Portable robots for upper-limb rehabilitation after stroke: a systematic review and meta-analysis

BACKGROUND

Robot-assisted upper-limb rehabilitation has been studied for many years, with many randomised controlled trials (RCTs) investigating the effects of robotic-assisted training on affected limbs. The current trend directs towards end-effector devices. However, most studies have focused on the effectiveness of rehabilitation devices, but studies on device sizes are relatively few.

GOAL

Systematically review the effect of a portable rehabilitation robot (PRR) on the rehabilitation effectiveness of paralysed upper limbs compared with non-robotic therapy.

METHODS

A meta-analysis was conducted on literature that included the Fugl-Meyer Assessment (FMA) obtained from the PubMed and Web of Science (WoS) electronic databases until June 2023.

RESULTS

A total of 9 studies, which included RCTs, were completed and a meta-analysis was conducted on 8 of them. The analysis involved 295 patients. The influence on upper-limb function before and after treatment in a clinical environment is analysed by comparing the experimental group using the portable upper-limb rehabilitation robot with the control group using conventional therapy. The result shows that portable robots prove to be effective (FMA: SMD = 0.696, 95% = 0.099 to.293, p < 0.05).

DISCUSSION

Both robot-assisted and conventional rehabilitation effects are comparable. In some studies, PRR performs better than conventional rehabilitation, but conventional treatments are still irreplaceable. Smaller size with better portability has its advantages, and portable upper-limb rehabilitation robots are feasible in clinical rehabilitation.

CONCLUSION

Although portable upper-limb rehabilitation robots are clinically beneficial, few studies have focused on portability. Further research should focus on modular design so that rehabilitation robots can be decomposed, which benefits remote rehabilitation and household applications.

Electrochemical platform produced by 3D printing for analysis of small volumes using different electrode materials

Fused deposition modeling (FDM) 3D printing is a promising additive manufacturing technique to produce low-cost disposable electrochemical devices. However, the print of devices like well-known screen-printed electrodes (all electrodes on the same device) is difficult using the available technology (few materials available for production of working electrodes). In this paper we present a procedure to produce disposable and robust electrochemical devices by FDM 3D printing that allows reproducible analysis of small volumes (50-2000 μL). The device consists of just two printed parts that allow easy coupling of different conductive materials for using as disposable or non-disposable working electrodes with reproducible geometric area. Printed counter and pseudo-reference electrodes can also be easily fitted into the microcell. Moreover, conventional counter (platinum wire) and mini reference electrodes can also be used. As a proof of concept, paracetamol, cocaine and uric acid were used as model analytes using different materials as working electrodes. Linear calibration curves (r > 0.99) with similar slopes (0.29 ± 0.01 μA μmol L-1; RSD = 3.4%) were obtained by square wave voltammetry (SWV) using a complete printed system and different volumes of standard solutions of paracetamol (50, 100, and 200 μL). For uric acid, a linear range of 10-125 μmol L-1 (r > 0.99), was obtained using differential pulse voltammetry as the electrochemical technique and a disposable laser-induced graphene base as the working electrode. With the coupling of boron-doped diamond working electrode, screening tests were successfully performed in seized cocaine samples with selective detection of cocaine in the presence of its most common adulterants. The production cost per unit of a complete electrochemical system is around US 5.00. In large-scale production, only the working electrode needs to be replaced while the microcell and counter/pseudo reference electrodes do not need to be discarded.

Generalizability and portability of natural language processing system to extract individual social risk factors

OBJECTIVE

The objective of this study is to validate and report on portability and generalizability of a Natural Language Processing (NLP) method to extract individual social factors from clinical notes, which was originally developed at a different institution.

MATERIALS AND METHODS

A rule-based deterministic state machine NLP model was developed to extract financial insecurity and housing instability using notes from one institution and was applied on all notes written during 6 months at another institution. 10% of positively-classified notes by NLP and the same number of negatively-classified notes were manually annotated. The NLP model was adjusted to accommodate notes at the new site. Accuracy, positive predictive value, sensitivity, and specificity were calculated.

RESULTS

More than 6 million notes were processed at the receiving site by the NLP model, which resulted in about 13,000 and 19,000 classified as positive for financial insecurity and housing instability, respectively. The NLP model showed excellent performance on the validation dataset with all measures over 0.87 for both social factors.

DISCUSSION

Our study illustrated the need to accommodate institution-specific note-writing templates as well as clinical terminology of emergent diseases when applying NLP model for social factors. A state machine is relatively simple to port effectively across institutions. Our study. showed superior performance to similar generalizability studies for extracting social factors.

