Rapid Decision Research Articles

Telemedicine networks for acute stroke: an analysis of global coverage, gaps and opportunities.

Despite the proven efficacy of telestroke in improving clinical outcomes by providing access to specialized expertise and allowing rapid expert hyperacute stroke management and decision-making, detailed operational evidence is scarce, especially for less developed or lower - income regions. We aimed to map the global telestroke landscape and characterize existing networks. We employed a four-tiered approach to comprehensively identify telestroke networks, primarily involving engagement with national stroke experts, stroke societies, and international stroke authorities. A carefully designed questionnaire was then distributed to the leaders of all identified networks to assess these networks' structures, processes, and outcomes. We identified 254 telestroke networks distributed across 67 countries. High-income countries (HICs) concentrated 175 (69%) of the networks. No evidence of telestroke services was found in 58 (30%) countries. From the identified networks, 88 (34%) completed the survey, being 61 (71%) located in HICs. Network set-up was highly heterogenous, ranging from 17 (22%) networks with more than twenty affiliated hospitals, providing thousands of annual consultations using purpose-built highly specialized technology, to 11 (13%) networks with fewer than 120 consultations annually using generic videoconferencing equipment. Real-time video and image transfer was employed in 64 (75%) networks, while 62 (74%) conducting quality monitoring. Most networks established in the last three years were located in low- and middle-income countries (LMICs). This comprehensive global survey of telestroke networks found significant variation in network coverage, set-up, and technology use. Most services are in HICs, and few services are in LMICs, although an emerging trend of new networks in these regions marks a pivotal moment in global telestroke care. The wide variation in quality monitoring practices across networks, with many failing to report key performance metrics, underscores the urgent need for standardized, resource-appropriate quality assurance measures that can be adapted to diverse settings.

Multi-UAV Escape Target Search: A Multi-Agent Reinforcement Learning Method

The multi-UAV target search problem is crucial in the field of autonomous Unmanned Aerial Vehicle (UAV) decision-making. The algorithm design of Multi-Agent Reinforcement Learning (MARL) methods has become integral to research on multi-UAV target search owing to its adaptability to the rapid online decision-making required by UAVs in complex, uncertain environments. In non-cooperative target search scenarios, targets may have the ability to escape. Target probability maps are used in many studies to characterize the likelihood of a target’s existence, guiding the UAV to efficiently explore the task area and locate the target more quickly. However, the escape behavior of the target causes the target probability map to deviate from the actual target’s position, thereby reducing its effectiveness in measuring the target’s probability of existence and diminishing the efficiency of the UAV search. This paper investigates the multi-UAV target search problem in scenarios involving static obstacles and dynamic escape targets, modeling the problem within the framework of decentralized partially observable Markov decision process. Based on this model, a spatio-temporal efficient exploration network and a global convolutional local ascent mechanism are proposed. Subsequently, we introduce a multi-UAV Escape Target Search algorithm based on MAPPO (ETS–MAPPO) for addressing the escape target search difficulty problem. Simulation results demonstrate that the ETS–MAPPO algorithm outperforms five classic MARL algorithms in terms of the number of target searches, area coverage rate, and other metrics.

Collaborative Real-World Evidence Among Regulators: Lessons and Perspectives.

The International Coalition of Medicines Regulatory Authorities (ICMRA), comprising 38 global medicines regulatory authorities, collaborates on shared challenges, notably during the COVID-19 pandemic. This article focuses on the ICMRA COVID-19 Real-World Evidence (RWE) and Observational Studies Working Group. The Working Group aimed to address challenges related to RWE and observational studies during the pandemic, resulting in impactful studies and ICMRA statements on international collaboration for RWE and COVID-19 vaccine safety. Reflecting on 3 years of collaboration, the Working Group surveyed members for insights, and recommendations were formulated to enhance research preparedness, collaboration, and response to future public health emergencies. The lessons learned highlight the importance of global collaborations, governance structures for rapid decision-making, and effective utilization of existing networks. Recommendations include the establishment of an international governance structure, a "coalition of the willing" for swift research collaboration, dedicated sub-groups, periodic workshops, common protocols, joint timelines, and data model templates, leveraging existing infrastructure, and strengthening outreach for transparency and engagement. The Working Group envisions repurposing into an RWE strategic and operational entity, contributing to global public health emergency response mechanisms. In conclusion, the Working Group's success lies in effective communication, collaborative research, and leveraging existing infrastructure, with ongoing contributions to global emergency response mechanisms.

