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A new tool for the solution of electromigrative separations in paper-based microfluidics devices is presented. The implementation is based on a recently published complete mathematical model for describing these types of separations, and was developed on top of the open-source toolbox electroMicroTransport, based on OpenFOAM® , inheriting all its features as native 3D problem handling, support for parallel computation, and a GNU GPL license . The presented tool includes full support for paper-based electromigrative separations (including EOF and the novel mechanical and electrical dispersion effects), compatibility with a well-recognized electrolyte database, and a novel algorithm for computing and controlling the electric current in arbitrary geometries. Additionally, the installation on any operating system is available due to its novel installation option in the form of a Docker image. A validation example with data from literature is included, and two extra application examples are provided, including a 2D free-flow IEF problem, which demonstrates the capabilities of the toolbox for dealing with computational and physicochemical modeling challenges simultaneously. This tool will enable efficient and reliable numerical prototypes of paper-based electrophoretic devices to accompany the contemporary fast growth in paper-based microfluidics.

Aiming at the problem of easy tampering and difficult integrity authentication of paper text documents, this paper proposes a robust content authentication method for printed documents based on text watermarking scheme resisting print-and-scan attack. Firstly, an authentication watermark signal sequence related to content of text document is generated based on the Logistic chaotic map model; then, the authentication watermark signal sequence is embedded into printed paper document by using a robust text watermarking scheme; finally, the watermark information is extracted from scanned image of paper document, and compared with the authentication watermark information calculated in real time by the text document content obtained by OCR technology, thereby performing content integrity authentication of the paper text documents. Experimental results show that our method can achieve the robust content integrity authentication of paper text documents, and can also accurately locate the tampering position. In addition, the document after embedding the watermark information has a good visual effect, and the text watermarking scheme has a large information capacity.

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application. Methods and procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients’ demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared. Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86–0.99 and 0.79–0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively. Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

Highlights • In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from the clinical needs to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease on the national level. We focus on three specific use-cases for which AI systems can be built: from the early disease detection, the management of the disease in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical features. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead., In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead.

In this paper, we propose an end-to-end approach to addressing QRS complex detection and measurement of Electrocardiograph (ECG) paper using convolutional neural networks (CNNs). Unlike conventional detection solutions that convert images to digital data, our method can directly detect QRS complex in images using Faster-RCNN, then the R-peak can be located and measured through a CNN. Validated by clinical ECG data in the St.-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database and real ECG paper from Peking University People’s Hospital, the proposed method can achieve the recall of 98.32%, the precision of 99.01% in detecting and 0.012 mv of mean absolute error in measuring. Experimental results demonstrate the superior performance of our method over conventional solutions, which would pave the way to detect and measure ECG paper using CNNs.

An accurate diagnosis of clinically distinct subgroups of aggressive mature B cell lymphomas is crucial for the choice of proper treatment. Presently, precise recognition of these disorders relies on the combination of morphological, immunophenotypical, and cytogenetic/molecular features. The diagnostic workup in such situations implies the application of costly and time-consuming analyses, which are not always required, since an intensified treatment option is reasonably reserved to fit patients. The Italian Group of Haematopathology proposes herein a practical algorithm for the diagnosis of aggressive mature B cell lymphomas based on a stepwise approach, aimed to select cases deserving molecular analysis, in order to optimize time and resources still assuring the optimal management for any patient.

