Effects of Data Scrubbing on Reliability in Storage Systems Silent data corruptions, which are induced by latent sector errors, phantom writes, DMA parity errors and so on, can be detected by explicitly issuing a read command to a disk controller and comparing the corresponding data with their checksums. Because some of the data stored in a storage system may not be accessed for a long time, there is a high chance of silent data corruption occurring undetected, resulting in data loss. Therefore, periodic checking of the entire data in a storage system, known as data scrubbing, is essential to detect such silent data corruptions in time. The errors detected by data scrubbing will be recovered by the replica or the redundant information maintained to protect against permanent data loss. The longer the period between data scrubbings, the higher the probability of a permanent data loss. This paper proposes a Markov failure and repair model to conservatively analyze the effect of data scrubbing on the reliability of a storage system. We show the relationship between the period of a data scrubbing operation and the number of data replicas to manage the reliability of a storage system by using the proposed model. Most storage systems today make certain disk drive reliability assumptions and build redundancy mechanisms such as RAID to compensate for complete disk failures. However, the inability to either temporarily or permanently access data from certain sectors can also affect the MTTDL (Mean Time To Data Loss). Such incidents are often referred to as latent sector errors because the disk drive does not report any error until the particular sector is accessed. The impact of latent sector errors on the MTTDL in RAID systems is well known. Hafner et al. pointed out that a single latent sector error can lead to data loss during RAID group reconstruction after a disk failure. Similarly, Baker et al. developed new RAID equations that account for latent sector errors when calculating the MTTDL.

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Mitral annulus segmentation from four-dimensional ultrasound using a valve state predictor and constrained optical flow Measurement of the shape and motion of the mitral valve annulus has proven useful in a number of applications, including pathology diagnosis and mitral valve modeling. Current methods to delineate the annulus from four-dimensional (4D) ultrasound, however, either require extensive overhead or user-interaction, become inaccurate as they accumulate tracking error, or they do not account for annular shape or motion. This paper presents a new 4D annulus segmentation method to account for these deficiencies. The method builds on a previously published three-dimensional (3D) annulus segmentation algorithm that accurately and robustly segments the mitral annulus in a frame with a closed valve. In the 4D method, a valve state predictor determines when the valve is closed. Subsequently, the 3D annulus segmentation algorithm finds the annulus in those frames. For frames with an open valve, a constrained optical flow algorithm is used to the track the annulus. The only inputs to the algorithm are the selection of one frame with a closed valve and one user-specified point near the valve, neither of which needs to be precise. The accuracy of the tracking method is shown by comparing the tracking results to manual segmentations made by a group of experts, where an average RMS difference of 1.67±0.63mm was found across 30 tracked frames. The mitral valve annulus is a fibrous saddle-shaped structure that anchors the mitral leaflets, and is critical to proper cardiac function. It is typically viewed in the clinical setting using four-dimensional (3D+time) ultrasound (4DUS), as ultrasound is inexpensive, portable, and nonionizing. The shape and motion of the annulus are used in a number of applications, most commonly for pathology diagnosis, as it has been shown that the there is a strong correlation of annular shape and motion to several pathologies [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] . It is also used as boundary conditions for mitral valve models [11] [12] [13] [14] [15] [16] , to assess valve function [17, 18] , for surgical planning [19] , and for implant design [20] .. Methods to delineate annular shape and motion include tracking implanted radiopaque markers with fluoroscopy [10, [21] [22] [23] , tracking implanted sonomicrometry transducers [24] , and time consuming manual segmentation from imaging [1-9, [25] [26] [27] . Few automated methods have been developed to delineate its four-dimensional (4D) shape from ultrasound. There have been a number of semiautomatic methods to track the annulus in 2D ultrasound that generally require manual initialization of the mitral anatomy, after which the annulus points are tracked using some form of either block or template matching, optical flow, active contours, dynamic programming, or a combination of these methods [28] [29] [30] [31] . One of the first methods to track the annulus in 4DUS is the work by Veronesi et al., 2006 [32] , which uses a combination of optical flow and block matching to track manually initialized annulus points in 4D. This method, however, does not constrain the motion of the annulus, leading the authors to add that "manual corrections were performed when required," revealing that the method accumulates tracking error that causes the delineations to become inaccurate. Ionasec et al., 2010 [33] delineate the 4D annulus by fitting a mitral valve model to 4DUS data using machine learning techniques. The method, while accurate, requires an expansive database of thousands of manually delineated features (that took the authors two years to compile), making the method inaccessible to most. Current 4D annulus delineation methods either require extensive user interaction, do not take into account the shape or motion of the annulus, or accumulate excessive tracking error. To address these issues, we present a new semi-automated 4D mitral annulus segmentation (4DMAS) method for delineating the annulus in a 4DUS sequence (Figure 1) . From the user, it requires only the selection of one frame with a closed valve and one point near the valve. Using these inputs and our previously-presented 3D mitral annulus segmentation (3DMAS) method for closed mitral valves [34] , the annulus in the selected frame is found. A valve state predictor then uses this annulus to determine those frames that contain a closed valve versus those that contain an open valve. For those containing a closed valve, the 3DMAS method is again used. For those containing an open valve, a constrained optical flow formulation based on the Lucas & Kanade method [35] is used to track the annulus. In taking into account both the valve state and the shape and motion of the annulus in the optical flow method, the 4DMAS method limits the accumulation of segmentation error.. The details of the 4DMAS method are presented in Section 2. An analysis of the 4DMAS method is presented in Section 3, which includes studies that examine the accuracy of the valve state predictor (Section 3.1) and the accuracy of the constrained optical flow method (Section 3.2). The mitral annulus is an important cardiac structure that is widely studied both clinically and for research purposes. The presented method, being that it is accurate and requires little user interaction, could serve to expedite a number of new studies. Knowing the valve state in a frame could also prove valuable in future work to determine the temporal location of a frame relative to the cardiac cycle without using an EKG signal. This could be useful information in related segmentation efforts, such as mitral leaflet or left ventricle segmentation. Also, because the presented method can determine valve state and switch between direct segmentation and tracking, it does not accumulate substantial tracking error, and could be used to analyze longer ultrasound sequences than those studied.. Future work will also include addressing the limitations on the images for which the 4DMAS method will successfully and accurately segment the mitral annulus -in particular, the limitation that the entire mitral annulus needs to be contained in the ultrasound volume throughout the cardiac cycle. Currently, to obtain such an ultrasound sequence, EKG-gated full volume reconstructions are acquired. However, this consequently lowers the frame rate. It would be beneficial to design methods to construct large field-of-view images with high temporal resolution. The presented 4D mitral annulus segmentation method is an accurate and robust means of delineating the annulus throughout a 4DUS sequence. It finds the 4D annulus by first using a valve state predictor to determine those frames that contain a closed valve versus those that contain an open valve. We found that the valve state predictor was on average 99.2% accurate in identifying closed valves and 100% accurate in identifying open valves. For closed valves, our previously presented 3D annulus segmentation method is used to find the annulus. For open valves, a constrained optical flow algorithm is used that takes into account annular shape and motion. In comparing the tracking results of the CLKOF algorithm to manually tracked points, we found an average RMS difference of 1.67±0.63mm. While the CLKOF algorithm tended to outperform the experts, the difference between the two groups was not statistically significant (p = 0.062), suggesting that the algorithm performed at least as well as the experts. The 4D annulus segmentation method requires from the user only the selection of one frame with a closed valve and one point near the valve in that frame. We showed that resulting 4D annulus is essentially independent of these inputs. The 4DMAS method consists of three major components: a valve state predictor, an annulus segmentation algorithm (3DMAS method), and an annulus tracking algorithm (CLKOF method). As we presented validation of the 3DMAS algorithm in [34] , we present here an analysis of the valve state predictor and annulus tracking method. In the analysis, we used seven anonymized clinical transesophageal echocardiography sequences (iE33 Echocardiography System with X7-2t transesophageal probe, Philips Healthcare, Andover, MA, USA). To capture the entire mitral valve throughout the ultrasound sequence, these had to be acquired as EKG-gated full volume reconstructions. Volume dimensions were roughly 200×200×200 voxels with a resolution of 0.5-0.75 mm/voxel. More details about the images and valves can be seen in Table 1 .. An example of the results of the 4DMAS method are shown in Figure 4 for the example sequence shown in Figure 2 . As a comparison, also shown in the figure are the results when tracking is performed with the original LKOF method versus the CLKOF method, both using the window sizes and iterations as described. This highlights the stability of the CLKOF method versus the LKOF method for annulus tracking in ultrasound.. The algorithm was coded mostly in MATLAB (The Math-Works, Natick, MA, USA), but used C++ for both computing parts of the thin tissue detector and for the implementation of the max-flow algorithm. In the validation studies, the parameter values used in the 3DMAS algorithm are the same as those listed in [34] . The approximate time to compute the annulus for closed and open valves (64-bit PC, 3.0 GHz Intel Core 2 Duo processor with 4GB of RAM) was roughly 90 and 30 seconds, respectively, which includes the time to determine valve states. Figure 4 : Comparison of the resulting 4D annulus delineation for the example 4DUS sequence shown in Figure 2 when tracking is performed using the original LKOF method (i.e. minimizing (2)) versus the CLKOF method (i.e. minimizing (3)).

