Flexiplex: a versatile demultiplexer and search tool for omics data

MOTIVATION

The process of analyzing high throughput sequencing data often requires the identification and extraction of specific target sequences. This could include tasks, such as identifying cellular barcodes and UMIs in single-cell data, and specific genetic variants for genotyping. However, existing tools, which perform these functions are often task-specific, such as only demultiplexing barcodes for a dedicated type of experiment, or are not tolerant to noise in the sequencing data.

RESULTS

To overcome these limitations, we developed Flexiplex, a versatile and fast sequence searching and demultiplexing tool for omics data, which is based on the Levenshtein distance and thus allows imperfect matches. We demonstrate Flexiplex's application on three use cases, identifying cell-line-specific sequences in Illumina short-read single-cell data, and discovering and demultiplexing cellular barcodes from noisy long-read single-cell RNA-seq data. We show that Flexiplex achieves an excellent balance of accuracy and computational efficiency compared to leading task-specific tools.

AVAILABILITY AND IMPLEMENTATION

Flexiplex is available at https://davidsongroup.github.io/flexiplex/.

Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping

In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster's distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluster from our client to process cotton image data in a distributed and parallel fashion, from pre-processing to bloom detection and spatio-temporal map creation. Additionally, we present a comparison of our four-node cluster performance with centralized, one-, two-, and three-node clusters. This work is the first to develop a distributed computing pipeline for high-throughput cotton phenotyping in field-based agriculture.

How missing value imputation is confounded with batch effects and what you can do about it

In data-processing pipelines, upstream steps can influence downstream processes because of their sequential nature. Among these data-processing steps, batch effect (BE) correction (BEC) and missing value imputation (MVI) are crucial for ensuring data suitability for advanced modeling and reducing the likelihood of false discoveries. Although BEC-MVI interactions are not well studied, they are ultimately interdependent. Batch sensitization can improve the quality of MVI. Conversely, accounting for missingness also improves proper BE estimation in BEC. Here, we discuss how BEC and MVI are interconnected and interdependent. We show how batch sensitization can improve any MVI and bring attention to the idea of BE-associated missing values (BEAMs). Finally, we discuss how batch-class imbalance problems can be mitigated by borrowing ideas from machine learning.

Neural Activity and Information Processing Capacity of Neuronal Culture

Whether artificial or living, neural networks perform tremendously diverse kinds of information processing, such as visual feature extraction, speech recognition, motor control, and so on. Some studies have evaluated the computational ability of living neural networks based on the performances of specific tasks, yet could not comprehensively grasp the versatile ability. In this study, we investigated dissociated culture of neurons as a physical reservoir, which generates diverse outputs through linear regression, or readout, of the dynamical states. Based on the theory of reservoir computing, the potential computational capabilities of neuronal culture were evaluated by the information processing capacity (IPC), which indicates how a target function can be achieved from the given dynamics. As a result, we found that the neuronal culture exhibited significant IPC and that IPC varied with the inter-step interval (ISI), the time step of reservoir computing. The cultures exhibited a memory capacity of 10 time steps for computation, and this memory capacity decayed at an ISI of 5 ms or shorter. In addition, the IPC had a significant positive correlation with the intensity of the evoked response relative to spontaneous activity. The multiple regression model with evoked response and ISI showed the positive effect of evoked response and 30 ms as the best ISI for IPC. These results suggest that the distinct evoked response and the optimal time step to interact with the neuronal culture are key factors to uncover computational resources from the neuronal system.

Molecular information technology with DNA

Qian and Winfree constructed complex biochemical circuits with computation capability from scratch, demonstrating the programmability of biomolecules. One day, programming molecular information processing may be just like how electronic machines are programmed today, with exciting applications in nanoscale science and biotechnology.

Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis

Using computer vision through artificial intelligence (AI) is one of the main technological advances in dentistry. However, the existing literature on the practical application of AI for detecting cephalometric landmarks of orthodontic interest in digital images is heterogeneous, and there is no consensus regarding accuracy and precision. Thus, this review evaluated the use of artificial intelligence for detecting cephalometric landmarks in digital imaging examinations and compared it to manual annotation of landmarks. An electronic search was performed in nine databases to find studies that analyzed the detection of cephalometric landmarks in digital imaging examinations with AI and manual landmarking. Two reviewers selected the studies, extracted the data, and assessed the risk of bias using QUADAS-2. Random-effects meta-analyses determined the agreement and precision of AI compared to manual detection at a 95% confidence interval. The electronic search located 7410 studies, of which 40 were included. Only three studies presented a low risk of bias for all domains evaluated. The meta-analysis showed AI agreement rates of 79% (95% CI: 76-82%, I2 = 99%) and 90% (95% CI: 87-92%, I2 = 99%) for the thresholds of 2 and 3 mm, respectively, with a mean divergence of 2.05 (95% CI: 1.41-2.69, I2 = 10%) compared to manual landmarking. The menton cephalometric landmark showed the lowest divergence between both methods (SMD, 1.17; 95% CI, 0.82; 1.53; I2 = 0%). Based on very low certainty of evidence, the application of AI was promising for automatically detecting cephalometric landmarks, but further studies should focus on testing its strength and validity in different samples.

"Are we there yet?" Ten persistent hazards and inefficiencies with the use of medication administration technology from the perspective of practicing nurses

OBJECTIVES

(1) Characterize persistent hazards and inefficiencies in inpatient medication administration; (2) Explore cognitive attributes of medication administration tasks; and (3) Discuss strategies to reduce medication administration technology-related hazards.

MATERIALS AND METHODS

Interviews were conducted with 32 nurses practicing at 2 urban, eastern and western US health systems. Qualitative analysis using inductive and deductive coding included consensus discussion, iterative review, and coding structure revision. We abstracted hazards and inefficiencies through the lens of risks to patient safety and the cognitive perception-action cycle (PAC).

