Software Platform Research Articles

Covalent Labeling Automated Data Analysis Platform for High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis Platform for Covalent Labeling Experiments.

Covalent labeling methods coupled to mass spectrometry have emerged in recent years for studying the higher order structure of proteins. Quantifying the extent of modification of proteins in multiple states (i.e., ligand free vs ligand-bound) can provide information on protein interaction sites and regions of conformational change. Though there are several software platforms that are used to quantify the extent of modification, the process can still be time-consuming, particularly for proteome-wide studies. Here, we present an open-source software for quantitation called Covalent labeling Automated Data Analysis Platform for high Throughput in R (coADAPTr). coADAPTr tackles the need for more efficient data analysis in covalent labeling mass spectrometry for techniques such as hydroxyl radical protein footprinting (HRPF). Traditional methods like Excel's Power Pivot (PP) are cumbersome and time-intensive, posing challenges for large-scale analyses. coADAPTr simplifies analysis by mimicking the functions used in the previous quantitation platform using PowerPivot in Microsoft Excel but with fewer steps, offering proteome-wide insights with enhanced graphical interpretations. Several features have been added to improve the fidelity and throughput compared to those of PowerPivot. These include filters to remove any duplicate data and the use of the arithmetic mean rather than the geometric mean for quantitation of the extent of modification. Validation studies confirm coADAPTr's accuracy and efficiency while processing data up to 200 times faster than conventional methods. Its open-source design and user-friendly interface make it accessible for researchers exploring intricate biological phenomena via HRPF and other covalent labeling MS methods. coADAPTr marks a significant leap in structural proteomics, providing a versatile and efficient platform for data interpretation. Its potential to transform the field lies in its seamless handling of proteome-wide data analyses, empowering researchers with a robust tool for deciphering complex structural biology data.

Devastation of the cerrado of mato grosso do sul and the advance of arenization in the pardo river watershed

Despite the significant biodiversity in the Cerrado, the process of occupation, subsequent economic cycles, and intensification of agriculture in a predatory manner have caused profound alterations in this biome. This study analyzed changes in land use and land cover in the Cerrado of Mato Grosso do Sul between 1985 and 2022 by mapping and also investigating the formation and dynamics of sand dunes in the Bauru sedimentary basin, focusing on the Pardo River watershed. MapBiomas Network data, collection 8, were used for land use and land cover analysis. Arenization foci cartography was conducted using brightness index 2 based on Sentinel-2 satellite images in Sentinel Application Platform software. The historical analysis of land use and cover in the Cerrado of Mato Grosso do Sul revealed a rapid transformation of the landscape, with a drastic reduction of native vegetation and an intensification of large-scale monocultures. Between 1985 and 2022, the Cerrado of Mato Grosso Sul lost 4.6 million hectares of native vegetation (forest formations, savannahs, grasslands, and wetlands), representing 24.54% of this territory. Recent changes in land use include pasture-soybean and pasture-eucalyptus. In the Pardo River watershed, arenization foci cover an area of 17,834.34 hectares, with varying dimensions ranging from less than 1 hectare to 376.41 hectares. The arenization process occurs in different slope compartments, such as the base, slope, and interfluves. The physical and chemical characteristics of Quartzipsamments, such as low organic carbon content in soil, are associated with high rates of surface exposure and weakened load-dependent structures, leading to degradation. Arenization occurs where natural vegetation has been suppressed and soil has been depleted by poorly managed production processes.

Preoperative Planning for Shoulder Arthroplasty is Feasible with Computed Tomography at Lower-Than-Conventional Radiation Doses