O B B O O B O O O O O O O O O O O O O O O O O B I B O O O O O O O O O O O O B I O O O O O O B O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O B B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B B O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B I B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O B B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O B O B O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B B O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O


On the performance of binary extremal self-dual codes Abstract. The decoding error probability of a code C measures the quality of performance when C is used for error correction in data transmission. In this note we compare different types of codes with regard to the decoding error probability. From a pure mathematical point of view binary extremal self-dual codes of type II deserve particular attention. They are related to unimodular even lattices, provide 5-designs, and often have interesting automorphism groups. In this paper we investigate how good they perform if used for error correction in data transmission. To measure the performance we take the decoding error probability and assume that bounded distance decoding is used for correction of errors.. The notation thoughout the paper is standard and can be taken from [8] . A binary self-dual code C is called of type II if for all codewords c ∈ C the weight wt(c) is divisible by 4. Otherwise, i.e., 2 | wt(c) for all c ∈ C and there is a codeword c with 4 wt(c) we say that C is of type I. A binary self-dual code of length n and minimum distance d satisfies. n 24 + δ where δ = 4 if n ≡ 22 mod 24 and δ = 6 if n ≡ 22 mod 24 (see [11] , [10] ). We call C extremal if the bound is attained. Furthermore, if 24 | n then an extremal self-dual code is always of type II as Rains has shown in [11] . Finally, extremal codes of type II do not exist for large n according to a result of Mallows and Sloane [9] . More precisely, due to Zhang [13] , the length n is bounded by 3928. Apart from section 2 all codes are binary.

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Investigating the relationship between QoS and QoE in a mixed desktop/handheld gaming setting Innovative software and system development requires a multidisciplinary approach where technical, objectively quantifiable Quality of Service aspects are integrated with and correlated to subjective Quality of Experience measures such as usability, user expectations and experiences. To date, QoS and QoE research has however mainly been carried out in an isolated manner. Their exact correlation consequently remains unclear. Using a collaborative multi-platform game as case study, we intend to investigate this relationship and hope to bridge the divide that currently exists between social, user-oriented research and technically-oriented application development by formulating recommendations and guidelines. To implement the QoS and QoE optimizations, we will rely on the NIProxy, a network proxy equipped with network traffic shaping and service provision functionality. Quality of Service (QoS) is a well-established concept in software and system engineering. It is a measure of technological excellence as it denotes the capability of systems to guarantee that a certain level of performance will be met. Quality of Experience (QoE) on the other hand deals with user expectation, satisfaction and overall experience. Compared to QoS, it is a subjective metric that involves human dimensions and that is hence not easily quantified or objectively measured. A common approach to obtain QoE feedback is through qualitative user studies.. Despite their different perspective, QoS and QoE are unquestionably related. The level of service provided by an application or software system is sure to influence the user satisfaction. On the other hand, a high QoS level not necessarily yields an excellent QoE. As a result, there is a need to thoroughly chart the relationship between both concepts. To date however, QoS/QoE research has largely been carried out in a fragmented manner, focusing on measuring and optimizing either objective technical QoS parameters or subjective QoE dimensions. In the former category, [1] for instance presents a methodology for devising a cross-layer performance-energy trade-off, however without considering the user satisfaction. An example in the latter category is the study described in [2] , which relates video quality to user experience in the context of a video conferencing system, without linking the findings to objective QoS factors that can be applied to network services. Our goal is to bridge the QoS/QoE gap and to uncover the correlation between both concepts. Within this gaming context, we plan to monitor and optimize QoS as well as QoE parameters and to investigate their correlation and the way they influence each other. QoS measurement and optimization will take place on both network and device level. On the network level, we will deploy probes to deduce network capacity and performance (in terms of throughput, error rate, latency, . . . ), perform network traffic shaping and bandwidth brokering to ensure optimal use of the available capacity, deliberately adapt network traffic during dissemination, and so on. Regarding device QoS parameters, factors that will be considered include device capabilities and features (such as audiovisual codec support) and performance-related statistics (achieved frame rate, battery lifetime, CPU utilization, etcetera). Concerning QoE, we will evaluate mechanisms which allow users to specify their preferences as well as other contextual information (e.g. environmental factors) and we will examine the ability to determine such information implicitly (e.g. automated user profiling). To obtain QoE-related data, we plan to organize qualitative user studies in a living lab test environment that is representative for the real-life setting in which the application is to be used. The goal of these studies will be to determine user expectations (e.g. by interviewing users before the gaming session is started), to attempt to quantify user satisfaction (e.g. through observation during play) and to collect user feedback (for instance through a questionnaire or post-gaming interview).. Based on the data collected in this particular gaming context, we will attempt to identify the mutual influence QoS and QoE parameters have on each other and we will try to translate our findings into a set of recommendations and guidelines for designers and developers.