CONCLUSION

Rule-based NLP model to extract social factors from clinical notes showed strong portability and generalizability across organizationally and geographically distinct institutions. With only relatively simple modifications, we obtained promising performance from an NLP-based model.

Portable and integrated microfluidic flow control system using off-the-shelf components towards organs-on-chip applications

Organ-on-a-chip (OoC) devices require the precise control of various media. This is mostly done using several fluid control components, which are much larger than the typical OoC device and connected through fluidic tubing, i.e., the fluidic system is not integrated, which inhibits the system's portability. Here, we explore the limits of fluidic system integration using off-the-shelf fluidic control components. A flow control configuration is proposed that uses a vacuum to generate a fluctuation-free flow and minimizes the number of components used in the system. 3D printing is used to fabricate a custom-designed platform box for mounting the chosen smallest footprint components. It provides flexibility in arranging the various components to create experiment-specific systems. A demonstrator system is realized for lung-on-a-chip experiments. The 3D-printed platform box is 290 mm long, 240 mm wide and 37 mm tall. After integrating all the components, it weighs 4.8 kg. The system comprises of a switch valve, flow and pressure controllers, and a vacuum pump to control the diverse media flows. The system generates liquid flow rates ranging from 1.5 [Formula: see text]Lmin[Formula: see text] to 68 [Formula: see text]Lmin[Formula: see text] in the cell chambers, and a cyclic vacuum of 280 mbar below atmospheric pressure with 0.5 Hz frequency in the side channels to induce mechanical strain on the cells-substrate. The components are modular for easy exchange. The battery operated platform box can be mounted on either upright or inverted microscopes and fits in a standard incubator. Overall, it is shown that a compact integrated and portable fluidic system for OoC experiments can be constructed using off-the-shelf components. For further down-scaling, the fluidic control components, like the pump, switch valves, and flow controllers, require significant miniaturization while having a wide flow rate range with high resolution.

Smartphone-based on-site detection of hydrogen peroxide in milk by using a portable ratiometric fluorescent probe

The on-site detection of hydrogen peroxide (H₂O₂) is important for maintaining food safety as the ingestion of H₂O₂ can lead to serious pathological conditions. However, most reported fluorescent probes require a fluorometer to ensure readable signal output and reliable detection result. Consequently, the fluorescent detection of H₂O₂ can be realized only within a standard laboratory setting. Herein, we report a novel supramolecular strategy that can successfully convert the typical off-on response to H₂O₂ into a ratiometric response, which allows the on-site detection of H₂O₂ when used in conjunction with a smartphone-based 3D-printed miniaturized testing system. This method has acceptable sensitivity, good anti-interference ability, and desirable accuracy compared to a standard detection method. More importantly, this portable ratiometric method can be used to detect H₂O₂ residue in commercial milk samples with the simple testing apparatuses.

A fluorescence aptasensor based on hybridization chain reaction for simultaneous detection of T-2 toxins and zearalenone(1)

It is extremely necessary to establish a rapid and high-throughput method to detect mycotoxins in food, because grains and cereals are greatly vulnerable to mycotoxins before and after harvest. In this study, we developed a portable aptasensor based on streptavidin magnetic microspheres (MMPs) and hybridization chain reaction (HCR) to simultaneously detect T-2 toxin and zearalenone (ZEN) in corn and oat flour. The MMPs compete with the aptamer for binding, which releases more H0 and triggers HCR with the H1 intermediate modified using 6-FAM and BHQ-1 and the unmodified H2. Subsequently, placing the HCR system corresponding to T-2 and ZEN in a constant-temperature fluorescence detector resulted in well-recovered fluorescence of the HCR products. T-2 and ZEN exhibited good fluorescence response in the dynamic range of 0.001-10 ng mL^-1 and 0.01-100 ng mL^-1 with detection limits of 0.1 pg mL^-1 and 1.2 pg mL^-1, respectively. In addition, this strategy achieved the selective detection of T-2 and ZEN in the spiked corn and oat flour samples. The results are also in good agreement with those obtained using commercial ELISA kits. This developed aptasensor with the characteristics of simple operation and portability has the application potential of establishing sensitive and portable field detection of various mycotoxins.