RESULTS

Persistent safety hazards and inefficiencies related to MAT organized around the PAC cycle included: (1) Compatibility constraints create information silos; (2) Missing action cues; (3) Intermittent communication flow between safety monitoring systems and nurses; (4) Occlusion of important alerts by other, less helpful alerts; (5) Dispersed information: Information required for tasks is not collocated; (6) Inconsistent data organization: Mismatch of the display and the user's mental model; (7) Hidden medication administration technologies (MAT) limitations: Inaccurate beliefs about MAT functionality contribute to overreliance on the technology; (8) Software rigidity causes workarounds; (9) Cumbersome dependencies between technology and the physical environment; and (10) Technology breakdowns require adaptive actions.

DISCUSSION

Errors might persist in medication administration despite successful Bar Code Medication Administration and Electronic Medication Administration Record deployment for reducing errors. Opportunities to improve MAT require a deeper understanding of high-level reasoning in medication administration, including control over the information space, collaboration tools, and decision support.

CONCLUSION

Future medication administration technology should consider a deeper understanding of nursing knowledge work for medication administration.

Barcode Medication Administration Technology to Prevent Medication Errors

Medication errors cause harm to patients at any point along the medication administration process and can be prevented. Barcoding medication administration (BCMA) is effective as a clinical decision support system (CDSS) to avoid errors. This viewpoint proposes the implementation of BCMA to avoid potential adverse events. The opinion piece gives an overview of BCMA, reviews the current literature on its effectiveness, and sheds light on the associated challenges and how to overcome them. The objective of this article is to increase awareness regarding BCMA and how it can decrease patient morbidity and mortality, enhance safety, and lower overall hospital-associated costs by preventing medication errors. Key Words: Bar-code medication administration, Medication errors, Adverse drug events, Patient safety.

Barcode Medication Administration Technology to Prevent Medication Errors

Medication errors cause harm to patients at any point along the medication administration process and can be prevented. Barcoding medication administration (BCMA) is effective as a clinical decision support system (CDSS) to avoid errors. This viewpoint proposes the implementation of BCMA to avoid potential adverse events. The opinion piece gives an overview of BCMA, reviews the current literature on its effectiveness, and sheds light on the associated challenges and how to overcome them. The objective of this article is to increase awareness regarding BCMA and how it can decrease patient morbidity and mortality, enhance safety, and lower overall hospital-associated costs by preventing medication errors. Key Words: Bar-code medication administration, Medication errors, Adverse drug events, Patient safety.

A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload

OBJECTIVE

The aim of the study is to introduce an innovative use of bar code medication administration (BCMA) data, medication pass analysis, that allows for the examination of nurse staffing and workload using data generated during regular nursing workflow.

METHODS

Using 1 year (October 1, 2014-September 30, 2015) of BCMA data for 11 acute care units in one Veterans Affairs Medical Center, we determined the peak time for scheduled medications and included medications scheduled for and administered within 2 hours of that time in analyses. We established for each staff member their daily peak-time medication pass characteristics (number of patients, number of peak-time scheduled medications, duration, start time), generated unit-level descriptive statistics, examined staffing trends, and estimated linear mixed-effects models of duration and start time.

RESULTS

As the most frequent (39.7%) scheduled medication time, 9:00 was the peak-time medication pass; 98.3% of patients (87.3% of patient-days) had a 9:00 medication. Use of nursing roles and number of patients per staff varied across units and over time. Number of patients, number of medications, and unit-level factors explained significant variability in registered nurse (RN) medication pass duration (conditional R2  = 0.237; marginal R2  = 0.199; intraclass correlation = 0.05). On average, an RN and a licensed practical nurse (LPN) with four patients, each with six medications, would be expected to take 70 and 74 minutes, respectively, to complete the medication pass. On a unit with median 10 patients per LPN, the median duration (127 minutes) represents untimely medication administration on more than half of staff days. With each additional patient assigned to a nurse, average start time was earlier by 4.2 minutes for RNs and 1.4 minutes for LPNs.

CONCLUSION

Medication pass analysis of BCMA data can provide health systems a means for assessing variations in staffing, workload, and nursing practice using data generated during routine patient care activities.

Informatics Opportunities and Challenges in Medical Imaging: A Journey

The role of the field of informatics in medical imaging is vital; novel or adapted informatics' core methods can be employed to realise innovative information processing and engineering of medical images. As such, imaging informatics can assist in the interpretation of image-based, clinically recorded evidence. This, in turn, leads to the generation of associated actionable knowledge to achieve precision medicine practice. The discipline of informatics has the power to transform data to useful clinical information patterns of observable evidence and, subsequently to generate actionable knowledge in terms of diagnosis, prognosis, and disease management. This paper presents the author's personal viewpoint and distinct contributions to innovations in the acquisition and collection of imaging data; storage, retrieval, and management of imaging information objects; quantitative analysis, classification, and dissemination of imaging observable evidence.

Toward a General Framework for Multimodal Big Data Analysis

Multimodal Analytics in Big Data architectures implies compounded configurations of the data processing tasks. Each modality in data requires specific analytics that triggers specific data processing tasks. Scalability can be reached at the cost of an attentive calibration of the resources shared by the different tasks searching for a trade-off with the multiple requirements they impose. We propose a methodology to address multimodal analytics within the same data processing approach to get a simplified architecture that can fully exploit the potential of the parallel processing of Big Data infrastructures. Multiple data sources are first integrated into a unified knowledge graph (KG). Different modalities of data are addressed by specifying ad hoc views on the KG and producing a rewriting of the graph containing merely the data to be processed. Graph traversal and rule extraction are this way boosted. Using graph embeddings methods, the different ad hoc views can be transformed into low-dimensional representation following the same data format. This way a single machine learning procedure can address the different modalities, simplifying the architecture of our system. The experiments we executed demonstrate that our approach reduces the cost of execution and improves the accuracy of analytics.

Spontaneous emergence of computation in network cascades

Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning) and neuroscience. Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the form of computational cascades. We explain the emergent inverse relationship between the computational complexity of the motifs and their rank-ordering by function probabilities due to motifs, and its relationship to symmetry in function space. We also show that the optimal fraction of inhibition observed here supports results in computational neuroscience, relating to optimal information processing.

