Mobilizing health equity through Computable Biomedical Knowledge (CBK): a call to action to the library, information sciences, and health informatics communities.

The twin pandemics of COVID-19 and structural racism brought into focus health disparities and disproportionate impacts of disease on communities of color. Health equity has subsequently emerged as a priority. Recognizing that the future of health care will be informed by advanced information technologies including artificial intelligence (AI), machine learning, and algorithmic applications, the authors argue that to advance towards states of improved health equity, health information professionals need to engage in and encourage the conduct of research at the intersections of health equity, health disparities, and computational biomedical knowledge (CBK) applications. Recommendations are provided with a means to engage in this mobilization effort.

Clinician and computer: a study on doctors' perceptions of artificial intelligence in skeletal radiography.

BACKGROUND: Traumatic musculoskeletal injuries are a common presentation to emergency care, the first-line investigation often being plain radiography. The interpretation of this imaging frequently falls to less experienced clinicians despite well-established challenges in reporting. This study presents novel data of clinicians' confidence in interpreting trauma radiographs, their perception of AI in healthcare, and their support for the development of systems applied to skeletal radiography. METHODS: A novel questionnaire was distributed through a network of collaborators to clinicians across the Southeast of England. Over a three-month period, responses were compiled into a database before undergoing statistical review. RESULTS: The responses of 297 participants were included. The mean self-assessed knowledge of AI in healthcare was 3.68 out of ten, with significantly higher knowledge reported by the most senior doctors (Specialty Trainee/Specialty Registrar or above = 4.88). 13.8% of participants reported an awareness of AI in their clinical practice. Overall, participants indicated substantial favourability towards AI in healthcare (7.87) and in AI applied to skeletal radiography (7.75). There was a preference for a hypothetical system indicating positive findings rather than ruling as negative (7.26 vs 6.20). CONCLUSIONS: This study identifies clear support, amongst a cross section of student and qualified doctors, for both the general use of AI technology in healthcare and in its application to skeletal radiography for trauma. The development of systems to address this demand appear well founded and popular. The engagement of a small but reticent minority should be sought, along with improving the wider education of doctors on AI.

Polypharmacy and Drug Interactions in the COVID-19 Pandemic.

The COVID-19 pandemic generated a great impact on health systems. We compared evolution, polypharmacy, and potential drug-drug interactions (P-DDIs) in COVID-19 and non-COVID-19 hospitalizations during first wave of pandemic. Prescriptions for hospitalized patients ≥ 18 years (COVID-19 and non-COVID-19 rooms) between April and September 2020 were included. The computerized medical decision support system SIMDA and the physician order entry system Hdc.DrApp.la were used. Patients in COVID-19 rooms were divided into detectable and non-detectable, according to real-time reverse transcription polymerase chain reaction (RT-PCR). Number of drugs, prescribed on day 1, after day 1, and total; polypharmacy, excessive polypharmacy, and P-DDIs were compared. 1,623 admissions were evaluated: 881 COVID-19, 538 detectable and 343 non-detectable, and 742 non-COVID-19. Mortality was 15% in COVID-19 and 13% in non-COVID-19 (RR [non-COVID-19 vs. COVID-19]: 0.84 [95% CI] [0.66-1.07]). In COVID-19, mortality was 19% in detectable and 9% in non-detectable (RR: 2.07 [1.42-3.00]). Average number of drugs was 4.54/patient (SD ± 3.06) in COVID-19 and 5.92/patient (±3.24) in non-COVID-19 (p<0.001) on day 1 and 5.57/patient (±3.93) in COVID-19 and 9.17/patient (±5.27) in non-COVID-19 (p<0.001) throughout the hospitalization. 45% received polypharmacy in COVID-19 and 62% in non-COVID-19 (RR: 1.38 [1.25-1.51]) and excessive polypharmacy 7% in COVID-19 and 14% in non-COVID-19 (RR: 2.09 [1.54-2.83]). The frequency of total P-DDIs was 0.31/patient (±0.67) in COVID-19 and 0.40/patient (±0.94) in non-COVID-19 (p=0.022). Hospitalizations in the COVID-19 setting are associated with less use of drugs, less polypharmacy and less P-DDIs. Detectable patients had higher mortality.

Predicting factors of clinically significant urological anomalies after initial urinary tract infection among 2- to 24-month-old children.

