examine the cost-effectiveness of potentially more accurate or less complex tests . Although these tests may be more expensive than those in use in most districts, their simplicity and accuracy may save downstream costs. One of the major limitations of this study is the lack of quality assurance at regional and supra-regional level for common laboratory tests. These were the laboratories responsible for determining the target values of tests distributed to peripheral laboratories. To overcome this problem, regional laboratories tested the samples several times before determining the target values and they also exchanged samples with each other as an inter-regional check on results. Plans are in place to expand this into an inclusive national quality monitoring system for higher level laboratories.We have confirmed previous work showing that at peripheral health facilities haemoglobin using manual methods is the most inaccurate test . Safety To our knowledge this is the first external laboratory quality assurance programme in sub-Saharan Africa to demonstrate that it is feasible to achieve widespread coverage of peripheral health laboratories and staff. Through this programme an effective and practical model has been developed for improving the accuracy of common laboratory tests in Ghana. The model is based on a national network of senior technicians who implemented and supervised a continuous cycle of test monitoring and targeted training for all laboratory staff in their regions. The programme's success was dependent on the quality and commitment of the national network of laboratory supervisors. Participation in the programme was itself a strong motivational force for the supervisors. Because of the supportive attitude of the supervisors, the laboratory staff viewed the monitoring visits as educational rather than punitive and this ensured cooperation when remedial action was required. Health planners can use the lessons learnt from this programme to introduce measures to improve morale and job satisfaction of key health workers especially those in the neglected laboratory services. Components of this model have already been adopted by other countries for their laboratory programmes and could be adapted for other health disciplines in poorer countries.Several steps are necessary before this model could be sustainably implemented. The network of supervisors needs to have pro-active long-term support from all stakeholders, combined with career and promotion packages. Health managers need to provide secure funding for laboratory quality assurance programmes at all levels. This includes establishing internal quality control measures and external validation systems that are linked into national and international external quality monitoring schemes. To be successful, this model will require prioritization of the laboratory service by policy makers at national and supra-national level and adequate representation of the laboratories in decision-making processes. Implementing a quality assurance system for laboratories in poorer countries is expensive and logistically complicated. Managers will need to balance cost against quality, taking into account that it is those who are most vulnerable to ill health who can least afford to bear the brunt of the consequences of inaccurate laboratory results.IB and VB designed the outline framework for the programme. IB documented and collated results. VB was the local programme coordinator. AAA provided an overview and ensured the programme was compatible with Ministry of Health policies. All authors read and approved the final manuscript.This study was supported by the Ministry of Health in Ghana and the Malaria Knowledge Programme  at the Liverpool School of Tropical Medicine. The Department for International Development had no role in study design, the collection, analysis and interpretation of data, in the writing of the report or in the decision to submit the paper for publication and accepts no responsibility for the information or views expressed.


Low birth weight  is a strong predictor of infant mortality. Yet low birth weight, in isolation, is uninformative since it is comprised of two intertwined components: preterm delivery and reduced fetal growth. Through nonparametric logistic regression models, we examine the effects of gestational age, fetal growth, and maternal smoking on neonatal mortality.We derived data on over 10 million singleton live births delivered at ≥ 24 weeks from the 1998–2000 U.S. natality data files. Nonparametric multivariable logistic regression based on generalized additive models was used to examine neonatal mortality (deaths within the first 28 days) in relation to fetal growth , gestational age, and number of cigarettes smoked per day. All analyses were further adjusted for the confounding effects due to maternal age and gravidity.The relationship between standardized birth weight and neonatal mortality is nonlinear; mortality is high at low z-score birth weights, drops precipitously with increasing z-score birth weight, and begins to flatten for heavier infants. Gestational age is also strongly associated with mortality, with patterns similar to those of z-score birth weight. Although the direct effect of smoking on neonatal mortality is weak, its effects  appear to be largely mediated through reduced fetal growth and, to a lesser extent, through shortened gestation. In fact, the association between smoking and reduced fetal growth gets stronger as pregnancies approach term.Our study provides important insights regarding the combined effects of fetal growth, gestational age, and smoking on neonatal mortality. The findings suggest that the effect of maternal smoking on neonatal mortality is largely mediated through reduced fetal growth. This isDisentangling the intricate pathways of gestational age and fetal growth to neonatal mortality gets even more complicated by the consideration of a third factor – maternal smoking during pregnancy. Smoking has been clearly associated with poor reproductive outcomes, including increased risk of preterm birth, stillbirth, and a range of other outcomes -6. Receni.e., z-score birth weight), gestational age, and smoking during pregnancy. We applied nonparametric logistic regression based on generalized additive models to examine neonatal mortality in relation to 3 factors.To better understand the relationship among these indices of "fetal wellbeing", we examined neonatal mortality in relation to standardized birth weight , assembled by the National Center for Health Statistics of the Centers for Disease Control and Prevention .Information on smoking during pregnancy was available in two forms on the vital statistics data: one as an indicator variable (yes or no), and the other as a continuous variable denoting the number of cigarettes smoked per day during pregnancy. Both of these smoking measures were based on maternal self-report. Information on smoking patterns across different trimesters in pregnancy was not available on the vital records.Fetal growth was defined as birth weight-for-gestational age, and was expressed as gestational age-specific birth weight z-score. This z-score construct is interpreted as units of standard deviations from the population-specific mean birth weight at each gestational age. The z-score or standardized birth weight follows a Gaussian distribution with mean 0 and variance 1.e.g., infant delivered at 26 weeks with a birth weight of 4,000 g) on neonatal mortality.In addition to the full analysis, we also examined in a sub-analysis the impact of implausible birth weight/gestational age combinations on overall results. These implausible birth weight/gestational ages were identified if infants' birth weights were outside the gestational age-specific birth weight cutoffs . This waThere