Development of the Leapfrog Group's Bar Code Medication Administration Standard to Address Hospital Inpatient Medication Safety

ABSTRACT

Medication errors are the most common type of error in hospitals and reflect a leading cause of avoidable harm to patients. Bar code medication administration (BCMA) systems are a technology designed to help intercept medication errors at the point of medication administration. This article describes the process of developing, testing, and refining a standard for BCMA adoption and use in U.S. hospitals, as measured through the Leapfrog Hospital Survey. Building on the published literature and an expert panel's collective experience in studying, implementing, and using BCMA systems, the expert panel recommended a standard with 4 key domains. Leapfrog's BCMA standard provides hospitals with a "how-to guide" on what best practice looks like for using BCMA to ensure safe medication administration at the bedside.

Wearable Cardiorespiratory Sensors for Aerospace Applications

Emerging Air Traffic Management (ATM) and avionics human-machine system concepts require the real-time monitoring of the human operator to support novel task assessment and system adaptation features. To realise these advanced concepts, it is essential to resort to a suite of sensors recording neurophysiological data reliably and accurately. This article presents the experimental verification and performance characterisation of a cardiorespiratory sensor for ATM and avionics applications. In particular, the processed physiological measurements from the designated commercial device are verified against clinical-grade equipment. Compared to other studies which only addressed physical workload, this characterisation was performed also looking at cognitive workload, which poses certain additional challenges to cardiorespiratory monitors. The article also addresses the quantification of uncertainty in the cognitive state estimation process as a function of the uncertainty in the input cardiorespiratory measurements. The results of the sensor verification and of the uncertainty propagation corroborate the basic suitability of the commercial cardiorespiratory sensor for the intended aerospace application but highlight the relatively poor performance in respiratory measurements during a purely mental activity.

Quantum guessing games with posterior information

Quantum guessing games form a versatile framework for studying different tasks of information processing. A quantum guessing game with posterior information uses quantum systems to encode messages and classical communication to give partial information after a quantum measurement has been performed. We present a general framework for quantum guessing games with posterior information and derive structure and reduction theorems that enable to analyze any such game. We formalize symmetry of guessing games and characterize the optimal measurements in cases where the symmetry is related to an irreducible representation. The application of guessing games to incompatibility detection is reviewed and clarified. All the presented main concepts and results are demonstrated with examples.

Bio-Inspired In-Sensor Compression and Computing Based on Phototransistors

The biological nervous system possesses a powerful information processing capability, and only needs a partial signal stimulation to perceive the entire signal. Likewise, the hardware implementation of an information processing system with similar capabilities is of great significance, for reducing the dimensions of data from sensors and improving the processing efficiency. Here, it is reported that indium-gallium-zinc-oxide thin film phototransistors exhibit the optoelectronic switching and light-tunable synaptic characteristics for in-sensor compression and computing. Phototransistor arrays can compress the signal while sensing, to realize in-sensor compression. Additionally, a reservoir computing network can also be implemented via phototransistors for in-sensor computing. By integrating these two systems, a neuromorphic system for high-efficiency in-sensor compression and computing is demonstrated. The results reveal that even for cases where the signal is compressed by 50%, the recognition accuracy of reconstructed signal still reaches ≈96%. The work paves the way for efficient information processing of human-computer interactions and the Internet of Things.

Barcode Medication Administration Software Technology Use in the Emergency Department and Medication Error Rates

High-quality care during and after a medication process requires complete and accurate medication administration documentation. Veterans Affairs Medical Centers use barcode medication administration technology to document medication administered to Veterans throughout the inpatient and long-term care areas of the hospital. Barcode medication administration has demonstrated a reduction in medication administration errors; however, it is not commonly used in Veterans Affairs Medical Center clinical areas or emergency departments. During this study, only 39% of the recorded 165 Veterans Affairs Medical Centers that use barcode medication administration technology in their inpatient areas stated that barcode medication administration was also used in clinical areas of the hospital. Of these facilities, only 14% had implemented barcode medication administration in their emergency department. This study evaluated medication error rates before and after barcode medication administration technology was implemented in the emergency department of a Veterans Affairs Medical Center located in the Southeastern region of the United States. A total of 258 charts, 129 before and 129 after barcode medication administration technology implementation in the emergency department, were reviewed for Veterans who were evaluated and ordered to receive medication in the emergency department before transferring to an inpatient unit at the Veterans Affairs Medical Center where this study was conducted. A quantitative nonexperimental descriptive comparison demonstrated a 10.8% reduction in medication error rates and 21% reduction in the average number of medications given in error per chart after barcode medication administration technology was implemented in the emergency department. In addition to the study outcome, a potentially unsafe workaround was identified. Stakeholders that use barcode medication administration technology in their emergency departments would benefit from assessing the association between barcode medication administration use and medication administration error rates. However, assessing whether barcode medication administration technology remains useful and continues to provide safe medication administration practices for our Veterans is also recommended.

SpotClean adjusts for spot swapping in spatial transcriptomics data

Spatial transcriptomics is a powerful and widely used approach for profiling the gene expression landscape across a tissue with emerging applications in molecular medicine and tumor diagnostics. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind RNA. Ideally, unique molecular identifiers (UMIs) at a spot measure spot-specific expression, but this is often not the case in practice due to bleed from nearby spots, an artifact we refer to as spot swapping. To improve the power and precision of downstream analyses in spatial transcriptomics experiments, we propose SpotClean, a probabilistic model that adjusts for spot swapping to provide more accurate estimates of gene-specific UMI counts. SpotClean provides substantial improvements in marker gene analyses and in clustering, especially when tissue regions are not easily separated. As demonstrated in multiple studies of cancer, SpotClean improves tumor versus normal tissue delineation and improves tumor burden estimation thus increasing the potential for clinical and diagnostic applications of spatial transcriptomics technologies.

Evaluation of a Personal Digital Assistant Device Implementation for Barcode Medication Administration with Nurses Using a Likert Questionnaire

The majority of preventable medication errors occur at the administration stage. BCMA systems are used to improve safety and prevent errors in this stage. A variety of devices can be used for this purpose. Hospital Italiano de Buenos Aires is a high complexity medical center implementing a BCMA project since 2015. It is currently migrating to PDA devices for nurses. The objective of this work is to evaluate the implementation of these new devices in selected wards at HIBA using a self-reported questionnaire. From 318 contacted nurses, 58 answered the questionnaire (18.2% response rate). Overall, agreement was high among all statements regarding the new devices. Nurses valued especially the increased safety to reduce errors, improvements in previous hospital processes and achieving improvements in the flow and quality of patient care. Nurses recommended the use of the device in their sector, with a mean score of 4.6/5 and 91.3% agreement, highest in total. This proved to be a cost-effective method of evaluation of the newly implemented devices and acceptance by nurses. Measures to incorporate the remaining nurses' feedback should be considered.