Computed tomography (CT) offers a detailed assessment of the shoulder for preoperative shoulder arthroplasty planning; however, this technique exposes the patient to ionizing radiation. The purpose of this study was to prospectively evaluate the practicality of reducing the CT radiation dose compared to conventional dose levels for manual and preoperative planning software measurements for shoulder arthroplasty. A total of 10 shoulder CT examinations were performed for preoperative planning purposes on a dual x-ray source CT scanner. A specialized dose-split scan technique was utilized to reconstruct CT images corresponding to 100%, 70%, and 30% radiation dose relative to our institution's standard of care imaging protocol. Glenoid version, inclination, and humeral head subluxation were measured manually by three authors and by commercially available software platforms. These measurements were analyzed for agreement between the 100%, 70%, and 30% dose levels for each patient. Tolerances of 5° of glenoid version, 5° of glenoid inclination, and 10% humeral head subluxation were used as equivalent for preoperative planning. Automated measurements of 70% dose images were within 5° of version, 5° of inclination, and 10% subluxation in 95.0% of cases. Manual measurements of 70% RD images were within 5° of version for 90.0% of cases, 5° of inclination in 86.7% of cases, and 10% subluxation in 100% of cases. Automated measurements from the 30% dose images were within 5° of version, 5° of inclination, and 10% subluxation for 100% of cases. Manual measurements from the 30% dose images were within 5° of version for 86.7% of cases, 5° of inclination in 76.7% of cases, and 10% subluxation in 100% of cases. The mean absolute difference in software measurement of glenoid version (p = 0.96), glenoid inclination (p = 0.64), or humeral head subluxation (p = 0.09) or in aggregated manual mean absolute difference of version (p = 0.22), inclination (p = 0.31), or humeral head subluxation (p = 0.56) was not significant. Good to excellent reliability was determined by interclass correlation coefficients among the manual observers and automatic software platforms for measurements at all doses (P<0.001) CONCLUSIONS: The results indicate that both preoperative planning software platforms and human observers produced similar measurements of glenoid version, inclination, and humeral head subluxation from reduced-dose images compared to standard of care doses. By implementing reduced dose techniques in preoperative shoulder CT, the potential risks associated with radiation exposure could be reduced for patients undergoing shoulder arthroplasty.

Research and application of 3D spatial safety electronic fence technology based on beidou high-precision positioning in substation construction projects

Abstract In recent years, the Beidou Navigation Satellite System (BDS) has developed rapidly, achieving global coverage and being widely used in various fields. The high-precision positioning capability of the Beidou system provides strong technical support for a variety of application scenarios, especially in fields requiring high-accuracy location information, such as safety monitoring in substation engineering. Substations are critical components of the power system, and their operational safety is vital for the stable operation of the entire grid. However, due to the complexity of the equipment and harsh working environments in substation engineering, utilizing Beidou’s high-precision positioning capabilities can significantly enhance the safety management level of substation construction projects and reduce the occurrence of accidents. This paper primarily explores and investigates high-precision positioning-based three-dimensional (3D) spatial safety electronic fence technology for substation construction projects. The process begins with using laser radar to capture 3D scene information and extract key parts of the point cloud model. Comprehensive positioning sensors, integrating the Beidou Navigation Satellite System (BDS) and inertial navigation, are then installed on crane booms to achieve real-time monitoring of the boom’s posture. Finally, a software platform is established to calculate the real-time 3D spatial distance between the crane boom and surrounding sensitive facilities, ensuring the safety of large-scale construction machinery. Field test results validate the effectiveness of the proposed method.

An initial exploration of factors that may impact radiographer performance in reporting mammograms

ObjectivesIn the United Kingdom, radiographers with a qualification in image interpretation have interpreted mammograms since 1995. These radiographers work under the title of radiography advanced practitioners (RAP) or Consultant Radiographer. This study extends upon what has been very recently published by exploring further clinical, non-clinical and experiential factors that may impact the reporting performance of RAPs. MethodsFifteen RAPs interpreted an image test set of 60 2D mammograms of known truth using the Detected-X software platform. Unknown to the reader, twenty cases contained a malignancy. Sensitivity, specificity, lesion sensitivity, receiver operating characteristic (ROC) and jack-knife free response operating characteristic (AFROC) values were established for each RAP. Specific features that had significant impact on accuracy were identified using Student's-T and Mann Whitney tests. ResultsRAPs with more than 10 years' experience in image interpretation, compared to those with less than 10 years’ experience, demonstrated lower specificity (51.3% vs 84.8%, p = 0.0264), ROC (0.83 vs 0.91, p = 0.0264) and AFROC (0.75 vs 0.87, p = 0.0037) values. Further, higher sensitivity values of 90.7% were seen in those RAPs who had an eye test in the last year compared to those who had not, 82% (p = 0.021). Other changes are presented in the paper. ConclusionThese data reveal previously unidentified factors that impact the diagnostic efficacy of RAPs when interpreting mammographic images. Highlighting such findings will empower screening authorities to better examine ways of standardising performance and offer a baseline for performance benchmarks. Implications for practiceThis study for the first time performs an initial exploration of the factors that may be associated with RAP performance when interpreting screening mammograms.