were 11,677,103 singleton live births from which we excluded infants with missing birth weight or gestational age , and birth weight <500 g or gestational age <24 weeks n = 28,732). Since smoking data was not reported on vital statistics in California, Indiana, New York state, and South Dakota [,732. Sini.e., continuity of the dose-response function and its low-order derivatives [We examined the distributions of z-score birth weight, gestational age, and number of cigarettes smoked per day, and compared these distributions between the two groups of neonatal mortality. Neonatal mortality was then modeled using nonparametric logistic regression based on generalized additive models . GAM is ivatives . When coivatives  could bei.e., number of pregnancies). We examined the associations between neonatal mortality and each of the 3 factors z-score birth weight, gestational age, and number of cigarettes smoked per day separately. We then fit a full model for mortality after forcing all 3 predictors (in addition to the confounders) as described in the Appendix [see i.e., comparing the residual deviances from a full model to a model without the predictor). Under the large sample assumption, the deviance has an approximate chi-square distribution, with degrees-of-freedom for the test being the difference in the degrees of freedom between the nested models being compared. We also examined the distribution of partial residuals [All regression models were adjusted for the confounding effects due to maternal age and gravidity , none provided any additional insights that were different from a model that contained no interaction terms. Therefore, we did not consider assessing two-way interactions in the analysis.gam function based on the it loess scatterplot smoother [All statistical analyses were performed in S-Plus  version 6.2 on the UNIX . Nonparametric logistic regression models were fit using the smoother , using tThe overall neonatal mortality rate was 2.4 per 1,000 live births. The distribution of z-score birth weight among infants that died during the neonatal period was shifted more towards lower standardized birth weights than among those that survived the neonatal period .We first separately examined the effect of each of the 3 covariates standardized birth weight, gestational age, and number of cigarettes smoked per day, on neonatal mortality. This was done by fitting nonparametric logistic regression models (GAM). The univariable GAM strongly suggests that the unadjusted association between standardized z-score birth weight and neonatal mortality is nonlinear (not shown). The association between gestational age and neonatal mortality was also nonlinear, whereas the association between number of cigarettes smoked per day mortality was virtually flat. The adjusted smooth curves for these 3 covariates, along with their corresponding 95% point-wise confidence bands are displayed in Figure Since smoking was weakly associated with neonatal mortality, we examined if the effect of smoking on mortality was mediated through either standardized birth weight or gestational age (or both). We therefore modeled neonatal mortality in relation to these 2 covariates (and adjusted for confounders) within broad categories of smokers and nonsmokers Fig . CompareThe logistic regression models discussed thus far are based on the implicit assumption that the combined effects of standardized birth weight and gestational age are multiplicative on a logistic scale. We examined the sensitivity of this assumption by modelling neonatal mortality by allowing an interaction term between these two factors based on nonparametric smooth fit. The joint effect of standardized birth weight and gestational age on neonatal mortality reveals that both reduced fetal growth and early delivery result in increasing mortality risk, with the mortality plane progressively diminishing with increasing standardized birth weight and gestational age Fig .For decades, several researchers have focused on trying to understand the complex biological relationship among pregnancy duration, infant size, and neonatal mortality. Not only are gestational age and birth weight highly correlated, but both are powerful predictors of neonatal mortality -16. The The inverted "J"-shaped relationship between birth weight and mortality essentially holds for analyses relating to gestational age and mortality. While birth weight is considered a marker for fetal size, gestational age is thought of as an indicator of fetal maturity. Almost 3 decades ago, Susser and colleagues  proposedOn these similar lines, Herman and Hastie  examinedIt is widely acknowledged that smoking mothers give birth to infants that are lighter compared with those born to nonsmoking mothers. This reduction in birth weight is thought mainly to result in fetal growth restriction, as well as to shortened gestation ,20. AlthOur study has some limitations. First, errors in the estimation of gestational age ,26 are lApplication of generalized additive regression models to examine neonatal mortality appears useful towards understanding the complex biological relationship amongst the predictors. However, we make no claim that GAMs serve as adjuncts to other modeling approaches; on the contrary, we believe that GAMs can provide the first step toward modeling complex exposure-disease relationships.Our study provides important insights about the combined effects of gestational age, fetal growth, and smoking during pregnancy on neonatal mortality. Both standardized z-score birth weight and preterm delivery are strongly associated with neonatal mortality, and the effect of maternal smoking appears largely mediated largely through reduced fetal growth and, to a smaller extent, through shortened gestation.The author(s) declare that there are no competing interests.CVA and RWP conceived the idea for the study. CVA assembled the data and performed all statistical analyses. CVA drafted the manuscript with essential contributions from RWP. Both authors critically reviewed the manuscript, edited it for content, and revised it to the final form.Both authors have read and approve the final manuscript.The pre-publication history for this paper can be accessed here:Description of generalized additive models.Click here for file


It's no surprise that the Amazonian rainforest contains far more species than, say, the Siberian tundra. Over 50% of the world's species live in tropical rainforests, which cover just 6% to 7% of the earth's terrestrial surface. That the number of marine and terrestrial species declines with distance from the equator is a well-documented phenomenon called the latitudinal species diversity gradient. What's proven challenging, however, is figuring out what drives this pattern. Over 30 hypotheses have been proposed in the past two decades, but only four have garnered serious attention. These four focus on variables relating to area and energy factors, geographic constraints, and habitat diversity. Understanding the factors—both contemporary and ancient—responsible for the diversity gradient could help answer one of the fundamental questions in evolutionary ecology: what regulates species diversity? But teasing out the likely mechanisms behind this diversity has practical implications as well: mounting evidence suggests that ecological and climatic conditions influence the emergence, spread, and recurrence of infectious diseases. Global climate change is likely to aggravate climate-sensitive diseases in unpredictable ways.Increasingly, public health programs aimed at preventing and controlling disease outbreaks are considering aspects of the ecology of infectious diseases—how hosts, vectors, and parasites interact with each other and their environment. The hope is that by understanding how ecological factors impact the global