Diagnosis from an Epidemiological Point of View. The Example of COVID-19

A diagnostic test is anything that provides data about the patient's health or disease. All such tests can be formally evaluated for their accuracy and precision (sensitivity and specificity). Once a test has been performed, whether positive or negative, sensitivity and specificity are not the priority issues; the physician's dilemma is whether or not the patient has the disease, once the test result is known. This is the positive and negative predictive value. However, the positive predictive value depends on the prevalence: when prevalence is high, the positive predictive value of the test increases and, as a consequence, there are fewer false positives and more false negatives. When prevalence is low, the opposite occurs: the positive predictive value of the test decreases, and there will be more false positives and fewer false negatives. Furthermore, diagnoses are generally not determined and labeled in isolation; rather, diagnoses are applied sequentially (Bayesian inference). Thus, prior knowledge facilitates rapid decision-making that is generally correct by increasing the pre-test diagnostic probability. When tests are performed in series (one after the other), specificity and positive predictive value are maximized, but sensitivity and negative predictive value are reduced. Thus, we have the example of COVID-19. For a low prevalence scenario (such as the current one), assuming a specificity of 98%, a positive test ensures the diagnosis in at least 2/3 of patients. And a negative result practically rules it out. When the incidence of COVID-19 is high. For example, with a prevalence of 30% or in practice when we are in some "COVID-19 wave", a positive result ensures the diagnosis in 94% but a negative result can occur in up to 10% of patients. On the other hand, a second test after the first gives a more reliable result if its results go in the same direction.

Precision Offloading in Edge Computing: Leveraging Predictive Model

The intended effect of the investigation is to provide sophisticated prediction and decision-making models in order to optimize service delivery and improve the Quality of Experience (QoE) for users. This research tackles the problems that are associated with job offloading in edge computing settings. In order to reduce service latency and improve overall performance, the Bi-Directional Long Short-Term Memory (B-LSTM) model is used. This model provides the ability to forecast task creation and server load. In order to accommodate the particular qualities of different devices, the Selective Objective Offloading Decision (SOOD) approach is presented. This method makes use of the TOPSIS methodology to turn server assessment into a decision-making issue that involves several criteria. A considerable increase of 98.4% in user quality of experience is achieved by the SOOD paradigm. In addition, the Rapid Offloading Decision (ROD) model is presented in order to manage unexpected work patterns. This is accomplished by using the log information of surrounding devices, which results in instantaneous and dependable offloading choices. Through the usage of prediction algorithms and selective decision-making, this research gives a complete strategy to improving the efficiency of edge computing. The goal of this technique is to maximize the utilization of servers and the user experience.

Technology Focus: Data Analytics (October 2024)