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Use of the Global Trigger Tool in patient safety improvement efforts: Nordic experiences Abstract The Global Trigger Tool (GTT) developed by the Institute for Healthcare Improvement is a method for retrospective patient record review based on the use of 'triggers'-signals of potential adverse events that have caused patient harm. The method has the purpose of patient safety measurement and monitoring among adult inpatient populations and has been increasingly popular among Nordic countries. Use of the GTT in the Nordic area has been part of broader legal and policy actions and initiatives supportive of patient safety promotion and is being used to establish also national level estimates of patient safety incidents. Limitations of the method are its dependency on quality of documentation and the varying inter-rater reliability observed in many studies. Strengths of the GTT are its ability to detect larger numbers, as well as different types of adverse events when compared to other incident detection methods, hence it is a good addition to the palette of means for organizational patient safety monitoring. Research on reliability, usefulness and implementation approaches of the GTT, including its automation, is ongoing in the Nordic countries and is expected to generate useful input for the international patient safety community. The IHI Global Trigger Tool (GTT) is a retrospective method for monitoring patient safety levels within a healthcare provider organization. Its aim is to enable longitudinal comparisons and assessment of implemented patient safety measures and support the identification of target areas for improvement. A distinct feature of the IHI Trigger Tool methodology is its focus on actual harm (restricted to physical injury) inflicted to patients (Griffin and Resar 2009; IHI 2011) . The underlying rationale is that surveillance of events that have led to harm is a more focused and hence more effective approach to developing a strategy for injury reduction . The method is paper-based, in other words, it does not require or depend on the use of health information systems, although many have identified the benefits of integration with the electronic patient record (Classen et al. 2011; Naessens et al. 2010) . Use of the GTT seems to be on the rise at least in the USA and in the Nordic countries, as part of large organizational, as well as national patient safety programmes. In this paper, we focus on the current status of GTT implementation for purposes of patient safety monitoring in Nordic hospitals, including experimentations for further development of electronic tools. Moreover, methodological issues and potential limitations and strengths of the tool as identified through a review of the literature are reflected upon and discussed against the backdrop of the authoring team's practical experiences. Compared to full patient record review, GTT places a smaller demand on resources as a result of reviewing a smaller number of records, with a higher probability of containing actual adverse events. The tendency of the GTT to identify a larger number of adverse events as opposed to other detection methods has been attributed to the broad definition of adverse events used by the method, which includes also events present on admission, as well as less serious than sentinel events. It should be noted, however, that these comparisons have originated in the USA and have focused on event detection methods such as voluntary reporting systems of sentinel events and the AHRQ Patient Safety Indicators. There is very scarce evidence comparing the GTT with other patient safety assessment methods in a European context, where, for example, the rate of voluntary organizational incident reports seems to be much higher than that reported by US healthcare providers. Therefore, at this point, it is not possible to say whether the method's benefits will be equally prominent in the European context as well. In addition to its ability to detect larger number of events than other assessment methods, comparative studies also indicate that the GTT may identify different types of adverse events. In the light of these observations, it appears that use of the trigger tool approach can supplement incident reporting and other assessment methods when the aim is a comprehensive picture of the level of patient safety 2 The matter of preventability is receiving now more attention by the IHI, as demonstrated in the interview of David Classen (24) , where he is also proposing to enlarge the concept by including mitigability and ameliorability-aspects which become relevant as automation of the GTT progresses.. incidents within an organization. This echoes the position of the IHI (White Paper) that: ''…hospitals should use the IHI Global Trigger Tool as one part of a learning system that includes other component measures, such as voluntarily reported errors, surgical site infections, and other outcome measures'' (Griffin and Resar 2009 ). The necessity for utilizing a palette of methods to monitor and improve patient safety has been echoed in the publication of both scientists and organizations in the field (Rosen 2005; Battles 2005; Ferranti et al. 2008; Ö hrn et al. 2011) .. It should be kept in mind that the GTT, just as the rest of the IHI trigger tools family is a relatively new technology. A recent review of the literature (Doupi 2012 ) located only nine papers specific to the IHI GTT, mostly published during the last 2-3 years. The articles concerned the tool's development and evaluation, performance features, comparisons with other methods and examples of utilization either within or across large health systems or in national level programs. None of the studies had the purpose of formal validation of the tool. There is therefore a need for caution when using the method, as well as further research on its reliability and fitness for specific purposes.. Acknowledging the controversies and still ongoing discussion around the tool's methodological soundness, the coordinating bodies in both Norway and Sweden-where the GTT has been employed on a national scale, have refrained from using the results as a benchmarking instrument. Rather, it has been made explicit that the focus is on each individual hospital's development over time.. Whether the focal point of use should be the whole organization or individual hospital departments is also a question of great interest. The potential for increased learning and more effective interventions when the method is brought closer to frontline staff has been recognized, but several methodological issues still remain to be resolved.. Yet another area of unclarity is the suitability of the GTT in the analysis of mortality statistics. Following the publication of the national level analysis results in Norway, intensive discussions have ensued as to whether the GTT is a valid method for identifying and estimating harmful incidents that contribute to a patient's death. Generally, the literature shows very low inter-rater agreement on such decisions, while the subject has not been specifically studied for the GTT. As a result, it has been decided that the respective rates for 2011 in Norway will not be published before consistency of the methodology has been confirmed.. The original inspiration for the current generation of trigger tools was work on automated trigger systems. Now, after almost a decade of development, IHI and the developers of GTT are placing again their hopes for future success and more widespread adoption of the tool on the computerization of medical records (Classen et al. 2011 ).. Many of the groups reporting their experience with the paper-based GTT also refer to the need of a tool integrated with electronic patient record systems (Good et al. 2011) . Indicatively, in a recent interview, Dr. Classen communicated that IHI has already proof-tested the automation of the GTT in all 'leading EMR vendors at various health systems'' (AHRQ 2012).. At a minimal level of computerization, which is also the view presented in the IHI White Paper (Griffin and Resar 2009 ), triggers-particularly medications and laboratory values-can be directly captured from a patient information system (once the random selection of records has happened), thus speeding up the review process. Such a trigger system can be viewed as a 'first generation' example, since the objective is still the post hoc identification of harm. As the sophistication and capacity of electronic systems improves, the closer the implementation of trigger systems moves to the possibility of intervening to an adverse patient safety event before it causes harm to the patient (concurrent and real-time systems) or even before it even happens (interventionist trigger system) (AHRQ 2008) . Such applications though require on the one hand the establishment of a notification and reaction system well fitted to the organizational workflow, and on the other hand, verification of their accuracy in order to avoid false alarms and ensure relevance for clinical decisions.. We have presented an overview of the current uses of and experiences with the GTT methodology in the Nordic countries, where significant emphasis is being placed on patient safety through ongoing national level programmes and initiatives. We have drawn on the experience of the authoring team, all of whom have functioned in key expert positions in their respective countries and thus closely followed pertinent activities, combined with the evidence provided by a systematic review of the literature on the GTT and automated trigger tools. However, we have not attempted to perform a systematic comparison of the way each Nordic country has proceeded in implementing the GTT, neither have we performed any form of quantitative analysis across national data. Rather, our exploration of the current status of affairs has laid the ground for such approaches in the future.. It should also be noted that this paper focuses exclusively on trigger tools in the hospital environment. Therefore, it is not possible to say how well the GTT or other trigger tool methodology is suited for use in other levels of healthcare services-such as primary care centers, nursing homes, etc. Evidence on this subject exists, and its analysis can be the focus of future research. Work on implementation and research on the GTT is ongoing in the Nordic countries and will certainly continue generating valuable contributions to patient safety measurement methodology.. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