distribution of parasitic and infectious diseases, public health officials can predict and contain future outbreaks. Even though parasitic and infectious organisms account for a major fraction of the biological diversity on the planet, few studies have analyzed the factors affecting the spatial distribution of these organisms or attempted to quantify their contribution to biodiversity. In this issue, Vanina Guernier, Michael Hochberg, and Jean-François Guégan address the influence of ecological factors on the biological diversity and distribution of parasitic and infectious diseases and find that climatic factors are the most important determinant of the global distribution of human pathogens.The current understanding of human disease and availability of complete datasets on many parasitic and infectious diseases, the researchers explain, present a unique opportunity to explore the relationship between parasitic and infectious disease species richness  and latitude. This information, in turn, can help identify potential factors that affect diversity gradients. After compiling epidemiological data on 332 different human pathogens across 224 countries, Guernier et al. used sophisticated statistical modeling methods to identify and characterize the influence of a number of potential contributing factors on species richness. After adjusting the model to control for cofactors that might influence the relationship between latitude and species richness indirectly rather than directly , the researchers confirmed that, on average (seven times out of ten), tropical areas harbor a larger number of pathogen species than more temperate areas. In other words, the species richness of human pathogens follows the same pattern seen in other species.These results, Guernier et al. argue, suggest that the latitudinal species diversity gradient “might be generated in large part by biotic interactions.” This in turn indicates that current estimates of species diversity, which ignore parasites and infectious organisms, are “substantially underestimated.” The authors went on to explore groupings of individual pathogen species within larger parasitic and infectious disease communities along the gradient and found that species present at northern latitudes are a subset of those present in equatorial areas, rather than a different set of species . Since nestedness is strongly associated with latitude, which is typically used as a proxy for a range of climatic factors, the researchers investigated the relationship between various climatic variables and pathogen diversity. The climatic variable most strongly correlated with diversity was the maximum range of precipitation of a region.The finding that climatic factors are largely responsible for the spatial distribution of human pathogens has important implications for predicting and managing future infectious disease outbreaks. These results counter the conventional assumption that socioeconomic conditions are the most important factor in controlling disease, indicating that global climate change could have far more significant effects on global patterns of disease, with diseases once relegated to the tropics migrating to temperate zones, for example. Identifying the links between ecology and disease, however, could lay the foundation for effective preventive strategies.


Drosophila protein-protein interaction map was constructed using the LexA system, complementing a previous map using the GAL4 system and adding many new interactions.A  Drosophila proteins. Although these data are a valuable resource for insights into protein networks, they cover only a fraction of the expected number of interactions.Maps depicting binary interactions between proteins can be powerful starting points for understanding biological systems. A proven technology for generating such maps is high-throughput yeast two-hybrid screening. In the most extensive screen to date, a Gal4-based two-hybrid system was used recently to detect over 20,000 interactions among Drosophila open reading frames to construct arrays for a LexA-based two-hybrid system. We screened the arrays using a novel pooled mating approach, initially focusing on proteins related to cell-cycle regulators. We detected 1,814 reproducible interactions among 488 proteins. The map includes a large number of novel interactions with potential biological significance. Informative regions of the map could be highlighted by searching for paralogous interactions and by clustering proteins on the basis of their interaction profiles. Surprisingly, only 28 interactions were found in common between the LexA- and Gal4-based screens, even though they had similar rates of true positives.To complement the Gal4-based interaction data, we used the same set of Drosophila and other organisms.The substantial number of new interactions discovered here supports the conclusion that previous interaction mapping studies were far from complete and that many more interactions remain to be found. Our results indicate that different two-hybrid systems and screening approaches applied to the same proteome can generate more comprehensive datasets with more cross-validated interactions. The cell-cycle map provides a guide for further defining important regulatory networks in  Protein-protein interactions have an essential role in a wide variety of biological processes. A wealth of data has emerged to show that most proteins function within networks of interacting proteins, and that many of these networks have been conserved throughout evolution. Although some of these networks constitute stable multi-protein complexes while others are more dynamic, they are all built from specific binary interactions between individual proteins. Maps depicting the possible binary interactions among proteins can therefore provide clues not only about the functions of individual proteins but also about the structure and function of entire protein networks and biological systems.Saccharomyces cerevisiae. One protein is fused to a DNA-binding domain (BD) and the other is fused to a transcription activation domain (AD). An interaction between the two proteins results in activation of reporter genes that have upstream binding sites for the BD. To map interactions among large sets of proteins, the BD and AD expression vectors are placed initially into different haploid yeast strains of opposite mating types. Pairs of BD and AD fused proteins can then be tested for interaction by mating the appropriate pair of yeast strains and assaying reporter activity in the resulting diploid cells [One of the most powerful technologies used in recent years for mapping binary protein interactions is the yeast two-hybrid system . In a yeid cells . Large aid cells -6. Most Caenorhabditis elegans and Drosophila melanogaster [in vivo. Unfortunately, this is a common feature of all high-throughput methods for generating interaction data, including affinity purification of protein complexes and computational methods to predict protein interactions [High-throughput two-hybrid screens have been used to map interactions among proteins from bacteria, viruses, yeast, and most recently, nogaster -10. Analnogaster -13. One ractions -14. A soractions -13. ThusC. elegans [A second shortcoming of the large-scale screens has been the high rate of false negatives, or missed interactions. This is evident from comparing the high-throughput data with reference data collected from published low-throughout studies. Such comparisons with two-hybrid maps from yeast  and C. e elegans , for exa elegans ,12. For  elegans . These r elegans . Thus, tDrosophila proteins by using two different yeast two-hybrid systems  and different screening strategies. Results from the screens using the Gal4 system have already been published [et al. successfully amplified 