Background Electrocardiograms (ECGs) are routinely recorded and stored in a variety of paper or scanned image format. Current ECG recording machines record ECG on graph paper, also it provides digitized ECG signal along with automated cardiovascular diagnosis (CVD). However, such recording machines cannot analyse preserved paper ECG records as it requires input in terms of digitized signal. Therefore, it is important to extract ECG signal from these preserved paper ECG records using digitization method. There are different paper degradations that adversely affect digitization process. The purpose of this work is to perform an image enhancement and digitization of the degraded ECG images to extract continuous ECG signal. Methods In this paper, we propose entropy-based bit plane slicing (EBPS) algorithm in which pre-processing is done using dominant color detection and local bit plane slicing. Maximum entropy based adaptive bit plane selection is applied to the pre-processed image. Discontinuous ECG correction (DECGC) is then done to produce continuous ECG signal. Results The algorithm is tested on 836 different degraded paper ECG records obtained from various diagnostic centers. After analysis with 101 known ground truth ECG signals the accuracy, sensitivity, specificity and overall F-measure of ECG is 99.42%, 99.69%, 99.81% and 99.26% respectively. The RMS error and correlation between the extracted digitized signal and ground truth for 101 cases is 0.040 and 99.89% respectively. Conclusions The EBPS method is able to remove all types of degradation in paper ECG records to generate a uniform digitized signal. Instead of manual measurement and prediction from archived paper ECG records, automated prediction (using already existing cardiovascular diagnosis software) is possible with the help of extracted digitized signal obtained using proposed digitization method, which will also help retrospective cardiovascular analysis.

In the last 15 years, the segmentation of vessels in retinal images has become an intensively researched problem in medical imaging, with hundreds of algorithms published. One of the de facto benchmarking data sets of vessel segmentation techniques is the DRIVE data set. Since DRIVE contains a predefined split of training and test images, the published performance results of the various segmentation techniques should provide a reliable ranking of the algorithms. Including more than 100 papers in the study, we performed a detailed numerical analysis of the coherence of the published performance scores. We found inconsistencies in the reported scores related to the use of the field of view (FoV), which has a significant impact on the performance scores. We attempted to eliminate the biases using numerical techniques to provide a more realistic picture of the state of the art. Based on the results, we have formulated several findings, most notably: despite the well-defined test set of DRIVE, most rankings in published papers are based on non-comparable figures; in contrast to the near-perfect accuracy scores reported in the literature, the highest accuracy score achieved to date is 0.9582 in the FoV region, which is 1% higher than that of human annotators. The methods we have developed for identifying and eliminating the evaluation biases can be easily applied to other domains where similar problems may arise.

Efficient and safe detection of Bacillus anthracis spores (BAS) is a challenging task especially in bio-terror scenarios where the agent is concealed. We provide a proof-of-concept for the identification of concealed BAS inside mail envelopes using short-wave infrared hyperspectral imaging (SWIR-HSI). The spores and two other benign materials are identified according to their typical absorption spectrum. The identification process is based on the removal of the envelope signal using a new automatic new algorithm. This method may serve as a fast screening tool prior to using classical bioanalytical techniques.

Electrocardiography (ECG) is essential in many heart diseases. However, some ECGs are recorded by paper, which can be highly noisy. Digitizing the paper-based ECG records into a high-quality signal is critical for further analysis. We formulated the digitization problem as a segmentation problem and proposed a deep learning method to digitize highly noisy ECG scans. Our method extracts the ECG signal in an end-to-end manner and can handle different paper record layouts. In the experiment, our model clearly extracted the ECG waveform with a Dice coefficient of 0.85 and accurately measured the common ECG parameters with more than 0.90 Pearson's correlation. We showed that the end-to-end approach with deep learning can be powerful in ECG digitization. To the best of our knowledge, we provide the first approach to digitize the least informative noisy binary ECG scans and potentially be generalized to digitize various ECG records.

Author summary Conferences are great venues for disseminating algorithmic bioinformatics results, but they unfortunately do not offer an opportunity to make major revisions in the way that journals do. As a result, it is not possible for authors to fix mistakes that might be easily correctable but nevertheless can cause the paper to be rejected. As a reviewer, I wish that I had the opportunity to tell the authors, “Hey, you forgot to do this really important thing, without which it is hard to accept the paper, but if you could go back and fix it, you might have a great paper for the conference.” This lack of a back and forth can be especially problematic for first-time submitters or those from outside the field, e.g., biologists. In this article, I outline Ten Simple Rules to follow when writing an algorithmic bioinformatics conference paper to avoid having it rejected.

