Effects of Implementing a Barcode Information Management System on Operating Room Staff: Comparative Study.

BACKGROUND: Barcode information management systems (BIMS) have been implemented in operating rooms to improve the quality of medical care and administrative efficiency. Previous research has demonstrated that the Agile development model is extensively used in the development and management of information systems. However, the effect of information systems on staff acceptance has not been examined within the context of clinical medical information management systems. OBJECTIVE: This study aimed to explore the effects and acceptance of implementing a BIMS in comparison to the original information system (OIS) among operating and supply room staff. METHODS: This study was a comparative cohort design. A total of 80 staff members from the operating and supply rooms of a Northern Taiwan medical center were recruited. Data collection, conducted from January 2020 to August 2020 using a mobile-based structured questionnaire, included participant characteristics and the Information Management System Scale. SPSS (version 20.0, IBM Corp) for Windows (Microsoft Corporation) was used for data analysis. Descriptive statistics included mean, SD, frequency, and percentage. Differences between groups were analyzed using the Mann-Whitney U test and Kruskal-Wallis test, with a P value <.05 considered statistically significant. RESULTS: The results indicated that the BIMS generally achieved higher scores in key elements of system success, system quality, information quality, perceived system use, perceived ease of use, perceived usefulness, and overall quality score; none of these differences were statistically significant (P>.05), with the system quality subscale being closest to significance (P=.06). Nurses showed significantly better perceived system use than technicians (1.58, SD 4.78 vs -1.19, SD 6.24; P=.02). Significant differences in perceived usefulness were found based on educational level (P=.04) and experience with OIS (P=.03), with junior college-educated nurses and those with over 6 years of OIS experience reporting the highest perceived usefulness. CONCLUSIONS: The study demonstrates that using the Agile development model for BIMS is advantageous for clinical environments. The high acceptance among operating room staff underscores its practicality and broader adoption potential. It advocates for continued exploration of technology-driven solutions to enhance health care delivery and optimize clinical workflows.

Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review.

Pregnancy monitoring is always essential for pregnant women and fetuses. According to the report of WHO (World Health Organization), there were an estimated 287,000 maternal deaths worldwide in 2020. Regular hospital check-ups, although well established, are a burden for pregnant women because of frequent travelling or hospitalization. Therefore, home-based, long-term, non-invasive health monitoring is one of the hot research areas. In recent years, with the development of wearable sensors and related data-processing technologies, pregnancy monitoring has become increasingly convenient. This article presents a review on recent research in wearable sensors, physiological data processing, and artificial intelligence (AI) for pregnancy monitoring. The wearable sensors mainly focus on physiological signals such as electrocardiogram (ECG), uterine contraction (UC), fetal movement (FM), and multimodal pregnancy-monitoring systems. The data processing involves data transmission, pre-processing, and application of threshold-based and AI-based algorithms. AI proves to be a powerful tool in early detection, smart diagnosis, and lifelong well-being in pregnancy monitoring. In this review, some improvements are proposed for future health monitoring of pregnant women. The rollout of smart wearables and the introduction of AI have shown remarkable potential in pregnancy monitoring despite some challenges in accuracy, data privacy, and user compliance.

Implementing a Barcode-Based Whole-Life Cycle Management Process to Improve Robotic Instrument Tracking.

Minimally invasive surgery can involve the use of robotics to improve patient outcomes. Some robotic systems require special instruments with a designated number of uses. In China, during the reprocessing of the robotic instruments, health care personnel determined that the existing tracking processes were inadequate. They conducted a quality improvement project with the goal of establishing a barcode-based standardized process for tracking robotic instruments. They implemented technology that generated a unique identifier each time a robotic instrument was reprocessed after use. Nurses scanned the identifier when surgeons used the instrument. The findings included the increased accuracy of use documentation and decreases in untraceable sterilization and use records, charging concerns, and average daily and monthly inventory times. An increase in adverse event reports associated with robotic instruments also was noted. The use of barcode technology for robotic instrument tracking continues at the facility and may be expanded for additional specialty instruments.