Analysis of Electronic and Thermoelectric Properties of Janus Materials Based on Molybdenum

Abstract Thermoelectric devices, which directly convert heat into electrical energy, hold great potential for efficient energy transformation. With the abundant availability of heat energy, global research has increasingly focused on developing thermoelectric materials that enhance conversion efficiency. The performance of these materials is often evaluated using the Figure-of-Merit (ZT), a measure influenced by variables such as the Seebeck coefficient, electrical conductivity, and thermal conductivity. High-performing materials typically exhibit a strong Power Factor (PF) and a high ZT value. This study investigates the thermoelectric properties of Janus materials based on molybdenum, utilizing a computational approach. We employed density functional theory (DFT) to solve Schrödinger’s equations and Boltzmann transport theory through Quantum ESPRESSO and BoltzTraP2 software platforms. The results demonstrate that the studied Janus compounds possess stable structures. The electronic properties indicate direct band gaps of 1.58 eV for MoSSe (a Janus structure combining molybdenum, sulfur, and selenium), 1.04 eV for MoSTe (a combination of molybdenum, sulfur, and tellurium), and 1.3 eV for MoSeTe (a combination of molybdenum, selenium, and tellurium). Indirect band gaps were found to be 0.23 eV for MoTeO, 0.8 eV for MoSeO, and 1.12 eV for MoSO. Among the materials studied, MoSSe exhibited the highest thermoelectric properties, with a power factor of 0.003 W/mK2 for p-type and 0.0031 W/mK2 for n-type. These findings suggest that Janus MoSSe is a promising candidate for the development of 2D thermoelectric devices, potentially advancing thermoelectric technology.

A web-based platform for studying the impact of artificial intelligence in video capsule endoscopy.

Objective: Integrating artificial intelligence (AI) solutions into clinical practice, particularly in the field of video capsule endoscopy (VCE), necessitates the execution of rigorous clinical studies. Methods: This work introduces a novel software platform tailored to facilitate the conduct of multi-reader multi-case clinical studies in VCE. The platform, developed as a web application, prioritizes remote accessibility to accommodate multi-center studies. Notably, considerable attention was devoted to user interface and user experience design elements to ensure a seamless and engaging interface. To evaluate the usability of the platform, a pilot study is conducted. Results: The results indicate a high level of usability and acceptance among users, providing valuable insights into the expectations and preferences of gastroenterologists navigating AI-driven VCE solutions. Conclusion: This research lays a foundation for future advancements in AI integration within clinical VCE practice.

Family history and cancer risk study (FOREST): A clinical trial assessing electronic patient-directed family history input for identifying patients at risk of hereditary cancer

BackgroundHereditary cancer syndromes cause a high lifetime risk of early, aggressive cancers. Early recognition of individuals at risk can allow risk-reducing interventions that improve morbidity and mortality. Family health history applications that gather data directly from patients could alleviate barriers to risk assessment in the clinical appointment, such as lack of provider knowledge of genetics guidelines and limited time in the clinical appointment. New approaches allow linking these applications to patient health portals and their electronic health records (EHRs), offering an end-to-end solution for patient-input family history information and risk result clinical decision support for their provider. MethodsWe describe the design of the first large-scale evaluation of an EHR-integrable, patient-facing family history software platform based on the Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART on FHIR) standard. In our study, we leverage an established implementation science framework to evaluate the success of our model to facilitate scalable, systematic risk assessment for hereditary cancers in diverse clinical environments in a large pragmatic study at two sites. We will also evaluate the success of the approach to improve the efficiency of downstream genetic counseling resulting from pre-counseling pedigree generation. ConclusionsOur research study will provide evidence regarding a new care delivery model that is scalable and sustainable for a variety of medical centers and clinics. Trial registrationThis study was registered on ClinicalTrials.gov under NCT05079334 on 15 October 2021.