12,278 Drosophila open reading frames (ORFs) and subcloned a majority of them into the Gal4 BD and Gal4 AD expression vectors by recombination in yeast. They screened the arrays using a library-screening approach and detected 20,405 interactions involving 7,048 proteins. To extend these results we subcloned the same amplified Drosophila ORFs into vectors for use in the LexA-based two-hybrid system, and constructed arrays of BD and AD yeast strains for high-throughput screening. Our expectation was that maps generated with these arrays would include interactions missed in previous screens, and would also partially overlap the Gal4 map, providing opportunities for cross-validation.We set out to map interactions among the approximately 14,000 predicted ublished . In thatInitially, we screened for interactions involving proteins that are primarily known or suspected to be cell-cycle regulators. We chose cell-cycle proteins as a starting point for our interaction map because cell-cycle regulatory systems are known to be highly conserved in eukaryotes, and because previous results have suggested that the cell-cycle regulatory network is centrally located within larger cellular networks . This isDrosophila full-length ORFs from the Gal4 project [Escherichia coli that fortuitously activates transcription in yeast [Drosophila ORF insert . For all strains involved in an interaction reported here, the plasmid was isolated and the insert was sequenced to verify the identity of the ORF.We used the same set of 12,278 amplified  project  to generin yeast . In the in yeast . Previouin yeast ,19. The in yeast ,6, and tAs a first step toward generating a LexA-based protein-interaction map, we chose 152 BD-fused proteins that were either known or homologous to regulators of the cell cycle or DNA damage repair . We used all 152 proteins as 'baits' to screen the 12,000-member AD array. We used a pooled mating approach  in whichAltogether we detected interactions with 106 of the 152 baits, which resulted in a protein-interaction map with 1,814 unique interactions among the products of 488 genes . The map includes interactions that were already known or that could be predicted from known orthologous or paralogous interactions (see below). The map also includes a large number of novel interactions, including many involving functionally unclassified proteins.in vivo experimental verification impracticable. Thus, we set out to assess the quality of the data by examining the topology of the interaction map, by looking for enrichment of genes with certain functions, and by comparing the LexA map with other datasets. First we examined the topology of the interaction map, because recent studies have shown that cellular protein networks have certain topological features that correlate with biological function [k) varies over a broad range (from 1 to 84) and the distribution of proteins with k interactions follows a power law, similar to previously described protein networks [As is common with data derived from high-throughput screens, the number of novel interactions detected was large, making direct function . In our networks ,21. Mostnetworks , charactnetworks ,6,16. Monetworks -25. ThusP < 0.00002). In the AD list, these two functions are enriched four- and threefold, respectively (Table P < 0.001). Thus, the enrichment for proteins and pairs of interacting proteins annotated with the same function suggests that many of the novel interactions will be biologically significant. It also suggests that the map will be useful for predicting the functions of novel proteins on the basis of their connections with proteins having known functions, as described for other interaction maps [Next we assessed the list of proteins in the interaction map to look for enrichment of proteins or pairs of proteins with particular functions. An interaction map with a high rate of biologically relevant interactions should have a high frequency of interactions between pairs of proteins previously thought to be involved in the same biological process. Among the 488 proteins in the map, 153 have been annotated with a putative biological function using the Gene Ontology (GO) classification system ,27. Becaion maps ,28.Direct comparison of the LexA cell-cycle map with the Gal4 data revealed that only 28 interactions were found in common between the two screens Table . Moreovep < 10-6; Table 6 random networks having the same BD proteins, total interactions and topology as the LexA map, and found that none of these random maps shared more than two interactions in common with the Gal4 map. To assess the relative quality of the 28 common interactions we used the confidence scores assigned to them by Giot et al. [P < 0.0001), indicating that the overlap of the two datasets is significantly enriched for biologically relevant interactions. Thus, the detection of interactions by both systems could be used as an additional measure of reliability. The surprisingly small number of common interactions, however, severely limits the opportunities for cross-validation, and suggests that both datasets are far from comprehensive.Although only 28 interactions were found in both the Gal4 map and our map, this rate of overlap is significantly greater than expected by chance . This supports the notion that these datasets are enriched for true-positive interactions relative to randomly selected pairs of proteins. We found that the fractions of LexA- and Gal4-derived interactions that overlap with these datasets are similar ; andterlogs' ,33. As ein vivo [in vivo (for example Cdc2-CycE [in vivo (Cdk4-CycD) and two that do not occur in vivo [Further inspection of the LexA cell-cycle interaction map revealed biologically informative interactions and additional insights for interpreting high-throughput two-hybrid data. For example, we expected to observe interactions between cyclins and cyclin-dependent kinases (Cdks), which have been shown to interact by a number of assays. Our interaction map includes six proteins having greater than 40% sequence identity to Cdk1 . A map of all the interactions involving these proteins reveals that they are multiply connected with several cyclins . Similadc2-CycE , includi in vivo .in vivo. Such patterns are consistent with potential interactions between members of different protein families, even though they do not reveal the precise pair of proteins that interact in vivo. This class of informative false positives may be common in two-hybrid data where the interaction is assayed out of biological context. Experimentally reproducible interactions, whether or not they occur in vivo, can be used to discover interacting protein motifs or domains [in vivo interactions and functions of specific pairs of interacting proteins.Thus, while some of these interactions are false positives in the strictest sense, the data is informative nevertheless, as it clearly demonstrates a high incidence of paralogous interactions - where pairs of interacting proteins each have paralogs, some combinations of which also interact  domains ,36. TheyThe Cdk subgraph also illustrates that proteins with similar interaction profiles may have related functions or structural features. To look for other groups of proteins having similar interaction profiles we used a hierarchical clustering algorithm to cluster BD and AD fusion proteins according to their interactions . The resulting clustergram reveals several groups of proteins with similar interaction profiles Figure . One of Thus, the Skp cluster provides another example of how proteins with similar interaction profiles may be structurally or functionally related, and how such clusters may be enriched for biologically relevant interactions. This is consistent with previous results showing that protein pairs often have related functions if they have