Implementation of barcode medication administration (BMCA) technology on infusion pumps in the operating rooms.

BACKGROUND: Medication administration errors (MAEs) are a major cause of morbidity and mortality. An updated barcode medication administration (BCMA) technology on infusion pumps is implemented in the operating rooms to automate double check at a syringe exchange. OBJECTIVE: The aim of this mixed-methods before-and-after study is to understand the medication administrating process and assess the compliance with double check before and after implementation. METHODS: Reported MAEs from 2019 to October 2021 were analysed and categorised to the three moments of medication administration: (1) bolus induction, (2) infusion pump start-up and (3) changing an empty syringe. Interviews were conducted to understand the medication administration process with functional resonance analysis method (FRAM). Double check was observed in the operating rooms before and after implementation. MAEs up to December 2022 were used for a run chart. RESULTS: Analysis of MAEs showed that 70.9% occurred when changing an empty syringe. 90.0% of MAEs were deemed to be preventable with the use of the new BCMA technology. The FRAM model showed the extent of variation to double check by coworker or BCMA.Observations showed that the double check for pump start-up changed from 70.2% to 78.7% postimplementation (p=0.41). The BCMA double check contribution for pump start-up increased from 15.3% to 45.8% (p=0.0013). The double check for changing an empty syringe increased from 14.3% to 85.0% (p<0.0001) postimplementation. BCMA technology was new for changing an empty syringe and was used in 63.5% of administrations. MAEs for moments 2 and 3 were significantly reduced (p=0.0075) after implementation in the operating rooms and ICU. CONCLUSION: An updated BCMA technology contributes to a higher double check compliance and MAE reduction, especially when changing an empty syringe. BCMA technology has the potential to decrease MAEs if adherence is high enough.

Improving Administration and Documentation of Enteral Nutrition Support Therapy in a Veteran Affairs Health Care System: Use of Medication Administration Record and Bar Code Scanning Technology.

OBJECTIVES: The goal of this project was to evaluate and improve the ordering, administration, documentation, and monitoring of enteral nutrition therapies within the inpatient setting in a Veteran's Health Administration system. METHODS: An interdisciplinary team of clinicians reviewed the literature for best practices and revised the process for enteral nutrition support for hospitalized veterans. Interventions included training staff, revising workflows to include scanning patients and products, including enteral nutrition orders within the medication administration record (MAR), and using the existing bar code medication administration system for administration, documentation, and monitoring. Baseline and postprocess improvement outcomes over a year period were collected and analyzed for quality improvement opportunities. RESULTS: Before process change, only 60% (33/55) of reviewed enteral nutrition orders were documented and 40% (22/55) were not documented in the intake flowsheet of the electronic health record. In the year after adding enteral nutrition therapies to the MAR and using bar code scanning, a total of 3807 enteral nutrition products were evaluated. One hundred percent of patients were bar code scanned, 3106/3807 (82%) products were documented as given, 447/3807 (12%) were documented as held (with comments), 12/3807 (<1%) were documented as missing/unavailable, and 242/3807 (6%) were documented as refused. CONCLUSIONS: Inclusion of enteral nutrition order sets on the MAR and using bar code scanning technology resulted in sustained improvements in safety, administration, and documentation of enteral therapies for hospitalized veterans.

Performance of QR Code Detectors near Nyquist Limits.

For the interacting with real world, augmented reality devices need lightweight yet reliable methods for recognition and identification of physical objects. In that regard, promising possibilities are offered by supporting computer vision with 2D barcode tags. These tags, as high contrast and visually well-defined objects, can be used for finding fiducial points in the space or to identify physical items. Currently, QR code readers have certain demands towards the size and visibility of the codes. However, the increase of resolution of built-in cameras makes it possible to identify smaller QR codes in the scene. On the other hand, growing resolutions cause the increase to the computational effort of tag location. Therefore, resolution reduction in decoders is a common trade-off between processing time and recognition capabilities. In this article, we propose the simulation method of QR codes scanning near limits that stem from Shannon's theorem. We analyze the efficiency of three publicly available decoders versus different size-to-sampling ratios (scales) and MTF characteristics of the image capture subsystem. The MTF we used is based on the characteristics of real devices, and it was modeled using Gaussian low-pass filtering. We tested two tasks-decoding and locating-and-decoding. The findings of the work are several-fold. Among others, we identified that, for practical decoding, the QR-code module should be no smaller than 3-3.5 pixels, regardless of MTF characteristics. We confirmed the superiority of Zbar in practical tasks and the worst recognition capabilities of OpenCV. On the other hand, we identified that, for borderline cases, or even below Nyquist limit where the other decoders fail, OpenCV is still capable of decoding some information.