One-class model with two decision thresholds for the rapid detection of cashew nuts adulteration by other nuts

A green screening method to determine cashew nut adulteration with Brazilian nut, pecan nut, macadamia nut and peanut was proposed. The method was based on the development of a one-class soft independent modelling of class analogy (SIMCA) model for non-adulterated cashew nuts using near-infrared (NIR) spectra obtained with portable equipment. Once the model is established, the assignment of unknown samples depends on the threshold established for the authentic class, which is a key aspect in any screening approach. The authors propose innovatively to define two thresholds: lower model distance limit and upper model distance limit. Samples with distances below the lower threshold are assigned as non-adulterated with a 100% probability; samples with distance values greater than the upper threshold are assigned as adulterated with a 100% probability; and samples with distances within these two thresholds will be considered uncertain and should be submitted to a confirmatory analysis. Thus, the possibility of error in the sample assignment significantly decreases. In the present study, when just one threshold was defined, values greater than 95% for the optimized threshold were obtained for both selectivity and specificity. When two class thresholds were defined, the percentage of samples with uncertain assignment changes according to the adulterant considered, highlighting the case of peanuts, in which 0% of uncertain samples was obtained. Considering all adulterants, the number of samples that were submitted to a confirmatory analysis was quite low, 5 of 224 adulterated samples and 3 of 56 non-adulterated samples.

On-demand biomanufacturing through synthetic biology approach

Biopharmaceuticals including protein therapeutics, engineered protein-based vaccines and monoclonal antibodies, are currently the mainstay products of the biotechnology industry. However, the need for specialized equipment and refrigeration during production and distribution poses challenges for the delivery of these technologies to the field and low-resource area. With the development of synthetic biology, multiple studies rewire the cell-free system or living cells to impact the portable, on-site and on-demand manufacturing of biomolecules. Here, we review these efforts and suggest future directions.

Portability of natural language processing methods to detect suicidality from clinical text in US and UK electronic health records

Background

In the global effort to prevent death by suicide, many academic medical institutions are implementing natural language processing (NLP) approaches to detect suicidality from unstructured clinical text in electronic health records (EHRs), with the hope of targeting timely, preventative interventions to individuals most at risk of suicide. Despite the international need, the development of these NLP approaches in EHRs has been largely local and not shared across healthcare systems.

Methods

In this study, we developed a process to share NLP approaches that were individually developed at King's College London (KCL), UK and Weill Cornell Medicine (WCM), US - two academic medical centers based in different countries with vastly different healthcare systems. We tested and compared the algorithms' performance on manually annotated clinical notes (KCL: n = 4,911 and WCM = 837).

Results

After a successful technical porting of the NLP approaches, our quantitative evaluation determined that independently developed NLP approaches can detect suicidality at another healthcare organization with a different EHR system, clinical documentation processes, and culture, yet do not achieve the same level of success as at the institution where the NLP algorithm was developed (KCL approach: F1-score 0.85 vs. 0.68, WCM approach: F1-score 0.87 vs. 0.72).

Limitations

Independent NLP algorithm development and patient cohort selection at the two institutions comprised direct comparability.

Conclusions

Shared use of these NLP approaches is a critical step forward towards improving data-driven algorithms for early suicide risk identification and timely prevention.

One step construction of an electrochemical sensor for melamine detection in milk towards an integrated portable system

Excessive intake of melamine (MEL) can be harmful to human health, and it is important to establish a rapid and accurate MEL detection method. As the electrochemical activity of MEL is very low, ferrocenylglutathione (Fc-ECG) was used as an electron transfer mediator to assist with the detection of MEL using screen-printed carbon electrode (SPCE). This modified electrode (MEL/Fc-ECG/SPCE) was prepared by stepwise drop-casting and was fully characterized. Results showed that MEL significantly enhanced signal of Fc-ECG/SPCE sensor due to the three p-π conjugated double bonds that facilitated electron transfer. Under optimal conditions, the sensor exhibits two linearities in the range of 0.20-2.00 μM and 8.00-800 μM, with a sensitivity of 15.03 μA·μM^-1·cm^-2. The selectivity, stability, and reproducibility were investigated and successfully used to detect MEL in raw milk and confirms safety verification of foods. Moreover, a portable testing platform was designed for MEL detection based on a CH32 chip.