BFF and cellhashR: analysis tools for accurate demultiplexing of cell hashing data

MOTIVATION

Single-cell sequencing methods provide previously impossible resolution into the transcriptome of individual cells. Cell hashing reduces single-cell sequencing costs by increasing capacity on droplet-based platforms. Cell hashing methods rely on demultiplexing algorithms to accurately classify droplets; however, assumptions underlying these algorithms limit accuracy of demultiplexing, ultimately impacting the quality of single-cell sequencing analyses.

RESULTS

We present Bimodal Flexible Fitting (BFF) demultiplexing algorithms BFFcluster and BFFraw, a novel class of algorithms that rely on the single inviolable assumption that barcode count distributions are bimodal. We integrated these and other algorithms into cellhashR, a new R package that provides integrated QC and a single command to execute and compare multiple demultiplexing algorithms. We demonstrate that BFFcluster demultiplexing is both tunable and insensitive to issues with poorly behaved data that can confound other algorithms. Using two well-characterized reference datasets, we demonstrate that demultiplexing with BFF algorithms is accurate and consistent for both well-behaved and poorly behaved input data.

AVAILABILITY AND IMPLEMENTATION

cellhashR is available as an R package at https://github.com/BimberLab/cellhashR. cellhashR version 1.0.3 was used for the analyses in this manuscript and is archived on Zenodo at https://www.doi.org/10.5281/zenodo.6402477.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clinical Issues-May 2022

Using the AORN staffing formula Key words: safe OR staffing, direct care, indirect care, on-call, full-time equivalents (FTEs). RN first assistant application prerequisites Key words: registered nurse first assistant (RNFA), certification, curriculum, education requirements, CNOR. Differences between preceptors and mentors Key words: novice nurse, expert, career development, professional growth, assimilation. Unique device identifier requirements Key words: global unique device identification database (GUDID), automatic identification and data capture (AIDC) technology, medical device, barcode, integration.

Building FAIR Functionality: Annotating Events in Time Series Data Using Hierarchical Event Descriptors (HED)

Human electrophysiological and related time series data are often acquired in complex, event-rich environments. However, the resulting recorded brain or other dynamics are often interpreted in relation to more sparsely recorded or subsequently-noted events. Currently a substantial gap exists between the level of event description required by current digital data archiving standards and the level of annotation required for successful analysis of event-related data across studies, environments, and laboratories. Manifold challenges must be addressed, most prominently ontological clarity, vocabulary extensibility, annotation tool availability, and overall usability, to allow and promote sharing of data with an effective level of descriptive detail for labeled events. Motivating data authors to perform the work needed to adequately annotate their data is a key challenge. This paper describes new developments in the Hierarchical Event Descriptor (HED) system for addressing these issues. We recap the evolution of HED and its acceptance by the Brain Imaging Data Structure (BIDS) movement, describe the recent release of HED-3G, a third generation HED tools and design framework, and discuss directions for future development. Given consistent, sufficiently detailed, tool-enabled, field-relevant annotation of the nature of recorded events, prospects are bright for large-scale analysis and modeling of aggregated time series data, both in behavioral and brain imaging sciences and beyond.

The coordinated development of manufacturing industry and logistics industry in the Yangtze River Economic Belt: Empirical study by stages based on Haken Model

It has great significance for improving the logistics service ability of the Yangtze River economic belt, optimizing the industrial structure of manufacturing industry, and realizing the integrated development of the Yangtze River economic belt to explore the collaborative evolution of logistics industry and manufacturing industry in the Yangtze River economic belt, and identify the leading position of the collaborative development of the two industries, so as to. Based on the Haken Model, this paper summarizes the coevolution law of logistics industry and manufacturing industry in the Yangtze River economic belt through two-stage empirical analysis, and identifies the order parameters of the co-development of logistics industry and manufacturing industry. The results show that the overall degree of coordination between the logistics industry and the manufacturing industry in the Yangtze River economic belt is high. And the order parameter has been changed from manufacturing industry in 2003–2009 to logistics industry in 2010–2017. The gap between regions has been reduced, and the western region has the advantage of post development.

Integrated information as a common signature of dynamical and information-processing complexity

The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress. Nonetheless, given the shared theoretical goals between both approaches, it is reasonable to conjecture the existence of underlying common signatures that capture interesting behavior in both dynamical and information-processing systems. Here, we argue that a pragmatic use of integrated information theory (IIT), originally conceived in theoretical neuroscience, can provide a potential unifying framework to study complexity in general multivariate systems. By leveraging metrics put forward by the integrated information decomposition framework, our results reveal that integrated information can effectively capture surprisingly heterogeneous signatures of complexity-including metastability and criticality in networks of coupled oscillators as well as distributed computation and emergent stable particles in cellular automata-without relying on idiosyncratic, ad hoc criteria. These results show how an agnostic use of IIT can provide important steps toward bridging the gap between informational and dynamical approaches to complex systems.

The Potential Role of Smart Infusion Devices in Preventing or Contributing to Medication Administration Errors: A Descriptive Study of 2 Data Sets

OBJECTIVES

Errors in medication administration are common, with many interventions suggested to reduce them. For intravenous infusion-related errors, "smart infusion devices" incorporating dose error reduction software are widely advocated. Our aim was to explore the role of smart infusion devices in preventing or contributing to medication administration errors using retrospective review of 2 complementary data sets that collectively included a wide range of errors with different levels of actual or potential harm.

METHODS

We reviewed 216 medication administration errors identified from an observational study in clinical practice and 123 medication incidents involving infusion devices reported to a national reporting system. The impact of smart infusion devices in preventing or contributing to these errors was assessed by the research team and an expert panel.

