Automatic ICD-10-CM coding via Lambda-Scaled attention based deep learning model.

The International Classification of Diseases (ICD) serves as a global healthcare administration standard, with one of its editions being ICD-10-CM, an enhanced diagnostic classification system featuring numerous new codes for specific anatomic sites, co-morbidities, and causes. These additions facilitate conveying the complexities of various diseases. Currently, ICD-10 coding is widely adopted worldwide. However, public hospitals in Pakistan have yet to implement it and automate the coding process. In this research, we implemented ICD-10-CM coding for a private database and named it Clinical Pool of Liver Transplant (CPLT). Additionally, we proposed a novel deep learning model called Deep Recurrent-Convolution Neural Network with a lambda-scaled Attention module (DRCNN-ATT) using the CPLT database to achieve automatic ICD-10-CM coding. DRCNN-ATT combines a bi-directional long short-term memory network (bi-LSTM), a multi-scale convolutional neural network (MS-CNN), and a lambda-scaled attention module. Experimental results demonstrate that deep recurrent convolutional neural network (DRCNN) faces attention score vanishing problem with a standard attention module for automatic ICD coding. However, adding a lambda-scaled attention module resolves this issue. We evaluated DRCNN-ATT model using two distinct datasets: a private CPLT dataset and a public MIMIC III top 50 dataset. The results indicate that the DRCNN-ATT model outperformed various baselines by generating 0.862 micro F1 and 0.25 macro F1 scores on CPLT dataset and 0.705 micro F1 and 0.655 macro F1 scores on MIMIC III top 50 dataset. Furthermore, we also deployed our model for automatic ICD-10-CM coding using ngrok and the Flask APIs, which receives input, processes it, and then returns the results.

NeuronBridge: an intuitive web application for neuronal morphology search across large data sets.

BACKGROUND: Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome's structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities. RESULTS: Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine. CONCLUSIONS: NeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org .

Adapting an Isobaric Tag-Labeled Yeast Peptide Standard to Develop Targeted Proteomics Assays.

Targeted proteomics strategies present a streamlined hypothesis-driven approach to analyze specific sets of pathways or disease related proteins. goDig is a quantitative, targeted tandem mass tag (TMT)-based assay that can measure the relative abundance differences for hundreds of proteins directly from unfractionated mixtures. Specific protein groups or entire pathways of up to 200 proteins can be selected for quantitative profiling, while leveraging sample multiplexing permits the simultaneous analysis of up to 18 samples. Despite these benefits, implementing goDig is not without challenges, as it requires access to an instrument application programming interface (iAPI), an elution order and spectral library, a web-based method builder, and dedicated companion software. In addition, the absence of an example test assay may dissuade researchers from testing or implementing goDig. Here, we repurpose the TKO11 standard─which is commercially available but may also be assembled in-lab─and establish it as a de facto test assay for goDig. We build a proteome-wide goDig yeast library, quantify protein expression across several gene ontology (GO) categories, and compare these results to a fully fractionated yeast gold-standard data set. Essentially, we provide a guide detailing the goDig-based quantification of TKO11, which can also be used as a template for user-defined assays in other species.

Inserting Pre-analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics with Sample Multiplexing.

GoDig, a platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standards, is a powerful, flexible, and easy-to-use method that uses tandem mass tags to increase sample throughput up to 18-fold relative to label-free methods. Though the protein-level success rates of GoDig are high, the peptide-level success rates are more limited, hampering assays of harder-to-quantify proteins and site-specific phenomena. To guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates. In this mode, two or more chromatographic priming runs are automatically performed to improve the accuracy and precision of target elution orders, followed by analytical runs which quantify targets. Using priming runs, success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy expression profiles across four human cell lines.

Apigenin intervenes in liver fibrosis by regulating PKM2-HIF-1α mediated oxidative stress.