A comprehensive study on the digestion, absorption, and metabolization of tropane alkaloids in human cell models

Tropane alkaloids (TAs) are toxic compounds with potent anticholinergic effects. Herbal infusions are among the most contaminated food commodities; however, the fate of TAs after ingestion remains poorly understood. This study presents a comprehensive investigation into the absorption, and metabolism of five TAs (atropine, scopolamine, tropine, homatropine, and apoatropine) following the digestion of contaminated tea.In vitro human cell models were employed, including gastric (NCI-N87), intestinal (Caco-2:HT29-MTX), and hepatic (HEP-G2) cells. TAs were found to be highly absorbed in the intestinal epithelium, while gastric cells exhibited poor absorption. Metabolism was studied using a custom-made database, revealing that it occurs predominantly in intestinal cells, involving hydroxylation and methylation reactions.Cell metabolomics was conducted using annotation, fragment simulation, and statistical software platforms. Significant statistical differences were observed for 40 tentatively identified compounds. MetaboAnalyst 5.0 was employed to discern the most disturbed metabolic pathways, with amoniacids biosynthesis pathways and TCA cycles being the most affected. These pathways are involved in responses to cellular metabolic stress, neurotransmitter production, cellular energy generation, and the regulation of oxidative stress response. The findings of this study enhance our understanding of the fate of TAs after ingestion, their metabolization and their effects at the cellular level.

Privacy Policies on the Fediverse: A Case Study of Mastodon Instances

Free and open source social platform software has dramatically lowered the barrier to entry for anyone to set up and administer their own social network. This new population of social network administrators thus assume data management responsibilities for sociotechnical systems. Administrators have the power to customize this software, including data collection and data retention, potentially leading to radically different privacy policies. To better understand the characteristics — e.g., the variability, prohibitions, and permissions — of privacy policies on these new social networking platforms, we have conducted a case study of Mastodon. We performed a text analysis of 351 privacy policies and a survey of 104 Mastodon administrators. While most administrators used the default policy that ships with the Mastodon software, we observed that approximately ten percent of our sample tailored their privacy policies to their instances and that some administrators conflated codes of conduct with privacy policies. Our findings suggest the existing market-based individualistic frameworks for thinking about privacy policies do not adequately address this emerging community.

Дослідження проблем швидкодії програмних додатків

The article presents a study of methods for optimizing the performance of software applications aimed at identifying the most effective combination of internal and external factors that maximize the objective function. A generalized mathematical model is described, which includes the main factors affecting performance, such as computation time, the number of input/output operations, the number of computational operations, algorithm complexity, the volume of data processed, the use of parallelism, the architecture of hardware and software platforms, and code efficiency. The importance of using specialized libraries and tools to accelerate computational processes, which is critically important for achieving high performance in modern software systems, is emphasized. The developed approaches were implemented in software, allowing for the practical evaluation of the proposed methods. Software modules were created to analyze the impact of various factors on performance, considering the specifics of particular tasks and execution environments. The test results demonstrated significant potential for performance improvement through optimization at both the code level and the hardware architecture level. Particular attention is given to the study of memory management, addressing potential challenges that negatively impact performance. The necessity of using a caching system and avoiding duplication of immutable information is highlighted. The identified scenarios are independent of any specific implementation and can therefore be integrated into the developing recommendation system. The research has practical significance, offering comprehensive solutions for optimizing the performance of software systems that can be applied in industrial high-load environments. Further research will focus on expanding the functionality of the recommendation system, integrating more complex optimization models, and conducting large-scale computational experiments to validate the results under real-world conditions.

A MATLAB platform for production planning in batch processing firms producing items that are jointly replenished

Rising levels of poverty have prompted manufacturers of non-durable consumer goods to explore opportunities at the lower end of the wealth spectrum. To enhance affordability for low-income consumers, these goods are often packaged in various sizes, including single-serve sachets. While numerous models and algorithms have been developed to determine the optimal frequency of manufacturing runs and packaging setups for the simultaneous replenishment of different pack sizes, there is a notable lack of literature addressing their implementation by small and medium-sized enterprises (SMEs) in this sector. This gap may be attributed to the absence of accessible source codes, a shortage of model experts, and the complexity involved in manual calculations. To address this issue, we developed a user-friendly MATLAB software platform based on Goyal’s algorithm, tailored specifically for SMEs. The software prioritizes simplicity, interactivity, and flexible visual displays. It was validated against examples from existing literature and through randomly generated scenarios. Users can easily compute the optimal number of manufacturing setups, packaging frequencies for each pack size, and the associated costs by inputting relevant data such as manufacturing setup, inventory holding and packaging setup costs, and demand for each pack size. Importantly, users do not need expertise in modeling to operate the software. However, it is designed for single-product deterministic environments; future research should explore multi-product and stochastic modeling scenarios.