a significantly larger number of common interacting partners than expected by chance ,38. Theset al. [The interaction profile data is statistically confirmed by domain-pairing data, which shows that certain pairs of domains are found within interacting pairs of proteins more frequently than expected by chance Table . These iet al. , revealeDrosophila protein interaction map that was generated using the Gal4 yeast two-hybrid system [Drosophila ORFs, using the same PCR amplification products used in the Gal4 project, into the LexA two-hybrid vectors. Initially, we used a novel pooled mating approach [Proteome-wide maps depicting the binary interactions among proteins provide starting points for understanding protein function, the structure and function of protein complexes, and for mapping biological pathways and regulatory networks. High-throughput approaches have begun to generate large protein-interaction maps that have proved useful for functional studies, but are also often plagued by high rates of false positives and false negatives. Several analyses have shown that the set of interactions detected by more than one high-throughout approach is enriched for biologically relevant interactions, suggesting that the application of multiple screens to the same set of proteins results in higher-confidence, cross-validated interactions -13. Suchd system . We consapproach  to screeC. elegans proteins were shown to cover only about 10-13% of the corresponding reference datasets [The level of coverage for a high-throughput screen can be estimated by determining the percentage of a reference dataset that was detected; reference sets have been derived from published low-throughput experiments, for example, which are considered to have relatively low false-positive rates. High-throughput two-hybrid data for yeast and datasets ,10,13. TAlthough the relative contribution of these two factors is difficult to estimate, results from screens to map interactions among yeast proteins suggest that the major reason for the lack of coverage is that the screens are incomplete. Complete screens would identify all interactions that could possibly be detected by a given method; ideally therefore, two complete screens using the same method would identify all the same interactions. However, the rate of overlap among the different yeast proteome screens is low, even though they used very similar two-hybrid systems. Moreover, the overlap between screens is not significantly greater than the rate at which they overlap any reference set ,10. ThisDrosophila proteins are consistent with those for the yeast proteins. We used the LexA system to detect 1,814 reproducible interactions to complement the 20,439 interactions previously detected in a proteome-wide screen using the Gal4 system [Drosophila datasets are far from complete and that many more interactions could be detected by additional two-hybrid screening.The rates of coverage and completeness from our high-throughput two-hybrid screening with 4 system . The oveet al. estimated that at least 11% of the Gal4 interactions are likely to be biologically relevant, based on the prediction accuracy of their statistical model [The actual number of interactions that might be detected by complete two-hybrid screening might be roughly estimated from the partially overlapping datasets, as was performed for accurate estimation of the number of genes in the human genome ,46. In tin vivo. Using the approach described here, the interactions are shown to be reproducible during the one-on-one two-hybrid assays that are used to record reporter activity scores, suggesting that we have minimized the frequency of technical false positives.False-positive interactions have been classified as technical or biological . A technin vivo, but that nevertheless have some biological basis for being detected and are potentially useful for guiding future experiments. In our data, for example, the Cdk and Skp proteins each interact with a different group of targets, which in turn interact with multiple Cdk or Skp proteins. From this data alone, we would accurately predict that Cdk proteins interact with cyclins, and that Skp proteins interact with F-box proteins, even though only some of the specific combinations are true in vivo partners. Similarly, from analysis of domain pairs in the LexA dataset, other patterns are evident, such as homeobox domains being associated with both protein kinase and cyclin domains  of interest. This narrows down the number of potential assays necessary to establish function for a given gene from hundreds to just a handful; conversely, when studying a specific function, such as the cell cycle, interaction data can identify which few genes, selected from thousands, may have a role in the process. Just as the sequencing of various genomes has not allowed unambiguous ascription of biological function to the majority of the identified genes, mapping of an interactome by high-throughput methods does not allow final assignment of interaction capacity or of higher functionality to a protein. This requires additional experiments, guided by these and other high-throughput data. The results presented here show that extending and combining different two-hybrid datasets will allow further refinement of the selection of functional analyses to be performed for each protein of the proteome.MAL62 promoter [GAL1 promoter was pJZ4-NRT, which was constructed from pJG4-5 [ADH1 terminator with the CYC1 terminator and inserting the 5' and 3' recombination tags (5RT1 and 3RT1 [S. cerevisiae) strain RFY231 (MAT trp1::hisG his3 ura3-1 leu2::3Lexop-LEU2) and RFY206 (Mata his3Δ200 leu2-3 lys2Δ201 ura3-52 trp1Δ::hisG) were previously described [lacZ reporter plasmid pSH18-34 [Yeast two-hybrid vectors used are related to those originally described for the LexA system . The vecpromoter . The vecm pJG4-5  by replaand 3RT1 ) into thand 3RT1 . Yeast  in yeast . We begat et al. . For thelacZ reporter plasmids, and containing both galactose and maltose to induce expression of the AD and BD fusions, respectively. The plates also lacked leucine to assay for expression of the LEU2 reporter, and contained X-Gal (40 μg/ml) to assay for expression of lacZ. These plates were photographed after 5 days at 30°C and interactions were scored as described [LEU2 and lacZ reporters were assayed on separate plates: growth on plates lacking leucine was scored from 0 (no growth) to 3 (heavy growth); the extent of blue on the X-Gal plates was scored from 0 (white) to 5 (dark blue). After re-testing interactions  the AD plasmids from interacting AD strains were rescued in bacteria and clones were sequenced to verify insert identity. Cloned plasmids were then reintroduced into RFY231 and used in all possible combinations of one-on-one mating operations with the appropriate BD strains to repeat the interaction assay a third time. The same set of BDs was also used to screen a pool of all approximately 13,000 AD strains using a library screening approach as described in the Additional data files. All interaction data from both screens are listed in Additional data file 3 and are also available at [The BD fused proteins used as baits in our screens are listed in Additional data file 2. The AD array was screened using a two-phase pooled mating approach . First, escribed . In the lable at ,50 and alable at  in the Plable at .Drosophila interlogs of yeast or worm interactions, a list of Drosophila proteins belonging to eukaryotic clusters of orthologous groups (KOGs) [S. cerevisiae) proteins, derived mostly from high-throughput yeast two-hybrid screens [The interaction profiles for the BD fused proteins and AD fused proteins were independently clustered and are plotted in Figure s (KOGs)  was obtas (KOGs) . Each fl screens ,55 and f screens ,57, was  screens ,59.Drosophila proteins belonging to those KOGs. Similarly, Drosophila-worm (C. elegans) interlogs were determined using the list of interactions between worm proteins determined by high-throughput yeast two-hybrid screening [Drosophila genetic interactions were obtained from Flybase [Drosophila Release 3.1 genome [For the interactions determined by precipitation of complexes, two lists were generated. One list includes the binary interactions between the bait protein and every protein that was co-precipitated, but not between the precipitated proteins (hub and spoke model). The second list included all possible binary interactions among the members of a complex (matrix model). The lists were each used to generate a list of interactions between KOG pairs, which in turn was used to generate a list of potential interactions between pairs of creening . Drosoph Flybase ,60. To c1 genome  and thesP values represented the number of times that the observed number of overlapping interactions was detected in 50,000 iterations of a random network, divided by 50,000. In most cases P < 0.0002 . Additional methods are in Additional data file 1. To compare the number of common interactions between the LexA and Gal4 maps with the number expected by chance, we generated 106 random LexA maps and found that they never contained more than two interactions in common with the Gal4 map; thus, the P-value for the 28 common interactions is significantly less than 10-6.Fifty thousand such random networks were generated for each two-hybrid dataset, and then compared with the yeast interlogs, worm interlogs, and genetic interactions to determine the amount of overlap expected by chance. P-values for overlap among datasets, and Supplementary Table 6, Interactions from the LexA and Gal4 screens that successfully used the same BD bait proteins; Additional data file The following additional data are available with the online version of this paper. Additional data file Supplementary Materials and methodsClick here for additional data fileSupplementary Table 1: BD 'baits' used in the LexA screensClick here for additional data fileSupplementary Table 2: Interactions detected in the LexA screensClick here for additional data fileSupplementary Table 3: Enrichment of Gene Ontology classes (the complete list)Click here for additional data fileSupplementary Table 4: Enrichment of Domain pairs (the complete list)Click here for additional data fileP-values for overlap among datasetsa nd Supplementary Table 6: Interactions from the LexA and Gal4 screens that successfully used the same BD bait proteinsSupplementary Table 5: Click here for additional data fileSupplementary Figure 1: Interaction maps of other clustersClick here for additional data fileSupplementary Figure 2: Proteins clustered by interaction profileClick here for additional data fileThe legends to Supplementary Figures 1 and 2Click here for additional data file


Non-linearities in observed log-ratios of gene expressions, also known as intensity dependent log-ratios, can often be accounted for by global biases in the two channels being compared. Any step in a microarray process may introduce such offsets and in this article we study the biases introduced by the microarray scanner and the image analysis software.By scanning the same spotted oligonucleotide microarray at different photomultiplier tube (PMT) gains, we have identified a channel-specific bias present in two-channel microarray data. For the scanners analyzed it was in the range of 15–25 . The observed bias was very stable between subsequent scans of the same array although the PMT gain was greatly adjusted. This indicates that the bias does not originate from a step preceding the scanner detector parts. The bias varies slightly between arrays. When comparing estimates based on data from the same array, but from different scanners, we have found that different scanners introduce different amounts of bias. So do various image analysis methods. We propose a scanning protocol and a constrained affine model that allows us to identify and estimate the bias in each channel. Backward transformation removes the bias and brings the channels to the same scale. The result is that systematic effects such as intensity dependent log-ratios are removed, but also that signal densities become much more similar. The average scan, which has a larger dynamical range and greater signal-to-noise ratio than individual scans, can then be obtained..The study shows that microarray scanners may introduce a significant bias in each channel. Such biases have to be calibrated for, otherwise systematic effects such as intensity dependent log-ratios will be observed. The proposed scanning protocol and calibration method is simple to use and is useful for evaluating scanner biases or for obtaining calibrated measurements with extended dynamical range and better precision. The cross-platform R package aroma, which implements all described methods, is available for free from  A microarray, also known as a gene chip, has well defined regions that each consists of immobilized sequences of DNA, which each is unique to a specific gene. These regions are referred to as probes  = 0. Because we want to do inference based on μc,i, it must be possible to find the inverse of fc(·), which (at least in theory) is possible if it is strictly increasing.where fc(·), high quality calibration data from several stages along the microarray process is required. Here we will consider a simpler case. Split the overall measurement function into two parts. The first part xc,i = gc models, in channel c, the amount of light from spot i that enters the photomultiplier tube (PMT)  To quantify the foreground and the background signals, the scanned images  were analyzed using both the Axon GenePix Pro v4.1.1 software (Axon Instruments) and the Spot v2 software ,24. We fFor both Spot and GenePix the median spot pixel intensity was used as foreground signal. Background estimates were not considered in this analysis. No spot signals were discarded. and  data can be calibrated using backward transformation. LetGiven estimates of be the backward transformed observed signal and the rescaled error terms, respectively. The affine Model (2) can then be rewritten asMoreover, leti in channel c. Now, if , thenbe the average backward transformed signal for gene  and  are known. Thus, if (8) is applied with estimates of  and  that are consistent as I → ∞, and the error terms have zero mean, the mean of the backward transformed signals will converge to xc,i as I grows. Even though  is not observable, we can estimate it consistently by increasing the number of scans K. Inspection of the residuals of calibrated signals (not shown) indicates that the variance of the calibrated noise is independent of PMT setting, that is . Assuming independent noise terms, the variance of the sample mean decrease with K aswhen all xc,i with increasing I and K.In summary, we obtain consistent estimates (up to a multiplicative constant) of all Finally, signals that are saturated by the scanner have to be excluded before calculating the average. If the quantified signal for a spot happens to be saturated in all scans, then that spot is marked as saturated, which still may be informative when compared to other non-saturated signals.All further analysis was carried out using R ,26 and tGJ and JVC carried out the practical microarray laboratory work and the scanning of hybridized arrays. Image analysis using GenePix software was carried out by JVC whereas HB carried out image analysis using Spot software. HB performed all the statistical analysis and conceived the constrained affine model used to identify and estimate channel-specific bias in microarray data. All authors participated in the design of the study and approved the final manuscript.