RESULTS

The data suggest that use of any infusion device rather than gravitational administration may have prevented 13% of observed errors and 8% of reported incidents; additional reductions may be possible with standalone smart infusion devices, and further potential reductions with smart infusion devices integrated with electronic prescribing and barcode administration systems. An estimated 52% to 73% of errors that occurred with traditional infusion pumps could be prevented with such integrated smart infusion devices. In the few cases where smart infusion devices were used, these contributed to errors in 2 of 58 observed errors and 7 of 8 reported incidents.

CONCLUSIONS

Smart infusion devices not only prevent some medication administration errors but can also contribute to them. Further evaluation of such systems is required to make recommendations for policy and practice.

Identifying Health Information Technology Usability Issues Contributing to Medication Errors Across Medication Process Stages

OBJECTIVE

Different health information technology (health IT) systems are intended to support medication ordering, reviewing, and administration. We sought to identify the types of medication errors associated with health IT use, whether they reached the patient, where in the medication process those errors occurred, and the specific usability issues contributing to those errors.

METHODS

Patient safety event reports from more than 595 healthcare facilities entered between January 2013 and September 2018 were analyzed. We computationally identified reports associated with health IT intended to support the medication process, including computerized provider order entry, electronic medication administration record, and barcode medication administration. From these, 2700 reports were manually reviewed to determine the type of medication error, medication process stage, and health IT usability issue.

RESULTS

Of the 2700 manually reviewed reports, 1508 (55.9%) described a medication error that was associated with health IT use and 750 (49.7%) reached the patient. Improper dose errors were frequent (1214 of 1508, 80.5%) with most errors during ordering (673 of 1508, 44.6%) and reviewing medications (639 of 1508, 42.4%). Most health IT-associated medication error reports described usability issues (n = 1468 of 1508, 97.3%) including data entry, workflow support, and alerting. Data entry usability issues impacted few medication process stages, whereas workflow support and alerting impacted several stages.

CONCLUSIONS

Health IT usability issues are a prevalent contributing factor to medication errors, many of which reach the patient. Data entry, workflow support, and alerting should be prioritized during usability and safety optimization efforts.

Modeling and performance analysis of shuttle-based compact storage systems under parallel processing policy

Short response time for order processing is important for modern warehouses, which can be potentially achieved by adopting appropriate processing policy. The parallel processing policy have advantages in improving performance of many autonomous storage and retrieval systems. However, researchers tend to assume a sequential processing policy managing the movement of independent resources in shuttle-based compact storage systems. This paper models and analyses a single-tier of specialized shuttle-based compact storage systems under parallel processing policy. The system is modeled as a semi-open queueing network with class switching and the parallel movement of shuttles and the transfer car is modeled using a fork-join queueing network. The analytical model is validated against simulations and the results show our model can accurately estimate the system performance. Numerical experiments and a real case are carried out to compare the performance of parallel and sequential processing policies. The results suggest a critical transaction arrival rate and depth/width ratio, below which the sequential processing policy outperforms the parallel processing policy. However, the advantage of sequential processing policy is decreasing with the increasing of shuttle number, transaction arrival rate and depth/width ratio. The results also suggest an optimal depth/width ratio with a value of 1.75 for minimizing the expected throughput time in the real system. Given the current system configurations, the parallel processing policy should be considered when the number of shuttles is larger than 2 or the transaction arrival rate is larger than 24 per hour.

Mesmerize is a dynamically adaptable user-friendly analysis platform for 2D and 3D calcium imaging data

Calcium imaging is an increasingly valuable technique for understanding neural circuits, neuroethology, and cellular mechanisms. The analysis of calcium imaging data presents challenges in image processing, data organization, analysis, and accessibility. Tools have been created to address these problems independently, however a comprehensive user-friendly package does not exist. Here we present Mesmerize, an efficient, expandable and user-friendly analysis platform, which uses a Findable, Accessible, Interoperable and Reproducible (FAIR) system to encapsulate the entire analysis process, from raw data to interactive visualizations for publication. Mesmerize provides a user-friendly graphical interface to state-of-the-art analysis methods for signal extraction & downstream analysis. We demonstrate the broad scientific scope of Mesmerize's applications by analyzing neuronal datasets from mouse and a volumetric zebrafish dataset. We also applied contemporary time-series analysis techniques to analyze a novel dataset comprising neuronal, epidermal, and migratory mesenchymal cells of the protochordate Ciona intestinalis.

The "Narratives" fMRI dataset for evaluating models of naturalistic language comprehension

The "Narratives" collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging.

High throughput quantification of short nucleic acid samples by capillary electrophoresis with automated data processing

Analysis of catalytic activity of nucleic acid enzymes is crucial for many applications, ranging from biotechnology to the search for antiviral drugs. Commonly used analytical methods for quantifying DNA and RNA reaction products based on slab-gel electrophoresis are limited in throughput, speed, and accuracy. Here we report the optimization of high throughput methods to separate and quantify short nucleic acid reaction products using DNA sequencing instruments based on capillary electrophoresis with fluorescence detection. These methods afford single base resolution without requiring extensive sample preparation. Additionally, we show that the utility of our system extends to quantifying RNA products. The efficiency and reliability of modern instruments offers a large increase in throughput but complications due to variations in migration times between capillaries required us to develop a computer program to normalize the data and quantify the products for automated kinetic analysis. The methods presented here greatly increase sample throughput and accuracy and should be applicable to many nucleic acid enzymes.

A novel feature selection algorithm based on damping oscillation theory

Feature selection is an important task in big data analysis and information retrieval processing. It reduces the number of features by removing noise, extraneous data. In this paper, one feature subset selection algorithm based on damping oscillation theory and support vector machine classifier is proposed. This algorithm is called the Maximum Kendall coefficient Maximum Euclidean Distance Improved Gray Wolf Optimization algorithm (MKMDIGWO). In MKMDIGWO, first, a filter model based on Kendall coefficient and Euclidean distance is proposed, which is used to measure the correlation and redundancy of the candidate feature subset. Second, the wrapper model is an improved grey wolf optimization algorithm, in which its position update formula has been improved in order to achieve optimal results. Third, the filter model and the wrapper model are dynamically adjusted by the damping oscillation theory to achieve the effect of finding an optimal feature subset. Therefore, MKMDIGWO achieves both the efficiency of the filter model and the high precision of the wrapper model. Experimental results on five UCI public data sets and two microarray data sets have demonstrated the higher classification accuracy of the MKMDIGWO algorithm than that of other four state-of-the-art algorithms. The maximum ACC value of the MKMDIGWO algorithm is at least 0.5% higher than other algorithms on 10 data sets.