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments., Comment: Article source data is available here: https://github.com/scipy/scipy-articles

Background and Objective Cardiotocography (CTG) is the most popular prenatal diagnostic examination, which includes continuous monitoring of foetal heart rate (FHR, bpm) and uterine contraction (UC, mmHg) signals. Compared with CTG paper reports, digitized reports have better storage, transmission and retrieval capabilities, in addition to being able to assess foetal health. However, most of the existing digitization methods extract signals from paper reports with colour background grids, and they cannot extract signals completely from paper reports with binary background grids, which are widely used in clinical CTG monitoring. Moreover, the existing digitization algorithms often neglect the image distortion caused by the imaging equipment. Methods To overcome the above drawbacks, a digitization method for CTG paper reports with binary background grids taken by smartphones is proposed in this paper. In the stage of removing the grid background, a region merger based on super-pixels and an improved binary line mask removal are designed. Then, signal extraction is performed separately according to the different states of the image column. Through a projection map used to synchronize the signal, the distortion effect of the mobile phone is removed. Results The experimental results show that the average correlation coefficient (ρ) between the recovery signal obtained by the proposed method and the reference signal is 0.9855±0.0108 for FHR and 0.9866 ± 0.1020 for UC, and the root mean square errors (RMSE) of FHR and UC processed by the proposed method are 1.0366 ± 0.4953 and 2.0355 ± 1.0246, and the mean absolute errors (MAE) of FHR and UC processed by the proposed method are 0.8735 ± 0.0684 and 1.4991 ± 0.2837, which are higher than those of the existing digitization methods. Compared with clinical signals, no significant difference is found in the feature of digitization CTG. Conclusion The proposed digitization method is a promising useful tool to realize the electronization of CTG signal.

In this article we show the development process to create a programming course for the first-year students. These young people are very prompt to be influenced by technology, they are constantly immersed in smartphones, connected 24/7. This raises the question; how can we interest a well-connected student to learn programming? Some years ago, we used to have paper and pencil to teach ”algorithms” while trying to bring a quick start to programming activities on basic science and engineering. Nowadays this approach is not viable, we need to use the same tools and platforms used by the students to access social media and the Internet in general, we need to use the computer directly to show them a programming language and simple tools for development. This paper describes the main goal of this course, bring the tools a student needs from simple to complex stage, based in case study methodology. In the first sessions, students are being monitored and guided by the teacher at all times. Later on, and progressively reaching the end of the course, the students will have less time with the teachers and more independence in order to develop self-learning skills.

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing.

The plotting of data into graphs should be a mandatory step in all data analysis as part of a descriptive statistics exercise, since it gives the researcher an overview of the shape and nature of the data. Moreover, outlier values may be identified, which may be incorrect data, or true outliers, from which important findings (and publications) may arise. This exercise should always precede inferential statistics, when possible, and this paper in the Early Human Development WASP series provides some pointers for doing so in Microsoft Excel™.

Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of a scholarly paper. A higher-order weighted quantum PageRank algorithm is also developed to address the behavior of multiple step citation flow. Capturing the citation dynamics with higher-order dependencies reveals the actual impact of papers, including necessary self-citations that are sometimes excluded in prior studies. Quantum PageRank is utilized in this paper to help differentiating nodes whose PageRank values are identical. Refereed/Peer-reviewed

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs.

Plotting data into graphs is a crucial step in data analysis as part of an initial descriptive statistics exercise since it gives the researcher an overview of the shape and nature of the data. Outlier values may also be identified, and these may be incorrect data, or true and important outliers. This paper explains how to access Microsoft Excel's Analysis Toolpak and provides some pointers for the utilisation of the histogram tool within the Toolpak.

The aim of this study was to compare the depiction ability of small grayscale contrasts in ink-jet printouts of digital radiographs on different print media with CRT monitor. A CCD-based digital cephalometric image of a stepless aluminum wedge containing 50 bur holes of different depth was cut into 100 isometric images. Each image was printed on glossy paper and on transparent film by means of a high-resolution desktop inkjet printer at specific settings. The printed images were viewed under standardized conditions, and the perceptibility of the bur holes was evaluated and compared to the perceptibility on a 17-in CRT monitor. Thirty observers stated their blinded decision on a five-point confidence scale. Areas (Az) under receiver operating characteristics curves were calculated and compared using the pair wise sign tests. Overall agreement was estimated using Cohen’s kappa device and observer bias using McNemar’s test. Glossy paper prints and monitor display revealed significantly higher (P

Good scientific writing is essential to career development and to the progress of science. A well-structured manuscript allows readers and reviewers to get excited about the subject matter, to understand and verify the paper’s contributions, and to integrate these contributions into a broader context. However, many scientists struggle with producing high-quality manuscripts and typically get little training in paper writing. Focusing on how readers consume information, we present a set of 10 simple rules to help you get across the main idea of your paper. These rules are designed to make your paper more influential and the process of writing more efficient and pleasurable.