Applying color recognition techniques to achieve low-cost portable digital board functionalities

Digital teaching support devices, like digital boards, are becoming increasingly popular to improve concept learning, as they allow manipulating represented objects in new and very innovative ways if compared with traditional boards. However, their acquisition cost (device, software licenses, additional hardware), and the lack of deployment flexibility to adapt to certain classroom configurations, are the main problems that prevents them to be used in a substantial number of educational environments or slow their adoption rate. This research shows that a color recognition algorithm, applied to projected images captured by consumer-grade webcams, can successfully provide functionalities equivalent to typical digital boards on low-budget educational environments. This way, a pointer object is recognized by its color, and its actions are interpreted at real time to be converted to standard UI actions in the computer screen, as a standard input device. The prototype has been also designed to be usable in classroom environments of different sizes, available space, or configuration, maximizing reutilization of existing elements in the classroom. The research prototype was successfully deployed in a real classroom using modest hardware, inexpensive requirements, and very flexible setup options, adapting both to student and teacher needs.

Half a century later and we're back where we started: How the problem of locality turned in to the problem of portability

In the 1970s, Lewontin sparked a debate about a problem of locality, by making the case that any given heritability estimate is local to the original population and environment studied, and could not be generalized to other populations and environments. Nearly 50 years later, a new problem of portability has emerged: the predictive accuracy of polygenic scores diminishes when applied to populations whose characteristics are different from the original population sample. This paper briefly reviews the nature of each problem and analyzes their similarities and differences in three areas: 1) conceptual underpinnings, 2) causal explanations, and 3) practical, social, and political implications. Although conceptually and methodologically different from the problem of locality in important respects, the problem of portability facing contemporary genomics today should come as no surprise, as it is an inevitable outcome of the kinds of problematic inferences detailed by Lewontin nearly half a century ago.

Compressed sensing for reduced hardware footprint in medical ultrasound

In this work, a compressed sensing method to reduce hardware complexity of ultrasound imaging systems is proposed and experimentally verified. We provide clinical evaluation of the method with a possible high compression rates (up to 64 RF signals compressed into a single channel on receive) which uses elastic net estimation for decoding stage. This allows a reduction in size and power consumption of the front-end electronics with only a minor loss in image quality. We demonstrate an 8-fold receive channel count reduction with a 3.16 dB and 3.64 dB mean absolute error for gallbladder and kidney images, respectively, as well as 7.4% increase in the contrast-to-noise ratio for kidney images and 0.1% loss in the contrast-to noise ratio for gallbladder images, on average. The proposed method may enable a fully portable ultrasonic device with virtually no loss in image quality as compared to a full size clinical scanner to be constructed.

Automated ICD coding via unsupervised knowledge integration (UNITE)

OBJECTIVE

Accurate coding is critical for medical billing and electronic medical record (EMR)-based research. Recent research has been focused on developing supervised methods to automatically assign International Classification of Diseases (ICD) codes from clinical notes. However, supervised approaches rely on ICD code data stored in the hospital EMR system and is subject to bias rising from the practice and coding behavior. Consequently, portability of trained supervised algorithms to external EMR systems may suffer.

METHOD

We developed an unsupervised knowledge integration (UNITE) algorithm to automatically assign ICD codes for a specific disease by analyzing clinical narrative notes via semantic relevance assessment. The algorithm was validated using coded ICD data for 6 diseases from Partners HealthCare (PHS) Biobank and Medical Information Mart for Intensive Care (MIMIC-III). We compared the performance of UNITE against penalized logistic regression (LR), topic modeling, and neural network models within each EMR system. We additionally evaluated the portability of UNITE by training at PHS Biobank and validating at MIMIC-III, and vice versa.

RESULTS

UNITE achieved an averaged AUC of 0.91 at PHS and 0.92 at MIMIC over 6 diseases, comparable to LR and MLP. It had substantially better performance than topic models. In regards to portability, the performance of UNITE was consistent across different EMR systems, superior to LR, topic models and neural network models.

CONCLUSION

UNITE accurately assigns ICD code in EMR without requiring human labor, and has major advantages over commonly used machine learning approaches. In addition, the UNITE attained stable performance and high portability across EMRs in different institutions.