In recent years, advancements in artificial intelligence, particularly the revolutionary applications of deep-learning-based computer vision and large language models, have significantly affected the oil and gas industry. These technological developments have underscored the growing importance and diversity of data analytics across various aspects of the industry’s daily operations. Maintaining this momentum, data analytics practitioners in the industry have reexamined existing workflows and realized the substantial benefits that artificial intelligence brings, including increased efficiency and expedited turnaround times. Organizations have allocated resources to develop a big-data-driven, multidisciplinary platform that leverages artificial intelligence and machine learning to enhance rapid and robust decision-making in asset management and risk mitigation. Integrated data-management platforms use both proprietary data and public domain data to streamline and automate processes that otherwise could be tedious and time-consuming. Successful case studies have been shared in the literature on topics such as candidate screening and production forecasting. A prominent area of focus garnering extensive attention is the integration of artificial intelligence and domain knowledge, exemplified by physics-informed machine learning. Several innovative workflows using proxy or surrogate models in conjunction with machine learning and deep learning have been published. These workflows demonstrate promising test cases that extract valuable insights from physics-based modeling results and provide production forecasts through a statistical approach. Undoubtedly, we can anticipate a surge in research and publications on this topic in the coming years, which will contribute significantly to reshaping our industry. As advancements in generative artificial intelligence and big data continue to accelerate, swiftly adapting and pivoting toward digital transformation will be essential in the emerging digital era. Recommended additional reading at OnePetro: www.onepetro.org. SPE 217665 Well Construction in the Era of Big Data: It’s Not Data Analytics, It’s Engineering With Data by G.S. Payette, ExxonMobil, et al. IPTC 24126 Deep-Learning-Based Long Short-Term Memory for Production Data Analysis of Hydraulically Fractured Wells by Fengyuan Zhang, China University of Petroleum-Beijing, et al. URTeC 3857064 Application of Data Analytics to Chemometric Analysis of Conventionally Produced Oil Samples From the Delaware Basin by Timothy Carpp, Texas A&M University, et al.

Future armed conflicts - A new point of view

This article focusses on future wars and the resulting consequences for the development of the armed forces. The purpose of this study is to indicate the course of future armed conflicts on the base of the forecasts presented by the largest research centres as well as to signal the need to look at them from a new point of view, disregarding the current tense situation in the world. The subjects of the research are armed conflicts, and above all, the possible ways of conducting them in the future, as well as the domains in which they will be fought. The authors assume that the causes and forms of future armed conflicts will evolve. Changes will depend on the time and place of the outbreak of conflicts, the technology used, as well as the scale of their play. It is also assumed that future armed conflicts will take place in all domains, between equal opponents or between great powers and smaller states or organizations, which are weaker to them in every respect. In the paper, the authors took an attempt to answer the following questions: How will the geopolitical and economic situation in the world change, also in the context of progressing climate change? How will armed conflicts evolve in a changing world? How will future wars play out and what capabilities should military forces have to fight them? According to the analysis, the authors claim that future armed conflicts will: take place in all domains; require a rapid situation assessment and decisions; conducted in a complex environment; include hybrid strategies; increasingly difficult to resolve; non-linear; took the form of asymmetric activities; periodically conducted without electronic devices (an opponent can disable devices or track them); implemented by modular units capable of conducting independent activities at the lowest levels; saturated with robots and drones.

Development of a machine learning model to support low cost real-time Legionella monitoring in premise plumbing systems

Legionella pneumophila (L. pneumophila) is a pathogenic bacterium primarily known for causing Legionnaires’ Disease which is known for high mortality rates, particularly in the elderly. With caseloads continuing to increase, further research is needed to improve our understanding of optimized sampling schema and safe limits of L. pneumophila, in part to target improved treatment options and realistic population-level risk modeling. Particularly in healthcare and other high-risk locations these become crucial and time sensitive needs. Therefore, we conceptualized this research as a means of incorporating easily measured physiochemical water quality parameters and generalization of the unique ecology of building water systems to build a computational model that can allow for more rapid and accurate decision making. This research uses the specific machine learning (ML) method called statistical learning theory to incorporate concentration of host cells, such as native amoeba, and physiochemical water quality parameters to estimate the probability of observing ranges of Legionella gene copy concentrations. Using data from previously published research on Legionella prevalence in a large building, our ML method trains the model on the relative impacts of physiochemical parameters on likely amoeba host cell occurrences. The model is expanded to estimate host cell concentrations using correlations and regressions operated through LASSO algorithms. After categorization variables from these results are then used to inform a logistic regression to provide an estimate of the probability of Legionella gene copy concentration ranges. In summary, conventional results generated by logistic regression and multiple linear regression quantified the associations among ecological conditions in the water and ability to predict a likely range of Legionella concentration in a management focused way. Further, two ML methods, PCA and LASSO, demonstrated feasibility in accurate real-time monitoring of Legionella through physiochemical indicators as evidenced with good accuracy of predictions based for validation results. Furthermore results demonstrate the vital need to account for the impact of water quality on building on host cells, and via their quantified water microbial ecology, not just Legionella concentrations.