AIM: To find more effective criteria to identify clinically significant urological anomalies after initial urinary tract infection among children. METHODS: Children aged 2-24 months with an initial urinary tract infection were consecutively recruited in a Japanese hospital from 2013 to 2019. Voiding cystourethrography, 99mTc dimercaptosuccinic acid scan and ultrasound were intended to perform for all cases. Clinically significant urological anomalies were defined as high-grade vesicoureteral reflux, obstructive and abnormal urinary tract lesions, need for surgical intervention, renal hypoplasia and scarring. Using classification and regression tree analysis, we sought the associated factors. We developed new criteria with these factors, retrospectively applied them to the original data, and calculated the sensitivity and specificity. RESULTS: One hundred sixty-seven patients were eligible, and 39 had clinically significant urological anomalies. Classification and regression tree analysis showed that the associated factors were non-E. coli infections, serum creatinine levels and ultrasound abnormalities. When the gold standards were performed on children with non-E. coli infections or serum creatinine levels ≥0.21 mg/dl, sensitivity and specificity were 0.82 and 0.68, respectively. CONCLUSION: The criteria including non-E. coli infections and high-normal or higher serum creatinine levels may efficiently predict clinically significant urological anomalies after initial urinary tract infections.

Abnormal cortical regions and subsystems in whole brain functional connectivity of mild cognitive impairment and Alzheimer's disease: a preliminary study.

The disease roots of Alzheimer's disease (AD) are unknown. Functional connection (FC) methodology based on functional MRI data is an effective lever to investigate macroscopic neural activity patterns. However, regional properties of brain architecture have been less investigated by special markers of graph indexes in general mental disorders. In terms of the set of the abnormal edges in the FCs matrix, this paper introduces the strength index (S-scores) of region centrality on the principle of holism. Then, the important process is to investigate the S-scores of regions and subsystems in 36 healthy controls, 38 mild cognitive impairment (MCI) patients and 34 AD patients. At the edge level, abnormal FCs is numerically increasing progressively from MCI to AD brains. At the region level, the CUN.L, PAL.R, THA.L, and TPOsup.R regions are highlighted with abnormal S-scores in MCI patients. By comparison, more regions are abnormal in AD patients, which are PreCG.L, INS.R, DCG.L, AMYG.R, IOG.R, FFG.L, PoCG.L, PCUN.R, TPOsup.L, MTG.L, and TPOmid.L. Importantly, the regions in DMN have abnormal S-scores in AD groups. At the module level, the S-scores of frontal, parietal, occipital lobe, and cerebellum are found in MCI and AD patients. Meanwhile, the abnormal lateralization is inferred because of the S-scores of left and top hemisphere in the AD group. Though this is strictly a contrastive study, the S-score may be a meaningful imaging marker for excavating AD psychopathology.

Development and Evaluation of a Method for Automated Detection of Spreading Depolarizations in the Injured Human Brain.

BACKGROUND: Spreading depolarizations (SDs) occur in some 60% of patients receiving intensive care following severe traumatic brain injury and often occur at a higher incidence following serious subarachnoid hemorrhage and malignant hemisphere stroke (MHS); they are independently associated with worse clinical outcome. Detection of SDs to guide clinical management, as is now being advocated, currently requires continuous and skilled monitoring of the electrocorticogram (ECoG), frequently extending over many days. METHODS: We developed and evaluated in two clinical intensive care units (ICU) a software routine capable of detecting SDs both in real time at the bedside and retrospectively and also capable of displaying patterns of their occurrence with time. We tested this prototype software in 91 data files, each of approximately 24 h, from 18 patients, and the results were compared with those of manual assessment ("ground truth") by an experienced assessor blind to the software outputs. RESULTS: The software successfully detected SDs in real time at the bedside, including in patients with clusters of SDs. Counts of SDs by software (dependent variable) were compared with ground truth by the investigator (independent) using linear regression. The slope of the regression was 0.7855 (95% confidence interval 0.7149-0.8561); a slope value of 1.0 lies outside the 95% confidence interval of the slope, representing significant undersensitivity of 79%. R2 was 0.8415. CONCLUSIONS: Despite significant undersensitivity, there was no additional loss of sensitivity at high SD counts, thus ensuring that dense clusters of depolarizations of particular pathogenic potential can be detected by software and depicted to clinicians in real time and also be archived.

Wearable Inertial Gait Algorithms: Impact of Wear Location and Environment in Healthy and Parkinson's Populations.