Leveraging Qualitative Analysis Software to Complement Traditional Qualitative Research Approaches for Curriculum Design: Results of a Needs Assessment Study

ABSTRACT Educational programs are continually challenged with maintaining a curriculum that meets the current and future needs of the industry they serve, and accreditation requirements. Research methodologies of interviews, focus groups, and surveys to accomplish these requirements have a long-standing documented history in the literature. Interviews and focus groups can provide collection of rich information, but have limited sample size, and greater potential for participant and researcher bias. Survey research can be more objective and reach larger population, but results are limited to the type and quality of questions posed by the researchers, and honesty of the participant responses. Technological advances in data collection and analysis have been improving research methodologies for decades. This paper describes a research approach for conducting a program needs assessment. Using structured focus groups and qualitative artificial intelligence software, MAXQDA, the methodology identified 15 themes for activities commonly performed by entry-level construction management graduates. Within the themes, 17 common types of knowledge, and 15 types of skills were identified. Key knowledge included Terms & Symbols, Project Documents, Materials & Methods, Software Platforms, Scopes of Work, and Contract Structures. The top skills identified were Oral & Written Communication, Using/Learning Software/Technology, Collaboration/Teamwork, Analyzing/Interpreting Documents, and Personal Organization/Time Management.

Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging. This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software engineering tasks. We conduct a comprehensive empirical study using five established datasets to assess three open-source bLLMs in zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs, using sLLMs to learn contextual embeddings of text from software platforms. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions. bLLMs can also achieve excellent performance under a zero-shot setting. However, when ample training data is available or the dataset exhibits a more balanced distribution, fine-tuned sLLMs can still achieve superior results.

An Impact of CRM at Max Fashion Retail Limited

Effective customer relationship management (CRM) is essential for maintaining competitive advantage in the retail fashion industry. At Max Fashion, CRM strategies have been designed to enhance customer loyalty, streamline communication, and improve overall customer satisfaction. This study examines the various CRM practices employed by Max Fashion, including the use of customer data analytics, personalized marketing campaigns, and loyalty programs. By integrating technology such as CRM software and social media platforms, the company aims to better understand customer preferences and purchasing behaviour. The research highlights how these strategies contribute to increased customer retention and long-term business success. Through a combination of qualitative interviews with Max Fashion's management team and quantitative data analysis from customer feedback, the study identifies the key factors driving CRM effectiveness. This research provides valuable insights for fashion retailers seeking to implement or refine their own CRM strategies in an increasingly digital marketplace

Making PBPK models more reproducible in practice.

Systems biology aims to understand living organisms through mathematically modeling their behaviors at different organizational levels, ranging from molecules to populations. Modeling involves several steps, from determining the model purpose to developing the mathematical model, implementing it computationally, simulating the model's behavior, evaluating, and refining the model. Importantly, model simulation results must be reproducible, ensuring that other researchers can obtain the same results after writing the code de novo and/or using different software tools. Guidelines to increase model reproducibility have been published. However, reproducibility remains a major challenge in this field. In this paper, we tackle this challenge for physiologically-based pharmacokinetic (PBPK) models, which represent the pharmacokinetics of chemicals following exposure in humans or animals. We summarize recommendations for PBPK model reporting that should apply during model development and implementation, in order to ensure model reproducibility and comprehensibility. We make a proposal aiming to harmonize abbreviations used in PBPK models. To illustrate these recommendations, we present an original and reproducible PBPK model code in MATLAB, alongside an example of MATLAB code converted to Systems Biology Markup Language format using MOCCASIN. As directions for future improvement, more tools to convert computational PBPK models from different software platforms into standard formats would increase the interoperability of these models. The application of other systems biology standards to PBPK models is encouraged. This work is the result of an interdisciplinary collaboration involving the ELIXIR systems biology community. More interdisciplinary collaborations like this would facilitate further harmonization and application of good modeling practices in different systems biology fields.