Adapting electronic health records-derived phenotypes to claims data: Lessons learned in using limited clinical data for phenotyping

Algorithms for identifying patients of interest from observational data must address missing and inaccurate data and are desired to achieve comparable performance on both administrative claims and electronic health records data. However, administrative claims data do not contain the necessary information to develop accurate algorithms for disorders that require laboratory results, and this omission can result in insensitive diagnostic code-based algorithms. In this paper, we tested our assertion that the performance of a diagnosis code-based algorithm for chronic kidney disorder (CKD) can be improved by adding other codes indirectly related to CKD (e.g., codes for dialysis, kidney transplant, suspicious kidney disorders). Following the best practices from Observational Health Data Sciences and Informatics (OHDSI), we adapted an electronic health record-based gold standard algorithm for CKD and then created algorithms that can be executed on administrative claims data and account for related data quality issues. We externally validated our algorithms on four electronic health record datasets in the OHDSI network. Compared to the algorithm that uses CKD diagnostic codes only, positive predictive value of the algorithms that use additional codes was slightly increased (47.4% vs. 47.9-48.5% respectively). The algorithms adapted from the gold standard algorithm can be used to infer chronic kidney disorder based on administrative claims data. We succeeded in improving the generalizability and consistency of the CKD phenotypes by using data and vocabulary standardized across the OHDSI network, although performance variability across datasets remains. We showed that identifying and addressing coding and data heterogeneity can improve the performance of the algorithms.

A new aptamer/black phosphorous interdigital electrode for malachite green detection

Malachite Green (MG), a cationic triphenylmethane dye, has adverse effects on the immune and reproductive system. Thus, it is essential to develop a rapid, sensitive and high-selective method for determination of MG. Black phosphorus (BP) has high charge-carrier mobility (∼1000 cm² V^-1 s^-1) and high adsorption capacity for cationic dyes (i.e. MG) through both electrostatic and hydrophobic interactions. Thus, it potentially plays as a high-sensitive sensing platform for detecting MG. However, BP degrades within 12 h under humid condition, which limits its applications. To overcome this issue, cysteine (CYS) is used for protecting BP from oxidation and ceasing its degradation. To the best of our knowledge, it is the first time that CYS is used to functionalize BP, and a silicon interdigital electrode is fabricated with the functionalized BP and aptamer. The BP-based interdigital electrode shows a lowest detection limit of 0.3 ng L^-1 toward MG. This work provides a new route to prepare a large scale and selective biosensor for MG monitoring on site in future.

Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network

BACKGROUND

Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms.

METHODS

We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category.

RESULTS

A total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 ± 1.38. Specifically, the average knowledge (K) score is 0.64 ± 0.66, interpretation (I) score is 0.33 ± 0.55, and programming (P) score is 0.40 ± 0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks.

CONCLUSION

This study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks.

Portable ultrasound imaging system with super-resolution capabilities

This paper discusses an ultrasound technique where the echo signals from the array of transducer elements are compressed to as few as two RF channels while still in analog domain, with a much simplified front-end electronics. The method can achieve resolutions well beyond the diffraction limit, which is set by the excitation signal wavelength and numerical aperture of the imaging system. The fundamental principle that underlies this model based imaging technique is the preservation of the spatial frequency information content of the recorded echo signals with the help of pseudo-random apodization function followed by summation. A Verasonics V1 ultrasonic scanner is used to conduct experiments using an anechoic cyst made from gel phantom, immersed in degassed water. The estimated images were compared to those obtained using traditional B-mode delay-and-sum imaging available with the Verasonics V1 ultrasound machine. The estimated images using the proposed imaging technique showed a contrast ratio of 0.96 and Full-Width-Half-Maximum (FWHM) of about half the wavelength at a depth of 9.1 cm and at 1.875 MHz center frequency while the traditional delay and sum images had a contrast ratio of 0.62 and FWHM of about 5.5 wavelengths.

Development of a powered variable-stiffness exoskeleton device for elbow rehabilitation

Robot-assisted movement training by means of exoskeleton devices has been proven to be an effective method for post-stroke patients to recover their motor function. However, in order to be used in home-based rehabilitation, the kinematic structure of a wearable exoskeleton device should provide portability and make allowances for the natural joint range of motion for the user. Additionally, the actuated stiffness of the target joint is desired to be adjustable in accordance with the specific impairment level of the patient's upper limb. In this paper, we present a novel portable exoskeleton device which could provide support for rehabilitation patients with variable actuated stiffness in the elbow joint. It has five passive degrees of freedom to guarantee the user's natural joint range of motion and intra-subject variability, as well as an integrated variable stiffness actuator (VSA) which can adjust the joint stiffness independently by moving the pivot position. An elbow power-assist trial with different actuated joint stiffnesses was tested on a healthy subject to evaluate the functionality of the proposed device. By regulating the joint stiffness, the proposed device could provide variable power assistance for the wearer's elbow movements.