Wearable inertial measurement units (IMUs) are used in gait analysis due to their discrete wearable attachment and long data recording possibilities within indoor and outdoor environments. Previously, lower back and shin/shank-based IMU algorithms detecting initial and final contact events (ICs-FCs) were developed and validated on a limited number of healthy young adults (YA), reporting that both IMU wear locations are suitable to use during indoor and outdoor gait analysis. However, the impact of age (e.g., older adults, OA), pathology (e.g., Parkinson's Disease, PD) and/or environment (e.g., indoor vs. outdoor) on algorithm accuracy have not been fully investigated. Here, we examined IMU gait data from 128 participants (72-YA, 20-OA, and 36-PD) to thoroughly investigate the suitability of ICs-FCs detection algorithms (1 × lower back and 1 × shin/shank-based) for quantifying temporal gait characteristics depending on IMU wear location and walking environment. The level of agreement between algorithms was investigated for different cohorts and walking environments. Although mean temporal characteristics from both algorithms were significantly correlated for all groups and environments, subtle but characteristically nuanced differences were observed between cohorts and environments. The lowest absolute agreement level was observed in PD (ICC2,1 = 0.979, 0.806, 0.730, 0.980) whereas highest in YA (ICC2,1 = 0.987, 0.936, 0.909, 0.989) for mean stride, stance, swing, and step times, respectively. Absolute agreement during treadmill walking (ICC2,1 = 0.975, 0.914, 0.684, 0.945), indoor walking (ICC2,1 = 0.987, 0.936, 0.909, 0.989) and outdoor walking (ICC2,1 = 0.998, 0.940, 0.856, 0.998) was found for mean stride, stance, swing, and step times, respectively. Findings of this study suggest that agreements between algorithms are sensitive to the target cohort and environment. Therefore, researchers/clinicians should be cautious while interpreting temporal parameters that are extracted from inertial sensors-based algorithms especially for those with a neurological condition.

Transparency as design publicity: explaining and justifying inscrutable algorithms.

In this paper we argue that transparency of machine learning algorithms, just as explanation, can be defined at different levels of abstraction. We criticize recent attempts to identify the explanation of black box algorithms with making their decisions (post-hoc) interpretable, focusing our discussion on counterfactual explanations. These approaches to explanation simplify the real nature of the black boxes and risk misleading the public about the normative features of a model. We propose a new form of algorithmic transparency, that consists in explaining algorithms as an intentional product, that serves a particular goal, or multiple goals (Daniel Dennet's design stance) in a given domain of applicability, and that provides a measure of the extent to which such a goal is achieved, and evidence about the way that measure has been reached. We call such idea of algorithmic transparency "design publicity." We argue that design publicity can be more easily linked with the justification of the use and of the design of the algorithm, and of each individual decision following from it. In comparison to post-hoc explanations of individual algorithmic decisions, design publicity meets a different demand (the demand for impersonal justification) of the explainee. Finally, we argue that when models that pursue justifiable goals (which may include fairness as avoidance of bias towards specific groups) to a justifiable degree are used consistently, the resulting decisions are all justified even if some of them are (unavoidably) based on incorrect predictions. For this argument, we rely on John Rawls's idea of procedural justice applied to algorithms conceived as institutions.

Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study.

BACKGROUND: There is a need for shorter-length assessments that capture patient questionnaire data while attaining high data quality without an undue response burden on patients. Computerized adaptive testing (CAT) and classification and regression tree (CART) methods have the potential to meet these needs and can offer attractive options to shorten questionnaire lengths. OBJECTIVE: The objective of this study was to test whether CAT or CART was best suited to reduce the number of questionnaire items in multiple domains (eg, anxiety, depression, quality of life, and social support) used for a needs assessment procedure (NAP) within the field of cardiac rehabilitation (CR) without the loss of data quality. METHODS: NAP data of 2837 CR patients from a multicenter Cardiac Rehabilitation Decision Support System (CARDSS) Web-based program was used. Patients used a Web-based portal, MyCARDSS, to provide their data. CAT and CART were assessed based on their performances in shortening the NAP procedure and in terms of sensitivity and specificity. RESULTS: With CAT and CART, an overall reduction of 36% and 72% of NAP questionnaire length, respectively, was achieved, with a mean sensitivity and specificity of 0.765 and 0.817 for CAT, 0.777 and 0.877 for classification trees, and 0.743 and 0.40 for regression trees, respectively. CONCLUSIONS: Both CAT and CART can be used to shorten the questionnaires of the NAP used within the field of CR. CART, however, showed the best performance, with a twice as large overall decrease in the number of questionnaire items of the NAP compared to CAT and the highest sensitivity and specificity. To our knowledge, our study is the first to assess the differences in performance between CAT and CART for shortening questionnaire lengths. Future research should consider administering varied assessments of patients over time to monitor their progress in multiple domains. For CR professionals, CART integrated with MyCARDSS would provide a feedback loop that informs the rehabilitation progress of their patients by providing real-time patient measurements.