Apigenin (API) is a natural flavonoid compound with antioxidant, anti fibrotic, anti-inflammatory and other effects, but there is limited research on the effect of API on liver fibrosis. This study aims to explore the effect and potential mechanism of API on liver fibrosis induced by CCl4 in mice. The results indicate that API reduces oxidative stress levels, inhibits hepatic stellate cell (HSC) activation, and exerts anti liver fibrosis effects by regulating the PKM2-HIF-1α pathway. We observed that API alleviated liver tissue pathological damage and collagen deposition in CCl4 induced mouse liver fibrosis model, promoting the recovery of liver function in mice with liver fibrosis. In addition, the API inhibits the transition of Pyruvate kinase isozyme type M2 (PKM2) from dimer to tetramer formation by regulating the EGFR-MEK1/2-ERK1/2 pathway, thereby preventing dimer from entering the nucleus and blocking PKM2-HIF-1α access. This change leads to a decrease in malondialdehyde (MDA) and Catalase (CAT) levels and an increase in glutathione (GSH), superoxide dismutase (SOD), glutathione peroxidase (GSH-PX) levels, as well as total antioxidant capacity (T-AOC) in the liver of liver fibrosis mice. At the same time, API downregulated the expression of α-smooth muscle actin (α-SMA), Vimentin and Desmin in the liver tissue of mice with liver fibrosis, inhibited the activation of HSC, and reduced collagen deposition. These results indicate that API can inhibit HSC activation and alleviate CCl4 induced liver fibrosis by inhibiting the PKM2-HIF-1α pathway and reducing oxidative stress, laying an important foundation for the development and clinical application of API as a novel drug for treating liver fibrosis.

Unraveling the Potential of Vitamin B3-Derived Salts with a Salicylate Anion as Dermal Active Agents for Acne Treatment.

This study is focused on the utilization of naturally occurring salicylic acid and nicotinamide (vitamin B3) in the development of novel sustainable Active Pharmaceutical Ingredients (APIs) with significant potential for treating acne vulgaris. The study highlights how the chemical structure of the cation significantly influences surface activity, lipophilicity, and solubility in aqueous media. Furthermore, the new ionic forms of APIs, the synthesis of which was assessed with Green Chemistry metrics, exhibited very good antibacterial properties against common pathogens that contribute to the development of acne, resulting in remarkable enhancement of biological activity ranging from 200 to as much as 2000 times when compared to salicylic acid alone. The molecular docking studies also revealed the excellent anti-inflammatory activity of N-alkylnicotinamide salicylates comparable to commonly used drugs (indomethacin, ibuprofen, and acetylsalicylic acid) and were even characterized by better IC50 values than common anti-inflammatory drugs in some cases. The derivative, featuring a decyl substituent in the pyridinium ring of nicotinamide, exhibited efficacy against Cutibacterium acnes while displaying favorable water solubility and improved wettability on hydrophobic surfaces, marking it as particularly promising. To investigate the impact of the APIs on the biosphere, the EC50 parameter was determined against a model representative of crustaceans─Artemia franciscana. The majority of compounds (with the exception of the salt containing the dodecyl substituent) could be classified as "Relatively Harmless" or "Practically Nontoxic", indicating their potential low environmental impact, which is essential in the context of modern drug development.

Development of Cholinium-Based API Ionic Liquids with Enhanced Drug Solubility: Biological Evaluation and Interfacial Properties.

We report an efficient sustainable two-step anion exchange synthetic procedure for the preparation of choline API ionic liquids (Cho-API-ILs) that contain active pharmaceutical ingredients (APIs) as anions combined with choline-based cations. We have evaluated the in vitro cytotoxicity for the synthesized compounds using three different cells lines, namely, HEK293 (normal kidney cell line), SW480, and HCT 116 (colon carcinoma cells). The solubility of APIs and Cho-API-ILs was evaluated in water/buffer solutions and was found higher for Cho-API-ILs. Further, we have investigated the antimicrobial potential of the pure APIs, ILs, and Cho-API-ILs against clinically relevant microorganisms, and the results demonstrated the promise of Cho-API-ILs as potent antimicrobial agents to treat bacterial infections. Moreover, the aggregation and adsorption properties of the Cho-API-ILs were observed by using a surface tension technique. The aggregation behavior of these Cho-API-ILs was further supported by conductivity and pyrene probe fluorescence. The thermodynamics of aggregation for Cho-API-ILs has been assessed from the temperature dependence of surface tension. The micellar size and their stability have been studied by dynamic light scattering, transmission electron microscopy, and zeta potential. Therefore, the duality in the nature of Cho-API-ILs has been explored with the upgradation of their physical, chemical, and biopharmaceutical properties, which enhance the opportunities for advances in pharmaceutical sciences.