O O O B I I O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Delay Attack-Resilient Clock Synchronization for Wireless Sensor Networks This paper presents the design and performance evaluation of a delay attack-resilient clock synchronization scheme (abbreviated to DARCS) for wireless sensor networks. In order to provide both synchronization accuracy and robustness, we propose a novel three-way handshake-based protocol, which completely excludes non-deterministic factors such as random backoff durations and unexpected hardware interrupts in a software manner and, in this way, the node can accurately estimate the relative clock offset and the end-to-end delay between a pair of nodes. Consequently, DARCS makes it possible to correct time synchronization errors as well as to detect delay attacks precisely. The simulation results show that DARCS achieves a higher synchronization accuracy and is more resilient to delay attacks than the other popular time synchronization schemes. Time synchronization is crucial for many operations and applications of wireless sensor networks (WSNs), such as time slot allocation in TDMA, duty cycle scheduling in CSMA, environmental monitoring, object tracking, and so on [1] . The related works can be classified into two main approaches: receiver-receiver synchronization (RRS) and sender-receiver synchronization (SRS). RBS [2] and FTSP [3] belong to the former type, and TPSN [4] , LTS [5] , and TS/MS [6] to the latter. The RRS approach uses a "third party" for synchronization, where the nodes in the network synchronize their clocks with the time of arrival by receiving the reference beacon broadcasted from this third party node. In the SRS approach, a pair of nodes exchange the timing message in a two-way handshake pattern when they want to synchronize with each other. Noh et al. [12] proposed a third synchronization approach, called pairwise broadcast synchronization (PBS). PBS is a hybrid version of the above two approaches, in which a pair of super nodes exchange timing messages in an SRS manner, and the nearby neighbors overhear them and estimate the time offset and skew. Its extensions [13] , [14] are included in this approach. Manuscript These existing schemes commonly have the following limitations when it comes to meeting the requirements of general WSN applications. First of all, because of the uncertainty of the packet delay, due to such factors as the Tx-FIFO buffer loading time, random backoff, and unexpected hardware interrupts, which act as non-negligible sources of error in the calculation of the clock offset, the desired level of time accuracy cannot be achieved. Even if the buffer loading time is assumed to be deterministic, the random backoff that is performed after time stamping the packet at the MAC is highly variable and, thus, would completely overshadow other delays in practice. Moreover, a hardware interrupt occurring before the transmission of the packet would severely degrade the performance of the time synchronization procedure. In order to minimize the repercussions resulting from these non-deterministic factors, FTSP [3] and TPSN [4] realize MAC-layer time stamping in their hardware, i.e. the Mica Atmel (AVR) MCU platform [7] , where the node can write its clock time on the timing packet from the PHY to the MAC layer when it is about to be transmitted from the radio module. However, this MAC-layer time stamping based time synchronization is both hardware-dependent and MAC/PHY-layer dependent. Thus, it is not applicable to WSNs with other hardware platforms and cannot be a general solution for WSNs.. Second, none of the existing schemes was designed with security in mind. Thus, they cannot be applied as is to applications in hostile environments (such as a military battlefield or security monitoring area), where security is critical. In [8] , Song et al. identified the four possible attacks that may occur in a WSN environment, i.e., the masquerade attack, replay attack, message manipulation attack, and delay attack. The first three attacks can be addressed by cryptographic techniques. However, to prevent a delay attack, specific countermeasures are needed. Ganeriwal et al. [9] proposed a secure pairwise synchronization protocol, where the node can detect pulse-delay attacks by comparing the calculated end-to-end delay with the maximal estimated delay. However, as mentioned above, since the calculated delay includes uncertainties resulting from such factors as random backoffs and hardware interrupts, the solution provided by these techniques suffers from detection errors caused by these uncertainties.. In this paper, we propose a delay attack-resilient clock synchronization scheme (abbreviated to DARCS) which is a software-based solution and is applicable to general WSN nodes regardless of the kind of hardware mote platform. In Copyright c 2012 The Institute of Electronics, Information and Communication Engineers DARCS, the node captures the outgoing or incoming time of the timing packets and then provides its counterpart with this information instead of using MAC-layer time stamping. In this way, DARCS can thoroughly exclude the uncertainties caused by random backoff and hardware interrupts, resulting in high precision performance. The nodes in the network can obtain a precisely calculated relative offset and end-toend delay, by means of which they can effectively detect delay attacks from malicious nodes.. In what follows, we describe the design and performance of the proposed scheme in detail. This paper presents DARCS, which is a delay attackresilient clock synchronization scheme for wireless sensor networks. To reconcile the two objectives of robustness and accuracy, DARCS involves the following salient features: 1) A three-way handshake-based protocol, which enables the time offset and end-to-end delay to be precisely estimated by exchanging the incoming/outgoing times of a timing message without using hardware-dependent MAC-layer time stamping, and 2) Delay attack detection, which provides the exact level of detection for delay attacks by malicious nodes with a minor fault rate. The performance evaluation shows that the proposed scheme has better accuracy than the existing protocols and effectively defends against delay attacks.