Micapipe: A pipeline for multimodal neuroimaging and connectome analysis.

Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.

An Efficient Data Acquisition and Processing Scheme for Wireless Multimedia Sensor Networks.

This study studies the problem of efficient multimedia data acquisition and decreasing whole energy expenditure of wireless multimedia sensor networks and proposes a three-step multimedia data acquisition and wireless energy supplement strategy. Firstly, for network partition, this study proposes a network partition scheme based on vicinity likeness and distance of sensor nodes (VLD), which divides the whole sensor network into multiple regions. The physical links inside the region are dense and concentrated, while the link connections between regions are sparse. Disconnecting the connections between regions hardly affects the data transmission of sensor nodes. Secondly, this study proposes an efficient data acquisition and processing scheme for wireless multimedia sensor network ASS. Compared with other anchor selection schemes, this scheme has obvious performance advantages. Then, the problem of minimizing network energy expenditure is defined, and the optimal sensor node data perception rate and network link transmission rate of the optimization function are obtained by dual decomposition and sub-gradient method. Finally, in the case of a given network energy threshold, the performance of the overall strategy in this study is verified by comparing the amount of data collected by the base station.

Mindful Workarounds in Bar Code Medication Administration.

Bar-Coded Medication Administration systems (BCMA) are often used with workarounds. These workarounds are usually judged against standard operating procedures or the use of the technology as 'designers' intended'. However, some workarounds may be reasonable and justified to prevent safety errors. In this conceptual paper, we clarify BCMA safety mechanisms and provide a framework to identify workarounds with BCMA that nullify the error prevention mechanisms inherent in the technology design and process. We also highlight the importance of understanding the purpose behind a nurse's workaround in BCMA, focusing on the notion of mindful (thoughtful) workarounds that have the potential to improve patient safety.

Standardization of cellular therapy terminology, coding and labeling: a review.

The 1990s saw rapid growth in international activity in hematopoietic cell transplantation. As national donor registries were established and international collaboration increased, a need to transfer cellular therapy products across national borders emerged. A lack of international standards for identification, terminology and labeling resulted in significant challenges for import and export. Twenty years of effort by a large group of experts supported by professional societies and accreditation bodies has today achieved a high degree of standardization. This review highlights the main landmarks in this journey and serves as a reminder of the importance of taking the "long view" when working toward international standardization. It demonstrates the need for continual maintenance and enhancement of standards to meet the changing needs of the cell therapy industry and highlights recent developments in ISBT 128.

Pea-KD: Parameter-efficient and accurate Knowledge Distillation on BERT.

Knowledge Distillation (KD) is one of the widely known methods for model compression. In essence, KD trains a smaller student model based on a larger teacher model and tries to retain the teacher model's level of performance as much as possible. However, existing KD methods suffer from the following limitations. First, since the student model is smaller in absolute size, it inherently lacks model capacity. Second, the absence of an initial guide for the student model makes it difficult for the student to imitate the teacher model to its fullest. Conventional KD methods yield low performance due to these limitations. In this paper, we propose Pea-KD (Parameter-efficient and accurate Knowledge Distillation), a novel approach to KD. Pea-KD consists of two main parts: Shuffled Parameter Sharing (SPS) and Pretraining with Teacher's Predictions (PTP). Using this combination, we are capable of alleviating the KD's limitations. SPS is a new parameter sharing method that increases the student model capacity. PTP is a KD-specialized initialization method, which can act as a good initial guide for the student. When combined, this method yields a significant increase in student model's performance. Experiments conducted on BERT with different datasets and tasks show that the proposed approach improves the student model's performance by 4.4% on average in four GLUE tasks, outperforming existing KD baselines by significant margins.