Predicting 35-Cl electric field gradient tensors in crystalline solids using cluster and fragment-corrected planewave density functional theory.

Planewave-corrected methods have proven effective for accurately modeling nuclear magnetic resonance (NMR) parameters in crystalline systems. Recent work extended the application of planewave-corrected calculations beyond the second row, predicting EFG tensor parameters for 35Cl using a simple molecular correction to projector augmented-wave (PAW) density functional theory (DFT). Here we extend this work using fragment and cluster-based calculations coupled with polarizable continuum (PCM) methods to improve further the accuracy of planewave-corrected 35Cl EFG tensor calculations. Benchmark data from a test set comprised of 105 individual 35Cl EFG tensor principal components for chlorine-containing molecular crystals and crystalline chloride salts shows fragment-corrected planewave calculations using the PBE0 hybrid density functional improve the accuracy of predicted EFG tensor components by 30 % relative to traditional planewave calculations. We compare the influence of different geometry optimization methods and density functionals on the accuracy of predicted 35Cl EFG tensor parameters. Four cases of spectral assignment are presented to demonstrate the utility of improving the accuracy of predicted 35Cl EFG tensor parameters.

Comparison of permitted daily exposure (PDE) values for active pharmaceutical ingredients (APIs) - Evidence of a robust approach.

Permitted Daily Exposure Limits (PDEs) are set for Active Pharmaceutical Ingredients (APIs) to control cross-contamination when manufacturing medicinal products in shared facilities. With the lack of official PDE lists for pharmaceuticals, PDEs have to be set by each company separately. Although general rules and guidelines for the setting of PDEs exist, inter-company variations in the setting of PDEs occur and are considered acceptable within a certain range. To evaluate the robustness of the PDE approach between different pharmaceutical companies, data on PDE setting of five marketed APIs (amlodipine, hydrochlorothiazide, metformin, morphine, and omeprazole) were collected and compared. Findings show that the variability between PDE values is within acceptable ranges (below 10-fold) for all compounds, with the highest difference for morphine due to different Point of Departures (PODs) and Adjustment Factors (AFs). Factors of PDE variability identified and further discussed are: (1) availability of data, (2) selection of POD, (3) assignment of AFs, (4) route-to-route extrapolation, and (5) expert judgement and differences in company policies. We conclude that the investigated PDE methods and calculations are robust and scientifically defensible. Additionally, we provide further recommendations to harmonize PDE calculation approaches across the pharmaceutical industry.

Racial Biases Associated With Pulse Oximetry: Longitudinal Social Network Analysis of Social Media Advocacy Impact.

BACKGROUND: Pulse oximetry is a noninvasive method widely used in critical care and various clinical settings to monitor blood oxygen saturation. During the COVID-19 pandemic, its application for at-home oxygen saturation monitoring became prevalent. Further investigations found that pulse oximetry devices show decreased accuracy when used on individuals with darker skin tones. This study aimed to investigate the influence of X (previously known as Twitter) on the dissemination of information and the extent to which it raised health care sector awareness regarding racial disparities in pulse oximetry. OBJECTIVE: This study aimed to explore the impact of social media, specifically X, on increasing awareness of racial disparities in the accuracy of pulse oximetry and to map this analysis against the evolution of published literature on this topic. METHODS: We used social network analysis drawing upon Network Overview Discovery and Exploration for Excel Pro (NodeXL Pro; Social Media Research Foundation) to examine the impact of X conversations concerning pulse oximetry devices. Searches were conducted using the Twitter Academic Track application programming interface (as it was known then). These searches were performed each year (January to December) from 2012 to 2022 to cover 11 years with up to 52,052 users, generating 188,051 posts. We identified the nature of influencers in this field and monitored the temporal dissemination of information about social events and regulatory changes. Furthermore, our social media analysis was mapped against the evolution of published literature on this topic, which we located using PubMed. RESULTS: Conversations on X increased health care awareness of racial bias in pulse oximetry. They also facilitated the rapid dissemination of information, attaining a substantial audience within a compressed time frame, which may have impacted regulatory action announced concerning the investigation of racial biases in pulse oximetry. This increased awareness led to a surge in scientific research on the subject, highlighting a growing recognition of the necessity to understand and address these disparities in medical technology and its usage. CONCLUSIONS: Social media platforms such as X enabled researchers, health experts, patients, and the public to rapidly share information, increasing awareness of potential racial bias. These platforms also helped connect individuals interested in these topics and facilitated discussions that spurred further research. Our research provides a basis for understanding the role of X and other social media platforms in spreading health-related information about potential biases in medical devices such as pulse oximeters.