O O O O O B I I O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


moult:

An

R

Package

to

Analyze

Moult

in

Birds

Moult

is

the

process

by

which

birds

replace

their

feathers.

It

is

a

costly

process

in

terms

of

energy

and

reduced

flight

ability

but

necessary

for

the

maintenance

of

the

plumage

and

its

functions.

Because

birds

generally

avoid

to

moult

while

engaged

with

other

energy

demanding

activities

such

as

breeding

and

migration,

the

analysis

of

moult

data

gives

insight

into

how

birds

fit

this

life

stage

into

the

annual

cycle,

on

time

constraints

in

the

annual

cycle,

and

on

the

effects

of

environmental

variables

on

the

timing

of

moult.

The

analysis

of

moult

data

requires

non-standard

statistical

techniques.

More

than

years

ago

Underhill

and

Zucchini

developed

a

likelihood

approach

for

estimating

duration,

mean

start

date

and

variation

in

start

date

of

a

population

of

moulting

birds.

However,

use

of

these

models

has

been

limited,

mainly

due

to

the

lack

of

user-friendly

software.

The

moult

package

for

R

implements

the

Underhill-Zucchini

models,

allowing

the

user

to

specify

moult

models

in

a

regression

type

formula.

In

addition

the

functions

allow

the

moult

parameters

(duration,

and

mean

and

variation

in

start

date)

to

depend

on

explanatory

variables.

We

here

describe

the

package,

give

a

brief

summary

of

the

theory

and

illustrate

the

models

on

two

datasets

included

in

the

package.

Many

bird

ringers

collect

data

on

the

progression

of

moult.

Moult

data

can

provide

insights

into

the

energy

allocation

under

time

constraints

involved

in

the

annual

cycle

of

different

bird

populations,

and

these

data

are

relatively

easy

to

collect

compared

to

the

timing

of

breeding

and

migration

(Newton

However,

the

analysis

of

moult

data

has

been

slowed

initially

by

the

lack

of

appropriate

statistical

methods

to

analyze

these,

and

after

Underhill

and

Zucchini

developed

their

likelihood

models

(referred

to

as

UZ

models

henceforth),

by

the

lack

of

software

that

could

run

these

models

in

a

convenient

user-friendly

way..

Here

we

show

how

to

fit

the

UZ

moult

models

using

the

R

package

moult

(available

from

the

Comprehensive

R

Archive

Network

at

We

briefly

summarize

the

theory

underlying

the

moult

models

as

developed

by

Underhill

and

Zucchini

and

extended

in

Underhill,

Zucchini,

and

Summers

We

describe

the

implementation

in

R

(R

Core

Team

and

illustrate

the

use

of

the

models

using

some

examples.

More

than

years

ago

Underhill

and

Zucchini

published

an

improved

method

to

analyze

moult

in

birds.

These

UZ

models,

and

the

probit

models

used

by

Rothery

and

Newton

are

still

considered

the

best

way

to

analyze

moult.

However,

easy-to-use

software

to

fit

the

UZ

models

was

not

widely

available.

The

moult

package

for

R

aims

to

provide

all

functions

required

for

the

analysis

of

moult

data

in

an

easy-to-use

format.

Here,

we

have

demonstrated

its

functionality.

In

addition

to

the

original

moult

models

published

in

Underhill

and

Zucchini

and

Underhill

et

al.

in

moult

the

duration,

mean

and

standard

deviation

of

start

of

moult

parameters

can

be

modelled

as

functions

of

explanatory

variables.

This

will

allow

researchers

to

ask

and

answer

important

ecological

questions,

and

can

be

seen

as

a

further

advantage

of

the

UZ

models:

because

duration

and

mean

start

date

(and

standard

deviation

in

start

date)

are

the

parameters

of

the

likelihood,

it

is

straightforward

to

extend

the

likelihood

to

model

these

parameters

as

functions

of

covariates..

The

UZ

moult

models

can

be

sensitive

to

certain

types

of

data

problems.

One

of

these

problems

is

individuals

that

start

moulting

markedly

later

or

earlier

than

the

bulk

of

the

population.

Such

outliers

can

result

in

large

estimates

of

the

standard

deviation

parameter

σ,

and

may

even

lead

to

non-convergence.

A

second,

common,

problem

is

the

presence

of

zero

(not

started)

or

(completed)

moult

scores

throughout

the

season.

In

our

experience,

for

example,

with

the

weaver

data

in

this

manuscript,

it

is

not

straightforward

to

choose

cut-off

points

to

decide

whether

a

bird

not

in

moult

has

all

old

or

all

new

feathers,

and

estimates

can

be

sensitive

to

these

cut-off

points.

Optimization

of

the

log-likelihood

can

be

sensitive

to

starting

values,

especically

for

standard

deviation

estimates,

so

it

is

advisable

to

try

different

starting

values

for

complex

models..

The

critical

assumption

made

by

the

UZ

models

of

constant

rate

of

change

in

moult

index,

or

a

linear

increase

in

moult

index

over

time,

may

not

hold

in

reality

(Summers,

Swann,

and

Nicoll

Newton

and

Rothery

This

assumption

is

not

required

when

using

the

probit

model

Rothery

and

Newton

or

the

UZ

models

with

type

data.

Newton

and

Rothery

compared

the

UZ

models

for

data

types

to

and

found

that

type

models

gave

more

accurate

estimates

of

timing

and

duration

of

moult,

especially

when

the

linearity

assumption

was

not

met.

The

probit

model

and

the

UZ

type

model

are

almost

identical.

The

advantage

of

the

UZ

model

is

that,

because

start

and

duration

of

moult

are

the

parameters

being

estimated

directly,

it

is

easier

to

add

covariates.