Tools for supporting solution scattering during the COVID-19 pandemic

During the COVID-19 pandemic, synchrotron beamlines were forced to limit user access. Performing routine measurements became a challenge. At the Life Science X-ray Scattering (LiX) beamline, new instrumentation and mail-in protocols have been developed to remove the access barrier to solution scattering measurements. Our efforts took advantage of existing instrumentation and coincided with the larger effort at NSLS-II to support remote measurements. Given the limited staff-user interaction for mail-in measurements, additional software tools have been developed to ensure data quality, to automate the adjustments in data processing, as users would otherwise rely on the experience of the beamline staff, and produce a summary of the initial assessments of the data. This report describes the details of these developments.

Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance

BACKGROUND

Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown.

RESULTS

Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A, which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding.

CONCLUSION

Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.

Weighting or aggregating? Investigating information processing in multi-attribute choices

Multi-attribute choices are commonly analyzed in economics to value goods and services. Analysis assumes individuals consider all attributes, making trade-offs between them. Such decision-making is cognitively demanding, often triggering alternative decision rules. We develop a new model where individuals aggregate multi-attribute information into meta-attributes. Applying our model to a choice experiment (CE) dataset, accounting for attribute aggregation (AA) improves model fit. The probability of adopting AA is greater for: homogenous attribute information; participants who had shorter response time and failed the dominance test; and for later located choices. Accounting for AA has implications for welfare estimates. Our results underline the importance of accounting for information processing rules when modelling multi-attribute choices.

Identification of spectral signature for in situ real-time monitoring of smoltification

We describe the use of an optical hyperspectral sensing technique to identify the smoltification status of Atlantic salmon (Salmo salar) based on spectral signatures, thus potentially providing smolt producers with an additional tool to verify the osmoregulatory state of salmon. By identifying whether a juvenile salmon is in the biological freshwater stage (parr) or has adapted to the seawater stage (smolt) before transfer to sea, negative welfare impacts and subsequent mortality associated with failed or incorrect identification may be reduced. A hyperspectral imager has been used to collect data in two water flow-through and one recirculating production site in parallel with the standard smoltification evaluations applied at these sites. The results from the latter have been used as baseline for a machine-learning algorithm trained to identify whether a fish was parr or smolt based on its spectral signature. The developed method correctly classified fish in 86% to 100% of the cases for individual sites, and had an overall average classification accuracy of 90%, thus indicating that analysis of spectral signatures may constitute a useful tool for smoltification monitoring.

Fast and Frugal: Information Processing Related to The Coronavirus Pandemic

This research focuses on three factors that influence how individuals cognitively process information related to the coronavirus outbreak. Guided by dual-process theories of information processing, we establish how the two different information processing modes (system 1: heuristic processing; system 2: systematic processing) are influenced by individuals' responsibility attribution, discrete negative emotions, and risk perception. In an experiment, participants were exposed to a news article that either blames China (n = 445) or does not blame China (n = 498) for the pandemic. Results reveal that exposure to the responsibility attribution frame led individuals to engage in more heuristic processing, but it did not influence systematic processing. Discrete negative emotions and risk perception mediated the relationship between responsibility attribution and information processing. The indirect relationships suggest a more intricate process underlying heuristic processing and systematic processing. In particular, information processing styles seem to be determined by social judgment surrounding the coronavirus pandemic.

Precisely Encoded Barcodes through the Structure-Fluorescence Combinational Strategy: A Flexible, Robust, and Versatile Multiplexed Biodetection Platform with Ultrahigh Encoding Capacities

With the rapid development of suspension array technology, microbeads-based barcodes as the core element with sufficient encoding capacity are urgently required for high-throughput multiplexed detection. Here, a novel structure-fluorescence combinational encoding strategy is proposed for the first time to establish a barcode library with ultrahigh encoding capacities. Based on the never revealed transformability of the structural parameters (e.g., porosity and matrix component) of mesoporous microbeads into scattering signals in flow cytometry, the enlargement of codes number has been successfully realized in combination with two other fluorescent elements of fluorescein isothiocyanate isomer I (FITC) and quantum dots (QDs). The barcodes are constructed with precise architectures including FITC encapsulated within mesopores and magnetic nanoparticles as well as QDs immobilized on the outer surface to achieve the ultrahigh encoding level of 300 accompanied with superparamagnetism. To the best of knowledge, it is the highest record of single excitation laser-based encoding capacity up to now. Moreover, a ten-plexed tumor markers bioassay based on the tailored-designed barcodes has been evaluated to confirm their feasibility and effectiveness, and the results indicate that the barcodes platform is a promising and robust tool for practical multiplexed biodetection.

The Effects of Bar-coding Technology on Medication Errors: A Systematic Literature Review

BACKGROUND

The bar-coding technology adoptions have risen drastically in U.S. health systems in the past decade. However, few studies have addressed the impact of bar-coding technology with strong prospective methodologies and the research, which has been conducted from both in-pharmacy and bedside implementations.

OBJECTIVE

This systematic literature review is to examine the effectiveness of bar-coding technology on preventing medication errors and what types of medication errors may be prevented in the hospital setting.

METHODS

A systematic search of databases was performed from 1998 to December 2016. Studies measuring the effect of bar-coding technology on medication errors were included in a full-text review. Studies with the outcomes other than medication errors such as efficiency or workarounds were excluded. The outcomes were measured and findings were summarized for each retained study.

RESULTS

A total of 2603 articles were initially identified and 10 studies, which used prospective before-and-after study design, were fully reviewed in this article. Of the 10 included studies, 9 took place in the United States, whereas the remaining was conducted in the United Kingdom. One research article focused on bar-coding implementation in a pharmacy setting, whereas the other 9 focused on bar coding within patient care areas. All 10 studies showed overall positive effects associated with bar-coding implementation.