Utilizing ChatGPT for On-the-Fly Decision-Making in Autonomous Systems

In the realm of autonomous systems, rapid and efficient decision-making is critical to ensure optimal performance and adaptability in dynamic environments. This paper explores the application of ChatGPT, a large-scale language model, in on-the-fly decision-making within autonomous systems. By leveraging ChatGPT's capabilities in natural language processing, contextual understanding, and real-time response generation, the study demonstrates how it can support autonomous agents in interpreting situations, generating actionable insights, and making informed decisions. The integration of ChatGPT into autonomous systems provides a flexible and scalable approach to improving decision accuracy and response times in unpredictable scenarios. This research investigates the benefits, challenges, and potential use cases, highlighting the model's contributions to enhancing autonomy in various fields such as robotics, self-driving vehicles, and intelligent drones.

Development of a Machine Learning Framework for Real-Time PMI (Post-Mortem Interval) Estimation in Field Forensics

Accurate estimation of the Post-Mortem Interval (PMI) is critical in forensic investigations, aiding in determining the time of death. However, traditional PMI estimation methods, often reliant on physiological observations and environmental factors, face significant limitations in accuracy and efficiency, especially in field conditions. This paper presents the development of a machine learning (ML) framework designed for real-time PMI estimation, integrating multimodal sensor data to address the challenges encountered in field forensics. Our framework utilizes environmental and physiological features, including body temperature, ambient humidity, and biochemical decomposition markers, to predict PMI with high precision. The ML model, trained on historical forensic data, is deployed on a real-time processing platform, enabling rapid analysis and decision-making in resource- constrained environments. The system is optimized for field operations, incorporating low-power hardware and edge computing capabilities to provide forensic investigators with reliable PMI estimates on-site. Through a series of controlled experiments simulating forensic scenarios, our framework demonstrates a significant improvement in PMI accuracy compared to traditional methods, while maintaining low latency for real-time applications. This research highlights the potential of machine learning to revolutionize forensic practices, offering a scalable and adaptive solution for time-sensitive investigations. Here are some relevant keywords for the development of a machine learning framework for real-time PMI (Post- Mortem Interval) estimation in field forensics: Keywords: Field Forensics, Real-Time Machine Learning, Body Decomposition Stages, Machine Learning in Forensic Science, Artificial Intelligence for PMI Analysis, Sensor Data in PMI Estimation, Deep Learning for PMI Estimation, Automated Forensic Analysis, Data Acquisition in Field Forensics.

Digital 3D Hologram Generation Using Spatial and Elevation Information

The evolution of cartography poses challenges in representing three-dimensional terrain accurately on traditional two-dimensional maps. Providing an accurate 3D view of the area, coupled with essential geographic information, is vital for rapid and accurate decision-making in emergency management and response. Holography offers a promising solution by providing immersive three-dimensional visualizations. The field of hologram mapping, although novel, is still developing. Given its nascent stage, several limitations are evident. This study addresses one such limitation—inaccuracies in distance measurement—by presenting a hologram map that integrates two-dimensional and three-dimensional information. Accurate distance information on maps is critical for operational success. We aimed to improve hologram maps by integrating contour lines. Our approach allows users to measure distances from near-perpendicular angles while viewing 3D features from other perspectives. We review current advancements in hologram mapping, highlight existing limitations, and introduce our innovative solution designed to enhance both accuracy and usability. Our experiment resulted in a hologram map that accurately depicts a 3D environment, integrates contour lines, and allows for distance and slope angle measurements. The hologram map fills the research gap by providing accurate 3D visualization and distance measurement, signifying a major advancement in hologram mapping.

Artificial intelligence in cardiology: a peek at the future and the role of ChatGPT in cardiology practice.