B O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O


The importance of accounting for the number of co-authors and their order when assessing research performance at the individual level in the life sciences Accurate measurement of research productivity should take account of both the number of co-authors of every scientific work and of the different contributions of the individuals. For researchers in the life sciences, common practice is to indicate such contributions through position in the authors list. In this work, we measure the distortion introduced to bibliometric ranking lists for scientific productivity when the number of co-authors or their position in the list is ignored. The field of observation consists of all Italian university professors working in the life sciences, with scientific production examined over the period [2004][2005][2006][2007][2008]. The outcomes of the study lead to a recommendation against using indicators or evaluation methods that ignore the different authors' contributions to the research results. Evaluating the productive efficiency of research organizations and individual scientists is an exercise that is as important as it is delicate. The principle indicator of efficiency for almost any activity is the labor productivity, or in very simple terms, the relationship between output produced in a defined period and the hours of labor expended to produce it. As for any measurement system, that for research productivity is subject to limits and approximations, which must be duly taken into account considering the field and the intended use of the results. In particular, research activity has certain characteristics that make it notably complicated to carry out accurate and robust measurement of labor productivity. We first observe the intangible nature of the output, and also consider that such outputs are generally obtained through collaboration of various individuals, who may or may not be from the same organization or nation, and who may cooperate by contributing resources, experience and competencies that are both similar and complementary. In evaluating the scientific activity of a researcher or organization it is thus fundamental to identify the true contribution that the individual or institution has provided to the various research results in which they have from time to time participated. In the scientific fields where codification of results is primarily through publication in scientific journals, indexed in such databases as Web of Science (WoS) or Scopus, bibliometrics can be conveniently applied for large scale evaluation of productivity. In this case, the contribution of scientists and organizations to the individual publications can be recognized through the analysis of co-authorships. 2 In the life sciences, in particular, widespread practice is for the authors to indicate the various contributions to the results of the published research by the positioning of the names in the authors list.. In this work, we propose to measure the distortions encountered in the evaluation of research productivity for single individuals in Biology and Medicine when no consideration is given to the co-authors of a research work or to their order in the list.. As much as taking account of both of these factors in comparative measurement of research productivity would seem logical, and even mandatory under the theory of production, it is not at all rare that they are partially or completely ignored. In national research evaluation exercises with peer-review techniques, this is standard practice: for example in the UK Research Assessment Exercises (RAE) and in the Italian Triennial Evaluation Exercise (VTR), the peer evaluators are only called to judge the level of excellence of the products that the researchers submit, independent of true entity of the author's contribution to their accomplishment. The same is true of the national exercises that, while conducted with bibliometric techniques, examine only a share of the entire output (see the current Research Quality Evaluation exercise, VQR, in Italy). Even famous and widely used bibliometric performance indicators, such as the h-index (Hirsch, 2005) and the g-index (Egghe, 2006) , totally ignore any consideration of the contributions of the individual authors to the scientific product. Little attention has been paid to advice from the inventors themselves, such as that from Hirsch (2005) , who warned that "subfields with typically large collaborations (e.g., high-energy experiment) will exhibit larger h values", and further recommended that "in cases of large differences in the number of co-authors, it may be useful in comparing different individuals to normalize h by a factor that reflects the average number of co-authors".. Little attention has also been paid to the specific corrections proposed, such as the simple division of the h-index by the average number of co-authors included in the Hirsch core (Batista et al., 2006; Egghe, 2008; Schreiber, 2009 Schreiber, , 2010 , or consideration of the actual number of co-authors and the scientists' relative position in the byline (Wan et al., 2008) . In spite of the above intrinsic limits, we still see major bibliometric databases such as WoS and Scopus provide the h-index of every author, and it is this that scientists widely use to compare their personal performance against that of their peers, to the point that this index has now become the regulated reference threshold for access to a professorial career in Italy, both for candidates and for members of the national competition commissions (Ministerial decree 344, 4 August 2011).. In the literature, various scholars have addressed the theme of the analysis of coauthorship in evaluating scientists' research performance. Van Hooydonk (1997) pointed out that the impact of a research unit can dramatically be affected by the counting procedures. Carbone (2011) holds that "in general fractional counting is preferred because this does not increase the total weight of a single paper", and suggests that "the best way to define a fractional counting of authorship is to divide the number of citations received by each paper by the square root of the number of co-authors". As early as 1968, Zuckerman studied the patterns of name ordering in cases of multiple authorship involving Nobel laureates, and concluded that "ordering of author's names is an adaptive device which symbolizes their relative contributions to research". Based on a random selection of 5,686 chemistry papers from Current Contents volumes, Vinkler (2000) observed "only a slight preference for the alphabetical listing of authors over other rankings". In a previous work, Lukovits and Vinkler (1995) suggested that coauthors should declare their individual contributions to the research as percentages, and also introduced a simple equation for calculating individual contribution scores for coauthors of multi-authored papers. More recently Verhagen et al. (2003) proposed a Quantitative Uniform Authorship Declaration (QUAD) System that permits the reader to rapidly identify who contributed what. According to Bhandari et al. (2003) "the answer, in the tradition of scientific transparency, is for authors to decide together their individual contributions and disclose these to their readers". The author order "can reveal subtle patterns of scientific collaboration and provide insights on the nature of credit assignment among co-authors" (He et al., 2012) . Trueba and Guerrero (2004) proposed a formula that assigns relative values to each co-author according to their position in the list. Laurance (2006) suggests that "the individual making the greatest intellectual contribution is the lead author, followed sequentially by those making progressively lesser contributions. In addition, the final-author slot is sometimes reserved for a lab head or project initiator, who