Systematic DOE Approach for Dry Granulation Study at Early Clinical Stage of Novel Drugs: An Industrial Case.

In order to introduce a cost-effective strategy method for commercial scale dry granulation at the early clinical stage of drug product development, we developed dry granulation process using formulation without API, fitted and optimized the process parameters adopted Design of Experiment (DOE). Then, the process parameters were confirmed using one formulation containing active pharmaceutical ingredient (API). The results showed that the roller pressure had significant effect on particle ratio (retained up to #60 mesh screen), bulk density and tapped density. The roller gap had significant influence on particle ratio and specific energy. The particle ratio was significantly affected by the mill speed (second level). The tabletability of the powder decreased after dry granulation. The effect of magnesium stearate on the tabletability was significant. In the process validation study, the properties of the prepared granules met the requirements for each response studied in the DOE. The prepared tablets showed higher tensile strength, good content uniformity of filled capsules, and the dissolution profiles of which were consistent with that of clinical products. This drug product process development and research strategies could be used as a preliminary experiment for the dry granulation process in the early clinical stage.

Research on Automated Modeling Technology of Sluice Gate Project Based on BIM Technology.

In order to improve the efficiency and accuracy of the modeling and design work of the sluice gate project, this paper proposes an automatic generation template of the sluice gate project with customized semantics and project layout scheme, aiming at realizing the rapid assembling of all kinds of components of the sluice gate project. In the construction process, this paper first starts from basic parametric modeling and proposes constraints as the basis of modeling. Subsequently, a template library framework is developed based on the constraints to ensure that the generated templates have a high degree of standardization and consistency. Finally, an efficient and flexible template library is successfully constructed by using the customized classes and functions of Revit API, which provides powerful technical support for the modeling and design work of sluice gate engineering. This achievement helps to promote the informationization and intelligent development of the water conservancy engineering industry, and its versatility and scalability also make it have a wide range of application prospects in other water conservancy engineering fields.

Channel Features and API Frequency-Based Transformer Model for Malware Identification.

Malicious software (malware), in various forms and variants, continues to pose significant threats to user information security. Researchers have identified the effectiveness of utilizing API call sequences to identify malware. However, the evasion techniques employed by malware, such as obfuscation and complex API call sequences, challenge existing detection methods. This research addresses this issue by introducing CAFTrans, a novel transformer-based model for malware detection. We enhance the traditional transformer encoder with a one-dimensional channel attention module (1D-CAM) to improve the correlation between API call vector features, thereby enhancing feature embedding. A word frequency reinforcement module is also implemented to refine API features by preserving low-frequency API features. To capture subtle relationships between APIs and achieve more accurate identification of features for different types of malware, we leverage convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. Experimental results demonstrate the effectiveness of CAFTrans, achieving state-of-the-art performance on the mal-api-2019 dataset with an F1 score of 0.65252 and an AUC of 0.8913. The findings suggest that CAFTrans improves accuracy in distinguishing between various types of malware and exhibits enhanced recognition capabilities for unknown samples and adversarial attacks.

Adoption of Deep-Learning Models for Managing Threat in API Calls with Transparency Obligation Practice for Overall Resilience.