CONCLUSIONS

The results of this review show that bar-coding technology may reduce medication errors in hospital settings, particularly on preventing targeted wrong dose, wrong drug, wrong patient, unauthorized drug, and wrong route errors.

Improving Staff Experience With Vaccine Data Entry With 2D Barcode Scanning

BACKGROUND

Small fonts on vaccine labels make manually recording vaccine data in patient records time-consuming and challenging. Vaccine 2-dimensional (2D) barcode scanning is a promising alternative to manually recording these data.

PROBLEM

While vaccine 2D barcode scanning assists in data entry, adoption of scanning technology is still low.

APPROACH

Pilot sites (n = 27) within a health system scanned 2D barcodes to record vaccine data for 6 months. The time to record through scanning and nonscanning methods was measured for 13 vaccinators at 9 sites. A survey was administered to participants across all sites about their experience.

OUTCOMES

On average, 22 seconds were saved per vaccine scanned versus entered manually (7 vs 29 seconds, respectively). Participants reported preference for scanning over other vaccine entry options and identified benefits of scanning.

CONCLUSION

Expanded use of 2D barcode scanning can meaningfully improve clinical practices by improving efficiency and staff satisfaction during vaccine data entry.

Bipartite Synchronization of Multiple Memristor-Based Neural Networks With Antagonistic Interactions

In this article, by introducing a signed graph to describe the coopetition interactions among network nodes, the mathematical model of multiple memristor-based neural networks (MMNNs) with antagonistic interactions is established. Since the cooperative and competitive interactions coexist, the states of MMNNs cannot reach complete synchronization. Instead, they will reach the bipartite synchronization: all nodes' states will reach an identical absolute value but opposite sign. To reach bipartite synchronization, two kinds of the novel node- and edge-based adaptive strategies are proposed, respectively. First, based on the global information of the network nodes, a node-based adaptive control strategy is constructed to solve the bipartite synchronization problem of MMNNs. Secondly, a local edge-based adaptive algorithm is proposed, where the weight values of edges between two nodes will change according to the designed adaptive law. Finally, two simulation examples validate the effectiveness of the proposed adaptive controllers and bipartite synchronization criteria.

The Impacts of Face Mask Use on Standard Automated Perimetry Results in Glaucoma Patients

PURPOSE

The coronavirus disease 2019 (COVID-19) spread rapidly worldwide, causing a severe outbreak. Because the disease is easily transmitted, face masks are a vital tool to slow the spread. The aim of this study is to investigate the impacts of face mask use on standard automated perimetry (SAP) results in glaucoma patients.

MATERIALS AND METHODS

All follow-up glaucoma patients who underwent SAP between May and October 2020 were enrolled in this study. In patients with low test reliability and/or visual field changes, SAP was repeated after repositioning and taping patients' face masks.

RESULTS

A total of 127 patients (59 female and 68 male) with a mean age of 59.8±10.3 years were included in the study. While 101 patients (79.5%) wore surgical face masks, 26 patients (20.5%) wore cloth face masks. Low SAP reliability appeared in 23 patients (18.1%), and inferior visual field defects were present in 3 patients (2.4%). The main effects of poorly fitting face masks on SAP reliability were increased fixation losses and false-positive errors (for both, P=0.001). Low SAP reliability was significantly higher in patients wearing cloth face masks than in those wearing surgical face masks (47.8% vs. 9.9%; P=0.0001). The face mask-related fogging of eyeglasses before SAP is a strong predictor of fogging of the trial lenses-related low SAP reliability (odds ratio: 27, 95% confidence interval: 5.48-132.92, P=0.0001). In all repeated SAPs, the patients' reliability parameters improved, and inferior visual field artifacts disappeared.

CONCLUSIONS

Unsuitable face masks can cause either visual field artifacts, which may be interpreted as glaucoma progression or low test reliability. Taping the face masks' upper edges is an effective technique to prevent visual field artifacts and obtain good test reliability.

Understanding On-Campus Interactions With a Semiautomated, Barcode-Based Platform to Augment COVID-19 Contact Tracing: App Development and Usage

BACKGROUND

The COVID-19 pandemic has forced drastic changes to daily life, from the implementation of stay-at-home orders to mandating facial coverings and limiting in-person gatherings. While the relaxation of these control measures has varied geographically, it is widely agreed that contact tracing efforts will play a major role in the successful reopening of businesses and schools. As the volume of positive cases has increased in the United States, it has become clear that there is room for digital health interventions to assist in contact tracing.

OBJECTIVE

The goal of this study was to evaluate the use of a mobile-friendly app designed to supplement manual COVID-19 contact tracing efforts on a university campus. Here, we present the results of a development and validation study centered around the use of the MyCOVIDKey app on the Vanderbilt University campus during the summer of 2020.

METHODS

We performed a 6-week pilot study in the Stevenson Center Science and Engineering Complex on Vanderbilt University's campus in Nashville, TN. Graduate students, postdoctoral fellows, faculty, and staff >18 years who worked in Stevenson Center and had access to a mobile phone were eligible to register for a MyCOVIDKey account. All users were encouraged to complete regular self-assessments of COVID-19 risk and to key in to sites by scanning a location-specific barcode.

RESULTS

Between June 17, 2020, and July 29, 2020, 45 unique participants created MyCOVIDKey accounts. These users performed 227 self-assessments and 1410 key-ins. Self-assessments were performed by 89% (n=40) of users, 71% (n=32) of users keyed in, and 48 unique locations (of 71 possible locations) were visited. Overall, 89% (202/227) of assessments were determined to be low risk (ie, asymptomatic with no known exposures), and these assessments yielded a CLEAR status. The remaining self-assessments received a status of NOT CLEAR, indicating either risk of exposure or symptoms suggestive of COVID-19 (7.5% [n=17] and 3.5% [n=8] of self-assessments indicated moderate and high risk, respectively). These 25 instances came from 8 unique users, and in 19 of these instances, the at-risk user keyed in to a location on campus.