A Blockchain-Based Consent Platform for Active Assisted Living: Modeling Study and Conceptual Framework.

BACKGROUND: Recent advancements in active assisted living (AAL) technologies allow older adults to age well in place. However, sensing technologies increase the complexity of data collection points, making it difficult for users to consent to data collection. One possible solution for improving transparency in the consent management process is the use of blockchain, an immutable and timestamped ledger. OBJECTIVE: This study aims to provide a conceptual framework based on technology aimed at mitigating trust issues in the consent management process. METHODS: The consent management process was modeled using established methodologies to obtain a mapping of trust issues. This mapping was then used to develop a conceptual framework based on previous monitoring and surveillance architectures for connected devices. RESULTS: In this paper, we present a model that maps trust issues in the informed consent process; a conceptual framework capable of providing all the necessary underlining technologies, components, and functionalities required to develop applications capable of managing the process of informed consent for AAL, powered by blockchain technology to ensure transparency; and a diagram showing an instantiation of the framework with entities comprising the participants in the blockchain network, suggesting possible technologies that can be used. CONCLUSIONS: Our conceptual framework provides all the components and technologies that are required to enhance the informed consent process. Blockchain technology can help overcome several privacy challenges and mitigate trust issues that are currently present in the consent management process of data collection involving AAL technologies.

The use of bibliography enriched features for automatic citation screening.

CONTEXT: Citation screening (also called study selection) is a phase of systematic review process that has attracted a growing interest on the use of text mining (TM) methods to support it to reduce time and effort. Search results are usually imbalanced between the relevant and the irrelevant classes of returned citations. Class imbalance among other factors has been a persistent problem that impairs the performance of TM models, particularly in the context of automatic citation screening for systematic reviews. This has often caused the performance of classification models using the basic title and abstract data to ordinarily fall short of expectations. OBJECTIVE: In this study, we explore the effects of using full bibliography data in addition to title and abstract on text classification performance for automatic citation screening. METHODS: We experiment with binary and Word2vec feature representations and SVM models using 4 software engineering (SE) and 15 medical review datasets. We build and compare 3 types of models (binary-non-linear, Word2vec-linear and Word2vec-non-linear kernels) with each dataset using the two feature sets. RESULTS: The bibliography enriched data exhibited consistent improved performance in terms of recall, work saved over sampling (WSS) and Matthews correlation coefficient (MCC) in 3 of the 4 SE datasets that are fairly large in size. For the medical datasets, the results vary, however in the majority of cases the performance is the same or better. CONCLUSION: Inclusion of the bibliography data provides the potential of improving the performance of the models but to date results are inconclusive.

Counting Bites With Bits: Expert Workshop Addressing Calorie and Macronutrient Intake Monitoring.