may have made little direct contribution to the paper but deserves some vague honor nonetheless". In practice, different patterns are followed in ordering the authors list, from simple alphabetical order to sequences that signal the varying importance of the contributions from individual authors, a pattern which is particularly common in the life sciences.. There is increasing agreement among bibliometricians on the desirability of taking account of co-authorship through fractional counting, though there are still differences over the most appropriate fraction to assign to each co-author.. This work is not precisely concerned with establishing the most appropriate value to assign to contributions from co-authors in the life sciences. Rather after choosing fixed, but potentially "fine-tunable", criteria to assign different weight to the various positions in the list, the objective we set is to measure the extent of the distortion in performance ranking when the number of co-authors and their order are totally ignored. In Italy, there are no fixed guide-lines establishing the order of names in the authors list for the life sciences, even though some important academic lobbying bodies have officially pronounced themselves in favor 3 . The Italian National University Council states that the medical sciences are characterized by "scientific works that are prevailingly by multiple authors, in which the first and last authors are generally the leader of the specific research and the leader of the entire research group, and where in certain fields the second name indicates the co-leader of the specific research". In effect, wide-spread practice is that the position of first author falls to the "idea generator" and person who executes the bulk of the work, while the last position is assigned to the overall workinggroup leader. In the case of multiple authors from more than one institution, with similar contributions to the research, the indication of second and second-last authors also becomes significant. In general, if the position of the first author is assigned to one organization, the last name listed will be that of the group leader from the other institution, and the positions of second and second-last authors are then assigned in the opposite manner.. In the current work we will calculate the research performance of individual professors and draw up a total of six ranking lists, three for each of two types of bibliometric indicators, based on number of publications and number of citations: i) a list that takes account of both number of co-authors of each publication and their position in the list; ii) a list that does not consider position; iii) one that does not consider co-authorship in any way. The field of observation is the 2004-2008 research production by professors in Biology and Medicine from all Italian universities.. The next section of our paper illustrates the methodology and dataset used for the analyses. Section 3 presents the results concerning the correlations between the ranking lists, the analysis of shifts in position in the classifications, and a deeper examination concerning the "above-median" and top 10% of scientists. The work concludes with a summary of the results and the authors' considerations. There is a rapidly increasing tendency to evaluate the research activity of individual scientists, research groups, and entire organizations, as a support instrument for improving efficiency in national research systems. Evaluation at the individual level informs recruitment, incentive systems and selective resource allocation. Bibliometrists are thus called on to refine techniques and indicators that can render the evaluation tools every more accurate and robust. The different contributions of the specific authors in realizing scientific advancement through co-authorship must certainly be taken account in individual evaluation. In the life sciences, there are widespread and consolidated practices to signal the different contributions of the individual authors to the research results. Many bibliometric indicators and national evaluation exercises ignore this important specificity of the life sciences, failing to consider the order of the co-authors and even their number. In this work we have indicated an order of magnitude for the distortion in performance ranking that occurs under such circumstances.. The benchmark indicators we used to measure labor productivity are the less refined weighted fractional output, WFO, and the more sophisticated weighted fractional impact, WFI. Beginning from each of the main indicators, we measured another four: two, FO and FI, eliminate the weighting that takes account of position in the co-authors list; a further two, O and I, eliminate the fractional count that provides for the number of co-authors. Comparison to the ranking list for each indictor, for each field of research and academic rank, permitted us to reveal the shift from the respective benchmarks.. The extent of distortion is considerable, even for the indicators that retain consideration of co-authors but do not take account of their different contributions: in the case of the measures based on impact indicators (comparison WFI-FI), the average shift in percentile rank is 7.8 for Biology and 8.8 for Medicine, with peaks of maximum shift in rank of up to 53.8 (in BIO/01) for Biology and 56.7 (in MED/06), for Medicine.. The distortions are still more evident when the indicators do not take account of the number of co-authors (comparison WFI-I): the average shift in percentile rank is 10.6 for Biology and 11.2 for Medicine, with peaks of maximum rank shift equal to 81.5 in BIO/18 and 57.5 in MED/30. Not taking account of the number of co-authors would result in a full 31.8% of the top 10% scientists in Biology and 31.3% of those in Medicine no longer being recognized as such, while 14.6% of above-median researchers in Biology and 16.4% of those in Medicine would then be classified as below the median. Similar values are seen in the comparison based on indicators of output.. In a context where collaboration in research is ever more the norm (98.2% of Italian university articles in Biology and Medicine are in co-authorship and 93.6% show more than two authors), it is evident that using bibliometric indicators that ignore the contribution of each individual author leads to the introduction of distortions that can: a) notably reduce the accuracy and reliability of the measure; b) undermine the effectiveness of the entire evaluation process; and, according to the uses made, c) compromise the results at the micro-and macro-economic level.. The evidence from this study should inspire caution concerning the use of widelydiffused indicators in the scientific world (such as the h-index and others) that do not take account, among other considerations, of the number of co-authors. The same can be said for peer-review methodologies applied to the evaluation of individuals and organizations that, in the analysis of the quality of a share of scientific production (such as in the UK RAE and others), then ignore the order and number of co-authors in the products evaluated. In this section we will present the results from the comparisons between the different productivity ranking lists. Comparisons are between the ranking lists derived from the same type of productivity indicator: those based on simple publication count (WFO, FO, O) and those based on standardized citations (WFI, FI, I ). The ranking lists are prepared for each SDS and academic rank. We begin with the correlation analyses between the ranking lists; continue with the analyses of shifts in position when changing from ranking under one indicator to rankings under another, and conclude with a deeper analysis of the shifts in position for the above-median and top 10% of scientists.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