A data flow process for confidential data and its application in a health research project.

BACKGROUND: The use of linked healthcare data in research has the potential to make major contributions to knowledge generation and service improvement. However, using healthcare data for secondary purposes raises legal and ethical concerns relating to confidentiality, privacy and data protection rights. Using a linkage and anonymisation approach that processes data lawfully and in line with ethical best practice to create an anonymous (non-personal) dataset can address these concerns, yet there is no set approach for defining all of the steps involved in such data flow end-to-end. We aimed to define such an approach with clear steps for dataset creation, and to describe its utilisation in a case study linking healthcare data. METHODS: We developed a data flow protocol that generates pseudonymous datasets that can be reversibly linked, or irreversibly linked to form an anonymous research dataset. It was designed and implemented by the Comprehensive Patient Records (CPR) study in Leeds, UK. RESULTS: We defined a clear approach that received ethico-legal approval for use in creating an anonymous research dataset. Our approach used individual-level linkage through a mechanism that is not computer-intensive and was rendered irreversible to both data providers and processors. We successfully applied it in the CPR study to hospital and general practice and community electronic health record data from two providers, along with patient reported outcomes, for 365,193 patients. The resultant anonymous research dataset is available via DATA-CAN, the Health Data Research Hub for Cancer in the UK. CONCLUSIONS: Through ethical, legal and academic review, we believe that we contribute a defined approach that represents a framework that exceeds current minimum standards for effective pseudonymisation and anonymisation. This paper describes our methods and provides supporting information to facilitate the use of this approach in research.

ORPER: A Workflow for Constrained SSU rRNA Phylogenies.

The continuous increase in sequenced genomes in public repositories makes the choice of interesting bacterial strains for future sequencing projects ever more complicated, as it is difficult to estimate the redundancy between these strains and the already available genomes. Therefore, we developed the Nextflow workflow "ORPER", for "ORganism PlacER", containerized in Singularity, which allows the determination the phylogenetic position of a collection of organisms in the genomic landscape. ORPER constrains the phylogenetic placement of SSU (16S) rRNA sequences in a multilocus reference tree based on ribosomal protein genes extracted from public genomes. We demonstrate the utility of ORPER on the Cyanobacteria phylum, by placing 152 strains of the BCCM/ULC collection.

A robust bitmap-based real-time position tracking algorithm for rats in radial arm maze tests.

This paper aims to develop a position tracking algorithm by which a rat in a radial arm maze can be accurately located in real time. An infrared (IR) night-vision camera was hung above the maze to capture IR images of the rat. The IR images were binarized and then duplicated for subsequent intersection and opening operations. Due to simple operations and a high robustness against the noise spots formed by the droppings of the rat, it took just minutes to process more than 9000 frames, and an accuracy above 99% was reached as well. The maze was intruded by an experimenter to further test the robustness, and the accuracy slightly fell to 98%. For comparison purposes, the same experiments were carried out using a pre-trained YOLO v2 model. The YOLO counterpart gave an accuracy beyond 97% in the absence and in the presence of the intruder. In other words, this work slightly outperformed the YOLO counterpart in terms of the accuracy in both cases, which indicates the robustness of this work. However, it took the YOLO counterpart an hour or so to locate a rat contained in the frames, which highlights the contribution of this work.

A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder.

Dimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we present the single-cell graph autoencoder (scGAE), a dimensionality reduction method that preserves topological structure in scRNA-seq data. scGAE builds a cell graph and uses a multitask-oriented graph autoencoder to preserve topological structure information and feature information in scRNA-seq data simultaneously. We further extended scGAE for scRNA-seq data visualization, clustering, and trajectory inference. Analyses of simulated data showed that scGAE accurately reconstructs developmental trajectory and separates discrete cell clusters under different scenarios, outperforming recently developed deep learning methods. Furthermore, implementation of scGAE on empirical data showed scGAE provided novel insights into cell developmental lineages and preserved inter-cluster distances.