Recent advances in synthetic biology of cyanobacteria

Cyanobacteria are attractive hosts that can be engineered for the photosynthetic production of fuels, fine chemicals, and proteins from CO₂. Moreover, the responsiveness of these photoautotrophs towards different environmental signals, such as light, CO₂, diurnal cycle, and metals make them potential hosts for the development of biosensors. However, engineering these hosts proves to be a challenging and lengthy process. Synthetic biology can make the process of biological engineering more predictable through the use of standardized biological parts that are well characterized and tools to assemble them. While significant progress has been made with model heterotrophic organisms, many of the parts and tools are not portable in cyanobacteria. Therefore, efforts are underway to develop and characterize parts derived from cyanobacteria. In this review, we discuss the reported parts and tools with the objective to develop cyanobacteria as cell factories or biosensors. We also discuss the issues related to characterization, tunability, portability, and the need to develop enabling technologies to engineer this "green" chassis.

META-pipe cloud setup and execution

META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the service to all ELIXIR users. We describe our approach for setting up and executing the functional analysis of META-pipe on additional academic and commercial clouds. Our goal is to provide a powerful analysis service that is easy to use and to maintain. Our design therefore uses a distributed architecture where we combine central servers with multiple distributed backends that execute the computationally intensive jobs. We believe our experiences developing and operating META-pipe provides a useful model for others that plan to provide a portal based data analysis service in ELIXIR and other organizations with geographically distributed compute and storage resources.

META-pipe cloud setup and execution

META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the service to all ELIXIR users. We describe our approach for setting up and executing the functional analysis of META-pipe on additional academic and commercial clouds. Our goal is to provide a powerful analysis service that is easy to use and to maintain. Our design therefore uses a distributed architecture where we combine central servers with multiple distributed backends that execute the computationally intensive jobs. We believe our experiences developing and operating META-pipe provides a useful model for others that plan to provide a portal based data analysis service in ELIXIR and other organizations with geographically distributed compute and storage resources.

META-pipe cloud setup and execution

META-pipe is a complete service for the analysis of marine metagenomic data. It provides assembly of high-throughput sequence data, functional annotation of predicted genes, and taxonomic profiling. The functional annotation is computationally demanding and is therefore currently run on a high-performance computing cluster in Norway. However, additional compute resources are necessary to open the service to all ELIXIR users. We describe our approach for setting up and executing the functional analysis of META-pipe on additional academic and commercial clouds. Our goal is to provide a powerful analysis service that is easy to use and to maintain. Our design therefore uses a distributed architecture where we combine central servers with multiple distributed backends that execute the computationally intensive jobs. We believe our experiences developing and operating META-pipe provides a useful model for others that plan to provide a portal based data analysis service in ELIXIR and other organizations with geographically distributed compute and storage resources.

Towards generalizable entity-centric clinical coreference resolution

OBJECTIVE

This work investigates the problem of clinical coreference resolution in a model that explicitly tracks entities, and aims to measure the performance of that model in both traditional in-domain train/test splits and cross-domain experiments that measure the generalizability of learned models.

METHODS

The two methods we compare are a baseline mention-pair coreference system that operates over pairs of mentions with best-first conflict resolution and a mention-synchronous system that incrementally builds coreference chains. We develop new features that incorporate distributional semantics, discourse features, and entity attributes. We use two new coreference datasets with similar annotation guidelines - the THYME colon cancer dataset and the DeepPhe breast cancer dataset.

RESULTS

The mention-synchronous system performs similarly on in-domain data but performs much better on new data. Part of speech tag features prove superior in feature generalizability experiments over other word representations. Our methods show generalization improvement but there is still a performance gap when testing in new domains.

DISCUSSION

Generalizability of clinical NLP systems is important and under-studied, so future work should attempt to perform cross-domain and cross-institution evaluations and explicitly develop features and training regimens that favor generalizability. A performance-optimized version of the mention-synchronous system will be included in the open source Apache cTAKES software.

CloudBridge: a Simple Cross-Cloud Python Library

With clouds becoming a standard target for deploying applications, it is more important than ever to be able to seamlessly utilise resources and services from multiple providers. Proprietary vendor APIs make this challenging and lead to conditional code being written to accommodate various API differences, requiring application authors to deal with these complexities and to test their applications against each supported cloud. In this paper, we describe an open source Python library called CloudBridge that provides a simple, uniform, and extensible API for multiple clouds. The library defines a standard 'contract' that all supported providers must implement, and an extensive suite of conformance tests to ensure that any exposed behavior is uniform across cloud providers, thus allowing applications to confidently utilise any of the supported clouds without any cloud-specific code or testing.