Artificial intelligence has increasingly become an integral part of our daily activities. ChatGPT, a natural language processing technology developed by OpenAI, is widely used in various industries, including healthcare. The application of ChatGPT in healthcare is still evolving, with studies exploring its potential in clinical decision-making, patient education, workflow optimization, and scientific literature. ChatGPT could be exploited in the medical field to improve patient education and information, thus increasing compliance. ChatGPT could facilitate information exchange on major cardiovascular diseases, provide clinical decision support, and improve patient communication and education. It could assist the clinician in differential diagnosis, suggest appropriate imaging modalities, and optimize treatment plans based on evidence-based guidelines. However, it is unclear whether it will be possible to use ChatGPT for the management of patients who require rapid decisions. Indeed, many drawbacks are associated with the daily use of these technologies in the medical field, such as insufficient expertise in specialized fields and a lack of comprehension of the context in which it works. The pros and cons of its use have been explored in this review, which was not written with the help of ChatGPT.

Parameter Optimization and Capacitance-Based Monitoring of In Situ Cell Detachment in Microcarrier Cultures

This study delves into the scale-down optimization of the in situ cell detachment process for MA 104 cells cultivated on Cytodex 1 microcarriers (MCs). Through a systematic exploration, critical operational parameters—the agitation speed, incubation time, Trypsin–EDTA volume and corresponding activity, and washing steps—were identified as key factors influencing the efficiency and scalability of in situ cell detachment in microcarrier-based cell culture. Maintaining an appropriate agitation speed (1.25 × Njs, minimum agitation speed at which no microcarriers remain stationary for the signification period of 5 s), optimizing the Trypsinization incubation time (up to 60 min), and implementing multiple washing steps (two times) post-medium removal were found to be crucial for efficient cell detachment and subsequent growth. Our study demonstrates the feasibility of reducing the final Trypsin volume to 50 mL per gram of microcarrier while maintaining a Trypsin activity above 380 USP/mL. These conditions ensure complete cell dissociation and improve the cost effectiveness in large-scale productions. Additionally, we introduced real-time monitoring using a capacitance sensor during in situ cell detachment. This method has proven to be an effective process analytical technology (PAT) tool for tracking the cell detachment progress and efficiency. It allows for the prediction of cell detachment based on signals recorded between 3 and 7 min of Trypsinization, enabling rapid process decisions without the need for offline sampling, thereby enhancing the overall process control. This systematic approach not only optimizes in situ cell detachment processes but also has significant implications for the scalability and efficiency of microcarrier-based cell culture systems.

Project SIGNAL: A Dashboard for Supporting Community Confidence in Making Data-Driven Decisions.

Data dashboards have emerged as critical tools for surveillance and informing resource allocation. Despite their utility and popularity during COVID-19, there is a growing need to understand what tools and training are tailored to nonprofit community-based organizations that may partner with public health officials. In June 2021, the Rhode Island Department of Health and Brown University partnered to create Project SIGNAL (Spatiotemporal Insights to Guide Nuanced Actions Locally), which utilizes spatiotemporal analytics to identify Rhode Island's largest disparities in COVID-19-related outcomes (eg, testing, diagnosis, vaccinations) at the neighborhood level. Results were hosted in an interactive online dashboard (signal-ri.org) designed using principles of the CDC Clear Communication Index. The target audience included a network of 15 geographic areas called Health Equity Zones, funded by the health department to provide critical grassroots public health programs to address social, health, and economic outcomes in their communities. To disseminate the dashboard, a 6-hour virtual workshop series was created to train leaders to use the dashboard and increase their confidence in understanding common public health data terminology and concepts and better prepare attendees for rapid decision making during future public health emergencies. The Project SIGNAL dashboard was launched in August 2022 and has been accessed over 7500 times. A total of 84 community leaders were trained to use this dashboard, increasing their confidence in applying common public health metrics to make decisions about their COVID-19-related activities. While several studies have outlined best practices for data dashboards, this is among the first to examine incorporating these practices into a spatiotemporal decision tool designed specifically for community organizations. Project SIGNAL demonstrates that by incorporating design best practices and pairing data dashboards with hands-on training, we can empower community leaders to utilize advanced spatiotemporal methods to identify health disparities and take localized action.