Some of the major results of future goals of an automatic program synthesis project are described in the two papers that comprise this document. The first paper gives a detailed algorithm for synthesizing a computer program from a trace of its behavior. Since the algorithm involves a search, the length of time required to do the synthesis of nontrivial programs can be quite large. Techniques are given for preprocessing the trace information to reduce enumeration, for pruning the search using a failure memory technique, and for utilizing multiple traces to the best advantage. The results of numerous tests are given to demonstrate the value of the techniques. The other paper gives a brief overview of the automatic programming system within which the above algorithm is being used. This system is still under development. (Author/CH)

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user's expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.

To the Editor We applaud the work by Katayev and colleagues1 reflected in their article on data mining for reference intervals. The development of robust automated tools to perform data mining in a standardized manner represents a significant step forward in the field. We would like to point out that the authors’ application of the Hoffmann2 method seems to deviate from the original in one important aspect. The original Hoffmann method involved manually plotting data on normal probability paper. When the cumulative frequency distribution of a Gaussian data set is plotted on this type of paper, a straight line is obtained. As Hoffmann pointed out, even when a data set includes a significant minority of data from a …

Motivation: Converting the vast quantity of free-format text found in journals into a concise, structured format makes the researcher's quest for information easier. Recently, several information extraction systems have been developed that attempt to simplify the retrieval and analysis of biological and medical data. Most of this work has used the abstract alone, owing to the convenience of access and the quality of data. Abstracts are generally available through central collections with easy direct access (e.g. PubMed). The full-text papers contain more information, but are distributed across many locations (e.g. publishers' web sites, journal web sites and local repositories), making access more difficult. In this paper, we present BioRAT, a new information extraction (IE) tool, specifically designed to perform biomedical IE, and which is able to locate and analyse both abstracts and full-length papers. BioRAT is a Biological Research Assistant for Text mining, and incorporates a document search ability with domain-specific IE. Results: We show first, that BioRAT performs as well as existing systems, when applied to abstracts; and second, that significantly more information is available to BioRAT through the full-length papers than via the abstracts alone. Typically, less than half of the available information is extracted from the abstract, with the majority coming from the body of each paper. Overall, BioRAT recalled 20.31% of the target facts from the abstracts with 55.07% precision, and achieved 43.6% recall with 51.25% precision on full-length papers. Availability: The software and documentation can be found at http://bioinf.cs.ucl.ac.uk/biorat