System-to-system communication via Application Programming Interfaces (APIs) plays a pivotal role in the seamless interaction among software applications and systems for efficient and automated service delivery. APIs facilitate the exchange of data and functionalities across diverse platforms, enhancing operational efficiency and user experience. However, this also introduces potential vulnerabilities that attackers can exploit to compromise system security, highlighting the importance of identifying and mitigating associated security risks. By examining the weaknesses inherent in these APIs using security open-intelligence catalogues like CWE and CAPEC and implementing controls from NIST SP 800-53, organizations can significantly enhance their security posture, safeguarding their data and systems against potential threats. However, this task is challenging due to evolving threats and vulnerabilities. Additionally, it is challenging to analyse threats given the large volume of traffic generated from API calls. This work contributes to tackling this challenge and makes a novel contribution to managing threats within system-to-system communication through API calls. It introduces an integrated architecture that combines deep-learning models, i.e., ANN and MLP, for effective threat detection from large API call datasets. The identified threats are analysed to determine suitable mitigations for improving overall resilience. Furthermore, this work introduces transparency obligation practices for the entire AI life cycle, from dataset preprocessing to model performance evaluation, including data and methodological transparency and SHapley Additive exPlanations (SHAP) analysis, so that AI models are understandable by all user groups. The proposed methodology was validated through an experiment using the Windows PE Malware API dataset, achieving an average detection accuracy of 88%. The outcomes from the experiments are summarized to provide a list of key features, such as FindResourceExA and NtClose, which are linked with potential weaknesses and related threats, in order to identify accurate control actions to manage the threats.

Integrated Portable and Stationary Health Impact-Monitoring System for Firefighters.

The multi-layered negative effects caused by pollutants released into the atmosphere as a result of fires served as the stimulus for the development of a system that protects the health of firefighters operating in the affected area. A collaborative network comprising mobile and stationary Internet of Things (IoT) devices that are furnished with gas sensors, along with a remote server, constructs a resilient framework that monitors the concentrations of harmful emissions, characterizes the ambient air quality of the vicinity where the fire transpires, adopting European Air Quality levels, and communicates the outcomes via suitable applications (RESTful APIs and visualizations) to the stakeholders responsible for fire management decision making. Different experimental evaluations adopting separate contexts illustrate the operation of the infrastructure.

Sustainable Synthesis of the Active Pharmaceutical Ingredient Atenolol in Deep Eutectic Solvents.

Atenolol, one of the top five best-selling drugs in the world today used to treat angina and hypertension, and to reduce the risk of death after a heart attack, faces challenges in current synthetic methods to address inefficiencies and environmental concerns. The traditional synthesis of this drug involves a process that generates a large amount of waste and other by-products that need disposal. This study presents a one-pot DES-based sustainable protocol for synthesizing atenolol. The use of the DES allowed the entire process to be conducted with no need for additional bases or catalysts, in short reaction times, under mild conditions, and avoiding chromatographic purification. The overall yield of atenolol was 95%. The scalability of the process to gram-scale production was successfully demonstrated, emphasizing its potential in industrial applications. Finally, the 'greenness' evaluation, performed using the First Pass CHEM21 Metrics Toolkit, highlighted the superiority in terms of the atom economy, the reaction mass efficiency, and the overall process mass intensity of the DES-based synthesis compared with the already existing methods.

Greenery planning for urban air pollution control based on biomonitoring potential: Explicit emphasis on foliar accumulation of particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs).

In this study, efficiencies of eight indigenous plants of Baishnabghata Patuli Township (BPT), southeast Kolkata, India, were explored as green barrier species and potentials of plant leaves were exploited for biomonitoring of particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs). The present work focused on studying PM capturing abilities (539.32-2766.27 µg cm-2) of plants (T. divaricata, N. oleander and B. acuminata being the most efficient species in retaining PM) along with the estimation of foliar contents of PM adhered to leaf surfaces (total sPM (large + coarse): 526.59-2731.76 µg cm-2) and embedded within waxes (total wPM (large + coarse): 8.73-34.51 µg cm-2). SEM imaging used to analyse leaf surfaces affirmed the presence of innate corrugated microstructures as main drivers for particle capture. Accumulation capacities of PAHs of vehicular origin (total index, TI > 4) were compared among the species based on measured concentrations (159.92-393.01 µg g-1) which indicated T. divaricata, P. alba and N. cadamba as highest PAHs accumulators. Specific leaf area (SLA) of plants (71.01-376.79 cm2 g-1), a measure of canopy-atmosphere interface, had great relevance in PAHs diffusion. Relative contribution (>90%) of 4-6 ring PAHs to total carcinogenic equivalent and potential as well as 5-6 ring PAHs to total mutagenic equivalent and potential had also been viewed with respect to benzo[a]pyrene. In-depth analysis of foliar traits and adoption of plant-based ranking strategies (air pollution tolerance index (APTI) and anticipated performance index (API)) provided a rationale for green belting. Each of the naturally selected plant species showed evidences of adaptations during abiotic stress to maximize survival and filtering effects for reductive elimination of ambient PM and PAHs, allowing holistic management of green spaces.