CRISPR-based platform for clinical diagnosis of Neisseria gonorrhoeae and simultaneous tracking of ceftriaxone-resistant FC428 clone

The global dissemination of the ceftriaxone-resistant Neisseria gonorrhoeae FC428 clone poses substantial challenges in the control and treatment of gonorrhoea, resulting in heightened morbidity rates and the increased risk of public health emergencies. However, currently, the identification of N. gonorrhoeae FC428 clone is labor-intensive and laboratory-based, which is not suitable for rapid on-site analyses and effective decision making. Herein, an Equipment-free CRISPR/Cas12a assisted RPA platform is developed for rapid duplex detection (E-card), which provides a solution for early and effective clinical diagnosis of N. gonorrhoeae, particularly the simultaneous tracking of ceftriaxone-resistant FC428 clone to allow reasonable and timely antimicrobial therapy. The E-card achieves 1 copy/reaction for N. gonorrhoeae screening and 10 copies/reaction for ceftriaxone-resistant FC428 clone detection within 40 min. The accuracy of N. gonorrhoeae and FC428 identification has been validated by concordance rates of 100 % and 98 % between the E-card platform and PCR-sequencing, respectively. This convenient CRISPR-based disposable molecular diagnostic platform can serve as a powerful tool to rapid detection of N. gonorrhoeae and its ceftriaxone-resistant FC428 clone, enabling effective assessment of infection, effective medicine decision making and the mitigation of health burdens.

Phylogenetic Diversity, Antibiotic Resistance, and Virulence of Escherichia coli Strains from Urinary Tract Infections in Algeria.

Urinary tract infections (UTIs) caused by Escherichia coli represent a significant public health concern due to the high virulence and antimicrobial resistance exhibited by these pathogens. This study aimed to analyze the phylogenetic diversity and antibiotic resistance profiles of Uropathogenic E. coli (UPEC) strains isolated from UTI patients in Algeria, focusing on virulence factors such as extended β-lactamase (ESBL) production, biofilm formation, and hemolytic activity. Phylogenetic grouping of 86 clinical imipenem resistant E. coli isolates showed the prevalence of group B2 (48.9%), followed by groups E (22.1%), unknown (12.8%), A (8.1%), and B1 (4.7%), and Clade I, D, Clade I, or Clade II (1.2%). The highest resistance rates were observed towards amoxicillin (86.04%), ticarcillin (82.55%), piperacillin (73.25%), nitrofurantoin (84.88%), and trimethoprim-sulfamethoxazole (51.16%). Notably, 69.8% of UPEC strains were multidrug-resistant (MDR) and 23.2% were extensively drug-resistant (XDR). Additionally, 48.9%, 42%, and 71% of strains demonstrated ESBL production, hemolytic activity, and weak biofilm production, respectively. Continuous monitoring and characterization of UPEC strains are essential to track the spread of the most resistant and virulent phylogenetic groups over time, facilitating rapid therapeutic decisions to treat infections and prevent the emergence of new resistant organisms, helping choose the most effective antibiotics and reducing treatment failure.

Hyperspectral reflectance and machine learning for multi-site monitoring of cotton growth

Hyperspectral measurements can help with rapid decision-making and collecting data across multiple locations. However, there are multiple data processing methods (Savisky-Golay [SG], first derivative [FD], and normalization) and analyses (partial least squares regression [PLS], weighted k-nearest neighbor [KKNN], support vector machine [SVM], and random forest [RF]) that can be used to determine the best relationship between physical measurements and hyperspectral data. In the current study, FD was the best method for data processing and SVM was the best model for predicting average cotton (Gossypium spp. Malvaceae) height and nodes. However, the combination of FD and RF were best at predicting cotton leaf area index, canopy cover, and chlorophyll content across the growing season. Additionally, results from models developed by both SVM and RF were closely related to pseudo-CHIME satellite wavebands, where in-situ hyperspectral data were matched to the spectral resolutions of a future hyperspectral satellite. The information and results presented will aid producers and other members of the cotton industry to make rapid and meaningful decisions that could result in greater yield and sustainable intensification.