Background and AimsThe critical appraisal of the evidence presented in research papers is an early step in the process of evidence-based design (EBD), which aims at transferring research findings into design practice (Stichler, 2010a). However, because architects and designers are often not trained in research methods, they may feel somewhat overwhelmed by the terms used in the description of the methodology of scientific papers. Therefore, when designing healthcare facilities, they may not feel confident discussing the evidence available from research studies with members of the medical professions, who often display a profound knowledge of research methods. Also, architects and designers may encounter difficulties in evaluating the quality of the evidence resulting from numerous research papers, which sometimes arrive at conflicting results.The purpose of this article is to develop a simple method to appraise the quality of research papers suitable for EBD. The most common study designs will be explained, and an algorithm, which could be helpful for guiding architects and designers when assessing the quality of evidence, will be introduced.Appraising the Quality of Evidence in the Process of EBDIn building design, there is a broad spectrum of evidence sources, such as experts' opinions, guidelines, observational studies, and systematic reviews. In the design process, it is necessary to determine which sources are scientifically valid and how the resulting findings are to be incorporated best into design decisions. The methodology of EBD offers a way of acquiring, appraising, weighting, and transferring the existing evidence into design practice (Rosswurm & Larrabee, 1999). This circular process is shown in Figure 1.The circle of the evidence-based design process starts with the definition of the research question and the development of a research strategy in step one. In the second step, relevant papers are acquired and selected. In the third step, which comprises the critical appraisal of the research studies' quality, this article comes into use by providing a simple algorithm for architects, designers, and other practitioners. It should be noted that this appraisal only rates the methodological quality of a given study. When "weighing the evidence" in step four, the results of several studies on the chosen research question are combined. Additionally, external factors, such as an estimation of the evidence's consistency, relevance, and external validity, are taken into account (SIGN 50, 2008). In step five, design recommendations, which are often scaled between weak and strong (Guyatt et al., 2008a), are developed. This judgment is made on the basis of the quality of the evidence (see step three) and its assigned weight (see step four). In the sixth and last step, the design decisions made on the basis of the design recommendations are implemented and evaluated.As it has been outlined, the critical appraisal of the quality of scientific evidence is a crucial step in EBD, answering the question to what extent the results of a research study are valid. However, what determines the "quality of evidence"? In general, it is defined as "the extent to which a study's design, conduct, and analysis has minimized selection, measurement, and confounding bias" (West et al., 2002). The better a study manages to protect against bias and error, the higher the quality of the results produced. Methods to protect against bias are, among others, group allocation, randomization, and blinding. Group allocation means dividing the participants into at least two groups: a treatment group and a control group. Randomization is the masked, random allocation of participants to the treatment or the control group. Blinding also refers to the group allocation: in the best case, during the intervention, neither the participant nor the researcher knows which group a participant belongs to (a doubleblind study). …

The similarity of documents in a large database of published Fractals articles was examined for redundancy. Three different text matching techniques were used on published Abstracts to identify redundancy candidates, and predictions were verified by reading full text versions of the redundancy candidate articles. A small fraction of the total articles in the database was judged to be redundant. This was viewed as a lower limit, because it excluded cases where the concepts remained the same, but the text was altered substantially. Far more pervasive than redundant publications were publications that did not violate the letter of redundancy but rather violated the spirit of redundancy. There appeared to be widespread publication maximization strategies. Studies that resulted in one comprehensive paper decades ago now result in multiple papers that focus on one major problem, but are differentiated by parameter ranges, or other stratifying variables. This 'paper inflation' is due in large part to the increasing use of metrics (publications, patents, citations, etc) to evaluate research performance, and the researchers' motivation to maximize the metrics.

A new system by which paper-recorded EEG can be converted to computer treatable digital data was developed. It consists simply of an image scanner and a microcomputer. The performance of the system was evaluated and found to perform well in data reconstruction. The system makes it possible to apply various computer analyses to EEGs recorded on paper.

The importance of writing well can never be overstated for a successful professional career, and the ability to write solid papers is an essential trait of a productive researcher. Writing and publishing a paper has its own life cycle; properly following a course of action and avoiding missteps can be vital to the overall success not only of a paper but of the underlying research as well. Here, we offer ten simple rules for writing and publishing research papers. As a caveat, this essay is not about the mechanics of composing a paper, much of which has been covered elsewhere, e.g., [1], [2]. Rather, it is about the principles and attitude that can help guide the process of writing in particular and research in general. In this regard, some of the discussion will complement, extend, and refine some advice given in early articles of this Ten Simple Rules series of PLOS Computational Biology [3]–[8].

This paper promotes the concept of active models in image-guided interventions. We outline the limitations of the rigid body assumption in image-guided interventions and describe how intraoperative imaging provides a rich source of information on spatial location of anatomical structures and therapy devices, allowing a preoperative plan to be updated during an intervention. Soft tissue deformation and variation from an atlas to a particular individual can both be determined using non-rigid registration. Established methods using free-form deformations have a very large number of degrees of freedom. Three examples of deformable models--motion models, biomechanical models and statistical shape models--are used to illustrate how prior information can be used to restrict the number of degrees of freedom of the registration algorithm and thus provide active models for image-guided interventions. We provide preliminary results from applications for each type of model.

We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three pre-defined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields., Comment: 18 pages, 9 figures