CONCLUSIONS

Digital contact tracing tools may be useful in assisting organizations to identify persons at risk of COVID-19 through contact tracing, or in locating places that may need to be cleaned or disinfected after being visited by an index case. Incentives to continue the use of such tools can improve uptake, and their continued usage increases utility to both organizational and public health efforts. Parameters of digital tools, including MyCOVIDKey, should ideally be optimized to supplement existing contact tracing efforts. These tools represent a critical addition to manual contact tracing efforts during reopening and sustained regular activity.

Task Similarity Estimation Through Adversarial Multitask Neural Network

Multitask learning (MTL) aims at solving the related tasks simultaneously by exploiting shared knowledge to improve performance on individual tasks. Though numerous empirical results supported the notion that such shared knowledge among tasks plays an essential role in MTL, the theoretical understanding of the relationships between tasks and their impact on learning shared knowledge is still an open problem. In this work, we are developing a theoretical perspective of the benefits involved in using information similarity for MTL. To this end, we first propose an upper bound on the generalization error by implementing the Wasserstein distance as the similarity metric. This indicates the practical principles of applying the similarity information to control the generalization errors. Based on those theoretical results, we revisited the adversarial multitask neural network and proposed a new training algorithm to learn the task relation coefficients and neural network parameters automatically. The computer vision benchmarks reveal the abilities of the proposed algorithms to improve the empirical performance. Finally, we test the proposed approach on real medical data sets, showing its advantage for extracting task relations.

Metabolomics approach based on LC-HRMS for the fast screening of iron(II)-chelating peptides in protein hydrolysates

Production of iron-chelating peptides from protein hydrolysates requires robust and adequate screening methods to optimize their purification and subsequently valorize their potential antioxidant properties. An original methodology was developed for direct and sensitive screening of iron(II)-chelating peptides based on ion-pair reverse phase liquid chromatography (IP-RPLC) coupled to high-resolution mass spectrometry (HRMS). Peptide mixture was first added to iron(II) solution to form iron(II)-peptide complexes. Then IP-RPLC-HRMS analysis was conducted on this iron-peptide mixture and on the iron-free peptide solution for comparative mass spectra analysis. This protocol, initially applied to a range of low molecular weight standard peptides, allowed detection of [(Peptide-H)+⁵⁶Fe^II]⁺ complex ion for iron(II)-chelating peptides (GGH, EAH, DAH, βAH, DMH, DTH, DSH). GGH was added in complex peptide mixtures and targeted analysis of [(GGH-H)+⁵⁶Fe^II]⁺ complex showed a limit of detection (LOD) below 0.77 mg L^-1 of GGH. This protocol was finally tested in combination with metabolomics software and additional digital processing for non-targeted search for iron(II)-chelating peptides. Applicability of this new screening methodology has been validated by detection of GGH as iron(II)-chelating peptide when added at 0.77 mg L^-1 in casein hydrolysate. Graphical abstract.

The open EEGLAB portal Interface: High-Performance computing with EEGLAB

EEGLAB signal processing environment is currently the leading open-source software for processing electroencephalographic (EEG) data. The Neuroscience Gateway (NSG, nsgportal.org) is a web and API-based portal allowing users to easily run a variety of neuroscience-related software on high-performance computing (HPC) resources in the U.S. XSEDE network. We have reported recently (Delorme et al., 2019) on the Open EEGLAB Portal expansion of the free NSG services to allow the neuroscience community to build and run MATLAB pipelines using the EEGLAB tool environment. We are now releasing an EEGLAB plug-in, nsgportal, that interfaces EEGLAB with NSG directly from within EEGLAB running on MATLAB on any personal lab computer. The plug-in features a flexible MATLAB graphical user interface (GUI) that allows users to easily submit, interact with, and manage NSG jobs, and to retrieve and examine their results. Command line nsgportal tools supporting these GUI functionalities allow EEGLAB users and plug-in tool developers to build largely automated functions and workflows that include optional NSG job submission and processing. Here we present details on nsgportal implementation and documentation, provide user tutorials on example applications, and show sample test results comparing computation times using HPC versus laptop processing.

Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons

This article proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (MuST) representation of input AER events and a spiking neural network (SNN) using spike-timing-dependent plasticity (STDP) for object recognition with MuST. MuST extracts the features contained in both the spatial and temporal information of AER event flow, and forms an informative and compact feature spike representation. We show not only how MuST exploits spikes to convey information more effectively, but also how it benefits the recognition using SNN. The recognition process is performed in an unsupervised manner, which does not need to specify the desired status of every single neuron of SNN, and thus can be flexibly applied in real-world recognition tasks. The experiments are performed on five AER datasets including a new one named GESTURE-DVS. Extensive experimental results show the effectiveness and advantages of the proposed approach.

The Robson classification for caesarean section-A proposed method based on routinely collected health data

BACKGROUND

With an increasing rate of caesarean sections as well as rising numbers of multiple pregnancies, valid classifications for benchmarking are needed. The Robson classification provides a method to group cases with caesarean section in order to assess differences in outcome across regions and sites. In this study we set up a novel method of classification by using routinely collected health data. We hypothesize i that routinely collected health data can be used to apply complex medical classifications and ii that the Robson classification is capable of classifying mothers and their corresponding newborn into meaningful groups with regard to outcome.

METHODS AND FINDINGS

The study was conducted at the coding department and the department of obstetrics and gynecology Inselspital, University Hospital of Bern, Switzerland. The study population contained inpatient cases from 2014 until 2017. Administrative and health data were extracted from the Data Warehouse. Cases were classified by a Structured Query Language code according to the Robson criteria using data from the administrative system, the electronic health record and from the laboratory system. An automated query to classify the cases according to Robson could be implemented and successfully validated. A linkage of the mother's class to the corresponding newborn could be established. The distribution of clinical indicators was described. It could be shown that the Robson classes are associated to outcome parameters and case related costs.

CONCLUSIONS

With this study it could be demonstrated, that a complex query on routinely collected health data would serve for medical classification and monitoring of quality and outcome. Risk-stratification might be conducted using this data set and should be the next step in order to evaluate the Robson criteria and outcome. This study will enhance the discussion to adopt an automated classification on routinely collected health data for quality assurance purposes.

Data analysis and modeling pipelines for controlled networked social science experiments

There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.