Mapeamento de buscas eletrônicas dos medicamentos mais populares na pandemia da COVID-19 no Brasil / Mapeo de búsquedas electrónicas de las drogas más populares en la pandemia COVID-19 en Brasil / Mapping of electronic searches for the most popular drugs in the COVID-19 pandemic in Brazil

O estudo objetivou mapear as buscas eletrônicas dos medicamentos mais populares na pandemia da COVID-19 no Brasil. Trata.se de um estudo exploratório, retrospectivo e misto. Os dados foram coletados em julho de 2020 através do Google Trends®, filtrados a partir dos últimos 90 dias de pesquisa, que estivessem relacionados aos medicamentos, ivermectina, cloroquina, hidroxicloroquina, dexametasona e azitromicina. Para identificação dos noticiários mais visitados, utilizou.se o Google News®. A população brasileira realizou buscas eletrônicas com os cinco medicamentos investigados, porém houve predomínio da cloroquina e ivermectina. Ademais, conforme a doença evoluiu pelos Estados brasileiros foi perceptível a influência de pesquisas científicas e do governo na busca por esses medicamentos. Ressalta.se que é fundamental para a gestão da pandemia que as estratégias de comunicação sejam traçadas, alicerçadas na responsabilidade social e na perspectiva do empoderamento popular com foco na identificação de notícias falsas e no uso consciente das informações adquiridas virtualmente(AU)

El estudio tuvo como objetivo mapear las búsquedas electrónicas de los medicamentos más populares en la pandemia COVID-19 en Brasil. Se trata de un estudio exploratorio, retrospectivo y mixto. Los datos fueron recolectados en julio de 2020 a través de Google Trends®. Se seleccionaron en los últimos 90 días de investigación los que estaban relacionados con los medicamentos ivermectina, cloroquina, hidroxicloroquina, dexametasona y azitromicina. Para identificar las noticias más visitadas se utilizó Google News®. La población brasileña realizó búsquedas electrónicas entre los cinco fármacos investigados, pero predominaron la cloroquina y la ivermectina. Además, a medida que la enfermedad evolucionó en los estados brasileños, la influencia de la investigación científica y gubernamental en la búsqueda de estos fármacos fue notable. Es de destacar que es fundamental para el manejo de la pandemia que se elaboren estrategias de comunicación, basadas en la responsabilidad social y la perspectiva del empoderamiento popular, con foco en la identificación de noticias falsas y el uso consciente de la información adquirida virtualmente(AU)

The study aimed to map electronic searches for most popular drugs in the COVID-19 pandemic in Brazil. This is an exploratory, retrospective and mixed study. The data were collected in July 2020 through Google Trends®, filtered from the last 90 days of research, wich were related to drugs, ivermectin, chloroquine, hydroxychloroquine, dexamethasone and azithromycin. To identify the most visited newscasts, Google News® was used. The Brazilian population conducted electronic searches among the five investigated drugs, but chloroquine and ivermectin predominated. Furthermore, as the disease evolved in Brazilian states, the influence of scientific and government research in the search for these drugs was noticeable. It is noteworthy that it is essential for the management of the pandemic that communication strategies be drawn up, based on social responsibility and the perspective of popular empowerment, with a focus on the identification of false news and the conscious use of information acquired virtually(AU)

An automatic screening method for strabismus detection based on image processing.