Best-Practice Biomarker Testing of Oesophago-Gastric Cancer in the UK: Expert Consensus Recommendations Developed Using a Modified Delphi

AimsOesophago-gastric cancers (OGCs) are amongst the most commonly diagnosed malignancies worldwide and are associated with high disease-related mortality. Predictive biomarkers are molecules that can be objectively measured and used to indicate a likely response to therapeutic intervention, thus facilitating individualised cancer therapy. However, there remains variation in uptake and implementation of biomarker testing across the UK. Materials and methodsWe conducted a modified Delphi study to formulate consensus recommendations for best-practice biomarker testing of OGC in the UK. We employed two rounds of online questionnaires followed by a virtual consensus meeting. Biomarkers for discussion included HER2, MSI/MMR, and PD-L1. Topics comprised the overall biomarker pathway, pre-analytical, analytical, and post-analytical considerations, including challenges in current practice. ResultsTwenty-six and eighteen participants completed the first and second round Delphi questionnaire, respectively, with an even split of pathologists and oncologists from across the UK. There was consensus (>80% agreement) across several topics, including the requirements for standardisation of the pathway, which must include coordination throughout the tissue journey, requirements for a quality-assured process to ensure accuracy and validity of testing, plus the need for clear, detailed information on the pathology report to support treatment decisions. There was consensus amongst oncologists regarding reflex testing of all biomarkers depending on histology; however, concerns over capacity in relation to workload and availability of pathologists were evident among the pathologists. Overall, participants were in the opinion that reflex testing improves the speed of treatment decisions and improves patient care. ConclusionThe recommendations reflect best-practices and should be implemented to support rapid multidisciplinary team decision-making within oesophago-gastric cancer. Results reflect the need for standardisation and demonstrate the challenges faced in clinical practice by those requesting and testing biomarkers for oesophago-gastric cancer, suggesting significant concerns relating to pathologist capacity.

Neural Control of Naturalistic Behavior Choices.

In the natural world, animals make decisions on an ongoing basis, continuously selecting which action to undertake next. In the lab, however, the neural bases of decision processes have mostly been studied using artificial trial structures. New experimental tools based on the genetic toolkit of model organisms now make it experimentally feasible to monitor and manipulate neural activity in small subsets of neurons during naturalistic behaviors. We thus propose a new approach to investigating decision processes, termed reverse neuroethology. In this approach, experimenters select animal models based on experimental accessibility and then utilize cutting-edge tools such as connectomes and genetically encoded reagents to analyze the flow of information through an animal's nervous system during naturalistic choice behaviors. We describe how the reverse neuroethology strategy has been applied to understand the neural underpinnings of innate, rapid decision making, with a focus on defensive behavioral choices in the vinegar fly Drosophila melanogaster.

Digital technology adoption and radical and incremental innovations: evidence from the Chinese wind power industry

Purpose Digital technologies open up unprecedented opportunities for the Chinese wind power industry to make rapid and comprehensive decisions. However, the relationship between digital technology adoption and radical and incremental innovations has not been empirically assessed. In addition, reconfiguration capability is the ability of firms to transform and respond to changes. How such an organizational capability influences the effectiveness of digital technology adoption is a black box. In response, this study aims to assess the relationship between digital technology adoption and radical and incremental innovations in the Chinese wind power industry and elucidate the moderating role of reconfiguration capability. Design/methodology/approach Based on the data of listed companies in the Chinese wind power industry from 2006 to 2020, this study constructs regression models and validates the hypotheses. Findings The correlation between digital technology adoption and incremental innovation in the wind power industry in China is significantly positive, but the relationship between digital technology adoption and radical innovation is not significant. In addition, reconfiguration capability significantly enhances the incentive effect of digital technology adoption on incremental innovation. Originality/value To the best of the authors’ knowledge, this study is one of the earliest to explore the heterogeneous relationships between digital technology adoption and radical and incremental innovations in emerging economies, advancing the theoretical insights into how digital transformation can foster different categories of technological innovations. Moreover, this study embeds dynamic capability theory into digital transformation research by exploring the boundary conditions for the effectiveness of digital technology adoption from the perspective of organizational dynamic capability, thereby expanding the boundaries of existing knowledge.