BACKGROUND: Conventional diet assessment approaches such as the 24-hour self-reported recall are burdensome, suffer from recall bias, and are inaccurate in estimating energy intake. Wearable sensor technology, coupled with advanced algorithms, is increasingly showing promise in its ability to capture behaviors that provide useful information for estimating calorie and macronutrient intake. OBJECTIVE: This paper aimed to summarize current technological approaches to monitoring energy intake on the basis of expert opinion from a workshop panel and to make recommendations to advance technology and algorithms to improve estimation of energy expenditure. METHODS: A 1-day invitational workshop sponsored by the National Science Foundation was held at Northwestern University. A total of 30 participants, including population health researchers, engineers, and intervention developers, from 6 universities and the National Institutes of Health participated in a panel discussing the state of evidence with regard to monitoring calorie intake and eating behaviors. RESULTS: Calorie monitoring using technological approaches can be characterized into 3 domains: (1) image-based sensing (eg, wearable and smartphone-based cameras combined with machine learning algorithms); (2) eating action unit (EAU) sensors (eg, to measure feeding gesture and chewing rate); and (3) biochemical measures (eg, serum and plasma metabolite concentrations). We discussed how each domain functions, provided examples of promising solutions, and highlighted potential challenges and opportunities in each domain. Image-based sensor research requires improved ground truth (context and known information about the foods), accurate food image segmentation and recognition algorithms, and reliable methods of estimating portion size. EAU-based domain research is limited by the understanding of when their systems (device and inference algorithm) succeed and fail, need for privacy-protecting methods of capturing ground truth, and uncertainty in food categorization. Although an exciting novel technology, the challenges of biochemical sensing range from a lack of adaptability to environmental effects (eg, temperature change) and mechanical impact, instability of wearable sensor performance over time, and single-use design. CONCLUSIONS: Conventional approaches to calorie monitoring rely predominantly on self-reports. These approaches can gain contextual information from image-based and EAU-based domains that can map automatically captured food images to a food database and detect proxies that correlate with food volume and caloric intake. Although the continued development of advanced machine learning techniques will advance the accuracy of such wearables, biochemical sensing provides an electrochemical analysis of sweat using soft bioelectronics on human skin, enabling noninvasive measures of chemical compounds that provide insight into the digestive and endocrine systems. Future computing-based researchers should focus on reducing the burden of wearable sensors, aligning data across multiple devices, automating methods of data annotation, increasing rigor in studying system acceptability, increasing battery lifetime, and rigorously testing validity of the measure. Such research requires moving promising technological solutions from the controlled laboratory setting to the field.

Fast and scalable distributed deep convolutional autoencoder for fMRI big data analytics.

In recent years, analyzing task-based fMRI (tfMRI) data has become an essential tool for understanding brain function and networks. However, due to the sheer size of tfMRI data, its intrinsic complex structure, and lack of ground truth of underlying neural activities, modeling tfMRI data is hard and challenging. Previously proposed data modeling methods including Independent Component Analysis (ICA) and Sparse Dictionary Learning only provided shallow models based on blind source separation under the strong assumption that original fMRI signals could be linearly decomposed into time series components with corresponding spatial maps. Given the Convolutional Neural Network (CNN) successes in learning hierarchical abstractions from low-level data such as tfMRI time series, in this work we propose a novel scalable distributed deep CNN autoencoder model and apply it for fMRI big data analysis. This model aims to both learn the complex hierarchical structures of the tfMRI big data and to leverage the processing power of multiple GPUs in a distributed fashion. To deploy such a model, we have created an enhanced processing pipeline on the top of Apache Spark and Tensorflow, leveraging from a large cluster of GPU nodes over cloud. Experimental results from applying the model on the Human Connectome Project (HCP) data show that the proposed model is efficient and scalable toward tfMRI big data modeling and analytics, thus enabling data-driven extraction of hierarchical neuroscientific information from massive fMRI big data.

Overcoming the data drought: exploring general practice in Australia by network analysis of big data.

OBJECTIVES: To investigate the organisation and characteristics of general practice in Australia by applying novel network analysis methods to national Medicare claims data. DESIGN: We analysed Medicare claims for general practitioner consultations during 1994-2014 for a random 10% sample of Australian residents, and applied hierarchical block modelling to identify provider practice communities (PPCs). PARTICIPANTS: About 1.7 million patients per year. MAIN OUTCOME MEASURES: Numbers and characteristics of PPCs (including numbers of providers, patients and claims), proportion of bulk-billed claims, continuity of care, patient loyalty, patient sharing. RESULTS: The number of PPCs fluctuated during the 21-year period; there were 7747 PPCs in 2014. The proportion of larger PPCs (six or more providers) increased from 32% in 1994 to 43% in 2014, while that of sole provider PPCs declined from 50% to 39%. The median annual number of claims per PPC increased from 5000 (IQR, 40-19 940) in 1994 to 9980 (190-23 800) in 2014; the proportion of PPCs that bulk-billed all patients was lowest in 2004 (21%) and highest in 2014 (29%). Continuity of care and patient loyalty were stable; in 2014, 50% of patients saw the same provider and 78% saw a provider in the same PPC for at least 75% of consultations. Density of patient sharing in a PPC was correlated with patient loyalty to that PPC. CONCLUSIONS: During 1994-2014, Australian GP practice communities have generally increased in size, but continuity of care and patient loyalty have remained stable. Our novel approach to the analysis of routinely collected data allows continuous monitoring of the characteristics of Australian general practices and their influence on patient care.

Multi-Robot Assembly Strategies and Metrics.