PURPOSE: This study aims to provide an automatic strabismus screening method for people who live in remote areas with poor medical accessibility. MATERIALS AND METHODS: The proposed method first utilizes a pretrained convolutional neural network-based face-detection model and a detector for 68 facial landmarks to extract the eye region for a frontal facial image. Second, Otsu's binarization and the HSV color model are applied to the image to eliminate the influence of eyelashes and canthi. Then, the method samples all of the pixel points on the limbus and applies the least square method to obtain the coordinate of the pupil center. Lastly, we calculated the distances from the pupil center to the medial and lateral canthus to measure the deviation of the positional similarity of two eyes for strabismus screening. RESULT: We used a total of 60 frontal facial images (30 strabismus images, 30 normal images) to validate the proposed method. The average value of the iris positional similarity of normal images was smaller than one of the strabismus images via the method (p-value<0.001). The sample mean and sample standard deviation of the positional similarity of the normal and strabismus images were 1.073 ± 0.014 and 0.039, as well as 1.924 ± 0.169 and 0.472, respectively. CONCLUSION: The experimental results of 60 images show that the proposed method is a promising automatic strabismus screening method for people living in remote areas with poor medical accessibility.

Doctor Recommendation Model Based on Ontology Characteristics and Disease Text Mining Perspective.

BACKGROUND: Patients can access medical services such as disease diagnosis online, medical treatment guidance, and medication guidance that are provided by doctors from all over the country at home. Due to the complexity of scenarios applying medical services online and the necessity of professionalism of knowledge, the traditional recommendation methods in the medical field are confronting with problems such as low computational efficiency and poor effectiveness. At the same time, patients consulting online come from all sides, and most of them suffer from nonacute or malignant diseases, and hence, there may be offline medical treatment. Therefore, this paper proposes an online prediagnosis doctor recommendation model by integrating ontology characteristics and disease text. Particularly, this recommendation model takes full consideration of geographical location of patients. OBJECTIVE: The recommendation model takes the real consultation data from online as the research object, fully testifying its effectiveness. Specifically, this model would make recommendation to patients on department and doctors based on patients' information of symptoms, diagnosis, and geographical location, as well as doctor's specialty and their department. METHODS: Utilizing crawler technique, five hospital departments were selected from the online medical service platform. The names of the departments were in accordance with the standardized department names used in real hospitals (e.g., endocrinology, dermatology, gynemetrics, pediatrics, and neurology). As a result, a dataset consisting of 20000 consultation questions by patients was built. Through the application of Python and MySQL algorithms, replacing semantic dictionary retrieval or word frequency statistics, word vectors were utilized to measure similarity between patients' prediagnosis and doctors' specialty, forming a recommendation framework on medical departments or doctors based on the above-obtained sentence similarity measurement and providing recommendation advices on intentional departments and doctors. RESULTS: In the online medical field, compared with the traditional recommendation method, the model proposed in the paper is of higher recommendation accuracy and feasibility in terms of department and doctor recommendation effectiveness. CONCLUSIONS: The proposed online prediagnosis doctor recommendation model integrates ontology characteristics and disease text mining. The model gives a relatively more accurate recommendation advice based on ontology characteristics such as patients' description texts and doctors' specialties. Furthermore, the model also gives full consideration on patients' location factors. As a result, the proposed online prediagnosis doctor recommendation model would improve patients' online consultation experience and offline treatment convenience, enriching the value of online prediagnosis data.

High-speed serial deep learning through temporal optical neurons.

Deep learning is able to functionally mimic the human brain and thus, it has attracted considerable recent interest. Optics-assisted deep learning is a promising approach to improve forward-propagation speed and reduce the power consumption of electronic-assisted techniques. However, present methods are based on a parallel processing approach that is inherently ineffective in dealing with the serial data signals at the core of information and communication technologies. Here, we propose and demonstrate a sequential optical deep learning concept that is specifically designed to directly process high-speed serial data. By utilizing ultra-short optical pulses as the information carriers, the neurons are distributed at different time slots in a serial pattern, and interconnected to each other through group delay dispersion. A 4-layer serial optical neural network (SONN) was constructed and trained for classification of both analog and digital signals with simulated accuracy rates of over 79.2% with proper individuality variance rates. Furthermore, we performed a proof-of-concept experiment of a pseudo-3-layer SONN to successfully recognize the ASCII codes of English letters at a data rate of 12 gigabits per second. This concept represents a novel one-dimensional realization of artificial neural networks, enabling a direct application of optical deep learning methods to the analysis and processing of serial data signals, while offering a new overall perspective for temporal signal processing.

Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases.

While metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.