Clinical significance of RETN gene expression and rs3219175 G > a polymorphism in cancer

The inflammatory cytokine resistin, which is encoded by the RETN gene, plays a variety of roles in cancer. This study aimed to assess the relationship between RETN gene expression and cancer stage, survival prognosis, immune infiltration, and drug sensitivity, and whether the rs3219175 G > A polymorphism affected the expression of the RETN gene and cancer risk. The clinical significance of RETN gene expression and the rs3219175 polymorphism in cancer was analyzed by the GSCA platform, GTEx database and STATA software. The results showed that RETN gene expression was associated with the stage of thyroid carcinoma, survival prognosis and immune infiltration of certain cancers, and sensitivity to multiple drugs. The rs3219175 polymorphism could influence the expression of the RETN gene in a wide range of tissues. Furthermore, RETN gene rs3219175 polymorphism was significantly associated with cancer risk [GA vs. GG: OR = 2.27, 95%CI = 1.26–4.09; (GA + AA) vs. GG: OR = 2.23, 95%CI = 1.28–3.88; A vs. G: OR = 1.72, 95%CI = 1.15–2.58]. In conclusion, the current study suggested that resistin might serve as a prognostic marker and therapeutic target for certain cancers, and the rs3219175 polymorphism might be used as a marker for predicting cancer risk.

The Gompertz model for biohydrogen production kinetics: Origin, application and solving methods

The Gompertz model has been extensively employed in describing biohydrogen kinetics. In this study, the Gompertz model, including its origins, modifications, application for describing biohydrogen kinetics, and solving methods, was explored in detail. The Gompertz model has undergone modifications to enhance its applicability in biological processes. The Lay-modified Gompertz model, tailored explicitly for modeling bioproduction, demonstrates versatility in predicting crucial factors, contributing to a deeper understanding of production efficiency. The applications of this model in biohydrogen production were summarized. Additionally, the solving methods for the Gompertz model were presented for different software platforms, in which Microsoft Excel, Origin, SigmaPlot, and GraphPad Prism offer user-friendly interfaces. The MATLAB-based method is more versatile, allowing users to solve the Gompertz model without the need for relatively accurate initial parameter values. The Excel sheet and MATLAB file are provided in Supplementary Material. This study enhances the understanding and practical application of the Gompertz model in describing biohydrogen production kinetics.

Getting rapid diagnostic test data into the appropriate hands by leveraging pharmacy staff and a clinical surveillance platform: a case study from a US community hospital

Abstract Objectives To outline the procedural implementation and optimization of rapid diagnostic test (RDT) results for bloodstream infections (BSIs) and to evaluate the combination of RDTs with real-time antimicrobial stewardship team (AST) support plus clinical surveillance platform (CSP) software on time to appropriate therapy in BSIs at a single health system. Methods Blood culture reporting and communication were reported for four time periods: (i) a pre-BCID [BioFire® FilmArray® Blood Culture Identification (BCID) Panel] implementation period that consisted of literature review and blood culture notification procedure revision; (ii) a BCID implementation period that consisted of BCID implementation, real-time results notification via CSP, and creation of a treatment algorithm; (iii) a post-BCID implementation period; and (iv) a BCID2 implementation period. Time to appropriate therapy metrics was reported for the BCID2 time period. Results The mean time from BCID2 result to administration of effective antibiotics was 1.2 h (range 0–7.9 h) and time to optimal therapy was 7.6 h (range 0–113.8 h) during the BCID2 Panel implementation period. When comparing time to optimal antibiotic administration among patients growing ceftriaxone-resistant Enterobacterales, the BCID2 Panel group (mean 2.8 h) was significantly faster than the post-BCID Panel group (17.7 h; P = 0.0041). Conclusions Challenges exist in communicating results to the appropriate personnel on the healthcare team who have the knowledge to act on these data and prescribe targeted therapy against the pathogen(s) identified. In this report, we outline the procedures for telephonic communication and CSP support that were implemented at our health system to distribute RDT data to individuals capable of assessing results, enabling timely optimization of antimicrobial therapy.

A Quantum Image Secret Sharing Scheme Based on Designated Multi‐Verifier Signature

AbstractThis paper presents a quantum image secret sharing scheme based on designated multi‐verifier signature. First, share images are generated by a 2D hyperchaotic system incorporating sinusoidal mapping, Henon mapping, and Cubic mapping using the cascade modulation method. Quantum Arnold scrambling and diffusion are performed on the secret image. Subsequently, the concept of designated multi‐verifier signature is introduced. In the secret image sharing phase, the pixel values of the share images are used to sign the secret image, and the signed secret image and share images are sent to a trusted third party and designated verifiers, respectively. In this scheme, no carrier images are required, and the share images held by the participants do not contain any information about the secret image. Finally, simulation experiments and security analysis are conducted on IBM Qiskit platform and Matlab software.