We present a survey of multi-robot assembly applications and methods and describe trends and general insights into the multi-robot assembly problem for industrial applications. We focus on fixtureless assembly strategies featuring two or more robotic systems. Such robotic systems include industrial robot arms, dexterous robotic hands, and autonomous mobile platforms, such as automated guided vehicles. In this survey, we identify the types of assemblies that are enabled by utilizing multiple robots, the algorithms that synchronize the motions of the robots to complete the assembly operations, and the metrics used to assess the quality and performance of the assemblies.

Computer-assisted technologies used in oral rehabilitation and the clinical documentation of alleged advantages - a systematic review.

The objective of this systematic review is to identify current computer-assisted technologies used for managing patients with a need to re-establish craniofacial appearance, subjective discomfort and stomatognathic function, and the extent of their clinical documentation. Electronic search strategies were used for locating clinical studies in MEDLINE through PubMed and in the Cochrane library, and in the grey literature through searches on Google Scholar. The searches for commercial digital products for use in oral rehabilitation resulted in identifying 225 products per November 2016, used for patient diagnostics, communication and therapy purposes, and for other computer-assisted applications in context with oral rehabilitation. About one-third of these products were described in about 350 papers reporting from clinical human studies. The great majority of digital products for use in oral rehabilitation has no clinical documentation at all, while the products from a distinct minority of manufacturers have frequently appeared in more or less scientific reports. Moore's law apply also to digital dentistry, which predicts that the capacity of microprocessors will continue to become faster and with lower cost per performance unit, and innovative software programs will harness these improvements in performance. The net effect is the noticeable short product life cycle of digital products developed for use in oral rehabilitation and often lack of supportive clinical documentation. Nonetheless, clinicians must request clinically meaningful information about new digital products to assess net benefits for the patients or the dental professionals and not accept only technological verbiage as a basis for product purchases.

Aortic Curvature Instead of Angulation Allows Improved Estimation of the True Aorto-iliac Trajectory.

OBJECTIVE: Supra- and infrarenal aortic neck angulation have been associated with complications after endovascular aortic aneurysm repair. However, a uniform angulation measurement method is lacking and the concept of angulation suggests a triangular oversimplification of the aortic anatomy. (Semi-)automated calculation of curvature along the center luminal line describes the actual trajectory of the aorta. This study proposes a methodology for calculating aortic (neck) curvature and suggests an additional method based on available tools in current workstations: curvature by digital calipers (CDC). METHODS: Proprietary custom software was developed for automatic calculation of the severity and location of the largest supra- and infrarenal curvature over the center luminal line. Twenty-four patients with severe supra- or infrarenal angulations (≥45°) and 11 patients with small to moderate angulations (<45°) were included. Both CDC and angulation were measured by two independent observers on the pre- and postoperative computed tomographic angiography scans. The relationships between actual curvature and CDC and angulation were visualized and tested with Pearson's correlation coefficient. The CDC was also fully automatically calculated with proprietary custom software. The difference between manual and automatic determination of CDC was tested with a paired Student t test. A p-value was considered significant when two-tailed α < .05. RESULTS: The correlation between actual curvature and manual CDC is strong (.586-.962) and even stronger for automatic CDC (.865-.961). The correlation between actual curvature and angulation is much lower (.410-.737). Flow direction angulation values overestimate CDC measurements by 60%, with larger variance. No significant difference was found in automatically calculated CDC values and manually measured CDC values. CONCLUSION: Curvature calculation of the aortic neck improves determination of the true aortic trajectory. Automatic calculation of the actual curvature is preferable, but measurement or calculation of the curvature by digital calipers is a valid alternative if actual curvature is not at hand.

Demystifying computer science for molecular ecologists.

In this age of data-driven science and high-throughput biology, computational thinking is becoming an increasingly important skill for tackling both new and long-standing biological questions. However, despite its obvious importance and conspicuous integration into many areas of biology, computer science is still viewed as an obscure field that has, thus far, permeated into only a few of the biology curricula across the nation. A national survey has shown that lack of computational literacy in environmental sciences is the norm rather than the exception [Valle & Berdanier (2012) Bulletin of the Ecological Society of America, 93, 373-389]. In this article, we seek to introduce a few important concepts in computer science with the aim of providing a context-specific introduction aimed at research biologists. Our goal was to help biologists understand some of the most important mainstream computational concepts to better appreciate bioinformatics methods and trade-offs that are not obvious to the uninitiated.