High-throughput methods identify an overwhelming number of protein-protein interactions. However, the limited accuracy of these methods results in the false identification of many spurious interactions. Accordingly, the resulting interactions are regarded as hypothetical and computational methods are needed to increase their confidence. Several methods have recently been suggested for this purpose including co-expression as a confidence measure for interacting proteins, but their performance is still quite poor.S. cerevisiae interactions, and is shown to overcome limitations of previous methods. We present specific examples of known and putative interactions that are detected by our method and not by previous methods, and suggest that they represent transient interactions that might have been conserved and stabilized in other species.We introduce a novel computational method for verification of protein-protein interactions based on the co-expression of orthologs of interacting partners. The performance of our method is analysed using known S. cerevisiae as well as in other species. This approach may be especially useful for species with no available expression profiles and for transient interactions.Co-expression of orthologous protein-pairs can be used to increase the confidence of hypothetical protein-protein interactions in  Protein-protein interactions (PPIs) have a central role in most biological processes, and identifying these interactions is an important goal of biological research. PPIs are the subject of extensive experimental studies, but the majority of them remain unknown. In the last few years, high-throughput techniques were developed for the identification of PPIs on a genomic scale. Yeast two-hybrid ,2 and maIt was previously shown that interacting pairs of proteins are often correlated in their expression profiles ,12. The S. cerevisiae, in order to later compare it to our new method. High quality S. cerevisiae expression data is available for many conditions, making it an ideal organism for the use of co-expression for validation of PPIs. We extracted a reference set of 1656 known interaction from the MIPS database [S. cerevisiae conditions was used to compare the levels of co-expression between the reference set and the random set (see methods).Former studies that used co-expression to identify PPIs did not explicitly examine its predictive power, or did not use a random set of protein-pairs as control for evaluating its performance. We thus carried out an analysis to evaluate the predictive power of this approach for database , and genS. cerevisiae yeast two hybrid sets [The results of this analysis are summarized in Figure rid sets ,6) resulWe verified that the performance of this method is largely independent of the exact set of conditions that is used, and that filtering the conditions or choosing them specifically for each pair of proteins does not improve the performance (not shown).S. cerevisiae PPIs only correspond to a small fraction of the biologically meaningful interactions, and the situation is much worse for other species. Consequently, this method has so far been based only on S. cerevisiae PPIs, including putative ones, to predict interactions in other organisms. Giot et al. used putative S. cerevisiae PPIs from mass spectrometric analysis to verify Drosophila PPIs found by yeast two-hybrid. Only 65 out of the ~2000 Drosophila putative interactions were identified as having an orthologous interaction in S. cerevisiae. This set was then used to train a statistical model for assignment of confidence scores to putative PPIs. Li et al. used putative S. cerevisiae PPIs gathered from several sources to predict C. elegans PPIs (rather than verify an existing set of putative PPIs). Out of the 5534 predicted C. elegans PPIs, only 949 were identified as having an orthologous interaction in S. cerevisiae [Another approach that was proposed to verify or predict PPIs is based on conservation of interactions ,15,16. IThe use of conserved interactions to verify a putative set of PPIs is therefore very limited, since only a small fraction of the putative set would have a known orthologous interaction. Furthermore, using putative PPIs in order to increase the coverage of this approach will decrease its accuracy and introduce many more false positives.In order to overcome the limitations of the two methods described above, we propose to integrate them and detect PPIs by orthologous co-expression, i.e. co-expression of the orthologs of the interacting partners Figure . A conseascomycota species [The use of orthologous co-expression for verification of PPIs is also supported by three previous observations. First, in order to preserve their interaction and functionality, interacting partners should co-evolve . Sequenc species , and canS. cerevisiae orthologs from five species . Orthologous pairs of the protein-pairs in the reference and random sets were identified by BLAST [E. coli and D. melanogaster are even higher than the percentage of PPIs with significant co-expression in S. cerevisiae , so the percentages of PPIs that can be predicted by each organism is lower than 7% for all five organisms Figure . To overe Figure .S. cerevisiae co-expression and orthologous co-expression of the true interactions in the test set is only 0.34. This means that the two methods are complementary, and that except for detecting interactions between co-expressed proteins, orthologous co-expression can also detect interactions between proteins that are not co-expressed in S. cerevisiae, but their corresponding orthologous are co-expressed in other species. Examples of known interactions from the test set with low co-expression in S. cerevisiae but high orthologous co-expression are shown in Table S. cerevisiae is very low or even negative, but the orthologous co-expression is high in at least two species, such that they are easily detected by our approach.The correlation between S. cerevisiae and orthologous co-expression, we proceeded by adding the orthologous co-expression to S. cerevisiae co-expression , TAFIIs, SAGA complex (Spt-Ada-Gcn5-acetyltransferase), CCR4 complex, RSC complex, SRB complex (kornberg's mediator) and SWI/SNF complex. Assuming all pair-wise interactions in these complexes, we compared the percentage of protein-pairs with significant t Figure .S. cerevisiae co-expression at identifying interactions in the reference set, but the differences in performance increase considerably when transient complexes are examined. In the combined set of 764 transient interactions, orthologous co-expression identifies almost three times (2.68) more interactions