QCScreen: a software tool for data quality control in LC-HRMS based metabolomics Background: Metabolomics experiments often comprise large numbers of biological samples resulting in huge amounts of data. This data needs to be inspected for plausibility before data evaluation to detect putative sources of error e.g. retention time or mass accuracy shifts. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, proper quality control checks (e.g. for precision, signal drifts or offsets) are crucial prerequisites to achieve reliable and comparable results within and across experimental measurement sequences. Software tools can support this process. Results: The software tool QCScreen was developed to offer a quick and easy data quality check of LC-HRMS derived data. It allows a flexible investigation and comparison of basic quality-related parameters within user-defined target features and the possibility to automatically evaluate multiple sample types within or across different measurement sequences in a short time. It offers a user-friendly interface that allows an easy selection of processing steps and parameter settings. The generated results include a coloured overview plot of data quality across all analysed samples and targets and, in addition, detailed illustrations of the stability and precision of the chromatographic separation, the mass accuracy and the detector sensitivity. The use of QCScreen is demonstrated with experimental data from metabolomics experiments using selected standard compounds in pure solvent. The application of the software identified problematic features, samples and analytical parameters and suggested which data files or compounds required closer manual inspection. In state-of-the-art metabolomics studies, the development and application of appropriate quality control measures such as QC data processing algorithms are more and more recognized to be crucial prerequisites for obtaining comparable results over longer time periods, across different sample type categories and biological experiments and also between different laboratories [20] . In this respect the presented QCScreen software was developed to process LC-HRMS data sets originating from metabolomics studies and aims to offer a tool for quick visual, easy-to-interpret and descriptive inspection of basic data quality-related parameters such as the stability of chromatographic peak shape, retention time, MS detector sensitivity and mass accuracy. Based on a predefined list of target features, the program can be used to process both multiple QC sample types as well as other experimental samples. Thus, QCScreen provides a good basis for assessing data suitability or to identify the need for developing and applying corrective functions prior to time consuming, detailed data processing and subsequent statistical analysis. Under ideal conditions such as thoroughly selected parameters for sample preparation and LC-HRMS measurements as well as highly stable instrument performance, only minor random fluctuations can be expected to contribute to the overall data variability observed in a metabolomics study. In practice however, the aim to capture as many metabolites as possible in a single measurement is reflected by the use of generic, less accurate technical workflows, which can finally result in significant contributions to both the overall variability as well as bias of the LC-HRMS data. This can also be exemplified with the outcome of the above illustrated study. It is obvious that a combined data evaluation of the biological experiment across the inspected measurement sequences is not straight forward and requires additional processing of data such as normalization of every measurement sequence e.g. by the use of pooled matrix QC samples. For biological interpretation it is important to consider raw data of the complete biological Fig. 3 Graphical illustration of feature area, retention time and mass accuracy for jasmonic acid. The illustration of the monitored parameter values of feature area indicates a separation (offset) of the two measurement sequences due to an alteration of instrument sensitivity, a. b shows a stable chromatographic process and c clearly illustrates a stable m/z offset in S1 and an m/z drift in S2, which can also be seen in the other compounds of the tested standard QC sample (Additional file 1) experiment. Otherwise, compounds could possibly be missed, be wrongly annotated or lead to falsely classified as being significantly differing between experimental conditions.. Compared to existing QC evaluation methods embedded in workflows or comprehensive software tools that perform complex algorithms e.g. to reduce analytical variation by automated QC batch correction, QCScreen was developed based on conventional QC charts and enables QC checks without complex statistics. The software tool computes basic performance criteria calculated from the parameter values retention time, feature area and m/z value and illustrates the data compared to predefined constraints. For every parameter, the user is able to adjust tolerance limits to define how much the measured values are allowed to deviate from the given values to be still considered acceptable.. As exemplified above, QCScreen allowed an immediate identification of problematic features, parameters and samples. A closer look at the results generated by QCScreen emphasized outliers and offsets and revealed a change of instrument sensitivity as well as mass accuracy between the investigated measurement sequences.. Other comprehensive software (e.g. [13] ) also offer a wide range of analysis tools including QC and multivariate analysis and takes a pre-processed quantitation data matrix containing metabolites and samples as input. Pre-processed data matrices are also required by [14] , which allows inspecting data for inconsistency or drifts. In contrast, QCScreen does not require prior feature extraction and peak area integration, but directly starts from the LC-HRMS raw data. QCScreen is flexible and allows for comparison of any number of features within one or multiple sequences and multiple sample types within these sequences. The presented software tool is suited to process centroid LC-HRMS full scan data in the mzXML format [16] and automatically extracts and integrates target features. Thus, QCScreen can be used with many different LC-HRMS platforms and aims to assess quality-related parameters of any of this kind of data. For future applications it would be desirable to extend QCScreen by implementing an option for automated correction functions of experimental shifts of retention time and mass accuracy. Biological experiments often include measurements over more than one measurement sequence. For time series experiments involving several biological replicates per time point, organism and condition, measurements can extend over several days or even weeks, as was the case for the presented biological experiment, which also served as basis for the development of QCScreen. In such cases, parameters like retention time, mass accuracy and feature area may drift or shift due to changes in MS instrument sensitivity or chromatographic separation. QCScreen has been developed to monitor stability and to promote a comparative analysis of all results over multiple measurement sequences. Many metabolomics studies involve the measurement of large numbers of experimental samples, which makes it necessary to combine analytical data originating from different measurements, several sequences or sometimes even from different experiments.. The comprehensive evaluation of the generated measurement data is a laborious and time-consuming task. In case of liquid chromatography-high resolution mass spectrometry (LC-HRMS) detector sensitivity drifts, retention time or mass accuracy shifts within or across different measurement sequences do frequently occur in practice and can further complicate data evaluation. Software tools which enable a rapid check of data quality and consistency can greatly support the decision if a particular data set is suitable for further data processing and subsequent statistical analysis. Such tools are therefore of general interest in metabolomics. Stable isotopically labelled analytes [1] [2] [3] or labelled biological samples [4, 5] and various types of control samples are widely used for quality control (QC) [6, 7] . Internal standardisation can be regarded the gold standard for QC purposes as this technique enables "within" sample compensation of ion suppression and technical variability as for example reviewed in [8] . In contrast to that, QC samples such as solvent blanks, selected standard mixtures and representative pooled matrix samples can be used for "between sample" standardisation. Typically, QC samples are analysed periodically within and across measurement sequences to monitor analytical sensitivity, precision, stability or sample carry-over of the analytical process [9] . Based on the different (QC) sample categories, both multivariate [10] [11] [12] [13] and univariate [9] methods have been described to evaluate and illustrate the analytical performance of individual samples and analytical features in the LC-HRMS data set(s) under investigation. Obviously, visual inspection of short-and long-term stability of critical parameters such as the MS signal abundance, retention time and mass accuracy constitutes the first and most straightforward step in quickly assessing data integrity. While some of the available comprehensive software programs for the processing of metabolomics data support different QC checks [7, [13] [14] [15] , to the best of the authors knowledge no free, standalone software tool is available that provides a quick and intuitive assessment of quality in LC-HRMS raw data from several measurement sequences based on the inspection of basic quality-related parameters.. The presented software QCScreen is a novel, freely available software tool that allows the flexible investigation of selected feature parameters to assess data quality within and across LC-HRMS measurement sequences. QCScreen inspects LC-HRMS data for the presence and analytical behaviour of user -definable target features. Both graphical and tabular outputs of the different, evaluated quality parameters, which allow for a quick check, are automatically generated in support of both descriptive as well as prescriptive feature-based analytical quality assessment. QCScreen can be used to detect and examine predefined analytical features from full scan LC-HRMS data with the aim to provide a quick and easy-interpretable graphical overview of data quality.. Data files referring to specified sample categories (e.g. blanks, QC samples, QC standards, matrix QCs or experimental samples) originating from one or multiple measurement sequences can be selected and inspected using predefined features. For the features and samples under investigation, QCScreen visualizes the stability and precision of the chromatographic separation step, MS sensitivity, m/z value and mass accuracy (±ppm) over time. The generated graphical illustrations and a coloured quality overview table enable quickly spotting putatively problematic data, i.e. features, samples or measurement sequences. QCScreen was implemented in the Python programming language (v. 2.6) and uses the Qt framework for the graphical user interface (GUI), R Version 2.12 and SQlite for data management.. QCScreen loads the centroided raw data in the mzXML format [16] . The GUI of the software tool contains a main area for selection of measurement sequences, sample type categories and features to be investigated and provides the option to define parameters, display settings as well as tolerance limits for the parameters. The then observed feature characteristics are evaluated against predefined or data-based performance criteria. For each feature, QCScreen creates an illustration of extracted ion chromatogram (EIC), retention time (tR), mass-to-charge ratio (m/z), mass accuracy and feature area across the provided data files. Finally, evaluation results of all tested features and parameters are saved in tabular form.. In the following the implemented sections are described.

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