Streamlining Research Metrics Compilation Reports: An Automated Approach Using Scopus and Clarivate APIs.

This article examines the development and implementation of a customized Python script utilizing the Elsevier Scopus and Clarivate Web of Science Journal Citation Reports Application Programming Interfaces (APIs). The aim was to streamline and expedite the labor-intensive process of collecting research metrics, which were traditionally compiled manually by librarians at the University of Miami Miller School of Medicine Louis Calder Memorial Library. The script significantly reduces the time and effort required to generate comprehensive reports on research productivity, thereby enabling more efficient resource allocation and aiding in faculty evaluations.

Assessment of spatial air quality on the East Coast of Peninsular Malaysia utilizing environmetric techniques.

The presence of harmful substances in the atmosphere poses significant risks to the environment and public health. These pollutants can come from natural sources like dust and wildfires, or from human activities such as industrial, transportation, and agricultural practices. The objective of this study was to assess air quality on the East Coast of Peninsular Malaysia by analyzing historical data from the Department of Environment, Malaysia. Daily measurements of PM10, O3, SO2, NO2, and CO were collected from eight monitoring stations over 11 years (2011-2021) and analyzed using environmetric techniques. Hierarchical agglomerative cluster analysis (HACA) classified two stations as belonging to the high pollution cluster (HPC), three stations as part of the moderate pollution cluster (MPC), and three stations as the low pollution cluster (LPC). Discriminant analysis revealed a correct assignment rate of 90.50%, indicating that all five parameters were able to differentiate pollution levels with high significance (p < 0.0001). Principal component analysis (PCA) was conducted to validate the pattern of air quality variables in relation to the identified clusters (HPC, MPC, and LPC). The results showed that two verifactors (VFs) were extracted in HPC and LPC, while three VFs were identified in MPC. The cumulative variance explained by the PCA for HPC, MPC, and LPC was 69.43%, 82.32%, and 62.16%, respectively. Finally, an artificial neural network (ANN) was used to forecast the air pollutant index (API) levels, using the R2 and RMSE performance metrics. The PCA-MLP Model A yielded an R2 value of 0.8470 and an RMSE of 6.6470, while PCA-MLP Model B achieved an R2 value of 0.8591 and an RMSE of 6.3000, both indicating a significant and strong correlation.

OpenFIBSEM: A universal API for FIBSEM control.

This paper introduces OpenFIBSEM, a universal API to control Focused Ion Beam Scanning Electron Microscopes (FIBSEM). OpenFIBSEM aims to improve the programmability and automation of electron microscopy workflows in structural biology research. The API is designed to be cross-platform, composable, and extendable: allowing users to use any portion of OpenFIBSEM to develop or integrate with other software tools. The package provides core functionality such as imaging, movement, milling, and manipulator control, as well as system calibration, alignment, and image analysis modules. Further, a library of reusable user interface components integrated with napari is provided, ensuring easy and efficient application development. OpenFIBSEM currently supports ThermoFisher and TESCAN hardware, with support for other manufacturers planned. To demonstrate the improved automation capabilities enabled by OpenFIBSEM, several example applications that are compatible with multiple hardware manufacturers are discussed. We argue that OpenFIBSEM provides the foundation for a cross-platform operating system and development ecosystem for FIBSEM systems. The API and applications are open-source and available on GitHub (https://github.com/DeMarcoLab/fibsem).