than S. cerevisiae co-expression. Moreover, for five out of the nine transient complexes, orthologous co-expression identifies at least three times more interactions than S. cerevisiae co-expression, while the opposite occurs only in one complex – RSC, which is also the smallest complex examined. These results suggest that orthologous co-expression is especially useful for detection of transient interactions.Orthologous co-expression is slightly better than S. cerevisiae, while their corresponding orthologs are co-expressed in other species  in S. cerevisiae involved in cell cycle transitions [CDC28 interacts with different proteins at different stages of the cell cycle, including G1 and B-type cyclins  and CDC6. Indeed, no detectable co-expression is found between CDC28 and its interacting partners (Table CLNs). In contrast, CDC28 has several orthologs in higher eukaryotes , each devoted to specific processes or tissues [H. sapiens, D. melanogaster and C. elegans  are highly co-expressed with the corresponding orthologs of CDC6 and the B-type cyclins  is only known to be a part of the TFIID complex, while a second ortholog (TAF5L) is known to be in both TFIID, and the human equivalent of SAGA [TAF5 and the other proteins in human TFIID is higher than that of yeast TAF5 and the other proteins in yeast TFIID (not shown). complex . However of SAGA . As expe3. The opposite case of two S. cerevisiae paralogs with only one ortholog in higher eukaryotes, though less common, may also help to identify PPIs. The nascent polypeptide associated complex (NAC), consists of an alpha subunit (EGD2) and a beta subunit (either EGD1 or BTT1) [BTT1 is not co-expressed with EGD2, presumably since EGD1 and BTT1 are alternating beta subunits that bind both the ribosome and the alpha subunit (EGD2). In contrast, D. melanogaster and C. elegans have only one known orthologous beta subunit, which are co-expressed with the corresponding orthologs of EGD2 (Table or BTT1) . BTT1 isTable SMT3 is the only SUMO gene in S. cerevisiae, which is known to modify TOP2 (DNA Topoisomerase II) and other proteins [SUMO1, SUMO2, and SUMO3. As suggested by the model in Figure SMT3 is not co-expressed with TOP2, but one of its human orthologs (SUMO1), is highly co-expressed with the human ortholog of TOP2 . The first method can only be applied to organisms with expression data and its performance depends on the amount and quality of that data. Our method overcomes this limitation by integrating sequence and expression data from other organisms. It can thus be applied to any sequenced organism, particularly for those without available expression data, thereby replacing the missing data. Moreover, it performs better than the former method even for The proposed method also overcomes the limitation of the interologs approach, namely the small fraction of interactions that is known to date. Our method uses expression rather than interaction data, which makes it capable of giving evidence for a much larger number of interactions.mRNA expression profiles are being generated by many different labs for a wide range of organisms. The improved quality of existing expression profiles as well as the addition of profiles for other organisms will improve the performance of our method. Further improvements can be achieved by giving different weights to the co-expression from different organisms (not shown). A weight can be given to each organism according to the reliability of its expression profiles, or according to its evolutionary distance from the studied organism.S. cerevisiae, and used that to predict PPIs in S. cerevisiae. This approach is complementary to the one that we have proposed. Thus, mRNA expression should be used directly when possible, even from relatively distant species (such as D. melanogaster), and CAI should be used from closely related species without available expression data.During the writing of this manuscript, a related approach was suggested . Based oFinally, the methods described here are still not accurate enough to verify specific PPIs, but they provide additional evidences and are useful for assessment and filtering of high-throughput PPIs data sets, in order to produce smaller sets of higher confidence, and direct further investigations. Complementary methods should be combined to create a general scheme for verification of putative PPIs, for example by considering only those interactions that are verified by at least two or three methods  or usingS. cerevisiae, with highly characterized expression profiles, orthologous co-expression may be combined with co-expression of the actual proteins, whereas for other, less studied organisms, it may replace the missing expression profiles. Notably, this method is especially useful for detection of transient interactions which presents a known weakness of most prediction methods. The success of this method also implies that PPIs tend to be conserved in different organisms, even as distant as yeast and human, further supporting the use of comparative approaches in proteomics.We have shown that expression data from multiple organisms can be used to increase the confidence of hypothetical PPIs by considering co-expression of orthologs of the presumed interacting partners. For organisms such as Interactions sets – a reference set of S. cerevisiae interactions was extracted from the MIPS (Munich Information Center for Protein Sequences) PPI database [database  at 22/01database  since thdatabase . The tramRNA expression data – datasets for six organisms were collected from different sources, as described in [ribed in , and canribed in . All datExpression correlation – cosine correlation over the entire expression data of each organism was used as a measure of co-expression. Former analysis suggested that cosine correlation is the optimal measure of co-expression for the purpose of detecting PPIs [ing PPIs . Many geing PPIs .Orthologous proteins – orthologs were found using blastp [-7, and alignment length threshold of 0.3. The ortholog with the most significant p-value that had available expression data was used to measure co-expression. Other studies had used a reciprocal best-hit BLAST search for finding orthologous; we use a less strict criterion in order to apply the orthologous co-expression method to more protein-pairs.g blastp  with a PP-values and Significance – by sampling 100,000 protein pairs we determined p-values for S. cerevisiae and orthologous co-expression as the fraction of pairs with equal or greater correlation of expression profiles; P-values of 0.05 (not corrected for multiple testing) were used as thresholds for significance.